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

Albarado, Kevin; Hartfield, Roy J.; Hurston, B. Wade; Jenkins, Rhonald M.

doi:10.2514/6.2011-5798

Cited by 11 publications

(5 citation statements)

References 6 publications

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“…The use of Genetic Algorithm/Pattern Search/Particle Swarm Optimization developed by Hartfield, Jenkins and Albarado [28] and the Evolving Swarm Optimizer developed by Jenkins have been used effectively to produce results. The optimization method of Hartfield, Jenkins and Albarado combines the attributes of particle swarm strategy of Kennedy and Eberhart with the direct search method technique of Hooke and Jeeves [28]. The technique has been proven to be successful in matching solid rocket motor performances.…”

Section: Pressure Distribution Optimizationmentioning

confidence: 99%

Automated Nozzle Design through Axis-Symmetric Method of Characteristics Coupled with Chemical Kinetics

Young¹

2012

48th AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

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A method of designing axis-symmetric nozzles with included finite-rate chemical kinetic has been developed. The use of the axis-symmetric method of characteristics coupled with finite-rate chemistry allows for the design and performance prediction of nozzles for liquid rocket engines. The use of a particle swarm / pattern search optimizer will enable viable pressure distributions to initiate the axis-symmetric method of characteristics. The method allows for the nozzle boundary to be formed through a full method of characteristics solution. The contour formed allows for a smooth throat, shock free nozzle. This development allows the process for designing rocket nozzles to be automated, facilitating the design of axis-symmetric nozzles to meet a specific performance goal, while simplifying the effort of the designer. This paper describes the chemical kinetic coupled axis-symmetric method of characteristics, and the associated automated nozzle design methodology.

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Section: Pressure Distribution Optimizationmentioning

confidence: 99%

Automated Nozzle Design through Axis-Symmetric Method of Characteristics Coupled with Chemical Kinetics

Young¹

2012

48th AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

Self Cite

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“…Since the 21st century, with the emergence and broad application of heuristic optimization algorithms such as the particle swarm optimization (PSO) and the genetic algorithm (GA), it has become possible to solve the reverse design practically. Albarado [4] uses a hybrid optimizer combining pattern search and particle swarm to minimize the square sum of the gap between the designed pressure curve and the given one. Yücel [5] uses the GA to optimize a finocyl grain to ensure that the error of the thrust curve is minimum.…”

Section: Introductionmentioning

confidence: 99%

Reverse Design of Solid Propellant Grain for a Performance-Matching Goal: Shape Optimization via Evolutionary Neural Network

et al. 2022

Aerospace

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The reverse design of solid propellant grain for a performance-matching goal, one of the most challenging directions of the solid rocket motor designing work, is limited by the traditional semi-empirical parameter-driven optimization methods based on some predefined grain configurations. Grain designers call for a new method that can automatically provide brand-new grain shapes beyond the traditional ones. In this work, a shape optimization method based on the evolutionary neural network is proposed to achieve the reverse design of two-dimensional (2D) grains. Firstly, the modified ellipse-form eikonal equation is solved by using the finite element method to realize the burn-back analysis of 2D grains in any shape on a fixed unstructured mesh. Then, the neural network is introduced to determine the spatial distribution of the propellant to define the grain shape. The hyperparameters of the network are continuously evolved with the aid of the genetic algorithm. Finally, the optimal grain shape that matches the performance goal most is obtained. The method is verified in different scenarios. The result shows that the design can precisely match the given pressure-time curve of star grains and slotted-tube grains. Furthermore, the method can automatically evolve a new dendritic-shaped grain that matches the given dual-thrust pressure-time curve. Since the reverse design uses the concept of shape optimization, it does not require any pre-selection of the grain shape, and the designers shall be free from defining different kinds of geometric parameters for specific grain configurations. Consequently, the method has the potential to apply in the reconstruction of an actual grain and the conceptual design of innovative grain configurations.

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“…In this emerging filed, several efforts have been made. [10][11][12] Albarado 10 developed a performance matching design method, driven by a pattern search/particle swarm optimization (PSO) algorithm.…”

Section: Introductionmentioning

confidence: 99%

Solid rocket motor design employing an efficient performance matching approach

Wang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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Solid rocket motor design matching the thrust requirement of the overall vehicle has become an exigent task. In the present research, an efficient performance matching optimization approach is proposed and applied to the solid rocket motor design. To obtain reliable solid rocket motor designs, refined performance evaluation is accomplished successfully based on the coupling of the level set grain burnback analysis and one-dimensional ballistic simulation by the time-stepping algorithm. Due to the heavy computing burden of the coupled performance solver, a novel progressive matching optimization approach is proposed. This approach depends on partial but not full-burn thrust profile to eliminate inferior candidates, predict superior candidates, advance the simulation and drive the candidates converging to the specified performance. Validation of the proposed solid rocket motor design approach is carried out by two test cases possessing different thrust requirements, and results indicate that the proposed approach is practical and efficient.

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

Cited by 11 publications

References 6 publications

Automated Nozzle Design through Axis-Symmetric Method of Characteristics Coupled with Chemical Kinetics

Automated Nozzle Design through Axis-Symmetric Method of Characteristics Coupled with Chemical Kinetics

Reverse Design of Solid Propellant Grain for a Performance-Matching Goal: Shape Optimization via Evolutionary Neural Network

Solid rocket motor design employing an efficient performance matching approach

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