Thermophysical Properties Of Helium Research Articles

Thermophysical properties of helium and hydrogen mixtures under high pressure predicted by ab-initio calculations: Implications for Saturn and Jupiter planets

The thermodynamic and physical properties of Helium (He) and Hydrogen (H) mixtures are crucial form an astrophysical perspective. Nowadays, it is well known that these two elements constitute roughly 95% of the matter in the solar system. The high-pressure equation of He state doped with H is calculated in the framework of density functional theory (DFT) for three crystallographic structures. Namely, the Body Centered Cubic (BCC), Face Centered Cubic (FCC), and hexagonal close packed (HCP) structures have been studied. The equations of state (EOS) are provided for the He 0.90 H 0.10 and He 0.70 H 0.30 mixtures and the energy of the same mixtures are calculated with the earlier structures. The band structure for pressures of 0, 75 and 150 GPa were estimated by our calculations for the presumed crystal structures in the case of pure He and He 0.70 H 0.30 mixture for comparison. The k x − k y cuts of the Bloch Spectral Functions (BSF) are presented for the pure He and He 0.70 H 0.30 mixture for the three structures to explore the impact of doping on electronic properties. In addition, electrical resistivity calculations of the mentioned structures were carried out for mixtures with 10, 15, 20 and 30% of H. At final stage, the enthalpy (ΔH) of these latest mixtures have been deduced for the three crystal structures.

Prediction of Thermophysical Properties of Helium Using Linear Prediction and Artificial Neural Networks

Thermophysical properties of helium are significant in practical applications. However, the values of properties vary under different circumstances, which may have bad impacts on practical productions and applications. In our study, computational models like Linear Prediction and Artificial Neural Networks (ANNs) are applied to predict the thermophysical properties of the chemical substances. By analyzing 50 data groups using Linear Prediction, General Regression Neural Network (GRNN) and Multilayer Feedforward Neural Network (MLFN) methods, 9 models were successfully established to predict the thermophysical properties of helium, including density, energy, enthalpy, entropy, isochoric heat capacity, isobaric heat capacity, viscosity, thermal conductivity and dielectric constant. Within permissible error range (30% tolerance), our models were proved to be robust and accurate which indicates that ANN models can be used to predict the thermophysical properties of helium.

Estimating the intensity of heat transfer to supercritical helium when the specific variation of the thermophysical properties is taken into account

We carry out an analysis of the rational choice of the rules for averaging the thermophysical properties of helium, and on the basis of this analysis we propose a generalizing function for calculating the intensity of heat transfer.