Journal articles: 'Numerical optimisation'

1

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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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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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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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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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.2017.10006778.

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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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Pinto, Mark Christhian Barrueta, and Yongbo Li. "Numerical investigation on welding process parameters optimisation using multi-objective optimisation technique." International Journal of Advanced Operations Management 12, no. 3 (2020): 195. http://dx.doi.org/10.1504/ijaom.2020.10032145.

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Pinto, Mark Christhian Barrueta, and Yongbo Li. "Numerical investigation on welding process parameters optimisation using multi-objective optimisation technique." International Journal of Advanced Operations Management 12, no. 3 (2020): 195. http://dx.doi.org/10.1504/ijaom.2020.109788.

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Odstrcil, M., J. Mlynar, T. Odstrcil, B. Alper, and A. Murari. "Modern numerical methods for plasma tomography optimisation." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment 686 (September 2012): 156–61. http://dx.doi.org/10.1016/j.nima.2012.05.063.

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Xiang, qin. "An improved firefly algorithm for numerical optimisation." International Journal of Computing Science and Mathematics 6, no. 2 (2015): 201. http://dx.doi.org/10.1504/ijcsm.2015.069466.

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Guo, Bao-Yu, Ke-Jun Dong, and Ai-Bing Yu. "Optimisation of sputnik distributor using numerical method." International Journal of Computational Fluid Dynamics 23, no. 2 (February 2009): 119–35. http://dx.doi.org/10.1080/10618560902740618.

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Berteau, J. Ph, J. Ph Pastre, S. Mesure, M. Pithioux, G. Bolini, and P. Chabrand. "Optimisation of brace treatment with numerical modelling." Computer Methods in Biomechanics and Biomedical Engineering 12, sup1 (August 2009): 45–46. http://dx.doi.org/10.1080/10255840903065399.

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G., W., and Philippe G. Ciarlet. "Introduction to Numerical Linear Algebra and Optimisation." Mathematics of Computation 55, no. 191 (July 1990): 395. http://dx.doi.org/10.2307/2008817.

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Dai, Chaohua, Jiajun Ouyang, Weirong Chen, Yunfang Zhu, and Lei Ma. "Human group optimiser for global numerical optimisation." International Journal of Bio-Inspired Computation 6, no. 5 (2014): 333. http://dx.doi.org/10.1504/ijbic.2014.065586.

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Naar, Raphaëlle, and François Bay. "Numerical optimisation for induction heat treatment processes." Applied Mathematical Modelling 37, no. 4 (February 2013): 2074–85. http://dx.doi.org/10.1016/j.apm.2012.04.058.

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Caseiro, João F. M., Robertt Angelo Fontes Valente, António Andrade-Campos, and Renato Natal Jorge. "Optimisation of Tubular Hydroforming Processes for Wrinkling and Thinning Control." Key Engineering Materials 473 (March 2011): 159–67. http://dx.doi.org/10.4028/www.scientific.net/kem.473.159.

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This paper is focused on the development and implementation of an innovative optimisation algorithm for the wrinkling and thinning control during the hydroforming of complex metallic parts. A straightforward numerical algorithm for simultaneous tracking and evaluation of the initiation of wrinkling/thinning defects was implemented, together with a numerical simulation program based on the Finite Element Method (FEM). After undesirable wrinkling/thinning patterns are identified during FEM simulation, the developed optimisation procedure (called Hybrid Differential Evolution Particle Swarm Optimisation - HDEPSO) is responsible to automatically correct the process input parameters, in order to achieve the successful forming of the desired part. In the end, the combined procedure (optimisation methodology + FEM) proved to be able to lead to a suitable numerical simulation and design tool for industrial hydroformed metallic tubular parts.

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Düğenci, Muharrem, and Mehmet Emin Aydin. "A honeybees-inspired heuristic algorithm for numerical optimisation." Neural Computing and Applications 32, no. 16 (October 16, 2019): 12311–25. http://dx.doi.org/10.1007/s00521-019-04533-x.

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Abstract Swarm intelligence is all about developing collective behaviours to solve complex, ill-structured and large-scale problems. Efficiency in collective behaviours depends on how to harmonise the individual contributors so that a complementary collective effort can be achieved to offer a useful solution. The main points in organising the harmony remain as managing the diversification and intensification actions appropriately, where the efficiency of collective behaviours depends on blending these two actions appropriately. In this paper, a hybrid bee algorithm is presented, which harmonises bee operators of two mainstream well-known swarm intelligence algorithms inspired of natural honeybee colonies. The parent algorithms have been overviewed with many respects, strengths and weaknesses are identified, first, and the hybrid version has been proposed, next. The efficiency of the hybrid algorithm is demonstrated in comparison with the parent algorithms in solving two types of numerical optimisation problems; (1) a set of well-known functional optimisation benchmark problems and (2) optimising the weights of a set of artificial neural network models trained for medical classification benchmark problems. The experimental results demonstrate the outperforming success of the proposed hybrid algorithm in comparison with two original/parent bee algorithms in solving both types of numerical optimisation benchmarks.

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Susantez, Çiğdem, and Aldélio Bueno Caldeira. "Genetic Algorithm Optimisation of a TNT Solidification Model." Defence Science Journal 69, no. 6 (December 16, 2019): 545–49. http://dx.doi.org/10.14429/dsj.69.14037.

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The control of the solidification process of energetic materials is important to prevent manufacturing defects in high explosive ammunitions. The present work aims to propose an optimisation procedure to determine the value of the model parameter, avoiding the traditional trial and error approach. In this work, the solidification of TNT has been numerically modelled employing apparent heat capacity method and the model parameter was optimised using genetic algorithm. One dimensional numerical model has been solved in Comsol Multiphysics Modeling Software and the genetic algorithm code was written in Matlab. The Neumann’s analytical solution of the solidification front was used as a reference to build the fitness function, following the inverse problems concepts. The optimum model parameter has been predicted after 20 generations and among 30 candidate solutions for each generation. The numerical solution performed with the optimised model parameter has agreed with the analytical solution, indicating the feasibility of the proposed procedure. The discrepancy was 3.8 per cent when maximum difference between analytical and numerical solutions was observed.

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Guha, A. "Optimisation of aero gas turbine engines." Aeronautical Journal 105, no. 1049 (July 2001): 345–58. http://dx.doi.org/10.1017/s0001924000012264.

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Abstract A systematic methodology for the thermodynamic optimisation of civil bypass engines (turbofan or advanced propulsors) is presented, which would be useful for designing air-breathing engines based on “clean-sheet analysis”. The process starts with establishing an optimum specific thrust for the engine based on an economic analysis (installation constraints, noise regulations etc. also need to be considered). The task of the optimisation process is then to find the combination of optimum values of fan pressure ratio, overall pressure ratio, bypass ratio and turbine entry temperature concurrently that maximises overall efficiency at the fixed specific thrust. This procedure is quite different from the usual single-variable parametric performance studies which do not give proper optimum values and may involve large excursion in the value of the specific thrust unacceptable for a particular mission. Additionally, several, simple and explicit, analytical relations are derived here from fundamental principles, which perform well against numerical optimisation performed by a specialist computer program employing iterative and advanced search techniques. The analytical relations accelerate the optimisation process and offer physical insight. Present numerical computations with real gas properties have established new concepts in turbofan optimisation (for example, the existence of an optimum bypass ratio and optimum turbine entry temperature). The question of optimum jet velocity has been addressed. An analytical expression for the optimum jet velocity at a given bypass ratio has been derived which performs well against numerical optimisation results.

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23

Colloud, Floren. "Advanced modelling of human movements using numerical optimisation." Movement & Sport Sciences 90, no. 4 (2015): 1. http://dx.doi.org/10.3917/sm.090.0001.

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Lógó, J., and E. Pintér. "Numerical Methods in Probabilistic Topology Optimisation: A Review." Computational Technology Reviews 5 (September 4, 2012): 79–108. http://dx.doi.org/10.4203/ctr.5.3.

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Herty, Michael, and Veronika Schleper. "Time discretizations for numerical optimisation of hyperbolic problems." Applied Mathematics and Computation 218, no. 1 (September 2011): 183–94. http://dx.doi.org/10.1016/j.amc.2011.05.116.

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26

Hiltcher, Yann, Michèle Guingand, and Jean Pierre de Vaujany. "Numerical simulation and optimisation of worm gear cutting." Mechanism and Machine Theory 41, no. 9 (September 2006): 1090–110. http://dx.doi.org/10.1016/j.mechmachtheory.2005.10.016.

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Colloud, Floren. "Advanced modelling of human movements using numerical optimisation." Movement & Sport Sciences - Science & Motricité, no. 90 (2015): 1–6. http://dx.doi.org/10.1051/sm/2016001.

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28

Roshanaei, M. M., C. Lucas, and A. R. Mehrabian. "Adaptive beamforming using a novel numerical optimisation algorithm." IET Microwaves, Antennas & Propagation 3, no. 5 (2009): 765. http://dx.doi.org/10.1049/iet-map.2008.0188.

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Xue, Gang, Wenbin Song, Simon J. Cox, and Andy Keane. "Numerical Optimisation as Grid Services for Engineering Design." Journal of Grid Computing 2, no. 3 (September 2004): 223–38. http://dx.doi.org/10.1007/s10723-004-6742-9.

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Boudinar, M. Ben, W. T. Hanbury, and S. Avlonitis. "Numerical simulation and optimisation of spiral-wound modules." Desalination 86, no. 3 (August 1992): 273–90. http://dx.doi.org/10.1016/0011-9164(92)80038-b.

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Draa, Amer, Samira Bouzoubia, and Imene Boukhalfa. "A sinusoidal differential evolution algorithm for numerical optimisation." Applied Soft Computing 27 (February 2015): 99–126. http://dx.doi.org/10.1016/j.asoc.2014.11.003.

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Fischer, M., and X. Jiang. "Numerical optimisation for model evaluation in combustion kinetics." Applied Energy 156 (October 2015): 793–803. http://dx.doi.org/10.1016/j.apenergy.2015.05.002.

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Khurana, M., and H. Winarto. "Development and validation of an efficient direct numerical optimisation approach for aerofoil shape design." Aeronautical Journal 114, no. 1160 (October 2010): 611–28. http://dx.doi.org/10.1017/s0001924000004097.

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Abstract Intelligent shape optimisation architecture is developed, validated and applied in the design of high-altitude long endurance aerofoil (HALE). The direct numeric optimisation (DNO) approach integrating a geometrical shape parameterisation model coupled to a validated flow solver and a population based search algorithm are applied in the design process. The merit of the DNO methodology is measured by computational time efficiency and feasibility of the optimal solution. Gradient based optimisers are not suitable for multi-modal solution topologies. Thus, a novel particle swarm optimiser with adaptive mutation (AM-PSO) is developed. The effect of applying the PARSEC and a modified variant of the original function, as a shape parameterisation model on the global optimal is verified. Optimisation efficiency is addressed by mapping the solution topology for HALE aerofoil designs and by computing the sensitivity of aerofoil shape variables on the objective function. Variables with minimal influence are identified and eliminated from shape optimisation simulations. Variable elimination has a negligible effect on the aerodynamics of the global optima, with a significant reduction in design iterations to convergence. A novel data-mining technique is further applied to verify the accuracy of the AM-PSO solutions. The post-processing analysis, to swarm optimisation solutions, indicates a hybrid optimisation methodology with the integration of global and local gradient based search methods, yields a true optima. The findings are consistent for single and multi-point designs.

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Shariatmadar, Keivan, and Mark Versteyhe. "Numerical Linear Programming under Non-Probabilistic Uncertainty Models — Interval and Fuzzy Sets." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 28, no. 03 (May 21, 2020): 469–95. http://dx.doi.org/10.1142/s0218488520500191.

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This paper considers a linear optimisation problem under uncertainty with at least one element modelled as a non-probabilistic uncertainty. The uncertainty is expressed in the coefficient matrices of constraints and/or coefficients of goal function. Previous work converts such problems to classical (linear) optimisation problems and eliminates uncertainty by converting the linear programming under uncertainty problem to a decision problem using imprecise probability and imprecise decision theory. Our aim here is to generalise this approach numerically and present three methods to calculate the solution. We investigate what numerical results can be obtained for interval and fuzzy types of uncertainty models and compare them to classical probabilistic cases — for two different optimality criteria: maximinity and maximality. We also provide an efficient method to calculate the maximal solutions in the fuzzy set model. A numerical example is considered for illustration of the results.

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Ramakrishna, Varanasi, Shinadam Rama Krishna, and PVS Ganesh Kumar. "Experimental and Numerical Investigation of Engine Foundation for Vibration Reduction." Defence Science Journal 68, no. 6 (October 31, 2018): 604. http://dx.doi.org/10.14429/dsj.68.12705.

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<p>The purpose of this study is to minimise frequency response of engine foundation using topology optimisation. The study involves vibration response estimation of an existing marine engine foundation, validation of estimations with measurements and estimation of reduction in vibration response after optimisation. Initially, solid model of baseline model is generated using dimensions of the existing foundation measured by a laser line probe coordinate measuring machine. Harmonic analysis is used to find the vibration response of the foundation. These results are experimentally validated by the measurements on the foundation using the vibration testing. Frequency response topology optimisation is then carried out on the baseline model to reduce vibration response with specified constraints and objective function. Subsequently, harmonic analysis is performed on the topology optimised design to verify the reduction in vibration response. From these results, it is observed that considerable frequency response is reduced with modified design compared to baseline model.</p>

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Merklein, Marion, Maren Johannes, M. Biasutti, and Michael Lechner. "Numerical Optimisation of a Shear Specimen Geometry According to ASTM." Key Engineering Materials 549 (April 2013): 317–24. http://dx.doi.org/10.4028/www.scientific.net/kem.549.317.

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Complex material models used for the numerical representation of forming processes need in addition to tension and compression tests also shear tests to completely analyse the behaviour of the material under different loading conditions. There are two concepts of shear test one uses symmetrical specimens with two shear zones (according to Miyauchi) the other one specimens with a single shear zone. In both cases, a homogeneous distribution of the strain in the shear zone is essential for the validity of the shear test. Therefore, the length and width of the shear zone in a single shear specimen made from high strength steel according to the ASTM standard geometry were varied. Analysing the resulting strain distribution numerically an optimised sample geometry with a more uniform strain distribution than the ASTM standard was achieved. The numerical results were also validated with experimental shear tests.

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Kijanski, Wojciech, and Franz-Joseph Barthold. "Two-scale shape optimisation based on numerical homogenisation techniques and variational sensitivity analysis." Computational Mechanics 67, no. 4 (March 6, 2021): 1021–40. http://dx.doi.org/10.1007/s00466-020-01955-6.

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AbstractThis contribution presents a theoretical and computational framework for two-scale shape optimisation of nonlinear elastic structures. Particularly, minimum compliance optimisation problems with composite (matrix-inclusion) microstructures subjected to static loads and volume-type design constraints are focused. A homogenisation-based FE$$^2$$ 2 scheme is extended by an enhanced formulation of variational (shape) sensitivity analysis based on Noll’s intrinsic, frame-free formulation of continuum mechanics. The obtained overall two-scale sensitivity information couples shape variations across micro- and macroscopic scales. A numerical example demonstrates the capabilities of the proposed variational sensitivity analysis and the (shape) optimisation framework. The investigations involve a mesh morphing scheme for the design parametrisation at both macro- and microscopic scales.

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Haas, Titus, Sascha Weikert, and Konrad Wegener. "MPCC-Based Set Point Optimisation for Machine Tools." International Journal of Automation Technology 13, no. 3 (May 5, 2019): 407–18. http://dx.doi.org/10.20965/ijat.2019.p0407.

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Numerical control code is typically used for manufacturing a workpiece using machine tools. Most state-of-the-art approaches decouple the set point optimisation into two steps: the geometry and the feed rate optimisation that does not necessarily result in time-optimal set points for the desired geometry. Given the originally programmed geometry through the numerical control code, dynamic constraints of the machine tool, and maximum permissible contour error for the optimisation, a model predictive contouring control based set point optimisation approach is developed to generate time-optimal set points for machine tools globally. A suitable error definition and its linearisation are used whereby the optimisation problem can be represented by a quadratic programming problem with linear constraints. Compared to most state-of-the-art methods, a direct approach is presented and no previous geometry optimisation step is required. Depending on the demands of accuracy, different maximum contour error constraints and penalisation as well as various maximum permissible axis velocities and accelerations are presented and tested on a test bench. The method is shown to be adaptable to different demands on the set points, and the contour errors can be affected by either the constraints or penalising factors.

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Porsev, V. I., A. I. Sivkov, and E. P. Voroshilin. "Estimating angular velocities of a ballistic object using radial acceleration." Journal of «Almaz – Antey» Air and Space Defence Corporation, no. 2 (June 30, 2016): 30–35. http://dx.doi.org/10.38013/2542-0542-2016-2-30-35.

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We developed an algorithm for estimating the total velocity vector of a ballistic object based on its radial acceleration, without employing numerical optimisation methods. We supply a comparison between the efficiency of the algorithm functioning and an existing optimisation method.

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40

Sun, Liang, and Bing Wang. "Robust Optimisation Approach for Vehicle Routing Problems with Uncertainty." Mathematical Problems in Engineering 2015 (2015): 1–8. http://dx.doi.org/10.1155/2015/901583.

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We formulated a solution procedure for vehicle routing problems with uncertainty (VRPU for short) with regard to future demand and transportation cost. Unlike E-SDROA (expectation semideviation robust optimisation approach) for solving the proposed problem, the formulation focuses on robust optimisation considering situations possibly related to bidding and capital budgets. Besides, numerical experiments showed significant increments in the robustness of the solutions without much loss in solution quality. The differences and similarities of the robust optimisation model and existing robust optimisation approaches were also compared.

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Kumar, Puneet, Amalanathan Paul, and M. Anil Kumar. "Risk Optimisation Analytics." International Journal of Social Ecology and Sustainable Development 12, no. 2 (April 2021): 48–62. http://dx.doi.org/10.4018/ijsesd.2021040103.

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Risk optimization using business analytics is gaining momentum in India over the last decade. In order to tap this huge opportunity, most of the startups are getting into the analytics field engaging in financial market survey to provide their customers with valid data. The objective of this study is to help “Sai Builders” in solving their portfolio investment problem as well as sinking funds problem using linear programming and to obtain the total optimum returns by satisfying all constraints. The authors solve the problem of minimizing portfolio risk measures. In addition, the expected return of the portfolio is maximized subject to the aforementioned risk measures. By using numerical experiments, they illustrate the impact of these risk measures on portfolio optimization. The analysis is done by using Excel Solver, and the optimum solution is achieved.

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Lombardi, G., and A. Vicini. "Induced drag prediction for wing-tail and canard configurations through numerical optimisation." Aeronautical Journal 98, no. 976 (July 1994): 199–206. http://dx.doi.org/10.1017/s0001924000049733.

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Abstract A computational procedure has been developed in order to predict aerodynamic interference between lifting surfaces, and to devise configurations which best meet given aerodynamic requirements. The procedure, which couples an aerodynamic solver with a numerical optimisation routine, is useful in the preliminary design of aircraft. The essential features of the aerodynamic code and of the optimisation routine are described, along with the coupling criteria. Some of the most significant predictions obtained in induced-drag minimisation for wing-tail and canard configurations are described and discussed.

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Brastein, Ole Magnus, Bernt Lie, Carlos F. Pfeiffer, and Nils-Olav Skeie. "Estimating uncertainty of model parameters obtained using numerical optimisation." Modeling, Identification and Control: A Norwegian Research Bulletin 40, no. 4 (2019): 213–43. http://dx.doi.org/10.4173/mic.2019.4.3.

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Laurens, Jean-Marc, Margot Remaud, and Pierre-Michel Guilcher. "Preliminary Parameter Characterization for Numerical Optimisation of Ducted Propellers." Maritime Technology and Research 1, no. 2 (May 21, 2019): Manuscript. http://dx.doi.org/10.33175/mtr.2019.183390.

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In this paper, a preliminary parameter characterization for the numerical optimisation of ducted propellers was performed. The ENSTA Bretagne in-house solver used is based on the potential flow theory. Although the potential flow solver is able to solve unsteady problems, in this preliminary study only steady state flow problems are considered. Different parameters were analysed, such as the gap between the propeller tip and the inner duct surface as well as the propeller location in the duct tube. The analyses were carried out on a standard advance coefficient range. A quick study showed that a neutral NACA profile for the duct section could provide higher performance predictions than the classical accelerating Kort nozzle 19A.

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Rebuffel, V., J. Tabary, M. Wozniak, and E. Juliac. "Optimisation of acquisition parameters for radiography using numerical simulation." Insight - Non-Destructive Testing and Condition Monitoring 49, no. 10 (October 2007): 585–88. http://dx.doi.org/10.1784/insi.2007.49.10.585.

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46

Pepelnjak, T., G. Gantar, and K. Kuzman. "Numerical simulations in optimisation of product and forming process." Journal of Materials Processing Technology 115, no. 1 (August 2001): 122–26. http://dx.doi.org/10.1016/s0924-0136(01)00744-0.

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47

Guilbaud, M., S. Huberson, and M. Voisine. "Numerical optimisation of slalom canoe and kayak hulls performances." Journal of Biomechanics 39 (January 2006): S549. http://dx.doi.org/10.1016/s0021-9290(06)85261-9.

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48

Grazzini, Giuseppe, and Andrea Rocchetti. "Numerical optimisation of a two-stage ejector refrigeration plant." International Journal of Refrigeration 25, no. 5 (August 2002): 621–33. http://dx.doi.org/10.1016/s0140-7007(01)00063-9.

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49

Larsson, L., B. Regnström, K. J. Han, G. Bark, and N. Bathfield. "Numerical optimisation of propeller-hull configurations at full scale." Journal of Marine Engineering & Technology 5, no. 1 (January 2006): 27–33. http://dx.doi.org/10.1080/20464177.2006.11020193.

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50

Korycki, Ryszard, and Izabella Krucińska. "Numerical Optimisation of Thickness of Composite Bonnet for Neonates." Autex Research Journal 16, no. 4 (December 1, 2016): 196–204. http://dx.doi.org/10.1515/aut-2015-0039.

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Abstract Head is the most sensitive body part of neonate. Head that is considerably uncovered causes the significant heat and moisture loss from the skin to the surrounding areas. The main goal is to optimise the thickness of a multilayer composite textile bonnet to secure the optimal skin parameters. Problem is solved using both sensitivity analysis and material derivative concept. An arbitrary objective functional is introduced, its first-order sensitivity is formulated by means of a direct approach. Numerical application is the thickness optimisation of a composite bonnet made of different textile materials.

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Journal articles on the topic 'Numerical optimisation'

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