Length: 1 days
To learn more about the ASME Verification and Validation Symposium, please click HERE. To download the course brochure, please click HERE.
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 seminar 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 are assumed to be 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 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.
Each attendee will receive a copy of the book, Verification and Validation in Scientific Computing, Cambridge University Press, 2010, written by Dr. William Oberkampf and Dr. Christopher Roy, which this course closely follows.
You Will Learn 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
- Explain procedures for model accuracy assessment
- Explain 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
This course benefits model developers, computational analysts, code developers, experimentalists, and software engineers. Managers directing this work and project engineers relying on computational simulations for decision-making will also find this course to be beneficial. The course will discuss the responsibilities of organizations and individuals serving in various positions where computational simulation software, mathematical models, and simulation results are produced. An undergraduate or advanced degree in engineering or the physical sciences is recommended. Training and experience in computational simulation or experimental testing is also helpful.