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Open Access
Article
Publication date: 21 April 2022

Myeongjin Kim and Joo Hyun Moon

This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance.

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Abstract

Purpose

This study aims to introduce a deep neural network (DNN) to estimate the effective thermal conductivity of the flat heat pipe with spreading thermal resistance.

Design/methodology/approach

A total of 2,160 computational fluid dynamics simulation cases over up to 2,000 W/mK are conducted to regress big data and predict a wider range of effective thermal conductivity up to 10,000 W/mK. The deep neural networking is trained with reinforcement learning from 10–12 steps minimizing errors in each step. Another 8,640 CFD cases are used to validate.

Findings

Experimental, simulational and theoretical approaches are used to validate the DNN estimation for the same independent variables. The results from the two approaches show a good agreement with each other. In addition, the DNN method required less time when compared to the CFD.

Originality/value

The DNN method opens a new way to secure data while predicting in a wide range without experiments or simulations. If these technologies can be applied to thermal and materials engineering, they will be the key to solve thermal obstacles that many longing to overcome.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 33 no. 2
Type: Research Article
ISSN: 0961-5539

Keywords

Open Access
Article
Publication date: 13 September 2021

Silvio Rendon

This paper aims to weigh the restrictions to job creation imposed by labor market imperfections with respect to financial market imperfections. The authors want to see which…

1116

Abstract

Purpose

This paper aims to weigh the restrictions to job creation imposed by labor market imperfections with respect to financial market imperfections. The authors want to see which restriction is more severe, and thus assess which is more powerful in creating permanent employment if it were removed.

Design/methodology/approach

A structural estimation is performed. The policy rules of the dynamic programming model are integrated into a simulated maximum likelihood procedure by which the model parameters are recovered. Data come from the CBBE (Balance Sheet data from the Bank of Spain). Identification of key parameters comes mainly from the observation of debt variation and sluggish adjustment to permanent labor.

Findings

Long-run permanent employment increases up to 69% when financial constraints are removed, whereas permanent employment only increases up to 54% when employment protection or firing costs are eliminated. The main finding of this paper is that the long-run expansion of permanent employment is larger when financial imperfections are removed than when firing costs are removed, even when there are important wage increases that moderate these employment expansions.

Social implications

The removal of firing costs has been suggested by several economists as a result of the analysis of labor market imperfections. These policies, however, face the strong opposition of labor unions. This paper shows that the goals of permanent job creation can be accomplished without removing employment protection but by means of enhancing financial access to firms.

Originality/value

The connection between financial constraints and employment has been studied in recent years, motivated by the Great Recession. However, there is no assessment of how financial and labor market imperfections compare with each other to restrict permanent job creation. This comparison is crucial for policy analysis. This study is an attempt to fill out this gap in the economic literature. No previous research has attempted to perform this very important comparison.

Details

Applied Economic Analysis, vol. 30 no. 89
Type: Research Article
ISSN:

Keywords

Open Access
Article
Publication date: 3 February 2022

Shian Li, Zhi Yang, Yihui Liu, Qiuwan Shen, Guogang Yang and Bengt Ake Sunden

The purpose of this paper is to investigate the heat and mass transport characteristics in microchannel reactors with non-uniform catalyst distributions.

Abstract

Purpose

The purpose of this paper is to investigate the heat and mass transport characteristics in microchannel reactors with non-uniform catalyst distributions.

Design/methodology/approach

A two-dimensional model is developed to study the heat and mass transport characteristics in microchannel reactors. The heat and mass transport processes in the microchannel reactors with non-uniform catalyst distribution in the catalytic combustion channel are also studied.

Findings

The simulated results are compared in terms of the distributions of species mole fraction, temperature and reaction rate for the conventional and new designed reactors. It is found that the chemical reaction, heat and mass transport processes are significantly affected and the maximum temperature in the reactor is also greatly reduced when a non-uniform catalyst distribution is applied in the combustion catalyst layer.

Practical implications

This study can improve the understanding of the transportation characteristics in microchannel reactors with non-uniform catalyst distributions and provide guidance for the design of microchannel reactors.

Originality/value

The design of microchannel reactors with non-uniform catalyst distributions can be used in methane steam reforming to reduce the maximum temperature inside the reactor.

Details

International Journal of Numerical Methods for Heat & Fluid Flow, vol. 32 no. 10
Type: Research Article
ISSN: 0961-5539

Keywords

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