Optimal Design of a Class of Welded Structures Based on Design for Latitude

1985 ◽  
Vol 107 (4) ◽  
pp. 482-487 ◽  
Author(s):  
E. Sandgren ◽  
G. Gim ◽  
K. M. Ragsdell
Keyword(s):  
Optimal Design ◽  
Objective Function ◽  
Gradient Method ◽  
Low Cost ◽  
Optimal Solution ◽  
Beam Structures ◽  
Welded Structures ◽  
Reduced Gradient Method ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

The minimization of the sensitivity of a design to variations in uncontrollable parameters is illustrated. The procedure is applied to the design of a class of welded beam structures which results in a low-cost design with minimal sensitivities. Dominant constraints are chosen which contain variations of the uncontrollable parameters. A dual objective function is formed and tradeoff curves are presented from which the optimal solution is selected. The minimization is carried out using the generalized reduced gradient method and other applications are presented.

The Generalized Reduced Gradient Method: A Reliable Tool for Optimal Design

1977 ◽  
Vol 99 (2) ◽  
pp. 394-400 ◽  
Author(s):  
G. A. Gabriele ◽  
K. M. Ragsdell
Keyword(s):  
Optimal Design ◽  
Gradient Method ◽  
Theoretical Development ◽  
Test Problems ◽  
Basic Algorithm ◽  
Reduced Gradient Method ◽  
Fortran Code ◽  
Reduced Gradient ◽  
Efficient Code ◽  
Generalized Reduced Gradient

This paper is a presentation of a method, called the Generalized Reduced Gradient Method, which has not received wide attention in the engineering design literature. Included is a theoretical development of the method, a description of the basic algorithm, and additional recommendations to produce an efficient code. A Fortran code employing this theory was written and tested on the Eason and Fenton [1] test problems, illustrating the method to be efficient and reliable.

Multi-objective traction optimization of vehicles in loose dry sand using the generalized reduced gradient method

2016 ◽  
Vol 64 ◽  
pp. 46-57 ◽  
Author(s):  
Joe D. Robinson ◽  
Farshid Vahedifard ◽  
Masoud Rais-Rohani ◽  
George L. Mason ◽  
Jody D. Priddy
Keyword(s):  
Gradient Method ◽  
Multi Objective ◽  
Reduced Gradient Method ◽  
Reduced Gradient ◽  
Dry Sand ◽  
Generalized Reduced Gradient

Optimal Design of a Mechanism Used for Opening and Shutting a Ship’s Hatch Cover

1984 ◽  
Vol 106 (4) ◽  
pp. 503-509
Author(s):  
Koichi Ito ◽  
Tadashi Kuroiwa ◽  
Shinsuke Akagi
Keyword(s):  
Optimal Design ◽  
Nonlinear Optimization ◽  
Optimization Problem ◽  
Numerical Study ◽  
Optimal Solution ◽  
Optimization Method ◽  
Gradient Algorithm ◽  
Linkage Mechanism ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

A nonlinear optimization method is proposed to design a linkage mechanism used for opening and shutting a ship’s hatch cover. Considering the maximum force of the oil cylinder necessary to move the hatch cover as the objective function to be minimized, the design problem to determine the optimal configuration of linkage mechanism is formulated as a nonlinear optimization problem of minimax type. It it shown that the optimal solution can be derived by adopting the generalized reduced gradient algorithm together with a linkage statical simulation model, and the effectiveness of the algorithm is ascertained through a numerical study.

Functional Dependence and the Method of Alternate Formulations in Optimal Design

1992 ◽  
Vol 114 (4) ◽  
pp. 596-602
Author(s):  
C. R. Hammond ◽  
G. E. Johnson
Keyword(s):  
Optimal Design ◽  
Functional Dependence ◽  
Problem Formulation ◽  
System Model ◽  
Decision Variable ◽  
Reduced Gradient Method ◽  
State Variable ◽  
Mathematical Properties ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

In an earlier article in this journal we introduced the Method of Alternate Formulations (MAF). MAF is a nonnumerical approach to constrained optimal design implemented with symbolic mathematics. The MAF problem formulation is the same as is used by the generalized reduced gradient method. There are usually many ways to partition the design vector into decision variable and state variable components and so there are usually many different alternate formulations for the objective function and constraints. Each alternate formulation contains all of the information about the physical system. Yet all other mathematical properties (e.g., convexity, linearity, scaling, etc.) can change. It has been observed that some of the alternate formulations that should exist based strictly on the theory of combinations cannot be obtained. In this paper, we show that this phenomenon occurs whenever there is functional dependence in the system model. Several examples are used to show how functional dependence affects the search for the solution by MAF. Prediction of functional dependence at the outset informs the designer which formulations cannot exist. This allows the designer to concentrate effort (more productively) on other formulations of the problem.

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