The most successful engineering projects begin with discovery—conceiving a rich array of ideas to solve a problem or address a need—then move on to methodically explore which candidates are most promising for development and refinement. But the power of such discovery and exploration is too often sacrificed to schedule pressures and resource constraints, compounded by digital toolset gaps and limitations. The result is familiar: engineers conceive two or three design alternatives, then rely on intuition, best guesses and handbook formulas to choose one that looks reasonably promising and not too risky to implement—without really knowing whether it’s the best, most cost-effective or most robust solution attainable.
An emerging answer to this quandary is design space exploration—both a family of methods and a rapidly evolving category of software tools that are beginning to radically advance the capabilities of engineers and multidisciplinary engineering teams to discover an array of feasible design concepts early; quickly and fluently evaluate sensitivities, variants and tradeoffs; then select the best design candidate and implement it.
Design space exploration lets engineers systematically and automatically investigate very large numbers of design alternatives to identify those with the most promising performance values. Many of the quantitative methods that underpin design space exploration have been long known—and sometimes applied, in cases where the attendant costs in expertise, time and labor could be justified. What’s changing now is the way fresh software technologies are at last transforming these powerful but formerly difficult-to-apply methods into practical everyday engineering aids.
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