Internal Temperature Distribution Research Articles

Experimental and numerical investigations on the intermittent heat transfer performance of phase change material (PCM)-based heat sink with triply periodic minimal surfaces (TPMS)

The excessive heat generated during the operation of electronic components leads to significant temperature increases, posing a significant risk to their service life. For the problem of efficient thermal management of electronic devices in confined spaces experiencing high heat flow, a scheme using triply periodic minimal surfaces and a phase change material-based active–passive cooling heat sink is proposed to control the temperature of electronic equipment. The apparent heat capacity method is employed to simulate the intermittent process of the phase change material-based heat sink. The optimization based on Box-Behnken Design and Nondominated Sorting Genetic Algorithm II is analyzed to obtain an improved triply periodic minimal surface structure. The effect of thermal performance (including base temperature, liquid fraction, and Grashof number) of an intermittent heat sink based on an improved structure is discussed. Visualization and temperature testing platforms are established. Through visualization experiments, the internal temperature and liquid fraction distribution of the heat sink are obtained, and the simulation results are in good agreement with the test results. The results show that the base temperature, liquid fraction, and Grashof number achieve a steady periodic variation during the intermittent process. Specifically, the base temperature remains stable in the range of 314 K to 340 K, the liquid fraction stabilizes between 0.8 and 1.0, and the Grashof number stabilizes between 100 and 1000. At heating power of 30 W or below, the base temperature of the heat sink exhibits a steady periodic variation. At the heating power of 40 W, the maximum base temperature of the heat sink exceeds 370 K.

Influence factors of frictional heat between bristles and rotor for a multi-stage brush seal

Frictional heat generates at frictional surface caused by contact between bristles of brush seal and rotor, which leads to early failure of the seal, especially downstream stage of multi-stage brush seal (MBS). In this paper, a three-dimensional tube bundle model of the MBS was established to study change of heat flux on the frictional surface among stages, internal temperature distribution of bristles and leakage flow characteristics of the seal. Moreover, three control strategies (i.e., increasing height of backing plate to rotor hbp, decreasing interference between bristles and rotor Δr and decreasing axial width of bristle pack wab) were analyzed to further investigate influence mechanism of the frictional heat. Results showed that the frictional heat at bristle tips mainly depend on formation of frictional heat caused by friction between bristle tip and rotor, transfer of heat from upstream stage to downstream one and dissipation of heat with flow of the airflow passing through the seal. For a traditional MBS with same structural single-stage seal units, a remarkable increasing bristle temperature at bristle tip of downstream stage and an increasing vortex area of airflow with higher temperature in chambers implied accumulation of the frictional heat at the downstream stages. Increase of hbp enhanced dissipation of the heat due to increasing passage area of airflow. Both decrease of Δr and decrease of wab obviously reduced bristle temperatures at downstream stages due to decreasing formation of the frictional heat. Furthermore, the decrease of wab had a lower bristle temperature compared to the increase of hbp, indicating a higher impact of the frictional heat formation on bristle temperature at downstage stages.

Numerical Study on Electromagnetic Thermal Performance of Non-Metallic Armoured Optoelectronic Cable Winch System

Non-metallic armoured optoelectronic cable winch systems (NAOCWSs) play critical roles in facilitating signal transmission and powering subsea equipment. Due to the varying depths in these applications, deploying the entire cable length is unnecessary. However, the portion of the cable that remains coiled around the winch can generate an electromagnetic field, which may interfere with signal transmission and induce electromagnetic heating. This can lead to elevated temperatures within the system, affecting the cable’s lifespan. Consequently, this study examines the distributions of magnetic and temperature fields within the NAOCWS with different currents (10–30 A) and numbers of winding layers (1–10). Findings indicate that the magnetic flux density (MFD) changes periodically, and the period is closely related to the distance between the cables. At the centre of the cable, the flux density is minimum. Temperature distribution correlates with both current amplitude and the number of winding layers, where an increase in either parameter amplifies the temperature variance between the edge and intermediate cables within the same layer. The current does not affect the internal temperature distribution pattern. With the number of winding layers determined, the layer where the highest temperature of the system is located is well defined and does not vary with current.

Prediction of thermal runaway for a lithium-ion battery through multiphysics-informed DeepONet with virtual data

A surrogate model that predicts thermal runaway (TR) of lithium-ion batteries (LIBs) fast and accurately is essential, yet complex phenomena of TR present significant challenges to achieving adequate performance in both aspects, particularly as traditional finite element models (FEMs) incur significant time and cost. This study proposes a multiphysics-informed deep operator network (MPI-DeepONet) with encoders to address these issues. This proposed neural network aims to predict TR under various thermal abuse conditions, offering a fast and accurate TR prediction surrogate model. In this study, MPI-DeepONet with encoders is trained with virtual data from a multiphysics FEM to overcome the scarcity of actual TR data. The architecture of DeepONet solves interpolation and extrapolation problems, allowing predictions across multiple thermal abuse conditions once trained. The neural network is further enhanced by the supervision of energy balance and chemical reaction equations, ensuring accurate and robust predictions despite limited data by effectively capturing the complex phenomena of TR. Quantitative analysis, compared against actual experiments and ablation studies, confirms the effectiveness of the proposed neural network. Notably, MPI-DeepONet achieves a mean RMSE of 13.2 °C for temperature predictions in the test set, significantly outperforming the 25.4 °C RMSE of purely data-driven DeepONet. This improvement highlights the importance of integrating multiphysics constraints into the neural network. The generality of the proposed neural network is further evidenced by accurate TR prediction in both LFP and NMC cells. The features deployed on the proposed neural network enable real-time quantification of internal temperature distribution and dimensionless concentration of the key components in LIBs, which are challenging to measure directly, achieving speeds at least 10,000 times faster than FEM. The proposed neural network provides comprehensive information for advanced battery management systems to ensure safety and reliability in LIBs, accelerating the digital twin of electric transportation systems through artificial intelligence transformation.

Numerical study of heat transfer characteristics of intermittent flow cold storage surface heat exchanger

The intermittent flow cold storage surface heat exchanger is a key component of the new pulse tube expansion cryogenic refrigerator, which is responsible for the periodic heat transfer of cold and hot helium, and its heat exchange efficiency directly affects the thermodynamic efficiency of the whole machine. In this study, a three-dimensional model of the heat exchanger unit is established, and numerical simulation is employed to investigate its heat exchange characteristics. The analysis focuses on understanding the internal temperature distribution patterns within the heat exchanger and explores the influence of wall materials on its heat transfer characteristics. Results show that the existence of the temperature lag loop minimizes the effective heat transfer temperature difference, consequently decreasing the heat exchanger’s efficiency. Moreover, the choice of wall material significantly affects the performance of the heat exchanger, with optimal thermal conductivity and volumetric specific heat enhancing its efficiency. This research provides valuable insights for future design and improvement of similar heat exchangers.

Experimental investigation on the stability and heat transfer enhancement of phase change materials composited with nanoparticles and metal foams

Conventional phase change materials (PCMs) possess the challenge of low thermal conductivity, which hinders the energy exchange rate and storage efficiency. PCMs composited nanoparticles and metal foams have been a proven solution to enhance the thermal characteristics of PCMs. In this work, three enhanced PCM composites including nanoparticle/paraffin, metal-foam/paraffin and nanoparticle/metal-foam/paraffin were prepared using fusion blending and vacuum impregnation method. The effects of different nanoparticles with different mass fractions on the stability and heat storage and release characteristics of nanoparticle/paraffin were investigated. Compared with paraffin, the melting time and solidification time of nano-metal/paraffin are reduced by a maximum of 30.18 % and 43.15 %, respectively. In addition, the effect of pore density of metal foam on natural convective heat transfer process was evaluated. The results showed that the stability in descending order is as follows: nano‑aluminum, nano‑copper and nano‑silver. The optimal mass fraction of nanoparticles with good stability was 1.5 wt% (copper), 1.0 wt% (silver), 1.5 wt% (aluminum), respectively. The metal foam makes the internal temperature distribution of PCMs more uniform in the heat transfer process, while inhibiting the convective heat transfer process. With the increase of pore density, the effect on natural convection heat transfer process rises. When the PPI (pores per linear inch) of foamed metal is greater than 40, the natural convection process can be inhibited, obviously. The foam metal has a more significant effect on the heat transfer performance of nanoparticle/metal-foam/paraffin than that of the nanoparticles. The economic benefits of nanoparticle/paraffin, metal-foam/paraffin and nanoparticle and metal foam composite phase change materials are progressively decrease, with MFP being suitable for applications with high economic budgets.

Thermal boundary conditions for heat transfer analysis of bridges considering non-uniform distribution of internal air temperature by computational fluid dynamics

Heat transfer analysis has been used to calculate the temperature distribution in bridges. Thermal boundary conditions play a critical role in this analysis. However, existing studies on thermal boundary conditions simplify the air temperature inside the bridge deck as uniform, which is not realistic and thus causes inaccurate simulation results. This study proposes a new approach to thermal boundary conditions in the heat transfer analysis of bridges. For the first time, computational fluid dynamics is used to calculate non-uniform air temperatures inside the bridge deck. In addition, non-approximate heat exchange equations for long-wave radiation are also incorporated into the approach. The techniques are applied to the 1377-m main span Tsing Ma Suspension Bridge to calculate the internal air temperatures of a deck segment. Transient heat transfer analysis is then conducted to calculate the time-dependent temperature distribution of the segment. As compared with the field monitoring results, the proposed approach can simulate the temperature distribution of the bridge with an average discrepancy of 0.88 °C and is more accurately than other existing approaches.

Modeling the propagation of internal thermal runaway in lithium-ion battery

The trend toward high capacity and huge size in lithium-ion batteries has made it necessary to investigate the internal thermal characteristics. In this study, a thermal runaway model was developed to describe lithium-ion batteries' internal thermal characteristics. Moreover, triggering energy was proposed as a critical feature for evaluating and characterizing the thermal runaway under diverse thermal abuse situations, with large differences among characteristic temperatures. Finally, the effects of battery configuration on thermal runaway behaviors were investigated. The modeling results showed that internal temperature distribution can be divided into four characteristic stages with two jelly rolls, and the application of more numerous and thinner cells inside a battery can accelerate the propagation of thermal runaway. The experimental results showed that the ratio of triggering energy of self-heat onset to total self-heat generation remained consistent in an adiabatic environment. The mean value of the ratio was 24.5%, indicating that lithium iron phosphate batteries obtain most of the energy (generally 80%) from internal exothermic reactions during adiabatic thermal abuse. The triggering energy of thermal runaway remained constant when various heating powers were applied to one of the batteries' laterals (about 20.8% of theoretical energy contained inside lithium iron phosphate batteries). Triggering energy can provide new insights into the modeling of thermal runaway mechanisms and propagation.

Temperature prediction and scheduling of data center based on segmented neural network

Task scheduling based on temperature perception is beneficial for avoiding hotspots and optimizing the internal temperature distribution of data centers. However, the accuracy of task scheduling largely depends on the accuracy of temperature prediction. There are many features that affect the accuracy of temperature prediction in data centers, and the variation periods of these features vary greatly. Traditional machine learning models are difficult to accurately fit them. Therefore, this article proposes a step-by-step temperature prediction algorithm based on Gated Recurrent Unit (GRU). This algorithm establishes prediction models for important parameters such as CPU utilization and air conditioning temperature that affect temperature prediction, and uses the outputs of these two models as inputs for the server temperature prediction model to better fit the changes of feature values. The model combines the principle of thermal locality and integrates the temperature of upper and lower servers for joint modeling. Experiments show that our prediction model can accurately predict the inlet temperature evolution of the server with dynamic workload. RSME reaches 0.278 and the average prediction temperature difference is 0.633, which is much higher than the traditional model. In addition, this article also propose a minimum temperature difference scheduling algorithm based on temperature prediction model, which can effectively reduce the number of servers running at high temperature and low temperature in the data center, make the temperature of the data center more balanced and achieve better energy-saving compared with other baseline algorithms.

Heat Convection in a Channel-Opened Cavity with Two Heated Sources and Baffle

Heat Convection in a Channel-Opened Cavity with Two Heated Sources and Baffle

Two-dimensional inverse problem of fire location in the closed goaf of coal mine based on optical fiber sensors.

Monitoring the temperature to determine the fire source locations is essential for controlling the spontaneous combustion in the goaf. Optical fiber sensors are employed to measure the temperature distribution in the goaf. However, due to changes in the geological conditions and the influence of the falling rocks in the goaf, only sensors on the upper side of the uncompacted goaf, due to inclination and coal pillar, may remain. Unilateral sensors are located on the upper side of the goaf, while fire occurs in the center. To investigate the issue with linear unilateral sensors, a two-dimensional inverse method has been developed to determine the location of fire sources by considering heat transfer after a fire inside the goaf. The equations were theoretically solved using Green's function method to obtain the internal temperature distribution of the physical model of the goaf. Sensitivity analysis identified the most crucial parameters in the process of spontaneous heating at different temperature. The fire source location can be determined using a loop method based on the model calculations. We considered a case to validate the model. Accurately identifying the fire source location in the goaf using the unilateral sensors has an essential theoretical and practical significance for fire prevention and fighting.

A model predictive fault‐tolerant control strategy for non‐redundant T‐inverters with multiphysics‐circuit coupling loss analysis

SummaryThis paper presents a multiobjective optimal power prediction fault‐tolerant control (MO2PPC) approach for mitigating DC link neutral‐point voltage imbalance and current ripple problems in three‐level T‐type inverter (3LT2I). Additionally, an auxiliary thyristor is employed to isolate and reconstruct the faulty phase into an 8ST2I for ensuring uninterrupted operation during phase failure. A method called injected bias current‐MO2PPC (IBC‐MO2PPC) fault‐tolerant strategy is proposed for this structure. This approach eliminates the need for DC or AC current sensors to reconstruct the three‐phase current, thereby reducing neutral‐point oscillation caused by phase deficiency and ensuring full power output after failure. Additionally, nonfaulty phase power loss is compared and analyzed to verify the stability of this method following a phase fault. The internal losses and temperature distribution of an IGBT module in a bridge arm before and after a fault are verified through a multiphysics‐circuit model in COMSOL‐Simulink coupling simulation presented last. The model shows reduced conduction losses and overall lower temperature distribution in the IGBT module after fault reconstruction. Experiments and multiphysics simulations have demonstrated that this approach displays exceptional robustness and sustainability throughout the entire fault cycle.

Structure optimization design of gasket test rig based on response surface model

In order to enhance the temperature regulation response speed of the gasket test rig and reduce the hysteresis of the temperature control system, structural optimization is implemented in the hardware part of the test rig. Firstly, a multi-physical field coupling method is employed for comprehensive performance testing of high-temperature and high-pressure gaskets, establishing a heat-fluid-solid coupling simulation and analysis model based on ANSYS. Then, internal temperature distribution of the gasket test device is calculated considering initial and boundary conditions. Next, data is collected using Latin hypercube sampling method, and optimal structural parameter combinations are determined through a multi-objective optimization approach utilizing response surface method and improved genetic algorithm. Finally, collected data is further utilized with response surface method and improved genetic algorithm multi-objective optimization technique to obtain optimal structural parameter combinations for the gasket test device which are verified by heat-fluid-solid coupling simulation. The temperatures tested before and after optimization are analyzed for comparison purposes. The results demonstrate that optimized gasket test rig significantly enhances its temperature control performance.

3D Electrothermal Model for Internal Temperature Distribution in Lithium-Ion Battery Cell and Module

In recent years, lithium-ion batteries (LIBs) have been massively developed in many applications as they provide high energy and power densities, high efficiency and long lifespan compared to other battery technologies. Despite remarkable improvements, Li-ion batteries still face thermal issues, which cause performance drop, limited calendar life, safety concerns and temperature-caused degradation. Therefore, proper thermal management for a LIB is necessary to control its temperature and to extend its lifetime. Internal temperature measurement remains very challenging for LIB. So, electrothermal LIB models, developed to forecast both electrical performances and temperature distributions, seems to be a powerful and complementary tool to experiments, in particular to obtain battery temperatures in use. Most of models, assume simplified approach considering that heat sources are either located in the center of the cell or uniformly distributed in the volume of the cell and so do not consider the real internal geometry of the cells.In this work, a 3D electrothermal model of a large commercial prismatic NMC-type lithium-ion cell (25 Ah) is developed based on the internal geometry reconstructed from tomography. The electrothermal model, developed with the COMSOL Multiphysics® software with the real internal geometry as well as the anisotropy of the thermal properties, accounts for irreversible and reversible heat sources. The entropy variation profiles were estimated by Entroview™ using their patented method. The validation of the model is performed thank to temperature measurement during charge and discharge at several currents in a climatic chamber at 25°C. Simulation results highlight exothermal and endothermal behaviors related to the entropy change along with the charge and discharge stages. Moreover, the internal temperature distribution is thoroughly impacted by the internal geometry of the cell and the anisotropy of the thermal properties.Finally, the 3D electrothermal model is extended to a module of 12 cells in series (12S) to evaluate the heterogeneities of internal temperatures considering the real geometry of each cell. Once validated, simulation (as shown of figure 1) underline the heterogeneous temperature distribution at the module scale and the importance of having an efficient battery thermal management system to limit an excessive increase of the temperatures which can cause performance drops until irreversible degradations. Figure 1

A study on the temperature characteristics in laser-assisted cutting of cemented carbide

To investigate the temperature characteristics of Laser-Assisted Cutting (LAC) in relation to cemented carbide, a theoretical model for LAC temperature was established based on heat conduction theory. Finite element simulations were conducted to analyze the surface temperature distribution and internal temperature distribution of cemented carbide during the LAC process. And an empirical formula for the LAC temperature of cemented carbide was obtained through temperature testing, and the model was experimentally validated. In addition the influences of the cutting temperature on the material machinability were experimentally studied. The results showed that the factors affecting cutting temperature ranked in order of importance are laser power, laser traversal speed, laser spot diameter, distance between cutting edge and spot center, cutting depth, and cutting speed. The cutting temperature trends were consistent under different processing parameters. In comparison with traditional cutting, the LAC method reduced the radial thrust force (Fx), tangential force (Fy), and axial thrust force (Fz) by 20.00, 27.14, and 53.19% respectively. The surface roughness of the workpiece was reduced by 50%, resulting in a flat and smooth surface with improved machinability.

Elevated temperature properties of foam concrete: Experimental study, numerical simulation, and theoretical analysis

Foam concrete, as an inorganic cellular material possessing exceptional thermal insulation and fire resistance properties, emerges as a promising candidate for thermal insulation. To achieve a more precise evaluation of its performance under elevated temperatures, the present study comprehensively investigates the thermal characteristics of foam concrete with a combined experimental, numerical, and theoretical approach. Firstly, a high-temperature test is conducted to investigate the influence of temperature and foam concrete density on its macroscopic appearance and internal temperature distribution. Meanwhile, the thermal conductivity of foam concrete at various densities and temperatures is directly measured with a thermal conductivity analyzer. Subsequently, to acquire comprehensive specific heat and thermal conductivity parameters of foam concrete, with a numerical model established within ABAQUS, the genetic algorithm is applied to identify these thermal parameters based on the measurement data from the elevated temperature test. In addition, CT scanning and 3D reconstruction of foam concrete before and after exposure to elevated temperatures are utilized to extract pore characteristics. This facilitates the thermal conductivity calculation using theoretical models, enabling the determination of the thermal conductivity of foam concrete above 600 °C. Through a comparison of the experimental, numerical, and theoretical results, it is recommended to consider the variation of specific heat in three dehydration stages due to the observed notable dehydration. Due to the effect of cracks, the numerically determined thermal conductivity is slightly higher compared to the experimentally and theoretically determined thermal conductivity. Based on the analysis, an improved formula predicting the specific heat of foam concrete, and a formula determining the thermal conductivity of foam concrete subjected to elevated temperature are proposed.

Creeping thermocapillary motion of a Newtonian droplet suspended in a viscoelastic fluid

In this work we consider theoretically the problem of a Newtonian droplet moving in an otherwise quiescent infinite viscoelastic fluid under the influence of an externally applied temperature gradient. The outer fluid is modelled by the Oldroyd-B equation, and the problem is solved for small Weissenberg and Capillary numbers in terms of a double perturbation expansion. We assume microgravity conditions and neglect the convective transport of energy and momentum. We derive expressions for the droplet migration speed and its shape in terms of the properties of both fluids. In the absence of shape deformation, the droplet speed decreases monotonically for sufficiently viscous inner fluids, while for fluids with a smaller inner-to-outer viscosity ratio, the droplet speed first increases and then decreases as a function of the Weissenberg number. For small but finite values of the Capillary number, the droplet speed behaves monotonically as a function of the applied temperature gradient for a fixed ratio of the Capillary and Weissenberg numbers. We demonstrate that this behaviour is related to the polymeric stresses deforming the droplet in the direction of its migration, while the associated changes in its speed are Newtonian in nature, being related to a change in the droplet’s hydrodynamic resistance and its internal temperature distribution. When compared to the results of numerical simulations, our theory exhibits a good predictive power for sufficiently small values of the Capillary and Weissenberg numbers.

A multi‐layer thermo‐mechanical coupling hysteretic model for high damping rubber bearings at low temperature

AbstractHigh damping rubber (HDR) bearings have been widely applied in seismic isolation of structures. However, the complex behavior of HDR bearings is influenced by thermal mechanism coupled with the seismic response of the structure, particularly at low ambient temperatures. In order to express the hysteretic restoring force behavior of HDR bearings involving temperature dependence, heating effect, and heat transfer with an improved accuracy, a multi‐layer thermo‐mechanical coupling model was developed. The thermal mechanism was illustrated by considering the heating effect and heat transfer in the multi‐layer thermal‐coupled model. A nonlinear hysteretic model including the temperature dependence of the stress‐strain relationship of rubber was incorporated. Quasi‐static cyclic loading was conducted for parameter identification and real‐time hybrid simulation tests were carried out for model validation. The model accurately represented the time‐dependent thermal mechanism within the bearing, considering the non‐uniform distribution of internal temperature. Numerical results exhibited good agreement with the experimental results, including the hysteresis curve, temperature history, and temperature distribution. The model demonstrated improved accuracy in computing the hysteretic behavior compared with an existing simple model.

Ultrasonic measurement of temperature distributions in extreme environments: Electrical power plants testing in utility-scale steam generators

Thermal heterogeneities within energy conversion and storage, material processing, nuclear processes, aerospace, and military applications are often inaccessible to characterization by insertion sensors. When sensor deployment is possible, conventional pointwise temperature probes quickly degrade when inserted into harsh environments typical of such processes. We developed spatially-resolved ultrasonic thermometry to noninvasively measure the spatial distributions of thermal properties in such applications, even when sizable thermal gradients are present. Our method divides the path of ultrasonic propagation into segments bound by echogenic features, which create echoes in pulse-echo mode, encoding the information about interior temperature distributions. We use the acquired ultrasonic responses to estimate the internal temperature distributions by solving an inverse problem or concatenating segmental estimates. This work describes the implementation and industrial testing of the developed method at a coal-fired electrical power generation plant. We inserted an echogenically segmented Inconel 625 waveguide into the combustion zone of the utility-scale boiler and continuously acquired ultrasonic data while keeping sensitive components away from the damaging combustion environment. The accuracy of the time-dependent temperature distributions reconstructed from the ultrasonic measurements was comparable to that of thermocouples. The resiliency of ultrasonic thermometry to harsh combustion conditions was far superior to conventional insertion sensors. The measurements obtained during plant operation captured daily steam generation cycles in response to changing customer demand and intermittent contributions of renewable power sources to the power grid. These measurements have revealed new insights into the relationship between the dynamic power generation load and the conditions inside the steam generator. The successful industrial testing of spatially-resolved ultrasonic thermometry in solids indicates that the developed technology has matured to become an attractive alternative to conventional sensing in solving challenging problems of long-term thermal characterizations in extreme environments.

Data-driven modeling of long temperature time-series to capture the thermal behavior of bridges for SHM purposes

Bridges experience complex heat propagation phenomena that are governed by external thermal loads, such as solar radiation and air convection, as well as internal factors, such as thermal inertia and geometrical properties of the various components. This dynamics produces internal temperature distributions which cause changes in some measurable structural responses that often surpass those produced by any other load acting on the structure or by the insurgence or growth of damage. This article advocates the use of regression models that are capable of capturing the dynamics buried within long sequences of temperature measurements and of relating that to some measured structural response, such as strain as in the test structure used in this study. Two such models are proposed, namely the multiple linear regression (MLR) and a deep learning (DL) method based on one-dimensional causal dilated convolutional neural networks, and their ability to predict strain is evaluated in terms of the coefficient of determination R2. Simple linear regression (LR), which only uses a single temperature reading to predict the structural response, is also tested and used as a benchmark. It is shown that both MLR and the DL method largely outperform LR, with the DL method providing the best results overall, though at a higher computational cost. These findings confirm the need to consider the evolution of temperature if one wishes to setup a temperature-based data-driven strategy for the SHM of large structures such as bridges, an example of which is given and discussed towards the end of the article.