Accurate Thermal Model Research Articles

A Novel Thermal Network Model Used for Temperature Calculation and Analysis on Brushless Doubly-Fed Generator With Winding Encapsulating Structure

In recent years, magnetic-barrier rotor has been put forward for brushless doubly-fed generator (BDFG) application owing to its desirable performance, such as high power density and strong magnetic coupling ability. However, it also causes significant losses induced by harmonics and high temperature problem. Hence, it is a major challenge to evaluate Bref_DFG temperature rise fast and accurately. So far, aiming at magnetic-barrier rotor, there has been no accurate thermal network model established achieving this goal. In addition, wingding end encapsulating technology has been used for a few years and its heat dissipation potential is determined by the materials. Therefore, the application of novel material in winding encapsulating structure is desirable to improve the heat dissipation potential. In this paper, an equivalent thermal network model, thermal resistance models of magnetic-barrier rotor, and a winding encapsulating structure with novel materials are further established for thermal analysis. Subsequently, temperature distributions for BDFG components are also calculated by finite element method, while the results are compared with those by analytical method. The BDFG prototype is manufactured with experimental tests performed. The correctness of the equivalent thermal network models proposed in this paper is verified by the test results.

Simplified Thermal Model and Optimal Control for Chilled Water Pipe in Air-Conditioning System

As an important subsystem in air-conditioning system, water system connects the chiller and terminal equipment. Therefore, it is necessary to develop an accurate thermal model for chilled water pipe to create the suitable indoor temperature and humidity environment. In this paper, the thermal model of the pipe was considered by utilizing the simplified thermal time-delay state-space model with the mass, energy balance, and heat consumption equations. Based on this improved model, the preview control as control strategy for water pipe temperature was proposed, and its robustness and stability were discussed. Subsequently, the performances of this model and control strategy were tested in a fan-coil system simulating with MATLAB and TRNSYS (Transient System Simulation Program). Explicitly, the results show that this model accurately predicts the thermal characteristic, and the average mean squared errors for water temperature were 11.14% and 12.82%, respectively. Meanwhile, the tracking effect of valve controller was better than the control strategy with no preview control.

Fast and Accurate Temperature-Dependent Current Modeling of HBTs Using the Dimension Reduction Method

Empirical models have been widely and successfully used in device modeling in the past few decades. However, they are becoming increasingly intricate to accurately capture the complex thermal effects in semiconductor devices. Therefore, the aim of this work is to utilize a general dimension-reduction method to quickly and accurately construct large-signal models of semiconductor devices with consideration of thermal effects. In general, the junction voltage dimension is represented by empirical functions, whereas the junction temperature dimension is described by the first-order Taylor series approximation. The final analytical current model is a combination of two independent sets of empirical functions. These functions are constructed from pulsed I-V measurements at different ambient temperatures. The percentages of different components are controlled by the thermal level. Two commercial InGaP/GaAs heterojunction bipolar transistors are investigated to verify the effectiveness of this method. The large-signal models are implemented in Advanced Design System. Excellent agreement is achieved between measurement and simulation of the I-V characteristics, S-parameters, and power sweeps. The dimension reduction method is able to effectively reduce the number of equations and parameters because the temperature dimension is expanded by using a Taylor series. In addition, this method would be applied to the thermal modeling of various devices based on new materials or process technologies. Accordingly, the dimension reduction method is powerful and useful in fast and accurate thermal modeling of microwave semiconductor devices.

Impacts of thermos-physical properties on plate-fin multi-stream heat exchanger design in cryogenic process for CO2 capture

Oxy-fuel combustion is one of the most promising technologies for CO2 capture for power plants. In oxy-fuel combustion plants, cryogenic process can be applied for CO2 purification because the main impurities in flue gas are non-condensable gases. The multi-stream plate-fin heat exchanger is one of the most important components in the CO2 cryogenic system. In-depth understanding of the impacts of property on the heat exchanger is of importance for appropriate design. In order to investigate the impacts of properties on sizing the heat exchanger and to further identify the key properties to be prioritized for the property model development, this paper presented the design procedure for the plate-fin multi-stream heat exchanger for the CO2 cryogenic process. Sensitivity study was conducted to analyze the impacts of thermos-physical properties including density, viscosity, heat capacity and thermal conductivity. The results show that thermal conductivity has the most significant impact and hence, developing a more accurate thermal conductivity model is more important for the heat exchanger design. In addition, even though viscosity has less significant impact compared to other properties, the larger deviation range of current viscosity models may lead to higher uncertainties in volume design and annual capital cost of heat exchanger.

Furnace thermal efficiency modeling using an improved convolution neural network based on parameter-adaptive mnemonic enhancement optimization

Thermal efficiency is a very important factor in furnaces. Accurate thermal efficiency modeling plays a key role in optimizing and improving thermal efficiency. Industrial process data own complex characteristics of high nonlinearity and time series. Convolutional neural network (CNN) has been widely used to build models due to the strong ability in processing high-nonlinear and time-series data. However, in the traditional CNN method, the repeatability of calculations is not considered when updating weights; only the latest updated weights are reserved. As a result, some important computation experience is abandoned, which weakens the performance of CNN. In order to solve this problem, an improved CNN method integrating with parameter-adaptive mnemonic enhancement optimization (AMEO-CNN) is proposed in this paper. In the proposed AMEO-CNN, previous computational experiences are adaptively reserved to minimize output errors using optimized weights. The continuity of the algorithm and the anisotropy of the initial values are proved through theoretical analyses. In order to validate the performance of the proposed AMEO-CNN model, thermal efficiency modeling simulations of an industrial heating furnace are carried out. Compared with other three models, simulation results show that the proposed AMEO-CNN model can achieve higher modeling accuracy, which verifies the effectiveness of the proposed AMEO-CNN method.

Local clothing thermal properties of typical office ensembles under realistic static and dynamic conditions

An accurate local thermal sensation model is indispensable for the effective development of personalized conditioning systems in office environments. The output of such a model relies on the accurate prediction of local skin temperatures, which in turn depend on reliable input data of the local clothing thermal resistance and clothing area factor. However, for typical office clothing ensembles, only few local datasets are available in the literature. In this study, the dry thermal resistance was measured for 23 typical office clothing ensembles according to EN-ISO 15831 on a sweating agile manikin. For 6 ensembles, the effects of different air speeds and body movement were also included. Local clothing area factors were estimated based on 3D scans. Local differences can be found between the measured local insulation values and local area factors of this study and the data of other studies. These differences are likely due to the garment fit on the manikin and reveal the necessity of reporting clothing fit parameters (e.g., ease allowance) in the publications. The increased air speed and added body movement mostly decreased the local clothing insulation. However, the reduction is different for all body parts, and therefore cannot be generalized. This study also provides a correlation between the local clothing insulation and the ease allowance for body parts covered with a single layer of clothing. In conclusion, the need for well-documented measurements is emphasized to get reproducible results and to choose accurate clothing parameters for thermo-physiological and thermal sensation modeling.

A Universal Scalable Thermal Resistance Model for Compact Large-Signal Model of AlGaN/GaN HEMTs

This paper presents a universal scalable thermal resistance model for AlGaN/GaN high electron-mobility transistors (HEMTs), which can be easily implemented into compact large-signal models and is very suitable for rapid device modeling. It contains all the key HEMT device geometric parameters such as gate length, gate width, gate pitch, and different layer thicknesses. The temperature-dependent conductivities of different materials are taken into consideration. The via-holes effects on the thermal resistance of a device are also included, which is considered for the first time in a compact largesignal model. Two uniform parameters are used to simplify the complicated analytical closed-form expression of thermal resistance, which is difficult to be implemented into the compact model. Another two uniform parameters are used to simplify the complicated transcendental function of the temperature rise with respect to the temperature-dependent thermal conductivity. Thus a simple, accurate, and scalable thermal resistance model can be obtained. The results are verified by comparing with experimental and finite-element numerical simulations. The dc I-V characteristics and large-signal performances of HEMTs devices with different gate lengths 0.15, 0.25, and 0.4 μm are used for further verifications. The proposed thermal resistance model can also be used to optimize the structural geometry of a device to obtain minimum thermal resistance.

Solid and Vapor Chamber Integrated Heat Spreaders: Which to Choose and Why

The performance and reliability of semiconductor electronics require that thermal management hardware be used to adequately cool devices, components, and packages. A vital component of many devices, including but not limited to PAs, CPUs, GPUs, and LEDs, is the integrated heat spreader (IHS). IHSs are implemented to spread the heat from the small electronic component to a larger area in order to facilitate sufficiently low thermal resistance active or passive cooling to the final fluid-cooled heat sink in the stack. One question that still needs to be addressed for electronics packaging is “Under what circumstances do vapor chamber heat spreaders outperform solid IHSs?” This paper is aimed at providing a straightforward approach to addressing this question, specifically during the early “scoping” design stage. Sufficiently accurate steady-state thermal network models for both solid and vapor chamber IHSs are proposed for estimating the overall stack resistance of a package, including TIM1, IHS, TIM2, and convective heat sink. In this way, the performance of both heat spreading technologies can be compared and contrasted over a range of geometric and operating parameters without having to resort to complex numerical models, which may require specialized expertise and/or be too expensive and time-consuming for early-stage design phases of electronic packages.

Application of artificial neural networks to near-instant construction of short-term g-functions

The concept of a g-function is frequently used to simplify the simulation of a ground heat exchanger composed of several boreholes. The technique is suitable for modelling long-term thermal interactions, although it suffers from a lack of accuracy in the short term. Alternatively, models based on the theory of thermal resistances and capacities provide a more accurate solution for the short term but at the cost of a long solution time that prevents an efficient iterative use. In this work, an artificial neural network designed to rapidly and accurately construct the short-term g-function of a ground heat exchanger is presented. To assemble a large training set, 15 000 simulations are performed with an accurate thermal resistance and capacity model. The boundary conditions used to solve the model allow direct construction of the short-term g-function for the outlet fluid temperature. A multilayer perceptron architecture is proposed in conjunction with a methodology designed to reduce training time and increase the accuracy of the neural network. The output of the resulting network is compared with numerical and experimental data sets. The construction of a short-term g-function by an artificial neural network is 10 000 times faster and achieved in a few milliseconds. A source code combining efficiently a short- and long-term g-function is published with the paper.

Combined ab initio and empirical model of the thermal conductivity of uranium, uranium-zirconium, and uranium-molybdenum

In this work we developed a practical and general modeling approach for thermal conductivity of metals and metal alloys that integrates ab initio and semiempirical physics-based models to maximize the strengths of both techniques. The approach supports creation of highly accurate, mechanistic, and extensible thermal conductivity modeling of alloys. The model was demonstrated on {\alpha}-U and U-rich U-Zr and U-Mo alloys, which are potential fuels for advanced nuclear reactors. The safe use of U-based fuels requires quantitative understanding of thermal transport characteristics of the fuel. The model incorporated both phonon and electron contributions, displayed good agreement with experimental data over a wide temperature range, and provided insight into the different physical factors that govern the thermal conductivity under different temperatures. This model is general enough to incorporate more complex effects like additional alloying species, defects, transmutation products, and noble gas bubbles to predict the behavior of complex metallic alloys like U-alloy fuel systems under burnup.

Online estimation of thermal parameters based on a reduced wide-temperature-range electro-thermal coupled model for lithium-ion batteries

To improve the accuracy of thermal model, optimize the design of heat dissipation system and evaluate online the thermal management system, an online estimation method of key thermal parameters is developed from carefully designed experiments, rather than being taken from the literature or the empirical value. The accurate prediction of heat generation, which is based on a reduced wide-temperature-range electro-thermal coupled model, is presented under easily obtainable alternating current excitation at different temperatures. To circumvent some inherent errors, a combined experimental/computational approach to simultaneously estimate the specific heat capacity and thermal resistance is proposed using quasi step power, with which the identification time is significantly reduced. The identified values of specific heat capacity and thermal resistance are validated with high accuracy. The adaptability validation is carried out under different temperatures and cooling conditions, as well as using different battery chemistries, indicating that the proposed method is generic. The in-situ methodology, thanks to good robustness on the colored noise, is capable of providing a promising candidate for accurate thermal modeling, on-board evaluation of battery thermal safety, and advanced design of thermal management system for electric vehicles.

Development of Accurate BSIM4 Noise Parameters for CMOS 0.13-µm Transistors in Below 3-GHz LNA Application

Accurate transistor thermal noise model is crucial in IC design as it allows accurate selection of transistors for specific frequency application. The accuracy of the model is represented by the similarity between the simulated and the measured noise parameters (NPs). This work was based on a problem faced by a foundry concerning the dissimilarities between the measured and simulated NPs, especially minimum noise figure (NF<sub>min</sub>) for frequencies below 3 GHz.

Direct Coupling Method for Coupled Field-Circuit Thermal Model of Electrical Machines

A direct coupling method for coupled field-circuit thermal model of electrical machines is proposed, by which the temperature distributions in the lumped-parameter thermal network and finite-element region are solved simultaneously without iterative calculation procedure. Each thermal resistance in the lumped-parameter thermal network is seen as a one-dimensional finite element. The lumped-parameter thermal network and the finite-element region are linked with the two kinds of boundaries, namely: equivalent convective and equivalent temperature. The users can take advantage of both methods together for more accurate and efficient thermal modeling of the electrical machines using the direct coupling method. An outer-rotor permanent magnet machine with an uneven distribution of ac copper loss in the winding region is used to highlight the benefits of the proposed method. The proposed method is verified by comparing with the indirect method and experiments as well.

Numerical study of countermeasure against thermal stimuli for HMX-based polymer-bonded explosives

ABSTRACTAn accurate thermal decomposition model of Octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine (HMX)-based polymer-bonded explosives is indispensable to the design of countermeasure against environmental thermal stimuli for certain explosive devices. However, the complicated chemical reactions occurring in decomposition of HMX-based polymer-bonded explosives pose great challenge to scientists. In this study, the thermal ignition kinetic model proposed by Tarver is implemented to study thermal decomposition of HMX-based polymer-bonded explosives using commercial software Abaqus, which does not only consider the thermal decomposition of HMX but also the polymer binders. The simulation results are compared to ODTX and Scaled Thermal Explosion Experiment (STEX) and reasonably good agreements are achieved. Then the thermal decomposition model is applied to analyze an explosive device exposed to environmental thermal stimuli. Furthermore, countermeasure against environmental thermal stimuli is suggested and analyzed quantitatively for the explosive device. The time to explosion and environmental temperature before ignition is calculated and analyzed for the explosive device under various heating rates.

A novel second-order thermal model of Stirling engines with consideration of losses due to the speed of the crack system

Very accurate second-order thermal models have been developed for the thermal simulation of Stirling engines in recent years. One of the last ones is the comprehensive polytropic model of Stirling engine called the CPMS model. The accuracy of the CPMS model was found to be sufficient for the nominal operation of a prototype Stirling engine known as the GPU-3 engine. Nevertheless, the accuracy of the CPMS model was drastically reduced at high rotational speeds of the engine. In this paper, power loss and pressure change due to the inertial force of the crank system were integrated into the CPMS thermal model in order to compensate inaccuracy of the CPMS model at high rotational speeds. Moreover, the effect of rotational speed on the gas temperature in heater and cooler was also incorporated. A precise model for evaluating the mechanical friction loss was also employed and compared with the simple frictional model of the simple frictional model used in the CPMS. The model was examined on the GPU-3 engine, and it was found that it has superior accuracy compared to the previous thermal model over the entire working regime of the GPU-3 engine.

Depth and thermal sensor fusion to enhance 3D thermographic reconstruction.

Three-dimensional geometrical models with incorporated surface temperature data provide important information for various applications such as medical imaging, energy auditing, and intelligent robots. In this paper we present a robust method for mobile and real-time 3D thermographic reconstruction through depth and thermal sensor fusion. A multimodal imaging device consisting of a thermal camera and a RGB-D sensor is calibrated geometrically and used for data capturing. Based on the underlying principle that temperature information remains robust against illumination and viewpoint changes, we present a Thermal-guided Iterative Closest Point (T-ICP) methodology to facilitate reliable 3D thermal scanning applications. The pose of sensing device is initially estimated using correspondences found through maximizing the thermal consistency between consecutive infrared images. The coarse pose estimate is further refined by finding the motion parameters that minimize a combined geometric and thermographic loss function. Experimental results demonstrate that complimentary information captured by multimodal sensors can be utilized to improve performance of 3D thermographic reconstruction. Through effective fusion of thermal and depth data, the proposed approach generates more accurate 3D thermal models using significantly less scanning data.

Prediction model and experimental validation for the thermal deformation of motorized spindle

The thermal deformation of motorized spindle has a great influence on the precision of numerical control (NC) machine tools. Thus, it is crucial to predict the thermal deformation in the design and operation control phase by numerical simulation and improve the precision of NC machine tools. In order to achieve this, an accurate thermal deformation prediction model for motorized spindle is required. In this paper, a model for predicting the thermal error of motorized spindle based on finite element method and parameter optimization is proposed. Levenberg-Marquardt (LM) method is applied to optimize the heat transfer coefficient of motorized spindle by using surface temperature data measured. The optimized heat transfer coefficient is then taken as one of the boundary condition of the finite element model. The boundary conditions about heat of the finite element model are obtained by energy loss experiment. The proposed model is validated by experimental results, and the results have shown well correlation.

An accurate thermal performance modeling and simulation method for motorized spindle of machine tool based on thermal contact resistance analysis

Motorized spindle is an inevitable choice of precision machine, but it has a prominent heat problem for its specific configuration of build-in motor. The paper proposed temperature distribution and thermal deformation modeling and a thermal-structural coupling simulation analysis method for a motorized spindle system from a vertical machining center. In order to establish an accurate finite element model, the boundary condition of joint thermal contact resistance (TCR) was taken into account. The paper detailed on how to obtain the TCR value which is related to the machine tool parameters by means of experimental and theoretical methods, and carried out a simulation experiment for the purpose of acquiring a quantitative analysis of the TCR influence on the temperature results. In order to verify the effectiveness of the TCR relevant methods, the experiment was carried out. The agreement between the experimental results and the simulation results showed that the proposed method can more accurately simulate the thermal characteristics of the motorized spindle.

An optimised logarithmic discretisation approach for accurate and efficient compact thermal models

The accuracy of building energy simulations is of considerable interest as discrepant results can elicit adverse financial and environment consequences. The physical and temporal scales considered within building energy applications necessitate compact modelling approaches. The prediction accuracy of such simulations is intrinsically linked with the ability to predict the thermal responses of structural elements. The optimal means of representing these components such that accurate solutions are ensured at minimal computational cost remains unclear. The current study seeks to optimise the spatial placement of nodes through assessing and reporting results pertaining to a logarithmic spatial discretisation method. Contour plots are presented to intuitively determine optimal discretisation levels and time steps required for accurate thermal response predictions. This is achieved by comparing numerical solutions of varying discretisation levels with benchmark analytical solutions. Results are reported in terms of governing dimensionless parameters, Biot and Fourier numbers, to ensure generality of findings. Furthermore, spatial and temporal discretisation errors are separated and assessed independently. Finally, models derived using the proposed guidance achieve high levels of prediction accuracy for typically encountered boundary conditions with buildings.

Empirical Parameter Identification for a Hybrid Thermal Model of a High-Speed Permanent Magnet Synchronous Machine

An accurate thermal model will commonly require empirical parameter identification, specifically for the convection coefficients and interface resistances. A high-speed permanent magnet synchronous machine test platform, equipped with various temperature and power measuring equipment, is used to determine these parameters. Specifically, two tests, a dc injection test and rotational test with no load connected, were performed. The results were compared with a lumped thermal model and the parameters updated until an acceptable match was achieved. There were significant differences in the temperature rise when activating forced air cooling, thus significantly influencing the convection coefficients. Also, a significant difference in the interface resistances showed that in these high-speed machines, doing only the dc injection test will not give accurate interface resistance values. The work is novel through combining systematic empirical parameter identification to determine the convection coefficients and interface resistances for a machine where the end windings are cooled by forced tangential air flow.