Evaluating different types of artificial neural network structures for performance prediction of compact heat exchanger

Shojaeefard, Mohammad Hassan; Zare, Javad; Tabatabaei, Amir; Mohammadbeigi, Hassan

doi:10.1007/s00521-016-2302-z

Cited by 15 publications

(5 citation statements)

References 29 publications

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“…After output data is compared with the other results, the iteration process is finished and the closest estimate is obtained. There are various studies focusing on ANN at theoretically, such as Shojaeefard et al [22], Li et al [23]. A diagram of a multilayer feed forward neural network model structure used in this study is shown in Fig.…”

Section: Methodsmentioning

confidence: 99%

Performance Estimation of Organic Rankine Cycle by Using Soft Computing Technics

Kovacı¹,

Şahin²,

Dikmen³

et al. 2017

International Journal of Engineering &Amp; Applied Sciences

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Section: Methodsmentioning

confidence: 99%

Performance Estimation of Organic Rankine Cycle by Using Soft Computing Technics

Kovacı¹,

Şahin²,

Dikmen³

et al. 2017

International Journal of Engineering &Amp; Applied Sciences

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“…Fully connected ANN [5][6][7][8][9][10] [11] SVR, RBNN, Kriging [9] [ 12,13] CFD models are increasingly being used for predictive design, even in critical applications such as nuclear reactor thermal-hydraulics [14], provided that rigorous verification and validation practices are adhered to. In the context of fin-tube bundles, CFD models can provide heat transfer and pressure drop coefficients in a consistent and time efficient manner.…”

Section: Data Source Experimental Correlation or Cfdmentioning

confidence: 99%

A Machine Learning Approach to Correlation Development Applied to Fin-Tube Bundle Heat Exchangers

et al. 2018

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Heat exchanger designers need reliable thermal-hydraulic correlations to optimize heat exchanger designs. This work combines an adaptive sampling method with a Computational Fluid Dynamics (CFD) simulator to obtain increased accuracy and validity range of heat transfer and pressure drop predictions using a limited number of data points. Correlation efficacy was evaluated based on a steam generator case study. The sensitivity to the design parameters was analyzed in detail. The RMSE (root mean square error) of the developed correlations were reduced, through CFD sampling, from 28% to 15% for pressure drop, and from 33% to 25% heat transfer, compared to regression on experimental data only. The best reference correlations have RMSE values of 43% and 33% on pressure drop and heat transfer, respectively, on an independent validation set. Indeed, a radically different fin-tube geometry was suggested for the case study, compared to results using the Escoa correlations.The developed correlations show good to excellent agreement with trends in the CFD model. The quantitative error of predicted heat transfer and pressure drop coefficients at the case study optimum, however, was as large as those of the Escoa correlations. More data are likely needed to improve accuracy for compact heat exchanger designs further.

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“…and Shojaeefard et al (Shojaeefard et al, 2017) focused on the prediction of cooling capacity in heat exchangers. While Li et al employed a Response Surface Methodology (RSM)-based Neural Network (NN) model, Shojaeefard et al evaluated different Artificial Neural Network (ANN) structures in their model.…”

mentioning

confidence: 99%

Recent advances in the applications of machine learning methods for heat exchanger modeling—a review

Zou,

Hirokawa,

et al. 2023

Front. Energy Res.

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Heat exchanger modeling has been widely employed in recent years for performance calculation, design optimizations, real-time simulations for control analysis, as well as transient performance predictions. Among these applications, the model’s computational speed and robustness are of great interest, particularly for the purpose of optimization studies. Machine learning models built upon experimental or numerical data can contribute to improving the state-of-the-art simulation approaches, provided careful consideration is given to algorithm selection and implementation, to the quality of the database, and to the input parameters and variables. This comprehensive review covers machine learning methods applied to heat exchanger applications in the last 8 years. The reviews are generally categorized based on the types of heat exchangers and also consider common factors of concern, such as fouling, thermodynamic properties, and flow regimes. In addition, the limitations of machine learning methods for heat exchanger modeling and potential solutions are discussed, along with an analysis of emerging trends. As a regression classification tool, machine learning is an attractive data-driven method to estimate heat exchanger parameters, showing a promising prediction capability. Based on this review article, researchers can choose appropriate models for analyzing and improving heat exchanger modeling.

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Evaluating different types of artificial neural network structures for performance prediction of compact heat exchanger

Cited by 15 publications

References 29 publications

Performance Estimation of Organic Rankine Cycle by Using Soft Computing Technics

Performance Estimation of Organic Rankine Cycle by Using Soft Computing Technics

A Machine Learning Approach to Correlation Development Applied to Fin-Tube Bundle Heat Exchangers

Recent advances in the applications of machine learning methods for heat exchanger modeling—a review

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