Verifying and Validating Simulation Models
Abstract
This presentation is a highlevel discussion of the Verification and Validation (V&V) of computational models. Definitions of V&V are given to emphasize that “validation” is never performed in a vacuum; it accounts, instead, for the current stateofknowledge in the discipline considered. In particular comparisons between physical measurements and numerical predictions should account for their respective sources of uncertainty. The differences between error (bias), aleatoric uncertainty (randomness) and epistemic uncertainty (ignorance, lackof knowledge) are briefly discussed. Four types of uncertainty in physics and engineering are discussed: 1) experimental variability, 2) variability and randomness, 3) numerical uncertainty and 4) modelform uncertainty. Statistical sampling methods are available to propagate, and analyze, variability and randomness. Numerical uncertainty originates from the truncation error introduced by the discretization of partial differential equations in time and space. Modelform uncertainty is introduced by assumptions often formulated to render a complex problem more tractable and amenable to modeling and simulation. The discussion concludes with highlevel guidance to assess the “credibility” of numerical simulations, which stems from the level of rigor with which these various sources of uncertainty are assessed and quantified.
 Authors:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Publication Date:
 Research Org.:
 Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
 Sponsoring Org.:
 USDOE National Nuclear Security Administration (NNSA)
 OSTI Identifier:
 1170703
 Report Number(s):
 LAUR1521344
 DOE Contract Number:
 AC5206NA25396
 Resource Type:
 Technical Report
 Country of Publication:
 United States
 Language:
 English
 Subject:
 Mathematics & Computing(97)
Citation Formats
Hemez, Francois M. Verifying and Validating Simulation Models. United States: N. p., 2015.
Web. doi:10.2172/1170703.
Hemez, Francois M. Verifying and Validating Simulation Models. United States. doi:10.2172/1170703.
Hemez, Francois M. 2015.
"Verifying and Validating Simulation Models". United States.
doi:10.2172/1170703. https://www.osti.gov/servlets/purl/1170703.
@article{osti_1170703,
title = {Verifying and Validating Simulation Models},
author = {Hemez, Francois M.},
abstractNote = {This presentation is a highlevel discussion of the Verification and Validation (V&V) of computational models. Definitions of V&V are given to emphasize that “validation” is never performed in a vacuum; it accounts, instead, for the current stateofknowledge in the discipline considered. In particular comparisons between physical measurements and numerical predictions should account for their respective sources of uncertainty. The differences between error (bias), aleatoric uncertainty (randomness) and epistemic uncertainty (ignorance, lackof knowledge) are briefly discussed. Four types of uncertainty in physics and engineering are discussed: 1) experimental variability, 2) variability and randomness, 3) numerical uncertainty and 4) modelform uncertainty. Statistical sampling methods are available to propagate, and analyze, variability and randomness. Numerical uncertainty originates from the truncation error introduced by the discretization of partial differential equations in time and space. Modelform uncertainty is introduced by assumptions often formulated to render a complex problem more tractable and amenable to modeling and simulation. The discussion concludes with highlevel guidance to assess the “credibility” of numerical simulations, which stems from the level of rigor with which these various sources of uncertainty are assessed and quantified.},
doi = {10.2172/1170703},
journal = {},
number = ,
volume = ,
place = {United States},
year = 2015,
month = 2
}

This report describes recent progress toward one of the principal objectives of the Nuclear Waste Treatment Program (NWTP) at the Pacific Northwest Laboratory (PNL): to establish relationships between vitrification process control and glass product quality. during testing of a vitrification system, it is important to show that departures affecting the product quality can be sufficiently detected through process measurements to prevent an unacceptable canister from being produced. Meeting this goal is a practical definition of a successful sampling, data analysis, and process control strategy. A simulation model has been developed and preliminarily tested by applying it to approximate operation ofmore »

Verifying usefulness of engineering process models applied to forecasting. Final report
The concept of production has been a central theme in the development of economics. Economists have continually strived to include engineering information in their forecasts and analyses. Energy economists have been extremely active in the use of engineering process models for economic analysis. Recent research efforts have developed statistical methods for transferring the process information. This report explores the validity of these efforts and suggests alternatives. The authors review classic economic production theory and recent efforts to use statistical output from process models in forecasting applications. An alternative application of functions known as cubic splines is developed. An errorminimizing nonstatisticalmore » 
The role of global cloud climatologies in validating numerical models. Final technical report, 1 April 198931 March 1993
The purpose of this work is to estimate sampling errors of areatime averaged rain rate due to temporal samplings by satellites. In particular, the sampling errors of the proposed low inclination orbit satellite of the Tropical Rainfall Measuring Mission (TRMM) (35 deg inclination and 350 km altitude), one of the sun synchronous polar orbiting satellites of NOAA series (98.89 deg inclination and 833 km altitude), and two simultaneous sun synchronous polar orbiting satellitesassumed to carry a perfect passive microwave sensor for direct rainfall measurementswill be estimated. This estimate is done by performing a study of the satellite orbits and themore » 
Development of PUNDA (Parametric Universal Nonlinear Dynamics Approximator) Models for SelfValidating KnowledgeGuided Modelling of Nonlinear Processes in Particle Accelerators \& Industry
The difficult problems being tackled in the accelerator community are those that are nonlinear, substantially unmodeled, and vary over time. Such problems are ideal candidates for modelbased optimization and control if representative models of the problem can be developed that capture the necessary mathematical relations and remain valid throughout the operation region of the system, and through variations in system dynamics. The goal of this proposal is to develop the methodology and the algorithms for building highfidelity mathematical representations of complex nonlinear systems via constrained training of combined firstprinciples and neural network models. 
User's Manual for Data for Validating Models for PV Module Performance
This user's manual describes performance data measured for flatplate photovoltaic (PV) modules installed in Cocoa, Florida, Eugene, Oregon, and Golden, Colorado. The data include PV module currentvoltage curves and associated meteorological data for approximately oneyear periods. These publicly available data are intended to facilitate the validation of existing models for predicting the performance of PV modules, and for the development of new and improved models. For comparing different modeling approaches, using these public data will provide transparency and more meaningful comparisons of the relative benefits.