Fast Temperature Research Articles

Categorization of Beef Longissimus Lumborum and Gluteus Medius Muscles Based on Metabolic Attributes Is More Informative Than Muscle pH

Muscle metabolism is generally monitored using muscle pH. However, pH does not account for all metabolic effects on meat quality. We evaluated the effectiveness of agglomerative hierarchical clustering in creating clusters of beef longissimus and gluteus medius muscles based on metabolic traits. Beef carcasses (n = 100) were selected at grading based on longissimus thoracis pH (< 5.6, 5.60 to 5.74, 5.75 to 5.9, and > 5.9). Metabolic traits characterizing oxidative and glycolytic metabolism were measured on each muscle. A subset of longissimus lumborum muscles were placed in an in vitro glycolytic system with 2 temperature decline rates to evaluate glycolytic efficiency. Gluteus medius muscles exhibited more oxidative metabolism than longissimus lumborum muscles. Metabolic traits measured in one muscle were generally positively correlated to the same trait measured in the other muscle. Clustering of metabolic traits within each muscle produced similar dendrograms. Clustering of longissimus lumborum muscles based on metabolic traits produced 4 distinct clusters (High pH, Glycolytic, Chaperone, and Soluble). Clustering of the high pH was generally, but not totally, in agreement with classifications based on pH. The remaining longissimus lumborum clusters did not differ in pH. Similar to the longissimus lumborum clusters, the gluteus medius clusters included High pH and Glycolytic clusters and a cluster with low values for protein solubility and peroxiredoxin 2 abundance. In the in vitro system, pH decline was affected by a cluster × temperature decline rate interaction (P < 0.05). The soluble cluster had the least extensive pH decline under the fast temperature decline but had the most rapid pH decline at the slower pH decline. These results indicate that clustering muscles based on several metabolic factors was more effective than categorizing muscles based on muscle pH. Metabolic variation identified by clustering was related to differences in the glycolytic machinery that can be differentially impacted by chilling rate.

A fast real-time motor temperature estimation method taking less than 0.2 s

A fast real-time motor temperature estimation method taking less than 0.2 s

Platform for laser-driven X-ray diagnostics of heavy-ion heated extreme states of matter

We report on commissioning experiments at the high-energy, high-temperature (HHT) target area at the GSI Helmholtzzentrum für Schwerionenforschung GmbH, Darmstadt, Germany, combining for the first time intense pulses of heavy ions from the SIS18 synchrotron with high-energy laser pulses from the PHELIX laser facility. We demonstrate the use of X-ray diagnostic techniques based on intense laser-driven X-ray sources, which will allow probing of large samples volumetrically heated by the intense heavy-ion beams. A new target chamber as well as optical diagnostics for ion-beam characterization and fast pyrometric temperature measurements complement the experimental capabilities. This platform is designed for experiments at the future Facility for Antiproton and Ion Research in Europe GmbH (FAIR), where unprecedented ion-beam intensities will enable the generation of millimeter-sized samples under high-energy-density conditions.

X-Ray Diffraction Line Broadening of Irradiated Zr-2.5Nb Alloys

The evolution of the mechanical properties of Zr-2.5Nb pressure tubing during irradiation is dependent on dislocation loop densities that are represented by the broadening of X-ray diffraction lines. Empirical models for the integral breadth of the diffraction peaks as a function of operating conditions have been developed to predict the mechanical properties of CANDU reactor pressure tubes as a function of fast neutron flux, time and temperature. Apart from predicting mechanical property changes based on integral breadth measurements, a new model has been developed to retrospectively deduce abnormal operating temperatures of ex-service pressure from the measured line broadening. The application of integral breadth measurements to assess mechanical properties and temperature variations in pressure tubes is described and discussed in terms of the implications for pressure tube integrity.

Temperature-responsive metamaterials made of highly sensitive thermostat metal strips.

Temperature-responsive metamaterials have remarkable shape-morphing ability during thermal energy conversion. However, integrating the thermal shape programmability, wide-working temperature range, fast temperature response, and actuation into metamaterials remains challenging. Here, we introduce using thermostat metal strips to assemble metamaterials with desirable and balanced temperature-responsive properties, and we systematically investigate the thermal deformation performance. Achieving 70 to 80% of the designed strain requires only 5 seconds of heating. A thermal strain of around 30% is achieved for the assembled metamaterials, surpassing other bimetallic metamaterials by a magnitude of 100 to 200. The actuation capacity of thermostat metal strips exceeds 26 times their weight. Further, by leveraging the highly programmable thermal deformation, the tuneable bandgap range is 3847 to 40,000 hertz. These fully integrated mechanical performances in the multiphysics have great application potential, for example, as soft actuators and soft robots in intelligent structure systems, vibration isolation and noise reduction in hypersonic vehicles, and unique thermal deformation in precision instruments.

(Invited) Hyper-Multimodal Sensing with Heater-Integrated Fluidic Resonators

Mechanical resonators with embedded fluidic channels (hereafter referred to as fluidic resonators), known as suspended micro/nanochannel resonators (SMRs or SNRs) [1, 2], allow precise and sensitive measurements of single micro/nanoparticles or single cells in transit, adsorbed biomolecules and liquid analytes. To date, the majority of studies using fluidic resonators have been performed at a fixed steady-state temperature close to room temperature, since temperature modulation inevitably changes the resonance frequency of the fluidic resonators, one of the key measurement variables.To maintain physiologically relevant conditions or to perform measurements at different temperatures, fluidic resonators have been heated. Initially, slow heating methods such as heating bath circulation or hot plate were used, followed by fast photothermal heating to raise the temperature of the fluidic resonators and the sample together. Of note, the major drawbacks of fast photothermal heating are the need for a complicated optical system and tedious alignment, and less precise and less quantitative temperature modulation. Integration of heating electrodes into fluidic resonators during device fabrication is considered to be the best option, although fabrication complexity and cost are expected to increase or device yield may be compromised. However, if heating electrodes are successfully integrated into fluidic resonators, new measurement modalities such as microcalorimetry and thermogravimetry would be enabled and a variety of coupled phenomena between dynamics, fluid mechanics and heat transfer could be effectively studied.Recently, we have developed heater-integrated fluidic resonators (HFRs), which allow fast, quantitative, alignment-free and wide-range temperature modulation [3]. Because the integrated heater can also act as a resistive thermometer, HFRs can track temperature and resonant frequency as they are heated by resistive thermometry and resonant densitometry. With and without a dispensing nozzle, HFRs are used for atomised spray dispensing, thermophysical property measurements and microchannel boiling studies. These are the first demonstrations of transient operation of fluidic resonators over a significantly extended temperature range. The performance of HFRs has been further enhanced by reducing mechanical and thermal losses in vacuum [4].Our current focus is on hyper-multimodal measurements with HFRs, providing primary properties (mass density, dynamic viscosity, thermal conductivity and specific heat) and secondary properties (thermal diffusivity, kinematic viscosity and Prandtl number) of various liquid samples at elevated temperatures. Once cooperatively combined with machine learning, such hyper-multimodal measurements appear to be exclusively useful for the identification and analysis of liquid mixtures. Measured spectra of mechanical and thermophysical properties of mixtures can be separated into corresponding spectra of individual components. We envisage that these will become the basis of mechanical and thermophysical spectroscopy, enabling separation-free analysis of liquid mixtures.

Design of honeycomb-imitated composite hydrophobic aerogel and applications for multifunctional water cleaning

It is a huge challenge to integrate comprehensive properties in photothermal materials for multifunctional application in various aspects of water cleaning and purification. In this study, inspired by honeycombs, a polysiloxane/polyimide/carbon composite hydrophobic aerogel (Bio-SPC aerogel) is fabricated using a blending-crosslinking-directional freezing-gradient heating method, and exhibits reasonable structure and component regulation for multiple applications such as oil–water separation, photothermal crude absorption and water evaporation. Primary vertical porous channels and the secondary structure of fibrous carbon on the aerogel’s cell walls reduce light reflection and provide a large light absorption area, respectively, promoting photothermal conversion properties. Another secondary structure of micro-nano pores enhances heat preservation. Through convenient pre-processing and directional freeze-drying, methyltrimethoxysilane is hydrolysis-condensed and bound to the structure skeleton formed using polyimide precursor and bacterial cellulose biomass, coating on the surface to mimic beeswax’s enhanced hydrophobicity and mechanical strength. The aerogel exhibits a mechanical strength of 32 kPa at 80 % strain and good structural stability over 500 cycles, as well as efficient light-to-heat conversion with a fast temperature increase (reaching 75.3℃ in 30 s) and a maximum stable temperature of nearly 100℃. Its solvent absorption capacity is 74.2 g/g and it has a flux retention rate of 71 % after 10 cycles of oil–water separation. Under sunlight irradiation, the aerogel absorbs crude oil more quickly during cleanup. The assembled aerogel evaporator has an average evaporation rate 4.2 times that of bulk water and 96.6 % efficiency. The evaporation performance is stable in high-concentration saline water and long-term cycles, with a maximum rate of 2.675 kg m-2 h−1 outdoors. This novel honeycomb-inspired composite aerogel shows promise for multifunctional applications, including oily water cleaning, crude cleanup, seawater desalination, and heavy metal or organic dye removal from polluted water.

Experimental evaluation of barium bromide-ammonia equilibrium lines

This work has experimentally evaluated the position of thermochemical equilibrium lines of barium bromide (BaBr2) reacting with ammonia (NH3). BaBr2 samples, contained in discs of Expanded Natural Graphite, were placed in an experimental Large Temperature Jump rig and a new methodology, termed the Fast Temperature Ramp, was used to reveal adsorption and desorption reactions. Reaction onset points were plotted to generate equilibrium lines and calculate reaction enthalpies and entropies. Significant hysteresis between adsorption and desorption reactions was found, with the degree of hysteresis increasing as the salt becomes more deammoniated. The hysteresis effect was greatest for BaBr2(2–1) and BaBr2(1–0) reactions and also increased at higher pressures, exceeding 20 °C at 900 kPa for BaBr2(1–0). Adsorption reactions were found to occur over a very small temperature range, giving rise to a single transition ‘zone’ less than 20 °C wide. TGA experiments confirmed the position of equilibrium lines, although they were not successful in differentiating between two of the four ammoniated states. The close position of all equilibrium lines gives rise to a temperature ‘zone’ encompassing all four reactions, and in future work it may be advantageous to consider BaBr2 as a pseudo single-transition salt instead.

Propagation behavior of wetting front and its fluctuation characteristics during subcooled jet impingement quenching

Jet impingement quenching as one of the most effective cooling methods is widely applied in many industrial fields. When a subcooled jet strikes the hot surface, the wetting front propagates outside after the wetting delay accompanied by high-frequency fluctuations. To elucidate the propagation behavior of the wetting front and its fluctuation characteristics, the quenching experiments that an upward water jet with different velocities and subcoolings impacted onto a hot nickel block with an initial temperature of 280°C were conducted. The transient transition boiling associated with the intermittent wet and dry situations around the wetting front was quantitatively analyzed based on the visual observation and fast response surface temperature measurement technique. The liquid-solid contact period near the wetting front, under the late transition boiling regime, spans the range of 494–590 µs. This time interval is comparable to the period of disturbance waves induced by the Plateau-Rayleigh instability of the jet. Furthermore, fluctuation amplitude of the wetting front diminishes from 0.8 to 0.2 mm as it propagates. The order of magnitude and the trend of variation in the fluctuation amplitude with respect to the wetting front radius align with theoretical calculations without considering boiling heat transfer effects.

Hybrid model-based predictive HVAC control through fast prediction of transient indoor temperature fields

Efforts to reduce energy demand in the building sector have prompted a focus on the operational control of HVAC systems. Despite extensive research on HVAC control based on temperature prediction models, existing approaches often rely on node-based or average temperature predictions, which lack the detailed temperature distribution data necessary for accurate control, especially in transient situations with both spatial and temporal variations. This study introduces a precise HVAC control method based on a fast temperature field prediction model. By combining the single-step prediction response coefficient (SPRC) method with Convolutional Neural Network (CNN) architecture, sub-temperature field prediction models for multiple independent heat sources were constructed and integrated to achieve fast temperature field predictions. Subsequently, utilizing the predicted temperature field, air conditioning operation parameters were optimized and controlled to minimize energy consumption. Application of the proposed method in real building scenarios demonstrated the temperature field predictions closely aligned with computational fluid dynamics (CFD) simulations, achieving a mean absolute error (MAE) of 0.27 °C and a root mean square error (RMSE) of 0.24 °C. Furthermore, this model achieved a notable 57.8 % improvement in prediction accuracy compared to models relying solely on single-step prediction responses. Additionally, the model predictive control based on the hybrid model's temperature field predictions significantly reduced the runtime of the HVAC system by 18.18 % while maintaining temperatures within the comfort range throughout the operation period. The method presents a promising avenue for optimizing HVAC operations and minimizing energy consumption in building environments, thereby contributing to sustainable building management practices.

Adjustment of temperature sensitivity using hydrophilic materials to develop superabsorbent polymers based on poly(N‐isopropyl acrylamide) as a temperature‐sensitive material

AbstractPoly(N‐isopropyl acrylamide) (PNIPAm) is a temperature‐sensitive material with a fast temperature response and a low critical solubilization temperature (LCST) of approximately 32°C, but it swells only 20–40 times after absorbing water. To enhance its water‐absorption properties, we introduced hydrophilic monomers into the NIPAm monomer to prepare a ternary copolymerized superabsorbent polymer. In addition, we characterized this copolymer using various techniques and analyzed its temperature‐sensitive and water‐absorbing properties. The results showed that the optimal synthesis conditions consisted of m(NIPAm):m(AMPS) = 4, 1.5% initiator, 0.3% cross‐linking agent, and 1.0% quaternized ammonium chitosan, which ensured the significant temperature sensitivity of the water‐absorbent resin. Under these conditions, the swelling ratio of the water‐absorbing resin reached 235.3 times, the LCST value was 44.6°C, the water retention rate was 84.9% at 25°C after 12 h, and the shrinkage ratio reached 81.7% at 60°C after 3 h, and 97.6% after 12 h. The resulting material could be rapidly dehydrated at high temperatures and presented clear temperature‐sensitive characteristics while maintaining excellent water‐absorbing properties.

Long-term thermal aging effects in ferritic-martensitic steel HT9

The ferritic-martensitic steel HT9 is a candidate material for fuel cladding and core components in advanced nuclear reactors, such as sodium-cooled fast reactors, thanks to their high temperature mechanical properties and low susceptibility to irradiation induced swelling phenomena. However, thermal stability and elevated temperature microstructural evolution in these alloys may impact their long-term behavior and reliability. In this work, the effects of thermal aging on the microstructural and mechanical properties of HT9 have been investigated through complementary electron microscopy, synchrotron X-ray diffraction, microhardness, and thermodynamic modeling. Plates of HT9 were aged up to 50 kh at relevant sodium-cooled fast reactor operational temperatures (360 °C - 700 °C). Trends in microstructure as a function of aging time and temperature were apparent from qualitative and quantitative analysis. These observations were further supported by thermodynamic modeling of the bulk and precipitate phases. Specific phases observed include BCC Fe, FCC M23C6, HCP and FCC MX phase and Laves M2X phase. Through the application of our multi-scale and multi-modal approach, clear information on the aging mechanism of HT9 was obtained, allowing for a more informed prediction, and understanding of the long-term behavior, performance and thermal stability of ferritic-martensitic alloys exposed to elevated temperatures.

Effect of fast charging on degradation and safety characteristics of lithium-ion batteries with LiFePO4 cathodes

Fast charging compatibility is a desirable feature for batteries to shape a promising future in our rechargeable world. Enabling fast charging of advanced energy-dense Li-ion batteries for growing electric vehicle (EV) markets depends on the breakthrough of material chemistry and optimization of charging strategy. Lithium iron phosphate (LiFePO4, or LFP) is a pivotal cathode material in state-of-the-art EV batteries due to the merits of high thermal stability, long cycle lifetime, and high-temperature performance. However, degradation-safety interactions of LFP-based Li-ion batteries under fast charging conditions and low temperatures remain elusive. In this study, we cycle LFP cells under different fast charging strategies and thermal environments to understand their effects on degradation pathways, where the capacity, voltage, temperature, coulombic inefficiency, internal resistance, and impedance spectroscopy are evaluated. Half-cell studies are implemented to assess electrode-level capacity retentions. Post-mortem analyses are conducted to reveal physicochemical changes in electrodes. Accelerating rate calorimeter tests are carried out to measure evolutions of cell-level thermal instabilities after fast charging tests. This research article highlights the significant roles of graphite-centric lithium plating and LFP-centric transition metal dissolution in driving substantial electrochemical degradations, suggesting that a mild low temperature does not necessarily reduce the LFP cell lifetime.

Accurate high-temperature profile sensing with dense multipoint arrays of regenerated fiber Bragg gratings

A spectrally and spatially dense sensor array consisting of 15 regenerated fiber Bragg gratings (RFBGs) over a length of 30 mm is presented for precise and fast multipoint temperature sensing up to 700°C. For the first time, it could be shown that with a dense fiber Bragg grating (FBG)-based sensor array the accuracy requirements of Class 1 thermocouples could be achieved and even exceeded. This also represents the highest spatial density of FBG-based high-temperature multipoint sensing reported so far. The mitigation of broadband losses during the regeneration process was studied, revealing the advantages of the low broadband loss characteristics of the RFBGs, especially when larger numbers of measuring points are required. Low measurement uncertainties were achieved by a new, improved calibration methodology and by an analysis of interferences of an FBG with the side lobes of spectrally neighboring FBGs as well as their suppression by a suitable arrangement of the Bragg wavelengths within the array and corresponding data processing. The capabilities of this multipoint sensor technique were demonstrated by resolving the temperature profile within the calibration volume of a calibration furnace and by resolving the temporal and spatial temperature gradients within the flame of a Bunsen burner. The results are of great importance for fiber-optic sensing of high-temperature profiles in real-world applications.

Experimental demonstration of transition from J × B heating to stochastic heating in ultrashort high intensity laser foil interaction

We experimentally demonstrate the transition of fast electron generation mechanism from J × B heating to stochastic heating by varying preplasma scale length in the interaction of ultrashort (∼25 fs) high intensity (∼3–4 × 1019 W/cm2) laser with thin foil. At sharp plasma density laser interaction (contrast ∼2 × 10−10 at 1 ns, L/λ ≪ 1), fast electrons were observed along the laser propagation direction demonstrating J × B heating. Interestingly, fast electron temperature in this case was less than ponderomotive scaling. The reasons were identified to be the small excursion length of electron compared to laser wavelength in sharp density interaction along with energy loss while escaping through the rear surface. A simplistic model has been proposed to understand the energy loss mechanism from the rear surface. Next, preplasma was introduced gradually by varying the amplified spontaneous emission contrast and additional picosecond prepulse at different delays. It resulted in an increase in the energy and temperature of fast electrons. Most importantly, at larger scale length (L/λ ≫ 1), fast electron temperature beyond the ponderomotive limit was observed. The temperature scales with scale length as T∝L0.59 and shows a saturation effect at longer scale length. The results indicate a gradual change in the fast electron generation mechanism to stochastic heating producing superponderomotive energy. Particle-in-cell simulation also very well reproduces our experimental findings.

Multi-Objective optimal scheduling of energy Hubs, integrating different solar generation technologies considering uncertainty

For a few decades, operators of energy systems have sought to achieve appropriate frameworks due to energy crises and rapid growth in energy requirements. In this regard, this study presents a multi-objective optimization model for an energy hub (EH) designed to manage a diverse energy portfolio. The EH receives electricity, natural gas, hydrogen, seawater, and solar energy as inputs, aiming to satisfy electricity, heating, and freshwater demands at the output port while considering a limited available area. The model incorporates the selection of the optimal solar energy technology (photovoltaics, parabolic dish, or parabolic trough collector) through a comprehensive evaluation encompassing technical, economic, and environmental aspects. To achieve optimal scheduling of the EH’s production units, the model factors in forecasts of solar energy availability alongside electrical, heat, and water load demands. The evaluation of the EH’s performance is conducted through a multi-objective framework considering social welfare, CO2 emissions, voltage stability margin (VSM), a newly proposed simplified fast temperature stability index (SFTSI), and a similarly novel simplified fast pressure stability index (SFPSI). The optimization problem is formulated within a MATLAB environment and solved using a multi-objective Archimedes optimization algorithm across five distinct case studies, each characterized by a varying designated area for solar energy generation. The effectiveness of the proposed model and optimization technique is validated through test systems, with the obtained results demonstrating significant improvements compared to a baseline scenario. These improvements include a 36.18% reduction in CO2 emissions, a 14.22% increase in total social welfare, and reductions in the average values of VSM, SFTSI, and SFPSI when incorporating all solar energy technologies.

Dynamic modeling and model predictive control for printed-circuit heat exchanger in supercritical CO2 power cycle

The supercritical carbon dioxide (sCO2) Brayton cycle, known for its superior thermal efficiency and compact turbomachinery size, can utilize various heat sources. The printed-circuit heat exchangers (PCHE) used in the power cycle, particularly the recuperators and cooler, significantly influence the thermal efficiency of the cycle. However, they exhibit intricate characteristics attributed to large thermal inertia and nonlinear heat transfer. This paper explores the one-dimensional dynamic modeling and control issues for PCHE. The established dynamic PCHE models are verified through different scenarios. Then, a novel control strategy is proposed for fast temperature reference tracking and disturbance rejection. The composite controller includes three parts: the main control is based on the principle of model predictive control (MPC); Gaussian process (GP) regression modeling is incorporated as the feedforward; and the disturbance observer (DOB) is introduced for unexpected disturbance estimation and compensation. Compared with traditional controllers, simulations demonstrate that for reference tracking the settling time of the proposed method can be saved by more than 50%, and for disturbance rejection the recovery time can be reduced by more than 30%. The results indicate the presented strategy can ensure the safe and effective operation of the sCO2 power cycle.

Carbon Materials With Conductivity Gradients Allow Dynamic Screening of Steep Temperature Differences Along Thin Films

AbstractCarbon materials comprise a wide range of microstructures and excellent electrical and thermal properties while being cost‐effective and readily available. They can be obtained through carbothermal processes at high temperatures, starting from cellulose. Catalytically active compounds, for example, iron salts, strongly influence the carbon microstructure during the graphitization process. Different degrees of structural order can, therefore, be achieved by adjusting the concentration of the iron salts. An infusion withdrawal impregnation approach is used on filter paper to prepare a continuous gradient of the carbon microstructure. This structural change is accompanied by a continuous variation of the closely related electrical and thermal transport properties. Even more, the synergistic interplay of local sheet resistance and thermal diffusivity results in the formation of switchable temperature gradients when an external current is applied. Steady state temperature differences of up to 80 °C are observed along the centimeter‐scaled samples. The controllable temperature gradient formation will be of great interest for applications requiring a fast temperature screening. Furthermore, the temperature gradient can be imposed onto other materials, which will be particularly relevant for advanced thin film characterization applications.

Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling

The paper describes the implementation of 3D numerical simulation in machine learning models used in infrared thermographic nondestructive testing. The enhancement of generalizability of such models emerges as a decisive factor for producing trust-worthy test results. First, it is demonstrated that the models trained on datasets with fixed parameters yield limited defect detection capabilities. The concept of training datasets, which include subtle variations in material thickness, thermal conductivity, as well as various combinations of material density and heat capacity, provides the best learning results and a noticeable ability to identify defects in all test datasets. Second, the model robustness in respect to noise is explored to demonstrate its ability to withstand additive and multiplicative random noise. Third, potentials of some known techniques of thermographic data processing, such as Thermographic Signal Reconstruction, Fast Fourier Transform and Temperature Contrast, are examined. In particular, the use of the Temperature Contrast data ensured sensitivity (True Positive Rate) better than 98% across all test datasets.

Powder bed fusion with electron beam: The interplay of sintering, porosity, and coordination number in modelling the powder thermal conductivity through a novel tortuosity formulation

For the powder bed fusion with electron beam (PBF-EB) additive manufacturing, properties such as the thermal conductivity of the material surrounding the melting area are critical. Thermal conductivity is influenced by the extremely high temperature reached in a short time and distributed in the building area. This fast temperature growth produces sintering phenomena and the creation of a neck between the particles. Because of this sintering, measuring the thermal conductivity at the process conditions is challenging.This paper proposes an analytical formulation for estimating the effective powder bed thermal conductivity at the PBF-EB conditions, introducing a novel modelling strategy for the tortuosity factor. In a changing net of sintered powder particles, the proposed model for the tortuosity factor considers the neck evolution and the complexity of the heat transfer due to the several heat paths possible through the particle net. To show the effectiveness of the proposed model, the thermal conductivity is evaluated for three 3D structures characterised by an increasing number of powder particles and heat path complexity: a simple cubic, a body centred cubic and a portion of a powder bed. It is shown that thermal conductivity strongly depends on the arrangement of the particles in 3D space, the structure density and the complexity of the heat diffusion path (tortuosity). Also, the numerical results from the proposed model show good agreement when compared with finite element analysis and experimental literature data.