Adaptive Thermal Model Research Articles

Adaptive thermal model for real-time peak temperature and cooling rate prediction in laser material processing

Laser materials processing (LMP) is highly sensitive to small disturbances in the process inputs, resulting in changes in the thermal dynamics, particularly, in the peak temperature and cooling rate. These thermal dynamic changes in turn vary the consistency of the material and the mechanical properties of the processed material. There are numerous models to estimate and control these thermal dynamics in LMP processes. However, none of these existing models can deal with thermal dynamic changes in real-time. In this research, an adaptive thermal model is developed that can deal with disturbances in real-time. A two-dimensional thermal model of the laser heat treatment (LHT) process is developed for real-time prediction of the peak temperature and cooling rate. In addition, an optimization algorithm is developed to make the thermal model adaptive to thermal dynamic changes in real-time. The adaptive thermal model has been implemented on an LMP setup, and multiple LHT experiments have been performed to study and validate the performance of the adaptive thermal model. Results show that the adaptive thermal model can effectively deal with the thermal dynamic changes in real-time, resulting in accurate peak temperature and cooling rate predictions during LHT. This work can be considered as an important step towards the development of accurate and computationally efficient models for real-time prediction and control of LMP.

Adaptive thermal model for loading of transformers in low carbon electricity distribution networks

The uptake of low carbon technologies, particularly Electric Vehicles (EVs) and Heat-Pumps (HPs), at the low voltage (LV) distribution network, in the quest of cutting down on greenhouse gas (GHG) emissions in the transportation and residential sectors, has the potential to cause general load increase and may lead to higher and longer peak load demand. This development can, as hinted in previous studies, pose a real challenge of capacity overloading to transformers at the LV distribution network of electricity system. Prolonged periods of transformer overloading could lead to premature transformer failure and shortens transformer's life expectancy. A direct solution to addressing transformer overloading is the upgrading of the transformer capacity. However, the number of LV distribution transformers in electricity system to be upgraded and the resources needed for such operation make the solution less desirable to the Distribution Network Operators (DNOs). Therefore, it is important to develop cost-effective solutions for the optimal utilization of the existing transformer capacity. Adaptive thermal loading of transformers is one of such solutions. This paper focusses on the Adaptive Thermal Loading (ATL) of transformers in LV distribution networks with considerable penetration level of EVs and HPs. The thermal model of a 500-kVA, 11/0.415-kV (no load), 50-Hz, Dyn11, ONAN mineral oil filled, free breathing, ground mounted transformer serving a real and typical urban LV network in the United Kingdom (UK) is developed based on IEC 60,076–7:2005 standard and used as the case study. A method of adaptive thermal loading of the transformer is presented to examine its capacity performance when serving the future load of the LV network following the integration of projected uptake figures of EVs and HPs for the years 2020, 2030, 2040 and 2050 into the network. Given the load and temperature forecasts of a day, the method aims at optimizing, considering the real and present conditions of the operating environment, the overall daily transformer capacity utilization that gives maximum daily return on investment without undermining reliability of supply and normal life expectancy of the transformer. Results show improved performances of the transformer when the adaptive thermal loading method is used.

Reducing the Operating Energy of Buildings in Arid Climates through an Adaptive Approach

Due to its excessive energy consumption, the building sector contributes significantly to greenhouse gas (GHG) emissions. The type of thermal comfort models used to maintain the comfort of occupants has a direct influence on forecasting heating and cooling demands and plays a critical role in reducing actual energy usage in the buildings. In this research, a typical residential building was simulated to compare the heating and cooling loads in four different Jordanian climates when using an adaptive thermal model versus the constant setting of temperature limits for air-conditioning systems (19–24 °C). The air-conditioning system with constant temperature settings worked to sustain thermal comfort inside the building, resulting in a significantly increased cooling and heating load. By contrast, significant energy savings were achieved using the temperature limits of an adaptive thermal model. These energy savings equated to 1533, 6276, 3951, and 3353 kWh, which represented 29.3%, 80.5%, 48.5%, and 67.5% of the total energy used for heating and cooling for zones one, two, three, and four, respectively.

Field study on adaptive thermal comfort models for nursing homes in the Mediterranean climate

Nursing homes are designed and operated to meet general thermal specifications outlined by existing standards. This paper presents adaptive thermal comfort models for nursing homes based on the field survey administered in 100 common rooms of five nursing homes in the Mediterranean climate. The survey included simultaneous measurements of outdoor and indoor environmental parameters and an assessment of the occupants’ thermal comfort sensations using questionnaires. In total, 1,921 subjective questionnaires were obtained. The analysis focused on: Building Operation Mode (naturally ventilated and air-conditioned mode (cooling and heating)); and type of occupant (residents and non-residents (caregivers and therapists)). In naturally ventilated rooms residents were found to be more adaptive than what EN and ASHRAE 55:2020 standards propose (Tc (naturally ventilated) = 0.26 Trm + 18.83 (R2 = 0.81)). Residents in air-conditioned rooms were found to be less sensitive to outdoor conditions (Tc (air-conditioned) = 0.16Trm + 20.41 (R2 = 0,91)) than in naturally ventilated rooms. Both adaptive thermal models fall in limits set by these standards but in the lower acceptable levels. These adaptive thermal comfort models for nursing homes will allow extending the use of natural ventilation and the adoption of setpoint temperatures when air-conditioning is needed with the consequent reduction of heating and cooling use.

A Real-Time Adaptive IGBT Thermal Model Based on an Effective Heat Propagation Path Concept

The information of junction temperature is crucial for the operational management of insulated-gate bipolar transistor (IGBT) modules. In practice, the junction temperature is typically estimated by using an electrothermal model. IGBT modules are subject to various aging processes during operation, some of which, e.g., substrate solder crack, change the thermal impedance of the IGBT module. However, few works in the literature have included the aging effect on the online thermal behavior modeling of IGBT modules. This article proposes an effective heat propagation path (EHPP)-based real-time adaptive thermal model for IGBT modules, where the EHPP is proposed to quantify the impact of substrate solder cracks on the heat propagation inside the IGBT modules. A straightforward relationship between substrate solder crack and the degree of nonuniformity of case temperature distribution is established. This relationship is then used to approximate the EHPP of the IGBT module in different substrate solder health conditions in real time using the measured nonuniformity of case temperature distribution. Based on the change of the EHPP, the parameters of a thermal equivalent circuit (TEC) model, e.g., an improved Cauer-type TEC, are adjusted online and in real time to track the thermal behavior changes of the IGBT modules caused by substrate solder cracks, leading to a real-time substrate-solder-aging-adaptive thermal model. The proposed real-time adaptive thermal model is validated by simulation studies and experimental tests for a commercial IGBT module.

Study on an adaptive thermal comfort model with K-nearest-neighbors (KNN) algorithm

Compared with the static thermal comfort models like predicted mean vote (PMV) model, adaptive thermal models have a wider range of adaptability. The traditional concept of adaptive thermal comfort is that residents actively adapt to environmental changes. In this paper, a K-nearest neighbors (KNN)-based thermal comfort model is developed to establish a personalized adaptive thermal comfort environment to adapt to the preferences of the occupants. The KNN-based thermal comfort model can adjust the thermal comfort boundary for one specific individual person according to the changing environmental conditions. An artificial intelligent (AI) environmental controller has been built for studying the KNN-based thermal comfort model. 34 volunteers have been invited for testing the effectiveness of the KNN-based thermal comfort model. The test results manifested that the percent accuracy of the KNN model with 1000 sets of training data could reach up to 88.31% and can meet practical demand. The proposed thermal comfort model can help different people establish their personal indoor thermal comfort environment and promote the development of the intelligent and personalized air-conditioning systems.

Thermal Perception in Mild Climate: Adaptive Thermal Models for Schools

A comprehensive assessment of indoor environmental conditions is performed on a representative sample of classrooms in schools across southern Spain (Mediterranean climate) to evaluate the thermal comfort level, thermal perception and preference, and the relationship with HVAC systems, with a comparison of seasons and personal clothing. Almost fifty classrooms were studied and around one thousand pool-surveys distributed among their occupants, aged 12 to 17. These measurements were performed during spring, autumn, and winter, considered the most representative periods of use for schools. A new proposed protocol has been developed for the collection and subsequent analysis of data, applying thermal comfort indicators and using the most frequent predictive models, rational (RTC) and adaptive (ATC), for comparison. Cooling is not provided in any of the rooms and natural ventilation is found in most of the spaces during midseasons. Despite the existence of a general heating service in almost all classrooms in the cold period, the use of mechanical ventilation is limited. Heating did not usually provide standard set-point temperatures. However, this did not lead to widespread complaints, as occupants perceive the thermal environment as neutral—varying greatly between users—and show a preference for slightly colder environments. Comparison of these thermal comfort votes and the thermal comfort indicators used showed a better fit of thermal preference over thermal sensation and more reliable results when using regional ATC indicators than the ASHRAE adaptive model. This highlights the significance of inhabitants’ actual thermal perception. These findings provide useful insight for a more accurate design of this type of building, as well as a suitable tool for the improvement of existing spaces, improving the conditions for both comfort and wellbeing in these spaces, as well as providing a better fit of energy use for actual comfort conditions.

The Impact of the Thermal Comfort Models on the Prediction of Building Energy Consumption

Building energy assessment software/programs use various assumptions and types of thermal comfort models to forecast energy consumption. This paper compares the results of using two major thermal comfort models (adaptive thermal comfort and the predicted mean vote (PMV) adjusted by the expectancy factor) to examine their influence on the prediction of the energy consumption for several full-scale housing experimental modules constructed on the campus of the University of Newcastle, Australia. Four test modules integrating a variety of walling types (insulated cavity brick (InsCB), cavity brick (CB), insulated reverse brick veneer (InsRBV), and insulated brick veneer (InsBV)) were used for comparing the time necessary for cooling and heating to maintain internal thermal comfort for both models. This research paper exhibits the benefits of adopting the adaptive thermal model for building structures. It shows the effectiveness of this model in helping to reduce energy consumption, increasing the thermal comfort level for the buildings, and therefore reducing greenhouse emissions.

The Significance of Temperature Based Approach Over the Energy Based Approaches in the Buildings Thermal Assessment

Abstract The design of low energy buildings requires accurate thermal simulation software to assess the heating and cooling loads. Such designs should sustain thermal comfort for occupants and promote less energy usage over the life time of any building. One of the house energy rating used in Australia is AccuRate, star rating tool to assess and compare the thermal performance of various buildings where the heating and cooling loads are calculated based on fixed operational temperatures between 20 °C to 25 °C to sustain thermal comfort for the occupants. However, these fixed settings for the time and temperatures considerably increase the heating and cooling loads. On the other hand the adaptive thermal model applies a broader range of weather conditions, interacts with the occupants and promotes low energy solutions to maintain thermal comfort. This can be achieved by natural ventilation (opening window/doors), suitable clothes, shading and low energy heating/cooling solutions for the occupied spaces (rooms). These activities will save significant amount of operating energy what can to be taken into account to predict energy consumption for a building. Most of the buildings thermal assessment tools depend on energy-based approaches to predict the thermal performance of any building e.g. AccuRate in Australia. This approach encourages the use of energy to maintain thermal comfort. This paper describes the advantages of a temperature-based approach to assess the building’s thermal performance (using an adaptive thermal comfort model) over energy based approach (AccuRate Software used in Australia). The temperature-based approach was validated and compared with the energy-based approach using four full scale housing test modules located in Newcastle, Australia (Cavity Brick (CB), Insulated Cavity Brick (InsCB), Insulated Brick Veneer (InsBV) and Insulated Reverse Brick Veneer (InsRBV)) subjected to a range of seasonal conditions in a moderate climate. The time required for heating and/or cooling using the adaptive thermal comfort approach and AccuRate predictions were estimated. Significant savings (of about 50 %) in energy consumption in minimising the time required for heating and cooling were achieved by using the adaptive thermal comfort model.

An Experimental Study of a Lithium Ion Cell Operation at Low Temperature Conditions

Lithium-ion (Li-ion) batteries are widely used for various applications such as telecommunication, automotive, and stationary applications. With their wide range of safe operating temperatures (i.e. -10 °C to 50 °C), the Li-ion is preferred over other types of matured battery technologies such as lead acid and nickel-cadmium (NiCd). Nevertheless, operating the Li-ion batteries at cold climate conditions can potentially harm the batteries and lead to issues such as degradation and reduction in their capacity and power density. This paper aims to experimentally investigate the behavior of a Li-ion cell operating at low temperatures (i.e. -15 °C to 25 °C) with respect to its charging and discharging behavior. It was observed that at sub-zero temperatures (i.e. -5 °C, -10 °C and -15 °C) the Li-ion cell's capacity is reduced due to the impedance effect which then increases the cell's internal resistance. Moreover, at such low temperatures the best state of charge (SOC) of the cell (i.e. during charging mode) has reduced to about 7-23% of its maximum initial SOC (i.e. 100%). To complement the experimental finding, an existing simplified adaptive thermal model was used to obtain the discharge curves at various current rates based on the function of extracted charge (Qout). The discharge curve of equilibrium potential (Eeq) is then extrapolated towards zero current in order to obtained the overpotential heat generation curve based on the discharge current of the cell. The result showed a good agreement to the discharge curves that were obtained experimentally. Likewise, with the finding of cell voltage (E), current (I) and temperature (T) that were obtained experimentally, the thermal behavior of the cell in respect of its internal temperature is predicted and represented by comparing both the simulated and experimental cell internal temperatures.

The Environmental Design of Working Spaces in Equatorial Highlands Zones: The Case of Bogotá

Recent empirical investigations have indicated that the majority of occupants in office buildings would appreciate contact with the external environment, especially in cities where the climate is mild for part of the year. Supported by the possibilities of adaptive thermal models, the design of naturally ventilated buildings has been elaborated since the decade of 1990s. More communal areas rather than private ones are demanded due to the importance of social interaction and knowledge transfer among employees. In this context, this paper investigates the possibility of daylight and thermal comfort in naturally ventilated working environments, located in cities of mild climatic conditions, by redefining the parameters of a façade’s design and exploring coupling strategies with the outdoors. For this purpose, the city of Bogotá (Latitude 4°7′ N), in Colombia, a place with great potential for passive strategies, is taken as the geographic context of this research, which is supported by fieldwork with occupants of 37 office buildings and analytical work. The survey revealed that being close to a window is valued by the majority. Furthermore, 50% would like to have informal areas and outdoor spaces attached to their working environments. In additithe analytical studies showed how the combination of a set of environmental design strategies, including a schedule for coupling and decoupling of indoor spaces with the outdoors and a variation of occupancy density, made thermal comfort possible in free running working spaces in Bogotá.

Thermal Modeling of Cylindrical LiFePO<sub>4</sub> Batteries

Thermal management of Li-ion batteries is important because of the high energy content and the risk of rapid temperature development in the high current range. Reliable and safe operation of these batteries is seriously endangered by high temperatures. It is important to have a simple but accurate model to evaluate the thermal behavior of batteries under a variety of operating conditions and be able to predict the internal temperature as well. To achieve this goal, a radial-axial model is developed to investigate the evolution of the temperature distribution in cylindrical Li-ion cells. Experimental data on LiFePO4 cylindrical Li-ion batteries are used to determine the overpotentials and to estimate the State-of-Charge-dependent entropies from the previously developed adaptive thermal model [1]. The heat evolution is assumed to be uniform inside the battery. Heat exchange from the battery surfaces with the ambient is non-uniform, i.e. depends on the temperature of a particular point at the surface of the cell. Furthermore, the model was adapted for implementation in battery management systems. It is shown that the model can accurately predict the temperature distribution inside the cell in a wide range of operating conditions. Good agreement with the measured temperature development has been achieved. Decreasing the heat conductivity coefficient during cell manufacturing and increasing the heat transfer coefficient during battery operation suppresses the temperature evolution. This modified model can be used for the scale-up of large size batteries and battery packs.

Thermal modeling of power transformers using evolving fuzzy systems

Thermal models for distribution transformers with core immersed in oil are of utmost importance for transformers lifetime study. The hot spot temperature determines the degradation speed of the insulating paper. High temperatures cause loss of mechanical stiffness, generating failures. Since the paper is the most fragile component of the transformer, its degradation determines the lifetime limits. Thus, good thermal models are needed to generate reliable data for lifetime forecasting methodologies. It is also desired that thermal models are able to adapt to cope with changes in the transformer behavior due to structural changes, maintenance and so on. In this work we apply an evolving fuzzy model to build adaptive thermal models of distribution transformers. The model used is able to adapt its parameters and also its structure based on a stream of data. The proposed model is evaluated using actual data from an experimental transformer. The results suggest that evolving fuzzy models are a promising approach for adaptive thermal modeling of distribution transformers.

SU‐GG‐J‐164: Real‐Time Magnetic Resonance Imaging (MRI) Guidance and Thermal Modeling to Focus Hyperthermia Delivery

Purpose: Hyperthermia is an effective adjuvant modality for treatment of locally advanced cancer. However, focusing heat in the tumor using an external microwave applicator can be difficult, due to electromagnetic wave reflections at tissue interfaces. We present a methodology to steer heating towards the desired focus in real‐time, using MR thermal images for feedback. Method and Materials: The treatment control platform is based on repeated MR proton resonance frequency shift thermal imaging of the treatment volume over the course of the treatment. A cylindrical applicator with 4 independent pairs of dipole patch antennas (140MHz) is used as a heat source. The control process consists of iteratively constructing and updating a model for the heated object. At each iteration, the current model is employed to compute the optimal antenna settings (settings that the model predicts to focus heating in the target). These settings are applied to obtain the next thermal image, which is utilized in the next iteration to update the model. Thus, the algorithm progressively steers focusing while updating the model. We report on the convergence efficiency of the algorithm and importance of prior system knowledge (pretreatment simulations). Results: The experiments conducted on a cylindrical muscle‐equivalent phantom demonstrated that, for the 4‐antenna applicator, 16 iterations were sufficient to converge to the optimal thermal coverage of tumor. If prior knowledge was used, only 12 iterations were necessary to reach convergence from a starting focus that is positionally rotated 90° with respect to the desired focus. For a smaller positional rotation of 50°, 3 iterations were sufficient for convergence. The ratio of tumor to normal tissue heating after convergence improved by a factor of 3–6, compared to the initial ratio. Conclusion: Real‐time adaptive thermal modeling enables fast convergence to the optimal treatment of the patient and corrects for dynamic system changes.

Adaptive thermal modeling: a concept for measurement of local blood perfusion in heated tissues.

A new Adaptive Thermal Modeling (ATM) method for the measurement of local tissue blood perfusion rate is introduced. The method is based on a two-phase numerical technique. The first phase includes a fast, finite difference scheme for solution of the transient temperature field. The second phase involves iterative corrections of the perfusion until the modeled temperatures coincide with those measured by the temperature sensors. The results obtained from computer generated "data", as well as from laboratory experiments demonstrate the potential capability of the ATM method to continuously measure local perfusion rates in heated tissues. Rigorous analysis of the technique is planned for the near future so that it can be applied to in vivo measurements of local tissue blood perfusions.