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Download Model Verification and Validation in Engineering Mechanics: Theory and Applications of Uncertainty Quantification and Predictive Accuracy fb2, epub

by François Hemez,Scott Doebling,Mark Anderson

Download Model Verification and Validation in Engineering Mechanics: Theory and Applications of Uncertainty Quantification and Predictive Accuracy fb2, epub

ISBN: 0470859342
Author: François Hemez,Scott Doebling,Mark Anderson
Language: English
Publisher: Wiley; 1 edition (September 4, 2009)
Pages: 624
Category: Computer Science
Subcategory: IT
Rating: 4.1
Votes: 917
Size Fb2: 1332 kb
Size ePub: 1357 kb
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Author Hemez, Francois, Doebling, Scott, Anderson, Mark.

Author Hemez, Francois, Doebling, Scott, Anderson, Mark. ISBN13: 9780470860434. More Books . ABOUT CHEGG.

Model verification and validation (V&V) is an enabling methodology for the development of computational models that can be used to make . Verification and Validation in Computational Sciencend Engineering.

Model verification and validation (V&V) is an enabling methodology for the development of computational models that can be used to make engineering predictions with quantified confidence. Model V&V procedures are needed to reduce the time, cost and risk associated with component and full-scale testing of products, materials, and engineered systems. Quantifying the confidence and predictive accuracy of model calculations provides the decision-maker with the information necessary for making high-consequence decisions. Hemez, etc. Concepts of Model Verification and Validation.

Model verification and validation (V&V) is an enabling methodology for the . Model V&V procedures are needed by government and industry to reduce the time, cost, and risk associated with full-scale testing of products, materials, and weapon systems. Model verification and validation are the primary processes for quantifying and building credibility in numerical models.

Model verification and validation (VV thus, V&V cannot prove that a model is correct . Therefore, engineers seeking to develop credible predictive models.

Model verification and validation (VV thus, V&V cannot prove that a model is correct and accurate for all possible scenarios, but, rather, it can provide evidence that the model is sufficiently accurate for its intended us. In all applications of practical interest, the calculations involved in obtaining solutions with the model require a computer code, . finite element or finite difference analysis. Therefore, engineers seeking to develop credible predictive models critically need model V&V guidelines and procedures.

Thacker BH, Doebling SW, Hemez F, Anderson MC, Pepin JE, and Rodriguez EA (2004) Concepts of model . Atamturktur S, Hemez FM, Laman JA (2012) Uncertainty quantification in model verification and validation as applied to large scale historic masonry monuments.

Thacker BH, Doebling SW, Hemez F, Anderson MC, Pepin JE, and Rodriguez EA (2004) Concepts of model verification and validation. Los Alamos National Laboratory, Los Alamos, NM, LA-14167Google Scholar. Eng Struct 43:221–234CrossRefGoogle Scholar.

Model verification and validation (V&V) are essential before a model can . François M. Hemez1 and. The application illustrated in Figures 4-5 represents the highly transient response of a threaded joint assembly due to explosive loading.

Model verification and validation (V&V) are essential before a model can be implemented in practice. th. For Publication in the Proceedings of IMAC-XIX, the 19 International Modal Analysis Conference. February 5-8, 2001, Kissimmee, Florida. The explicit finite element model features . million elements, 480 contact pairs and more than 6 million degrees of freedom. Nonlinearity arises in the form of pre-load, contact mechanics, material modeling and thermal coupling.

Verification and validation of computer simulation models is conducted during the development of a simulation model with the ultimate goal of producing an accurate and credible model. Simulation models are increasingly being used to solve problems and to aid in decision-making

Chapter: 5 Model Validation and Prediction. In some cases, the verification effort can effectively eliminate the uncertainty due to solution and coding errors, leaving only the first three sources of uncertainty.

Chapter: 5 Model Validation and Prediction. Likewise, if the computational model runs very quickly, one could evaluate the model at any required input setting, eliminating the need to estimate what the model would have produced at an untried input setting. The process of validation and prediction, explored in previous publications (. Klein et a. 2006; NRC, 2007, Chapter 4), is described in this chapter from a more mathematical perspective.

5 ABSTRACT Model verification and validation (V&V) is an enabling methodology for the development of computational models that can be used to make engineering predictions with quantified confidence.

Model validation: model parameter and measurement uncertainty. Evidence theory for engineering applications. Verification, validation, and predictive capability in computational engineering and physics. Journal of Heat Transfer. Hills, R. G. and Leslie, I. (2003). Statistical Validation of Engineering and Scientific Models: Validation Experiments to Application. In Engineering Design Reliability Handbook. Nikolaidis, . Ghiocel, D. M. and Singhal, S. (ed. New York, NY, CRC Press: 29. Oberkampf, W. L. and Trucano, T. (2002).

Model validation attempts to assess whether a mathematical or computer model is appropriate and capable of providing accurate and credible predictions. Numerical models have been developed to predict such complex phenomena as crash dynamics and acoustic levels in the automotive industry; virtual design and prototyping in the aerospace industry; simulation of earthquake dynamics; and the assessment of nuclear weapons reliability. Simulation-based analysis also has the advantage of being more cost-effective than actual physical experimentation. This book: Includes real-world engineering applications Assesses the predictive accuracy of numerical models over a specific design domain Encompasses the four main model variation topics: definition of features and metrics; domain of validation; uncertainty quantification and meta-modeling Educates the engineer, researcher or graduate student to the issue of predictability using computer models Addresses critical questions at a time when demands in terms of performance and accuracy are higher than ever

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