Estimation of heat transfer coefficients in continuous casting under large disturbance by Gaussian kernel particle swarm optimization method

2017 ◽  
Vol 111 ◽  
pp. 1087-1097 ◽  
Author(s):  
Xiaochuan Luo ◽  
Qiqi Xie ◽  
Yuan Wang ◽  
Cuie Yang
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Continuous Casting ◽  
Particle Swarm ◽  
Optimization Method ◽  
Gaussian Kernel ◽  
Transfer Coefficients ◽  
Swarm Optimization ◽  
Heat Transfer Coefficients ◽  
Large Disturbance

scholarly journals Parallelized Particle Swarm Optimization to Estimate the Heat Transfer Coefficients of a Series of Vegetable Oils in Comparison with Typical Fast Petroleum Quench Oil Quenchant

2019 ◽  
Author(s):  
Rosa L. Simencio Otero ◽  
Sándor Szénási ◽  
Zoltán Fried ◽  
Imre Felde ◽  
Jônatas M. Viscaino ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Particle Swarm Optimization ◽  
Vegetable Oils ◽  
Particle Swarm ◽  
Cooling Curve ◽  
Processing Unit ◽  
Transfer Coefficients ◽  
Swarm Optimization ◽  
Heat Transfer Coefficients ◽  
Petroleum Oil

Abstract An inverse solver for the estimation of the temporospacial heat transfer coefficients (HTCs), without using prior information of the thermal boundary conditions, was used for immersion quenching into a series of vegetable oils and two commercial petroleum oil quenchants. The Particle Swarm Optimization method was used on near-surface temperature-time cooling curve data obtained with the so-called Tensi multithermocouple 12.5 mm diameter x 45 mm Inconel 600 probe. The fitness function to be minimized by a particle swarm optimization (PSO) approach is defined by the deviation of the measured and calculated cooling curves. The PSO algorithm was parallelized and implemented on a Graphics Processing Unit architecture. This paper describes in detail the PSO methodology to compare and differentiate the potential quenching properties attainable with a series of vegetable oils including: cottonseed, peanut, canola, coconut, palm, sunflower, corn and a soybean oil vs a typical accelerated petroleum oil quenchant.

scholarly journals Thermal Analysis of Cylindrical Parabolic Trough Collector Using Particle Swarm Optimization

2021 ◽  
pp. 6-10
Author(s):  
Manoj Kumar Kushwaha ◽  
Anup Kumar Rajak
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Particle Swarm Optimization ◽  
Base Fluid ◽  
Particle Swarm ◽  
Heat Transfer Analysis ◽  
Optimized Design ◽  
Parabolic Trough ◽  
Transfer Coefficients ◽  
Swarm Optimization

For about the first instance, a revolutionary Multi-objective Particle Swarm Optimization (PSO) method is applied to achieve a relatively close CPTC design for improving batter thermal performance and output warmth. Optimized design of parabolic trough collectors serves an important part in today's marketplace and is a rising area of concern for experts and academics. Particle swarm optimization (PSO) is an unique approach for enhancing a solar power trough collector's heat transfer analysis. Nanofluid has received a lot of interest as a way to increase convective heat transfer. They surpassed the previously used fluid. The thermal conductivity and convection temperature transfer coefficients are increased by employing nanofluid as the heat transport fluid and changing the base fluid. Particle concentration, thermal dispersion consistency, and nanofluid dimension are the important parameters for enhancing thermal conductivity, according to a prior study. As a preliminary step, keep these points in mind. Except perhaps the concentration fluid intake velocity and pipe diameter, the optimal solution value is determined using the tested model characteristics and the Dimethyl Polysiloxane Molecular as a base fluid. The results are exhibited at different temperatures. To determine if the recommended solution approach is appropriate, the outcomes of two research publications are compared

Sistem Kontrol Optimal Fuzzy-Particle Swarm Optimization

2018 ◽  
Vol 9 (1) ◽  
pp. 1-5
Author(s):  
Fachrudin Hunaini ◽  
Imam Robandi ◽  
Nyoman Sutantra
Keyword(s):  
Fuzzy Logic ◽  
Particle Swarm Optimization ◽  
Control System ◽  
Membership Function ◽  
Fuzzy Logic Control ◽  
Particle Swarm ◽  
Optimization Method ◽  
Swarm Optimization ◽  
Motion Error ◽  
Logic Control

Fuzzy Logic Control (FLC) is a reliable control system for controlling nonlinear systems, but to obtain optimal fuzzy logic control results, optimal Membership Function parameters are needed. Therefore in this paper Particle Swarm Optimization (PSO) is used as a fast and accurate optimization method to determine Membership Function parameters. The optimal control system simulation is carried out on the automatic steering system of the vehicle model and the results obtained are the vehicle's lateral motion error can be minimized so that the movement of the vehicle can always be maintained on the expected trajectory

Sign in / Sign up

Export Citation Format

Share Document