Assessment Criteria for Computational Fluid Dynamics Validation Benchmark Experiments

[+] Author and Article Information
William Oberkampf

ASME Member, Consultant, 5112 Hidden Springs Trail, Georgetown, TX, 78633

Barton L. Smith

ASME Member, Professor, Dept. of Mechanical and Aerospace Engineering, Utah State University, Logan, UT, 84322

1Corresponding author.

ASME 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 so that the predictive accuracy of the model can be critically determined. This article proposes a framework for assessing validation experiments for computational fluid dynamics regarding information content, data completeness, and uncertainty quantification. This framework combines two previously published concepts: the strong-sense benchmark for validation experiments and the modeling assessment procedure referred to as the Predictive Capability Maturity Method. The 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. 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 validation experiment, it is appropriate for critical assessment of modern computational fluid dynamics (CFD) simulations.

Copyright (c) 2017 by ASME
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