Predictions of heat transfer coefficients of supercritical carbon dioxide using the overlapped type of local neural network

2005 ◽  
Vol 48 (12) ◽  
pp. 2483-2492 ◽  
Author(s):  
Junghui Chen ◽  
Kuan-Po Wang ◽  
Ming-Tsai Liang
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Supercritical Carbon

Effect of Turbulent Prandtl Number on Convective Heat Transfer to Turbulent Upflow of Supercritical Carbon Dioxide

2009 ◽  
Author(s):  
Majid Bazargan ◽  
Mahdi Mohseni
Keyword(s):  
Heat Transfer ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Prandtl Number ◽  
Convection Heat Transfer ◽  
Turbulent Prandtl Number ◽  
Upward Flow ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Supercritical Carbon

A two-dimensional model is developed to simultaneously solve the momentum and energy equations and thus predict convection heat transfer to an upward flow of supercritical carbon dioxide in a round tube. The effect of the turbulent Prandtl number, Prt, on heat transfer coefficients has been extensively studied. A number of constant values of Prt, as well as a number of suggested equations accounting for variations of Prt with flow conditions, have been examined. The investigation has been carried out for both regimes of enhanced and deteriorated heat transfer. The results of this study show that the increase of Prt, even in the viscous sublayer, cause the heat transfer coefficients to decrease. The models of Prt leading to best agreement with experiments in either regimes of heat transfer were recognized. From the effect Prt has on heat transfer coefficients, it has been deduced that the buoyancy effects in upward flow of a supercritical fluid causes the Prt to decrease and hence the heat transfer coefficients to increase.

scholarly journals Measuring Heat Transfer Coefficients for Supercritical Carbon Dioxide Cycle Recuperators

2020 ◽  
Author(s):  
Matthew Searle ◽  
James Black ◽  
Douglas Straub ◽  
Edward Robey ◽  
M. Yip ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Transfer Coefficients ◽  
Heat Transfer Coefficients ◽  
Supercritical Carbon

Comparison of Existing Supercritical Carbon Dioxide Heat Transfer Correlations for Horizontal and Vertical Bare Tubes

2012 ◽  
Author(s):  
Prabu Surendran ◽  
Sahil Gupta ◽  
Tiberiu Preda ◽  
Igor Pioro
Keyword(s):  
Heat Transfer ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Wall Temperature ◽  
Statistical Error ◽  
Heat Fluxes ◽  
Transfer Coefficients ◽  
Bulk Fluid ◽  
Heat Transfer Correlations ◽  
Supercritical Carbon

This paper presents a thorough analysis of ability of various heat transfer correlations to predict wall temperatures and Heat Transfer Coefficients (HTCs) against experiments on internal forced-convective heat transfer to supercritical carbon dioxide conducted by Koppel [1], He [2], Kim [3] and Bae [4]. It should be noted the Koppel dataset was taken from a paper which used the Koppel data but was not written by Koppel. All experiments were completed in bare tubes with diameters from 0.948 mm to 9 mm for horizontal and vertical configurations. The datasets contain a total of 1573 wall temperature points with pressures ranging from 7.58 to 9.59 MPa, mass fluxes of 400 to 1641 kg/m2s and heat fluxes from 20 to 225 kW/m2. The main objective of the study was to compare several correlations and select the best of them in predicting HTC and wall temperature values for supercritical carbon dioxide. This study will be beneficial for analyzing heat exchangers involving supercritical carbon dioxide, and for verifying scaling parameters between CO2 and other fluids. In addition, supercritical carbon dioxide’s use as a modeling fluid is necessary as the costs of experiments are lower than supercritical water. The datasets were compiled and calculations were performed to find HTCs and wall and bulk-fluid temperatures using existing correlations. Calculated results were compared with the experimental ones. The correlations used were Mokry et al. [5], Swenson et al. [6] and a set of new correlations presented in Gutpa et al. [7]. Statistical error calculations were performed are presented in the paper.

Measurements of Heat Transfer Coefficients From Supercritical Carbon Dioxide Flowing in Horizontal Mini/Micro Channels

2002 ◽  
Vol 124 (3) ◽  
pp. 413-420 ◽  
Author(s):  
S. M. Liao ◽  
T. S. Zhao
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
Constant Temperature ◽  
Transfer Coefficients ◽  
Circular Tubes ◽  
Horizontal Tubes ◽  
Micro Channels ◽  
Supercritical Carbon

Heat transfer from supercritical carbon dioxide flowing in horizontal mini/micro circular tubes cooled at a constant temperature has been investigated experimentally. Six stainless steel circular tubes having inside-diameters of 0.50 mm, 0.70 mm, 1.10 mm, 1.40 mm, 1.55 mm, and 2.16 mm were tested. Measurements were carried out for the pressures ranging from 74 to 120 bar, the temperatures ranging from 20 to 110°C, and the mass flow rates ranging from 0.02 to 0.2 kg/min. It is found that the buoyancy effect was still significant, although supercritical CO2 was in forced motion through the horizontal tubes at Reynolds numbers up to 105. The experimental results also indicate that the existing correlations developed in the previous studies for large tubes deviate significantly from the experimental data for the present mini/micro tubes. Based on the experimental data, a correlation was developed for the axially averaged Nusselt number in terms of appropriate dimensionless parameters for forced convection of supercritical carbon dioxide in horizontal mini/micro tubes cooled at a constant temperature.

Application of Artificial Neural Network for the Heat Transfer Investigation Around a High-Pressure Gas Turbine Rotor Blade

2011 ◽  
Author(s):  
Ibrahim Eryilmaz ◽  
Sinan Inanli ◽  
Baris Gumusel ◽  
Suha Toprak ◽  
Cengiz Camci
Keyword(s):  
Neural Network ◽  
Heat Transfer ◽  
Artificial Neural Network ◽  
High Pressure ◽  
Rotor Blade ◽  
Incidence Angle ◽  
Transfer Coefficients ◽  
Experimental Conditions ◽  
Heat Transfer Coefficients ◽  
Artificial Neural

This paper presents the preliminary results of using artificial neural networks in the prediction of gas side convective heat transfer coefficients on a high pressure turbine blade. The artificial neural network approach which has three hidden layers was developed and trained by nine inputs and it generates one output. Input and output data were taken from an experimental research program performed at the von Karman Institute for Fluid Dynamics by Camci and Arts [5,6] and Camci [7]. Inlet total pressure, inlet total temperature, inlet turbulence intensity, inlet and exit Mach numbers, blade wall temperature, incidence angle, specific location of measurement and suction/pressure side specification of the blade were used as input parameters and calculated heat transfer coefficient around a rotor blade used as output. After the network is trained with experimental data, heat transfer coefficients are interpolated for similar experimental conditions and compared with both experimental measurements and CFD solutions. CFD analysis was carried out to validate the algorithm and to determine heat transfer coefficients for a closely related test case. Good agreement was obtained between CFD results and neural network predictions.

Sign in / Sign up

Export Citation Format

Share Document