Multiple Temperature Sensors Research Articles

HALOBATES: An autonomous surface vehicle for high-resolution mapping of the sea-surface microlayer and near-surface layer on essential climate variables

Abstract Essential climate variables (ECVs) are physical, chemical, or biological parameters supporting the characterization of the climate system. The oceanic ECVs temperature and salinity have been defined for the sea surface (upper 1 mm) and bulk water (upper 2 meters). Direct observation and sampling of the sea surface from ships and buoys is limited due to the potential loss of integrity of the sea surface and contamination by the research platform. The design, development, and operation of a research vehicle with autonomous capabilities based on commercially available components are described in this study. The autonomous operation and fully automated sampling enable high-resolution mapping of the sea surface microlayer (SML), i.e., the upper < 1mm layer, and near-surface layer (< 1.2 m). Sampling the SML is based on a rotating glass disk sampler. The autonomous surface vehicle HALOBATES has been equipped with multiple conductivity, temperature, and depth sensors (CTDs) in a flow-through system to measure both sea surface and bulk temperature and salinity. The performance of the autopilot under various scenarios is demonstrated. Typical anomalies of ECVs within the SML and near-surface layer are evident and forced by both atmospheric and oceanic processes. Data analyses reveal HALOBATES' ability to resolve thermohaline structure on different spatial and temporal scales, for example, small-scale freshwater lenses and thermohaline changes across oceanic fronts. The integration of an Acoustic Doppler Current Profiler supports the interpretation of thermohaline structures. HALOBATES is a platform with cutting-edge technology for understanding climate-related processes between the ocean and atmosphere and providing high-resolution sea surface data for validating satellite products of ECVs.

Experimental investigation of time-dependent electrical load effects through multipoints in-situ measurement of temperature and relative humidity of PEMFC bipolar plate under transient operation

In this study, multiple micro-relative humidity and temperature (RH/T) sensors were installed in the anode and cathode channels of a proton exchange membrane fuel cell (PEMFC) bipolar plate to simultaneously observe and analyze the variation in RH/T behaviors during transient operation. In order to simulate the acceleration and deceleration process, the electrical load was ramped from 10 to 40 A and then back to 10 A, with time intervals of 0, 2, and 5 s. The data corresponding to different acceleration/deceleration time intervals were compared and analyzed to assess the time-dependent effects of load changes. The analyzed data provide visual information, including the transient voltage response, transient behavior of temperature and water distribution, and evolution of RH/T within PEMFC. The results indicate that the acceleration and deceleration interval times significantly influence the temperature and water distribution within PEMFC during transient operation. Shorter acceleration times lead to more rapid changes in temperature and humidity within the PEMFC active area, resulting in higher transient temperature and water concentration. This finding explains why the output voltage decreases more when the load changes with a short time interval. This research enhances our understanding of transient temperature and water behavior, providing a foundation for further research and development of effective operational strategies and vehicle PEMFC designs.

Thermal error modelling of gear measuring instrument based on principal component regression

As gear measuring instruments (GMI) are frequently used at the production site, the influence of temperature on their accuracy and stability becomes non-negligible. The existing standards and technologies only consider the error modelling and compensation problem when the GMI is in a uniform temperature field and is not suitable for the production site environment with a large temperature change rate and large temperature gradient. The paper delves into the problems of GMI thermal error modelling at the production site. First, the temperatures of the GMI and its surroundings were measured using multiple temperature sensors. The parallelism thermal errors between centres and the Z-axis (referred to as parallelism thermal errors) and the y-direction spatial thermal errors (referred to as spatial thermal errors) were also measured by the probe and laser interferometer synchronously. Then, the models of them were established based on principle component regression (PCR). As evidenced by the experimental results, the fitting determination coefficient of the models are both greater than 97%, and the prediction determination coefficient is greater than 90%, demonstrating that the models are precise and robust.

Determination of flow velocities using fiber-optic temperature measurements

A new flow measuring technique is introduced to measure liquid flow velocities under harsh circumstances in environments with dirt, high pressures and elevated temperatures as in boreholes within the earth’s crust. A glass fiber embedded in a cable with heating wires measures the temperature within the heated cable with fiber-optic temperature sensing. Similar to hot-wire anemometry (HWA), the velocity dependence of convective heat transfer is exploited to measure the velocity around the cable as a cylinder in crossflow. In the first experiment, a borehole-mimicking test rig and a realistic prototype of a borehole probe were built and the flow along the borehole axis was investigated. The concept of this new measurement technique was proven, since the expected Nusselt-Reynolds characteristic of a cylinder in crossflow has been successfully measured. Furthermore, a temperature profile model across the cables cross section has been developed to account for the unexpectedly low ranges of Nusselt number. The model accuracy has been addressed with a second experiment, where a straight segment of a custom-built heated cylinder was placed in a water channel perpendicular to the flow direction. The upstream flow speed during this set of measurements was recorded using particle image velocimetry (PIV), while multiple temperature sensors in the channel, on the probe’s sheath and within the probe delivered the information for the heat transfer model.

Research on Thermal Error Modeling Method of Machine Tool Spindle Based on Optimized BP Neural Network

Addressing the limitations of the single-temperature measurement point monitoring for detecting the temperature changes in the CNC machine tool spindle, and the shortcomings of the thermal error model based on back propagation neural network (BP) in accuracy, convergence and robustness. This paper studies the thermal error identification model and method of spindle based on multiple temperature sensors. An Adaptive particle swarm algorithm-back-propagation neural network (IAPSO-BP) model for thermal error identification of principal axes is proposed. To enhance modeling accuracy and comprehensively monitor the temperature information of the machine tool spindle, the input of this model is generated by processing the data collected through five temperature sensors. The IAPSO algorithm is employed for the automatic identification of BP parameters, reduce manual intervention, and enhancing the model’s capacity for generalization.

Estimation of thermal input in thermite reaction for innovative wellbore plugging & abandonment techniques

In this work, a methodology is presented to simulate heat transfer in wellbores for plugging & abandonment operations, where the thermal input is provided by a thermite reaction. The operation of burning a thermite column inside the well's production tubing to form a metal plug is studied. The objective is to eliminate the need to simulate the thermite domain and chemical reaction during the process, simplifying the physical model and reducing computational cost. In this model, it is assumed that the thermal input from the thermite reaction is provided to the model through multiple heat flux boundary conditions along the inner wall of the production tubing. The unknown heat flux from the thermite to the inner wall of the tube is estimated by solving an inverse heat conduction problem (IHCP). The Adaptive Function Specification Method is used to estimate multiple heat flux functions at the boundary through information from multiple temperature sensors located on the outer surface along the tube's height. A characteristic behavior of the heat flux curve was verified in all segments of the tube, and the average heat flux curve was used to simulate heat transfer during a 15 m thermite burning process inside a well.

Robust Electro-Thermal Modeling of Lithium-Ion Batteries for Electrified Vehicles Applications

Lithium-ion battery (LIBs) packs represent the most expensive and safety-critical components in any electric vehicle, requiring accurate real-time thermal management. This task falls under the battery management system (BMS), which plays a crucial role in ensuring the longevity, safety, and optimal performance of batteries. The BMS accurately monitors cell temperatures and prevents thermal runaway by leveraging multiple temperature sensors; however, adding a temperature sensor to each individual cell is not practical and increases the total cost of the EV. This paper provides three key original contributions: (1) the development and optimization of a new efficient electro-thermal battery model that accurately estimates the LIB voltage and temperature, which reduces the required number of temperature sensors; (2) the investigation of the ECM parameters’ dependency on the state of charge (SOC) at a wide range of ambient temperatures, including cold temperatures; (3) the testing and validation of the proposed electro-thermal model using real-world dynamic drive cycles and temperature ranges from −20 to 25 °C. Results indicate the effectiveness of the proposed electro-thermal model, which shows good estimation accuracy with an average error of 50 mV and 0.5 °C for the battery voltage and surface temperature estimation, respectively.

Soft Wearable Thermal Devices Integrated with Machine Learning

AbstractCore body temperature (CBT) is a vital parameter that provides insight into individuals' overall health. However, existing methods to monitor CBT are mainly invasive and limited to applications in operating rooms. This work reports a soft wearable thermal device with low power operation to accurately monitor the core temperature and overcome these limitations. The thermal device comprises multiple temperature sensors separated with insulating materials of different thermal conductivities. The design provides a well‐defined thermal gradient to characterize the heat flux across the device. Thermal simulation of the devices with finite element analysis provides guidelines on the device design. Experimental studies involving tissue phantom and human subjects characterize and validate the device performance. A machine learning approach can account for heterogeneous, hard‐to‐measure parameters among individuals such as tissue thermal conductivity and heat generation rate. The machine learning algorithms can be trained to accurately quantify the core temperature in human subjects using the zero‐heat‐flux device measured temperature as a reference. The results show that the mean core temperature difference between the zero‐heat‐flux and the devices is 0.01 °C with 95% limits of agreement in the range of −0.08 °C and 0.1 °C.

A scoping study on remelting process of a debris bed in the lower head of reactor pressure vessel

Coolability and retention of core melt (corium) in the lower head of reactor pressure vessel (RPV) has been accepted as a severe accident management strategy to maintain the reactor pressure vessel (RPV) integrity of a light water reactor. To qualify the in-vessel melt retention strategy, lots of studies have been performed to investigate the natural convection heat transfer of a melt pool in the lower head. However, little work has been attributed to the precursory phase of the melt pool, i.e., the remelting process of a debris bed which is formed in the lower head at the very beginning of corium relocation from the core the lower head. The present study is motivated to conduct an experimental study on the debris remelting process. For this purpose, a dedicated test facility named COREM (COrium REMelting) is conceived and constructed, which features internal heating of electromagnetic induction and visualization of debris remelting dynamics. The test section is a semicircular vessel representing a slice of scaled-down RPV lower head, whose front and back walls are made of transparent tempered glass which facilitate visualization and induction heating of the debris bed. Fiber probes with multiple optical temperature sensors are mounted in the semicircular wall of the test section to measure its temperature distribution. In the scoping test, n-octanol and Wood's metal are selected as the simulant materials of metallic and oxidic components of corium, respectively, and their particles ware loaded in the test section to form a debris bed. This paper presents the first two scoping tests which have been performed with the COREM facility so far, under different mixing ratios of two debris materials. The measured data include the photography of debris remelting processes and the temperatures in the debris bed and the semicircular wall. Based on the temperature distributions, heat flux profile along the semicircular vessel wall is also estimated. The scoping tests well reproduce the dynamic process of debris remelting, with two distinct stages of fusion of n-octanol and Wood’s metal, i.e., melting successively from low to high melting-point debris particles. During the remelting process, the vessel wall temperatures increase with the polar angle firstly and then decrease gradually, leading to the highest temperature appearing in the middle of the lower layer of the stratified molten pool which is finally formed.

Design of Multi-Channel Temperature Acquisition System Based on STM32

The change of electrolyte temperature in electrochemical machining results in the change of conductivity, and then affect the machining accuracy. A multi-channel temperature acquisition system based on STM32 was designed in order to monitor the temperature distribution in the process of processing, Adopting the structure of "Main Controller + Function Module", the acquisition module is composed of multiple DS18B20 temperature sensors, and the STM32F103RCT6 single chip microcomputer is used as the system control center to complete the temperature acquisition and storage, the precision of temperature data acquisition is improved significantly by digital filtering anti-interference processing. In order to verify the reliability and stability of the acquisition system, the temperature measurement and acquisition test is carried out. The test results show that the system has rapid response, strong operability and high acquisition accuracy.

Safety, feasibility, and application of intraperitoneal gas-based hyperthermia beyond 43°C in the treatment of peritoneal metastasis: An in-vivo pilot study.

Background43°Celsius (C) is currently the highest temperature used in the treatment of peritoneal metastasis (PM). Despite sufficient data on water- based hyperthermic solutions in PM treatment, there is currently no information on gas-based hyperthermia extending beyond 43°C. This study is the first to provide in-vivo data on different organ systems during and after intraperitoneal gas-based hyperthermia beyond 43°C. The aim of this study is to explore in-vivo feasibility, safety, and efficacy of this novel concept from a biological perspective.MethodsFor this study, three swine were subjected to laparoscopy and subsequent gas-based intraperitoneal hyperthermia at 48°, 49° and 50°C under a high-flow air stream. Intraoperative data from multiple temperature sensors were analysed. Additionally, intraoperative anaesthesiologic and gasometrical data was analysed. Postoperatively, swine were monitored for one week and laboratory work-up was performed on postoperative days 1, 3 and 7.ResultsDuring gas-based intraperitoneal hyperthermia, anesthesiologic parameters did not exhibit critical values. No intra- or postoperative complications were observed. Distinct temperature measurements on the skin, cystohepatic triangle and esophagus did not display any temperature increase. Postoperative laboratory workup did not show any changes in hemoglobin, white blood cell count, platelets, or kidney function.DiscussionBased on our data, there are no safety concerns for the application of gas-based hyperthermia between 48 - 50°C. In fact, no critical systemic temperature increase was observed. With respect to possible limitations, further in-vivo studies are required to evaluate whether gas-based intraperitoneal hyperthermia may be a therapeutic option for PM patients.

High-temperature Pt–Al2O3 composite nano-thick interdigital electrodes for surface acoustic wave sensors

High-temperature stability of ultra-thin film electrodes is vital for surface acoustic wave (SAW) devices in harsh environments. This paper proposed the Pt–Al2O3 composite Nano-thick interdigital electrodes (IDEs) for langasite-based high-temperature SAW sensors. The sandwich structure of the IDEs was composed of Pt layers and Al2O3 armors. The armor consisting of barrier and coating layers was introduced to reduce the agglomeration, oxidation, and interdiffusion of IDEs at high temperatures. Capacitive bonding pads were proposed to transmit radiofrequency signals through the closed armor. The composite IDEs (226 nm thickness) had good structural stability and impurities concentration less than 1% at 1000 °C (3 h, air condition). Improved quality factors of 8805, and matched electrical impedance of 51 Ω and −16° were achieved through the electroacoustic optimized IDEs. Finally, multiple SAW temperature sensors using the composite IDEs presented fitting errors less than 1% and hysteresis errors below 4% in 80–1000 °C exceeded 9 h, which was 150 °C higher than existing ones. The proposed composite IDEs enable SAW devices to be applicated in 80–1000 °C.

Determination Of Flow Velocities Using Fiber-Optic Temperature Measurements

A novel flow measuring technique is introduced to measure under harsh circumstances in environments with dirt, high pressures and elevated temperatures as in boreholes within the earth's crust. A glass fiber embedded in a cable with heating wires measures the temperature within the heated cable with distributed temperature sensing (DTS). Similar to Hot Wire Anemometry, the velocity dependence of convective heat transfer is exploited to measure the velocity of the cable as a cylinder in crossflow. A borehole-mimicking test rig and a realistic prototype of a borehole probe were built and the flow along the borehole axis was investigated. The expected Nusselt Reynolds characteristic of a cylinder in crossflow has been measured which proves the concept of this novel measurement technique. Challenges arise with the insufficient spatial resolution of DTS measurements and in the heat transfer modeling because the temperature profile of the cables cross-section must be taken into account. More detailed investigations and developments are planned to elevate this measurement technique from the current proof-of-concept stage to a reliable flow measurement technique. As a first step, the DTS technique will be extended the application of fiber Bragg grating (FBG) temperature measurements at specific points along the glass fiber. Subsequently a straight segment of a new hybrid cable will be placed in a water channel perpendicular to the flow direction. The Flow will be precisely specified using Particle Image Velocimetry and multiple temperature sensors in the channel and on the cable's sheath will deliver the information for enhanced heat transfer modelling.

Real-Time Construction of Thermal Model Based on Multimodal Scene Data

In commercial buildings, the total consumption of central air conditioning accounts for about 40%–50%. However, at present, the initial design value of building Heating Ventilation and Air Conditioning (HVAC) is usually far greater than the actual refrigeration value of refrigeration demand, which will lead to great energy consumption waste. Moreover, the operation of HVAC affects the thermal comfort of users, so it is necessary to establish a thermal model for the scene to control. The thermal model describes the temperature of the scene in different environments. So it is very important to design a thermal model to calculate the scene in real time. Because the flow of people, the opening of windows, the ventilation of the scene and other parameters influence the change of thermal state in the scene environment, these parameters are complicated to model. Human disturbance will lead to the instability of the state of the scene environment. The inconsistency of its thermal model will lead to energy allocation tracking strategies in different regions. To solve this problem, We propose a thermal model for building thermal comfort using a multimodal analysis framework. This paper analyzes multiple temperature and humidity sensors and area image by multimodal combination and processes the image and sensor data by combining CNN and LSTM. Our results show that when the thermal model analyzed by this method is deployed in a building in the south of China, the MSE accuracy of the local effect of temperature field prediction reaches 99%, and its AMAX reaches 94%, so the running stability of the model in the scene is high. In addition, the research shows that the thermal model analysis framework can make the Internet of Things (IoT) in buildings more intelligent, and it can be combined with this thermal model to improve human comfort, make it easier to deploy in each hot zone, and have a better overall energy-saving effect.

Thermodynamic and metastable colorimetric states of polydiacetylenes composite films and their applications for deciphering secret codes and sensing temperature

Electrospun polydiacetylenes (PDAs) composite materials are favorable for practical applications. However, a further understanding of the colorimetric changes of the composite systems in terms of thermodynamic and metastable states resulting from the interactions among multiple components is normally neglected. Herein, we functionalized diacetylenes with a variety of head groups and used them to prepare electrospun films with polyethylene oxide (PEO) as the matrix. Detailed investigations have been performed on the thermochromic properties of the PDA/PEO composite films to profile their general energy landscapes that describe the identified metastable and thermodynamic colorimetric states. Underlying mechanisms for the generation of multiple energy minima were proposed on the basis of chemical structures of PDA head groups. Utilizing the fined-tuned chromic responsiveness of the PDA/PEO composite films under thermo- and pH stimulations, as well as the features of thermodynamic and metastable states, we fabricated delicate devices for secret codes with multiple deciphering pathways and temperature sensors with a high-temperature recording function.

Estimation of temperature detection delay in a fiber optic gyroscope sensing coil

The use of algorithmic temperature compensation requires that the external temperature sensor data and the thermal response of the fiber optic sensor should be synchronized. The paper considers an approach to estimate the temperature detection delay for the external temperature sensor in a sensing coil assembly of a fiber-optic gyroscope. The delay estimation is based on a cross-correlation of temperature data from an external temperature sensor and temperature data of an optical fiber segment obtained by distributed temperature measurement based on optical frequency reflectometry. The results include the estimation of temperature detection delay between the sensing coil and the temperature sensor. The described approach allows evaluating temperature detection delay in a sensing coil assembly of a fiber-optic gyroscope. In case of using multiple temperature sensors, the delay for each temperature sensor can be estimated and taken into account to improve the efficiency of the thermal drift compensation of the fiber optic gyroscope.

Real-Time Estimation of 2-D Temperature Distribution in Lithium-Ion Pouch Cells

Thermal management is an integral part of battery management systems due to the effect of temperature on safety, lifetime, and efficiency of batteries. Therefore, a reliable real-time estimation algorithm is required to estimate the temperature distribution in battery cells based on available measurements. Temperature estimation in pouch-type cells is especially challenging due to the nonuniform distribution along length and breadth. Motivated by this issue, we study effective sensor placement and estimation algorithm design for pouch cells in this article. Specifically, we explore two scenarios: Scenario 1 where multiple temperature sensors are available, and Scenario 2 where only one temperature sensor is available. For Scenario 1, we find the minimum number of sensors required and their effective locations, whereas for Scenario 2, we find the effective location of the single sensor that maximizes the state observability. We employ the Gramian observability analysis for this study. Subsequently, we design sliding mode observer-based real-time algorithms for distributed temperature estimation in both scenarios. Finally, we illustrate the performance of the proposed estimation algorithms through extensive experimental and simulation studies.

TempSense: A Four-Channel Temperature Logger Based on a USB Bridge and CircuitPython

Accurate temperature measurements are needed to monitor and reduce energy use and greenhouse gas emissions in building, industrial, and other processes. This paper describes a temperature sensor instrument, named TempSense, developed for classroom demonstrations of thermal phenomena. The instrument can monitor, record, and visualize data from multiple temperature sensors using a K-type amplifier and thermocouples. It has an accuracy of ±2 deg C from -200 to +700 deg C and an accuracy of ±6 deg C from –270 to +1372 deg C. The instrument uses a USB bridge and the Python language to interface with open-source thermocouple amplifiers without the need for byte-level programming. Its design allows novice experimenters to inspect the build and operation and even customize it to their needs. TempSense can be extended via Python libraries to provide internet communication, visualization, and analysis.

Multiscale Analysis to Determine the Sensitive Zones for Temperature Sensor Location in Tubular Reactors

AbstractThe sensitivity of benchmark tubular reactors subject to temperature and concentration of external perturbations was investigated. Two case studies were evaluated, a pseudo‐homogeneous axial dispersion dimensionless tubular reactor and a heterogeneous tubular reactor to produce maleic anhydride. For the dimensionless reactor model, the highest sensitivities in the reactor were located at the center and at the outlet for temperature and concentration disturbances, respectively, while for the heterogeneous reactor model the sensitivity does not undergo significant changes with feed disturbances. When variations in the most sensitive position occur, a cascade controller can be improved using multiple temperature sensors. The evaluation on weighting assignment of the temperature measurements for the cascade control scheme is also described.

Stream Temperature and Environment Relationships in a Semiarid Riparian Corridor

This study examined the relationship between stream temperature and environmental variables in a semiarid riparian corridor in northcentral Oregon, USA. The relationships between riparian vegetation cover, subsurface flow temperature, and stream temperature were characterized along an 800 m reach. Multiple stream temperature sensors were located along the reach, in open and closed canopy areas, with riparian vegetation cover ranging from 4% to 95%. A support vector regression (SVR) model was developed to assess the relationship between environmental characteristics and stream temperature at the larger valley scale. At the reach scale, results show that air temperature was highly correlated with stream temperature (Pearson’s r = 0.97), and no significant (p < 0.05) differences in stream temperature levels were found among sensor locations, irrespective of percent vegetation cover. Channel subsurface temperature levels from an intermittent flow tributary were generally cooler than those in the perennial stream in the summer and warmer during winter months, indicating that the tributary may have a localized moderating effect on stream temperature. At the valley scale, results from the SVR model showed that air temperature, followed by streamflow, was the strongest variable influencing stream temperature. Also, riparian area land cover showed little effect on stream temperature along the entire riparian corridor. This research indicates that air temperature, subsurface flow, and streamflow are important variables affecting the stream temperature variability observed in the study area.