Shape Optimization Using Feature-Based CAD Systems and Adjoint Methods

DOCTOR OF PHILOSOPHY Shape optimization using feature-based CAD systems and adjoint methods Agarwal, Dheeraj Award date: 2018 Awarding institution: Queen's University Belfast Link to publication Terms of use All those accessing thesis content in Queen’s University Belfast Research Portal are subject to the following terms and conditions of use • Copyright is subject to the Copyright, Designs and Patent Act 1988, or as modified by any successor legislation • Copyright and moral rights for thesis content are retained by the author and/or other copyright owners • A copy of a thesis may be downloaded for personal non-commercial research/study without the need for permission or charge • Distribution or reproduction of thesis content in any format is not permitted without the permission of the copyright holder • When citing this work, full bibliographic details should be supplied, including the author, title, awarding institution and date of thesis Take down policy A thesis can be removed from the Research Portal if there has been a breach of copyright, or a similarly robust reason. If you believe this document breaches copyright, or there is sufficient cause to take down, please contact us, citing details. Email: [email protected] Supplementary materials Where possible, we endeavour to provide supplementary materials to theses. This may include video, audio and other types of files. We endeavour to capture all content and upload as part of the Pure record for each thesis. Note, it may not be possible in all instances to convert analogue formats to usable digital formats for some supplementary materials. We exercise best efforts on our behalf and, in such instances, encourage the individual to consult the physical thesis for further information. Download date: 24. Sep. 2021 Shape optimization using feature-based CAD systems and adjoint methods Dheeraj Agarwal, MEng June 2018 School of Mechanical and Aerospace Engineering Queen’s University Belfast A thesis submitted for the degree of Doctor of Philosophy This thesis is dedicated to the memory of my father Mukesh Kumar Agarwal (02/06/1956 – 17/05/2012) Abstract Abstract The key issue restricting the use of computer-aided design (CAD) models within an optimization framework is that there is no clear definition of how the change in CAD parameters effect the model’s performance in terms of optimizing for certain objective functions (for e.g. minimum pressure loss, minimum drag, maximum lift etc.). In this thesis, an automated optimization process is presented, which uses the parameters defining the features in a feature-based CAD model as design variables. This process exploits adjoint methods for the computation of gradients, which predicts how the objective function changes for an infinitesimally small movement of each surface mesh node in the normal direction. The use of adjoint methods results in a computational cost that is essentially independent of the number of design variables, making it ideal for optimization in a large parameter space. The success of any shape optimization methodology depends on the choice of parameters and can sometimes stifle the creation of high performing innovative solutions. Parametric effectiveness is a measure that rates the ability of the parameters in a model to change its shape in the optimum way. Here, the optimum shape change is that suggested by the adjoint sensitivity on the model boundary. Herein, an automated approach is developed to compute the parametric effectiveness of CAD model parameters. In cases where the parametric effectiveness is low, a novel methodology is shown which automatically adds the optimum features to the CAD model feature tree, and thus increases the design freedom of the model. In this thesis, the optimization framework is developed to exploit the capabilities of modern CAD systems to add geometrical constraints to the optimization process including minimum thickness, constant volume and packaging constraints. The packaging constraints are imposed by the adjacent components in the CAD model product assembly which the component being optimized is not allowed to violate. The applicability of the developed approaches is demonstrated on a range of CAD models created in CATIA V5 for 2D and 3D finite element and computational fluid dynamics problems. During this research, the ability to carry out optimization directly on the CAD models created in commercial CAD systems has been enhanced. In addition, it has been shown that the additional shape flexibility imparted to the model by inserting additional “optimum” CAD features, leads to a better optimized i Abstract component than would have been possible using the original model. Lastly, it has been shown that an optimization process can be configured to respect CAD assembly constraints, resulting in an optimized geometry that does not violate the space occupied by other components in the product assembly. ii Acknowledgements Acknowledgements First and foremost, I wish to thank my supervisors Dr. Trevor T. Robinson and Prof. Cecil G. Armstrong for giving me the opportunity to work under their expert guidance. They have been a source of continuous motivation throughout this work, and I am indebted to them for providing an excellent atmosphere to carry out my research. It has been a humbling experience working with them, to say the least. I also wish to acknowledge the financial support by the European Union HORIZON 2020 Framework Programme for Research and Innovation under Grant Agreement No. 642959. I’d like to extend my sincere appreciation to all the members of the Finite Element Modelling Group at Queen’s University Belfast for providing support and creating an enjoyable office atmosphere. Finally, I am very grateful to my family in India, this submission would not have been possible without their extraordinary care and support. Last but not the least I would like to pay special thanks to my wife Mrs. Richa Agarwal for her unconditional support and understanding. iii Table of Contents Table of Contents Abstract ......................................................................................................................... i Acknowledgements ..................................................................................................... iii Table of Contents ........................................................................................................ iv List of Figures ............................................................................................................. vi Nomenclature ............................................................................................................. xii Chapter 1 Introduction ................................................................................................. 1 1.1 Thesis outline ..................................................................................................... 4 Chapter 2 Literature review ......................................................................................... 5 2.1 Optimization methods ........................................................................................ 5 2.2 Adjoint methods ................................................................................................. 8 2.3 Design parameterization ................................................................................... 10 2.4 Parametric design velocity ............................................................................... 13 2.5 CAD feature modelling .................................................................................... 17 2.6 Software Used .................................................................................................. 20 2.7 Research methodology ..................................................................................... 21 2.8 Thesis aims and objectives ............................................................................... 23 2.9 Summary .......................................................................................................... 23 Chapter 3 Design velocity and gradient computation ................................................ 25 3.1 Introduction ...................................................................................................... 25 3.2 Design velocity computation ............................................................................ 26 3.3 Validation of design velocity ........................................................................... 33 3.4 Gradient computation ....................................................................................... 38 3.5 Validation of Gradients .................................................................................... 39 3.6 Summary .......................................................................................................... 45 Chapter 4 CAD-based adjoint optimization ............................................................... 47 4.1 Sequential least square programming (SLSQP) ............................................... 47 4.2 Automated optimization framework ................................................................ 48 4.3 NACA0012 aerofoil ......................................................................................... 49 4.4 ONERA M6 Wing ............................................................................................ 54 4.5 NLR 7301 High lift case .................................................................................. 61 4.6 Summary .........................................................................................................

Load more