Optimal Design of Traction Drive Continuously Variable Transmissions

An optimal design technique for minimum power loss in traction drive continuously variable transmissions is developed. The general forms of the objective function and constraint equations are derived, and the formulated optimal design problems are implemented in a nonlinear programming algorithm. Kinematic analysis and optimal design problem formulation are performed for a selected traction drive configuration as an example of the procedures.

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An Adaptive Sequential Linear Programming Algorithm for Optimal Design Problems With Probabilistic Constraints

Volume 2: 31st Design Automation Conference, Parts A and B ◽

10.1115/detc2005-84489 ◽

2005 ◽

Cited By ~ 2

Author(s):

Kuei-Yuan Chan ◽

Steven J. Skerlos ◽

Panos Y. Papalambros

Keyword(s):

Linear Programming ◽

Optimal Design ◽

Probabilistic Constraints ◽

Programming Algorithm ◽

Sequential Linear Programming ◽

Design Problems ◽

Reliability Based Design ◽

Balance Accuracy ◽

New Strategy ◽

Solution Methods

Optimal design problems with probabilistic constraints, often referred to as Reliability-Based Design Optimization (RBDO) problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using both first order and second order approximations, and applies a filter-based SLP algorithm to reach the optimum. Simple numerical examples show promise for increased accuracy without sacrificing efficiency.

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An Adaptive Sequential Linear Programming Algorithm for Optimal Design Problems With Probabilistic Constraints

Journal of Mechanical Design ◽

10.1115/1.2337312 ◽

2006 ◽

Vol 129 (2) ◽

pp. 140-149 ◽

Cited By ~ 30

Author(s):

Kuei-Yuan Chan ◽

Steven J. Skerlos ◽

Panos Papalambros

Keyword(s):

Linear Programming ◽

Optimal Design ◽

Optimization Problems ◽

Probabilistic Constraints ◽

Programming Algorithm ◽

Sequential Linear Programming ◽

Design Problems ◽

Reliability Based Design ◽

New Strategy ◽

Solution Methods

Optimal design problems with probabilistic constraints, often referred to as reliability-based design optimization problems, have been the subject of extensive recent studies. Solution methods to date have focused more on improving efficiency rather than accuracy and the global convergence behavior of the solution. A new strategy utilizing an adaptive sequential linear programming (SLP) algorithm is proposed as a promising approach to balance accuracy, efficiency, and convergence. The strategy transforms the nonlinear probabilistic constraints into equivalent deterministic ones using both first order and second order approximations, and applies a filter-based SLP algorithm to reach the optimum. Simple numerical examples show promise for increased accuracy without sacrificing efficiency.

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Process Optimal Design in Forging by Genetic Algorithm

Journal of Manufacturing Science and Engineering ◽

10.1115/1.1406954 ◽

2002 ◽

Vol 124 (2) ◽

pp. 397-408 ◽

Cited By ~ 22

Author(s):

J. S. Chung ◽

S. M. Hwang

Keyword(s):

Genetic Algorithm ◽

Optimal Design ◽

Process Design ◽

Process Model ◽

Hot Forging ◽

Objective Functions ◽

Optimal Design Problem ◽

Design Problems ◽

Design Iteration ◽

Design Variables

A genetic algorithm based approach is presented for process optimal design in forging. In this approach, the optimal design problem is formulated on the basis of the integrated thermo-mechanical finite element process model so as to cover diverse design variables and objective functions, and a genetic algorithm is adopted for conducting design iteration for optimization. The process model, the formulation for process optimal design, and the genetic algorithm are described in detail. The approach is applied to several selected process design problems in cold and hot forging.

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Relaxation for an optimal design problem with linear growth and perimeter penalization

Proceedings of the Royal Society of Edinburgh Section A Mathematics ◽

10.1017/s0308210513001479 ◽

2015 ◽

Vol 145 (2) ◽

pp. 223-268 ◽

Cited By ~ 3

Author(s):

Graça Carita ◽

Elvira Zappale

Keyword(s):

Optimal Design ◽

Integral Representation ◽

Linear Growth ◽

Functions Of Bounded Variation ◽

Optimal Design Problem ◽

Design Problems ◽

Admissible Solutions ◽

Integral Energy ◽

Perimeter Penalization ◽

Limit Energy

This paper is devoted to the relaxation and integral representation in the space of functions of bounded variation for an integral energy arising from optimal design problems. The presence of a perimeter penalization is also considered in order to avoid non-existence of admissible solutions and, in addition, this leads to an interaction in the limit energy. More general models have also been taken into account.

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APPLICATION OF IMPROVED COLLABORATIVE OPTIMIZATION METHOD TO MECHANICAL MULTI-OBJECTIVE OPTIMAL DESIGN PROBLEM

Journal of Advanced Manufacturing Systems ◽

10.1142/s021968671250014x ◽

2012 ◽

Vol 11 (02) ◽

pp. 151-157 ◽

Cited By ~ 1

Author(s):

FENGTAO WEI ◽

LI SONG ◽

YAN LI ◽

KUN SHI

Keyword(s):

Optimal Design ◽

Efficient Method ◽

Design Problem ◽

Optimization Method ◽

Collaborative Optimization ◽

Flow Chart ◽

Optimal Design Problem ◽

Design Problems ◽

Multi Objective ◽

Result Show

In order to solve the mechanical multi-objective optimal design problems, the basic idea and flow chart of collaborative optimization method are introduced in this paper. In view of the shortcomings that exist in standard collaborative optimization method, this method has been improved by applying the dynamic slack factor method. Taking a mechanical multi-objective optimal design of spring as an example, the multi-objective optimal design problem has been solved by the improved collaborative optimization method. The process and result show that the improved collaborative optimization method has higher accuracy and efficiency. This paper has provided an efficient method to solve the complicated mechanical multi-objective optimal design problems.

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