The most effective use of MDO is not as a “push-button” tool where one can specify the problem and get the “best” answer. Instead, MDO is a valuable complement to other design tools, and as such should be constructed to provide relevant information for informed design decisions rather than just a solution to a problem. Rather than being used to eke out a 5% improvement in the design solution (where model fidelity is often an issue anyway), MDO ought to be used as a way of gaining insight to the design space, quantitatively identifying trades and finding innovative design options.Often, in practical applications, it is the solution concept suggested by the optimizer but not the actual details of the design that are most interesting. In this research, we are developing methodology to improve the way in which designers can use MDO. By coupling the optimizer with a visualization framework, we aim to automatically convey important information about the design space, such as:
(*) What path did the optimizer take from the initial to the final solution -- what design decisions and tradeoffs were made and why? (*) Which constraints are active, and which constraints are particularly constraining to the design? (*) What is the sensitivity of the solution to various problem parameters?