A CFD-Based Shape Design Optimization Process of Fixed Flow Passages in a Francis Hydro Turbine

In recent times, optimization began to be popular in the turbomachinery field. The development of computational fluid dynamics (CFD) analysis and optimization technology provides the opportunity to maximize the performance of hydro turbines. The optimization techniques are focused mainly on the rotating components (runner and guide vane) of the hydro turbines. Meanwhile, fixed flow passages (stay vane, casing, and draft tube) are essential parts for the proper flow uniformity in the hydro turbines. The suppression of flow instabilities in the fixed flow passages is an inevitable process to ensure the power plant safety by the reduction of vortex-induced vibration and pressure pulsation in the hydro turbines. In this study, a CFD-based shape design optimization process is proposed with response surface methodology (RSM) to improve the flow uniformity in the fixed flow passages of a Francis hydro turbine model. The internal flow behaviors were compared between the initial and optimal shapes of the stay vane, casing, and the draft tube with J-Groove. The optimal shape design process for the fixed flow passages proved its remarkable effects on the improvement of flow uniformity in the Francis hydro turbine.

Shape Design Optimization of a Robot Arm Using a Surrogate-Based Evolutionary Approach

In the design optimization of robot arms, the use of simulation technologies for modeling and optimizing the objective functions is still challenging. The difficulty is not only associated with the large computational cost of high-fidelity structural simulations but also linked to the reasonable compromise between the multiple conflicting objectives of robot arms. In this paper we propose a surrogate-based evolutionary optimization (SBEO) method via a global optimization approach, which incorporates the response surface method (RSM) and multi-objective evolutionary algorithm by decomposition (the differential evolution (DE ) variant) (MOEA/D-DE) to tackle the shape design optimization problem of robot arms for achieving high speed performance. The computer-aided engineering (CAE) tools such as CAE solvers, computer-aided design (CAD) Inventor, and finite element method (FEM) ANSYS are first used to produce the design and assess the performance of the robot arm. The surrogate model constructed on the basis of Box–Behnken design is then used in the MOEA/D-DE, which includes the process of selection, recombination, and mutation, to optimize the robot arm. The performance of the optimized robot arm is compared with the baseline one to validate the correctness and effectiveness of the proposed method. The results obtained for the adopted example show that the proposed method can not only significantly improve the robot arm performance and save computational cost but may also be deployed to solve other complex design optimization problems.