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CHAPTER 6: Three-Dimensional CFD Optimization of the Dual-Stage Organic RIT I. Choice of the independent design variables and their ranges (i.e. turbine geometry parameters) analogous to the mean-line input optimization parameters (Tables Table 5-2 and 5-3). II. Definition of the objective function (OF) that quantifies the RIT performance together with the constraints (if any). III. Search algorithm for finding the optimum combination of the design variables corresponding to the optimum OF. Similar to the optimization strategy in chapter five, to avoid convergence to sub- optimal solutions (local optima), genetic algorithm (GA) was employed as the search mechanism to ensure convergence to global optimum. However, GA requires numerous function evaluations before reaching the global optimum. Simultaneously, the RANS solver itself (i.e. ANSYS CFX) requires substantial computational time depending on the mesh resolution (Table 3-3). Therefore, integration of the RANS solver with the GA optimization could be prohibitive in terms of computer effort and became ineffective for finding the global optimum. To improve such drawback, it is possible to utilize fast but approximate prediction methods such as Meta-models (approximation functions) which are then further verified and refined by the accurate RANS solver. Meta-model is a mathematical function which performs with a very low computational cost, the same task as the high fidelity tool such as CFD RANS solvers (Pasquale et al. 2013). Hence, it is possible to build a Meta-model of the original RANS solver that can be integrated in the optimization loop (i.e. GA) instead of the RANS solver. Consequently, the performance evaluation by the Meta-model is no more time-consuming and the number of function evaluations performed by the GA is no longer critical. The Meta-model not only needed to be fast but must also be accurate to drive the GA to the real optimum. The accuracy of 218 | P a g ePDF Image | SMALL-SCALE RADIAL INFLOW TURBINE FOR WHR ORC
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