Thermal performance of six different types of wavy-fins

Khaled, A.-R. A.

doi:10.1108/hff-06-2014-0174

Cited by 12 publications

(22 citation statements)

References 27 publications

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“…This table shows that the method used is extremely convergent, and the results are accurate to maximum of four or five decimal places. Also, Table 2 provides the validation of the performed PINN calculations with the published work of Khaled 54 . The result of the PINN excellently converges with the compared outcomes.…”

Section: Resultsmentioning

confidence: 99%

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Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Chandan,

Saadeh,

Qazza

et al. 2024

Sci Rep

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Fins are widely used in many industrial applications, including heat exchangers. They benefit from a relatively economical design cost, are lightweight, and are quite miniature. Thus, this study investigates the influence of a wavy fin structure subjected to convective effects with internal heat generation. The thermal distribution, considered a steady condition in one dimension, is described by a unique implementation of a physics-informed neural network (PINN) as part of machine-learning intelligent strategies for analyzing heat transfer in a convective wavy fin. This novel research explores the use of PINNs to examine the effect of the nonlinearity of temperature equation and boundary conditions by altering the hyperparameters of the architecture. The non-linear ordinary differential equation (ODE) involved with heat transfer is reduced into a dimensionless form utilizing the non-dimensional variables to simplify the problem. Furthermore, Runge–Kutta Fehlberg’s fourth–fifth order (RKF-45) approach is implemented to evaluate the simplified equations numerically. To predict the wavy fin's heat transfer properties, an advanced neural network model is created without using a traditional data-driven approach, the ability to solve ODEs explicitly by incorporating a mean squared error-based loss function. The obtained results divulge that an increase in the thermal conductivity variable upsurges the thermal distribution. In contrast, a decrease in temperature profile is caused due to the augmentation in the convective-conductive variable values.

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Section: Resultsmentioning

confidence: 99%

“…Considering the aforesaid-stated premises, the governing equation is expressed as follows (Poornima et al 53 , Khaled 54 and Ramesh et al 55 ): …”

Section: Formulation Of the Problemmentioning

confidence: 99%

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Chandan,

Saadeh,

Qazza

et al. 2024

Sci Rep

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show abstract

“…Figure 1 illustrates the physical depiction of SWWF. The following governing equation provides the mathematical representation of the wavy fin problem (Kundu and Yook [61], and Khaled [64]).…”

Section: Formulation Of the Problemmentioning

confidence: 99%

Evolutionary Computing for the Radiative–Convective Heat Transfer of a Wetted Wavy Fin Using a Genetic Algorithm-Based Neural Network

Poornima,

Sarris,

Chandan

et al. 2023

Biomimetics

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Evolutionary algorithms are a large class of optimization techniques inspired by the ideas of natural selection, and can be employed to address challenging problems. These algorithms iteratively evolve populations using crossover, which combines genetic information from two parent solutions, and mutation, which adds random changes. This iterative process tends to produce effective solutions. Inspired by this, the current study presents the results of thermal variation on the surface of a wetted wavy fin using a genetic algorithm in the context of parameter estimation for artificial neural network models. The physical features of convective and radiative heat transfer during wet surface conditions are also considered to develop the model. The highly nonlinear governing ordinary differential equation of the proposed fin problem is transmuted into a dimensionless equation. The graphical outcomes of the aspects of the thermal profile are demonstrated for specific non-dimensional variables. The primary observation of the current study is a decrease in temperature profile with a rise in wet parameters and convective-conductive parameters. The implemented genetic algorithm offers a powerful optimization technique that can effectively tune the parameters of the artificial neural network, leading to an enhanced predictive accuracy and convergence with the numerically obtained solution.

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“…For instance, in Ref. [6], various fins having different form of sinusoidal profiles studied. The writer compared the wavy fins in terms of fin efficiency and two different performance indicators.…”

Section: Introductionmentioning

confidence: 99%

The Effect of Using Sinusoidal Profile in Fins on Thermal Performance

Sertel¹,

Bilen²

2019

IJHT

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The aim of the present study is to investigate the effect of using sinusoidal profile in fins on temperature distribution and heat transfer enhancement. The longitudinal fins of rectangular, triangular, wavy rectangular and wavy triangular profiles are studied using analytical and numerical solution techniques. In addition to known profiles, wavy triangular longitudinal fins are proposed as a new fin configuration. The temperature distribution of wavy triangular fins is obtained using Shooting Method along with Cash-Karp Runge Kutta Method. Besides, CFD analyses in which laminar flow regime is concerned at different air velocities is utilized for the comparison of heat transfer rates and convective heat transfer coefficients. CFD results show that heat transfer convection coefficient values for wavy rectangular fins are the lowest ones among the investigated fins. CFD and analytical results indicate that for lower convective coefficients, the dominancy of triangular fins having wave form on plate type triangular fins is more noticeable than the dominancy of wavy rectangular fins on plate type rectangular fins. Also, further heat transfer augmentation can be provided for wavy triangular fins at higher amplitude lengths. For instance, heat transfer enhancement by 15.3 % can be achieved using wavy triangular fins with thickness of 2 cm, amplitude length of 0.21 cm and three-wave profile in comparison with triangular fins having same dimensions.

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Thermal performance of six different types of wavy-fins

Cited by 12 publications

References 27 publications

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Predicting the thermal distribution in a convective wavy fin using a novel training physics-informed neural network method

Evolutionary Computing for the Radiative–Convective Heat Transfer of a Wetted Wavy Fin Using a Genetic Algorithm-Based Neural Network

The Effect of Using Sinusoidal Profile in Fins on Thermal Performance

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