Optimal Design of Integrated Heat Pipe Air-Cooled System Using TLBO Algorithm for SiC MOSFET Converters

Alizadeh, Maryam; Rodriguez, Romina; Bauman, Jennifer; Emadi, Ali

doi:10.1109/ojpel.2020.2985700

Cited by 13 publications

(3 citation statements)

References 23 publications

(36 reference statements)

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“…In order to improve the convergence speed of BP neural networks, we propose a method to optimize BP neural networks using Teaching-Learning-Based Optimization Algorithm. Teaching-Learning-Based Optimization Algorithm (TLBO) is a crowd-based, inspirational, random group intelligence algorithm [17]. Similar to other evolutionary algorithms, the approach also has iterative processes.…”

Section: Deep Bp Neural Network Algorithm Optimized By Tlbomentioning

confidence: 99%

Student Achievement Prediction Model based on TLBO-BP

Hou,

Deng,

et al. 2023

Machine Learning &Amp; Applications

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Today, educational prediction analysis has become an important tool for educational institutions to analyze students, and the performance of students in educational prediction analysis is a critical feedback. However, there are serious challenges in dealing with multifactor datasets to improve the convergence and accuracy of predicting student performance. Therefore, this article comprehensively analyzes machine learning technologies, analyzes the community activities of middle-term students, and explores the impact of community activities on middle-aged students' practical skills. This paper uses Random Forest and Pearson Correlation Coefficient to analyze the relevance of the impact of community activities on student hands-on performance and use Teaching and Learning Algorithms (TLBO) to optimize Back propagation (BP) neural networks applied to predict students' future trends. The results show that the TLBO-BP model can more accurately predict dynamic changes in student performance and predict high accuracy in simple patterns.

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Section: Deep Bp Neural Network Algorithm Optimized By Tlbomentioning

confidence: 99%

Student Achievement Prediction Model based on TLBO-BP

Hou,

Deng,

et al. 2023

Machine Learning &Amp; Applications

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“…In which they sought to reduce the distortion (THD). The main reason for reducing the THD is the problems that occur when a high THD percenta problems are as follows: Power Electronics [19] 2020 DAB converter TLBO used to design the system with a minimum volume inverter and achieve maximum power efficiency TLBO has better results and a faster convergence rate [20] 2019 PFC and cloud data logger monitoring TLBO used to optimize the capacitor bank and compensate power factor TLBO reduced the time to find the best solution and obtained the power factor near the desired level [21] 2017 Load dispatch with TLBO TLBO used to reduce energy production costs in power plants through economical load dispatching TLBO provides lower-cost and high-quality solutions to the problem of economical freight forwarding [22] 2015 Design of a hybrid energy system TLBO is used to design a hybrid system that can supply a typical household load.…”

Section: The Tlbo Algorithm In Power Electronicsmentioning

confidence: 99%

Teaching–Learning-Based Optimization Algorithm Applied in Electronic Engineering: A Survey

et al. 2022

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This paper presents a survey of the articles published in the period 2013–2021 related to the application of the teaching–learning-based optimization (TLBO), which reproduces the dynamics that occur in a classroom with the teacher and the student. This paper uses the algorithm to optimize some objective functions related to the design in the electronics field. A total of 62 papers were reviewed and some graphs were generated to summarize the most relevant of these articles. These have been classified into five categories based on the areas of electronic engineering, such as power electronics, control, electromagnetism, digital electronics, and analogue electronics. Electronic engineering has been becoming increasingly relevant in world technological development, for example, in electric vehicles or generating electricity from renewable energy sources to counteract the environmental impact that non-renewable sources generate. The TLBO algorithm has attracted the interest of a large number of researchers due to its efficiency, speed, and low initialization parameter requirements. This article is composed of two stages, the first is a summary of the information on electronics, in general, encompassing all its areas, and the second focuses on the algorithm applied to multilevel inverters; for each stage, graphs and tables are shown.

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“…In the air-cooled system, the heat dissipation of heat source is limited by the contact area between heat source and spreader. M. Alisadeh et al [16] add heat pipe to transfer heat from compact space to feasible cooling space in power electronics packaging, so as to achieve higher fan efficiency and lower inverter packaging volume. H.L.…”

Section: Introductionmentioning

confidence: 99%

Heat Transfer Analysis of Flat Heat Pipe With Enhanced Microchannel Shape

Chen

Zhang

2021

IEEE Access

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Flat heat pipe has many excellent characteristics such as high thermal conductivity, reversibility of heat flow direction and so on. In this paper, a flat heat pipe model with multiple microchannels is established, and the effects of different microchannel shapes and different materials on the heat transfer efficiency of flat heat pipe are studied. Then, based on the general rectangular and inverted trapezoidal microchannel shape, the enhancement is carried out. That is to fill a number of materials in the bottom of the microchannel to change the bottom shape of the microchannel. In the steady-state thermal simulation of flat heat pipe, the axial temperature of the flat heat pipe with enhanced microchannel shape is lower than that of the flat heat pipe with general microchannel, which shows that the enhanced microchannel shape is helpful to the heat dissipation of flat heat pipe. The fluid simulation results show that the enhanced rectangular microchannel shape can accelerate the current velocity of working fluid. Therefore, the flat heat pipe with enhanced microchannel shape has better heat transfer performance. In order to obtain the local temperature of flat heat pipe quickly, the transient analytical solution is provided. In this paper, the temperature field calculated by using transient analytical solution is compared with that simulated by COMSOL. The results show that the maximum error between the results of discrete heat source model and continuous heat source model calculated by transient solution and COMSOL simulation results is less than 2%.INDEX TERMS Flat heat pipe, microchannel shape, heat dissipation, analytical solution.

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Optimal Design of Integrated Heat Pipe Air-Cooled System Using TLBO Algorithm for SiC MOSFET Converters

Cited by 13 publications

References 23 publications

Student Achievement Prediction Model based on TLBO-BP

Student Achievement Prediction Model based on TLBO-BP

Teaching–Learning-Based Optimization Algorithm Applied in Electronic Engineering: A Survey

Heat Transfer Analysis of Flat Heat Pipe With Enhanced Microchannel Shape

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