Application of machine learning methods for investigating the heat transfer enhancement performance in a circular tube with artificial roughness

Koroleva, A P; Kuzmenkov, N V; Frantcuzov, M S

doi:10.1088/1742-6596/1675/1/012008

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…The limitations are the same as those of ANN created by Akdag et al (2016). Another study was also proposed by Koroleva et al (2020) to investigate heat transfer enhancement performance using computational fluid dynamics and artificial neural networks.…”

Section: Comparison Of Previous Studies and Current Modelsmentioning

confidence: 99%

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Stacked Heterogeneous Ensemble Learning Model in Mixed Convection Heat Transfer from a Vertically Oscillating Flat Plate

Akçay

Buyrukoğlu

Akdag

2023

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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In this study, the effects of mixed convection heat transfer from a moving vertical flat plate with an experimental and stacked heterogeneous ensemble learning approach are analyzed. In the experimental work, the effects on both natural and forced convection of dimensionless oscillation amplitude (Ao), dimensionless oscillation frequency (Wo) and Rayleigh number (Ra) are investigated. In the experiments, the vertical movement of the plate is provided by a flywheel-motor assembly. The average Nusselt numbers (Nu) on the fixed plate and the moving plate surface were obtained. Additionally, this study is focused on the prediction of heat transfer of a moving flat plate using single-based algorithms (Gradient Boosting, AdaBoost, Multilayer Per-ceptron) and a stacked heterogeneous ensemble learning model. The statistical per-formance of the single-based algorithms and the stacked ensemble model is meas-ured in the prediction of mixed convection heat transfer. The results show that the stacked-based ensemble learning model yielded the MSE = 2.01, RMSE = 1.42, MAE = 1.1 and R2 = 0.99 values. Overall, this study reveals that the proposed stacked en-semble machine learning model can be used successfully for modeling convection heat transfer of a moving plate.

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Section: Comparison Of Previous Studies and Current Modelsmentioning

confidence: 99%

“…The used ANN provided a better MAE (0.316) score than the Linear Regressor. Koroleva et al (2020) investigated heat transfer enhancement performance using computational fluid dynamics and artificial neural networks. 5.17×10 -5 MSE score is obtained through the proposed approach.…”

Section: Introductionmentioning

confidence: 99%

Stacked Heterogeneous Ensemble Learning Model in Mixed Convection Heat Transfer from a Vertically Oscillating Flat Plate

Akçay

Buyrukoğlu

Akdag

2023

Osmaniye Korkut Ata Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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“…Koroleva et al presented a study that uses a hybrid approach combining computational fluid dynamics and artificial neural networks to investigate heat transfer enhancement performance in pipes. 34 They discussed the use of artificial roughness elements, such as transverse rectangular ribs, to enhance heat transfer, highlighting the need to find optimal rib roughness parameters for each specific case. The study proposes the use of machine learning methods, such as artificial neural networks, to accurately predict major parameters such as Nusselt number, friction factor, and thermal hydraulic performance, which can be difficult and costly to determine otherwise.…”

Section: Introductionmentioning

confidence: 99%

LBM curved boundary treatments for pulsatile flow on convective heat transfer and friction factor in corrugated channels

Aslan,

Ozsaban,

Kucur

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The present research investigates heat transfer and the flow characteristics of periodically corrugated wavy channels numerically under pulsatile flow conditions. The numerical method used here is Lattice Boltzmann Method (LBM), and the validation of the study is done by Ansys-Fluent which is finite volume based commercial Computational Fluid Dynamics (CFD) code. For modeling walls, bounce-back method, namely, staircase method and three different curved boundary treatments, which are extrapolation, Filippova-Hänel (FH) and Mei-Luo-Shy (MLS), are used. For modeling constant temperature at walls, staircase method and the same curved wall treatments are used. Corrugated channels have a sharp wavy peak, and its inclination angle is 30°. Two different minimum channel heights are considered, which are 5 and 10 mm in corrugated channels. Flow regime is assumed as laminar (50 < Re < 300) and Prandtl number is kept as 0.7. Four kinds of different sinusoidal pulsatile flows are used with a combination of two different dimensionless frequencies and dimensionless amplitudes. For varying Reynolds number range, Nusselt number and friction factor are calculated. Narrow channel creates higher Nusselt number and friction factor than wide channel. At low Reynolds number, Nusselt number does not changed with pulsatile flow conditions, however at high Reynolds number cases of lower dimensionless frequency and higher dimensionless amplitude produce higher Nusselt numbers. Lower dimensionless frequency cases produce higher Nusselt number than higher dimensionless frequency cases. Pulsatile flow conditions have no effect on friction factor and for narrow and wide channel. Nusselt number prediction of FVM is close to STR and EXT for all cases of narrow channels, and close to the FH, MLS and EXT for all cases wide channel. Correlation equations for Nusselt number and friction factor are constructed by deep neural network (DNN) algorithm.

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“…The hybrid approach for heat transfer enhancement using computational fluid dynamics and artificial neural networks was implemented. An artificial neural network (ANN) was implemented to find optimum rib roughness due to low computational cost and prediction error of less than 1.5% [22]. The random forest algorithm was used to predict the convection heat transfer coefficients for a cooling channel unified with adjustable rib coarseness [23].…”

Section: Introductionmentioning

confidence: 99%

Experimental Thermohydraulic Assessment of Novel Curved Ribs for Heat Exchanger Tubes: A Machine Learning Approach

Deshmukh,

Lahane,

Sumant

et al. 2023

Aerospace

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Heat transfer enhancement using curved ribs of different cross sections, viz., square, rectangular, triangular, and circular, is a crucial study for designing heat-exchanging devices for various applications, and their thermohydraulic performance prediction using machine learning technique is a vital part of the modern world. An experimental study on using curved ribs suitable for heat transfer enhancement for the circular tube is presented for turbulent airflow with Reynolds numbers varying from 10,000 to 50,000. The machine learning methodology is used to predict the thermohydraulic performance assessment of curved ribs. The square cross-sectioned curved ribs produce the highest performance factor R3 of 1.5 to 2.65 to the equivalent Reynolds number Rec value of 20,000. It is observed that most of the curved rib configurations show a performance ratio R3 maximum and are suitable at a low Reynolds number value. At moderate and high Reynolds number values, the performance factor values decrease due to a rise in the pressure drop values for a few curved rib configurations. An artificial neural network (ANN) model predicts with an accuracy of 95% with the present study experimental values for the heat transfer performance indicators like average heat transfer enhancement Nua/Nus, average heat transfer enhancement fa/fs, and performance ratio R3, i.e., Nua/Nuc.

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Application of machine learning methods for investigating the heat transfer enhancement performance in a circular tube with artificial roughness

Cited by 7 publications

References 6 publications

Stacked Heterogeneous Ensemble Learning Model in Mixed Convection Heat Transfer from a Vertically Oscillating Flat Plate

Stacked Heterogeneous Ensemble Learning Model in Mixed Convection Heat Transfer from a Vertically Oscillating Flat Plate

LBM curved boundary treatments for pulsatile flow on convective heat transfer and friction factor in corrugated channels

Experimental Thermohydraulic Assessment of Novel Curved Ribs for Heat Exchanger Tubes: A Machine Learning Approach

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