New strategies for the aerodynamic design optimization of aeronautical configurations through soft-computing techniques

Premio Extraordinario de Doctorado de la UAH en 2013Lozano Rodríguez, Carlos, codir.This thesis deals with the improvement of the optimization process in the aerodynamic design of aeronautical configurations. Nowadays, this topic is of great importance in order to allow the European aeronautical industry to reduce their development and operational costs, decrease the time-to-market for new aircraft, improve the quality of their products and therefore maintain their competitiveness. Within this thesis, a study of the state-of-the-art of the aerodynamic optimization tools has been performed, and several contributions have been proposed at different levels: -One of the main drawbacks for an industrial application of aerodynamic optimization tools is the huge requirement of computational resources, in particular, for complex optimization problems, current methodological approaches would need more than a year to obtain an optimized aircraft. For this reason, one proposed contribution of this work is focused on reducing the computational cost by the use of different techniques as surrogate modelling, control theory, as well as other more software-related techniques as code optimization and proper domain parallelization, all with the goal of decreasing the cost of the aerodynamic design process. -Other contribution is related to the consideration of the design process as a global optimization problem, and, more specifically, the use of evolutionary algorithms (EAs) to perform a preliminary broad exploration of the design space, due to their ability to obtain global optima. Regarding this, EAs have been hybridized with metamodels (or surrogate models), in order to substitute expensive CFD simulations. In this thesis, an innovative approach for the global aerodynamic optimization of aeronautical configurations is proposed, consisting of an Evolutionary Programming algorithm hybridized with a Support Vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size, geometry parameterization sensitivity and techniques for design of experiments are discussed and the potential of the proposed approach to achieve innovative shapes that would not be achieved with traditional methods is assessed. -Then, after a broad exploration of the design space, the optimization process is continued with local gradient-based optimization techniques for a finer improvement of the geometry. Here, an automated optimization framework is presented to address aerodynamic shape design problems. Key aspects of this framework include the use of the adjoint methodology to make the computational requirements independent of the number of design variables, and Computer Aided Design (CAD)-based shape parameterization, which uses the flexibility of Non-Uniform Rational B-Splines (NURBS) to handle complex configurations. The mentioned approach is applied to the optimization of several test cases and the improvements of the proposed strategy and its ability to achieve efficient shapes will complete this study

New strategies for the aerodynamic design optimization of aeronautical configurations through soft-computing techniques

Premio Extraordinario de Doctorado de la UAH en 2013Lozano Rodríguez, Carlos, codir.This thesis deals with the improvement of the optimization process in the aerodynamic design of aeronautical configurations. Nowadays, this topic is of great importance in order to allow the European aeronautical industry to reduce their development and operational costs, decrease the time-to-market for new aircraft, improve the quality of their products and therefore maintain their competitiveness. Within this thesis, a study of the state-of-the-art of the aerodynamic optimization tools has been performed, and several contributions have been proposed at different levels: -One of the main drawbacks for an industrial application of aerodynamic optimization tools is the huge requirement of computational resources, in particular, for complex optimization problems, current methodological approaches would need more than a year to obtain an optimized aircraft. For this reason, one proposed contribution of this work is focused on reducing the computational cost by the use of different techniques as surrogate modelling, control theory, as well as other more software-related techniques as code optimization and proper domain parallelization, all with the goal of decreasing the cost of the aerodynamic design process. -Other contribution is related to the consideration of the design process as a global optimization problem, and, more specifically, the use of evolutionary algorithms (EAs) to perform a preliminary broad exploration of the design space, due to their ability to obtain global optima. Regarding this, EAs have been hybridized with metamodels (or surrogate models), in order to substitute expensive CFD simulations. In this thesis, an innovative approach for the global aerodynamic optimization of aeronautical configurations is proposed, consisting of an Evolutionary Programming algorithm hybridized with a Support Vector regression algorithm (SVMr) as a metamodel. Specific issues as precision, dataset training size, geometry parameterization sensitivity and techniques for design of experiments are discussed and the potential of the proposed approach to achieve innovative shapes that would not be achieved with traditional methods is assessed. -Then, after a broad exploration of the design space, the optimization process is continued with local gradient-based optimization techniques for a finer improvement of the geometry. Here, an automated optimization framework is presented to address aerodynamic shape design problems. Key aspects of this framework include the use of the adjoint methodology to make the computational requirements independent of the number of design variables, and Computer Aided Design (CAD)-based shape parameterization, which uses the flexibility of Non-Uniform Rational B-Splines (NURBS) to handle complex configurations. The mentioned approach is applied to the optimization of several test cases and the improvements of the proposed strategy and its ability to achieve efficient shapes will complete this study

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

Parallel surrogate-assisted global optimization with expensive functions – a survey

Surrogate assisted global optimization is gaining popularity. Similarly, modern advances in computing power increasingly rely on parallelization rather than faster processors. This paper examines some of the methods used to take advantage of parallelization in surrogate based global optimization. A key issue focused on in this review is how different algorithms balance exploration and exploitation. Most of the papers surveyed are adaptive samplers that employ Gaussian Process or Kriging surrogates. These allow sophisticated approaches for balancing exploration and exploitation and even allow to develop algorithms with calculable rate of convergence as function of the number of parallel processors. In addition to optimization based on adaptive sampling, surrogate assisted parallel evolutionary algorithms are also surveyed. Beyond a review of the present state of the art, the paper also argues that methods that provide easy parallelization, like multiple parallel runs, or methods that rely on population of designs for diversity deserve more attention.United States. Dept. of Energy (National Nuclear Security Administration. Advanced Simulation and Computing Program. Cooperative Agreement under the Predictive Academic Alliance Program. DE-NA0002378

Development and application of an optimisation architecture with adaptive swarm algorithm for airfoil aerodynamic design

The research focuses on the aerodynamic design of airfoils for a Multi-Mission Unmanned Aerial Vehicle (MM-UAV). Novel shape design processes using evolutionary algorithms (EA) and a surrogate-based management system are developed to address the identified issues and challenges of solution feasibility and computational efficiency associated with present methods. Feasibility refers to the optimality of the converged solution as a function of the defined objectives and constraints. Computational efficiency is a measure of the number of design iterations needed to achieve convergence to the theoretical optimum. Airfoil design problems are characterised by a multi-modal solution topology. Present gradient-based optimisation methods do not converge to an optimal profile, hence solution feasibility is compromised. Population-based optimisation methods including the Genetic Algorithm (GA) have been used in the literature to address this issue. The GA can achieve solution feasibility, yet it is computationally time-intensive, hence efficiency is compromised. Novel EAs are developed to address the identified shortcomings of present methods. A variant to the original Particle Swarm Optimisation algorithm (PSO) is presented. Novel mutation operators are implemented which facilitate the transition of the search particles toward a global solution. The methodology addresses the limited search performance of the original PSO algorithm for multi-modal problems, while maintaining acceptable computational efficiency for aerodynamic design applications. Demonstration of the developed principles confirmed the merits of the proposed design approach. Airfoil optimisation for a low-speed flight profile achieved drag performance improvement that is lower than a off-the-shelf shape designed for the intent role. Acceptable computational efficiency is achieved by restricting the optimisation phase to promising solution regions through the development of a novel, design variable search space mapping structure. The merit of the optimisation framework is further confirmed by transonic airfoil design for high-speed missions. The wave drag of the established optima is lower than the identified, off-the-shelf benchmark. Concurrently significant computational time-savings are achieved relative to the design methodologies present in the literature. A novel surrogate-assisted optimisation framework by the definition of an Artificial Neural Network with a pattern recognition model is developed to further improve the computational efficiency. This has the potential of enhancing the aerodynamic shape design process. The measure of computational efficiency is critical in the development of an optimisation algorithm. Airfoil design simulations presented required 80\% fewer design iterations to achieve convergence than the GA. Computational time-savings spanning days was achieved by the innovative algorithms developed relative to the GA. Hence, computational efficiency of the developed processes is confirmed. Aircraft shape design simulations involve three-dimensional configurations which require excessive computational effort due to the use of high-fidelity solvers for flow analysis in the optimisation process. It is anticipated that the confirmed computational efficiency performance of the design structure presented on two-dimensional cases will be transferable to three-dimensional shape design problems. It is further expected that the novel principles will be applicable for analysis within a multidisciplinary design structure for the development of a MM-UAV

Machine Learning in Aerodynamic Shape Optimization

Machine learning (ML) has been increasingly used to aid aerodynamic shape optimization (ASO), thanks to the availability of aerodynamic data and continued developments in deep learning. We review the applications of ML in ASO to date and provide a perspective on the state-of-the-art and future directions. We first introduce conventional ASO and current challenges. Next, we introduce ML fundamentals and detail ML algorithms that have been successful in ASO. Then, we review ML applications to ASO addressing three aspects: compact geometric design space, fast aerodynamic analysis, and efficient optimization architecture. In addition to providing a comprehensive summary of the research, we comment on the practicality and effectiveness of the developed methods. We show how cutting-edge ML approaches can benefit ASO and address challenging demands, such as interactive design optimization. Practical large-scale design optimizations remain a challenge because of the high cost of ML training. Further research on coupling ML model construction with prior experience and knowledge, such as physics-informed ML, is recommended to solve large-scale ASO problems

Facilitating the Use of Optimisation in the Aerodynamic Design of Axial Compressors

There is commercial pressure to design axial compressors exhibiting high levels of performance more quickly. This is despite the performance of these machines approaching an asymptote in recent years, with further gains becoming increasingly difficult to achieve. One tool that can be used to help is optimisation, effectively harnessing the speed of computational analysis to accelerate the design process and unlock additional performance improvements. The greatest potential for optimisation exists at the preliminary design stage, however, current methodologies struggle when applied at this early point in the design process due to inadequate problem formulations, an inability to fulfil the role of enhancing designer understanding and a lack of high-fidelity analysis due to computational cost. The goal of this thesis is to facilitate the use of optimisation in the preliminary aerodynamic design of axial compressors by developing an improved methodology that overcomes these limitations. The multiple dominance relations (MDR) formulation enables a larger number of performance parameters to be incorporated in a way that accurately reflects the desires of the designer. This is implemented within a Tabu Search (TS) that is capable of providing interpretable design development information to enhance designer understanding. The combined MDRTS algorithm, overcoming the limitations associated with formulation and understanding, outperforms existing methods when applied to analytic, aerofoil and six-stage axial compressor test cases, generating computational savings of up to 80%. Multi-fidelity techniques are used to accelerate the search by conducting analysis on a "need-to-know'' basis. Computational savings of over 70% are observed compared to the single-fidelity version of the algorithm across the analytic, aerofoil and six-stage axial compressor test cases, enabling high-fidelity analysis to be employed in a computationally efficient manner. The resultant methodology represents a novel and inherently flexible multi-level multi-fidelity optimisation technique. Application to an N-stage axial compressor test case, in which the optimiser is given control over the number of stages in the machine, demonstrates the capabilities of the accelerated MDRTS approach. The complex design space is effectively navigated, generating computational savings of over 90% compared to existing methodologies and producing designs that are more likely to be of interest to the designer. Interpretable design development information is also provided for this problem to enhance designer understanding. These results show that the improved methodology successfully facilitates the use of optimisation in the preliminary aerodynamic design of axial compressors, overcoming the problems associated with formulation, understanding and speed that limit existing approaches

Optimisation of aspects of rotor blades using computational fluid dynamics

This work presents a framework for the optimisation of various aspects of rotor blades in forward flight. The literature survey suggests that the quest for such a method is generating much research as more performance is obtainable from current designs. With increasing computational power and efficient methods, this can be of practical use to the helicopter industry. The proposed method employs CFD in conjunction with metamodels such as artificial neural networks (ANNs) and kriging interpolation, and a non-gradient based optimiser, in the form of genetic algorithms (GAs), for optimisation. The approach is demonstrated using several cases, including the optimisation of linear twist of rotors in hover (a steady case) and the optimisation of rotor sections in forward flight (an unsteady case); other cases include transonic aerofoils, wing and rotor tip planforms. For rotor tip planforms, first a simple rectangular rotor in hover was optimised. Then the developed method was used to optimise the anhedral and sweep of the UH60-A rotor blade in forward flight while constraining its hover performance and the final rotor optimisation was for a BERP-like rotor in forward flight, also constraining hover performance. For each case, a parameterisation method was defined, a specific objective function created using the initial CFD data and the metamodel was used for evaluating the objective function during the optimisation using the GAs. The obtained results suggest optima in agreement with engineering intuition but provide precise information about the shape of the final lifting surface and its performance. The results were checked by comparison with the Pareto subset of data and the metamodels were also validated with high-fidelity CFD data. Neither was sensitive to the employed techniques with substantial overlap between the outputs of the selected methods. The main CPU cost was associated with the population of the CFD database necessary for the metamodel. To improve this further, the Harmonic Balance alternative for obtaining the CFD data (as opposed to Time Marching) was used to increase efficiency and reduce clock time for the BERP-like tip optimisation. The novelty of this method is the use of a metamodel in conjunction with high-fidelity CFD data so that high-resolution performance improvements can be captured efficiently using a non-gradient based method

Advances in Evolutionary Algorithms

With the recent trends towards massive data sets and significant computational power, combined with evolutionary algorithmic advances evolutionary computation is becoming much more relevant to practice. Aim of the book is to present recent improvements, innovative ideas and concepts in a part of a huge EA field