Abstract
This work demonstrates the use of Latin Hypercube Sampling and Proper Orthogonal Decomposition in combination with a Radial Basis Function model to perform on vehicle prediction coupled fluid–thermal–structure. We regarded the Mach number, flight altitude and angle of attack as input parameters and established a rapid prediction model. The basic process of numerical simulation of the hypersonic vehicle coupled fluid–thermal–structure was studied to obtain the database of pressure coefficient, heat flux, structural temperature and structural stress as the sample data to train this prediction method. The prediction error was analyzed. The prediction results showed that the data-driven method proposed in this paper based on proper orthogonal decomposition and radial basis function could be used for predicting vehicle coupled fluid–thermal–structure with good efficiency.
Highlights
A hypersonic vehicle is a challenging research area
We demonstrate the use of Latin Hypercube Sampling (LHS) and Proper Orthogonal Decomposition (POD) in combination with a Radial Basis Function (RBF) model to perform a prediction of the physical quantities coupled fluid–thermal–structure
We briefly review the theory of the POD and RBF Interpolation, followed by the rapid data-driven prediction method of a vehicle coupled fluid–thermal– structure, which are employed throughout this paper
Summary
A hypersonic vehicle is a challenging research area. This is due to the fact that researchers need to deal with a large flight envelope, rapidly changing aerodynamic coefficients, as well as coupling between the structure and the aerodynamics of the vehicle [1]. Pramote Dechaumphai et al [12] carried out a study of a numerical simulation on leading edges coupled fluid–thermal–structure, and compared this with the experiment result Many researchers such as Hirschel [2], Anderson [3], and so on had gone into studies on the aerodynamic heat of hypersonic vehicles. Bui Thanh Tan [17] proposed to interpolate the POD coefficient under the condition of the known incoming flow state (such as Mach number, angle of attack) corresponding to the pressure coefficient distribution This method can quickly obtain the approximate pressure coefficient distribution of the unknown flow field and greatly improves the calculation efficiency. We demonstrate the use of Latin Hypercube Sampling (LHS) and Proper Orthogonal Decomposition (POD) in combination with a Radial Basis Function (RBF) model to perform a prediction of the physical quantities coupled fluid–thermal–structure
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