Accurate Temperature Measurements Research Articles

Accurate temperature measurement of miniaturization NMR sensor in vacuum environment

—The nuclear magnetic resonance (NMR) sensor is one of the core sensors in the inertial-grade navigation system, which has the advantages of higher precision and smaller volume. The vapor cell is the sensitive unit of NMR sensor, which is easily affected by temperature. In the process of miniaturization, NMR sensor needs to be reduced in size, which leads to poor thermal insulation performance. Thus, the heat transfer increases between cell and atmosphere environment, and the temperature uniformity of cell gets worse. The average temperature of cell is far from the operating temperature we require. In order to accurately measure the temperature of cell, a vacuum chamber is designed for NMR sensor. The vacuum chamber can provide a vacuum environment with 2.2 × 10−2Pa to isolate NMR sensor from the influence of atmosphere environment temperature. A fitting curve model is established to analyze the actual temperature in atmosphere by measuring the spin polarizability of alkali metal atoms. With this model, the temperature measurement error decreased by 2.45%, and temperature control will also be more accurate. Furthermore, the temperature uniformity simulated by ANSYS has improved by 83.33% in vacuum. It's important to provide a vacuum environment for the cell in miniaturization NMR sensor. If not, the fitting curve model also guarantees the accurate of temperature measurement and control.

The nonlinear dynamics of magnetic nanoparticles: A thermometry in complex magnetic fields

In this Letter, we propose a thermometry method for magnetic nanoparticles in complex magnetic fields. Complex magnetic fields result in intricate magnetization that can be influenced by temperature and relaxation mechanisms. We derive a set of approximate equations from the results of a numerical simulation of the nonlinear dynamic magnetic response to describe the relationship between particle temperature and the cubic susceptibility phase under complex magnetic fields. The thermometry of various magnetic nanoparticles in suspended or immobilized states is achieved with measured susceptibility. The experimental results show that the temperature measurement accuracy is less than 0.1 K for temperatures ranging from 309 to 333 K. Furthermore, the combination of complex magnetic fields and suspension samples typically dominated by Brown relaxation leads to errors of less than 0.05 K, suggesting that the significant relaxation process improves the accuracy of the temperature measurements. The method also has potential for noninvasive and quantitative temperature monitoring in magnetic particle imaging with complex magnetic fields.

Research on accurate non-contact temperature measurement method for telescope mirror.

Non-contact temperature measurement for a solar telescope mirror is critical for improving the mirror seeing and thermal deformation of solar telescopes, a long-standing challenge in astronomy. This challenge arises from the telescope mirror's inherent weak thermal radiation, often overwhelmed by reflected background radiations due to its high reflectivity. In this work, an infrared mirror thermometer (IMT) is equipped with a thermally-modulated reflector, and a measurement method based on an equation for extracting mirror radiation (EEMR) has been developed for probing the accurate radiation and temperature of the telescope mirror. Using this approach, we can extract the mirror radiation from the instrumental background radiation via the EEMR. This reflector has been designed to amplify the mirror radiation signal incident on the infrared sensor of IMT, while inhibiting the radiation noise from the ambient environment. In addition, we also propose a set of evaluation methods for IMT performance based on EEMR. The results reveal that the temperature measurement accuracy of IMT to the solar telescope mirror using this measurement method can be achieved better than ±0.15°C.

Optimizing MRT data processing via comparative analysis of SA and PSO algorithms: a simulation and numerical study.

The unknown emissivity still poses a significant challenge in the data processing of Multispectral radiation thermometry (MRT). In order to achieve global optimal solution with fast convergence speed and strong robustness, a systematic comparative analysis of particle swarm optimization (PSO) and simulated annealing (SA) algorithms in the application in MRT is presented in this paper. The simulations of six hypothetical emissivity models were compared, and the results indicate that the PSO algorithm is superior to the SA algorithm in accuracy, efficiency and stability. The measured data of the surface temperature of rocket motor nozzle is simulated by the PSO algorithm, the maximum absolute error and the maximum relative error are 16.27 K and 0.65%, and the calculation time is less than 0.3 s. The superior performance of the PSO algorithm indicates that it can be well used in data processing for accurate temperature measurement in MRT, and the method proposed in this paper can be extended to other multispectral systems and applied to various industrial processes under high temperature conditions.

Fast and easy radiometric calibration method integration time insensitive for infrared thermography

The radiometric calibration of an infrared camera is a crucial and unavoidable step to obtain an accurate temperature measurement. The calibration does the link between the sensor digital levels and the actual radiometric temperature. Although this step is critical and necessary to obtain meaningful measurement, it is a complex and time-consuming procedure, which requires many experimental data acquisitions and an algorithm to process them. This article proposes a method based on the radiance data processing allowing an easily and quickly radiometric calibration. Thanks to the proposed method, an integration time insensitive radiometric calibration is obtained from only two different observed steady and homogeneous thermal conditions (using a black body or equivalent).

Remote sensing of human skin temperature by AI speckle pattern analysis

Analysis of dynamic differential speckle patterns, scattered from human tissues illuminated by a laser beam, has been found by many researchers to be applicable for noncontact sensing of various biomedical parameters. The COVID-19 global pandemic brought the need for massive rapid-remote detection of a fever in closed public spaces. The existing non-contact temperature measurement methods have a significant tradeoff between the measurement distance and accuracy. This paper aims to prove the feasibility of an accurate temperature measurement system based on speckle patterns analysis, enabling the sensing of human temperature from an extended distance greater than allowed by the existing methods. In this study, we used speckle patterns analysis combined with artificial intelligence (AI) methods for human temperature extraction, starting with fever/no fever binary classification and continuing with temperature measurement at higher resolution.

Using the U.S. Climate Reference Network to Identify Biases in Near- and Subsurface Meteorological Fields in the High-Resolution Rapid Refresh (HRRR) Weather Prediction Model

Abstract The ability of high-resolution mesoscale models to simulate near-surface and subsurface meteorological processes is critical for representing land–atmosphere feedback processes. The High-Resolution Rapid Refresh (HRRR) model is a 3-km numerical weather prediction model that has been used operationally since 2014. In this study, we evaluated the HRRR over the contiguous United States from 1 January 2021 to 31 December 2021. We compared the 1-, 3-, 6-, 12-, 18-, 24-, 30-, and 48-h forecasts against observations of air and surface temperature, shortwave radiation, and soil temperature and moisture from the 114 stations of the U.S. Climate Reference Network (USCRN) and evaluated the HRRR’s performance for different geographic regions and land cover types. We found that the HRRR well simulated air and surface temperatures, but underestimated soil temperatures when temperatures were subfreezing. The HRRR had the largest overestimates in shortwave radiation under cloudy skies, and there was a positive relationship between the shortwave radiation mean bias error (MBE) and air temperature MBE that was stronger in summer than winter. Additionally, the HRRR underestimated soil moisture when the values exceeded about 0.2 m3 m−3, but overestimated soil moisture when measurements were below this value. Consequently, the HRRR exhibited a positive soil moisture MBE over the drier areas of the western United States and a negative MBE over the eastern United States. Although caution is needed when applying conclusions regarding HRRR’s biases to locations with subgrid-scale land cover variations, general knowledge of HRRR’s biases will help guide improvements to land surface models used in high-resolution weather forecasting models. Significance Statement Weather forecasters rely upon output from many different models. However, the models’ ability to represent processes happening near the land surface over short time scales is critical for producing accurate weather forecasts. In this study, we evaluated the High-Resolution Rapid Refresh (HRRR) model using observations from the U.S. Climate Reference Network, which currently includes 114 reference climate observing stations in the contiguous United States. These stations provide highly accurate measurements of air temperature, precipitation, soil temperature, and soil moisture. Our findings helped illustrate conditions when the HRRR performs well, but also conditions in which the HRRR can be improved, which we expect will motivate ongoing improvements to the HRRR and other weather forecasting models.

FBG temperature measurement error compensation method based on LSTM and attention mechanism

Real-time temperature measurement of power cables is an important part of the power system, and improving the accuracy of temperature measurement is a key factor of the temperature measurement system. The measurement of existing measurement methods is susceptible to interference and has certain errors. In this paper, an error compensation model is established by using a two-layer LSTM network to view the dynamic error in the process of FBG temperature measurement, and the attention mechanism is used to capture the relevant parameters of temperature change, so as to improve the model effect. The experimental results show that the compensation effect of the two-layer LSTM-Focus model is increased by 44.4% compared with the single LSTM model, which can meet the actual measurement requirements.

Thermal analysis by variable temperature infrared spectroscopy with a button sample holder and Peltier heating/cooling

An apparatus and methodology for variable temperature infrared spectroscopy measurements of neat samples contained in a button sample holder are described. Sample heating and cooling are achieved by applying voltage to stacked Peltier thermoelectric devices. Between 0 and 150°C, samples can be heated and cooled at 2°C s-1 rates, facilitating temperature step heating/cooling profiles with minimal delay between isothermal infrared spectrum measurements. Examples of correlating temperature-dependent spectral variations with specific sample changes are provided for α-quartz heating/cooling, ibuprofen melting, and acetylsalicylic acid thermal decomposition. Trends in α-quartz infrared spectra obtained with a step heating/cooling temperature profile are used to evaluate spectrum measurement reproducibility. Detection of vibration band intensity variations of less than 1% resulting from a 10°C sample temperature increment illustrates the measurement sensitivity. By comparing infrared spectra obtained at different temperatures, reversible and irreversible sample changes are identified. Infrared spectra acquired during linear ramp heating are employed to determine the ibuprofen melting point, which confirms the temperature measurement accuracy of the apparatus. Selective analysis is demonstrated by determining isoconversion effective activation energies for processes involved in the thermal decomposition of the acetylsalicylic acid component of a commercial pharmaceutical tablet.

Analysis and optimization of temperature measurement distance on the infrared temperature measurement accuracy of a building envelope

Due to the measuring distance, there are variable degrees of temperature measurement differences when infrared temperature monitoring technology is employed to take the temperature of a building envelope, which can directly lead to distortion in the detection of the building envelope temperature. In order to address this issue, temperature data from 20 sets of different measurement locations were acquired through field testing analyzing the building envelope and the interior latex-painted wall. By fitting polynomials to the temperature error and distance curves, a model of the temperature-distance compensation function was produced. According to the results, it is possible to precisely estimate the temperature of latex-painted interior walls at a distance of 0.25 meters using an instrument. The temperature-distance compensation model is executed as the measurement distance increases, resulting in a maximum error rate of 1% for temperature readings after compensation. Before adjustment, the error rate in measuring temperature was 4.9%; after adjustment, it is 3.9%.

Lateral scanning Raman scattering lidar for accurate measurement of atmospheric temperature and water vapor from ground to height of interest: publisher's note.

This publisher's note contains corrections to Opt. Lett.48, 2595 (2023).10.1364/OL.488924.

Quick and accurate optical thermometry based on chemometrics model strategy in Na0.5Gd0.5TiO3: Er3+

Current optical temperature measurements are confined to materials with high sensitivity, while probes with fluorescence intensity ratio (FIR) sensitivity above 12% K−1 still confront severe challenges. Therefore, we propose a novel strategy to obtain temperatures in one step from the temperature-dependent emission spectra by establishing a chemometrics model instead of calculating FIR values to improve temperature measurement accuracy of materials with a low FIR sensitivity. Here, Er3+-doped Na0.5Gd0.5TiO3 with low sensitivity and the monotonic variation of FIR is used as a temperature sensing material. The temperature-dependent spectra are preprocessed by the moving average filter method, and the useful information is extracted from the preprocessed spectra by the synergy interval partial least squares (siPLS) algorithm. The extracted spectra are then combined with the partial least squares regression (PLSR), the principal component regression (PCR), the multiple linear regression (MLR), and the support vector machine regression (SVM) respectively to build chemometrics models. The errors between the predicted and the actual temperatures of different models are evaluated to verify the feasibility of the proposed strategy and to select the optimal temperature measurement model for Na0.5Gd0.5TiO3: Er3+. The results show small errors and high correlation coefficients over 0.98 between the predicted and the actual temperatures of all models, indicating the applicability of the chemometrics model strategy in optical thermometry. Finally, the best model (a combination of siPLS-PCR + SVM) and the periodic testing of FIR with temperature may make Na0.5Gd0.5TiO3: Er3+ a candidate for optical thermometer with rapidity, durability and accuracy.

Thin-Wire Thermocouple Design for Exhaust Gas Temperature Pulse Measurements in Internal Combustion Engines

<div>Accurate exhaust gas temperature (EGT) measurements are vital in the design and development process of internal combustion engines (ICEs). The unsteady ICE exhaust flow and thermal inertia of commonly used sheathed thermocouples and resistance thermometers require high bandwidth EGT pulse measurements for accurate cycle-resolved and mean EGTs. The EGT pulse measurement challenge is typically addressed using exposed thin-wire resistance thermometers or thermocouples. The sensor robustness to response tradeoff limits ICE tests to short durations over a few exhaust conditions. Larger diameter multiwire thermocouples using response compensation potentially overcomes the tradeoff. However, the literature commonly adopts weaker slack wire designs despite indications of coated weld taut wires being robust. This study experimentally evaluates the thin-wire thermocouple design placed in the exhaust of a heavy-duty diesel engine over wide-ranging exhaust conditions for improving both sensor robustness and accuracy of the measured EGT. The assessed design parameters included the wire diameter (51 μm to 254 μm), the exposed wire length, and the wires placed slack or taut with coated weld faces. All taut wires with ceramic-coated weld faces endured over 3 h of engine operation, while similar diameter slack wires (51 μm and 76 μm) were sensitive to the exhaust condition and exposed wire length. Reducing the wire diameter from 76 μm to 51 μm significantly impacted response improvements as evidenced at certain test conditions by a peak-peak EGT increase of 92 °C, a mean EGT drop of 26 °C, and a doubling of the sensitivity of mean EGT cycle-to-cycle variations to ±12 °C. Increasing the exposed wire length showed less significant response improvements. The Type-K thin-wire thermocouples showed negligible drift, thereby indicating the possibility of using smaller and longer wires built taut with coated weld faces for improved accuracy of EGT measurements in ICEs.</div>

High Sensitivity Temperature Sensing of Long-Period Fiber Grating for the Ocean

In this study, a new temperature sensor with high sensitivity was achieved by four-layer Ge and B co-doped long-period fiber grating (LPFG) based on the mode coupling principle. By analyzing the mode conversion, the influence of the surrounding refractive index (SRI), the thickness and the refractive index of the film on the sensitivity of the sensor is studied. When 10 nm-thick titanium dioxide (TiO2) film is coated on the surface of the bare LPFG, the refractive index sensitivity of the sensor can be initially improved. Packaging PC452 UV-curable adhesive with a high-thermoluminescence coefficient for temperature sensitization can realize high-sensitivity temperature sensing and meet the requirements of ocean temperature detection. Finally, the effects of salt and protein attachment on the sensitivity are analyzed, which provides a reference for the subsequent application. The sensitivity of 3.8 nm/°C in the range of 5–30 °C was achieved for this new sensor, and the resolution is about 0.00026 °C, which is over 20 times higher than ordinary temperature sensors. This new sensor meets the accuracy and range of general ocean temperature measurements and could be used in various marine monitoring and environmental protection applications.

Design of OMC-Sagnac Loop Using PDMS and Different Package Structures to Improve Sensing Performance and Optimize the Ill-Conditioned Matrix

In the process of ocean exploration, highly accurate and sensitive measurements of seawater temperature and pressure significantly impact the study of seawater’s physical, chemical, and biological processes. In this paper, three different package structures, V-shape, square-shape, and semicircle-shape, are designed and fabricated, and an optical microfiber coupler combined Sagnac loop (OMCSL) is encapsulated in these structures with polydimethylsiloxane (PDMS). Then, the temperature and pressure response characteristics of the OMCSL, under different package structures, are analyzed by simulation and experiment. The experimental results show that structural change hardly affects temperature sensitivity, and square-shape has the highest pressure sensitivity. In addition, with an input error of 1% F.S., temperature and pressure errors were calculated, which shows that a semicircle-shape structure can increase the angle between lines in the sensitivity matrix method (SMM), and reduce the effect of the input error, thus optimizing the ill-conditioned matrix. Finally, this paper shows that using the machine learning method (MLM) effectively improves demodulation accuracy. In conclusion, this paper proposes to optimize the ill-conditioned matrix problem in SMM demodulation by improving sensitivity with structural optimization, which essentially explains the cause of the large errors for multiparameter cross-sensitivity. In addition, this paper proposes to use the MLM to solve the problem of large errors in the SMM, which provides a new method to solve the problem of the ill-conditioned matrix in SMM demodulation. These have practical implications for engineering an all-optical sensor that can be used for detection in the ocean environment.

Vacancy defects reducing the accuracy and reliability of NTC sensors

The negative temperature coefficient (NTC) thermal sensor has received much attention for temperature sensing, which aims to achieve accurate temperature measurement by using the electrical signal generated by its temperature change. The perovskite (1-x)CaMn0.05Zr0.95O3-xCaMnO3 (x = 0, 0.1, and 0.2) composite ceramics were reported for the first time. Furthermore, their structure, microscopic morphology, and device performance were systematically evaluated. It was revealed that the sensor performance could be tuned by controlling the CaMnO3 ratio in the low resistance phase. The phase structure and crystal structures (cell volume and cell parameters a, b, and c) of the composite ceramics were defined using x-ray diffraction (XRD) refinement. Scanning electron microscopy (SEM)/Mapping revealed dense and uniform micromorphology. The ln(ρ) and 1000/T were quite linear, and the aging drift rate was as low as 1.89% after aging at 900 °C for 600 h. Importantly, a novel double hopping mechanism and a cation vacancy defect diffusion model were proposed to reveal the electron transport mechanism in the sensor lattice and elaborate the physical mechanism of the effect of cation vacancy defects on sensor resistance drift. In conclusion, this study prepared an NTC thermal sensor with ultra-high stability in a high temperature environment by rational design, providing a fresh idea for subsequently developing a high temperature NTC sensor.

In situ temperature measurement of a deforming interface with thermoelectricity

Accurate temperature measurement is critical to understanding the thermomechanical conditions and microstructural evolution that materials experience during severe plastic deformation (SPD). Others have previously measured the interface between a non-deforming tool and metal workpiece using thermocouples, infrared measurement, and the thermoelectric principle. Measuring temperature within the deforming material itself, especially at a high-shear deforming interface, has proven to be extremely difficult, thus limiting the application of these measurements to fundamental SPD research. In this study, the thermoelectric principle is used to directly measure temperature at the interface between copper and nickel billets undergoing SPD. This is done by utilizing the deforming materials themselves as two halves of a thermocouple junction. The spatial resolution of this measurement is shown via microscopy to be less than 1 micron. Temperature changes are sensed almost instantly, with time delays on the order of the data logger frequency (0.01 seconds). Process variations that are both very brief (<0.05 seconds) and minute (<3°C) are able to be detected by this method.

Lateral scanning Raman scattering lidar for accurate measurement of atmospheric temperature and water vapor from ground to height of interest.

A novel lateral scanning Raman scattering lidar (LSRSL) system is proposed, aiming to realize the accurate measurement of atmospheric temperature and water vapor from the ground to a height of interest and to overcome the effect of a geometrical overlap function of backward Raman scattering lidar. A configuration of the bistatic lidar is employed in the design of the LSRSL system, in which four horizontally aligned telescopes mounted on a steerable frame to construct the lateral receiving system are spatially separated to look at a vertical laser beam at a certain distance. Each telescope, combined with a narrowband interference filter, is utilized to detect the lateral scattering signals of the low- and high-quantum-number transitions of the pure rotational Raman scattering spectra and vibrational Raman scattering spectra of N2 and H2O. The profiling of lidar returns in the LSRSL system is performed by the elevation angle scanning of the lateral receiving system, in which the intensities of the lateral Raman scattering signals at each setting of elevation angles are sampled and analyzed. Preliminary experiments are carried out after the construction of a LSRSL system in Xi'an city, whose retrieval results and statistical error analyses present a good performance in the detection of atmospheric temperature and water vapor from the ground to a height of 1.11 km and show the feasibility for combination with backward Raman scattering lidar in atmospheric measurement.

Normal spectral emissivity characteristics of tungsten in the mid-infrared region

Tungsten is an important material constituting the first wall of the Experimental Advanced Superconducting Tokamak due to its excellent corrosion resistance. The temperature distribution of the first wall during the discharge process are the key to ensuring the safe and stable operation of the EAST. Therefore, it is necessary to explore the dependence of material emissivity on temperature and wavelength for improving temperature measurement accuracy. This work quantitatively explores the normal spectral emissivity of tungsten within the 3 μm to 5 μm in the temperature range between 100°C and 500°C based on the Fourier transform infrared spectrometer. The dependence of emissivity on wavelength and temperature are experimentally analyzed, and the possible functional relationship between the emissivity of tungsten and the surface temperature and the wavelength are established respectively. This work could lay the foundation for the application of multi-wavelength thermometry on temperature measurement of the first wall of the EAST.

Design of Low Power Temperature Sensor Based on 180 nm Complementary Metal Oxide Semiconductor Technology

CMOS temperature sensor is widely used in power monitoring system, power consumption is an important index. The digital filter power consumption is one of the main sources of the temperature sensor power consumption, and limiting the Digital filter power consumption becomes an important method to realize the low power consumption of the temperature sensor. Based on this, a low power digital filter for CMOS temperature sensors is designed, and a precision adaptive digital filter is proposed, the filter is cascaded by a recursive CIC filter and a FIR filter based on a shift adder, the order of CIC filter and FIR filter can be adjusted according to the difference between the measured temperature and the threshold temperature range set by the user, when the measured temperature is outside the threshold temperature range, the operation unit in the filter is selectively switched off, which makes the power consumption of the filter decrease. For the temperature range that does not need to be monitored, the requirement of temperature measurement accuracy is usually not high, if high-precision temperature monitoring is still carried out, it will have a lot of unnecessary power consumption, in this paper, an adaptive precision digital filter is used to solve the problem. In order to further reduce the power consumption of the temperature sensor, according to the characteristics of the slow change of the temperature signal, a single temperature conversion combined with idle off mode is adopted, the FIR filter power consumption is reduced by 5.5% by optimizing the single temperature conversion operation. The temperature sensor is realized by 180 nm CMOS process. The results show that the sensor can achieve an accuracy of 0.47 °C in the temperature range of −55–115 °C when the measured temperature is in the threshold temperature range, under 1.8 V supply voltage, the power consumption of the digital part of the sensor is 20.15 μw. When the measured temperature is outside the threshold temperature range, the power consumption of the digital part of the sensor can be reduced by 11.3%.