Response surfaces: designs and analyses
Response surfaces: designs and analyses
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Response Surface Methodology: Process and Product in Optimization Using Designed Experiments
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
| Hi-index | 0.00 |
Nowadays, the use of computational fluid dynamics (CFD) in the design of valves is very common. Despite the continuing growth of computing capability, the computational cost of complex three-dimensional CFD analysis of butterfly valve maintains high, therefore, the CFD analysis-based optimization becomes more time-consuming and computational expensive. In this paper, a comparative study on the use of multiple approximate models including polynomial response surface, Kriging model, support vector regression and radial basis neural networks, which have been well used for a variety of engineering optimizations, is performed for the prediction and optimization of fluid performance of a butterfly valve. Several types of error analysis corresponding to the four surrogate models are compared to identify the final optimum result and which model is more proper for this case. This study gives a deep insight into the use of multiple surrogate models for the design and optimization of a butterfly valve.