Solid Rocket Motor Design Using Hybrid Optimization

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

doi:10.1155/2012/987402

Cited by 8 publications

(5 citation statements)

References 10 publications

(17 reference statements)

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“…[3] reinvigorated hybrid optimization techniques theory through a study in 2012 to enhance the efficiency of solid rocket motor design using hybrid optimization techniques. The researchers combined optimization algorithms including GA, PSO, and simulated annealing to optimize key parameters such as propellant composition, nozzle geometry, and chamber pressure [3]. The study revealed that the hybrid optimization approach outperformed individual optimization algorithms, producing optimal solid rocket motor designs.…”

Section: Theoretical Frameworkmentioning

confidence: 99%

Enhancing Hydro Power Plant Efficiency through Hybrid-Optimization Approach

Enyiukwu, E. I. and Omeje, C. O.

2024

JCMPS

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The inclusion of hydroelectric power is crucial to Nigeria's overall energy mix, playing a significant role in electricity generation. However, the Shiroro hydro plant, one of the main facilities located on the Kaduna River, is currently facing operational obstacles due to deteriorating infrastructure and inadequate maintenance practices. To overcome these challenges and improve efficiency within Nigeria's hydroelectric power sector, a hybrid-optimization approach has been proposed. This study sought to enhance the efficiency of the Shiroro hydro plant by implementing this innovative method. To achieve our objectives and address pertinent research questions, a mixed research method combining primary and secondary data was employed. The analysis included hydropower modeling and hydro-turbine input-output modeling. Three optimizer models, namely the particle swarm optimizer (PSO), Ant colony optimizer (ACO), and Artificial bee colony optimizer (ABCO), were utilized to formulate objective functions and task representations. The study involved comparing the daily output and fitness response of the Shiroro hydro plant through swarm optimizer iterations. The findings revealed a clear correlation between the turbine's power output and the water flow rate and water column height, suggesting that altering these factors could significantly improve the plant's performance. The comparison of the PSO, ACO, and ABCO models demonstrated that PSO and ABCO generated optimal or near-optimal solutions, while ACO produced suboptimal results. Consequently, the study concluded that enhancing the Shiroro hydro plant's output was feasible by increasing the water flow rate and column height. Additionally, the utilization of PSO and ABCO models proved to be an effective means of accurately predicting the turbine's output. As a result, the study recommended the integration of hybrid optimization techniques to monitor and identify any deviations in the Shiroro hydro plant's daily power output. This approach would enable prompt maintenance to be carried out, preventing significant damage to the plant. Ultimately, this research contributes valuable insights into improving the efficiency and performance of Nigeria's Shiroro hydro plant.

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Section: Theoretical Frameworkmentioning

confidence: 99%

Enhancing Hydro Power Plant Efficiency through Hybrid-Optimization Approach

Enyiukwu, E. I. and Omeje, C. O.

2024

JCMPS

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“…The optimal design with a more complicated area profile than a neutral profile is performed to confirm the usefulness of the proposed grain design method [34]. This study used regressive-progressive (RP) grain to confirm whether various classification models are helpful for complex grain design.…”

Section: Rp Grain Optimal Design Using Multiclass Svmmentioning

confidence: 99%

A Study on the Optimal Design Method for Star-Shaped Solid Propellants through a Combination of Genetic Algorithm and Machine Learning

Oh,

Roh,

Lee

2023

Aerospace

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This study was focused on the configuration design of a star grain by using machine learning in the optimal design process. The key to optimizing the grain design is aimed at obtaining a set of configuration variables that satisfy the requirements. The optimization problem consists of an objective area profile subject to certain constraints and an objective function that quantitatively calculates the design level. Designers must formulate suitable optimization problems to achieve an optimal design. However, because a method to alleviate the influence of the sliver section is not yet available, the optimization problem is typically solved based on experience, which is time- and effort-intensive. Consequently, a more practical and objective grain design method must be developed. In this study, an optimal design method using machine learning was developed to increase the convenience and success rate. A support vector machine was used to train a classification model that predicts a class. The classification model was used to alleviate the influence of the sliver zone and correct the search problem to ensure that an optimal solution existed in the region satisfying the requirements. The proposed method was validated through star grain optimal design using the genetic algorithm. The optimization was performed considering the area profiles, and the effectiveness of the proposed method was demonstrated by the enhanced accuracy.

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“…Various attempts have been made to combine global and local optimization algorithms in order to overcome the challenges of using them separately [10,[17][18][19][20][21]. Most of these studies used a combination of a global (e.g., GA) and a local optimization algorithm, which can be referred to as a hybrid optimization algorithm.…”

Section: Hybrid Optimization Algorithm For Thrust Allocationmentioning

confidence: 99%

A Thrust Allocation Method for Efficient Dynamic Positioning of a Semisubmersible Drilling Rig Based on the Hybrid Optimization Algorithm

Zhao

Roh

2015

Mathematical Problems in Engineering

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A thrust allocation method was proposed based on a hybrid optimization algorithm to efficiently and dynamically position a semisubmersible drilling rig. That is, the thrust allocation was optimized to produce the generalized forces and moment required while at the same time minimizing the total power consumption under the premise that forbidden zones should be taken into account. An optimization problem was mathematically formulated to provide the optimal thrust allocation by introducing the corresponding design variables, objective function, and constraints. A hybrid optimization algorithm consisting of a genetic algorithm and a sequential quadratic programming (SQP) algorithm was selected and used to solve this problem. The proposed method was evaluated by applying it to a thrust allocation problem for a semisubmersible drilling rig. The results indicate that the proposed method can be used as part of a cost-effective strategy for thrust allocation of the rig.

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Solid Rocket Motor Design Using Hybrid Optimization

Cited by 8 publications

References 10 publications

Enhancing Hydro Power Plant Efficiency through Hybrid-Optimization Approach

Enhancing Hydro Power Plant Efficiency through Hybrid-Optimization Approach

A Study on the Optimal Design Method for Star-Shaped Solid Propellants through a Combination of Genetic Algorithm and Machine Learning

A Thrust Allocation Method for Efficient Dynamic Positioning of a Semisubmersible Drilling Rig Based on the Hybrid Optimization Algorithm

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