Multidisciplinary Optimization Framework for High Speed Train using Robust Hybrid GA-PSO Algorithm
Jonathan Black (Committee Member), Haibo Dong (Committee Member), George Huang (Committee Chair), Ravi Penmetsa (Committee Co-chair), Norihiko Watanabe (Committee Member)
Doctor of Philosophy (PhD)
High speed trains are the most efficient means of public transportation. However the speed of the train needs to be increased (> 350 km/hr) to cover large distances in a short time to make it accessible to large population. With the increase in speed, number of issues related to efficiency, safety and comfort like the aerodynamic drag, structural strength, as well as the noise levels inside and outside of the train etc. need to be considered in the design of the high speed trains. Hence making it a multi disciplinary design problem. There are a large number of parameters from different disciplines that need to be tuned to identify the best design. The parameters need to be optimized to identify the best design configuration that meets the design requirements. This requires the use of robust and efficient optimization algorithms. Evolutionary algorithms have been used extensively in the engineering design optimization problems, but they suffer from a drawback of lack of robustness. One of the objectives of this research is to address the robustness issue of currently available optimization algorithms. A hybrid GA-PSO algorithm combining the benefits of both the original algorithms GA and PSO is proposed in this research. The hybrid GA-PSO algorithm was observed to be robust and accurate based upon the tests. The computer simulations required to complete the optimization of this problem are expensive both in terms of computational resources as well as time. To minimize the computational effort an adaptive surrogate model based on kriging was used during optimization. The accuracy of the surrogate model was checked during the optimization process using the parameter called expected improvement value (EIV) and is updated whenever found to be inadequate. The optimization algorithm combined with the adaptive surrogate modeling technique is tested on Branin function and is found to be robust and efficient.
The optimization of a high speed train is an MDO problem. The MDO problem can be simplified significantly if the problem can be decoupled thereby reducing the complexity of the problem. The objectives considered while finding the optimum design of the high speed train are aerodynamic drag for efficiency, structural strength for safety, and generated noise for human comfort. The objective for comfort, noise levels both inside and outside the train can be used as a decoupling objective between the aerodynamic and structural optimization. The optimization is performed sequentially. First step involves performing the shape optimization which identifies the optimum aerodynamic shape and structural optimization is performed on the optimum shape to identify the structure strong enough to withstand the aerodynamic loads with the least mass. A multi objective shape optimization is performed to identify the aerodynamic shape which induces least drag and generates least aerodynamic noise. Aerodynamic shape optimization requires the construction of new CAD models and some preprocessing to generate the computational mesh before the shape is analyzed. This step becomes complicated and is a hurdle when trying to automate the optimization process. Shape optimization is performed by using the shape control parameters on computational mesh and deforming the mesh along with the surface to obtain the optimum shape using commercial mesh deformation software, Sculptor. This approach was tested on a 2-D model before using it on a 3-D train model. Shape optimization is performed using a commercial CFD solver SC/Tetra. Since shape optimization is performed using mesh deformation software, there is an additional step of preparing the structure after the shape optimization is completed. Time averaged pressure loads acting on the structure are simulated using the optimum shape of the train and are mapped onto the structure. Structural optimization is performed to identify the structure t...
Department or Program
Ph.D. in Engineering
Year Degree Awarded
Copyright 2011, all rights reserved. This open access ETD is published by Wright State University and OhioLINK.