Heat transfer analysis in a longitudinal porous trapezoidal fin by non-Fourier heat conduction model: An application of artificial neural network with Levenberg–Marquardt approach

Goud, J. Suresh; Srilatha, Pudhari; Kumar, R. S. Varun; Sowmya, G.; Gamaoun, Fehmi; Nagaraja, K.V.; Singh, Sandeep; Khan, Umair; Eldin, Sayed M.

doi:10.1016/j.csite.2023.103265

Cited by 27 publications

(2 citation statements)

References 43 publications

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“…The FEA simulation results show that conductivity, k, the heat transfer rate of sawdust, is calculated using equation (2) per heat conduction formula (Fourier's law) [31], [32], [33].…”

Section: Resultsmentioning

confidence: 99%

Analysis of the Propagation of Temperature and Thermal Conductivity of Sawdust Pyrolysis Process with Modeling Fea and Experiment

David Rey,

Mujiyono,

Nurhadiyanto

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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The study uses densified sawdust with heat propagation and thermal conductivity to achieve better pyrolysis and gasification processes. Analysis of sawdust's heat propagation and thermal conductivity during pyrolysis was developed with Finite Element Analysis (FEA). The thermal conductivity values of sawdust (k) obtained from FEA are 1.49, 1.3, 1.1, and 0.7 W/m C considering the sawdust heated with a triggering temperature (T0) 535 ºC and reaches a constant temperature of T1 = 265 ºC, T2 = 165 ºC, T3 = 115, and T4 = 95 ºC respectively after 20 minutes. The temperature distribution of sawdust heated shows between the experimental and FEA results is quite the same; however, differences on graph T1 show the experimental thermal conductivity (k) value is distinct from the simulated thermal conductivity (k) value due to a significant change that occurred as sawdust transformed into charcoal at minute 70, where the distance between point T1 and the trigger point (T0) is 40mm and after 180 minutes the sawdust had turned into charcoal completely. Furthermore, 31 grams (6.46%) of sawdust underwent pyrolysis in this experiment. The findings can aid future research and development in this field and provide valuable insight into the duration of time a bio stove utilizing compacted sawdust can produce a sustainable flame and be used as a reference for future experiments.

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“…The FEA simulation results show that conductivity, k, the heat transfer rate of sawdust, is calculated using equation (2) per heat conduction formula (Fourier's law) [31], [32], [33].…”

Section: Resultsmentioning

confidence: 99%

Analysis of the Propagation of Temperature and Thermal Conductivity of Sawdust Pyrolysis Process with Modeling Fea and Experiment

David Rey,

Mujiyono,

Nurhadiyanto

et al. 2024

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Stimulated by the working of the human brain, ANNs simulate the process of learning and pattern recognition, which is advantageous for solving several complex problems. In comparison to traditional statistical regression methods, ANN exhibits superior predictive ability and can autonomously identify meaningful patterns and features in data without prior knowledge or explicit insight (Cui et al [30] and Goud et al [31]). Additionally, they can iteratively improve performance over many training cycles and rapidly estimate overall statistics when applied to datasets containing numerous variables.…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Analysis of Heat Transfer Behavior of Porous Wavy Fin with Radiation and Convection by Using a Machine Learning Technique

Kumar,

Nimmy,

Nagaraja

et al. 2023

Symmetry

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The impact of convection and radiation on the thermal distribution of the wavy porous fin is examined in the present study. A hybrid model that combines the differential evolution (DE) algorithm with an artificial neural network (ANN) is proposed for predicting the heat transfer of the wavy porous fin. The equation representing the thermal variation in the wavy porous fin is reduced to its dimensionless arrangement and is numerically solved using Rung, e-Kutta-Fehlberg’s fourth-fifth order method (RKF-45). The study demonstrates the effectiveness of this hybrid model, and the results indicate that the proposed approach outperforms the ANN model with parameters obtained through grid search (GS), showcasing the superiority of the hybrid DE-ANN model in terms of accuracy and performance. This research highlights the potential of utilizing DE with ANN for improved predictive modeling in the heat transfer sector. The originality of this study is that it addresses the heat transfer problem by optimizing the selection of parameters for the ANN model using the DE algorithm.

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Thermal analysis of natural convection in rectangular porous fin wetted with CNTs nanoparticles and thermal radiation

Thimlapura Nagaraju,

Linganna,

Varma

et al. 2024

Z Angew Math Mech

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In the present investigation, the phenomenon of heat conduction in rectangular shaped porous fin wetted with nanofluid (a mixture of carbon nanotube [CNT] with water as base liquid) is examined using the local thermal non‐equilibrium (LTNE) paradigm. The heat transport mechanism involving the nanofluid and solid phases is represented by the dimensional thermal governing ordinary differential equations (TGODEs). These equations are transformed into nonlinear ordinary differential equations (ODEs) using relevant non‐dimensional variables. To solve the resultant dimensionless TGODEs, probabilists collocation method with Hermite polynomials (PCMHPs) is utilized. This study of temperature analysis has examined the characteristics of internal and exterior radiation, convection, and thermal conductivity to determine the attributes affecting heat transfer. For both the nanofluid and solid phase aspects, temperature distribution characteristics are revealed in tables and graphs. Subsequently, it is determined that as surface‐ambient radiation parameter levels decreased, the temperature profile of both solid and nanofluid phase augmented. The temperature variance among the solid and nanofluid phases decreased with an escalation in the wet porous parameter. The numerical outcomes illustrate that the presented PCMHP approach is not only convenient to execute but also provides accurate results.

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Heat transfer analysis in a longitudinal porous trapezoidal fin by non-Fourier heat conduction model: An application of artificial neural network with Levenberg–Marquardt approach

Cited by 27 publications

References 43 publications

Analysis of the Propagation of Temperature and Thermal Conductivity of Sawdust Pyrolysis Process with Modeling Fea and Experiment

Analysis of the Propagation of Temperature and Thermal Conductivity of Sawdust Pyrolysis Process with Modeling Fea and Experiment

Analysis of Heat Transfer Behavior of Porous Wavy Fin with Radiation and Convection by Using a Machine Learning Technique

Thermal analysis of natural convection in rectangular porous fin wetted with CNTs nanoparticles and thermal radiation

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