A Loss-model-based Efficiency Optimization Control Method for Induction Traction System of High-speed Train under Emergency Self-propelled Mode

Increasing attention has been paid to the efficiency improvement of the induction traction system of high-speed trains due to the high demand for energy saving. In emergency self-propelled mode, however, the dc-link voltage and the traction power of the motor are significantly reduced, resulting in decreased traction efficiency due to the low load and low speed operations. Aiming to tackle this problem, a novel efficiency improved control method is introduced to the emergency mode of high-speed train traction system in this paper. In the proposed method, a total loss model of induction motor considering the behaviors of both iron and copper loss is established. An improved iterative algorithm with decreased computational burden is then introduced, resulting in a fast solving of the optimal flux reference for loss minimization at each control period. In addition, considering the parameter variation problem due to the low load and low speed operations, a parameter estimation method is integrated to improve the controller's robustness. The effectiveness of the proposed method on efficiency improvement at low voltage and low load conditions is demonstrated by simulated and experimental results.

Investigation on One-Dimensional Loss Models for Predicting Performance of Multistage Centrifugal Compressors in Supercritical CO Brayton Cycle

The main compressor in a supercritical carbon dioxide(SCO2)Brayton cycle works near the critical point where the physical properties of CO_(2)are far away from the ideal gas.To investigate the effectiveness of the conventional one-dimensional(1D)loss models for predicting the performance of compressors working in such nontraditional conditions,detailed comparisons of 1D predicted performance,experimental data and threedimensional CFD results are made.A 1D analysis method with enthalpy and total pressure based loss system is developed for multistage SCO2 centrifugal compressors,and it is firstly validated against the experimental results of a single stage SCO2 centrifugal compressor from the Sandia National Laboratory.A good agreement of pressure ratios with experiments can be achieved by the 1D method.But the efficiency deviations reveal the potential deficiencies of the parasitic loss models.On the basis of the validation,a two-stage SCO2 centrifugal compressor is employed to do the evaluation.Three-dimensional CFD simulations are performed.Detailed comparisons are made between the CFD and the 1D results at different stations located in the compressor.The features of the deviations are analyzed in detail,as well as the reasons that might cause these deviations.

Robust Fingerprint Construction Based on Multiple Path Loss Model (M-PLM) for Indoor Localization

A robust radio map is essential in implementing a fingerprint-based indoor positioning system(IPS).However,the offline site survey to manually construct the radio map is time-consuming and labour-intensive.Various interpolation techniques have been proposed to infer the virtual fingerprints to reduce the time and effort required for offline site surveys.This paper presents a novel fingerprint interpolator using a multi-path loss model(MPLM)to create the virtual fingerprints from the collected sample data based on different signal paths from different access points(APs).Based on the historical signal data,the poor signal paths are identified using their standard deviations.The proposed method reduces the positioning errors by smoothing out the wireless signal fluctuations and stabilizing the signals for those poor signal paths.By consideringmultipath signal propagations from different APs,the inherent noise from these signal paths can be alleviated.Firstly,locations of the signal data with standard deviations higher than the threshold are identified.The new fingerprints are then generated at these locations based on the proposed M-PLM interpolation function to replace the old fingerprints.The proposed technique interpolates virtual fingerprints based on good signal paths with more stable signals to improve the positioning performance.Experimental results show that the proposed scheme enhances the positioning accuracy by up to 44%compared to the conventional interpolation techniques such as the Inverse DistanceWeighting,Kriging,and single Path LossModel.As a result,we can overcome the site survey problems for IPS by building an accurate radio map with more reliable signals to improve indoor positioning performance.

A study on temperature monitoring method for inverter IGBT based on memory recurrent neural network

The power module of the Insulated Gate Bipolar Transistor(IGBT)is the core component of the traction transmission system of high-speed trains.The module's junction temperature is a critical factor in determining device reliability.Existing temperature monitoring methods based on the electro-thermal coupling model have limitations,such as ignoring device interactions and high computational complexity.To address these issues,an analysis of the parameters influencing IGBT failure is conducted,and a temperature monitoring method based on the Macro-Micro Attention Long Short-Term Memory(MMALSTM)recursive neural network is proposed,which takes the forward voltage drop and collector current as features.Compared with the traditional electricalthermal coupling model method,it requires fewer monitoring parameters and eliminates the complex loss calculation and equivalent thermal resistance network establishment process.The simulation model of a highspeed train traction system has been established to explore the accuracy and efficiency of MMALSTM-based prediction methods for IGBT power module junction temperature.The simulation outcomes,which deviate only 3.2% from the theoretical calculation results of the electric-thermal coupling model,confirm the reliability of this approach for predicting the temperature of IGBT power modules.

Modified Pressure Loss Model for T-junctions of Engine Exhaust Manifold

The T-junction model of engine exhaust manifolds significantly influences the simulation precision of the pressure wave and mass flow rate in the intake and exhaust manifolds of diesel engines. Current studies have focused on constant pressure models, constant static pressure models and pressure loss models. However, low model precision is a common disadvantage when simulating engine exhaust manifolds, particularly for turbocharged systems. To study the performance of junction flow, a cold wind tunnel experiment with high velocities at the junction of a diesel exhaust manifold is performed, and the variation in the pressure loss in the T-junction under different flow conditions is obtained. Despite the trend of the calculated total pressure loss coefficient, which is obtained by using the original pressure loss model and is the same as that obtained from the experimental results, large differences exist between the calculated and experimental values. Furthermore, the deviation becomes larger as the flow velocity increases. By improving the Vazsonyi formula considering the flow velocity and introducing the distribution function, a modified pressure loss model is established, which is suitable for a higher velocity range. Then, the new model is adopted to solve one-dimensional, unsteady flow in a D6114 turbocharged diesel engine. The calculated values are compared with the measured data, and the result shows that the simulation accuracy of the pressure wave before the turbine is improved by 4.3% with the modified pressure loss model because gas compressibility is considered when the flow velocities are high. The research results provide valuable information for further junction flow research, particularly the correction of the boundary condition in one-dimensional simulation models.

A Discrete State Event Driven Simulation based Losses Analysis for Multi-terminal Megawatt Power Electronic Transformer

At present,power electronic transformers(PETs)have been widely used in power systems.With the increase of PET capacity to the megawatt level.the problem of increased losses need to be taken seriously.As an important indicator of power electronic device designing,losses have always been the focus of attention.At present,the losses are generally measured through experiments,but it takes a lot of time and is difficult to quantitatively analyze the internal distribution of PET losses.To solve the above problems,this article first qualitatively analyzes the losses of power electronic devices and proposes a loss calculation method based on pure simulation.This method uses the Discrete State Event Driven(DSED)modeling method to solve the problem of slow simulation speed of large-capacity power electronic devices and uses a loss calculation method that considers the operating conditions of the device to improve the calculation accuracy.For the PET prototype in this article,a losses model of the PET is established.The comparison of experimental and simulation results verifies the feasibility of the losses model.Then the losses composition of PET was analyzed to provide reference opinions for actual operation.It can help pre-analyze the losses distribution of PET,thereby providing a potential method for improving system efficiency.

Prediction Model of Soil Nutrients Loss Based on Artificial Neural Network

On the basis of Artificial Neural Network theory, a back propagation neural network with one middle layer is building in this paper, and its algorithms is also given, Using this BP network model, study the case of Malian-River basin. The results by calculating show that the solution based on BP algorithms are consis- tent with those based multiple - variables linear regression model. They also indicate that BP model in this paper is reasonable and BP algorithms are feasible.

Closed-Form Models of Accuracy Loss due to Subsampling in SVD Collaborative Filtering

We postulate and analyze a nonlinear subsampling accuracy loss(SSAL)model based on the root mean square error(RMSE)and two SSAL models based on the mean square error(MSE),suggested by extensive preliminary simulations.The SSAL models predict accuracy loss in terms of subsampling parameters like the fraction of users dropped(FUD)and the fraction of items dropped(FID).We seek to investigate whether the models depend on the characteristics of the dataset in a constant way across datasets when using the SVD collaborative filtering(CF)algorithm.The dataset characteristics considered include various densities of the rating matrix and the numbers of users and items.Extensive simulations and rigorous regression analysis led to empirical symmetrical SSAL models in terms of FID and FUD whose coefficients depend only on the data characteristics.The SSAL models came out to be multi-linear in terms of odds ratios of dropping a user(or an item)vs.not dropping it.Moreover,one MSE deterioration model turned out to be linear in the FID and FUD odds where their interaction term has a zero coefficient.Most importantly,the models are constant in the sense that they are written in closed-form using the considered data characteristics(densities and numbers of users and items).The models are validated through extensive simulations based on 850 synthetically generated primary(pre-subsampling)matrices derived from the 25M MovieLens dataset.Nearly 460000 subsampled rating matrices were then simulated and subjected to the singular value decomposition(SVD)CF algorithm.Further validation was conducted using the 1M MovieLens and the Yahoo!Music Rating datasets.The models were constant and significant across all 3 datasets.

Combining RUSLE model and the vegetation health index to unravel the relationship between soil erosion and droughts in southeastern Tunisia

Droughts and soil erosion are among the most prominent climatic driven hazards in drylands,leading to detrimental environmental impacts,such as degraded lands,deteriorated ecosystem services and biodiversity,and increased greenhouse gas emissions.In response to the current lack of studies combining drought conditions and soil erosion processes,in this study,we developed a comprehensive Geographic Information System(GIS)-based approach to assess soil erosion and droughts,thereby revealing the relationship between soil erosion and droughts under an arid climate.The vegetation condition index(VCI)and temperature condition index(TCI)derived respectively from the enhanced vegetation index(EVI)MOD13A2 and land surface temperature(LST)MOD11A2 products were combined to generate the vegetation health index(VHI).The VHI has been conceived as an efficient tool to monitor droughts in the Negueb watershed,southeastern Tunisia.The revised universal soil loss equation(RUSLE)model was applied to quantitatively estimate soil erosion.The relationship between soil erosion and droughts was investigated through Pearson correlation.Results exhibited that the Negueb watershed experienced recurrent mild to extreme drought during 2000–2016.The average soil erosion rate was determined to be 1.8 t/(hm2•a).The mountainous western part of the watershed was the most vulnerable not only to soil erosion but also to droughts.The slope length and steepness factor was shown to be the most significant controlling parameter driving soil erosion.The relationship between droughts and soil erosion had a positive correlation(r=0.3);however,the correlation was highly varied spatially across the watershed.Drought was linked to soil erosion in the Negueb watershed.The current study provides insight for natural disaster risk assessment,land managers,and stake-holders to apply appropriate management measures to promote sustainable development goals in fragile environments.

System-L evel Efficiency Optimization of a Linear Induction Motor Drive System

Linear induction motors are superior to rotary induction motors in direct drive systems because they can generate direct forward thrust force independent of mechanical transmission.However,due to the large air gap and cut-open magnetic circuit,their efficiency and power factor are quite low,which limit their application in high power drive systems.To attempt this challenge,this work presents a system-level optimization method for a single-sided linear induction motor drive system.Not only the motor but also the control system is included in the analysis.A system-level optimization method is employed to gain optimal steady-state and dynamic performances.To validate the effectiveness of the proposed optimization method,experimental results on a linear induction motor drive are presented and discussed.

Evolution of seismic risk management for insurance over the past 30 years

During the past 30 years, there has been spectacular growth in the use of risk analysis and risk management tools developed by engineers in the financial and insurance sectors. The insurance, the reinsurance, and the investment banking sectors have enthusiastically adopted loss estimation tools developed by engineers in developing their business strategies and for managing their financial risks. As a result, insurance/reinsurance strategy has evolved as a major risk mitigation tool in managing catastrophe risk at the individual, corporate, and government level. This is particularly true in developed countries such as US, Western Europe, and Japan. Unfortunately, it has not received the needed attention in developing countries, where such a strategy for risk management is most needed. Fortunately, in the last five years, there has been excellent focus in developing ＂Insur Tech＂ tools to address the much needed ＂Insurance for the Masses＂, especially for the Asian Markets. In the earlier years of catastrophe model development, risk analysts were mainly concerned with risk reduction options through engineering strategies, and relatively little attention was given to financial and economic strategies. Such state-of-affairs still exists in many developing countries. The new developments in the science and technologies of loss estimation due to natural catastrophes have made it possible for financial sectors to model their business strategies such as peril and geographic diversification, premium calculations, reserve strategies, reinsurance contracts, and other underwriting tools. These developments have not only changed the way in which financial sectors assess and manage their risks, but have also changed the domain of opportunities for engineers and scientists.This paper will address the issues related to developing insurance/reinsurance strategies to mitigate catastrophe risks and describe the role catastrophe risk insurance and reinsurance has played in managing financial risk due to natural catastrophes. Historical losses and the share of those losses covered by insurance will be presented. How such risk sharing can help the nation share the burden of losses between tax paying public, the ＂at risk＂ property owners, the insurers and the reinsurers will be discussed. The paper will summarize the tools that are used by the insurance and reinsurance companies for estimating their future losses due to catastrophic natural events. The paper will also show how the results of loss estimation technologies developed by engineers are communicated to the business flow of insurance/reinsurance companies. Finally, to make it possible to grow ＂Insurance for the Masses - IFM＂, the role played by parametric insurance products and Insur Tech tools will be discussed.

A Study of Propagation in Shenzhen City

Surveying the propagation path loss behavior of Shenzhen city and from the data recorded we can obtain the city’s path loss slopes, which can be comparatively regarded as the reliable bases of system design.

New mathematical modelings of the human liver and hearing loss systems with fractional derivatives

Fractional derivatives are significant mathematical instruments that have been practiced to model real phenomena in different areas of science.Through the current investigation study,we develop the Human Liver and Hearing Loss models by employing three fractional operators called Atangana-Baleanu-Caputo,Caputo and Caputo-Fabrizio derivatives.The numerical techniques regarding the basic theories of fractional analysis considered to obtain the approximate solutions of the offered systems.Indeed,approximate results are presented for showing the efficacy of the methods during graphs of solutions.To see the performance of the suggested models,different fractional orders are tested.

Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data

Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.

A spanwise loss model for axial compressor stator based on machine learning

In the early stage of aircraft engine design, the through-flow method is an important tool for designers. The accuracy of the through-flow method depends heavily on the accuracy of the loss model. However, most existing models cannot(or cannot well) provide the spanwise loss distribution. To construct an effective spanwise loss model, both turbomachinery knowledge and machine learning skills were used in this paper. A large number of numerical simulations were carried out to build a database containing more than 1000 compressor cascade numerical samples. Secondary flow intensity was introduced as the independent variable to carry out feature engineering. A model containing a selector based on support vector machine regression and estimators based on K-nearest neighbor regression was constructed. Numerical test set and design data of two former highpressure core compressors were used for validation. Results suggest that the spanwise loss model show good consistency with both numerical test set and data of two former compressors. It can reflect the influence of secondary flow and can also predict both value and trend of total pressure loss coefficient well, with mean absolute error general around or less than 1% and R^(2)(coefficient of determination) more than 0.8 on the test set. Especially when dealing with loss coefficient at midspan position, the model shows even better performance, with R^(2)over 0.97 on the test set. And the selector of the model can well classify the samples, predict the intensity of secondary flow and help estimators to capture the phenomenon that end-wall secondary flow extends to the mid-span.

Precise Electro-thermal Power Loss Model of a Three-level ANPC Inverter with Hybrid Si/SiC Switches

Hybrid Si/SiC switches constituting a parallel connection of a lower current rated SiC MOSFET and a higher current rated Si IGBT are becoming very attractive solution for designing high frequency and high-power density power electronic converters.Due to the complementary nature of Si IGBT devices(smaller inverter cost and smaller conduction loss)and SiC devices(smaller switching loss and higher junction temperature capability),these novel switch device configurations enable a good tradeoff between cost and efficiency for high power converter applications.One such recent application of hybrid Si/SiC switches for efficiency-cost optimization is an Si/SiC hybrid switch based ANPC inverter proposed in Ref.[30].In Ref.[30]the topology structure,modulation strategy and the efficiency-cost benefits of the proposed ANPC inverter is presented.In this paper a precise electro-thermal power loss model for this ANPC inverter topology will be presented based on the modulation strategy of the inverter and the operating characteristics of the Si/SiC hybrid switches.The power loss model development takes into account how the current sharing between the two internal devices of the Si/SiC hybrid switches and their corresponding gate control method affects their power loss.A brief introduction to the topology structure and operation principle of the Si/SiC based ANPC inverter is first highlighted to provide context for readers and then a detailed description of the proposed electro-thermal power loss model is presented.The precision of the electro-thermal power loss model introduced in this paper is then validated using experimentally measured energy loss,device temperature and inverter efficiency data.

Some discussions on the prediction model of three-dimensional shock losses

Wennerstrom and Puterbaugh （1984） presented a model for predicting the shock loss in a compressor blade row that took into account the three-dimemionality of the shock surface. Both the measured data and the numerical solution show that the shock surface is almost normal to the relative flow on each S1 stream surface near the design flow condition. The shock surface is oblique in the spanwise direction because of the sweep of

Uncertainty Reduction Through Data Management in the Development, Validation, Calibration, and Operation of a Hurricane Vulnerability Model

Catastrophe models estimate risk at the intersection of hazard,exposure,and vulnerability.Each of these areas requires diverse sources of data,which are very often incomplete,inconsistent,or missing altogether.The poor quality of the data is a source of epistemic uncertainty,which affects the vulnerability models as well as the output of the catastrophe models.This article identifies the different sources of epistemic uncertainty in the data,and elaborates on strategies to reduce this uncertainty,in particular through identification,augmentation,and integration of the different types of data.The challenges are illustrated through the Florida Public Hurricane Loss Model(FPHLM),which estimates insured losses on residential buildings caused by hurricane events in Florida.To define the input exposure,and for model development,calibration,and validation purposes,the FPHLM teams accessed three main sources of data:county tax appraiser databases,National Flood Insurance Protection(NFIP)portfolios,and wind insurance portfolios.The data from these different sources were reformatted and processed,and the insurance databases were separately cross-referenced at the county level with tax appraiser databases.The FPHLM hazard teams assigned estimates of natural hazard intensity measure to each insurance claim.These efforts produced an integrated and more complete set of building descriptors for each policy in the NFIP and wind portfolios.The article describes the impact of these uncertainty reductions on the development and validation of the vulnerability models,and suggests avenues for data improvement.Lessons learned should be of interest to professionals involved in disaster risk assessment and management.

Overview of Planar Magnetics for High-frequency Resonant Converters

With the continuous development of power supplies toward miniaturization,light weights,and high levels of integration,research on high-frequency resonant conversion based on planar magnetics is becoming extensive.Combining the soft-switching characteristics of resonant converters with those of wide bandgap devices,the switching frequency can be increase to the MHz range,and the power density of the entire system can be improved considerably.However,higher switching frequencies impose new requirements for the structural design,loss distribution,and common mode(CM)noise suppression of passive magnetic components.Herein,a thorough survey of the-state-of-the-art of planar magnetics in high-frequency resonant converters is conducted.Printed circuit board winding-based planar magnetics,magnetic integration,and power-loss optimization strategies are summarized in detail.Suppression methods for CM noise in high-frequency planar magnetics are also clarified and discussed.An insight view into the future development of planar magnetics for high-frequency resonant converters is presented.

Stator re-stagger optimization in multistage axial compressor

As a widely applied technique in multistage axial compressors,variable stator vanes(VSV)can flexibly rematch the blade rows to fulfil a variety of aerodynamic performance requirements,such as high efficiency and wide surge margin.The purpose of this paper is to develop an optimization method to quickly determine VSV settings during the preliminary design phase.A mean-line method with a model calibration procedure is adopted to evaluate compressor performance,and the NSGA-II algorithm is employed for automatic optimization.The developed optimization system is then employed to determined re-stagger arrangement in a multistage compressor.A single-speed optimization with performance constraints of specific operating point and a multi-speed optimization with different control laws are conducted.Results are compared with available experimental re-stagger scheme,which verifies the effectiveness of the re-stagger optimization method.Moreover,method is proposed to determine operating parameters of a working point with a user-defined pressure ratio or mass flow rate after variable geometry.