9783843355193 - computationally intelligent cfd: solving potential, viscous and non-newtonian fluid flow problems using real-coded genetic algorithms de bourisli, raed (5 resultados)
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Taschenbuch. Condición: Neu. Computationally Intelligent CFD | Solving Potential, Viscous and Non-Newtonian Fluid Flow Problems Using Real-Coded Genetic Algorithms | Raed Bourisli | Taschenbuch | 224 S. | Englisch | 2010 | LAP LAMBERT Academic Publishing | EAN 9783843355193 | Verantwortliche Person für die EU: BoD - Books on Dem…and, In de Tarpen 42, 22848 Norderstedt, info[at]bod[dot]de | Anbieter: preigu.
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Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, , AlemaniaBuchWeltWeit Ludwig Meier e.K.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -In this work, the concept of evolutionary search is utilized as a versatile CFD solver. Specifically, a real-coded genetic algorithm, mimicking the natural evolution process, is used to minimize the residuals resulting from a finite… difference discretization. While most gradient-based methods can suffer from divergence or slow convergence, the evolutionary solver's heuristic nature allows it to avoid solving the resulting systems of equations, thereby precluding many convergence difficulties and avoiding stiffness-related problems. Furthermore, these stochastic optimization techniques work around many stability issues in computational fluid dynamics. A number of new, unitary as well as binary, GA operators are proposed, explained and tested, along with the more traditional crossover and mutation operators. Also, new GA-customized refinement strategy and a GA-window approach are proposed which helps reduce time requirements. The GA is used to successfully solve problems involving a potential flow, a viscous flow via the Navier-Stokes equations, and a power-law non-Newtonian flow. The GA-solver is shown to be able to solve problems that the gradient-based method could not. 224 pp. Englisch.
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Taschenbuch. Condición: Neu. This item is printed on demand - Print on Demand Titel. Neuware -In this work, the concept of evolutionary search is utilized as a versatile CFD solver. Specifically, a real-coded genetic algorithm, mimicking the natural evolution process, is used to minimize the residuals resulting from a finite dif…ference discretization. While most gradient-based methods can suffer from divergence or slow convergence, the evolutionary solver''s heuristic nature allows it to avoid solving the resulting systems of equations, thereby precluding many convergence difficulties and avoiding stiffness-related problems. Furthermore, these stochastic optimization techniques work around many stability issues in computational fluid dynamics. A number of new, unitary as well as binary, GA operators are proposed, explained and tested, along with the more traditional crossover and mutation operators. Also, new GA-customized refinement strategy and a GA-window approach are proposed which helps reduce time requirements. The GA is used to successfully solve problems involving a potential flow, a viscous flow via the Navier-Stokes equations, and a power-law non-Newtonian flow. The GA-solver is shown to be able to solve problems that the gradient-based method could not.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 224 pp. Englisch.
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Librería: AHA-BUCH GmbH, Einbeck, AlemaniaAHA-BUCH GmbH
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Taschenbuch. Condición: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - In this work, the concept of evolutionary search is utilized as a versatile CFD solver. Specifically, a real-coded genetic algorithm, mimicking the natural evolution process, is used to minimize the residuals resulting from a finite diff…erence discretization. While most gradient-based methods can suffer from divergence or slow convergence, the evolutionary solver's heuristic nature allows it to avoid solving the resulting systems of equations, thereby precluding many convergence difficulties and avoiding stiffness-related problems. Furthermore, these stochastic optimization techniques work around many stability issues in computational fluid dynamics. A number of new, unitary as well as binary, GA operators are proposed, explained and tested, along with the more traditional crossover and mutation operators. Also, new GA-customized refinement strategy and a GA-window approach are proposed which helps reduce time requirements. The GA is used to successfully solve problems involving a potential flow, a viscous flow via the Navier-Stokes equations, and a power-law non-Newtonian flow. The GA-solver is shown to be able to solve problems that the gradient-based method could not.
