Browsing by Author "Gnanasekaran, N."

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3D coupled conduction-convection problem using in-house heat transfer experiments in conjunction with hybrid inverse approach
(Emerald Group Holdings Ltd., 2019) Vishweshwara, P.S.; Kumar, M.K.; Gnanasekaran, N.; Mahalingam, A.
Purpose: Many a times, the information about the boundary heat flux is obtained only through inverse approach by locating the thermocouple or temperature sensor in accessible boundary. Most of the work reported in literature for the estimation of unknown parameters is based on heat conduction model. Inverse approach using conjugate heat transfer is found inadequate in literature. Therefore, the purpose of the paper is to develop a 3D conjugate heat transfer model without model reduction for the estimation of heat flux and heat transfer coefficient from the measured temperatures. Design/methodology/approach: A 3 D conjugate fin heat transfer model is solved using commercial software for the known boundary conditions. Navier–Stokes equation is solved to obtain the necessary temperature distribution of the fin. Later, the complete model is replaced with neural network to expedite the computations of the forward problem. For the inverse approach, genetic algorithm (GA) and particle swarm optimization (PSO) are applied to estimate the unknown parameters. Eventually, a hybrid algorithm is proposed by combining PSO with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method that outperforms GA and PSO. Findings: The authors demonstrate that the evolutionary algorithms can be used to obtain accurate results from simulated measurements. Efficacy of the hybrid algorithm is established using real time measurements. The hybrid algorithm (PSO-BFGS) is more efficient in the estimation of unknown parameters for experimentally measured temperature data compared to GA and PSO algorithms. Originality/value: Surrogate model using ANN based on computational fluid dynamics simulations and in-house steady state fin experiments to estimate the heat flux and heat transfer coefficient separately using GA, PSO and PSO-BFGS. © 2019, Emerald Publishing Limited.
3D Numerical Modelling of Turbulent Flow in a Channel Partially Filled with Different Blockage Ratios of Metal Foam
(Isfahan University of Technology, 2024) Narkhede, A.; Gnanasekaran, N.
The aim of the present research work is to understand the intricacies of fluid flow through a rectangular channel that has been partially filled with a metal foam block of different blockage ratio (0.16-1), with a pore density (5-30 Pores Per Inch i.e. PPI), along with varying inlet velocity (6.5-12.5 m/s). For the porous region, numerical solutions are acquired using the Darcy Extended Forchheimer model. The Navier-Stokes equation is used in the non-porous zone. Different flow behaviours were seen as a function of PPI, height, and inlet velocity. The pressure drop increases with inlet velocity, PPI, and block height, with a maximum value of approximately 4.5 kPa for the case of 30 PPI, 12.5 m/s, and a blockage ratio of 1. Results show that the existence and location of the formation of eddies depends on the inlet velocity, PPI, and blockage ratio. Such studies have been reported less and will aid research on forced convection through a channel partially filled with metal foam and optimisation studies between increased heat transmission and the additional pressure drop for the same by providing a detailed fluid flow analysis. © (2024), (Isfahan University of Technology). All Rights Reserved.
A Bayesian inference approach: estimation of heat flux from fin for perturbed temperature data
(Springer India, 2018) Kumar, H.; Gnanasekaran, N.
This paper reports the estimation of the unknown boundary heat flux from a fin using the Bayesian inference method. The setup consists of a rectangular mild steel fin of dimensions 250×150×6 mm3 and an aluminium base plate of dimensions 250×150×8 mm3. The fin is subjected to constant heat flux at the base and the fin setup is modelled using ANSYS14.5. The problem considered is a conjugate heat transfer from the fin, and the Navier–Stokes equation is solved to obtain the flow parameters. Grid independence study is carried out to fix the number of grids for the study considered. To reduce the computational cost, computational fluid dynamics (CFD) is replaced with artificial neural network (ANN) as the forward model. The Markov Chain Monte Carlo (MCMC) powered by Metropolis–Hastings sampling algorithm along with the Bayesian framework is used to explore the estimation space. The sensitivity analysis of the estimated temperature with respect to the unknown parameter is discussed to know the dependency of the temperature with the parameter. This paper signifies the effect of a prior model on the execution of the inverse algorithm at different noise levels. The unknown heat flux is estimated for the surrogated temperature and the estimates are reported as mean, Maximum a Posteriori (MAP) and standard deviation. The effect of a-priori information on the estimated parameter is also addressed. The standard deviation in the estimation process is referred to as the uncertainty associated with the estimated parameters. © 2018, Indian Academy of Sciences.
A combined ANN-GA and experimental based technique for the estimation of the unknown heat flux for a conjugate heat transfer problem
(Springer Verlag service@springer.de, 2018) Kumar, M.K.; Vishweshwara, P.S.; Gnanasekaran, N.; Balaji, C.
The major objectives in the design of thermal systems are obtaining the information about thermophysical, transport and boundary properties. The main purpose of this paper is to estimate the unknown heat flux at the surface of a solid body. A constant area mild steel fin is considered and the base is subjected to constant heat flux. During heating, natural convection heat transfer occurs from the fin to ambient. The direct solution, which is the forward problem, is developed as a conjugate heat transfer problem from the fin and the steady state temperature distribution is recorded for any assumed heat flux. In order to model the natural convection heat transfer from the fin, an extended domain is created near the fin geometry and air is specified as a fluid medium and Navier Stokes equation is solved by incorporating the Boussinesq approximation. The computational time involved in executing the forward model is then reduced by developing a neural network (NN) between heat flux values and temperatures based on back propagation algorithm. The conjugate heat transfer NN model is now coupled with Genetic algorithm (GA) for the solution of the inverse problem. Initially, GA is applied to the pure surrogate data, the results are then used as input to the Levenberg- Marquardt method and such hybridization is proven to result in accurate estimation of the unknown heat flux. The hybrid method is then applied for the experimental temperature to estimate the unknown heat flux. A satisfactory agreement between the estimated and actual heat flux is achieved by incorporating the hybrid method. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.
A Markov Chain Monte Carlo-Metropolis Hastings Approach for the Simultaneous Estimation of Heat Generation and Heat Transfer Coefficient from a Teflon Cylinder
(Taylor and Francis Ltd. michael.wagreich@univie.ac.at, 2018) Kumar, H.; Kumar, S.; Gnanasekaran, N.; Balaji, C.
This paper reports the use of Markov Chain Monte Carlo (MCMC) and Metropolis Hastings (MH) approach, to solve an inverse heat transfer problem. Three-dimensional, steady state, conjugate heat transfer from a Teflon cylinder of dimensions 100 mm diameter and 100 mm length with uniform volumetric internal heat generation is considered. The goal is to estimate volumetric heat generation and heat transfer coefficient, given the temperature data at certain fixed location on the surface of the cylinder. The internal volumetric heat generation is specified as input and the temperature and heat transfer coefficient values are obtained by a numerical solution to the governing equation. The temperature values also depend on heat transfer coefficient which is obtained by solving Navier–Stokes equation to obtain flow information. In order to reduce the computational cost, a neural network is trained from the computational fluid dynamics simulations. This is posed as an inverse problem wherein volumetric heat generation and heat transfer coefficient are unknown but the temperature data is known by conducting experiments. The novelty of the paper is the simultaneous determination of volumetric heat generation and heat transfer coefficient for the experimentally measured steady-state temperatures from a Teflon cylinder using MCMC-MH as an inverse model in a Bayesian framework and finally, the estimates are reported in terms of mean, maximum a posteriori, and the standard deviation which is the uncertainty associated with the estimated parameters. © 2018 Taylor & Francis Group, LLC.
A neural network based method for estimation of heat generation from a teflon cylinder
(Global Digital Central, 2016) Kumar, S.; Kumar, H.; Gnanasekaran, N.
The paper reports the estimation of volumetric heat generation (qv) from a Teflon cylinder. An aluminum heater, which acts as a heat source, is placed at the center of the Teflon cylinder. The problem under consideration is modeled as a three dimensional steady state conjugate heat transfer from the Teflon cylinder. The model is created and simulations are performed using ANSYS FLUENT to obtain temperature data for the known heat generation qv. The numerical model developed using ANSYS acts as a forward model. The inverse model used in this work is Artificial Neural Network (ANN). Estimation of heat generation is carried out by minimizing the error between the simulated temperature and the experimental/surrogated temperature. The efficacy of the ANN method is explored for the estimation of unknown heat generation as both forward model and inverse model. The concept of Asymptotic Computational Fluid Dynamics (ACFD) is introduced as a fast forward model which is obtained by performing CFD simulations. The unknown heat generation is estimated for the surrogated data using ANN. In order to mimic experiments, noise is added to the surrogated data and estimation of heat generation is also carried out for the perturbed/noise added temperature data. © 2016, Global Digital Central. All rights reserved.
A New Forward Model Approach for a Mild Steel Fin under Natural Convection Heat Transfer
(Elsevier Ltd, 2015) Kulkarni, A.S.; Kumar, H.; Gnanasekaran, N.
This paper reports the correlation for temperature of the mild steel fin which is subjected to heat flux at its base. The study is performed on a two dimensional, steady state and laminar flow model. The numerical model is restricted to natural convection and the fluid under consideration is air. A rectangular mild steel fin (250 mm x 150 mm x 6 mm), aluminium base plate (250 mm x 150 mm x 8 mm) and an extended geometry representing the ambient air condition is modelled and simulated using ANSYS 14.5. Grid independence study is carried out to fix the number of grids in order to find the optimum number of nodes for carrying out simulations. The heat flux (q) at the bottom of the base plate is varied to study temperature distribution, surface heat transfer coefficient (h) and velocity profile of the flow in the boundary layer around the fin. All these parameters are studied by inclining the model at various angles. A multiple regression analysis is carried out to obtain correlation for the temperature in terms of angle of inclination and the heat flux. The main objective of the work is, proposing a model for the estimation of heat flux or heat transfer coefficient from the fin thereby reducing the computational cost of the forward model in the field of inverse heat transfer. Â© 2015 The Authors.
A novel framework for the estimation of interfacial heat transfer coefficient using Bat algorithm during solidification of metal casting
(Toronto Metropolitan University, 2019) Vishweshwara, P.S.; Gnanasekaran, N.; Arun, M.
In the present work, the interfacial heat transfer coefficient (IHTC) at the mold metal interface is estimated during solidification of Al-4.5wt%Cu alloy using ANN-Bat-Bayesian framework. The forward model comprises of a one dimensional transient governing equation for the solidification of metal casting and is solved using explicit finite difference scheme with the available IHTC correlation from the literature. Within the range of values of constants of IHTC correlation, a set of numerical simulation is performed and corresponding temperature output is trained using Artificial Neural Network (ANN). The network created acts a fast forward model replacing the FDM scheme during IHTC estimation thus reducing computational time. Bat algorithm is used as inverse method along with the Bayesian framework, that drives towards the accurate retrieval of unknown parameters. Â© 2019, Toronto Metropolitan University. All rights reserved.
A novel optimized liquid cooled heat sink integrated with 3D lattice structure under different blockage ratios
(Elsevier Ltd, 2025) Narkhede, A.; Gnanasekaran, N.; Yadav, A.K.
In this numerical work, investigation is focused on thermo-hydraulic nature of a periodic metal foam-integrated heat sink with an octet lattice-structure topology. Heat sink is partially filled with octet structure based periodic metal foam having 2.5 mm unit cell length with blockage ratios of 0.25/0.5/0.75/1, porosity of 0.83/0.87/0.91, and flow velocity of 0.02–0.05 m/s for electronic thermal management. The effect of porosity and blockage ratio on the wall temperature and pressure gradient of the heat sink is examined. Among all configurations, the lowest value of wall temperature of 311.24 K and the highest value of pressure gradient of 5091 Pa/m are observed for the case of blockage ratio 1, porosity 0.83, and flow velocity of 0.05 m/s. Additionally, the thermo-hydraulic performance enhancement owing to the partly packed configuration is observed based on the enhancement ratio and thermo-hydraulic performance parameter (THPP). The highest enhancement ratio is observed for the case with a blockage ratio of 1, porosity of 0.83, and a velocity of 0.02 m/s. The thermal design with a velocity of 0.03 m/s, a blockage ratio of 0.75, and a porosity of 0.83 is considered the optimal design in accordance with the THPP, which has a value of approximately 1.7. © 2025 Elsevier Ltd
A Parametric Study on Mixed Convection in a Vertical Channel in the Presence of Wire Mesh
(Taylor and Francis Ltd., 2021) Kotresha, B.; Gnanasekaran, N.
A numerical study on mixed convection is carried out through a partially filled brass wire mesh in a vertical channel. A heater embedded with aluminum plates is placed at the center of the vertical channel. The aluminum heater assembly is wrapped with brass wire mesh to facilitate more heat transfer. The vertical channel that consists of aluminum heater assembly with the brass wire mesh is considered as the numerical model. Local thermal non-equilibrium and Darcy extended Forchheimer models are used to accomplish the numerical simulations for thermal and flow characteristics of the considered domain. The aim of the study is to find out the optimum filling of the brass wire mesh in the channel which gives a higher heat transfer rate with low pumping power of the fluid. In the present analysis, three different filling conditions of wire mesh are considered: (i) fully filled channel, (ii) 70% filled channel, and (iii) 40% filled channel. From the results, it is inferred that the vertical channel partially filled with 70% of wire mesh porous medium predicts 89% of heat transfer of the completely filled channel with 41% reduced pressure loss. As a result, the proposed parametric study is good enough to prove that the partly filled wire mesh can be used in the thermal applications where augmentation of heat transfer is required with less pressure drop. © 2020 Taylor & Francis Group, LLC.
A review of lattice boltzmann method computational domains for micro-and nanoregime applications
(Begell House Inc., 2020) Narendran, G.; Arumuga Perumal, A.P.; Gnanasekaran, N.
In the last two decades, microscale and nanoscale devices have received much interest due to the inevitable performance and their numerous applications not only in the field of fluid flow and heat transfer but also in bio-technology, bio-medical engineering, etc. In many situations, besides the conventional experiments and theoretical analysis, computations have emerged as a valuable tool for investigating the fluid transport and heat transfer phenomena. The lattice Boltzmann method (LBM) has emerged as an important option for micro-and nanoscale devices due to the fact that the LBM is well established for the range of Knudsen number. A comparative study on several working fluids used in the field of micro-and nanodevices such as microchannel, micro-cavity, microboiling, and nanochannel is categorized. Various aspects of nanofluids used in natural convection with different cavity configurations, flow boiling, immiscible fluids, liquid–vapor phase change are also critically reviewed. Different remarks and findings of available numerical results with several investigated parameters were summarized. © 2020 Begell House, Inc.
A review on recent advances in microchannel heat sink configurations
(Bentham Science Publishers, 2018) Narendran, G.; Gnanasekaran, N.; Arumuga Perumal, D.A.
A qualitative observation has been undergone to review the various geometries of a microchannel that has been reported for the last two decades in literature majorly for the application of high power devices. Recent research on microchannel is more focused on numerical and experimental work with various configurations of the heat sink. In this paper, a comparative work on different flow geometries used in the microchannel and their influence on heat transfer and pressure drop is investigated with the brief representation of different working fluids used in microchannel heat sink for the purpose of electronic cooling and their associated performance characteristics with various examined parameters. Background: The microchannel cooling is an established cooling technique for high power electronic components which effectively enhances the performance of the high power devices. Objective: This article presents a general overview of microchannels with novel constructional bifurcations structures with related patents. Further, the influential parameter on thermal and flow characteristics with greater depth is also reviewed by authors. Methods: This review directs by presenting standard and benchmark investigation in the microchannel and different working parameters continued with recent studies. Further, it is addressed with the application of electronic cooling with latest patents using bifurcations and fractal microchannels. Result: The current situation of 3D cooling requires a robust cooling system to accommodate increased heat flux without compromising the packaging. Moreover, the recently developed patents also evolved with improved thermal load handling under constrained packaging. Conclusion: The advanced microchannel cooling with an optimized fluid handling system with effective packaging results in a highly effective heat dissipation system. © 2018 Bentham Science Publishers.
A smart and sustainable energy approach by performing multi-objective optimization in a minichannel heat sink for waste heat recovery applications
(Elsevier Ltd, 2023) Narendran, G.; Jadhav, P.H.; Gnanasekaran, N.
Minichannel heat sink is widely used in waste heat recovery systems for their compactness and ability to recover heat effectively from high heat flux applications. However, the heat recovery efficiency is constrained by the flow configurations resulting in flow maldistribution. Numerous neural network combined evolutionary algorithms have been used to reduce pressure drop and flow maldistribution factors in the literature. But it is very challenging to assign appropriate weights to these parameters with no physical significance between them for optimization studies. To overcome this, TOPSIS-based optimization studies have been used in the current work to reduce the flow maldistribution factor (ϕ) and increase the Nusselt number (Nu) with ribs and inclined structures. Four Minichannel designs are studied to assess the channel heat recovery efficiency from small-scale incinerators using water and Graphene oxide (GO) nanofluid for three different volume fractions of GO-0.02%, GO-0.07%, and GO-0.12%. The motive is to determine an optimal nanofluid volume fraction and a suitable Minichannel configuration for the given heat flux. The TOPSIS method handles five criteria, including the combination of weightage for the maldistribution factor and Nusselt number. For criteria I ((ϕ)min: (Nu)max = 0.0:1.0) maximum weightage is given to heat transfer, the ribbed channel has gained a higher performance score for GO-0.07% nanofluid volume fraction. For criteria V ((ϕ)min: (Nu)max = 1.0:0.0) maximum weightage is given to maldistribution reduction, the ribbed inclined channel has gained with significantly higher performance score for all the studied nanofluid volume fractions. Further, the study is extended to determine the heat recovery efficiency, and it is found that with the increase in mass flow rate and nanofluid volume fraction, the heat recovery efficiency increases significantly. In particular, the maximum heat recovery efficiency of 66% was obtained for ribbed Minichannel using GO-0.12% nanofluid. © 2023 Elsevier Ltd
A Surrogate Forward Model Using Artificial Neural Networks inÂ Conjunction with Bayesian Computations for 3D Conduction-Convection Heat TransferÂ Problem
(Springer, 2020) Kumar, M.K.; Vishweshwara, P.S.; Gnanasekaran, N.
The present work describes the determination of heat flux at the boundary for a conjugate heat transfer problem based on a coupled three-dimensional conduction-convection fin numerical model, also referred to as complete model. The model is developed using commercially available software and solved along with Navierâ€“Stokes equation in order to acquire the required temperature distribution. An inverse analysis is proposed by treating the boundary heat flux as unknown while the temperatures of the fin are known. The inverse analysis is greatly accomplished with the help of Bayesian framework that combines the solution of the forward model and the simulated measurements. Markov chain Monte Carlo (MCMC) is applied to explore the sample space that drives samples to proper convergence and the selection or acceptance of the new samples is performed using Metropolisâ€“Hastings algorithm. Thus, the novelty of the present work is the use of artificial neural network (ANN) as surrogate model, that not only retains the full nature of the complete model but also acts as a fast forward model in the inverse analysis, within the Bayesian framework that quantifies the uncertainty of heat flux. The results of the present work emphasize that even for noise-added temperature data the final estimates are very close to the actual values and the uncertainty of the unknown heat flux is reported in terms of standard deviation accompanied by mean and maximum a posteriori (MAP). Â© 2020, Springer Nature Singapore Pte Ltd.
A synergistic combination of Asymptotic Computational Fluid Dynamics and ANN for the estimation of unknown heat flux from fin heat transfer
(Elsevier B.V., 2018) Kumar, H.; Gnanasekaran, N.
This paper deals with conjugate heat transfer from a rectangular fin. The problem consists of mild steel (250 × 150 × 6 mm) fin placed vertically on aluminium base (250 × 150 × 4 mm). The aluminium plate is subjected to an unknown heat flux at the base. The fin set-up is modelled using ANSYS fluent 14.5. The fin geometry is surrounded by extended domain filled with air so as to account for natural convection conjugate heat transfer. Grid independence study is carried out to fix the number of grids. A simple correlation using Asymptotic Computational Fluid Dynamics (ACFD) is developed and the same is used as a forward model to obtain the temperature distribution considering heat flux as the input. The problem is treated as an inverse problem in which a non-iterative method, ANN is used as the inverse model to estimate the unknown heat flux from the information of temperature. The results of the forward model and the ANN predicted values are in close agreement with error less than 1%. Effect of noise on the unknown parameter is also studied extensively. © 2017 Faculty of Engineering, Alexandria University
A Synergistic Combination of Thermal Models for Optimal Temperature Distribution of Discrete Sources Through Metal Foams in a Vertical Channel
(American Society of Mechanical Engineers (ASME) infocentral@asme.org, 2019) Kotresha, B.; Gnanasekaran, N.
This paper discusses about the numerical prediction of forced convection heat transfer through high-porosity metal foams with discrete heat sources in a vertical channel. The physical geometry consists of a discrete heat source assembly placed at the center of the channel along with high thermal conductivity porous metal foams in order to enhance the heat transfer. The novelty of the present work is the use of combination of local thermal equilibrium (LTE) model and local thermal nonequilibrium (LTNE) model for the metal foam region to investigate the temperature distribution of the heat sources and to obtain an optimal heat distribution so as to achieve isothermal condition. Aluminum and copper metal foams of 10 PPI having a thickness of 20 mm are considered for the numerical simulations. The metal foam region is considered as homogeneous porous media and numerically modeled using Darcy Extended Forchheimer model. The proposed methodology is validated using the experimental results available in literature. The results of the present numerical solution indicate that the excess temperature of the bottom heat source reduces by 100 °C with the use of aluminum metal foam. The overall temperature of the vertical channel reduces based on the combination of LTE and LTNE models compared to only LTNE model. The results of excess temperature for both the empty and the metal foam filled vertical channels are presented in this work. © 2019 by ASME.
Accelerating MCMC using model reduction for the estimation of boundary properties within Bayesian framework
(Pleiades journals, 2019) Gnanasekaran, N.; Kumar, M.K.
In this work, Artificial Neural Network (ANN) and Approximation Error Model (AEM) are proposed as model reduction methods for the simultaneous estimation of the convective heat transfer coefficient and the heat flux from a mild steel fin subject to natural convection heat transfer. The complete model comprises of a three-dimensional conjugate heat transfer from fin whereas the reduced model is simplified to a pure conduction model. On the other hand, the complete model is then replaced with ANN model that acts as a fast forward model. The modeling error that arises due to reduced model is statistically compensated using Approximation Error Model. The estimation of the unknown parameters is then accomplished using the Bayesian framework with Gaussian prior. The sampling space for both the parameters is successfully explored based on Markov chain Monte Carlo method. In addition, the convergence of the Markov chain is ensured using Metropolis–Hastings algorithm. Simulated measurements are used to demonstrate the proposed concept for proving the robustness; finally, the measured temperatures based on in-house experimental setup are then used in the inverse estimation of the heat flux and the heat transfer coefficient for the purpose of validation. © Springer Nature Singapore Pte Ltd. 2019.
Analysis of forced convection heat transfer through graded PPI metal foams
(Pleiades journals, 2019) Kotresha, B.; Gnanasekaran, N.
A forced convection heat transfer through high porosity graded Pores per inch (PPI) metal foam heat exchanger is numerically solved in this paper. The physical domain of the problem consists of a heat exchanger system attached to the bottom of a horizontal channel to absorb heat from the exhaust gas leaving the system. Two different pore densities of the metal foam 20 and 40 along with two different metal foam materials are considered for the enhancement of heat transfer in the present numerical investigation. The metal foam heat exchanger is considered as a homogeneous porous medium and is modeled using Darcy Extended Forchheirmer model. The heat transfer through the metal foam porous media is solved by using local thermal equilibrium (LTE) model. The effect of graded pore density and graded thermal conductivity is investigated and compared with the nongraded PPI metal foam. The heat exchanger system is simulated over a velocity range of 6–30 m/s. The pressure drop decreases for the graded pore density metal foams compared to the higher PPI metal foam and also increases with increase in the fluid inlet velocity. The results of temperature and velocity distribution for the graded and nongraded metal foams are compared and discussed elaborately. © Springer Nature Singapore Pte Ltd. 2019.
Analysis of functionally graded metal foams for the accomplishment of heat transfer enhancement under partially filled condition in a heat exchanger
(Elsevier Ltd, 2023) Jadhav, P.H.; Gnanasekaran, N.; Mobedi, M.
The use of partially filled high porosity graded aluminum and copper foams is explored to satisfy both heat transfer and pressure drop in a heat exchanger. Both positive and negative orientations are accomplished for the enhancement of heat transfer with reduction in the pressure drop. The present research includes three different configurations M1, M2 and M3 (porous layer inner diameter = 0.06 m, 0.04 m, and 0.02 m, respectively, while outer diameter = 0.10 m) partially filled with positive (i.e., increasing, 20/45 PPI) negative (i.e., decreasing, 45/20 PPI) and compound (i.e., 45 Cu/20 Al PPI) graded porous layer thickness. Each configuration involves three different graded porous layer to present the optimum graded porous layer thickness. The thermo-hydrodynamic characteristics are apprehended by using Darcy Extended Forchheimer (DEF) flow and local thermal non-equilibrium (LTNE) models for the partially filled graded porous structure and k-ω turbulence model is accomplished in open passage flow of the conduit. The decreasing graded foam located inside the models M1 and M2 performed 1.68%–12.85% and 13.42%–23.32% higher heat transfer rate compared to without graded metal foam of models M2 and M3, respectively accompanied with 55.43%–84.02% and 35.69%–50.31% lesser pumping power. © 2022 Elsevier Ltd
Analyzing the impact of nanofluid flow rate and thermal conductivity on response time in a compact heat spreader-integrated microchannel heat sink
(Springer Science and Business Media B.V., 2024) Narendran, G.; Gnanasekaran, N.; Arumuga Perumal, D.
In microchannel-based cooling devices, the response time exhibits distinctive variations owing to the heterogeneous integration of the heat source and the heat sink. These variations accompanied by flow maldistribution attributes to local temperature gradients are often referred to as flow-induced high-temperature zones and develop an uneven temperature distribution in microchannel heat sinks. To explore this phenomenon, we have designed an experimental setup featuring an in-house rectangular microchannel with an integrated heat spreader. In this study, we use a nanofluid comprised of graphene oxide (GO) and water as the working fluid, aiming to understand the thermo-hydrodynamics of the heat sink for various channel aspect ratios. The experimental results show that the heat wave propagation in the heat spreader is highly directional and influenced by the nanofluids flow rate and thermal conductivity. The study demonstrated that bulk fluid diffusion of GO nanofluid increased the temperature of the heat spreader by 30%. In the case of working fluid temperature, it increased by 35% for water and 52% for GO-0.12%. © Akadémiai Kiadó Zrt 2024.