Exchange inlet optimization by genetic algorithm for improved RBCC performance

Chorkawy, G.; Etele, Jason

doi:10.1016/j.actaastro.2017.05.035

Cited by 12 publications

(3 citation statements)

References 47 publications

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“…Optimization designs on the two-dimensional and three-dimensional inlets have generally been extensively studied by using the response surface method and the genetic algorithm to obtain a higher total pressure recovery and more uniform outflow at the inlet exit [19][20][21]. The issues affecting the performance of the inlet are considered comprehensively in the above optimization methods, but they are too complicated by consuming a lot of computational resources, making it difficult to reveal the general rules of shock wave configuration.…”

Section: Introductionmentioning

confidence: 99%

Lagrange Optimization of Shock Waves for Two-Dimensional Hypersonic Inlet with Geometric Constraints

Yue

et al. 2022

Aerospace

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The present paper focuses on the Lagrange optimization of shock waves for a two-dimensional hypersonic inlet by limiting the cowl internal angle and inlet length. The results indicate the significant influences of geometric constraints on the configuration of shock waves and performances of an inlet. Specifically, the cowl internal angle mainly affects the internal compression section; the inlet length affects both the internal and external compression sections where the intensity of internal and external compression shock waves shows a deviation of equal. In addition, the performances of optimized inlets at off-design points are further numerically simulated. A prominent discovery is that a longer inlet favors a higher total pressure recovery at the positive AOA; conversely, a shorter inlet can increase the total pressure recovery at the negative AOA.

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Section: Introductionmentioning

confidence: 99%

Lagrange Optimization of Shock Waves for Two-Dimensional Hypersonic Inlet with Geometric Constraints

Yue

et al. 2022

Aerospace

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“…Adaptive tuning of PID sliding surface parameters based on improved genetic algorithm (IGA). Genetic algorithm (GA) has the characteristics of a simple algorithm, parallel processing and global optimal solution (Tripp, 2010;Chen et al, 2013;Chorkawy and Etele, 2017). Both Mahdi (Mahdi, 2014) and Dastranj (Dastranj et al, 2011) used GA to set PID controller parameters and verified by experiment and simulation.…”

Section: Introductionmentioning

confidence: 99%

The adaptive sliding mode control using improved genetic algorithm tuning PID controller for the planetary rover

Yuan

Guo

et al. 2021

AEAT

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Purpose The purpose of this paper is to resolve the problem of the dynamic response performance of the driving control system for a six-wheeled planetary rover. An adaptive sliding mode controller based on an improved genetic algorithm (IGA) to tune PID sliding surface parameters was used in the driving control system of the planetary rover. Design/methodology/approach First, the mathematical model of planetary rover driving control is established. Second, according to sliding mode variable structure control, an equivalent controller and a disturbance controller are constructed to solve the problem of a multi-disturbance nonlinear driving control system of planetary rovers and an IGA is used to tune PID parameters. Findings Simulation results show that the proposed control algorithm improves the accuracy of the driving control system and optimizes the smoothness of rover motion control. Practical implications The controller based on the IGA to tune PID sliding surface parameters has good self-adaptability and real-time controllability for the control object which is difficult to present a precise mathematical model. Originality/value The advanced control method is adopted to solve the uncertainty and external interference of planetary rovers in a complex environment. The mathematical model of the six-wheeled rover is established as the control object and the uncertainty and external disturbance of the model are considered. The controller based on IGA has good adaptability and real-time performance and the control algorithm can be used to drive robots in complex environments.

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“…By randomly altering the alleles of genes, the GAs can effectively avoid trap situations and maintain sufficient variance in the population [24,25], whereas the probability of mutation is around 1%, since a high probability of mutation reduces the GA to a random search function.…”

Section: Journal Of Sensorsmentioning

confidence: 99%

Optimization of Magnetic-Grating-Like Stroke-Sensing Cylinder Based on Response Quality Evaluation Algorithm

Guo

Liu

et al. 2018

Journal of Sensors

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The measurement of hydraulic cylinder displacement has been addressed from different fields. The detection principle of magnetic grating is able to realize the high integration and accuracy. In this paper, a signal response quality evaluation algorithm for devising and optimizing a high-accuracy displacement measuring system is proposed. On the basic of signal response quality evaluation method, structure variables are optimized to enhance the working performance. By defining the parameters, an optimum structure cylinder prototype is made and tested to provide better estimates. Experimental results on working characteristic are presented to verify the effectiveness of the optimized structure. The efficiency of the proposed signal response quality evaluation function is therefore demonstrated through the working performance.

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Exchange inlet optimization by genetic algorithm for improved RBCC performance

Cited by 12 publications

References 47 publications

Lagrange Optimization of Shock Waves for Two-Dimensional Hypersonic Inlet with Geometric Constraints

Lagrange Optimization of Shock Waves for Two-Dimensional Hypersonic Inlet with Geometric Constraints

The adaptive sliding mode control using improved genetic algorithm tuning PID controller for the planetary rover

Optimization of Magnetic-Grating-Like Stroke-Sensing Cylinder Based on Response Quality Evaluation Algorithm

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