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The real-time thermal–mechanical–frictional coupling characteristics of bearings are critical to the accuracy, reliability, and life of entire machines. To obtain the real-time dynamic characteristics of ball bearings, a novel model to calculate point contact dynamic friction in mixed lubrication was firstly presented in this work. The model of time-varying thermal contact resistance under fit between the ring and the ball, between the ring and the housing, and between the ring and the shaft was established using the fractal theory and the heat transfer theory. Furthermore, an inverse thermal network method with time-varying thermal contact resistance was presented. Using these models, the real-time thermal–mechanical–frictional coupling characteristics of ball bearings were obtained. The effectiveness of the presented models was verified by experiment and comparison.

In the presented paper a thermal network approach was proposed for a quick and accurate study of the thermal performance of two interconnected and unsymmetrically energized switchgears. The method ...

The reliability analysis of traction inverters is of great interest due to the use of new semi-conductor devices and inverter topologies in electric vehicles (EVs). Switching devices in the inverter are the most vulnerable component due to the electrical, thermal and mechanical stresses based on various driving conditions. Accurate stress analysis of power module is imperative for development of compact high-performance inverter designs with enhanced reliability. Therefore, this paper presents an inverter reliability estimation approach using an enhanced power loss model developed considering dynamic and transient influence of power semi-conductors. The temperature variation tracking has been improved by incorporating power module component parameters in an LPTN model of the inverter. A 100 kW EV grade traction inverter is used to validate the developed mathematical models towards estimating the inverter performance and subsequently, predicting the remaining useful lifetime of the inverter against two commonly used drive cycles.

To effectively find an optimal solution in the development process of electrical machines, it is essential to predict machine iron loss and thermal behavior in the predesign stage. Most existing publications only consider thermal behavior by roughly limiting the maximum current density. However, especially for high-speed application, iron loss can also be an essential part in thermal-behavior estimation. This paper proposes a simplified model to efficiently calculate iron loss and thermal behavior in the design procedure of switched reluctance machines. For this purpose, a maximum coenergy loop control method is implemented to facilitate the estimation of flux waveform and machine torque. Three different iron loss calculation methods are compared in terms of accuracy and time. Besides, a simplified lumped parameter thermal network is applied for machine sizing. Furthermore, the impact of number of turns and peak magnetomotive force on the losses and machine sizing is discussed. The efficiency and power density of various machine geometries are comparatively analyzed. Finally, the accuracy of the simplified model is verified by comparison with finite element simulations and experiment results.

As the core components of mechanical power system, triple-phase asynchronous motor and reducer are required strictly for temperature control. In this paper, the triple-phase asynchronous motor and the reducer are regarded as a coupling system, and thermal network method is used to predict the temperature field distribution of the coupling system. The predicted temperature of the thermal network method is consistent with the experimental result and the finite-element analysis. Furthermore, analysis shows that motor output power, coefficient of friction between teeth and lubricating oil parameters have a great effect on reducing the temperature of the coupling system.

Thermal management is a critical task of battery control to ensure the safe, efficient, and enduring performance of the battery system, which can be considered as an interconnected thermal network of cells. The basis of thermal management is the estimation of temperature and its gradient across the battery system, which has received extensive attention in the literature. However, existing works neglect two important constraints in practical battery systems: 1) limited number of available sensors and 2) presence of system uncertainty such as parameter error. This paper is the first to investigate robust battery system temperature estimation under sparse sensing and system uncertainty. We first propose a framework consisting of optimization problems at three different levels: 1) evaluation of the worst case estimation performance (error) under uncertainty; 2) robust observer design to minimize the worst case error; and 3) optimization of sensor locations. Two robust estimation methods are then used to solve the problem. The system uncertainty considered in this paper is the unknown resistance variability among battery cells, but the methodology can be applied to address other types of uncertainty. It is shown that the designed observers could guarantee and improve the robustness and reliability of estimation by significantly reducing the worst case estimation errors induced by uncertainty.

In this paper, a new temperature observer topology is presented which overcomes the shortcomings of previous ones and achieves a higher accuracy, and a more robust disturbance rejection. It makes use of the Gopinath-style flux observer and combines a lumped-parameter thermal network operating at low speeds and a flux-based permanent magnet temperature observer operating at medium and high speeds. Simulation and experimental results on a 50 kW permanent magnet motor show a performance enhancement over standard topologies; particularly, a superior disturbance rejection to voltage estimation errors. A detailed analysis of the optimal controller tuning is also presented. Furthermore, a Kalman filter is incorporated to account for sensor noise and model uncertainties. Experimental results show an effective fusion of independent temperature estimation methods leading to a superior accuracy compared to the previously investigated approaches. Moreover, the Kalman filter-based fusion offers the capability of detecting temperature-related system failures, e.g., cooling circuit malfunctions.

It is difficult to meet the engineering requirements for a high-torque-density direct drive (HTDDD) permanent magnet synchronous machine (PMSM) using the thermal load design method for traditional motors. In this study, a waterway structure design method for a HTDDD PMSM depending on the motor thermal capacity is proposed based on a temperature cycling module. The design program of the motor waterway structure is based on a waterway thermal calculation and an equivalent thermal network method. The waterway structure can be rapidly designed by cyclic checking of the temperature at each node in the motor. A fluid-solid coupled temperature field simulation is used to analyze the influences of the thermal capacity, the waterway structure, and the water velocity on the temperature rise and the cooling effect of the motor. Finally, the algorithm is used to create a prototype, and the proposed method is experimentally validated.

The flexible generation of electrical energy is heavily dependent on hydropower. Advanced control systems may offer an enhanced operational security. During power system disturbances, a generator may need to operate beyond its limits to maintain a stable operation. This increased performance can be obtained if the total thermal capacity of the generator is exploited by applying optimal control theory in the automatic voltage regulator. A low-order model is required for online implementation and prediction of critical thermal stresses in the machine. Periodic extension of the generator capability diagram has been historically overlooked. In this paper, a thermal network for air-cooled hydrogenerators for use in real-time monitoring and optimal control is proposed and validated. The thermal model is extended with a heat exchanger model. The dynamic model is developed from an energy balance of the system and verified through finite-element method (FEM) simulations and temperature measurements in a case study. Transient simulation results of the online low-order model are in good agreement with measurements. Finally, a satisfactory simulator speed is obtained which confirms the feasibility of this approach in real-world applications.

Double U-pipe boreholes are the second most used type of ground heat exchangers after single U-pipe boreholes. The borehole thermal resistance is a key design and performance parameter for the doub...

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.

This research aims to bring together thermal modelling and machine learning approaches to improve the understanding on the operation and fault detection of a wind turbine gearbox. Recent fault detection research has focused on machine learning, black box approaches. Although it can be successful, it provides no indication of the physical behaviour. In this paper, thermal network modelling was applied to two datasets using SCADA (Supervisory Control and Data Acquisition) temperature data, with the aim of detecting a fault one month before failure. A machine learning approach was used on the same data to compare the results to thermal modelling. The results found that thermal network modelling could successfully detect a fault in many of the turbines examined and was validated by the machine learning approach for one of the datasets. For that same dataset, it was found that combining the thermal model losses and the machine learning approach by using the modelled losses as a feature in the classifier resulted in the engineered feature becoming the most important feature in the classifier. It was also found that the results from thermal modelling had a significantly greater effect on successfully classifying the health of a turbine compared to temperature data. The other dataset gave less conclusive results, suggesting that the location of the fault and the temperature sensors could impact the fault-detection ability.

In district energy applications, implementation of management strategies is crucial to achieve reductions in primary energy consumption and carbon dioxide emissions. The development of optimization tools to upgrade the operation of smart energy systems should take into account all the relevant elements of these complex infrastructures. In this paper, a global optimization approach, applied to district heating, cooling and electricity networks interconnected to each other, is proposed. The suggested approach combines the optimization of the production side, useful to understand how it is convenient to produce heat, cold and electricity, with demand-side management for district heating customers. This is reached by using a bi-level optimization structure, exploiting the genetic algorithm and linear programming. A physical model of the district heating network is included in the procedure to accurately reproduce the effects of demand-side management. The tool can be applied to different objective functions. In this paper, a multi-objective optimization is carried out with two different objective functions: the operation cost and the carbon dioxide emissions. Results show that, by choosing an intermediate trade-off among the two goals, it would be possible to have a 12% reduction in the emissions at the expense of a 25% increase in the operating cost.

© 1982-2012 IEEE. Multisector machines reveal a high fault-tolerant capability, since failure events can be isolated by de-energizing the faulty sector, while the healthy ones contribute in delivering the required power. This article is focused on the thermal analysis of multisector three-phase machines in healthy and faulty operations. First, a 3-D lumped parameter thermal network (LPTN) of a single sector is developed and finetuned against experimental data, through a genetic algorithm for identifying the uncertain parameters. According to the operating conditions, the varying housing surface temperature affects the heat exchanged to the ambient. Hence, an analytical formula is proposed to adjust the natural convection coefficient value depending on the operating condition. Then, the 3-D LPTN, modeling the whole machine, is built aiming at investigating the thermal behavior during faulty conditions. Finally, the complete 3-D LPTN is employed for predicting the machine thermal performance under several faulty conditions. Furthermore, the current overload experienced by the healthy sector (in order to keep the same torque level as during the pre-fault operation) is determined, in accordance with the magnet wire thermal class. The effectiveness of the 3-D LPTN in predicting the temperature is experimentally demonstrated.

An important trend of Building Integrated Solar Thermal (BIST) system is to improve the aesthetic aspect of the solar collector to meet the requirement of architectural style and energy collection. Painting on the glass cover in an appropriate method is a simple and practical way. In this study, a halftone coating was used to print a red brick wall pattern on the glass cover. A series of comparative experiments were carried out to test the effect of the coating on the thermal behavior of the solar collector. In heat collection processes, compared with the solar collector with blank cover plate, the one with coated cover plate has lower absorber plate temperature and higher cover plate temperature. The lower the solar irradiance, the smaller the effect of color coating on the solar collector. Compared with the uncoated surface, the coated surface is more sensitive to solar irradiation. In the same heat collection process, compared with the solar collector coated on the outer surface of the cover plate, the one coated on the inner surface has 0.8 &deg, C higher heat absorber plate temperature and 5% lower top heat loss.

A novel winding embedded liquid cooling (WELC) concept is proposed to improve thermal management of high power density slotless motor. This concept introduces thermal plastic based liquid cooling channels through the non-magnetic thermal plastic winding support for slotless motor. A comprehensive analysis on key thermal paths from all thermal sources to the ambient for the proposed WELC concept is presented. This paper demonstrates the efficacy of the WELC concept for a 120 kW slotless motor using computational fluid dynamics (CFD). A lumped parameter thermal network (LPTN) has also been developed for the WELC concept which has been validated using CFD analysis. It is found that the WELC concept can achieve a continuous current density of 23.3 A(rms)/mm2, which is 50% higher than that of a conventional axial water jacket cooling.

This paper quantifies the accuracy of thermal stress modeling in PV inverters under real mission profile operation. The estimated thermal stress profiles obtained from a lumped thermal network under one-day mission profiles are compared with the experimental measurement. According to the results, the average estimation error is well below 1.5 % even under highly dynamics mission profile conditions.

To aid in the integration of renewable and residual energy sources in the energy system, energy flexibility is required. By charging and discharging energy storage, energy flexibility can be created and heat demand and heat generation can be matched in time. One possible source of energy flexibility is the thermal capacity of the water in district heating network pipes. Effective use of this thermal energy storage requires efficient techniques to determine the available flexibility. The goal of this paper is to determine the required level of detail of a substation model to characterise network flexibility through simulation. The substation models differ in the assumptions that are made and range from a detailed, non-linear model to a simple, linear model. To analyse the results, we identify different phases occurring during a network flexibility activation. By determining if reduced models are as effective in reproducing important flexibility characteristics as more detailed and computationally expensive models, network flexibility characterisation can be simplified and sped up. Results show that the network flexibility can be adequately characterised even with very simple models, provided correct assumptions are made.

International audience; The concept of a single-input/multi-output thermal network was proposed by the Development of Libraries of Physical models for an Integrated design environment (DELPHI) consortium more than twenty years ago. The present work highlights the recent improvements made to efficiently derive a low-computing-effort model from a fully detailed numerical model and to characterize its performances. The temperature predictions of a deduced ball-grid-array (BGA) dynamic compact thermal model are compared to those of a realistic three-dimensional representation, including the large set of internal copper traces, as well as its board structure, which has been validated by experiment. The current study discloses a method for creating an amalgam reduced-order modal model (AROMM) for that electronic component family that allows the preservation of the geometry integrity and shortening scenarios computation. Typically, the AROMM method reduces by a factor of 600 the computation time needed to obtain the solution while keeping the error on the maximum temperature below 2%. Then, a meta-heuristic optimization is run to derive a more practical low-order resistor capacitor model that enables a thermo-fluidic analysis at the board level. Based on the calibrated numerical model, a novel AROMM method was investigated in order to address the chip behavior submitted to multiple heat sources. The first results highlight the capability to enforce a non-uniform power distribution on the upper surface of the silicon chip. Thus, the chip design layout can be analyzed and optimized to prevent thermal and reliability issues.

The volume of magnetic components for space applications, directly related to the launch cost, and their performance are critically influenced by their capability to dissipate the internal electromagnetic power losses. This is the reason why accurate thermal models are required. Furthermore, these models need to be simple and versatile to allow a fast analysis of many different designs. In this study, a specific methodology to analyze inductors and transformers by thermal networks and a simplification based on the Thevenin&rsquo, s theorem leading to a simple equation are proposed. The specific details to address thermal modelling for space magnetic components are discussed. A generic 50 W Flyback transformer for space applications is analyzed in this study and experimental validation is provided, showing that the proposed method leads to a deviation in the temperature estimation between 1 ∘ C and 5 ∘ C, which is considered quite a good result from the literature review carried out.

Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 ∘ C, considering one year of data with different weather conditions.

This study presents results of modelling of heat transfer in low-power synchronous motors with internal permanent magnets (IPM). The analysis is conducted on the basis of equivalent thermal network as well as the magnetostatic finite element analysis for determination of usual electromagnetic loss components. The approach which consistently combines the two mentioned models allows for effective analysis of heat transfer in the machine at the designing stage. Special attention is focused on the method for homogenisation of a mush-wound winding and sheet stack. The results of computer simulations are positively verified by measurements carried out on the physical motor.

Nowadays, the development of electric vehicle equipped with a two-speed automatic transmission has become a hotspot. As well known, the automatic transmission operates with power loss including gear meshing loss, bearing loss, and oil churning loss. This paper focuses on the bulk temperature prediction of a two-speed automatic transmission using thermal network method. An integrated model, including an efficiency model and a heat balance model, is proposed, which makes it possible to predict power loss, bulk temperature, and temperature distributions under different conditions. In the efficiency model, each part of power losses from gear meshes is studied to calculate the summation of mechanical power losses in the transmission, including losses of gear meshing, bearing and oil churning. In the heat balance model, the entire gearbox is divided into elements with a uniform temperature connected by thermal resistances which account for conduction, convection, and radiation, based upon the first law of thermodynamics for transient conditions. The effectiveness of bulk temperature prediction using thermal network method is validated by the comparison between simulation results and the experimental data. Consequently, this study on heat transfer characteristics, thermal characteristics, and bulk temperature prediction of the two-speed automatic transmission has significant academic and application values.

Traditional overload protection depends on pre-set values and cannot reflect the current tolerance capability of the line. If the current of a line exceeds the setting value of its overload protect...

Many convection problems entail more than two isothermal boundaries. In previous work, a resistor-network model was proposed for this class, multi-temperature convection problems. A technique dubbed dQdT was also developed to obtain the paired convective resistances that characterize the thermal network of a multi-temperature convection problem. In the present paper an extension of the Newton law of cooling is proposed as a general formulation of multi-temperature convection in terms of multiple driving temperature differences. Most notably, the proposed formulation eliminates the need for an effective temperature difference. The formulation is characterized by functionality coefficients which give the relation between a heat transfer rate and one of the temperature differences. These coefficients can be obtained using the dQdT technique. The new formulation and the application of the dQdT technique are demonstrated for classical three-temperature convection problems. The connection between the extended Newton formulation and the resistor-network model of multi-temperature convection is also discussed. It is shown that dQdT can be used to determine the applicability of the resistor-network model of convection.

High performance computing promotes the solution of large mathematical models of complex mechanical systems and lays a foundation for the theoretical analysis of complicated and difficult problems in the mechanical field. At the same time, the high reliability transmission of wireless data makes it possible for a variety of motion state mechanical systems to measure and control. In this paper, we present the energy loss model of the wet axle transmission system based on bond graph theory, and obtain the heat source boundary condition of the thermal analysis of the wet axle by calculating the mathematical model of the energy loss. Moreover, thermal network method is proposed to analysis heat accumulation and disequilibrium of multistage closed wet axle, by analyzing heat source distribution and heat transfer characteristics among machine elements, oil and air, finally the mathematic model of thermal analysis is established. The thermal numerical calculation of the thermal network for the complex mechanical system and the verification of the whole vehicle test for the results of the thermal calculation have been realized, then thermal optimization is done for wet axle heat equilibrium. Theory and experiment results indicate that heat accumulation in wet hub and heat disequilibrium of different parts are severe especially under efficient operating condition; thermal network calculation and optimization results verified by real vehicle experiment are true and valid. The research proves that high performance computer and high reliable wireless data transmission technology have promoted the optimization design of complex mechanical system.

The flat-plate solar collector has been treated as a regulation object. An analog model of dynamics of the collector, based on the equivalent thermal network (ETN) method, has been proposed. The solar collector has been presented as an electrical circuit and has been calculated as a four-terminal network. Model was based on the construction and operating parameters of the collector, which allowed to shape the dynamics of work already at the stage of its design. An innovative solution has been proposed that has not yet appeared in the subject. The analog model has been verified on the digital model determined by experiment with using the parametric identification method. The models are to be used in designing of systems and algorithms for controlling of operation of hybrid supply systems. Transfer functions and step responses for both collector models (analog and digital) has been analyzed.

Rolling element bearing is an essential component in mechanical transmission because it reduces friction between two rotating parts. Two main approaches to evaluate power losses are proposed in literature: (i) global engineering models using few input data; (ii) local models which are more accurate but need much more information on rolling element bearing geometry. Based on thermal network approach, an intermediate model is developed in this study. This new model allows obtaining lumped information (temperature of rings) with a minimum of input data (external geometry only) and by using global power loss models. This intermediate model is developed for angular contact ball bearing under oil jet lubrication for high speed application. Thermal network results are compared with experimental findings.

A ventilated wall module is an outdoor air-intake device that can clean the drawn air, reduce heat loss or gain, and insulate outdoor noises. It contains a solar board, air cavities, and an air fil...

Energy conversion and management : ECM 151, 158-169 (2017). doi:10.1016/j.enconman.2017.08.072, Published by Elsevier Science, Amsterdam [u.a.]

Nanofluids have found crucial presence in heat transfer applications with their promising characteristics that can be controlled as per requirements. Nanofluids possess unique characteristics that have attracted many researchers over the past two decades to design new thermal systems for different engineering applications. Mono nanofluids, prepared with a single kind of nanoparticles, possess certain specific benefits owing to the properties of the suspended nanoparticle. However to further improve the characteristics of nanofluids, that could possess a number of favourable characteristics, researchers developed a new generation heat transfer fluid called hybrid nanofluid. Hybrid nanofluids are prepared either by dispersing dissimilar nanoparticles as individual constituents or by dispersing nanocomposite particles in the base fluid. Hybrid nanofluids may possess better thermal network and rheological properties due to synergistic effect. Researchers, to adjudge the advantages, disadvantages and their suitability for diversified applications, are extensively investigating the behavior and properties of these hybrid nanofluids. This review summarizes the contemporary investigations on synthesis, thermo-physical properties, heat transfer characteristics, hydrodynamic behavior and fluid flow characteristics reported by researchers on different hybrid nanofluids. This review also outlines the applications and challenges associated with hybrid nanofluid and makes some suggestions for future scope of research in this area.

Optimal design and operation of district heating networks require accurate, but simple models to allow fast simulation. This paper describes the analytical derivation of such a model based on existing work regarding heat loss and dynamic temperature profile calculations in literature. The most important addition from this work is the incorporation and investigation of heat transfer from supply to return side in double pipes, something which is often (over)simplified in more commonly used models. The paper presents the mathematical derivations and the assumptions made in detail for the case of steady-state heat losses in double pipes. The resulting model is carefully examined in a parameter study, from which a number of interesting conclusions can be drawn. Firstly, the heat losses are found to be nearly independent of the mass flow rate in the range of mass flow rates usually encountered in thermal network pipes. The remaining heat loss calculation is simply based on temperature levels and thermal resistance factors, determined by the pipe dimensions and materials. Furthermore, it is found that heat losses from supply to return side should be incorporated in the analysis for better accuracy of the results, even more so with the increasing popularity of twin pipes with common insulation. ispartof: International Journal of Thermal Sciences vol:117 pages:316-327 status: published

District heating networks play an important role in the heating and cooling sector, serving up to 60% of the citizens in some countries. The availability of a thermal network supplying multiple users allows producing heat from different sources and multiple technologies. The possibility of relying on different solutions allows the system manager to optimize the heat generation by choosing the best unit for each operation condition. This choice is based on a deep knowledge of heat load profiles, that are related to users’ behavior, network performances and control logics. This paper provides an analysis of a DH system operation over ten heating seasons, with the aim of highlighting the main characteristics of the heat load variations and finding the fundamental drivers for heat load prediction. Although the system has seen a significant development throughout the years, the specific energy consumption has been found to be comparable on the whole duration of the analysis. Two main patterns are highlighted, based on the different operation settings along the hours of the day and the outdoor temperature as the main weather driver for building's heat demand.

A novel CHP configuration is presented, which is fueled by low-temperature geothermal energy and delivers heat to a district heating (DH) system.This so-called “Preheat-parallel” configuration has a higher net electrical power output ( W net ) and a higher exergetic plant efficiency ( η ex ) than the convenient series and parallel configurations for the connection to a state-of-the-art 75/50 DH system.For the considered cases, W net and η ex are 1.3–6.4% and 0.4–1.9%-pts higher than for the parallel configuration, respectively.The highest values correspond to the highest heat demand.With respect to the series configuration W net and η ex are 2.1–9.9% and 0.7–3.0%-pts higher, respectively, where the highest values correspond to the lowest heat demand.Furthermore, the optimal CHP configuration - series, parallel or “Preheat-parallel” - is discussed.The optimal configuration depends on the DH system requirements.Supply and return temperatures in the range of T supply = 40 – 110 ° C and T return = 30 – 70 ° C are considered.We conclude that the series and parallel configurations have the best performance for the connection to low-temperature and high-temperature DH systems, respectively.However, for a wide range of T supply and T return , the “Preheat-parallel” configuration is the most appropriate.The preheating-effect is the main feature of the “Preheat-parallel” configuration, and is more useful for a large temperature difference T supply - T return and for low values of T return .Furthermore, we found that for high heat demands and small temperature differences T supply - T return , the “Preheat-parallel” or series configurations might perform better than the parallel configuration for the connection to a high-temperature DH system.

Modelling, simulation and optimization of an isolated building separated from the district in which they operate is no longer of interest as a view point of improved efficiency, economic benefits and exploitation of renewable energy resources. Instead, district energy systems have the capacity to obtain several benefits, regarding the practical, environmental and safety by taking advantage of large poly-generation energy conversion technologies. The use of optimization techniques to design such high-efficient systems is strongly motivated by minimizing of the cost for the required infrastructures, minimizing emission, and maximizing the generation or efficiency but is particularly challenging because of the technical characteristics and the size of the real world applications. In this paper, different types of optimization problems, constraints and techniques as well as the optimization tools used in district energy systems are discussed.

In view of the time-varying complexity of the heat source for the ball screw feed system, this paper proposes an adaptive inverse problem-solving method to estimate the time-varying heat source and temperature field of the feed system under working conditions. The feed system includes multiple heat sources, and the rapid change of the moving heat source increases the difficulty of its identification. This paper attempts to develop a numerical calculation method for identifying the heat source by combining the experiment with the optimization algorithm. Firstly, based on the theory of heat transfer, a new dynamic thermal network model was proposed. The temperature data signal and the position signal of the moving nut captured by the sensors are used as input to optimize the solution of the time-varying heat source. Then, based on the data obtained from the experiment, finite element software parametric programming was used to optimize the estimate of the heat source, and the results of the two heat source prediction methods are compared and verified. The other measured temperature points obtained by the experiment were used to compare and verify the inverse method of this numerical calculation, which illustrates the reliability and advantages of the dynamic thermal network combined with the genetic algorithm for the inverse method. The method based on the on-line monitoring of temperature sensors proposed in this paper has a strong application value for heat source and temperature field estimation of complex mechanical structures.

Prediction of junction temperatures in power semiconductor modules is essential to improve reliability of the device and prevent module failures due to thermal stress. Lumped parameter network is a popular approach for temperature modeling. Calibration of the thermal model is based on thermal measurements of the junction temperatures that are difficult to obtain. We aim to combine the knowledge of internal model structure and as little measurements as possible. Specifically, we use a state space thermal model with structure determined by the module layout, and propose to use the Expectation-Maximization algorithm from that can utilize data from different incomplete experiments. The identification procedure is introduced in detail in this paper and the applicability of the proposed approach is demonstrated on simulated and experimental data.

Under abnormal electrical supply conditions, induction motors can easily reach an overheating condition. A continuous operation under thermal stress significantly decrements the motors useful lifetime. In this paper a short-time thermal transient analysis, based on a Lumped Parameter Thermal Network (LPTN), is performed in order to evaluate the thermal behaviour of an asynchronous machine operating under supply voltage unbalance. Three types of supply unbalance are considered in this paper: unbalanced voltage amplitude, unbalanced phase, and a combination of both. The analytical model is used to estimate the temperature in different points of the stator. This model presents a reasonable compromise between simplicity, accuracy, and rapidity. The obtained results are validated using an experimental prototype.

Multi-domain electro-thermal-optical models of LEDs are required so that their thermal and optical behavior may be predicted during a luminaire design process. Today, no standardized approach exists for the extraction of such models. Therefore, models are not readily provided by LED suppliers to end-users. This results in designers of LED-based luminaires wasting time on LED characterization and ad hoc model extraction themselves. The Delphi4LED project aims to address these deficiencies by identifying standardizable methodologies to extract both electro-optical and thermal compact models of LEDs that together can be used in a multi-domain simulation context. This article describes a methodology to extract compact thermal models of LEDs that are dynamic, in that they accommodate transient thermal effects, and are boundary condition-independent, in that their accuracy is independent of their thermal operating environment. Such models are achieved by first proposing an equivalent thermal nodal network topology. The thermal resistances and capacitances of that network are identified by means of optimization so that the transient thermal response of the network matches that of either an equivalent calibrated 3D thermal model or a transient thermal measurement of a physical sample. The accuracy of the thermal network is then verified by comparing the thermal compact model with a 3D detailed model, which predicts thermal responses within a 3D system-level model.

This paper presents an analytical thermal model of a synchronous reluctance motor (SynRMs) to predict the temperature of its different parts. For the analytical calculation, a lumped parameter thermal network (LPTN) is developed and analyzed. To validate the developed model, measurements are carried out on an experimental setup including the analyzed machine. Finally, the test results are compared with the analytical results to assess the accuracy of the model.

In an electrical machine design, thermal management plays a key role in improving the performance and reducing size. End-windings are commonly identified as the machine hot-spot. Hence, lowering and predicting end-windings temperature are crucial tasks in thermal management of electrical machines. This paper proposes and investigates a noninvasive but effective cooling method that aims for a uniform cooling of a machine's winding by implementing direct cooling on its end-windings. Modeling and experimental results show that a 25% hot-spot temperature reduction on a particular application can be achieved. To analyze the proposed technique in detail, an accurate but computationally economic lumped parameter thermal network is developed. Comparison between a “standard” thermal network and its simplified equivalent (with less nodes) is presented where the models are developed and fine-tuned based on experimental data. All the above is used to investigate the potential of the proposed end-winding cooling method with different configurations of the methodology.

The high temperature rise of grease lubricated angular contact ball bearing under high speed operation will affect the working accuracy of the bearing, and even lead to the loss of accuracy. In this paper, a friction heat generation model for high-speed grease lubricated angular contact ball bearing was established. Based on the quasi-static analysis model, the thermal expansion of the bearing components is brought into the quasi-static equilibrium equation, and the modified quasi-static analysis model of high-speed grease lubricated ACBB is obtained. Under grease lubrication conditions, a local bearing heat-generation model was employed to assess power losses in different contact zones, in which bearing contact parameters, external loads, and rotation speeds conditions were fully considered. Moreover, the temperature distribution of grease lubricated high-speed bearing was analyzed by the multi node thermal network method. Through the analysis model of bearing dynamic and thermal characteristics considering the influence of thermal expansion established, bearing contact parameters have significant differences. The calculated values of outer ring temperature of grease lubricated angular contact ball bearing is in good agreement with the experimental values. The model can predict the temperature values of grease lubricated angular contact ball bearing under axial load at high speed.

The proper planning of rest periods in response to the availability of parking spaces at rest areas is an important issue for haulage companies as well as traffic and road administrations. We present a case study of how You Only Look Once (YOLO)v5 can be implemented to detect heavy goods vehicles at rest areas during winter to allow for the real-time prediction of parking spot occupancy. Snowy conditions and the polar night in winter typically pose some challenges for image recognition, hence we use thermal network cameras. As these images typically have a high number of overlaps and cut-offs of vehicles, we applied transfer learning to YOLOv5 to investigate whether the front cabin and the rear are suitable features for heavy goods vehicle recognition. Our results show that the trained algorithm can detect the front cabin of heavy goods vehicles with high confidence, while detecting the rear seems more difficult, especially when located far away from the camera. In conclusion, we firstly show an improvement in detecting heavy goods vehicles using their front and rear instead of the whole vehicle, when winter conditions result in challenging images with a high number of overlaps and cut-offs, and secondly, we show thermal network imaging to be promising in vehicle detection.

A thermomechanical model of the FZG test rig is presented. The numerical model is based on the thermal network method and takes into account power losses due to tooth friction, rolling element bearings (REBs), oil churning, and shaft seals. Some measurements underline that REB rings run at different temperatures. To investigate this difference, several REB models are proposed and compared to measurements. Their influence on the global thermal behavior of the gear unit is discussed and analyzed.

District heating systems provide the heat generated in a centralized location to a set of users for their residential and commercial heating requirements. Heat distribution is generally obtained by using hot water or steam flowing through a closed network of insulated pipes and heat exchange stations at the users’ locations. The use of optimization techniques for the strategic design of such networks is strongly motivated by the high cost of the required infrastructures but is particularly challenging because of the technical characteristics and the size of the real world applications. We present a mathematical model developed to support district heating system planning. The objective is the selection of an optimal set of new users to be connected to an existing thermal network, maximizing revenues and minimizing infrastructure and operational costs. The model considers steady state conditions of the hydraulic system and takes into account the main technical requirements of the real world application. Results on real and randomly generated benchmark networks are discussed.

Transient analysis by thermal network method for complex structures like the spindle-bearing system is essential but insufficient. In this paper, the network approach was developed for spindle transient analysis in consideration of thermal-structure interaction. Firstly, the radial and axial deformation of spindle system during assembling process, deformation by thermal extension and centrifugal effect were all obtained. Then the transient analysis was deduced based on traditional steady model, the thermal-deformation coupling and some other time-varying parameters. Experiment results indicate that temperature by steady model is of large deviation, while by the transient model is much more accurate. Finally, the temperature rising curves, the balance time of the spindle system and temperature rise feature for continuous working conditions were all achieved and discussed.

Nowadays, flexibility through energy storage constitutes a key feature for the optimal management of energy systems. Concerning thermal energy, Latent Heat Thermal Storage (LHTS) units are characterized by a significantly higher energy density with respect to sensible storage systems. For this reason, they represent an interesting solution where limited space is available. Nevertheless, their market development is limited by engineering issues and, most importantly, by scarce knowledge about LHTS integration in existing energy systems. This study presents a new modeling approach to quickly characterize the dynamic behavior of an LHTS unit. The thermal power released or absorbed by a LHTS module is expressed only as a function of the current and the initial state of charge. The proposed model allows simulating even partial charge and discharge processes. Results are fairly accurate when compared to a 2D finite volume model, although the computational effort is considerably lower. Summarizing, the proposed model could be used to investigate optimal LHTS control strategies at the system level. In this paper, two relevant case studies are presented: (a) the reduction of the morning thermal power peak in District Heating systems, and (b) the optimal energy supply schedule in multi-energy systems.

This paper proposes an approach to develop building dynamic thermal models that are of paramount importance for controller application. In this context, controller requires a low-order, computationally efficient, and accurate models to achieve higher performance. An efficient building model is developed by having proper structural knowledge of low-order model and identifying its parameter values. Simplified low-order systems can be developed using thermal network models using thermal resistances and capacitances. In order to determine the low-order model parameter values, a specific approach is proposed using a stochastic particle swarm optimization. This method provides a significant approximation of the parameters when compared to the reference model whilst allowing low-order model to achieve 40% to 50% computational efficiency than the reference one. Additionally, extensive simulations are carried to evaluate the proposed simplified model with solar radiation and identified model parameters. The developed simplified model is afterward validated with real data from a case study building where the achieved results clearly show a high degree of accuracy compared to the actual data.

Many research works have demonstrated that taking the combined cooling, heating and power system (CCHP) as the core equipment, an integrated energy system (IES), which provides multiple energy flows by a combination of different energy production equipment can bring obvious benefit to energy efficiency, CO2 emission reduction and operational economy in urban areas. Compared with isolated IES, an integrated energy micro-grid (IEMG) which is formed by connecting multiple regions’ IES together, through a distribution and thermal network, can further improve the reliability, flexibility, cleanliness and the economy of a regional energy supply. Based on the existing IES model, this paper describes the basic structure of IEMG and built an IEMG planning model. The planning was based on the mixed integer linear programming. Economically, construction planning configuration are calculated by using known electricity, heating and cooling loads information and the given multiple equipment selection schemes. Finally, the model is validated by a case study, which includes heating, cooling, transitional and extreme load scenarios, proved the feasibility of planning model. The results show that the application of IEMG can effectively improve the economy of a regional energy supply.