Uncertainties In Design Variables Research Articles

A ROBUST OPTIMIZATION PROCEDURE WITH VARIATIONS ON DESIGN VARIABLES AND CONSTRAINTS

This paper describes a procedure that incorporates manufacturing and operational variances to achieve designs with robust and optimal performance. The procedure optimizes the expected value of a performance characteristic subject to a set of constraints. It uses concepts from statistical design of experiments to approximate the expected value of a performance characteristic. The procedure incorporates uncertainties in design variables and variations in constraints due to the uncertainty in design variables. This paper discusses the following three methods to incorporate variations in constraints: 1) A method using heuristics that evalutes constraints at the worst combinations of design variables, 2) A method with built-in constraint variation that models constraints using first order Taylor expansion, and 3) A method based on differentiating the K KT optimality conditions. The design of spur and helical gears with minimum transmission error serves as the target application. The key gear design research is...

Reliability-based structural optimal design using the Neumann expansion technique

The reliability-based structural optimization formulated using the advanced first order second moment (AFOSM) method contains a suboptimization process. This corresponds to obtaining the most probable failure point of the failure constraint and requires excessive computational work to solve the random state equation with random parameters in the stiffness matrix. This paper presents a numerically efficient method to reduce the computational effort by using the Neumann expansion technique to deal with the random state equation in the suboptimization process. Several examples of truss and beam structures with uncertainties in design variables and loads, including non-normal distributions, are taken. The results show the method can give accurate solutions and reduce computation time.

A parametric system study for the selection of RFP fusion reactor design point

Based on simple physics, engineering, and economic models among various design parameters of reversed-field pinch (RFP) fusion reactors, a parametric system study program has been developed giving cost of electricity (COE) for a given set of design parameters or a set of design parameters giving the minimum COE. This program has been used extensively to select design points and evaluate the sensitivity of physics, engineering, economic and operational parameters in the REPUTER RFP fusion reactor design study effort. Several key features emerged from the study in relation to high power density RFP fusion reactors with solid breeder blanket. They are: (1) selection of normal conductor for TF and PF coils to reduce the thickness of radiation shielding, (2) selection of a total first wall loading of 18 MW/m 2 considering the potential economic merit and nonlinear effects which might shorten the lifetime of the first wall, and (3) a maintenance policy of package replacement of the fusion power core, including TF coils, to significantly reduce maintenance time. The uncertainty range of COE was evaluated based on the sensitivity and uncertainty of design variables and parameters. The result warned us of the use of COE evaluated in this program for the purpose of comparison of economy with the result of other fusion system studies.

A General Method for Evaluating the Effects of Uncertainties in Design Variables on Core Thermal Performance

A general method was developed for evaluating the effects of uncertainties in design variables on the ability of a core to meet various transient and steady-state limiting thermal criteria. The performance characteristics of interest are evaluated by digital computer at carefully selected points in the parameter regions of interest, and the results are fitied by least squares to a polynomial approximation. Statistical information and/or limiting values may be readily applied to this approximation to yield a bound on the probability that the thermal design limits will not be exceeded. (auth)