Pub Date : 2002-01-01DOI: 10.1115/DETC2002/DAC-34129
P. A. Simionescu, D. Beale
A method of inspecting the design space of multivariable objective functions is proposed. By scanning 1 or 2 of the variables at a constant step while partially minimizing or maximizing the function with respect to the remaining variables, sets of points are generated that can be fiarther used in producing 2D or 3D diagrams. A number of examples are given for showing the usefialness of the method in studying the design space of objective functions and of the constraint activity. All graphs are produced with an in-house program that allows generation of logarithmically spaced level-curve diagrams and accurately truncating fimction surfaces over the z-axis at specified heights.
{"title":"New Concepts in Graphic Visualization of Objective Functions","authors":"P. A. Simionescu, D. Beale","doi":"10.1115/DETC2002/DAC-34129","DOIUrl":"https://doi.org/10.1115/DETC2002/DAC-34129","url":null,"abstract":"A method of inspecting the design space of multivariable objective functions is proposed. By scanning 1 or 2 of the variables at a constant step while partially minimizing or maximizing the function with respect to the remaining variables, sets of points are generated that can be fiarther used in producing 2D or 3D diagrams. A number of examples are given for showing the usefialness of the method in studying the design space of objective functions and of the constraint activity. All graphs are produced with an in-house program that allows generation of logarithmically spaced level-curve diagrams and accurately truncating fimction surfaces over the z-axis at specified heights.","PeriodicalId":132526,"journal":{"name":"Volume 2: 28th Design Automation Conference","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122404315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2002-01-01DOI: 10.1115/DETC2002/DAC-34140
Lusine Baghdasaryan, Wei Chen, T. Buranathiti, Jian Cao
Model validation has become a primary means to evaluate accuracy and reliability of computational simulations in engineering design. Mathematical models enable engineers to establish what the most likely response of a system is. However, despite the enormous power of computational models, uncertainty is inevitable in all model-based engineering design problems, due to the variation in the physical system itself, or lack of knowledge, and the use of assumptions by model builders. Therefore, realistic mathematical models should contemplate uncertainties. Due to the uncertainties, the assessment of the validity of a modeling approach must be conducted based on stochastic measurements to provide designers with the confidence of using a model. In this paper, a generic model validation methodology via uncertainty propagation is presented. The approach reduces the number of physical testing at each design setting to one by shifting the evaluation effort to uncertainty propagation of the computational model. Response surface methodology is used to create metamodels as less costly approximations of simulation models for uncertainty propagation. The methodology is illustrated with the examination of the validity of a finite-element analysis model for predicting springback angles in a sample flanging process.
{"title":"Model Validation via Uncertainty Propagation Using Response Surface Models","authors":"Lusine Baghdasaryan, Wei Chen, T. Buranathiti, Jian Cao","doi":"10.1115/DETC2002/DAC-34140","DOIUrl":"https://doi.org/10.1115/DETC2002/DAC-34140","url":null,"abstract":"Model validation has become a primary means to evaluate accuracy and reliability of computational simulations in engineering design. Mathematical models enable engineers to establish what the most likely response of a system is. However, despite the enormous power of computational models, uncertainty is inevitable in all model-based engineering design problems, due to the variation in the physical system itself, or lack of knowledge, and the use of assumptions by model builders. Therefore, realistic mathematical models should contemplate uncertainties. Due to the uncertainties, the assessment of the validity of a modeling approach must be conducted based on stochastic measurements to provide designers with the confidence of using a model. In this paper, a generic model validation methodology via uncertainty propagation is presented. The approach reduces the number of physical testing at each design setting to one by shifting the evaluation effort to uncertainty propagation of the computational model. Response surface methodology is used to create metamodels as less costly approximations of simulation models for uncertainty propagation. The methodology is illustrated with the examination of the validity of a finite-element analysis model for predicting springback angles in a sample flanging process.","PeriodicalId":132526,"journal":{"name":"Volume 2: 28th Design Automation Conference","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2002-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128333910","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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