Liquid propellant engine conceptual design by using a fuzzy-multi-objective genetic algorithm (MOGA) optimization method

Mirshams, Mehran; Naseh, Hassan; Taei, Hojat; Fazeley, H. R.

doi:10.1177/0954410014521390

Cited by 3 publications

(2 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The GA was formally introduced in 1970s by John Holland and discussed by many researchers. [33][34][35] GAs apply re-combination operators on large sets (populations) of potential solutions to reach an optimum one. Their ability to search an entire design space makes them suitable for handling optimization problems with highly nonlinear objective functions and many local optima.…”

Section: Optimization Algorithmsmentioning

confidence: 99%

Spacecraft mission design optimization under uncertainty

Jafarsalehi

Fazeley

Mirshams

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

Self Cite

View full text Add to dashboard Cite

The design of space systems is a complex and multidisciplinary process. In this study, two deterministic and nondeterministic approaches are applied to the system design optimization of a spacecraft which is actually a small satellite in low Earth orbit with remote sensing mission. These approaches were then evaluated and compared. Different disciplines such as mission analysis, payload, electrical power supply, mass model, and launch manifest were properly combined for further use. Furthermore, genetic algorithm and sequential quadratic programming were employed as the system-level and local-level optimizers. The main optimization objective of this study is to minimize the resolution of the satellite imaging payload while there are several constraints. A probabilistic analysis was performed to compare the results of the deterministic and nondeterministic approaches. Analysis of the results showed that the deterministic approaches may lead to an unreliable design (because of leaving little or no room for uncertainties), while using the reliability-based multidisciplinary design optimization architecture, all probabilistic constraints were satisfied.

show abstract

Section: Optimization Algorithmsmentioning

confidence: 99%

Spacecraft mission design optimization under uncertainty

Jafarsalehi

Fazeley

Mirshams

2016

Proceedings of the Institution of Mechanical Engineers, Part C:

Self Cite

View full text Add to dashboard Cite

show abstract

“…Zhou et al [16] modelled the optimal starting and regulating characteristics of an electric pump for LPRE using the GA technique. A fuzzy Multi-Objective GA (MOGA) technique has been applied to a GG cycle [17] as well as on a hybrid rocket [18] with the objectives of maximizing altitude and minimizing Gross Lift-Off Weight (GLOW). Orgeira-Crespo et al [19] compiled a database of existing LPREs and examined conceptual multistage LVs based on those existing engines.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Optimization Techniques and Objective Functions Using Gas Generator and Staged Combustion LPRE Cycles

et al. 2022

View full text Add to dashboard Cite

This paper compares various optimization techniques and objective functions to obtain optimum rocket engine performances. This research proposes a modular optimization framework that provides an optimum design for Gas Generator (GG) and Staged Combustion (SC) Liquid Propellant Rocket Engines. This process calculates the ideal rocket engine performance by applying seven different optimization techniques: Simulated Annealing (SA), Nelder Mead (NM), Cuckoo Search Algorithm (CSA), Particle Swarm Optimization (PSO), Pigeon-Inspired Optimization (PIO), Genetic Algorithm (GA) and a novel hybrid GA-PSO technique named GA-Swarm. This new technique combines the superior search capability of GA with the efficient constraint matching capability of PSO. This research also compares objective functions to determine the most suitable function for GG and SC cycle rocket engines. Three single objective functions are used to minimize the Gross Lift-Off Weight and to maximize Specific Impulse and the Thrust-to-Weight ratio. A fourth multiobjective function is used to simultaneously maximize both Specific Impulse and Thrust-to-Weight ratio. This framework is validated against a pump-fed rocket, and results are within 1% of the actual rocket engine mass. The results of this research indicate that PSO and GA-Swarm produce optimum results for all objective functions. Finally, the most suitable objective function to use while comparing these two cycles is the Gross Lift-Off Weight.

show abstract

Multidisciplinary design optimization of an aircraft by using knowledge-based systems

Setayandeh

Babaei

2020

Soft Comput

View full text Add to dashboard Cite

Liquid propellant engine conceptual design by using a fuzzy-multi-objective genetic algorithm (MOGA) optimization method

Cited by 3 publications

References 43 publications

Spacecraft mission design optimization under uncertainty

Spacecraft mission design optimization under uncertainty

Comparison of Optimization Techniques and Objective Functions Using Gas Generator and Staged Combustion LPRE Cycles

Multidisciplinary design optimization of an aircraft by using knowledge-based systems

Contact Info

Product

Resources

About