Quality and trust in industrial CFD - A European initiative

Hutton, A. G.; Casey, Michael

doi:10.2514/6.2001-656

Cited by 14 publications

(6 citation statements)

References 4 publications

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“…Generally, industrial customers tend to use computational models in more pragmatic ways than do code developers and researchers. [96] The rigorous application of computational models by code developers and researchers is often a luxury not available to industrial customers, who are constrained by the time and costs of industry design procedures. These constraints have consequences on the level of requirements that industry can put on a simulation.…”

Section: Practical Applications In Industrial Settingsmentioning

confidence: 99%

Verification, validation, and predictive capability in computational engineering and physics

Oberkampf

Trucano

Hirsch

2004

Applied Mechanics Reviews

507

197

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Developers of computer codes, analysts who use the codes, and decision makers who rely on the results of the analyses face a critical question: How should confidence in modeling and simulation be critically assessed? Verification and validation (V&V) of computational simulations are the primary methods for building and quantifying this confidence. Briefly, verification is the assessment of the accuracy of the solution to a computational model. Validation is the assessment of the accuracy of a computational simulation by comparison with experimental data. In verification, the relationship of the simulation to the real world is not an issue. In validation, the relationship between computation and the real world, i.e., experimental data, is the issue.-3 -This paper presents our viewpoint of the state of the art in V&V in computational physics. (In this paper we refer to all fields of computational engineering and physics, e.g., computational fluid dynamics, computational solid mechanics, structural dynamics, shock wave physics, computational chemistry, etc., as computational physics.) We do not provide a comprehensive review of the multitudinous contributions to V&V, although we do reference a large number of previous works from many fields. We have attempted to bring together many different perspectives on V&V, highlight those perspectives that are effective from a practical engineering viewpoint, suggest future research topics, and discuss key implementation issues that are necessary to improve the effectiveness of V&V. We describe our view of the framework in which predictive capability relies on V&V, as well as other factors that affect predictive capability. Our opinions about the research needs and management issues in V&V are very practical: What methods and techniques need to be developed and what changes in the views of management need to occur to increase the usefulness, reliability, and impact of computational physics for decision making about engineering systems?We review the state of the art in V&V over a wide range of topics, for example, prioritization of V&V activities using the Phenomena Identification and Ranking Table (PIRT), code verification, software quality assurance (SQA), numerical error estimation, hierarchical experiments for validation, characteristics of validation experiments, the need to perform nondeterministic computational simulations in comparisons with experimental data, and validation metrics. We then provide an extensive discussion of V&V research and implementation issues that we believe must be addressed for V&V to be more effective in improving confidence in computational predictive capability. Some of the research topics addressed are development of improved procedures for the use of the PIRT for prioritizing V&V activities, the method of manufactured solutions for code verification, development and use of hierarchical validation diagrams, and the construction and use of validation metrics incorporating statistical measures. Some of the implementation topics address...

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Section: Practical Applications In Industrial Settingsmentioning

confidence: 99%

Verification, validation, and predictive capability in computational engineering and physics

Oberkampf

Trucano

Hirsch

2004

Applied Mechanics Reviews

507

197

View full text Add to dashboard Cite

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“…When judged by the results of numerousvalidationstudies, 1¡4 this lack of con dence in CFD is not surprising. To quote Hutton and Casey, 2 "the results of such exercises can be highly user-dependenteven when the same CFD software with the same models is being used." Oftentimes, when a number of users do obtain the same results, these results do not agree with the experimental data.…”

mentioning

confidence: 99%

Grid convergence error analysis for mixed-order numerical schemes

Roy

2001

15th AIAA Computational Fluid Dynamics Conference

139

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New developments are presented in the area of grid convergence error analysis for mixed-order numerical schemes. A mixed-order scheme is de ned as a numerical method where the formal order of the truncation error varies either spatially, for example, at a shock wave, or for different terms in the governing equations, for example, third-order convection with second-order diffusion. The case examined is the Mach 8 inviscid ow of a calorically perfect gas over a spherically blunted cone. This ow eld contains a strong bow shock wave, where the formally second-order numerical scheme is reduced to rst order via a ux-limiting procedure. The proposed mixedorder error analysis method allows for nonmonotonic behavior in the solution variables as the mesh is re ned. Nonmonotonicity in the local solution variables is shown to arise from a cancellation of rst-and second-order error terms for the present case. An error estimator is proposed based on the mixed-order analysis and is shown to provide good estimates of the actual error when the solution converges nonmonotonicallywith grid re nement.

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“…While RE is a simple, elegant and general mathematical idea that works on approximations that have uniform convergence properties described by a Taylor expansion, its use on multi-scale problems is questionable. In Computational Fluid Dynamics (CFD) [7,8,21,26,27,30,31,32,35] the applicability of RE is limited by the fact that meshes might not be fine enough to satisfy accurately the a priori convergence estimates that are only asymptotic in nature. Furthermore, the order of convergence of a CFD code is often space dependent and eventually parameter dependent [16,35].…”

mentioning

confidence: 99%

On Optimized Extrapolation Method for Elliptic Problems with Large Coefficient Variation

Garbey

Shyy

2007

Journal of Algorithms & Computational Technology

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A posteriori error estimators are fundamental tools for providing confidence in the numerical computation of PDEs. In this paper we present a new technique that produces global a posteriori error estimates based on an optimized extrapolation method. The choice of the objective function as well as the representation of the unknown weight function in the extrapolation formula is discussed. This paper focuses on applications governed by the elliptic problem div(ρΔu)= f, with Dirichlet boundary conditions. Special attention is given to problems where the positive coefficient ρ exhibits large variations throughout the domain or f contains some singular source terms. These features are commonly encountered in physical problems including heat transfer with heterogeneous material properties and pressure solver in multiphase flows with large ratio of density between fluids.

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Quality and trust in industrial CFD - A European initiative

Cited by 14 publications

References 4 publications

Verification, validation, and predictive capability in computational engineering and physics

Verification, validation, and predictive capability in computational engineering and physics

Grid convergence error analysis for mixed-order numerical schemes

On Optimized Extrapolation Method for Elliptic Problems with Large Coefficient Variation

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