A transonic turbine airfoil design is optimized using an artificial intelligence engineering design shell coupled with an inviscid, adaptive grid, CFD solver. The objective of the optimization is to minimize the downstream static pressure variation resulting from the trailing edge shock structure. Cascade test results verify the analytical predictions. Techniques are described which were used to couple the optimization shell to the 2-D turbine airfoil shape to allow the search for optimal designs and indicate the quality of those designs. The emphasis of the discussion is upon the application of these techniques rather than the physical details of the resulting blade design. NOMENCLATURE

This paper describes a new software approach to the preliminary design of aircraft engine turbines. A hybrid artificial intelligence and numerical-optimization-based design shell called Engineous was used to capture some basic turbine preliminary design knowledge, manipulate turbine design parameters, execute a turbine performance prediction program and its preprocessors, and analyze results. Engineous automatically supplements incomplete human design knowledge with symbolic and numerical search techniques when needed. This approach produced designs with higher predicted performance gains than the existing manual design process in a tenth of the turnaround time and has yielded new insights into turbine design. A comparison of turbine designs obtained by designers and by Engineous is presented here along with an overview of Engineous system architecture.

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