Online Training Course

Verification, Validation and Uncertainty Quantification in Scientific Computing

11 - 14 September | Online (Webex)

Engineering systems must increasingly rely on computational simulation for predicted performance, reliability, and safety. Computational analysts, designers, decision makers, and project managers who rely on simulation must have practical techniques and methods for assessing simulation credibility. This short course presents modern terminology and effective procedures for verification of numerical simulations, validation of mathematical models, and uncertainty quantification of nondeterministic simulations.

The techniques presented in this course are applicable to a wide range of engineering and science applications, including fluid dynamics, heat transfer, solid mechanics, and structural dynamics. The mathematical models considered are given in terms of partial differential or integral equations, formulated as initial and boundary value problems. The computer codes that implement the mathematical models can use any type of numerical method (e.g., finite volume, finite element) and can be developed by commercial, corporate, government, or research organizations. A framework is provided for incorporating a wide range error and uncertainty sources identified during the modeling, verification, and validation processes with the goal of estimating the total prediction uncertainty of the simulation.

While the focus of the course is on modeling and simulation, experimentalists will benefit from a detailed discussion of techniques for designing and conducting high quality validation experiments. Application examples are primarily taken from the fields of fluid dynamics and heat transfer, but the techniques and procedures apply to all application areas in engineering and science. The course closely follows the course instructors’ book, Verification and Validation in Scientific Computing, Cambridge University Press (2010).

Upon completion of this course, attendees will be able to:

Define the objectives of verification, validation, and uncertainty quantification
Implement procedures for code verification and software quality assurance
Implement procedures for solution verification, i.e., numerical error estimation
Plan and design validation experiments
Understand procedures for model accuracy assessment
Comprehend the concepts and procedures for non-deterministic simulation
Identify sources of uncertainty, such as aleatory and epistemic uncertainties
Recognize the goals of model parameter calibration/updating
Interpret local and global sensitivity analyses
Recognize the practical difficulties in implementing VVUQ technologies

Who Should Attend?

Model developers, computational analysts, code developers, software engineers, and experimentalists working with computational analysts. Managers directing simulation work and project engineers relying on computational simulations for decision-making will also find this course beneficial.

Course Program

Introduction, Background, and Motivation

Terminology and Fundamental Concepts

Brief history of terminology
Present definitions and interpretations
Alternate definitions used by related communities
Additional important terms
Who should conduct verification, validation, and uncertainty quantification?

Code Verification

Software engineering
Criteria and definitions–Order of accuracy
Traditional exact solutions
Method of manufactured solutions

Solution Verification

Round-off error
Iterative convergence
Iterative error estimation
Discretization error estimation
Reliability of discretization error estimators
Discretization error and uncertainty estimation
Solution adaptation procedures

Validation Experiments

Validation fundamentals
Validation experiment hierarchy
Validation experiments vs. traditional experiments
Six characteristics of validation experiments
Detailed example of a wind tunnel validation experiment

Model Accuracy Assessment

What are validation metrics?
Various approaches to validation metrics
Recommended characteristics for validation metrics
Identification of model discrepancy
Cumulative distribution function approach

Predictive Capability of Modeling and Simulation

Identify all sources of uncertainty
Characterize each source of uncertainty
Estimate solution error in system responses of interest
Estimate total uncertainty in system responses of interest
Procedures for updating model parameters
Types of sensitivity analysis

Final Topics

Planning and prioritization in modeling and simulation
Maturity assessment of modeling and simulation
Practical difficulties in implementing VVUQ technologies

PSE Competencies addressed by this training course

V&V-SIMMsy9	Design a test for analysis validation purposes.
V&V-SIMMsy8	Formulate a series of smaller studies, benchmarks or experimental tests in support of a simulation modelling strategy.
V&V-SIMMsy7	Prepare a validation plan in support of a FEA study.
V&V-SIMMkn6	State simulation V&V principles
V&V-SIMMkn15	List relevant physical tests and their characteristics to calibrate or validate simuation.
V&V-SIMMev8	Train engineering staff in validation techniques
V&V-SIMMev7	Design appropriate verification and validation procedures in support of simulation.
V&V-SIMMev11	Design test/analysis correlation processes, and select analysis validation criteria.
V&V-SIMMev10	Assess model/analysis validity from test/analysis correlation studies
V&V-SIMMco9	Explain the term model calibration.
V&V-SIMMco8	Explain the term code verification.
V&V-SIMMco7	Explain the term solution verification.
V&V-SIMMco6	Explain the terms Verification and Validation.
V&V-SIMMco32	Understand simulation error assessment methodologies and the concept of simulation predictive maturity.
V&V-SIMMap6	Perform test /analysis correlation studies
V&V-SIMMap5	Perform model calibration from tests
V&V-SIMMap4	Perform basic model checks
V&V-SIMMap3	Conduct validation studies in support of simulation.
V&V-SIMMan7	Analyze test data to support validation activities
V&V-SIMMan6	Analyze simulation results to support validation activities.
PROBkn8	List types of uncertainty
PROBkn5	List typical random sampling techniques.
PROBkn3	List the characteristics of a typical probability distribution
PROBkn10	List the benefits from probabilistic finite element analysis.
PROBkn1	List typical sources of uncertainty in a reliability assessment
PROBco9	Explain the relationship between the Normal Probability Density Function and the Cumulative Density Function.
PROBco8	Explain how probabilistic sensitivities can be used to guide product design.
PROBco7	Describe how variability in an analysis input quantity may be characterised.
PROBco2	Describe the difference between epistemic and aleatoric uncertainty and how they can be quantified
PROBco11	Describe Monte Carlo simulation.
PROBco1	Explain the term non-deterministic.
MG-SIMMsy15	Implement efficient versioning process for the simulation tools used by your company.
MG-SIMMev9	Evaluate and benchmark external supplier validation approach
MG-SIMMev14	Assess simulation solution maturity and readiness levels for a new project.
MG-SIMMco33	Understand software versioning processes
MG-SIMMco1	Understand the need and relevance of analysis specifications.
MG-SIMMap25	Apply appropriate procedures for controlling the quality of simulation work
MG-SIMMap18	Monitor tool (code) verification for the relevant project and intended use
MESMev1	Select appropriate validation measures.
MESMco9	Discuss the uncertainties typically present in analyses and explain how these are handled.
FEAsy8	Prepare a validation plan in support of a FEA study.
FEAkn4	Define the meaning of adaptive mesh refinement
FEAkn13	State the word length or arithmetic precision of calculations for any system used.
FEAev5	Manage verification and validation procedures in support of FEA.
FEAco3	Explain the term solution residual.
FEAco12	Outline a common method employed to solve the large sets of sparse symmetric common in FEA.
CFDsy3	Formulate a plan to address the uncertainty in input data or modelling when using a CFD code for a design study.
CFDkn7	List the main sources of error and uncertainty that may occur in a CFD calculation.
CFDco12	Review the issues associated with the estimation of total uncertainty in a flow simulation.

Verification and Validation in Engineering Simulation

Course Information

Course Start and End Times:
10:00 AM to 3:00 PM ET
(This includes two short coffee breaks and lunch break)

Course Fee

Non NAFEMS members: USD 1895 / person*
NAFEMS member: USD 1495 / person*
Included in the fees are digital course notes and a certificate.
* plus VAT if applicable.

In-house Course
This course can be booked as in-house course. Please request a quote.

Cancellation Policy

Up to 6 weeks before course starts: free of charge;
Up to one week before: 75 %;
Later and no show: 100 %.

Course Cancellation
If not enough participants we keep the right to cancel the course one week before. The course can be canceled also in case of force majeure. In these cases the course fees will be returned.

Accreditation Policy

The course is agreed and under control of NAFEMS Education and Training Working Group.

Still Curious?

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