State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Novel anisotropic continuum-discrete damage model capable of representing localized failure of massive structures. Part II: identification from tests under heterogeneous stress field

In Part I of this paper we have presented a simple model capable of describing the localized failure of a massive structure. In this part, we discuss the identification of the model parameters from two kinds of experiments: a uniaxial tensile test and a three-point bending test. The former is used only for illustration of material parameter response dependence, and we focus mostly upon the latter, discussing the inverse optimization problem for which the specimen is subjected to a heterogeneous stress field.Comment: 18 pages, 12 figures, 6 table

Hybrid Intelligent Optimization Methods for Engineering Problems

The purpose of optimization is to obtain the best solution under certain conditions. There are numerous optimization methods because different problems need different solution methodologies; therefore, it is difficult to construct patterns. Also mathematical modeling of a natural phenomenon is almost based on differentials. Differential equations are constructed with relative increments among the factors related to yield. Therefore, the gradients of these increments are essential to search the yield space. However, the landscape of yield is not a simple one and mostly multi-modal. Another issue is differentiability. Engineering design problems are usually nonlinear and they sometimes exhibit discontinuous derivatives for the objective and constraint functions. Due to these difficulties, non-gradient-based algorithms have become more popular in recent decades. Genetic algorithms (GA) and particle swarm optimization (PSO) algorithms are popular, non-gradient based algorithms. Both are population-based search algorithms and have multiple points for initiation. A significant difference from a gradient-based method is the nature of the search methodologies. For example, randomness is essential for the search in GA or PSO. Hence, they are also called stochastic optimization methods. These algorithms are simple, robust, and have high fidelity. However, they suffer from similar defects, such as, premature convergence, less accuracy, or large computational time. The premature convergence is sometimes inevitable due to the lack of diversity. As the generations of particles or individuals in the population evolve, they may lose their diversity and become similar to each other. To overcome this issue, we studied the diversity concept in GA and PSO algorithms. Diversity is essential for a healthy search, and mutations are the basic operators to provide the necessary variety within a population. After having a close scrutiny of the diversity concept based on qualification and quantification studies, we improved new mutation strategies and operators to provide beneficial diversity within the population. We called this new approach as multi-frequency vibrational GA or PSO. They were applied to different aeronautical engineering problems in order to study the efficiency of these new approaches. These implementations were: applications to selected benchmark test functions, inverse design of two-dimensional (2D) airfoil in subsonic flow, optimization of 2D airfoil in transonic flow, path planning problems of autonomous unmanned aerial vehicle (UAV) over a 3D terrain environment, 3D radar cross section minimization problem for a 3D air vehicle, and active flow control over a 2D airfoil. As demonstrated by these test cases, we observed that new algorithms outperform the current popular algorithms. The principal role of this multi-frequency approach was to determine which individuals or particles should be mutated, when they should be mutated, and which ones should be merged into the population. The new mutation operators, when combined with a mutation strategy and an artificial intelligent method, such as, neural networks or fuzzy logic process, they provided local and global diversities during the reproduction phases of the generations. Additionally, the new approach also introduced random and controlled diversity. Due to still being population-based techniques, these methods were as robust as the plain GA or PSO algorithms. Based on the results obtained, it was concluded that the variants of the present multi-frequency vibrational GA and PSO were efficient algorithms, since they successfully avoided all local optima within relatively short optimization cycles

APPLICATION OF GENETIC ALGORITHMS AND CFD FOR FLOW CONTROL OPTIMIZATION

Active flow control is an area of heightened interest in the aerospace community. Previous research on flow control design processes heavily depended on trial and error and the designers knowledge and intuition. Such an approach cannot always meet the growing demands of higher design quality in less time. Successful application of computational fluid dynamics (CFD) to this kind of control problem critically depends on an efficient searching algorithm for design optimization. CFD in conjunction with Genetic Algorithms (GA) potentially offers an efficient and robust optimization method and is a promising solution for current flow control designs. Current research has combined different existing GA techniques and motivation from the two-jet GA-CFD system previously developed at the University of Kentucky propose the applications of a real coded Continuous Genetic Algorithm (CGA) to optimize a four-jet and a synthetic jet control system on a NACA0012 airfoil. The control system is an array of jets on a NACA0012 airfoil and the critical parameters considered for optimization are the angle, the amplitude, the location, and the frequency of the jets. The design parameters of a steady four-jet and an unsteady synthetic jet system are proposed and optimized. The proposed algorithm is built on top of CFD code (GHOST), guiding the movement of jets along the airfoils upper surface. The near optimum control values are determined within the control parameter range. The current study of different Genetic Algorithms on airfoil flow control has been demonstrated to be a successful optimization application

Statistical and Computational Tradeoff in Genetic Algorithm-Based Estimation

When a Genetic Algorithm (GA), or a stochastic algorithm in general, is employed in a statistical problem, the obtained result is affected by both variability due to sampling, that refers to the fact that only a sample is observed, and variability due to the stochastic elements of the algorithm. This topic can be easily set in a framework of statistical and computational tradeoff question, crucial in recent problems, for which statisticians must carefully set statistical and computational part of the analysis, taking account of some resource or time constraints. In the present work we analyze estimation problems tackled by GAs, for which variability of estimates can be decomposed in the two sources of variability, considering some constraints in the form of cost functions, related to both data acquisition and runtime of the algorithm. Simulation studies will be presented to discuss the statistical and computational tradeoff question.Comment: 17 pages, 5 figure

Active Control of Shocks and Sonic Boom Ground Signal

The manipulation of a flow field to obtain a desired change is a much heightened subject. Active flow control has been the subject of the major research areas in fluid mechanics for the past two decades. It offers new solutions for mitigation of shock strength, sonic boom alleviation, drag minimization, reducing blade-vortex interaction noise in helicopters, stall control and the performance maximization of existing designs to meet the increasing requirements of the aircraft industries. Despite the wide variety of the potential applications of active flow control, the majority of studies have been performed at subsonic speeds. The active flow control cases were investigated in transonic speed in this study. Although the active flow control provides significant improvements, the sensibility of aerodynamic performance to design parameters makes it a nontrivial and expensive problem, so the designer has to optimize a number of different parameters. For the purpose of gaining understanding of the active flow control concepts, an automated optimization cycle process was generated. Also, the optimization cycle reduces cost and turnaround time. The mass flow coefficient, location, width and angle were chosen as design parameters to maximize the aerodynamic performance of an aircraft. As the main contribution of this study, a detailed parametric study and optimization process were presented. The second step is to appraise the practicability of weakening the shock wave and thereby reducing the wave drag in transonic flight regime using flow control devices such as two dimensional contour bump, individual jet actuator, and also the hybrid control which includes both control devices together, thereby gaining the desired improvements in aerodynamic performance of the air-vehicle. After this study, to improve the aerodynamic performance, the flow control and shape parameters are optimized separately, combined, and in a serial combination. The remarkable part of all these studies is both gradient and non-gradient optimization techniques were used to find the global optimum point. The second part of this study includes investigation of the possibility of weakening the shock strength and the reduction of far field signature by using off-body energy addition. The main obstacle for flying supersonically over land is the detrimental effects of sonic boom on general public and structures. The shock waves generated from various parts of an aircraft flying at supersonic speed, coalesce to form a classic sonic boom acoustic signature, \u27N\u27 wave associated with the sonic boom on the ground. High pressure was imposed on certain parts of the computational domain to simulate the pulsed laser effects, and then the propagation and interaction of this pulsed shock with shock waves generated from the diamond shaped model were investigated. Optimization of the location and the power of the pulsed shock were achieved using the non-gradient optimization technique. The main contribution of this study is the optimization of the parameters of pulsed shock

Genetic Algorithm and its Variants: Theory and Applications

The Genetic Algorithm is a popular optimization technique which is bio-inspired and is based on the concepts of natural genetics and natural selection theories proposed by Charles Darwin. The Algorithm functions on three basic genetic operators of selection, crossover and mutation. Based on the types of these operators GA has many variants like Real coded GA, Binary coded GA, Sawtooth GA, Micro GA, Improved GA, Differential Evolution GA. This paper discusses a few of the forms of GA and applies the techniques to the problem of Function optimization and System Identification. The paper makes a comparative analysis of the advantages and disadvantages of the different types of GA. The computer simulations illustrate the results. It also makes a comparison between the GA technique and Incremental LMS algorithm for System Identification