High Precision Temperature Research Articles

Implementation of Linear Regression Method for Calibration and Temperature Measurement on PT100 Temperature Sensor

The PT100 temperature sensor, a type RTD (resistance temperature detector), is widely employed in industries, particularly for steam boiler temperature regulation. Precise temperature measurement is vital for operational efficiency and safety maintenance. Utilizing linear regression for calibrating and measuring temperature with the PT100 sensor integrated with Arduino Uno and LCD I2C is proposed to enhance accuracy. Testing involved assembling the PT100 sensor with Arduino Uno and LCD I2C. Test data were employed for linear regression to derive the calibration equation. Measurements were conducted by comparing PT100 sensor readings with a digital thermometer across a temperature range of 30ºC to 75ºC at 5ºC intervals, with each temperature point tested five times. Results exhibit good accuracy in temperature measurement with the PT100 sensor, featuring low error and percentage error. The standard deviation indicates consistent measurement. Integration of the PT100 temperature sensor with Arduino Uno and LCD I2C, coupled with linear regression for calibration, yields a precise temperature measurement system. Testing against a digital thermometer demonstrates highly accurate outcomes. This developed system finds application in high-precision temperature measurement for steam boiler operations.

A ±0.15 °C (3σ) Inaccuracy CMOS Smart Temperature Sensor from −40 °C to 125 °C with a 10 ms Conversion Time-Leveraging an Adaptative Decimation Filter in 65 nm CMOS Technology

This paper presents the design and implementation of a highly accurate smart temperature sensor designed in 65 nm CMOS technology. The sensor exhibits a ±0.15 °C (3σ) error across a wide temperature range from −40 °C to 125 °C, catering to diverse application needs. Leveraging advanced CMOS technology, the sensor employs an adaptive decimation filter that allows us to control the conversion time, ensuring that the accuracy of the conversion is maintained even in challenging conditions. The proposed sensor architecture integrates advanced techniques for temperature sensing for improved accuracy and reliability. Through meticulous circuit design and the usage of dynamic element matching, chopping, and calibration/trimming, the sensor demonstrates exceptional performance characteristics, making it suitable for various industrial, automotive, and consumer electronics applications demanding high precision temperature monitoring.

High sensitivity optical fiber temperature sensor based upon a polydimethylsiloxane filled Fabry-Perot interferometer

A high-sensitivity temperature sensor based upon an optical fiber Fabry-Perot interferometer (FPI) filled with polydimethylsiloxane (PDMS) is reported that employed a single mode fiber (SMF) and a hollow core fiber (HCF) to enhance the sensitivity. The sensing characteristics were investigated from 20 °C to 40 °C. The experimental results demonstrate that the sensor is sensitive to temperature due to the high thermal optical coefficient (TOC) and high thermal expansion coefficient (TEC) of PDMS, achieving a high sensitivity of 2.155 nm/°C. The designed sensor shows great potential for high-precision temperature sensing applications due to its high sensitivity, compact structure, and simple preparation.

Development path of digital cultural industry by integrating proportional integral and differential algorithm from the perspective of cultural innovation

The digital culture business benefits from quick technical updates, digital manufacturing, communication networks, and customized consumption, which promotes new supply and consumption. The innovation and development of the digital cultural industry cannot be separated from the physical industry. The efficient production of the physical industry can ensure the innovative development of digital cultural industry. Therefore, to improve the production efficiency of physical industry, this research proposes a temperature control model combining whale optimization algorithm and fuzzy proportional integral differential algorithm based on the traditional temperature control in ceramic firing and wine making process. Firstly, fuzzy control theory is used to improve the proportional integral differential algorithm, and a fuzzy proportional integral differential model with adaptive adjustment function is established. On this basis, to quickly confirm the optimal parameters of the proportional integral differential model, the whale optimization algorithm is introduced to further optimize its weight factors. It was verified that the temperature control adjustment model combining whale optimization algorithm and fuzzy proportional integral differential model was 2.77 s, which was shortened by 1.11 s, 2.37 s and 3.13 s, respectively than the other three models. Therefore, the constructed temperature control model can effectively realize high-precision intelligent temperature control in industrial production, laying the cornerstone for promoting the innovative development of digital culture industry. The proposed model can improve the production efficiency of physical industries, promote digital transformation, and optimize the innovative development of digital cultural industries.

Research on a smart bra design strategy and platform for breast health risk monitoring

To reduce the risk of breast disease in women, we proposed a new smart bra design scheme. We implanted high-precision temperature sensors into the bra for real-time data monitoring, and used multipoint comparison technology to obtain early warnings of breast lesions based on abnormal temperature data. The proposed monitoring system can perform functions such as real-time data feedback, periodic report recording, remote consultation, health management, and electronic medical record generation. Compared with previous approaches, the functionality of this smart bra is not affected by dynamic body movements or environmental temperature, and its monitoring range for breast diseases is wider. This study promotes the monitoring and prevention of breast diseases in women, leading to a shift from disease treatment to disease prevention.

Highly Sensitive Balloon-like Fiber Interferometer Based on Ethanol Coated for Temperature Measurement.

A highly sensitivity balloon-like fiber interferometer based on ethanol coating is presented in this paper. The Mach-Zehnder interferometer is formed by bending a single-mode fiber to a balloon-like structure and nested in the Teflon tube. Then, an ethanol solution was filled into the tube of the balloon-like fiber interferometer by the capillary effect. Due to the high sensitivity of the refractive index (RI) of ethanol solutions to temperature, when the external temperature varies, the optical path difference changes. The change in temperature can be detected by the shift in the interference spectrum. Limited by the size of the balloon-like structure, three kinds of these structures with different sensitive lengths were prepared to select the best parameters. The sensitive lengths were 10, 15 and 20 mm, respectively, and the RI detection performance of each structure in 10~26% NaCl solutions was investigated experimentally. The results show that when the sensitive length is 20 mm, the RI sensitivity of the sensor is the highest, which is 212.88 nm/RIU. Ultimately, the sensitive length filled with ethanol is 20 mm. The experimental results show that the temperature sensitivity of the structure is 1.145 nm/°C in the range of 28.1 °C~35 °C, which is 10.3 times higher than that of an unfilled balloon-like structure (0.111 nm/°C). The system has the advantages of low cost and easy fabrication, which can potentially be used in high-precision temperature monitoring processes.

A temperature sensor based on multi-beam capture and interference

In this study, we proposed and validated a temperature fiber sensor based on optical signal capture Mach-Zehnder Interferometer (OSC-MZI) with high-precision temperature measurement capabilities. By creating three air cavities on a continuously offset-spliced no-core fiber (NCF), the optical signal experiences reflection and divergence within these cavities, which enhances the optical path of propagating light. Gradually, filling these NCF cavities with polymethyl methacrylate (PMMA) microspheres to increase the light coupling efficiency and as a sensing unit. Due to the directly contact between the optical fiber waveguide and PMMA, the state of incident lights is easily influenced by the refractive index (RI) variations of PMMA. When the environmental temperature changes, RI of the PMMA with high thermal-optic coefficient shows marked change, which finally leads to the obvious wavelength shift of interference dips in the transmission spectrum. Experimental results show that MZI coated with the polymer materials achieves a temperature sensitivity of up to 2.04 nm/°C within the temperature range of 25–50 °C. Furthermore, as an interference-based MZI sensor, its total length is only 4.75 mm. The proposed OSC-MZI provides a new approach for combining optical fiber sensors with polymer materials.

High-sensitivity ratiometric thermometers of Yb3+/Er3+/Tm3+ co-doped LuScO3 single crystal fibers based on non-thermally coupled energy levels

Fluorescence thermometry manifests tremendous attention for their potential applications in the temperature sensing, especially for combining wide operating temperature range with high sensitivity. However, the use of thermally coupled energy levels (TCLs) for temperature measurement has strict requirements for energy difference, which limits the sensitivity of detection. In order to satisfy higher sensitivity sensing, the pair of non-thermally coupled energy levels (NTCLs) of 4S3/2 and 3H4 has been innovatively designed. Herein, the sesquioxide Yb,Er,Tm co-doped LuScO3 single crystal fibers (SCFs) had been delicately grown by Laser Heated Pedestal Growth (LHPG) method. The fluorescence intensity ratio (FIR) thermometry was realized based on the Er3+:4S3/2→4I15/2 (545–593 nm) and Tm3+:3H4→3H6 (750–850 nm) NTCLs. Such an obvious FIR-temperature change through the NTCLs presents an outstanding optical thermometric performance, yielding the high temperature sensing with wide range. The maximum value of absolute sensitivity was 2 orders of magnitude higher than the temperature measurement using TCLs. More importantly, the sesquioxide Yb,Er,Tm:LuScO3 SCFs exhibit superior thermal stability that will be helpful to obtain high precision temperature measurement. Hence, our findings might offer some useful inspiration for exploring high-sensitivity luminescent thermometers and enriching the research of temperature measuring materials.

Temperature field reconstruction based on femtosecond laser prepared of multiwavelength fibre bragg grating array

Temperature field measurements are of great significance in industrial production, scientific research, and other fields. Contact temperature measurements usually achieve high spatial resolution by increasing the layout density of the sensors. However, in practical applications, achieving high-density sensor placement is often difficult. Therefore, when the number of sensors is limited, it is necessary to perform function fitting or interpolation of the temperature of the sampling points to reconstruct the temperature field distribution. This study proposed a temperature field reconstruction method based on femtosecond laser prepared multiwavelength fibre Bragg grating (FBG) array. A multiwavelength FBG array was prepared using the femtosecond laser phase mask method combined with stress stretching, and applied for measuring the temperature field distribution in a tubular high-temperature furnace. Cubic spline interpolation and backpropagation (BP) neural networks were used to construct two-dimensional temperature field models for the temperature field distribution data measured by the FBGs, and the prediction accuracies of the two models were compared. The test results show that the root mean square error of the temperature field distribution constructed using the BP neural network is 0.7333 °C, which is approximately 23.18% of the predicted results of the cubic spline interpolation model, indicating that it is a high-precision temperature field reconstruction method.

Bias-voltage dependence of structure in Mn–Ni–O/mica flexible films for high precise temperature sensing up to 600 °C

Mechanically deformable flexible temperature sensors are being used to fabricate next-generation smart electronics, but improving their high-temperature application range is still a major challenge. In this work, in pursuit of excellent performance, a series of Mn–Ni–O ultrathin flexible thermal thin films with dual-phase structure were successfully prepared by magnetron sputtering using the bias voltage adjustment method. The interactions of grain density, ionic valence and phase structure on the performance of high-temperature NTC are emphasized. By overcoming the significant drawbacks of the flexible thermal films such as poor stability and narrow temperature range under high temperature conditions, excellent thermal performance with a wide temperature range from 200 °C to 600 °C and a resistance drift rate as low as 1.5 % has been achieved. This opens up innovative prospects for the design and application of flexible high-temperature thermal materials.

Joint identification method for thermal–mechanical coupling loads

The crucial role in evaluating structural fatigue life, implementing health monitoring, and optimizing lightweight and reliable design is played by load identification technology. With the development of the aviation industry towards high-speed aircraft, the challenges under high temperature and high load conditions have been intensified. Unfortunately, in environments characterized by the coexistence of force and heat, difficulties are faced by existing load identification methods in achieving key decoupling and accurate identification of force and temperature fields. To address this gap, the Joint Identification Method (JIM), a new method that integrates force and thermal strain patterns, is introduced in this article. This method has been developed through in-depth research on “joint physical fields” and “joint basis functions.” Multiple physical fields for load identification are systematically decomposed by our method, each field is theoretically deduced, and they are seamlessly reassembled using the same structural spatial field. Through the method, mechanical strain and thermal strain are separated in high-precision temperature field reconstruction, and a basis function method for identifying the full field load distribution under a few measurement points is obtained. To verify the accuracy of our proposed Joint Identification Method (JIM), five experiments were designed, and the results consistently demonstrated the superiority of our method in accuracy (with an error always below 5%), efficiency (avoiding a large number of redundant calculations in traditional Duhamel methods), and reliability in static and dynamic load static heat identification. In the end, accurate, efficient, and reliable identification of the structural strain field, structural load field, and temperature field were achieved by our method.

Simplified decoy-state quantum key distribution based on planar lightwave circuit integration module

The BB84 protocol with decoy states for quantum key distribution (QKD) has generated significant interest due to its theoretical unconditional security. Presently, the most of QKD systems demonstrated rely on bulk optics. Nevertheless, in upcoming large-scale commercial availability, it is imperative to have highly integrated, consistently performing, and cost-effective systems. Photonic integrated circuits (PICs) based on silica planar lightwave circuits (PLCs) play a crucial role in QKD due to its mature manufacturing process, excellent stability, low loss, and high integration level. We develop a simplified BB84 time-bin QKD experiment system that can significantly improve the key rate because it does not require time basis decoding. And two silica PLC asymmetric Mach-Zehnder interferometer (AMZI) module are used with a high-precision temperature control system (±0.01 °C). Three quantum states |ψ0⟩, |ψ1⟩ and |ψ+⟩ are encoded and decoded with the module. The system achieves an estimated key rate about 3.4 Mbps using an analytical model with one decoy state at a clock repetition rate of 1.25 GHz. Finally, we experimentally obtain a key rate of 531 kbps over a 20 km single-mode fiber (SMF) at a clock repetition rate of 156.25 MHz.

High-precision and wide-range temperature measurement and control system of satellite-borne calibration blackbody

The accurate measurement and control of blackbody temperature can play an important role in radiometric calibration. A novel circuit suitable for the aerospace environment is designed to measure and control the temperature of the blackbody. A coarse-fine measurement method considering the wire resistance and self-heating effect of the platinum resistance is presented to calculate the resistance values of the platinum resistors. The precision of temperature measurement is improved using a line fitting algorithm with error calibration and temperature drift error compensation. A fuzzy self-adaptive PID controller is designed to improve the temperature control effect. The performances of the developed system are implemented on an FPGA/A3PE3000-FG484I controller board. The test results show that the device temperature measurement precision can reach ±0.03K for the entire temperature range 238 K to 367 K, the temperature stability is better than 0.02 K/20 s, and the temperature uniformity is better than 0.1 K.

High-precision temperature retrieval algorithm and verification for mesospheric airglow spectrum photometer

High-precision temperature retrieval algorithm and verification for mesospheric airglow spectrum photometer

The Coupled Temperature Field Model of Difficult-to-Deform Mg Alloy Foil High-Efficiency Electro-Rolling and Experimental Study

In response to the challenging difficult-to-deform of magnesium foils, a high-efficiency and high-precision electro-rolling temperature field coupled model is established. This model is designed to simulate the non-annealing electric rolling (NAER) process of Mg foils under conditions of high current density, rapid temperature rise rates, and large temperature gradients. Firstly, a coupled temperature field difference model for the guide roller, roll, and Mg foil is established, based on the equipment for NAER and the electrification conditions. The Joule heat, distortion heat, and friction heat in the electric rolling process were precisely considered. Secondly, considering the peculiarity of the heat source and the heat transfer mechanism during NAER, the influence of the dynamic boundary conditions on the instantaneous temperature of the Mg foil was analyzed, which was closer to the actual situation. The experimental results show that the original model can accurately simulate the transient temperature change in Mg foils during NAER, and the error between the predicted value and the measured value is within 7.1%. According to the calculation of the model, the microstructure of completely recrystallized magnesium foil with a grain size of 4.61 μm and a texture strength of 11.3 can be obtained at an inlet temperature of 250 °C.

High precision temperature measurement for cryogenic temperature sensors based on deep learning technology

Nonlinear error compensation is a significant factor that affects the measurement accuracy of temperature sensors. Cryogenic temperature sensors require a precise calibration technique to achieve accurate temperature measurements. BP (Back Propagation) neural networks are not suitable for sensor temperature prediction due to slow convergence and poor learning ability. In this study, a Chebyshev polynomial-based BiLSTM (Bi-directional Long Short-Term Memory) algorithm (C-BiLSTM) is proposed to improve the accuracy of measurement. Firstly, we demonstrate vacuum packaged temperature sensors based on zirconium oxynitride (ZrOxNy) thin films with ultra-high sensitivity at cryogenic temperatures. Secondly, a dataset consisting of sensor resistance and temperature values was obtained from five above-mentioned sensors, which contains 29 calibration points in the temperature range of 16 K-300 K measured by the Calibration System. Then, the dataset was divided into two temperature ranges (16 K-54.358 K and 40 K-300 K). In the range 16–54.358 K, 14 set-points are selected as training set and 3 set-points as testing set. In the range 40–300 K, 12 set-points are selected as training set and 2 set-points as testing set. Thirdly, a neural network model was built and trained using the TensorFlow framework. By comparing C-BiLSTM we proposed with the BP neural network and BiLSTM, the results show that the C-BiLSTM model converges faster and greatly improves the prediction accuracy after adding Chebyshev polynomial features. The fitting error and prediction error are less than 1 mK in the temperature range of 16 K-40 K. They can also keep less than 10 mK even at the wide temperature range of 40 K-300 K, which is a significant improvement respect to improving the accuracy of temperature measurement.

Surface plasmon resonance temperature sensor based on the conjoined-tube hollow-core anti-resonant fiber with ultra-high temperature sensitivity

A surface plasmon resonance (SPR) temperature sensor based on the conjoined-tube hollow-core anti-resonant fiber (HC-ARF) is designed and analyzed. The conjoined-tube HC-ARF contains two connecting tubes with a cross arrangement in the cladding. The SPR temperature sensor is constructed by inserting a metal into one of the inner layer tubes and injecting a thermo-sensitive liquid into the hollow core of the HC-ARF to enhance the temperature sensitivity by exploiting the SPR effect. The effects of the structural parameters and thermo-sensitive media and metals on the sensing properties such as the temperature sensitivity, peak loss, resolution, amplitude sensitivity, and figure of merit (FOM) are analyzed systematically. Numerical analysis reveals ultra-high temperature sensitivity of 38.8 nm/°C and FOM of 673.84∘C−1, which are approximately 10 times higher than those of sensors described in the recent literature. In addition, the sensor is capable of detecting a wide temperature range from −5∘C to 60°C with good linearity. The SPR temperature sensor with high precision, a wide temperature detection range, a simple and easily modifiable structure, as well as good manufacturing tolerance has large potential in high-precision temperature monitoring in the petrochemical and biomedical industries.

Temperature-Automated Calibration Methods for a Large-Area Blackbody Radiation Source.

High-precision temperature control of large-area blackbodies has a pivotal role in temperature calibration and thermal imaging correction. Meanwhile, it is necessary to correct the temperature difference between the radiating (surface of use) and back surfaces (where the temperature sensor is installed) of the blackbody during the testing phase. Moreover, large-area blackbodies are usually composed of multiple temperature control channels, and manual correction in this scenario is error-prone and inefficient. At present, there is no method that can achieve temperature-automated calibration for a large-area blackbody radiation source. Therefore, this article is dedicated to achieving temperature-automated calibration for a large-area blackbody radiation source. First, utilizing two calibrated infrared thermometers, the optimal temperature measurement location was determined using a focusing algorithm. Then, a three-axis movement system was used to obtain the true temperature at the same measurement location on a large-area blackbody surface from different channels. This temperature was subtracted from the blackbody's back surface. The temperature difference was calculated employing a weighted algorithm to derive the parameters for calibration. Finally, regarding experimental verification, the consistency error of the temperature measurement point was reduced by 85.4%, the temperature uniformity of the surface source was improved by 40.4%, and the average temperature measurement deviation decreased by 43.8%. In addition, this system demonstrated the characteristics of strong environmental adaptability that was able to perform temperature calibration under the working conditions of a blackbody surface temperature from 100 K to 573 K, which decreased the calibration time by 9.82 times.

Thermally stable Peltier controlled vacuum chamber for electrical transport measurements.

The design, manufacture, and characterization of an inexpensive, temperature-controlled vacuum chamber with millikelvin stability for electrical transport measurements at and near room temperature is reported. A commercially available Peltier device and a high-precision temperature controller are used to actively heat and cool the sample space. The system was designed to minimize thermal fluctuations in spintronic and semiconductor transport measurements, but the general principle is relevant to a wide range of electrical measurement applications. The main issues overcome are the mounting of a sample with a path of high thermal conductivity through to the Peltier device and the heat sinking of the said Peltier device inside a vacuum. A copper slug is used as the mount for a sample, and a large copper block is used as a thermal feedthrough before a passive heat sink is used to cool this block. The Peltier device provides 20W of heating and cooling power, achieving a maximum range of 30K below and 40K above the ambient temperature. The temperature stability is within 5 mK at all set points with an even better performance above the ambient temperature. A vacuum pressure of 10-8 hPa is achievable. As a demonstration, we present experimental results from current-induced electrical switching of a CuMnAs thin film. Transport measurements with and without the Peltier control emphasize the importance of a constant temperature in these applications. The thermal lag between the sample space measurement and the sample itself is observed through magnetoresistance values measured during a temperature sweep.

Intelligent control of electric vehicle air conditioning system based on deep reinforcement learning

The existing AC system of EVs consumes a lot of energy in summer, resulting in reduced range. Additionally, the cabin temperature (Tcabin) fluctuates due to varying driving conditions. In this paper, an innovative cooling strategy is designed to address the above drawbacks using deep reinforcement learning algorithms. The primary objective of this strategy is to achieve high-precision temperature control while concurrently minimizing the energy consumption of the AC system. The strategy combines compressor speed (Ncompressor) and blower airflow (Bairflow) control to optimize the AC system's performance. The RL controller minimizes energy consumption by reducing Ncompressor while increasing Bairflow, thus ensuring a constant cooling capacity (Q). And it effectively reduces temperature fluctuations within the cabin. Finally, the RL control strategy was compared with on/off control and proportional integral differential (PID) control. The results showed that RL control performed well in the multi-objective optimization of the AC system. In terms of temperature control, it effectively reduces the amount of overshooting, and the minimum temperature fluctuation achieved through RL control is merely 0.07 °C, exhibiting an 84.4 % and 86.5 % decrease compared to traditional control methods. The average absolute temperature error stands at 0.06–0.07℃, maintaining a remarkable precision in preserving the target temperature. Furthermore, RL control not only ensures superior energy efficiency but also reduces energy consumption by up to 5.98 % and 7.65 % compared with traditional control methods.