Accurate Thermal Conductivity Research Articles

Analysis of a Novel Method for Generating 3D Mesh at Contact Points in Packed Beds

This study comprehensively analyzes the impact of the novel HybridBridge method, developed by the authors, for generating a 3D mesh at contact points within packed beds within the effective thermal conductivity. It compares HybridBridge with alternative methodologies, highlights its superiority and outlines potential applications. The HybridBridge employs two independent geometry parameters to facilitate optimal flow mapping while maintaining physically accurate effective thermal conductivity and ensuring high mesh quality. A method is proposed to estimate the HybridBridge radius for a defined packed bed and cap height, enabling a presimulative determination of a suitable radius. Numerical analysis of a body-centered-cubic unit cell with varied HybridBridges is conducted alongside previous simulations involving a simple-cubic unit cell. Additionally, a physically based resistance model is introduced, delineating effective thermal conductivity as a function of the HybridBridge geometry and porosity. An equation for the HybridBridge radius, tailored to simulation parameters, is derived. Comparison with the unit cells and a randomly packed bed reveals an acceptable average deviation between the calculated and utilized radii, thereby streamlining and refining the implementation of the HybridBridge methodology.

Thermal conductivity prediction of Al[formula omitted]O[formula omitted]-doped tetragonal YSZ coatings using deep learning

Thermal barrier coatings, including Al2O3-doped tetragonal yttria-stabilized zirconia (t-YSZ) coatings are vital in diverse applications. Thermal conductivity is a key property, but prediction often requires some complex experiments. In this study, a novel dual-structure feature extraction coupled with a multi-scale attention fusion network (RCFNet) is introduced for accurate thermal conductivity prediction using electron backscattered diffraction (EBSD) microstructure images. The method requires minimal data, and the result shows high accuracy with a Mean Square Error (MSE) of 0.003 W(m⋅k)−1 and an R-squared (R2) value of 0.81. Furthermore, the model reveals specific correlations between microstructural characteristics and thermal conductivity, facilitating rapid structure selection for specific thermal conductivity.

Effective thermal conductivity of granular soils: a review of influencing factors and prediction models towards an investigation framework through multiscale characters

The effective thermal conductivity of soil is important to geo-engineering applications, and it is controlled by factors across different length scales. Through a comprehensive review of these factors, we found that while other more traditional factors have been well studied, there is still a lack of characterisation of soil microscale and mesoscale structures and their influence on effective thermal conductivity. In addition, after reviewing the models available in the literature for soil effective thermal conductivity prediction, it was found that compared with empirical and theoretical models, machine learning models can account for the influence of multi-scale factors; however, research into them is scarce. To overcome the limitations of previous research, we proposed a framework that can investigate the factors influencing soil effective thermal conductivity at multiple scales. It includes the impact of soil structural factors at micro to mesoscale, and this impact is integrated with the influence from other factors for accurate thermal conductivity prediction.

The poor reliability of thermal conductivity data in the aerogel literature: a call to action!

Aerogels are an exciting class of materials with record-breaking properties including, in some cases, ultra-low thermal conductivities. The last decade has seen a veritable explosion in aerogel research and industry R&D, leading to the synthesis of aerogels from a variety of materials for a rapidly expanding range of applications. However, both from the research side, and certainly from a market perspective, thermal insulation remains the dominant application. Unfortunately, continued progress in this area suffers from the proliferation of incorrect thermal conductivity data, with values that often are far outside of what is possible within the physical limitations. This loss of credibility in reported thermal conductivity data poses difficulties in comparing the thermal performance of different types of aerogels and other thermal superinsulators, may set back further scientific progress, and hinder technology transfer to industry and society. Here, we have compiled 519 thermal conductivity results from 87 research papers, encompassing silica, other inorganic, biopolymer and synthetic polymer aerogels, to highlight the extent of the problem. Thermal conductivity data outside of what is physically possible are common, even in high profile journals and from the world’s best universities and institutes. Both steady-state and transient methods can provide accurate thermal conductivity data with proper instrumentation, suitable sample materials and experienced users, but nearly all implausible data derive from transient methods, and hot disk measurements in particular, indicating that under unfavorable circumstances, and in the context of aerogel research, transient methods are more prone to return unreliable data. Guidelines on how to acquire reliable thermal conductivity data are provided. This paper is a call to authors, reviewers, editors and readers to exercise caution and skepticism when they report, publish or interpret thermal conductivity data.Graphical

Accurate Thermal Conductivity Measurements of Porous Thin Films by Time-Domain Thermoreflectance.

Accurate measurements of the thermal conductivity (κ) of porous thin films are still limited due to challenges to deposit flat and continuous metal transducers on porous samples, a necessity for many thermal measurement techniques for nanostructures. In this paper, we introduce an approach based on time-domain thermoreflectance (TDTR) to accurately and conveniently measure κ of porous thin films by transferring a flat and smooth metal film unto porous samples as the transducer for TDTR measurements. We demonstrate our approach by measuring κ of a series of microscale holey SiO2 films with diameters of 1-3.5 μm and porosity of 13-50%. To achieve a measurement uncertainty of <12%, we ensure that the metal transducer films are sufficiently stiff and establish good thermal contact with the holey SiO2 samples. Our κ measurements agree well with calculations of κ from effective medium theory. Our approach could provide a convenient way to further investigate the thermal transport properties of porous films.

An interpretable deep learning strategy for effective thermal conductivity prediction of porous materials

The effective thermal conductivity of porous materials plays a pivotal role in advancing novel thermal insulation materials. Existing theoretical prediction methods are computationally demanding with restricted applicability, and deep learning methods lack interpretability. More importantly, current approaches fail to explore the material structures themselves and leverage those insights to improve the accuracy of thermal conductivity prediction. Herein, we for the first time propose an interpretable deep learning approach with feedback for thermal conductivity prediction. A 3D convolutional neural network is trained on material structures-thermal conductivity data, and heat maps are generated from weighted feature maps to elucidate the prediction processes. Through image processing, incongruous regions are eliminated, resulting in porous structures with improved reasonability. Consequently, more accurate effective thermal conductivities can be obtained. This work may offer a novel predictive approach that refines both the material structure and the predictive model, thereby achieving more precise thermal conductivity predictions for porous materials.

Computing of temperature-dependent thermal conductivity and viscosity correlation for solar energy and turbulence appliances via artificial neuro network algorithm

The growing popularity of artificial intelligence approaches has led to their application in a wide range of engineering fields. The most widely used artificial intelligence tool, artificial neural networks, can be used to predict data with high accuracy. An artificial neural network approach is being used to predict effective and accurate thermal conductivity and viscosity models for hybrid nanofluid systems. Here, new types of correlations relating to the thermophysical properties of Fly Ash–Cu nanoparticles with diameter sized 15.2[Formula: see text]nm and which are temperature-dependent are developed. The highest thermal conductivity and viscosity values were obtained for hybrid nanofluids with a mixture ratio of 20:80, with maximum amplification exceeding 83.2% and 65%, respectively, over the base fluid. The Fly Ash–Cu/water hybrid nanofluid’s viscosity and thermal conductivity are evaluated for a concentration range of 0–4%. The evaluation of the Fly Ash–Cu/water hybrid nanofluids system at concentrations ranging from 0 to 4% most likely entails a scientific or engineering study aimed at understanding the behavior and properties of this nanofluid mixture. Nanoparticles can agglomerate or settle in the base fluid over time, compromising the stability of the nanofluids. Researchers may be interested in determining how varied quantities of Fly Ash and Cu nanoparticles affect the nanofluid’s stability and sedimentation behavior. The heat transfer potential is examined within the optimistic range of temperatures of 30–80∘C. Many fruitful results for turbulence and solar energy have been drawn. The Mouromtseff number achieved an optimal value for all concentration levels. The heat transfers of turbulent flow and thermal conductivity of hybrid nanofluids increase with the augmented values of concentrations and temperature. Researchers found an increase in thermal conductivity of hybrid nanofluids at 0–4% concentrations, potentially impacting heat transfer applications. The conclusion explores the potential integration of the developed correlations and neural network model into practical engineering or industrial applications involving solar energy and turbulence appliances. In this work, we extend the work of Kanti et al. [Sol. Energy Mater. Sol. Cells 234 (2022) 111423] which is on the properties of water-based fly ash-copper hybrid nanofluid for solar energy applications.

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.

Extreme sensitivity of higher-order interatomic force constants and thermal conductivity to the energy surface roughness of exchange-correlation functionals

In this Letter, we report that the fourth-order interatomic force constants (4th-IFCs) are significantly sensitive to the energy surface roughness of exchange-correlation (XC) functionals in density functional theory calculations. This sensitivity, which is insignificant for the second- (2nd-) and third-order (3rd-) IFCs, varies for different functionals in different materials and can cause misprediction of thermal conductivity by several times of magnitude. As a result, when calculating the 4th-IFCs using the finite difference method, the atomic displacement needs to be taken large enough to overcome the energy surface roughness, in order to accurately predict phonon lifetime and thermal conductivity. We demonstrate this phenomenon on a benchmark material (Si), a high-thermal conductivity material (BAs), and a low thermal conductivity material (NaCl). For Si, we find that the LDA, PBE, and PBEsol XC functionals are all smooth to the 2nd- and 3rd-IFCs but all rough to the 4th-IFCs. This roughness can lead to a prediction of nearly one order of magnitude lower thermal conductivity. For BAs, all three functionals are smooth to the 2nd- and 3rd-IFCs, and only the PBEsol XC functional is rough for the 4th-IFCs, which leads to a 40% underestimation of thermal conductivity. For NaCl, all functionals are smooth to the 2nd- and 3rd-IFCs but rough to the 4th-IFCs, leading to a 70% underprediction of thermal conductivity at room temperature. With these observations, we provide general guidance on the calculation of 4th-IFCs for an accurate thermal conductivity prediction.

Machine learning interatomic potentials as efficient tools for obtaining reasonable phonon dispersions and accurate thermal conductivity: A case study of typical two-dimensional materials

The accurate description of phonon dispersion of two-dimensional (2D) materials demonstrates significance in many research fields of condensed matter physics. In this paper, we systematically calculate the phonon spectra and transport properties of six representative 2D materials (encompassing single-element and binary compounds with flat, buckled, and puckered backbone geometries) by means of density functional theory (DFT) and two machine learning interatomic potentials [MLIPs, on-the-fly machine learning potential (FMLP), and moment tensor potential (MTP)]. The results show that the acoustic out-of-plane flexural (ZA) dispersion of the 2D materials are always and easily exhibiting non-quadratic dispersion phenomena near the center of the Brillouin zone by using the pure DFT calculation method. This phenomenon contradicts physics and reflects intuitively from the non-zero group velocity at Γ point. However, no matter which MLIP (FMLP/MTP) the calculation is based on, it could solve such behavior perfectly, where the ZA mode conforms to the quadratic dispersion relationship in the long-wavelength limit. Our results further demonstrate that compared to the pure DFT calculation, the FMLP and MTP method could quickly and relatively accurately obtain the lattice thermal conductivities of graphene, silicene, phosphorene, SiC, MoS2, and GeS. The findings presented in this work provide a solution about the pseudophysical phenomenon of ZA dispersions in 2D materials with the pure DFT calculation, which will greatly facilitate research areas such as phonon thermal transport, flexural mechanics, and electron–acoustic coupling.

Predictive modelling of thermal conductivity in single-material nanofluids: a novel approach

BackgroundThis research introduces a novel approach for modelling single-material nanofluids, considering the constituents and characteristics of the fluids under investigation. The primary focus of this study was to develop models for predicting the thermal conductivity of nanofluids using a range of machine learning algorithms, including ensembles, trees, neural networks, linear regression, Gaussian process regressors, and support vector machines.The main body of the abstractTo identify the most relevant features for accurate thermal conductivity prediction, the study compared the performance of established feature selection algorithms, such as minimum redundancy maximum relevance, Ftest, and RReliefF, a newly proposed feature selection algorithm. The novel algorithm eliminated features lacking direct implications for fluid thermal conductivity. The selected features included temperature as a thermal property of the fluid itself, multiphase features such as volume fraction and particle size, and material features including nanoparticle material and base fluid material, which could be fixed based on any two intensive properties. Statistical methods were employed to select the features accordingly.ResultsThe results demonstrated that the novel feature selection algorithm outperformed the established approaches in predicting the thermal conductivity of nanofluids. The models were evaluated using fivefold cross-validation, and the best model was the model based on the proposed feature selection algorithm that exhibited a root-mean-squared error of validation of 1.83 and an R-squared value of 0.94 on validation set. The model achieved a root-mean-squared error of 1.46 and an R-squared value of 0.97 for the test set.ConclusionsThe developed predictive model holds practical significance by enabling nanofluids' numerical study and optimisation before their creation. This model facilitates the customisation of conventional fluids to attain desired fluid properties, particularly their thermal properties. Additionally, the model permits the exploration of numerous nanofluid variations based on permutations of their features. Consequently, this research contributes valuable insights to the design and optimisation of nanofluid systems, advancing our understanding and application of thermal conductivity in nanofluids and introducing a novel and methodological approach for feature selection in machine learning.

Thermal conductivity measurement using modulated photothermal radiometry for nitrate and chloride molten salts

Molten salts are being used or explored for thermal energy storage and conversion systems in concentrating solar power and nuclear power plants. Thermal conductivity of molten salts is an important thermophysical property dictating the performance and cost of these systems, but its accurate measurement has been challenging, as evidenced by wide scattering of existing data in literature. The corrosive and conducting nature of these fluids also leads to time consuming sample preparation processes of many contact-based measurements. Here, we report the measurement of thermal conductivity of molten salts using a modulated photothermal radiometry (MPR) technique, which is a laser-based, non-contact, frequency-domain method adopted for molten salts for the first time. By unitizing the advantages of front side sensing of frequency-domain measurements and the vertical holder orientation, the technique can minimize the natural convection and salt creeping effects, thus yielding accurate molten salt thermal conductivity . The MPR technique is first calibrated using standard molten materials including paraffin wax and sulfur. It is then applied on measuring pure nitrate salts (NaNO3 and KNO3), solar salt (NaNO3–KNO3 mixture), and chloride salt (NaCl–KCl–MgCl2). The measurement results are compared with data from literature, especially those obtained from laser flash analysis (LFA). Our results demonstrate that the MPR is a convenient and reliable technique of measuring thermal conductivity of molten salts. Accurate thermal conductivity data of molten salts will be valuable in developing the next-generation high-temperature thermal energy storage and conversion systems.

A new model to predict soil thermal conductivity

Thermal conductivity is a basic parameter of soil heat transferring, playing an important role in many fields including groundwater withdrawal, ground source heat pump, and heat storage in soils. However, it usually requires a lot of time and efforts to obtain soil thermal conductivity. To conveniently obtain accurate soil thermal conductivity, a new model describes the relationship between soil thermal conductivity (λ) and degree of saturation (Sr) was proposed in this study. Dry soil thermal conductivity (λdry) and saturated soil thermal conductivity (λsat) were described using a linear expression and a geometric mean model, respectively. A quadratic function with one constant was added to calculate λ beyond the lower λdry and upper λsat limit conditions. The proposed model is compared with five other frequently used models and measured data for 51 soil samples ranging from sand to silty clay loam. Results show that the proposed model match the measured data well. The proposed model can be used to determine soil thermal conductivity of a variety of soil textures over a wide range of water content.

Investigation on liquid thermal conductivity of ethylene glycol (EG)/water mixtures: A comparative experimental and molecular dynamics simulation study

Ethylene glycol (EG) and water mixtures can be used as the antifreeze, refrigerant and coolant due to their good environmental, thermodynamic and transport performance. The mixture’s accurate thermal conductivity is indispensable for the optimal design of the relevant heat exchangers. However, thermal conductivity of EG/water mixtures is lacking and is seen to limit the further industrial application. In this paper, the transient hot wire method (THW) was used to measure the thermal conductivities of five EG/water mixtures (with ethylene glycol mass fraction wEG = 0, 0.25, 0.50, 0.75, 1.00) in the temperature range from 253.15 K to 353.15 K and pressures up to 8.0 MPa. A comparison was made between the present experimental data and literature values, yielding a good agreement. The AADs are within 2.65% for EG/water mixture systems. For convenience of engineering calculations, the present experimental data were correlated as a function of temperature and pressure. The average absolute deviations (AADs) between the experimental thermal conductivities and the calculations are 0.50%, 0.10%, 0.60%, 0.40% and 0.32% for mixture systems with ethylene glycol mass fraction wEG = 0.00, 0.25, 0.50, 0.75, 1.00, respectively. In addition, non-equilibrium molecular dynamics was applied to study the thermal conductivities of ethylene glycol/water binary mixtures based on PCFF (polymer consistent force field) and SPC/E (extended simple point charge) model, showing a good agreement with the experimental data. This study can provide fundamental thermal conductivity data for the heat transfer application of ethylene glycol aqueous solution in the industrial field.

Development of a molten salt thermal conductivity model and database for advanced energy systems

A theoretical model for predicting the temperature-dependent thermal conductivity of pure molten salts, both simple and complex, is presented. The model is based on kinetic theory and incorporates Einstein’s concept of minimum thermal conductivity. The proposed formulation can consider the magnitude of thermal conductivity for separate salts, including complex and polymerizing salts. The model’s thermal conductivity predictions were compared to reliable experimental data in the literature and a previously recommended thermal conductivity model using the Bland–Altman method. The comparison showed accurate thermal conductivity predictions relative to the reliable experimental data, with an average deviation of 10% or less. The model’s predictions were also compared to the experimental data on an individual basis for halide, divalent halide, carbonate, nitrate, nitrite, sulfate, and hydroxide molten salts, demonstrating reliable predictions for the molten salts studied and improved accuracy over the previous model. Lastly, a database of simple and complex molten salts, with the necessary parameters for modeling their thermal conductivity, is recommended.

Ultra-low lattice thermal conductivity induces high-performance thermoelectricity in Janus group-VIA binary monolayers: A comparative investigation

In this paper, the electrical transport, thermal transport, and thermoelectric properties of two new Janus STe2 and SeTe2 monolayers are systematically studied by first-principles calculations, as well as the comparative with available literature’s results using different methods. It is found that the Seebeck coefficient and conductivity have opposite dependence on temperature, and we illustrate this phenomenon in detail. The decrease of the thermoelectric power factor (PF) with temperature originates from the decrease in conductivity. To obtain accurate and convergent lattice thermal conductivity, the root mean square (RMS) is calculated to obtain a reasonable cutoff radius for the calculation of third-order forces. Janus STe2 and SeTe2 monolayers exhibit ultra-low lattice thermal conductivity of 0.2 and 0.133 W/mK at 300 K, which result from the strong coupling effect between the acoustic mode and the low-frequency optical branch, low phonon group velocity, small phonon lifetime, and large anharmonicity. Consequently, ultra-high ZT values of 2.11 (2.09) and 3.28 (4.24) for n-type(p-type) carrier doping of STe2 and SeTe2 are obtained, indicating that they are promising thermoelectric materials.

Error factors in precise thermal conductivity measurement using 3ω method for wire samples

In designing thermoelectric and heat transfer devices on the micrometer scale, the accurate thermal conductivity measurement is very important, and a variety of measurement methods have been developed so far. Among them, the 3ω method is one of the best for conductive wires because it can directly measure thermal conductivity without measuring density or specific heat, and also in the same direction as electrical or thermoelectric property. However, previous studies have not sufficiently considered the effects of ambient pressure and the conductive adhesive used to attach the sample to the electrode, which may hinder accurate measurement. In this study, using a thin gold wire as a test sample, the influence of ambient pressure and the length of conductive adhesive along the sample has been investigated quantitatively as major factors of systematic errors in the 3ω method. When the pressure was increased in the transition flow region, the measured apparent thermal conductivity increased. An analytical model for the low-pressure gas heat conduction is proposed to quantitatively explain the pressure dependence. The measured value also increased when the length of the conductive adhesive exceeded 20% of the sample length. This work has revealed that the ambient should be evacuated to the molecular flow region and the length of conductive adhesive be less than 20% of the sample length. The guidelines proposed here will help researchers in various fields to more accurately determine the thermal conductivity of micrometer-scale wires.

First-principles based computational framework for the thermal conductivity of complex intermetallics: The case study of MgZn2 and Mg4Zn7

Complex intermetallics usually exist as second phases in metal alloys. How these second phases can affect the thermal conductivity of alloys is generally unknown because the intrinsic thermal transport properties of these complex intermetallic compounds are quite less explored. In this work, we propose a computational framework based on first-principles calculations to study the electron and phonon thermal transport in complex intermetallics. Two typical intermetallics, i.e., MgZn2 and Mg4Zn7, are studied as prototypes. The rigorous mode-level first-principles calculations are first carried out to study the thermal transport of MgZn2. The calculations not only provide accurate thermal conductivity results, but also allow to prove that the constant relaxation time approximation and the Slack model work quite well in complex intermetallics. Then these two models are combined with first-principles calculations to predict the thermal transport properties for Mg4Zn7. Our results show that the directional average thermal conductivities for MgZn2 and Mg4Zn7 are 53.9 and 21.9 W/mK, significantly smaller than those of their elemental counterparts. Electrons are found to be the main heat carriers in these compounds, leading to a nearly temperature-independent thermal conductivity. Phonon thermal conductivity is negligible due to large unit cells and weak metallic bondings. Our work provides reliable thermal conductivity values for MgZn2 and Mg4Zn7. The computational framework developed in this work can also be further extended to study the electrical and thermal transport of other complex intermetallics.

Investigation on thermal conductivity of decamethylcyclopentasiloxane (D5) and decamethyltetrasiloxane (MD2M) in the liquid phase

In this work, the liquid thermal conductivities of Decamethylcyclopentasiloxane (D5) and Decamethyltetrasiloxane (MD2M) were measured by using the transient hot wire method. The measurement was conducted in the temperature range of 253.15 K to 353.15 K and pressures ranging from 0.1 MPa to 8.0 MPa, with an uncertainty of 3.0 % in the present measurements. Besides, an empirical correlation was used to represent the experimental data. The Average Absolute Deviation (AAD) between the experimental data and calculations for D5 and MD2M are 0.34 % and 0.31 %, respectively. This study provides accurate liquid thermal conductivities of D5 and MD2M. The present experimental thermal conductivity data are useful for the design and optimization of the relevant heat exchange system.

A novel heat-pulse probe for measuring soil thermal conductivity: Field test under different tillage practices

Accurate soil thermal conductivity (λ) measurements are needed in experimental agri-environmental research. This study design and build of new heat-pulse probe (HPP) based on transient state technology to measure λ. The HPP consists of three main components: an electronic control system, a measurement chamber, and sample rings. The performance of the new HPP for in-situ λ measurements is compared to estimates from measurable soil physical properties (pedotransfer function). Tests were conducted in clay loam and loam soils at three depths. λ measurements by the HPP were affected by tillage practice, fertilizer treatment, soil depth, and soil type. No significant differences in λ measurements by the HPP and estimates from a pedotransfer function were found between tillage practices. There were positive correlations between their values at three soil depths: R2 = 0.92 at 0–5 cm depth, and R2 = 0.88 at both 5–10 and 10–15 cm depths. The standard deviation from the HPP measurements were 0.061, 0.077, and 0.080 W·m−1·K−1 at 0–5, 5–10, and 10–15 cm depths, respectively. In contrast, the pedotransfer function estimates had standard deviations of 0.085, 0.660, and 0.083 W·m−1·K−1, respectively. It was found that conventional tillage increases temperature flow in soils compared to no-tillage because of decreasing soil bulk density (ρb) and consequently higher porosity. The proposed HPP is a low-cost and energy-efficient device, with wide applicability under a range of conditions. It is highly recommended for measuring λ clay loam and loam soils; however, more research is needed to determine its value with other soil types.