Courses Category: Aerospace R&D

Synopsis:

Computational modeling is becoming more prevalent in the engineering analysis and design process. This increased reliance means that one must understand the accuracy of the computational models. A first step in understanding the accuracy is to identify the model input parameters for which the computational model is most sensitive. The course will specifically focus on the following techniques for determining sensitivity information: differentiation of analytical models, finite difference of computational models, complex step method, software differentiation, sensitivity equation methods, adjoint methods, and sampling methods (Monte Carlo and Latin Hypercube). Techniques for propagating uncertainty in model inputs through computational models will also be presented. Uncertainty propagation techniques that use the sensitivity information (first-order techniques) and more general techniques based on sampling are covered in the course. The final topic covered is validation of computational models. Validation is a process to assess the accuracy of computational models by comparing to experimental data.

Key Topics:

• Insights and application of sensitivity analysis
• Approaches for computing sensitivity coefficients
• Uncertainty propagation through computational models
• Sampling-based methods for propagating uncertainty and performing sensitivity analysis
• Methodology for validation of computational models

Who Should Attend:

This course is intended for engineering analysts that are faced with determining the sensitivity of computational models to parameters in their models. The minimum background is a BS in engineering (or related field). Managers directing the activities of staff responsible for sensitivity analysis would also benefit from this course.