Surface Temperature Measurements Research Articles

Study on Temperature Cascade ELM Inversion Method for 110 kV Single-Core Cable Intermediate Joints

The accurate calculation of the hotspot temperature of the cable intermediate joint can effectively guarantee the safe operation of the transmission and distribution network. This paper addresses the limitations of the current method of estimating hotspot temperature solely from surface temperature measurements. Specifically, we focus on a 110 kV single-core cable as our subject of study. We started by establishing a simulation model for the temperature field at the intermediate joint to generate data samples. Subsequently, the NCA (neighborhood component analysis) algorithm was employed to select the optimal measurement points on the cable’s surface. This allowed determination of the quantity and location of characteristic points. Finally, we developed a cascading inversion model, which consists of a radial inversion model and an axial inversion model, based on the extreme learning machine algorithm. The example results show that the mean squared error of hotspot temperature obtained by cascade inversion and direct inversion is 6.95 and 24.71, respectively, indicating that cascade inversion can effectively improve the inversion accuracy.

Theoretical and numerical investigation of introduced errors in surface temperature measurements of pistons applied in internal combustion engine

DMP (Differences in Material Properties) often exists between temperature sensors and combustion chamber components of ICE (Internal Combustion Engine), resulting in IEs (Introduced Errors) in the temperature measuring process. In this paper, we present a comprehensive theoretical analysis of the correlation between introduced errors on chamber components and the DMP. The analysis process considers the characteristics of the chamber's heat transfer BC (Boundary Condition). Then, simulations are used to investigate the introduced error of hardness plug and thermocouple sensors on the top surface of an ICE aluminum alloy piston. The theoretical analysis results indicate that heat transfer BC can be segmented into the time-averaged and fluctuation parts, and IEs are linearly superimposed under these two parts of BCs. The heat conductivity impacts the time-averaged error and the product of heat conductivity, density, and heat capacity impacts the fluctuation error. The experimental and simulation results indicate that the max time-average error of the hardness plug on piston is 2.4 °C, with a fluctuation introduced error of 3.6 °C and a total introduced error of 6 °C; and the time-average introduced error of thermocouple sensor is 9.4 °C, with a fluctuation error of 5.8 °C and a total introduced error of 15.2 °C. Finally, a fluctuation error correction method is proposed to reduce error. After correction, the error is reduced by 71 % and 66 % for hardness plug and thermocouple, respectively.

Abstract PO1-07-12: Inverse modeling with surface temperature accurately detects the presence of breast cancer

Abstract Breast cancer affects over 250,000 women in the United States every year. Patient outcomes including overall survival significantly decline with large sized tumors and nodal involvement on initial presentation. The introduction of mammogram screening has been instrumental in the early detection of cancer. However, it has lower sensitivity in dense breast tissue and limited specificity which leads to unnecessary additional invasive testing. Several methods such as magnetic resonance imaging (MRI), computed tomography (CT) and ultrasound imaging are used as adjunctive methods. However, there is still a need for cost-effective techniques without additional radiation exposure for breast cancer screening for use with mammography. We propose an adjunctive screening method using surface temperature measurements and inverse breast modeling for early detection of breath cancer. Methods: We enrolled patients who presented with mammogram detected breast tumors that were confirmed to be cancerous lesions by biopsy. We used prone position steady state infrared imaging of bilateral breasts to measure surface temperatures and multi-view MRI images to create patient specific 3D breast models. The inverse technique applies Levenberg-Marquard algorithms (LMA) and utilizes commercial software for thermal transport modeling. The inverse technique predicts a heat generation map normalized for baseline surface temperatures to localize the presence of an underlying tumor. Results: A total of 25 breast tumors (diameter range from 5 to 27 mm) from 24 patients were included in the analysis with a median patient age of 67 years. One patient had bilateral breast tumors. Mammogram data showed inclusion of mixed breast tissue composition including dense and extremely dense breast tissue. LMA accurately detected the tumor in all 25 patients with maximum absolute errors of 7 mm in location of the tumor in the breast and 1.78 mm in diameter. The accuracy of detection was not affected by tumor histology which included invasive ductal and lobular carcinomas, ductal and lobular carcinoma in-situ and atypical ductal hyperplasia. No heat sensitive tumors were detected in the 23 contralateral breasts which acted as internal negative controls. Conclusion: Infrared temperature profiles and inverse modeling successfully detected malignant breast tumors with no missed tumors or false positive results and could be used as adjunctive screening along with mammography, especially in patients with dense breast tissue. Citation Format: Nithya Sritharan, Carlos Gutierrez, Isaac Perez-Raya, Jose-Luis Gonzalez-Hernandez, Alyssa Owens, Donnette Dabydeen, Lori Medeiros, Satish Kandlikar, Pradyumna Phatak. Inverse modeling with surface temperature accurately detects the presence of breast cancer [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-07-12.

Research on Thermocouple Integration Method of Hollow Thin-Walled Turbine Rotor Blade Surface

The hollow thin-walled structure is increasingly used in advanced aero-engine turbine rotor blades, and the traditional surface temperature measurement method based on groove buried couple is no longer applicable. In this paper, a new thermocouple integration method is proposed. The laser additive manufacturing process is used to generate thermocouple embedding channels on the surface of turbine blades. After the thermocouple is embedded, it is pre-fixed, and the thermocouple protection is realized by supersonic flame spraying technology. The strength of the thermocouple integrated structure of the turbine blade is verified by finite element analysis. Based on CFD analysis, the influence of thermocouple integration on the efficiency, pressure ratio and temperature distribution of the turbine blade is studied. The high-speed rotation test shows that the thermocouple integrated structure has high strength and reliability and meets the measurement requirements. This method can provide technical support for the surface temperature measurement of hollow thin-walled turbine rotor blades.

Estimation of thermophysical properties of a pouch-type Li-ion battery using an inverse methodology

The growing popularity of electric vehicles highlights the crucial role of batteries. Effective battery thermal management is crucial for improving performance, reliability, and safety, especially in tropical areas where overheating is a key challenge. This necessitates a comprehensive understanding of the thermophysical properties of batteries. The present study concerns the estimation of the temperature-dependent orthotropic thermophysical properties (kx , ky , kz , cp ) of the active material in a pouch-type Li-ion battery using an inverse methodology. An experimental study is conducted on a commercial AMP20M1HD-A Li-ion battery to measure the surface temperature at various locations using thermocouples. The forward model consists of the three-dimensional unsteady conduction problem and is solved in COMSOL using experimental boundary conditions. The data generated is used to train an Artificial Neural Network, which acts as a replacement for the forward model. The Metropolis Hasting-Markov Chain Monte Carlo algorithm along with the Bayesian inference inverse model is used for analyzing the posterior distribution and the average estimates for thermophysical properties are obtained. The temperature dependence study shows a significant correlation between temperature and battery thermophysical properties. The accuracy of the employed inverse model is validated by obtaining the surface temperature using the estimated thermophysical properties and comparing it with the measured surface temperature.

Lunar North Polar Cold Traps Based on Diurnally and Seasonally Varying Temperatures

Lunar cold traps are defined by extremely low sublimation rates, such that water ice could have accumulated in them. Here time-averaged sublimation rates are calculated for the north polar region of the Moon based on over 14 years of Diviner surface temperature measurements. Data for each spatial pixel are binned according to subsolar (diurnal) and ecliptic (seasonal) longitude. The cold trap area poleward of 80°N is about 32% larger when defined by a time-average sublimation rate instead of by peak temperature. Apparently sunlit cold traps are identified, e.g., in Lenard Crater, where modeling of direct illumination reveals that the Sun briefly rises above the horizon each Draconic year. The true cold trap area is smaller than what can be determined from Diviner data. Also presented are north polar maps for the potential sublimation rate of relic buried ice and for subsurface cold trapping.

Review of the Capacity to Accurately Detect the Temperature of Human Skin Tissue Using the Microwave Radiation Method.

Microwave radiometry (MWR) is instrumental in detecting thermal variations in skin tissue before anatomical changes occur, proving particularly beneficial in the early diagnosis of cancer and inflammation. This study concisely traces the evolution of microwave radiometers within the medical sector. By analyzing a plethora of pertinent studies and contrasting their strengths, weaknesses, and performance metrics, this research identifies the primary factors limiting temperature measurement accuracy. The review establishes the critical technologies necessary to overcome these limitations, examines the current state and prospective advancements of each technology, and proposes comprehensive implementation strategies. The discussion elucidates that the precise measurement of human surface and subcutaneous tissue temperatures using an MWR system is a complex challenge, necessitating an integration of antenna directionality for temperature measurement, radiometer error correction, hardware configuration, and the calibration and precision of a multilayer tissue forward and inversion method. This study delves into the pivotal technologies for non-invasive human tissue temperature monitoring in the microwave frequency range, offering an effective approach for the precise assessment of human epidermal and subcutaneous temperatures, and develops a non-contact microwave protocol for gauging subcutaneous tissue temperature distribution. It is anticipated that mass-produced measurement systems will deliver substantial economic and societal benefits.

Higher accuracy estimation of the weighted mean temperature (Tm) using GPT3 model with new grid coefficients over China

The weighted mean temperature (Tm) is an essential parameter in the field of Global Navigation Satellite System (GNSS) meteorology, as it enables the conversion of zenith wet delay to atmospheric precipitable water vapor (PWV). The existing Tm models, such as the GPT3 model, utilize the empirical annual and semi-annual amplitudes of Tm at grid points along with corresponding trigonometric functions to estimate Tm, making it difficult to provide a more detailed description of the daily variation of Tm. Therefore, the measured surface temperature and water vapor pressure were introduced to calculate the new series of grid coefficients using the least-square method, which enables the GPT3 model to estimate Tm with higher accuracy. In this process, the ERA5 reanalysis data derived from ECMWF from 2001 to 2010 was utilized to compute Tm values, and the new grid coefficient obtained by introducing only surface temperature was considered as scheme #1 and that achieved by introducing both surface temperature and surface water vapor pressure was regarded as scheme #2. The Tm data of 2023 calculated both by the radiosonde and ERA5 reanalysis data were utilized to assess the new grid coefficients for Tm estimation, and the results show that the grid coefficients based on these two surface parameters outperformed those only using the temperature. In the compassion using the ERA5 data, the bias/MAE/RMSE with the two types of new grid coefficients are −0.09/1.89/2.41 K and − 0.07/1.82/2.31 K, respectively, and the improvements reach to 50%/32.5%/34.2% and 61.1%/35%/36.9% compared with GPT3 model. In the comparison with radiosonde data, the scheme #1 and #2 performed a 0.55 K(13.8%) and 0.68 K(17.1%) improvement in the mean RMSE of stations compared with the GPT3 model. Note that the proposed two schemes can effectively improve the accuracy of estimating Tm in the comparison of regions, epochs and seasons with the Bevis and GPT3 model. Furthermore, the impact of Tm on PWV can be improved by the proposed two schemes, which was demonstrated using the theoretical RMSE and relative error of PWV.

Temperature imaging inside fluid devices using a ratiometric near infrared (NIR-II/III) fluorescent Y2O3: Nd3+, Yb3+, Er3+ nanothermometer

Luminescence thermometry is a non-contact method that can measure surface temperatures and the temperature of the area where the fluorescent probe is located, allowing temperature distribution visualizations with a camera. Ratiometric fluorescence thermometry, which uses the intensity ratio of fluorescence peaks at two wavelengths with different fluorescence intensity dependencies, is an excellent method for visualizing temperature distributions independent of the fluorophore spatial concentration, excitation light intensity and absolute fluorescence intensity. Herein, Nd3+/Yb3+/Er3+-doped Y2O3 nanomaterials with a diameter of 200 nm were prepared as phosphors for temperature distribution measurement of fluids at different temperatures. The advantages of this designed fluorescent material include non-aggregation in water and the fact that its near-infrared (NIR) fluorescence excitation (808 nm) is not absorbed by water, thereby minimizing sample heating upon irradiation. Under optical excitation at 808 nm, the ratio of the fluorescence intensities of Yb3+ (IYb; 975 nm) and Er3+ (IEr; 1550 nm), which exhibited different temperature responses, indicated the temperature distribution inside the fluid device. Thus, this technique using Nd3+/Yb3+/Er3+-doped Y2O3 is expected to be applied for temperature distribution mapping analysis inside fluidic devices as a ratiometric NIR fluorescence thermometer, which is unaffected by laser-induced heating.Graphical abstract

Urban morphology clustering analysis to identify heat-prone neighbourhoods in cities

Exposure to heat is a major health concern to urban populations. Cities aim to reduce outdoor thermal stress by adapting the built environment, but the spatial heterogeneity within cities makes it difficult to establish universal mitigation strategies. We present a methodology that identifies the hottest neighbourhoods in a city and links them to underlying patterns in urban form and function, to derive heat mitigation measures for individual neighbourhoods according to their characteristics, mitigation potential, and average surface temperature. The method applies k-means clustering and is applicable to any city using available datasets on surface cover and building morphology, as well as globally available satellite measurements of surface temperatures. Here, we present a heat-mitigation analysis for the city of Zurich. The clustering differentiates seven neighbourhood types, including two types of residential areas, modern neighbourhoods with high-rise buildings, historical districts, and industrial zones. The hottest temperatures are in neighbourhoods with extensive impervious ground cover such as railway tracks and airport parking. Surface temperatures strongly correlate with impervious surface cover and vegetation cover for all neighbourhoods, with building cover only for non-industrial built neighbourhoods, and with sky-view factor for all neighbourhoods except those with large vegetation cover. Historical, modern, and industrial neighbourhoods are particular heat-prone, and increasing vegetation for evaporative cooling is a suggested mitigation strategy for all. Modern and industrial areas could benefit from shading through increase of tree cover, while historical centres may adapt vertical greening as suitable heat mitigation strategy.

Accuracy assessment of land surface temperature retrievals from remote sensing imagery: pixel-based, single and multi-channel methods

Land Surface Temperature (LST) measurements using remote sensing data are conducted using various methods, including the Single Channel Method (SCM) and the Multi-Channel Method (MCM). Emissivity, which significantly affects LST, is determined using methods such as the Correction-Based Emissivity Method (CBEM) and the Non-Linear Based Emissivity Method (NBEM). Given these factors, validating LST measurements is crucial for assessing the accuracy of data processing results. This study aims to compare LST data retrieved using SCM and MCM with various emissivity calculation methods and assess their accuracy compared to single measurements with an infrared thermometer, measurements using a grid plot, and measurements using a thermal camera with the assistance of an Unmanned Aerial Vehicle (UAV). The results show that SCMJM&S and SCMAC did not produce optimal accuracy (>2.5K) when using the emissivity estimates from CBEM and NBEM. In contrast, MCM that adopted MCMSko, MCMQin, and MCMMao generally produced high-accuracy LST with an average difference of <1 K from actual temperatures, specifically in shrub pixels. MCM with the NDVI threshold method (NDVITHM) showed excellent results in distinguishing between vegetation and non-vegetation objects. In conclusion, MCM provides more accurate LST than SCM. Nevertheless, selecting the appropriate method should consider heterogeneity, homogeneity, and regional physiography.

ROOT SURFACE TEMPERATURE MEASUREMENT DURING ROOT CANAL OBTURATION

Prolonged exposure to elevated temperatures exceeding 47°C, which can occur during root canal obturation, can cause damage of both dental and bone tissues. In order to study the temperature distribution on the surface of the tooth root a temperature measuring device with cold-junction compensation is proposed. For in vitro measurement of the temperature distribution on the surface of the tooth, 8 thermocouples placed in direct contact with the cementum of the tooth were used. In order to eliminate the cold-junction temperature variations, the temperature equilibration device and RTD were used. The suggested linear approximation for the thermocouples' conversion function provides a nonlinearity relative error of less than 0.05% for K-type thermocouples and 0.07% for J-type thermocouples over the temperature range from 20 to 60°C.

Strategic Integration of a Vegetative Component on a Metal Roof Base: An Evaluation of Its Impacts on Thermal and Acoustic Performance in the Tropics

This paper attempts to ascertain the thermal and acoustic impacts of introducing a vegetative roof layer on insulated and uninsulated metal roofs for tropical climates, through field measurements in Skudai, Johor, Malaysia, that were conducted for both dry and wet days. Four small-scale roof modules were tested, namely an uninsulated metal roof (uiMDR), an insulated metal roof (iMDR), and two identical corresponding modules with an additional vegetative component (uiGR and iGR, respectively). Outdoor ambient temperature (Tamb) was the most influential correlated variable affecting the roof outer surface temperature (RTOS) in 50% of the assessed scenarios. On the selected dry day, the inter-quartile ranges (IQR) of iGR, iMDR, uiGR, and uiMDR were 6.21 °C, 8.32 °C, 6.69 °C, and 1.66 °C, respectively; the IQRs were 1.6 °C, 4.11 °C, 2.59 °C, and 1.78 °C, respectively, on the selected wet day. Based on design U-value calculations, iGR was better than iMDR and uiMDR for both dry and wet days. The U-value of uiGR was also better than iMDR under dry-day conditions. The Wilcoxon signed-rank test also indicated a statistically significant difference in the roof inner surface temperature (RTIS) measurements (p-value = 0.00) during Malaysian daylight hours, between 8.00 a.m. and 6.00 p.m., regardless of the weather. In terms of sound level reduction under dry-day conditions, the Kruskal–Wallis and Wilcoxon signed-rank tests showed statistically significant differences in sound level reductions, with iGR and uiGR performing better than iMDR and uiMDR (p-values = 0.00). The sound level reductions for iGR, iMDR, and uiGR were 51%, 32%, and 31%, respectively, while uiMDR experienced sound level amplifications by 6%, possibly due to the acoustic resonance effect. This proof of concept may encourage a broader application of extensive GRs in Malaysia using metal roofs, beyond the conventional RC base construction method.

Validation of Thermal Imaging and the ALT-70 Prediction Model to Differentiate Cellulitis From Pseudocellulitis

Cellulitis is misdiagnosed in up to 30% of cases due to mimic conditions termed pseudocellulitis. The resulting overuse of antibiotics is a threat to patient safety and public health. Surface thermal imaging and the ALT-70 (asymmetry, leukocytosis, tachycardia, and age ≥70 years) prediction model have been proposed as tools to help differentiate cellulitis from pseudocellulitis. To validate differences in skin surface temperatures between patients with cellulitis and patients with pseudocellulitis, assess the optimal temperature measure and cut point for differentiating cellulitis from pseudocellulitis, and compare the performance of skin surface temperature and the ALT-70 prediction model in differentiating cellulitis from pseudocellulitis. This prospective diagnostic validation study was conducted among patients who presented to the emergency department with acute dermatologic lower extremity symptoms from October 11, 2018, through March 11, 2020. Statistical analysis was performed from July 2020 to March 2021 with additional work conducted in September 2023. Temperature measures for affected and unaffected skin were obtained. Cellulitis vs pseudocellulitis was assessed by a 6-physician, independent consensus review. Differences in temperature measures were compared using the t test. Logistic regression was used to identify the temperature measure and associated cut point with the optimal performance for discriminating between cellulitis and pseudocellulitis. Diagnostic performance characteristics for the ALT-70 prediction model, surface skin temperature, and both combined were also assessed. The final sample included 204 participants (mean [SD] age, 56.6 [16.5] years; 121 men [59.3%]), 92 (45.1%) of whom had a consensus diagnosis of cellulitis. There were statistically significant differences in all skin surface temperature measures (mean temperature, maximum temperature, and gradients) between cellulitis and pseudocellulitis. The maximum temperature of the affected limb for patients with cellulitis was 33.2 °C compared with 31.2 °C for those with pseudocellulitis (difference, 2.0 °C [95% CI, 1.3-2.7 °C]; P < .001). The maximum temperature was the optimal temperature measure with a cut point of 31.2 °C in the affected skin, yielding a mean (SD) negative predictive value of 93.5% (4.7%) and a sensitivity of 96.8% (2.3%). The sensitivity of all 3 measures remained above 90%, while specificity varied considerably (ALT-70, 22.0% [95% CI, 15.8%-28.1%]; maximum temperature of the affected limb, 38.4% [95% CI, 31.7%-45.1%]; combination measure, 53.9% [95% CI, 46.5%-61.2%]). In this large diagnostic validation study, significant differences in skin surface temperature measures were observed between cases of cellulitis and cases of pseudocellulitis. Thermal imaging and the ALT-70 both demonstrated high sensitivity, but specificity was improved by combining the 2 measures. These findings support the potential of thermal imaging, alone or in combination with the ALT-70 prediction model, as a diagnostic adjunct that may reduce overdiagnosis of cellulitis.

Analysis method of explosive electromagnetic radiation energy

AbstractThe energy of electromagnetic radiation from explosions is coupled to electronic equipment circuits, disrupting the initiation sequence or causing failure in an increasing number of cases, which seriously affects the stability of weapon systems. There is a significant difference between the characteristics of the explosive electromagnetic radiation signals and modulated electromagnetic signals. The electric field intensity and signal power cannot directly represent the magnitude of the explosive electromagnetic radiation energy, and traditional electromagnetic signal analysis methods are unsuitable for explosion electromagnetic signal analysis. To solve this problem, the mechanism of explosive electromagnetic radiation was first analyzed. Through verification experiments of the explosion electromagnetic intensity and temperature, it was concluded that there is a strong correlation between the explosion plasma temperature and electromagnetic intensity. The temperature of the explosive plasma is derived based on the measured surface temperature of the explosive fireball, a functional relationship is established between the energy of the explosive plasma and the temperature of the plasma, and plasma energy is introduced as a parameter for electric field intensity correction. The interference signal analysis method based on eye diagram is used to calibrate the electromagnetic radiation damage ability, achieving quantitative analysis of the interference degree of electromagnetic radiation energy on the signal, and providing a new approach for the analysis of explosive electromagnetic radiation energy.

A cost-effective approach to measurements of fluorophore temperature sensitivity and temperature change with reasonable accuracy

The demand for measuring fluorophore temperature sensitivity and temperature change in chemical or biological samples has spurred the search for effective methods. While infrared (IR) light-based thermal devices are popular, they are limited to surface temperature measurement. Fluorescence-based thermometry, which utilizes intensity, lifetime, polarization, and spectrum change, provides the temperature information directly from the samples and can have high temporal and spatial resolution. However, measuring fluorescence can be tricky and expensive. A cost-effective approach to achieving reasonable accuracy is highly desired. This study introduces such an approach, employing a light-emitting diode (LED) for fluorophore excitation and a laser diode (LD) for sample heating, with a phone camera recording fluorescence changes. A data processing method converts the video into digital data, processed through digital filters. Utilizing a small-volume cuvette enhances heating efficiency. This study serves as a practical guide for inexperienced individuals, including students, instructors, and researchers, facilitating entry into the field and navigating the complexities of fluorescence-based thermometry.

Band Heater Heat Flux Characterization Using Inverse Heat Conduction Problem Models

Abstract An experimental apparatus was constructed to correlate water flowrate and temperature rise under an external band heater. Due to the physical characteristics of the band heater, its transient heating behavior is unknown. This paper investigates the application of inverse heat conduction problem (IHCP) methods to characterize the heat flux from the band heater. Three experiments with different heating times (5, 10, and 20 s) and no flowrate were conducted to measure the transient temperature under the 400 W band heater. Type-T thermocouples measure surface temperature at the centerline of the band heater. The experimental results are computed with five different heat conduction models. The models are chosen to identify how the heat flux response varies from a simplified to a realistic model. Additionally, the results of the experimental heat flux are compared to the manufacturer band heater data (58.9 kW/m2) for each model. The minimum time needed for the heater to fully energize the system is from 10 to 12 s. The residuals for each model are analyzed and used to evaluate the appropriateness of the five different models. The results show that the use of simpler models can achieve results similar to those of complex models, with less time and computational cost.

Subcutaneous temperature prediction during cryogenic jet cooling by surface temperature measurements and RNNs

Accurate information of subcutaneous temperature is crucial to the effectiveness and safety of a variety of medical procedures by energy-based devices, such as cooling treatments. Numerous strategies for invasive subcutaneous temperature monitoring have been investigated; however, they are impractical mainly due to their requirements for precise heat transfer modeling. In the case of cooling treatment by cryogenic substances, heat transfer rate largely varies with their phase change and procedure condition, which prevents accurate prediction of subcutaneous temperature. In this work, we propose a non-invasive, accurate, and practical prediction of subcutaneous temperature during CO2 spray jet cooling without numerical modeling by utilizing recurrent neural networks (RNNs) with surface temperature measurements for the first time. A patchable temperature sensor array on a flexible PCB is used to measure surface temperature. With this measured surface temperature, RNNs are developed with two functionalities: extraction of thermophysical properties (thermal conductivity and thermal diffusivity) of the target substrate and prediction of temperature response at 1-mm depth from the surface, which is the typical location of pain points. Five polymer thin films are used with the reference thermocouple embedded inside to train RNNs. A porcine skin is used to validate the RNNs, which show a prediction accuracy of 99.2%.

GLAZING SYSTEMS WITH SILICA AEROGEL FOR OPTIMIZING ENERGY CONSUMPTION IN HOTEL BUILDING

Due to their high energy consumption hotel buildings have a significant impact on the environment. Such constructions are particularly energy-intensive demanding environments so as to meet the various needs of occupants and ensure their comfort. Guests tend to consume more energy during their stays than they do in their own homes (water, electricity, light, temperature...). In recent years, Algeria has seen a significant increase in the construction of hotels, many of which are concentrated in the Mediterranean region, which experiences particularly high humidity levels in the summer and winter. The new generation of building materials used to enhance comfort and reduce building energy consumption, are progressively sought after all over the world. This work, which studied the performance of silica aerogels for the glazing system of hotel rooms in a Mediterranean climate, can provide a database for hotel designers, an investigation combining experimental work (air and surface temperature measurements and thermography) and numerical simulation (using TRNsys V17) was conducted on an existing hotel building located in Béjaia. The results revealed the insertion of silica aerogel within a 6 mm thick glazing sheet decreases significantly the heating and cooling requirements of the study indoor environment.

Exceptions to Fourier’s law at the macroscale

The usual basis to analyze heat transfer within materials is the equation formulated 200 years ago, Fourier's law, which is identical mathematically to the mass diffusion equation, Fick's law. Revisiting this assumption regarding heat transport within translucent materials, performing the experiments in vacuum to avoid air convection, we compare the model predictions to infrared-based measurements with nearly mK temperature resolution. After heat pulses, we find macroscale non-Gaussian tails in the surface temperature profile. At steady state, we find macroscale anomalous hot spots when the sample is topographically rough, and this is validated by using two additional independent methods to measure surface temperature. These discrepancies from Fourier's law for translucent materials suggest that internal radiation whose mean-free-path is millimeters interacts with defects to produce small heat sources that by secondary emission afford an additional, non-local mode of heat transport. For these polymer and inorganic glass materials, this suggests unique strategies of heat management design.