Academic literature on the topic 'Extreme Model-Driven Design'
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Journal articles on the topic "Extreme Model-Driven Design"
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Khan, Rafflesia, Alexander Schieweck, Ciara Breathnach, and Tiziana Margaria. "Historical Civil Registration Record Transcription Using an eXtreme Model Driven Approach." Proceedings of the Institute for System Programming of the RAS 33, no. 3 (2021): 123–42. http://dx.doi.org/10.15514//ispras-2021-33(3)-10.
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Modelling is considered as a universal approach to define and simplify real-world applications through appropriate abstraction. Model-driven system engineering identifies and integrates appropriate concepts, techniques, and tools which provide important artefacts for interdisciplinary activities. In this paper, we show how we used a model-driven approach to design and improve a Digital Humanities dynamic web application within an interdisciplinary project that enables history students and volunteers of history associations to transcribe a large corpus of image-based data from the General Register Office (GRO) records. Our model-driven approach generates the software application from data, workflow and GUI abstract models, ready for deployment.
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Khan, Rafflesia, Alexander Schieweck, Ciara Breathnach, and Tiziana Margaria. "Historical Civil Registration Record Transcription Using an eXtreme Model Driven Approach." Proceedings of the Institute for System Programming of the RAS 33, no. 3 (2021): 123–42. http://dx.doi.org/10.15514/ispras-2021-33(3)-10.
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Modelling is considered as a universal approach to define and simplify real-world applications through appropriate abstraction. Model-driven system engineering identifies and integrates appropriate concepts, techniques, and tools which provide important artefacts for interdisciplinary activities. In this paper, we show how we used a model-driven approach to design and improve a Digital Humanities dynamic web application within an interdisciplinary project that enables history students and volunteers of history associations to transcribe a large corpus of image-based data from the General Register Office (GRO) records. Our model-driven approach generates the software application from data, workflow and GUI abstract models, ready for deployment.
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Larsen, Gunner Chr, and Kurt S. Hansen. "Statistical Model of Extreme Shear." Journal of Solar Energy Engineering 127, no. 4 (February 18, 2005): 444–55. http://dx.doi.org/10.1115/1.2035702.
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In order to continue cost-optimization of modern large wind turbines, it is important to continuously increase the knowledge of wind field parameters relevant to design loads. This paper presents a general statistical model that offers site-specific prediction of the probability density function (PDF) of turbulence driven short-term extreme wind shear events, conditioned on the mean wind speed, for an arbitrary recurrence period. The model is based on an asymptotic expansion, and only a few and easily accessible parameters are needed as input. The model of the extreme PDF is supplemented by a model that, on a statistically consistent basis, describes the most likely spatial shape of an extreme wind shear event. Predictions from the model have been compared with results from an extreme value data analysis, based on a large number of full-scale measurements recorded with a high sampling rate. The measurements have been extracted from ”Database on Wind Characteristics” (http:∕∕www.winddata.com∕), and they refer to a site characterized by a flat homogeneous terrain. The comparison has been conducted for three different mean wind speeds in the range of 15-19m∕s, and model predictions and experimental results are consistent, given the inevitable uncertainties associated with the model as well as with the extreme value data analysis.
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Jeong, Dae Il, Alex J. Cannon, and Robert J. Morris. "Projected changes to wind loads coinciding with rainfall for building design in Canada based on an ensemble of Canadian regional climate model simulations." Climatic Change 162, no. 2 (May 21, 2020): 821–35. http://dx.doi.org/10.1007/s10584-020-02745-y.
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Abstract Strong wind coinciding with rainfall is an important weather phenomenon in many science and engineering fields. This study investigates changes in hourly extreme driving rain wind pressure (DRWP)—a climatic variable used in building design in Canada—for future periods of specified global mean temperature change using an ensemble of a Canadian regional climate model (CanRCM4) driven by the Canadian Earth system model (CanESM2) under the Representative Concentration Pathway 8.5 scenario. Evaluation of the model shows that the CanRCM4 ensemble reproduces hourly extreme wind speeds and rainfall (> 1.8 mm/h) occurrence frequency and the associated design (5-year return level) DRWP across Canada well when compared with 130 meteorological stations. Significant increases in future design DRWP are projected over western, eastern, and northern Canada, with the areal extent and relative magnitude of the increases scaling approximately linearly with the amount of global warming. Increases in future rainfall occurrence frequency are driven by the combined effect of increases in precipitation amount and changes in precipitation type from solid to liquid due to increases in air temperature; these are identified as the main factors leading to increases in future design DRWP. Future risk ratios of the design DRWP are highly dependent on those of the rainfall occurrence, which shows large increases over the three regions, while they are partly affected by the increases in future extreme wind speeds over western and northeastern Canada. Increases in DRWP can be an emerging risk for existing buildings, particularly in western, eastern, and northern Canada, and a consideration for managing and designing buildings across Canada.
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Murray, Lee T., Eric M. Leibensperger, Clara Orbe, Loretta J. Mickley, and Melissa Sulprizio. "GCAP 2.0: a global 3-D chemical-transport model framework for past, present, and future climate scenarios." Geoscientific Model Development 14, no. 9 (September 24, 2021): 5789–823. http://dx.doi.org/10.5194/gmd-14-5789-2021.
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Abstract. This paper describes version 2.0 of the Global Change and Air Pollution (GCAP 2.0) model framework, a one-way offline coupling between version E2.1 of the NASA Goddard Institute for Space Studies (GISS) general circulation model (GCM) and the GEOS-Chem global 3-D chemical-transport model (CTM). Meteorology for driving GEOS-Chem has been archived from the E2.1 contributions to phase 6 of the Coupled Model Intercomparison Project (CMIP6) for the pre-industrial era and the recent past. In addition, meteorology is available for the near future and end of the century for seven future scenarios ranging from extreme mitigation to extreme warming. Emissions and boundary conditions have been prepared for input to GEOS-Chem that are consistent with the CMIP6 experimental design. The model meteorology, emissions, transport, and chemistry are evaluated in the recent past and found to be largely consistent with GEOS-Chem driven by the Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA-2) product and with observational constraints.
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Wang, Xinmei, Yifei Wang, and Tao Wu. "The Review of Electromagnetic Field Modeling Methods for Permanent-Magnet Linear Motors." Energies 15, no. 10 (May 13, 2022): 3595. http://dx.doi.org/10.3390/en15103595.
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Permanent-magnet linear motors (PMLMs) are widely used in various fields of industrial production, and the optimization design of the PMLM is increasingly attracting attention in order to improve the comprehensive performance of the motor. The primary problem of PMLM optimization design is the establishment of a motor model, and this paper summarizes the modeling of the PMLM electromagnetic field. First, PMLM parametric modeling methods (model-driven methods) such as the equivalent circuit method, analytical method, and finite element method, are introduced, and then non-parametric modeling methods (data-driven methods) such as the surrogate model and machine learning are introduced. Non-parametric modeling methods have the characteristics of higher accuracy and faster computation, and are the mainstream approach to motor modeling at present. However, surrogate models and traditional machine learning models such as support vector machine (SVM) and extreme learning machine (ELM) approaches have shortcomings in dealing with the high-dimensional data of motors, and some machine learning methods such as random forest (RF) require a large number of samples to obtain better modeling accuracy. Considering the modeling problem in the case of the high-dimensional electromagnetic field of the motor under the condition of a limited number of samples, this paper introduces the generative adversarial network (GAN) model and the application of the GAN in the electromagnetic field modeling of PMLM, and compares it with the mainstream machine learning models. Finally, the development of motor modeling that combines model-driven and data-driven methods is proposed.
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Huang, Shenghong, Qiusheng Li, Man Liu, Fubin Chen, and Shun Liu. "Numerical Simulation of Wind-Driven Rain on a Long-Span Bridge." International Journal of Structural Stability and Dynamics 19, no. 12 (December 2019): 1950149. http://dx.doi.org/10.1142/s0219455419501499.
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Wind-driven rain (WDR) and its interactions with structures is an important research subject in wind engineering. As bridge spans are becoming longer and longer, the effects of WDR on long-span bridges should be well understood. Therefore, this paper presents a comprehensive numerical simulation study of WDR on a full-scale long-span bridge under extreme conditions. A validation study shows that the predictions of WDR on a bridge section model agree with experimental results, validating the applicability of the WDR simulation approach based on the Eulerian multiphase model. Furthermore, a detailed numerical simulation of WDR on a long-span bridge, North Bridge of Xiazhang Cross-sea Bridge is conducted. The simulation results indicate that although the loads induced by raindrops on the bridge surfaces are very small as compared to the wind loads, extreme rain intensity may occur on some windward surfaces of the bridge. The adopted numerical methods and rain loading models are validated to be an effective tool for WDR simulation for bridges and the results presented in this paper provide useful information for the water-erosion proof design of future long-span bridges.
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Cheng, Qi, Shuchun Wang, and Xifeng Fang. "Intelligent design technology of automobile inspection tool based on 3D MBD model intelligent retrieval." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 235, no. 10-11 (March 2, 2021): 2917–27. http://dx.doi.org/10.1177/09544070211000174.
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The existing process equipment design resource utilization rate in automobile industry is low, so it is urgent to change the design method to improve the design efficiency. This paper proposed a fast design method of process equipment driven by classification retrieval of 3D model-based definition (MBD). Firstly, an information integration 3D model is established to fully express the product information definition and to effectively express the design characteristics of the existing 3D model. Through the classification machine-learning algorithm of 3D MBD model based on Extreme Learning Machine (ELM), the 3D MBD model with similar characteristics to the auto part model to be designed was retrieved from the complex process equipment case database. Secondly, the classification and retrieval of the model are realized, and the process equipment of retrieval association mapping with 3D MBD model is called out. The existing process equipment model is adjusted and modified to complete the rapid design of the process equipment of the product to be designed. Finally, a corresponding process equipment design system was developed and verified through a case study. The application of machine learning to the design of industrial equipment greatly shortens the development cycle of equipment. In the design system, the system learns from engineers, making them understand the design better than engineers. Therefore, it can help any user to quickly design 3D models of complex products.
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Qin, Guodong, Huapeng Wu, and Aihong Ji. "Equivalent Dynamic Analysis of a Cable-Driven Snake Arm Maintainer." Applied Sciences 12, no. 15 (July 26, 2022): 7494. http://dx.doi.org/10.3390/app12157494.
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In this paper, we investigate a design method for a cable-driven snake arm maintainer (SAM) and its dynamics modelling. A SAM can provide redundant degrees of freedom and high structural stiffness, as well as high load capacity and a simplified structure ideal for various narrow and extreme working environments, such as nuclear power plants. However, their serial-parallel configuration and cable drive system make the dynamics of a SAM strongly coupled, which is not conducive to accurate control. In this paper, we propose an equivalent dynamics modelling method for the strongly coupled dynamic characteristics of each joint cable. The cable traction dynamics are forcibly decoupled using force analysis and joint torque equivalent transformation. Then, the forcibly equivalent dynamic model is obtained based on traditional series robot dynamic modelling methods (Lagrangian method, etc.). To verify the correctness of the equivalent dynamics, a simple model-based controller is established. In addition, a SAM prototype is produced to collect joint angles and cable forces at different trajectories. Finally, the results of the equivalent dynamics control simulation and the prototype tests demonstrate the validity of the SAM structural design and the equivalent dynamics model.
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Gu, Ziyu, Shuwei Pang, Wenxiang Zhou, Yuchen Li, and Qiuhong Li. "An Online Data-Driven LPV Modeling Method for Turbo-Shaft Engines." Energies 15, no. 4 (February 9, 2022): 1255. http://dx.doi.org/10.3390/en15041255.
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The linear parameter-varying (LPV) model is widely used in aero engine control system design. The conventional local modeling method is inaccurate and inefficient in the full flying envelope. Hence, a novel online data-driven LPV modeling method based on the online sequential extreme learning machine (OS-ELM) with an additional multiplying layer (MLOS-ELM) was proposed. An extra multiplying layer was inserted between the hidden layer and the output layer, where the hidden layer outputs were multiplied by the input variables and state variables of the LPV model. Additionally, the input layer was set to the LPV model’s scheduling parameter. With the multiplying layer added, the state space equation matrices of the LPV model could be easily calculated using online gathered data. Simulation results showed that the outputs of the MLOS-ELM matched that of the component level model of a turbo-shaft engine precisely. The maximum approximation error was less than 0.18%. The predictive outputs of the proposed online data-driven LPV model after five samples also matched that of the component level model well, and the maximum predictive error within a large flight envelope was less than 1.1% with measurement noise considered. Thus, the efficiency and accuracy of the proposed method were validated.
Dissertations / Theses on the topic "Extreme Model-Driven Design"
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AIRO', FARULLA GIUSEPPE. "Design and development of methodologies, technologies, and tools to support people with disabilities." Doctoral thesis, Politecnico di Torino, 2017. http://hdl.handle.net/11583/2678711.
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Assistive Technologies (ATs) is an umbrella term that includes, from the one hand, assistive, adaptive, and rehabilitative devices for people with disabilities and, from the other hand, the process needed to select, locate, and use them. ATs promote greater independence by enabling people to perform tasks that they were formerly unable to accomplish (or had great difficulty accomplishing) by providing enhancements to, or changing methods of interacting with, the technology needed to accomplish such tasks. Researching on ATs means to focus both on the individuals, the users, the design, and the consecutive development of any kind of technology that could ease, or even improve, everyday life of disabled, elderly people, and people who are following rehabilitative programs.
This dissertation spans on ATs that, starting from a common root and deriving from the realm of Information Technology, have been applied and deployed to several groups of individuals with disabilities. Starting from the issue of detecting hand poses, gestures, and signs for enabling novel paradigms for human-machine interaction, three approaches for hand tracking and gesture recognition from single markerless observation have been developed. The first approach comprises machine learning techniques and optimized features to boost performances. The second one comprises a 3D model of a human hand and optimization techniques. The third approach applies machine learning and statistical techniques on top of technology specifically designed for tracking human hands.
Starting from these results, hand gesture recognition has then been proposed to enable new interaction paradigms, suitable for individuals with disabilities, in the eld of Human-Robot collaboration. A reliable real time protocol to remotely control anthropomorphic robotic actuators has been implemented. This protocol allows the user to send commands to one (or many) robotic actuator by simply moving his/her hand; it has been designed, modeled, and formally validated resorting to a knowledge-driven agile approach.
This dissertation proposes two use cases enabled from the outcomes of the research activities. The former one is a remote communication system for deafblind individuals based on Sign Languages (SLs) with tactile feedback. With the support of SL experts, I have identified a list of fundamental hand movements and gestures to be recognized accurately. The developed algorithms were successfully tested involving 80+ volunteers (both proficient and not in SLs). This communication system is ready to be used concurrently by many people, allowing 1-to-many communication. In addition, it supports different input (cameras and sensors for non-invasive markerless hand tracking) and output (upper-limb anthropomorphic robotic interfaces) systems. The latter one is a telerehabilitation setup for upper-limb post-stroke rehabilitation, comprising vision-based input and a hand exoskeleton.
Knowledge derived from the research activities has been applied to two projects, whom outcomes are discussed in this dissertation, as well. The former one lies in the realm of character recognition and aims at improving accessibility of mathematical and scientific documents for blind and deafblind individuals. The latter one aims at developing inclusive interfaces to a web platform under development for preserving and disseminating the cultural heritage of deaf and deafblind communities.
All the research activities presented in this dissertation have involved a strict and direct contact with end-user associations and persons who benefit from the results of the research itself, and have been widely discussed and tested with them.
Book chapters on the topic "Extreme Model-Driven Design"
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Gelbard, Roy. "Software Modeling Processes." In Handbook of Research on Modern Systems Analysis and Design Technologies and Applications, 169–78. IGI Global, 2009. http://dx.doi.org/10.4018/978-1-59904-887-1.ch013.
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Applications require short development cycles and constant interaction with customers. Requirement gathering has become an ongoing process, reflecting continuous changes in technology and market demands. System analysis and modeling that are made at the initial project stages are quickly abandoned and become outmoded. Model driven architecture (MDA), rapid application development (RAD), adaptive development, extreme programming (XP), and others have resulted in a shift from the traditional waterfall model. These methodologies attempt to respond to the needs, but do they really fulfill their objectives, which are essential to the success of software development? Unified modeling language (UML) was created by the convergence of several well-known modeling methodologies. Despite its popularity and the investments that have been made in UML tools, UML is not yet translatable into running code. Some of the problems that have been discovered have to do with the absence of action semantics language and its size. This chapter reviews and evaluates the UML evolution (UML2, xUML), providing criteria and requirements to evaluate UML and the xUML potential to raise levels of abstraction, flexibility, and productivity enhancement. At the same time, it pinpoints its liabilities that keep it from completely fulfilling the vision of software development through a continuous exactable modeling process, considered to be the future direction for modeling and implementation.
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Lopez-Franco, Michel, Alma Y. Alanis, Nancy Arana-Daniel, and Carlos Lopez-Franco. "Artificial Higher Order Neural Networks for Modeling MIMO Discrete-Time Nonlinear System." In Artificial Higher Order Neural Networks for Modeling and Simulation, 30–43. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2175-6.ch002.
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In this chapter, a Recurrent Higher Order Neural Network (RHONN) is used to identify the plant model of discrete time nonlinear systems, under the assumption that all the state is available for measurement. Then the Extended Kalman Filter (EKF) is used to train the RHONN. The applicability of this scheme is illustrated by identification for an electrically driven nonholonomic mobile robot. Traditionally, modeling of mobile robots only considers its kinematics. It has been well known that the actuator dynamics is an important part of the design of the complete robot dynamics. However, most of the reported results in literature do not consider all parametric uncertainties for mobile robots at the actuator level. This is due to the modeling problem becoming extremely difficult as the complexity of the system dynamics increases, and the mobile robot model includes the uncertainties of the actuator dynamics as well as the uncertainties of the robot kinematics and dynamics.
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Chattoraj, Shovan L. "Debris Flow Modelling and Risk Assessment of Selected Landslides from Uttarakhand- Case Studies using Earth Observation Data." In Advances in Geospatial Technologies, 111–21. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-1814-3.ch006.
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Presently demand for process based modelling of mass movements encompassing snow avalanche, debris flows, landslides, mud flows and rock falls has increased manifold due to their devastating effect and mitigation challenges of disasters caused by such phenomena. Debris flows are multi-phase gravity-driven flows consisting of randomly dispersed interacting phases and therefore, are, extremely challenging to predict the dynamics, run-out distance and area of inundation related to such events which will facilitate mitigation as well as evaluation of simulated scenarios. The numerical simulation model predicts the motion of a geophysical mass movement from head to base in three dimensions. These process-based 3-D models can be utilized for better understanding of vulnerability of a complex mountainous terrain and design appropriate civil engineering structures to withstand the impact of potential flows like Ukhimath, Uttarkashi and Kedarnath.
Conference papers on the topic "Extreme Model-Driven Design"
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Steffen, Bernhard, Sven Jorges, Christian Wagner, and Tiziana Margaria. "Maintenance, or the 3rd dimension of eXtreme model-driven design." In 2009 IEEE International Conference on Software Maintenance (ICSM). IEEE, 2009. http://dx.doi.org/10.1109/icsm.2009.5306281.
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Blum, Niklas, Thomas Magedanz, Jan KleeBen, and Tiziana Margaria. "Enabling eXtreme Model Driven Design of Parlay X-based Communications Services for End-to-End Multiplatform Service Orchestrations." In 2009 14th IEEE International Conference on Engineering of Complex Computer Systems (ICECCS 2009). IEEE, 2009. http://dx.doi.org/10.1109/iceccs.2009.34.
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Blum, Niklas, Thomas Magedanz, and Tiziana Margaria. "Rapid service creation using eXtreme Model Driven Design for real-time communications services on top of Next Generation Networks." In 2009 13th International Conference on Intelligence in Next Generation Networks (ICIN): "Beyond the Bit Pipes". IEEE, 2009. http://dx.doi.org/10.1109/icin.2009.5357109.
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Wada, Ryota, Philip Jonathan, Takuji Waseda, and Shejun Fan. "Estimating Extreme Waves in the Gulf of Mexico Using a Simple Spatial Extremes Model." In ASME 2019 38th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/omae2019-95442.
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Abstract We seek to characterize the behavior of extreme waves in the Gulf of Mexico, using a 109 year-long wave hindcast (GOMOS). The largest waves in this region are driven by strong winds from hurricanes. Design of offshore production systems requires the estimation of extreme metocean conditions corresponding to return periods from 1 year to 10,000 years and beyond. For extrapolation to long return periods, estimation using data for around 100 years from a single location will incur large uncertainties. Approaches such as spatial pooling, cyclone track-shifting and explicit track modeling have been proposed to alleviate this problem. The underlying problem in spatial pooling is the aggregation of dependent data and hence underestimation of uncertainty using naïve analysis; techniques such as block-bootstrapping can be used to inflate uncertainties to more realistic levels. The usefulness of cyclone track-shifting or explicit track modeling is dependent on the appropriateness of the physical assumptions underpinning such a model. In this paper, we utilize a simple spatial statistical model for extreme value estimation of significant wave height under tropical cyclones, known as STM-E, proposed in Wada et al. (2018). The STM-E model was developed to characterize extreme waves offshore Japan, also dominated by tropical cyclones. The method relies on the estimation of two distributions from a sample of data, namely the distribution of spatio-temporal maximum (STM) and the exposure (E). In the current work, we apply STM-E to extreme wave analysis in Gulf of Mexico. The STM-E estimate provides a parsimonious spatially-smooth distribution of extreme waves, with smaller uncertainties per location compared to estimates using data from a single location. We also discuss the estimated characteristics of extreme wave environments in this region.
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Mercelis, Peter, Marc Dufour, Ariel Alvarez Gebelin, Vincent Gruwez, Sarah Doorme, Marc Sas, and Gert Leyssen. "Generation of Multivariate Wave Conditions as Input for a Probabilistic Level III Breakwater Design." In ASME 2014 33rd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/omae2014-24143.
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For an offshore LNG project situated in the estuary of the Rio de la Plata nearby Montevideo, Uruguay, it was required to verify the deterministic design of the protective rubble mound breakwater and the jetty infrastructure with a level three probabilistic design. Therefore, in first instance extreme site conditions were required both in front of and behind the breakwater. To obtain these conditions, the first step is to extrapolate the offshore variables in order to translate them to the breakwater location. All the possible combinations of extreme wind, water level and waves are quantified with a probability of occurrence. A combination of univariate extreme value distributions, copula’s and regression is used to describe the multivariate statistical behaviour of the offshore variables. The main variable is the wind velocity, as in the area of concern extreme wave conditions are wind driven. The secondary variable is water level. Wind velocity and water levels are only correlated for some wind directions. For these directions, wind velocity and water level extreme value distributions are linked through a multivariate Gumbel Copula. The wave height at the model boundaries was taken into account by a regression function with the extreme wind velocity at the offshore location and the wave period by a regression function with the wave height. This way 1515 synthetic events were selected and simulated with the spectral wave model SWAN, each of which a frequency of occurrence is calculated for. However, due to refraction and diffraction effects of the approach channel (in the area of concern water depths are limited to about 7 m and the navigation channel has a depth of about 14 m), the port basin and the breakwater itself, the spectral wave model SWAN is not sufficient to accurately calculate the local wave conditions in the entire area of interest. Therefore a non-linear Boussinesq wave model (i.e. Mike 21 BW) was set up in addition, using input from the spectral model at the boundary and including the navigation channel of more than 12 km long. Combining both models, significant wave heights are obtained on both the seaward side and the leeside of the breakwater with corresponding frequencies of occurrence. This approach allows the determination of conditional return periods and generates the site conditions required for a probabilistic level three design of the breakwater and the jetty infrastructure taking for example the joint probabilities between waves and water levels fully into account as needed for overtopping or failure calculations.
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Evan-lwanowski, R. M., and Chu-Ho Lu. "Transitions Through Period Doubling Route to Chaos." In ASME 1991 Design Technical Conferences. American Society of Mechanical Engineers, 1991. http://dx.doi.org/10.1115/detc1991-0328.
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Abstract The Duffing driven, damped, “softening” oscillator has been analyzed for transition through period doubling route to chaos. The forcing frequency and amplitude have been varied in time (constant sweep). The stationary 2T, 4T… chaos regions have been determined and used as the starting conditions for nonstationary regimes, consisting of the transition along the Ω(t)=Ω0±α2t,f=const., Ω-line, and along the E-line: Ω(t)=Ω0±α2t;f(t)=f0∓α2t. The results are new, revealing, puzzling and complex. The nonstationary penetration phenomena (delay, memory) has been observed for a single and two-control nonstationary parameters. The rate of penetrations tends to zero with increasing sweeps, delaying thus the nonstationary chaos relative to the stationary chaos by a constant value. A bifurcation discontinuity has been uncovered at the stationary 2T bifurcation: the 2T bifurcation discontinuity drops from the upper branches of (a, Ω) or (a, f) curves to their lower branches. The bifurcation drops occur at the different control parameter values from the response x(t) discontinuities. The stationary bifurcation discontinuities are annihilated in the nonstationary bifurcation cascade to chaos — they reside entirely on the upper or lower nonstationary branches. A puzzling drop (jump) of the chaotic bifurcation bands has been observed for reversed sweeps. Extreme sensitivity of the nonstationary bifurcations to the starting conditions manifests itself in the flip-flop (mirror image) phenomena. The knowledge of the bifurcations allows for accurate reconstruction of the spatial system itself. The results obtained may model mathematically a number of engineering and physical systems.
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Idogun, Akpevwe Kelvin, Ruth Oyanu Ujah, and Lesley Anne James. "Surrogate-Based Analysis of Chemical Enhanced Oil Recovery – A Comparative Analysis of Machine Learning Model Performance." In SPE Nigeria Annual International Conference and Exhibition. SPE, 2021. http://dx.doi.org/10.2118/208452-ms.
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Abstract Optimizing decision and design variables for Chemical EOR is imperative for sensitivity and uncertainty analysis. However, these processes involve multiple reservoir simulation runs which increase computational cost and time. Surrogate models are capable of overcoming this impediment as they are capable of mimicking the capabilities of full field three-dimensional reservoir simulation models in detail and complexity. Artificial Neural Networks (ANN) and regression-based Design of Experiments (DoE) are common methods for surrogate modelling. In this study, a comparative analysis of data-driven surrogate model performance on Recovery Factor (RF) for Surfactant-Polymer flooding is investigated with seven input variables including Kv/Kh ratio, polymer concentration in polymer drive, surfactant slug size, surfactant concentration in surfactant slug, polymer concentration in surfactant slug, polymer drive size and salinity of polymer drive. Eleven Machine learning models including Multiple Linear Regression (MLR), Ridge and Lasso regression; Support Vector Regression (SVR), ANN as well as Classification and Regression Tree (CART) based algorithms including Decision Trees, Random Forest, eXtreme Gradient Boosting (XGBoost), Gradient Boosting and Extremely Randomized Trees (ERT), are applied on a dataset consisting of 202 datapoints. The results obtained indicate high model performance and accuracy for SVR, ANN and CART based ensemble techniques like Extremely Randomized Trees, Gradient Boost and XGBoost regression, with high R2 values and lowest Mean Squared Error (MSE) values for the training and test dataset. Unlike other studies on Chemical EOR surrogate modelling where sensitivity was analyzed with statistical DoE, we rank the input features using Decision Tree-based algorithms while model interpretability is achieved with Shapely Values. Results from feature ranking indicate that surfactant concentration, and slug size are the most influential parameters on the RF. Other important factors, though with less influence, are the polymer concentration in surfactant slug, polymer concentration in polymer drive and polymer drive size. The salinity of the polymer drive and the Kv/Kh ratio both have a negative effect on the RF, with a corresponding least level of significance.
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Scholten, James R. "A Magnetic Bearing Suspension System for High Temperature Gas Turbine Applications: Control System Design." In ASME 1997 International Gas Turbine and Aeroengine Congress and Exhibition. American Society of Mechanical Engineers, 1997. http://dx.doi.org/10.1115/97-gt-029.
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A practical magnetic-bearing control system has been designed based upon modeling and simulation of the dynamics of a jet engine turbine shaft and bearing system. Simulations include models for flexible rotor dynamics, magnetic actuators, auxiliary touchdown bearings, ordinary and extraordinary external loads, and disturbances from rotor imbalance, stator vibration, and noise. The shaft model includes a motor-generator which acts as an uncontrolled negative stiffness. The control system is decentralized, running independently for each of the five physical axes of control (1 axial, 4 radial). The fundamental algorithm is classical PID: proportional for broadband stiffness, integrator (with anti-windup) for high load-carrying capacity, and derivative to dampen disturbances. Additional phase lead is provided via a first-order pole-zero pair. The vibration due to rotor imbalance is eliminated by an autobalancing algorithm. Compensation for magnetic actuator non-linearity and varying rotor-stator gap is provided by feedback of sensed magnetic flux, using sensor coils built into the actuator. The control design can be readily implemented using a commercial Digital Signal Processing system. The magnetic bearing actuators will be driven with commercial power amplifiers via customized front-end electronics. Based upon simulations, the design goal has been achieved of keeping the shaft within two mils of its desired location at the magnetic bearings, under all normal loads. Under extreme external loads, the capacity of the magnetic bearings will be exceeded and touchdown will occur upon backup mechanical bearings. Simulation shows that the control design handles this critical event, which determines the force slew rate required from the actuators.
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Mackay, Ed B. L., and Jon P. Hardwick. "Joint Extremes of Waves and Currents at Tidal Energy Sites in the English Channel." In ASME 2022 41st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/omae2022-79348.
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Abstract Efficient and resilient design of tidal turbines requires knowledge of the environmental conditions which they will be exposed to over the course of their design life. Several sites in the English Channel have been identified by technology developers for potential deployments of tidal farms. These sites are exposed to strong tidal currents and large wave conditions. At sites of interest for tidal energy extraction, the largest currents are primarily driven by astronomical forcing and can be predicted from harmonic analysis of relatively short datasets. In contrast, wave conditions are stochastic in nature and require long hindcasts to accurately estimate extreme conditions. Moreover, at sites relevant to tidal energy, currents have a significant influence on the wave conditions. This necessitates that extremes of waves and currents are assessed using joint probabilistic models, in order to specify combinations of waves and currents to be used in the structural design of tidal turbines. In this work we use a coupled wave-current model of the English Channel to create a 31-year hindcast of conditions. We examine the joint distribution of wave and current conditions for tidal energy sites near the Isle of Wight (UK) and in the Alderney Race, off the coast of France. We construct 3D environmental contours of current speed, significant wave height and relative direction between the waves and currents. It is shown that the largest waves occur when waves and currents are in opposing directions. The directional misalignment between waves and currents is examined and the potential impact that this may have on the design of tidal turbines is discussed.
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Aubault, Alexia, Christian Cermelli, and Dominique Roddier. "Parametric Optimization of a Semi-Submersible Platform With Heave Plates." In ASME 2007 26th International Conference on Offshore Mechanics and Arctic Engineering. ASMEDC, 2007. http://dx.doi.org/10.1115/omae2007-29391.
Full textAbstract:
The hydrodynamic responses of a semi-submersible platform are driven by its mass properties and geometric parameters, e.g. column size, spacing, draft and pontoon size. The mooring system also influences the platform responses. Heave plates added to the base of each column have been proposed to enhanced stability of semi-submersible platforms, particularly in the lower payload range. Optimization of a platform typically involves a compromise among a large number of factors including the structural weight, vertical, horizontal motion and rotations in operating and extreme sea-states, airgap, mooring size, etc. Optimization methods are reviewed. The complexity of the problem leads to the choice of a genetic algorithm presented herein. To allow systematic platform optimization assuming primary project parameters are given, i.e. payload, waterdepth, environmental conditions. A simplified hydrodynamic model is developed to capture the parametric sensitivity of the platform responses to primary design parameters.
Reports on the topic "Extreme Model-Driven Design"
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An Input Linearized Powertrain Model for the Optimal Control of Hybrid Electric Vehicles. SAE International, March 2022. http://dx.doi.org/10.4271/2022-01-0741.
Full textAbstract:
Models of hybrid powertrains are used to establish the best combination of conventional engine power and electric motor power for the current driving situation. The model is characteristic for having two control inputs and one output constraint: the total torque should be equal to the torque requested by the driver. To eliminate the constraint, several alternative formulations are used, considering engine power or motor power or even the ratio between them as a single control input. From this input and the constraint, both power levels can be deduced. There are different popular choices for this one control input. This paper presents a novel model based on an input linearizing transformation. It is demonstrably superior to alternative model forms, in that the core dynamics of the model (battery state of energy) are linear, and the non-linearities of the model are pushed into the inputs and outputs in a Wiener/Hammerstein form. The output non-linearities can be approximated using a quadratic model, which creates a problem in the linear-quadratic framework. This facilitates the direct application of linear control approaches such as LQR control, predictive control, or Model Predictive Control (MPC). The paper demonstrates the approach using the ELectrified Vehicle library for sImulation and Optimization (ELVIO). It is an open-source MATLAB/Simulink library designed for the quick and easy simulation and optimization of different powertrain and drivetrain architectures. It follows a modelling methodology that combines backward-facing and forward-facing signal path, which means that no driver model is required. The results show that the approximated solution provides a performance that is very close to the solution of the original problem except for extreme parts of the operating range (in which case the solution tends to be driven by constraints anyway).