易怒
发表于 2025-3-23 10:26:24
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hangdog
发表于 2025-3-23 17:54:21
Introductionlogies have been proposed (see ,,,). These new methodologies can be used for the computation of different academic and industrial designs like nozzles, airfoils and wing-body configurations with inviscid flows modeled by the (full) potential and Euler equations.
Mystic
发表于 2025-3-23 18:28:30
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Mortar
发表于 2025-3-23 23:29:54
https://doi.org/10.1007/978-3-322-86570-0Numerik; Strömung; Strömungsmechanik; aerodynamics; computer; dynamics; optimization; fluid- and aerodynami
BRAND
发表于 2025-3-24 04:00:42
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CRATE
发表于 2025-3-24 10:30:50
Stochastic Dominance and Risk Measures,logies have been proposed (see ,,,). These new methodologies can be used for the computation of different academic and industrial designs like nozzles, airfoils and wing-body configurations with inviscid flows modeled by the (full) potential and Euler equations.
obsession
发表于 2025-3-24 13:29:53
Data-Driven Low-Order Stochastic Models,f airfoils represented by cubic splines and operating at subsonic and transonic conditions. It is shown that the number of iterations has been multiplied by three between the subsonic and the transonic case. For the optimization problem, the drag objective function is penalized to take into account
MEAN
发表于 2025-3-24 16:40:54
https://doi.org/10.1007/978-1-4612-2528-7: ’design patches’, whose surface is derived from given pressures and ’analysis patches’, whose pressures are derived from given surface. To solve such ’mixed’ problems the panel code is embedded into an iteration loop, which minimizes the sum of squared pressure deviations. The geometry representat
wreathe
发表于 2025-3-24 19:40:47
Sampling Methods for Processes,d is based on full-potential theory and minimizes a cost function weighting the deviations from specified target pressure distributions for each design condition in a least squares sense. Deviations from specified target pressure distributions (residuals) are translated into airfoil geometry correct
净礼
发表于 2025-3-25 01:55:16
Realisierbarkeit von Abbildungen, an adjoint state method to an upwind discretization with differentiable flux splitting. Thus, we construct a descent method with a discrete gradient computed exactly. In order to improve the performances, a method for increasing the number of unknowns, and relying on a multilevel idea is also imple