scholarly journals Decoupling of Control and Force Objective in Adjoint-Based Fluid Dynamic Shape Optimization

AIAA Journal ◽  
2019 ◽  
Vol 57 (9) ◽  
pp. 4110-4114 ◽  
Author(s):  
Niklas Kühl ◽  
Peter M. Müller ◽  
Arthur Stück ◽  
Michael Hinze ◽  
Thomas Rung
Author(s):  
Peter Marvin Müller ◽  
Niklas Kühl ◽  
Martin Siebenborn ◽  
Klaus Deckelnick ◽  
Michael Hinze ◽  
...  

AbstractWe introduce a novel method for the implementation of shape optimization for non-parameterized shapes in fluid dynamics applications, where we propose to use the shape derivative to determine deformation fields with the help of the $$p-$$ p - Laplacian for $$p > 2$$ p > 2 . This approach is closely related to the computation of steepest descent directions of the shape functional in the $$W^{1,\infty }-$$ W 1 , ∞ - topology and refers to the recent publication Deckelnick et al. (A novel $$W^{1,\infty}$$ W 1 , ∞ approach to shape optimisation with Lipschitz domains, 2021), where this idea is proposed. Our approach is demonstrated for shape optimization related to drag-minimal free floating bodies. The method is validated against existing approaches with respect to convergence of the optimization algorithm, the obtained shape, and regarding the quality of the computational grid after large deformations. Our numerical results strongly indicate that shape optimization related to the $$W^{1,\infty }$$ W 1 , ∞ -topology—though numerically more demanding—seems to be superior over the classical approaches invoking Hilbert space methods, concerning the convergence, the obtained shapes and the mesh quality after large deformations, in particular when the optimal shape features sharp corners.


2019 ◽  
Vol 30 (6) ◽  
pp. 3307-3321 ◽  
Author(s):  
Mohammad Reza Pakatchian ◽  
Hossein Saeidi ◽  
Alireza Ziamolki

Purpose This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics. Design/methodology/approach The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry. Findings 3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent. Originality/value The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.


2019 ◽  
Vol 141 (8) ◽  
Author(s):  
Giacomo Persico ◽  
Pablo Rodriguez-Fernandez ◽  
Alessandro Romei

This paper presents a novel tool for the shape optimization of turbomachinery blade profiles operating with fluids in non-ideal thermodynamic conditions and in complex flow configurations. In novel energy conversion systems, such as organic Rankine cycles or supercritical CO2 cycles, the non-conventional turbomachinery layout as well as the complex thermodynamics of the working fluid complicate significantly the blade aerodynamic design. For such applications, the design of turbomachinery may considerably benefit from the use of systematic optimization methods, especially in combination with high-fidelity computational fluid dynamics (CFD), as it is shown in this paper. The proposed technique is implemented in the shape-optimization package FORMA (Fluid-dynamic OptimizeR for turbo-Machinery Aerofoils) developed in-house at the Politecnico di Milano. FORMA is constructed as a combination of a generalized geometrical parametrization technique based on B-splines, a CFD solver featuring turbulence models and arbitrary equations of state, and multiple surrogate-based evolutionary strategies based on either trust-region or training methods. The application to the re-design of a supersonic turbine nozzle shows the capabilities of applying a high-fidelity optimization, consisting of a 50% reduction in the cascade loss coefficient and in an increased flow uniformity at the inlet of the subsequent rotor. Two alternative surrogate-based evolutionary strategies and different fitness functions are tested and discussed, including nonlinear constraints within the design process. The optimization study reveals relevant insights into the design of supersonic turbine nozzles as well on the performance, reliability, and potential of the proposed design technique.


Author(s):  
Joris S. M. Vergeest ◽  
Chensheng Wang ◽  
Yu Song ◽  
Imre Horva´th

Automatic processing of shape information requires the selection of a representation form of shape. Shape modeling is based on a choice of shape type, which is the joint specification of representation form and a set of operations. In shape applications, such as shape design and shape optimization, it is not sufficient to maintain a static shape type. Depending on the specific needs during the application, i.e. depending on the modeling context, the appropriate shape type might be continuously varying. Programmed systems can handle static shape types relatively well. However, to support dynamic shape typing a number of fundamental problems need to be understood and solved. An approach to dynamic typing of freeform shapes is presented. Theoretical issues will be described and some concrete examples and initial experimental results will be presented.


2007 ◽  
Vol 36 (3) ◽  
pp. 307-317 ◽  
Author(s):  
Jianping Zhang ◽  
Shuguang Gong ◽  
Yunqing Huang ◽  
Aihong Qiu ◽  
Renke Chen

Author(s):  
Yanhui Duan ◽  
Zhaolin Fan ◽  
Wenhua Wu ◽  
Ti Chen

AbstractIn this paper, global optimization design of a transonic compressor 3D blade (Rotor 37) has been carried out by a self-developed aerodynamic shape optimization (ASO) platform based on improved parallel synchronous particle swarm optimization (PSPSO). To improve the performance of PSPSO, coefficient of variation (COV) based attenuation method with new parameters is proposed and then validated by optimization tests. Flow field of blade is calculated by an in-house computational fluid dynamic (CFD) code called PMB3D-Turbo, which is validated by Rotor 37. Choosing Rotor 37 as the case, optimization object is to maximize the peak adiabatic efficiency, meanwhile constraining mass flow and total pressure ratio. The solutions show that, the ASO platform is effective to transonic compressor blade and variations of thickness distribution near the trailing edge can help improve the adiabatic efficiency of a transonic compressor blade.


Author(s):  
Jinglu Li ◽  
Peng Wang ◽  
Xu Chen ◽  
Huachao Dong

Currently developed underwater gliders can be roughly divided into the two types:traditional configuration and unconventional configuration. As a type of underwater gliders with unconventional configuration, a blended-wing-body (BWB) underwater glider has better fluid dynamic performances because of its unique shape. However, it is difficult to design the shape of the BWB underwater glider that has excellent hydrodynamic performances. Therefore, it is of great significance to optimize its shape, which this paper carries out by using the free-form deformation (FFD). The complete and automatic shape optimization framework is established by jointly using FFD parameterization method, CFD solver, optimization algorithm and mesh deformation method. The framework is used to optimize the shape of a BWB underwater glider. The average drag coefficient of the BWB underwater glider during its sinking and floating in one working period is used as the objective function to optimize its shape, with the volume constraints considered. The optimization results show that the gliding performance of the BWB underwater glider is remarkably enhanced.


2019 ◽  
Vol 30 (9) ◽  
pp. 4241-4257
Author(s):  
Franck Mastrippolito ◽  
Stephane Aubert ◽  
Frédéric Ducros ◽  
Martin Buisson

Purpose This paper aims to improve the radial basis fuction mesh morphing method. During a shape optimization based on computational fluid dynamic (CFD) solvers, the mesh has to be changed. Two possible strategies are re-meshing or morphing. The morphing one is advantageous because it preserves the mesh connectivity, but it must be constrained. Design/methodology/approach RBF mesh deformation is one of the most robust and accurate morphing method. Using a greedy algorithm, the computational cost of the method is reduced. To evaluate the morphing performances, a rib shape optimization is performed using the NSGA-II algorithm coupled to kriging metamodels based on CFD. The morphing method is then compared to a re-meshing strategy. Findings The authors propose a method, based on Schur complement, to speed-up the greedy process. By using the information of the previous iteration, smaller linear systems are solved and time is saved. The optimization results highlight the interest of using a morphing-based metamodel regarding the resolution time and the accuracy of the interpolated solutions. Originality/value A new method based on Schur complement is addressed to speed-up the greedy algorithm and successfully applied to a shape optimization.


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