Design-oriented identification of critical times in transient response
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Engineering topics
Publications and source records attributed to Grandhi, R. V..
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Optimum structural design problems generally employ constraints which are parametric in terms of space and time variables. A parametric constraint may be replaced by equivalent critical point constraints at its local minima for optimization applications. In complex structures, accurate identification of such critical points is computationally expensive due to the cost of finite element analyses. Three techniques are described for efficiently and accurately identifying critical points for space- and time-dependent parametric constraints. An adaptive search technique and a spline interpolation technique are developed for exactly known response. A least squares spline approximation is suggested for noisy behavior. A helicopter tail-boom structure subjected to transient loading is used as an example to demonstrate the techniques described. All three techniques are shown to be computationally efficient for critical point identification and the least squares approximation also removes noise from the data. The case of multiple constraints per element is shown to be particularly suited to the use of spline techniques.
This paper is a survey of structural shape optimization with an emphasis on techniques dealing with shape optimization of the boundaries of two and three dimensional bodies. Attention is focused on the special problems of structural shape optimization which are due to a finite element model which must change during the optimization process. These problems include the requirement for sophisticated automated mesh generation techniques and careful choice of design variables. They also include special problems in obtaining sufficiently accurate sensitivity derivatives.
Two techniques are presented for reducing the computational effort in identifying the critical time points. One approach is an adaptive search technique, well suited for the case where the response is exactly known. The other technique, useful for noisy response, is based on a least-squares spline approximation of the response. The possibility of grouping several closely spaced local peaks to identify a single super peak from each group is also investigated. The computational efficiency of the techniques proposed here is illustrated by two examples.