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Готові списки джерел за темами / Computationnal geometry

Добірка наукової літератури з теми "Computationnal geometry"

Автор: Grafiati

Опубліковано: 8 червня 2024

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Статті в журналах з теми "Computationnal geometry":

1

Toma, Milan, Satvinder K. Guru, Wayne Wu, May Ali, and Chi Wei Ong. "Addressing Discrepancies between Experimental and Computational Procedures." Biology 10, no. 6 (June 15, 2021): 536. http://dx.doi.org/10.3390/biology10060536.

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Imaging subject-specific heart valve, a crucial step to its design, has experimental variables that if unaccounted for, may lead to erroneous computational analysis and geometric errors of the resulting model. Preparation methods are developed to mitigate some sources of the geometric error. However, the resulting 3D geometry often does not retain the original dimensions before excision. Inverse fluid–structure interaction analysis is used to analyze the resulting geometry and to assess the valve’s closure. Based on the resulting closure, it is determined if the geometry used can yield realistic results. If full closure is not reached, the geometry is adjusted adequately until closure is observed.

2

Bayer, Tomáš. "The importance of computational geometry for digital cartography." Geoinformatics FCE CTU 3 (April 12, 2008): 15–24. http://dx.doi.org/10.14311/gi.3.2.

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This paper describes the use of computational geometry concepts in the digital cartography. It presents an importance of 2D geometric structures, geometric operations and procedures for automated or semi automated simplification process. This article is focused on automated building simplification procedures, some techniques are illustrated and discussed. Concrete examples with the requirements to the lowest time complexity, emphasis on the smallest area enclosing rectangle, convex hull or self intersection procedures, are given. Presented results illustrate the relationship of digital cartography and computational geometry.

3

Cafaro, Carlo. "Geometric algebra and information geometry for quantum computational software." Physica A: Statistical Mechanics and its Applications 470 (March 2017): 154–96. http://dx.doi.org/10.1016/j.physa.2016.11.117.

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4

Moussiaux, A., and Ph Tombal. "Geometric calculus: A new computational tool for Riemannian geometry." International Journal of Theoretical Physics 27, no. 5 (May 1988): 613–21. http://dx.doi.org/10.1007/bf00668842.

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5

Veltkamp, Remco C. "Generic Geometric Programming in the Computational Geometry Algorithms Library." Computer Graphics Forum 18, no. 2 (June 1999): 131–37. http://dx.doi.org/10.1111/1467-8659.00363.

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6

ASANO, Tetsuo. "Computational Geometry." Journal of Japan Society for Fuzzy Theory and Systems 13, no. 2 (2001): 130–38. http://dx.doi.org/10.3156/jfuzzy.13.2_2.

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7

O'Rourke, Joseph. "Computational geometry." ACM SIGACT News 23, no. 2 (May 1992): 26–28. http://dx.doi.org/10.1145/130956.130957.

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8

O'Rourke, J. "Computational Geometry." Annual Review of Computer Science 3, no. 1 (June 1988): 389–411. http://dx.doi.org/10.1146/annurev.cs.03.060188.002133.

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9

Agarwal, Pankaj K., and Joseph O'Rourke. "Computational geometry." ACM SIGACT News 29, no. 3 (September 1998): 27–32. http://dx.doi.org/10.1145/300307.300310.

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10

Lee, D. T. "Computational geometry." ACM Computing Surveys 28, no. 1 (March 1996): 27–31. http://dx.doi.org/10.1145/234313.234325.

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Дисертації з теми "Computationnal geometry":

1

Baer, Lawrence H. "Numerical aspects of computational geometry." Thesis, McGill University, 1992. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=22507.

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This thesis is concerned with the numerical issues resulting from the implementation of geometric algorithms on finite precision digital computers. From an examination of the general problem and a survey of previous research, it appears that the central problem of numerical computational geometry is how to deal with degenerate and nearly degenerate input. For some applications, such as solid modeling, degeneracy is often intended but we cannot always ascertain its existence using finite precision. For other applications, degenerate input is unwanted but nearly degenerate input is unavoidable. Near degeneracy is associated with ill-conditioning of the input and can lead to a serious loss of accuracy and program failure. These observations lead us to a discussion of problem condition in the context of computational geometry. We use the Voronoi diagram construction problem as a case study and show that problem condition can also play a role in algorithm design.

2

Hussain, R. "Computational geometry using fourier analysis." Thesis, De Montfort University, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.391483.

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3

Eades, Patrick Fintan. "Uncertainty Models in Computational Geometry." Thesis, University of Sydney, 2020. https://hdl.handle.net/2123/23909.

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In recent years easily and cheaply available internet-connected devices have enabled the collection of vast amounts of data, which has driven a continued interest in efficient, elegant combinatorial algorithms with mathematical guarantees. Much of this data contains an inherent element of uncertainty; whether because of imperfect measurements, because the data contains predictions about the future, or because the data is derived from machine learning algorithms which are inherently probabilistic. There is therefore a need for algorithms which include uncertainty in their definition and give answers in terms of that uncertainty. Questions about the most likely solution, the solution with lowest expected cost or a solution which is correct with high probability are natural here. Computational geometry is the sub-field of theoretical computer science concerned with developing algorithms and data structures for geometric problems, that is problems involving points, distances, angles and shapes. In computational geometry uncertainty is included in the location of the input points, or in which potential points are included in the input. The study of uncertainty in computational geometry is relatively recent; earlier research concerned imprecise points, which are known to appear somewhere in a geometric region. More recently the focus has been on points whose location, or presence, is given by a probability distribution. In this thesis we describe the most commonly used uncertainty models which are the subject of ongoing research in computational geometry. We present specific problems in those models and present new results, both positive and negative. In Chapter 3 we consider universal solutions, and show a new lower bound on the competitive ratio of the Universal Traveling Salesman Problem. In Chapter 4 we describe how to determine if two moving entities are ever mutually visible, and how data structures can be repeatedly queried to simulate uncertainty. In Chapter 5 we describe how to construct a graph on uncertain points with high probability of being a geometric spanner, an example of redundancy protecting against uncertainty. In Chapter 6 we introduce the online ply maintenance problem, an online problem where uncertainty can be reduced at a cost, and give an optimal algorithm.

4

Pirzadeh, Hormoz. "Computational Geometry with the Rotating Calipers." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0027/MQ50856.pdf.

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5

Doskas, Michael. "Various stabbing problems in computational geometry." Thesis, McGill University, 1987. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=66153.

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6

Pătrașcu, Mihai. "Computational geometry through the information lens." Thesis, Massachusetts Institute of Technology, 2007. http://hdl.handle.net/1721.1/40526.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (p. 111-117).
This thesis revisits classic problems in computational geometry from the modern algorithmic perspective of exploiting the bounded precision of the input. In one dimension, this viewpoint has taken over as the standard model of computation, and has led to a powerful suite of techniques that constitute a mature field of research. In two or more dimensions, we have seen great success in understanding orthogonal problems, which decompose naturally into one dimensional problems. However, problems of a nonorthogonal nature, the core of computational geometry, have remained uncracked for many years despite extensive effort. For example, Willard asked in SODA'92 for a o(nlg n) algorithm for Voronoi diagrams. Despite growing interest in the problem, it was not successfully solved until this thesis. Formally, let w be the number of bits in a computer word, and consider n points with O(w)-bit rational coordinates. This thesis describes: * a data structure for 2-d point location with O(n) space, and 0( ... )query time. * randomized algorithms with running time 9 ... ) for 3-d convex hull, 2-d Voronoi diagram, 2-d line segment intersection, and a variety of related problems. * a data structure for 2-d dynamic convex hull, with O ( ... )query time, and O ( ... ) update time. More generally, this thesis develops a suite of techniques for exploiting bounded precision in geometric problems, hopefully laying the foundations for a rejuvenated research direction.
by Mihai Pǎtraşcu.
S.M.

7

Selmi-Dei, Fabio Pakk. "Um visualizador para uma extensão de CGAL ao plano projetivo orientado." [s.n.], 2005. http://repositorio.unicamp.br/jspui/handle/REPOSIP/276388.

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Orientadores: Pedro Jussieu de Rezende
Dissertação (mestrado) - Universidade Estadual de Campinas, Instituto de Computação
Made available in DSpace on 2018-08-04T08:54:01Z (GMT). No. of bitstreams: 1 Selmi-Dei_FabioPakk_M.pdf: 2287860 bytes, checksum: 97e2fc68f82f1ee33b0e737ed3b9f831 (MD5) Previous issue date: 2005
Resumo: Visualizadores são softwares capazes de gerar, através de recursos gráficos computacionais, figuras geométricas a partir de estruturas de dados e seus estados. Suas imagens facilitam a compreensão e depuração de algoritmos, bem como aumentam a intuição do usuário sobre os objetos geométricos e o espaço que os abriga. O presente trabalho descreve o projeto e a criação de um visualizador geométrico para uma extensão de CGAL ao plano projetivo orientado ('T POT 2'). CGAL é uma biblioteca de algoritmos geométricos e estruturas de dados desenvolvida por um consórcio de universidades com o objetivo de ser uma ferramenta de fácil acesso usada no desenvolvimento de aplicações que necessitem resolver problemas geométricos em 'R POT 2'. Através do trabalho [dO04], esta biblioteca foi estendida para incorporar 'T POT 2', preservando sua robustez, corretude e confiabilidade. O plano projetivo orientado é um espaço geométrico estritamente maior que o plano cartesiano 'R POT 2', porém com geometria semelhante. Uma das principais características de 'T POT 2' é o uso de coordenadas homogêneas sinaladas, o que permite lidar com o conceito de pontos no infinito de maneira homogênea ao tratamento dos pontos do plano, possibilitando o projeto de algoritmos geométricos que não mais precisam tratar separadamente muitos casos particulares, tornando-os mais simples e sucintos. Neste contexto, o visualizador aqui descrito tem por finalidade a criação de um ambiente de visualização que permite a observação das características intrínsecas à geometria projetiva orientada, o que é de grande benefício para o usuário-programador da extensão de CGAL para 'T POT 2'
Abstract: A graphical viewer is a software that enables the display of geometric figures from data structures and their varying states. The images it provides improve comprehension, make debugging easier and raise the users' intuition regarding geometric objects and their embedding space. The present work describes the design and creation of a geometrical viewer for an oriented projective plane ('T POT 2') extension of CGAL. CGAL is a library of geometric algorithms and data structures developed by a consortium of universities with the goal of producing an easy-to-use tool for building applications that require problem solving in 'R POT 2'. In [dO04], Oliveira describes an extension of this library that incorporates 'T POT 2' into CGAL, while adhering to its robustness, correctness and reliability. The oriented projective plane is a geometric space strictly larger than the Cartesian plane R2, though with similar geometry. One of the main features of 'T POT 2' is the use of signed homogeneous coordinates, which enables one to work with points at infinity in a way similar to working with proper points on the plane, allowing for the design of algorithms that no longer need to handle many particular cases, making them simpler and shorter. In this context, the viewer described here has the purpose of providing a visualization system that allows for the perception of the intrinsic characteristics of the oriented projective geometry, which is of great benefit to programmers of the extension of CGAL to 'T POT 2'
Mestrado
Geometria Computacional
Mestre em Ciência da Computação

8

Lundqvist, Samuel. "Computational algorithms for algebras." Doctoral thesis, Stockholm : Department of Mathematics, Stockholm University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-31552.

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Diss. (sammanfattning) Stockholm : Stockholms universitet, 2009.
At the time of doctoral defence, the following papers were unpublished and had a status as follows: Paper 3: Manuscript. Paper 4: Manuscript. Paper 5: Manuscript. Paper 6: Manuscript. Härtill 6 uppsatser.

9

Murri, Riccardo. "Computational techniques in graph homology of the moduli space of curves." Doctoral thesis, Scuola Normale Superiore, 2013. http://hdl.handle.net/11384/85723.

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The object of this thesis is the automated computation of the rational (co)homology of the moduli spaces of smooth marked Riemann surfaces Mg;n. This is achieved by using a computer to generate a chain complex, known in advance to have the same homology as Mg;n, and explicitly spell out the boundary operators in matrix form. As an application, we compute the Betti numbers of some moduli spaces Mg;n. Our original contribution is twofold. In Chapter 3, we develop algorithms for the enumeration of fatgraphs and their automorphisms, and the computation of the homology of the chain complex formed by fatgraphs of a given genus g and number of boundary components n. In Chapter 4, we describe a new practical parallel algorithm for performing Gaussian elimination on arbitrary matrices with exact computations: projections indicate that the size of the matrices involved in the Betti number computation can easily exceed the computational power of a single computer, so it is necessary to distribute the work over several processing units. Experimental results prove that our algorithm is in practice faster than freely available exact linear algebra codes. An effective implementation of the fatgraph algorithms presented here is available at http://code.google.com/p/fatghol. It has so far been used to compute the Betti numbers of Mg;n for (2g + n) 6 6. The Gaussian elimination code is likewise publicly available as open-source software from http://code.google.com/p/rheinfall.

10

Scibilia, Francesco. "Explicit Model Predictive Control:Solutions Via Computational Geometry." Doctoral thesis, Norges teknisk-naturvitenskapelige universitet, Institutt for teknisk kybernetikk, 2010. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-11627.

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The thesis is mainly focused on issues involved with explicit model predictive control approaches. Conventional model predictive control (MPC) implementation requires at each sampling time the solution of an open-loop optimal control problem with the current state as the initial condition of the optimization. Formulating the MPC problem as a multi-parametric programming problem, the online optimization effort can be moved offline and the optimal control law given as an explicitly defined piecewise affine (PWA) function with dependence on the current state. The domain where the PWA function is defined corresponds to the feasible set which is partitioned into convex regions. This makes explicit MPC solutions into promising approaches to extend the scope of applicability of MPC schemes. The online computation reduces to simple evaluations of a PWA function, allowing implementations on simple hardware and with fast sampling rates. Furthermore, the closed form of the MPC solutions allows offline analysis of the performance, providing additional insight of the controller behavior. However, explicit MPC implementations may still be prohibitively costly for large optimization problems. The offline computational effort needed to solve the multiparametric optimization problem may be discouraging, and even the online computation needed to evaluate a complex PWA controller may cause difficulties if low-cost hardware is used. The first contribution of this thesis is to propose a technique for computing approximate explicit MPC solutions for the cases where optimal explicit MPC solutions are impractical due to the offline computational effort needed and their complexity for online evaluations. This technique is based on computational geometry, a branch of computer science which focuses heavily on computational complexity since the algorithms are intended to be used on large data-sets. The approximate solution is suboptimal only over the subregion of the feasible set where constraints are active. In this subregion, the ineffective optimal explicit MPC solution is replaced by an approximation based on Delaunay tessellations and is computed from a finite number of samples of the exact solution. Finer tessellations can be obtained in order to achieve a desired level of accuracy Successively, the thesis presents a twofold contribution concerned with the computation of feasible sets for MPC and their suitable approximations. First, an alternative approach is suggested for computing the feasible set which uses set relations instead of the conventional orthogonal projection. The approach can be implemented incrementally on the length of the MPC prediction horizon, and proves to be computationally less demanding than the standard approach. Thereafter, an algorithm for computing suitable inner approximations of the feasible set is proposed, which constitutes the main contribution. Such approximations are characterized by simpler representations and preserve the essential properties of the feasible set as convexity, positive invariance, inclusion of the set of expected initial states. This contribution is particularly important in the context of finding less complex suboptimal explicit MPC solutions, where the complexity of the feasible set plays a decisive role. The last part of the thesis is concerned with robustness of nominal explicit MPC solutions to model uncertainty. In the presence of model mismatch, when the controller designed using the nominal model is applied to the real plant, the feasible set may lose its invariance property, and this means violation of constraints. Also, since the PWA control law is designed only over the feasible set, there is the technical problem that the control action is undefined if the state moves outside of this set. To deal with this issue, a tool is proposed to analyze how uncertainty on the model affects the PWA control law computed using the nominal model. Given the linear system describing the plant and the PWA control law, the algorithm presented considers the polytopic model uncertainty and constructs the maximal robust feasible set, i.e. the largest subset of the feasible set which is guaranteed to be feasible for any model in the family of models described by the polytopic uncertainty. The appendix of the thesis contains two additional contributions which are only marginally related to the main theme of the thesis. MPC approaches are often implemented as state feedback controllers. The state variables are not always measured, and in these cases a state estimation approach has to be adopted to obtain the state from the measurements. The two contributions deal with state estimation in two different applications, but not with the explicit goal of being used in MPC approaches.

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Книги з теми "Computationnal geometry":

1

Lin, Ming C., and Dinesh Manocha, eds. Applied Computational Geometry Towards Geometric Engineering. Berlin, Heidelberg: Springer Berlin Heidelberg, 1996. http://dx.doi.org/10.1007/bfb0014474.

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2

Márquez, Alberto, Pedro Ramos, and Jorge Urrutia, eds. Computational Geometry. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-34191-5.

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3

de Berg, Mark, Marc van Kreveld, Mark Overmars, and Otfried Cheong Schwarzkopf. Computational Geometry. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04245-8.

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4

de Berg, Mark, Otfried Cheong, Marc van Kreveld, and Mark Overmars. Computational Geometry. Berlin, Heidelberg: Springer Berlin Heidelberg, 2008. http://dx.doi.org/10.1007/978-3-540-77974-2.

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5

Preparata, Franco P., and Michael Ian Shamos. Computational Geometry. New York, NY: Springer New York, 1985. http://dx.doi.org/10.1007/978-1-4612-1098-6.

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6

de Berg, Mark, Marc van Kreveld, Mark Overmars, and Otfried Schwarzkopf. Computational Geometry. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03427-9.

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7

Pawar, Akhilesh. Computational Geometry. New Delhi, India: Campus Books International, 2011.

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8

1944-, Toussaint Godfried T., ed. Computational geometry. New York: IEEE, 1992.

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9

1944-, Toussaint Godfried T., ed. Computational geometry. Amsterdam: North-Holland, 1985.

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10

Bokowski, Jürgen. Computational synthetic geometry. Berlin: Springer-Verlag, 1989.

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Частини книг з теми "Computationnal geometry":

1

Edelsbrunner, Herbert. "Geometric structures in computational geometry." In Automata, Languages and Programming, 201–13. Berlin, Heidelberg: Springer Berlin Heidelberg, 1988. http://dx.doi.org/10.1007/3-540-19488-6_117.

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2

Beichl, Isabel M., Javier Bernal, Christoph Witzgall, and Francis Sullivan. "Computational Geometry." In Encyclopedia of Operations Research and Management Science, 241–46. Boston, MA: Springer US, 2013. http://dx.doi.org/10.1007/978-1-4419-1153-7_142.

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3

de Berg, Mark, Marc van Kreveld, Mark Overmars, and Otfried Cheong Schwarzkopf. "Computational Geometry." In Computational Geometry, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-04245-8_1.

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4

Skiena, Steven S. "Computational Geometry." In Texts in Computer Science, 621–76. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-54256-6_20.

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5

Komzsik, Louis. "Computational geometry." In Applied Calculus of Variations for Engineers, 155–73. Third edition. | Boca Raton, FL : CRC Press/Taylor and Francis, [2020]: CRC Press, 2019. http://dx.doi.org/10.1201/9781003009740-9.

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6

Forišek, Michal, and Monika Steinová. "Computational Geometry." In Explaining Algorithms Using Metaphors, 31–57. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-5019-0_3.

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7

Skiena, Steven S. "Computational Geometry." In The Algorithm Design Manual, 562–619. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-84800-070-4_17.

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8

de Berg, Mark, Marc van Kreveld, Mark Overmars, and Otfried Schwarzkopf. "Computational Geometry." In Computational Geometry, 1–17. Berlin, Heidelberg: Springer Berlin Heidelberg, 1997. http://dx.doi.org/10.1007/978-3-662-03427-9_1.

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9

Wagon, Stan. "Computational Geometry." In Mathematica in Action, 399–422. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-0-387-75477-2_16.

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10

Wagon, Stan. "Computational Geometry." In Mathematica® in Action, 485–506. New York, NY: Springer New York, 1999. http://dx.doi.org/10.1007/978-1-4612-1454-0_24.

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Тези доповідей конференцій з теми "Computationnal geometry":

1

Chazelle, Bernard. "Computational geometry." In the twenty-sixth annual ACM symposium. New York, New York, USA: ACM Press, 1994. http://dx.doi.org/10.1145/195058.195110.

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2

Conte, A., V. Demichelis, F. Fontanella, and I. Galligani. "Computational Geometry." In Workshop. WORLD SCIENTIFIC, 1993. http://dx.doi.org/10.1142/9789814536370.

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3

Castelli, Mauro, Luca Manzoni, Ivo Gonçalves, Leonardo Vanneschi, Leonardo Trujillo, and Sara Silva. "An Analysis of Geometric Semantic Crossover: A Computational Geometry Approach." In 8th International Conference on Evolutionary Computation Theory and Applications. SCITEPRESS - Science and Technology Publications, 2016. http://dx.doi.org/10.5220/0006056402010208.

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4

Aggarwal, Alok, Bernard Chazelle, Leo Guibas, Colm O'Dunlaing, and Chee Yap. "Parallel computational geometry." In 26th Annual Symposium on Foundations of Computer Science (sfcs 1985). IEEE, 1985. http://dx.doi.org/10.1109/sfcs.1985.42.

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5

Karasik, Y. B., and M. Sharir. "Optical computational geometry." In the eighth annual symposium. New York, New York, USA: ACM Press, 1992. http://dx.doi.org/10.1145/142675.142723.

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6

Lanzagorta, Marco, and Jeffrey K. Uhlmann. "Quantum computational geometry." In Defense and Security, edited by Eric Donkor, Andrew R. Pirich, and Howard E. Brandt. SPIE, 2004. http://dx.doi.org/10.1117/12.541624.

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7

Chan, Timothy. "Computational Geometry for Non-Geometers: Recent Developments on Some Classical Problems." In Proceedings of the Twenty-Second Annual ACM-SIAM Symposium on Discrete Algorithms. Philadelphia, PA: Society for Industrial and Applied Mathematics, 2011. http://dx.doi.org/10.1137/1.9781611973082.110.

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8

Karasik, Y. B., and M. Sharir. "The power of geometric duality and Minkowski sums in optical computational geometry." In the ninth annual symposium. New York, New York, USA: ACM Press, 1993. http://dx.doi.org/10.1145/160985.161168.

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9

Alliez, Pierre, and Andreas Fabri. "Computational geometry algorithms library." In ACM SIGGRAPH ASIA 2009 Courses. New York, New York, USA: ACM Press, 2009. http://dx.doi.org/10.1145/1665817.1665821.

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Alliez, Pierre, Andreas Fabri, and Efi Fogel. "Computational geometry algorithms library." In ACM SIGGRAPH 2008 classes. New York, New York, USA: ACM Press, 2008. http://dx.doi.org/10.1145/1401132.1401160.

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Звіти організацій з теми "Computationnal geometry":

1

Hansen, Mark D. Results in Computational Geometry: Geometric Embeddings and Query- Retrieval Problems. Fort Belvoir, VA: Defense Technical Information Center, November 1990. http://dx.doi.org/10.21236/ada230380.

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2

Zolnowsky, J. Topics in Computational Geometry. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1453953.

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3

Michalski, A,, D. Andersson, R. Rossi, and C. Soriano. D7.1 DELIVERY OF GEOMETRY AND COMPUTATIONAL MODEL. Scipedia, 2021. http://dx.doi.org/10.23967/exaqute.2021.2.020.

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Анотація:

This document describes the industrial application, on which the developments of the project are implemented, and the CFD set-up. The developments are implemented over six analysis cases with increasing complexity starting from a 2D geometry with mean wind inflow to a 3D geometry with turbulent inflow and real-time shape optimization. The application represents the CAARC tall building model, which has served as a benchmark model for many studies since the 1970’s when it was first developed. Base moments (bending and torsional moments) of the building are extracted for validation by comparison of the results with the benchmark study. Page 3 of 19 Deliverable 7.1

4

Thompson, David C., Joseph Maurice Rojas, and Philippe Pierre Pebay. Computational algebraic geometry for statistical modeling FY09Q2 progress. Office of Scientific and Technical Information (OSTI), March 2009. http://dx.doi.org/10.2172/984161.

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5

Kipnis, Shlomo. Three Methods for Range Queries in Computational Geometry. Fort Belvoir, VA: Defense Technical Information Center, March 1989. http://dx.doi.org/10.21236/ada210830.

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6

Dobkin, David. AASERT: Software Tools for Experimentation in Computational Geometry. Fort Belvoir, VA: Defense Technical Information Center, February 2001. http://dx.doi.org/10.21236/ada391643.

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7

Magnuson, Alan, Christopher Deschenes, and Ali Merchant. Automated Preparation of Geometry for Computational Applications Final Report. Fort Belvoir, VA: Defense Technical Information Center, January 2011. http://dx.doi.org/10.21236/ada542742.

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8

Stiller, Peter. Algebraic Geometry and Computational Algebraic Geometry for Image Database Indexing, Image Recognition, And Computer Vision. Fort Belvoir, VA: Defense Technical Information Center, October 1999. http://dx.doi.org/10.21236/ada384588.

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9

Desbrun, Mathieu, and Marin Kobilarov. Geometric Computational Mechanics and Optimal Control. Fort Belvoir, VA: Defense Technical Information Center, December 2011. http://dx.doi.org/10.21236/ada564028.

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10

Salari, K., and M. McWherter-Payne. Computational Flow Modeling of a Simplified Integrated Tractor-Trailer Geometry. Office of Scientific and Technical Information (OSTI), September 2003. http://dx.doi.org/10.2172/15006457.

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