Optimal Design of Coldplates for CPU Coolers

2007 ◽  
Author(s):  
C. N. Rasmussen ◽  
C. B. Terp
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Plate Thickness ◽  
Source Area ◽  
Cold Plate ◽  
Plate Material ◽  
Cfd Simulations ◽  
Total Thermal Resistance ◽  
Effective Heat Transfer Coefficient

We here present the results of an effort to determine the influence of the parameters affecting cold-plate performance and to minimize the total thermal resistance of a CPU cold-plate. An analysis based on analytical calculations and CFD simulations shows that spreading resistance is affected not only by the heat source area, cold-plate thickness and thermal conductivity of the cold-plate material, but also by the effective heat transfer coefficient of the interface between cold-plate and cooling liquid. Different options for optimizing heat transfer through the cold-plate are discussed.

An Experimental Study on the Convective Heat Transfer Behaviour of Diamond Nanofluids in Electronic Cooling Applications

2018 ◽  
Author(s):  
Farzin Mashali ◽  
Ethan M. Languri ◽  
Jim Davidson ◽  
David Kerns ◽  
Fahad Alkhaldi
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Experimental Study ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Base Fluid ◽  
Convective Heat Transfer Coefficient ◽  
Cold Plate

This study presents the convective heat transfer coefficient of 0.05 wt.% diamond nanofluids containing functionalized nanodiamond dispersed in a base fluid deionized (DI) water flowing in a conduction cold plate under turbulent flow conditions, experimentally. The conduction cold plate was heated via six cartridge heaters with a constant heat transfer rate. The primary experimental study has been implemented to investigate the thermal conductivity of diamond nanofluids which showed a higher effective thermal conductivity than that of the base fluid. In addition, nanofluid was flowed in a closed system with heating at the heat exchanger and cooling via a cooling tank to keep the inlet temperature constant to explore the convection heat transfer properties of diamond nanofluids. Results indicate that the convective heat transfer coefficient and Nusselt number of diamond nanofluid are higher than that of the DI water in a same flow rate, and these properties increased with increase in Reynolds number.

scholarly journals Influence of molten salt-(FLiNaK) thermophysical properties on a heated tube using CFD RANS turbulence modeling of an experimental testbed

2019 ◽  
Vol 5 ◽  
pp. 16
Author(s):  
Ramiro Freile ◽  
Mark Kimber
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Molten Salt ◽  
Thermophysical Properties ◽  
Material Properties ◽  
Thermal Hydraulics ◽  
Cfd Simulations ◽  
The Heat Transfer Coefficient

In a liquid fuel molten salt reactor (MSR) a key factor to consider upon its design is the strong coupling between different physics present such as neutronics, thermo-mechanics and thermal-hydraulics. Focusing in the thermal-hydraulics aspect, it is required that the heat transfer is well characterized. For this purpose, turbulent models used for FLiNaK flow must be valid, and its thermophysical properties must be accurately described. In the literature, there are several expressions for each material property, with differences that can be significant. The goal of this study is to demonstrate and quantify the impact that the uncertainty in thermophysical properties has on key metrics of thermal hydraulic importance for MSRs, in particular on the heat transfer coefficient. In order to achieve this, computational fluid dynamics (CFD) simulations using the RANS k-ω SST model were compared to published experiment data on molten salt. Various correlations for FLiNaK’s material properties were used. It was observed that the uncertainty in FLiNaK’s thermophysical properties lead to a significant variance in the heat coefficient. Motivated by this, additional CFD simulations were done to obtain sensitivity coefficients for each thermophysical property. With this information, the effect of the variation of each one of the material properties on the heat transfer coefficient was quantified performing a one factor at a time approach (OAT). The results of this sensitivity analysis showed that the most critical thermophysical properties of FLiNaK towards the determination of the heat transfer coefficient are the viscosity and the thermal conductivity. More specifically the dimensionless sensitivity coefficient, which is defined as the percent variation of the heat transfer with respect to the percent variation of the respective property, was −0.51 and 0.67 respectively. According to the different correlations, the maximum percent variations for these properties is 18% and 26% respectively, which yields a variation in the predicted heat transfer coefficient as high as 9% and 17% for the viscosity and thermal conductivity, respectively. It was also demonstrated that the Nusselt number trends found from the simulations were captured much better using the Sieder Tate correlation than the Dittus Boelter correlation. Future work accommodating additional turbulence models and higher fidelity physics will help to determine whether the Sieder Tate expression truly captures the physics of interest or whether the agreement seen in the current work is simply reflective of the single turbulence model employed.

Development of a New Correlation and Post Processing of Heat Transfer Coefficient and Pressure Drop of Functionalized COOH MWCNT Nanofluid by Artificial Neural Network

2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Heat Transfer Coefficient ◽  
Pressure Drop ◽  
Transfer Coefficient ◽  
Regression Coefficient ◽  
New Correlation ◽  
The Neural Network

Background: Because of nanofluids applications in improvement of heat transfer rate in heating and cooling systems, many researchers have conducted various experiments to investigate nanofluid's characteristics more accurate. Thermal conductivity, electrical conductivity, and heat transfer are examples of these characteristics. Method: This paper presents a modeling and validation method of heat transfer coefficient and pressure drop of functionalized aqueous COOH MWCNT nanofluids by artificial neural network and proposing a new correlation. In the current experiment, the ANN input data has included the volume fraction and the Reynolds number and heat transfer coefficient and pressure drop considered as ANN outputs. Results: Comparing modeling results with proposed correlation proves that the empirical correlation is not able to accurately predict the experimental output results, and this is performed with a lot more accuracy by the neural network. The regression coefficient of neural network outputs was equal to 99.94% and 99.84%, respectively, for the data of relative heat transfer coefficient and relative pressure drop. The regression coefficient for the provided equation was also equal to 97.02% and 77.90%, respectively, for these two parameters, which indicates this equation operates much less precisely than the neural network. Conclusion: So, relative heat transfer coefficient and pressure drop of nanofluids can also be modeled and estimated by the neural network, in addition to the modeling of nanofluid’s thermal conductivity and viscosity executed by different scholars via neural networks.

Experimental Convective Heat Transfer With Nanofluids

2008 ◽  
Author(s):  
S. Kabelac ◽  
K. B. Anoop
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Turbulent Regime ◽  
Heat Transfer Characteristics ◽  
Forced Convective Heat Transfer ◽  
Transfer Characteristics

Nanofluids are colloidal suspensions with nano-sized particles (<100nm) dispersed in a base fluid. From literature it is seen that these fluids exhibit better heat transfer characteristics. In our present work, thermal conductivity and the forced convective heat transfer coefficient of an alumina-water nanofluid is investigated. Thermal conductivity is measured by a steady state method using a Guarded Hot Plate apparatus customized for liquids. Forced convective heat transfer characteristics are evaluated with help of a test loop under constant heat flux condition. Controlled experiments under turbulent flow regime are carried out using two particle concentrations (0.5vol% and 1vol %). Experimental results show that, thermal conductivity of nanofluids increases with concentration, but the heat transfer coefficient in the turbulent regime does not exhibit any remarkable increase above measurement uncertainty.

Investigations on Transient Natural Convection in Boron Nitride-Mineral Oil Nanofluid Systems

2012 ◽  
Author(s):  
Shijo Thomas ◽  
C. B. Sobhan ◽  
Jaime Taha-Tijerina ◽  
T. N. Narayanan ◽  
P. M. Ajayan
Keyword(s):  
Heat Transfer ◽  
Thermal Conductivity ◽  
Heat Transfer Coefficient ◽  
Natural Convection ◽  
Boron Nitride ◽  
Transfer Coefficient ◽  
Convective Heat Transfer ◽  
Convective Heat ◽  
Convective Heat Transfer Coefficient ◽  
Mineral Oil

Nanofluids are suspensions or colloids produced by dispersing nanoparticles in base fluids like water, oil or organic fluids, so as to improve their thermo-physical properties. Investigations reported in recent times have shown that the addition of nanoparticles significantly influence the thermophysical properties, such as the thermal conductivity, viscosity, specific heat and density of base fluids. The convective heat transfer coefficient also has shown anomalous variations, compared to those encountered in the base fluids. By careful selection of the parameters such as the concentration and the particle size, it has been possible to produce nanofluids with various properties engineered depending on the requirement. A mineral oil–boron nitride nanofluid system, where an increased thermal conductivity and a reduced electrical conductivity has been observed, is investigated in the present work to evaluate its heat transfer performance under natural convection. The modified mineral oil is produced by chemically dispersing boron nitride nanoparticles utilizing a one step method to obtain a stable suspension. The mineral oil based nanofluid is investigated under transient free convection heat transfer, by observing the temperature-time response of a lumped parameter system. The experimental study is used to estimate the time-dependent convective heat transfer coefficient. Comparisons are made with the base fluid, so that the enhancement in the heat transfer coefficient under natural convection situation can be estimated.

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