Effective Thermal Parameters Research Articles

Thermal and mechanical properties of AlSi7Mg matrix syntactic foams reinforced by Al2O3 or SiC particles in matrix

Materials with complex inner structure can be challenging to characterise in an effective way. The effective material parameters significantly depend on the structure, and thus on the interface properties. In the present paper, we focus on metal matrix syntactic foams, and we attempt to determine their effective thermal parameters. We supplement the results with their mechanical properties as well. For thermal characterisation, we use the heat pulse experiment to study their transient thermal response. We observed deviation from Fourier's law in multiple situations, and thus we propose an evaluation procedure for such experimental data using the Guyer–Krumhansl heat equation. We provide an iterative procedure to efficiently find the effective thermal diffusivity values provided by the Guyer–Krumhansl and Fourier equations based on the measured transient response. Furthermore, we studied the effective parameters, the specific heat, mass density, thermal conductivity and thermal diffusivity. We found that the standard estimations significantly overestimate the thermal conductivity. Furthermore, we propose a method for deducing a more reliable thermal diffusivity and thermal conductivity based on the transient temperature history, i.e., we propose to estimate Fourier's thermal diffusivity based on averaging the parameters of the Guyer-Krumhansl equation according to the observed diffusive time scales. We also found that for such metal matrix syntactic foams, even with knowing the constituents, the contact thermal resistance makes more challenging to provide a prior estimate for the effective thermal conductivity, but this difficulty can be resolved using proper effective thermal diffusivity values.

Microresonator Effective Thermal Parameters Definition via Thermal Modes Decomposition

High-Q optical microresonators are particularly efficient practical tools of modern applied optics and photonics. Using them, one inevitably faces the problem of thermal effects. Accurate determination of effective thermal parameters of high-Q microresonators (effective thermal relaxation rate and optical absorption rate) is of particular importance for developing microresonator-based devices. Our investigation looks into diverse methodologies to estimate these effective parameters for such systems, ultimately revealing a divergence between the commonly employed simplified model, the direct numerical approach, and classical analytical formulas. We introduce a novel approach to calculate effective parameters based on the decomposition of the thermal field into microresonator thermal modes, which inherently considers the intricate geometry and material anisotropy inherent in microresonators, as well as the influence of external conditions. The method for the accurate determination of the effective thermal parameters of the microresonator for corresponding thermal modes is developed. As a result of applying this method, we modified the classical approach for the simulation of thermal effects in optical microresonators for better agreement with the numerical simulations. By accounting for the complexities of microresonator shapes, material properties, and external factors, our proposed method contributes to a more accurate understanding of thermal dynamics and enhances the predictive capabilities of simulations for these systems. We demonstrated the application of this method on the example of integrated microring resonators, but it can be used to analyze thermal effects in other microresonator platforms.

Analysis of the remote laser cutting process induced damage in carbon fibre reinforced polymers

AbstractIn machining technologies for fibre reinforced polymers, remote laser cutting plays a special role as it can minimize the drawbacks of conventional laser cutting methods, in particular thermally induced damage such as charred edges and matrix evaporation. In this contribution, a thermal simulation model for the remote laser cutting process is presented. With the aim of a universally applicable model that works independently of the selected carbon fibres, matrix system, and fibre content, the effective thermal material parameters are calculated with a rule of mixture from the microscopic parameters for fibre, matrix, and process gas. The evaporation of matrix and fibre material is modelled with phase transitions. The moving laser spot is modelled with a Gaussian distribution as a heat input on the upper side. The emerging process gas is assumed to absorb a part of the laser power according to the Lambert‐Beer law.

A comprehensive numerical model for the pyrolysis of intumescent polymers: Application to EVA-ATH compounds

A detailed numerical pyrolysis model is presented for the thermal degradation of ethylene–vinylacetate copolymers filled with alumina-trihydrate (EVA-ATH). The formulation involves effective thermal parameters and surface radiative emissivity that continuously follow the material evolution, deduced from its evolving morphology along the degradation, as characterized and modelled based on microtomographic imaging. The comparison of simulated and actual cone calorimeter experiments suggests that the ATH dehydration occurs on a broader range of temperature than generally assumed. The analysis of the thermal balance during the material degradation shows that only 40% of the incident flux is absorbed by the material due to the radiative behaviour of alumina, 13% is released at the backface, and the ATH dehydration represents 70% of the pyrolysis endothermicity. These results confirm the importance of the front- and back-face boundary conditions in reference experiments, and the need of an improved modelling of the ATH chemical degradation scheme and kinetics.

Performance optimization of heat-exchanger with delta-wing tape inserts using machine learning

Nusselt number (Nu), friction factor (f), and thermal performance factor (η) are effective thermal parameters in determining the robustness of thermal management systems. However, a precise prediction of these parameters is a challenge due to complicated fluid and thermal behaviors of thermal systems, such as heat exchangers. In this study, we propose and develop a machine learning-based approach for predicting the heat transfer performance of a heat exchanger that employs delta-wing tape inserts. An aggregated database, containing 300 data points, is obtained from seven sources that include two working fluids. The wing-width ratio (w/W), pitch ratio (p/W), attack angle (α), Reynolds number (Re), and tube length (L) are in the ranges of 0.31 to 0.83, 0.95 to 1.65, 30° to 70°, 4,000 to 22,000, and 1,200 to 2,500 mm, respectively. Nu, f, and ηare predicted using an artificial neural network (ANN) model based on a universal aggregated database divided into training and test datasets. Optimization is performed, and an ANN model architecture is selected, consisting of input parameters (w/W, Re, α, p/W, and L) and hidden layers (10,10,10) that predict the test data with a mean absolute error (MAE) of 0.002393 for f, 1.821792 for Nu, and 0.00507 for η. The robustness of the developed ANN model is analyzed by precluding the databases from the training datasets together and is utilized to predict these excluded datasets. Furthermore, multiple linear regression analysis is adopted to measure and validate the efficiency of the proposed model. From the study results, it is evident that the proposed ANN method can provide valuable guidance to accurately predict the heat transfer characteristics with the lowest variance. Here, the 5–10–10–10-1 configuration is verified as the optimal configuration for the proposed model as it has the lowest MAE of 0.002393.

Drying of porous media: an algorithm to determine effective parameters

The drying process plays an important role in various industries such as chemical engineering, food, agriculture, and construction. The drying process is a complex one due to the phenomena of heat and mass transfer, which take place simultaneously. In principle, these transport processes can be modelling using continuous and discrete approaches. Following the continuous approach, porous media was simulated as continuous, and effective parameters for heat and mass transfer were employed. In this continuous model, the effective diffusivity and effective thermal conductivity parameters had strong effects on the results. The purpose of this research was to develop an algorithm to determine the effective parameters of the drying of porous media by using a continuous model.

Effect of viscous-Pasternak foundation on laser-excited microbeams via modified thermoelastic MGT model

Due to the wide variety of applications in many fields of science and engineering, the subject of foundations is considered highly attractive to many investigators. Foundations are also essential in maintaining microstructural systems during oscillations. In the current paper, a modified model of a microbeam resting on a viscous Pasternak foundation under the influence of axial tension will be presented. The microbeam is described as an Euler-Bernoulli beam as it was heated by a very short laser pulse. Based on the Moore-Gibson-Thompson (MGT) model of non-Fourier-heat-conduction and thermal field effect, the equation governing the vibration of the thermoelastic microbeam was derived. An analytical solution is presented for the studied field variables based on the Laplace transform method, in addition to an approximate numerical technique for calculating inverse transformations. The effects of different effective factors on the deflection, temperature, and thermal stresses are demonstrated, including viscoelastic damping structures, damping coefficients, laser pulse heating, and stiffness of the viscoelastic substrate. Furthermore, the estimated outcomes are compared with those obtained in the previous literature to ensure that the derived formulas are acceptable. Additional numerical results are summarized in the tables and presented graphically in order to make comparisons and reveal the effect of all effective thermal parameters. Finally, the importance of the thermo-elastic MGT model over the previous corresponding models was explored.

A Fractal Model for Effective Thermal Conductivity in Complex Geothermal Media

Thermal conductivity is an important macroscopic thermo-physical parameter due to its significant effects on the temperature field distribution and heat flow magnitude in the material at heat conduction equilibrium. However, because of the extremely complex pore structure and disordered pore distribution, a well-accepted relationship between effective thermal conductivity (ETC) and geometric structural parameters is still lack. In this study, a novel fractal model with variation pore diameter is established systematically based on the assumption that the rough elements of wall surface, pore size distribution and capillary tortuosity follow the fractal scaling law. Thermal-electrical analogy is introduced to predict the ETC of unsaturated geothermal media. The proposed model explicitly relates the ETC to the microstructural parameters (relative roughness, porosity, fractal dimensions and radius fluctuation amplitude) and fluid properties. The proposed model is validated by comparing with existing experimental data. A parametric analysis is performed for presenting the effects of the structural parameters and fluid properties on the ETC. The results show that pore structure has significant effect on ETC of unsaturated porous media. ETC gradually decreases with the increment of porosity, relative roughness, and fractal dimensions. The present study improves the accuracy in predicting ETC and sheds light on the heat transfer mechanisms of geothermal media.

Effective thermal conductivity of particulate composites for time-varying temperature fields

A homogeneous conductive medium containing random sets of identical spherical inclusions is considered. The self-consistent effective medium method is used for solution of the homogenization problem and calculation of the effective thermal conductivity parameters of the composite. For time-varying fields, the effective conductivity parameters are non-local operators with respect to time. The effective medium method allows reducing the homogenization problem to solution of a system of non-linear equations for the Laplace transforms of the kernels of these operators. An efficient numerical algorithm of solution of this system is proposed. Examples of construction of these operators for the composites with copper and glass phases are considered. The Green functions of the corresponding effective media are compared with the Green function of the media with static effective parameters of thermal conductivity.

Real-Time Predictive Temperature Measurement in Oil Pipeline: Modeling and Implementation on Embedded Wireless Sensing Devices

In pipeline management, the advent of Internet of Things (IoT) technology enables effective pipeline maintenance with the deployment of embedded sensing units, the basic requirement of which is to make accurate and real-time measurement. Practically, it is impossible to directly measure the internal temperature of the pipe by laying conventional sensors in the oil. This work proposed a novel indirect testing framework to perform real-time and accurate temperature monitoring in oil pipes by solving the inverse problem based on pipe wall's temperature field. The contribution includes three aspects: 1) A rigorous theory was developed to validate the principle of equivalence for heat transfer in pipes. A single-layer boundary-equivalent diffusion model with effective thermal parameters exhibited excellent performance in predicting temperature change in a multilayer structure by simulation. 2) We established a microprocessor-enabled predictive temperature measurement modality to precisely determine the time-dependent temperature evolution in a multilayer composite pipe based on explicit analytical expressions. 3) The correctness and applicability of the modality were verified experimentally on a real oil pipe and a commercial device. The results show that precision of the predictive measurement modality reaches 0.3 K and 0.002 K/s for stable and linear-rising internal fluid temperature, respectively.

Pressure Effect on Order-Disorder Ferroelectric Transition in a Hydrogen-Bonded Metal-Organic Framework.

Perovskite-like ABX3 metal-organic frameworks (MOFs) have gathered great interest due to their intriguing chemical and physical properties, including their magnetism, ferroelectricity, and multiferroicity. Pressure is an effective thermal parameter in tuning related properties in MOFs due to the adjustable organic framework. Though spectrum experiments have been made on the structural evolution during decompression, there is a lack of electrical studies on the order-disorder ferroelectric transition in the metal-organic frameworks under pressure. In this work, we use a static pyroelectric current measurement, a dynamic dielectric method combined with a Raman scattering technique with applying in situ pressure, to explore the order-disorder ferroelectric transition in [(CH3)2NH2]Co(HCOO)3. The ferroelectric transition vanishes around the external pressure of 1.6 GPa, emerging with a new paraelectric phase. Another phase transition was observed at 6.32 GPa, mainly associated with the distortive transition of DMA+ cations. A phenomenological theory of ferroelectricity vanishing at 1.6 GPa for [(CH3)2NH2]Co(HCOO)3 is also discussed. Our study gives a comprehensive understanding in the pressure tuning of ferroelectric properties in hybrid inorganic-organic materials.

Thermal decomposition kinetics analysis of the oil sludge using model-based method and model-free method

Oil sludge (OS) is one of the main waste in the petrochemical industry, and serious consequences can result from the improper disposal of this waste. Pyrolysis is an effective method to dispose of OS and to recover the high heating value combustibles from it. The pyrolysis of three OS samples was carried out in a nitrogen atmosphere at heating rates of 5°C/min, 10°C/min, and 20°C/min, respectively. The TG-DTG/DSC-DDSC curves show that the thermal decomposition of OS has been divided into 5 stages, and each stage has unique thermal decomposition characteristics. Two model-free methods were used to calculate the apparent activation energy at a conversion rate from 0.05 to 0.95, and based on 10 solid-state mechanisms of thermal decomposition, the model-based method was used to fit the thermal decomposition mechanisms exposed to the three heating rates. The results show that the high heating rate is beneficial for the pyrolysis of OS. The apparent activation energy varies irregularly with the conversion rate, and the apparent activation energy error calculated by the FWO method is smaller. For the three samples, OS-1, OS-2 and OS-3, the average apparent activation energies calculated by the FWO method are 40.39kJ/mol, 38.01kJ/mol, and 85.53kJ/mol, respectively. The mechanism function of OS thermal decomposition changed, and the process of thermal decomposition should be described by a three-step thermal decomposition model. During 140.48–331.88°C, the fitting correlation coefficients of the two-dimensional phase interfacial reaction mechanism under the three heating rates were all above 0.99. During 400.00–556.57°C, the fitting result of the three-dimensional nucleation and growth mechanism is better than fitting results of other models. During 583.82–676.60°C, the two-dimensional diffusion mechanism has a good fitting regression. Significantly, model-based method and model-free method provide effective and reliable thermal decomposition kinetics parameters and models as a theoretical support for optimization of OS industrial pyrolysis and treatment of OS.

Parameter identification algorithm for ground source heat pump systems

This paper presents a new parameter identification (PI) algorithm for estimating effective and detailed thermal parameters of ground source heat pump systems using data obtained from the well-known thermal response test. The PI comprises an iterative scheme coupling a semi-analytical forward model to an inverse model. The forward model is formulated based on the spectral element method to simulate transient 3D heat flow in ground source heat pump (GSHP) systems, and the inverse model is formulated based on the interior-point optimization method to minimize the system objective function. Compared to existing interpretation tools for the thermal response test, the proposed PI algorithm has several advanced features, including: it can handle fluctuating heat pump power and inlet temperatures; interpret data obtained from multiple heat injection or extraction signals; produce accurate backcalculation for short and long duration experiments; and handle multilayer systems. The PI algorithm is tested against synthesized data, using a wide range of random noise, and versus an available laboratory experiment. The computational results show that the PI algorithm is accurate, stable and exhibiting relatively high convergence rate.

Soft and hard computation methods for estimation of the effective thermal conductivity of sands

Thermal properties of sand are of importance in numerous engineering and scientific applications ranging from energy storage and transportation infrastructures to underground construction. All these applications require knowledge of the effective thermal parameters for proper operation. The traditional approaches for determination of the effective thermal property, such as the thermal conductivity are based on very costly, tedious and time-consuming experiments. The recent developments in computer science have allowed the use of soft and hard computational methods to compute the effective thermal conductivity (ETC). Here, two computation methods are presented based on soft and hard computing approaches, namely, the deep neural network (DNN) and the thermal lattice element method (TLEM), respectively, to compute the ETC of sands with varying porosity and moisture content values. The developed models are verified and validated with a small data set reported in the literature. The computation results are compared with the experiments, and the numerical results are found to be within reasonable error bounds. The deep learning method offers fast and robust implementation and computation, even with a small data set due to its superior backpropagation algorithm. However, the TLEM based on micro and meso physical laws outperforms it at accuracy.

Estimation of effective thermal parameters of heating sources based on dynamic measurments in smart home

This work is devoted to the method of determining the effective thermal parameters of heating sources in a smart home, which involves a combination of algorithms for data analysis and the equation of the physical process of heat transfer. The use of such parameters allows one to create software and hardware solutions for modeling the thermal map of the house, as well as to analyze energy consumption using the machine learning models. Since, for the most part, the total consumption of heating energy is known, it is of interest to determine the part of the energy that corresponds to the individual heating sources. To this end, the article proposes a mathematical model and algorithm for estimating the effective thermal characteristics of heating sources based on the heat transfer equation and data analysis approaches that can be used to obtain information about individual heating sources. The task of determining such parameters is reduced to two stages. At the first stage, using the finite-difference approach to the heat transfer equation, the effective thermal parameter of the heating sources is determined. Further, according to the data of energy consumption and distributions of room temperatures and temperatures on the surface of heating elements, by applying data analysis methods, an algorithm for estimating individual effective thermal characteristics of heating elements installed in rooms is proposed.

Temperature measurements along a vertical borehole heat exchanger: A method comparison

The standard thermal response tests (TRT) provide integral and effective thermal parameters of the ground in the vicinity of borehole heat exchangers (BHE). However, typical ground properties are heterogeneously distributed. As a result, advanced TRT such as distributed and enhanced TRT are growing in popularity as they provide more spatial information of the thermal properties. Thus, the objective of this study is to compare various instruments to measure the depth-dependent temperatures using standard Pt100-sensors, fiber optical thermometers and novel instruments such as Geowire, Geoball and GEOsniff®. The investigations are carried out in a 30 m length test borehole. The results showed an excellent agreement between both the Geowire and GEOsniff® in comparison with Pt100-sensors with a root mean squared error of 0.10 and 0.09 K, respectively. The results also suggest that the novel instruments have various advantages over the standard sensors and fiber optics. For example, with the novel instruments comparable, accurate, inexpensive, instantaneous and higher spatial resolution temperature measurements are obtained. Finally, the outcome of this study provides a guide for choosing the adequate temperature measurement along a BHE thus generally improving the evaluation of advanced TRT, while potentially increasing efficiency and economic viability of ground-source heat pump systems.

Thermal behaviour of nanosilver doped gallium flexible conductor for biomedical application

Gallium has been of interest in the medical community in the past decades due to its flexibility, non-toxic and anti-microbial property. In addition to its ductility, gallium makes a reasonable choice for the approach of a flexible conductor but it has a relatively low electrical conductivity as compared with other common metals which restricts usage in bio-sensors as well as other biomedical applications. In order to address such issue, a novel method was investigated using nanosilver doped gallium for enhancing charge transfer ability in the gallium metal matrix. Nanosilver has been ideal dopant due to its characteristics of highest electrical conductivity where nanoscale enables better diffusibility into the gallium matrix. The experiments were carried out to identify the effective thermal parameters which were temperature and heating duration for nanosilver doped gallium at three different temperatures, 250°C, 500°C, and 1000°C. Nevertheless, each heating duration were also in three different level, 6 hours, 12 hours, and 24 hours. All samples were tested in terms of surface morphology using an optical microscope. It was revealed that among the three temperatures, 1000°C showed better homogeneous surface morphology. It was also revealed that longer heating time promotes higher diffusion of nanosilver particles into gallium. The effective heating duration was observed to be 24 hours in terms of better homogeneity of surface morphology. Nevertheless, such findings had laid the foundation of better doping process of nanosilver into gallium to achieve a flexible conductor that can widely benefit in biomedical application.

Study on the Reliability of Thermal Parameter for Air Bricks Based on the ANSYS Software

Because of the different heat transfer mechanism between air brick and solid brick, the effective thermal parameter need to test many times. This paper used the ANSYS software to simulate the thermal parameter of air brick. By comparing the simulated result with test result for three kind of air bricks, improving that using ANSYS software to simulate the thermal parameter for air brick is reliability.

Dynamic thermo-mechanical coupled simulation of statistically inhomogeneous materials by statistical second-order two-scale method

In this paper, a statistical second-order two-scale (SSOTS) method is developed to simulate the dynamic thermo-mechanical performances of the statistically inhomogeneous materials. For this kind of composite material, the random distribution characteristics of particles, including the shape, size, orientation, spatial location, and volume fractions, are all considered. Firstly, the representation for the microscopic configuration of the statistically inhomogeneous materials is described. Secondly, the SSOTS formulation for the dynamic thermo-mechanical coupled problem is proposed in a constructive way, including the cell problems, effective thermal and mechanical parameters, homogenized problems, and the SSOTS formulas of the temperatures, displacements, heat flux densities and stresses. And then the algorithm procedure corresponding to the SSOTS method is brought forward. The numerical results obtained by using the SSOTS algorithm are compared with those by classical methods. In addition, the thermo-mechanical coupling effect is studied by comparing the results of coupled case with those of uncoupled case. It demonstrates that the coupling effect on the temperatures, heat flux densities, displacements, and stresses is very distinct. The results show that the SSOTS method is valid to predict the dynamic thermo-mechanical coupled performances of statistically inhomogeneous materials.

Influence of Rolling on the Thermal Diffusivity of Metal Alloys by Photothermal Infrared Radiometry

The thermal diffusivities of metallic foils subjected to cold-rolling processes have been studied by photothermal radiometry in a thermal transmission and reflection configuration. In this work, measurements were conducted on foils of Al, Cu, and stainless steel (V2A) that were subjected stepwise to cold-rolling process, reducing the sample of around 1 mm, as prepared, to approximately 0.1 mm. It was found that the effect of the cold-rolling is manifested as a relationship between the relative diffusivity and the relative thickness, both with respect to their corresponding initial values. This empirical relationship consists of a linear decrease of the relative diffusivity with the negative of the logarithm of the relative thickness of the sample. Within the approximation of small deformations, this behavior is consistent with linear diminishing of the thermal diffusivity with the degree of rolling. An influence of rolling on both the thermal diffusivity and effusivity was previously observed for a polycrystalline NiTi shape memory alloy with a nearly equi-atomic composition. Due to the thermal-diffusivity behavior of metal alloys due to rolling, a simple microscopic model is proposed to explain this influence upon the effective thermal parameters within the framework of a one-dimensional heat flow perpendicular to the foil surface. The model assumes the reduction of grain sizes and the consequent increase of grain interfaces during rolling as responsible for a larger effective thermal resistance. Numerical results are shown using the available polycrystalline NiTi for both the thermal-diffusivity and thermal-effusivity values.