Prediction Of Thermophysical Properties Research Articles

Statistical Analysis and Accurate Prediction of Thermophysical Properties of ZnO-MWCNT/EG-Water Hybrid Nanofluid Using Several Artificial Intelligence Methods

Statistical Analysis and Accurate Prediction of Thermophysical Properties of ZnO-MWCNT/EG-Water Hybrid Nanofluid Using Several Artificial Intelligence Methods

Local structure and thermophysical property prediction for CaCl2-KCl molten salt with machine learning potentials

CaCl2-KCl molten salt, due to its excellent physical and chemical properties, is considered to have great application prospects in the fields of concentrated solar power (CSP) systems and preparation of metallic calcium. Structure information and thermophysical property have been simulated at various temperatures to explore the effect of the temperature on CaCl2-KCl molten salt. The reliability of machine learning potential molecular dynamics (MLPMD) simulation results is studied by comparison with the first-principles molecular dynamics (FPMD) simulations and experiments. Moreover, the MLPMD simulations lower the barrier to understanding and designing molten salts due to their high efficiency and accuracy.

Prediction of thermo-physical properties and evaluation of thermal energy storage performance of phase change materials based on silicone rubber/graphite (Q/G) composites

ABSTRACT Phase change composite materials (PCCMs) are a type of thermal energy storage system known for their high latent heat of fusion. In this study, PCCM samples based on peroxide-cured silicone rubber (Q) were prepared using a simple soaking procedure in molten Paraffin Wax (PW). At any fixed amount of peroxide, the rubber base samples were filled with varying amounts of graphite powder (G), 4 to 8 Wt.%, to improve the thermo-physical properties. The effect of peroxide and graphite content was then evaluated on the PW loading, PW leakage, and thermal storage performance of the resulting Q/G/PW composites. It was found that the highest ratio of the PW loading to PW leakage occurs at a peroxide content of 6 Wt.%. Moreover, the graphite particles demonstrate a significant role in PW trapping inside the rubber structure and lowering the PW leakage. Finally, the thermal studies showed that the Q/G/PW composite containing 6 wt.% of peroxide and 4 wt.% of graphite has the highest latent heat of 111.5 J/g with a PW loading of 15.2%. This sample also had the lowest thermal diffusivity and the slowest cooling and heating rate.

High throughput screening of thermal interface materials by machine learning

Till now, it remains a challenge for effective prediction and screening of novel materials with high thermal conductivity, as well as further optimization of the interface thermal resistance. Normally, people have to spend long time on tedious calculations when predicting and screening these materials. In this paper, I combined machine learning with molecular dynamics simulations to investigate the thermal conductive properties of materials with the aim of significantly reducing computational consumption. I first applied molecular dynamics simulations to obtain the relevant properties of materials, then generated models for predicting physical properties by machine learning, and finally made predictions of thermophysical properties of materials. The use of machine learning significantly reduces the prediction time compared to direct molecular dynamics simulations. Especially when the XGBoost and the neural network models are employed, the prediction efficiency is significantly improved. This work guides a new way for the future screening of high-performance thermal interface materials.

Molecular dynamics simulations for the prediction of thermophysical properties of plutonium-based molten salts

Molten salt nuclear reactors are promising technologies for sustainable energy sources. In this context, understanding the physicochemical properties of molten salts is essential to the development, usage, and maintenance of reactors. Classical molecular simulations allow estimating the macroscopic properties of molten salts at a relatively low computational cost. In this work, we develop a new force field for plutonium-based molten salts. We validate our classical model by comparing molecular dynamics results with experimental measurements, obtaining a quantitative agreement. Our work demonstrates the reliability and transferability of classical force fields to represent the physicochemical properties of molten salts.

A Review of Artificial Intelligence Methods in Predicting Thermophysical Properties of Nanofluids for Heat Transfer Applications

This present review explores the application of artificial intelligence (AI) methods in analysing the prediction of thermophysical properties of nanofluids. Nanofluids, colloidal solutions comprising nanoparticles dispersed in various base fluids, have received significant attention for their enhanced thermal properties and broad application in industries ranging from electronics cooling to renewable energy systems. In particular, nanofluids’ complexity and non-linear behaviour necessitate advanced predictive models in heat transfer applications. The AI techniques, which include genetic algorithms (GAs) and machine learning (ML) methods, have emerged as powerful tools to address these challenges and offer novel alternatives to traditional mathematical and physical models. Artificial Neural Networks (ANNs) and other AI algorithms are highlighted for their capacity to process large datasets and identify intricate patterns, thereby proving effective in predicting nanofluid thermophysical properties (e.g., thermal conductivity and specific heat capacity). This review paper presents a comprehensive overview of various published studies devoted to the thermal behaviour of nanofluids, where AI methods (like ANNs, support vector regression (SVR), and genetic algorithms) are employed to enhance the accuracy of predictions of their thermophysical properties. The reviewed works conclusively demonstrate the superiority of AI models over the classical approaches, emphasizing the role of AI in advancing research for nanofluids used in heat transfer applications.

Thermophysical property prediction of anion-functionalized ionic liquids for CO2 capture

We develop a machine learning framework for predicting the density, viscosity, and heat capacity of a family of anion-functionalized ionic liquids for CO2 capture, specifically those with tetraalkylphosphonium cations and aprotic N-heterocyclic anions (AHAs). We screen several feature sets using group contribution-based (GC) descriptors and descriptors extracted from COSMO-RS sigma profiles (SP) to build Support Vector Regression (SVR) and Gradient-Boosted Regression (GBR) machine learning models. Viscosities and densities were modeled based on data sets containing nearly 60 ILs each. The best fit for viscosity used GC-based descriptors and the SVR model, achieving a test set %AARD of 12.5% and R2 of 0.989. Density was modeled using these same descriptors with the SVR model framework and was fitted with a test set %AARD of 1.0%. Heat capacity was fit as a function of molar volume and temperature, a general trend observed for all ILs in a family. Heat capacity predictions could then be made using the density SVR model with a test set accuracy of 3.0 %AARD. With these results, we have developed predictive models which can potentially be used in the design of new advanced ionic liquids for carbon capture.

Detailed experimentation and prediction of thermophysical properties in lauric acid-based nanocomposite phase change material using artificial neural network

The thermal conductivity (TC) of a nanocomposite phase change material (NPCM) may be improved by adding nanostructured materials to a Phase Change Material (PCM). To assess the heat transfer rate during the process of phase change, such as melting and freezing, an accurate TC prediction of NPCM is required. A Field Emission Scanning Electron Microscope (FESEM) was utilized to examine the nanoparticle morphological study, and X-Ray Diffraction (XRD) analysis evaluated the crystalline structure. NPCMs were verified using Fourier Transform Infrared Spectroscopy (FTIR). The goal of this research is to create an Artificial Neural Network (ANN) that guesses the TC and viscosity of Lauric Acid (LA) embedded with dispersed copper oxide (CuO) and aluminium oxide (Al2O3). A multi-layered feed-forward ANN (MLFFANN) is trained using the Levenberg-Marquardt (LM) backpropagation algorithm. There are 130 experimental datasets in total, obtained from experiments with nanoparticle mass fractions ranging from 1.25 to 10 wt%. The minimum mean square error (MSE) for TC and viscosity is 4.6815 × 10−5 and 2.4681 × 10−5, respectively. The average absolute deviation (AAD) for TC and viscosity is 0.004249 and 0.003596, respectively, while the mean absolute percentage error (MAPE) is 2.1835 % and 4.8197 % for TC and viscosity, and the correlation coefficients(R) are 0.992 and 0.975, respectively. The largest percentage variation between experimental values and ANN computed values for the liquid and solid phases, respectively, is 3.54 % and 0.832 %. It demonstrates that the constructed ANN prediction model predicts the increased TC and viscosity of NPCM for different nanoparticle loadings, temperatures, and oxide nanoparticles.

Optimized ANFIS models based on grid partitioning, subtractive clustering, and fuzzy C-means to precise prediction of thermophysical properties of hybrid nanofluids

Applying machine learning algorithms in the prediction of nanofluids' thermophysical properties such as density, viscosity, thermal conductivity (TC), and specific heat capacity (SHC), can lead to cost and time reduction in practical applications. The present research aimed to accurately predict the thermophysical properties of water-based oxide-MWCNT hybrid nanofluids by adopting a whole search strategy for structural/training optimization of the ANFIS models with different types of clustering techniques, including grid partitioning (GP), subtractive clustering (SC), and fuzzy c-means (FCM). To evaluate the optimized ANFIS models various statistical criteria, ARD-based pie charts, MOD plots, violin graphs, and well-known theoretical/experimental correlations were employed. The results revealed that the optimal GP-ANFIS model performed better than other ANFIS approaches in modeling the nanofluid's specific heat capacity (R = 0.99992 and MAPE = 0.0359%) and thermal conductivity (R = 0.99833 and MAPE = 0.2177%). Also, the optimal SC-based ANFIS approach presented the highest precision model for the nanofluids density (R = 0.99886 and MAPE = 0.0369%) and viscosity (R = 0.99887 and MAPE = 0.4206%). The sensitivity analysis indicated that inputs of nanoparticles density, solid volume fraction, nanoparticles SHC, and nanoparticles TC are the most influential parameters in predicting nanofluid density, viscosity, specific heat capacity, and thermal conductivity, respectively.

Unified model for the prediction of thermophysical properties of nanometals

It is very important to predict the thermophysical properties of engineered material at nanoscale. To predict thermophysical properties of material at nanoscale is still a great challenge. Although many efforts have been done by researchers and scientists in this field, even the exact model for the prediction and explanation of the thermophysical properties of nanometals is still awaited. In our current work we have suggested unified model to forecast the thermophysical properties of nanometals such as specific heat, melting temperature, melting enthalpy & melting entropy which are calculated with the help of cohesive energy model including shape effect and the structure of materials at nanoscale. The relaxation factor induced due to dangling bond on the surface of nanomaterials is taken into consideration. The results obtained using this model is verified with the available experimental results of the thermophysical properties of nanometals such as Silver (Ag), Aluminium (Al), Gold (Au), and Copper (Cu). The present model can also be used for the prediction of change of the thermophysical properties of other metals at their size and shape in nanoscale.

Correlations for prediction of hydrogen gas viscosity and density for production, transportation, storage, and utilization applications

Accurate determination of hydrogen transport properties is essential for hydrogen production, transportation, storage, and utilization. Different Equations of State (EoS) are commonly used to calculate hydrogen thermophysical properties. However, EoS approaches are usually implicit and require numerous calculations and could be very time consuming. Therefore, the end users often desire the predictions of thermophysical properties using simple empirical correlations, which can be considered a practical solution to reduce the computational burden of EoS calculations. This study presents new explicit empirical correlations using symbolic regression analysis of available experimental data to calculate hydrogen viscosity and density. The developed viscosity correlation provides accurate predictions over the temperature range of 100–2130 K for dilute gas and 14 (H2 triple point) −1000 K for hydrogen gas up to 220 MPa. The results show an average absolute deviation (AAD) of 1.06% in the predicted gas viscosity, with the largest deviation in the vicinity of the critical point. The dilute gas viscosity was also predicted with an AAD of 0.467%. The density correlation represents a high prediction accuracy with an AAD of 0.26% over the temperature and pressure ranges of 150–423 K and 0.1–220 MPa, respectively. The developed correlations offer a higher prediction accuracy and find applications in hydrogen production, transportation, storage, and utilization value chain.

Prediction of thermophysical properties of hybrid nanofluids using machine learning algorithms

Prediction of thermophysical properties of hybrid nanofluids using machine learning algorithms

Reliable prediction of thermophysical properties of nanofluids for enhanced heat transfer in process industry: a perspective on bridging the gap between experiments, CFD and machine learning

Reliable prediction of thermophysical properties of nanofluids for enhanced heat transfer in process industry: a perspective on bridging the gap between experiments, CFD and machine learning

Effect of four-phonon scattering on anisotropic thermal transport in bulk hexagonal boron nitride by machine learning interatomic potential

The highly anisotropic thermal conductivity in layered materials is crucial for a broad range of applications such as thermal management of electronic devices, thermal insulation, and thermoelectrics. Understanding of anisotropic thermal transport in layered materials largely depends on atomistic simulations based on density functional theory (DFT) or empirical potentials, which however suffer either low computational efficiency or accuracy. Recently, machine learning interatomic potentials (MLIPs) are emerging as a powerful tool to bridge the gap. Despite the recent progress in developing MLIPs, little attention has been paid to constructing a potential that can accurately predict the thermal properties of layered materials, which is more challenging compared with the case of isotropic materials because of the highly anisotropic bonding and weak van der Waals interactions in layered materials. Here, we introduce a MLIP within the Gaussian approximation potential (GAP) framework for bulk hexagonal boron nitride (h-BN) with a typical layered structure. We demonstrate that the GAP can well predict the highly anisotropic phonon transport properties and thermal conductivity of bulk h-BN with DFT-level accuracy at 3 orders of magnitude reduced cost. Particularly, we investigate the effect of four-phonon scattering on the thermal conductivity of bulk h-BN using the GAP. The suppression of thermal conductivity by four-phonon scattering is negligible below 300 K due to the orders of magnitude weaker four-phonon scattering than three-phonon scattering, which is mainly attributed to the breaking of the reflection symmetry selection rule and the splitting of the flexural acoustic branch in bulk h-BN. As temperature increases, four-phonon scattering results in a larger reduction in thermal conductivity, which increases up to 21.64% (in-plane) and 35.71% (cross-plane) at 1000 K. Atomistic simulations based on MLIPs are expected to be able to greatly promote the understanding of phonon transport and the prediction of thermophysical properties in layered materials.

Comparison of Force Fields for the Prediction of Thermophysical Properties of Long Linear and Branched Alkanes.

The prediction of thermophysical properties at extreme conditions is an important application of molecular simulations. The quality of these predictions primarily depends on the quality of the employed force field. In this work, a systematic comparison of classical transferable force fields for the prediction of different thermophysical properties of alkanes at extreme conditions, as they are encountered in tribological applications, was carried out using molecular dynamics simulations. Nine transferable force fields from three different classes were considered (all-atom, united-atom, and coarse-grained force fields). Three linear alkanes (n-decane, n-icosane, and n-triacontane) and two branched alkanes (1-decene trimer and squalane) were studied. Simulations were carried out in a pressure range between 0.1 and 400 MPa at 373.15 K. For each state point, density, viscosity, and self-diffusion coefficient were sampled, and the results were compared to experimental data. The Potoff force field yielded the best results.

Estimating Thermophysical Rock Properties from Electromagnetic Sounding Data and Laboratory Measurements

Abstract—Possibilities to construct the sections of thermal conductivity, specific heat capacity, as well as heat flow density at the surface, using electromagnetic sounding data and laboratory measurements on core samples, have been studied. It has been shown that the neural network prediction of thermophysical properties from electromagnetic sounding data and laboratory measurements enables estimating these properties not only below boreholes but also in the interwell space, with relative accuracy around 6–10%. Sections of “dry” and “wet” thermal conductivity, specific heat capacity and a profile of the heat flow density at the surface of the study area, along magnetotelluric sounding profile, have been built.

Theoretical predictions of thermophysical properties of BiSn liquid alloys at 600 K

Theoretical predictions of thermophysical properties of BiSn liquid alloys at 600 K

Thermophysical properties of n-dodecane over a wide temperature and pressure range via molecular dynamics simulations with modification methods

The working range of hydrocarbon fuel has gradually expanded to high pressure/high temperature conditions above the critical point with the proposal of “dual carbon” strategy. The calculation accuracy of fuel thermophysical properties in the power systems has become a major difficulty. In this study, the density, internal energy, enthalpy, entropy, thermal conductivity and viscosity of n-dodecane molecular system were calculated using the SKS-UA force field in the isothermal-isobaric (NPT) ensemble via molecular dynamics (MD) simulations in the range of temperatures from 298 K to 2000 K and pressures from 1.2773 atm to 3000 atm. Three modification methods in terms of stability modification, kernel density estimation (KDE) modification and zero potential energy surface modification were proposed to improve the calculation accuracy. Average absolute relative deviation (AARD) and standard error (SE) were employed to perform error and uncertainty analysis on existing and non-existent thermophysical properties data of n-dodecane, respectively. The results indicated that the modified results for the six thermophysical properties are better than the original ones. The modified density, internal energy, enthalpy, entropy, and thermal conductivity were in good agreement with the experimental values, while the viscosity is underestimated at low temperature. The change in thermophysical properties was enormous near the critical point. Among the conditioned factors, pressure had a greater impact on density and thermal conductivity, while internal energy, enthalpy and entropy were more sensitive to temperature. Viscosity changed significantly when the temperatures were below the critical point. This modification method offers high-precision calculation results, inspiring the prediction of thermophysical properties over a wide temperature and pressure range.

Investigation on heat conduction of multi-sized sintered-ceramic powders based on Monte-Carlo method

Thermophysical property prediction of sintered lunar regolith plays a crucial role in developing extraterrestrial thermal processing techniques for In-situ Resource Utilization (ISRU) on the lunar surface since the Moon has been regarded as an outpost of deep space exploration. It is widely established that well-designed thermal processing can improve the mechanical and thermal properties of powdered material for industrial applications. Varying particle sizes and compositions significantly impact the sintering neck formation and heat transfer between solid particles. Previous studies have developed several parametric expressions for thermal conductivity between sintered mono-sized metallic or oxide particles. However, only a few concentrated on evaluating the thermal conductivity of sintered bodies with multi-sized powders. In this study, an analytical model for predicting the Effective Thermal Conductivity (ETC) of sintered ceramic samples fabricated using bi-sized particles was developed. The sintering contact between grains was estimated by an algorithm that couples the Monte-Carlo method with the neck formation theory. Here, samples of two oxides, alumina and silica, with different granularity fractions were prepared to simulate the lunar regolith. Volume ratios of small (radius of 5 μm) to large (radius of 22.5 μm) were 4:1, 2:1, and 1:2, respectively. The presented model was validated by ETC data measured by a special-designed apparatus, as well as the specific surface area within porous samples through the BET technique. The simulated results agree well with the measured data of samples with a single chemical component (RMSE=8.1%). However, the predicted ETC values for the mixture of two oxides (50/50 Vol.%) were about twice the measured results. The formation of mullite, verified by X-Ray Diffraction (XRD) analysis, was probably the main reason for the overvalued modeling ETC. Furthermore, the presented model was applied to analyze the sensitivity of ETC to important factors of ceramic mixtures. The results showed that the curvature deficiency between multi-sized particles influences the neck growth rate of the sintering necks compared with the mono-sized scenario.

Not from Scratch: Predicting Thermophysical Properties through Model-Based Transfer Learning Using Graph Convolutional Networks.

In this study, a framework for the prediction of thermophysical properties based on transfer learning from existing estimation models is explored. The predictive capabilities of conventional group-contribution methods and traditional machine-learning approaches rely heavily on the availability of experimental datasets and their uncertainty. Through the use of a pretraining scheme, which leverages the knowledge established by other estimation methods, improved prediction models for thermophysical properties can be obtained after fine-tuning networks with more accurate experimental data. As our experiments show, for the case of critical properties of compounds, this pipeline not only improves the performance of the models on commonly found organic structures but can also help these models generalize to less explored areas of chemical space, where experimental data is scarce, such as inorganics and heavier organic compounds. Transfer learning from estimation models data also allows for graph-based deep learning models to create more flexible molecular features over a bigger chemical space, which leads to improved predictive capabilities and can give insights into the relationship between molecular structures and thermophysical properties. The generated molecular features can discriminate behavior discrepancy between isomers without the need of additional parameters. Also, this approach shows better robustness to outliers in experimental datasets.