A New Condensation Heat Transfer Model Based on the Flow Regime in a Nearly Horizontal Pipe.

Author(s):  
Taehwan Ahn ◽  
Jae-jun Jeong ◽  
Kyong-ho Kang ◽  
Jong Cheon ◽  
Byong-jo Yun
2006 ◽  
Vol 128 (10) ◽  
pp. 1050-1059 ◽  
Author(s):  
Todd M. Bandhauer ◽  
Akhil Agarwal ◽  
Srinivas Garimella

A model for predicting heat transfer during condensation of refrigerant R134a in horizontal microchannels is presented. The thermal amplification technique is used to measure condensation heat transfer coefficients accurately over small increments of refrigerant quality across the vapor-liquid dome (0<x<1). A combination of a high flow rate closed loop primary coolant and a low flow rate open loop secondary coolant ensures the accurate measurement of the small heat duties in these microchannels and the deduction of condensation heat transfer coefficients from measured UA values. Measurements were conducted for three circular microchannels (0.506<Dh<1.524mm) over the mass flux range 150<G<750kg∕m2s. Results from previous work by the authors on condensation flow mechanisms in microchannel geometries were used to interpret the results based on the applicable flow regimes. The heat transfer model is based on the approach originally developed by Traviss, D. P., Rohsenow, W. M., and Baron, A. B., 1973, “Forced-Convection Condensation Inside Tubes: A Heat Transfer Equation For Condenser Design,” ASHRAE Trans., 79(1), pp. 157–165 and Moser, K. W., Webb, R. L., and Na, B., 1998, “A New Equivalent Reynolds Number Model for Condensation in Smooth Tubes,” ASME, J. Heat Transfer, 120(2), pp. 410–417. The multiple-flow-regime model of Garimella, S., Agarwal, A., and Killion, J. D., 2005, “Condensation Pressure Drop in Circular Microchannels,” Heat Transfer Eng., 26(3), pp. 1–8 for predicting condensation pressure drops in microchannels is used to predict the pertinent interfacial shear stresses required in this heat transfer model. The resulting heat transfer model predicts 86% of the data within ±20%.


Author(s):  
XinMei Shi ◽  
Daan M. Maijer ◽  
Guy Dumont

Controlling and eliminating defects, such as macro-porosity, in die casting processes is an on-going challenge for manufacturers. Current strategies for eliminating defects focus on the execution of a pre-set casting cycle, die structure design or the combination of both. To respond to process variability and mitigate its negative effects, advanced process control methodologies may be employed to dynamically adjust the operational parameters of the process. In this work, a finite element heat transfer model, validated by comparison with experimental data, has been developed to predict the evolution of temperatures and the volume of liquid encapsulation in an experimental casting process. A virtual process, made up of the heat transfer model and a wrapper script for communication, has been employed to simulate the continuous operation of the real process. A stochastic state-space model, based on data from measurements and the virtual process, has been developed to provide a reliable representation of this virtual process. The parameters of the deterministic portion result from system identification of the virtual process, whereas the parameters of the stochastic portion arise from the analysis and comparison of measurement data with virtual process data. The resulting state-space model, which can be extended to a multi-input multi-output model, will facilitate the design of a model-based controller for this process.


2013 ◽  
Vol 45 (6) ◽  
pp. 759-766 ◽  
Author(s):  
YUN-JE CHO ◽  
SEOK KIM ◽  
BYOUNG-UHN BAE ◽  
YUSUN PARK ◽  
KYOUNG-HO KANG ◽  
...  

Author(s):  
Anilchandra Attaluri ◽  
Robert Ivkov ◽  
Ronghui Ma ◽  
Liang Zhu

A coupled theoretical framework comprising a suspension of nanoparticles transport in porous media model and a heat transfer model is developed to address nanoparticle redistribution during heating. Nanoparticle redistribution in biological tissues during magnetic nanoparticle hyperthermia is described by a multi-physics model that consists of five major components: (a) a fully saturated porous media model for fluid flow through tissue; (b) nanoparticle convection and diffusion; (c) heat transfer model based on heat generation by local nanoparticle concentration; (d) a model to predict tissue thermal damage and corresponding change to the porous structure; and (e) a nanoparticle redistribution model based on the dynamic porosity and diffusion diffusivity. The integrated model has been used to predict the structural damage in porous tumors and its effect on nanoparticle-induced heating in tumors. Thermal damage in the vicinity of the tumor center that is predicted by the Arrhenius equation increases from 14% with 10 minutes of heating to almost 99% after 20 minutes. It then induces an increased tumor porosity that increases nanoparticle diffusivity by seven-fold. The model predicts thermal damage induced by nanoparticle redistribution increases by 20% in the radius of the spherical tissue region containing nanoparticles. The developed model has demonstrated the feasibility of enhancing nanoparticle dispersion from injection sites using targeted thermal damage.


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