Machine learning for heat transfer correlations

Kwon, Beomjin; Ejaz, Faizan; Hwang, Leslie K.

doi:10.1016/j.icheatmasstransfer.2020.104694

Cited by 87 publications

(28 citation statements)

References 17 publications

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“…In comparison with neural networks, RF is less computationally expensive and can effectively estimate the importance scores of input features, i.e., RF is a useful algorithm for feature importance ranking 82,83 . In spite of the higher computational cost for training, the ANN offers better model accuracy and performance, if the model is well optimized.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

“…RF learning algorithm creates multiple decision trees on input data and then selects the mean predictions of each decision tree as the best solution 82,83 . We use the data of 11 pore structural features and corresponding effective reaction rates as input to train a bagged ensemble of decision trees to estimate the importance value for each pore structural feature.…”

Section: Machine Learning Methodsmentioning

confidence: 99%

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Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Liu

Kwon

Kang

2022

Preprint

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Large discrepancies between well-mixed reaction rates and effective reactions rates estimated under fluid flow conditions have been a major issue for predicting reactive transport in porous media systems. In this study, we introduce a framework that accurately predicts effective reaction rates directly from pore structural features by combining 3D pore-scale numerical simulations with machine learning (ML). We first perform pore-scale reactive transport simulations with fluid-solid reactions in hundreds of porous media and calculate effective reaction rates from pore-scale concentration fields. We then train a Random Forests model with 11 pore structural features and effective reaction rates to quantify the importance of structural features in determining effective reaction rates. Based on the importance information, we train artificial neural networks with varying number of features and demonstrate that effective reaction rates can be accurately predicted with only three pore structural features, which are specific surface, pore sphericity, and coordination number. Finally, global sensitivity analyses using the ML model elucidates how the three structural features affect effective reaction rates. The proposed framework enables accurate predictions of effective reaction rates directly from a few measurable pore structural features, and the framework is readily applicable to a wide range of applications involving porous media flows.

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Section: Machine Learning Methodsmentioning

confidence: 99%

Section: Machine Learning Methodsmentioning

confidence: 99%

Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Liu

Kwon

Kang

2022

Preprint

Self Cite

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“…Researchers across the globe have found machine learning to be very useful. Lindqvist et al [58], Baghban et al [59], Kwon et al [60] and Krishnayatra et al [61] employed a machine learning approach for the development of correlations and predicting the thermal performance for heat exchangers. Baghban et al [59] employed the machine learning approach for predicting the thermal performance of a coiled heat exchanger.…”

Section: Introductionmentioning

confidence: 99%

“…The multilayer perceptron artificial neural network, adaptive neuro-fuzzy inference system and least squares support vector machine model were employed to predict the Nusselt number; they reported that the least squares support vector machine model predicted the results with the best accuracy. Kwon et al [60] employed a random forest algorithm for predicting the heat transfer coefficient by training and testing the machine learning model and reported that the machine learning model predicts the heat transfer coefficient with a high accuracy, i.e., of 96.6%. Ahmadi et al [62] employed neural networking for predicting the friction factor in a car radiator while using CuO-water nanofluid as a working agent.…”

Section: Introductionmentioning

confidence: 99%

Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape

et al. 2021

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A circular tube fitted with novel corrugated spring tape inserts has been investigated. Air was used as the working fluid. A thorough literature review has been done and this geometry has not been studied previously, neither experimentally nor theoretically. A novel experimental investigation of this enhanced geometry can, therefore, be treated as a new substantial contribution in the open literature. Three different spring ratio and depth ratio has been used in this study. Increase in thermal energy transport coefficient is noticed with increase in depth ratio. Corrugated spring tape shows promising results towards heat transfer enhancement. This geometry performs significantly better (60% to 75% increase in heat duty at constant pumping power and 20% to 31% reduction in pumping power at constant heat duty) than simple spring tape. This paper also presented a statistical analysis of the heat transfer and fluid flow by developing an artificial neural network (ANN)-based machine learning (ML) model. The model is evaluated to have an accuracy of 98.00% on unknown test data. These models will help the researchers working in heat transfer enhancement-based experiments to understand and predict the output. As a result, the time and cost of the experiments will reduce. The results of this investigation can be used in designing heat exchangers.

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“…Machine learning is widely used for classification, regression and clustering in various fields such as predictive analysis, computer vision and risk analysis [31][32][33][34]. So, machine learning is used to analyze climate change for the risk of forest fires [35,36] Deterministic models can be used to predict the spread of forest fires. There are many models for the spread of forest fires, from simple geometric models [37] to more complex ones [38][39][40].…”

Section: Introductionmentioning

confidence: 99%

Parallel Implementation of the Algorithm to Compute Forest Fire Impact on Infrastructure Facilities of JSC Russian Railways

2021

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Forest fires have a negative impact on the economy in a number of regions, especially in Wildland Urban Interface (WUI) areas. An important link in the fight against fires in WUI areas is the development of information and computer systems for predicting the fire safety of infrastructural facilities of Russian Railways. In this work, a numerical study of heat transfer processes in the enclosing structure of a wooden building near the forest fire front was carried out using the technology of parallel computing. The novelty of the development is explained by the creation of its own program code, which is planned to be put into operation either in the Information System for Remote Monitoring of Forest Fires ISDM-Rosleskhoz, or in the information and computing system of JSC Russian Railways. In the Russian Federation, it is forbidden to use foreign systems in the security services of industrial facilities. The implementation of the deterministic model of heat transfer in the enclosing structure with the complexity of the algorithm O (2N2 + 2K) is presented. The program is implemented in Python 3.x using the NumPy and Concurrent libraries. Calculations were carried out on a multiprocessor cluster in the Sirius University of Science and Technology. The results of calculations and the acceleration coefficient for operating modes for 1, 2, 4, 8, 16, 32, 48 and 64 processes are presented. The developed algorithm can be applied to assess the fire safety of infrastructure facilities of Russian Railways. The main merit of the new development should be noted, which is explained by the ability to use large computational domains with a large number of computational grid nodes in space and time. The use of caching intermediate data in files made it possible to distribute a large number of computational nodes among the processors of a computing multiprocessor system. However, one should also note a drawback; namely, a decrease in the acceleration of computational operations with a large number of involved nodes of a multiprocessor computing system, which is explained by the write and read cycles in cache files.

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Machine learning for heat transfer correlations

Cited by 87 publications

References 17 publications

Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Machine Learning to Predict Effective Reaction Rates in 3D Porous Media From Pore Structural Features

Heat and Fluid Flow Analysis and ANN-Based Prediction of A Novel Spring Corrugated Tape

Parallel Implementation of the Algorithm to Compute Forest Fire Impact on Infrastructure Facilities of JSC Russian Railways

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