Pub Date : 2021-01-01DOI: 10.1615/int.j.uncertaintyquantification.2021035424
S. Nandi, T. Singh
{"title":"GLOBAL SENSITIVITY ANALYSIS MEASURES BASED ON STATISTICAL DISTANCES","authors":"S. Nandi, T. Singh","doi":"10.1615/int.j.uncertaintyquantification.2021035424","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2021035424","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67531111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1615/int.j.uncertaintyquantification.2021036467
J. Quick, R. King, M. T. Henry de Frahan, S. Ananthan, M. Sprague, P. Hamlington
{"title":"Field Sensitivity Analysis of Turbulence Model Parameters for Flow Over a Wing","authors":"J. Quick, R. King, M. T. Henry de Frahan, S. Ananthan, M. Sprague, P. Hamlington","doi":"10.1615/int.j.uncertaintyquantification.2021036467","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2021036467","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67531259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021033482
D. Jarušková, A. Pázman
{"title":"Methods for approximating distribution of unknown parameters estimates with application in material thermophysics","authors":"D. Jarušková, A. Pázman","doi":"10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021033482","DOIUrl":"https://doi.org/10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021033482","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021035536
Krushna Shinde, P. Feissel, Sébastien Destercke
{"title":"DEALING WITH INCONSISTENT MEASUREMENTS IN INVERSE PROBLEMS: SET-BASED APPROACH","authors":"Krushna Shinde, P. Feissel, Sébastien Destercke","doi":"10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021035536","DOIUrl":"https://doi.org/10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021035536","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"308 1","pages":"59-84"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67531151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1615/int.j.uncertaintyquantification.2021033543
Z. Gong, F. DiazDelaO, P. O. Hristov, M. Beer
{"title":"HISTORY MATCHING WITH SUBSET SIMULATION","authors":"Z. Gong, F. DiazDelaO, P. O. Hristov, M. Beer","doi":"10.1615/int.j.uncertaintyquantification.2021033543","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2021033543","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1615/int.j.uncertaintyquantification.2020034192
C. R. D. Silva, Roberto M. F. Squarcio, J. L. Cavichiolo, João Morais da Neto
{"title":"APPLICATION OF THE λ NEUMANN-MONTE CARLO METHODOLOGY FOR QUANTIFICATION OF THE UNCERTAINTY OF THE PROBLEM OF STOCHASTIC BENDING OF KIRCHHOFF PLATES","authors":"C. R. D. Silva, Roberto M. F. Squarcio, J. L. Cavichiolo, João Morais da Neto","doi":"10.1615/int.j.uncertaintyquantification.2020034192","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2020034192","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"11 1","pages":"85-97"},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67530937","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021034779
Wenzhe Xu, D. Williamson, P. Challenor
{"title":"LOCAL VORONOI TESSELLATIONS FOR ROBUST MULTIWAVE CALIBRATION OF COMPUTER MODELS","authors":"Wenzhe Xu, D. Williamson, P. Challenor","doi":"10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021034779","DOIUrl":"https://doi.org/10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021034779","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67531059","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021036657
Yian Chen, M. Anitescu
{"title":"Scalable Gaussian Process Analysis for Implicit Physics-Based Covariance Models","authors":"Yian Chen, M. Anitescu","doi":"10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021036657","DOIUrl":"https://doi.org/10.1615/INT.J.UNCERTAINTYQUANTIFICATION.2021036657","url":null,"abstract":"","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67531270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-01DOI: 10.1615/int.j.uncertaintyquantification.2021037714
A. Strand, J. Kjølaas, T. Bergstrøm, I. Steinsland, L. Hellevik
The prediction uncertainty in simulators for industrial processes is due to uncertainties in the input variables and uncertainties in specification of the models, in particular the closure laws. In this work, the uncertainty in each closure law was modeled as a random variable and the parameters of its distribution were optimized to correctly quantify the uncertainty in predictions. We have developed two methods for optimization, based on the integrated quadratic distance and the energy score. The proposed methods were applied to the commercial multiphase flow simulator LedaFlow with the liquid volume fraction and pressure gradient as output variables. Two datasets were analyzed. Both describe two-phase gas-liquid flow, but are otherwise fundamentally different. One is gas-dominated stratified/annular flow and the other is liquiddominated slug flow. The closure law for the gas-wall friction factor is decisive for the gas-dominated predictions, and the estimated relative standard deviation is 4.5% or 8.0% depending on method. The liquid-dominated study showed that the liquid-wall friction factor and the slug bubble velocity are the closure laws with the greatest impact. Moreover, the estimated relative standard deviation in the liquid-wall friction factor is 5%, and the deviation in the slug bubble velocity is 4%. We used direct measurements of the slug bubble velocity to validate the estimated uncertainty.
{"title":"Closure Law Model Uncertainty Quantification","authors":"A. Strand, J. Kjølaas, T. Bergstrøm, I. Steinsland, L. Hellevik","doi":"10.1615/int.j.uncertaintyquantification.2021037714","DOIUrl":"https://doi.org/10.1615/int.j.uncertaintyquantification.2021037714","url":null,"abstract":"The prediction uncertainty in simulators for industrial processes is due to uncertainties in the input variables and uncertainties in specification of the models, in particular the closure laws. In this work, the uncertainty in each closure law was modeled as a random variable and the parameters of its distribution were optimized to correctly quantify the uncertainty in predictions. We have developed two methods for optimization, based on the integrated quadratic distance and the energy score. The proposed methods were applied to the commercial multiphase flow simulator LedaFlow with the liquid volume fraction and pressure gradient as output variables. Two datasets were analyzed. Both describe two-phase gas-liquid flow, but are otherwise fundamentally different. One is gas-dominated stratified/annular flow and the other is liquiddominated slug flow. The closure law for the gas-wall friction factor is decisive for the gas-dominated predictions, and the estimated relative standard deviation is 4.5% or 8.0% depending on method. The liquid-dominated study showed that the liquid-wall friction factor and the slug bubble velocity are the closure laws with the greatest impact. Moreover, the estimated relative standard deviation in the liquid-wall friction factor is 5%, and the deviation in the slug bubble velocity is 4%. We used direct measurements of the slug bubble velocity to validate the estimated uncertainty.","PeriodicalId":48814,"journal":{"name":"International Journal for Uncertainty Quantification","volume":"1 1","pages":""},"PeriodicalIF":1.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"67531288","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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