Accurate Thermal Model Research Articles

Dynamic thermal simulation of a tree-shaped district heating network based on discrete event simulation

The computational complexity involved in modelling district heating (DH) networks impedes the integration of network operations into comprehensive DH system studies. We developed a flexible, accurate, and fast dynamic thermal simulation model utilising discrete event simulation (DES). This model is versatile, suitable for any tree-shaped DH network with a central heating plant and can estimate node temperatures and calculate pipe heat losses. The speed of the model is improved via using variable time steps and by incorporating two advanced techniques: lazy evaluation and a customised priority queue. To further improve the computational speed, we developed a technique to eliminate redundant sampling points. This model was tested and demonstrated excellent consistency with actual measurements. Remarkably, reducing sampling points can speed up the simulation by a factor of three without compromising the temperature accuracy. A 72-day simulation of a network with 102 pipes was completed within 0.219 s. Our findings highlight the significant potential of the DES model for large-scale dynamic network simulations and offer a promising solution for DH network simulations and system optimisation.

Research on Temperature-Rise Characteristics of Motor Based on Simplified Lumped-Parameter Thermal Network Model

The thermal management of a driving motor is related to the performance and economy of the vehicle. The lumped-parameter thermal network (LPTN) method can provide a model basis for motor temperature-rise control and effectively shorten the development cycle of motor thermal performance design. In this study, an 80 kw water-cooled permanent magnet synchronous motor was used and the accurate thermal model of a motor was built using the LPTN. On the basis of the accurate thermal model, the simplified thermal model of a motor was obtained by reducing the complexity of the model. The temperature-rise test of the end winding and magnetic steel of the motor was carried out under some working conditions. The test conditions were selected according to continuous external characteristics and operating characteristics of the motor. Compared with the experimental results, the temperature-rise error of the two thermal models was less than 5%. The temperature-rise error of the simplified thermal model was less than 3% compared with the accurate thermal model. Therefore, the simplified thermal model can be used to quickly predict the temperature rise of the motor.

Thermal modeling and analysis of multi-mover motors considering mover quantity and dynamic states

—Multi-mover motors are widely used in logistics transportation systems. However, the unique number of movers and variations in motion states complicate the distribution of loss and thermal characteristics, thereby increasing the difficulty of calculating temperature rise. In this paper, a winding loss calculation method that considers the multi-condition and dynamic characteristics of multi-mover motors is proposed. The convective heat transfer coefficient (CHTC) is calculated using computational fluid dynamics (CFD) and response surface methodology (RSM), with a detailed analysis of velocity distribution characteristics. The interactive effects of mover speed, acceleration, mover quantity, and the distance between adjacent movers on the CHTC are investigated. A simplified yet accurate thermal modeling is developed, reducing the required time for a single operating condition from 4 h to 0.5 h, with an error of only 4 %. Through both single variable and multivariable analyses, the thermal characteristics of multi-mover motors under different conditions are revealed. Finally, a prototype is created and tested under various operating conditions. The discrepancies between the experimental and calculated values are within 5 %, validating the accuracy of the proposed model and analysis.

Novel accurate steady-state thermal resistance model for power chips embedded in TTSVs heat dissipation array

a novel oblique pyramid equivalent heat dissipation model is proposed for theoretically analyzing heat dissipation capacity of power chips with embedded TTSVs. Extra carefully researching correction of oblique thermal resistance, additional lateral thermal resistance, and thermal resistance of TSV insulation sleeve, the accurate theoretical thermal resistance model was established. Setting the FEA simulating results based on COMSOL Multiphysics as benchmark, heat dissipation effects of variation of radius of TTSV (r), thickness of the power chip (H0), and side length of the single cell (L0) have been numerically investigated. The maximum temperature relative error of the proposed model with FEA simulated results is not more than 0.2 %, 1 %, and 0.5 % for variation of r, H0, and L0, respectively. The proposed model has been theoretically and numerically proven to be effective and accurate.

Developing building-specific, occupant-centric thermal comfort models: A methodological approach

This study addresses the research problem of accurately predicting thermal comfort in buildings by developing occupant-centric, building-specific models. Traditional models like PMV and adaptive models often fall short in reflecting individual comfort preferences. This research aims to create more accurate and personalised thermal comfort models by considering human-related factors such as clothing, mood, and activities, along with environmental variables like temperature and humidity. The methodology involved collecting data through two self-reporting campaigns using mobile and smartwatch applications in a university building in The Hague. Regression and classification models were developed, achieving accuracy rates of 72 % and 89 % respectively, which surpass the performance of traditional models. The findings indicate that human factors significantly influence thermal comfort, with mood and clothing being particularly impactful. Seasonal variations were also accounted for, emphasising the need for periodic data collection to capture changes in occupant behaviour and environmental conditions. The key contribution of this research lies in its ability to enhance the accuracy of thermal comfort predictions, leading to more effective and user-centric HVAC system operations. This approach not only has the potential to improve the comfort of building occupants but also has implications for energy efficiency, as HVAC systems can be better tailored to actual comfort needs.

Investigation on the thermal characteristics of double-driven hydrostatic lead screw micro-feed system

The micro-feed system of the dual-drive hydrostatic lead screw (DDHLS) can overcome the accuracy limitations inherent in traditional hydrostatic lead screw through the process of differential synthesis, thereby achieving exceedingly low speeds and exceptional precision in feed operations. Nevertheless, due to the complex structural form of DDHLS, both heat generation and heat dissipation are different from conventional hydrostatic functional components. Therefore, for DDHLS micro-feed system, it is necessary to deeply investigate its thermal performance and evolution rules to improve its thermal stability. Based on the analysis of multi-field coupling, the accurate thermal model of the DDHLS micro-feed system is formulated. Taking into account the variable viscosity of the oil film and the heat dissipation conditions within the oil film region, the thermal behavior of DDHLS feed system is analyzed systematically. The accuracy of the thermal performance model of DDHLS feed system is verified by experiments. The findings indicate that the DDHLS micro-feed system has better thermal performance than the dual-axis differential feed system based on rolling components. Furthermore, optimizing the heat dissipation pathway of the DDHLS can significantly mitigate the thermal effects exerted by the hydrostatic nut assembly on the screw.

Thermal analysis of the HTS transformer in the fault current condition considering the tape roughness effect

High temperature superconducting (HTS) transformers have many advantages over conventional transformers in terms of volume, weight, and total efficiency. However, fault conditions limit the electro-thermal performance of an HTS transformer. Based on this, we investigate the thermal modeling of the HTS transformer in the fault-current state. For thermal modeling, heat transfer and parameters affecting it such as surface roughness and bubble behavior have been considered. At first, an accurate thermal model for a 10 kVA HTS transformer has been prepared. Experimental results show that the penalty of this model is about 2.5 K in the hot-spot point (HSP) temperature in fault-current conditions. In a second step, the impact of a roughness on the heat transfer and mass transfer rate in nucleated boiling mode is investigated. Results show that surface roughness decreases the HSP temperature by more than 10 K in fault-current conditions. These results have significant impacts on protecting HTS windings against quenching.

Multi-objective optimization of a novel phase-change thermal storage unit based on theoretical modeling and genetic algorithm

Phase-change thermal storage technologies are becoming increasingly indispensable in the field of renewable energy, and researchers have been striving to achieve high thermal storage performance in storage devices. To enhance the application potential of phase-change thermal storage units based on micro heat pipe arrays, theoretical analysis is applied to optimize the operating parameters and the fin structure, addressing the research gap caused by insufficient theoretical analysis. Therefore, an accurate thermal resistance network model is developed, and machine learning algorithms are employed for the multi-objective optimization of the fin structure. The optimal heights, widths, and thicknesses in two directions of the fins are 42.2, 3.7, 0.3, and 0.8 mm, respectively, resulting in a 15.8 % increase in charging power compared to the initial structure without increasing metal consumption. Additionally, it is recommended that the design flow rate be increased by 26 kg/h for each additional thermal storage unit connected in series. Finally, a phase-change thermal storage device is designed for a solar water heating system, and its applicability is simulated and analyzed. This study provides design guidance for this novel thermal storage device and promotes its application in the field of renewable energy.

Quantitative assessment of the extent of damage to timber structures during the full course of a real fire using the energy equivalence method

In this study, an innovative method, the Energy Equivalence Method (EEM), is proposed in the context of the Performance-Based Fire Engineering (PBFE) framework for quantitatively assessing the degree of damage of timber structures during the full process of a real fire. To verify the validity of the method, glued laminated timber (GLT) specimens and GLT columns were selected as the subjects of fire action, and a series of equivalent fire experiments were conducted by using a parametric fire model (referred to as the DNI fire model), which is currently used in Germany, to simulate real fire scenarios. By comparing the equivalence of charring depth (CD), pyrolysis zone temperature, and residual load-carrying capacity of the test specimens after the action of the DNI fire and its equivalent standard fire, the validity of the EEM in quantifying the degree of damage to timber members after the DNI fire was fully verified. Subsequently, based on the fire experimental data, accurate thermal and structural models were developed, and these models were utilized to further validate the effectiveness of the EEM in quantifying the extent of damage to timber members under the action of a DNI fire over an arbitrary time period. The results of the study show that the proposed EEM not only can be used to assess the extent of structural damage during fire action and the residual load carrying capacity of the structure after fire action, but also promotes the transition from standard fire safety design to the PBFE design.

Grey-box modeling for thermal dynamics of buildings under the presence of unmeasured internal heat gains

An accurate grey-box building thermal model is an essential component for prediction-based control of split air conditioners. As the thermal performance of building envelope improves, the internal heat gains play an increasingly noteworthy influence on the building’s thermal dynamics. However, the internal heat gains are usually not measured, which makes the conventional data-dependent identification methods of building thermal models fail. To improve the accuracy of building thermal models under unmeasured internal heat gains, a two-step parameter identification framework for resistance–capacitance (RC) models is proposed in this paper. First, the time-invariant building model parameters are estimated by designing a novel optimization objective function. Next, the time-varying internal heat gains are identified based on the mismatch in predictions of the estimated building model from the first step and the measured temperature. The effectiveness of the method is evaluated using data from a simulation case and an experiment case. The results show that the proposed method outperforms the conventional identification method as well as two other identification methods that handle the unmeasured internal heat gains. Besides, the internal heat gains estimated by the proposed method can capture the trend of the actual internal loads well.

Thermo-electric modeling and analysis of lithium-ion battery pack for E-mobility

AbstractElectric Vehicles (EVs) have emerged as a viable and environmentally sustainable alternative to traditional internal combustion vehicles by utilizing a clean energy source. The advancement and expansion of electric cars rely on the progress of electrochemical batteries. The utilization of Lithium-Ion Batteries is widespread primarily because of its notable energy density. Changes influence the performance of these batteries in temperature. The Thermal Management System of the battery is one of the very important systems in EVs to improve the performance and life of the battery. The geometrical spacing of the cell modules is considered identical for a more accurate comparison of temperature distribution. For better cooling and heat dissipation, the battery pack’s two sides are kept entirely open to facilitate the inflow of air. In this work, active BTMS solutions are selected and analyzed using the development of three-dimensional free, open-source OpenFOAM computational fluid dynamics simulations for accurate thermal modeling and hotspot zones in cylindrical battery packs. The outcome of the simulations is compared using parameters like temperature distribution in battery cells, battery modules, and heat generation. Among all the cell temperature zones, the temperature maximum is near the sixth cell of the module depth. OpenFOAM results validated with the existing literature’s experimental and Ansys results. Air cooling is utilized for cooling performance because of its relatively simple structure and lightweight.

Control-oriented thermal model and load identification for circulating cooling water system in precision process applications

With the increasing precision in machining and measurement, there is a growing demand for improved temperature stability in cooling water systems used in industrial processes. Accurate thermal models and load states are essential for developing efficient controllers to enhance temperature performance. This paper focuses on the development of thermal models for various components of circulating water systems in precision water-cooling processes. It presents a control-oriented thermal model framework that segregates the water circulation cycles and defines system inputs and processes. A novel identification algorithm is proposed to address thermal inertia and cyclic nonlinearity by extracting the cyclic period and cycle change rate. The algorithm can simultaneously estimate the state–space model and unmeasurable load disturbances. A validation experimental setup was constructed, and the results demonstrate that, for different flow scenarios, the estimated load and thermal model parameters align well, with correlations exceeding 0.85 and error ratios close to 10%. Additionally, the circulating water output temperature can be accurately predicted. The fitting degrees surpass 84% within five cycles, with correlations exceeding 0.97 and error standard deviations below 0.22 °C.

Accurate Time-Efficient Thermal Modeling and Discretization Methodology Applied to Electric Machines

The increasing power density of electric machines for electric vehicles necessitates accurate thermal analysis in design and real-time condition monitoring through simulations with computation times not exceeding one minute. Existing thermal models are either accurate and complex while requiring large computation times or they are fast but lack accuracy. To address these challenges, a novel hybrid modeling methodology is proposed, combining 3D lumped parameter thermal networks with thermal resistances extracted from 2D finite element models (FEM) to reduce computation demands while preserving accuracy. Further, a methodology is introduced to define the thermal network discretization for a desired threshold accuracy while minimizing computation time. Analysis of this discretization methodology demonstrates that the desired accuracy can be reliably achieved with limited computational effort. Verification of the modeling methodology against 3D FEM in OpenFOAM shows key component temperature deviations of less than 0.67 °C in steady-state simulations and 0.75 °C in transient simulations, comparable to the 3D FEM uncertainty of 0.39 °C. The computation times for steady-state and transient simulations are 18.3 s and 43.5 s respectively compared to 44 h and 175 h for 3D FEM. This highlights the time efficiency of the proposed methodology while maintaining accuracy for key component temperatures.

A Multi-Objective Evolutionary Approach to Discover Explainability Tradeoffs when Using Linear Regression to Effectively Model the Dynamic Thermal Behaviour of Electrical Machines

Modelling and controlling heat transfer in rotating electrical machines is very important as it enables the design of assemblies (e.g., motors) that are efficient and durable under multiple operational scenarios. To address the challenge of deriving accurate data-driven estimators of key motor temperatures, we propose a multi-objective strategy for creating Linear Regression (LR) models that integrate optimised synthetic features. The main strength of our approach is that it provides decision makers with a clear overview of the optimal tradeoffs between data collection costs, the expected modelling errors and the overall explainability of the generated thermal models. Moreover, as parsimonious models are required for both microcontroller deployment and domain expert interpretation, our modelling strategy contains a simple but effective step-wise regularisation technique that can be applied to outline domain-relevant mappings between LR variables and thermal profiling capabilities. Results indicate that our approach can generate accurate LR-based dynamic thermal models when training on data associated with a limited set of load points within the safe operating area of the electrical machine under study.

Numerical Investigation of Thermal Energy Storage Systems for Collective Heating of Buildings

This study aims to investigate and identify the most effective thermal energy storage (TES) system configuration for the collective heating of buildings. It compares three TES technologies, i.e., sensible, latent, and cascade latent shell and tube storage, and examines their respective performances. A fast and accurate lumped thermal dynamic model to efficiently simulate TES system performances under different operation conditions is developed. The validation of this model’s accuracy is achieved by aligning numerical findings with data from prior experimental studies. Key findings indicated that the latent and cascade latent shell and tube storage systems demonstrate superior thermal energy storage capacities compared to the sensible configuration. Using a single-phase change material (PCM) tank increases the duration of constant thermal power storage by about 50%, and using a cascade PCM tank further enhances this duration by approximately 65% compared to the sensible TES case. Moreover, the study revealed that adjusting the PCM composition within the cascade TES significantly influenced both thermal power storage durations and pumping energy consumption. In summary, the recommended cascade PCM configuration for collective heating of buildings offers a balanced solution, ensuring prolonged stable thermal power production, elevated HTF outlet temperatures, and improved energy efficiency, presenting promising prospects for enhancing TES systems in district heating applications.

Nonlinear compact thermal modeling of self-adaptability for GaN high-electron-mobility-transistors using Gaussian process predictor and ensemble Kalman filter

Thermal issue has been regarded as one of the bottlenecks for GaN high-electron-mobility transistor (HEMT) performance and reliability, which highlights the importance of accurate thermal modeling. In the present work, we propose a GP (Gaussian process)-resistor–capacitor compact thermal model integrated with the ensemble Kalman filter (EnKF) to handle the nonlinear problems attributed to the temperature-dependent properties of GaN HEMTs under large-signal working conditions. The GP predictor is employed for the nonlinear correction term, with strong ability and extendibility to characterize various temperature-dependent relations resulting from different design configurations and materials. The model is identified via the EnKFs by inputting a sequence of channel temperature oscillations induced by imposing a large-signal continuous wave heating source to the device. Furthermore, an adaptation mode is devised for the in situ and timely update of the model parameters to adapt to the thermal variability of GaN devices, avoiding storing a large amount of historical data and repeated offline regressions. The validation of our modeling scheme is conducted through the case study on GaN-on-SiC HEMT’s detailed 3D finite element method simulations.

A brief survey on heat generation in lithium-ion battery technology

The powertrain in electric vehicles typically comprises various components, including lithium-ion batteries (LIBs), a battery management system, an energy converter, an electric motor, and a mechanical transmission system. Electric vehicles utilize the electrical energy stored in LIBs to efficiently drive the motors efficiently. LIBs find widespread use in portable electronic devices like laptops, mobile phones, and other electronic appliances, with potential applications in the automotive sector. To examine the thermal performance of LIBs across diverse applications and establish accurate thermal models for batteries, it is essential to understand heat generation. Numerous researchers have proposed various methods to determine the heat generation of LIBs through comprehensive experimental laboratory measurements. This study comprehensively explores diverse experimental and modeling techniques used to analyze the thermal behavior and heat generation of LIBs.

Quantifying the Effects of Temperature and Depth of Discharge on Li-Ion Battery Heat Generation: An Assessment of Resistance Models for Accurate Thermal Behavior Prediction

Li-ion batteries (LiBs) are widely adopted in electric vehicles (EVs) owing to their superior properties, such as high energy density, low discharge rate, long lifespan, and lightweight construction. Since the battery pack is the sole energy source for an EV, its performance is critical for optimal vehicle operation. However, the battery's calendar life, cycle life, and overall performance are significantly affected by temperature variations. The Li-ion batteries used in EVs may encounter challenging working conditions, leading to thermal problems such as significant capacity and power loss. In contrast, thermal runaways can occur at temperatures above a specific threshold, leading to severe health deterioration and sometimes catastrophic safety hazards such as fires and explosions. As the temperature significantly impacts Li-ion batteries, a battery thermal management system that can efficiently dissipate heat is crucial to ensure the battery's optimal performance and longevity. Hence, it is crucial to develop accurate algorithms for battery thermal management systems to precisely and dynamically estimate the temperature dynamics of the batteries integrated within the battery pack.While experimental data can be used to estimate battery temperatures, the dynamic and diverse operating conditions of electric vehicles (EVs) present a significant challenge. Therefore, accurately predicting thermal response within batteries is critical. Various thermal models have been developed to predict the thermal behavior of batteries and quantify the amount of heat generated. The simplified thermal model only considers joule heating and reversible entropic heating. However, more accurate physics-based models consider reversible heat caused by the side reactions, heat generated by mass transport loss, and even mixing-induced heat. The amount of heat generated inside a Li-ion battery is determined by its equivalent internal resistance, open circuit voltage, and entropy change, which are in turn influenced by temperature and depth of discharge (DoD). To the best of the authors' knowledge, previous research on the heat generation of Li-ion batteries has been limited in some respects. Specifically, there has been little investigation into the combined impact of temperature and depth of discharge (DoD) across a wide temperature range. Most studies have been conducted under ambient temperature conditions, and only a few have focused on high temperatures within a narrow range with low discharge rates.Thus, this study aims to address the research gap regarding the impact of temperature and depth of discharge (DoD) on heat generation in Li-ion batteries by analyzing these parameters using a transient battery thermal model. The research intends to improve the accuracy and precision of battery thermal behavior prediction, which has broad implications for battery-powered applications. This study aims to evaluate the impact of different resistance models on heat generation in Li-ion batteries, explicitly comparing a constant resistance model with a model that considers resistance as a function of temperature and depth of discharge (DoD). Investigating the interdependent impact of battery temperature and DoD on heat generation is crucial to create an accurate battery thermal model with high fidelity. The current study uses a two-dimensional battery thermal model to comprehensively analyze thermal behavior of a LiFePO4-20Ah Li-ion pouch cell. In this research study, heat generation in a Li-ion battery is evaluated by estimating the internal resistance and entropic change obtained from experimentation. The energy equation is then solved using the finite difference method in MATLAB to obtain the transient thermal response of the battery. The developed transient electrothermal model is validated against experimental data under varying C rates to assess the accuracy and precision of the proposed model. The simulation results show that the thermal response obtained considering the effect of temperature and DoD on heat generation shows more accurate results than the constant resistance values. The thermal behavior of a LiFePO4 pouch cell, considering constant values for heat generation, has a maximum relative error of roughly 19.99% compared to experimental data at a 4C discharge rate. While this maximum relative error was reduced to 6.29% when considering the effect of temperature and DoD on heat generation. In the constant resistance model, more significant errors can be attributed to the fact that the resistance of a Li-ion battery varies with the depth of discharge (DoD). While the initial discharge phase of the battery exhibits minimal changes in resistance values, a substantial increase in resistance occurs during the final stages of discharge. This contrasts with the actual behavior of Li-ion batteries, which demonstrate significant variations in resistance values throughout the discharge process. Thus, coupling the effects of DoD and temperature on heat generation is necessary to accurately predict the thermal behavior of Li-ion battery. Figure 1

Thermal error modeling of servo axis based on optimized LSSVM with gray wolf optimizer algorithm

Thermal error of a CNC machine tool has a serious effect on its machining accuracy. In order to reduce the thermal error, establishing an accurate and robust thermal error model is necessary. A new data-driven thermal error model is proposed based on gray wolf optimizer (GWO) and least squares support vector machine (LSSVM). While, three temperature-sensitivity points (TSPs) were picked by grouping search method. With optimizing the hyperparameters γ and σ2 by GWO algorithm, LSSVM is applied to thermal error modeling, which has advantages in dealing with small samples and nonlinear data. The experiments were conducted, and the results showed that the error prediction model constructed by GWO-LSSVM achieved an accuracy of 94.39 % meeting the requirements for error compensation. Then, the stability of the proposed error model is verified. Finally, the LSSVM model is compared with MLR and BP models commonly used in error modeling, and the advantages and applicable occasions are analyzed through experiments.

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

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