Application of Response Surface Model on Aerodynamic Characteristic of Airfoil

2014 ◽  
Vol 610 ◽  
pp. 70-73
Author(s):  
Cheng Long Liu ◽  
Hui Zhu ◽  
Zhi Gang Yang
Keyword(s):  
Response Surface ◽  
Aerodynamic Drag ◽  
Lift Coefficient ◽  
Response Surface Model ◽  
Surface Model ◽  
Aerodynamic Drag Coefficient ◽  
Sample Points ◽  
Computational Resources ◽  
Development And Validation ◽  
Selection Of

To reduce the computational resources, experimental design and response surface method (RSM) were employed to investigate the aerodynamic drag coefficient (CD) and lift coefficient (CL) of airfoils by using CFD methods. The selection of sample points, development and validation of response surface model and the effects of different sample points on approximation model were discussed. The results indicates that the number and distribution of sample points have a significant impact on model accuracy and optimize results. Also this method can greatly reduce calculation amount.

Finite Element Analysis and Multi-Objective Optimization of the Precision Horizontal Machining Center Bed

2014 ◽  
Vol 889-890 ◽  
pp. 130-134
Author(s):  
Xue Yan Li ◽  
Wen Tie Niu ◽  
Jun Qiang Wang ◽  
Ling Jun Xue
Keyword(s):  
Response Surface ◽  
Response Surface Model ◽  
Least Square ◽  
Surface Model ◽  
Multi Objective Optimization ◽  
Element Analysis ◽  
Maximum Deformation ◽  
Multi Objective ◽  
Machining Center ◽  
Sample Points

In order to improve dynamic and static performance of the precision horizontal machining center, the method of multi-objective optimization based on the response surface model was applied for optimizing design of the bed structure. The design variables were the layout parameters of the rib plates. Sample points were obtained by the Box-Behnken design experiment, and responses of sample points were analyzed by SAMCEF. The maximum deformation of guide rails and the low-order natural frequency were extracted to fit the response surface model by least square method. The layout parameters of the rib plates were optimized through the application of multi-objective genetic algorithms. Then, relationship between the lightening holes and the performance were analyzed to determine the suitable diameter. The results verify the validity of the optimization method, and the paper provides methodological guidance for optimization of machine tool structural parts.

Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence around a Generic Vehicle Model

2014 ◽  
Vol 989-994 ◽  
pp. 3468-3472 ◽  
Author(s):  
Cheng Wu ◽  
Yi Ping Wang ◽  
Xue Yang
Keyword(s):  
Flow Field ◽  
Drag Coefficient ◽  
Turbulence Model ◽  
Aerodynamic Drag ◽  
Lift Coefficient ◽  
Turbulence Models ◽  
Separation Flow ◽  
Rans Turbulence Models ◽  
Aerodynamic Drag Coefficient ◽  
Selection Of

For vehicle external aerodynamic computation, the selection of the turbulence model is very important. In current research, ten RANS turbulence models were introduced to compute the time-averaged flow field around the Ahmed model with 25° backlight angle. In order to evaluate the feasibility of the turbulence model, the results were compared with the related published experimental data. The results showed that the two equations RANS turbulence models were more favorable to compute the vehicle external flow field, but parts of the two equations turbulence model just could predict the aerodynamic drag coefficient or lift coefficient effectively. However, the results further revealed that the realizable k-ε could obtain the more accurate drag coefficient and lift coefficient simultaneously, and simulate the complex separation flow in the wake.

Optimization and design method for a rough rear surface on the notchback MIRA model

2017 ◽  
Vol 232 (10) ◽  
pp. 1297-1309 ◽  
Author(s):  
Yi Yang ◽  
Di Zhang ◽  
Zheng Liu
Keyword(s):  
Drag Coefficient ◽  
Rough Surface ◽  
Aerodynamic Drag ◽  
Design Method ◽  
Rear Surface ◽  
Latin Hypercube Sampling ◽  
Vehicle Body ◽  
Surface Model ◽  
Aerodynamic Drag Coefficient ◽  
Sample Points

In order to explore the optimization and design method of the rough surface on a vehicle body, a pitted rough surface, a convex rough surface and a grooved rough surface are respectively arranged at the rear of the MIRA model. Computational fluid dynamics and wind tunnel tests are adopted to analyse the relationships between the aerodynamic drag and the factors of the rough surface. The distance L and the height H of the surface roughness are considered as the design variables, and the aerodynamic drag coefficient CD is the objective evaluation function. Sample points are obtained by the Latin hypercube sampling method, and the kriging approximate model is set up according to the sample points and the response values. Then the multi-island genetic algorithm is used as the optimization algorithm to find the optimum solution. The aerodynamic effects of the rough surface model, with optimization and without optimization, are compared and analysed. Experimental and simulation results show that the aerodynamic drag coefficient further decreases after parameter optimization and that the optimization method is valid and general. The results obtained may provide a helpful reference and guidance for future rough surface applications on a vehicle body.

Sign in / Sign up

Export Citation Format

Share Document