Heat transfer analysis of an inclined longitudinal porous fin of trapezoidal, rectangular and dovetail profiles using cascade neural networks

Khan, Naveed Ahmad; Sulaiman, Muhammad; Alshammari, Fahad Sameer

doi:10.1007/s00158-022-03350-6

Cited by 16 publications

(7 citation statements)

References 51 publications

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“…The stochastic performances using the global/local search operators have been presented for the stiff, grim, complicated, and nonlinear differential systems 43 . Few famous applications of these solvers are coronavirus differential models 44 , food chain models 45 , 46 , transmission of heat in radiative, convective, and moving rod using the thermal conductivity 47 , longitudinal porous fin of trapezoidal 48 , wireless channels 49 , HIV systems 50 , delayed differential model 51 , 52 , thermal explosion models 53 and third order nonlinear singular system 54 .…”

Section: An Overview: Stochastic Operatorsmentioning

confidence: 99%

RETRACTED ARTICLE: A fractional order nonlinear model of the love story of Layla and Majnun

Sabir

Saïd

2023

Sci Rep

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In this study, a fractional order mathematical model using the romantic relations of the Layla and Majnun is numerically simulated by the Levenberg–Marquardt backpropagation neural networks. The fractional order derivatives provide more realistic solutions as compared to integer order derivatives of the mathematical model based on the romantic relationship of the Layla and Majnun. The mathematical formulation of this model has four categories that are based on the system of nonlinear equations. The exactness of the stochastic scheme is observed for solving the romantic mathematical system using the comparison of attained and Adam results. The data for testing, authorization, and training is provided as 15%, 75% and 10%, along with the twelve numbers of hidden neurons. Furthermore, the reducible value of the absolute error improves the accuracy of the designed stochastic solver. To prove the reliability of scheme, the numerical measures are presented using correlations, error histograms, state transitions, and regression.

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Section: An Overview: Stochastic Operatorsmentioning

confidence: 99%

RETRACTED ARTICLE: A fractional order nonlinear model of the love story of Layla and Majnun

Sabir

Saïd

2023

Sci Rep

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“…The novel machine learning of NN-BLMA is easy to apply, handles nonlinear problems, and is also a gradient-free technique that converges faster than other machine learning technique [ 45 , 46 , 47 , 48 ]. Algorithm 1: Pseudocode of NN-BLMA.

…”

Section: Design Methodologymentioning

confidence: 99%

A Numerical Study of the Dynamics of Vector-Born Viral Plant Disorders Using a Hybrid Artificial Neural Network Approach

Alhakami

Umar

Sulaiman

et al. 2022

Entropy

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Most plant viral infections are vector-borne. There is a latent period of disease inside the vector after obtaining the virus from the infected plant. Thus, after interacting with an infected vector, the plant demonstrates an incubation time before becoming diseased. This paper analyzes a mathematical model for persistent vector-borne viral plant disease dynamics. The backpropagated neural network based on the Levenberg–Marquardt algorithm (NN-BLMA) is used to study approximate solutions for fluctuations in natural plant mortality and vector mortality rates. A state-of-the-art numerical technique is utilized to generate reference data for obtaining surrogate solutions for multiple cases through NN-BLMA. Curve fitting, regression analysis, error histograms, and convergence analysis are used to assess accuracy of the calculated solutions. It is evident from our simulations that NN-BLMA is accurate and reliable.

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“…FGMs are materials whose composition and properties change gradually, allowing tailored thermal properties. Although previous research on FGM fins has mostly focused on conduction-convection fins, where the conduction parameter was assumed to be a function of fin length, it is important to consider other important thermal parameters that cannot be neglected 7 . For example, Campo and Wolko made a study of rectangular fins with temperature-dependent power law variations of thermal conductivity and emissivity 8 .…”

Section: Introductionmentioning

confidence: 99%

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials

Sulaiman,

Khalaf,

Khan

et al. 2024

Sci Rep

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This paper presents a study investigating the performance of functionally graded material (FGM) annular fins in heat transfer applications. An annular fin is a circular or annular structure used to improve heat transfer in various systems such as heat exchangers, electronic cooling systems, and power generation equipment. The main objective of this study is to analyze the efficiency of the ring fin in terms of heat transfer and temperature distribution. The fin surfaces are exposed to convection and radiation to dissipate heat. A supervised machine learning method was used to study the heat transfer characteristics and temperature distribution in the annular fin. In particular, a feedback architecture with the BFGS Quasi-Newton training algorithm (trainbfg) was used to analyze the solutions of the mathematical model governing the problem. This approach allows an in-depth study of the performance of fins, taking into account various physical parameters that affect its performance. To ensure the accuracy of the obtained solutions, a comparative analysis was performed using guided machine learning. The results were compared with those obtained by conventional methods such as the homotopy perturbation method, the finite difference method, and the Runge–Kutta method. In addition, a thorough statistical analysis was performed to confirm the reliability of the solutions. The results of this study provide valuable information on the behavior and performance of annular fins made from functionally graded materials. These findings contribute to the design and optimization of heat transfer systems, enabling better heat management and efficient use of available space.

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Heat transfer analysis of an inclined longitudinal porous fin of trapezoidal, rectangular and dovetail profiles using cascade neural networks

Cited by 16 publications

References 51 publications

RETRACTED ARTICLE: A fractional order nonlinear model of the love story of Layla and Majnun

RETRACTED ARTICLE: A fractional order nonlinear model of the love story of Layla and Majnun

A Numerical Study of the Dynamics of Vector-Born Viral Plant Disorders Using a Hybrid Artificial Neural Network Approach

Machine learning-based prediction of heat transfer performance in annular fins with functionally graded materials

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