The Experts below are selected from a list of 150 Experts worldwide ranked by ideXlab platform
John R. Thome - One of the best experts on this subject based on the ideXlab platform.
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a new method for reducing local Heat Transfer Data in multi microchannel evaporators
International Journal of Thermal Sciences, 2017Co-Authors: Houxue Huang, Navid Borhani, Nicolas Lamaison, John R. ThomeAbstract:Abstract Measuring the strong local variation in Heat Transfer coefficients in multi-microchannel evaporators is related to the inverse Heat conduction problem (IHCP). As the local flow Heat Transfer coefficients change greatly in magnitude from single-phase liquid at the entrance to a peak in slug flow and then to a minimum at the transition to the onset of annular flow and finally a new substantial rise up to the outlet, a significant Heat spreading occurs due to the Heat Transfer process itself, and this has to be accounted for when processing the Data. Until now, IHCP has not been introduced in the experimental study of Heat Transfer in such evaporators when reducing local experimental Data. In this paper, a new method for processing experimental local Heat Transfer Data by solving the 3D IHCP is proposed. This method is then applied and validated using two sets of single- and two-phase flow experimental Data obtained with infrared (IR) camera temperature measurements. The 14 400 raw pixel temperatures per image from the IR camera are first pre-processed by a filtering technique to remove the noise and then to smooth the Data, where the IR camera has undergone a prior inhouse pixel by pixel insitu temperature calibration. Three filtering techniques (Wiener filter, spline smooth, and polynomial surface fitting) are compared. The polynomial surface fitting technique was shown to be more suitable for the current type of Data set. Then the 3D IHCP is solved based on a finite volume method using the TDMA (Tridiagonal Matrix Algorithm) solver with a combination of Newton-Raphson iteration and a local energy balance method. Furthermore, the present 3D TDMA method (named as 3D TDMA) is compared with three other post-processing methods currently used in the literature, among which the present one is found to be more accurate for reducing the local Heat Transfer Data in multi-microchannel evaporators.
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Micro-channel flow boiling Heat Transfer of R-134a, R-236fa, and R-245fa
Microfluidics and Nanofluidics, 2009Co-Authors: Lorenzo Consolini, John R. ThomeAbstract:The rapid development of micro-thermal technologies has conveyed an increasing interest on convective boiling in micro-channels. Although there is general agreement that these systems may be able to dissipate potentially very high Heat fluxes per unit volume, their Heat Transfer characteristics are still unclear and require investigation. The present study illustrates Heat Transfer Data for flow boiling in a single micro-channel, for two channel diameters, namely, 510 and 790 μm, three fluids, namely, R-134a, R-236fa and R-245fa, mass velocities from 300 to 2,000 kg/m^2 s, and Heat fluxes up to 200 kW/m^2. Stable flow boiling Heat Transfer Data are analyzed through a parametric investigation, and are also confronted with measurements in the presence of two-phase oscillatory instabilities, which were found to significantly change the trends with respect to vapor quality.
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flow boiling in horizontal tubes part 2 new Heat Transfer Data for five refrigerants
Journal of Heat Transfer-transactions of The Asme, 1998Co-Authors: Nakhle Kattan, John R. Thome, Daniel FavratAbstract:A summary of a comprehensive experimental study on flow boiling Heat Transfer is presented for five refrigerants (R134a, R123, R402A, R404A and R502) evaporating inside plain horizontal, copper tube test sections. The test Data were obtained for both 12.00 mm and 10.92 mm diameters using hot water as the Heating source. Besides confirming known trends in flow boiling Heat Transfer Data as a function of test variables, it was also proven that the Heat flux level at the dryout point at the top of the tube in annular flow has a very significant downstream effect on Heat tranfer coefficients in the annular flow regime with partial dryout.
Bernard P A Grandjean - One of the best experts on this subject based on the ideXlab platform.
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a neural network methodology for Heat Transfer Data analysis
International Journal of Heat and Mass Transfer, 1991Co-Authors: Jules Thibault, Bernard P A GrandjeanAbstract:Abstract Neural networks have been until very recently a topic of academic research. Recent developments of powerful learning algorithms and the increasing number of applications in a great number of disciplines suggest that neural networks can provide useful tools for modelling and correlating practical Heat Transfer problems. This paper presents an introduction to computing with neural networks. To evaluate the potential of neural networks for correlating Heat Transfer Data, three different examples are solved, using a three-layer feedforward neural network. Two different learning algorithms, including the traditional backpropagation algorithm, are used to teach the neural network. It is shown that neural networks can be used to adequately correlate Heat Transfer Data.
Jun Su Park - One of the best experts on this subject based on the ideXlab platform.
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material design of a film cooling system using experimental Heat Transfer Data
International Journal of Heat and Mass Transfer, 2012Co-Authors: Jiwoon Song, Jun Su ParkAbstract:Abstract The present study numerically calculates the temperature and thermal stress distributions near a normal cooling hole. We evaluate the effects of material properties on thermal damage by using local Heat Transfer Data from previous experiments. The experimental results are converted into the surface’s Heat Transfer coefficients and the adiabatic wall temperature while using surface boundary conditions. The calculated results reveal that the thermal stresses depend on the main stream temperature and the material properties. To predict the maximum thermal stress near the normal cooling hole, we provide a single correlation consisting of the material properties and the main stream temperature.
Jules Thibault - One of the best experts on this subject based on the ideXlab platform.
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a neural network methodology for Heat Transfer Data analysis
International Journal of Heat and Mass Transfer, 1991Co-Authors: Jules Thibault, Bernard P A GrandjeanAbstract:Abstract Neural networks have been until very recently a topic of academic research. Recent developments of powerful learning algorithms and the increasing number of applications in a great number of disciplines suggest that neural networks can provide useful tools for modelling and correlating practical Heat Transfer problems. This paper presents an introduction to computing with neural networks. To evaluate the potential of neural networks for correlating Heat Transfer Data, three different examples are solved, using a three-layer feedforward neural network. Two different learning algorithms, including the traditional backpropagation algorithm, are used to teach the neural network. It is shown that neural networks can be used to adequately correlate Heat Transfer Data.
Jiwoon Song - One of the best experts on this subject based on the ideXlab platform.
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material design of a film cooling system using experimental Heat Transfer Data
International Journal of Heat and Mass Transfer, 2012Co-Authors: Jiwoon Song, Jun Su ParkAbstract:Abstract The present study numerically calculates the temperature and thermal stress distributions near a normal cooling hole. We evaluate the effects of material properties on thermal damage by using local Heat Transfer Data from previous experiments. The experimental results are converted into the surface’s Heat Transfer coefficients and the adiabatic wall temperature while using surface boundary conditions. The calculated results reveal that the thermal stresses depend on the main stream temperature and the material properties. To predict the maximum thermal stress near the normal cooling hole, we provide a single correlation consisting of the material properties and the main stream temperature.