Development and Use of Engineering Standards for Computational Fluid Dynamics for Complex Aerospace Systems

Computational fluid dynamics (CFD) and other advanced modeling and simulation (M&S) methods are increasingly relied on for predictive performance, reliability and safety of engineering systems. Analysts, designers, decision makers, and project managers, who must depend on simulation, need practical techniques and methods for assessing simulation credibility. The AIAA Guide for Verification and Validation of Computational Fluid Dynamics Simulations (AIAA G-077-1998 (2002)), originally published in 1998, was the first engineering standards document available to the engineering community for verification and validation (V&V) of simulations. Much progress has been made in these areas since 1998. The AIAA Committee on Standards for CFD is currently updating this Guide to incorporate in it the important developments that have taken place in V&V concepts, methods, and practices, particularly with regard to the broader context of predictive capability and uncertainty quantification (UQ) methods and approaches. This paper will provide an overview of the changes and extensions currently underway to update the AIAA Guide. Specifically, a framework for predictive capability will be described for incorporating a wide range of error and uncertainty sources identified during the modeling, verification, and validation processes, with the goal of estimating the total prediction uncertainty of the simulation. The Guide's goal is to provide a foundation for understanding and addressing major issues and concepts in predictive CFD. However, this Guide will not recommend specific approaches in these areas as the field is rapidly evolving. It is hoped that the guidelines provided in this paper, and explained in more detail in the Guide, will aid in the research, development, and use of CFD in engineering decision-making.

[1]  A. O'Hagan,et al.  Bayesian calibration of computer models , 2001 .

[2]  Hugh W. Coleman,et al.  Comprehensive Approach to Verification and Validation of CFD Simulations—Part 1: Methodology and Procedures , 2001 .

[3]  Christopher J. Roy,et al.  Verification and Validation in Scientific Computing: Design and execution of validation experiments , 2010 .

[4]  William L. Oberkampf,et al.  Joint Computational/Experimental Aerodynamics Research on a Hypersonic Vehicle, Part 1: Experimental Results , 1991 .

[5]  Scott Ferson,et al.  Arithmetic with uncertain numbers: rigorous and (often) best possible answers , 2004, Reliab. Eng. Syst. Saf..

[6]  Timothy G. Trucano,et al.  Verification and validation benchmarks , 2008 .

[7]  Christopher J. Roy,et al.  Verification and Validation in Scientific Computing , 2010 .

[8]  Wei Chen,et al.  Bayesian Validation of Computer Models , 2009, Technometrics.

[9]  Asit P. Basu,et al.  Probabilistic Risk Analysis , 2002 .

[10]  D. Bingham,et al.  Computer Model Calibration or Tuning in Practice , 2006 .

[11]  Vladik Kreinovich,et al.  Monte-Carlo-Type Techniques for Processing Interval Uncertainty, and Their Potential Engineering Applications , 2006, Reliab. Comput..

[12]  Wei Chen,et al.  A better understanding of model updating strategies in validating engineering models , 2009 .

[13]  N. Sinha,et al.  Error Quantification for Computational Aerodynamics Using an Error Transport Equation , 2007 .

[14]  V. Ebrahimipour,et al.  A synergetic approach for assessing and improving equipment performance in offshore industry based on dependability , 2006, Reliab. Eng. Syst. Saf..

[15]  Christopher J. Roy,et al.  Review of code and solution verification procedures for computational simulation , 2005 .

[16]  Karen A. F. Copeland Design and Analysis of Experiments, 5th Ed. , 2001 .

[17]  Hugh W. Coleman,et al.  Uncertainties and CFD Code Validation , 1997 .

[18]  Vicente J. Romero,et al.  Calibration and Uncertainty Analysis for Computer Simulations with Multivariate Output , 2008 .

[19]  Kenneth M. Hanson,et al.  A framework for assessing uncertainties in simulation predictions , 1999 .

[20]  James O. Berger,et al.  A Bayesian analysis of the thermal challenge problem , 2008 .

[21]  Matthew F. Barone,et al.  Measures of agreement between computation and experiment: Validation metrics , 2004, J. Comput. Phys..

[22]  Brian Williams,et al.  A Bayesian calibration approach to the thermal problem , 2008 .

[23]  Patrick J. Roache,et al.  Verification and Validation in Computational Science and Engineering , 1998 .

[24]  Timothy G. Trucano,et al.  Verification and validation. , 2005 .

[25]  Hugh W. Coleman,et al.  Experimentation, Validation, and Uncertainty Analysis for Engineers , 2009 .

[26]  A. OHagan,et al.  Bayesian analysis of computer code outputs: A tutorial , 2006, Reliab. Eng. Syst. Saf..

[27]  R. Ghanem,et al.  Stochastic Finite Elements: A Spectral Approach , 1990 .

[28]  J. C. Helton,et al.  Uncertainty and sensitivity analysis in the presence of stochastic and subjective uncertainty , 1997 .

[29]  Wei Chen,et al.  A Design-Driven Validation Approach Using Bayesian Prediction Models , 2008 .

[30]  J. Ghosh,et al.  An Introduction to Bayesian Analysis: Theory and Methods , 2006 .

[31]  W. Oberkampf,et al.  Model validation and predictive capability for the thermal challenge problem , 2008 .

[32]  P. Roache Perspective: A Method for Uniform Reporting of Grid Refinement Studies , 1994 .

[33]  J. Oden,et al.  A Posteriori Error Estimation in Finite Element Analysis , 2000 .

[34]  Paul D. Arendt,et al.  Quantification of model uncertainty: Calibration, model discrepancy, and identifiability , 2012 .

[35]  Leonard E. Schwer,et al.  An overview of the PTC 60/V&V 10: guide for verification and validation in computational solid mechanics , 2007, Engineering with Computers.

[36]  Timothy G. Trucano,et al.  Verification and Validation in Computational Fluid Dynamics , 2002 .

[37]  Jon C. Helton,et al.  Extension of Latin hypercube samples with correlated variables , 2008, Reliab. Eng. Syst. Saf..

[38]  T. Lancaster An Introduction to Modern Bayesian Econometrics , 2004 .

[39]  Lars-Erik Lindgren,et al.  Constitutive modelling and parameter optimisation , 2003 .

[40]  J. Oden,et al.  A Posteriori Error Estimation in Finite Element Analysis: Oden/A Posteriori , 2000 .

[41]  William L. Oberkampf,et al.  Guide for the verification and validation of computational fluid dynamics simulations , 1998 .

[42]  Christopher J. Roy,et al.  A comprehensive framework for verification, validation, and uncertainty quantification in scientific computing , 2011 .

[43]  Christopher J. Roy,et al.  Review of Discretization Error Estimators in Scientific Computing , 2010 .

[44]  James O. Berger,et al.  A Framework for Validation of Computer Models , 2007, Technometrics.

[45]  J. Z. Zhu,et al.  The superconvergent patch recovery and a posteriori error estimates. Part 2: Error estimates and adaptivity , 1992 .

[46]  Laura Painton Swiler,et al.  Calibration, validation, and sensitivity analysis: What's what , 2006, Reliab. Eng. Syst. Saf..

[47]  T. G. Carne,et al.  Model correlation and updating of a nonlinear finite element model using crush test data , 1999 .

[48]  S. Ferson,et al.  Different methods are needed to propagate ignorance and variability , 1996 .

[49]  T. Trucano,et al.  Verification, Validation, and Predictive Capability in Computational Engineering and Physics , 2004 .

[50]  H. Christopher Frey,et al.  Probabilistic Techniques in Exposure Assessment: A Handbook for Dealing with Variability and Uncertainty in Models and Inputs , 1999 .

[51]  Jon C. Helton,et al.  Alternative representations of epistemic uncertainty , 2004, Reliab. Eng. Syst. Saf..

[52]  Terry Speed Statistics for Experimenters: Design, Innovation, and Discovery (2nd ed.) , 2006 .

[53]  Ivo Babuška,et al.  A systematic approach to model validation based on Bayesian updates and prediction related rejection criteria , 2008 .

[54]  Jonathan Rougier,et al.  Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations , 2007 .

[55]  Margaret J. Robertson,et al.  Design and Analysis of Experiments , 2006, Handbook of statistics.

[56]  Dave Higdon,et al.  Combining Field Data and Computer Simulations for Calibration and Prediction , 2005, SIAM J. Sci. Comput..

[57]  Max Henrion,et al.  Uncertainty: A Guide to Dealing with Uncertainty in Quantitative Risk and Policy Analysis , 1990 .

[58]  W. T. Tucker,et al.  Sensitivity in risk analyses with uncertain numbers. , 2006 .