scholarly journals Inverse shape design method based on pressure and shear stress for separated flow via Elastic Surface Algorithm

Author(s):  
Mohammad Hossein Noorsalehi ◽  
Mahdi Nili-Ahmadabadi ◽  
Kyung Chun Kim
2021 ◽  
Vol 11 (11) ◽  
pp. 4845
Author(s):  
Mohammad Hossein Noorsalehi ◽  
Mahdi Nili-Ahmadabadi ◽  
Seyed Hossein Nasrazadani ◽  
Kyung Chun Kim

The upgraded elastic surface algorithm (UESA) is a physical inverse design method that was recently developed for a compressor cascade with double-circular-arc blades. In this method, the blade walls are modeled as elastic Timoshenko beams that smoothly deform because of the difference between the target and current pressure distributions. Nevertheless, the UESA is completely unstable for a compressor cascade with an intense normal shock, which causes a divergence due to the high pressure difference near the shock and the displacement of shock during the geometry corrections. In this study, the UESA was stabilized for the inverse design of a compressor cascade with normal shock, with no geometrical filtration. In the new version of this method, a distribution for the elastic modulus along the Timoshenko beam was chosen to increase its stiffness near the normal shock and to control the high deformations and oscillations in this region. Furthermore, to prevent surface oscillations, nodes need to be constrained to move perpendicularly to the chord line. With these modifications, the instability and oscillation were removed through the shape modification process. Two design cases were examined to evaluate the method for a transonic cascade with normal shock. The method was also capable of finding a physical pressure distribution that was nearest to the target one.


Author(s):  
M. H. Noorsalehi ◽  
M. Nili-Ahamadabadi ◽  
E. Shirani ◽  
M. Safari

In this study, a new inverse design method called Elastic Surface Algorithm (ESA) is developed and enhanced for axial-flow compressor blade design in subsonic and transonic flow regimes with separation. ESA is a physically based iterative inverse design method that uses a 2D flow analysis code to estimate the pressure distribution on the solid structure, i.e. airfoil, and a 2D solid beam finite element code to calculate the deflections due to the difference between the calculated and target pressure distributions. In order to enhance the ESA, the wall shear stress distribution, besides pressure distribution, is applied to deflect the shape of the airfoil. The enhanced method is validated through the inverse design of the rotor blade of the first stage of an axial-flow compressor in transonic viscous flow regime. In addition, some design examples are presented to prove the effectiveness and robustness of the method. The results of this study show that the enhanced Elastic Surface Algorithm is an effective inverse design method in flow regimes with separation and normal shock.


Author(s):  
Kisun Song ◽  
Kyung Hak Choo ◽  
Jung-Hyun Kim ◽  
Dimitri N. Mavris

In modern automotive industry market, there have been a lot of state-of-art methodologies to perform a conceptual design of a car; functional methods and 3D scanning technology are widely used. Naturally, the issues frequently boiled down to a trade-off decision making problem between quality and cost. Besides, to incorporate the design method with advanced optimization methodologies such as design-of-experiments (DOE), surrogate modeling, how efficiently a method can morph or recreate a vehicle’s shape is crucial. This paper accomplishes an aerodynamic design optimization of rear shape of a sedan by incorporating a reverse shape design method (RSDM) with the aforementioned methodologies based on CFD analysis for aerodynamic drag reduction. RSDM reversely recovers a 3D geometry of a car from several 2D schematics. The backbone boundary lines of 2D schematic are identified and regressed by appropriate interpolation function and a 3D shape is yielded by a series of simple arithmetic calculations without losing the detail geometric features. Besides, RSDM can parametrize every geometric entity to efficiently manipulate the shape for application to design optimization studies. As the baseline, an Audi A6 is modeled by RSDM and explored through CFD analysis for model validation. Choosing six design variables around the rear shape, 77 design points are created to build neural networks. Finally, a significant amount of CD reduction is obtained and corresponding configuration is validated via CFD.


2012 ◽  
Vol 479-481 ◽  
pp. 917-920
Author(s):  
Yong Ping Liu ◽  
Peng Fei Meng ◽  
Chi Bing Hu

According to the meshing principle of noncircular gears, the meshing characteristic and digital manufacturing technology of eccentric involute gears is studied. Based on analyzing the parameterized modeling of pitch curve, transmission characteristics and convex-concave property on eccentric involute gears, the transmission feasibility of this type gear is proved. Through tooth shape design, CAM calculation and processing simulation, the validity and manufacturability of theoretical design method on this type gear is proved. The research results can provide more systemic theory basis for the design, manufacture, measure and application of eccentric involute gears.


2011 ◽  
Vol 314-316 ◽  
pp. 594-598
Author(s):  
Min Xiao ◽  
Xue Dao Shu

Blank shape design is the prerequisite and foundation of optimization for the closed forming the high-neck flange. This paper obtained the design formulas of blank size with analyzing the mathematical model of flank blank based on the principle of volume invariably during the rolling process.The blank of a special flange was designed by this method which was validated by the numerical simulation under the DEFORM software. The results indicate that the product is qualified with the blank shape based on this method. These research conclusions can provide scientific basis for forming the high-neck flange with rolling method.


2012 ◽  
Vol 9 (1) ◽  
pp. 779-784
Author(s):  
Nien-Te Liu ◽  
Chang-Tzuoh Wu ◽  
Yang-Chih Lin

2013 ◽  
Vol 42 ◽  
pp. 55-69 ◽  
Author(s):  
Grégoire Fourrié ◽  
Laurent Keirsbulck ◽  
Larbi Labraga

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