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Relevant bibliographies by topics / Shape parametrisation

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Academic literature on the topic 'Shape parametrisation'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Shape parametrisation"

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BARTEL, JOHANN, and KRZYSZTOF POMORSKI. "JACOBI SHAPE TRANSITIONS WITHIN THE LSD MODEL AND THE SKYRME-ETF APPROACH." International Journal of Modern Physics E 17, no. 01 (January 2008): 100–109. http://dx.doi.org/10.1142/s0218301308009598.

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The "Modified Funny-Hills parametrisation" is used together with the Lublin-Strasbourg Drop Model to evaluate the stability of rotating nuclei. The Jacobi transition into triaxial shapes is studied. By a comparison with selfconsistent semiclassical calculations in the framework of the Extended Thomas-Fermi method, the validity of the present approach is demonstrated and possible improvements are indicated.

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Martinelli, Massimiliano, and François Beux. "Multi-level gradient-based methods and parametrisation in aerodynamic shape design." European Journal of Computational Mechanics 17, no. 1-2 (January 2008): 169–97. http://dx.doi.org/10.3166/remn.17.169-197.

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Mesnil, Romain, Cyril Douthe, Christiane Richter, and Olivier Baverel. "Fabrication-aware shape parametrisation for the structural optimisation of shell structures." Engineering Structures 176 (December 2018): 569–84. http://dx.doi.org/10.1016/j.engstruct.2018.09.026.

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Tomášik, Boris, Jakub Cimerman, and Christopher Plumberg. "Averaging and the Shape of the Correlation Function." Universe 5, no. 6 (June 13, 2019): 148. http://dx.doi.org/10.3390/universe5060148.

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A brief pedagogical introduction to correlation femtoscopy is given. We then focus on the shape of the correlation function and discuss the possible reasons for its departure from the Gaussian form and better reproduction with a Lévy stable distribution. With the help of Monte Carlo simulations based on asymmetric extension of the Blast-Wave model with resonances we demonstrate possible influence of averaging over many events and integrating over wide momentum bins on the shape of the correlation function. We also show that the shape is strongly influenced by the use of the one-dimensional parametrisation in the q i n v variable.

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Nerlo-Pomorska, B., K. Pomorski, J. Bartel, and H. Molique. "On Shape Isomers of Pt–Pb Isotopes in the 4D Fourier Parametrisation." Acta Physica Polonica B Proceedings Supplement 13, no. 3 (2020): 449. http://dx.doi.org/10.5506/aphyspolbsupp.13.449.

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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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Ugail, H., and M. J. Wilson. "Efficient shape parametrisation for automatic design optimisation using a partial differential equation formulation." Computers & Structures 81, no. 28-29 (November 2003): 2601–9. http://dx.doi.org/10.1016/s0045-7949(03)00321-3.

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Capitán-Agudo, Carlos, Beatriz Pontes, Pedro Gómez-Gálvez, and Pablo Vicente-Munuera. "Evolutionary 3D Image Segmentation of Curve Epithelial Tissues of Drosophila melanogaster." Applied Sciences 11, no. 14 (July 12, 2021): 6410. http://dx.doi.org/10.3390/app11146410.

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Analysing biological images coming from the microscope is challenging; not only is it complex to acquire the images, but also the three-dimensional shapes found on them. Thus, using automatic approaches that could learn and embrace that variance would be highly interesting for the field. Here, we use an evolutionary algorithm to obtain the 3D cell shape of curve epithelial tissues. Our approach is based on the application of a 3D segmentation algorithm called LimeSeg, which is a segmentation software that uses a particle-based active contour method. This program needs the fine-tuning of some hyperparameters that could present a long number of combinations, with the selection of the best parametrisation being highly time-consuming. Our evolutionary algorithm automatically selects the best possible parametrisation with which it can perform an accurate and non-supervised segmentation of 3D curved epithelial tissues. This way, we combine the segmentation potential of LimeSeg and optimise the parameters selection by adding automatisation. This methodology has been applied to three datasets of confocal images from Drosophila melanogaster, where a good convergence has been observed in the evaluation of the solutions. Our experimental results confirm the proper performing of the algorithm, whose segmented images have been compared to those manually obtained for the same tissues.

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Corrales, Marta Lucia, and Edilberto Cepeda-Cuervo. "A Bayesian Approach to Mixed Gamma Regression Models." Revista Colombiana de Estadística 42, no. 1 (January 1, 2019): 81–99. http://dx.doi.org/10.15446/rce.v42n1.69334.

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Gamma regression models are a suitable choice to model continuous variables that take positive real values. This paper presents a gamma regression model with mixed effects from a Bayesian approach. We use the parametrisation of the gamma distribution in terms of the mean and the shape parameter, both of which are modelled through regression structures that may involve fixed and random effects. A computational implementation via Gibbs sampling is provided and illustrative examples (simulated and real data) are presented.

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Nerlo-Pomorska, B., K. Pomorski, J. Bartel, and C. Schmitt. "Potential Energy Surfaces of Mercury up to Uranium Isotopes in the 4D Fourier Shape Parametrisation." Acta Physica Polonica B Proceedings Supplement 10, no. 1 (2017): 173. http://dx.doi.org/10.5506/aphyspolbsupp.10.173.

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Dissertations / Theses on the topic "Shape parametrisation"

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Leavers, Violet. "Shape parametrisation and object recognition in machine vision." Thesis, King's College London (University of London), 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.243898.

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Zhang, Xingchen. "CAD-based geometry parametrisation for shape optimisation using non-uniform rational B-splines." Thesis, Queen Mary, University of London, 2018. http://qmro.qmul.ac.uk/xmlui/handle/123456789/43186.

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With the continuous growth in computing power, numerical optimisation is increasingly applied in shape optimisation using Computational Fluid Dynamics (CFD). Since CFD computations are expensive, gradient-based optimisation is preferable when the number of design variables is large. In particular the recent progress with adjoint solvers is important, as these solvers allow to compute the gradients at constant computational cost regardless of the number of design variables, and as a consequence enable the use of automatically derived and rich design spaces. One of the crucial steps in shape optimisation is the parametrisation of the geometry, which directly determines the design space and thus the nal results. This thesis focuses on CAD-based parametrisations with the CAD model continuously updated in the design loop. An existing approach that automatically derives a parametrisation from the control points of a net of B-Spline patches is extended to include NURBS. Continuity constraints for water-tightness, tangency and curvature across patch interfaces are evaluated numerically and a basis for the resulting design space is computed using Singular Value Decomposition (SVD). A CAD-based shape optimisation framework is developed, coupling a flow solver, an adjoint solver, the in-house CAD kernel and a gradient-based optimiser. The flow sensitivities provided by the adjoint solver and the geometric sensitivities computed through automatic differentiation (AD) are assembled and provided to the optimiser. An extension to maintain the design space and hence enables use of a quasi-Newton method such as the BFGS algorithm is also presented and the convergence improvements are demonstrated. The framework is applied to three shape optimisation cases to show its effectiveness. The performance is assessed and analysed. The effect of parameters that can be chosen by the user are analysed over a range of cases and best practice choices are identified.

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Ugail, Hassan, and M. J. Wilson. "Efficient shape parametrisation for automatic design optimisation using a partial differential equation formulation." 2003. http://hdl.handle.net/10454/2255.

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Book chapters on the topic "Shape parametrisation"

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Bloor, M. I. G., and M. J. Wilson. "The Shape Parametrisation of an Aircraft Engine Nacelle and Pylon." In Geometric Modelling, 39–50. Vienna: Springer Vienna, 1998. http://dx.doi.org/10.1007/978-3-7091-6444-0_4.

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Ju, Yong Chul, Daniel Maurer, Michael Breuß, and Andrés Bruhn. "Direct Variational Perspective Shape from Shading with Cartesian Depth Parametrisation." In Mathematics and Visualization, 43–72. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-24726-7_3.

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Martinelli, M., and F. Beux. "Optimum shape design through multilevel gradient-based method using Bézier parametrisation." In Computational Fluid Dynamics 2006, 537–42. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-92779-2_84.

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Conference papers on the topic "Shape parametrisation"

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Jesudasan, R., and J. Mueller. "Cad-Based Adaptive Shape Parametrisation for Aerodynamic Shape Optimisation." In 14th WCCM-ECCOMAS Congress. CIMNE, 2021. http://dx.doi.org/10.23967/wccm-eccomas.2020.079.

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MARTINELLI, M., and F. BEUX. "MULTILEVEL GRADIENT METHOD WITH BÉZIER PARAMETRISATION FOR AERODYNAMIC SHAPE OPTIMISATION." In Selected Contributions from the 8th SIMAI Conference. WORLD SCIENTIFIC, 2007. http://dx.doi.org/10.1142/9789812709394_0038.

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Jesudasan, Rejish, Xingchen Zhang, and Jens-Dominik Mueller. "Adjoint Optimisation of Internal Turbine Cooling Channel Using NURBS-Based Automatic and Adaptive Parametrisation Method." In ASME 2017 Gas Turbine India Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gtindia2017-4669.

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A well-formulated design space parametrisation is the key to the success of design optimisation. Most parametrisation methods require manual set-up which typically results in a restricted design space and impedes the generation of superior designs which may be found outside this restricted envelope. In this work, we adopt a NURBS-based automatic and adaptive parametrisation approach where the optimisation begins in a coarser design space and adapts to finer parametrisation during the optimisation. Our approach takes CAD descriptions as input and to alter the shape perturbs the control points of the NURBS patches that form the boundary representation. Driven by adjoint sensitivity information the control net is adaptively enriched using knot insertion. The sensitivity-driven parametrisation method is applied here to reduce the pressure loss of a U-bend passage of a turbine blade serpentine cooling channel.

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Xu, Shenren, David Radford, Marcus Meyer, and Jens-Dominik Müller. "CAD-Based Adjoint Shape Optimisation of a One-Stage Turbine With Geometric Constraints." In ASME Turbo Expo 2015: Turbine Technical Conference and Exposition. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/gt2015-42237.

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An extension of the CAD-based parametrisation termed ‘NURBS-based parametrisation with complex constraints’, or NsPCC, is developed and applied to the aerodynamic shape optimisition of a one-stage high pressure turbine. NsPCC uses a test-point approach to impose various geometric constraints such as continuity, thickness and trailing edge radius constraint. To perform the shape optimisation using NsPCC, The surface sensitivity is first computed efficiently using a discrete adjoint solver. The displacements of the control points of the NURBS patches are used as the design variables and linked to the surface sensitivity through consistent application of Automatic Differentiation. A robust mesh deformation based on linear elasticity and further enhanced with sliding mesh capability is used to deform the mesh at each design step. Finally, the optimised rotor shape is exported as a STEP file. The method is demonstrated on a turbine stage where isentropic efficiency is improved by over 0.4% with both the inlet capacity and rotor reaction ratio deviation below the prescribed thresholds. Satisfaction of the G1 continuity, thickness and trailing radius constraints is verified.

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Andreou, Thomas, Craig White, Konstantinos Kontis, Shahrokh Shahpar, and Nicholas Brown. "Part 2: Design Optimisation Strategies for a Fuel Spray Nozzle." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-15431.

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Abstract Reducing emissions and improving fuel economy has become a key target for advanced aircraft engine platforms. The competitive aviation market requires a drastic reduction in current aircraft emission levels, which leads to the requirement of optimising the entire combustion process. Lean burn pre-mixed systems are an efficient way of reducing NOx emissions. Starting from a datum blade generated in a companion paper, shape optimisation strategies applied to a fuel spray nozzle have been explored. Making use of three different design space parametrisation methods and several optimisation algorithms, an optimised passage has been generated. The optimised design achieves a considerable pressure loss reduction in comparison to the baseline design.

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Vincekovic, Luka, Alistair John, Ning Qin, and Shahrokh Shahpar. "Exploring Topology Optimisation of High Pressure Turbine Blade Tips." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16059.

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Abstract This work presents the aerodynamic topology optimisation of high pressure turbine rotor blade tips. Before carrying out the topology optimisation on the blade tip, some initial tip design studies were carried out. The winglet shape was optimised using two different design space setups and parameter limits. The optimum winglet design features the largest overhangs and in the case of unconstrained optimisation proved to have 1.40% greater aerodynamic efficiency. Secondly, a radial basis function based parametrisation was set up to allow the creation of single squealer line using the flat tip blade as a baseline geometry. The optimum case proved to increase efficiency 0.46% compared to the flat tip. After that, a combination of winglet and topology free squealer tips was investigated for topology optimisation. The winglet tip was parametrized as in the winglet only optimisation cases and topology free squealer walls were created using mapping of radial basis function surfaces of different complexities. It is shown that by combining both winglet and novel squealer topology optimisation, better designs of different topologies can be produced.

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Lopez, Diego I., Tiziano Ghisu, and Shahrokh Shahpar. "Global Optimisation of a Transonic Fan Blade Through AI-Enabled Active Subspaces." In ASME Turbo Expo 2021: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2021. http://dx.doi.org/10.1115/gt2021-59166.

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Abstract The increased need to design higher performing aerodynamic shapes has led to design optimisation cycles requiring high-fidelity CFD models and high-dimensional parametrisation schemes. The computational cost of employing global search algorithms on such scenarios has typically been prohibitive for most academic and industrial environments. In this paper, a novel strategy is presented that leverages the capabilities of Artificial Neural Networks for regressing complex unstructured data, while coupling them with dimensionality reduction algorithms. This approach enables employing global-based optimisation methods on high-dimensional applications through a reduced computational cost. This methodology is demonstrated on the efficiency optimisation of a modern jet engine fan blade with constrained pressure ratio. The outcome is compared against a state-of-the-art adjoint-based approach. Results indicate the strategy proposed achieves comparable improvements to its adjoint counterpart with a reduced computational cost, and can scale better to multi-objective optimisation applications.

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