“…For this matter, the GA is attractive, specifically for its flexibility and accuracy. 18,21,22 However, these advantages are counterbalanced by the complexity of this methodology regarding the design space exploration. To lessen this issue, PSO techniques are seriously considered and investigated.…”
Section: Optimal Design Methodologymentioning
confidence: 99%
“…The PSO is relevant considering its low complexity (few optimization parameters) and its efficiency (fast convergence speed, short computation time, and good accuracy) compared to other optimization methods. 22,28,29 Figure 5.Particle swarm optimization algorithm.Figure 6.Calculation of each particle's move vector.…”
This paper presents an optimal design methodology enabling to exhibit the best parameters of a complex energy system combing several components and their related control parts. It is based on a particle swarm optimization technique for component sizing, combined with optimal control to consider energy management constraints. This approximate resolution is valuable since it allows to achieve a robust and effective optimal design using low computational resources: it enables to tackle large search spaces in engineering time constraints. The selected use case is a fuel cell/battery hybrid power source based on a power-split parallel architecture. Its performance index is defined as the fuel consumption. Regarding this objective, the drivetrain components size and the control parameters values are both strongly coupled and physically constrained. In this context, the methodology makes a tradeoff between component sizing and energy saving. Simulation results show the relevance and robustness of this approach regarding different driving cycles and operating conditions. It validates the replicability of this method to other optimization problems in the field of energy optimization. A comprehensive review of the simulation tests highlights the present limits of this optimization and provides new perspectives for future works.
“…For this matter, the GA is attractive, specifically for its flexibility and accuracy. 18,21,22 However, these advantages are counterbalanced by the complexity of this methodology regarding the design space exploration. To lessen this issue, PSO techniques are seriously considered and investigated.…”
Section: Optimal Design Methodologymentioning
confidence: 99%
“…The PSO is relevant considering its low complexity (few optimization parameters) and its efficiency (fast convergence speed, short computation time, and good accuracy) compared to other optimization methods. 22,28,29 Figure 5.Particle swarm optimization algorithm.Figure 6.Calculation of each particle's move vector.…”
This paper presents an optimal design methodology enabling to exhibit the best parameters of a complex energy system combing several components and their related control parts. It is based on a particle swarm optimization technique for component sizing, combined with optimal control to consider energy management constraints. This approximate resolution is valuable since it allows to achieve a robust and effective optimal design using low computational resources: it enables to tackle large search spaces in engineering time constraints. The selected use case is a fuel cell/battery hybrid power source based on a power-split parallel architecture. Its performance index is defined as the fuel consumption. Regarding this objective, the drivetrain components size and the control parameters values are both strongly coupled and physically constrained. In this context, the methodology makes a tradeoff between component sizing and energy saving. Simulation results show the relevance and robustness of this approach regarding different driving cycles and operating conditions. It validates the replicability of this method to other optimization problems in the field of energy optimization. A comprehensive review of the simulation tests highlights the present limits of this optimization and provides new perspectives for future works.
“…A multitude of applications have assessed the relevance of the PSO approach. It is able to provide high quality solutions in comparison with experiment or with other known methods [10,14,15,16]. Even if there is no clear link to physics or mathematics that could justify rationally this performance, the merit of the PSO algorithm is associated to the fact that it works satisfactorily in practice.…”
Section: Proposed Localization Procedures Based On Particle Swarm Optimentioning
This paper focuses on the inverse problem regarding force localization in the case of non punctual impacts occurring on elastic beams. Following the identification approach proposed to solve this problem and which is based on the reciprocity theorem, the impact location characteristics were determined by using Particle Swarm Optimization algorithm. To eliminate numerical trouble due to the trivial solutions appearing in this formulation, the fitness function was customized by introducing a set of weighting coefficients. Four different formulations of the fitness function were considered and their performances with regards to the number of sensors used and their positions were analyzed. This enabled to select the best combination of weighting coefficients to be used in the context of an impact force localization process based on Particle Swarm Optimization technique. Three sensors were found to be required and comparison with Genetic Algorithm has revealed effectiveness of the proposed method in terms of accuracy and computational time.
“…For example, Seyed et al (2012) utilized the PSO algorithm for optimizing thermo-economic analysis of combine gas turbine power plant. And Sadeghierad et al (2010) performed design of high-speed micro turbine generator which has high rotor speed and high frequency of the stator winding current by using the PSO algorithm for optimizing the efficiency of machine. Weiping et al (2013) carried out the forecasting of steam turbine heat rate which is important in assuring the economic, efficient, and safe operation by applying PSO algorithm in order to optimize the regularization parameter r and the kernel parameter σ 2 [6][7][8].…”
A turbo fan engine performance analysis and the optimization using particle swarm optimization(PSO) algorithm have been conducted to investigate the effects of major performance design parameters of an aircraft gas turbine engine. The FJ44-2C turbofan engine, which is widely used in the small business jet, CJ2 has been selected as the basic model. The design parameters consists of the bypass ratio, burner exit temperature, HP compressor ratio, fan inlet mass flow, and nozzle cooling air ratio. The sensitivity analysis of the parameters has been evaluated and the optimization of the parameters has been performed to achieve high net thrust or low specific fuel consumption.
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