Thermal Modeling Method Research Articles

Feed shaft thermal expansion error modeling based on principal component regression

In order to further improve the prediction accuracy of the thermal error model of the feed shaft of the gear grinding machine, this paper proposes a feed shaft thermal expansion error modeling method based on principal component regression. The feed shaft positioning error is decoupled by linear fitting to obtain the thermal expansion slope parameter, the position correlation of the feed shaft thermal expansion error is eliminated, and a regression model of the thermal expansion slope parameter and the temperature of all measuring points is established based on the principal component regression algorithm. Different from the traditional method, the principal component regression model does not require additional screening of temperature sensitive points, and the root mean square error mean and standard deviation of the group experimental prediction results can reach 2.0 μm/m、0.9μm/m, which has higher accuracy and stability than conventional methods.

Building thermal energy efficiency modeling based on light image inspection and super resolution algorithm in interior landscape design

With the intensification of global climate change and energy crisis, the thermal efficiency of buildings has become increasingly important. Interior landscape design not only affects living comfort, but also plays a key role in building energy efficiency. Therefore, it is of great practical significance to explore the modeling method of building thermal energy efficiency based on optical image inspection and super resolution algorithm. The light image inspection technique is used to obtain the data of light and temperature distribution inside the building. The super-resolution algorithm of deep learning is used to process the acquired low-resolution image to improve the clarity and detail of the image. Combined with the physical characteristics of the building, the thermal efficiency model of the building is constructed and multi-dimensional analysis is carried out. The experimental results show that the combination of optical image inspection and super resolution algorithm can effectively improve the accuracy of building thermal efficiency modeling. Compared with traditional methods, the prediction error of the model is reduced, and the recommended optimization scheme performs better in terms of energy consumption in different interior landscape design schemes. Therefore, the building thermal efficiency modeling method based on optical image inspection and super resolution algorithm provides a new idea for interior landscape design. Through accurate thermal efficiency analysis, it can provide more scientific decision-making basis for architectural designers, so as to realize the sustainable development of buildings and maximize the energy efficiency.

Development of S-domain experimental correlation thermal control model and rapid enumeration PID tuning method for uncooled Laser chip module

Uncooled semiconductor lasers are sensitive to ambient temperature variation which makes the PID thermal control for the uncooled semiconductor is complicated. In this work, a kind of enumeration PID tuning method based on S-domain experimental correlation thermal control model for the uncooled semiconductor lasers is introduced. Numeric validation and enumeration PID tuning of S-domain experimental correlation model for uncooled semiconductor are conducted. Results shows that the control accuracy prediction result of the S-domain experimental correlation model has a deviation of 3.6% in contrast with CAE simulation result. The PID tunning speed of rapid enumeration S-domain experimental correlation method is 17,000 times that of CAE method.

Recent Patents on Thermal Characteristic Analysis and Modeling of Machine Tools

Abstract: Modern machining machines are becoming more sophisticated and automated, but the problems of thermal deformation caused by machining have also come to the fore. CNC machine tools are used in high-speed, high-feed rate machining conditions, so the impact of thermal deformation factors will lead to a more prominent loss of accuracy. In this context, the design and manufacture of future CNC machine tools need to take further account of the errors caused by thermal deformation and the accompanying loss of accuracy. The principle of thermal characterisation and thermal error modeling of machine tools is to measure and reasonably analyse the machine tool at different temperature points and make corrections to the analysis data in order to get a value as close as possible to the real value and improve machining accuracy. Several representative patents and studies on thermal characterisation and thermal error modeling of machine tools at home and abroad are reviewed. This study presents a selection and summary of a large number of recent patents and studies on machine tool thermal characterisation and thermal error modeling, focusing on the selection of the machine tool thermal error measurement point area, thermal error modeling methods, spindle thermal displacement compensation, and other aspects, and discusses the future trends in the mainstream methods for reducing the thermal error of machine tools. The analysis and modeling of thermal errors in machine tools have an integral influence on further improvements in machining accuracy and efficiency in the future.

Modeling of machine tool thermal error based on factor extraction

Abstract In the process of modeling thermal errors in machine tools, optimizing the extraction of temperature information affects the prediction accuracy of the thermal error model. This paper proposes a thermal error modeling method based on factor extraction. Firstly, the temperature measurement points are optimized to determine the optimal number using finite element, fuzzy clustering, and correlation methods. Then, the common deformation factors in the machine tool temperature information are extracted through factor analysis, and they are used as independent variables for thermal error modeling. Finally, the method was applied to establish a model for thermal error model for a certain model of precision machine tool, and a regression model of the machine tool was obtained after factor extraction optimization, the effectiveness and feasibility of the proposed method in this paper were verified by comparing it with experimental data. The method proposed in this study provides a reference for modeling thermal errors in various types of machine tools in the future.

Thermal error modeling method of truss robot based on GA-LSTM

PurposeThe selection of sensitive temperature measurement points is the premise of thermal error modeling and compensation. However, most of the sensitive temperature measurement point selection methods do not consider the influence of the variability of thermal sensitive points on thermal error modeling and compensation. This paper considers the variability of thermal sensitive points, and aims to propose a sensitive temperature measurement point selection method and thermal error modeling method that can reduce the influence of thermal sensitive point variability.Design/methodology/approachTaking the truss robot as the experimental object, the finite element method is used to construct the simulation model of the truss robot, and the temperature measurement point layout scheme is designed based on the simulation model to collect the temperature and thermal error data. After the clustering of the temperature measurement point data is completed, the improved attention mechanism is used to extract the temperature data of the key time steps of the temperature measurement points in each category for thermal error modeling.FindingsBy comparing with the thermal error modeling method of the conventional fixed sensitive temperature measurement points, it is proved that the method proposed in this paper is more flexible in the processing of sensitive temperature measurement points and more stable in prediction accuracy.Originality/valueThe Grey Attention-Long Short Term Memory (GA-LSTM) thermal error prediction model proposed in this paper can reduce the influence of the variability of thermal sensitive points on the accuracy of thermal error modeling in long-term processing, and improve the accuracy of thermal error prediction model, which has certain application value. It has guiding significance for thermal error compensation prediction.

A simplified mathematical modeling approach for thermal runaway of Li(Ni0.8Co0.1Mn0.1)O2 pouch cells based on thermal runaway experimental data

Currently, the numerical modeling methods used to characterize the thermal runaway and propagation mechanisms in NCM811 pouch batteries still face numerous challenges. This is primarily due to the uncertainties in thermal runaway reactions and the immense computational demands encountered when modeling begins at the granular level of the cell. To address this issue, this study designed a series of experiments for an NCM811 pouch battery. By employing experimental data, reasonable assumptions were made regarding the combined effects of solid-state thermal conduction, electrochemical heating, and fluid-solid heat transfer through gas jet streams. A simplified thermal runaway mathematical modeling method based on experimental data was proposed, effectively tackling the problem. This framework was then applied to thermal runaway experiments on a single NCM811 battery used in this study, providing essential input parameters for three-dimensional numerical modeling of modules composed of multiple batteries in multiphysics simulation software. The simulation results derived from the proposed method were in good agreement with experimental data. This study can characterize the thermal runaway and thermal propagation characteristics of battery modules using single-cell thermal runaway experiments. Moreover, the proposed modeling approach not only predicted the experimental outcomes of the NCM811 pouch battery with reasonable accuracy but also provided smoother data, elucidating the highly fluctuating trends observed in experimental data. This method also offers guidance for designing fire prevention and smoke exhaust systems for battery packs. This study presents a novel approach for the mathematical modeling of thermal runaway and thermal propagation in battery modules using NCM811 pouch batteries, contributing to the ongoing development of lithium-ion battery module design.

Aggregate scheduling potential evaluation of large-scale air conditioning loads

As an adjustable load, the air conditioning (AC) load is a high-quality resource for power system operation regulation. However, due to the large number of AC loads and the heterogeneity among individual users, the scheduling potential of AC loads is difficult to accurately calculate. An aggregate scheduling potential evaluation of large-scale AC loads is proposed. Firstly, according to the operation characteristics of ACs, the aggregation models of central ACs and household split-type ACs are constructed by using the equivalent thermal parameter model and Monte Carlo sampling method respectively. Secondly, based on the proposed aggregation models of large-scale AC loads, the AC loads are rescheduled by resetting the set temperature, and the scheduling potential calculation model of central ACs and residential ACs is obtained. Finally, case studies show that AC loads have considerable scheduling potential when the indoor temperature is maintained within the range of human comfort.

A Study of Thermal Runaway Mechanisms in Lithium-Ion Batteries and Predictive Numerical Modeling Techniques

While thermal runaway characterization and prediction is an important aspect of lithium-ion battery engineering and development, it is a requirement to ensure that a battery system can be safe under normal operations and during failure events. This study investigated the current existing literature regarding lithium-ion battery thermal runaway characterization and predictive modeling methods. A thermal model for thermal runaway prediction was adapted from the literature and is presented in this paper along with a comparison of empirical data and predicted data using the model. Empirical data were collected from a Samsung 30Q 18650 cylindrical cell and from a large 20 Ah pouch cell format using accelerated rate calorimetry. The predictive model was executed in a macro-enabled Microsoft Excel workbook for simplicity and accessibility for the public. The primary purpose of using more primitive modeling software was to provide an accurate model that was generally accessible without the purchase of or training in a specific modeling software package. The modes of heat transfer during the thermal runaway event were studied and are reported in this work, along with insights on thermal management during a thermal runaway failure event.

Adaptive thermal error prediction for CNC machine tool spindle using online measurement and an improved recursive least square algorithm

Establishing models for predicting and compensating for spindle thermal errors is cost-effective and necessary to improve the accuracy of machine tools for smart manufacturing. However, the prediction performance of existing methods deteriorates significantly with dynamic working conditions of machine tools because training from static conditions leads to the inability to adapt to dynamic conditions. Therefore, an adaptive thermal error modeling method using online measurement and an improved recursive least square algorithm is proposed to fill this research gap, which updates the thermal error model adaptively to ensure that dynamic working conditions are learned in real time. Particularly, Spearman's rank correlation coefficient method is first adopted for temperature-sensitive point selection to capture the nonlinear relationship between temperature and thermal error variables. Furthermore, a variable-forgetting factor-based recursive least square (VFF-RLS) algorithm is proposed to improve the prediction performance, in which the proposed variable forgetting factor is adaptively updated according to real-time thermal error data collected by online measurement. The experimental results showed that the proposed VFF-RLS method can maintain a high prediction accuracy of 1.75 μm and robustness of 0.16 μm on both constant and dynamic working conditions. The effectiveness of the VFF-RLS method is validated by verification experiments.

MA-CNN based spindle thermal error modeling using the depth feature analysis with thermal error mechanism

The dynamic changes in the temperature fields make it unavoidable for the spindle to undergo thermal deformation. Thermal error modelling is helpful in enhancing the accuracy of machine tools. This paper proposes a spindle thermal error modelling method based on the mayfly algorithm (MA) for complex working conditions. First, different working conditions are set to obtain thermal deformation and temperature data. A thermal error model is established using MA. MA is used to optimize the number and size of convolutional kernels. Next, the depth features of the model are compared with the exponential thermal error model in logarithmic space. The model is compared with five other models. The results indicate that the MA-CNN model has a higher accuracy. Finally, the compensation is conducted in three directions. The results show that the average thermal error in the Z direction is reduced by 63.75 %, which demonstrates the good performance of the model.

Numerical investigation on thermal performance of phase change materials embedded in functionally graded metal foam

Latent heat thermal energy storage (LHTES) technology based on phase change materials (PCMs) embedded in metal foam has recently become a popular method to store thermal energy and release it to other processes with thermal energy demand, which has been applied in solar energy, electronic devices, waste heat recovery, etc. In this study, we proposed a novel design of metal foam with porosity changing over the horizontal and vertical coordinates. The thermal resistance model method was applied to explore the heat transfer mechanism and linear porosity was proved to be able to improve the convection intensity in different stages of melting. Also, metal foam with horizontal linear porosity or vertical linear porosity can both shorten the total melting time and increase the average power density, while composite linear porosity can combine the enhancing effect of these two structures and achieve an optimal enhancing effect. The optimal linear porosity gradient can enhance the average power density by 15.8 % compared to those of uniform porosity. Furthermore, the heat storage performance of linear porosity with different enclosure aspect ratios was also discussed. The linear porosity structure was proved to be able to balance the nonuniformity of melting caused by natural convection.

FEM and CFD thermal modeling of an axial-flux induction machine with experimental validation

Axial-flux electrical machines are ideal candidates as in-wheel motors for electrical vehicles (EVs). Due to their characteristics of high power density and compact structure, thermal management is vital for them. Lowering the temperature of the stator windings can protect the insulation material from rapid degradation and reduce the extra copper losses by decreasing their electrical resistance. Contrary to the widely reported axial-flux permanent magnet synchronous machines, thermal modeling methods of axial-flux induction machines are rarely seen in previous literature. Hence, the present work aims at investigating their thermal response based on both the finite element method (FEM) and computational fluid dynamics (CFD) techniques. In addition, a test rig is built to validate the computed results of these two thermal models with the experimental measurement. The CFD conjugate heat transfer analysis is found to be more accurate than the FEM thermal analysis in predicting the temperature distribution of different components in the machine and the temperature rise of the airflow, with lower than 5 ∘C average errors deviating from the corresponding measured data at three rotation speeds. Additionally, the CFD simulation is able to capture the backflow occurring near the outlets of the casing that has been found during the experiments.

Thermal modeling of high-power satellite power cable and analysis of bundling thermal effect

With the increase of satellite power, the current-carrying and heat dissipation of power cable is increasing significantly and the thermal safety problem becomes prominent. Therefore, the need for accurate modeling and fine design of power cable becomes increasingly urgent. In this paper, the thermal analysis modeling method for high-power cable bundle is proposed based on ground test data, and the heat transfer parameters at the installation and bundling points are obtained. At the same time, the thermal effect of cable bundling is quantitatively analyzed by simulating the splitting and binding power cable bundle. The result indicates that enhancing the radiation heat transfer between cables and the surrounding environment is the most effective means to improve the heat dissipation condition of cable bundles. The thermal model of high-power satellite power cable proposed in this paper can be used as an important basis for the prediction of on-orbit temperature and fine laying design of the power cable.

Physics-informed neural network for fast prediction of temperature distributions in cancerous breasts as a potential efficient portable AI-based diagnostic tool

This work presents the development of a novel Physics-Informed Neural Network (PINN) method for fast forward simulation of heat transfer through cancerous breast models. The proposed PINN method combines deep learning and physical principles to predict the temperature distributions in breast tissues and identify potential abnormal regions indicating the presence of tumors. The PINN model is normally trained by physics in terms of the residuals of the heat transfer equation, as well as boundary conditions with and without datasets of surface thermal imaging data concerning cancerous breast tissues, which can be used for future inverse thermal modeling to calculate tumor sizes and locations. The model is validated by comparing its predictions with those obtained by traditional Finite Element Analysis (FEA) for various cases. The comparison validates the PINN model as an accurate and fast method for thermal modeling and breast cancer diagnostic tool as the PINN simulation is found to be around 12 times faster than its FEM counterpart. The utilization of deep learning and physical principles in a diagnostic tool provides a non-invasive and safer alternative for breast self-examination compared to traditional methods such as mammography. These findings hold promise for the ongoing development of a new portable Artificial Intelligence (AI) tool for the early detection of breast cancer in breast self-examination as promoted by WHO, which is crucial for reducing mortality rates of breast cancer in the world.

Performance analysis and comparison of data-driven models for predicting indoor temperature in multi-zone commercial buildings

Building thermal models, which characterize the properties of a building’s envelope and thermal mass, are essential for accurate indoor temperature and cooling/heating demand prediction. Because of their flexibility and ease of use, data-driven models are increasingly used. This study compared and analyzed the performance of gray-box (resistance-capacitance) and black-box (recurrent neural network) models for predicting indoor air temperature in a real multi-zone commercial building. The developed resistance-capacitance model served as a benchmark model for which full sets of temporal data and building information were used as inputs. The recurrent neural network models were trained and tested assuming various available types and amounts of temporal data and known building physical information to investigate the effects of data and information availability. Feature importance analysis was conducted to select the key variables for different prediction targets under different scenarios. This research provides guidance in selecting an appropriate building thermal response modeling method based on the measured data availability, building physical information, and application.

A thermal error modeling method for CNC lathes based on thermal distortion decoupling and nonlinear programming

A thermal error modeling method for CNC lathes based on thermal distortion decoupling and nonlinear programming

A comparison of ultrasonic temperature monitoring using machine learning and physics-based methods for high-cycle thermal fatigue monitoring

Failure of pipe network components in so-called mixing zones due to high-cycle thermal fatigue (HCTF) can occur within nuclear power plants where fluids of different thermal and hydraulic properties interact. Given that the consequences of such failures are potentially deadly, a method to monitor HCTF non-invasively in real-time is expected to be of great use. This method may be realised by a technique to determine the inaccessible temperature distribution of a component since thermal gradients drive HCTF. Previous work showed that a physics-based method called the inverse thermal modelling (ITM) method can obtain the temperature distribution from external temperature and ultrasonic time of flight (TOF) measurements. This study investigated whether the long-short-term memory (LSTM) machine learning architecture could be a faster alternative to the ITM method for data inversion. On experimental data, a 25-member ensemble of LSTM networks achieved an ensemble median root mean square error (RMSE) of 1.04°C and an ensemble median mean error of 0.194°C (both relative to a resistance temperature device measurement). These values are similar to the ITM method which achieved a RMSE of 1.04°C and a mean error of 0.196°C. The single LSTM network and the ITM method achieved a computation-to-real-world time ratio of 0.008% and 14%, respectively demonstrating that both methods can invert data in real-time. Simulation studies revealed that LSTM performance is sensitive to small differences between the training and real-world parameters leading to unacceptable errors. However, these errors can be detected via an ensemble of independent networks and, corrected by simply adding a correction factor to the TOF prior to being input into the networks. The results show that LSTM has the potential to be an alternative to the ITM method; however, the authors favour ITM for temperature distribution monitoring given its interpretability.

A High-Resolution Analytical Thermal Modeling Method of Capacitor Bank Considering Thermal Coupling and Different Cooling Modes

Thermal stress is of crucial importance to capacitor reliability. However, for a capacitor bank, on spatial scale, the complex heat transfer modes are not clearly illustrated; and on time scale, the ESR aging is neither fully discussed in current evaluation methods. These could lead to a glaring error in the prediction of temperature distribution and lifetime estimation. Therefore, this paper proposed an analytical thermal modeling method with high-resolution for the capacitor bank, considering the thermal coupling effect between individual capacitors, as well as different cooling conditions and the heat variation caused by ESR aging. Firstly, the improved thermal state-space modeling method is proposed to universally describe a multiple heat source system. Then, based on heat transfer and fluid mechanic theories, the characterization method of thermal coupling effect in different cooling modes is discussed. Furthermore, the ESR aging model is applied in the electro-thermal analysis of capacitor bank, aiming to improve the accuracy of thermal calculation in long time scale. Finally, to alleviate the uneven temperature distribution in an in-line designed capacitor bank, a staggered design method is proposed followed by a case study, where a nine-capacitor bank is presented to validate the corresponding modeling method and optimization.

A time-varying state-space model for real-time temperature predictions in rack-based cooling data centers

Fast-growing data centers (DCs) require efficient cooling systems (such as rack-based cooling architectures) and control strategies to reduce operating costs and guarantee desired indoor conditions. Thus, this study proposed a novel real-time temperature prediction model for rack-based cooling DCs, in order to facilitate advanced control regarding cooling management and workload assignment. Specifically, a data-driven technology was introduced to estimate time-invariant model parameters, in order to avoid the time-consuming physics-based parameters extracting process. The mass conservation relationships were employed to update time-varying flow parameters in real-time to capture the nonlinear behaviors in DCs. Moreover, the proposed control-oriented thermal modeling method can model hot air recirculation and cold air bypass occurring simultaneously for the first time. The performance of the developed time-varying state-space model was validated by CFD simulation data. Additionally, the timeliness of modeling and temperature prediction was also investigated. The results show that the developed model achieves sufficient accuracy with a mean absolute error (MAE) equal to 0.28 °C, even for long prediction horizons and dynamic IT workloads. Also, the developed model has outstanding timeliness for advanced control techniques, in terms of less than 30 min for parameter identification and less than 10 s for temperature prediction.