intro-optim-with-surrogates

intro-optim-with-surrogates - Optimization...

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ptimization cle concept Optimization cycle concept efine region in design space 1. Define region in design space 2. Construct surrogates for expensive objective nction and constraints function and constraints 3. Perform optimization based on surrogates 4. Refine region of interest and go back to step 1 if convergence not achieved and another cycle is affordable
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eoretical considerations Theoretical considerations rocess may not converge to true (even local) Process may not converge to true (even local) optimum but algorithms that do are still quite limited (see publications by Natalia Alexandrov) It is extremely important to reduce size of design space (every function is quadratic in a small enough region) Choice between surrogates depends on density of sampling
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esign Space Refinement Design Space Refinement Design space refinement (DSR): process of narrowing own search by excluding down search by excluding regions because They obviously violate the constraints bjective function values in Objective function values in region are poor Benefits of DSR Prevent costly analysis of f ib ldi Madsen et al. (2000) infeasible designs Improve surrogate model accuracy Techniques Design space reduction Reasonable design space Design space windowing Rohani and Singh (2004)
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adial Turbine Preliminary Radial Turbine Preliminary Aerodynamic Design Optimization Yolanda Mack University of Florida, Gainesville, FL Raphael Haftka, University of Florida, Gainesville, FL Lisa Griffin, Lauren Snellgrove, and Daniel Dorney, NASA/Marshall Space Flight Center, AL Frank Huber, Riverbend Design Services, Palm Beach Gardens, FL Wei Shyy, University of Michigan, Ann Arbor, MI 42nd AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit 7 12 06
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adial Turbine Optimization Overview Radial Turbine Optimization Overview Perform optimization to improve efficiency of a compact radial turbine Increase turbine efficiency while aintaining low turbine weight maintaining low turbine weight Polynomial response surface approximations used to facilitate optimization Three stage optimization using 1 D Meanline code 1. Determination of feasible design ace space 2. Identify region of interest 3. Obtain high accuracy approximation for Pareto front identification
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intro-optim-with-surrogates - Optimization...

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