![]() ![]() Original Assignee Massachusetts Institute Of Technology Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.) ( en Inventor Thomas Sebastian Christopher Strem Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.) Granted Application number US15/803,961 Other versions US10836466B2 The CPU time cost can be saved by up to 90%, especially for the simulated system with a large number of elements and nodes.- Google Patents US20190135410A1 - Toroidal PropellerÄownload PDF Info Publication number US20190135410A1 US20190135410A1 US15/803,961 US201715803961A US2019135410A1 US 20190135410 A1 US20190135410 A1 US 20190135410A1 US 201715803961 A US201715803961 A US 201715803961A US 2019135410 A1 US2019135410 A1 US 2019135410A1 Authority US United States Prior art keywords propeller toroidal propellers elements design Prior art date Legal status (The legal status is an assumption and is not a legal conclusion. However, the CPU time cost does not increase very much with the increase of the level of h-adaptive mesh refinement. The overall mesh refinement causes the CPU time cost to greatly increase as the mesh is refined. Both methods can improve the computational accuracy with the refinement of mesh. The overall mesh refinement and the h-adaptive mesh refinement were justified with respect to the computational accuracy and the CPU time cost. The test cases show that the adaptive mesh is refined only in the localization region where the feature gradient is relatively high. The program was developed with h-type adaptive mesh refinement, and it was tested for several classical cases involving heat transfer, fluid mechanics and structural mechanics. Fortran 2003 was used in the development of this finite element program due to its advantages for scientific and engineering programming and its new object-oriented features. The framework is divided into several basic sets of classes that enable the code segments to be built according to the type of problem to be solved. We exemplarily demonstrate this process and its effectiveness using different approaches and systematically show how one should exploit some given properties of a PDE problem to arrive at an efficient compound discretisation.ĪBSTRACT In this paper, an object-oriented framework for numerical analysis of multi-physics applications is presented. This method not only helps to identify and assemble suitable schemes, but enables the unique combination of multiple methods on a per field basis. We introduce a fixed set of input parameters, amongst them for example the governing equations and the hard- ware configuration. We intro- duce some well defined restrictions to systematically guide an application expert through the process of classifying a given multiphysics problem, identifying suit- able numerical schemes and implementing them. Here, we propose a unifying scheme for grid based approximation methods to address this issue. Bespoke high performance schemes in particular pose a substantial hurdle for domain sci- entists regarding their theory and implementation. The resulting landscape of choices regarding numerical schemes for a given system of PDEs can thus easily appear daunting for an application expert that is famil- iar with the relevant physics, but not necessarily with the numerics. Especially within the last decades, there has been a consider- able push toward high performance numerical schemes to solve partial differential equations (PDEs) from the applied mathematics and numerics community. The accurate representation and prediction of physical phenomena through numerical computer codes remains to be a vast and intricate interdisciplinary topic of research. ![]()
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