Multipoint Shape Optimization Research Articles

Conditional U-Net을 이용한 다중 유동 조건에서의 효율적인 다점 형상 최적 설계 기법

In this study, Conditional U-Net model was used to predict the transonic flow of various shapes and flow conditions which shows large non-linearity, and compared with the POD-GPR model, a linear order reduction technique. In addition, multi-point shape optimization was performed based on the validated model. Both models showed an average error of around 1% for test data, but the accuracy of the Conditional U-Net prevailed for data with large non-linearity. It has also been demonstrated that applying gradient difference loss function is effective in improving accuracy of Conditional U-Net. When multi-point shape optimization was performed using the SL U-Net, the drag reduction at M=0.74 was 47.3%, a relatively small amount compared to the single-point shape optimization. But the performance of airfoil improved in most Mach number ranges. In terms of computational cost, the time required to predict one flow field using Conditional U-Net was measured to be about 0.05% of the FOM CFD. Therefore, it was found that the AI-based model developed in this study has sufficient accuracy and computational efficiency, and also is effectively applicable to multi-point optimization problems.

Multipoint Shape Optimization of Supersonic Waverider

Multipoint Shape Optimization of Supersonic Waverider

Coupled Multipoint Shape Optimization of Runner and Draft Tube of Hydraulic Turbines

This paper suggests a method of simultaneous multi-objective shape optimization of hydraulic turbine runner and draft tube (DT) with the objective to increase turbine efficiency in wide range of operating points (OPs). Runner and DT are the main sources of energy losses in hydraulic turbines. Coupling runner and DT in computational fluid dynamics (CFD) analysis enables correct statement of boundary conditions for efficiency evaluation, while simultaneous variation of these components allows more flexible adjustment of flow passage geometry. Detailed runner parameterization with 28 free geometrical parameters and DT parameterization with nine free parameters are given. Optimization problem is solved using multi-objective genetic algorithm (MOGA). For each variation of runner and DT shapes, flow field in wicket gate (WG), runner, and DT is simulated using steady-state Reynolds-Averaged Navier–Stokes (RANS) equations with k-e turbulence model. Energy-based boundary conditions are used for the calculations, allowing determination of efficiency of the whole turbine in correspondence with International Electrotechnical Commission (IEC) standard. Formulations of multiple OP efficiency objective functions and constraints are discussed in detail. To demonstrate the advantages of simultaneous runner and DT variation, two optimization problems are solved for a medium specific speed Francis turbine. Namely, single runner and coupled “runner–DT” optimizations are carried out. It is shown that optimized runner–DT geometry outperforms the result of single runner optimization by about 0.3% in terms of average efficiency, showing the potential of the developed approach to improve multiregime turbine characteristics in practical design optimization problems.

Multi-Point Shape Optimization of Airfoils at Low Reynolds Numbers

Multi-Point Shape Optimization of Airfoils at Low Reynolds Numbers