Traditional Thermal Model Research Articles

Optimal Dispatch of Regional Integrated Heating and Power System Based on Differential Thermal Inertia Model

As the physical carrier of Energy Internet, regional integrated energy system (RIES) has become an important role for improving comprehensive energy utilization efficiency. First, a subtle thermodynamic model of buildings and water-heating network was constructed based on differential thermal inertia model. Different from the traditional single-layer wall thermal inertial model, this paper constructed multi-layer wall thermal model. Then, an optimization scheme combining electricity and heat was established. The optimization results show that, compared with the traditional single-layer wall thermal inertia model, the proposed multi-layer wall thermal inertia model has better performance. The proposed comprehensive energy optimization scheme can reduce the cost of electricity while maintaining indoor comfort, and can provide a reference for the system operation status for distribution network dispatching.

An Efficient Thermal Model for Multifinger SiGe HBTs Under Real Operating Condition

In this work, we present a simple analytical model for electrothermal heating in multifinger bipolar transistors under realistic operating condition where all fingers are heating simultaneously. The proposed model intuitively incorporates the effect of thermal coupling among the neighboring fingers in the framework of self-heating bringing down the overall model complexity. Compared to the traditional thermal modeling approach for an ${n}$ -finger transistor where the number of circuit nodes increases as ${n}^{{2}}$ , our model requires only ${n}$ -number of nodes. The proposed model is scalable for any number of fingers and with different emitter geometries. The model is validated with 3-D thermal simulations and measured data from STMicroelectronics B4T technology. The Verilog-A implemented model simulates 40% faster than the conventional model in a transient simulation of a five-finger transistor.

Thermal Modeling and Calibration Method in Complex Temperature Field for Single-Axis Rotational Inertial Navigation System.

Single-axis rotational inertial navigation systems (single-axis RINSs) are widely used in high-accuracy navigation because of their ability to restrain the horizontal axis errors of the inertial measurement unit (IMU). The IMU errors, especially the biases, should be constant during each rotation cycle that is to be modulated and restrained. However, the temperature field, consisting of the environment temperature and the power heating of single-axis RINS, affects the IMU performance and changes the biases over time. To improve the precision of single-axis RINS, the change of IMU biases caused by the temperature should be calibrated accurately. The traditional thermal calibration model consists of the temperature and temperature change rate, which does not reflect the complex temperature field of single-axis RINS. This paper proposed a multiple regression method with a temperature gradient in the model, and in order to describe the complex temperature field thoroughly, a BP neural network method is proposed with consideration of the coupled items of the temperature variables. Experiments show that the proposed methods outperform the traditional calibration method. The navigation accuracy of single-axis RINS can be improved by up to 47.41% in lab conditions and 65.11% in the moving vehicle experiment, respectively.

Thermal models for avionics pod cabin based on Stochastic Configuration Network (SCN)

Abstract The thermal failure of airborne avionics equipment is not optimistic. It is very necessary to establish relatively accurate thermal models for predicting thermal response of avionics equipment under different flight conditions. Traditional thermal modeling methods are often difficult to obtain accurate temperature response in complex conditions. This has severely restricted the application of these models. However, the Stochastic Configuration Network (SCN) model based on random algorithm can weaken the heat transfer mechanism and pay attention to the mining of experimental data, so that a more accurate thermal relationship might be obtained. In this paper, the SCN was used to analyze the experimental data of the avionics pod with a Ram Air Turbine (RAT) cooling system. The thermal models based on the SCN were finally built for avionics pod. Compared with the commonly used Random Vector Functional Link Network (RVFLN) thermal models, the SCN thermal models not only inherit the advantages of simple network structure and low computational complexity, but also have some merits, such as the better learning performance and the less human intervention. The presented SCN models provide a way to predict the thermal response of avionics pod cabin under the full flight envelope for a fighter.

Modeling and Analysis of Thermal Resistances and Thermal Coupling Between Power Devices

The recent trend in the design of the high-power density power converter generally reduces the rate of the device cooling process. As a result, increased thermal coupling among devices exists. Based on measurements, a thermal coupling resistances network (TCRN) model is proposed in this article. Considering different spacings and current values at a fixed value of case temperature ( ${T} _{\text {c}}$ ), the relationships between the case-to-ambient thermal resistance ( ${R} _{\text {ca}}$ ) of individual power devices and their thermal coupling resistance ( ${R} _{\text {cp}}$ ) to the adjacent device are established. The close correspondence of ${T} _{\text {c}}$ from the calculation of the different spacing and experimental results obtained from a thermal coupling measurement platform confirms the established TCRN model and the relationships. Traditional thermal models do not consider the changes of ${R} _{\text {ca}}$ and also ignore the effect of thermal coupling among the adjacent devices. Compared with these models, the proposed thermal resistances modeling approach provides a better understanding of the thermal behavior of power devices.

A novel sunshine duration-based photothermal time model interprets the photosensitivity of flower maturity of pecan cultivars.

Although it is well-known and established that light plays important roles in plant development, up to now, there is no substantial improvements in how to deal with the light factor of spring phenology under natural condition. By monitoring the local meteorologic data and mature dates of two types (male and female) of flower from four pecan cultivars during 9years, it was found that the complementary pattern of growing degree day and sunshine duration helped to maintain a threshold of driving force related to the maturity of pecan flower during 9years. A novel photothermal time model based on the linear combination of growing degree day and sunshine duration was then proposed and validated to interpret the variance of mature dates of pecan cultivars. Comparative analysis showed that the new model had made extremely significant improvements to the traditional thermal time model. In addition, this model introduced the conversion coefficient K, which quantified the effect of light on the flowering drive, and revealed the differences of base temperature among cultivars. This was the first time that sunshine duration instead of photoperiod was adopted to develop into a verified model on spring phenological event of tree species. It will help to model the spring phenologies of other tree species more reasonably.

MPC-based interval number optimization for electric water heater scheduling in uncertain environments

In this paper, interval number optimization and model predictive control are proposed to handle the uncertain-but-bounded parameters in electric water heater load scheduling. First of all, interval numbers are used to describe uncertain parameters including hot water demand, ambient temperature, and real-time price of electricity. Moreover, the traditional thermal dynamic model of electric water heater is transformed into an interval number model, based on which, the day-ahead load scheduling problem with uncertain parameters is formulated, and solved by interval number optimization. Different tolerance degrees for constraint violation and temperature preferences are also discussed for giving consumers more choices. Furthermore, the model predictive control which incorporates both forecasts and newly updated information is utilized to make and execute electric water heater load schedules on a rolling basis throughout the day. Simulation results demonstrate that interval number optimization either in day-ahead optimization or model predictive control format is robust to the uncertain hot water demand, ambient temperature, and real-time price of electricity, enabling customers to flexibly adjust electric water heater control strategy.

Principles of energy conversion and noise characterization in air ventilation ducts exposed to solar radiation

The aim of this paper is to present a design tool with its guiding principles for energy conversion and noise characterization of an exterior rectangular duct often installed on building envelopes for pre-conditioning of fresh air. The energy conversion in an exterior rectangular duct is a function of solar irradiation, air gap width, mass flow rate and pressure, wall and air temperatures. A generalized two dimensional thermal analysis of an outdoor duct is presented by placement of surface and air nodes into two adjacent stacks of control volumes representing outer and inner walls of duct. A matrix solution procedure is adopted by constituting conjugate heat exchange of conduction, convection, radiation and ventilation heat transport. The rectangular duct model is built by a metallic exterior wall exposed to a steady heat flux generation due to solar heat gain and a well-insulated back panel as its counterpart wall. The model improves the results of traditional thermal models for the cases where: (i) there are sections of conjugate heat exchange; and (ii) stack effect due to thermal buoyancy necessitates 2-D nodal analysis for distant composite nodes. The design tool is supported with some numerical and experimental results of an airflow window with a photovoltaic (PV) solar wall installed in an outdoor test-room. Furthermore, some examples of noise characterization calculations are illustrated using devised noise measurement equations.

A machine learning approach to thermal conductivity modeling: A case study on irradiated uranium-molybdenum nuclear fuels

A deep neural network was developed for the purpose of predicting thermal conductivity with a case study performed on neutron irradiated nuclear fuel. Traditional thermal conductivity modeling approaches rely on existing theoretical frameworks that describe known, relevant phenomena that govern the microstructural evolution processes during neutron irradiation (such as recrystallization, and pore size, distribution and morphology). Current empirical modeling approaches, however, do not represent all irradiation test data well. Here, we develop a machine learning approach to thermal conductivity modeling that does not require a priori knowledge of a specific material microstructure and system of interest. Our approach allows researchers to probe dependency of thermal conductivity on a variety of reactor operating and material conditions. The purpose of building such a model is to allow for improved predictive capabilities linking structure-property-processing-performance relationships in the system of interest (here, irradiated nuclear fuel), which could lead to improved experimental test planning and characterization. The uranium-molybdenum system is the fuel system studied in this work, and historic irradiation test data is leveraged for model development. Our model achieved a mean absolute percent error of approximately 4% for the validation data set (when a leave-one-out cross validation approach was applied). Results indicate our model generalizes well to never before seen data, and thus use of deep learning methods for material property predictions from limited, historic irradiation test data is a viable approach. This work is at the frontier of the evolving paradigm in materials science, where machine learning methods are being applied to material property predictions in lieu of limited experimental data fitted to low-dimensionality phenomenological models. The work presented here aims to demonstrate the potential and limitations of machine learning in the field of materials science and material property modeling.

A Thermal Model of an IGBT Module Taking into Account Thermal Interconnections between Chips

An approach to development of the thermal model of an IGBT module based on the method of electro-thermal analogy is considered. Unlike traditional one-dimensional thermal models, a new model is supplemented with thermal interrelations between chips. The parameters of thermal interrelations were determined using studying the 3D-model of the power module in the ANSYS environment. The proposed model is intended to improve the quality of predicting the temperature of power module chips due to taking into account the thermal mutual influence of the chips.

A thermal model for calculating axial temperature distribution of overhead conductor under laboratory conditions

Most traditional thermal models of overhead conductor neglect the axial temperature distribution along the length of conductor. In order to provide a necessary foundation for calculating axial conductor temperature distribution in real weather conditions, a thermal model is proposed for the axial conductor temperature calculation under laboratory environment. This proposed model is established with the equivalent thermal network, and expressed as a nonlinear state-space equation. An identification method is presented to determine the conductor thermal parameters, which are non-weather related and hardly to be calculated theoretically. The interior-point algorithm is employed to optimize the objective function of parameter identification. The effectiveness of identification method and proposed thermal model are validated by experiment results. This study will provide the basic modelling principle, parameter identification method and conductor thermal parameters to develop the conductor thermal model under the real weather conditions in the future.

Coupled thermal model of wellbore and permafrost in Arctic regions

During drilling operations in the Arctic permafrost region, drilling mud with a higher temperature flowing in the wellbore can lead to thawing of the permafrost. Unpredictable thawing of the permafrost results in severe settling of the wellhead. In addition, the wellbore and permafrost interact as a result of the permafrost thaw. At present, researches on heat and mass transfer in permafrost around the wellbore are very limited. The traditional thermal models for wellbore and formation cannot actual simulate the temperature fields without considering the effect of permafrost thaw. In this regard, a coupled thermal model of the wellbore and permafrost is established considering the latent heat of fusion, water migration, and the change in thermal parameters. In order to reveal the interactions between wellbore and permafrost in Arctic permafrost regions, comparisons between the new model and traditional model are made through a series of numerical simulations. The temperature fields and water content are thoroughly analyzed through a case study. In addition, numerical simulations are carried out to evaluate the effect of cement thermal conductivity on the wellhead stability. The conclusions and suggestions in this paper can provide safety guidance for oil and gas exploration in Arctic regions.

A new cast-resin transformer thermal model based on recurrent neural networks

Abstract Thermal modeling in the transient condition is very important for cast-resin dry-type transformers. In the present research, two novel dynamic thermal models have been introduced for the cast-resin dry-type transformer. These models are based on two artificial neural networks: the Elman recurrent networks (ELRN) and the nonlinear autoregressive model process with exogenous input (NARX). Using the experimental data, the introduced neural network thermal models have been trained. By selecting a typical transformer, the trained thermal models are validated using additional experimental results and the traditional thermal models. It is shown that the introduced neural network based thermal models have a good performance in temperature prediction of the winding and the cooling air in the cast-resin dry-type transformer. The introduced thermal models are more accurate for the temperature analysis of this transformer and they will be trained easily. Finally, the trained and validated thermal models are employed to evaluate the life-time and the reliability of a typical cast-resin dry-type transformer.

New DTR Estimation Method Without Measured Solar and Wind Data

Dynamic thermal rating (DTR) of overhead transmission lines can provide a significant increase in transmission capacity compared to the static thermal rating. However, the DTR are usually estimated by the traditional thermal model of overhead conductor that is highly dependent on the solar, wind speed and wind direction data. Consequently, the estimated DTR would be unreliable and the safety of transmission lines would be reduced when the solar and wind sensors are out of function. To address this issue, this study proposed a novel thermal model of overhead conductor based on the thermal-electric analogy theory and Markov chain. Using this thermal model, the random variation of conductor temperature can be simulated with any specific current level and ambient temperature, even if the solar and wind sensors are out of function or uninstalled. On this basis, an estimation method was proposed to determine the DTR in the form of probability. The laboratory experiments prove that the proposed method can estimate the DTR reliably without measured solar and wind data.

Review of Arc Models in Distribution Networks

The incipient fault in underground cable is recognized as arc fault. Then the arc model selection is very important for the incipient fault detection. The arc features and some typical models have been introduced in detail, including traditional thermal based models, arc models in low voltage and models of arc in long free air. At last, the Kizilcay’s model is recommended to analyze the incipient fault in underground cable for its accuracy and widely utilized. Finally, some conclusions are summarized.

Simulation and experimental study on the thermally induced deformations of high-speed spindle system

In order to avoid the degeneration of high-speed spindle's machining accuracy in actual machining caused by the uneven distribution of temperature field at the design stage, a three-dimensional (3D) finite element analysis (FEA) model, which considered the combined influence of thermal contact resistance (TCR) and bearing stiffness on the accuracy of simulation results, was proposed to conduct transient thermal-structure interactive analysis of motorized spindles. And the method to calculate the boundary conditions used in the FEM model were discussed in detail, such as the heat loads, convective heat transfer, TCR and bearing stiffness. Based on the quasi-static mechanics analysis of rolling bearing, the transfer relationships among multiple variables in the equilibrium equation set of bearings were analyzed. Newton–Raphson algorithm, which regarded the contact angle as the iteration variable, was proposed to calculate the heat power and stiffness of bearings and improve the convergence of the algorithm, and the termination criteria of contact angle's searching trial calculation was proposed to improve the accuracy of the algorithm. The Weierstrass–Mandelbrot (W–M) function in fractal geometry was used to characterize the rough surface morphology of bearing rings. The fractal parameters were identified by the power spectrum method, and a contact deformation model of asperities was developed to calculate the contact parameters used in the TCR modeling. Then, the predictive model for TCR, which considered the combined effect of the morphology of bearing rings and the contact deformation of asperities, was proposed. Thermal equilibrium experiments were conducted to demonstrate the validity of the model. The results showed that the FEA model can accurately simulate the temperature field and thermal deformation of the spindle system and that the FEA model was much more accurate than the traditional thermal model of the high-speed spindle system which ignored TCR and bearing stiffness.

Thermal characteristics analysis and experimental study on the high-speed spindle system

In order to avoid the sudden failure of high-speed spindles in the actual machining process caused by an excessive temperature rise at the design stage, a three-dimensional (3D) finite element analysis (FEA) model was proposed to conduct transient thermal-structure interactive analysis of a high-speed spindle. The FEA model considered thermal contact resistance (TCR) at solid joints and bearing stiffness to improve the accuracy of traditional thermal models which ignored TCR. However, TCRs at solid joints and bearing stiffness were often ignored in traditional thermal models of high-speed spindles. This caused inaccuracies in traditional thermal models. The heat generation of the built-in motor was calculated based on the efficiency analysis method proposed by Bossmanns and Tu [1]. Based on the quasi-static mechanics analysis of rolling bearing, the heat generation and stiffness of bearings were calculated by applying the Newton-Raphson algorithm to improve the convergence. The Weierstrass-Mandelbrot (W-M) function, a function of fractal parameters, was used to characterize the rough surface morphology of bearing rings. The fractal parameters were identified by the structure function method and the measurement data of bearing ring’s surface morphology, and a contact mechanics model was developed to calculate the contact parameters used in the model of TCR. Then, a new predictive model for TCR was proposed based on M-T model. The above boundary conditions were applied to the FEA model, and thermal equilibrium experiments were conducted to validate the effectiveness of the model. The results showed that the FEA model was much more accurate than the traditional model which ignored TCRs at solid joints and bearing stiffness.

Modeling and Optimization of a Steam System in a Chemical Plant Containing Multiple Direct Drive Steam Turbines

Steam systems in some of China’s chemical plants usually contain multiple direct-drive steam turbines that provide mechanical power to pumps/compressors. When optimizing this system, a certain degree of deviation is found in the theoretical models of steam turbines. A more realistic steam turbine model is developed by improving the traditional thermal model using industry data. This model characterizes efficiency variations under different conditions. Boiler and other unit models are then simplified to allow the use of this model in optimization. By incorporating the models, a mixed-integer nonlinear programming (MINLP) model is formulated to perform the operation optimization. The proposed model considers electric power as the alternative energy source for lower-level mechanical power demands. Using the proposed optimization model on an ethylene plant, a maximum of 8.01% reduction in the total operation cost is achieved compared with the original operation strategies. This case study shows a successful application of the MINLP model in optimizing an actual chemical plant.

Thermal modelling and characteristics analysis of high speed press system

The traditional thermal model of presses neglects the effect of roughness between two contact surfaces on the contact conduction coefficient, which causes the analysis to have a low accuracy. The primary objective of this work is to propose an improved thermal model of the high speed press system with the thermal contact resistance between solid joints and the change of the heat generation power with the temperature of bearings considered to analyze its thermal characteristics. Based on the fractal model and the change of the heat generation power, a complete thermal model for the high speed press system is developed. The temperature histories and the time for reaching thermal equilibrium condition of the high speed press system are explored by the finite element (FE) method. The experimental results demonstrate that the complete model is more accurate than the model without considering the thermal contact resistance and the change of the heat generation power. The thermal expansion of the high speed press system is also investigated, which can provide a theoretical basis for design of structure and intelligent lubrication system to improve its precision.

DYNAMIC ENERGY MODELING OF AN EXPERIMENTAL BUILDING EQUIPPED WITH AN UNDERFLOOR AIR DISTRIBUTION (UFAD) SYSTEM

In this study, experimental and simulation studies were performed in a building equipped with an UFAD system in order to investigate the applicability of a new approach designed for prediction of the energy consumption of a residential building with an UFAD system. In this approach, a zonal model, Pressurized zOnal Model with the Air diffuser (POMA), was coupled with a thermal jet model and integrated into a traditional multizone thermal model. The coupled model was verified experimentally. This integrated model has the ability to take into account the characteristics of an UFAD system and thus accurately simulate its energy consumption. A case study was carried out using both approaches: Multi-zone approach and the new developed integrated zonal/jet/multizone model. A quantitative comparison, in terms of energy demand of a building with an UFAD system, shows that the difference can reach 14% and thus indicates that the traditional multizone modeling approach is not appropriate to use for UFAD system in the building energy prediction.