scholarly journals Determination of Radiative Heat Transfer Coefficient at High Temperatures Using a Combined Experimental-Computational Technique

2015 ◽  
Vol 15 (2) ◽  
pp. 85-91 ◽  
Author(s):  
Václav Kočí ◽  
Jan Kočí ◽  
Tomáš Korecký ◽  
Jiří Maděra ◽  
Robert Č Černý

Abstract The radiative heat transfer coefficient at high temperatures is determined using a combination of experimental measurement and computational modeling. In the experimental part, cement mortar specimen is heated in a laboratory furnace to 600°C and the temperature field inside is recorded using built-in K-type thermocouples connected to a data logger. The measured temperatures are then used as input parameters in the three dimensional computational modeling whose objective is to find the best correlation between the measured and calculated data via four free parameters, namely the thermal conductivity of the specimen, effective thermal conductivity of thermal insulation, and heat transfer coefficients at normal and high temperatures. The optimization procedure which is performed using the genetic algorithms provides the value of the high-temperature radiative heat transfer coefficient of 3.64 W/(m2K).

2021 ◽  
Author(s):  
Wenping Peng ◽  
Min Xu ◽  
Xiaoxia Ma ◽  
Xiulan Huai

Abstract Wall radiative heat transfer in inner straight fin tubes is very complex considering the coupling of heat conduction in fins and radiative heat transfer of medium with solid surfaces, influenced by a number of factors such as fin parameters, radiative pro perties and run conditions. In this study, a simplified method is used.The average radiative heat transfer between radiative medium and solid surfaces is firstly studied by simulation with fins assumed having a constant temperature. Then an approximate correlation of this radiative heat transfer coefficient is proposed using the traditional radiative heat transfer calculation method with a view coefficient, having a error within 15%. A calculation method of average wall radiative heat transfer coefficient is further proposed by fin theory with an average temperature of fin surface used to consider the varying of the temperature along the fin when the conductivity of fins is finite. Using the predicting method proposed, a method for design calculation of fins in tubes to optimize wall radiative heat transfer is also given with three dimensionless numbers of p/n, 2H/D and nt/pD defined. Three cases of are analyzed in detail based on the design calculation method. It is verified that the radiative heat transfer could be enhanced twice by introducing fins. Under the same h0, conductivity and emissivity are two important factors to choose the material for fins.The micro-fins or the special treatments on the tube wall are a best choice for the fin material having a relatively small conductivity.


Author(s):  
Sathish K. Gurupatham ◽  
Priyanka Velumani ◽  
Revathy Vaidhya

Abstract A detailed model of human thermoregulation and a numerical algorithm to predict thermal comfort is a novel field of research and has wide applications in the auto/transportation industry and in the heating, ventilating, and air-conditioning (HVAC) industry. Anatomically specific convective and radiative heat transfer coefficients for the human body will be required to understand the human thermal physiological and comfort models. It necessitates to create hygienic and thermally comfortable spaces for the best productivity of the users. The physiological nature of thermal comfort during a transient condition such as a physical exercise or travel in an automobile are not yet well understood. In this paper, thermography has been applied to measure the convective and radiative heat transfer coefficients which has not been done before. Three different recovery processes were considered after the running of a human model on a treadmill with a range of speeds starting from 2 miles/hour to 10 miles/hour for stretch of twenty minutes. The recovery process included, (a) fan-assisted cooling with an air velocity of 0.5 m/s for 30 minutes, (b) fan-assisted cooling with an air velocity of 1.5 m/s for 30 minutes, and (c) natural cooling with no assistance of fan for 30 minutes. Thermal images were taken for forehead, trunk, arms, hands, legs of the models and the convective heat transfer coefficient and radiative heat transfer coefficient were calculated. The human models included both male and female, and belonged to two different age groups of less than 15 and above 40 with a total of 24 participants. The results show that though the temperatures, measured using thermography, for various parts of the human body changed locally, the overall calculated radiative heat transfer coefficients matched with the ASHRAE handbook values, and the calculated convective heat transfer coefficient increased with the increase of air velocity, while the models cooled down after the workout. Interestingly, the skin temperature decreased, initially, as the exercise progressed. After the completion of exercise, the skin temperature exhibited a quick rise during the recovery period with a subsequent decrease in the temperature, later. This trend was the same with all different age groups and sex of the models. The results also confirm that thermal images can be relied on for calculating the convective and radiative heat transfer coefficients of the human body to determine the heat transfer rate.


2018 ◽  
Vol 14 (2) ◽  
pp. 104-112 ◽  
Author(s):  
Mohammad Hemmat Esfe ◽  
Somchai Wongwises ◽  
Saeed Esfandeh ◽  
Ali Alirezaie

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.


Author(s):  
S. Kabelac ◽  
K. B. Anoop

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.


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