Optimal placement of active/passive members in truss structures using simulated annealing

AIAA Journal ◽  
1991 ◽  
Vol 29 (8) ◽  
pp. 1327-1334 ◽  
Author(s):  
Gun-Shing Chen ◽  
Robin J. Bruno ◽  
Moktar Salama
Keyword(s):  
Simulated Annealing ◽  
Optimal Placement ◽  
Truss Structures

An Improved Shape Annealing Method for Truss Topology Generation

1994 ◽  
Author(s):  
Giridhar Reddy ◽  
Jonathan Cagan
Keyword(s):  
Simulated Annealing ◽  
Optimization Method ◽  
Geometric Constraints ◽  
Truss Structures ◽  
Model Design ◽  
Topology Generation ◽  
Truss Topology ◽  
Gradient Based ◽  
Annealing Algorithm ◽  
Annealing Method

Abstract A method for the design of truss structures which encourages lateral exploration, pushes away from violated spaces, models design intentions, and produces solutions with a wide variety of characteristics is introduced. An improved shape annealing algorithm for truss topology generation and optimization, based on the techniques of shape grammars and simulated annealing, implements the method. The algorithm features a shape grammar to model design intentions, an ability to incorporate geometric constraints to avoid obstacles, and a shape optimization method using only simulated annealing with more consistent convergence characteristics; no traditional gradient-based techniques are employed. The improved algorithm is illustrated on various structural examples generating a variety of solutions based on a simple grammar.

Optimal Placement of Controls for Truss Structures in Space

2012 ◽  
Vol 27 (5) ◽  
pp. 51-56 ◽  
Author(s):  
Dong-Xu Li ◽  
Wang Liu ◽  
Jian-Ping Jiang
Keyword(s):  
Optimal Placement ◽  
Truss Structures

Optimal design of truss structures by rescaled simulated annealing

2004 ◽  
Vol 18 (9) ◽  
pp. 1512-1518 ◽  
Author(s):  
Jungsun Park ◽  
Miran Ryu
Keyword(s):  
Simulated Annealing ◽  
Optimal Design ◽  
Truss Structures

Optimal placement of measurement points on large aircraft fuselage panels in digital assembly

2016 ◽  
Vol 231 (1) ◽  
pp. 73-84 ◽  
Author(s):  
Yunbo Bi ◽  
Weimiao Yan ◽  
Yinglin Ke
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Estimation Error ◽  
Optimal Placement ◽  
Measurement Point ◽  
Aircraft Fuselage ◽  
Simulated Annealing Genetic Algorithm ◽  
Adaptive Simulated Annealing ◽  
Assembly Error ◽  
Large Aircraft

A large aircraft fuselage panel is commonly composed of a variety of thin-walled components. Most of these components are large, thin and compliant, and they are also prone to some flexible deformation during assembly and remain deformed after assembly. Besides, many different fabrication and assembly manners are adopted in order to guarantee the complicated assembly relationships between each component. The above characteristics often cause large aircraft fuselage panels to exhibit low stiffness and weak strength, thereby inducing deformation during assembly. Since the posture of a large aircraft fuselage panel is commonly evaluated by matching the theoretical and actual positions of the measurement points placed on it, and its assembly deformation is also represented by the position errors of the measurement points, a reasonable measurement point placement is significant for the large aircraft fuselage panel in digital assembly. This article presents a method based on the D-optimality method and the adaptive simulated annealing genetic algorithm to optimize the placement of the measurement points which can cover more deformation information of the panel for effective assembly error diagnosis. By taking the principle of the D-optimality method, an optimal set of measurement points is selected from a larger candidate set through adaptive simulated annealing genetic algorithm. As illustrated by an example, the final measurement point configuration is more effective to maximize the determinant of the corresponding Fisher Information Matrix and minimize the estimation error of the assembly deformation than those obtained by other methods.

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