Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering · 2009 · 50 citations · 12 references
EngineeringComputational EngineeringMechanical EngineeringComputational Fluid DynamicsFinite Element AnalysisOptimizationControl ValvesFlow ModelingOptimization-based DesignButterfly ValvesCo-krigingFlow Rate CharacteristicsFlow Through ValvesPressure - Flow CharacteristicsSafety ValvesValve Performance ModelingValves
A butterfly valve is a type of flow control device, which is widely used to regulate a fluid flowing through a section of pipe. Currently, analyses and optimization are of special important in the design and usage of butterfly valves. For the analysis, finite element method (FEM) is often used to predict the safety of valve disc, and computational fluid dynamics (CFD) is commonly used to study the flow characteristics of valve. However, it is difficult to obtain accurate results for the optimization of butterfly valve due to the high non-linearities. For this reason, metamodels or surrogate model methods are extensively employed. This paper integrates metamodel with FEM and CFD analysis to optimize a traditional butterfly valve, where the weight of the valve disc is the design objective, and the strength safety of disc and the pressure loss coefficient of valve are constraints. Kriging model is employed as a surrogate model to formulate the objectives and constrains, and the orthogonal array is used as design of experiment to sample the computer analysis. The optimum results with the corresponding variable combinations for the valve disc are obtained easily by this method. Moreover, the structural and fluid analyses with the obtained optimum variable combinations are conducted again to verify the accuracy of the optimization method. The results demonstrate the capability and potential of this method, which integrates the Kriging model with FEM and CFD analysis, in solving the optimization of a butterfly valve.
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Design and Analysis of Computer Experiments
Jerome Sacks, William J. Welch, Toby J. Mitchell et al. · Statistical Science · 1989 · 7K citations · Full text
Smoothing noisy data with spline functions
Peter G. Craven, Grace Wahba · Numerische Mathematik · 1978 · 3.4K citations
Comparison of response surface and kriging models for multidisciplinary design optimization
Timothy W. Simpson, Farrokh Mistree, John J. Korte et al. · 7th AIAA/USAF/NASA/ISSMO Symposium on Multidisciplinary Analysis and Optimization · 1998 · 487 citations · Full text
Engineering, Computational Engineering, Mechanical Engineering +15