Thermophysical Properties of Hydrogen Mixtures Relevant for the Development of the Hydrogen Economy: Analysis of Available Experimental Data and Thermodynamic Models

2021 ◽  
Author(s):  
Daniel Lozano-Martín ◽  
Alejandro Moreau ◽  
David Vega-Maza ◽  
César R. Chamorro
Keyword(s):  
Experimental Data ◽  
Thermophysical Properties ◽  
Hydrogen Economy ◽  
Thermodynamic Models ◽  
Hydrogen Mixtures ◽  
Economy Analysis

Solid-Fluid Phase Equilibria Measurement for Mixtures of Methane, Carbon-Dioxide and N-Hexadecane

2021 ◽  
Author(s):  
Oluwakemi Victoria Eniolorunda ◽  
Antonin Chapoy ◽  
Rod Burgass
Keyword(s):  
Experimental Data ◽  
Carbon Dioxide ◽  
Equation Of State ◽  
Phase Equilibria ◽  
Activity Coefficients ◽  
Fluid Phase ◽  
Ternary Mixtures ◽  
Binary Interaction ◽  
Thermodynamic Models ◽  
Methane Carbon

Abstract In this study, new experimental data using a reliable approach are reported for solid-fluid phase equilibrium of ternary mixtures of Methane-Carbon-dioxide- n-Hexadecane for 30-73 mol% CO2 and pressures up to 24 MPa. The effect of varying CO2 composition on the overall phase transition of the systems were investigated. Three thermodynamic models were used to predict the liquid phase fugacity, this includes the Peng Robison equation of state (PR-EoS), Soave Redlich-Kwong equation of state (SRK-EoS) and the Cubic plus Association (CPA) equation of state with the classical mixing rule and a group contribution approach for calculating binary interaction parameters in all cases. To describe the wax (solid) phase, three activity coefficient models based on the solid solution theory were investigated: the predictive universal quasichemical activity coefficients (UNIQUAC), Universal quasi-chemical Functional Group activity coefficients (UNIFAC) and the predictive Wilson approach. The solid-fluid equilibria experimental data gathered in this experimental work including those from saturated and under-saturated conditions were used to check the reliability of the various phase equilibria thermodynamic models.

scholarly journals Electron Transport in Argon - Hydrogen Mixtures

1980 ◽  
Vol 33 (6) ◽  
pp. 975 ◽  
Author(s):  
GN Haddad ◽  
RW Crompton
Keyword(s):  
Experimental Data ◽  
Distribution Function ◽  
Cross Section ◽  
Transport Coefficients ◽  
Velocity Distribution Function ◽  
Significant Error ◽  
Cross Section Data ◽  
Section Data ◽  
Hydrogen Mixtures ◽  
Legendre Expansion

The transport coefficients υdr and D⊥/μ have been measured in mixtures of 0.5 % and 4 % hydrogen in argon. All measurements were made at 293 K. It is shown that for these mixtures the use of the solution of the Boltzmann equation based on the two-term Legendre expansion of the velocity distribution function introduces no significant error in the analysis of the transport data. All the experimental data have been predicted to within � 3.5 % using previously published cross section data.

Enhanced Condensation Heat-Transfer on Mini or Low Fin Tubes

2006 ◽  
Author(s):  
Adrian Briggs
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Thermophysical Properties ◽  
Heat Transfer Performance ◽  
Three Dimensional ◽  
Transfer Performance ◽  
Transfer Enhancement ◽  
Shell Side ◽  
Wide Range ◽  
Fin Tubes

This paper presents an overview of the use of low or mini-fin tubes for improving heat-transfer performance in shell-side condensers. The paper concentrates on, but is not limited to, the experimental and theoretical program in progress at Queen Mary, University of London. This work has so far resulted in an extensive data base of experimental data for condensation on single tubes, covering a wide range of tube geometries and fluid thermophysical properties and in the development of a simple to use model which predicts the majority of this data to within 20%. Work is progressing on the effects of vapor shear and on three-dimensional fin profiles; the later having shown the potential for even higher heat-transfer enhancement.

An Analytical Investigation of Free Convection Heat Transfer to Supercritical Water

1970 ◽  
Vol 92 (3) ◽  
pp. 345-350 ◽  
Author(s):  
E. S. Nowak ◽  
A. K. Konanur
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Free Convection ◽  
Supercritical Water ◽  
Thermophysical Properties ◽  
Convection Heat Transfer ◽  
Analytical Investigation ◽  
Convection Heat ◽  
Free Convection Heat ◽  
Vertical Flat Plate

Heat transfer to supercritical water (at 3400 psia in the pseudocritical region) by stable laminar free convection from an isothermal, vertical flat plate was analytically investigated. The actual variations with temperature of all or some of the thermophysical properties of supercritical water were taken into consideration. Fair agreement was found between the analytical values of this paper and existing experimental data.

scholarly journals Boiling Heat Transfer Evaluation in Nanoporous Surface Coatings

Nanomaterials ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 3383
Author(s):  
Uzair Sajjad ◽  
Imtiyaz Hussain ◽  
Muhammad Imran ◽  
Muhammad Sultan ◽  
Chi-Chuan Wang ◽  
...  
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Deep Learning ◽  
Thermophysical Properties ◽  
Boiling Heat Transfer ◽  
Parameters Estimation ◽  
Learning Method ◽  
Working Fluids ◽  
Coated Surfaces ◽  
Nanoporous Surfaces

The present study develops a deep learning method for predicting the boiling heat transfer coefficient (HTC) of nanoporous coated surfaces. Nanoporous coated surfaces have been used extensively over the years to improve the performance of the boiling process. Despite the large amount of experimental data on pool boiling of coated nanoporous surfaces, precise mathematical-empirical approaches have not been developed to estimate the HTC. The proposed method is able to cope with the complex nature of the boiling of nanoporous surfaces with different working fluids with completely different thermophysical properties. The proposed deep learning method is applicable to a wide variety of substrates and coating materials manufactured by various manufacturing processes. The analysis of the correlation matrix confirms that the pore diameter, the thermal conductivity of the substrate, the heat flow, and the thermophysical properties of the working fluids are the most important independent variable parameters estimation under consideration. Several deep neural networks are designed and evaluated to find the optimized model with respect to its prediction accuracy using experimental data (1042 points). The best model could assess the HTC with an R2 = 0.998 and (mean absolute error) MAE% = 1.94.

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