Aerodynamic optimization method based on Bezier curve and radial basis function

2016 ◽  
Vol 232 (3) ◽  
pp. 459-471
Author(s):  
Fan Yang ◽  
Zhufeng Yue ◽  
Lei Li ◽  
Weizhu Yang
Keyword(s):  
Radial Basis Function ◽  
Basis Function ◽  
Aerodynamic Performance ◽  
Bézier Curve ◽  
Design Parameters ◽  
Bezier Curve ◽  
Aerodynamic Design ◽  
Aerodynamic Optimization ◽  
Flight Vehicles ◽  
Radial Basis

Aerodynamic design is of great importance in the overall design of flight vehicles. In this study, an approach to aerodynamic design optimization is proposed by integrating Bezier curve parameterization and radial basis interpolation to enable large variation of aerodynamic profile during optimization. The Bezier curve uses the shape of a given airfoil and the radial basis function interpolation is applied to smoothly transfer the perturbation to the mesh in the whole flow field. Using design of experiments technique, the prominent design parameters that significantly affect the aerodynamic performance are determined. Aerodynamic optimizations are conducted for a wing airfoil and a blade airfoil to verify the efficiency of the proposed method. Genetic algorithm is employed in both single-objective and multiobjective design cases. Design results show that the present method can significantly improve the aerodynamic performance due to its capability to handle large shape changes of the airfoil. This work provides a useful and powerful tool to aerodynamic design with applications to various flight vehicles.

scholarly journals Path Planning Optimization for Driverless Vehicle in Parallel Parking Integrating Radial Basis Function Neural Network

2021 ◽  
Vol 11 (17) ◽  
pp. 8178
Author(s):  
Leiyan Yu ◽  
Xianyu Wang ◽  
Zeyu Hou ◽  
Zaiyou Du ◽  
Yufeng Zeng ◽  
...  
Keyword(s):  
Neural Network ◽  
Radial Basis Function ◽  
Basis Function ◽  
Bézier Curve ◽  
Bezier Curve ◽  
Control Points ◽  
Parallel Parking ◽  
Radial Basis ◽  
Initial Path ◽  
Continuous Curvature

To optimize performances such as continuous curvature, safety, and satisfying curvature constraints of the initial planning path for driverless vehicles in parallel parking, a novel method is proposed to train control points of the Bézier curve using the radial basis function neural network method. Firstly, the composition and working process of an autonomous parking system are analyzed. An experiment concerning parking space detection is conducted using an Arduino intelligent minicar with ultrasonic sensor. Based on the analysis of the parallel parking process of experienced drivers and the idea of simulating a human driver, the initial path is planned via an arc-line-arc three segment composite curve and fitted by a quintic Bézier curve to make up for the discontinuity of curvature. Then, the radial basis function neural network is established, and slopes of points of the initial path are used as input to train and obtain horizontal ordinates of four control points in the middle of the Bézier curve. Finally, simulation experiments are carried out by MATLAB, whereby parallel parking of driverless vehicle is simulated, and the effects of the proposed method are verified. Results show the trained and optimized Bézier curve as a planning path meets the requirements of continuous curvature, safety, and curvature constraints, thus improving the abilities for parallel parking in small parking spaces.

Direct Adaptive Control for a Certain Class of Nonlinear Systems Using Modified Radial Basis Function Neural Network

Volume 1 ◽  
2004 ◽  
Author(s):  
Hsuan-Ju Chen ◽  
Rongshun Chen
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Radial Basis Function ◽  
Basis Function ◽  
Controller Design ◽  
Rbf Neural Network ◽  
Design Parameters ◽  
State Variables ◽  
Rbf Network ◽  
Radial Basis

This paper proposes a direct adaptive controller for SISO affine nonlinear systems using Gaussian radial basis function (RBF) neural network (NN). The exact plant model is not necessary for composing the controller. If the plant is SISO, of affine form, without zero dynamics, and all the state variables are available, the controller is applicable under several mild assumptions. In this paper, the Gaussian RBF network (GRBFN) is modified to include pre-scale weights as its parameters for the input variables, which are also adapted in the control law. Pre-scaling the inputs is equivalent to extending or contracting the spectrum of the approximated function. With the modification, the spectrum along each coordinate of the domain can be scaled separately for approximating. The adaptation of the nonlinear parameters, including the variances, centers, and pre-scaling weights, are derived. Appropriate modification techniques are applied to the adaptation laws to ensure the robustness. The stability is analyzed with Lyapunov’s Theory. From the analysis, the effect of the controller design parameters is also examined. A simulation of an inverted pendulum control is demonstrated to show the effectiveness.

Multi-Objective Optimization of a Data Center Modeling Using Response Surface

2016 ◽  
Author(s):  
Long Phan ◽  
Cheng-Xian Lin
Keyword(s):  
Radial Basis Function ◽  
Response Surface ◽  
Thermal Management ◽  
Basis Function ◽  
Data Center ◽  
Data Centers ◽  
Maximum Temperature ◽  
Design Parameters ◽  
Running Time ◽  
Radial Basis

Energy consumption and thermal management have become key challenges in the design of large-scale data centers, where perforated tiles are used together with cold and hot aisles configuration to improve thermal management. Although full-field simulations using computational fluid dynamics and heat transfer (CFD/HT) tools can be applied to predict the flow and temperature fields inside data centers, their running time remain the biggest challenge to most modelers. In this paper, response surface methodology based on radial basis function is used to significantly reduce the running time for generating a large set of generations during a two-objective minimization process which uses the genetic algorithm as its main engine. Three design parameters including mass flow inlet, inlet temperature, and server heat load are investigated for a two-objective optimization. The goal is to minimize both the temperature difference and the maximum temperature inside the data center and search for a range of design parameters that satisfy both of these objectives. Numerous radial basis function models are studied and compared. Discussion on a more preferred scheme for the response surface construction is provided. Finally, a graph of Pareto font is generated showing the set of optimal designs in the objective space, and Pareto design validation is also performed.

Sign in / Sign up

Export Citation Format

Share Document