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Verification and validation benchmarks

Nuclear Engineering and Design

Oberkampf, William L.; Trucano, Timothy G.

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. © 2007 Elsevier B.V. All rights reserved.

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TYPE Conference YEAR 2008
OSTIScopus

Graph analysis with high-performance computing

Computing in Science and Engineering

Hendrickson, Bruce A.; Berry, Jonathan W.

Large, complex graphs arise in many settings including the Internet, social networks, and communication networks. To study such data sets, the authors explored the use of highperformance computing (HPC) for graph algorithms. They found that the challenges in these applications are quite different from those arising in traditional HPC applications and that massively multithreaded machines are well suited for graph problems. © 2008 IEEE.

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TYPE Journal Article YEAR 2008
ScopusOSTI
Results 8726–8750 of 9,998
Results 8726–8750 of 9,998