Validation and error estimation of computational models

Rebba, Ramesh; Mahadevan, Sankaran; Huang, Shupei

doi:10.1016/j.ress.2005.11.035

Cited by 125 publications

(77 citation statements)

References 9 publications

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“…Traditional graphical comparisons should be included; however, validation metrics should also be used. Because validation metrics are in an early stage of development, only a limited range of examples are available [4,19,[66][67][68][69][70][71][72][73][74][75][76][77][78]. Validation metric results should be computed for all the SRQs measured in the experiment so that objective information is complete rather than partial or biased toward those that "look good.…”

Section: Comparing Candidate Code Results With Validation Benchmarksmentioning

confidence: 99%

“…As is shown in the top portion of Fig. 4, methods for quantitative comparison, i.e., validation metrics, have become an active topic of research [4,19,[66][67][68][69][70][71][72][73][74][75][76][77][78]. High quality validation metrics must use statistical procedures to compare the results of code calculations with the measurements of validation experiments.…”

Section: Guidelinementioning

confidence: 99%

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Verification and validation benchmarks

Oberkampf

Trucano²

2008

Nuclear Engineering and Design

183

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Verification and validation (V&V) are the primary means to assess the accuracy and reliability of computational simulations. V&V methods and procedures have fundamentally improved the credibility of simulations in several high-consequence fields, such as nuclear reactor safety, underground nuclear waste storage, and nuclear weapon safety. Although the terminology is not uniform across engineering disciplines, code verification deals with assessing the reliability of the software coding, and solution verification deals with assessing the numerical accuracy of the solution to a computational model. Validation addresses the physics modeling accuracy of a computational simulation by comparing the computational results with experimental data. Code verification benchmarks and validation benchmarks have been constructed for a number of years in every field of computational simulation. However, no comprehensive guidelines have been proposed for the construction and use of V&V benchmarks. For example, the field of nuclear reactor safety has not focused on code verification benchmarks, but it has placed great emphasis on developing validation benchmarks. Many of these validation benchmarks are closely related to the operations of actual reactors at near-safety-critical conditions, as opposed to being more fundamental-physics benchmarks. This paper presents recommendations for the effective design and use of code verification benchmarks based on manufactured solutions, classical analytical solutions, and highly accurate numerical solutions. In addition, this paper presents recommendations for the design and use of validation benchmarks, highlighting the careful design of building-block experiments, the estimation of experimental measurement uncertainty for both inputs and outputs to the code, validation metrics, and the role of model calibration in validation. It is argued that the understanding of predictive capability of a computational model is built on the level of achievement in V&V activities, how closely related the V&V benchmarks are to the actual application of interest, and the quantification of uncertainties related to the application of interest.

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Section: Comparing Candidate Code Results With Validation Benchmarksmentioning

confidence: 99%

Section: Guidelinementioning

confidence: 99%

Verification and validation benchmarks

Oberkampf

Trucano²

2008

Nuclear Engineering and Design

183

View full text Add to dashboard Cite

show abstract

“…Mathematical theory and methods have been discussed in [4] for quantifying numerical error and model form error in computational mechanics models, but these methods require access to the original PDEs of the system. A simplified approach to error quantification for generic computational models has been developed in [5]. and surrogate model error, and some errors are deterministic, such as discretization error in FEA.…”

Section: Overviewmentioning

confidence: 99%

“…and surrogate model error, and some errors are deterministic, such as discretization error in FEA. In previous work, model form error is first explicitly expressed in terms of all error sources, and then it is quantified by sampling each of the error terms [5]. This approach has two significant drawbacks: first, in some cases it is not easy to find an analytical expression for model form error in terms of all error sources; second, the discretization error is treated as a random variable, which is incorrect, although we agree on the fact that in non-deterministic analysis correcting for this error would change the uncertainty of model prediction [5,6,7].…”

Section: Overviewmentioning

confidence: 99%