A CFD modeling of CPU cooling by eco-friendly nanofluid and fin heat sink passive cooling techniques

Siahchehrehghadikolaei, Soheil; Gholinia, M.; Ghadikolaei, S. S.; Lin, Cheng-Xian

doi:10.1016/j.apt.2022.103813

Cited by 44 publications

(7 citation statements)

References 36 publications

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“…This would require the development of a comprehensive multi physics simulation framework that incorporates the transport of nanoparticles, the porous medium, and the fluid. Furthermore, in the future, interested researchers may develop new AI-based integrated intelligent networks [53][54][55][56][57] for solving nonlinear stiff systems that present fluidic flow models [58][59][60][61][62].…”

Section: Future Recommendationsmentioning

confidence: 99%

Intelligent Bayesian regularization‐based solution predictive procedure for hybrid nanoparticles of AA7072‐AA7075 oxide movement across a porous medium

et al. 2023

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The research community has shown great interest for investigation in the nanofluids models involving Aluminum Alloys AA7072 and AA7072 +AA7075 due to their advantageous impact on heat transfer, physical and mechanical characteristic exploiting in broad engineering applications such as manufacturing of spacecraft, aircraft parts and building testing. The hybrid nanomaterial AA7072-AA7075 based fluidic system is investigated in this paper using an artificial neural network with Bayesian regularization scheme (ANNs-BRS). The derived partial differential equations (PDEs) are transformed into ordinary differential equations system ODEs and obtained the reference datasets for the estimated solution dynamics of hybrid nanofluidic system. For prominent parameters, the influence of flow on the temperature distribution and velocity plot are investigated. The performance on 80% training samples, 5% testing and 15% validation dataset for ANNs-BRS is well-established in terms of error histogram plots, regression analysis, and MSE based statistics. The results for entropy generation, Eckert number Ec, magnetic interaction parameter M, suction parameter S, and heat generation parameter 𝑄 are also discussed. The results show that the Eckert number Ec has the effect of slowing down the rate of heat transfer while increasing the temperature and increases in suction parameter causes decrease in temperature while increase in temperature profile due to enhancement of suction parameter.

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Section: Future Recommendationsmentioning

confidence: 99%

Intelligent Bayesian regularization‐based solution predictive procedure for hybrid nanoparticles of AA7072‐AA7075 oxide movement across a porous medium

et al. 2023

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“…In articles [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56] Ghadikolaei and his various coauthors made significant contributions to the literature by studying a wide range of flow problems. As instance, for central processing unit (CPU) cooling they employed hybrid passive techniques with the help of a computational fluid dynamics (CFD) modeling of heat transfer between the unique nature-based design heat sink and CGNPs/H 2 O eco-friendly nanofluid, CFD simulation of CPU cooling using passive heat sink cooling methods and environmentally friendly nanofluid.…”

Section: Introductionmentioning

confidence: 99%

Stability analysis of GO‐EG and GO‐blood base nanofluids by implementing of thermal radiation phenomena

Rehman,

Zeb,

Khun

et al. 2024

Z Angew Math Mech

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This work leaves to explore the stability of a 2‐D, time free, thermal boundary layer of graphene oxide ethylene glycol (GO‐EG,) where GO‐blood is taken as base nanofluids over a stretchable surface by executing a magnetic field (MHD), coupled stress effects, heat source/sink, and sun‐oriented radiations. This work is done due to the reason that nanofluids have a higher heat conductivity level rather than regular liquids. Nonlinear partial differential equations (N‐PDEs) are modeled utilizing the notable boundary layer equations. Dimensionless ordinary differential conditions (ODEs) are made from the created NPDEs by utilizing dimensional analysis and group theoretic approach. We examine the obtained ODEs through the notable homotopy analysis technique (HAM). The impacts of different parameters on temperature distribution and velocity profiles are shown graphically. However, some tables are constructed in support of the presented study to confirm the stability and convergence of the solutions. The main intentions in this work are to underline the enhancement of traditional heat transfer's thermal conductivity.

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“…In applications to increase heat transfer and physical quantities such as density, thermal conductivity, and thermal capacity; nanofluids play an important role in industry and technological processes. Eco-friendly nanofluids are useful in the process of CPU cooling [12,13]. Nanofluids play a key role in heat exchanger to recover waste energy from Diesel engine [14] and in microchannel heatsink for energy storage [15].…”

Section: Introductionmentioning

confidence: 99%

Regression analysis of horizontal magnetic field in thermophysical convective flow of nanofluid

Rauf,

Aslam,

Mushtaq

et al. 2024

Z Angew Math Mech

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The existence of magnetic field is beneficial to stabilize the fluid flow patterns in modern era industrial implications and engineering problems. A constant horizontal magnetized field is implemented to stabilize the swirl viscous fluid flow created by disk rotation. The heat transmission equation is expressed by the variable fluid property that the thermal conductivity is dependent on temperature. Buongiorno nanofluid model is applied to express the role of thermophoresis and Brownian movement. The flow governing equations are normalized through the von Kármán classical similarity variables. The impacts of magnetic and other physical parameters on velocity, thermal and concentration fields are investigated in numerical way using RKF‐45 (Runge–Kutta–Fehlberg) built‐in procedure. The critical part of horizontal magnetic field and dimensionless parameters onto physical quantities such as wall shears, thermal rate, and concentration rate are highlighted. Regression analysis is also performed to evaluate the impact of obtained numerical values of such physical quantities on flow rates. Unlike the familiar impacts of a uniformly applied transverse magnetic field that concerns the velocity field stabilizing and decreasing the thermal rate, it is established that the horizontal magnetized field is valuable for stabilizing/destabilizing the flow phenomenon. The results also matched with the published work in limiting case.

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A CFD modeling of CPU cooling by eco-friendly nanofluid and fin heat sink passive cooling techniques

Cited by 44 publications

References 36 publications

Intelligent Bayesian regularization‐based solution predictive procedure for hybrid nanoparticles of AA7072‐AA7075 oxide movement across a porous medium

Intelligent Bayesian regularization‐based solution predictive procedure for hybrid nanoparticles of AA7072‐AA7075 oxide movement across a porous medium

Stability analysis of GO‐EG and GO‐blood base nanofluids by implementing of thermal radiation phenomena

Regression analysis of horizontal magnetic field in thermophysical convective flow of nanofluid

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