Real-time Temperature Measurement Research Articles

Determination of the possibility of using a functional apparatus for the production of multi-component semi-finished products with a high degree of readiness

The object of research is a functional apparatus for the production of paste-like and powdered vegetable semi-finished products. The problem of combined heat and mass exchange treatment of vegetable raw materials are solved by using a functional apparatus for concentration and drying for processing and production complexes, farm lands. The expected effect during the approbation of the device is predicted under the condition of a functional approach combining the processes of concentration and drying with precise control of the temperature regime due to the use of a film-like resistive electronic heater of the radiating type. In the study, the approbation of a functional device for the production of multi-component vegetable semi-finished products of a high degree of readiness, with concentration and drying in the field of infrared heating at a temperature of 50 °C, was performed. Real-time temperature measurement was carried out by an automatic unit based on TRM, which ensured the accuracy of control at each stage. The temperature of the mashed mass during processing increased from 20.3 °С to 23.2 °С, reaching optimal conditions for forming a film with a thickness of 0.5 mm. In the rotary reboiler, the temperature in height varied from 25.7 °C to 50.1 °C, which contributed to the preservation of the properties of thermolabile raw materials. In the lower part of the apparatus, the temperature of the finished product was 49.8 °C, maintaining the necessary temperature regime for drying. The device creates a convective air flow (0.01 m/s) with the help of Peltier elements, which stabilizes the heat exchange at a temperature of 51.3 °C on the technical partition. The practical use of the functional apparatus will allow its use at processing and production complexes and directly at the places of collection of plant raw materials for resource-efficient production of semi-finished products with a high degree of readiness. These semi-finished products can act as independent functional products for people in extreme conditions or as recipe ingredients for the recipes of various food products.

Autonomous nervous system responses to environmental-level exposure to 5G's first deployed band (3.5GHz) in healthy human volunteers.

Following the global progressive deployment of 5G networks, considerable attention has focused on assessing their potential impact on human health. This study aims to investigate autonomous nervous system changes by exploring skin temperature and electrodermal activity (EDA) among 44 healthy young individuals of both sexes during and after exposure to 3.5GHz antenna-emitted signals, with an electrical field intensity ranging from 1 to 2V/m. The study employed a randomized, cross-over design with triple-blinding, encompassing both 'real' and 'sham' exposure sessions, separated by a maximum interval of 1week. Each session comprised baseline, exposure and postexposure phases, resulting in the acquisition of seven runs. Each run initiated with a 150s segment of EDA recordings stimulated by 10 repeated beeps. Subsequently, the collected data underwent continuous decomposition analysis, generating specific indicators assessed alongside standard metrics such as trough-to-peak measurements, global skin conductance and maximum positive peak deflection. Additionally, non-invasive, real-time skin temperature measurements were conducted to evaluate specific anatomical points (hand, head and neck). The study suggests that exposure to 3.5GHz signals may potentially affect head and neck temperature, indicating a slight increase in this parameter. Furthermore, there was a minimal modulation of certain electrodermal metrics after the exposure, suggesting a potentially faster physiological response to auditory stimulation. However, while the results are significant, they remain within the normal physiological range and could be a consequence of an uncontrolled variable. Given the preliminary nature of this pilot study, further research is needed to confirm the effects of 5G exposure.

Static in-situ curing characteristics of CFRP based on near infrared laser

Study on static in-situ curing characteristics of CFRP based on near infrared laserThe quick curing method of carbon fibre reinforced plastics (CFRP) is one of the hotspots in current research. A static in-situ curing method for CFRP prepreg based on near-infrared laser was put forward in this study. The in-situ curing structural characteristics and the mechanism of CFRP were investigated through real-time surface temperature measurement, COMSOL temperature field simulation, 3D measurement of curing morphology and resin curing degree test. The thermal conductivity of the CFRP along the fiber direction is considerably higher than that along the perpendicular fiber direction. As a result, the temperature profile in the plane takes on an elliptical shape. During the transfer, the temperature field gradually decreases, resulting in an ellipsoidal 3D high-temperature distribution. The different shrinkage phenomena in the different curing regions between the layers lead to an irregular ellipsoidal solidification morphology of the unidirectional CFRP. The temperature in the center of the heat affected zone increases as a power exponential function with time. The area and depth of the heat-affected zone increases with the laser power, and the curing area is positively correlated with the degree of curing. As a result, curing temperature governing equations based on laser power and layer thickness have been proposed, while relationship equations based on laser power, curing depth and curing morphology have been developed. In addition, prediction equations based on curing morphology have been developed for curing degree, in order to achieve precise curing of CFRP.

Real-time measurement of electron temperature using a coupled centre-tapped emissive probe and a Langmuir probe (CCTELP)

Abstract Conventional electrostatic probes are commonly used for measuring the plasma parameters like plasma density, electron temperature, plasma potential and floating potential respectively in a laboratory plasma to unveil physical processes of relevance to magnetosphere and fusion plasmas. With such probes, real-time measurement of electron temperature and its fluctuating AC component becomes very difficult. These measurements are significant as they directly give the measure of energy flux and hence can be utilized in estimating the turbulence-induced energy loss in any plasma system, especially useful for electron temperature gradient plasmas. Although Triple Langmuir Probe (TLP) diagnostics provide information on real-time measurement of electron temperature, this involves many complications. These complications of TLP are suitably overcome by the proposed diagnostic, which makes use of a combination of a closely separated centre tap emissive probe (CTEP) and Langmuir probe (LP) installed on a single probe head. The coupled centre-tapped emissive and Langmuir probe (CCTELP) provides direct measurement of electron temperature (T_e) and its fluctuations (T ̃_e). The design of the proposed diagnostic is very simple and it is almost free of rigorous control electronics and post-data analysis otherwise needed to estimate the electron temperature. These features make this diagnostic an additional and simple candidate for real-time measurement of electron temperature and its fluctuation. The paper will highlight details on this novel diagnostics, called CCTELP for the measurement in Maxwellian plasmas.

Automated Workflow Uses Pressure, Rate Measurements for Well Monitoring

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 218470, “A New Automated Workflow for Well Monitoring Using Permanent Pressure and Rate Measurements,” by Anton Shchipanov, SPE, NORCE; Boyu Cui, SPE, University of Stavanger; and Vitaly Starikov, SPE, Heriot-Watt University, et al. The paper has not been peer reviewed. _ The complete paper describes an integrated workflow for automated well monitoring using pressure and rate measurements obtained with permanent gauges and flowmeters. The workflow is based on time-lapse pressure transient analysis (PTA) and integrates the following components: virtual flowmetering (VFM), transient identification, feature extraction and pattern recognition in transient pressure responses, and assessment of well performance based on PTA metrics. The methodology behind the workflow combines different physics-informed and data-driven methods described in the paper. A New Workflow for Automated Well Monitoring The concept of the automated workflow described in the complete paper has been suggested in the literature. The workflow consists of four main steps (Fig. 1), and integrates the following components: - VFM - Pressure transient identification - PTA feature extraction and time-lapse pattern recognition - PTA metrics to obtain the well-performance profile VFM. The well flow rate is often measured more sparsely than its bottomhole or wellhead pressure for many reasons. However, the permanent downhole or wellhead gauges, when installed in a well, provide high-frequency real-time pressure and temperature measurements. This disparity in the pressure- and rate-measurement sampling rates is a problem for PTA, which requires accurate and synchronized input for flow rates in line with the pressure time series. VFM helps reconstruct missing flow-rate measurements using available pressure measurements. The automated well-monitoring workflow described in this paper would benefit from integration of a relatively simple but robust and scalable VFM model having modest input-data requirements. The steady-state vertical-lift-performance-based VFM model was chosen for use by the authors after testing. The model uses only the bottomhole and wellhead pressure difference, which fits well with the automated workflow requirements. Put simply, the VFM-developed algorithm identifies the suitable pressure and flow-rate samples to train the VFM model; estimates, filters, and verifies the evolution of the model coefficients; and predicts the missing flow-rate values using the pressure measurements where available. This prediction applies only to the time when the well is flowing; as for shut-in periods, the algorithm assigns zero rate values. Transient Identification. Proper transient identification is crucial for PTA because accurate identification of the starting break point of a transient and sequential synchronization with corresponding rate governs the reliability of PTA interpretation results. This is important not only for the transient in question but also for the well history preceding the transient. In the latter case, proper transient identification may help in reliable rate time allocation throughout the well history for common cases of uncertain rates. The workflow of the extended method consists of two main stages. First, the breakpoints indicating start and end of shut-in transients are detected using the topographic prominence max rotation (TPMR) method. This automatically provides detection of flowing transients between the shut-ins identified. Second, the breakpoints of multirate periods (assuming stepwise rate changes) are detected within the long flowing transients using the local minimum in rotation (LMIR) method. As a result, the well pressure history is divided into sequential flowing and shut-in transients, also differentiating flowing transients governed by stepwise rate changes. Neither TPMR nor LMIR methods require denoising, so raw data from permanent gauges may be used without any preprocessing.

In vitro heat insulation efficacy of 5% dextrose versus 0.9% saline during radiofrequency ablation.

This study compared the efficacy of heat insulation between 5% dextrose and 0.9% saline in radiofrequency ablation (RFA). Accordingly, temperature variations and maximum temperatures were assessed at identical distances and heat field distributions. Cubes of porcine liver tissue, measuring 10 mm across, were selected to precisely align the ablation boundary with the tissue boundary. An 18-gauge electrode with a 7-mm tip was inserted into each cube (10 per group) in a stainless-steel cup containing 40 mL of 5% dextrose or 0.9% saline. Fixed ablation was performed for 3 minutes using continuous mode at 30 W, simulating the typical thermal environment during thyroid RFA. Real-time temperature measurements were recorded by sensors positioned 0, 1, 3, and 5 mm from the cube's edge. A comparative analysis was conducted to assess the maximum temperature, temperature variation, and duration of temperatures exceeding 42℃. In both groups, the temperature curve declined with increasing distance from the edge of the ablated tissue. However, 0.9% saline exhibited higher maximum temperatures at 1, 3, and 5 mm compared to 5% dextrose (1 mm: 44.55°C±5.25°C vs. 34.68°C±3.07°C; 3 mm: 39.64°C±2.53°C vs. 29.22°C±2.21°C; 5 mm: 38.86°C±2.14°C vs. 28.74°C±2.51°C; all P<0.001). Considering a nerve injury threshold of 42°C, the 0.9% saline also displayed a greater proportion of samples reaching this temperature and a longer duration of temperatures exceeding it (P<0.05). The heat insulation efficacy of 5% dextrose at 1-5 mm exceeds that of 0.9% saline at identical distances and in a common thermal environment during thyroid RFA.

Highly stable lead-free perovskite glass for real-time 3D surface temperature measurement across the wide temperature range

3D Optical temperature sensors are highly valuable in various fields such as industry, medicine, and aerospace, as they allow for real-time detection of temperature distribution on surfaces. However, existing temperature sensor materials face challenges in achieving accurate detection across a wide range of temperatures from low to high. To overcome these issues, Cs2Zn1-xEuxBr4 perovskite B2O3–BaF2-Ga2O3 (BBG) borate glass was prepared for non-contact 3D optical temperature sensing. Cs2Zn1-xEuxBr4 perovskite B2O3–BaF2-Ga2O3 (BBG) borate glass improves the stability, thus expanding the temperature measurement range. Utilizing the fluorescence intensity ratio (FIR) method, based on the 613 nm (Eu3+: 5D0→7F2) and 592 nm (Eu3+: 5D0→7F1) emission bands, a FIR benchmark database was constructed for the entire temperature range from 10 K to 640 K. Temperature calculation equations related to FIR were derived, with a maximal relative sensitivity of 0.08 % K−1 at 180 K. The method for measuring the surface temperature of 3D curved aircraft is based on the rare earth perovskite borate glass. The temperature measured with this method is close to the thermal imaging temperature and has a good temperature measuring effect. The temperature measurement range is wider and more advantageous than previously reported ranges. These results indicate that Cs2Zn1-xEuxBr4-BBG perovskite borate glass temperature sensors have broad application prospects in many applications.

Composite structures with embedded fiber optic sensors: A smart propellant tank for future spacecraft applications

Modern spacecraft and launch vehicle design is more oriented towards reducing system-level design and assembly complexities. In order to maintain high overall system performance while reducing these complexities, the use of smart materials and smart structural components is a well-known practice and is currently of rising interest to space systems' designers. The paper discusses a concept of smart space structures, in particular, a carbon fiber composites structure embedded with Optical Fiber Sensors (OFS) for spacecraft and launch vehicle applications. This study highlights the operational requirements for such tank and the smart features enabled by the optical fiber sensors. For the latter aspect, a quantitative comparison between Fiber Bragg Grating sensors (FBGs) and Distributed Optical Fiber Sensors (DOFS) based on Optical Frequency Domain Reflectometry (OFDR) is presented to state their core performance parameters, such as the sensitivity, sensing range, dynamic measurement capability, and spatial resolution. The increased performance and reliability in harsh environments associated with fiber optic sensors come with a reduction in size, mass, and power consumption compared to the conventional electronic sensors. Optical fiber sensors embedded in carbon fiber structures have proven their capability in providing accurate real-time measurements of temperature and monitoring structural integrity while detecting precisely possible points of rupture and failure as discussed and demonstrated in the literature review. The applications of fiber optic sensing in smart propellant tanks may extend to detecting fluid leakage, also providing increased precision in propellant gauging through temperature mapping, and can be used in on-ground qualification, pre-flight testing, as well as in-orbit operation, condition, and structural health monitoring. The article presents a statement for an optimal FOS embedding approach in composite pressure vessels and discusses the related placement and orientation method for the fiber optic sensors, coupled with a one component simplified analytical stress-strain transfer model deriving the stress component along the maximum principal direction (i.e., σMaxPrincipal). The novel approach is believed to serve the optimal employment of embedded FOS in composite structures, e.g., pressure vessels and light-weight structures in spacecraft, among other applications. The simplified model is believed to pave the way for effective data interpretation and processing, utilizing the available limited computational resources on-board the spacecraft.

A particle filter-based approach for real-time temperature estimation in a lithium-ion battery module during the cooling-down process

Temperature exerts a remarkable influence on the safety, performance, and lifespan of lithium-ion (Li-ion) batteries. Consequently, the real-time monitoring of this variable within battery cells emerges as both crucial and a significant challenge for battery thermal management systems. This paper introduces an approach for real-time temperature estimation in a Li-ion battery module during the cooling-down process by applying the particle filter (PF) technique. A battery module comprising fifteen cylindrical lithium cobalt oxide cells connected in series was employed for experimentation. The battery module was arranged into three rows, the PF employs real-time temperature measurements from the first cell of each string, based on a fractal-time model and an artificial evolution approach for parameter estimation, to estimate two unknown parameters and the temperature of the remaining twelve cells. Furthermore, the temperature measured on the surface of each cell in the module was compared both the PF and computational fluid dynamics (CFD) approach. The results exhibit a favorable agreement between the cell temperature estimates derived from the PF, the CFD simulations, and their respective experimental measurements. Finally, the proposed approach facilitates real-time temperature monitoring in battery modules with a reduced number of temperature sensors, thereby implying a cost reduction in the data acquisition system.

Temperature transient analysis: Static formation temperature test (SFTT) an overview

When drilling a geothermal well, obtaining real-time measurements of the static formation temperature is impractical due to the cooling effect of drilling fluids on the surrounding rock, which masks the actual reservoir temperatures. To overcome this, the site engineers undertake a static formation temperature test (SFTT) to ascertain the stable reservoir temperature. The present work reviews existing methods for analysing data using five different temperature sets sourced from published literature. Based on our analysis, we provide recommendations on the application of the various analytical techniques. We recommend utilising the spherical method when using bottom-hole data, whilst, for any other depth, we recommend one of the cylindrical methods. These recommendations aim to enhance the accuracy and reliability of SFTT assessments during geothermal well drilling operations.

Impact of optical fiber-based photo-activation on dental composite polymerization

ObjectivesThe study aimed to introduce a novel two-step optical fiber-based photo-activation of dental resin-based composites (RBCs) for reducing polymerization shrinkage stress (PSS). MethodsProposed protocol design – in the first step, two flexible plastic optical fibers connected to a dental light curing unit (LCU), were used as light guides inserted into the filling to initiate low-irradiance polymerization from within; in the second step, fibers were extracted and remaining voids were filled with RBC, followed by conventional high-irradiance curing to finalize polymerization. Three bulk-fill RBCs were tested (Beautifil-Bulk Restorative, Filtek Bulk-fill Posterior, Tetric PowerFill) using tooth cavity models. Three non-invasive examination techniques were employed: Digital Holographic Interferometry, Infrared Thermography, and Raman spectroscopy for monitoring model deformation, RBC temperature change, and degree of conversion (DC), respectively. A control group (for each examined RBC) underwent conventional photo-activation. ResultsThe experimental protocol significantly reduced model deformation by 15 – 35 %, accompanied by an 18 – 54 % reduction in RBC temperature change, emphasizing the impact of thermal shrinkage on PSS. Real-time measurements of deformation and temperature provided indirect insights into reaction dynamics and illuminated potential mechanisms underlying PSS reduction. After a 24-hour dark-storage period, DC outcomes comparable to conventional curing were observed, affirming the clinical applicability of the method. ConclusionsProtocol involving the use of two 1.5 mm fibers in the first step (300 mW/cm2 x 10 s), followed by a second conventional curing step (1000 mW/cm2 x 10 s), is recommended to achieve the desired PSS reduction, while maintaining adequate DC and ensuring efficient clinical application. Clinical SignificanceObtained PSS reduction offers promise in potentially improving the performance of composite restorations. Additionally, leveraging the flexibility of optical fibers improves light guide approach for restorations on posterior teeth. Meanwhile, implementation in clinical practice is easily achievable by coupling the fibers with commercial dental LCUs using the provided plastic adapter.

Anomalously Large Heat Generation of Hydration Water under Microwave Irradiation.

Much attention has been paid to the biological effects of microwave irradiation. The hydration water surrounding a biomolecule is crucial in its biological reactions and functions. Therefore, it is important to know the response of hydration water to microwaves to understand their biological effects; however, the scarcity of studies about it often leads to speculations and debates about that effect. In this study, we have made real-time temperature measurements of reverse micellar solutions with their water droplet size from ∼2.3 to ∼9.5 nm using a waveguide system combined with a microwave generator at 2.45 GHz. The heat generated by water in reverse micelles has been observed to depend on their size. It is about 10 times larger than that of liquid water at their small sizes (<∼3.5 nm) and diminishes with further enlarging the size, approaching the water's value at their large sizes (∼10 nm). These results indicate that the heat generation behavior has an interfacial effect; specifically, the hydration water on the surfactant layer produces heat 10 times larger than bulk water. Moreover, the hydration number per surfactant molecule decreases in a core-shell model with increasing the reverse micelle size. These features are also reflected in the heat generation rate. Our findings may offer a new and fundamental perspective for studies on the biological effects of microwave irradiation.

Multi-spectral radiation thermometry based on the reconstructed spectral emissivity model

With the aim of solving the problem of detecting the true temperature of objects using multi-spectral radiation thermometry, this paper proposes a multi-spectral radiation thermometry method based on the reconstructed spectral emissivity model. Compared with traditional hypothetical spectral emissivity models, this model is a reconstruction emissivity model based on actual measured brightness and known wavelength quantities, which can improve the characterization of the real emissivity state of the measured object and not be affected by any hypothetical conditions or artificial preset parameters. In order to verify the effectiveness of the proposed method for detecting the temperature of real problems, a total of eight typical spectral emissivity models, spectral data of Si3N4 ceramics and ZrO2 ceramics, and measurement data of rocket motor casings are selected for experiments. The experimental results indicate that the true temperature calculated by the proposed method has a relative error of less than 1 %, and the calculation time is kept at the millisecond level, which meets the real-time multi-spectral temperature measurement requirements.

A FE Model for the Efficient Simulation of the Thermo-Elastic Machine Tool Behavior

In modern machine tools, the thermo-elastic positioning error has a large share on the overall machine tool error. A multitude of different approaches can be found in the literature to correct this error in order to improve the accuracy of the machine tool. However, an FE-based approach to model the thermo-elastic error of 5 axis machine tools is still an active field of research. Therefore, the goal of this work is the improvement of the machine tool accuracy by modelling the machine tool error using a FE model. The model uses real time measurement data of the thermo-elastic error using an R-Test, as well as real time temperature measurements of the machine structure.

Packaged WGM MBR sensor for high-performance temperature measurement using CNN-based multimode barcode images.

The whispering gallery mode (WGM) optical microresonator sensors are emerging as a promising platform for precise temperature measurements, driven by their excellent sensitivity, resolution and integration. Nevertheless, challenges endure regarding stability, single resonant mode tracking, and real-time monitoring. Here, we demonstrate a temperature measurement approach based on convolutional neural network (CNN), leveraging the recognition of multimode barcode images acquired from a WGM microbottle resonator (MBR) sensor with robust packaged microresonator-taper coupling structure (packaged-MTCS). Our work ensures not only a high sensitivity of -14.28 pm/℃ and remarkable resolution of 3.5 × 10-4 ℃ across a broad dynamic range of 96 ℃ but also fulfills the demands for real-time temperature measurement with an average detection accuracy of 96.85% and a speed of 0.68s per image. These results highlight the potential of high-performance WGM MBR sensors in various fields and lay the groundwork for stable soliton microcomb excitation through thermal tuning.

Fast data processing method for multispectral radiation thermometry based on Euclidean distance optimization.

This study presents a fast and accurate data processing method for multispectral radiation thermometry that can accurately measure the true temperature of steel materials without requiring a priori emissivity model. The method generates a temperature matrix by inputting emissivity values at different wavelengths and selects a reference vector from the matrix. Then, it rearranges the temperature matrices at other wavelengths and calculates the Euclidean distance between each column element of the rearranged matrix and the reference vector. The method uses an unconstrained optimization technique to minimize the Euclidean distance and obtain the true temperature and emissivity of the object simultaneously. We evaluate the performance of the method by simulation and experiment in the response band of 1.4 ∼ 2.5 µm and temperature range of 873 ∼ 1173 K. The simulation results indicate that the relative error of the inverted temperature is within 0.229%, and the average computation time is less than 112.301 ms. The experimental results show that the maximum temperature error during the measurement process is 0.813%. Our method provides a feasible and efficient solution for real-time temperature measurement of steel materials.

Investigation of SrB4O7:Tm2+ luminescence for temperature imaging with high sensitivity based on time-resolved luminescence.

In recent years, with the growing demand for non-contact and real-time optical temperature measurements, it has become imperative to develop new luminescent thermometry materials as well as novel temperature detection schemes with higher sensitivity. In this work, a series of Sr1-xB4O7:xTm2+ (x = 0.001-0.01) polycrystalline powder samples were prepared using a high temperature solid state reaction under ambient atmospheric conditions. The emission spectra and the luminescence decay curves of the red emission corresponding to the 4f125d → 4f13(2F7/2) transition of Tm2+ were recorded at intervals of 10 K from 280 K to 380 K. Both the emission intensity and the fluorescence lifetime exhibited remarkable temperature correlation in the studied temperature range, which reached the best relative temperature sensitivities of 3.55% K-1 and 3.86% K-1 at 363 K and 346 K, respectively. Furthermore, taking into account the highly sensitive variation of the fluorescence lifetime of Tm2+ in the SrB4O7 matrix as the temperature increased near room temperature, a time-resolved temperature measurement scheme was performed to realize real-time temperature field imaging. By utilizing the intensified charge coupled device (ICCD) camera with a time gate to acquire the integral intensity of distinct time intervals and calibrate the relationship between the ratio and the temperature, the experimental results demonstrated that this temperature measurement scheme can achieve a maximum relative sensitivity of 9.39% K-1 at 313 K, which significantly surpasses the relative sensitivity of the other two conventional temperature measurement schemes.

Machine-learning based process monitoring for automated composites manufacturing

AbstractAutomated fibre placement (AFP) is an advanced robotic manufacturing technique which can overcome the challenges of traditional composite manufacturing. The interlaminar strength of AFP-manufactured composites depends on the in-situ thermal history during manufacturing. The thermal history is controlled by the choice of processing conditions and improper interfacial temperatures may result in insufficient bonding. Being able to better predict such maintenance issues in real-time is an important focus of smart manufacturing and Industry 4.0 to improve manufacturing operations. The data analysis of real-time temperature measurements during AFP composites manufacturing requires the temperature profiles from Finite Element Analysis (FEA) based simulations of the AFP process to better predict the quality of layup. However, the FEA simulations of the AFP process are computationally expensive. This study focuses on developing a digital tool enabling real-time process monitoring and predictive maintenance of the AFP process. The digital tool constitutes a machine learning-based surrogate model based on results from Finite Element Analysis (FEA) simulations of the AFP process to predict the in-situ thermal profile during AFP manufacturing. Multivariate Linear Regression, Multivariate Polynomial Regression, Support Vector Machine, Random Forest and Artificial Neural Network (ANN)-based models are compared to conclude that ANN based surrogate model performs best by predicting the important parameters of thermal profiles with a mean absolute percentage error of 1.56% on additional test data and reducing the time by four orders of magnitude as compared to FEA simulations. The predicted thermal profile can be compared with the real-time in-situ temperatures during manufacturing to predict the quality of the layup. A GUI application is developed to provide predicted thermal profiles data for analysis in conjunction with real-time temperatures during manufacturing enabling monitoring and predictive maintenance of the AFP process and paving way for the development of a digital twin of the AFP composites manufacturing process.

Thermomechanical behavior and microstructural characteristics of 7055 aluminum alloy during friction stirring welding

This study employed a viscoplastic finite element model and re-meshing technique to investigate the thermomechanical response of 7055 aluminum alloy during friction stir welding (FSW). The stirring pin rotates at 800–1200 rpm and moves at 80–120 mm/min (i.e., welding speed) during the FSW process. The temperature field and viscoplastic flow were mathematically modeled based on a computational solid mechanics method and predicted by a three-dimensional coupled thermomechanical numerical simulation. To validate the simulation results, real-time temperature measurements at the welded joint were acquired via thermocouples embedded in the plate prior to the welding process. Additionally, optical microscopy and electron backscatter diffraction techniques were used to examine the metallographic structure and grain orientation of the welds, respectively. The results revealed a temperature difference of 5–10 ℃ between the advancing and retreating sides of the plate. The material on the advancing side was extruded upward in a circular motion and eventually reached the surface. Meanwhile, the material on the retreating side moved half a revolution around the pin and remained at the original depth of the plate. Complete dynamic recrystallization occurred in the weld nugget, resulting in the formation of fine equiaxed grains with random orientation. In the thermomechanically affected zone, the proximity to the weld nugget was associated with an increased proportion of high-angle grain boundaries and recrystallized structures. Simultaneously, the intensity of the deformation texture decreased, while the recrystallized texture showed an initial strengthening followed by a subsequent weakening. This comprehensive investigation contributes to a deeper understanding of the thermomechanical behavior and metallographic characterization of 7055 aluminum alloy during the FSW process.

Thermoelectric Nanofluidics Probing Thermal Heterogeneity inside Single Cells.

Accurate temperature measurement in one living cell is of great significance for understanding biological functions and regulation. Here, a nanopipet electric thermometer (NET) is established for real-time intracellular temperature measurement. Based on the temperature-controlled ion migration, the temperature change in solution results in altered ion mobilities and ion distributions, which can be converted to the thermoelectric responses of NET in a galvanostatic configuration. The exponential relationship between the voltage and the temperature promises highly sensitive thermoelectric responses up to 11.1 mV K-1, which is over an order of magnitude higher than previous thermoelectric thermometry. Moreover, the NET exhibits superior thermal resolution of 25 mK and spatiotemporal resolution of 100 nm and 0.9 ms as well as excellent stability and reproducibility. Benefiting from these unique features, both thermal fluctuations in steady-state cells and heat generation and dissipation upon drug administration can be successfully monitored, which are hardly achieved by current methods. By using NET, thermal heterogeneities of single cancer cells during immunotherapy were reported first in this work, in which the increased intracellular temperature was demonstrated to be associated with the survival benefit and resistance of cancer cells in immunotherapy. This work not only provides a reliable method for microscopic temperature monitoring but also gains new insights to elucidate the mechanism of immune evasion and therapeutic resistance.