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CHAPTER 6: Three-Dimensional CFD Optimization of the Dual-Stage Organic RIT Figure 6-3 Example of the DOE sample points (left), Meta-model fitted the samples (right) (Montgomery 2006) The effectiveness of the Kriging algorithm is based on the ability of its internal error estimator to improve Meta-model quality by generating refinement points and adding them to the areas in need of improvement (Martin et al. 2005). The goodness of fit for the created Kriging Meta-model can be measured based on a number of metrics such as coefficient of determination (should be close to unity) or maximum relative residual (should be close to zero). 6.2.3. Genetic algorithm optimization method As discussed in section 5.6, GA is based on the biological evolution in which a population of individuals (different geometries) is chosen randomly from the range of independent parameters and they are assessed based on their fitness value (aerodynamic performance). A new generation of populations is generated in a stochastic manner by breeding the selected members of the current population according to their fitness value. It goes through several iterations retaining the elite percentage of the individuals through each iteration allowing the individuals to genetically evolve until the global optimum is found. 222 | P a g ePDF Image | SMALL-SCALE RADIAL INFLOW TURBINE FOR WHR ORC
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