Design of Engine Cooling System Using Improved Particle Swarm Optimization Algorithm

Xu, Jin; Wang, Ran; Zhang, Qingxin; Cui, Tong; Li, Han; Pei, Lei; Quan, Xinran

doi:10.1109/jsen.2023.3294961

Cited by 4 publications

(1 citation statement)

References 34 publications

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“…Barakat has combined atomized cooling systems and Exhaust Gas Recirculation to improve gas turbine performance and increase the effectiveness of its cooling system in hot and humid regions [5]. There is another kind of cooling system that uses water as coolant and realizes the cooling of components at all levels of the gas turbine through the heat transfer effect of the heat exchanger [6][7][8]. These systems are typical process control systems, and usually have more than one controlled variable such as rate of flow, pipeline pressure, and coolant temperature, etc.…”

Section: Introductionmentioning

confidence: 99%

Application of Opposition-Based Learning Jumping Spider Optimization Algorithm in Gas Turbine Coupled Cooling System

Wang,

Li,

et al. 2023

Actuators

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A gas turbine cooling system is a typical multivariable, strongly coupled, nonlinear system; however, the randomness and large disturbances make it difficult to control the variables precisely. In order to solve the problem of precise process control for multi-input and multi-output coupled systems with flow, pressure, and temperature, this article conducts the following research: (1) Designing a secondary circuit for waste hot water and establishing a water-circulating gas turbine cooling system to improve the efficiency of waste heat utilization. (2) Identifying the coupled system model and establishing a mathematical model of the coupling relationship based on the characteristic data of input and output signals in the gas turbine cooling system. (3) Designing a coupled-system decoupling compensator to weaken the relationships between variables, realizing the decoupling between coupled variables. (4) An Opposition-based Learning Jumping Spider Optimization Algorithm is proposed to be combined with the PID control algorithm, and the parameters of the PID controller are adjusted to solve the intelligent control problems of heat exchanger water inlet flow rate, pressure, and temperature in the gas turbine cooling system. After simulation verification, the gas turbine cooling system based on an Opposition-based Learning Jumping Spider Optimization Algorithm can realize the constant inlet flow rate, with an error of no more than 1 m3/h, constant inlet water temperature, with an error of no more than 0.2 °C, and constant main-pipe pressure, with an error of no more than 0.01 MPa. Experimental results show that a gas turbine cooling system based on the Opposition-based Learning Jumping Spider Optimization Algorithm can accurately realize the internal variable controls. At the same time, it can provide a reference for decoupling problems in strongly coupled systems, the controller parameter optimization problems, and process control problems in complex systems.

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Section: Introductionmentioning

confidence: 99%

Application of Opposition-Based Learning Jumping Spider Optimization Algorithm in Gas Turbine Coupled Cooling System

Wang,

Li,

et al. 2023

Actuators

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Improved Particle Swarm Fuzzy PID Temperature Control for the Pellet Grills

Li,

Lu,

et al. 2024

IEEE Access

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To improve the stability and accuracy of temperature control in pellet grill, this article proposes a method that applies a Fuzzy PID controller optimized with Improved Particle Swarm Optimization (IPSO). Techniques such as Circle mapping are introduced to enhance the convergence speed and efficacy of the particle swarm algorithm (PSO). The optimized particle swarm algorithm is used for the identification of the transfer function of the pellet grill, as well as for the optimization of the quantification factor and the proportional factor within the Fuzzy PID controller. The IPSO optimized Fuzzy PID control strategy was then experimentally applied to a prototype pellet grill. The results show that the control system limits the overshoot to 1.6% and temperature fluctuations to within 1.83℉, demonstrating good stability and anti-interference capability, and significantly improving the effectiveness of temperature control. Therefore, the research findings provide a feasible solution for the temperature regulation of pellet grill.

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Nonintrusive Current Monitoring for Power Grids: An End-to-End Framework From Data Acquisition to Waveform Reconstruction

Shao,

Wen,

et al. 2024

IEEE Trans. Ind. Inf.

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Design of Engine Cooling System Using Improved Particle Swarm Optimization Algorithm

Cited by 4 publications

References 34 publications

Application of Opposition-Based Learning Jumping Spider Optimization Algorithm in Gas Turbine Coupled Cooling System

Application of Opposition-Based Learning Jumping Spider Optimization Algorithm in Gas Turbine Coupled Cooling System

Improved Particle Swarm Fuzzy PID Temperature Control for the Pellet Grills

Nonintrusive Current Monitoring for Power Grids: An End-to-End Framework From Data Acquisition to Waveform Reconstruction

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