Abstract
Computational fluid dynamics (CFD) plays an important role in investigating the flow in products. With the help of optimization algorithms, CFD-based optimization is increasingly applied in product development to improve the product design. Even though this approach is becoming increasingly mature, it is faced with the problem that the CFD solver is not able to correctly respond to the design changes under the batch mode, leading to incorrect simulation and optimization results. Besides, there is no work dedicated to dealing with the design points which are physically invalid during the optimization process. In this paper, the intelligent CFD solver is employed to analyze the flow at each design point and to set up the solver with the best fit simulation models. Based on correct simulation results, the physically invalid design points are automatically removed from the design space. Metamodeling is used to process the valid design space with simulation results provided by the intelligent solver and derive the optimum. A prototype system is developed incorporating ANSYS, Python, and MATLAB. The design optimization of a steam control valve is used as the case study to demonstrate how the proposed system works. The optimization is conducted based on the metamodel built by response surface model and radial basis function to verify the effectiveness of the proposed method.
Highlights
For the development of product involving fluid flow, computational fluid dynamics (CFD) is extensively applied to analyze the flow field and guide the design improvement
Incorporating optimization algorithms, CFD-based optimization [2] is increasingly employed to find the optimum based on CFD simulation results
A robust simulation model with accurate results will be generated for each specific design. Based on these simulation results, the physically invalid design points are removed from the design space to improve the efficiency and accuracy of the optimization
Summary
For the development of product involving fluid flow, computational fluid dynamics (CFD) is extensively applied to analyze the flow field and guide the design improvement. The input of the optimization algorithm is commonly the simulation results corresponding to the various design points [3]. Such simulations are usually conducted in batch mode to eliminate the idle time [4]. A robust simulation model with accurate results will be generated for each specific design. Based on these simulation results, the physically invalid design points are removed from the design space to improve the efficiency and accuracy of the optimization. The intelligent CFD solver and approximation-based optimization are demonstrated in Sections 4 and 5, respectively.
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