Thermal Network Model Research Articles

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.

Incorporating PCM-enabled thermal energy storage into 3D printable cementitious composites

This paper delineates the feasibility of incorporating microencapsulated phase change materials (mPCM) into 3D printable cementitious composite materials. A comprehensive experimental program was carried out to evaluate the impacts of mPCM on the printability, microstructures, mechanical and thermal properties of cementitious 3D printing ‘inks’. Results showed that the mPCM affected the printability of the cementitious ink material based on its physical properties (e.g., particle size) and volume loading – at lower volume loadings, mPCM increased the flowability of the cementitious ink material while leading to increased compressive strength and thermal conductivity for the hardened printed material. However, further increase in mPCM dosage led to a decrease in printability and, therefore, decrease in compressive strength and thermal conductivity as compared to the reference mixture. The results also showed that the inclusion of mPCM influence the printing parameters. In general, the inclusion of higher volume contents of mPCM necessitates a higher extrusion rate to achieve a desirable extrudability. Lastly, a thermal network model was formulated for 3D printed mPCM charged building components (e.g., wall). The study shows that microencapsulated PCM materials have good potential to be used in 3D printable cementitious mixtures for improving the thermal and energy performance of 3D printed buildings.

An optimized thermal network model to evaluate the thermal behavior on motorized spindle considering lubricating oil and contact factors

During the operation of the high-speed motorized spindle, the heating of motorized spindle components can cause thermal deformations, which worsens the work performance and shortens the life expectancy of the motorized spindle. In response to the above behaviors, the thermal network model for the motorized spindle in this investigation was built in which the lubricating oil properties and contact characteristics were considered. Next, according to Kirchhoff’s energy balance principle, the theoretical equations of steady-state temperature were established, and the temperature rise of the motorized spindle was obtained by Newton–Raphson method. Lastly, the influences of speed, cooling water flow rate, airflow rate, and other variables on the temperature were analyzed and verified by the experiment. The results show that the thermal network model including lubricating oil and contact factors can predict the temperature rise of motorized spindle accurately, which provides some references and suggestions in motorized spindle design.

Boundary Condition Independent Thermal Network Modeling of High-Frequency Power Transformers

With increasing global power demands and the trend of miniaturization of power converters, electronic components are being pushed toward their thermal limits. This has accentuated the need for accurate and low-cost thermal models of electronic components. Lumped Parameter Thermal Network Modeling (LPTNM) is commonly used to evaluate the thermal performance of electronic components, but unlike semiconductors, transformer LPTNM is not standardized in the academic community. Due to this lack of standardization, most proposed transformer thermal models have not been evaluated for Boundary Condition Independence (BCI). This study presents a BCI thermal modeling approach for power transformers based on LPTNM. A thermal network model is developed by discretizing the transformer geometry into smaller volumes and evaluating the thermal resistances by approximating the three-dimensional heat transfer problem as a piecewise one-dimensional model. The network model/geometry discretization is iteratively improved for a set of different boundary conditions, resulting in a BCI model. The modeling approach is experimentally and numerically validated on a PQ40/30 transformer operating in a 3.3 kW switch mode power supply. The BCI transformer thermal model closely matches both numerical and experimental results with a fraction of the computational cost of the numerical model. The proposed BCI modeling approach provides a low-cost alternative to detailed thermal models and can predict transformer thermal performance for varied applications.

Thermal Modelling an LED Bulb using IR Thermography and Bayesian Deconvolution

This study focuses on LED bulbs where LEDs are typically mounted on a metal core printed circuit board. A sample of LED bulbs having base E27 and a power range between 3.5-11.5 W have been measured. A thermal model will be found using thermal transient testing, infrared thermal measurements, and the Bayesian deconvolution. LED surface temperature will be measured without contact using a low-resolution IR imager. A test facility has been built which automatically switches power on and measures and records samples during the measurement cycle which typically lasts around one hour. This time duration was long enough for reaching the thermal steady-state condition. The procedure uses a measured thermal transient, first calculates the thermal impedance Zth, then the time constant spectrum Rζ(z) using the Bayesian deconvolution. An equivalent thermal network model will be found in Foster-presentation. This can be transformed to Cauer-presentation. The model is a driving point model for thermal impedance and includes thermal resistances and thermal capacitances. FEM simulation gives more detailed information about heat transfer paths and internal temperatures of an LED bulb.

Lumped parameter thermal network for thermal analysis of a rotor-excited axial flux switching machine with electromagnetic-thermal design

In this paper, a three-dimensional (3-D) lumped parameter thermal network (LPTN) model is presented for the first time for a rotor-excited axial-field flux-switching permanent magnet (RE-AFFSPM) machine. The 3-D LPTN predicts the steady-state temperature of different parts in various operating conditions. To enhance the LPTN accuracy and comprehensiveness, (i) the convection heat transfer in the internal and external areas, as well as radiation from the end-windings, (ii) core material anisotropic thermal conductivity, (iii) the equal thermal conductivity of the winding in the slot, and (iv) contact thermal resistances are all considered. Heat transfer coefficients are obtained mathematically to be applicable for the RE-AFFSPM machines with different parameters. A 3-D finite element method (FEM) is established to calculate the electromagnetic losses and thermal analysis with high accuracy. Eddy current losses in the stator core, rotor core, permanent magnets (PMs), and carriers, along with hysteresis losses in the stator and rotor cores, are calculated by 3-D FEM then coupled to the thermal analysis to predict the temperature distribution. By comparing the temperature results of the 3-D LPTN and 3-D FEM at various speeds, air–gap lengths, loading levels, and simultaneous variation of the current density and speed, the performance of the proposed 3-D LPTN is further investigated and verified. Results indicate that by the proposed 3-D LPTN, components temperature can be approximated with high accuracy in a lesser time than the 3-D FEM.

Hourly Simulation of an Earth-to-Air Heat Exchanger in a Low-Energy Residential Building

The earth-to-air heat exchanger (EAHE) is a well-founded and verified solution used in modern buildings both for heating and cooling purposes around the world. However, there is a lack of studies on operation of such devices cooperating with ventilation systems of buildings in hourly time step. In this study, the 5R1C thermal network model of a building from EN ISO 13790 was coupled with the EAHE model from EN 16798-5-1 to calculate hourly outlet air temperature. To improve the effectiveness of the considered solution, an additional algorithm was developed to choose between the EAHE outlet and ambient air as the source of ventilation air. Simulations were conducted in a spreadsheet for a low-energy single-family building. Ground temperature was compared with measurements taken in the considered location. The application of the EAHE with the proposed bypass resulted in a decrease in annual energy use for space heating and cooling from 14.82 GJ and 1.67 GJ to 12.74 GJ and 0.93 GJ, i.e., by 14% and 44%, respectively. Peak hourly heating and cooling thermal power decreased from 2.73 kW and 3.06 kW to 2.21 kW and 2.34 kW. Introduction of a bypass and switching between the EAHE and ambient air as the source of ventilation for the building resulted in annual energy savings of 123 kWh.

Flow-Assisted Evaporative Cooling for Electric Motor

This article examines a novel concept of flow-assisted latent heat-driven two-phase evaporative cooling (EC) confined in-between slot liner and active-winding of electric motor. Wicking microstructure-enhanced PDMS liner axially sucks coolant in the form of thin film between the PDMS liner and active-winding and eventually enables thin-film evaporation on the outer surface of the active-winding. Therefore, EC-based thermal management eliminates a major thermal resistance between the winding and the coolant and enhances the heat extraction from the winding without compromising the electromagnetic (EM) performance. Two-way coupled EM—computational fluid dynamics/heat transfer (CFD/HT) and EM—lumped parameter thermal network (LPTN) models have been developed to assess the electrothermal performance of the EC for steady and transient conditions. Taking a case study of a 125-kW jacket-cooled BMW i3 motor and dielectric coolant FC-84, EC is shown to be capable of handling a maximum steady-state rms current density of 26 A/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at an evaporative HT coefficient of 5000 W/ <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\text{m}^{2}\cdot \text {K}$ </tex-math></inline-formula> , which is about 78.7% higher compared to the traditional jacket cooling (JC). In the case of EC, a maximum steady-state and transient rms current density of 30 A/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> (106.2% higher compared to the JC) and 40.8 A/mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , respectively, have been realized by using high thermal conductivity epoxy (1.9 W/m.K) impregnation material. The thermal performance of the EC is also assessed and compared with JC over a dynamic drive cycle. Finally, a motorette testing has been performed to demonstrate the applicability of the proposed EC method and validate the developed modeling framework.

Modeling Method for Thermal Field of Turbulent Cooling Dry-Type On-Board Traction Transformer in EMUs

Adopting dry-type on-board traction transformer (OBTT) is one of the main ways to reduce the weight of electric traction systems, and its thermal characteristics are crucial for the safe operation of high-speed electric multiple units (EMUs). However, the existing thermal modeling methods either cannot realize the fast calculation of thermal characteristics of dry-type OBTT, or cannot accurately obtain the turbulence heat-transfer intensity when using the high-speed train-induced wind cooling. This article proposes an efficient method for dry-type OBTT thermal characteristics calculation. First, the thermal network of the winding area is proposed considering the thermal developing region of the air duct and the heat flow in the insulation paper between conductors. Then, the effects of key parameters on convective heat-transfer intensity are obtained by computational fluid dynamics (CFD) parametric sweeps, and then a novel local Nusselt number correlation for accurately calculating the turbulent convective thermal resistance is proposed. Finally, validation is performed with an experimental setup. The experimental results show that the computationally efficient thermal network model proposed in this article can quickly obtain the winding temperature distribution and the location of the hot-spot temperature. Moreover, the thermal analysis of a 6.3 MVA dry-type OBTT was conducted using the proposed modeling method.

The development of high temperature heat-pipe transient model for system analysis of heat pipe cooled microreactor

The miniaturization of nuclear reactor power is the direction of nuclear energy development in the future. A nuclear power system with a small solid core and cooled by a high-temperature alkali metal heat pipe has excellent safety performance. The start-up of the heat pipe cooled reactor depends on the heat pipe start-up characteristics from the frozen state, so transient analysis models of high-temperature heat pipe from the frozen state are established in this paper for the system characteristics analysis of the heat pipe cooled microreactor. The models include three stages of high-temperature heat pipe start-up from the frozen state: the molecular diffusion model of the free molecular stage, the flat-front model of the transitional flow process, and the 2-D thermal resistance network model of continuous flow. The energy conservation model is also used to consider the phase transition between solid, liquid, and gas. And The heat transfer limit model of heat pipe has been also coupled in the model. The models have been verified by the start-up experimental dates of the sodium heat pipe and the transient experimental dates of the water heat pipe. Then the models are applied to the system analysis code of the heat pipe reactor to analyze the transient experiment characteristics of a typical heat pipe reactor system. The results show that the predicted trend by HPMR system code is reasonable and the deviation with experimental data is less than 2%, which further demonstrates the applicability and effectiveness of the heat pipe transient models.

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

Validation and Parametric Investigations of an Internal Permanent Magnet Motor Using a Lumped Parameter Thermal Model

Abstract One of the key challenges for the electric vehicle industry is to develop high-power-density electric motors. Achieving higher power density requires efficient heat removal from inside the motor. In order to improve thermal management, a multiphysics modeling framework that is able to accurately predict the behavior of the motor, while being computationally efficient, is essential. This paper first presents a detailed validation of a lumped parameter thermal network (LPTN) model of an Internal Permanent Magnet synchronous motor within the commercially available motor-cad modeling environment. The validation is based on temperature comparison with experimental data and with more detailed finite element analysis (FEA). All critical input parameters of the LPTN are considered in detail for each layer of the stator, especially the contact resistances between the impregnation, liner, laminations, and housing. Finally, a sensitivity analysis for each of the critical input parameters is provided. A maximum difference of 4%—for the highest temperature in the slot-winding and the end-winding—was found between the LPTN and the experimental data. Comparing the results from the LPTN and the FEA model, the maximum difference was 2% for the highest temperature in the slot-winding and end-winding. As for the LPTN sensitivity analysis, the thermal parameter with the highest sensitivity was found to be the liner-to-lamination contact resistance.

Estimation of thermal conductivity of cemented sands using thermal network models

Effective thermal conductivity of soils can be enhanced to achieve higher efficiencies in the operation of shallow geothermal systems. Soil cementation is a ground improvement technique that can increase the interparticle contact area, leading to a high effective thermal conductivity. However, cementation may occur at different locations in the soil matrix, i.e. interparticle contacts, evenly or unevenly around particles, in the pore space or a combination of these. The topology of cementation at the particle scale and its influence on soil response have not been studied in detail to date. Additionally, soils are made of particles with different shapes, but the impact of particle shape on the cementation and the resulting change of effective thermal conductivity require further research. In this work, three kinds of sands with different particle shapes were selected and cementation was formed either evenly around the particles, or along the direction parallel or perpendicular to that of heat transfer. The effective thermal conductivity of each sample was computed using a thermal conductance network model. Results show that dry sand with more irregular particle shape and cemented along the heat transfer direction will lead to a more efficient thermal enhancement of the soil, i.e. a comparatively higher soil effective thermal conductivity.

Characteristics of effective thermal conductivity of porous materials considering thermal radiation: A pore-level analysis

Porous materials have attracted considerable attention in the fields of heat transfer and solar energy harvesting. Due to the complicated pore structures and multiple reflections of radiation beams at interior interfaces, the analysis of coupled radiative and conductive heat transfer in porous materials is complex and challenging. In this paper, heat transfer characteristics in porous materials considering the coupled modes of conduction and radiation are studied at pore level. A discrete ordinate ray-tracing (DORT) method is proposed to solve the radiative heat transfer in porous materials, which is then combined with the finite volume method to solve the energy balance equation. The geometry of porous structure is represented by the implicit function, which ensures the calculation accuracy of interface normal vectors during the solution of radiative heat transfer. A dimensional analysis is conducted and a governing dimensionless parameter (NSRC) is introduced, which well characterizes the scaling characteristics of the effective thermal conductivity (ETC). The proposed approach is demonstrated to be an effective way to simulate the coupled radiative-conductive heat transfer in porous materials at pore level. Due to the thermal radiation, the ETC for open-cell porous materials increases more dramatically with NSRC than that for closed-cell porous materials. A simple thermal network model is developed for both open- and closed-cell porous materials, which predicts the trends of the ETC with NSRC very well.

Fluid Flow and Thermal Analysis of an Axial Flux Permanent Magnet Eddy Current Brake

With the increasing requirements for miniaturization and high braking torque, it is now necessary to analyze the thermal characteristics to the same extent as the electromagnetic design for eddy current brakes. However, due to the lack of suitable convection heat transfer correlations for axial flux eddy current brakes, it brings difficulties to designers. To solve this problem, the correlations for an axial flux permanent magnet (PM) eddy current brake are derived using the computational fluid dynamics (CFD) and the least square method. The proposed correlations considered the changes in design parameters and temperatures of the eddy current brake, and the accuracy is verified by the CFD. Subsequently, convective heat transfer coefficients calculated through the correlations are assigned to a thermal network model of the eddy current brake, and the thermal network model is verified by experimental tests. The results show that the thermal network model can accurately predict temperatures of the eddy current brake. Since the time required to calculate convective heat transfer coefficients through the correlations is significantly less than that required through CFD, and the variation of the geometrical parameters and temperatures can be considered, these correlations are very suitable for use in the optimization process.

Self-Heating and Thermal Network Model for Complementary FET

In this article, we investigate the self-heating effect (SHE) of the Complementary FET (CFET) device and propose a cross-coupled thermal network model. For the investigation of thermal behavior, the intra- and inter-device crosstalk of CFET is evaluated qualitatively through the defined crosstalk coefficient. The results indicate that CFETs have more severe intra-device thermal crosstalk, almost twice that of CMOS. Moreover, the vertical structure makes the heat dissipation path of CFET devices significantly different in the two transition states (inputs “1” and “0”). Based on the thermal behavior characteristics of CFET and the necessity of high-precision and rapid prediction of thermal characteristics of the devices, a thermal network model is proposed, which can quickly respond to the heat source and be extended at the array level. In the last part, we analyze the influence of load capacitance and operating frequency on the self-heating of CFET, and SHE-related reliability lifetime is predicted based on the lattice temperature obtained from the thermal network. The prediction results show that the bias temperature instability (BTI) lifetime of CFET degrades by 84% compared with standard CMOS.

Influence of contact pressure on the thermal contact conductance of layered metallic sheets

For the optimisation of the annealing process of aluminium coils, simulation of the process is often performed. To simulate the process with higher accuracy, reliable input parameters are required, and thermal conductivity (thermal contact conductance) is one of them. In the present study, a method to measure the thermal conductivity and thermal contact conductance of metallic sheets were developed based on the steady-state comparative longitudinal heat flow. The apparatus was built with a compression test machine, and thus it allows to control the pressure to the sample and carry out the measurements at different contact pressure. An equipped heater allows to heat the sample to 573 K. To evaluate the thermal conductance at the interface, a thermal resistance network model was applied. The measurements were carried out with an aluminium alloy (AA3003 sheets). In addition to the thermal contact conductance measurements, the surface roughness of the sheets was also investigated. The semi-empirical equation for the relationship between thermal contact conductance and contact pressure was obtained based on the measurement results.

Deriving a thermal model from data by machine learning based on physical laws

We propose a method for creating a reduced-order model (ROM) for temperature prediction that is applicable to nonlinear time-variant systems, able to be created using a very small amount of data, and easy to interpret. Our nonlinear time-variant ROM is an extension of sparse identification of nonlinear dynamical systems, which was first proposed in 2016. Three machine learning methods are developed for automatically deriving a thermal network model from time-series data. Link relationships between temperature nodes and parameter settings, such as thermal resistance and heat capacity, are automatically inferred by machine learning. The effectiveness of the proposed method is demonstrated using the results of thermo-fluid analysis for a natural air-cooled power module in which thermal resistance is a function of temperature. The ROM was created using the results of 15 thermo-fluid analyses, with is fewer than the 25 independent variables in the ROM. The computational efficiency of the created ROM is very high, and unknown data, including extrapolated data, were predicted with error of less than 1 K. By using the proposed method, it is expected that design parameters can be fine-tuned and actual loads can be taken into account, and also that condition-based maintenance can be realized through real-time simulation.

2D hybrid magneto-thermal model for PM induction heating device with rotating movement

A new 2D-hybrid model in cylindrical coordinates intended for the study of a transient magneto-thermal coupled problem is proposed. This model is used to evaluate the spatiotemporal distribution of temperature in rotating aluminum billets heated by permanent magnets induction-heating device (PMIHD). The proposed hybrid model consists of coupling an exact analytical solution of the 2D magneto-harmonic field to a 2D transient thermal analysis where the temperature dependence of electrical and thermal physical properties of the heated billet is considered. Based on the separation variables technique, the analytical solution of magneto-harmonic problem serves to evaluate the induced current and the resulting power density loss distribution in the nonmagnetic billet. Furthermore, the 2D transient thermal analysis, based on the lumped thermal network model, is developed in radial and axial directions of the heated billet. In order to validate the 2D hybrid magneto-thermal model, the performances of the studied PMIHD are compared with those obtained with Finite Element Method (FEM).

Thermal design optimization of electronic circuit board layout with transient heating chips by using Bayesian optimization and thermal network model

This paper describes a method combining Bayesian optimization (BO) and a lumped-capacitance thermal network model that is effective for speeding up the thermal design optimization of an electronic circuit board layout with transient heating chips. As electronic devices have become smaller and more complex, the importance of thermal design optimization to ensure heat dissipation performance has increased. However, such a thermal design optimization is difficult because various trade-offs associated with packaging and transient temperature changes of heat-generating components must be considered. This study aims to improve the performance of thermal design optimization by artificial intelligence. BO using a Gaussian process was combined with the lumped-capacitance thermal network model, and its performance was verified. As a result, BO successfully found the ideal circuit board layout as well as particle swarm optimization (PSO) and genetic algorithm (GA) could. The CPU time for BO was 1/5 and 1/4 of that for PSO and GA. In addition, BO found a non-intuitive optimal solution in approximately 7 min from 10 million layout patterns. It was estimated that this was 1/1000 of the CPU time required for analyzing all layout patterns.