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1
Ching, Tan Kok. "Grid Survey Approach to Store Separation Trajectory Prediction." Journal of Aircraft 37, no. 4 (July 2000): 736–38. http://dx.doi.org/10.2514/2.2662.
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Hashemi, Seyed Mohammad, Seyed Ali Hashemi, Ruxandra Mihaela Botez, and Georges Ghazi. "Aircraft Trajectory Prediction Enhanced through Resilient Generative Adversarial Networks Secured by Blockchain: Application to UAS-S4 Ehécatl." Applied Sciences 13, no. 17 (August 22, 2023): 9503. http://dx.doi.org/10.3390/app13179503.
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This paper introduces a novel and robust data-driven algorithm designed for Aircraft Trajectory Prediction (ATP). The approach employs a Neural Network architecture to predict future aircraft trajectories, utilizing input variables such as latitude, longitude, altitude, heading, speed, and time. The model’s foundation is rooted in the Generative Adversarial Network (GAN) framework, known for its inherent generative capabilities, rendering it remarkably resilient against Adversarial Attacks. To enhance its credibility, the Blockchain is employed as a Ledger Technology (LT) to securely store legitimate predicted values utilized in subsequent trajectory predictions. The Blockchain ensures that only authorized and non-adversarial samples are stored in the blocks, rejecting any adversarial predictions. In the validation process, trajectory data for training the GAN model were generated through the UAS-S4 Ehécatl simulation model. The performance evaluation relies on the model’s resistance to adversarial attacks, measured by fooling rates. The results acquired affirm the excellent efficacy of the GAN model, Secured by Blockchain, approaching against adversarial attacks.
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Lee, Seungsoo, Minwoo Park, Kum Won Cho, and Jang Hyuk Kwon. "New Fully Automated Procedure for the Prediction of Store Trajectory." Journal of Aircraft 37, no. 6 (November 2000): 1038–49. http://dx.doi.org/10.2514/2.2709.
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KANDIEL, M., and M. ESSAWf. "PREDICTION OE STORE TRAJECTORY DROPPED 'FROM A HIGH SPEED AIRCRAFT." International Conference on Aerospace Sciences and Aviation Technology 1, CONFERENCE (May 1, 1985): 1–13. http://dx.doi.org/10.21608/asat.1985.26482.
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Razzaghi, Mohammad Mehdi. "Numerical study on trajectory of released store with manipulation of grid connections." Aircraft Engineering and Aerospace Technology 93, no. 5 (June 23, 2021): 768–75. http://dx.doi.org/10.1108/aeat-07-2020-0140.
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Purpose This study aims to present a moving grid method based on the manipulation of connections. Design/methodology/approach In this study, the grid’s connections were manipulated to simulate a released store’s displacement. The selected model in this research is the EGLIN test case. In the introduced method, connections are modified in specific nodes of the grid. Governing flow equations were solved with the finite volume method. The major characteristic of this technique is using the averaging method for calculating the flux of cells. Findings This method maintains the grid’s quality even in large displacements of the released store. The three-dimensional simulation was carried out in transonic and supersonic regimes. Comparison of the results with experimental data were highly satisfactory. Research limitations/implications Using this moving grid method is recommended for simulating other models. Practical implications Prediction of store trajectory released from air vehicles is one of the most critical issues under study especially in the design of new stores. Originality/value The most prominent advantage of this method is maintaining the grid quality simultaneous with large displacements of the released store.
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Lijewski, Lawrence E., and Norman E. Suhs. "Time-accurate computational fluid dynamics approach to transonic store separation trajectory prediction." Journal of Aircraft 31, no. 4 (July 1994): 886–91. http://dx.doi.org/10.2514/3.46575.
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Chakraborty, Debasis, K. Anandhanarayanan, Ankit Raj, Vaibhav Shah, and R. Krishnamurthy. "Separation Dynamics of Air-to-Air Missile and Validation with Flight Data." Defence Science Journal 68, no. 1 (December 18, 2017): 5. http://dx.doi.org/10.14429/dsj.68.11480.
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<p class="p1">Prediction of flight characteristics of a store in the vicinity of an aircraft is vitally important for ensuring the safety of the aircraft and effectiveness of the store to meet the mission objective. Separation dynamics of an agile air-to-air-Missile from a fighter aircraft is numerically simulated using an integrated store separation dynamics suite. Chimera cloud of points along with a grid-free Euler solver is used to obtain aerodynamic force on the missile and the force is integrated using a rigid body dynamics code to obtain the missile position. In the present work, the suite is applied to a flight test case and sensitivity of trajectory variables on launch parameters is studied. Further, the results of the suite are compared with the flight data. The predicted body rates and Euler angles of missile compare well with the flight data.<span class="Apple-converted-space"> </span></p><p class="p2"><span class="Apple-converted-space"> </span></p>
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Wang, Shanshan. "Enterprise Management Optimization by Using Artificial Intelligence and Edge Computing." International Journal of Distributed Systems and Technologies 13, no. 3 (July 1, 2022): 1–9. http://dx.doi.org/10.4018/ijdst.307994.
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In the internet era, huge data is generated every day. With the help of cloud computing, enterprises can store and analyze these data more conveniently. With the emergence of the internet of things, more hardware devices have accessed the network and produced massive data. The data heavily relies on cloud computing for centralized data processing and analysis. However, the rapid growth of data volume has exceeded the network throughput capacity of cloud computing. By deploying computing nodes at the edge of the local network, edge computing allows devices to complete data collection and preprocessing in the local network. Thus, it can overcome the problems of low efficiency and large transmission delay of cloud computing for massive native data. This paper designs a human trajectory training system for enterprise management. The simulation demonstrates that the system can support human trajectory tracing and prediction for enterprise management.
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Velychko, Dmytro, Benjamin Knopp, and Dominik Endres. "Making the Coupled Gaussian Process Dynamical Model Modular and Scalable with Variational Approximations." Entropy 20, no. 10 (September 21, 2018): 724. http://dx.doi.org/10.3390/e20100724.
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We describe a sparse, variational posterior approximation to the Coupled Gaussian Process Dynamical Model (CGPDM), which is a latent space coupled dynamical model in discrete time. The purpose of the approximation is threefold: first, to reduce training time of the model; second, to enable modular re-use of learned dynamics; and, third, to store these learned dynamics compactly. Our target applications here are human movement primitive (MP) models, where an MP is a reusable spatiotemporal component, or “module” of a human full-body movement. Besides re-usability of learned MPs, compactness is crucial, to allow for the storage of a large library of movements. We first derive the variational approximation, illustrate it on toy data, test its predictions against a range of other MP models and finally compare movements produced by the model against human perceptual expectations. We show that the variational CGPDM outperforms several other MP models on movement trajectory prediction. Furthermore, human observers find its movements nearly indistinguishable from replays of natural movement recordings for a very compact parameterization of the approximation.
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Zhang, Weini. "Research on Recognition Method of Basketball Goals Based on Image Analysis of Computer Vision." Journal of Sensors 2021 (September 20, 2021): 1–11. http://dx.doi.org/10.1155/2021/5269431.
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Moving target detection is involved in many engineering applications, but basketball has some difficulties because of the time-varying speed and uncertain path. The purpose of this paper is to use computer vision image analysis to identify the path and speed of a basketball goal, so as to meet the needs of recognition and achieve trajectory prediction. This research mainly discusses the basketball goal recognition method based on computer vision. In the research process, Kalman filter is used to improve the KCF tracking algorithm to track the basketball path. The algorithm of this research is based on MATLAB, so it can avoid the mixed programming of MATLAB and other languages and reduce the difficulty of interface design software. In the aspect of data acquisition, the extended EPROM is used to store user programs, and parallel interface chips (such as 8255A) can be configured in the system to output switch control signals and display and print operations. The automatic basketball bowling counter based on 8031 microprocessor is used as the host computer. After the level conversion by MAX232, it is connected with the RS232C serial port of PC, and the collected data is sent to the workstation recording the results. In order to consider the convenience of user operation, the GUI design of MATLAB is used to facilitate the exchange of information between users and computers so that users can see the competition results intuitively. The processing frame rate of the tested video image can reach 60 frames/second, more than 25 frames/second, which meet the real-time requirements of the system. The results show that the basketball goal recognition method used in this study has strong anti-interference ability and stable performance.
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Díaz-Casco, Manuel A., Blanca E. Carvajal-Gámez, Octavio Gutiérrez-Frías, and Fernando S. Osorio-Zúñiga. "Comprehensive—Model Based on Time Series for the Generation of Traffic Knowledge for Bus Transit Rapid Line 6 of México City." Electronics 11, no. 19 (September 24, 2022): 3036. http://dx.doi.org/10.3390/electronics11193036.
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Mobile sensor networks consist of different types of integrated devices that collect, disseminate, process and store information from the environments in which they are implemented. This type of network allows for the development of applications and systems in different areas for the generation of knowledge. In this paper, we propose a model called the Metrobus Arrival Prediction (MAP) model for predicting the arrival times of Line 6 buses of the bus rapid transit (BTR) system, known as the Metrobus, in Mexico City (CDMX). The network is composed of mobile and static nodes that collect data related to the speed and position of each Metrobus bus. These data are sent to the proposed time series model, which yields the Metrobus arrival time estimation. MAP allows the density of users projected during the day to be estimated with a time series model that uses the data collected and the historical data of each station. A comparison is made between the model results and the arrival time obtained with real-time traffic monitoring applications, such as Moovit and Google Maps. The proposed model, based on time series, takes the historical data (data of trajectory times) as reference to start the first arrival times. From these values, MAP feeds on the data collected through the sensor network. As the data are collected through the sensor network, the estimates present results, for example, the mean absolute error (MAE) of the expected time was less than 0.2 s and the root mean square error (RMSE) of the expected value was below 1 for the proposed model. Compared to real-time traffic platforms, it presents a value of 0.1650 of the average dispersion obtained in travel times. The obtained values provide certainty that the data shown presents results as accurately as a real-time platform that requires the data at the moments in which the traffic variations occur. Moreover, unlike other state-of-the-art models that rarely interact on the site, MAP requires a reduced number of variables, being an accessible tool for the implementation and scaling of real-time traffic monitoring.
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Wang, Ding, Boyang Liu, Pang-Ning Tan, and Lifeng Luo. "OMuLeT: Online Multi-Lead Time Location Prediction for Hurricane Trajectory Forecasting." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 01 (April 3, 2020): 963–70. http://dx.doi.org/10.1609/aaai.v34i01.5444.
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Hurricanes are powerful tropical cyclones with sustained wind speeds ranging from at least 74 mph (for category 1 storms) to more than 157 mph (for category 5 storms). Accurate prediction of the storm tracks is essential for hurricane preparedness and mitigation of storm impacts. In this paper, we cast the hurricane trajectory forecasting task as an online multi-lead time location prediction problem and present a framework called OMuLeT to improve path prediction by combining the 6-hourly and 12-hourly forecasts generated from an ensemble of dynamical (physical) hurricane models. OMuLeT employs an online learning with restart strategy to incrementally update the weights of the ensemble model combination as new observation data become available. It can also handle the varying dynamical models available for predicting the trajectories of different hurricanes. Experimental results using the Atlantic and Eastern Pacific hurricane data showed that OMuLeT significantly outperforms various baseline methods, including the official forecasts produced by the U.S. National Hurricane Center (NHC), by more than 10% in terms of its 48-hour lead time forecasts.
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Kim, Wonjik, and Osamu Hasegawa. "Time Series Prediction of Tropical Storm Trajectory Using Self-Organizing Incremental Neural Networks and Error Evaluation." Journal of Advanced Computational Intelligence and Intelligent Informatics 22, no. 4 (July 20, 2018): 465–74. http://dx.doi.org/10.20965/jaciii.2018.p0465.
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This study proposes a route prediction method using a self-organizing incremental neural network. The route trajectory is predicted from two location parameters (the latitude and longitude of the middle of a tropical storm) and the meteorological information (the atmospheric pressure). The method accurately predicted the normalized atmospheric pressure data of East Asia in the topological space of latitude and longitude, with low calculation cost. This paper explains the algorithms for training the self-organizing incremental neural network, the procedure for refining the datasets and the method for predicting the storm trajectory. The effectiveness of the proposed method was confirmed in experiments. With the results of experiments, possibility of prediction model improvement is discussed. Additionally, this paper explains the limitations of proposed method and brief solution to resolve. Although the proposed method was applied only to typhoon phenomena in the present study, it is potentially applicable to a wide range of global problems.
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Xi, Ke, and Chao Yan. "CFD Transonic Trajectory Predictions of Three-Store Ripple Release Using Chimera Method." Applied Mechanics and Materials 513-517 (February 2014): 4490–93. http://dx.doi.org/10.4028/www.scientific.net/amm.513-517.4490.
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The complicated unsteady flows with moving boundary were simulated numerically by coupling solving unsteady compressible Navier-Stokes equations and 6DOF rigid-body dynamics equations. The Chimera grid technology was used to handle the relative motion. The three-store ripple release of the wing-store configuration was simulated using this method. The computational results are in good agreement with data from other literature, showing that the method used has a strong applicability to complex multi-body separation problem.
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Zhang, Lixiang, Yian Zhu, Jiang Su, Wei Lu, Jiayu Li, and Ye Yao. "A Hybrid Prediction Model Based on KNN-LSTM for Vessel Trajectory." Mathematics 10, no. 23 (November 28, 2022): 4493. http://dx.doi.org/10.3390/math10234493.
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Trajectory prediction technology uses the trajectory data of historical ships to predict future ship trajectory, which has significant application value in the field of ship driving and ship management. With the popularization of Automatic Identification System (AIS) equipment in-stalled on ships, many ship trajectory data are collected and stored, providing a data basis for ship trajectory prediction. Currently, most of the ship trajectory prediction methods do not fully consider the influence of ship density in different sea areas, leading to a large difference in the prediction effect in different sea areas. This paper proposes a hybrid trajectory prediction model based on K-Nearest Neighbor (KNN) and Long Short-Term Memory (LSTM) methods. In this model, different methods are used to predict trajectory based on trajectory density. For offshore waters with a high density of trajectory, an optimized K-Nearest Neighbor algorithm is used for prediction. For open sea waters with low density of trajectory, the Long Short-Term Memory model is used for prediction. To further improve the prediction effect, the spatio-temporal characteristics of the trajectory are fully considered in the prediction process of the model. The experimental results for the dataset of historical data show that the mean square error of the proposed method is less than 2.92 × 10−9. Compared to the prediction methods based on the Kalman filter, the mean square error decreases by two orders of magnitude. Compared to the prediction methods based on recurrent neural network, the mean square error decreases by 82%. The advantage of the proposed model is that it can always obtain a better prediction result under different conditions of trajectory density available for different sea areas.
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Minh, Pham Thi, Hang Thi Nguyen, Thuy Kim Pham, and Gia Nguyen Hoang Cao. "An application of the multi-physical method of determining error of WRF models to simulate the track and intensity Usagi storm in 2018." Science & Technology Development Journal - Science of The Earth & Environment 5, no. 1 (May 30, 2021): first. http://dx.doi.org/10.32508/stdjsee.v5i1.547.
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This paper presents the test results of the WRF model error determination methods simulating the trajectory and intensity of storm Usagi in 2018. The study conducted three experiments: (1) The Combination of 11 options physical model, 21 composites, no increase in error correlation (MP); (2) Using a set of physical model, 21 composite components, multiplier growth factor l = 6.5 (MI); (3) Using a set of physical model, 21 compositions, no increase in error correlation (PF). Test results show that the multi-physics (MP) method has quite well simulated the intensity as well as the moving direction of the northern cold high pressure in the active Usagi storm area. As a result, The 2018 - Usagi 's trajectory and intensity is simulated in MP test better than in MI test and PF test. Specifically, at the 48-hour forecast term, the orbital prediction error of the MP test is below 350 km which is lower than the two tests (MI and PF), The orbital error in the MP test at the forecast term of 60 and 72 hours is 3-6% reduction in compared with the PF test, and in compared with the MI test, the orbital predictive error of the MP test decreased from 5% to 10% at the 12 hour to 72 hours forecast period. In terms of intensity, absolute error of Pmin (Vmax) in MP test always has lower value than two MI and PF tests. In particular, the absolute error of Vmax in the MP test decreased from 30-40% in compared to the other two trials at all forecasting terms, especially at the forecast term longer than 2 days. Thus, the multi-physics method can be a potential application of determining the error for the model to simulate the trajectory and intensity of storms affecting Vietnam.
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Hong, Keyyong, Sun-Young Kim, and Museok Song. "A REAL-TIME SIMULATION OF THE TRAJECTORY AND FATE OF SPILLED OIL AT SEA." International Oil Spill Conference Proceedings 1997, no. 1 (April 1, 1997): 573–77. http://dx.doi.org/10.7901/2169-3358-1997-1-573.
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ABSTRACT On the basis of the physical modeling of the trajectory and fate of spilled oil at sea, an integrated computer program was developed for the real-time prediction of spilled oil behavior. The trajectory model describes the movement of the surface spill center of mass, and the trajectory is mainly governed by the velocity of the wind and the surface current. For the fate model, weathering effects of spreading, evaporation, emulsification, and shoreline interaction are included. The Windows-based GUI has a flexible preprocessor that enables one to use measured field data as well as numerically generated data. For the realtime prediction, environmental data are stored in the database. The database on tidal and oceanic currents is produced by solving a Navier-Stokes equation based on the IAF finite difference method. The measured data are used for both the boundary condition of the governing equation and the calibration of the generated current field. An oil spill accident in the south coastal region of Korea is simulated and compared with the observed data. The simulation result indicates that the developed oil spill model gives a reasonable estimation of the route of spilled oil. Further improvement of the environmental database is required for the accurate prediction of spilled oil behavior.
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Tripicchio, Paolo, Matteo Unetti, Salvatore D’Avella, and Carlo Alberto Avizzano. "Smooth Coverage Path Planning for UAVs with Model Predictive Control Trajectory Tracking." Electronics 12, no. 10 (May 19, 2023): 2310. http://dx.doi.org/10.3390/electronics12102310.
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Within the Industry 4.0 ecosystem, Inspection Robotics is one fundamental technology to speed up monitoring processes and obtain good accuracy and performance of the inspections while avoiding possible safety issues for human personnel. This manuscript investigates the robotics inspection of areas and surfaces employing Unmanned Aerial Vehicles (UAVs). The contribution starts by addressing the problem of coverage path planning and proposes a smoothing approach intended to reduce both flight time and memory consumption to store the target navigation path. Evaluation tests are conducted on a quadrotor equipped with a Model Predictive Control (MPC) policy and a Simultaneous Localization and Mapping (SLAM) algorithm to localize the UAV in the environment.
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Al-Yahyai, Sultan, and Yassine Charabi. "Trajectory Calculation as Forecasting Support Tool for Dust Storms." Advances in Meteorology 2014 (2014): 1–6. http://dx.doi.org/10.1155/2014/698359.
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In arid and semiarid regions, dust storms are common during windy seasons. Strong wind can blow loose sand from the dry surface. The rising sand and dust is then transported to other places depending on the wind conditions (speed and direction) at different levels of the atmosphere. Considering dust as a moving object in space and time, trajectory calculation then can be used to determine the path it will follow. Trajectory calculation is used as a forecast supporting tool for both operational and research activities. Predefined dust sources can be identified and the trajectories can be precalculated from the Numerical Weather Prediction (NWP) forecast. In case of long distance transported dust, the tool should allow the operational forecaster to perform online trajectory calculation. This paper presents a case study for using trajectory calculation based on NWP models as a forecast supporting tool in Oman Meteorological Service during some dust storm events. Case study validation results showed a good agreement between the calculated trajectories and the real transport path of the dust storms and hence trajectory calculation can be used at operational centers for warning purposes.
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Shipilova, Elena, Egor Nekrylov, and Tat'yana Kurchenkova. "Analysis and modeling of online service user behavior trajectories using the RETENTIONEERING platform." Modeling of systems and processes 15, no. 4 (December 13, 2022): 82–93. http://dx.doi.org/10.12737/2219-0767-2022-15-4-82-93.
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The active development and widespread distribution of online stores and sales sites sets the task for marketers and IT specialists to analyze the results of sites and customer behavior to maximize store profits and predict the development of online sales. The article provides an analysis of the purchasing activity of Internet users of various categories in various periods, the postulates of online trading are formulated. To comprehensively analyze the impact of a variety of factors, predict the demand for goods, form additional recommendations and special support for purchases, the authors offer an integrated approach based on taking into account traditional components, and using the latest tools and software products for analyzing the behavior of online buyers. As a system of analysis, the Retentioneering platform is considered as the most representative and meets the modern requirements for business intelligence systems. The use of platform tools simplifies the processing and analysis of event flows, user behavior trajectories, user classification, allows you to create logical connections and functions for machine learning when predicting a user's category and behavior, as well as the likelihood of a target event - making a purchase based on previously collected data on user behavior. Based on a set of statistics on the behavior of online store buyers, the article discusses such tools for analyzing the Retentioneering platform as an interactive graph of visualization of the behavior trajectory, the matrix of steps and transitions, conversion funnels, clustering the behavior of vectoring user trajectories. Clustering methods use multivariate space convolution algorithms. The UMAP and t-SNE algorithms are considered as dimensionality reduction methods. The main stages and formulas of implementation of convolution algorithms are given, their advantages and disadvantages are considered. These algorithms simplify the process of finding global minima, and improve the quality of rendering. The described algorithms and methods allow you to analyze the behavior of visitors to the online store, combine users into clusters with a similar behavior strategy according to various target features, identify the most pressing problems and bottlenecks of the network platform.
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Wang, Yongming, and Xuguang Wang. "Prediction of Tornado-Like Vortex (TLV) Embedded in the 8 May 2003 Oklahoma City Tornadic Supercell Initialized from the Subkilometer Grid Spacing Analysis Produced by the Dual-Resolution GSI-Based EnVar Data Assimilation System." Monthly Weather Review 148, no. 7 (June 30, 2020): 2909–34. http://dx.doi.org/10.1175/mwr-d-19-0179.1.
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Abstract Explicit forecasts of a tornado-like vortex (TLV) require subkilometer grid spacing because of their small size. Most previous TLV prediction studies started from interpolated kilometer grid spacing initial conditions (ICs) rather than subkilometer grid spacing ICs. The tornadoes embedded in the 8 May 2003 Oklahoma City tornadic supercell are used to understand the impact of IC resolution on TLV predictions. Two ICs at 500-m and 2-km grid spacings are, respectively, produced through an efficient dual-resolution (DR) and a single-coarse-resolution (SCR) EnVar ingesting a 2-km ensemble. Both experiments launch 1-h forecasts at 500-m grid spacing. Diagnostics of data assimilation (DA) cycling reveal DR produces stronger and broader rear-flank cold pools, more intense downdrafts and updrafts with finer scales, and more hydrometeors at high altitudes through accumulated differences between two DA algorithms. Relative differences in DR, compared to SCR, include the integration from higher-resolution analyses, the update for higher-resolution backgrounds, and the propagation of ensemble perturbations along higher-resolution model trajectory. Predictions for storm morphology and cold pools are more realistic in DR than in SCR. The DR-TLV tracks match better with the observed tornado tracks than SCR-TLV in timing of intensity variation, and in duration. Additional experiments suggest 1) the analyzed kinematic variables strongly influence timing of intensity variation through affecting both low-level rear-flank outflow and midlevel updraft; 2) potential temperature analysis by DR extends the second track’s duration consistent with enhanced low-level stretching, delayed broadening large-scale downdraft, and (or) increased near-surface baroclinic vorticity supply; and 3) hydrometeor analyses have little impact on TLV predictions.
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Gramajo, German, and Praveen Shankar. "An Efficient Energy Constraint Based UAV Path Planning for Search and Coverage." International Journal of Aerospace Engineering 2017 (2017): 1–13. http://dx.doi.org/10.1155/2017/8085623.
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A path planning strategy for a search and coverage mission for a small UAV that maximizes the area covered based on stored energy and maneuverability constraints is presented. The proposed formulation has a high level of autonomy, without requiring an exact choice of optimization parameters, and is appropriate for real-time implementation. The computed trajectory maximizes spatial coverage while closely satisfying terminal constraints on the position of the vehicle and minimizing the time of flight. Comparisons of this formulation to a path planning algorithm based on those with time constraint show equivalent coverage performance but improvement in prediction of overall mission duration and accuracy of the terminal position of the vehicle.
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Georgieva, Hristina. "Aircraft noise level calculation during take-off." Aerospace Research in Bulgaria 32 (2020): 75–87. http://dx.doi.org/10.3897/arb.v32.e07.
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A mathematical model with 4 degree of freedom created in Matlab for aircraft departure trajectory is described in this article. As a reference aircraft a midsize commercial passenger aircraft similar to an Airbus A320 has been chosen. The aircraft is represented by the rigid body and the parameters of model are collected from Airbus and the simulated departure trajectory at the Munich airport is based on a Standard Instrumental Departure. A semi-empirical model of Stone for predicting the jet noise has been used. The proposed model is validated against 10 real flights obtained from aircraft noise and flight track monitoring system at Munich airport. The computed error between the real data and modelling is reported on. Obtained results are presented numerical and graphically. The observed effects of flight operational parameters affecting the aircraft noise emission level during take-off represent subjects of discussions in the paper.
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Descamps, L., D. Ricard, A. Joly, and P. Arbogast. "Is a Real Cyclogenesis Case Explained by Generalized Linear Baroclinic Instability?" Journal of the Atmospheric Sciences 64, no. 12 (December 1, 2007): 4287–308. http://dx.doi.org/10.1175/2007jas2292.1.
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Abstract Midlatitude cyclogenesis is currently often explained as resulting from the baroclinic instability of a jet flow. The present formulation of the theory, essentially resulting from the deep revision performed by Farrell, associates incipient cyclones with amplifying singular vectors of a linear propagator operator obtained by linearizing the relevant model equations (balanced or not) about a trajectory representing the jet flow alone. A major difficulty for transposing the theoretical framework to a real case, and then opening the way to quantitative verifications of the theory, is this separation of an actual realization of cyclogenesis into the cyclone as a perturbation on one side and its environment on the other. A methodology to obtain such a separation in a reasonably objective and dynamically consistent way is presented. It enables obtaining two diabatic primitive equation solutions about the 26 December 1999 intense storm, one that has the event and the other that has most of the characteristics of the exceptional baroclinic environment of that case, except the storm itself. It is then possible to employ the theoretical framework without further approximation and to compare the predicted unstable modes with the storm representing itself as a perturbation. Two aspects of the theory are especially studied. One is a comparison of the properties of the real and predicted systems, focusing on their structures. The other deals with the idea that precursor structures, although very different from the theoretical modes, trigger the cyclogenesis by exciting these modes. It appears that the classical predictions (scales, etc.) of such a theory are, for most of them, far away from the observed properties. It is clear that the structure of a singular vector has little to share with that of a real cyclone. Yet, a weaker, slower storm does occur as a result of applying the theory to the stormless trajectory.
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Ma, Congjun, Xiaoying Li, Guofei Xiang, and Songyi Dian. "A T-S Fuzzy Quaternion-Value Neural Network-Based Data-Driven Generalized Predictive Control Scheme for Mecanum Mobile Robot." Processes 10, no. 10 (September 29, 2022): 1964. http://dx.doi.org/10.3390/pr10101964.
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Four-mecanum-wheeled omnidirectional mobile robots (FMOMR) are widely used in many practical scenarios because of their high mobility and flexibility. However, the performance of trajectory tracking would be degenerated largely due to various reasons. To deal with this issue, this paper proposes a data-driven algorithm by using the T-S fuzzy quaternion-value neural network (TSFQVNN). TSFQVNN is tailored to obtain the controlled autoregressive integral moving average (CARIMA) model, and then the generalized predictive controller (GPC) is designed based on the CARIMA model. In this way, the spatial relationship between the three-dimensional pose coordinates can be preserved and training times can be reduced. Furthermore, the convergence of the proposed algorithm is verified by the Stone–Weierstrass theorem, and the convergence conditions of the algorithm are discussed. Finally, the proposed control scheme is applied to the three-dimensional (3D) trajectory tracking problem on the arc surface, and the simulation results prove the necessity and feasibility of the algorithm.
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Wang, Huandong, Changzheng Gao, Yuchen Wu, Depeng Jin, Lina Yao, and Yong Li. "PateGail: A Privacy-Preserving Mobility Trajectory Generator with Imitation Learning." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 12 (June 26, 2023): 14539–47. http://dx.doi.org/10.1609/aaai.v37i12.26700.
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Generating human mobility trajectories is of great importance to solve the lack of large-scale trajectory data in numerous applications, which is caused by privacy concerns. However, existing mobility trajectory generation methods still require real-world human trajectories centrally collected as the training data, where there exists an inescapable risk of privacy leakage. To overcome this limitation, in this paper, we propose PateGail, a privacy-preserving imitation learning model to generate mobility trajectories, which utilizes the powerful generative adversary imitation learning model to simulate the decision-making process of humans. Further, in order to protect user privacy, we train this model collectively based on decentralized mobility data stored in user devices, where personal discriminators are trained locally to distinguish and reward the real and generated human trajectories. In the training process, only the generated trajectories and their rewards obtained based on personal discriminators are shared between the server and devices, whose privacy is further preserved by our proposed perturbation mechanisms with theoretical proof to satisfy differential privacy. Further, to better model the human decision-making process, we propose a novel aggregation mechanism of the rewards obtained from personal discriminators. We theoretically prove that under the reward obtained based on the aggregation mechanism, our proposed model maximizes the lower bound of the discounted total rewards of users. Extensive experiments show that the trajectories generated by our model are able to resemble real-world trajectories in terms of five key statistical metrics, outperforming state-of-the-art algorithms by over 48.03%. Furthermore, we demonstrate that the synthetic trajectories are able to efficiently support practical applications, including mobility prediction and location recommendation.
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Zhao, Yuanzhe, Linjie Ren, Zhiming Liao, and Guobin Lin. "A Novel Model Predictive Direct Torque Control Method for Improving Steady-State Performance of the Synchronous Reluctance Motor." Energies 14, no. 8 (April 17, 2021): 2256. http://dx.doi.org/10.3390/en14082256.
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Due to the particularity of the synchronous reluctance motor (SynRM) structure, a novel high-performance model predictive torque control (MPTC) method was proposed to reduce the high torque ripple and improve the performance and efficiency of the motor. First, the precise parameters of the SynRM reflecting the magnetic saturation characteristics were calculated using finite element analysis (FEA) data, and the torque and flux linkage maximum torque per ampere (MTPA) trajectory was derived by considering the saturation characteristics. Then, an MPTC model of a SynRM with duty cycle control was established, the MTPA trajectory stored in a look-up table was introduced into the control model, and the duration of the active voltage vector in one control cycle was calculated by evaluating the torque error. Finally, an experimental platform based on a SynRM prototype was built, and various performance comparison experiments were carried out for the proposed MPTC method. The experimental results show that the proposed method could reduce the torque ripple of the motor, the performance of the motor was significantly improved under various working conditions, and its correctness and effectiveness were verified.
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Fu, Lei, Min Xu, Xiao Min An, and Xun Liang Yan. "Design and Optimization of Low-Thrust Orbit Transfers Using Hybrid Method." Advanced Materials Research 588-589 (November 2012): 335–39. http://dx.doi.org/10.4028/www.scientific.net/amr.588-589.335.
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A low-thrust guidance scheme, which is weighted combined by taking the optimum strategy of thrust allocation and the target deficits value into consideration for each orbital element, is developed. The presented guidance scheme is predictive in nature and does not rely on a stored reference trajectory or reference controls. The orbit transfer problem is converted into parametric optimization and utilizing a hybrid genetic algorithm. The minimum-time orbit transfer is considered. The influence of the Earth’s oblateness is taken into consideration in the simulation of minimum-time. A conclusion is drawn that the designed method presented here turns out to be an autonomous scheme because the information of target orbit is considered in the transfer process.
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McTaggart-Cowan, Ron, Lance F. Bosart, John R. Gyakum, and Eyad H. Atallah. "Hurricane Juan (2003). Part II: Forecasting and Numerical Simulation." Monthly Weather Review 134, no. 7 (July 1, 2006): 1748–71. http://dx.doi.org/10.1175/mwr3143.1.
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Abstract The landfall of Hurricane Juan (September 2003) in the Canadian Maritimes represents an ideal case in which to study the performance of operational forecasting of an intense, predominantly tropical feature entering the midlatitudes. A hybrid cyclone during its genesis phase, Juan underwent a tropical transition as it drifted slowly northward 1500 km from the east coast of the United States. Shortly after reaching its peak intensity as a category-2 hurricane, the storm accelerated rapidly northward and made landfall near Halifax, Nova Scotia, Canada, with maximum sustained winds of 44 m s−1. Although the forecasts and warnings produced by the U.S. National Hurricane Center and the Canadian Hurricane Centre were of high quality throughout Hurricane Juan’s life cycle, guidance from numerical weather prediction models became unreliable as the storm accelerated toward the coast. The short-range, near-surface forecasts from eight operational models during the crucial prelandfall portion of Juan’s track are investigated in this study. Despite continued improvements to operational numerical forecasting systems, it is shown that those systems not employing advanced tropical vortex initialization techniques were unable to provide forecasters with credible near-surface guidance in this case. A pair of regional forecasts, one successful and one from the failed model set, are compared in detail. Spurious asymmetries in the initial vortex of the deficient model are shown to hamper structural predictions and to cause nonnegligible track perturbations from the trajectory implied by the well-described deep-layer mean flow. The Canadian Mesoscale Compressible Community model is rerun with an improved representation of the hurricane’s vortex in the initial state. The hindcast produced following the tropical cyclone initialization contains reduced track, structure, and intensity errors compared with those generated by the model in real time. The enhanced initial intensity produces a direct improvement in the forecast storm strength throughout the period, and the symmetrization of the vortex eliminates the interactions that plague the operational system. The southeastward relocation of the implanted vortex to Juan’s observed location eliminates a significant northwestward track bias under the influence of a broad area of southerly steering flow. The study concludes that the initialization of Hurricane Juan’s structure and position adds value to numerical guidance even as the storm accelerates poleward at a latitude where the implantation of a quasi-symmetric vortex may not be generally valid.
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Hollan, Michael A., and Brian C. Ancell. "Ensemble Mean Storm-Scale Performance in the Presence of Nonlinearity." Monthly Weather Review 143, no. 12 (December 1, 2015): 5115–33. http://dx.doi.org/10.1175/mwr-d-14-00417.1.
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Abstract The use of ensembles in numerical weather prediction models is becoming an increasingly effective method of forecasting. Many studies have shown that using the mean of an ensemble as a deterministic solution produces the most accurate forecasts. However, the mean will eventually lose its usefulness as a deterministic forecast in the presence of nonlinearity. At synoptic scales, this appears to occur between 12- and 24-h forecast time, and on storm scales it may occur significantly faster due to stronger nonlinearity. When this does occur, the question then becomes the following: Should the mean still be adhered to, or would a different approach produce better results? This paper will investigate the usefulness of the mean within a WRF Model utilizing an ensemble Kalman filter for severe convective events. To determine when the mean becomes unrealistic, the divergence of the mean of the ensemble (“mean”) and a deterministic forecast initialized from a set of mean initial conditions (“control”) are examined. It is found that significant divergence between the mean and control emerges no later than 6 h into a convective event. The mean and control are each compared to observations, with the control being more accurate for nearly all forecasts studied. For the case where the mean provides a better forecast than the control, an approach is offered to identify the member or group of members that is closest to the mean. Such a forecast will contain similar forecast errors as the mean, but unlike the mean, will be on an actual forecast trajectory.
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Hamdan, Baida Abdulredha. "Neural Network Principles and its Application." Webology 19, no. 1 (January 20, 2022): 3955–70. http://dx.doi.org/10.14704/web/v19i1/web19261.
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Neural networks which also known as artificial neural networks is generally a computing dependent technique that formed and designed to create a simulation to the real brain of a human to be used as a problem solving method. Artificial neural networks gain their abilities by the method of training or learning, each method have a certain input and output which called results too, this method of learning works to create forming probability-weighted associations among both of input and the result which stored and saved across the net specifically among its data structure, any training process is depending on identifying the net difference between processed output which is usually a prediction and the real targeted output which occurs as an error, then a series of adjustments achieved to gain a proper learning result, this process called supervised learning. Artificial neural networks have found and proved itself in many applications in a variety of fields due to their capacity to recreate and simulate nonlinear phenomena. System identification and control (process control, vehicle control, quantum chemistry, trajectory prediction, and natural resource management. Etc.) In addition to face recognition which proved to be very effective. Neural network was proved to be a very promising technique in many fields due to its accuracy and problem solving properties.
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Carrió, Diego S. "Improving the predictability of the Qendresa Medicane by the assimilation of conventional and atmospheric motion vector observations. Storm-scale analysis and short-range forecast." Natural Hazards and Earth System Sciences 23, no. 2 (February 27, 2023): 847–69. http://dx.doi.org/10.5194/nhess-23-847-2023.
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Abstract. The coastal population in the western Mediterranean Basin is frequently affected by high-impact weather events that produce huge economic and human losses. Among the wide spectrum of maritime severe weather events, tropical-like Mediterranean cyclones (a.k.a. medicanes) draw particular attention, specially due to their poor predictability. The accurate prediction of this kind of event still remains a key challenge to the weather forecast community, mainly because of (i) errors in the initial conditions, (ii) lack of accuracy of modeling micro-scale physics processes and (iii) chaotic behavior inherent to numerical weather prediction models. The 7 November 2014 Qendresa Medicane, that took place over the Sicilian channel affecting the islands of Lampedusa, Pantelleria and Malta, was selected for this study because of its extremely low predictability behavior in terms of its track and intensity. To enhance the prediction of Qendresa, a high-resolution (4 km) ensemble-based data assimilation technique, known as ensemble Kalman filter (EnKF), is used. In this study, both in situ conventional and satellite-derived observations are assimilated with the main objective of improving Qendresa's model initial conditions and thus its subsequent forecast. The performance of the EnKF system and its impact on the Qendresa forecast are quantitatively assessed using different deterministic and probabilistic verification methods. A discussion in terms of the relevant physical mechanisms adjusted by the EnKF is also provided. Results reveal that the assimilation of both conventional and satellite-derived observations improves the short-range forecasts of the trajectory and intensity of Qendresa. In this context, the relevance of assimilating satellite-derived observations to improve the pre-convective estimation of Qendresa's upper-level dynamics is shown, which is key to obtain a realistic track and intensity forecast of this event.
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Potvin, Corey K., and Louis J. Wicker. "Assessing Ensemble Forecasts of Low-Level Supercell Rotation within an OSSE Framework." Weather and Forecasting 28, no. 4 (July 30, 2013): 940–60. http://dx.doi.org/10.1175/waf-d-12-00122.1.
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Abstract Under the envisioned warn-on-forecast (WoF) paradigm, ensemble model guidance will play an increasingly critical role in the tornado warning process. While computational constraints will likely preclude explicit tornado prediction in initial WoF systems, real-time forecasts of low-level mesocyclone-scale rotation appear achievable within the next decade. Given that low-level mesocyclones are significantly more likely than higher-based mesocyclones to be tornadic, intensity and trajectory forecasts of low-level supercell rotation could provide valuable guidance to tornado warning and nowcasting operations. The efficacy of such forecasts is explored using three simulated supercells having weak, moderate, or strong low-level rotation. The results suggest early WoF systems may provide useful probabilistic 30–60-min forecasts of low-level supercell rotation, even in cases of large radar–storm distances and/or narrow cross-beam angles. Given the idealized nature of the experiments, however, they are best viewed as providing an upper-limit estimate of the accuracy of early WoF systems.
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Mercorelli, Paolo. "A Multilevel Inverter Bridge Control Structure with Energy Storage Using Model Predictive Control for Flat Systems." Journal of Engineering 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/750190.
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The paper presents a novel technique to control the current of an electromagnetic linear actuator fed by a multilevel IGBT voltage inverter with dynamic energy storage. The technique uses a “cascade model predictive control (MPC),” which consists of two MPCs. A predictive control of the trajectory position predicts the optimal current, which is considered to be the desired current for the second MPC controller in which a hysteresis control technique is also integrated. Energy is stored in a capacitor using energy recovery. The current MPC can handle a capacitor voltage higher than the source voltage to guarantee high dynamic current and disturbance compensation. The main contribution of this paper is the design of an optimal control structure that guarantees a capacitor recharge. In this context, the approach is quite new and can represent a general emerging approach allowing to reduce the complexity of the new generation of inverters and, in the meantime, to guarantee precision and acceptable switching frequency. The proposed technique shows very promising results through simulations with real actuator data in an innovative transportation technology.
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Georgieva, Hristina, and Krasin Georgiev. "Modeling of aircraft jet noise in airports." MATEC Web of Conferences 234 (2018): 01003. http://dx.doi.org/10.1051/matecconf/201823401003.
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A mathematical model with 4 degree of freedom created in Matlab for aircraft final landing trajectory is described in this paper. A midsize commercial passenger aircraft similar to an Airbus A320 has been chosen as a reference aircraft. The parameters of model are obtained from Airbus, Eurocontrol and the approach procedure at the Munich airport is selected up from Jeppesen Airway manual. A semi-empirical model of Stone for predicting the jet noise has been used. The proposed model was validated against 10 real flights obtained from Aircraft noise monitoring at Munich airport. The computed error between the real data and modelling is reported on. Obtained results are presented numerical and graphically. The observed effects of aircraft speed, aircraft angle of descent and aircraft weight for reduction of aircraft jet noise in airports represent subjects of discussions in the paper.
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Hwang, Taegeon, Jiwon Kim, Dong-Ha Lee, and Jae-Cheol Lee. "Location Tracking of Drifting Container by Solitary Wave Load Using a Motion Analysis Program." Journal of Ocean Engineering and Technology 37, no. 4 (August 31, 2023): 158–63. http://dx.doi.org/10.26748/ksoe.2023.023.
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<i>Objects adrift can cause considerable damage to coastal infrastructure and property during tsunami and storm surge events. Despite the potential for harm, the drifting behavior of these objects remains poorly understood, thereby hindering effective prediction and mitigation of collision damage. To address this gap, this study employed a motion analysis program to track a drifting container's location using images from an existing laboratory experiment. The container's trajectory and velocity were calculated based on the positions of five markers strategically placed at its four corners and center. Our findings indicate that the container's maximum drift velocity and distance are directly influenced by the scale of the solitary wave and inversely related to the container's weight. Specifically, heavier containers are less likely to be displaced by solitary waves, while larger waves can damage coastal structures more. This study offers new insights into container drift behavior induced by solitary waves, with implications for enhancing coastal infrastructure design and devising mitigation strategies to minimize the risk of collision damage.</i>
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Bell, Samuel S., Savin S. Chand, Kevin J. Tory, and Christopher Turville. "Statistical Assessment of the OWZ Tropical Cyclone Tracking Scheme in ERA-Interim." Journal of Climate 31, no. 6 (March 2018): 2217–32. http://dx.doi.org/10.1175/jcli-d-17-0548.1.
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The Okubo–Weiss–Zeta (OWZ) tropical cyclone (TC) detection scheme, which has been used to detect TCs in climate, seasonal prediction, and weather forecast models, is assessed on its ability to produce a realistic TC track climatology in the ERA-Interim product over the 25-yr period 1989 to 2013. The analysis focuses on TCs that achieve gale-force (17 m s−1) sustained winds. Objective criteria were established to define TC tracks once they reach gale force for both observed and detected TCs. A lack of consistency between storm tracks preceding this level of intensity led these track segments to be removed from the analysis. A subtropical jet (STJ) diagnostic is used to terminate transitioning TCs and is found to be preferable to a fixed latitude cutoff point. TC tracks were analyzed across seven TC basins, using a probabilistic clustering technique that is based on regression mixture models. The technique grouped TC tracks together based on their geographical location and shape of trajectory in five separate “cluster regions” around the globe. A mean trajectory was then regressed for each cluster that showed good agreement between the detected and observed tracks. Other track measures such as interannual TC days and translational speeds were also replicated to a satisfactory level, with TC days showing limited sensitivity to different latitude cutoff points. Successful validation in reanalysis data allows this model- and grid-resolution-independent TC tracking scheme to be applied to climate models with confidence in its ability to identify TC tracks in coarse-resolution climate models.
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Dahl, Johannes M. L. "Near-Ground Rotation in Simulated Supercells: On the Robustness of the Baroclinic Mechanism*." Monthly Weather Review 143, no. 12 (December 1, 2015): 4929–42. http://dx.doi.org/10.1175/mwr-d-15-0115.1.
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Abstract This study addresses the robustness of the baroclinic mechanism that facilitates the onset of surface rotation in supercells by using two idealized simulations with different microphysics parameterizations and by considering previous results. In particular, the importance of ambient crosswise vorticity relative to baroclinically generated vorticity in the development of near-ground cyclonic vorticity is analyzed. The storms were simulated using the CM1 model in a kinematic base state characterized by a straight-line hodograph. A trajectory analysis spanning about 30 min was performed for a large number of parcels that contribute to near-surface vertical-vorticity maxima. The vorticity along these trajectories was decomposed into barotropic and nonbarotropic parts, where the barotropic vorticity represents the effects of the preexisting, substantially crosswise horizontal storm-relative vorticity. The nonbarotropic part represents the vorticity produced baroclinically within the storm. It was found that the imported barotropic vorticity attains a downward component near the surface, while the baroclinic vorticity points upward and dominates. This dominance of the baroclinic vorticity is independent of whether a single-moment or double-moment microphysics parameterization is used. A scaling argument is offered as explanation, predicting that the baroclinic vertical vorticity becomes increasingly dominant as downdraft strength increases.
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Kang, J., T. H. Lee, B. S. Koo, S. Y. Park, S. Lee, and T. H. Kim. "OP0151 PREDICTION OF RADIOGRAPHIC PROGRESSION IN PATIENTS WITH ANKYLOSING SPONDYLITIS: USING GROUP-BASED TRAJECTORY MODELING AND DECISION TREE ANALYSIS." Annals of the Rheumatic Diseases 81, Suppl 1 (May 23, 2022): 98.2–98. http://dx.doi.org/10.1136/annrheumdis-2022-eular.4433.
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BackgroundAnkylosing spondylitis (AS) tends to develop at a relatively young age and is characterized by long-term disease progression (1). During that time, various radiographic changes occur in the spine, which eventually lead to disability in the patient’s lifetime (2). Although the duration of the disease, aging, and passage of time are predicted to be highly associated with spinal progression of AS, it is difficult to predict its progression in the spine of patients.ObjectivesWe aimed to find ways to predict spinal progression over time in patients with AS and analyze its associated clinical factors.MethodsData from the medical records from a single center were extracted between 2001 and 2018. We analyzed the data on patients who fulfilled the modified New York Criteria for AS and had two or more sets of radiographs taken during the observation period. The modified Stoke Ankylosing Spondylitis Spinal Score (mSASSS) was estimated by two independent radiologists. Group-based trajectory modeling (GBTM) was used to classify patients into distinct subgroups of longitudinal mSASSS. And when these trajectories and statistically associated factors acted on a patient, which group the patient was most likely to belong to was predicted using a decision tree analysis.ResultsData on 1,125 patients were analyzed, and the trajectories were evaluated by dividing them into three groups based on duration of the disease. We confirmed that sex, age at diagnosis, ocular involvement and peripheral joint involvement were associated with the classified spinal progression trajectories. AS onset in older age and ocular involvement were associated with worse radiographic progression, while female sex and peripheral joint involvement were associated with slower radiographic progression (Figure 1 and Table 1).Table 1.Multivariate logistic regression analysis for predictors of mSASSS progression according to disease duration (class 2 and other classes)VariablesUnivariateMultivariableOROR 95% CIp-valueOROR 95% CIp-valueLowerUpperLowerUpperSex2.021.392.94<0.0012.411.384.210.002Age at diagnosis (10y)0.500.430.58<0.0010.510.420.61<0.001Ocular involvement0.610.470.80<0.0010.610.440.840.003Peripheral involvement2.141.652.79<0.0012.561.863.52<0.001HLA B27 positivity0.610.321.160.133Smoking*<0.0010.444Ex-smoker0.480.340.62<0.0010.780.521.160.218Smoker0.560.410.73<0.0010.840.571.230.364Baseline BASDAI1.000.921.090.950Baseline ESR1.001.001.000.013Log baseline ESR0.770.700.85<0.0010.780.690.88<0.001Baseline CRP0.980.941.030.476Log baseline CRP0.780.660.910.002¶¶¶¶Note: Bold text means statistically significant values.*Analysis with non-smoker as a reference.¶It was excluded when performing multivariate analysis due to multicollinearity.Figure 1.Longitudinal mSASSS trajectory groups for disease duration (A) and decision tree for three trajectory classes (B). (A) Time in month is shown along the x-axis, and logarithmic transformed total mSASSS is shown along the y-axis. The solid line represents the estimated mean in the same-colored area representing the 95% confidence interval. (B) In each terminal node, the class written in bold text is the class representing that node.ConclusionWe identified three patterns of radiographic progression according to duration of the disease. The progression trend of patients with AS identified in this study is expected to be helpful in the treatment and management of patients in actual clinical settings.References[1]Braun A, Saracbasi E, Grifka J, Schnitker J, Braun J. Identifying patients with axial spondyloarthritis in primary care: how useful are items indicative of inflammatory back pain? Ann Rheum Dis. 2011;70(10):1782-7.[2]Poddubnyy D, Listing J, Haibel H, Knuppel S, Rudwaleit M, Sieper J. Functional relevance of radiographic spinal progression in axial spondyloarthritis: results from the GErman SPondyloarthritis Inception Cohort. Rheumatology (Oxford). 2018;57(4):703-11.Disclosure of InterestsNone declared
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Volovoi, Vitali. "System Reliability at the Crossroads." ISRN Applied Mathematics 2012 (December 26, 2012): 1–36. http://dx.doi.org/10.5402/2012/850686.
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This paper surveys the current state of research related to the modeling and prediction of failures of engineering systems. It is argued that while greater understanding of the physics of failure has led to significant progress at the component level, there are significant challenges remaining at the system level. System reliability, a field of applied mathematics that addresses the latter challenges, is at a juncture where fundamental changes are likely. On the one hand, the traditional part of the field entered a phase of diminishing returns, largely having followed the trajectory of the Cold-War era technology development: golden years of rapid growth in the 1950s and 1960s, followed by maturation and slowing down in the ensuing decades. On the other hand, the convergence of several technologies related to data collection and processing, combined with important changes in engineering business and government priorities, has created the potential for a perfect storm that can revive and fundamentally transform the field; however, for this transformation to occur, some serious obstacles need to be overcome. The paper examines these obstacles along with several key areas of research that can provide enabling tools for this transformation.
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Lee, Dooyoul, Hwanjeong Cho, Min-Saeng Kim, and Kybeom Kwon. "Probabilistic Risk Analysis of Aircraft Self-Collisions: A Case Study." Aerospace 9, no. 2 (February 1, 2022): 80. http://dx.doi.org/10.3390/aerospace9020080.
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Airborne self-collisions occur primarily in military aircraft because of external stores and are frequently experienced by personnel operating these aircraft. In most cases, objects causing self-collisions are irregularly shaped and unstable. Consequently, the trajectories of these objects are uncertain. A framework for the probabilistic risk analysis of aircraft self-collisions is proposed in this study. Based on the probabilistic trajectory prediction model, methods for estimating the probability of collision (POC) and the corresponding risks were developed. Subsequently, a self-collision event involving an ejected gun shell was analyzed as a case study. A model considering random shell rotation, which continuously changes the drag characteristics and trajectories, was developed. Other uncertain factors associated with the aircraft and shell cases were considered. The most influential factors were selected based on the sensitivity analysis and were then used to calibrate the likelihood of the event using historical data. A Monte Carlo simulation, in conjunction with the probabilistic ballistic model, was performed to evaluate the POC. The POC was used to reflect the risk of engine failure up to the operational limit. The calculated risk indices were objective functions used for the design or operation optimization. Various risk measures were evaluated to reduce the incidence of failure and extend the aircraft’s flight envelope.
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Huang, Yi-Hsuan, Chun-Chieh Wu, and Yuqing Wang. "The Influence of Island Topography on Typhoon Track Deflection." Monthly Weather Review 139, no. 6 (June 1, 2011): 1708–27. http://dx.doi.org/10.1175/2011mwr3560.1.
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Abstract High-resolution simulations for Typhoon Krosa (2007) and a set of idealized experiments are conducted using a full-physics model to investigate the eminent deflection of typhoon track prior to its landfall over mountainous island topography. The terrain height of Taiwan plays the most important role in Typhoon Krosa’s looping motion at its landfall, while the surface properties, details in the topographic shape of Taiwan, and the cloud microphysics are shown to be secondary to the track deflection. A simulation with 3-km resolution and realistic model settings reproduces the observed Krosa’s track, while that with 9-km resolution fails, suggesting that high resolution to better resolve the typhoon–terrain interactions is important for the prediction and simulation of typhoon track deflection prior to landfall. Results from idealized experiments with model configurations mimicking those of Supertyphoon Krosa show that vortices approaching the northern and central topography are significantly deflected to the south before making sharp turns to the north, forming a kinked track pattern prior to and during landfall. This storm movement is consistent with the observed looping cases in Taiwan. Both real-case and idealized simulations show strong channel winds enhanced between the storm and the terrain when deflection occurs. Backward trajectory analyses support the concept of the channeling effect, which has been previously found to be crucial to the looping motion of Typhoon Haitang (2005) as well. However, the inner-core asymmetric ventilation flow does not match the movement of a deflected typhoon perfectly, partly because the steering flow is not well defined and could not completely capture the terrain-induced deflection in the simulation and in nature.
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Rech, Sabine, Rene Matias Arias, Simón Vadell, Dennis Gordon, and Martin Thiel. "Daily accumulation rates of floating debris and attached biota on continental and oceanic island shores in the SE Pacific: testing predictions based on global models." PeerJ 11 (July 27, 2023): e15550. http://dx.doi.org/10.7717/peerj.15550.
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Background Long-distance rafting on anthropogenic marine debris (AMD) is thought to have a significant impact on global marine biogeography and the dispersal of non-indigenous species. Therefore, early identification of arrival sites of AMD and its epibionts is crucial for the prioritization of preventive measures. As accumulation patterns along global coastlines are largely unstudied, we tested if existing oceanographic models and knowledge about upstream sources of litter and epibionts can be used as a simple and cost-efficient approach for predicting probable arrival sites of AMD-rafting biota in coastal zones. Methods Using the Southeast Pacific as a model system, we studied daily accumulation rates, composition, and minimum floating times of AMD with and without epibionts on seven sandy beaches, covering the oceanic environment (Rapa Nui/Easter Island) and three regions (south, centre, north) along the Chilean continental coast, over a minimum of 10 consecutive days, and we contrast our results with predictions from published models. Results Total AMD accumulation rates varied from 56 ± 36 (mean ± standard deviation) to 388 ± 433 items km−1 d−1 and differed strongly between regions, in accordance with local geomorphology and socioeconomic conditions (presence of larger cities and rivers upstream, main economic activities, etc.). Daily accumulation of items with pelagic epibionts (indicators of a pelagic trajectory) ranged from 46 ± 29 (Rapa Nui) to 0.0 items km−1 d−1 (northern continental region). Minimum floating times of rafts, as estimated from the size of pelagic epibionts, were longest in the South Pacific Subtropical Gyre’s (SPSG) centre region, followed by the high-latitude continental region under the influence of the onshore West Wind Drift, and decreased along the continental alongshore upwelling current, towards lower latitudes. Apart from pelagic rafters, a wide range of benthic epibionts, including invasive and cryptogenic species, was found on rafts at the continental beaches. Similarly, we present another record of local benthic corals Pocillopora sp., on Rapa Nui rafts. Discussion Our results agree with the predictions made by recent models based on the prevailing wind and surface current regimes, with high frequencies of long-distance rafting in the oceanic SPSG centre and very low frequencies along the continental coast. These findings confirm the suitability of such models in predicting arrival hotspots of AMD and rafting species. Moreover, storm surges as well as site-related factors seem to influence AMD arrival patterns along the Chilean continental coast and might cause the observed high variability between sampling sites and days. Our results highlight the possible importance of rafting as a vector of along-shore dispersal and range expansions along the SE Pacific continental coast and add to the discussion about its role in benthic species dispersal between South Pacific oceanic islands.
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Bongolan, V. P., J. E. Branzuela, and G. M. Torres. "POST-DISASTER RECOLONIZATION OF MANGROVE FORESTS WITH A STOCHASTIC AGENT-BASED MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W19 (December 23, 2019): 71–75. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w19-71-2019.
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Abstract. Mangrove forests in the Philippine coastline are susceptible to severe damage due to tropical storms. These mangrove forests provide a home for other plants and animals as well as providing resources for people living in coastal areas. Thus, it is important to promote proper conservation and judicious replanting in areas affected by storms. Since different species vary on their tolerance to physical conditions such as water salinity and soil composition, the appropriate genus must be used in reforestation efforts. This study aims to model the change in soil composition due to the introduction of a non-native species, Rhizophora mucronata, and restoring soil condition to aid recolonization of the existing native species, Avicennia and Sonneratia.The study uses an agent-based model for the prediction of the regenerative behaviour of mangrove stands consisting of the native species and the planted or non-native species in a fragmented habitat, with the use of spatio-temporal coloured noise to simulate stochastic seedling dispersal and subject to storm damage. The model uses Salmo and Juanico’s model for mangrove growth. Stochastic experiments were carried out in a shoreline habitat with an existing native population of varying ages and a larger population of planted, non-native seedlings. The GIS data of Bangrin Marine Protected Area was used to simulate the recovery trajectory of the stand after typhoon Chan-hom of 2009.
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Peng, Wei, Xinlei Zhang, and Xin Li. "Intelligent Behavior Data Analysis for Internet Addiction." Scientific Programming 2019 (November 29, 2019): 1–12. http://dx.doi.org/10.1155/2019/2753152.
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Internet addiction refers to excessive internet use that interferes with daily life. Due to its negative impact on college students’ study and life, discovering students’ internet addiction tendencies and making correct guidance for them timely is necessary. However, at present, the research methods used in analyzing students’ internet addiction are mainly questionnaires and statistical analysis, which relies on the domain experts heavily. Fortunately, with the development of the smart campus, students’ behavior data such as consumption and trajectory information in the campus are stored. With this information, we can analyze students’ internet addiction levels quantitatively. In this paper, we provide an approach to estimate college students’ internet addiction levels using their behavior data in the campus. In detail, we consider students’ addiction towards the internet is a hidden variable which affects students’ daily time online together with other behavior. By predicting students’ daily time online, we will find students’ internet addiction levels. Along this line, we develop a linear internet addiction (LIA) model, a neural network internet addiction (NIA) model, and a clustering-based internet addiction (CIA) model to calculate students’ internet addiction levels, respectively. These three models take the regularity of students’ behavior and the similarity among students’ behavior into consideration. Finally, extensive experiments are conducted on a real-world dataset. The experimental results show the effectiveness of our method, and it is also consistent with some psychological findings.
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Lussier, Louis L., Blake Rutherford, Michael T. Montgomery, Mark A. Boothe, and Timothy J. Dunkerton. "Examining the Roles of the Easterly Wave Critical Layer and Vorticity Accretion during the Tropical Cyclogenesis of Hurricane Sandy*." Monthly Weather Review 143, no. 5 (May 1, 2015): 1703–22. http://dx.doi.org/10.1175/mwr-d-14-00001.1.
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Abstract The tropical cyclogenesis sequence of Hurricane Sandy is examined. It is shown that genesis occurs within a recirculating Kelvin cat’s-eye flow of a westward-propagating tropical wave. The cat’s-eye flow is able to provide a protective environment for the mesoscale vortex to grow and is characterized by gradual column moistening and increased areal coverage of deep cumulus convection. These findings are generally consistent with a recently proposed tropical cyclogenesis sequence referred to as the “marsupial paradigm.” Sandy’s cyclogenesis provides a useful illustration of the marsupial paradigm within a partially open recirculating region, with the opening located south of the pouch center. It is suggested that the opening acts to enhance the genesis process because it is adjacent to an environment characterized by warm, moist air, conditions favorable for tropical cyclogenesis. From a dynamical perspective, accretion of low-level cyclonic vorticity filaments into the developing vortex from several sources (the South American convergence zone, an easterly wave located west of the pre-Sandy wave, and cyclonic vorticity generated along Hispaniola) is documented. Organization and growth of the nascent storm is enhanced by this accretion of cyclonic vorticity. A Lagrangian trajectory analysis is used to assess potential contributions to Sandy’s spinup from a Caribbean gyre and the easterly wave that formed Hurricane Tony. This analysis indicates that these features are outside of the Lagrangian flow boundaries that define the pre-Sandy wave and do not directly contribute to spinup of the vortex. Finally, the effectiveness of forecasts from the U.S. and European operational numerical weather prediction models is discussed for this case.
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Nallala, Jayakrupakar, Doriana Calabrese, Sarah Gosling, Allison Hall, Sarah Pinder, Ihssane Bouybayoune, Lorraine King, et al. "Abstract P2-08-07: Predicting DCIS prognosis using infrared and raman spectroscopy of breast calcifications and soft-tissue microstructure." Cancer Research 82, no. 4_Supplement (February 15, 2022): P2–08–07—P2–08–07. http://dx.doi.org/10.1158/1538-7445.sabcs21-p2-08-07.
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Abstract Introduction: Ductal carcinoma in situ (DCIS) is a potential precursor of invasive breast cancer. The uncertain trajectory of DCIS, to progress to invasive disease or to remain in situ, currently drives treatment, despite lack of proven benefit. Therefore, understanding the molecular features of the DCIS trajectory may prevent overtreatment of this disease. Breast calcifications are a common feature in DCIS and are seen mammographically in over 80% of cases. Calcifications have been largely characterised based only on x-ray morphology; their chemical composition, their association with the surrounding soft tissue and role in DCIS and invasive breast cancer biology is largely unexplored. In this regard, a bio-photonic approach, based on infrared (IR) and Raman spectroscopy in combination with machine learning, was used to study DCIS by probing the chemical composition of calcifications and the surrounding soft tissue in breast lesions. The main aim of the work is to identify molecular compositional changes in calcifications and in soft tissue that potentially accompany or drive the progression of DCIS to invasive breast cancer, or indicates a stable DCIS phenotype. Methods: Serial tissue sections from 303 patients with (i) ‘pure DCIS’ (DCIS without recurrence) (n=158), (ii) ‘DCIS with invasive recurrence’ (DCIS from a patient who subsequently was known to develop invasive disease) (n=123) and (iii) ‘DCIS plus invasive cancer contemporaneously’ (n=22), were measured using mid-IR imaging and Raman mapping. The same calcifications and soft tissue regions from specific DCIS ducts were targeted across the techniques on the serial sections. Spectral images were analysed using cluster analysis followed by unsupervised and supervised machine learning classification models to identify spectral features associated with the progression of DCIS to invasive breast cancer. Results: Segmentation of IR and Raman spectral images based on cluster analysis identified important histopathological features including calcifications, epithelium, necrotic areas, connective tissue and stroma based on the spectral heterogeneity. Based on analysis of Raman calcification data from 145 patients with (i) ‘pure DCIS’ (n=90) and (ii) ‘DCIS with invasive recurrence’ (n=55), an area under the receiver operating characteristic (AUROC) mean value of 85% was obtained in distinguish pure DCIS from DCIS that later recurred as invasive cancer. The calcification features appeared to indicate pathology specific changes in phosphate and carbonate content and appearance of magnesium whitlockite. Similar analysis of the surrounding soft tissue spectral features showed an AUROC mean value of 76%, which showed changes in protein secondary structure and content, particularly in the necrotic regions surrounding calcifications. In addition, classification models are being developed and refined from the IR spectral data, the initial results of which have shown an AUROC value of only 54% from the same patients’ data. Perspectives: It is anticipated that the current novel approaches allowing label-free measurement of calcifications and soft tissue will provide important cues in understanding DCIS prognosis and could be a promising way forward in determining DCIS management. Current and future efforts include identification of specific discriminatory spectral features for molecular and pathological correlation. Acknowledgments: This work was supported by Cancer Research UK and by KWF Kankerbestrijding (ref. C38317/A24043). Citation Format: Jayakrupakar Nallala, Doriana Calabrese, Sarah Gosling, Allison Hall, Sarah Pinder, Ihssane Bouybayoune, Lorraine King, Jeffrey Marks, Esther Lips, Thomas Lynch, Donna Pinto, Jelle Wesseling, Shelley Hwang, Keith Rogers, Nick Stone, on behalf of the Grand Challenge PRECISION consortium. Predicting DCIS prognosis using infrared and raman spectroscopy of breast calcifications and soft-tissue microstructure [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr P2-08-07.
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Thurnherr, Iris, Katharina Hartmuth, Lukas Jansing, Josué Gehring, Maxi Boettcher, Irina Gorodetskaya, Martin Werner, Heini Wernli, and Franziska Aemisegger. "The role of air–sea fluxes for the water vapour isotope signals in the cold and warm sectors of extratropical cyclones over the Southern Ocean." Weather and Climate Dynamics 2, no. 2 (April 14, 2021): 331–57. http://dx.doi.org/10.5194/wcd-2-331-2021.
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Abstract. Meridional atmospheric transport is an important process in the climate system and has implications for the availability of heat and moisture at high latitudes. Near-surface cold and warm temperature advection over the ocean in the context of extratropical cyclones additionally leads to important air–sea exchange. In this paper, we investigate the impact of these air–sea fluxes on the stable water isotope (SWI) composition of water vapour in the Southern Ocean's atmospheric boundary layer. SWIs serve as a tool to trace phase change processes involved in the atmospheric water cycle and, thus, provide important insight into moist atmospheric processes associated with extratropical cyclones. Here we combine a 3-month ship-based SWI measurement data set around Antarctica with a series of regional high-resolution numerical model simulations from the isotope-enabled numerical weather prediction model COSMOiso. We objectively identify atmospheric cold and warm temperature advection associated with the cold and warm sector of extratropical cyclones, respectively, based on the air–sea temperature difference applied to the measurement and the simulation data sets. A Lagrangian composite analysis of temperature advection based on the COSMOiso simulation data is compiled to identify the main processes affecting the observed variability of the isotopic signal in marine boundary layer water vapour in the region from 35 to 70∘ S. This analysis shows that the cold and warm sectors of extratropical cyclones are associated with contrasting SWI signals. Specifically, the measurements show that the median values of δ18O and δ2H in the atmospheric water vapour are 3.8 ‰ and 27.9 ‰ higher during warm than during cold advection. The median value of the second-order isotope variable deuterium excess d, which can be used as a measure of non-equilibrium processes during phase changes, is 6.4 ‰ lower during warm than during cold advection. These characteristic isotope signals during cold and warm advection reflect the opposite air–sea fluxes associated with these large-scale transport events. The trajectory-based analysis reveals that the SWI signals in the cold sector are mainly shaped by ocean evaporation. In the warm sector, the air masses experience a net loss of moisture due to dew deposition as they are advected over the relatively colder ocean, which leads to the observed low d. We show that additionally the formation of clouds and precipitation in moist adiabatically ascending warm air parcels can decrease d in boundary layer water vapour. These findings illustrate the highly variable isotopic composition in water vapour due to contrasting air–sea interactions during cold and warm advection, respectively, induced by the circulation associated with extratropical cyclones. SWIs can thus potentially be useful as tracers for meridional air advection and other characteristics associated with the dynamics of the storm tracks over interannual timescales.
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Bertran, Eduard, Paula Tercero, and Alex Sànchez-Cerdà. "UAV generalized longitudinal model for autopilot controller designs." Aircraft Engineering and Aerospace Technology 94, no. 3 (November 16, 2021): 380–91. http://dx.doi.org/10.1108/aeat-08-2020-0156.
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Purpose This paper aims to overcome the main obstacle to compare the merits of the different control strategies for fixed-wing unmanned aerial vehicles (UAVs) to assess autopilot performances. Up to now, the published studies of control strategies have been carried out over disperse models, thus being complicated, if not impossible, to compare the merits of each proposal. The authors present a worked benchmark for autopilots studies, consisting of generalized models obtained by merging UAVs’ parameters gathered from selected literature (journals) with other parameters directly obtained by the authors to include some relevant UAVs whose models are not provided in the literature. To obtain them it has been used a dedicated software (from U.S. Air Force). Design/methodology/approach The proposed models have been constructed by averaging both the main aircraft defining parameters (model derivatives) and pole-zero locations of longitudinal transfer functions. The suitability of the used methodologies has been checked from their capability to fit the short period and the phugoid modes. Previous analytical model arrangement has been required to match a uniform set of parameters, as the inner state variables are neither the same along the different published models nor between the additional models the authors have here contributed. Besides, moving models between the space state representation and transfer function is not just a simple averaging process, as neither the parameters nor the model orders are the same in the different published works. So, the junction of the models to a common set of parameters requires some residual’s computation and transient responses assessment (even Fourier analysis has been included to preserve the dominance of the phugoid) to keep the main properties of the models. The least mean squares technique has been used to have better fittings between SISO model parameters with state–space ones. Findings Both the SISO (Laplace) and state-space models for the longitudinal transfer function of an “averaged” fixed-wing UAV are proposed. Research limitations/implications More complicated situations, such as strong wind conditions, need another kind of models, usually based on finite element method simulation. These particular models apply fluid dynamics to study aerostructural aircraft aspects, such as flutter and other aerolastic aspects, the behavior under icing conditions or other distributed parameter problems. Even some models aim to control other aspects than the autopilot, such as the trajectory prediction. However, these models are not the most suitable for the basic UAV autopilot design (early design), so they are outside the objective of this paper. Obviously, the here-considered UAVs are not all the existing ones, but the number is large enough to consider the result as a reliable and realistic representation. The presented study may be seen as a stepping stone, allowing to include other UAVs in future works. Practical implications The proposed models can be used as benchmarks, or as a previous step to produce improved benchmarks, in order to have a common and realistic scenario the compare the benefits of the different control actions in UAV autopilots continuously presented in the published research. Originality/value A work with the scope of the presented one, merging model parameters from literature with other (often referred in papers and websites) whose parameters have been obtained by the authors has been never published.
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Stone, T. W., J. Bennett, D. H. S. Law, and J. A. Holmes. "Thermal Simulation With Multisegment Wells." SPE Reservoir Evaluation & Engineering 5, no. 03 (June 1, 2002): 206–18. http://dx.doi.org/10.2118/78131-pa.
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Summary The extension of a previously reported well model to compositional and thermal applications is discussed. This multisegment, multibranching wellbore model has been fully coupled to a commercial reservoir simulator that can operate in black-oil, compositional, or thermal modes. In this paper, the discussion will focus on thermal, heavy-oil applications in which simulation requires a better representation of the wellbore geometry and the physics of fluid flow and heat transfer. Introduction Gravity-drainage processes with possible steam (SAGD) or gas vapor (VAPEX) assistance and other recovery technologies often require the use of long horizontal wells with flow in an inner tubing and outer annulus.1–3 Thermal studies that simulate horizontal wells have been discussed by many authors. Recovery techniques include cyclicsteam projects,4–10 dual-well SAGD,11 and single-well SAGD.12 In these studies, the oils are heavy (970 to 1014 kg/m3; 14 to 8°API), with viscosities ranging from 2,000 cp at 32°C in California fields up to 1,000,000 cp at 12°C for oils found at the UTF project13 in the Athabasca tar sands deposit. These studies have, for the most part, used the conventional wellbore line source/sink model available in any thermal simulator. Simulation technology for horizontal wells has improved dramatically since the late 1980s. At this time, Stone et al.14 described a horizontal well model that featured a mechanistic multiphase fluid-flow model in the wellbore and allowed flow simultaneously in an inner tubing and outer annulus. This was designed to handle simulations in the near-wellbore region of a dual-well SAGD process and, because of the more detailed flow regime map, could not handle larger-scale simulations for stability reasons. Also during this time period, Long et al.15 carried out the Seventh SPE Comparative Solution Project concerning the modeling of horizontal wells in reservoir simulation. A variety of methods was used by the participants to model the inflow into the horizontal well model. These included the use of an inflow performance relationship (IPR) with a separate well model or direct coupling by modeling the well as part of the grid. Similarly, there were various wellbore hydraulics models ranging from a constant-pressure line sink to friction pressure-drop relations or simple functional fits of published holdup correlations. All of these horizontal well models were designed to run robustly and stably in large-scale field simulations. However, some were limited in their ability to calculate a multiphase pressure drop, others in not allowing the wellbore model geometry to correspond to the engineering design of the well rather than to the simulation grid. Some methods allowed multiphase pressure drops with explicit updates or other approximations. Recently, Tan et al.16 have described a fully coupled discretized thermal wellbore model with the ability to simulate flow in casing/annulus wellbore cells. Estimates of the relative flow rates are made based on phase saturations and straight-line relative permeability curves. These estimates are passed to a subroutine that calculates flow rates from the correlated Beggs et al.17 measurements. Wellbore cells are connected to reservoir cells. A multisegment well model that can simulate flow in advanced wells was discussed by Holmes et al.18,19 This model, implemented in a commercial black-oil simulator, is able to determine the local flowing conditions (the flow rate and pressure of each fluid) throughout the well. It allows for pressure losses along the wellbore and across any flow-control devices. In addition to being fully implicitly coupled, with crossflow modeling and the standard group control facilities, horizontal wells, multilateral wells, and "smart" wells containing flow-control devices can also be modeled. The trajectory is not constrained by the simulation grid. For example, the wellbore may run outside the grid or across layers. Properties and geometry can be updated at any time in the simulation. In this paper, we first describe the implementation and enhancements to the implicit multisegment well model discussed in Ref. 18 that allow this model to run in compositional and thermal modes. In these modes, the equation of state (EOS) or thermal K-value treatment of the fluid pressure/volume/temperature (PVT) is extended to the wellbore flow. Phase volumes are computed in each segment and are then used to calculate the multiphase pressure drop. In thermal mode, an enhancement allows the definition of heat transfer coefficients, which permit heat loss to the reservoir, to another segment, or to the overburden. Another enhancement allows individual segments to inject or produce fluids, which permits the direct modeling of gas lift, downhole water pumps, or circulating wells, available in any mode. It is important in compositional, and especially thermal, wellbore simulations to provide an accurate initial estimate of the well solution; otherwise, there can be convergence problems. A method for predicting the initial state within the well is also shown later. We then present four case studies. Each case study has been set up from published engineering analyses of fields in western Canada and California, U.S.A. The well model used in these studies is considerably more detailed than that in the original published simulation work. Not only are the wellbore hydraulics more accurately modeled with multiphase flow models, but the geometry of the wells is also specified in more detail. Wellbore geometry includes the ability to run the well outside the simulation grid, allowing the modeling of heat loss from a steam-injection well to the formation, between the surface and the simulation grid. Also, an undulating well trajectory can be specified and is demonstrated in one of the studies. Fluid flow down an inner tubing and back along an outer completed annulus is demonstrated in three of the studies, in which heat transfer occurs between the inner tubing and the outer annulus and between the annulus and the formation. Two of these studies contain a segment at the heel of a horizontal annulus that removes fluids to an external sink, allowing part of the circulating fluids to return to the surface while the remainder are injected, produced, or stored in the wellbore. Where possible, differences are shown between the multisegment model and a standard line source/sink model that demonstrate the effects of modeling the improved wellbore physics. Description of the Multisegment Well Model The multisegment well model reported by Holmes et al.18 was originally implemented in a black-oil simulator. It uses four main variables: a total fluid-flow rate through the segment, weighted fractional flows of both water and gas, and pressure in the segment.