CFD-based multi-objective aerodynamic optimization of a car rear diffuser

2021 ◽  
Author(s):  
Denis Peixoto ◽  
Bruno Carmo
Keyword(s):  
Aerodynamic Optimization ◽  
Multi Objective

Multi-objective structural optimization of a wind turbine blade using NSGA-II algorithm and FSI

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Ramazan Özkan ◽  
Mustafa Serdar Genç
Keyword(s):  
Structural Optimization ◽  
Wind Turbine ◽  
Turbine Blade ◽  
Wind Turbine Blade ◽  
Design Parameters ◽  
Aerodynamic Optimization ◽  
Nsga Ii ◽  
Content Type ◽  
Multi Objective ◽  
Optimization Study

Purpose Wind turbines are one of the best candidates to solve the problem of increasing energy demand in the world. The aim of this paper is to apply a multi-objective structural optimization study to a Phase II wind turbine blade produced by the National Renewable Energy Laboratory to obtain a more efficient small-scale wind turbine. Design/methodology/approach To solve this structural optimization problem, a new Non-Dominated Sorting Genetic Algorithm (NSGA-II) was performed. In the optimization study, the objective function was on minimization of mass and cost of the blade, and design parameters were composite material type and spar cap layer number. Design constraints were deformation, strain, stress, natural frequency and failure criteria. ANSYS Composite PrepPost (ACP) module was used to model the composite materials of the blade. Moreover, fluid–structure interaction (FSI) model in ANSYS was used to carry out flow and structural analysis on the blade. Findings As a result, a new original blade was designed using the multi-objective structural optimization study which has been adapted for aerodynamic optimization, the NSGA-II algorithm and FSI. The mass of three selected optimized blades using carbon composite decreased as much as 6.6%, 11.9% and 14.3%, respectively, while their costs increased by 23.1%, 29.9% and 38.3%. This multi-objective structural optimization-based study indicates that the composite configuration of the blade could be altered to reach the desired weight and cost for production. Originality/value ACP module is a novel and advanced composite modeling technique. This study is a novel study to present the NSGA-II algorithm, which has been adapted for aerodynamic optimization, together with the FSI. Unlike other studies, complex composite layup, fiber directions and layer orientations were defined by using the ACP module, and the composite blade analyzed both aerodynamic pressure and structural design using ACP and FSI modules together.

Multi-objective aerodynamic optimization design of high-speed train head shape

2017 ◽  
Vol 18 (11) ◽  
pp. 841-854 ◽  
Author(s):  
Liang Zhang ◽  
Ji-ye Zhang ◽  
Tian Li ◽  
Ya-dong Zhang
Keyword(s):  
High Speed ◽  
Optimization Design ◽  
High Speed Train ◽  
Head Shape ◽  
Aerodynamic Optimization ◽  
Multi Objective

Multi-Objective and Multi-Point Aerodynamic Optimization of Transonic Fan Blades

2014 ◽  
Author(s):  
Maryam Khelghatibana ◽  
Jean-Yves Trépanier ◽  
Christophe Tribes ◽  
Jason Nichols
Keyword(s):  
High Speed ◽  
Stokes Equations ◽  
Pressure Ratio ◽  
Operating Conditions ◽  
Optimized Design ◽  
Aerodynamic Optimization ◽  
Stall Margin ◽  
Choked Flow ◽  
Multi Objective ◽  
Transonic Fan

A multi-objective and multi-point optimization methodology is developed for aerodynamic design of transonic fan blades. The optimization method aims to increase design efficiency, near stall efficiency and stall margin while maintaining the required design pressure ratio and high speed choke margin. Numerical analyses are performed by solving three-dimensional Reynolds-Averaged Navier-Stokes equations combined with shear stress turbulence model. A multi-level blade parameterization is employed to modify the blade geometry. The proposed method is applied to redesign NASA rotor 67. First, an optimization case with considering two operating conditions at peak efficiency and near stall is performed to demonstrate the relation between near stall efficiency and stall margin. An investigation on Pareto optimal solutions of this optimization shows that the stall margin is increased with improving near stall efficiency. Then, in order to maintain the required choke margin, an operating point at high speed choked flow is added to the optimization process. A final optimized design is selected by considering the interaction of design requirements at all three operating points. The new design presents higher efficiency and stall margin without any reduction in the chocking mass flow rate.

Aerodynamic Optimization Design of Compressor Cascade Based on Parallel Multi-Objective Genetic Algorithm and Artificial Neural Network

2011 ◽  
Vol 138-139 ◽  
pp. 534-539
Author(s):  
Li Hai Chen ◽  
Qing Zhen Yang ◽  
Jin Hui Cui
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Optimization Design ◽  
Compressor Cascade ◽  
Aerodynamic Optimization ◽  
Navier Stokes Equation ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Artificial Neural

Genetic algorithm (GA) is improved with fast non-dominated sort approach and crowded comparison operator. A new algorithm called parallel multi-objective genetic algorithm (PMGA) is developed with the support of Massage Passing Interface (MPI). Then, PMGA is combined with Artificial Neural Network (ANN) to improve the optimization efficiency. Training samples of the ANN are evaluated based on the two-dimensional Navier-Stokes equation solver of cascade. To demonstrate the feasibility of the hybrid algorithm, an optimization of a controllable diffusion cascade is performed. The optimization results show that the present method is efficient and trustiness.

scholarly journals Multi-objective aerodynamic optimization of flying-wing configuration based on adjoint method

2021 ◽  
Vol 39 (4) ◽  
pp. 753-760
Author(s):  
Xiaodong Liu ◽  
Peiliang Zhang ◽  
Guanghong He ◽  
Yongen Wang ◽  
Xudong Yang
Keyword(s):  
Optimization Design ◽  
Adjoint Method ◽  
Stokes Equations ◽  
Design Method ◽  
Adjoint Equation ◽  
Geometric Constraints ◽  
Navier Stokes ◽  
Aerodynamic Design ◽  
Aerodynamic Optimization ◽  
Multi Objective

In order to solve the multi-objective multi-constraint design in aerodynamic design of flying wing, the aerodynamic optimization design based on the adjoint method is studied. In terms of the principle of the adjoint equation, the boundary conditions and the gradient equations are derived. The Navier-Stokes equations and adjoint aerodynamic optimization design method are adopted, the optimization design of the transonic drag reduction for the two different aspect ratio of the flying wing configurations is carried out. The results of the optimization design are as follows: Under the condition of satisfying the aerodynamic and geometric constraints, the transonic shock resistance of the flying wing is weakened to a great extent, which proves that the developed method has high optimization efficiency and good optimization effect in the multi-objective multi-constraint aerodynamic design of the flying wing.

Design strategies for multi-objective optimization of aerodynamic surfaces

2017 ◽  
Vol 34 (5) ◽  
pp. 1724-1753 ◽  
Author(s):  
Anand Amrit ◽  
Leifur Leifsson ◽  
Slawomir Koziel
Keyword(s):  
Pareto Front ◽  
Design Space ◽  
Optimization Problems ◽  
Computational Time ◽  
High Fidelity ◽  
Design Strategies ◽  
Aerodynamic Optimization ◽  
Content Type ◽  
Multi Objective ◽  
Aerodynamic Surfaces

Purpose This paper aims to investigates several design strategies to solve multi-objective aerodynamic optimization problems using high-fidelity simulations. The purpose is to find strategies which reduce the overall optimization time while still maintaining accuracy at the high-fidelity level. Design/methodology/approach Design strategies are proposed that use an algorithmic framework composed of search space reduction, fast surrogate models constructed using a combination of physics-based surrogates and kriging and global refinement of the Pareto front with co-kriging. The strategies either search the full or reduced design space with a low-fidelity model or a physics-based surrogate. Findings Numerical investigations of airfoil shapes in two-dimensional transonic flow are used to characterize and compare the strategies. The results show that searching a reduced design space produces the same Pareto front as when searching the full space. Moreover, as the reduced space is two orders of magnitude smaller (volume-wise), the number of required samples to setup the surrogates can be reduced by an order of magnitude. Consequently, the computational time is reduced from over three days to less than half a day. Originality/value The proposed design strategies are novel and holistic. The strategies render multi-objective design of aerodynamic surfaces using high-fidelity simulation data in moderately sized search spaces computationally tractable.

scholarly journals MULTI OBJECTIVE AERODYNAMIC OPTIMIZATION USING PARALLEL NASH EVOLUTIONARY/DETERMINISTIC HYBRID ALGORITHMS

2016 ◽  
Vol 42 ◽  
pp. 1660168
Author(s):  
ZHILI TANG
Keyword(s):  
Drag Reduction ◽  
Optimization Algorithm ◽  
Aerodynamic Drag ◽  
Hybrid Algorithms ◽  
Hybrid Optimization ◽  
Aerodynamic Optimization ◽  
Multi Objective ◽  
Hybrid Optimization Algorithm ◽  
Aerodynamic Drag Reduction ◽  
Global Parameters

This paper solved aerodynamic drag reduction of transport wing fuselage configuration in transonic regime by using a parallel Nash evolutionary/deterministic hybrid optimization algorithm. Two sets of parameters are used, namely globally and locally. It is shown that optimizing separately local and global parameters by using Nash algorithms is far more efficient than considering these variables as a whole.

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