Thermal Model Research Articles

Understanding the Microstructure Evolution during Laser Metal Deposition of HSS M4 Obtained from Various Building Strategies, through Thermal Modelling, and Both Microstructural and Mechanical Characterizations

Understanding the Microstructure Evolution during Laser Metal Deposition of HSS M4 Obtained from Various Building Strategies, through Thermal Modelling, and Both Microstructural and Mechanical Characterizations

Thermal Analysis of Acousto-Optic Modulators and Its Influence on Ultra-Stable Lasers

Acousto-optic modulators (AOMs) have been widely used in ultra-stable lasers (USLs) for optimizing its performances. A thermal theoretical model of the AOM, which is made by TeO2, was established. Based on the model, the temperature coefficients of the diffraction angle and efficiency were calculated to be 4.051 μrad/°C and 0.018%/°C. The influences of thermal effects of the AOM on USLs’ cavity coupling and frequency stability were firstly studied. A 1 °C temperature change in the AOM results in a 0.31 Hz frequency fluctuation of the laser within the USL cavity. Simulation and experimental results indicate that, to achieve USLs’ optimal performance, thermal effects of AOMs within the system must be addressed and managed.

Equivalent Heat Source Model of Thermal Relay Contact Based on Surface Roughness of Silver–Magnesium–Nickel Contact

In a sealed electromagnetic relay, the change in the surface roughness mainly depends on the collision wear between the contact and the moving reed and the ablation effect of the arc on the contact surface based on the strong correlation between the contact resistance and the surface roughness of the Ag-Mg-Ni contact. With a change in contact resistance, the contact temperature increase in a hermetically sealed electromagnetic relay (HSER) is greatly affected. Under extreme overload conditions, the contact surface is severely ablated by the arc, and the roughness increases rapidly with the number of cycles, which greatly affects the contact resistance of the contact surface and the reliability of the relay. A thermal model of a relay contact system based on the surface roughness of Ag-Mg-Ni contacts was established in this paper by analyzing the effect of an arc on the surface roughness of Ag-Mg-Ni contacts under heavy overload conditions. The arc image of the Ag-Mg-Ni contact was recorded using a double-axis arc photographing platform, and the moving track of the arc center under overload conditions was drawn. This paper explored the patterns of arc center movement on the contact surface and the effects of the arc on the surface roughness of the contacts by analyzing the probabilities of the arc center appearing in various locations. A mathematical model correlating the number of contact cycles with contact resistance was established. Subsequently, a finite element simulation model for the equivalent heat source of the contact was developed. The theoretical model error was less than 10%. The accuracy of the equivalent heat source model was verified by comparing the measured data with the simulation results.

Initial Performance Outlook on the Sliding Particle Receiver (SlideRec)

The next generation of central receivers is expected to reach high outlet temperatures of 800 °C and above, maintain stable operation and react to a varying incoming flux magnitude caused by hourly and seasonal variations, endure high-temperature operation, and remain cost-effective. Particle-based central receivers are being considered due to their high-temperature durability and favorable thermal properties of particle materials such as bauxite particles. This paper describes a new particle-based central receiver concept and provides an initial exploration of its performance in comparison with the existing CentRec® technology. Discrete Element Method (DEM) simulations demonstrated that a stable falling and sliding particle film can be achieved inside the SlideRec. The residence time of particle flow within the SlideRec is estimated to be higher than a falling particle receiver and similar to that of an obstructed-flow receiver. The results of the thermal model (incorporating reflective, radiative, convective and conductive heat losses) indicate that the SlideRec demonstrates a higher thermal efficiency than the CentRec® under an incoming aperture flux of between 0.1 MW/m2 and 1.7 MW/m2. The concept therefore is promising and is recommended for further experimental exploration.

A Thermal Impedance Model for IGBT Modules Considering the Nonlinear Thermal Characteristics of Chips and Ceramic Materials

The traditional method of calculating junction temperature does not consider the dependence of a material’s thermal conductivity on temperature, in which the thermal conductivity changes with temperature. However, with an increase in junction temperature, the temperature sensitivity (TS) will have a more significant impact on the actual temperature of chips. This study established an improved IGBT equivalent thermal impedance model that considers the nonlinear characteristics of the TS of chips and ceramic materials. The Fourier series analysis method was used to obtain the heat flux density curve, and then the heat diffusion angles of each layer were solved. Moreover, iterations were performed until the thermal conductivity and temperature of the chip and ceramic layers matched the nonlinear characteristics of the TS. When the power loss was less than 200 W, the maximum error of the junction temperature calculated by the proposed method considering TS was 3%, while the maximum error of the method without considering TS was 9.5%. Compared with the finite element simulation, the proposed method has a faster solving speed and high accuracy. The proposed method only requires the input material parameters, size parameters, and boundary conditions to solve the junction temperature, which has strong practicality and high accuracy.

Analysis of Key Factors Affecting Case-to-Ambient Thermal Resistance in Thermal Modeling of Power Devices

Analysis of Key Factors Affecting Case-to-Ambient Thermal Resistance in Thermal Modeling of Power Devices

Increased Formation of Trions and Charged Biexcitons by Above-Gap Excitation in Single-layer WSe2.

Two-dimensional semiconductors exhibit pronounced many-body effects and intense optical responses due to strong Coulombic interactions. Consequently, subtle differences in photoexcitation conditions can strongly influence how the material dissipates energy during thermalization. Here, using multiple excitation spectroscopies, we show that a distinct thermalization pathway emerges at elevated excitation energies, enhancing the formation of trions and charged biexcitons in single-layer WSe2 by up to 2× and 5× , respectively. Power- and temperature-dependent measurements lend insights into the origin of the enhancement. These observations underscore the complexity of excited state relaxation in monolayer semiconductors, provide insights for the continued development of carrier thermalization models, and highlight the potential to precisely control excitonic yields and probe nonequilibrium dynamics in 2D semiconductors.

Modeling Thermal Impedance of IGBT Devices Based on Fractional Calculus Techniques

The thermal impedance characteristics of insulated gate bipolar transistor (IGBT) modules are critical for the thermal management and design of electronic devices. This paper proposes a fractional-order equivalent thermal impedance model, which is inspired by the correlation between multi-time-scale dissipation characteristics of heat conduction processes and fractional calculus. The fractional-order equivalent thermal impedance model is derived based on the connection between fractional-order calculus and the Foster thermal network model in mathematical operations, with only two parameters to be identified: heat capacity C and fractional order α. Moreover, this paper provides a parameter identification method for the proposed fractional-order equivalent thermal impedance model based on the multi-objective particle swarm optimization (MOPSO) algorithm. In order to validate the effectiveness and superiority of this work, experiments and comparative works are provided in this paper. The results indicate that the fractional-order equivalent thermal impedance model can accurately describe the frequency domain characteristic curves of the thermal impedance of the Foster thermal network model for IGBT modules, with the difference between the amplitude frequency characteristics not exceeding 1 dB and the difference between the phase frequency characteristics not exceeding 1° within the operating frequency range of (1 kHz, 1 MHz).

Model‐Driven Manufacturing of High‐Energy‐Density Batteries: A Review

AbstractThe rapid advancement in energy storage technologies, particularly high‐energy density batteries, is pivotal for diverse applications ranging from portable electronics to electric vehicles and grid storage. This review paper provides a comprehensive analysis of the recent progress in model‐driven manufacturing approaches for high‐energy‐density batteries, highlighting the integration of computational models and simulations with experimental manufacturing processes to optimize performance, reliability, safety, and cost‐effectiveness. We systematically examine various modeling techniques, including electrochemical, thermal, and mechanical models, and their roles in elucidating the complex interplay of materials, design, and manufacturing parameters. The review also discusses the challenges and opportunities in scaling up these model‐driven approaches, addressing key issues such as model validation, parameter sensitivity, and the integration of machine learning and artificial intelligence for predictive modeling, process optimization, and quality assurance. By synthesizing current research findings and industry practices, this paper aims to outline a roadmap for future developments in model‐driven manufacturing of high‐energy density batteries, emphasizing the need for interdisciplinary collaboration and innovation to meet the increasing demands for energy storage solutions.

Data-driven pressure prediction for self-pressurization liquid hydrogen tank using transfer learning method

Prediction of pressure evolution in liquid hydrogen (LH2) tanks utilizing data-driven technology has gradually garnered significant attention. However, training an accurate data-driven model for pressure prediction in LH2 tanks is a challenge due to inadequate experimental data. To end this, this paper presents a Backpropagation Neural Network model for pressure prediction in LH2 tanks based on transfer learning on pre-trained models, using experimental data and results from thermodynamics models. Pre-trained models are firstly optimized by Bayesian optimization algorithms with large sample datasets from the thermal multi-zone model program and then performed the fine-tuning method with small sample datasets from experiments. The effectiveness of the proposed approach is validated by experimental data and numerical results. Compared with baseline models trained only with experimental data, the results demonstrate better model performance, with a maximum of 10.51% accuracy improvement. The proposed approach demonstrates considerable potential in facilitating the application of advanced machine learning algorithms for LH2 tank pressure prediction, significantly reducing the dependence on experimental data collection.

Spatio-temporal trends in the frost regime reveal late frost exposure to white spruce (Picea glauca [Moench] Voss) persists in northeastern America

Abstract The characteristics of the frost regime (intensity, frequency, and timing) contribute to shaping tree species adaptations and distribution as well as ecosystem productivity and functions. However, climate change increases the variability in extreme events; therefore, the different characteristics of the frost regime may diverge under climate change. Using the BioSim 11 software, we simulated daily air temperature at 512 locations over northeastern North America between 1901–2021 to determine how the spatio-temporal trends in the frost regime varied over this complex landscape and if spatio-temporal trends in extreme climatic events such as frosts are stronger compared to changes in aggregated climate variables such as mean annual air temperature and growing degree-days. We also used an eco-physiological model to conduct a case study focussing on white spruce to determine if trees are currently more exposed to growing season frosts than they were in the past by modelling the timings of budburst using the thermal time model. Our results showed that, at 67% of locations (343 locations), the day of the year of the last frost in spring (minimum daily air temperature<0 °C) occurred, on average, earlier by seven days during 2001–2021 compared to 1901–1920, whereas it occurred, on average, later by four days at 33% of locations (169 locations). The average temporal trends in frost occurrence were similar in magnitude to the average trends in aggregated climate variables; however, their variances were larger compared to the aggregated climate metrics, showing that the frost regime does not change uniformly throughout our study area. Our case study also revealed that white spruce remains exposed to late frosts of low and intermediate intensities (minimum daily air temperature <0; <−2 °C) compared to the past but exposure to high-intensity frosts (minimum daily air temperature <−4 °C) is rare. Since extreme events such as late frosts diverge in their response to climate change compared to aggregated climate variables, the mean annual temperature is not sufficient to predict how climate change will impact ecosystems through frost regimes.

Temperature and state-dependent electrical conductivity of soft biological tissue at hyperthermic temperatures

Objective: We present a physics-based, temperature and state-dependent electrical conductivity model for soft biological tissue under thermal therapies with a quantified damage parameter that represents the state of soft biological tissue (degree of denaturation). Most existing models consider electrical conductivity to be only temperature-dependent and evaluate tissue damage during post-processing after temperature calculation. Our model allows tissue damage to be coupled into the thermal model for a more accurate description of both RF ablation and electrosurgery. Methods: We model the denaturation process with an Arrhenius-type differential equation for chemical kinetics and a modified Stogryn equation for electrical conductivity under state transition. We present experimental data from two types of heating procedures at 128 kHz to validate and showcase the capability of our model. Results: Our model is able to capture the change in electrical conductivity during heating, cooling, and reheating procedures, which distinguishes different states and shows the irreversibility of denaturation. The model also accurately captures tissue change during slow cooking at a constant temperature, highlighting a state dependence. Conclusion: By incorporating state dependence into the model for electrical properties, we are able to capture the denaturation process more accurately and distinguish different degrees of damage. Our model allows the modeling of procedures involving repeated heating or cooling, which is impossible for models without a state dependence. While being able to adapt to patient-specific needs, the model can be used to improve planning and control in future robot-assisted surgeries to reduce unnecessary damage.

High-pressure thermal conductivity, speed of ultrasonic measurements and derived elastic modulus of sandstones with different porosity

The effect of pressure on thermal conductivity is an important for understanding the heat transfer processes in modeling applications in geothermal reservoirs and for optimization of the technology of geothermal energy extraction processes. The primary factors affecting the economics of any geothermal energy recovery process are the amount of heat present in the geothermal reservoir and rate of heat extraction, which strongly depend on the thermal properties of the reservoir rocks, which are functions of both temperature and pressure. The present paper aims to study the variation of thermal conductivity of five sandstone samples from the Germany Geothermal Field with various total (open and close) porosities of (6.8, 13.0, 15.44, 21.3, and 21.5%) with pressure up to 203 MPa, to overcome the existing lack of thermal conductivity data for the geothermal reservoir modeling. The improved steady-state heat-flow technique (contact method) was used to precisely (with an uncertainty of 4%) measure the thermal conductivity of the sandstone samples. Unlike previous studies, in the present work the effect of the contact thermal resistance on the measured values of thermal conductivity has been taken into account using a calibration procedure to increase the accuracy and reliability of the measured data. The results show that with the increase of the pressure at a fixed temperature of 293.15 K, the thermal conductivity of sandstone is linearly increasing with pressure from 0.52 to 1.73 GPa−1 depending on sandstone structural and mineralogical composition, porosity, and other characteristics, which falls in the same range reported by other authors for rock samples from different geothermal fields. The derived values of the thermal conductivity pressure coefficient are crucial for geothermal studies in order to effectively use the geothermal resources of the region, and are useful for scientific applications such as the development and testing of the accuracy, reliability, and predictive capability of existing thermal models of geothermal reservoirs. Based on the present experimental results, statistical analysis (correlation analysis) was revealed between the measured thermal conductivity of sandstone samples and open and close porosities. The role of closed and open porosities on the pressure dependence of thermal conductivity and elastic moduli is discussed. For the first time, we experimentally observed the difference of the influence of the open and closed porosities on the thermal conductivity and elastic properties of sandstones. It was experimentally confirmed that the effect of closed porosity on the thermal conductivity of sandstones is significantly greater than that of open porosity by 15 to 20%. The obtained results show that it is of great importance to study the changes in the thermal properties of the sandstones under pressure at realistic reservoir conditions for geothermal studies.

A Novel Cooling System for High-Speed Axial-Flux Machines Using Soft Magnetic Composites

Demand is high for small, lightweight, and power-dense machines. However, as power increases and size decreases, rejecting losses becomes more difficult. Many novel cooling systems have been developed, which have allowed machines to be made smaller while increasing power. This paper proposes a cooling system making use of soft magnetic composite (SMC) cores to improve cooling specifically in a high-speed axial-flux machine via the use of an integrated cooling channel in the SMC core. A series of experiments on a prototype machine are performed and the experimental data are used to determine a set of parameters for the FEA thermal model. Using the thermal FEA model, a comparison is completed with a traditional closed cooling system using laminated steels and an attached cooling plate.The SMC machine is then simulated at speeds up to 160 krpm and currents up to 8 A. To achieve the same coil temperature between the two designs, the laminated steel model required 4 MPa contact pressure at 10 krpm and 5 MPa contact pressure at 20 krpm. At the same time, the novel design removed approximately 20% more heat per shear air gap surface area and approximately 15% more heat per total machine surface area than the version with the attached cooling plate. Extending the operating range of the model to 160 krpm demonstrated that the maximum temperature rise remained below 180 °C.

Machine learning-based assessment of thermal comfort for the elderly in warm environments: Combining the XGBoost algorithm and human body exergy analysis

Many elderly people rarely own or use air conditioners because of low income and economising habits, causing them to live in warm thermal environments when heat waves and hot weather occur. Living in warm conditions worsens thermal discomfort and poses health risks this group. To investigate the thermal comfort and adaptation of the elderly, a total of 38 participants were recruited for two parts of experiments in a climate chamber: Part A collected thermal sensation vote (TSV) and physiological parameters for 30 min at 28, 30, and 32 °C, and Part B presented a 20-min cooling with fans (air velocities of 0.6 and 1.4 m/s) at the same temperature. Furthermore, we constructed a thermal comfort model for the elderly based on human body exergy analysis and the GBDT, AdaBoost, and XGBoost machine-learning algorithms. The results showed that the predicted mean vote considerably overestimated the actual TSV. The TSV and mean skin temperature were decreased by 0.1–0.5 scores and 0.4–0.5 °C by the behavioural adaptation of fan cooling. The predictive results showed that the XGBoost model performed better, with R2 score, mean absolute error (MAE), and mean squared error (MSE) of 81 %, 0.10, and 0.01. Exergy transfer from evaporation (Ex-Esk), mean skin temperature (mtsk), air velocity (va), and convective exergy transfer (Ex-C) contributed more to the feature importance in the SHAP value analysis. The current study has implications for investigating physiological comfort and age-friendly environmental designs for the elderly, providing new perspectives for thermal comfort evaluations.

Peridynamic Framework to Model Additive Manufacturing Processes

AbstractThe study presents a framework for analyzing Additive Manufacturing processes within the Peridynamics (PD) software PeriLab. This framewor k employs a mesh‐free, point‐based numerical approach to approximate the continuum PD equations. Implemented within this framework are thermal, thermo‐mechanical, and simple additive models. These models have been validated against analytical solutions, Finite Element (FE) models, and Peridigm simulations. To leverage the PD mesh‐free implementation, the study introduces a novel boundary detection algorithm. This algorithm is essential because the outer surface area may change during the manufacturing process. It operates without requiring surface or topology information, relying instead on the comparison of neighborhood volume to sphere volume. Additionally, the study introduces a wrapper that generates the mesh necessary for simulating the printing process, based on the G‐code machine input path. Finally, the study presents a comprehensive analysis of an L‐shaped profile utilizing the developed features, comparing the results with those obtained from an Abaqus solution.

Investigation of the Influence of Environmental Thermal Characteristics on Thermal Modes of Transparent Boxes

This paper presents the results of experiments to investigate the influence of thermal characteristics of the environment on the thermal modes of transparent boxes. To conduct experiments on the non-stationary thermal model of solar greenhouses developed by us, two identical transparent boxes with dimensions of 0.80 x 0.65 x 0.80 m were constructed. The transparent boxes have rectangular shapes. One transparent box has glass walls and the other with polyethylene walls. The influence of the thermal characteristics of the environment and the thermal conditions inside the transparent boxes with film and glass transparent walls are investigated. The experimental results show that at a maximum ambient air temperature of 42 °C on 27.06.2024 at 13:48 hours, the air temperature increases to 10% and 23% in transparent boxes with polyethylene and with glass walls, respectively, and at 05:10 hours, the humidity decreases to 8% and 11%, respectively. Thus, the influence of the thermal characteristics of the environment on the thermal conditions of transparent boxes with glass walls, at the maximum ambient temperature, is greater by 1.2 times than in transparent boxes with polyethylene walls, and humidity decreases by half.

School building-stock climate resilience: evaluating London’s school stock overheating performance

ABSTRACT Indoor environmental quality (IEQ) in classrooms is crucial for students’ health, wellbeing, and academic success. Rising outdoor temperatures due to climate change pose a significant risk – overheating in schools can negatively affect cognitive performance and health for both students and staff.This study examines the risk of overheating in primary and secondary schools in London, focusing on the impact of current and future climate scenarios. Using the UK Department for Education’s Building Bulletin 101 (BB101) as a framework, the study integrates data from various sources, including GIS form data and a national school survey, to develop a ‘one-by-one’ school stock thermal model called the Modelling Platform for Schools (MPS). This model, built using EnergyPlus, allows for the analysis of school thermal performance at national, regional, and individual levels.The results show that by the 2050s, under a medium emissions scenario, average summer temperatures in schools could reach nearly 30°C – 7% higher than current levels. The study also developed an overheating-risk regression model for London schools and explored potential mitigation strategies, such as extending summer holidays or adjusting school activities to times when overheating risk is lower.

Vitreous tissue cryopreservation using a blood vessel model and cryomacroscopy for scale-up studies: Observations and mathematical modeling

Successful long-term cryobanking of multicellular tissues and organs at deep subzero temperatures calls for the avoidance of ice cryoinjury by reliance upon ice-free cryopreservation techniques. However, the quality of the cryopreserved material is the direct result of its ability to survive a host of harmful mechanisms, chief among which is overcoming the trifecta effects of ice crystallization, toxicity, and mechanical stress. This study aims at exploring improved conditions to scale-up ice-free cryopreservation by combining DP6 as a base cryoprotective agent (CPA) solution with an array of synthetic ice modulators (SIMs). This study is conducted by integrating cryomacroscopy techniques, thermal modeling, solid mechanics analysis, and viability and contractility investigation to correlate physical effects, thermal outcomes, and cryobiology results. As an extension of previous work, this study aims at scale-up of established baseline blood vessel models, while comparing the relative toxicity and vitreous stability of 4 ml and 10 ml samples of DP6 containing either sucrose as a SIM, or the commercial synthetic ice blockers (X1000 and Z1000). Using that established protocol, the addition and removal of DP6+0.6M sucrose and DP6 + 1% X1000 + 1% Z1000 were both well tolerated in rabbit carotid and pig femoral artery models, when assessed for metabolic recovery and contractility. Using cryomacroscopy, it was demonstrated that DP6 + 0.6M sucrose provided a stable vitrification medium under marginal cooling and warming conditions that resulted in >50% survival rate. By contrast, DP6 + 1% X1000 + 1% Z1000 was subject to visible ice formation during cooling under the same thermal conditions, resulting in a significantly lower recovery of ∼20%. Thermal modeling is used in this study to verify the actual cooling and rewarming rates in the specimens, while thermomechanics analysis is used to explain why fractures were observed using cryomacroscopy when the specimens were contained in glass vials but not in plastic vials.

Discussion of “Thermal conductivity of porous building materials: An exploration of new challenges in fractal modelling solutions”

In November 2023 this journal published “Thermal conductivity of porous building ma­terials: An exploration of new challenges in fractal modelling solutions”. That paper gauges four fractal building materials’ thermal conductivity models, concluding that fractal-geometry-based approa­ches appear “a promising method” as they “demonstra­te high reliability in repro­ducing experimental data”. This discussion of the pa­per aims to shine a different light on the potential of fractal thermal conductivity models. It shows that good agreement with experimental data usually ori­ginates from calibration of various “physical” factors comprised in these models, with the fitted numbers commonly deviating from physical rea­lity. Moreover, exemplary instances reveal that good agreement with experimental data is obtained des­pite misinterpretation of measured outcomes or critical defects in model development, or, exceptio­nally, due to fabrication of validation information. This discussion does hence not share the pa­per’s positive opinion on the prospects of fractal-geometry-ba­sed ther­­mal conductivity models, and advises caution instead.