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2019 | OriginalPaper | Chapter

5. Experimental Credibility and Its Role in Model Validation and Decision Making

Authors : Sarah L. Kieweg, Walt R. Witkowski

Published in: Model Validation and Uncertainty Quantification, Volume 3

Publisher: Springer International Publishing

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Abstract

Experiments are a critical part of the model validation process, and the credibility of the resulting simulations are themselves dependent on the credibility of the experiments. The impact of experimental credibility on model validation occurs at several points through the model validation and uncertainty quantification (MVUQ) process. Many aspects of experiments involved in the development and verification and validation (V&V) of computational simulations will impact the overall simulation credibility. In this document, we define experimental credibility in the context of model validation and decision making. We summarize possible elements for evaluating experimental credibility, sometimes drawing from existing and preliminary frameworks developed for evaluation of computational simulation credibility. The proposed framework is an expert elicitation tool for planning, assessing, and communicating the completeness and correctness of an experiment (“test”) in the context of its intended use—validation. The goals of the assessment are (1) to encourage early communication and planning between the experimentalist, computational analyst, and customer, and (2) the communication of experimental credibility. This assessment tool could also be used to decide between potential existing data sets to be used for validation. The evidence and story of experimental credibility will support the communication of overall simulation credibility.

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Literature
1.
go back to reference ASME V&V 10-2006: Guide for Verification and Validation in Computational Solid Mechanics. ASME, New York (2006) ASME V&V 10-2006: Guide for Verification and Validation in Computational Solid Mechanics. ASME, New York (2006)
2.
go back to reference ASME V&V 20-2009: Standard for Verification and Validation in Computational Fluid Dynamics and Heat Transfer. ASME, New York (2009) ASME V&V 20-2009: Standard for Verification and Validation in Computational Fluid Dynamics and Heat Transfer. ASME, New York (2009)
3.
go back to reference ASME Performance Test Codes Supervisory Committee: ASME PTC 19.1–2013: Test Uncertainty. ASME, New York (2013) ASME Performance Test Codes Supervisory Committee: ASME PTC 19.1–2013: Test Uncertainty. ASME, New York (2013)
4.
go back to reference Coleman, H.W., Steele, W.G.: Experimentation, Validation, and Uncertainty Analysis for Engineers, 3rd edn. Wiley, New York (2009)CrossRef Coleman, H.W., Steele, W.G.: Experimentation, Validation, and Uncertainty Analysis for Engineers, 3rd edn. Wiley, New York (2009)CrossRef
5.
go back to reference What is Predictive Capability Maturity Model (PCMM) SAND2016-7399TR (UUR). Sandia National Laboratories, Livermore (2016) What is Predictive Capability Maturity Model (PCMM) SAND2016-7399TR (UUR). Sandia National Laboratories, Livermore (2016)
6.
go back to reference Oberkampf, W.L., Pilch, M., Trucano, T.G.: Predictive Capability Maturity Model for Computational Modeling and Simulations. SAND2007–5948 (UUR). Sandia National Laboratories, Livermore (2007)CrossRef Oberkampf, W.L., Pilch, M., Trucano, T.G.: Predictive Capability Maturity Model for Computational Modeling and Simulations. SAND2007–5948 (UUR). Sandia National Laboratories, Livermore (2007)CrossRef
7.
go back to reference Trucano, T.G., Pilch, M., Oberkampf, W.L.: General Concepts for Experimental Validation of ASCI Code Applications. SAND2002–0341 (UUR). Sandia National Laboratories, Livermore (2002)CrossRef Trucano, T.G., Pilch, M., Oberkampf, W.L.: General Concepts for Experimental Validation of ASCI Code Applications. SAND2002–0341 (UUR). Sandia National Laboratories, Livermore (2002)CrossRef
8.
go back to reference Hills, R.G., et al.: Development of a Fourth Generation Predictive Capability Maturity Model. SAND2013–8051 (UUR). Sandia National Laboratories, Livermore (2013) Hills, R.G., et al.: Development of a Fourth Generation Predictive Capability Maturity Model. SAND2013–8051 (UUR). Sandia National Laboratories, Livermore (2013)
9.
go back to reference Oberkampf, W.L., Smith, B.L.: Assessment Criteria for Computational Fluid Dynamics Validation Benchmark Experiments. in 52nd Aerospace Sciences Meeting. National Harbor, Maryland (2014) Oberkampf, W.L., Smith, B.L.: Assessment Criteria for Computational Fluid Dynamics Validation Benchmark Experiments. in 52nd Aerospace Sciences Meeting. National Harbor, Maryland (2014)
10.
go back to reference Lance, B.W., Harris, J.R., Smith, B.L.: Experimental validation benchmark data for computational fluid dynamics of mixed convection on a vertical flat plat. ASME J. Verification Validation Uncertainty Propagation. 1(September 2016), 021005 (2016)CrossRef Lance, B.W., Harris, J.R., Smith, B.L.: Experimental validation benchmark data for computational fluid dynamics of mixed convection on a vertical flat plat. ASME J. Verification Validation Uncertainty Propagation. 1(September 2016), 021005 (2016)CrossRef
11.
go back to reference Lance, B.W., Smith, B.L.: Experimental validation benchmark data for computational fluid dynamics of transient convection from forced to natural with flow reversal on a vertical flat plat. ASME J. Verification Validation Uncertainty Propagation. 1(September 2016), 031005 (2016)CrossRef Lance, B.W., Smith, B.L.: Experimental validation benchmark data for computational fluid dynamics of transient convection from forced to natural with flow reversal on a vertical flat plat. ASME J. Verification Validation Uncertainty Propagation. 1(September 2016), 031005 (2016)CrossRef
12.
go back to reference Elele, J., et al.: Applying Modeling and Simulation Verification, validation and Accreditation (VV&A) Techniques to Test and Laboratory Facilities, in ASME V&V Conference (2012) Elele, J., et al.: Applying Modeling and Simulation Verification, validation and Accreditation (VV&A) Techniques to Test and Laboratory Facilities, in ASME V&V Conference (2012)
Metadata
Title
Experimental Credibility and Its Role in Model Validation and Decision Making
Authors
Sarah L. Kieweg
Walt R. Witkowski
Copyright Year
2019
DOI
https://doi.org/10.1007/978-3-319-74793-4_5

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