Los Alamos National Laboratory is interested in developing high-energy-density physics validation capabilities for its multiphysics code xRAGE. xRAGE was recently updated with the laser package Mazinisin to improve predictability. We assess the current implementation and coupling of the laser package via validation of laser-driven, direct-drive spherical capsule experiments from the Omega laser facility. The ASME V&V 20-2009 standard is used to determine the model confidence of xRAGE, and considerations for high-energy-density physics are identified. With current modeling capabilities in xRAGE, the model confidence is overwhelmed by significant systematic errors from the experiment or model. Validation evidence suggests cross-beam energy transfer as a dominant source of the systematic error.
{"title":"Assessment of Model Confidence of a Laser Source Model in xRAGE Using Omega Direct-Drive Implosion Experiments","authors":"B. Wilson, A. Koskelo","doi":"10.1115/1.4043370","DOIUrl":"https://doi.org/10.1115/1.4043370","url":null,"abstract":"Los Alamos National Laboratory is interested in developing high-energy-density physics validation capabilities for its multiphysics code xRAGE. xRAGE was recently updated with the laser package Mazinisin to improve predictability. We assess the current implementation and coupling of the laser package via validation of laser-driven, direct-drive spherical capsule experiments from the Omega laser facility. The ASME V&V 20-2009 standard is used to determine the model confidence of xRAGE, and considerations for high-energy-density physics are identified. With current modeling capabilities in xRAGE, the model confidence is overwhelmed by significant systematic errors from the experiment or model. Validation evidence suggests cross-beam energy transfer as a dominant source of the systematic error.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4043370","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45412044","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}
A. Choudhary, William C. Tyson, Christopher J. Roy
In this study, an r-adaptation technique for mesh adaptation is employed for reducing the solution discretization error, which is the error introduced due to spatial and temporal discretization of the continuous governing equations in numerical simulations. In r-adaptation, mesh modification is achieved by relocating the mesh nodes from one region to another without introducing additional nodes. Truncation error (TE) or the discrete residual is the difference between the continuous and discrete form of the governing equations. Based upon the knowledge that the discrete residual acts as the source of the discretization error in the domain, this study uses discrete residual as the adaptation driver. The r-adaptation technique employed here uses structured meshes and is verified using a series of one-dimensional (1D) and two-dimensional (2D) benchmark problems for which exact solutions are readily available. These benchmark problems include 1D Burgers equation, quasi-1D nozzle flow, 2D compression/expansion turns, and 2D incompressible flow past a Karman–Trefftz airfoil. The effectiveness of the proposed technique is evident for these problems where approximately an order of magnitude reduction in discretization error (when compared with uniform mesh results) is achieved. For all problems, mesh modification is compared using different schemes from literature including an adaptive Poisson grid generator (APGG), a variational grid generator (VGG), a scheme based on a center of mass (COM) analogy, and a scheme based on deforming maps. In addition, several challenges in applying the proposed technique to real-world problems are outlined.
{"title":"Implementation and Assessment of a Residual-Based r-Adaptation Technique on Structured Meshes","authors":"A. Choudhary, William C. Tyson, Christopher J. Roy","doi":"10.1115/1.4043652","DOIUrl":"https://doi.org/10.1115/1.4043652","url":null,"abstract":"In this study, an r-adaptation technique for mesh adaptation is employed for reducing the solution discretization error, which is the error introduced due to spatial and temporal discretization of the continuous governing equations in numerical simulations. In r-adaptation, mesh modification is achieved by relocating the mesh nodes from one region to another without introducing additional nodes. Truncation error (TE) or the discrete residual is the difference between the continuous and discrete form of the governing equations. Based upon the knowledge that the discrete residual acts as the source of the discretization error in the domain, this study uses discrete residual as the adaptation driver. The r-adaptation technique employed here uses structured meshes and is verified using a series of one-dimensional (1D) and two-dimensional (2D) benchmark problems for which exact solutions are readily available. These benchmark problems include 1D Burgers equation, quasi-1D nozzle flow, 2D compression/expansion turns, and 2D incompressible flow past a Karman–Trefftz airfoil. The effectiveness of the proposed technique is evident for these problems where approximately an order of magnitude reduction in discretization error (when compared with uniform mesh results) is achieved. For all problems, mesh modification is compared using different schemes from literature including an adaptive Poisson grid generator (APGG), a variational grid generator (VGG), a scheme based on a center of mass (COM) analogy, and a scheme based on deforming maps. In addition, several challenges in applying the proposed technique to real-world problems are outlined.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4043652","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43736946","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}
A thorough code verification effort has been performed on a reduced order, finite element model for one-dimensional (1D) fluid flow convectively coupled with a three-dimensional (3D) solid, referred to as the “advective bar” model. The purpose of this effort was to provide confidence in the proper implementation of this model within the sierra/aria thermal response code at Sandia National Laboratories. The method of manufactured solutions (MMS) is applied so that the order of convergence in error norms for successively refined meshes and timesteps is investigated. Potential pitfalls that can lead to a premature evaluation of the model's implementation are described for this verification approach when applied to this unique model. Through observation of the expected order of convergence, these verification tests provide evidence of proper implementation of the model within the codebase.
{"title":"Verification of Advective Bar Elements Implemented in the sierra/aria Thermal Response Code","authors":"Brantley Mills, Adam C. Hetzler, Oscar Deng","doi":"10.1115/1.4041837","DOIUrl":"https://doi.org/10.1115/1.4041837","url":null,"abstract":"A thorough code verification effort has been performed on a reduced order, finite element model for one-dimensional (1D) fluid flow convectively coupled with a three-dimensional (3D) solid, referred to as the “advective bar” model. The purpose of this effort was to provide confidence in the proper implementation of this model within the sierra/aria thermal response code at Sandia National Laboratories. The method of manufactured solutions (MMS) is applied so that the order of convergence in error norms for successively refined meshes and timesteps is investigated. Potential pitfalls that can lead to a premature evaluation of the model's implementation are described for this verification approach when applied to this unique model. Through observation of the expected order of convergence, these verification tests provide evidence of proper implementation of the model within the codebase.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4041837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45678023","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}
Verification and validation (V&V) are necessary processes to ensure accuracy of the computational methods used to solve problems key to vast numbers of applications and industries. Simulations are essential for addressing impact cratering problems, because these problems often exceed experimental capabilities. Here, we show that the free Lagrange (FLAG) hydrocode, developed at Los Alamos National Laboratory (Los Alamos, NM), can be used for impact cratering simulations by verifying FLAG against two analytical models of aluminum-on-aluminum impacts at different impact velocities and validating FLAG against a glass-into-water laboratory impact experiment. Our verification results show good agreement with the theoretical maximum pressures, with relative errors as low in magnitude as 1.00%. Our validation results demonstrate FLAG's ability to model various stages of impact cratering, with crater radius relative errors as low as 3.48% and crater depth relative errors as low as 0.79%. Our mesh resolution study shows that FLAG converges at resolutions low enough to reduce the required computation time from about 28 h to about 25 min. We anticipate that FLAG can be used to model larger impact cratering problems with increased accuracy and decreased computational cost on current systems relative to other hydrocodes tested by Pierazzo et al. (2008, “Validation of Numerical Codes for Impact and Explosion Cratering: Impacts on Strengthless and Metal Targets,” MAPS, 43(12), pp. 1917–1938).
{"title":"Verification and Validation of the FLAG Hydrocode for Impact Cratering Simulations","authors":"W. Caldwell, A. Hunter, C. Plesko, S. Wirkus","doi":"10.1115/1.4042516","DOIUrl":"https://doi.org/10.1115/1.4042516","url":null,"abstract":"Verification and validation (V&V) are necessary processes to ensure accuracy of the computational methods used to solve problems key to vast numbers of applications and industries. Simulations are essential for addressing impact cratering problems, because these problems often exceed experimental capabilities. Here, we show that the free Lagrange (FLAG) hydrocode, developed at Los Alamos National Laboratory (Los Alamos, NM), can be used for impact cratering simulations by verifying FLAG against two analytical models of aluminum-on-aluminum impacts at different impact velocities and validating FLAG against a glass-into-water laboratory impact experiment. Our verification results show good agreement with the theoretical maximum pressures, with relative errors as low in magnitude as 1.00%. Our validation results demonstrate FLAG's ability to model various stages of impact cratering, with crater radius relative errors as low as 3.48% and crater depth relative errors as low as 0.79%. Our mesh resolution study shows that FLAG converges at resolutions low enough to reduce the required computation time from about 28 h to about 25 min. We anticipate that FLAG can be used to model larger impact cratering problems with increased accuracy and decreased computational cost on current systems relative to other hydrocodes tested by Pierazzo et al. (2008, “Validation of Numerical Codes for Impact and Explosion Cratering: Impacts on Strengthless and Metal Targets,” MAPS, 43(12), pp. 1917–1938).","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4042516","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48368305","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}
Establishing the credibility of computational fluid dynamics (CFD) models for multiphase flow applications is increasingly becoming a mainstream requirement. However, the established verification and validation (V&V) Standards have been primarily demonstrated for single phase flow applications. Studies to address their applicability for multiphase flows have been limited. Hence, their application may not be trivial and require a thorough investigation. We propose to adopt the ASME V&V 20 Standard and explore its applicability for multiphase flows through several extensions by introducing some of the best practices. In the current study, the proposed verification, validation, and uncertainty quantification (VVUQ) framework is presented and its preliminary application is demonstrated using the simulation of granular discharge through a conical hopper commonly employed in several industrial processes. As part of the proposed extensions to the V&V methodology, a detailed survey of subject matter experts including CFD modelers and experimentalists was conducted. The results from the survey highlighted the need for a more quantitative assessment of importance ranking in addition to a sensitivity study before embarking on simulation and experimental campaigns. Hence, a screening study followed by a global sensitivity was performed to identify the most influential parameters for the CFD simulation as the first phase of the process, which is presented in this paper. The results show that particle–particle coefficients of restitution and friction are the most important parameters for the granular discharge flow problem chosen for demonstration of the process. The identification of these parameters is important to determine their effect on the quantities of interest and improve the confidence level in numerical predictions.
{"title":"Toward the Development of a Verification, Validation, and Uncertainty Quantification Framework for Granular and Multiphase Flows—Part 1: Screening Study and Sensitivity Analysis","authors":"A. Gel, A. Vaidheeswaran, Jordan Musser, C. Tong","doi":"10.1115/1.4041745","DOIUrl":"https://doi.org/10.1115/1.4041745","url":null,"abstract":"Establishing the credibility of computational fluid dynamics (CFD) models for multiphase flow applications is increasingly becoming a mainstream requirement. However, the established verification and validation (V&V) Standards have been primarily demonstrated for single phase flow applications. Studies to address their applicability for multiphase flows have been limited. Hence, their application may not be trivial and require a thorough investigation. We propose to adopt the ASME V&V 20 Standard and explore its applicability for multiphase flows through several extensions by introducing some of the best practices. In the current study, the proposed verification, validation, and uncertainty quantification (VVUQ) framework is presented and its preliminary application is demonstrated using the simulation of granular discharge through a conical hopper commonly employed in several industrial processes. As part of the proposed extensions to the V&V methodology, a detailed survey of subject matter experts including CFD modelers and experimentalists was conducted. The results from the survey highlighted the need for a more quantitative assessment of importance ranking in addition to a sensitivity study before embarking on simulation and experimental campaigns. Hence, a screening study followed by a global sensitivity was performed to identify the most influential parameters for the CFD simulation as the first phase of the process, which is presented in this paper. The results show that particle–particle coefficients of restitution and friction are the most important parameters for the granular discharge flow problem chosen for demonstration of the process. The identification of these parameters is important to determine their effect on the quantities of interest and improve the confidence level in numerical predictions.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4041745","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44158556","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}
When making computational simulation predictions of multiphysics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis, a verification, validation, and uncertainty quantification (VVUQ) workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification (UQ) simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias is used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes use of computing resources.
{"title":"Separability of Mesh Bias and Parametric Uncertainty for a Full System Thermal Analysis","authors":"Benjamin Schroeder, H. Silva, K. Smith","doi":"10.1115/VVS2018-9339","DOIUrl":"https://doi.org/10.1115/VVS2018-9339","url":null,"abstract":"When making computational simulation predictions of multiphysics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis, a verification, validation, and uncertainty quantification (VVUQ) workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification (UQ) simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias is used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes use of computing resources.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44476214","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}
The purpose of this paper is not to present new results; rather, it is to show that the current approach to model validation is not consistent with the accepted mathematics of probability theory. Specifically, we argue that the Sandia V&V Challenge Problem is ill-posed in that the answers sought do not, mathematically, exist. We apply our arguments to show the types of mistakes present in the papers presented in the Journal of Verification, Validation and Uncertainty Quantification, Volume 1,1 along with the challenge problem. Further, we argue that, when the problem is properly posed, both the applicable methodology and the solution techniques are easily drawn from the well-developed mathematics of probability and decision theory. The unfortunate aspect of the challenge problem as currently stated is that it leads to incorrect and inappropriate mathematical approaches that should be avoided and corrected in the current literature.
{"title":"Models, Uncertainty, and the Sandia V&V Challenge Problem","authors":"G. Hazelrigg, G. Klutke","doi":"10.1115/VVS2018-9308","DOIUrl":"https://doi.org/10.1115/VVS2018-9308","url":null,"abstract":"\u0000 The purpose of this paper is not to present new results; rather, it is to show that the current approach to model validation is not consistent with the accepted mathematics of probability theory. Specifically, we argue that the Sandia V&V Challenge Problem is ill-posed in that the answers sought do not, mathematically, exist. We apply our arguments to show the types of mistakes present in the papers presented in the Journal of Verification, Validation and Uncertainty Quantification, Volume 1,1 along with the challenge problem. Further, we argue that, when the problem is properly posed, both the applicable methodology and the solution techniques are easily drawn from the well-developed mathematics of probability and decision theory. The unfortunate aspect of the challenge problem as currently stated is that it leads to incorrect and inappropriate mathematical approaches that should be avoided and corrected in the current literature.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/VVS2018-9308","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43786357","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}
M. Diez, R. Broglia, D. Durante, A. Olivieri, E. Campana, F. Stern
The objective of this work is to provide and use both experimental fluid dynamics (EFD) data and computational fluid dynamics (CFD) results to validate a regular-wave uncertainty quantification (UQ) model of ship response in irregular waves, based on a set of stochastic regular waves with variable frequency. As a secondary objective, preliminary statistical studies are required to assess EFD and CFD irregular wave errors and uncertainties versus theoretical values and evaluate EFD and CFD resistance and motions uncertainties and, in the latter case, errors versus EFD values. UQ methods include analysis of the autocovariance matrix and block-bootstrap of time series values (primary variable). Additionally, the height (secondary variable) associated with the mean-crossing period is assessed by the bootstrap method. Errors and confidence intervals of statistical estimators are used to define validation criteria. The application is a two-degrees-of-freedom (heave and pitch) towed Delft catamaran with a length between perpendiculars equal to 3 m (scale factor equal to 33), sailing at Froude number equal to 0.425 in head waves at scaled sea state 5. Validation variables are x-force, heave and pitch motions, vertical acceleration of bridge, and vertical velocity of flight deck. Autocovariance and block-bootstrap methods for primary variables provide consistent and complementary results; the autocovariance is used to assess the uncertainty associated with expected values and standard deviations and is able to identify undesired self-repetition in the irregular wave signal; block-bootstrap methods are used to assess additional statistical estimators such as mode and quantiles. Secondary variables are used for an additional assessment of the quality of experimental and simulation data as they are generally more difficult to model and predict than primary variables. Finally, the regular wave UQ model provides a good approximation of the desired irregular wave statistics, with average errors smaller than 5% and validation uncertainties close to 10%.
{"title":"Statistical Assessment and Validation of Experimental and Computational Ship Response in Irregular Waves","authors":"M. Diez, R. Broglia, D. Durante, A. Olivieri, E. Campana, F. Stern","doi":"10.1115/1.4041372","DOIUrl":"https://doi.org/10.1115/1.4041372","url":null,"abstract":"The objective of this work is to provide and use both experimental fluid dynamics (EFD) data and computational fluid dynamics (CFD) results to validate a regular-wave uncertainty quantification (UQ) model of ship response in irregular waves, based on a set of stochastic regular waves with variable frequency. As a secondary objective, preliminary statistical studies are required to assess EFD and CFD irregular wave errors and uncertainties versus theoretical values and evaluate EFD and CFD resistance and motions uncertainties and, in the latter case, errors versus EFD values. UQ methods include analysis of the autocovariance matrix and block-bootstrap of time series values (primary variable). Additionally, the height (secondary variable) associated with the mean-crossing period is assessed by the bootstrap method. Errors and confidence intervals of statistical estimators are used to define validation criteria. The application is a two-degrees-of-freedom (heave and pitch) towed Delft catamaran with a length between perpendiculars equal to 3 m (scale factor equal to 33), sailing at Froude number equal to 0.425 in head waves at scaled sea state 5. Validation variables are x-force, heave and pitch motions, vertical acceleration of bridge, and vertical velocity of flight deck. Autocovariance and block-bootstrap methods for primary variables provide consistent and complementary results; the autocovariance is used to assess the uncertainty associated with expected values and standard deviations and is able to identify undesired self-repetition in the irregular wave signal; block-bootstrap methods are used to assess additional statistical estimators such as mode and quantiles. Secondary variables are used for an additional assessment of the quality of experimental and simulation data as they are generally more difficult to model and predict than primary variables. Finally, the regular wave UQ model provides a good approximation of the desired irregular wave statistics, with average errors smaller than 5% and validation uncertainties close to 10%.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4041372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49017503","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}
Uncertainty quantification (UQ) is gaining in maturity and importance in engineering analysis. While historical engineering analysis and design methods have relied heavily on safety factors (SF) with built-in conservatism, modern approaches require detailed assessment of reliability to provide optimized and balanced designs. This paper presents methodologies that support the transition toward this type of approach. Fundamental concepts are described for UQ in general engineering analysis. These include consideration of the sources of uncertainty and their categorization. Of particular importance are the categorization of aleatory and epistemic uncertainties and their separate propagation through an UQ analysis. This familiar concept is referred to here as a “two-dimensional” approach, and it provides for the assessment of both the probability of a predicted occurrence and the credibility in that prediction. Unique to the approach presented here is the adaptation of the concept of a bounding probability box to that of a credible probability box. This requires estimates for probability distributions related to all uncertainties both aleatory and epistemic. The propagation of these distributions through the uncertainty analysis provides for the assessment of probability related to the system response, along with a quantification of credibility in that prediction. Details of a generalized methodology for UQ in this framework are presented, and approaches for interpreting results are described. Illustrative examples are presented.
{"title":"A General Methodology for Uncertainty Quantification in Engineering Analyses Using a Credible Probability Box","authors":"M. E. Ewing, B. Liechty, D. L. Black","doi":"10.1115/1.4041490","DOIUrl":"https://doi.org/10.1115/1.4041490","url":null,"abstract":"Uncertainty quantification (UQ) is gaining in maturity and importance in engineering analysis. While historical engineering analysis and design methods have relied heavily on safety factors (SF) with built-in conservatism, modern approaches require detailed assessment of reliability to provide optimized and balanced designs. This paper presents methodologies that support the transition toward this type of approach. Fundamental concepts are described for UQ in general engineering analysis. These include consideration of the sources of uncertainty and their categorization. Of particular importance are the categorization of aleatory and epistemic uncertainties and their separate propagation through an UQ analysis. This familiar concept is referred to here as a “two-dimensional” approach, and it provides for the assessment of both the probability of a predicted occurrence and the credibility in that prediction. Unique to the approach presented here is the adaptation of the concept of a bounding probability box to that of a credible probability box. This requires estimates for probability distributions related to all uncertainties both aleatory and epistemic. The propagation of these distributions through the uncertainty analysis provides for the assessment of probability related to the system response, along with a quantification of credibility in that prediction. Details of a generalized methodology for UQ in this framework are presented, and approaches for interpreting results are described. Illustrative examples are presented.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1115/1.4041490","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48983512","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}
U. Otgonbaatar, E. Baglietto, Y. Caffari, N. Todreas, G. Lenci
In this work, a general methodology and innovative framework to characterize and quantify representativeness uncertainty of performance indicator measurements of power generation systems is proposed. The representativeness uncertainty refers to the difference between a measurement value of a performance indicator quantity and its reference true value. It arises from the inherent variability of the quantity being measured. The main objectives of the methodology are to characterize and reduce the representativeness uncertainty by adopting numerical simulation in combination with experimental data and to improve the physical description of the measurement. The methodology is applied to an industrial case study for demonstration. The case study involves a computational fluid dynamics (CFD) simulation of an orifice plate-based mass flow rate measurement, using a commercially available package. Using the insight obtained from the CFD simulation, the representativeness uncertainty in mass flow rate measurement is quantified and the associated random uncertainties are comprehensively accounted for. Both parametric and nonparametric implementations of the methodology are illustrated. The case study also illustrates how the methodology is used to quantitatively test the level of statistical significance of the CFD simulation result after accounting for the relevant uncertainties.
{"title":"A Methodology for Characterizing Representativeness Uncertainty in Performance Indicator Measurements of Power Generating Systems","authors":"U. Otgonbaatar, E. Baglietto, Y. Caffari, N. Todreas, G. Lenci","doi":"10.1115/1.4041687","DOIUrl":"https://doi.org/10.1115/1.4041687","url":null,"abstract":"In this work, a general methodology and innovative framework to characterize and quantify representativeness uncertainty of performance indicator measurements of power generation systems is proposed. The representativeness uncertainty refers to the difference between a measurement value of a performance indicator quantity and its reference true value. It arises from the inherent variability of the quantity being measured. The main objectives of the methodology are to characterize and reduce the representativeness uncertainty by adopting numerical simulation in combination with experimental data and to improve the physical description of the measurement. The methodology is applied to an industrial case study for demonstration. The case study involves a computational fluid dynamics (CFD) simulation of an orifice plate-based mass flow rate measurement, using a commercially available package. Using the insight obtained from the CFD simulation, the representativeness uncertainty in mass flow rate measurement is quantified and the associated random uncertainties are comprehensively accounted for. Both parametric and nonparametric implementations of the methodology are illustrated. The case study also illustrates how the methodology is used to quantitatively test the level of statistical significance of the CFD simulation result after accounting for the relevant uncertainties.","PeriodicalId":52254,"journal":{"name":"Journal of Verification, Validation and Uncertainty Quantification","volume":null,"pages":null},"PeriodicalIF":0.6,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46649577","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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