|Tutor(s):||Dr William Oberkampf|
Pr Christopher Roy
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The ideal starting point for anyone interested in V&V and Uncertainty Quantification.
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).
Course attendees will be provided with a copy of the book Verification and Validation in Scientific Computing, Cambridge University Press (2010). The 780-page book provides a comprehensive and systematic development of the basic concepts, principles, and procedures for verification, validation, and uncertainty quantification for models and simulations. The book contains several examples of the most common procedures in VVUQ, including an example of the design and execution of a high quality validation experiment. Attendees will also be provided with an electronic (PDF) file and color print copies of over 270 short course slides presented during the course.
The contents are presented in eight lectures, tentatively organized as shown. The schedule allows for ample discussion and interaction with the instructors and other attendees. The instructors reserve the right to modify the contents to address the audience’s needs and preferences.
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.
Get in touch to discuss your next steps with our experienced training team. We can work closely with you to understand your specific requirements, cater for your specific industry sector or analysis type, and produce a truly personalised training solution for your organisation.
All NAFEMS training courses are entirely code independent, meaning they are suitable for users of any software package.
Courses are available to both members and non-members of NAFEMS, although member organisations will enjoy a significant discount on all fees.
NAFEMS course tutors enjoy a world-class reputation in the engineering analysis community, and with decades of experience between them, will deliver tangible benefits to you, your analysis team, and your wider organisation.
|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.|
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