Two Day Professional Short Course

Verification and Validation in Scientific Computing
 

About the Course

Now available as an online offering.

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 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. 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, including both aleatory and epistemic uncertainties
• Recognize the goals of model calibration/updating
• Interpret local and global sensitivity analyses
• Recognize the practical difficulties in implementing VVUQ techniques

Who Should Attend

This course benefits 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. 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 highly recommended. Training and experience in computational simulation of physical systems is also recommended.

Course Offering

This course can be offered as a 2 day in-person format or a 4 day fully online format via Zoom, see details below for schedule information. To discuss details and scheduling, contact Briana Blanchard bnblanch@vt.edu.

Outline of the Course (for in-person delivery)

Outline of the Course (for online delivery)