Model-based Support for Mutable Parametric Design Optimization

Traditional methods for parametric design optimization assume that the relations between performance criteria and design variables are known algebraic functions with fixed coefficients. However, the relations may be mutable, i.e., the functions and/or coefficients may not be known explicitly because they depend on input parameters and vary in different parts of the design space. We present a model-based reasoning methodology to support parametric, mutable, design optimization. First, we derive event models to represent the effects of the system's parameters on the material that flows through it. Next, we use these models to discover mutable relations between the system's design variables and its optimization criteria. We then present an algorithm that searches for "optimal" designs by employing sensitivity analysis techniques on the derived relations.