Real-time Temperature Research Articles

Cold Storage Temperature Monitoring with Trilinear Interpolation

The Internet of Things (IoT) is widely applied in agriculture and in-dustry. This article proposes a 3-dimensional temperature monitoring model for cold storage, enhancing the ability to observe and detect abnormal temperature changes within the storage. Using real-time temperature acquisition and applying trilinear interpolation techniques, this model determines temperatures at all loca-tions in the storage. It accepts temperature values and sensor locations, appropri-ately segmenting the cold storage space to apply trilinear interpolation and deter-mine the temperature value at every location in the storage. The model was tested based on different number of sensors and different sensor locations. Based on that, we provide an optimal solution for the number of sensors needed for cold storage.

Downhole flow control: A key for developing enhanced geothermal systems in horizontal wells

Field evidence indicates that only a few large fractures in enhanced geothermal systems (EGS) may dominate fluid flow and lead to the development of thermal short-circuiting. This problem would worsen in heat harvesting through horizontal wells with abundant fractures (induced or natural ones). To limit flow shortcuts that may cause early thermal breakthroughs, we propose a temperature-sensitive flow management system and explore its potential benefits during the development of EGS in horizontal wells. The proposed downhole flow management system consists of sensors for real-time temperature monitoring, and flow control devices for adjusting injection distribution in the wellbore. We evaluate the performance of such systems over 50 years of operation by numerical analysis. The results indicate that, when the proposed system is utilized, the produced fluid temperature will be increased by up to 60 K for a field-scale example. Besides, the application of the proposed flow management system can increase the cumulative heat extraction by up to 48.25 % (0.69 × 1016 J) after 50 years. The heat extraction efficiency can be improved by up to 94.16 %. During the EGS operation, it is suggested to implement multi-stage fluid control, but it is not recommended to reopen the fluid injection that has been previously shut down. Finally, the flow management system can mitigate the negative impacts of geological heterogeneities or the heterogeneity in fracture hydraulic conductivities. This paper proposes a new concept of flow management to integrate horizontal wells into efficient EGSs for power generation and heat extraction in the future.

THE TRAINING OF TEMPERATURE MONITORING SYSTEM WITH LM35 SENSOR BASED ON RESBEERY PI

Abstract. This community service activity aims to train the community on developing and implementing a temperature monitoring system using the LM35 sensor integrated with Raspberry Pi. This training was carried out to improve people's understanding and skills, especially in utilizing microcontroller-based technology and IoT (Internet of Things) in daily life, especially in the field of room temperature or environmental monitoring. This activity involves targets, for example, teachers, students, and technicians, at SMA AL-IRSYAD TEGAL, with training methods that include basic theories about temperature sensors, introduction to Raspberry Pi, and hands-on practice in assembling and programming temperature monitoring systems. The results of this training showed that participants could understand the basic concepts of temperature monitoring systems and could apply the knowledge gained in making simple devices that can measure and display temperature in real time. The activity evaluation showed that most of the participants felt helped by this training and showed high interest in further developing the system that had been studied. In the future, it is hoped that similar activities can continue to be carried out to empower the community to utilize technology for daily needs. Keywords: Community service, temperature monitoring system, LM35, Raspberry Pi, technology training.

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.

PEDOT:PSS-Based Wearable Flexible Temperature Sensor and Integrated Sensing Matrix for Human Body Monitoring.

Flexible temperature sensors have been widely used in electronic skins and health monitoring. Body temperature as one of the key physiological signals is crucial for detecting human body's abnormalities, which necessitates high sensitivity, quick responsiveness, and stable monitoring. In this paper, we reported a resistive temperature sensor designed as an ultrathin laminated structure with a serpentine pattern and a bioinspired adhesive layer, which was fabricated with a composite of poly(3,4-ethylenedioxythiophene)-poly(styrenesulfonate)/single-wall carbon nanotubes/reduced graphene oxide (PEDOT:PSS/SWCNTs/rGO) and polydimethylsiloxane (PDMS). The temperature sensor exhibited a high temperature sensitivity of 0.63% °C-1, coupled with outstanding linearity of 0.98 within 25-45 °C. Furthermore, it showed fast response and recovery speeds of 4.8 and 5.8 s, respectively, between 25 and 36 °C. It also demonstrated exceptional stability when subjected to stress and bending disturbances with the maximum bending interference deviation of 0.03%. Additionally, it displayed good cyclic stability over a broad temperature range from 25 to 85 °C, and the standard deviation at 25 °C is 0.14%. A series of experiments including blowing detection, respiratory monitoring with or without a mask, and during rest or sleep were conducted to show the potential of the flexible temperature sensors in human body monitoring. Furthermore, a 4 × 4 flexible temperature sensor matrix was integrated to detect and map objects such as wrenches and blood vessels through human hand skin. The results were consistent with those of infrared measurements. The flexible temperature sensor is capable of real-time temperature monitoring and has the potential in tracking human respiration, assessing sleep quality, and mapping the temperature of various objects.

PENGEMBANGAN SISTEM IOT UNTUK PEMANTAUAN KESEHATAN DOMBA DENGAN ALGORITMA C4.5 BERBASIS THINGSPEAK

Effective livestock health monitoring is one of the primary challenges in modern farming, especially in detecting early health disorders in sheep. This research aims to develop a sheep health monitoring system based on the Internet of Things (IoT) using the C4.5 algorithm and the ThingSpeak platform. The system collects vital sheep data such as body temperature, heart rate, sound, and physical activity in real time through sensors and microphones connected to IoT devices. The data is then transmitted to the ThingSpeak platform for analysis and storage. The C4.5 algorithm is used to build a decision model capable of classifying the health conditions of sheep based on collected parameters such as temperature, heart rate, and respiration. The processed data results are displayed in the form of graphs and warning notifications on the ThingSpeak platform, allowing farmers to monitor livestock health easily and responsively. The accuracy test yielded a 90% accuracy rate using a confusion matrix with a data sampling split of 80% for training data and 20% for testing data. This indicates that the system has a high level of accuracy in detecting sheep health conditions. Consequently, the system has the potential to assist farmers in improving the efficiency of livestock health monitoring automatically and in real time. Moreover, the application of IoT technology and the C4.5 algorithm in the livestock sector is expected to provide innovative solutions to support productivity and animal welfare.

Dual-sensing nano-yarns for real-time pH and temperature monitoring in smart textiles

Dual-sensing nano-yarns for real-time pH and temperature monitoring in smart textiles

Thermochromic Polymers in Food Packaging: A Comprehensive Systematic Review and Patent Landscape Analysis

This study addresses the gap in research on the application of thermochromic polymers (TPs) in food packaging and their potential for real-time temperature monitoring, aiding in the assessment of food quality and shelf-life. TPs exhibit a visible color change in response to temperature variations. A comprehensive systematic review (SR) across multiple engineering peer-review databases using predefined terms was conducted. Additionally, international patent databases were investigated using the same predefined terms. Independent experts reviewed the methodology to identify and address potential biases. A total of 288 eligible articles and 922 patents were identified. After a duplicate selection and extraction process according to the inclusion criteria, four related full-text publications were selected from the initial 288 articles, and five relevant patents were selected from the 922 patents. The qualitative review suggests that TPs hold significant promise as food packaging materials due to their unique physical properties. The study concludes that TPs offer valuable properties for the food packaging industry, meriting further investigation to exploit their benefits fully.

Effect of discharge thermal action in ECDM on electrochemical behaviours of matrix material and its recast layer in glycol-based solution

The interaction between thermal and electrochemical corrosion in the ECDM process remains unclear. This study clarifies the effect of discharge thermal action on electrochemical corrosion. Real-time temperature curves indicate that the solution temperature in the electrochemical corrosion zone increased significantly due to discharge thermal action. The corrosion resistance of the recast layer’s passive film is superior to that of the matrix due to the enrichment of Cr2O3. As the electrolyte temperature rises, the passive film thickens but shows lower polarization resistances and poorer corrosion resistance due to a loose and weak structure. The matrix material and its recast layer dissolved uniformly, resulting in a considerably flat surface in a glycol-based solution, with enhanced levelling efficiency due to discharge thermal action.

Real-time detection and temperature forecasting of large-space building fires using machine learning

Real-time detection and temperature forecasting of large-space building fires using machine learning

Predicting the subcutaneous temperature in cryolipolysis using deep operator networks

Accurate monitoring of subcutaneous temperature is crucial for the safety and efficacy of cryolipolysis. However, existing measurement and simulation methods often require trade-offs between accuracy, depth, and computational efficiency. This study introduces a novel deep learning architecture, ConvD-DeepONet, specifically designed to predict subcutaneous temperature fields with both high accuracy and efficiency. The model effectively captures spatial information and produces multi-dimensional output, owing to the innovative integration of convolutional layers and the decoder network. An average absolute error (MAE) of 0.0038 ℃ and a root mean square error (RMSE) of 0.0083 ℃ are achieved, resulting in over a 50 % reduction compared to the baseline models. Moreover, each prediction is completed in just 5.9 ms, rendering it 120 times faster than traditional finite element method simulations. These results indicate that ConvD-DeepONet is a promising tool for real-time subcutaneous temperature prediction, with the potential to enhance the safety and efficacy of cryolipolysis.

Coati optimization algorithm based Deep Convolutional Forest method for prediction of atmospheric and oceanic parameters

Droughts and floods are examples of extreme weather events that can result from changes in ocean temperature. Ocean temperature is a key component of the global open sea system. Currently, real-time sea surface temperature (SST) forecasts are generated by numerical models based on physics principles and influenced by boundary and initial conditions. These models generally perform better over large areas than at specific locations. To address this and improve prediction accuracy, particularly in high-precision areas, the Coati Optimization Algorithm-based Deep Convolutional Forest (COA-DCF) method is proposed. This optimization approach is utilized to train the Deep Convolutional Forest (DCF) classifier, which then applies the prediction strategy. The COA-DCF method forecasts ocean surface temperature anomalies by considering key variables such as SST, Sea Surface Height (SSH), soil moisture, and wind speed, using historical data ranging from 1 to 10 days across six different locations. The proposed method achieves improved accuracy with low Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) values, and a high Pearson’s correlation coefficient (r) of 0.493, 0.487, and 0.4733, respectively, thereby enhancing the overall performance of the deep learning model.

Photoacoustic thermal-strain measurement towards noninvasive and accurate temperature mapping in photothermal therapy

Photothermal therapy is a promising tumor treatment approach that selectively eliminates cancer cells while assuring the survival of normal cells. It transforms light energy into thermal energy, making it gentle, targeted, and devoid of radiation. However, the efficacy of treatment is hampered by the absence of accurate and noninvasive temperature measurement method in the therapy. Therefore, there is a pressing demand for a noninvasive temperature measurement method that is real-time and accurate. This article presents one such attempt based on thermal strain photoacoustic (PA) temperature measurement. The method was first modelled, and a circular array-based photoacoustic photothermal system was developed. Experiments with Indian ink as tumor simulants suggest that the temperature monitoring in this work achieves a precision of down to 0.3 °C. Furthermore, it is possible to accomplish real-time temperature imaging, providing accurate two-dimensional temperature mapping for photothermal therapy. Experiments were also conducted on human fingers and nude mice, validating promising potentials of the proposed method for practical implementations.

IoT-GChain: Internet of Things-Assisted Secure and Tractable Grain Supply Chain Framework Leveraging Blockchain

The grain supply chain is crucial for any nation’s self-sustainability due to its huge impact on food security, economic stability, and the livelihoods of several people. The path grain takes from farmers to consumers is opaque and complicated, due to which consumers cannot trust grain quality and its origin. Although blockchain is widely used for fair and secure transactions between farmers and buyers, issues related to transparency and traceability in the grain supply chain, such as counterfeiting and middlemen involvement, have not been adequately addressed. To tackle these issues, a blockchain-based solution is proposed that unites farmers, warehouses, government central and state agencies, transporters, and food corporations on a single platform to enhance transparency, traceability, and trust among all parties. This system involves minting a non-fungible token (NFT) corresponding to each lot of grain approved by government officials. The NFT comprises grain quality, type, temperature data from sensors, weight, and ownership information, which updates as the grain lot moves across the supply chain from central agencies to state agencies and so on. NFTs enable stakeholders to track the grain lot from cultivation to end-users, providing insights into grain conditions and quality. An Internet of Things-based circuit is designed using a Digital-output relative humidity & temperature (DHT22) sensor, which offers real-time temperature and humidity readings, and geolocation coordinates are gathered from the GPS module across the supply chain. Farmers can directly interact with warehouses to sell grains, eliminating the need for middlemen and fostering trust among all parties. The proposed four-tier framework is implemented and deployed on the Ethereum network, with smart contracts interacting with React-based web pages. Analysis and results of the proposed model illustrate that it is viable, secure, and superior to the existing grain supply chain system.

Giant Capacitive Energy Storage in High-Entropy Lead-Free Ceramics with Temperature Self-Check.

Considering the large demand for electricity in the era of artificial intelligence and big data, there is an urgent need to explore novel energy storage media with higher energy density and intelligent temperature self-check functions. High-entropy (HE) ceramic capacitors are of great significance because of their excellent energy storage efficiency and high power density (PD). However, the contradiction between configurational entropy and polarization in traditional HE systems greatly restrains the increase in energy storage density. Herein, the contradiction is effectively solved by regulating the octahedral tilt and cationic displacement in ABO3-type perovskite HE ceramics, i.e., (1-x)[0.6(Bi0.47Na0.47Yb0.03Tm0.01)TiO3-0.4(Ba0.5Sr0.5)TiO3]-xSr(Zr0.5Hf0.5)O3 (BNYTT-BST-xSZH). Combining the tape-casting process and cold isostatic pressing, the optimal BNYTT-BST-0.06SZH ceramic displays a large recoverable energy storage density (10.46 J cm-3) at 685kV cm-1 and a high PD (332.88 MW cm-3). More importantly, due to Tm/Yb codoping, abnormal fluorescent negative thermal expansion and excellent real-time temperature sensing are developed, thus the application of fault detection and warning in high-voltage transmission line systems is conceptualized. This study provides an effective strategy for enhancing the polarization of energy-storing HE ceramics and offers a promising material for overcoming the problems of insufficient capacitor density and thermal runaway in terminal communication.

Fiber temperature sensor based on dual-wavelength emitting Mg0.388Al2.408O4: Mn2+/Mn4+ phosphors for real-time temperature monitoring

Single doped Mg0.388Al2O3.408O4 phosphor was prepared and multivalent Mn ion (Mn2+ and Mn4+) emission was achieved in a single host. The structure, morphology, and luminescence characteristics of Mn ions in Mg0.388Al2O3.408O4 were discussed in detail, especially the temperature dependent emission characteristics of Mn ions. Based on the fluorescence intensity ratio (FIR) of Mn2+versus Mn4+ and the decay lifetime of Mn4+ emission as the temperature readout, a dual-mode optical temperature-sensing mechanism was proposed and studied in the temperature range of 293–473 K. The maximum relative sensitivities (Sr) are derived as 2.83 % K−1 (at 373 K based on FIR) and 3.40 % K−1 (at 473 K based on decay lifetime), respectively. The temperature sensing characteristics of Mg0.388Al2.408O4: Mn2+/Mn4+ powders in a full fiber system were studied, which can provide thermal-sensitive emissions at dual-wavelengths for stable ratiometric temperature sensing with good precision and repeatability. The constructed all fiber temperature sensing system has been applied in the on-line monitoring of CPU temperature.

A real-time temperature field prediction method for steel rolling heating furnaces based on graph neural networks

In the billet reheating process during steel rolling, the real-time and accurate prediction of the temperature field is a prerequisite for the dynamic regulation of the heating process, which is crucial for ensuring the quality of billet reheating and reducing the energy consumption of the reheating furnace. The most commonly used finite element thermal field simulation and analysis methods are unable to meet the demand for real-time prediction under dynamic working conditions. Furthermore, traditional machine learning methods struggle to handle irregular data that includes spatial location information, resulting in inaccurate temperature field predictions, which in turn affect the furnace control efficiency and the quality of the product. In light of these limitations, this study proposes a Graph Neural Network (GNN)-based temperature field prediction method for steel rolling reheating furnaces. This method considers the interactions between the nodes within the reheating furnace and constructs a temperature field topology graph to effectively capture these interactions, including heat conduction and convection. Additionally, in the feature fusion and state updating mechanism of the model, we introduce process parameters, such as the air-fuel ratio, as additional inputs to enhance the ability of the GNN to handle complex variables. This enables real-time accurate simulation of the internal temperature distribution of the reheating furnace. The experimental results demonstrate that the model prediction error is controlled within 2.9 % and the response time is maintained within 20 ms, thereby validating the reliability and efficacy of the method in practical applications.

Method for Producing Columnar Ice in Laboratory and Its Application

This study presents the design of a small open-circuit wind tunnel for laboratory use and a method for preparing columnar ice. The ice formation process was analyzed in terms of temperature and ice thickness variations under varying environmental temperatures and wind speeds. Observations revealed that as wind speed increased, the grain size of the columnar ice decreased. Key findings include the following: (1) the selection and validation of two cubic arcs for the wind tunnel contraction section, achieving an acceleration ratio of 6.7–6.8 and stable wind speeds of 1–10 m/s; (2) real-time temperature monitoring indicated rapid cooling before freezing and slower cooling post-freezing, with lower ambient temperatures and higher wind speeds accelerating the icing process; (3) the −1/2 power of grain size was found to be positively correlated with wind speed; and (4) the method’s feasibility for studying mechanical properties of polar columnar ice was confirmed. This technique offers a controlled approach for producing columnar ice in the laboratory, facilitating comprehensive research on ice properties and providing a foundation for future studies on the mechanical behavior of ice under windy polar conditions.

Fiber-optic drug delivery strategy for synergistic cancer photothermal-chemotherapy

Chemotherapy is one of the conventional treatments for cancer in clinical practice. However, poor delivery efficiency, systemic toxicity, and the lack of pharmacokinetic monitoring during treatment are the critical limitations of current chemotherapy. Herein, we reported a brand-new antitumor drug delivery strategy that harnesses an optical fiber endoscopically therapeutic probe. The fiber probe carries photosensitizers in the fiber core and antitumor agents on the fiber surface mediated by a temperature-responsive hydrogel film, giving rise to an activable photothermal-chemotherapy that orchestrates the localized hyperthermia and thermal-stimuli drug release to the tumor lesion. Furthermore, the dynamical drug release and in-situ temperature can be real-time supervised through the built-in fiber sensors, including the reflective Mach–Zehnder interferometer and fiber Bragg grating, to visualize the therapy process and thus improve the safety of treatment. Compared with conventional methods, the fiber-optic drug delivery can adequately take advantage of the chemotherapeutics through collaboratively recruiting the photoheating-mediated enhanced permeability and the hydrogel particle-assisted high drug retention, shedding new light on a “central-to-peripheral” drug pervasion and retention mechanism to destroy tumors completely. The fiber-optic chemotherapy strategy incorporates precise drug delivery, accurate controllability of drug release, high drug permeability and retention in tumor, low off-target rate, and real-time drug release and temperature feedback, performing a straightforward and precise photothermal-chemotherapy pathway. More than that, the proposed strategy holds tremendous promise to provide a revolutionized on-demand drug delivery platform for the highly efficient evaluation and screening of antitumor pharmaceuticals.

A time-series deep learning model for predicting concrete shrinkage and creep verified with in-situ and laboratory test data

This paper proposed a time series prediction method for concrete shrinkage and creep based on deep learning and the accuracy of the method was verified by field tests. Based on the experiment data in the Northwestern University (NU database), a deep learning model considering the material, environment, and history was constructed by integrating Gated Recurrent Units and Self-Attention mechanism (GSA model). To validated the model, short-term indoor and outdoor field tests of concrete shrinkage and creep were conducted. The proposed GSA model outperformed the LSTM model for both creep and shrinkage predictions. Moreover, the comparison between the GSA model prediction and the outdoor test data indicated that the model can accurately capture the real-time outdoor temperature and humidity variation through historical data. To predict the ten-year shrinkage and creep of concrete, a recursive multi-step method was proposed to extrapolate the short-term predictions of the GSA model. The effectiveness of the recursive multi-step method was validated against the long-term test data in the NU database. Overall, the proposed GSA model demonstrated high accuracy in both short-term and long-term predictions.