Investigation of a Smooth Local Correlation-based Transition Model in a Discrete-Adjoint Aerodynamic Shape Optimization Algorithm

2022 ◽  
Author(s):  
Michael G. Piotrowski ◽  
David W. Zingg
Keyword(s):  
Shape Optimization ◽  
Optimization Algorithm ◽  
Transition Model ◽  
Local Correlation ◽  
Aerodynamic Shape Optimization ◽  
Aerodynamic Shape ◽  
Discrete Adjoint

scholarly journals Discrete adjoint aerodynamic shape optimization using symbolic analysis with OpenFEMflow

2021 ◽  
Author(s):  
Ali Elham ◽  
Michel J. L. van Tooren
Keyword(s):  
Shape Optimization ◽  
Open Source ◽  
Symbolic Analysis ◽  
State Variables ◽  
Aerodynamic Shape Optimization ◽  
Computationally Efficient ◽  
Exact Integration ◽  
Aerodynamic Shape ◽  
Discrete Adjoint ◽  
Gradient Based

AbstractThe combination of gradient-based optimization with the adjoint method for sensitivity analysis is a very powerful and popular approach for aerodynamic shape optimization. However, differentiating CFD codes is a time consuming and sometimes a challenging task. Although there are a few open-source adjoint CFD codes available, due to the complexity of the code, they might not be very suitable to be used for educational purposes. An adjoint CFD code is developed to support students for learning adjoint aerodynamic shape optimization as well as developing differentiated CFD codes. To achieve this goal, we used symbolic analysis to develop a discrete adjoint CFD code. The least-squares finite element method is used to solve the compressible Euler equations around airfoils in the transonic regime. The symbolic analysis method is used for exact integration to generate the element stiffness and force matrices. The symbolic analysis is also used to compute the exact derivatives of the residuals with respect to both design variables (e.g., the airfoil geometry) and the state variables (e.g., the flow velocity). Besides, the symbolic analysis allows us to compute the exact Jacobian of the governing equations in a computationally efficient way, which is used for Newton iteration. The code includes a build-in gradient-based optimization algorithm and is released as open-source to be available freely for educational purposes.

Influence of Optimization Algorithm on Airfoil Shape Optimization of Aircraft Wings

2012 ◽  
Vol 232 ◽  
pp. 614-619
Author(s):  
R. Mukesh ◽  
K. Lingadurai ◽  
K. Elamvaluthi
Keyword(s):  
Particle Swarm Optimization ◽  
Shape Optimization ◽  
Optimization Algorithm ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Aerodynamic Shape Optimization ◽  
Swarm Optimization ◽  
Shape Optimization Problem ◽  
Aerodynamic Shape

Computational fluid dynamics (CFD) is one of the computer-based solution methods which are more widely employed in aerospace engineering. The computational power and time required to carry out the analysis increases as the fidelity of the analysis increases. Aerodynamic shape optimization has become a vital part of aircraft design in the recent years. The Method of search algorithms or optimization algorithms is one of the most important parameters which will strongly influence the fidelity of the solution during an aerodynamic shape optimization problem. Nowadays various optimization methods such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO) etc., are more widely employed to solve the aerodynamic shape optimization problems. In addition to the optimization method, the geometry parameterisation becomes an important factor to be considered during the aerodynamic shape optimization process. Generally if we want to optimize an airfoil we have to describe the airfoil and for that, we need to have at least hundred points of x and y co-ordinates. It is really difficult to optimize airfoils with this large number of co-ordinates. Nowadays many different schemes of parameter sets are used to describe general airfoil such as B-spline, Hicks- Henne Bump function, PARSEC etc. The main goal of these parameterization schemes is to reduce the number of needed parameters as few as possible while controlling the important aerodynamic features effectively. Here the work has been done on the PARSEC geometry representation method. The objective of this work is to introduce the knowledge of describing general airfoil using twelve parameters by representing its shape as a polynomial function. And also we have introduced the concept of Particle Swarm optimization Algorithm which is one kind of Non-Traditional Optimization technique to optimize the aerodynamic characteristics of a general airfoil for specific conditions. An aerodynamic shape optimization problem is formulated for NACA 2411 airfoil and solved using the method of Particle Swarm Optimization for 5.0 deg angle of attack. A MATLAB program has been developed to implement PARSEC, Panel Technique, and PSO Algorithm. This program has been tested for a standard NACA 2411 airfoil and optimized to improve its coefficient of lift. Pressure distribution and co-efficient of lift for airfoil geometries has been calculated using panel method. NACA 2411 airfoil has been generated using PARSEC and optimized for 5.0 deg angle of attack using PSO Algorithm. The results show that the particle swarm optimization scheme is more effective in finding the optimum solution among the various possible solutions.

An exploitation-enhanced multi-objective efficient global optimization algorithm for expensive aerodynamic shape optimizations

2021 ◽  
pp. 095441002110324
Author(s):  
Feng Deng ◽  
Ning Qin
Keyword(s):  
Global Optimization ◽  
Shape Optimization ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Efficient Global Optimization ◽  
Test Problems ◽  
Aerodynamic Shape Optimization ◽  
Aerodynamic Shape ◽  
Multi Objective ◽  
Complex Landscape

The traditional multi-objective efficient global optimization (EGO) algorithms have been hybridized and adapted to solving the expensive aerodynamic shape optimization problems based on high-fidelity numerical simulations. Although the traditional EGO algorithms are highly efficient in solving some of the optimization problems with very complex landscape, it is not preferred to solve most of the aerodynamic shape optimization problems with relatively low-degree multi-modal design spaces. A new infill criterion encouraging more local exploitation has been proposed by hybridizing two traditional multi-objective expected improvements (EIs), namely, statistical multi-objective EI and expected hypervolume improvement, in order to improve their robustness and efficiency in aerodynamic shape optimization. Different analytical test problems and aerodynamic shape optimization problems have been investigated. In comparison with traditional multi-objective EI algorithms and a standard evolutionary multi-objective optimization algorithm, the proposed method is shown to be more robust and efficient in the tests due to its hybrid characteristics, easier handling of sub-optimization problems, and enhanced exploitation capability.

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