Thermophysical Properties Of Liquid Research Articles

Liquid thermophysical properties of Ag-Si alloy based on deep learning potential

The knowledge of the thermophysical properties of liquid metals and alloys is essential for expanding the materials database and designing materials with good properties. In this work, we developed an interatomic potential using a deep neural network (DNN) algorithm for liquid Ag-Si alloys. Compared with ab initio molecular dynamics (AIMD) results, the DNN potential provided a good description of the information of energy, force, and structure features of the system in the simulated temperature range. Through this potential, we can obtain the thermophysical properties of different compositions of liquid alloys by simulation way. The computed thermophysical properties are in excellent agreement with the reported experimental data. The analysis of local structure indicates that the liquid ordering and stability strengthen upon cooling at the atomic level, eventually leading to an increase in thermophysical properties.

Rapid acquisition of liquid thermophysical properties from pure metals to quaternary alloys by proposing a machine learning strategy

The establishment of reliable materials genome databases involving the thermophysical properties of liquid metals and alloys promotes the progress of materials research and development, whereas acquiring these properties imposes great challenges on experimental investigation. Here, we proposed a deep learning method and achieved a deep neural network (DNN) interatomic potential for the entire Ti–Ni–Cr–Al system from pure metals to quaternary alloys. This DNN potential exhibited sufficient temperature and compositional transformability which extended beyond the training and provided the prediction of the liquid structure and thermophysical properties for metallic materials with both density functional theory accuracy and classic molecular dynamics efficiency. The predicted results agreed well with the reported experimental data. This work opens a feasible way to address the challenges of rapidly and accurately acquiring thermophysical properties data for liquid pure metals and multicomponent alloys, covering a broad temperature range from superheated to undercooled state.

Determining the Factors Affecting the Boiling Heat Transfer Coefficient of Sintered Coated Porous Surfaces

The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R2 = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R2 (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.

Thermophysical properties of liquid (U, Zr)O2 by molecular dynamics

ABSTRACT In-depth understanding of nuclear fuel behaviour under operation is critical to anticipate and prevent severe accidents. Loss-of-cooling events can lead to core meltdown, and formation of U–Zr–O liquids (the basic components of corium lavas), formed of molten fuel and cladding. To improve the knowledge on those liquid mixtures, we evaluate the ability of existing interatomic potentials (CRG and Yakub) for solid-phase (U, Zr)O to accurately reproduce density and heat capacity of molten UO and ZrO and their binary mixes within molecular dynamics simulations. Facing their limits, we determine a new set of parameters for the CRG potential through optimisation on a single experimental density point for ZrO (d=0.070 atoms/Å at T=2900 K). The proposed fitted potential shows good agreement during validation with experimental data when applied at high temperatures.

Thermophysical Properties of Liquid K–Pb Alloys

Thermophysical Properties of Liquid K–Pb Alloys

A high-fidelity approach to correlate the nucleate pool boiling data of roughened surfaces

The existing nucleate pool boiling correlations have theoretical footings and their usefulness is restricted by the failure to effectively account for the surface effect. To tackle this problem, a high-fidelity approach based on deep learning has been developed to predict the nucleate boiling surfaces subject to various surface roughness. The proposed model accounts for the effect of surface roughness, roughness fabrication method, surface material, surface inclination, saturation temperature, and pressure on the pool boiling performance of dielectric liquids, water, and refrigerants. The proposed method can accurately predict the boiling heat transfer performance of roughened surfaces by including the most influential surface characteristics, testing conditions, and liquid thermophysical properties into the architecture of the developed model. Correlation matrix identifies that heat flux, surface inclination, surface roughness, thermal conductivity of surface material, liquid saturation temperature, and pressure are the prime factors to affect the nucleate boiling heat transfer coefficient. Different neural networks (DNNs) are built and tested in order to find an optimal model based on an experimental dataset of 13000 data points. The final selected model can estimate the investigated parameter with a coefficient of determination (R2) = 0.994 and mean absolute error (MAE) = 0.65. The suggested method can be utilized to predict the boiling heat transfer performance of a variety of roughened surfaces subject to different working fluids and testing conditions.

Novel empirical correlation for ionanofluid PEC inside tube subjected to heat flux with application to solar energy

Ionic liquid-based nanofluids are a very novel group of fluids used for enhancing heat transfer in different applications, especially in solar energy ones where the parabolic trough pipe receiver is subjected to heat flux due to the sun beam radiation. In regard to this new class of fluids, no empirical correlations are available for their heat transfer capabilities inside tubes even if some research on local Nusselt number of regular flow is present in the current literature. This paper introduces new correlation for heat transfer and friction factor in pipes subjected to constant heat flux considering [C4mim] [NTf2] ionic liquid-based nanofluids flow. In addition, the performance evaluation criteria as an optimization parameter between heat transfer enhancement and pressure drop penalty have been evaluated. In this particular application, the flow is laminar, as recommended in low heat flux applications (solar beam radiation), with Reynolds number in the range of 100–2000 and nanoparticles volume concentration varying from 0 to 2.5%. In addition, according to the change in the [C4mim] [NTf2] ionic liquid thermophysical properties with temperature, the Prandtl number consequently has been changed. As an overall conclusion, the proposed correlations can be seen as favorable for the heat transfer enhancement estimation of the parabolic trough for the solar energy applications. Finally, this pioneering class of heat transfer fluids (ionic liquid-based nanofluids) reveals a great potential in advanced heat transfer applications; therefore, the new correlations aim to collaborate to this progress.

Optothermodynamic Method of Diagnosis of Absorption Coefficient in the Liquids

Optothermodynamic method of diagnosis of absorption coefficient of waves in inhomogeneous absorbing liquids has been developed temperature influence on wave’s absorption coefficient has been determined. Optothermodynamic method allows establishing relation between optic and thermophysical properties of liquid. Developed method is based on the application of energy conservation on the interaction of laser beams with inhomogeneous absorbing liquids.

Understanding decoupling mechanisms of liquid-mixture transport properties through regression analysis with structural perturbation

Optimization of liquid thermophysical properties is important for engineering applications; often achieved by mixing two or more liquids. An important issue is that properties tend to be coupled, which can be problematic if improvement of one property is accompanied by deterioration in another. Therefore, optimization is typically a compromise between properties, and it could be enhanced if they could be decoupled. Such decoupling however first requires an understanding of the common and distinct physical origins of each thermophysical property of interest. Here, we introduce a new approach to gain such understanding, combining molecular-simulation-based structural perturbation with regularized statistical analysis. Considering viscosity and thermal conductivity of a water–glycol mixture as a test-case, we identify the role of structure on each property, and highlight the important role that hydrogen bonding can play in such decoupling.

Experimental study of surface tension, specific heat and thermal diffusivity of liquid and solid titanium

The thermophysical properties of liquid and solid titanium such as the surface tension, specific heat and thermal diffusivity have been investigated over a wide temperature range. By using electromagnetic levitation and oscillating drop method, the surface tension of liquid titanium was measured in the temperature range of 1802–2188K. The viscosity and density of undercooled liquid titanium were calculated by some well-known models using the measured data as input. In addition, the specific heat of liquid titanium was determined over the experimental range using electromagnetic levitation and drop calorimetry obtaining the value of 33.64Jmol−1K−1. In addition, the thermal diffusivity of solid titanium was measured by laser flash method in the temperature range of 171–1080K.

Experiment and Numerical Modelling the Time of Plate-Shape Casting Solidification vs. Thermal Conductivity of Mould Material/ Eksperyment I Modelowanie Numeryczne Czasu Krzepniecia Odlewu Płyty W Funkcji Przewodnictwa Cieplnego Materiału Formy

Abstract The paper presents results of measuring thermal conductivity of sand mould material and time of castings solidification evaluated from cooling curves and from Nova Flow & Solid numerical calculations. During the experiments pure Al (99,95%) plate was cast into the sand moulds. The analysed variable parameter in numerical calculations was mould thermal conductivity of fixed value taken from the range 0.5-0.9 W/(mK). Other mould parameters (initial temperature, mass density, heat capacity) and thermo-physical properties of liquid and solid casting were taken invariable. Basing on the measurements it was stated that thermal conductivity of the moulding sand has complex temperature variability, especially during the water vaporization and the obtained dependence should be used in the numerical calculations to improve their accuracy.

Evaporation Induced Thermal Patterns in Fluid Layers: A Numerical Study

Liquid layers evaporating under the influence of a gas shear flow presents a non-uniform distribution of the evaporation rate all along the interface. Being the evaporation an endothermic process, a thermal gradient along the interface is generated and thermo-capillary flows are induced. Hence, two opposite mechanisms regulate the movement of the interface: the shear stress of the gas that entrains the interface in the direction of the flow and the thermo-capillary stress that forces the interface to move against the flow direction. The composition of these mechanisms at the interface generates an unstable thermal patterning. The dynamic evolution of the patterning and the relative evaporation rate are strongly influenced by the flow rate of inert gas, the layer thickness and the liquid thermo-physical properties. The goal of the present work is to study numerically how the evaporation process is influenced by the above-mentioned mechanisms. The focus will be on the evolution of the thermal patterning at the interface and the assessment of the main factors influencing the computed evaporation rate.

Heat Transfer in a Slurry Bubble Column Reactor: A Critical Overview

Studies of heat transfer in slurry bubble column reactors have been reviewed and observed differences analyzed based on available data. Heat transfer in these reactors is a strong function of some parameters and a weak function of others. The parameters significantly influencing heat transfer in these reactors are the superficial gas velocity, thermophysical properties of liquid and solid particles, and size and concentration of the particles. Moreover, the rate of change with a parameter is dependent on the operating flow regime, particle properties, and presence of internals. Of all of the parameters, the effect of the particles is more complex and inadequately understood because particles influence the flow regime transition and thermophysical and rheological properties of the suspension, which, in turn, affect the hydrodynamic behavior and associated heat-transfer characteristics. The effects of the column diameter and internals have been investigated by a limited number of studies. A comparison of available data shows that the effect of the column diameter on heat transfer diminishes above 0.3 m. This, however, requires confirmation from larger-diameter studies together with associated hydrodynamic studies and appropriate modeling. Literature correlations for the heat-transfer coefficient have been reviewed and their limitations and applicability discussed. Axial and radial variations of heat-transfer coefficients reported in literature studies require appropriate design considerations.

Nanofluid-based direct absorption solar collector

Solar energy is one of the best sources of renewable energy with minimal environmental impact. Direct absorption solar collectors have been proposed for a variety of applications such as water heating; however the efficiency of these collectors is limited by the absorption properties of the working fluid, which is very poor for typical fluids used in solar collectors. It has been shown that mixing nanoparticles in a liquid (nanofluid) has a dramatic effect on the liquid thermophysical properties such as thermal conductivity. Nanoparticles also offer the potential of improving the radiative properties of liquids, leading to an increase in the efficiency of direct absorption solar collectors. Here we report on the experimental results on solar collectors based on nanofluids made from a variety of nanoparticles (carbon nanotubes, graphite, and silver). We demonstrate efficiency improvements of up to 5% in solar thermal collectors by utilizing nanofluids as the absorption mechanism. In addition the experimental data were compared with a numerical model of a solar collector with direct absorption nanofluids. The experimental and numerical results demonstrate an initial rapid increase in efficiency with volume fraction, followed by a leveling off in efficiency as volume fraction continues to increase.

Estimation of heat transfer coefficient in bubble column reactors using support vector regression

The objective of this study was to develop a unified correlation for heat transfer coefficient ( h) in bubble columns without internals for various gas–liquid systems using support vector regression (SVR)-based modeling technique. From the data published in open literature, 366 data points from 10 open sources spanning the years 1976–2007 for h were collected. Generalized SVR-based models have been developed for the relationship between h and prominent design and operating parameters such as column and sparger geometry, gas–liquid thermo-physical properties, operating temperature, pressure, superficial gas velocity, etc. Further, this model for h has been uploaded on the link http://www.esnips.com/web/UICT-NCL. The proposed generalized SVR-based correlations for h has prediction accuracies of 98.56% and average absolute relative error (AARE) of 7.05%. Also, the SVR-based correlation showed much improved predictions when compared with those estimated by empirical correlations in the literature.

Thermophysical Properties of Liquid In–Sn Alloys

The surface tension of liquid In–Sn alloys was measured with three experimental techniques carried out in a protective atmosphere of a mixture of argon and hydrogen: tensiometric (in Chemnitz), and maximum bubble pressure and sessile drop (in Krakow). Attempts were undertaken to confirm the correlation of surface tension with electrical conductivity and viscosity and to compare them with literature data. The lack of such correlation or a weak one was observed, probably due to a slight negative departure of thermodynamic properties of liquid In–Sn alloys from ideal behavior. Both resistivity and viscosity correlated with the existence of In-rich β and Sn-rich γ phases of the In–Sn phase diagram. The mutual correlations of thermodynamic and physical properties, structure, and the type of phase diagram were confirmed previously for Li–Sn and Mg–Sn systems with evident negative thermodynamic departures from ideal behavior and with the occurrence of intermetallic compounds (IMCs) in the phase diagrams. Due to nearly the same values of surface tension and density of pure In and Sn, the concentration dependence on the surface tension and density was practically unchanged within an extensive range of temperatures in studies on Pb-free solders of binary and multicomponent alloys containing both metals. Thus, the beneficial influence of In on the wettability of In–Sn alloys was observed solely by the lowering of the contact angle.

Containerless Property Measurements of Liquid Palladium

Some thermophysical properties of liquid and supercooled palladium were measured using containerless techniques. Over the 1640–1875 K temperature interval, the density could be expressed as ρ(T)=10.66× 103 −0.77(T−Tm)(kg·m−3) and the ratio between the isobaric heat capacity and the hemispherical total emissivity could be rendered as Open image in new window (J·mol−1·K−1), where Tm=1828 K. The volume expansion coefficient was also determined as 7.2 × 10−5 K−1.

Thermophysical properties of liquid and supercooled ruthenium measured by noncontact methods

Several thermophysical properties of liquid and supercooled ruthenium were measured using electrostatic levitation. Over the 2225–2775 K temperature interval, the density can be expressed as ρ(T) = 10.75 × 103 – 0.56(T – Tm)(kg ⋅ m−3) with Tm = 2607 K. In addition, the surface tension can be expressed as σ(T) = 2.26 × 103 – 0.24(T – Tm)(mN ⋅ m−1) and the viscosity as η(T) = 0.60 exp[4.98 × 104/(RT)] (mPa ⋅ s) over the 2450–2725 K range. The isobaric heat capacity was estimated as CP(T) = 35.9 + 1.1 × 10−3(T – Tm)[(J/(mol K)] over the 2200–2750 K span by assuming a constant emissivity. The volume expansion coefficient, the enthalpy, and the entropy of fusion were also calculated as 5.2 × 10−5 K−1, 29.2 kJ ⋅ mol−1, and 11.2 J/(mol K).

Thermophysical properties of liquid and supercooled ruthenium measured by noncontact methods

Thermophysical properties of liquid and supercooled ruthenium measured by noncontact methods

静電浮遊炉を用いた溶融ニッケルの熱物性計測

Thermophysical properties of liquid and supercooled nickel were measured using electrostatic levitation. Over the 1420-1850 K temperature range, the density can be expressed as ρ(T )=7.89×103−0.65(T −Tm) (kg · m-3) with melting temperature Tm=1728 K. The isobaric heat capacity was estimated as Cp(T )=36.2+1.9×10-3(T −Tm) (J · mol-1·K-1) over the 1420-1850 K interval by assuming a constant emissivity. The volume expansion coefficient was also calculated as 8.2×10-5 K-1. In addition, the surface tension can be expressed as γ(T )=1739−2.2×10-1(T −Tm) (10-3 N · m-1) and the viscosity as η(T )=0.07 exp [6.7×104/(RT )](10-3Pa · s) over the 1553 to 1963 K temperature range.