Traditional Thermal Model Research Articles

Evaluating thermal sensation in outdoor environments - Different methods of coupling CFD and radiation modelling with a human body thermoregulation model

Thermal comfort is vital in outdoor spaces for social interaction and recreation. With increases in heat wave events and global warming, it becomes more essential to be able to assess heat stresses. Outdoor spaces have asymmetrical and transient environmental factors such as solar radiation, long-wave radiation and natural wind, which makes the thermal perception outdoors largely different from indoors. Traditional thermal comfort models have some limitations and less accuracy when applied to outdoor spaces. The current study focuses on developing a method to assess the human thermal interaction with the complex outdoor environment and the consequent thermal sensation. Computational Fluid Dynamics (CFD) and radiation modelling are coupled to the Joint System Thermoregulation (JOS-3) model to assess the skin temperature, based on which thermal sensation is predicted using the updated thermal comfort model developed by UC Berkeley. The proposed methodology is applied to assess the thermal sensation of human subjects standing in front of an outdoor radiant cooling hub. The important finding is that the one-way coupling, i.e., no skin temperature feedback to the CFD simulation, works reasonably well in the outdoor environment, and that convective heat transfer coefficients available from experimental studies can be coupled with CFD simulated environmental parameters to obtain more reliable convective heat exchange to avoid meshing the geometry of a human body in CFD and further reduce the computing cost.

Enhanced Thermal Modeling of Electric Vehicle Motors Using a Multihead Attention Mechanism

The rapid advancement of electric vehicles (EVs) accentuates the criticality of efficient thermal management systems for electric motors, which are pivotal for performance, reliability, and longevity. Traditional thermal modeling techniques often struggle with the dynamic and complex nature of EV operations, leading to inaccuracies in temperature prediction and management. This study introduces a novel thermal modeling approach that utilizes a multihead attention mechanism, aiming to significantly enhance the prediction accuracy of motor temperature under varying operational conditions. Through meticulous feature engineering and the deployment of advanced data handling techniques, we developed a model that adeptly navigates the intricacies of temperature fluctuations, thereby contributing to the optimization of EV performance and reliability. Our evaluation using a comprehensive dataset encompassing temperature data from 100 electric vehicles illustrates our model’s superior predictive performance, notably improving temperature prediction accuracy.

An Accurate Electro-Thermal Coupling Model of a GaAs HBT Device under Floating Heat Source Disturbances.

Taking into consideration the inaccurate temperature predictions in traditional thermal models of power devices, we undertook a study on the temperature rise characteristics of heterojunction bipolar transistors (HBTs) with a two-dimensional cross-sectional structure including a sub-collector region. We developed a current-adjusted polynomial electro-thermal coupling model based on investigating floating heat sources. This model was developed using precise simulation data acquired from SILVACO (Santa Clara, CA, USA). Additionally, we utilized COMSOL software (version 5.6) to simulate the temperature distribution within parallel power cells, examining further impacts resulting from thermal coupling. The research findings indicate that the rise in current induces modifications in the local carrier concentration, thereby prompting variations in the local electric field, including changes in the heat source's peak location and intensity. The device's peak temperature exhibits a non-linear trend regulated by the current, revealing an error margin of less than 1.5% in the proposed current-corrected model. At higher current levels, the drift of the heat source leads to an increase in the heat dissipation path and reduces the coupling strength between parallel devices. Experiments were performed on 64 GaAs (gallium arsenide) HBT-based power cells using a QFI infrared imaging system. Compared to the traditional temperature calculation model, the proposed model increased the accuracy by 6.84%, allowing for more precise predictions of transistor peak temperatures in high-power applications.

Research On Mg Doping in Nitride Semiconductor Materials

Summarized the achievements of various model research directions in nitride Mg doped semiconductors in this thesis. Due to the Internal properties of Mg acceptors which resulting the low ionization rate of acceptor, and the low hole mobility in heavily Mg doped nitrides, the conductivity of Mg doped nitrides is limited. At present, research is divided into three categories of models, one is a new type of polarization model. This model adopts a built-in electron polarization ionization acceptor dopant in the bulk uniaxial semiconductor crystal, and provides an attractive solution for the problem of P-type and n-type doping in broadband gap semiconductors. The other two are the traditional thermal activation model. There are mainly ultra-high-pressure-annealing (UHPA) and multicycle rapid thermal annealing (MRTA) respectively. Both of these schemes can activate more Mg impurities, resulting in higher conductivity in nitrides. The principles, advantages and disadvantages, and future development prospects will be explained of these three models in this thesis.

A thermal sensation model for naturally ventilated indoor environments based on deep learning algorithms

In recent years, with the emphasis on sustainability and energy efficiency, natural ventilation has attracted increasing interest from building designers. Natural ventilation is dependent on the outdoor environments which could change rapidly, and the traditional thermal sensation models such as the predicted mean vote (PMV) are not applicable, correspondingly. The deep belief neural network can reveal nonlinear patterns in processing big data, and it can be used to predict target data with high flexibility and accuracy. This study developed a deep belief neural network model for indoor thermal sensation prediction in naturally ventilated environments with outdoor environment parameters and human factors: outdoor air temperature, average radiant temperature, outdoor air relative humidity, outdoor wind speed, clothing thermal resistance, activity level, gender, age and weight collected in 10 semi-open classrooms and 5 laboratories in April and November when natural ventilation was used. The research compared the performance of deep belief neural networks with three neural networks: BP, Elman and fuzzy neural networks. Results showed that the deep belief neural network can enhance the performance of thermal sensation prediction of natural ventilated indoor environments. The research provides a more flexible and effective solution for thermal comfort prediction of natural ventilated indoor environments.

Improving IEC thermal model for oil natural air natural transformers using optimised parameters based on dynamic simulation

AbstractAccurate assessment of hot‐spot temperature is essential for the safe operation of power transformers. Existing dynamic thermal models cannot estimate hot‐spot temperature accurately since some input parameters are roughly determined by transformer capacity and cooling mode while ignoring the effect of winding structure, tank dimensions, and material physical properties. To improve the accuracy of temperature assessment, empirical parameters of the IEC thermal model including thermal constants, winding and oil exponents are optimised with the help of numerical simulation in this article. Based on energy conservation and heat transfer theory, a computational fluid dynamic (CFD) model of a transformer in oil natural air natural (ONAN) cooling mode is established. This CFD model simulates the entity's structure, sizes, and multi‐stage heat dissipation processes realistically, so it can more precisely calculate the dynamic hot‐spot temperature. According to the simulated temperature curves at different operating conditions, the thermal constants and oil exponent are estimated using non‐linear regression, and the winding exponent is optimised using linear regression. A case study is conducted on an ONAN transformer. It shows the improved IEC model with optimised parameters can more accurately evaluate hot‐spot temperature, and the absolute error is decreased by 2.4 K (38.7%) compared with traditional thermal models.

Anomalies in Infrared Outgoing Longwave Radiation Data before the Yangbi Ms6.4 and Luding Ms6.8 Earthquakes Based on Time Series Forecasting Models

Numerous scholars have used traditional thermal anomaly extraction methods and time series prediction models to study seismic anomalies based on longwave infrared radiation data. This paper selected bidirectional long short-term memory (BILSTM) as the research algorithm after analyzing and comparing the prediction performance of five time series prediction models. Based on the outgoing longwave radiation (OLR) data, the time series prediction model was used to predict the infrared longwave radiation values in the spatial area of 5° × 5° at the epicenter for 30 days before the earthquake. The confidence interval was used as the evaluation criterion to extract anomalies. The examples of earthquakes selected for study were the Yangbi Ms6.4-magnitude earthquake in Yunnan on 21 May 2021 and the Luding Ms6.8-magnitude earthquake in Sichuan on 5 September 2022. The results showed that the observed values of the Yangbi earthquake 15 to 16 days before the earthquake (5 May to 6 May) exceeded the prediction confidence interval over a wide area and to a large extent. This indicates a strong and concentrated OLR anomaly before the Yangbi earthquake. The observations at 27 days (9 August), 18 days (18 August), and 8 days (28 August) before the Luding earthquake exceeded the prediction confidence interval in a local area and by a large extent, indicating a strong and scattered OLR anomaly before the Luding earthquake. Overall, the method used in this paper extracts anomalies in both spatial and temporal dimensions and is an effective method for extracting infrared longwave radiation anomalies.

Physiological responses and data-driven thermal comfort models with personal conditioning devices (PCD)

Thermal comfort in uniform environments created by HVAC systems have been investigated for decades. However, the case in nonuniform microenvironments created by the personal conditioning device (PCD) is different because of the local thermal stimuli on occupants. This study used PCD to provide localized cooling for users based on their individual thermal preferences. We collected PCD users' wrist temperatures and heart rate variabilities (HRV), as well as thermal sensation/comfort questionnaires. Because of the nonuniform thermal stimuli, the physiological responses triggered by the PCD show unclear patterns, which are incompatible with traditional thermal comfort models developed for the uniform environment. Therefore, we utilized machine learning methods to model PCD users’ thermal comfort. By using SVM with RBF kernel based on multiple HRV indices in addition to wrist temperatures, the F1 score is improved by more than four times when compared to the model only based on wrist temperatures. This indicates the importance of HRV as physiological indices for the thermal comfort with nonuniform thermal stimuli. The best performances of our models are higher than 0.90 for both thermal sensation and comfort, which can provide a reliable solution in predicting the thermal sensation and comfort in nonuniform environments with the PCD.

Improving the cooling performance of cylindrical lithium-ion battery using three passive methods in a battery thermal management system

Developing a high-performance battery thermal management system (BTMS) to keep the temperature of lithium-ion battery (LIB) in a suitable range has become of great interest for electric vehicle (EV) applications. Hence, this study has been set out to design a BTMS by utilizing various combination of phase change material (PCM), metal foam, and fins that keeps the battery surface temperature at the lowest level in normal and harsh environmental conditions under discharging with 3C current rate. Considering these three passive methods, four different cases of BTMS have been designed separately. Moreover, the effects of various fin shapes including rectangular, triangular, trapezoidal, I-shape, and wavy fins are studied for the optimum BTMS. The two-equation local non-equilibrium thermal model has been used which is more precise than the traditional thermal equilibrium model in simulating heat transfer between the PCM and metal foam. The numerical results demonstrated that the optimum BTMS, which is the combination of PCM, metal foam, and fins (fourth case), can reduce the battery surface temperature by 3 K. Furthermore, the maximum delay of about 470 s in melting of the PCM has been reported for this case. Additionally, the applied fins in the fourth case are acting as a network of heat sources to spread heat in the middle of the system, and utilized metal foam can create a uniform distribution of heat between LIB and ambient. Analyzing the effect of different fin shapes on the performance of optimum BTMS showed that there are no remarkable changes between the battery surface temperature of various fin shapes and it would be hard to find a suitable shape of fins for all environmental conditions.

Analysis of thermal noise characteristics in 10 nm metal oxide semiconductor field effect transistor

<sec>Small size metal-oxide-semiconductor field effect transistor (MOSFET), owing to their high theoretical efficiency and low production cost, have received much attention and are at the frontier of transistors. At present, their development is bottlenecked by physical limits due to equal scaling down of devices, which requires further improvement in terms of materials choice and device fabrication. As the MOSFET devices scale down to nanometer scale, on the one hand, the resulting short channel effect affects severely the thermal noise property; on the other hand, it makes the ratio of thermal noise in the gate, source, drain and substrate regions become higher and higher. However, the traditional thermal noise model mainly considers thermal noise of large-size devices, and its model does not consider the channel saturation region. In view of this, it is necessary to establish a small size MOSFET thermal noise model and analyze its characteristics.</sec><sec>At present, there are some researches on MOSFET thermal noise, but they mainly focus on the thermal noise in channel region of large size nanoscale MOSFET. In the present work, according to the device structure and inherent thermal noise characteristics, we establish a thermal noise model for MOSFETs of 10 nm feature size. The model includes contributions of substrate region, gate-source-drain region, and channel region. In the channel region is also included the thermal noise related to the device saturation regime. Using such a model, the dependence of channel thermal noise and total thermal noise on the device bias condition and device parameters are investigated, evidencing the existence of thermal noise in the device saturation regime, which are consistent with the experimental results in the literature. The thermal noise increases with the gate voltage and source-drain voltage rising as the device structure shrinks. In a temperature range of 100–400 K, the thermal noise is basically on the order of 10<sup>21</sup>, indicating that the temperature has a great influence on the thermal noise. The thermal noise model established in this work can be applied to analyzing the noise performances of small size MOSFET devices, and the conclusions drawn from the present study are beneficial to improving the efficiency, lifetime, and response speed of MOSFETs on a nanometer scale.</sec>

Short-Duration Transient Temperature Distribution Prediction Model along Chip Vertical Path Applicable to Multi-Timescale Simulation

Based on extremely uneven temperature distribution along the insulated gate bipolar transistor (IGBT) chip vertical path during switching transients, a short-duration transient microsecond-scale prediction model applicable to multi-timescale simulation is presented in this paper. Traditional thermal models often take the chip active area as a uniform heat source to obtain a victual junction temperature (Tvj). In this paper, a discrete distributed heat source model combined with a thermal network model based on the sublayer division strategy is proposed to achieve an accurate temperature distribution description along the chip vertical path. Taking a 1700 V/3600 A IGBT module as an example, the proposed model can evaluate the short-duration transient temperature distribution along the chip vertical path and the error is less than 2 °C compared with the finite element model. Meanwhile, the model is applied to a single short-duration transient timescale and multi-timescale systems separately, and the simulation speed is increased by more than 80 times and 297 times, which verifies its validity and accuracy.

Research on the influence of secondary heat network characteristics on wind power consumption in the combined electric and heat system

With the large-scale investment of thermal power units in the “Three Norths” area, the coupling of the combined electric and heat system has become more and more close, leading to a huge number of abandoned wind. On the basis of the traditional thermal system model, physical characteristics of the secondary heat network are added, and an optimal scheduling model of the combined electric and heat system considering the operation characteristics of the secondary heat network is established. The influence of the operation characteristics of the secondary heat network on the heat loss and wind power consumption of the thermal system is studied. Based on this, different conditions are set up to study the influence of heat-exchange efficiency of heat exchange stations and the diameter of secondary heat network pipes on the combined electric and heat system. The simulation analysis shows that the heat network characteristics of the secondary heat network need to be considered in the scheduling analysis of the combined electric and heat system. In addition, the system air curtailment rate can be reduced by improving the heat exchange efficiency of the heat-exchange station and changing the diameter of the secondary heat network pipeline.

Experimental study on the mechanism of frequency-dependent heat in AC preheating of lithium-ion battery at low temperature

• Controlled experiments to explore the source of frequency-dependent heat have developed. • The heat generation mechanism of lithium-ion batteries under high-frequency AC excitation is different from those under low-frequency. • Frequency-dependent heat partly due to current harmonics, and partly from an unknown mechanism. Lithium-ion batteries suffer severe energy loss, significant pulse power decline, and reduced life cycle under cold weather. It is imperative to preheat the lithium-ion batteries for electric vehicles (EVs) at low temperatures. At present, various internal alternative current (AC) heating methods have been proposed to achieve fast and high-efficiency battery preheating. However, there is still confusion in the selection of the AC frequency. Researchers have found that the simplified Bernardi heat generation model is not always suitable for AC preheating, especially at high AC frequencies. Under these scenarios, a frequency-dependent heat is generated, making the traditional thermal model inapplicable. In this paper, the source of frequency-dependent heat is analyzed experimentally. Based on the electrochemical impedance spectrum (EIS) data and entropy thermal coefficient of the cell in the experiment, controlled experiments in different frequencies are developed to compare the cell heat generation rate. The experiments showed that the heat generation rate is highly correlated to its frequency. Further analysis show that the frequency-dependent heat is not due to electrochemical reaction, partly due to Joule effect by current harmonics, and partly from unknown mechanism (nominate “extra-heat”).

Analysis of temperature field of medium frequency transformer based on improved thermal network method

Medium frequency transformer (MFT) plays a key role in future power systems and new energy systems. It is very important for the safe operation of MFT to correctly calculate the temperature rise and hot spot position. According to the heat dissipation of transformer, the equivalent thermal network model of the core-type transformer is established in this paper. The traditional thermal network model ignores the heat dissipation of the magnetic core column and the inner surface of the yoke to the air. In this paper, the magnetic core column is divided into the air surrounding part and winding surrounding part. The heat dissipation on the inner surface of the yoke is further considered. The improved thermal network model is verified by finite element simulation. The results show that compared with the traditional thermal network model, the improved thermal network model proposed in this paper can more comprehensively analyse the temperature field distribution and calculate the node temperature more accurately.

Thermal Management of a 48 V Lithium-Ion Battery Pack by Semiconductor Refrigeration

At present, 48 V mild hybrid battery systems are widely used in hybrid electric vehicles to reduce fuel consumption and emissions. The battery pack often operates at high discharge/charge rates and requires an efficient and compact battery thermal management system (BTMS) to control its temperature, improve its electrical performance and extend its life. Due to their short start-up times and simple structures, semiconductors can provide rapid refrigeration and cool a battery quickly in response to sudden high current rates. Therefore, semiconductors were applied to the BTMS of a 48 V battery. The performance of the semiconductor-based BTMS was studied by simulation and experiment at high discharge rates (up to 9.375 C). Firstly, a thermal model of the BTMS was developed that integrates a resistance-based battery thermal model, a semiconductor thermal model and a three-dimensional fluid-solid coupled heat transfer model. Unlike a traditional thermal model, the proposed model considers the joint influences of SOC, temperature and current on battery resistance and improves the predictive precision of the battery’s thermal behaviour. The thermal model was verified by an experiment, with the results showing that it could precisely describe the temperature increase in the battery (maximum average absolute error within 0.9°C). Finally, the BTMS thermal model was applied to predict the cooling performance of the semiconductor BTMS at an ambient temperature of 37°C and high current rates (up to 9.375 C), which was compared with that of an air-cooled BTMS. The results demonstrate that the semiconductor-based BTMS achieves lower battery temperature than the air-cooled BTMS and ensures a temperature difference within the 48 V pack of <1.6°C.

Recognition and prediction of individual thermal comfort requirement based on local skin temperature

This study focuses on identifying individual thermal comfort requirement using infrared images. The primary objective is to verify the feasibility of infrared images in predicting thermal comfort parameters, and to propose corresponding prediction models for two different classification modes defined in this study. The relevant data for modeling was obtained via chamber experiments. In addition, multiple machine learning (ML) algorithms, such as logistic regression (LR), support vector machines (SVM), and random forest (RF), were applied to propose models in which the local skin temperature (forehead, eye, nose, cheek and ear) measured using the infrared camera is used as an input parameter and thermal sensation vote (TSV) is used as an output parameter. The area under curve (AUC) of each model on the test set was calculated and used as the main evaluation index. In order to compare with the traditional thermal comfort model, the accuracy was also concerned as an evaluation index. The result shows that the prediction accuracy of ML models based on local skin temperature is close to that of predicted mean vote (PMV) model and indicates significant feasibility for practical application. Among the models, the AUC and accuracy of LR model reflects a relative advantage in comparison with other ML models and PMV model. The selection of measurement sites is also discussed in this study, and suggestions for practical application are given according to the performance of the models based on selected algorithms.

Measurement of the convective heat transfer coefficient of the human body in the lift-up design

So far, the research on the convective heat transfer coefficient (hc) in outdoor thermal comfort has mainly employed CFD simulation and wind tunnel experiments, which are difficult to fully restore the complex microclimate wind environment. In the traditional thermal comfort model, the influence of turbulence intensity (TI) on the hc might be underestimated. This study aims to measure the hc of the human body surface in the outdoor environment. A thermal manikin was placed in a lift-up building. The ambient wind speed ranged from 0.5m/s to 4m/s, with the TI ranging from 4% to 55%. The experimental results show that under the same wind speed, the difference in hc between high and low TI can be up to 15%. Based on that, the regression formula for predicting hc related to wind speed and TI was proposed. This experimental study supplements the lack of field measurement of hc in outdoor thermal comfort research, which is helpful to improve the accuracy of the outdoor thermal comfort model.

Improving thermal performance of battery at high current rate by using embedded heat pipe system

This study establishes a resistance-based thermal model for the battery cooled by the heat pipe. Unlike the traditional thermal model, the proposed battery thermal model includes the influence of the battery temperature, state of charge (SOC) and current on the heat generation in the battery body. Moreover, it also integrates the heat generation inside the tabs to improve its prediction accuracy. The heat pipe model is developed based on the thermal resistance of its components, and this model is integrated with the battery thermal model to form the thermal model of the BTMS based on the embedded heat pipe system. Experiment are carried out to verify the prediction precision. At the level of the battery unit, the prediction error is within 1 °C. Then, the different heat pipe number, fin number and heat transfer coefficient at the condensation section of the heat pipe are used for the embedded heat pipe system in order to improve its cooling performance. According to the research results, increasing the heat pipe number can reduce the battery temperature and its difference. However, when the heat pipe number is over a certain threshold, both the battery temperature and temperature difference change little. Although the heat transfer coefficient has limited influence on the battery thermal behavior, increasing the heat transfer coefficient and fin number at the same time can reduces the battery temperature a lot. In addition, comparative studies show that the embedded heat pipe system significantly outperforms the bar-type heat pipe and flat heat pipe at an extreme high current rate.

Thermal error prediction of machine tool spindle using segment fusion LSSVM

The key temperature points are the input variables of the thermal error model for prediction and compensation of thermal errors for precision CNC machine tools. However, the revealed time-varying characteristics of the key temperature points may jeopardize the robust prediction. To this end, the segment fusion least squares support vector machine (SF-LSSVM) thermal error modeling method is proposed. Firstly, the temperature data and thermal error data are divided into different segments according to time. Then, using the LSSVM with excellent nonlinear mapping capabilities as the basic model, the sub LSSVM thermal error model building and the corresponding key temperature points selection in each segment are fulfilled with genetic algorithm (GA) in a wrapper manner to preserve the corresponding local prediction characteristics. Finally, pick some of or all the sub LSSVM thermal error models to fuse together as the final thermal error model which may incorporate both the local and global prediction characteristics. The modeling and prediction experiment results on the spindle thermal error of a horizontal machining center demonstrate that the mean root-mean-square error (RMSE) on 5 spindle speeds after compensation is only 3.1 μm. Comparing with two traditional thermal error models, the prediction performance of the present model is improved by up to 51%. This research casts new light on both the mechanism of key temperature points and the prediction method of thermal errors.

Comprehensive thermal resistance model of forced air cooling system for multiple power chips

It is necessary to design a heat sink according to the losses of chips in the application of power chips to maintain the temperature of the chip junction at a low level. Since multiple power chips are distributed on the same heat sink, the surface temperature of the heat sink is unevenly distributed, the chips located at the air outlet need to withstand higher temperatures. In order to describe the surface temperature of the heat sink accurately, a novel comprehensive thermal resistance of the forced air cooling system for multi-power chips was proposed. A temperature rise test by a static var generator (SVG) prototype was designed to prove the accuracy of the model proposed. The thermal resistance error of the model proposed in this paper is only 2%. Comparing with two traditional thermal models, the accuracy of the novel thermal resistance model is improved by more than 20%.