Solid Rocket Motor Performance Matching Using Pattern Search/Particle Swarm Optimization

2011 ◽  
Author(s):  
Kevin Albarado ◽  
Roy Hartfield ◽  
B. Hurston ◽  
Rhonald Jenkins
Keyword(s):  
Particle Swarm Optimization ◽  
Motor Performance ◽  
Particle Swarm ◽  
Pattern Search ◽  
Rocket Motor ◽  
Solid Rocket Motor ◽  
Swarm Optimization ◽  
Solid Rocket

Design Optimization for Fir-Tree Root of Turbine Blade Considering Manufacturing Variations

2013 ◽  
Vol 694-697 ◽  
pp. 2733-2737
Author(s):  
Qin Zhou ◽  
Ming Hui Zhang ◽  
Hui Yong Chen ◽  
Yong Hui Xie
Keyword(s):  
Particle Swarm Optimization ◽  
Turbine Blade ◽  
Equivalent Stress ◽  
Particle Swarm ◽  
Pattern Search ◽  
Design System ◽  
Contact Method ◽  
Swarm Optimization ◽  
Maximum Equivalent Stress ◽  
Manufacturing Variation

An optimization design system for fir-tree root of turbine blade has been developed in this paper. In the system, a parametric model of the blade and rim was established based on the parametric design language APDL, and nonlinear contact method was used for analysis by ANSYS, meanwhile some optimization algorithms, such as Pattern Search Algorithm, Genetic Algorithm, Simulated Annealing Algorithm and Particle Swarm Optimization, were adopted to control the optimizing process. Five cases of manufacturing variation in contact surfaces between root and rim were taken into account, and the design objective was to minimize the maximum equivalent stress of root-rim by optimizing eight critical geometrical dimensions of the root and rim. As a result, the maximum equivalent stress of root-rim decreases markedly after the optimization in all cases. In consideration of both precision and computing time, particle swarm optimization is assessed as the best algorithm to solve structure optimization problem in this work. Corresponding to five different cases of manufacturing variation, the maximum equivalent stress of root and rim reduces by 7%, 8%; 27%, 24%; 27%, 22%; 25%, 19%; 10%, 14% using the Particle Swarm Optimization.

scholarly journals Solid Rocket Motor Design Using Hybrid Optimization

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Kevin Albarado ◽  
Roy Hartfield ◽  
Wade Hurston ◽  
Rhonald Jenkins
Keyword(s):  
Hybrid Approach ◽  
Optimal Solution ◽  
Solution Space ◽  
Axial Direction ◽  
Pattern Search ◽  
Rocket Motor ◽  
Solid Rocket Motor ◽  
Thin Sections ◽  
Solid Rocket

A particle swarm/pattern search hybrid optimizer was used to drive a solid rocket motor modeling code to an optimal solution. The solid motor code models tapered motor geometries using analytical burn back methods by slicing the grain into thin sections along the axial direction. Grains with circular perforated stars, wagon wheels, and dog bones can be considered and multiple tapered sections can be constructed. The hybrid approach to optimization is capable of exploring large areas of the solution space through particle swarming, but is also able to climb “hills” of optimality through gradient based pattern searching. A preliminary method for designing tapered internal geometry as well as tapered outer mold-line geometry is presented. A total of four optimization cases were performed. The first two case studies examines designing motors to match a given regressive-progressive-regressive burn profile. The third case study studies designing a neutrally burning right circular perforated grain (utilizing inner and external geometry tapering). The final case study studies designing a linearly regressive burning profile for right circular perforated (tapered) grains.

Numerical Evaluation of the Effects of Inclusions on Solid Rocket Motor Performance

AIAA Journal ◽  
2020 ◽  
Vol 58 (9) ◽  
pp. 4028-4036
Author(s):  
F. Ponti ◽  
S. Mini ◽  
A. Annovazzi
Keyword(s):  
Motor Performance ◽  
Numerical Evaluation ◽  
Rocket Motor ◽  
Solid Rocket Motor ◽  
Solid Rocket

A Particle Swarm Optimization Algorithm Based on the Pattern Search Method

2012 ◽  
Vol 532-533 ◽  
pp. 1664-1669 ◽  
Author(s):  
Jun Li Zhang ◽  
Da Wei Dai
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Global Optimum ◽  
Pattern Search ◽  
Local Optimum ◽  
Effective Mechanism ◽  
Swarm Optimization

For the purpose of overcoming the premature property and low execution efficiency of the Particle Swarm Optimization (PSO) algorithm, this paper presents a particle swarm optimization algorithm based on the pattern search. In this algorithm， personal and global optimum particles are chosen in every iteration by a probability. Then, local optimization will be performed by the pattern search and then the original individuals will be replaced. The strong local search function of the pattern search provides an effective mechanism for the PSO algorithm to escape from the local optimum, which avoids prematurity of the algorithm. Simulation shows that this algorithm features a stronger function of global search than conventional PSO, so that the optimization process can be improved remarkably.

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