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

A novel implementation of computational aerodynamic shape optimisation using Modified Cuckoo Search

This paper outlines a new computational aerodynamic design optimisation algorithm using a novel method of parameterising a computational mesh using `control nodes'. The shape boundary movement as well as the mesh movement is coupled to the movement of user--defined control nodes via a Delaunay Graph Mapping technique. A Modified Cuckoo Search algorithm is employed for optimisation within the prescribed design space defined by the allowed range of control node displacement. A finite volume compressible Navier--Stokes solver is used for aerodynamic modelling to predict aerodynamic design `fitness'. The resulting coupled algorithm is applied to a range of test cases in two dimensions including aerofoil lift--drag ratio optimisation intake duct optimisation under subsonic, transonic and supersonic flow conditions. The discrete (mesh--based) optimisation approach presented is demonstrated to be effective in terms of its generalised applicability and intuitiveness

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

Multi-level and multi-objective design optimisation of a MEMS bandpass filter

Microelectromechanical system (MEMS) design is often complex, containing multiple disciplines but also conflicting objectives. Designers are often faced with the problem of balancing what objectives to focus upon and how to incorporate modeling and simulation tools across multiple levels of abstraction in the design optimization process. In particular due to the computational expense of some of these simulation methods there are restrictions on how much optimization can occur. In this paper we aim to demonstrate the application of multi-objective and multi-level design optimisation strategies to a MEMS bandpass filter. This provides for designers the ability to evolve solutions that can match multiple objectives. In order to address the problem of a computationally expensive design process a novel multi-level evaluation strategy is developed. In addition a new approach for bandpass filter modeling and optimization is presented based up the electrical equivalent circuit method. In order to demonstrate this approach a comparison is made to previous attempts to design similar bandpass filters. Results are comparable in design but at a significant reduction in functional evaluations, needing only 10,000 functional evaluations in comparison to 2.6 million with the previous work

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

Aerodynamic optimisation of a hypersonic reentry vehicle based on solution of the Boltzmann–BGK equation and evolutionary optimisation

Over the past decade there has been a surge in the interest, both academic and commercial, in supersonic and hypersonic passenger transport. This paper outlines an original approach for solving the problem of optimal design and configuration of a space vehicle operating in rarefied hypersonic flow. The approach utilises a novel flow solver based on the solution of the Boltzmann–BGK equation. For the first time this solver has been coupled to an evolutionary optimiser to assist in navigation of the unfamiliar hypersonic design space.The Boltzmann–BGK solver is rigorously tested on a number of examples and is shown to handle rarefied gas dynamics examples across a range of length scales. The examples, presented here for the first time, include: a Riemann–type gas expansion problem, drag prediction of a nano–particle and supersonic flow across an aerofoil. Finally the solver is coupled to the evolutionary optimiser Modified Cuckoo Search approach. The coupled solver–optimiser design tool is then used to explore the optimum configuration of the forebody of a generic space reentry vehicle under a range of design conditions.In all examples considered the flow solver produces valid solutions. It is also found that the evolutionary optimiser is successful in navigating the unfamiliar design space

Multielement polynomial chaos Kriging-based metamodelling for Bayesian inference of non-smooth systems

This paper presents a surrogate modelling technique based on domain partitioning for Bayesian parameter inference of highly nonlinear engineering models. In order to alleviate the computational burden typically involved in Bayesian inference applications, a multielement Polynomial Chaos Expansion based Kriging metamodel is proposed. The developed surrogate model combines in a piecewise function an array of local Polynomial Chaos based Kriging metamodels constructed on a finite set of non-overlapping subdomains of the stochastic input space. Therewith, the presence of non-smoothness in the response of the forward model (e.g.~ nonlinearities and sparseness) can be reproduced by the proposed metamodel with minimum computational costs owing to its local adaptation capabilities. The model parameter inference is conducted through a Markov chain Monte Carlo approach comprising adaptive exploration and delayed rejection. The efficiency and accuracy of the proposed approach are validated through two case studies, including an analytical benchmark and a numerical case study. The latter relates the partial differential equation governing the hydrogen diffusion phenomenon of metallic materials in Thermal Desorption Spectroscopy tests