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.
Skip Nav Destination
Article navigation
September 2017
Research-Article
Assessment Criteria for Computational Fluid Dynamics Model Validation Experiments
William L. Oberkampf,
William L. Oberkampf
Mem. ASME
W. L. Oberkampf Consulting,
5112 Hidden Springs Trail,
Georgetown, TX 78633
e-mail: wloconsulting@gmail.com
W. L. Oberkampf Consulting,
5112 Hidden Springs Trail,
Georgetown, TX 78633
e-mail: wloconsulting@gmail.com
Search for other works by this author on:
Barton L. Smith
Barton L. Smith
Mem. ASME
Professor
Department of Mechanical
and Aerospace Engineering,
Utah State University,
Logan, UT 84322
e-mail: bsmith@engineering.usu.edu
Professor
Department of Mechanical
and Aerospace Engineering,
Utah State University,
Logan, UT 84322
e-mail: bsmith@engineering.usu.edu
Search for other works by this author on:
William L. Oberkampf
Mem. ASME
W. L. Oberkampf Consulting,
5112 Hidden Springs Trail,
Georgetown, TX 78633
e-mail: wloconsulting@gmail.com
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
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. Sep 2017, 2(3): 031002 (14 pages)
Published Online: October 11, 2017
Article history
Received:
March 12, 2017
Revised:
August 31, 2017
Citation
Oberkampf, W. L., and Smith, B. L. (October 11, 2017). "Assessment Criteria for Computational Fluid Dynamics Model Validation Experiments." ASME. J. Verif. Valid. Uncert. September 2017; 2(3): 031002. https://doi.org/10.1115/1.4037887
Download citation file:
598
Views
0
Citations
Get Email Alerts
Cited By
A Methodology for the Efficient Quantification of Parameter and Model Uncertainty
J. Verif. Valid. Uncert
Verification of MOOSE/Bison's Heat Conduction Solver Using Combined Spatiotemporal Convergence Analysis
J. Verif. Valid. Uncert (June 2022)
On the Interpretation and Scope of the V&V 20 Standard for Verification and Validation in Computational Fluid Dynamics and Heat Transfer
J. Verif. Valid. Uncert (June 2022)
Verification and Validation: The Path to Predictive Scale-Resolving Simulations of Turbulence
J. Verif. Valid. Uncert (June 2022)
Related Articles
Integrated Design and Testing of an Anemometer for Autonomous Sail Drones
J. Dyn. Sys., Meas., Control (May,2018)
Perspective: Validation—What Does It Mean?
J. Fluids Eng (March,2009)
Frequency Response Characteristics of an Active Heat Flux Gage
J. Heat Transfer (August,1998)
Design of Experiments Using Uncertainty Information
J. Heat Transfer (August,1996)
Related Proceedings Papers
Related Chapters
Constructing Dynamic Event Trees from Markov Models (PSAM-0369)
Proceedings of the Eighth International Conference on Probabilistic Safety Assessment & Management (PSAM)
Instrument Calibration Methods
Metrology and Instrumentation: Practical Applications for Engineering and Manufacturing
Surface Analysis and Tools
Tribology of Mechanical Systems: A Guide to Present and Future Technologies