Machine Learning based algorithms for modeling natural convection fluid flow and heat and mass transfer in rectangular cavities filled with non-Newtonian fluids

Tizakast, Youssef; Kaddiri, Mourad; Lamsaadi, Mohamed; Makayssi, T.

doi:10.1016/j.engappai.2022.105750

Cited by 13 publications

(3 citation statements)

References 76 publications

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“…The tree models in Booster are implemented based on the Gradient Boosting algorithm, and the output from each tree model is added to the output of all previous tree models to improve prediction accuracy. 37 The final output is a weighted sum of all of the tree model outputs. The architecture of the model is shown in Figure 3, the training steps are as follows:…”

Section: Machine Learning Model and Data Creationmentioning

confidence: 99%

“…The core of XGBoost is the Booster, which consists of multiple tree models, each acting as a weak classifier for predicting the target variable. The tree models in Booster are implemented based on the Gradient Boosting algorithm, and the output from each tree model is added to the output of all previous tree models to improve prediction accuracy . The final output is a weighted sum of all of the tree model outputs.…”

Section: Machine Learning Model and Data Creationmentioning

confidence: 99%

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Comprehensive Investigation of Operating Parameters for Enhanced CO₂ Capture Using CaO Sorbent and Machine Learning

Wang,

Li,

et al. 2023

Energy Fuels

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The addition of sorbent to capture CO2 in steam methane reforming has become a method to reduce CO2 emissions in conventional hydrogen production. The aim of this paper is to propose a fast method for predicting the capture performance of sorbents by applying the eXtreme Gradient Boosting (XGBoost) method. First, the effects of inlet temperature, velocity, CO2 mass fraction, and initial material inventory height on the sorbent CaO capture efficiency and resulting flow regime are systematically analyzed, while an extensive database is constructed. Second, a comparative assessment is conducted to determine the relative significance of the four parameters in influencing the capture efficiency. Finally, the XGBoost model is trained and deployed to enhance the computational accuracy of capture efficiency predictions. The results emphasize that temperature has the most significant effect on capture efficiency. Increasing the temperature and decreasing the gas velocity helped to increase the capture efficiency. The initial material inventory height of 0.3 m proved to be favorable for CO2 capture compared to 0.1 and 0.2 m, especially under gas velocity conditions of 0.3 m/s. In addition, the XGBoost model can be used to quickly predict the CO2 capture efficiency of fluidized beds by using less training data and obtaining the prediction result with a high accuracy of R2 = 0.9699 in 2 s. This model has potential applications for the prediction of the sorbent capture efficiency.

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Section: Machine Learning Model and Data Creationmentioning

confidence: 99%

Section: Machine Learning Model and Data Creationmentioning

confidence: 99%

Comprehensive Investigation of Operating Parameters for Enhanced CO₂ Capture Using CaO Sorbent and Machine Learning

Wang,

Li,

et al. 2023

Energy Fuels

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show abstract

“…An ANN model accurately predicts the distribution of local Nusselt numbers with less computational time and cost than DNS. Youssef Tizakast et al 33 used Machine Learning models to study double‐diffusive natural convection in rectangular cavities filled with non‐Newtonian fluids due to the complexity of the problem. Four models (ANN, RF, GBDT, and XGBoost) were employed to predict flow intensity, average Nusselt number, and average Sherwood number, based on four dimensionless parameters.…”

Section: Introductionmentioning

confidence: 99%

Double diffusive Buoyancy‐driven flow in a fluid‐saturated elliptical annulus with a neural network‐based prediction of heat and mass transfer

et al. 2023

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This paper presents a numerical study of buoyancy‐driven double‐diffusive convection within an elliptical annulus enclosure filled with a saturated porous medium. An in‐house built FORTRAN code has been developed, and computations are carried out in a range of values of Darcy–Rayleigh number Ram (10 ≤ Ram ≤ 500), Lewis number Le (0.1 ≤ Le ≤ 10), and the ratio of buoyancy forces N (−5 ≤ N ≤ 5). In addition, three methods are used, namely the multi‐variable polynomial regression, the group method of data handling (GMDH), and the artificial neural network (ANN) for the predictions of heat and mass transfer rates. First, results are successfully validated with existing numerical and experimental data. Then, the results indicated that temperature and concentration distributions are sensitive to the Lewis number and thermal and mass plumes are developing in proportion to the Lewis number. Two particular values of Lewis number Le = 2.735 and Le = 2.75 captured the flow's transition toward an asymmetric structure with a bifurcation of convective cells. The average Nusselt number tends to have an almost asymptotic value for Le » 5. For the case of aiding buoyancies N > 1, the average Nusselt Number trueNu¯ $\bar{{Nu}}$ decreased by 33% when the Lewis number increased to its maximum value. Then, it increased by 10% when the Lewis number increased to Le = 1 for the case of opposing buoyancies N < 1 and then decreased by 33% when the Lewis number increased to its maximum value., contrary to the behavior of the average Sherwood number trueSh¯ $\bar{{Sh}}$ that increased by 700% for both cases N > 1 and N < 1. New correlations of trueNu¯ $\bar{{Nu}}$, and trueSh¯ $\bar{{Sh}}$ as a function of Ram, Le, and N are derived and compared with GMDH and ANN methods, and the ANN method showed higher performance for the prediction of trueNu¯ $\bar{{Nu}}$ and trueSh¯ $\bar{{Sh}}$ with R2 exceeding 0.99.

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Modeling of transient free convection flow in terms of permeability and thermal radiation

Hasan,

Khan,

Biswas

et al. 2024

J Therm Anal Calorim

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Machine Learning based algorithms for modeling natural convection fluid flow and heat and mass transfer in rectangular cavities filled with non-Newtonian fluids

Cited by 13 publications

References 76 publications

Comprehensive Investigation of Operating Parameters for Enhanced CO₂ Capture Using CaO Sorbent and Machine Learning

Comprehensive Investigation of Operating Parameters for Enhanced CO₂ Capture Using CaO Sorbent and Machine Learning

Double diffusive Buoyancy‐driven flow in a fluid‐saturated elliptical annulus with a neural network‐based prediction of heat and mass transfer

Modeling of transient free convection flow in terms of permeability and thermal radiation

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Machine Learning based algorithms for modeling natural convection fluid flow and heat and mass transfer in rectangular cavities filled with non-Newtonian fluids

Cited by 13 publications

References 76 publications

Comprehensive Investigation of Operating Parameters for Enhanced CO2 Capture Using CaO Sorbent and Machine Learning

Comprehensive Investigation of Operating Parameters for Enhanced CO2 Capture Using CaO Sorbent and Machine Learning

Double diffusive Buoyancy‐driven flow in a fluid‐saturated elliptical annulus with a neural network‐based prediction of heat and mass transfer

Modeling of transient free convection flow in terms of permeability and thermal radiation

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Product

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Comprehensive Investigation of Operating Parameters for Enhanced CO₂ Capture Using CaO Sorbent and Machine Learning

Comprehensive Investigation of Operating Parameters for Enhanced CO₂ Capture Using CaO Sorbent and Machine Learning