Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Hostname: page-component-6bf8c574d5-b4m5d Total loading time: 0 Render date: 2025-02-21T18:05:32.332Z Has data issue: false hasContentIssue false

10 - Model validation fundamentals

from Part IV - Model validation and prediction

Published online by Cambridge University Press:  05 March 2013

William L. Oberkampf  and
Christopher J. Roy
Christopher J. Roy
Affiliation:
Virginia Polytechnic Institute and State University
Get access

Summary

Because of the well-developed methodologies and techniques developed in metrology, experimental measurements are usually viewed as the best way to estimate a true value, if one exists. This level of trust and credibility in experimental measurements has been built over at least four millennia of learning, both from mistakes and successes. This is not meant to imply that experimental measurements always yield an accurate estimate of the true value. Experimental measurements can be inaccurate, or downright wrong, for many reasons. What is meant by the trustworthiness of an experimental measurement is that techniques for investigating its limitations, weaknesses, and uncertainties are generally well understood. When new experimental diagnostic techniques are developed they must be carefully investigated and understood. Where possible, comparisons need to be made with measurements from existing, better understood, techniques so that measurement uncertainty can be better quantified. As science and technology progresses, new measurement techniques can increase the confidence in measurement accuracy and also begin to measure physical quantities that were previously immeasurable.

An enlightening way of thinking about the trustworthiness of experimental measurements is to think of an experimental measurement as “asking a question of nature” (Hornung and Perry, 1998). When a measurement result is obtained, the result can be thought of as nature's answer to a question we have asked. We tend to believe that the answer obtained is the answer to the question we think we asked. However, this is not actually the case because there are always assumptions involved on our part. For example, when we measure the fluid velocity in a flow field, we believe we asked the question: given the flow field of interest, what is the velocity of the fluid at a certain point? Our intent is to ask nature the question that excludes or minimizes the effect of random or systematic errors in the measurement. If, however, there is significant random measurement error or if there is an unknown systematic error either in the measurement itself or in the data reduction procedure, then the question asked of nature is different from what we thought we asked. That is, nature answered the question that includes the random and systematic errors, whether large or small.

Type
Chapter
Information
Verification and Validation in Scientific Computing , pp. 371 - 408
Publisher: Cambridge University Press
Print publication year: 2010

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

Aeschliman, D. P. and Oberkampf, W. L. (1998). Experimental methodology for computational fluid dynamics code validation. AIAA Journal. 36(5), 733–741.CrossRefGoogle Scholar
AIAA (1998). Guide for the Verification and Validation of Computational Fluid Dynamics Simulations. AIAA-G-077–1998, Reston, VA, American Institute of Aeronautics and Astronautics.
Anderson, M. G. and Bates, P. D. (2001). Hydrological science: model credibility and scientific integrity. In Model Validation: Perspectives in Hydrological Science. Anderson, M. G. and Bates, P. D. (eds.). New York, John Wiley.Google Scholar
Anderson, M. G. and Bates, P. D., eds. (2001). Model Validation: Perspectives in Hydrological Science. New York, NY, John Wiley.Google Scholar
Balci, O., Ormsby, W. F., Carr, J. T., and Saadi, S. D. (2000). Planning for verification, validation, and accreditation of modeling and simulation applications. 2000 Winter Simulation Conference, Orlando FL, 829–839.CrossRef
Barber, T. J. (1998). Role of code validation and certification in the design environment. AIAA Journal. 36(5), 752–758.CrossRefGoogle Scholar
Benek, J. A., Kraft, E. M., and Lauer, R. F. (1998). Validation issues for engine–airframe integration. AIAA Journal. 36(5), 759–764.CrossRefGoogle Scholar
Bossel, H. (1994). Modeling and Simulation. 1st edn., Wellesley, MA, A. K. Peters.CrossRefGoogle Scholar
Chiles, J.-P. and Delfiner, P. (1999). Geostatistics: Modeling Spatial Uncertainty, New York, John Wiley.CrossRefGoogle Scholar
Cosner, R. R. (1995). CFD validation requirements for technology transition. 26th AIAA Fluid Dynamics Conference, AIAA Paper 95–2227, San Diego, CA, American Institute of Aeronautics and Astronautics.CrossRef
Hornung, H. G. and Perry, A. E. (1998). Personal communication.
Kleijnen, J. P. C. (1998). Experimental design for sensitivity analysis, optimization, and validation of simulation models. In Handbook of Simulation: Principles, Methodology, Advances, Application, and Practice. Banks, J. (ed.). New York, John Wiley: 173–223.CrossRefGoogle Scholar
Kleindorfer, G. B., O’Neill, L., and Ganeshan, R. (1998). Validation in simulation: various positions in the philosophy of science. Management Science. 44(8), 1087–1099.CrossRefGoogle Scholar
Lin, S. J., Barson, S. L., and Sindir, M. M. (1992). Development of evaluation criteria and a procedure for assessing predictive capability and code performance. Advanced Earth-to-Orbit Propulsion Technology Conference, Marshall Space Flight Center, Huntsville, AL.
Marvin, J. G. (1995). Perspective on computational fluid dynamics validation. AIAA Journal. 33(10), 1778–1787.CrossRefGoogle Scholar
McNish, A. G. (1962). The speed of light. Institute of Radio Engineers, Transactions on Instrumentation. I-11(3–4), 138–148.Google Scholar
Morgan, M. G. and Henrion, M. (1990). Uncertainty: a Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis. 1st edn., Cambridge, UK, Cambridge University Press.CrossRefGoogle Scholar
Morton, A. and Suarez, M. (2001). Kinds of models. In Model Validation: Perspectives in Hydrological Science. Anderson, M. G. and Bates, P. D. (eds.). New York, John Wiley.Google Scholar
Murray-Smith, D. J. (1998). Methods for the external validation of continuous systems simulation models: a review. Mathematical and Computer Modelling of Dynamics Systems. 4, 5–31.CrossRefGoogle Scholar
Oberkampf, W. L. and Aeschliman, D. P. (1992). Joint computational/experimental aerodynamics research on a hypersonic vehicle: Part 1, Experimental results. AIAA Journal. 30(8), 2000–2009.CrossRefGoogle Scholar
Oberkampf, W. L. and Barone, M. F. (2006). Measures of agreement between computation and experiment: validation metrics. Journal of Computational Physics. 217(1), 5–36.CrossRefGoogle Scholar
Oberkampf, W. L. and Trucano, T. G. (2000). Validation methodology in computational fluid dynamics. Fluids 2000 Conference, AIAA Paper 2000–2549, Denver, CO, American Institute of Aeronautics and Astronautics.CrossRef
Oberkampf, W. L. and Trucano, T. G. (2002). Verification and validation in computational fluid dynamics. Progress in Aerospace Sciences. 38(3), 209–272.CrossRefGoogle Scholar
Oberkampf, W. L. and Trucano, T. G. (2007). Verification and Validation Benchmarks. SAND2007–0853, Albuquerque, NM, Sandia National Laboratories.CrossRef
Oberkampf, W. L. and Trucano, T. G. (2008). Verification and validation benchmarks. Nuclear Engineering and Design. 238(3), 716–743.CrossRefGoogle Scholar
Oberkampf, W. L., Aeschliman, D. P., Henfling, J. F., and Larson, D. E. (1995). Surface pressure measurements for CFD code validation in hypersonic flow. 26th AIAA Fluid Dynamics Conference, AIAA Paper 95–2273, San Diego, CA, American Institute of Aeronautics and Astronautics.CrossRef
Oberkampf, W. L., Trucano, T. G., and Hirsch, C. (2004). Verification, validation, and predictive capability in computational engineering and physics. Applied Mechanics Reviews. 57(5), 345–384.CrossRefGoogle Scholar
Oreskes, N. and Belitz, K. (2001). Philosophical issues in model assessment. In Model Validation: Perspectives in Hydrological Science. Anderson, M. G. and Bates, P. D. (eds.). New York, John Wiley.Google Scholar
Oreskes, N., Shrader-Frechette, K., and Belitz, K. (1994). Verification, validation, and confirmation of numerical models in the earth sciences. Science. 263, 641–646.CrossRefGoogle ScholarPubMed
Pilch, M., Trucano, T. G., Moya, J. L., Froehlich, G. K., Hodges, A. L. and Peercy, D. E. (2001). Guidelines for Sandia ASCI Verification and Validation Plans – Content and Format: Version 2. SAND2000–3101, Albuquerque, NM, Sandia National Laboratories.
Pilch, M., Trucano, T. G., Peercy, D. E., Hodges, A. L., and Froehlich, G. K. (2004). Concepts for Stockpile Computing (OUO). SAND2004–2479 (Restricted Distribution, Official Use Only), Albuquerque, NM, Sandia National Laboratories.
Porter, J. L. (1996). A summary/overview of selected computational fluid dynamics (CFD) code validation/calibration activities. 27th AIAA Fluid Dynamics Conference, AIAA Paper 96–2053, New Orleans, LA, American Institute of Aeronautics and Astronautics.CrossRef
Refsgaard, J. C. (2000). Towards a formal approach to calibration and validation of models using spatial data. In Spatial Patterns in Catchment Hydrology: Observations and Modelling. Grayson, R. and Bloschl, G. (eds.). Cambridge, Cambridge University Press: 329–354.Google Scholar
Rizzi, A. and Vos, J. (1998). Toward establishing credibility in computational fluid dynamics simulations. AIAA Journal. 36(5), 668–675.CrossRefGoogle Scholar
Roache, P. J. (1998). Verification and Validation in Computational Science and Engineering, Albuquerque, NM, Hermosa Publishers.Google Scholar
Rykiel, E. J. (1996). Testing ecological models: the meaning of validation. Ecological Modelling. 90(3), 229–244.CrossRefGoogle Scholar
Sargent, R. G. (1998). Verification and validation of simulation models. 1998 Winter Simulation Conference, Washington, DC, 121–130.CrossRef
Sindir, M. M., Barson, S. L., Chan, D. C., and Lin, W. H. (1996). On the development and demonstration of a code validation process for industrial applications. 27th AIAA Fluid Dynamics Conference, AIAA Paper 96–2032, New Orleans, LA, American Institute of Aeronautics and Astronautics.CrossRef
Sindir, M. M. and Lynch, E. D. (1997). Overview of the state-of-practice of computational fluid dynamics in advanced propulsion system design. 28th AIAA Fluid Dynamics Conference, AIAA Paper 97–2124, Snowmass, CO, American Institute of Aeronautics and Astronautics.CrossRef
Trucano, T. G., Pilch, M., and Oberkampf, W. L. (2002). General Concepts for Experimental Validation of ASCI Code Applications. SAND2002–0341, Albuquerque, NM, Sandia National Laboratories.CrossRef
Youden, W. J. (1972). Enduring values. Technometrics. 14(1), 1–11.CrossRefGoogle Scholar
Zeigler, B. P., Praehofer, H. and Kim, T. G. (2000). Theory of Modeling and Simulation: Integrating Discrete Event and Continuous Complex Dynamic Systems. 2nd edn., San Diego, CA, Academic Press.Google Scholar
Zuber, N., Wilson, G. E., Ishii, M., Wulff, W., Boyack, B. E., Dukler, A. E., Griffith, P., Healzer, J. M., Henry, R. E., Lehner, J. R., Levy, S., and Moody, F. J. (1998). An integrated structure and scaling methodology for severe accident technical issue resolution: development of methodology. Nuclear Engineering and Design. 186(1–2), 1–21.CrossRefGoogle Scholar

Save book to Kindle

To save this book to your Kindle, first ensure [email protected] is added to your Approved Personal Document E-mail List under your Personal Document Settings on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part of your Kindle email address below. Find out more about saving to your Kindle.

Note you can select to save to either the @free.kindle.com or @kindle.com variations. ‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi. ‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.

Find out more about the Kindle Personal Document Service.

Available formats
×

Save book to Dropbox

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Dropbox.

Available formats
×

Save book to Google Drive

To save content items to your account, please confirm that you agree to abide by our usage policies. If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account. Find out more about saving content to Google Drive.

Available formats
×