Direct Temperature Measurements Research Articles

The generation mechanism of cutting heat and the theoretical prediction model for temperature on the rake face of the cutting tool in Zirconia ceramics

Cutting temperature and its distribution are crucial factors influencing tool strength and wear rate, due to the hardness and brittleness of Zirconia (ZrO2) ceramics, significant challenges arise in both direct temperature measurement in the cutting zone and theoretical analysis of cutting heat. Thus, focusing on the turning characteristics of ZrO2 ceramics, this study analyzes the mechanism of cutting heat generation and proposes utilizing thermodynamic state equations to determine the cutting heat source on rake face of tool. Based on the heat source method, a theoretically prediction model for temperature distribution on rake face is established. This model considers primary cutting parameters, workpiece material properties, crack fracture characteristics of the machined surface, and thermal characterizations of the tool material. The relationship between tool wear and cutting temperature is experimentally analyzed to determine the characteristic temperature that indicates the initial stage of tool wear. The validity of the theoretical model is verified, as the predicted results show high consistency with experimental results within the range of experimental parameters, with a relative error within 15.2 %.The results reveal the highest temperature during brittle cutting occurs within the cutting layer area, with the highest temperature occurring approximately 50 μm from the tool tip, followed by a gradual decrease beyond 150-200 μm. This study also demonstrates that cutting heat in ceramic turning does not solely originate from friction heat between tool flank-workpiece but also includes impact heat from tool rake face-workpiece, which under certain cutting parameters exerts a more significant influence on cutting temperature. This model can facilitate the selection and optimization of cutting process parameters for brittle materials and provide a theoretical basis for analyzing the relationship between tool thermal damage, thermophysical properties, and tool wear.

Ex Ante Construction of Flow Pattern Maps for Pulsating Heat Pipes

A novel methodology is proposed for the development of empirical flow pattern maps for pulsating heat pipes (PHPs), which relies on the concept of virtual superficial velocity of the liquid and vapour phases. The virtual superficial velocity of each phase is defined using solely the design and operational parameters of the pulsating heat pipe, allowing the resulting flow pattern map to serve as a predictive instrument. This contrasts with existing flow pattern maps that necessitate direct measurements of temperatures and/or velocities within one or more channels of the pulsating heat pipe. Specifically, the virtual superficial velocities are derived from the relative significance of the driving forces and the resistances encountered by each phase during flow. The proposed methodology is validated using flow visualisation datasets obtained from two separate experimental campaigns conducted on flat-plate polypropylene pulsating heat pipe prototypes featuring transparent walls and meandering channels with three turns, five turns, seven turns, and eleven turns, respectively. The PHP prototypes were tested for gravity levels ranging between 0 g and 1 g and heat inputs ranging from 5 W to 35 W. The proposed approach enables the identification of empirical boundaries for flow pattern transitions as well as the establishment of an empirical criterion for start-up.

Operating Key Factor Analysis of a Rotary Kiln Using a Predictive Model and Shapley Additive Explanations

The global smelting business of nickel using rotary kilns and electric furnaces is expanding due to the growth of the secondary battery market. Efficient operation of electric furnaces requires consistent calcine temperature in rotary kilns. Direct measurement of calcine temperature in rotary kilns presents challenges due to inaccuracies and operational limitations, and while AI predictions are feasible, reliance on them without understanding influencing factors is risky. To address this challenge, various algorithms including XGBoost, LightGBM, CatBoost, and GRU were employed for calcine temperature prediction, with CatBoost achieving the best performance in terms of MAPE and MLSE. The influential factors on calcine temperature were identified using SHAP from XAI in the context of the CatBoost model. SHAP effectively assesses model impacts, accounting for variable interdependencies, and offers visualization in high-dimensional contexts. Given the correlation and dimensionality of variables predicting calcine temperature, SHAP was preferred over Feature Importance or PDP for the analysis. By incorporating seven out of twenty operational factors like burner fuel and reductant feed rate, combustion conditions inside of the rotary kiln and RPM, the calcine temperature increased from 840 °C in 2023 to 910 °C by October 2024, concurrently reducing the electricity unit consumption of the electric furnace by 7.8%. Enhancements to the CatBoost algorithm will enable the provision of guidance values after optimizing key variables. It is expected that managing the rotary kiln’s calcine temperature according to the predictive model’s guidance values will allow for autonomous operation of the rotary kiln through inputting guidance values to the PLC.

An Approach to Estimate the Temperature of an Induction Motor under Nonlinear Parameter Perturbations Using a Data-Driven Digital Twin Technique

To monitor temperature as a function of varying inductance and resistance, we propose a data-driven digital twin approach for the rapid and efficient real-time estimation of the rotor temperature in an induction motor. By integrating differential equations with online signal processing, the proposed data-driven digital twin approach is structured into three key stages: (1) transforming the nonlinear differential equations into discrete algebraic equations by substituting the differential operator with the difference quotient based on the sampled voltage and current; (2) deriving approximate analytical solutions for rotor resistance and stator inductance, which can be utilized to estimate the rotor temperature; and (3) developing a general procedure for obtaining approximate analytical solutions to nonlinear differential equations. The feasibility and validity of the proposed method were demonstrated by comparing the test results with a 1.5 kW AC motor. The experimental results indicate that our method achieves a minimum estimation error that falls within the standards set by IEC 60034-2-1. This work provides a valuable reference for the overheating protection of induction motors where direct temperature measurement is challenging.

On the рossibility of multichannel optical backscattering sondes for joint balloon and lidar studies of the aerosol composition of the middle atmosphere

Aerosol backscattering sondes in the practice of aerological sounding, along with lidar observations, are used at night to study and monitor polar stratospheric clouds, tropospheric and stratospheric aerosol, cirrus clouds, pyroconvection, volcanic aerosol, as well as to verify remote methods and means of ground-based and satellite-based aerosol observations. For aerosol sondes, a simple two-wave measurement technique is used, which makes it possible to diagnose changes in aerosol composition by color index. The possibilities of the two-wave technique have limitations, which are discussed in this article. Aerological sounding combined with lidar observations expands the wavelength range for multi-wavelength studies, and direct measurements of atmospheric temperature increase the accuracy of aerosol sensing. The paper considers the application of 3 or more wavelenght techniques. Data from probe measurements using wavelengths of 470, 528, 850 and 940 nm and lidar sensing at wavelengths of 355 and 532 nm are presented.

Emergence hour-by-hour of r-process features in the kilonova AT2017gfo

The spectral features in the optical/near-infrared counterparts of neutron star mergers (kilonovae, KNe) evolve dramatically on hourly timescales. To examine the spectral evolution, we compiled a temporal series that was complete at all observed epochs from 0.5 to 9.4 days of the best optical/near-infrared (NIR) spectra of the gravitational-wave detected kilonova AT2017gfo. Using our analysis of this spectral series, we show that the emergence times of spectral features place strong constraints on line identifications and ejecta properties, while their subsequent evolution probes the structure of the ejecta. We find that the most prominent spectral feature, the 1 μm P Cygni line, appears suddenly, with the earliest detection at 1.17 days. We find evidence in this earliest feature for the fastest yet discovered kilonova ejecta component at 0.40–0.45c. Across the observed epochs and wavelengths, the velocities of the line-forming regions span nearly an order of magnitude, down to as low as 0.04–0.07c. The time of emergence closely follows the predictions for Sr II because Sr III combines rapidly under local thermal equilibrium (LTE) conditions. The transition time between the doubly and singly ionised states provides the first direct measurement of the ionisation temperature. This temperature is highly consistent with the temperature of the emitted blackbody radiation field at a level of a few percent. Furthermore, we find the KN to be isotropic in temperature, that is, the polar and equatorial ejecta differ by less than a few hundred Kelvin or ≲5%, in the first few days post-merger based on measurements of the reverberation time-delay effect. This suggests that a model with very simple assumptions, with single-temperature LTE conditions, reproduces the early kilonova properties surprisingly well.

Real-time measurement of electron temperature using a coupled centre-tapped emissive probe and a Langmuir probe (CCTELP)

Abstract Conventional electrostatic probes are commonly used for measuring the plasma parameters like plasma density, electron temperature, plasma potential and floating potential respectively in a laboratory plasma to unveil physical processes of relevance to magnetosphere and fusion plasmas. With such probes, real-time measurement of electron temperature and its fluctuating AC component becomes very difficult. These measurements are significant as they directly give the measure of energy flux and hence can be utilized in estimating the turbulence-induced energy loss in any plasma system, especially useful for electron temperature gradient plasmas. Although Triple Langmuir Probe (TLP) diagnostics provide information on real-time measurement of electron temperature, this involves many complications. These complications of TLP are suitably overcome by the proposed diagnostic, which makes use of a combination of a closely separated centre tap emissive probe (CTEP) and Langmuir probe (LP) installed on a single probe head. The coupled centre-tapped emissive and Langmuir probe (CCTELP) provides direct measurement of electron temperature (T_e) and its fluctuations (T ̃_e). The design of the proposed diagnostic is very simple and it is almost free of rigorous control electronics and post-data analysis otherwise needed to estimate the electron temperature. These features make this diagnostic an additional and simple candidate for real-time measurement of electron temperature and its fluctuation. The paper will highlight details on this novel diagnostics, called CCTELP for the measurement in Maxwellian plasmas.

Data-driven clustering learning modeling for semiconductor silicon single crystal growth process based on fuzzy C-means and DBN

The optimal operation of the semiconductor silicon single crystal (SSC) growth in an industrial single crystal furnace is contingent upon the precise measurement of both the crystal diameter and melt temperature. The Czochralski (CZ) silicon single crystal growth process (CZ-SSCGP) is a phase transition process from solid to liquid that takes place in a high-temperature, enclosed furnace designed for single crystal growth. The direct measurement of crystal diameter and melt temperature using sensors can pose a challenge due to the physical limitations of the furnace. This study introduces a novel approach for modeling clustering based on data that integrate fuzzy C-means (FCM) and deep belief network (DBN) techniques. The proposed method aims to estimate the online crystal diameter and melt temperature. The objective of this approach is to efficiently and precisely derive the CZ-SSCGP model. In order to address the negative impact of nonequilibrium properties of data on the model, it is advisable to employ FCM to produce numerous subsets of data that exhibit comparable operating conditions. This is especially important because data change over time and are different in different working situations. In order to enhance the precision of the fitting process for crystal diameter and melt temperature, we amalgamate the output outcomes of DBN that have been constructed for each individual subset. It is imperative to address the nonlinear and time-varying features manifested by the engineering data. Industrial data are utilized for conducting diverse data experiments and comparative analyses. The process takes into account the data’s susceptibility to contamination by outliers. The findings suggest that the method put forth exhibits superior precision and enhanced resilience in predicting crystal diameter and melt temperature compared to alternative modeling approaches.

Temperature Estimation of Lithium-Ion Batteries Using Electrochemical Techniques

With their high energy and high power density, lithium-ion batteries (LIBs) are widely used as power sources for electric vehicles and energy storage systems, and their demand continues to grow. However, the increasing energy and power density of LIBs leads to significant heat generation issues, and reflecting these safety concerns there have been a number of recent fires of high-performance batteries. Under these circumstances, it is increasingly important to have a reliable assessment of battery safety through precise diagnostics of battery state of health (SoH). As a matter of fact, the battery management system diagnoses and evaluates battery SoH by analyzing various parameters such as voltage, current, and surface temperature of battery, and among these there is no doubt that battery temperature is a critical safety factor. As LIBs become more high-performance and large-scale, the temperature distribution inside the battery becomes more uneven, which possibly degrades performance and eventually leads to failure, so it is very important to secure the uniformity of the battery temperature and at the same time to analyze the temperature inhomogeneity and sudden temperature changes inside the battery.A typical way to estimate battery temperature is to insert a temperature sensor into the battery or attach it to the battery surface. While these methods enable direct temperature measurement, inserting a sensor can mechanically affect the internal structure of the battery, and attaching a sensor to a surface can result in delayed detection of internal cell temperature changes and may not fully reflect the battery temperature especially if there is a large temperature distribution. As an alternative, temperature estimation using electrochemical impedance spectroscopy (EIS) has received significant attention. Although EIS analysis is an indirect method of obtaining internal cell information through the electrochemical signal, it has the great advantage that it is a non-destructive method that does not affect the cell structure, and the internal cell situation is fully reflected in the electrochemical signal. Nevertheless, typical temperature estimation methods using EIS are time-consuming because they require deriving temperature-related impedance components from impedance data measured over a wide frequency range (kHz to Hz). To solve this problem, Srinivasan pioneered a method that utilizes impedance values at a single frequency for temperature estimation, which uses a specific response signal that has a clear temperature dependence but is largely unaffected by other factors (such as state of charge (SoC)). While this method can effectively reduce the cost and time spent on temperature estimation using EIS and is one of the most promising methods of estimating cell internal temperature known to date, it has been reported that the accuracy of temperature estimation is low at high temperatures for reasons that are not yet fully understood.In this study, we diagnose the problems with the existing method of estimating the internal temperature of batteries through EIS and propose a way to improve the accuracy of temperature estimation at high temperatures. For this purpose, first, we measured the impedance at various SoCs and derived the characteristic frequency representing the reaction at the solid electrolyte interphase (SEI), which is the main reaction used for temperature estimation in the previous work. In addition, we thoroughly analyzed the impedance change with temperature to more clearly understand the resistance components contributing to the impedance at the characteristic frequency. The analysis showed that impedance value at characteristic frequency, which was thought to represent only one reaction at the SEI, contained signals from other reactions (e.g. interfacial charge transfer) at higher temperatures. This situation becomes more severe as the temperature increases, and is believed to be the reason why traditional impedance-based temperature estimation methods have limited accuracy at high temperatures. We also observed that increasing the characteristic frequency to avoid this signal overlap prevented signal disturbance due to charge transfer reactions, but instead introduced another signal disturbance due to stray inductance. Based on these analyses, this study proposes a new frequency value to be used for battery temperature estimation through EIS, and at the same time presents an impedance correction method that can reliably take only the impedance signal of a specific reaction with temperature dependence without interference from other reaction signals at that frequency. It is confirmed that the temperature estimation accuracy is greatly improved when using the method presented in this study compared to the existing temperature estimation method. In this presentation, we will critically discuss why existing methods have poor temperature estimation accuracy at high temperatures, and propose an impedance-based temperature estimation method that can achieve high accuracy over a wide operating temperature range.

Distribution of the acoustobrightness temperature satisfies heat equation

Passive acoustic thermometry (PAT), a method proposed for medical applications, involves direct measurement of the acoustobrightness temperature which is the intensity of the thermal acoustic radiation expressed in degrees. The hypothesis has been proposed that the acoustobrightness temperature measured on the surface of the object under study obeys the 2D heat equation whose parameters coincide or are uniquely connected with the parameters of the 3D heat equation governing the deep temperature of the object. Since in PAT a noise signal is measured, the achievement of an acceptable error level (0.5–1 K) requires a considerable integration time (about a minute). The proposed hypothesis will enable linking together the data obtained by acoustothermometric sensors separated in space, at different points in time. This, in turn, will lower the dimensionality of the problem of temperature distribution reconstruction and enable reducing the integration time without loss of accuracy. The proposed hypothesis has been confirmed experimentally and with the aid of computer simulation for the case where local hyperthermia of soft tissues of the human organism is modeled.

A Radial Decrease in Kinetic Temperature Measured with H2CO in 30 Doradus

Feedback from star formation is a critical component of the evolution of galaxies and their interstellar medium. At parsec scales internal to molecular clouds, however, the observed signatures of that feedback on the physical properties of CO-emitting gas have often been weak or inconclusive. We present subparsec observations of H2CO in the 30 Doradus region, which contains the massive star cluster R136 that is clearly exerting feedback on its neighboring gas. H2CO provides a direct measure of gas kinetic temperature, and we find a trend of decreasing temperature with projected distance from R136 that may be indicative of gas heating by the stars. While it has been suggested that mechanical heating affects H2CO-measured temperature, we do not observe any correlation between T K and line width. The lack of an enhancement in mechanical feedback close to R136 is consistent with the absence of a radial trend in gravitational boundedness seen the Atacama Large Millimeter/submillimeter Array CO observations. Estimates of cosmic-ray flux in the region are quite uncertain, but can plausibly explain the observed temperatures if R136 itself is the dominant local source of energetic protons. The observations presented here are also consistent with the H2CO-emitting gas near R136 being dominated by direct radiation from R136 and photoelectric heating in the photodissociation regions.

An experimental and modelling investigation on the water content of CO2and CO2-rich mixtures using the differential scanning hygrometry (DSH) method

Monitoring humidity downstream to conditioning facilities and during transportation is essential for avoiding hydrate deposition. However, water inline monitoring under high pressure is still challenging in the CCS industry. This study presents an experimental and modelling investigation for enhancing field monitoring and model predictions. Measurements are performed using the Differential Scanning Hygrometry (DSH). This novel analytical approach has been successfully tested for measuring dew/frost temperatures for carbon dioxide, CH4+CO2, and CO2-rich mixtures in equilibrium with hydrates, free water and ice. Moreover, the DSH method has been applied for direct HP equilibrium temperature measurements. Also, this work compares three modified versions of the classical SRK EoS with the Multi-Fluid Helmholtz Energy Approximation (MFHEA). This evaluation includes Huron-Vidal and the EMS mixing rules and the cubic-plus association (CPA) approach. A thorough fitting process was carried out and, overall, comparisons with the experimental data showed that SRK + EMS yielded results as satisfactory as sCPA.

Numerical Calculation and Direct Measurement of Local Hot Spot Temperatures in Transformer Clamping Plates

A verified multiphysics model is developed to simulate local hot spot temperatures in clamping structures. Stray field losses in clamping plates are obtained with the use of nonlinear impedance boundary condition through finite element method (FEM) while hot spot temperatures are simulated with the use of computational fluid dynamics (CFD). This multiphysics coupling is validated through direct measurements of local hot spot temperatures that were carried out with fiber optic temperature probes in real transformer. The local hot spot temperatures obtained with the simulations were in good agreement with the measured values, thus verifying overall approach and indirectly the calculation of local stray field losses.

CLASSY. IX. The Chemical Evolution of the Ne, S, Cl, and Ar Elements

To study the chemical evolution across cosmic epochs, we investigate Ne, S, Cl, and Ar abundance patterns in the Cosmic Origins Spectrograph Legacy Archive Spectroscopic SurveY (CLASSY). CLASSY comprises local star-forming galaxies (SFGs; 0.02 < z < 0.18) with enhanced star formation rates, making them strong analogues to high-z SFGs. With direct measurements of electron temperature, we derive accurate ionic abundances for all elements and assess ionization correction factors (ICFs) to account for unseen ions and derive total abundances. We find Ne/O, S/O, Cl/O, and Ar/O exhibit constant trends with gas-phase metallicity for 12+log(O/H) < 8.5 but significant correlation for Ne/O and Ar/O with metallicity for 12+log(O/H) > 8.5, likely due to ICFs. Thus, the applicability of the ICFs to integrated spectra of galaxies could bias results, underestimating true abundance ratios. Using CLASSY as a local reference, we assess the evolution of Ne/O, S/O, and Ar/O in galaxies at z > 3, finding no cosmic evolution of Ne/O, while the lack of direct abundance determinations for S/O and Ar/O can bias the interpretation of the evolution of these elements. We determine the fundamental metallicity relationship (FMR) for CLASSY and compare to the high-redshift FMR, finding no evolution. Finally, we perform the first mass–neon relationship analysis across cosmic epochs, finding a slight evolution to high Ne at later epochs. The robust abundance patterns of CLASSY galaxies and their broad range of physical properties provide essential benchmarks for interpreting the chemical enrichment of the early galaxies observed with the JWST.

Estimation of Temperature Distribution in the Brain Using the Temperatures of Blood Inflow and Outflow for Brain Temperature Control

Brain cells can be damaged by higher temperature in the brain, and brain temperature distribution monitoring system is required from the perspective of brain preservation. Brain tissue can be further damaged by direct measurement of temperature using some sensors. Therefore, indirect methods of measuring temperature are required. In this study, we propose a method for estimating the average metabolic rate of the entire brain based on the difference between the observed and estimated blood flow temperatures that can be measured during selective brain hypothermia. Our mathematical simulator can calculate the brain temperature distribution based on the estimated average metabolic rate, taking into account other parameters like heat transfer, metabolic heat of individual tissues, heat‐washout by blood flow, and environmental temperature. The algorithm for estimating the metabolic rate of mathematical master model substituted as a human head is confirmed using the mathematical slave model on the simulator. It is possible to present the temperature distribution by estimating metabolic heat production in the brain, considering the equilibrium between heat wash‐out by the blood flow and heat transfer. In addition, an appropriate initial value of brain temperature enables the accurate temperature management enough for clinical use even in the process of temperature estimation. The development of our proposed method will make it possible to construct a non‐invasive monitoring system that presents the estimated temperature distribution in the brain instead of the conventional invasive method. The new system is expected to be utilized in a various treatments related to some brain disorders. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Internal temperature estimation for lithium-ion batteries through distributed equivalent circuit network model

Lithium-ion cells experience significant internal thermal gradients during operation, with a direct impact on their safety, performance, cost and lifetime. The estimation of the internal temperature of cells is therefore particularly important. In this work, a 3D distributed electro-thermal model for internal temperature estimation is developed for a cylindrical cell (LG M50T, NMC811). The model is parameterized and comprehensively validated against experimental data for 21700 cells, including direct core temperature measurements. Multiple types of electrical load are considered, including constant current discharge, pulse discharge, drive cycle and instant discharge/charge switching. The developed model is used to estimate core temperature based on surface temperature measurement. The predictions are shown to have good accuracy at relatively low computational cost. We show that the widely adopted two-node lumped thermal estimation model is increasingly inaccurate for more aggressive discharges, when thermal gradients become higher. Compared to the standard two-node model, the distributed equivalent circuit network model predicts the effects of detailed internal cell structure (electrode, current collector, metal can and tab) and distributed internal heat generation. The results are of immediate interest to cell manufacturers and battery pack designers, while the modelling and parameterization framework is a useful tool for energy storage systems design.

The Application of a Non-Intrusive Methodology to Estimate Particle Egress Rate and Advective Heat Losses of a Falling Particle Receiver During On-Sun Tests

Abstract The direct measurement of particle temperatures and advective losses from solid particle receiver has been a topic of necessary interest to de-risk the technology and improve its performance. Due to the flow's transient and stochastic nature, it has presented a challenge to traditional thermometry and metrology methods. In this work, a simplified quantitative non-intrusive imaging methodology previously developed is applied to Sandia's falling particle receiver during on-sun tests to collect image sets, which could help estimate the average particle temperature and particle egress rate from the system. Further additions to the technique are presented to permit the estimation of the plume (air and particles) egress rate and total advective heat losses during on-sun operation. The falling particle receiver testing campaign in 2020 and 2021 allowed the team to capture multiple flow configurations and environmental conditions used to build a large database of data captures with different characteristics. Finally, the results captured were used to complete a regression study to gain further insight into the factors which impact the particle egress and the receiver's efficiency. Particle temperature, receiver flow configuration, and wind speed were found to be the most impactful factors in the study.

Heat Effects during the Operation of Lead-Acid Batteries

Thermal events in lead-acid batteries during their operation play an important role; they affect not only the reaction rate of ongoing electrochemical reactions, but also the rate of discharge and self-discharge, length of service life and, in critical cases, can even cause a fatal failure of the battery, known as “thermal runaway.” This contribution discusses the parameters affecting the thermal state of the lead-acid battery. It was found by calculations and measurements that there is a cooling component in the lead-acid battery system which is caused by the endothermic discharge reactions and electrolysis of water during charging, related to entropy change contribution. Thus, under certain circumstances, it is possible to lower the temperature of the lead-acid battery during its discharging. The Joule heat generated on the internal resistance of the cell due to current flow, the exothermic charging reaction, and above all, the gradual increase in polarization as the cell voltage increases during charging all contribute to the heating of the cell, overtaking the cooling effect. Of these three sources of thermal energy, Joule heating in polarization resistance contributes the most to the temperature rise in the lead-acid battery. Thus, the maximum voltage reached determines the slope of the temperature rise in the lead-acid battery cell, and by a suitably chosen limiting voltage, it is possible to limit the danger of the “thermal runaway” effect. The overall thermal conditions of the experimental cell are significantly affected by the ambient temperature of the external environment and the rate of heat transfer through the walls of the calorimeter. A series of experiments with direct temperature measurement of individual locations within a lead-acid battery uses a calorimeter made of expanded polystyrene to minimize external influences. A hitherto unpublished phenomenon is discussed whereby the temperature of the positive electrode was lower than that of the negative electrode throughout the discharge, while during charging, the order was reversed and the temperature of the positive electrode was higher than that of the negative electrode throughout the charge. The authors relate this phenomenon to the higher reaction entropy change of the active mass of the positive electrode than that of the negative electrode.

Non-invasive rotor temperature detection of magnetically levitated turbo molecular pump based on high-frequency inductance

The rotor temperature has a significant effect on the reliable operation of the magnetically levitated turbo molecular pump (MLTMP). Therefore, it is crucial to monitor the rotor temperature during the operation of the MLTMP to prevent irreversible demagnetization of the permanent magnet and improve production efficiency. However, direct measurement of rotor temperature is a challenge due to the complexity of installing a temperature sensor on the high speed spindle. This paper presents a non-intrusive method for detecting rotor temperature based on high-frequency(HF) inductance estimation. On this basis, a novel method is proposed that injects a HF rotating voltage into the stationary coordinate frame and introduces synchronous demodulation to achieve accurate estimation of the HF inductance. Finally, the experimental results conducted on the MLTMP test bench indicate that the proposed method can effectively detect the rotor temperature, and the temperature error is within 3 ∘C.

Acoustic imaging of stable double diffusion in the Madeira abyssal plain

Sub-mesoscale and mesoscale (i.e., 1–10 km and 10–200 km, respectively) ocean processes are highly relevant for the understanding of global circulation, mixing of water masses and energy exchange between ocean layers. However, the processes happening at these scales are hard to be characterized using direct measurements of temperature and salinity. Direct measurements are obtained from vertical probes and/or autonomous vehicles, which, despite their high vertical resolution, are sparsely located in space and therefore unable to capture spatial details at these scales. Seismic oceanography (SO) data have been successfully used to imaging and characterize the ocean at these spatial scales. These data represent indirect measurements of the ocean temperature and salinity along kilometric transects with high horizontal resolution (i.e., a near-synaptic view of the system under investigation), but lower vertical resolution when compared with direct observations. Despite its complex oceanographic setting, the Madeira Abyssal Plain is still largely uncharacterized due to the lack of direct observations. We show for the first time a comprehensive processing, modelling and interpretation of three 2-D seismic oceanography sections from this region. The data show coherent seismic reflection in space, depth and time and shed light into this oceanographic setting with an unprecedent horizontal resolution. The SO modelling and interpretation are combined with existing direct measurements and a quantitative method to correlate thermohaline staircases interpreted from conductivity-temperature-depth casts and seismic reflections is proposed. The results show the relatively stable presence of thermohaline staircases in simultaneously time and space between 1200 and 2000 m of water depth and their spatial variability and contribute to the generalization of SO in physical oceanography.