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Relevant bibliographies by topics / Numerical optimisation

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Academic literature on the topic 'Numerical optimisation'

Author: Grafiati

Published: 4 June 2021

Last updated: 9 June 2025

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Journal articles on the topic "Numerical optimisation"

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Diouf, Serigne, Mamadou M. Diop, and Alassane Sy. "NUMERICAL IMAGING USING NONLINEAR OPTIMISATION." International Journal of Numerical Methods and Applications 20, no. 1 (January 10, 2021): 1–16. http://dx.doi.org/10.17654/nm020010001.

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Theodore, N. D., and K. A. Padmanabhan. "Numerical optimisation of superplastic deformation." Journal of Materials Science 25, no. 4 (April 1990): 2133–43. http://dx.doi.org/10.1007/bf01045779.

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Wang, Wenjun, and Hui Wang. "An improved diversity-guided particle swarm optimisation for numerical optimisation." International Journal of Computing Science and Mathematics 5, no. 1 (2014): 16. http://dx.doi.org/10.1504/ijcsm.2014.059381.

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Tovar, Andres, Nelson Arzola de la Peña, and Alexander Gómez Cassab. "Multidisciplinary design optimisation techniques." Ingeniería e Investigación 27, no. 1 (January 1, 2007): 84–92. http://dx.doi.org/10.15446/ing.investig.v27n1.14785.

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Design optimisation of a multidisciplinary project in engineering involves the decomposition of a system into disciplines and the subsequent association of their contributions. This work was aimed at presenting the most common decomposition and association techniques currently used in multidisciplinary design optimisation (MDO). Amongst the decomposition techniques this work includes hierarchical and non-hierarchical approaches as well as the most popular numerical procedures. The association techniques include: one-level methods (e.g. all-at-once optimisation and simultaneous analysis and design), multilevel methods (e.g. concurrent subspace optimisation and collaborative optimisation) and robust design. This work also incorporates an illustrative numerical example.

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Grundmann, Jens, Niels Schütze, and Franz Lennartz. "Sustainable management of a coupled groundwater–agriculture hydrosystem using multi-criteria simulation based optimisation." Water Science and Technology 67, no. 3 (February 1, 2013): 689–98. http://dx.doi.org/10.2166/wst.2012.602.

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In this paper we present a new simulation-based integrated water management tool for sustainable water resources management in arid coastal environments. This tool delivers optimised groundwater withdrawal scenarios considering saltwater intrusion as a result of agricultural and municipal water abstraction. It also yields a substantially improved water use efficiency of irrigated agriculture. To allow for a robust and fast operation we unified process modelling with artificial intelligence tools and evolutionary optimisation techniques. The aquifer behaviour is represented using an artificial neural network (ANN) which emulates a numerical density-dependent groundwater flow model. The impact of agriculture is represented by stochastic crop water production functions (SCWPF). Simulation-based optimisation techniques together with the SCWPF and ANN deliver optimal groundwater abstraction and cropping patterns. To address contradicting objectives, e.g. profit-oriented agriculture vs. sustainable abstraction scenarios, we performed multi-objective optimisations using a multi-criteria optimisation algorithm.

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Giunta, A. A., V. Balabanov, D. Haim, B. Grossman, W. H. Mason, L. T. Watson, and R. T. Haftka. "Multidisciplinary optimisation of a supersonic transport using design of experiments theory and response surface modelling." Aeronautical Journal 101, no. 1008 (October 1997): 347–56. http://dx.doi.org/10.1017/s0001924000066045.

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AbstractThe presence of numerical noise in engineering design optimisation problems inhibits the use of many gradient-based optimisation methods. This numerical noise may result in the inaccurate calculation of gradients which in turn slows or prevents convergence during optimisation, or it may promote convergence to spurious local optima. The problems created by numerical noise are particularly acute in aircraft design applications where a single aerodynamic or structural analysis of a realistic aircraft configuration may require tens of CPU hours on a supercomputer. The computational expense of the analyses coupled with the convergence difficulties created by numerical noise are significant obstacles to performing aircraft multidisciplinary design optimisation. To address these issues, a procedure has been developed to create noise-free algebraic models of subsonic and supersonic aerodynamic performance quantities, for use in the optimisation of high-speed civil transport (HSCT) aircraft configurations. This procedure employs methods from statistical design of experiments theory and response surface modelling to create the noise-free algebraic models. Results from a sample HSCT design problem involving ten variables are presented to demonstrate the utility of this method.

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Ransing, R. S., S. Savino, and R. W. Lewis. "Numerical optimisation of tilt casting process." International Journal of Cast Metals Research 18, no. 2 (February 2005): 109–18. http://dx.doi.org/10.1179/136404605225022901.

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van der Heijden, F., W. Apperloo, and L. J. Spreeuwers. "Numerical optimisation in spot detector design." Pattern Recognition Letters 18, no. 11-13 (November 1997): 1091–97. http://dx.doi.org/10.1016/s0167-8655(97)00086-x.

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Krenek, Thorsten, Christopher Bacher, Günther Raidl, and Thomas Lauer. "Numerical Optimisation of Electro Hybrid Powertrains." MTZ worldwide 76, no. 3 (February 4, 2015): 46–52. http://dx.doi.org/10.1007/s38311-015-0007-9.

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Simić, Miloš. "Is the Bee Colony Optimisation algorithm suitable for continuous numerical optimisation?" International Journal of Metaheuristics 6, no. 4 (2017): 279. http://dx.doi.org/10.1504/ijmheur.2017.086978.

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Dissertations / Theses on the topic "Numerical optimisation"

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Routley, Paul Richard. "BiCMOS circuit optimisation." Thesis, University of Southampton, 1996. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.242271.

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Penev, Kalin. "Adaptive search heuristics applied to numerical optimisation." Thesis, Southampton Solent University, 2004. http://ssudl.solent.ac.uk/598/.

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The objective of the research project involves investigation of evolutionary computational methods, in particular analysis of population-based search heuristics, and abstraction of core cognition, which may lead to the design of a novel adaptive search algorithm capable of high performance and reliability. The thesis proposes a novel adaptive heuristic method called Free Search (FS). Free Search can be classified as a population-based evolutionary computational method. It gradually changes a set of solutions until satisfaction of certain criteria. The algorithm operates with real-value numbers. It is designed for continuous or partially discontinuous search space. Free Search harmonizes several advanced ideas, which lead to high overall performance. The study includes exploration of selected population-based evolutionary methods, namely : real-value coded Generic Algorithm BLX-a; Particle Swarm Optimisation (PSO); Ant Colony Optimisation (ACO); and Differential Evolution (DE). Common, substantial for the search purposes features, relationships and events are abstracted from the algorithms analysed. The events abstracted are generalised in a theoretical model of population-based heuristic search. The model supports significantly identification of essential advantages and disadvantages of population-based search algorithms and leads to establishment of a novel concept different from other evolutionary algorithms. Free Search together with GA BLX-a, PSO and DE are applied to heterogenous, numerical, non-linear, non-discrete, optimisation problems. The results are presented and discussed. A comparative analysis demonstrates better overall performance of FS than other explored methods. The capability of FS to cope with all tests illustrates a new quality - adaptation to the problem without concrete or specialised configuration of the search parameters. Free Search is tested aditionally with a hard, non-linear, constrained optimisation problem - the so-called bump problem. FS outperforms other methods applied to that problem. Results achieved from Free Search, currently unapproachable for other search algorithms are presented. Free Search opens a new area for research in the domain of adaptive intelligent systems. It can contribute also in general to Computer Science in the modelling of uncertain individual behaviour.

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Tian, Na. "Novel optimisation methods for numerical inverse problems." Thesis, University of Greenwich, 2011. http://gala.gre.ac.uk/9099/.

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Inverse problems involve the determination of one or more unknown quantities usually appearing in the mathematical formulation of a physical problem. These unknown quantities may be boundary heat flux, various source terms, thermal and material properties, boundary shape and size. Solving inverse problems requires additional information through in-situ data measurements of the field variables of the physical problems. These problems are also ill-posed because the solution itself is sensitive to random errors in the measured input data. Regularisation techniques are usually used in order to deal with the instability of the solution. In the past decades, many methods based on the nonlinear least squares model, both deterministic (CGM) and stochastic (GA, PSO), have been investigated for numerical inverse problems. The goal of this thesis is to examine and explore new techniques for numerical inverse problems. The background theory of population-based heuristic algorithm known as quantum-behaved particle swarm optimisation (QPSO) is re-visited and examined. To enhance the global search ability of QPSO for complex multi-modal problems, several modifications to QPSO are proposed. These include perturbation operation, Gaussian mutation and ring topology model. Several parameter selection methods for these algorithms are proposed. Benchmark functions were used to test the performance of the modified algorithms. To address the high computational cost of complex engineering optimisation problems, two parallel models of the QPSO (master-slave, static subpopulation) were developed for different distributed systems. A hybrid method, which makes use of deterministic (CGM) and stochastic (QPSO) methods, is proposed to improve the estimated solution and the performance of the algorithms for solving the inverse problems. Finally, the proposed methods are used to solve typical problems as appeared in many research papers. The numerical results demonstrate the feasibility and efficiency of QPSO and the global search ability and stability of the modified versions of QPSO. Two novel methods of providing initial guess to CGM with approximated data from QPSO are also proposed for use in the hybrid method and were applied to estimate heat fluxes and boundary shapes. The simultaneous estimation of temperature dependent thermal conductivity and heat capacity was addressed by using QPSO with Gaussian mutation. This combination provides a stable algorithm even with noisy measurements. Comparison of the performance between different methods for solving inverse problems is also presented in this thesis.

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Yang, Yong. "Efficient parallel genetic algorithms applied to numerical optimisation." Thesis, Southampton Solent University, 2008. http://ssudl.solent.ac.uk/631/.

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This research is concerned with the optimisation of multi-modal numerical problems using genetic algorithms (GAs). GAs use randomised operators operating over a population of candidate solutions to generate new points in the search space. As the scale and complexity of target applications increase, run time becomes a major inhibitor. Parallel genetic algorithms (PGAs) have therefore become an important area of research. Coarse-grained implementations are one of the most popular models and many researchers are concerned primarily with this area. The island model was the only one class of parallel genetic algorithm on the coarse-grained processing platform. There are indiscriminate overlaps between sub-populations in the island model even if there is no communication between sub-populations. In order to determine whether the removal of the overlaps between sub-populations is beneficial, the partition model based on domain decomposition was motivated and showed that it can offer superior performance on a number of two dimensional test problems. However the partition model has a certain scalability problem. The main contribution of this thesis is to propose and develop an alternative approach, which replicates the beneficial behaviour of the partition model using more scalable techniques. It operates in a similar manner to the island model, but periodically performs a clustering analysis on each sub-population. The clustering analysis is used to identify regions of the search space in which more than one sub-population are sampling. The overlapping clusters are then merged and redistributed amongst sub-populations so that only one sub-population has samples in that region of the search space. It is shown that these overlaps between sub-populations are identified and removed by the clustering analysis without a priori domain knowledge. The performance of the new approach employing the clustering analysis is then compared with the island model and the partition model on a number of non-trivial problems. These experiments show that the new approach is robust, efficient and effective, and removes the scalability problem that prevents the wide application of the partition model.

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Joubert, N. J. D. "Numerical design optimisation for the Karoo Array Telescope." Thesis, Stellenbosch : University of Stellenbosch, 2009. http://hdl.handle.net/10019.1/2727.

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Thesis (MScEng (Mechanical and Mechatronic Engineering))--University of Stellenbosch, 2009.<br>Although mass minimisation is an important application within structural optimisation, other applications include: (1) concept generation, (2) concept evaluation, (3) design for structural feasibility and (4) data matching. These applications, except data matching, are discussed and illustrated on a prototype design of the Karoo Array Telescope (KAT) antenna. The KAT passed through the design process and a full scale prototype was built, but was found to be too expensive. A detailed finite element model of the finalised design was considered as a test bed for reducing costs. Size-, shape- and topology optimisation are applied to three KAT components, while considering wind, temperature and gravity loads. Structural- and nonstructural constraints are introduced. Coupling of the structural optimisation code with an external analysis program to include non-structural responses and the parallelisation of the sensitivity calculations are presented. It is shown that if a finite element model is available, it is generally possible to apply structural optimisation to improve an existing design. A reduction of 2673 kg steel was accomplished for the existing KAT components. The total cost saving for the project will be significant, when considering that a large amount of antennas will be manufactured.

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Alqahtani, Mohammed Aeyed M. "Numerical Algorithms for Polynomial Optimisation Problems with Applications." Thesis, Curtin University, 2019. http://hdl.handle.net/20.500.11937/73582.

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In this thesis, we study tensor eigenvalue problems and polynomial optimization problems. In particular, we present a fast algorithm for computing the spectral radii of symmetric nonnegative tensors without requiring the partition of the tensors. We also propose some polynomial time approximation algorithms with new approximation bounds for nonnegative polynomial optimization problems over unit spheres. Furthermore, we develop an efficient and effective algorithm for the maximum clique problem.

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Rahbary, Asr M. A. "Computer assisted machine tool part-program optimisation." Thesis, Coventry University, 1990. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.279418.

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Fraser-Andrews, G. "Numerical techniques for singular optimal trajectories." Thesis, University of Hertfordshire, 1986. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.372080.

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The objectives of the subject-matter of this thesis were to appraise some methods of solving non-singular optimal control problems by their degree of success in tackling four chosen problems and then to try the most promising methods on chosen singular problems. In Part I of this thesis, the chosen problems are attempted by quasilinearisation, two versions of shooting, Miels's method, differential dynamic programming and two versions of parameterisation . Conclusions on the various methods are given. NOC shooting, developed by the Numerical Optimisation Centre of The Hatfield Polytechnic, and constrained optimisation were found to be very useful for non-singular problems. In Part 11, the properties and calculation of possible singular controls are investigated, then the two chosen methods used. It was found that NOC shooting was again very useful, provided the solution structure is known and that constrained parameterisation was invaluable for determining the solution structure and when shooting is impossible. Contributions to knowledge as as follows. In Part I, the relative merits of various methods are displayed, additions are made to the theory of parameterisation, shooting and quasilinearisation, the best solutions known of the chosen problems are produced and choices of optimisation parameters for one chosen problem, the satellite problem, are compared. The satellite problem has dependent state variables and the Maximum Principle is extended in Appendix III to cover this case . In Part II, a thorough survey of the properties of singular controls is given, the calculation of possible singular controls clarified and extended, the utility of the two chosen methods is displayed, the best solutions known of the Goddard problem obtained with improved understanding of transitions in soluti on structures , Cl problem studied with control dependent on the costate variables and singular solution structures found.

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Jones, R. "Numerical optimisation techniques applied to problems in continuum mechanics." Thesis, University of Nottingham, 1987. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.378760.

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Wang, Tao. "Numerical simulation and optimisation for shot peen forming processes." Thesis, University of Cambridge, 2003. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.620031.

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Books on the topic "Numerical optimisation"

1

D, Meegan, and Sprevak D, eds. An introduction to unconstrained optimisation. Bristol, England: A. Hilger, 1990.

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Treanţă, Savin. Variational analysis with applications in optimisation and control. Newcastle upon Tyne, UK: Cambridge Scholars Publishing, 2019.

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Bamberger, Alain. Analyse, optimisation et filtrage numériques: Anaylse numérique de l'équation de la chaleur. [Palaiseau, France]: Ecole polytechnique, Département de mathématiques appliquées, 1991.

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Pardalos, P. M. (Panos M.), 1954- author, ed. Numerical methods and optimization: An introduction. Boca Raton: CRC Press,Taylor & Francis Group, 2014.

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1946-, Woźniakowski Henryk, and European Mathematical Society, eds. Essays on the complexity of continuous problems. Zürich, Switzerland: European Mathematical Society, 2009.

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Ciarlet, Philippe G. Introduction à l'analyse numérique matricielle et à l'optimisation. Paris: Masson, 1985.

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1966-, Chen Yangquan, ed. Solving applied mathematical problems with MATLAB. Boca Raton: Taylor & Francis, 2009.

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Michalewicz, Zbigniew. Genetic Algorithms + Data Structures =: Evolution Programs. Berlin, Heidelberg: Springer Berlin Heidelberg, 1994.

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Michalewicz, Zbigniew. Algorytmy genetyczne + struktury danych = programy ewolucyjne. 3rd ed. Warszawa: Wydawnictwa Naukowo-Techniczne, 2003.

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International Workshop on Approximation Algorithms for Combinatorial Optimization Problems (3rd 2000 Saarbrücken, Germany). Approximation algorithms for combinatorial optimization: Third international workshop, APPROX 2000, Saarbrucken, Germany, September 5-8, 2000 : proceedings. Berlin: Springer, 2000.

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Book chapters on the topic "Numerical optimisation"

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Antia, H. M. "Optimisation." In Numerical Methods for Scientists and Engineers, 345–400. Gurgaon: Hindustan Book Agency, 2012. http://dx.doi.org/10.1007/978-93-86279-52-1_8.

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Woodford, C., and C. Phillips. "Optimisation." In Numerical Methods with Worked Examples: Matlab Edition, 169–95. Dordrecht: Springer Netherlands, 2012. http://dx.doi.org/10.1007/978-94-007-1366-6_8.

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Phan, Chi M. "One-Dimensional Optimisation." In Numerical Calculations for Process Engineering Using Excel VBA, 43–53. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003364511-4.

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Penev, Kalin, and Anton Ruzhekov. "Adaptive Intelligence Applied to Numerical Optimisation." In Numerical Methods and Applications, 280–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-18466-6_33.

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Penev, Kalin. "Demanded Scale for Modern Numerical Optimisation." In Numerical Methods and Applications, 271–78. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-32412-3_24.

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Mezura-Montes, Efrén, and Jorge Isacc Flores-Mendoza. "Improved Particle Swarm Optimization in Constrained Numerical Search Spaces." In Nature-Inspired Algorithms for Optimisation, 299–332. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-00267-0_11.

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Lutz, Thorsten, Andreas Sommerer, and Siegfried Wagner. "Parallel Numerical Optimisation of Adaptive Transonic Airfoils." In IUTAM Symposium Transsonicum IV, 265–70. Dordrecht: Springer Netherlands, 2003. http://dx.doi.org/10.1007/978-94-010-0017-8_40.

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Tavares, Rodrigo Machado, and Edwin R. Galea. "Numerical Optimisation Techniques Applied to Evacuation Analysis." In Pedestrian and Evacuation Dynamics 2008, 555–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-04504-2_49.

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Dobrinkova, Nina, Momchil Panayotov, and Peter Boyvalenkov. "Optimisation Techniques in Wildfire Simulations. Test Case Kresna Fire August 2017." In Numerical Methods and Applications, 72–79. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-10692-8_8.

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Vassiliadis, Panos, and Spiros Skiadopoulos. "Modelling and Optimisation Issues for Multidimensional Databases." In Notes on Numerical Fluid Mechanics and Multidisciplinary Design, 482–97. Cham: Springer International Publishing, 2000. http://dx.doi.org/10.1007/3-540-45140-4_32.

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Conference papers on the topic "Numerical optimisation"

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Keep., Joshua A., and Ingo H. Jahn. "Numerical Simulation of Blast Nozzles." In Coatings+ 2020, 1–12. SSPC, 2020. https://doi.org/10.5006/s2020-00027.

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Abstract In recent decades, lower density blasting media, such as garnet has become widely used. This has been in conjunction with existing blast nozzles, which are of a simple, un-optimised geometry. One means to design a more efficient blast nozzle is through the use of computational fluid dynamics (CFD) in conjunction with automated optimisation. This paper describes the development of a CFD model for simulating blast nozzles in the OpenFOAM environment, and quantifies the performance of two #6 blasting nozzle geometries operating with garnet. Simulation results indicate that the developed CFD model is suitable for blast nozzles operating with #80 garnet or finer.

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Banerjee, Siddhartha, and Jolly Xavier. "Optimisation of Nano Urchin Geometry for the Generation of Hot Electrons for Sensing: A Computational Study." In 2024 International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), 1–2. IEEE, 2024. http://dx.doi.org/10.1109/nusod62083.2024.10723561.

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Willich, Viktor, Niklas Himker, and Axel Mertens. "Dynamic Position Estimator for a PMSM Using Regular Current Sampling and Numerical Optimisation." In 2024 27th International Conference on Electrical Machines and Systems (ICEMS), 840–46. IEEE, 2024. https://doi.org/10.23919/icems60997.2024.10921281.

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Sharp, Andrew, Julian M. Niedermayer, Mobayode O. Akinsolu, Yuriy Vagapov, Shafiul Monir, and Anton Dianov. "Numerical Optimisation of Air-cooled Heat Sink Geometry to Improve Temperature Gradient of Power Semiconductor Modules." In 2024 XV International Symposium on Industrial Electronics and Applications (INDEL), 1–6. IEEE, 2024. https://doi.org/10.1109/indel62640.2024.10772679.

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Elibol, Armagan, Nuno Gracia, and Rafael Garcia. "Reducing the optimisation effort in batch mosaicing." In NUMERICAL ANALYSIS AND APPLIED MATHEMATICS ICNAAM 2012: International Conference of Numerical Analysis and Applied Mathematics. AIP, 2012. http://dx.doi.org/10.1063/1.4756281.

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Groot, J. A. W. M., C. G. Giannopapa, and R. M. M. Mattheij. "Numerical Optimisation of Blowing Glass Parison Shapes." In ASME 2009 Pressure Vessels and Piping Conference. ASMEDC, 2009. http://dx.doi.org/10.1115/pvp2009-77946.

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Industrial glass blowing is an essential stage of manufacturing glass containers, i.e. bottles or jars. An initial glass preform is brought into a mould and subsequently blown into the mould shape. Over the last few decades, a wide range of numerical models for forward glass blow process simulation have been developed. A considerable challenge is the inverse problem: to determine an optimal preform from the desired container shape. A simulation model for blowing glass containers based on finite element methods has previously been developed [1, 2]. This model uses level set methods to track the glass-air interfaces. In previous work of the authors [3] a numerical method was introduced for optimising the shape of the preform. The optimisation method aims at minimising the error in the level set representing the inner container surface. The objective of this paper is to analyse the inverse problem by means of an analytical approximation of the flow problem and to improve the performance of the optimisation method previously introduced. In particular an initial guess of the preform for the iterative optimisation algorithm is constructed from the approximate solution of the inverse problem. The main goals of this work are the analysis of the inverse problem and the development of the optimisation method in consideration of the application to containers of industrial relevance.

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Zhan, Jie, Yifu Han, Allan Fogwill, Kongjie Wang, Hossein Hejazi, Ruijian He, and Zhangxin Chen. "A Novel Integrated Numerical Simulation Scheme for Transient Gas Flow in Shale Matrix." In SPE Symposium: Production Enhancement and Cost Optimisation. Society of Petroleum Engineers, 2017. http://dx.doi.org/10.2118/189199-ms.

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Seyr, Martin, and Stefan Jakubek. "Dynamic trajectory generation via numerical multi-objective optimisation." In 2007 American Control Conference. IEEE, 2007. http://dx.doi.org/10.1109/acc.2007.4282153.

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Keskin, Akin. "Application of Numerical Optimisation to Support Engineering Design." In 58th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference. Reston, Virginia: American Institute of Aeronautics and Astronautics, 2017. http://dx.doi.org/10.2514/6.2017-1545.

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Huétink, J., A. H. van den Boogaard, H. J. M. Geijselears, and T. Meinders. "Numerical Forming Simulations and Optimisation in Advanced Materials." In MATERIALS PROCESSING AND DESIGN; Modeling, Simulation and Applications; NUMIFORM '07; Proceedings of the 9th International Conference on Numerical Methods in Industrial Forming Processes. AIP, 2007. http://dx.doi.org/10.1063/1.2740825.

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Reports on the topic "Numerical optimisation"

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Nobile, F., Q. Ayoul-Guilmard, S. Ganesh, M. Nuñez, A. Kodakkal, C. Soriano, and R. Rossi. D6.5 Report on stochastic optimisation for wind engineering. Scipedia, 2022. http://dx.doi.org/10.23967/exaqute.2022.3.04.

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This report presents the latest methods of optimisation under uncertainties investigated in the ExaQUte project, and their applications to problems related to civil and wind engineering. The measure of risk throughout the report is the conditional value at risk. First, the reference method is presented: the derivation of sensitivities of the risk measure; their accurate computation; and lastly, a practical optimisation algorithm with adaptive statistical estimation. Second, this method is directly applied to a nonlinear relaxation oscillator (FitzHugh–Nagumo model) with numerical experiments to demonstrate its performance. Third, the optimisation method is adapted to the shape optimisation of an airfoil and illustrated by a large-scale experiment on a computing cluster. Finally, the benchmark of the shape optimisation of a tall building under a turbulent flow is presented, followed by an adaptation of the optimisation method. All numerical experiments showcase the open-source software stack of the ExaQUte project for large-scale computing in a distributed environment.

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