Programme
The programme is divided in two days:
Thursday 20 sep. 2012 from 8h30 until 18h00 and Friday 21 sep. 2012
from 8h30 until 18h00.
Course programme
- High-level Overview of Verification and Validation
- Platform and code development environment at Los Alamos
- An example of computational physics simulation ("urban consequence" project)
- High-level organization of the V&V program at Los Alamos
- Perspectives for "ExaFlops" computing
- An Application of V&V to Wind Turbine
Simulations
- The "intelligent wind turbines" project at Los Alamos
- Code verification of the finite element software
- Simulation of blade vibration with bounds of numerical uncertainty
- Sensitivity analysis of the numerical simulations
- Calibration of the model using statistical emulators
- Final test-analysis correlation and validation assessment
- Code and Solution Verification
- Code verification with simple test problems
- How to define benchmark problems
- Method of manufactured solutions
- The concepts of Modified Equation Analysis (MEA), consistency, and convergence
- Truncation error and Richardson�s extrapolation applied to numerical solutions
- The Grid Convergence Index
- Design of Computer Experiments
- Principles of the design of (physical or computer) experiments
- Full-factorial and fractional factorial designs
- Orthogonal arrays and the central composite design
- 2^(n-k) designs
- Statistical aliasing and sparse designs
- Sensitivity Analysis, Effect Screening, and Surrogate
Modeling
- Rationale for effect screening ("where is an observed variability coming from?")
- Effect screening using a design of computer experiments
- Analysis-of-variance (ANOVA)
- Main effect and total effect sensitivity indices
- Surrogate modeling using a design of computer experiments
- Low-order, polynomial emulators
- Kriging emulators and Gaussian process models
- Sampling and the Propagation of Parametric
Uncertainty
- Sampling methods for the forward propagation of (parametric) uncertainty
- Monte Carlo, stratified sampling, Latin Hypercube Sampling (LHS)
- Convergence of statistical estimates
- Markov Chain Monte Carlo (MCMC) sampling for inference uncertainty quantification
- Fast probability integrators for reliability analysis
- Test-analysis Correlation and Validation Metrics
- Concepts of response features and validation metrics
- Statistical tests that account for probabilistic uncertainty
- Metrics based on the principal component decomposition
- Parameter calibration ("what is it? what are the dangers?")
- Model calibration under uncertainty
- An End-to-end Example of Verification and
Validation
- Engineering example of transient dynamics simulations
- Verification of the finite element software
- Design and execution of computer experiments (predictions)
- Design of physical experiments (measurements)
- Effect screening and identification of statistically most-significant inputs
- Small-scale validation experiments
- Uncertainty propagation and final validation assessment
