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Research Papers

Assessment Criteria for Computational Fluid Dynamics Model Validation Experiments

[+] Author and Article Information
William L. Oberkampf

Mem. ASME
W. L. Oberkampf Consulting,
5112 Hidden Springs Trail,
Georgetown, TX 78633
e-mail: wloconsulting@gmail.com

Barton L. Smith

Mem. ASME
Professor
Department of Mechanical
and Aerospace Engineering,
Utah State University,
Logan, UT 84322
e-mail: bsmith@engineering.usu.edu

1Corresponding author.

An earlier version of this article was presented at the AIAA Science and Technology Forum and Exposition, National Harbor, MD, Jan. 13–17, 2014, as Paper No. AIAA-2014-0205.Manuscript received March 12, 2017; final manuscript received August 31, 2017; published online October 11, 2017. Assoc. Editor: Yassin A. Hassan.

J. Verif. Valid. Uncert 2(3), 031002 (Oct 11, 2017) (14 pages) Paper No: VVUQ-17-1013; doi: 10.1115/1.4037887 History: Received March 12, 2017; Revised August 31, 2017

Validation assesses the accuracy of a mathematical model by comparing simulation results to experimentally measured quantities of interest. Model validation experiments emphasize obtaining detailed information on all input data needed by the mathematical model, in addition to measuring the system response quantities (SRQs) so that the predictive accuracy of the model can be critically determined. This article proposes a framework for assessing model validation experiments for computational fluid dynamics (CFD) regarding information content, data completeness, and uncertainty quantification (UQ). This framework combines two previously published concepts: the strong-sense model validation experiments and the modeling maturity assessment procedure referred to as the predictive capability maturity method (PCMM). The model validation experiment assessment requirements are captured in a table of six attributes: experimental facility, analog instrumentation and signal processing, boundary and initial conditions, fluid and material properties, test conditions, and measurement of system responses, with four levels of information completeness for each attribute. The specifics of this table are constructed for a generic wind tunnel experiment. Each attribute’s completeness is measured from the perspective of the level of detail needed for input data using direct numerical simulation of the Navier–Stokes equations. While this is an extraordinary and unprecedented requirement for level of detail in a model validation experiment, it is appropriate for critical assessment of modern CFD simulations.

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Grahic Jump Location
Fig. 1

Sequence of measurement signals, processing, and recording

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