Permanent Magnet Temperature Estimation for PMSMs Using Virtual Position-offset Injection

This paper proposes a virtual position-offset injection based permanent magnet temperature estimation approach for permanent magnet synchronous machines(PMSMs). The concept of virtual position-offset injection is mathematically transforming the machine model to a virtual frame with a position-offset. The virtual frame temperature estimation model is derived to calculate the permanent magnet temperature(PMT) directly from the measurements with computation efficiency. The estimation model involves a combined inductance term, which can simplify the establishment of saturation compensation model with less measurements. Moreover, resistance and inverter distorted terms are cancelled in the estimation model, which can improve the robustness to the winding temperature rise and inverter distortion. The proposed approach can achieve simplified computation in temperature estimation and reduced memory usage in saturation compensation. While existing model-based approaches could be affected by either the need of resistance and inverter information or complex saturation compensation. Experiments are conducted on the test machine to verify the proposed approach under various operating conditions.

Noninvasive temperature estimation by detecting echo-strain change including thermal expansion

This article studies the feasibility of noninvasive temperature estimation by detecting echo-strain including thermal expansion in therapeutic ultrasound treatment. This technique evaluates distributions of echo-strain and temperature inside the tissue by detecting echo signals pre- and post-heating, in combination with the temperature dependence of sound speed and thermal expansion. In the computer simulation and experimental study, echo signals pre- and post- heating are acquired and then the temperature elevation is evaluated by correlation analysis. Results demonstrate that this technique can effectively extend the measured temperature range up to 75℃ with an accuracy of±2 ℃.

Online temperature estimation of Shell coal gasification process based on extended Kalman filter

Obtaining the temperature inside the gasifier of a Shell coal gasification process(SCGP)in real-time is very important for safe process operation.However,this temperature cannot be measured directly due to the harsh operating condition.Estimating this temperature using the extended Kalman filter(EKF)based on a simplified mechanistic model is proposed in this paper.The gasifier is partitioned into three zones.The quench pipe and the transfer duct are seen as two additional zones.A simplified mechanistic model is developed in each zone and formulated as a state-space representation.The temperature in each zone is estimated by the EKF in real-time.The proposed method is applied to an industrial SCGP and the effectiveness of the estimated temperatures is verified by a process variable both qualitatively and quan-titatively.The prediction capability of the simplified mechanistic model is validated.The effectiveness of the proposed method is further verified by comparing it to a Kalman filter-based single-zone temperature estimation method.

Air Temperature Estimation with MODIS Data over the Northern Tibetan Plateau

Time series of MODIS land surface temperature(Ts) and normalized difference vegetation index(NDVI) products,combined with digital elevation model(DEM) and meteorological data from 2001 to 2012,were used to map the spatial distribution of monthly mean air temperature over the Northern Tibetan Plateau(NTP). A time series analysis and a regression analysis of monthly mean land surface temperature(Ts) and air temperature(Ta) were conducted using ordinary linear regression(OLR) and geographical weighted regression(GWR). The analyses showed that GWR,which considers MODIS Ts,NDVI and elevation as independent variables,yielded much better results [RAdj2> 0.79; root-mean-square error(RMSE) =0.51℃–1.12℃] associated with estimating Tacompared to those from OLR(RAdj2= 0.40-0.78; RMSE = 1.60℃–4.38℃).In addition,some characteristics of the spatial distribution of monthly Taand the difference between the surface and air temperature(Td) are as follows. According to the analysis of the 0℃ and 10℃ isothermals,Tavalues over the NTP at elevations of 4000–5000 m were greater than 10℃ in the summer(from May to October),and Tavalues at an elevation of3200 m dropped below 0℃ in the winter(from November to April). Taexhibited an increasing trend from northwest to southeast. Except in the southeastern area of the NTP,T d values in other areas were all larger than 0℃ in the winter.

MODIS-based air temperature estimation in the southeastern Tibetan Plateau and neighboring areas

Climatic conditions are difficult to obtain in high mountain regions due to few meteorological stations and, if any, their poorly representative location designed for convenient operation. Fortunately, it has been shown that remote sensing data could be used to estimate near-surface air temperature （Ta） and other climatic conditions. This paper makes use of recorded meteorological data and MODIS data on land surface temperature （Ts） to estimate monthly mean air temperatures in the southeastern Tibetan Plateau and its neighboring areas. A total of 72 weather stations and 84 MODIS images for seven years （2001 to 2007） are used for analysis. Regression analysis and spatio-temporal analysis of monthly mean Ts vs. monthly mean Ta are carried out, showing that recorded Ta is closely related to MODIS Ts in the study region. The regression analysis of monthly mean Ts vs. Ta for every month of all stations shows that monthly mean Ts can be rather accurately used to estimate monthly mean Ta （R2 ranging from 0.62 to 0.90 and standard error between 2.25℃ and 3.23℃）. Thirdly, the retrieved monthly mean Ta for the whole study area varies between 1.62℃ （in January, the coldest month） and 17.29℃ （in July, the warmest month）, and for the warm season （May-September）, it is from 13.1℃ to 17.29℃. Finally, the elevation of isotherms is higher in the central mountain ranges than in the outer margins; the 0℃ isotherm occurs at elevation of about 4500±500 m in October, dropping to 3500±500 m in January, and ascending back to 4500±500 m in May next year. This clearly shows that MODIS Ts data combining with observed data could be used to rather accurately estimate air temperature in mountain regions.

Analysis of electron temperature,impurity transport and MHD activity with multi-energy soft x-ray diagnostic in EAST tokamak

A new edge tangential multi-energy soft x-ray（ME-SXR） diagnostic with high temporal（≤ 0.1 ms） and spatial（~1 cm） resolution has been developed for a variety of physics topics studies in the EAST tokamak plasma. The fast edge electron temperature profile（approximately from r a~ 0.6 to the scrape-off layer） is investigated using ME-SXR diagnostic system. The data process was performed by the ideal ‘multi-foil＇ technique, with no priori assumptions of plasma profiles. Reconstructed ME-SXR emissivity profiles for a variety of EAST experimental scenarios are presented here for the first time. The applications of the ME-SXR for study of the effects of resonant magnetic perturbation on edge localized modes and the first time neon radiating divertor experiment in EAST are also presented in this work. It has been found that neon impurity can suppress the 2/1 tearing mode and trigger a 3/1 MHD mode.

Modeling Daily Average Temperatures in a Coastal Site of Central Africa: An Analysis of Seasonal Divisions

The seasonality and day-to-day variation of near-surface temperature patterns can greatly control nearly all physical and biological processes though temperature predictions at such scales remain challenging. This paper implements a simple analytical approach in order to generate daily average temperatures which implicitly accounts for surface heating and drivers through a comprehensive representation of station-based temperature records on a universal standard calendar propagated by the earth’s dynamics features. The modeled and observed pattern of daily temperatures exhibits a close agreement with the level of strength agreement exceeding 0.56. The extreme high and low values of the observed temperature patterns are equally well captured although model underestimates the probability of temperatures around the two modal peaks (~25.6℃ and 27.5℃). Additionally, a theoretical thermal-based division led to the identification of six seasons, including two hot and cold periods along with two pairs of mixed hot-cold. The theoretical division proposed here appears to be a good approximation for the understanding of rainfall seasonality in this area.

MODIS-based estimation of air temperature of the Tibetan Plateau

The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.

Distributed Unknown Input and State Estimation for Nonlinear Multi-agent Systems with Applications to Battery Management

This paper proposes a novel filtering algorithm for simultaneous estimation of unknown inputs and states of a class of nonlinear discrete-time heterogeneous multi-agent systems.Based on the Taylor approximation of the nonlinear multiagent system,a distributed semi-cooperative switch-mode filter is developed to achieve the minimum-variance unbiased(MVU)estimation of the unknown inputs and states.Compared with the conventional decentralized EKF-based unknown input filter,the proposed distributed filter has a more relaxed existence condition of the filter,which makes it more applicable in reality.This new type of filter is then successfully applied to the simultaneous estimation of state of charge(SOC)and temperature of a battery pack for battery management of electric vehicles and grid-tied energy storage systems.

Discussion on heat source mechanism and geothermal system of Qinghai Gonghe-Guide Basin

The Qinghai Gonghe-Guide Basin together with the alternatively distributed mountainous region shows characteristics that the conductive geothermal resource of the basin has high geothermal gradient, the granite occurs in the bottom of borehole for geothermal exploration, and the convective hot springs in the basin-edge uplift fracture are in zonal distribution and with high-temperature geothermal water. There are still some divergences about the heat source mechanism of the basin. In this paper, queries to the view of mantle-derived heat source have been put forward, coming up with geochemical evidences to prove that the radiogenic heat of granite is the heat source within the mantle. Additionally, temperature curve is drawn based on the geothermal boring and geochemical geothermometer has been adopted for an estimation of the temperature and depth of the geothermal reservoir, it has been found that the surrounding mountains belong to the medium-temperature geothermal system while the area within the basin belongs to the high-temperature geothermal system with the temperature of borehole bottom reaching up to 175-180 ℃. In this paper, discussions on the problems existing in the calculation of geothermal gradient and the differences generated by the geothermal system have been carried out.

Comparison of ArcGIS and SAS Geostatistical Analyst to Estimate Population-Weighted Monthly Temperature for US Counties

Temperature changes are known to have significant impacts on human health. Accurate estimates of population-weighted average monthly air temperature for US counties are needed to evaluate temperature＇s association with health behaviours and disease, which are sampled or reported at the county level and measured on a monthly--or 30-day--basis. Most reported temperature estimates were calculated using ArcGIS, relatively few used SAS. We compared the performance of geostatistical models to estimate population-weighted average temperature in each month for counties in 48 states using ArcGIS v9.3 and SAS v 9.2 on a CITGO platform. Monthly average temperature for Jan-Dec 2007 and elevation from 5435 weather stations were used to estimate the temperature at county population centroids. County estimates were produced with elevation as a covariate. Performance of models was assessed by comparing adjusted R2, mean squared error, root mean squared error, and processing time. Prediction accuracy for split validation was above 90% for 11 months in ArcGIS and all 12 months in SAS. Cokriging in SAS achieved higher prediction accuracy and lower estimation bias as compared to cokriging in ArcGIS. County-level estimates produced by both packages were positively correlated （adjusted R2 range=0.95 to 0.99）; accuracy and precision improved with elevation as a covariate. Both methods from ArcGIS and SAS are reliable for U.S. county-level temperature estimates; However, ArcGIS＇s merits in spatial data pre-processing and processing time may be important considerations for software selection, especially for multi-year or multi-state projects.

Spatio-temporal reconstruction of air temperature maps and their application to estimate rice growing season heat accumulation using multi-temporal MODIS data

The accumulation of thermal time usually represents the local heat resources to drive crop growth.Maps of temperature-based agro-meteorological indices are commonly generated by the spatial interpolation of data collected from meteorological stations with coarse geographic continuity.To solve the critical problems of estimating air temperature(T a) and filling in missing pixels due to cloudy and low-quality images in growing degree days(GDDs) calculation from remotely sensed data,a novel spatio-temporal algorithm for T a estimation from Terra and Aqua moderate resolution imaging spectroradiometer(MODIS) data was proposed.This is a preliminary study to calculate heat accumulation,expressed in accumulative growing degree days(AGDDs) above 10 ℃,from reconstructed T a based on MODIS land surface temperature(LST) data.The verification results of maximum T a,minimum T a,GDD,and AGDD from MODIS-derived data to meteorological calculation were all satisfied with high correlations over 0.01 significant levels.Overall,MODIS-derived AGDD was slightly underestimated with almost 10% relative error.However,the feasibility of employing AGDD anomaly maps to characterize the 2001-2010 spatio-temporal variability of heat accumulation and estimating the 2011 heat accumulation distribution using only MODIS data was finally demonstrated in the current paper.Our study may supply a novel way to calculate AGDD in heat-related study concerning crop growth monitoring,agricultural climatic regionalization,and agro-meteorological disaster detection at the regional scale.

Fuzzy C-means Rule Generation for Fuzzy Entry Temperature Prediction in a Hot Strip Mill

Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy C-means algorithm was evaluated for rule base generation for fuzzy and fuzzy grey-box temperature estimation. Experimental data were collected from a real- life mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The perform- ance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy C-means generated rule-bases improve temperature estimation; however, the best results are obtained when fuzzy C-means algorithm, grey-box modeling and learning functions are combined. Application of fuzzy C-means rule generation brings improvement on performance of up to 72%.

Accurate 3D Thermal Network Development for Direct-drive Outer-rotor Hybrid-PM Flux-switching Generator

Heat and thermal problems are major obstacles to achieving high power density in compact permanent magnet(PM)topologies.Consequently,a comprehensive,accurate,and rapid temperature rise estimation method is required for novel electric machines to ensure safe and reliable operations.A unique three-dimensional(3D)lumped parameter thermal network(LPTN)is presented for accurate thermal modeling of a newly developed outer-rotor hybrid-PM flux switching generator(OR-HPMFSG)for direct-drive applications.First,the losses of the OR-HPMFSG are calculated using 3D finite element analysis(FEA).Subsequently,all machine components considering the thermal contact resistance,anisotropic thermal conductivity of materials,and various heat flow paths are comprehensively modeled based on the thermal resistances.In the proposed 3-D LPTN,internal nodes are considered to predict the average temperature as well as the hot spots of all active and passive components.Experimental measurements are performed on a prototype OR-HPMFSG to validate the efficiency of the 3-D LPTN.A comparison of the results at various operating points between the developed 3-D LPTN,experimental test,and FEA indicates that the 3-D LPTN quickly approximates the hotspot and mean temperature of all components under both transient and steady states with high accuracy.

Development of Non-Quenched Prehardened Steel for Large Section Plastic Mould

In order to meet the demand of prehardened steel for large section plastic mould and save energy, a nonquenched prehardened （NQP） steel is developed. The temperature field of a large block is researched by finite element method simulation and 9 test steels are designed in the laboratory. Their microstructures and hardness are investigated when they are air cooled and control cooled at cooling rate similar to the simulation. The result shows that the hardness uniformity through section is closely correlated to bainitic hardenability for the NQP steel, and the hardness of one test steel （0.27C-1.95Mn-1.04Cr-0.45Mo-0.1V） fluctuates between HRC 40 and 43 under both cooling conditions. The test steel has better machinability compared with C45 steel, and the NQP steel is produced successfully in the factory based on the laboratory results. Its microstructure is bainite, and it is distributed uniformly through the size of 460 mm×800 mm×3 200 mm.