Accounting for Test Variability through Sizing Local Domains in Sequential Design Optimization with Concurrent Calibration-Based Model Validation
Abstract
We have recently proposed a new method for combined design optimization and calibration-based validation using a sequential approach with variable-size local domains of the design space and statistical bootstrap techniques. Our work was motivated by the fact that model validation in the entire design space may be neither affordable nor necessary. The method proceeds iteratively by obtaining test data at a design point, constructing around it a local domain in which the model is considered valid, and optimizing the design within this local domain. Due to test variability, it is important to know how many tests are needed to size each local domain of the sequential optimization process. Conducting an unnecessarily large number of tests may be inefficient, while a small number of tests may be insufficient to achieve the desired validity level. In this paper, we introduce a technique to determine the number of tests required to account for their variability by sizing the local domains accordingly. The goal is to achieve a desired level of model validation in each domain using the correlation between model data at the center and any other point in the local domain. The proposed technique is illustrated by means of a piston design example.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2013
- Accession Number
- ADA590078
Entities
People
- David Gorsich
- Dorin Drignei
- Michael Kokkolaras
- Vijitashwa Pandey
- Zissimos P. Mourelatos
Organizations
- Oakland University