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Limit and shakedown theorems are exact theories of classical plasticity for the direct computation of safety factors or of the load carrying capacity under constant and varying loads. Simple versions of limit and shakedown analysis are the basis of all design codes for pressure vessels and pipings. Using Finite Element Methods more realistic modeling can be used for a more rational design. The methods can be extended to yield optimum plastic design. In this paper we present a first implementation in FE of limit and shakedown analyses for perfectly plastic material. Limit and shakedown analyses are done of a pipe–junction and a interaction diagram is calculated. The results are in good correspondence with the analytic solution we give in the appendix.
Praezise Streuparametermessungen sind der Schluessel zur Modellierung elektrischer Schaltungen
(1997)
The Lehrstuhl für Wärmeübertragung und Klimatechnik at the Aachen University of Technology operates several test facilities to investigate fundamentals of heat and mass transfer supported by the Deutsche Forschungsgemeinschaft, the Minister für Forschung und Technologie and the industry. In order to get high-resolution and reproselection of hardware components is as critical as the design of software routines to perform successful and accurate measurements. In the following the development of the measurement and control system for three different test facilities is presented and discussed. Special attention is given to the education of students within the framework of laboratories and scientific experiments.
This paper addresses the pixel based recognition of 3D objects with bidirectional associative memories. Computational power and memory requirements for this approach are identified and compared to the performance of current computer architectures by benchmarking different processors. It is shown, that the performance of special purpose hardware, like neurocomputers, is between one and two orders of magnitude higher than the performance of mainstream hardware. On the other hand, the calculation of small neural networks is performed more efficiently on mainstream processors. Based on these results a novel concept is developed, which is tailored for the efficient calculation of bidirectional associative memories. The computational efficiency is further enhanced by the application of algorithms and storage techniques which are matched to characteristics of the application at hand.