Alternative approximation concepts for space frame synthesis

An automated structural design methodology has been devised which simultaneously considers design criteria associated with both linear elastic and crashworthiness loading conditions. This method is developed within the context of a nonlinear mathematical programming based structural optimization capability using an efficient two-phased crashworthiness analysis technique. Specially constructed nonlinear approximations for the crashworthiness constraints are employed to further reduce the computational burden during the optimization process. This methodology is demonstrated on an automobile structural design problem. It is shown that more mass efficient designs can be obtained by simultaneously considering elastic and crashworthiness design criteria as compared to a sequential approach in which the structure is first designed for the elastic loads and then modified to satisfy the crashworthiness criteria.

Section: Design Methodologymentioning

confidence: 99%

Structural optimization with crashworthiness constraints

Lust

1992

“…restated the conventional frame design problem in terms of the RSP taken as intermediate variables, eliminating the CSD during optimization and recovering them through linear approximations. Lust and Schmit (1986) compared results where the problem was stated in terms of CSD only to results where intermediate variables including the RSP were included. Their results were slightly better when RSP were used as intermediate variables.…”

Section: I~(x) ~ Fi(x) = R{ri[xi(x)] )mentioning

confidence: 99%

Approximation concepts for optimum structural design — a review

Barthélémy

Haftka

1993

485

177

This paper reviews the basic approximation concepts used in structural optimization. It also discusses some of the most recent developments in that area since the introduction of approximation concepts in the nfid-seventies. The paper distinguishes between local, medium-range and global approximations; it covers function approximations and problem approximations. It shows that, although the lack of comparative data established on reference test cases prevents an accurate assessment, there have been significant improvements. The largest number of developments have been in the areas of local function approximations and use of intermediate variable and response quantities. It appears also that some new methodologies emerge which could greatly benefit from the introduction of new computer architectures.

“…To illustrate the power of the algorithm presented here, some typical structures [for details see the works by Schmit and Miura (1976); Lust and Schmit (1985)] have been optimized. The computational results have shown that the efficiency and convergency of the method are satisfactory when compared to other well-known methods.…”

Section: O M P U T a T I V E Examplesmentioning

confidence: 99%

Two-level multipoint constraint approximation concept for structural optimization

Hai

Xia

1995

A two-level multipoint approximation concept is proposed. Based upon the values and the first-order derivatives of the critical constraint functions at the points obtained in the procedure of optimization, explicit functions approximating the primal constraint functions have been created. The nonlinearities of the approximate functions are controlled to be near those of the constraint functions in their expansion domains. Based on the principle above, the first-levd sequence of explicitly approximate problems used to solve the primarily structural optimization problem are constructed. Each of them is approximated again by the second-level sequence of approximate problems, which are formed by using the linear Taylor series expansion and then solved efficiently with dual theory. Typical numerical examples including optimum design for trusses and frames are solved to illustrate the power of the present method. The computational results show that the method is very efficient and no intermediate/generalized design variable is required to be selected. It testifies to the adaptability and generality of the method for complex problems.