Array Of Temperature Sensors Research Articles

In-Situ High Temperature and Large Strain Monitoring During a Copper Casting Process Based on Regenerated Fiber Bragg Grating Sensors

In-situ temperature and strain during copper casting process were monitored for the first time based on regenerated fiber Bragg grating (RFBG) sensors, with the maximum temperature up to 1100 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^{\circ }$</tex-math></inline-formula> C and the largest compressive strain approximately −14 000 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\mu$</tex-math></inline-formula> <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\varepsilon$</tex-math></inline-formula> . A generalized fifth-order polynomial calibration function was used to convert the measured Bragg wavelengths from RFBG into temperature values. With an RFBG temperature sensor array, the temperature distribution as a function of time was obtained, revealing how the temperature gradient changed during the casting process. In-situ strain was measured by an RFBG strain sensor in direct contact with copper, showing the strain progression inside the copper at different temperatures during the casting. The RFBG-based in-situ monitoring method provides a new way for measuring multiple parameters in high temperature casting, which can be used for improving the design of the casting mold and upgrading the casting quality. Furthermore, our investigation lays a good foundation for fiber-embedded smart metal structures as it shows that fiber sensors can be embedded in copper casts without special surface treatment.

Comparison of fiber-optic distributed temperature sensing and high-sensitivity sensor spatial surveying of stream temperature

Measuring surface water temperature spatial variability is needed to estimate the interaction between surface water and groundwater, evaluate fish habitat and thermal inertia, and to estimate streamflow frequency and duration. Fiber optic distributed temperature sensing (FO-DTS) has been used in rivers and lakes, providing high-resolution and sensitive temperature monitoring over large temporal and spatial scales. However, in streams with cobbly or bedrock-lined streambeds and variable bathymetry, use of FO-DTS to measure temperature close to the surface water and groundwater interface can be challenging if even feasible. FO-DTS can also be costly, involve difficult installations, and require an advanced understanding of the technology, calibration, and data processing. In this study, we compared FO-DTS stream temperature survey results to an alternative temperature survey method employing a towed transect of high-resolution temperature loggers spaced at 1-m and transported in the stream along the study reach, to measure the spatial distribution of stream-water temperature in East Fork Poplar Creek near Oak Ridge, Tennessee, USA. We assessed the applicability and limitations of the two methods, and quantitatively compared in-situ temperature survey results measured simultaneously with each method. Regression results showed strong temporal and spatial correlation between the two methods. Differences were only elevated near the stream banks in areas that were coincident with correlation slope deviations from unity, which was attributed to shallower water and lower data density. Kriging standard errors were also low at channel center with minor increases near the stream banks. The results suggested that the array of the individual temperature sensors can provide a practical alternative to FO-DTS for thermal characterization of surface water, providing slightly lower spatial and temporal resolution, but with higher accuracy of temperature measurement, with greater simplicity, and with a broader range of conditions where it may be applied.

Nickel oxide-nickel heterostructures point to cheaper, efficient wearable temperature sensors

Wearable prototype pushes performance of temperature sensor arrays with stability lasting more than three months.

Robust self-gated-carriers enabling highly sensitive wearable temperature sensors

Wearable temperature sensors can obtain and convey accurate temperature information on the human body and objects. However, most reported wearable temperature sensors suffer from limitations, such as high fabrication cost and low working efficiency, sensing performance, and scalability, which impede their broad application in wearable fields. In this work, a new type of metal–semiconductor–metal (MSM) self-gated device based on NiO/Ni heterostructure frameworks is fabricated and employed in wearable high-performance temperature sensors. The MSM self-gated device is prepared via a facile and controllable in situ oxidation method and demonstrates efficient charge transportation, excellent thermal conductivity [3.74 W (m K−1)], and high thermal diffusivity (9.39 mm2 s−1). The obtained temperature sensors exhibit high sensitivity (−5.04% °C−1), wide working range (−15 to 80 °C), and excellent stability (more than three months) and have been applied to monitor the temperature of objects and the human body. In particular, a flexible temperature sensor array is fabricated to measure the spatially resolved temperature distribution. The array demonstrates the bright application prospects of the sensors in the fields of human–machine interfaces and intelligent medical systems.

FBG-Based Temperature Sensors for Liquid Identification and Liquid Level Estimation via Random Forest.

This paper proposed a liquid level measurement and classification system based on a fiber Bragg grating (FBG) temperature sensor array. For the oil classification, the fluids were dichotomized into oil and nonoil, i.e., water and emulsion. Due to the low variability of the classes, the random forest (RF) algorithm was chosen for the classification. Three different fluids, namely water, mineral oil, and silicone oil (Kryo 51), were identified by three FBGs located at 21.5 cm, 10.5 cm, and 3 cm from the bottom. The fluids were heated by a Peltier device placed at the bottom of the beaker and maintained at a temperature of 318.15 K during the entire experiment. The fluid identification by the RF algorithm achieved an accuracy of 100%. An average root mean squared error (RMSE) of 0.2603 cm, with a maximum RMSE lower than 0.4 cm, was obtained in the fluid level measurement also using the RF algorithm. Thus, the proposed method is a feasible tool for fluid identification and level estimation under temperature variation conditions and provides important benefits in practical applications due to its easy assembly and straightforward operation.

Build-in compact and efficient temperature sensor array on field programmable gate array

Digital sensor arrays based on ring oscillators are used to monitor the temperature of Field Programmable Gate Array (FPGA) chips, but traditional sensor structures using binary frequency counters consume excessive hardware logic resources. This paper proposed a compact temperature sensor array that used low-decoding-complexity linear feedback shift registers (LFSR) as a frequency counter to efficiently count the ring oscillator frequency. We implemented an experimental system on a Spartan-6 FPGA, and the logic resource consumption of the entire system was 927 Look-Up-Tables (LUTs) with an overhead of only 10%. The single sampling time was approximately 40 μs. The proposed temperature sensor array has good temperature sensing capabilities, and the measured error is ±0.9°C(3σ).

Spatial seated occupancy detection in offices with a chair-based temperature sensor array

Sensors used to monitor indoor environmental conditions and utility consumption can result in well-defined working and living spaces. Real-time spatiotemporal occupancy detection techniques used to track humans indoors paired with these data can be used to better understand the role of occupants on indoor air quality and building energy consumption. This study introduces a novel occupancy sensing technique whereby a chair-based temperature sensor array is used to detect the presence of occupants seated in a living laboratory open-plan office. Seat surface temperatures were tracked for twenty individuals over seven months using K-type thermocouples with battery-powered dataloggers secured to office chairs in known locations. The temperature differential between the occupant and seat surface enables for rapid conduction and thus detection of one's seated presence. Seat surface temperature time-series were converted to binary seated occupancy for each chair and totaled to achieve a spatial map of room seated occupancy with a time resolution of 15 s. Trends in spatiotemporal seated occupancy profiles in the office were evaluated for seven months using the novel technique. This highly localized form of occupancy detection offers several advantages compared to delocalized sensing techniques, including visualization of spatial occupancy patterns over time; determination of individual seated occupancy histories visualized in the form of “occupancy barcodes; ” quantification of total seated hours per occupant in different spatial zones across varying time-scales; and characterization of diurnal and weekly trends in seated occupancy probabilities categorized by an occupant's relative level of presence.

Femtosecond laser micro-fabricated flexible sensor arrays for simultaneous mechanical and thermal stimuli detection

Flexible sensor matrix with functionalities for multiple stimuli detection has prospect applications in structural health monitoring, internet of things, soft robotics, etc. However, fabrication of the sensor matrix in a simple, highly efficient, and scalable manner is still a challenge. To address this, a femtosecond (fs) laser micro-fabrication method is developed to fabricate a sensor matrix integrated with temperature and pressure sensor arrays. Through the programmable control of laser irradiation manner including scanning velocity, laser power, and laser irradiation intervals, the temperature and pressure sensor arrays were fabricated by the fs laser. The temperature sensors have a temperature coefficient of resistance of −0.52%/°C, higher than commercial temperature sensor based on platinum. The pressure sensors deliver a sensitivity of −2.01 kPa−1, a response speed of 0.030 s, a detection window from 0.001 to 80 kPa, and high mechanical stability. When the fs laser micro-fabricated pressure and temperature sensor arrays are integrated together, the sensor matrix could simultaneously detect external mechanical and thermal stimuli.

A Spherical Temperature Sensor Array Design for Near-Surface Atmospheric Temperature Studies

AbstractThe observation accuracy of the surface air temperature less than 0.1 K is a requirement, stated by the meteorological and climatological community. However, the accuracy of a temperature sensor inside a shield is affected by a number of factors including solar radiation, wind speed, upwelling longwave radiation, air density, sun elevation angle, sun azimuth angle, underlying surface, precipitation, moisture, structure, and coating of the radiation shield. Due to these factors, the temperature error of the temperature sensor may be much larger than 1 K under adverse conditions. To improve the observation accuracy, this paper proposed a spherical temperature sensor array. A series of analytical calculations based on a computational fluid dynamics (CFD) method is performed to verify the design principle of this sensor array. The calculation results show that the temperature error ratio can be assumed as a constant. To verify the accuracy of this sensor array, simulations and observation experiments are conducted. The simulation results show that the mean difference between the temperature provided by this sensor array and the reference air temperature is 0.072 K. The field experiment results show that a root-mean-square error (RMSE) and a mean absolute error (MAE) between the temperature provided by this sensor array and the reference air temperature are 0.173 and 0.153 K, respectively.

39‐3: Distinguished Student Paper: In‐Pixel Temperature Sensor for High‐Luminance Active‐Matrix Micro‐LED Display using LTPO TFTs

We propose an in‐pixel temperature sensor using LTPO TFTs for high luminance AM micro‐LED displays. By taking advantage of the different off‐current characteristics of p‐type LTPS TFTs and n‐type a‐IGZO TFTs under temperature change, we designed and fabricated a temperature sensor consists of only LTPO TFTs without additional sensing component or material. The fabricated sensor exhibits excellent temperature sensitivity of up to 71.8 mV/°C. In addition, a 64 x 64 temperature sensor array with 3T sensing pixel and integrated gate driver has also been fabricated, which demonstrates potential approach for maxing out the performance of high‐luminance AM micro‐LED display with real‐time in‐pixel temperature monitoring.

Ultrathin Highly Flexible Featherweight Ceramic Temperature Sensor Arrays.

We fabricated highly flexible Sr- and Ni-doped perovskite SmMnO3 thermistor film sensor arrays on an ultrathin (5 μm thick) and lightweight (21 mg) polyimide sheet for healthcare monitoring devices. The Ag nanowire and nanoparticle-impregnated carbon microcone array, which was prepared by precisely controlled surface laser carbonization of polyimide, showed sufficiently low resistance as a bottom electrode and good stability against sharp bending angles. The dot-shaped (diameter: 900 μm) perovskite thermistor film with a thickness of 900 nm was crystallized by pulsed ultraviolet laser irradiation of a precursor film printed with perovskite nanoparticle dispersion ink, and the film functioned well as the thermistor with a thermistor constant of 2820 K. The thermistor sensor sheet exhibited rapid responses to temperature variation and high stability in the temperature cycle tests over 1000 cycles between room temperature and 80 °C. The bending durability for a bending angle of 60° with a small bending radius (500 μm) was also high. During the bending test over 1000 cycles, the monitoring temperature variation was suppressed only within 0.1 °C. This ultrathin sensor array sheet can be mounted on surfaces with shape variations, and we used the sensor for real-time monitoring in healthcare to detect precise temperature variations on the human skin during physical exercise.

A Force and Temperature Sensor Array Based on 3-D Field-Coupled Thin-Film Transistors for Tactile Intelligence

Tactile sensors that acquire force and temperature information are key to machine tactile intelligence. In this article, an active tactile sensor incorporating an array of 3-D field-coupled thin-film transistors (FCTFTs) with a $4 \times 4$ pixelated polyvinylidene fluoride (PVDF)-based force and temperature sensors is studied. In order to enhance sensitivity and reduce power consumption, a 3-D FIN-shaped FCTFT is designed and a subthreshold operation is implemented. We have demonstrated its functions in human–machine interfacing, object characterization and recognition, force and temperature mapping, and dynamic force tracing. Such a tactile sensor array is promising for applications in machine tactile intelligence and humanoid robotics.

In‐pixel temperature sensor for high‐luminance active matrix micro‐light‐emitting diode display using low‐temperature polycrystalline silicon and oxide thin‐film‐transistors

AbstractWe propose an in‐pixel temperature sensor using low‐temperature polycrystalline silicon and oxide (LTPO) thin‐film transistor (TFTs) for high‐luminance active matrix (AM) micro‐light‐emitting diode (LED) displays. By taking advantage of the different off‐current characteristics of p‐type LTPS TFTs and n‐type a‐IGZO TFTs under temperature change, we designed and fabricated a temperature sensor consists of only LTPO TFTs without additional sensing component or material. The fabricated sensor exhibits excellent temperature sensitivity of up to 71.8 mV/°C. In addition, a 64 × 64 temperature sensor array with 3T sensing pixel and integrated gate driver has also been fabricated, which demonstrates potential approach for maxing out the performance of high‐luminance AM micro‐LED display with real‐time in‐pixel temperature monitoring.

Sensor Array Enables Accurate Profiling of Produced Fluids During Drillstem Tests

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 29749, “Determining Produced-Fluid Properties for Accurate Production Profiling During a Drillstem Test Using Thermal-Imaging Technology,” by David Lavery and David Fyfe, Metrol, and Abu Rashid Hasan, SPE, Texas A&amp;M University, prepared for the 2019 Offshore Technology Conference Brasil, Rio de Janeiro, 29–31 October. Copyright 2019 Offshore Technology Conference. Reproduced by permission. This paper describes the use of a downhole temperature-sensor array during a commingled drillstem test (DST) to determine the density of produced fluids accurately. In a typical DST that uses only downhole pressure gauges, any fluid contacts between the pressure gauges would be missed and the produced-fluid density calculated would be erroneous. The complete paper demonstrates the importance of taking fluid properties into account when determining the zonal flow-rate contributions using the mass-enthalpy method. Introduction Downhole temperature-sensor-array data provide accurate fluid contact depths during buildup periods of the DST that typically cannot be observed in pressure gradients. Determination of these fluid contacts permits the calculation of individual produced-fluid densities. In a case study described in the complete paper, the deepest perforated zone produced a fluid with a higher density than that seen in shallower perforated intervals. The higher density of the produced brine caused the wellbore fluids to slug during the flow periods with a measurable response in pressure and temperature data. If this difference in the fluid properties is not taken into account, zonal-allocation flow rate will be in error because it relies on density and specific heat capacity. Qualitative assessment of the temperature-array data identified producing zones and clearly highlighted different fluid interfaces in detail that would remain hidden if the pressure gauges were relied upon solely. High-Resolution Temperature Measurement During DST Operations The deployment of the downhole temperature-sensor array during DSTs provides an in-depth look into reservoir characteristics that go beyond traditional downhole pressure-gauge measurements. The temperature sensors, contained within steel tubing and clamped to the outside of the tubing-conveyed perforating guns, cover the three perforated intervals in this case study. Two temperature sensor arrays were run, each with a sensor spacing of 0.6 m and offset with an effective sensor spacing of 0.3 m. Throughout the DST, the readings from the temperature sensor arrays were recorded on downhole memory devices at 1-minute intervals. The temperature measurement covered the entire perforated interval. High-resolution pressure gauges are placed above and below the perforated interval and above and below the tester valve. Using an acoustic communication system, real-time temperature and pressure data were available during the DST so that observations across the entire test could be made. Zonal Allocation of Produced Reservoir Fluids From Mass-Enthalpy Balance The mass-enthalpy method of calculating zonal contributions relies on intervals higher in the wellbore producing at a lower temperature than deeper producing intervals. The higher- temperature contributions from the deepest producing intervals will flow up the wellbore while losing some heat to the colder wellbore. The rate of cooling is dependent on the flow velocity. The shallower producing intervals contribute at a lower temperature than the mixture and will cool down the mixture. This method is detailed in the complete paper.

Sensitive Wearable Temperature Sensor with Seamless Monolithic Integration

Accurate temperature field measurement provides critical information in many scientific problems. Herein, a new paradigm for highly sensitive, flexible, negative temperature coefficient (NTC) thermistor-based artificial skin is reported, with the highest temperature sensing ability reported to date among previously reported NTC thermistors. This artificial skin is achieved through the development of a novel monolithic laser-induced reductive sintering scheme and unique monolithic structures. The unique seamless monolithic structure simultaneously integrates two different components (a metal electrode and metal oxide sensing channel) from the same material at ambient pressure, which cannot be achieved by conventional heterogeneous integration through multiple, complex steps of photolithography or vacuum deposition. In addition to superior performance, electronic skin with high temperature sensitivity can be fabricated on heat-sensitive polymer substrates due to the low-temperature requirements of the process. As a proof of concept, temperature-sensitive artificial skin is tested with conformally attachable physiological temperature sensor arrays in the measurement of the temperatures of exhaled breath for the early detection of pathogenic progression in the respiratory system. The proposed highly sensitive flexible temperature sensor and monolithic selective laser reductive sintering are expected to greatly contribute to the development of essential components in various emerging research fields, including soft robotics and healthcare systems.

Hyporheic Zone Microbiome Assembly Is Linked to Dynamic Water Mixing Patterns in Snowmelt‐Dominated Headwater Catchments

AbstractTerrestrial and aquatic elemental cycles are tightly linked in upland fluvial networks. Biotic and abiotic mineral weathering, microbially mediated degradation of organic matter, and anthropogenic influences all result in the movement of solutes (e.g., carbon, metals, and nutrients) through these catchments, with implications for downstream water quality. Within the river channel, the region of hyporheic mixing represents a hot spot of microbial activity, exerting significant control over solute cycling. To investigate how snowmelt‐driven seasonal changes in river discharge affect microbial community assembly and carbon biogeochemistry, depth‐resolved pore water samples were recovered from multiple locations around a representative meander on the East River near Crested Butte, CO, USA. Vertical temperature sensor arrays were also installed in the streambed to enable seepage flux estimates. Snowmelt‐driven high river discharge led to an expanding zone of vertical hyporheic mixing and introduced dissolved oxygen into the streambed that stimulated aerobic microbial respiration. These physicochemical processes contributed to microbial communities undergoing homogenizing selection, in contrast to other ecosystems where lower permeability may limit the extent of mixing. Conversely, lower river discharge conditions led to a greater influence of upwelling groundwater within the streambed and a decrease in microbial respiration rates. Associated with these processes, microbial communities throughout the streambed exhibited increasing dissimilarity between each other, suggesting that the earlier onset of snowmelt and longer periods of base flow may lead to changes in the composition (and associated function) of streambed microbiomes, with consequent implications for the processing and export of solutes from upland catchments.

Empirical evaluation of two-dimensional unsteady hydraulic models for applications in floodplain forest ecology

ABSTRACT Two-dimensional unsteady hydraulic models can be attractive tools for characterizing the inundation dynamics relevant to floodplain forest ecosystems because of their ability to make dynamic, spatially continuous predictions. Such models are challenging to evaluate because they require spatially and temporally extensive datasets that are expensive or logistically difficult to collect. As a result, there are few examples of model evaluation methods intended for ecological applications in terrestrial or semi-terrestrial areas. Here, we demonstrated one evaluation approach using 3D arrays of low-cost temperature sensors to characterize wetted extent, depth and duration. Simulations of observed flood events in two geomorphically distinct river reach exhibited strong agreement with indirect measurements of wetted extent (>80%). Errors in depth prediction (average 24–29 cm) were similar to those of other ecological applications. Discrepancies in simulated flood durations were almost exclusively less than 24 h in length, which we interpreted as acceptable for the intended model applications. We found that unsteady 2D hydraulic models can capture a level of reality in wetted extent, depth and duration appropriate for characterizing key environmental gradients thought to drive forest composition. We also demonstrated a spatio-temporal model evaluation method that can be tailored to specific ecological research questions.

A Flexible Temperature Sensor Array with Polyaniline/Graphene-Polyvinyl Butyral Thin Film.

Thermal-resistance temperature sensors generally employ temperature-sensitive materials as active layers, which are always deposited on a flexible substrate to improve flexibility. Such a temperature sensor is usually integrated in wearable devices with other sensors, such as pressure sensors and stretchable sensors. In prior works, the temperature and pressure sensors are usually located in different layers in a multifunction sensor, which results in a complicated fabrication process, as well as a large thickness of devices. Meanwhile, many temperature sensors are based on large areas of non-transparent materials, leading to difficulties in integrating display applications. In this paper, we demonstrate a flexible temperature sensor based on polyaniline/graphene (GPANI)–polyvinyl butyral (PVB) thin film and indium tin oxides (ITO)- polyethylene terephthalate (PET) substrates. The GPANI particles embedded in PVB film not only contribute to temperature detection, but also response to external pressures, due to weak deformations. In addition, the thin composite film (2.7 μm) highly improved the transparency. By optimizing the device structure, the sensor integrates temperature and pressure detection into one single layer, which shows a wide temperature range of 25–80 °C, a pressure range of 0–30 kPa, and a high transparency (>80%). The temperature sensor offers great potential for applications in emerging wearable devices and electronic skins.

Computational Fluid Dynamic Design and Experimental Study of a Temperature Sensor Array Used in Climate Reference Station

Accurate air temperature measurements are demanded for climate change research. However, air temperature sensors installed in a screen or a radiation shield have traditionally resisted observation accuracy due to a number of factors, particularly solar radiation. Here we present a novel temperature sensor array to improve the air temperature observation accuracy. To obtain an optimum design of the sensor array, we perform a series of analyses of the sensor array with various structures based on a computational fluid dynamics (CFD) method. Then the CFD method is applied to obtain quantitative radiation errors of the optimum temperature sensor array. For further improving the measurement accuracy of the sensor array, an artificial neural network model is developed to learn the relationship between the radiation error and environment variables. To assess the extent to which the actual performance adheres to the theoretical CFD model and the neural network model, air temperature observation experiments are conducted. An aspirated temperature measurement platform with a forced airflow rate up to 20 m s−1 served as an air temperature reference. The average radiation errors of a temperature sensor equipped with a naturally ventilated radiation shield and a temperature sensor installed in a screen are 0.42° and 0.23°C, respectively. By contrast, the mean radiation error of the temperature sensor array is approximately 0.03°C. The mean absolute error (MAE) between the radiation errors provided by the experiments and the radiation errors given by the neural network model is 0.007°C, and the root-mean-square error (RMSE) is 0.009°C.

A low power IoT architecture for the monitoring of chemical emissions

&lt;p class="Abstract"&gt;&lt;span lang="EN-US"&gt;In this work, a low-power IoT architecture for the monitoring of chemical emissions is presented. This system is expected to be employed to set up monitoring infrastructures in industrial plants or public buildings. The proposed system has been designed to embed different sensors. In particular, each sensor node manages a humidity sensor and an array of temperature and electrochemical gas sensors for the detection of carbon monoxide (CO), nitrogen oxides (NOx), and oxygen (O2). Moreover, it exploits some dedicated processing algorithms to mitigate the dependence of the sensor response on temperature. The sensor node has been designed to minimise power consumption as much as possible, and it is provided with LoRa LPWAN connectivity, which allows for wide-area data transmission. Tests carried out in urban areas proved that a 3 km communication range is achievable in noisy environments. A network architecture and a data acquisition and management structure are then described. A multilayer modular topology that combines the features of LoRa technology with shorter and larger range telecommunication channels in order to develop an IoT framework that can be customised according to the physical and technical features of the deployment scenario&lt;/span&gt;&lt;/p&gt;