Littérature scientifique sur le sujet « Representative Surrogate Problems »
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Articles de revues sur le sujet "Representative Surrogate Problems"
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Bao, Lin, Xiaoyan Sun, Yang Chen, Guangyi Man et Hui Shao. « Restricted Boltzmann Machine-Assisted Estimation of Distribution Algorithm for Complex Problems ». Complexity 2018 (1 novembre 2018) : 1–13. http://dx.doi.org/10.1155/2018/2609014.
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A novel algorithm, called restricted Boltzmann machine-assisted estimation of distribution algorithm, is proposed for solving computationally expensive optimization problems with discrete variables. First, the individuals are evaluated using expensive fitness functions of the complex problems, and some dominant solutions are selected to construct the surrogate model. The restricted Boltzmann machine (RBM) is built and trained with the dominant solutions to implicitly extract the distributed representative information of the decision variables in the promising subset. The visible layer’s probability of the RBM is designed as the sampling probability model of the estimation of distribution algorithm (EDA) and is updated dynamically along with the update of the dominant subsets. Second, according to the energy function of the RBM, a fitness surrogate is developed to approximate the expensive individual fitness evaluations and participates in the evolutionary process to reduce the computational cost. Finally, model management is developed to train and update the RBM model with newly dominant solutions. A comparison of the proposed algorithm with several state-of-the-art surrogate-assisted evolutionary algorithms demonstrates that the proposed algorithm effectively and efficiently solves complex optimization problems with smaller computational cost.
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Wu, Mengtian, Jin Xu, Pengjie Hu, Qianyi Lu, Pengcheng Xu, Han Chen et Lingling Wang. « An Adaptive Surrogate-Assisted Simulation-Optimization Method for Identifying Release History of Groundwater Contaminant Sources ». Water 14, no 10 (23 mai 2022) : 1659. http://dx.doi.org/10.3390/w14101659.
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The simulation-optimization method, integrating the numerical model and the evolutionary algorithm, is increasingly popular for identifying the release history of groundwater contaminant sources. However, due to the usage of computationally intensive evolutionary algorithms, traditional simulation-optimization methods always require thousands of simulations to find appropriate solutions. Such methods yield a prohibitive computational burden if the simulation involved is time-consuming. To reduce general computation, this study proposes a novel simulation-optimization method for solving the inverse contaminant source identification problems, which uses surrogate models to approximate the numerical model. Unlike many existing surrogate-assisted methods using the pre-determined surrogate model, this paper presents an adaptive surrogate technique to construct the most appropriate surrogate model for the current numerical model. Two representative cases about identifying the release history of contaminant sources are used to investigate the accuracy and robustness of the proposed method. The results indicate that the proposed adaptive surrogate-assisted method effectively identifies the release history of groundwater contaminant sources with a higher degree of accuracy and shorter computation time than traditional methods.
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Zhao, Hongbo, Lin Zhang, Jiaolong Ren, Meng Wang et Zhiqiang Meng. « AdaBoost-Based Back Analysis for Determining Rock Mass Mechanical Parameters of Claystones in Goupitan Tunnel, China ». Buildings 12, no 8 (22 juillet 2022) : 1073. http://dx.doi.org/10.3390/buildings12081073.
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The back analysis is an effective tool to determine the representative values of rock mass mechanical properties in rock engineering. The surrogate model is widely used in back analyses since analytical or numerical models are usually unavailable for practical engineering problems. This study proposes a novel back analysis framework by adopting the AdaBoost algorithm for deriving the surrogate model. Moreover, the simplicial homology global optimization (SHGO) algorithm, which is robust and applicable for a black-box global problem, is also integrated into the framework. To evaluate the performance, an experimental tunnel in Goupitan Hydropower Station, China, is introduced, and the representative rheological properties of the surrounding rock are obtained by applying the proposed framework. Then the computed displacements based on the acquired properties via both surrogate and numerical models are compared with field measurements. By taking triple-day data, the discrepancy between the calculated and field-measured displacements is less than 0.5 mm This validates the reliability of the obtained properties and the feasibility of the proposed framework. As an AdaBoost-based method, the proposed framework is sensitive to noise and outliers in the data, the elimination of which is recommended before application.
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Sala, Ramses, Niccolò Baldanzini et Marco Pierini. « Representative surrogate problems as test functions for expensive simulators in multidisciplinary design optimization of vehicle structures ». Structural and Multidisciplinary Optimization 54, no 3 (4 mai 2016) : 449–68. http://dx.doi.org/10.1007/s00158-016-1410-9.
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Lin, Dalue, Haogan Huang, Xiaoyan Li et Yuejiao Gong. « Empirical Study of Data-Driven Evolutionary Algorithms in Noisy Environments ». Mathematics 10, no 6 (15 mars 2022) : 943. http://dx.doi.org/10.3390/math10060943.
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For computationally intensive problems, data-driven evolutionary algorithms (DDEAs) are advantageous for low computational budgets because they build surrogate models based on historical data to approximate the expensive evaluation. Real-world optimization problems are highly susceptible to noisy data, but most of the existing DDEAs are developed and tested on ideal and clean environments; hence, their performance is uncertain in practice. In order to discover how DDEAs are affected by noisy data, this paper empirically studied the performance of DDEAs in different noisy environments. To fulfill the research purpose, we implemented four representative DDEAs and tested them on common benchmark problems with noise simulations in a systematic manner. Specifically, the simulation of noisy environments considered different levels of noise intensity and probability. The experimental analysis revealed the association relationships among noisy environments, benchmark problems and the performance of DDEAs. The analysis showed that noise will generally cause deterioration of the DDEA’s performance in most cases, but the effects could vary with different types of problem landscapes and different designs of DDEAs.
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Gilbody, Simon M., Allan O. House et Trevor A. Sheldon. « Outcomes research in mental health ». British Journal of Psychiatry 181, no 1 (juillet 2002) : 8–16. http://dx.doi.org/10.1192/bjp.181.1.8.
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BackgroundOutcomes research involves the secondary analysis of data collected routinely by clinical services, in order to judge the effectiveness of interventions and policy initiatives. It permits the study of large databases of patients who are representative of ‘real world’ practice. However, there are potential problems with this observational design.AimsTo establish the strengths and limitations of outcomes research when applied in mental health.MethodA systematic review was made of the application of outcomes research in mental health services research.ResultsNine examples of outcomes research in mental health services were found. Those that used insurance claims data have information on large numbers of patients but use surrogate outcomes that are of questionable value to clinicians and patients. Problems arise when attempting to adjust for important confounding variables using routinely collected claims data, making results difficult to interpret.ConclusionsOutcomes research is unlikely to be a quick or cheap means of establishing evidence for the effectiveness of mental health practice and policy.
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Jones, D. A., et K. J. Sene. « A Bayesian approach to flow record infilling and extension for reservoir design ». Hydrology and Earth System Sciences 3, no 4 (31 décembre 1999) : 491–503. http://dx.doi.org/10.5194/hess-3-491-1999.
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Abstract. A Bayesian approach is described for dealing with the problem of infilling and generating stochastic flow sequences using rainfall data to guide the flow generation process, and including bounded (censored) observed flow and rainfall data to provide additional information. Solutions are obtained using a Gibbs sampling procedure. Particular problems discussed include developing new procedures for fitting transformations when bounded values are available, coping with additional information in the form of values, or bounds, for totals of flows across several sites, and developing relationships between annual flow and rainfall data. Examples are shown of both infilled values of unknown past river flows, with assessment of uncertainty, and realisations of flows representative of what might occur in the future. Several procedures for validating the model output are described and the central estimates of flows, taken as a surrogate for historical observed flows, are compared with long term regional flow and rainfall data.
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Bronstert, A., et A. Bárdossy. « The role of spatial variability of soil moisture for modelling surface runoff generation at the small catchment scale ». Hydrology and Earth System Sciences 3, no 4 (31 décembre 1999) : 505–16. http://dx.doi.org/10.5194/hess-3-505-1999.
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Abstract. A Bayesian approach is described for dealing with the problem of infilling and generating stochastic flow sequences using rainfall data to guide the flow generation process, and including bounded (censored) observed flow and rainfall data to provide additional information. Solutions are obtained using a Gibbs sampling procedure. Particular problems discussed include developing new procedures for fitting transformations when bounded values are available, coping with additional information in the form of values, or bounds, for totals of flows across several sites, and developing relationships between annual flow and rainfall data. Examples are shown of both infilled values of unknown past river flows, with assessment of uncertainty, and realisations of flows representative of what might occur in the future. Several procedures for validating the model output are described and the central estimates of flows, taken as a surrogate for historical observed flows, are compared with long term regional flow and rainfall data.
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Dalton, James P., Benedict Uy, Narisa Phummarin, Brent R. Copp, William A. Denny, Simon Swift et Siouxsie Wiles. « Effect of common and experimental anti-tuberculosis treatments onMycobacterium tuberculosisgrowing as biofilms ». PeerJ 4 (22 novembre 2016) : e2717. http://dx.doi.org/10.7717/peerj.2717.
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Much is known regarding the antibiotic susceptibility of planktonic cultures ofMycobacterium tuberculosis, the bacterium responsible for the lung disease tuberculosis (TB). As planktonically-grownM. tuberculosisare unlikely to be entirely representative of the bacterium during infection, we set out to determine how effective a range of anti-mycobacterial treatments were againstM. tuberculosisgrowing as a biofilm, a bacterial phenotype known to be more resistant to antibiotic treatment. Light levels from bioluminescently-labelledM. tuberculosisH37Rv (strain BSG001) were used as a surrogate for bacterial viability, and were monitored before and after one week of treatment. After treatment, biofilms were disrupted, washed and inoculated into fresh broth and plated onto solid media to rescue any surviving bacteria. We found that in this phenotypic stateM. tuberculosiswas resistant to the majority of the compounds tested. Minimum inhibitory concentrations (MICs) increased by 20-fold to greater than 1,000-fold, underlying the potential of this phenotype to cause significant problems during treatment.
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Salmina, A. V. « ON THE NEED FOR SOCIOLOGICAL RESEARCH ON THE INTRODUCTION OF ASSISTED REPRODUCTIVE TECHNOLOGIES IN BELARUS ». Journal of the Grodno State Medical University 19, no 4 (12 septembre 2021) : 451–56. http://dx.doi.org/10.25298/2221-8785-2021-19-4-451-456.
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Background. At present there are no scientifically substantiated data on the problems of introducing assisted reproductive technologies (ART) in the Republic of Belarus. The relevance of developing approaches, organizing opinion polls and processing data on a representative sample of Belarusians does not raise doubts in view of the relationship between social attitudes in the field of reproductive health and the national security of the country. Purpose. Substantiation of medical and sociological study of ART in the population of the Republic of Belarus. Material and methods. The bibliographic analysis included the study of Russian and foreign experience in assessing the sociological aspects of reproductology (materials of Springer Link, Oxford University Press, The New England Journal of Medicine, The British Medical Journal, the SCOPUS database of Elsevier, the EBSCO platform), as well as the analysis of the legislation of the Republic of Belarus in the field of reproductology. Results. In the country, the interests of the party that wants to become a parent (surrogate motherhood, donation) are respected as much as possible. The medicalized approach to the definition of ART methods in Belarus is typical, as in other post-Soviet countries, which are characterized by a classical (nuclear) understanding of the family. Taking into account the current trends in the development of the market for reproductive technologies and those techniques that are used in reproductive centers of the Republic of Belarus, it is necessary in the legislative framework to provide for the rules and possibilities of using such methods as hatching (dissection of the embryo membrane), intracytoplasmic sperm injection, intracytoplasmic sperm injection after selection according to morphological criteria, preimplantation diagnostics. Conclusions. The following areas are relevant for Belarus: 1) study of the awareness of the population of the Republic of Belarus about ART; 2) assessment of social trust in ART on the part of the population; 3) development of technologies for positive reproductive attitudes in society, including the use of ART.
Thèses sur le sujet "Representative Surrogate Problems"
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SALA, RAMSES. « Towards efficient multidisciplinary design optimization for car body structures ». Doctoral thesis, 2016. http://hdl.handle.net/2158/1042892.
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The aim of the here presented research activity is to contribute to the identification and development of efficient strategies for multidisciplinary design optimization of vehicle structures involving, crashworthiness, vibro-acoustic and lightweight design criteria. The literature survey at the start of this activity, identified: that although a large variety of optimization strategies and methods are described in the literature, only few comparisons or guidelines are available for the selection of efficient optimization algorithms or methods for vehicle optimization related problems, involving the mentioned design criteria.
In this work, several state of the art optimization algorithms for multidisciplinary design optimization have been selected and are systematically compared for their efficiency on applications that typically occur within a car body design optimization context. Although these comparisons mainly involved existing methods, the resulting comparisons on the industrially relevant application of vehicle design related optimization problems extended the currently available literature. The results could serve as initial guidelines for practitioners in industry and as a starting point for further research.
In the optimization literature, there are many test functions/problems available that are commonly used for comparative assessments of optimization algorithms. These test problems are however difficult to relate to industrially relevant problems and vice versa. A novel Representative Surrogate Problem approach is developed in the scope of this work, which could be summarized as a strategy to construct optimization test problems, based on response characteristics of real-world problems. The approach is presented and investigated for its application to car body design problems.
Inspired by the response characterization strategies and results, a novel test function generation method based on the composition of random fields is presented. The resulting method is a contribution to the field op global optimization in general and not restricted to automotive applications. This method enables the construction of synthetic optimization problems with various interesting function features. Due to the parameterized nature of the method, these test functions enable structured investigations on the influence of particular problem features on the performance of optimization algorithms. Compared to existing test functions the method provides a further step towards systematic problem feature orientated performance analysis of meta-heuristic optimization methods, which contributes to the analysis, selection and development of optimization algorithms for non-convex optimization problems.
The overall results of the performed comparisons and case studies with the developed methods showed that significant gains in optimization efficiency can be achieved by selecting suitable optimization algorithms, and tuned parameter settings for optimization problem formulations relevant to car body design. The comparison results, stressed the need to take into account optimization efficiency, whereas in many case studies in the literature, optimization algorithms are selected without proper justification. The presented results and methods are relevant for practitioners in industry and for further research on the development of optimization algorithms for complex problems.
Actes de conférences sur le sujet "Representative Surrogate Problems"
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Messner, M. C., T. L. Sham et Yanli Wang. « N-Bar Problems As Approximations to the Bree Problem ». Dans ASME 2018 Pressure Vessels and Piping Conference. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/pvp2018-84106.
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The Bree solution to the problem of a ratcheting cylinder under constant pressure and cyclic thermal load is a fundamental result in nuclear engineering and widely used as the technical basis for the ASME Boiler and Pressure Vessel Code and other design methods. However, because the loading conditions in the Bree problem are difficult to achieve experimentally there have been relatively few works experimentally examining the problem and extending it to other relevant design situations, for example cladded components. In contrast, 2-bar problems are widely studied experimentally and are relatively easy to setup. These 2-bar problems are thought to be representative of Bree-type geometries, but a formal connection has not been demonstrated. This work formally establishes the connection between the Bree cylinder and an n-bar problem — a coupled bar experiment with, in general, more than two bars linked in parallel. The connection suggests that n-bar experiments using a fairly limited number of bars might be an experimentally-accessible setup that better represents ratcheting phenomenon in actual nuclear pressurized components. Such experiments could test surrogate cladded or multi-material components by using bars of different materials. Finally, this work suggests control schemes that yield optimally efficient n-bar experiments — experiments that best replicates a Bree cylinder with a limited number of bars.
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Kung, Pei-Ching, Mahtab Heydari et Bruce L. Tai. « A Neural Network-Based Framework of Real-Time Heat Map Generation for Surgical Hand-Held Drilling ». Dans ASME 2022 17th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/msec2022-85693.
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Abstract Understanding heat generation can help improve one’s surgical drilling skill to avoid thermal injury. Surgical drilling is mostly done manually, so it can be time-consuming to create personalized thermal models to assess each drilling. For this reason, this paper presents a framework for 2D real-time heat map generation for a moving, varying heat source problem based on neural networks (NN) and linear time-invariant system (LTI). In this framework, several location-specific heat maps and their temporal responses are calculated by finite element analysis (FEA) and trained through NN to build a surrogate model. The total heat map of any given moving heat source can be generated by the superposition of a series of location-specific heat maps along the moving path. The NN training shows a correlation over 99%, indicating a highly representative surrogate model. The validation study of comparing two FEA-based moving heat source problems with the framework predicted results show overall good agreement. Error sources and improvement methods are discussed in this paper.
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Chen, Ru, Ronghao Liang, Lu Zhou et Jiesheng Min. « A Sensitivity Analysis of Condensation Phenomena for a Passive Containment Cooling System by Using Code_Saturne Coupled With OpenTURNS ». Dans 2022 29th International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icone29-92164.
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Abstract Computation-expensive problems have become more common in nuclear industry, originating from expensive analysis with the aim of reaching high accuracy. To overcome this challenge, surrogate modelling techniques are often proposed. Passive containment cooling system (PCCS) is an essential pattern to ensure the containment integrity in the case of accidental conditions for AP600 reactor, so determination of the most influential parameter as regards the heat transfer rate is of great significance to the optimization design and security analysis of nuclear power plants. This paper contributes to uncertainty quantification in condensation phenomena for PCCS. The purpose is to create a surrogate model instead of a deterministic model for sensitivity analysis on the account of saving computational time. The case used is an experiment carried out in 1998 for which the data is publicly available and was selected here as a showcase of the meta-model construction. In this paper, the database conducted by Anderson in 1998 is used to validate the computational fluid dynamics (CFD) model. By coupling Open-source CFD code code_saturne and uncertainty analysis code OpenTURNS from the SALOME Platform, a representative sample of input and output parameters is obtained using the design of experiments (DoE) technique. Thus, a surrogate model, including kriging and polynomial chaos metamodel, is constructed. In this way, sensitivity analysis of condensing efficiency is performed, which demonstrates the propagation of modeling uncertainty of condensation phenomena.
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Hinz, Brandon J., Karim H. Muci-Küchler et Pauline M. Smith. « Distribution of Bacteria in Simplified Surrogate Extremities Shot With Small Caliber Projectiles ». Dans ASME 2013 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/imece2013-64803.
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Experiments were conducted to determine bacteria distribution trends in wound cavities of simplified surrogate extremities shot using small caliber projectiles. Two different shapes of targets, cylindrical and square, were used in this study. Cylindrical targets are more representative of an extremity but create difficulties while conducting tests due to inconsistent cavity lengths and optical distortions. Square targets, which are not as susceptible to the problems mentioned above, could be used in place of cylindrical ones if their shape does not significantly affect the distribution of bacteria within the wound cavity. Surface contamination of the targets in the experiments was represented using a circular piece of filter paper moistened with a solution with a known amount of Escherichia coli strain K-12. The projectiles used were 11.43-mm (0.45-in) caliber round nose projectiles shot from a commercially available air rifle. The permanent cavities were extracted from the targets and sliced into small, evenly spaced segments and the area surrounding the permanent cavities was removed with a biopsy punch. The radial tears that were made by the formation of the temporary cavity and surround the permanent cavity were removed using a scalpel. The permanent cavity and radial tears for each section were processed and plated on agar plates. Commercial software was used to count the number of colony forming units on each plate and the percentage of the total bacterial colony count per segment was determined. High speed video and motion analysis software was used to qualitatively and quantitatively compare the temporary cavities in the cylindrical and square targets. The data from the experiments showed that the bacteria distribution trends for the cylindrical and square targets were similar even though the maximum openings of the temporary cavity at the entrance and exit locations were higher for the cylindrical ones. For both target shapes, the bacterium was evenly distributed between the permanent cavity and the radial tears in the middle sections of the “wound tracks.” In addition, significantly higher amounts of bacterium were found in the entrance and exit segments compared with the rest of the segments in the “wound tracks”.
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Wang, Zhenzhen, Jincong He, William J. Milliken et Xian-Huan Wen. « Fast History Matching and Optimization Using a Novel Physics-Based Data-Driven Model : An Application to a Diatomite Reservoir ». Dans SPE Western Regional Meeting. SPE, 2021. http://dx.doi.org/10.2118/200772-ms.
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Abstract Full-physics models in history matching and optimization can be computationally expensive since these problems usually require hundreds of simulations or more. We have previously implemented a physics-based data-driven network model with a commercial simulator that serves as a surrogate without the need to build the 3-D geological model. In this paper, we reconstruct the network model to account for complex reservoir conditions of mature fields and successfully apply it to a diatomite reservoir in the San Joaquin Valley (SJV) for rapid history matching and optimization. The reservoir is simplified into a network of 1-D connections between well perforations. These connections are discretized into grid blocks and the grid properties are calibrated to historical production data. Elevation change, saturation distribution, capillary pressure, and relative permeability are accounted for to best represent the mature field conditions. To simulate this physics-based network model through a commercial simulator, an equivalent 2-D Cartesian model is designed where rows correspond to the above-mentioned connections. Thereafter, the history matching can be performed with the Ensemble Smoother with Multiple Data Assimilation (ESMDA) algorithm under a sequential iterative process. A representative model after history matching is then employed for well control optimization. The network model methodology has been successfully applied to the waterflood optimization for a 56-well sector model of a diatomite reservoir in the SJV. History matching result shows that the network model honors field-level production history and gives reasonable matches for most of the wells, including pressure and flow rate. The calibrated ensemble from the last iteration of history matching yields a satisfactory production prediction, which is verified by the remaining historical data. For well control optimization, we select the P50 model to maximize the Net Present Value (NPV) in 5 years under provided well/field constraints. This confirms that the calibrated network model is accurate enough for production forecasts and optimization. The use of a commercial simulator in the network model provided flexibility to account for complex physics, such as elevation difference between wells, saturation non-equilibrium, and strong capillary pressure. Unlike traditional big-loop workflow that relies on a detailed characterization of geological models, the proposed network model only requires production data and can be built and updated rapidly. The model also runs much faster (tens of seconds) than a full-physics model due to the employment of much fewer grid blocks. To our knowledge, this is the first time this physics-based data-driven network model is applied with a commercial simulator on a field waterflood case. Unlike approaches developed with analytic solutions, the use of commercial simulator makes it feasible to be further extended for complex processes, e.g., thermal or compositional flow. It serves as an useful surrogate model for both fast and reliable decision-making in reservoir management.
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Thoe, Samantha, et Joshua D. Summers. « Correlating Problem/Process Exam Question Complexity to Anticipated Effort : A Modeling Protocol ». Dans ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12301.
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This paper presents the initial investigation of the use of complexity as a surrogate for problem difficulty in predicting the effort or point value of an exam problem. In previous research, complexity of graph-based models has been used to predict market value of products using function models and to predict assembly time from connectivity graphs. This research investigates the potential of applying graphical representations and complexity metrics for exam problem solutions using expert assigned values as an appropriate method to offer point values for new exam questions. The factors and sources of problem difficulty are examined and compared to the structural complexity of a graphical representation of the problem solution. Specifically, this paper presents a protocol for developing the graphical representation. Multiple participants used the protocol to create graphical models of three exam questions to test and validate the usability of the protocol. A secondary protocol was tested to improve the rater agreement for use of the protocol. This protocol will be used for transforming exam problems into graphical models that can be analyzed with the connectivity complexity metrics. These metrics will be used to create predictive models for point assignments based on historical data.
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Guzas, Emily L., Stephen E. Turner, Matthew Babina, Brandon Casper, Thomas N. Fetherston et Joseph M. Ambrico. « Validation of a Surrogate Model for Marine Mammal Lung Dynamics Under Underwater Explosive Impulse ». Dans ASME 2019 Verification and Validation Symposium. American Society of Mechanical Engineers, 2019. http://dx.doi.org/10.1115/vvs2019-5143.
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Abstract Primary blast injury (PBI), which relates gross blast-related trauma or traces of injury in air-filled tissues or those tissues adjacent to air-filled regions (rupture/lesions, contusions, hemorrhaging), has been documented in a number of marine mammal species after blast exposure [1, 2, 3]. However, very little is known about marine mammal susceptibility to PBI except in rare cases of opportunistic studies. As a result, traditional techniques rely on analyses using small-scale terrestrial mammals as surrogates for large-scale marine mammals. For an In-house Laboratory Independent Research (ILIR) project sponsored by the Office of Naval Research (ONR), researchers at the Naval Undersea Warfare Center, Division Newport (NUWCDIVNPT), have undertaken a broad 3-year effort to integrate computational fluid-structure interaction techniques with marine mammal anatomical structure. The intent is to numerically simulate the dynamic response of a marine mammal thoracic cavity and air-filled lungs to shock loading, to enhance understanding of marine mammal lungs to shock loading in the underwater environment. In the absence of appropriate test data from live marine mammals, a crucial part of this work involves code validation to test data for a suitable surrogate test problem. This research employs a surrogate of an air-filled spherical membrane structure subjected to shock loading as a first order approximation to understanding marine mammal lung response to underwater explosions (UNDEX). This approach incrementally improves upon the currently used one-dimensional spherical air bubble approximation to marine mammal lung response by providing an encapsulating boundary for the air. The encapsulating structure is membranous, with minimal simplified representation not accounting for marine mammal species-specific and individual animal differences in tissue composition, rib mechanics, and mechanical properties of interior lung tissue. NUWCDIVNPT partnered with the Naval Submarine Medical Research Laboratory (NSMRL) to design and execute a set of experiments to investigate the shock response of an air-filled rubber dodgeball in a shallow underwater environment. These tests took place in the 2.13 m (7-ft) diameter pressure tank at the University of Rhode Island, with test measurements including pressure data and digital image correlation (DIC) data captured with high-speed cameras in a stereo setup. The authors developed 3-dimensional computational models of the dodgeball experiments using Dynamic System Mechanics Advanced Simulation (DYSMAS), a Navy fluid-structure interaction code. DYSMAS models of a variety of different problems involving submerged pressure vessel structures responding to hydrostatic and/or UNDEX loading have been validated against test data [4]. Proper validation of fluid structure interaction simulations is quite challenging, requiring measurements in both the fluid and structure domains. This paper details the development of metrics for comparison between test measurements and simulation results, with a discussion of potential sources of uncertainty.
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Tournemenne, Robin, Jean-François Petiot, Bastien Talgorn et Michael Kokkolaras. « Brass Instruments Design Using Physics-Based Sound Simulation Models and Surrogate-Assisted Derivative-Free Optimization ». Dans ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2016. http://dx.doi.org/10.1115/detc2016-59532.
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This paper presents a method for design optimization of brass wind instruments. The shape of a trumpet’s bore is optimized to improve intonation using a physics-based sound simulation model. This physics-based model consists of an acoustic model of the resonator (input impedance), a mechanical model of the excitator (the lips of a virtual musician) and a model of the coupling between the excitator and the resonator. The harmonic balance technique allows the computation of sounds in a permanent regime, representative of the shape of the resonator according to control parameters of the virtual musician. An optimization problem is formulated, in which the objective function to be minimized is the overall quality of the intonation of the different notes played by the instrument (deviation from the equal-tempered scale). The design variables are the physical dimensions of the resonator. Given the computationally expensive function evaluation and the unavailability of gradients, a surrogate-assisted optimization framework is implemented using the mesh adaptive direct search algorithm (MADS). Surrogate models are used both to obtain promising candidates in the search step of MADS and to rank-order additional candidates generated by the poll step of MADS. The physics-based model is then used to determine the next design iterate. Two examples (with two and five design optimization variables, respectively) are presented to demonstrate the approach. Results show that significant improvement of intonation can be achieved at reasonable computational cost. The implementation of this method for computer-aided instrument design is discussed, considering different objective functions or constraints based on intonation but also on the timbre of the instrument.
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Gogu, Christian, Raphael T. Haftka, Satish K. Bapanapalli et Bhavani V. Sankar. « Reducing the Number of Variables in a Response Surface Approximation : Application to Thermal Design ». Dans ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-34674.
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Response surface approximations (RSA) are a common tool in engineering, often constructed based on finite element (FE) simulations. For some design problems, the FE models can involve a high number of parameters. However it is advantageous to construct the RSA as function of a small number of variables. The purpose of this paper is to demonstrate that a significant reduction in the number of variables needed for an RSA is possible through physical reasoning, dimensional analysis and global sensitivity analysis. This approach is demonstrated for a transient thermal problem, but it is applicable to any FE based surrogate model construction. The thermal problem considered is the design of an integrated thermal protection system (ITPS) for spacecraft reentry where an RSA of the maximum bottom face temperature was needed. The FE model used to evaluate the maximum temperature depended on 15 parameters of interest for the design: 9 thermal material properties and 6 geometric parameters of the ITPS panel. A small number of assumptions simplified the thermal equations allowing easy nondimensionalization, which together with a global sensitivity analysis showed that the maximum temperature mainly depends on only two nondimensional parameters. These were selected to be the design variables of the RSA for maximum temperature. The RSA was still fitted to the original non-simplified FE simulations. Having only two variables allowed a dense design of experiments thus providing a very good quality of fit. Consequently the major error remaining in the RSA is due to the fact that the two nondimensional variables account for only part (albeit the major part) of the dependence on the original 15 variables. This error was checked and good agreement was found. The two-dimensional nature of the RSA allowed graphical representation, which was used for material selection from among hundreds of possible materials for the design optimization of an ITPS panel.
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Khadke, Kunal R., Weigang An et Andrés Tovar. « Ceramic Matrix Composite Materials by Design Using Robust Variable Fidelity Optimization ». Dans ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-13348.
Texte intégralRésumé :
Ceramic matrix composites (CMC) have been widely studied to tailor desired properties at high temperatures. However, research applications involving design tool development for multi-phase material design are at an early stage of development. While numerical CMC modeling provides significant insight on the material performance, the computational cost of the numerical simulations and the type of variables involved in these models are a hindrance for the effective application of design methods. This technical challenge heightens with the need of considering the uncertainty of material processing and service. For this reason, few design researchers have addressed the design paradox that accompanies the rapid design space expansion in CMC material design. The objective of this research is to establish a tractable approach for CMC design considering uncertainty. Traditionally, surrogate models of statistical data are incorporated in the design strategy. An alternative to surrogate modeling is the use of lower fidelity models, which captures some of the physics of the problem and avoids the generation of uncertainty quantification. A variable fidelity optimization (VFO) management framework is incorporated in this research. In the proposed VFO method, a high-fidelity, cohesive, finely meshed finite-element model guides the coarsely meshed, low-fidelity model towards the optimal material design. Uncertainty in CMC material processing (multiphase nucleation and growth) is quantified using a stochastic material microstructural lattice model. The lattice model is verified with laboratory processed microstructures. Dimension reduction for reduction of the number of random variables under consideration. Linear data transformation and principal component analysis (PCA) is traditionally used in dimension reduction. However, nonlinear dimension reduction techniques are better handle complex nonlinear data. This work incorporates Maximum Variance Unfolding (MVU) that preserves global properties of the original data in the low-dimensional representation. The proposed methodology is applied to the optimal distribution of the matrix and the disperse phases in the composite structure. Results are demonstrated in the design of silicon carbide (SiC) fibers in a silicon-nitride (Si3N4) matrix for maximum fracture energy. The results provide a reference for SiC-Si3N4 nanocomposite.