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.

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 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.

The Actuarial Data Intelligent Based Artificial Neural Network (ANN) Automobile Insurance Inflation Adjusted Frequency Severity Loss Reserving Model

This study proposes a novel approach for estimating automobile insurance loss reserves utilizing Artificial Neural Network (ANN) techniques integrated with actuarial data intelligence. The model aims to address the challenges of accurately predicting insurance claim frequencies, severities, and overall loss reserves while accounting for inflation adjustments. Through comprehensive data analysis and model development, this research explores the effectiveness of ANN methodologies in capturing complex nonlinear relationships within insurance data. The study leverages a data set comprising automobile insurance policyholder information, claim history, and economic indicators to train and validate the ANN-based reserving model. Key aspects of the methodology include data preprocessing techniques such as one-hot encoding and scaling, followed by the construction of frequency, severity, and overall loss reserving models using ANN architectures. Moreover, the model incorporates inflation adjustment factors to ensure the accurate estimation of future loss reserves in real terms. Results from the study demonstrate the superior predictive performance of the ANN-based reserving model compared to traditional actuarial methods, with substantial improvements in accuracy and robustness. Furthermore, the model’s ability to adapt to changing market conditions and regulatory requirements, such as IFRS17, highlights its practical relevance in the insurance industry. The findings of this research contribute to the advancement of actuarial science and provide valuable insights for insurance companies seeking more accurate and efficient loss reserving techniques. The proposed ANN-based approach offers a promising avenue for enhancing risk management practices and optimizing financial decision-making processes in the automobile insurance sector.

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.

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.

Application of artificial neural network for switching loss modeling in power IGBTs

The modeling of switching loss in semiconductor power devices is important in practice for the prediction and evaluation of thermal safety and system reliability.Both simulation-based behavioral models and data processing-based empirical models are difficult and have limited applications.Although the artificial neural network(ANN) algorithm has often been used for modeling, it has never been used for modeling insulated gate bipolar transistor(IGBT) transient loss.In this paper, we attempt to use the ANN method for this purpose, using a customized switching loss test bench.We compare its performance with two conventional curve-fitting models and verify the results by experiment.Our model is generally superior in calculation speed, accuracy, and data requirement, and is also able to be extended to loss modeling for all kinds of semiconductor power devices.

Anti-apoptotic treatment in mouse models of age-related hearing loss

Age-related hearing loss (AHL), or presbycusis, is the most common neurodegenerative disorder and top communication deficit of the aged population. Genetic predisposition is one of the major factors in the development of AHL. Generally, AHL is associated with an age-dependent loss of sensory hair cells, spiral ganglion neurons and stria vascularis cells in the inner ear. Although the mechanisms leading to genetic hearing loss are not completely understood, caspase-family proteases function as important signals in the inner ear pathology. It is now accepted that mouse models are the best tools to study the mechanism of genetic hearing loss or AHL. Here, we provide a brief review of recent studies on hearing improvement in mouse models of AHL by anti-apoptotic treatment.

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Magnetostrictive and Kinematic Model Considering the Dynamic Hysteresis and Energy Loss for GMA

Due to the influence of magnetic hysteresis and energy loss inherent in giant magnetostrictive materials （GMM）, output displacement accuracy of giant magnetostrictive actuator （GMA） can not meet the precision and ultra precision machining. Using a GMM rod as the core driving element, a GMA which may be used in the field of precision and ultra precision drive engineering is designed through modular design method. Based on the Armstrong theory and elastic Gibbs free energy theory, a nonlinear magnetostriction model which considers magnetic hysteresis and energy loss characteristics is established. Moreover, the mechanical system differential equation model for GMA is established by utilizing D＇Alembert＇s principle. Experimental results show that the model can preferably predict magnetization property, magnetic potential orientation, energy loss for GMM. It is also able to describe magnetostrictive elongation and output displacement of GMA. Research results will provide a theoretical basis for solving the dynamic magnetic hysteresis, energy loss and working precision for GMA fundamentally.

Numerical and experimental analysis of AC loss for CFETR CS model coil

The central solenoid(CS) is an important component of China Fusion Engineering Test Reactor, for producing, forming and stabilizing plasma in the superconducting tokamak. It is a complicated work to design and manufacture the large superconducting CS magnet, so it is meaningful to design a central solenoid model coil(CSMC) and analyze its electromagnetic properties in advance. In this paper, the structure, design parameters and magnetic field distribution of the CS model coil are discussed. The peak power of radial and axial turn conductors and time bucket loss are analyzed by using piecewise-linear method. The CSMC AC loss with different Nb3 Sn CICCs and AC loss of ITER CS coil are compared. The special electrometric method to measure AC loss of the CS model coil for future reference is presented.

Model-based predictive controller design for a class of nonlinear networked systems with communication delays and data loss

This paper discusses the model-based predictive controller design of networked nonlinear systems with communica- tion delay and data loss. Based on the analysis of the closed-loop networked predictive control systems, the model-based networked predictive control strategy can compensate for communication delay and data loss in an active way. The designed model-based predictive controller can also guarantee the stability of the closed-loop networked system. The simulation re- suits demonstrate the feasibility and efficacy of the proposed model-based predictive controller design scheme.

All Admissible Linear Estimators under Quadratic Loss in Multivariate Model

For multivariate linear model Y=XΘ+ε, ~N(0, σ 2ΣV), this paper is concerned with the admissibility of linear estimators of estimable function SXΘ in the class of all estimators. All admissible linear estimators of SXΘ are given under each of four definitions of admissibility.

An Animal Model of Sudden Onset Sensorineural Hearing Loss with Vestibular Function Disturbances Induced By Mitochondrial Toxin

Objective To establish an animal model of sudden onset sensorineural hearing loss (SSNHL) to study its mechanisms. Materials and methods The inner ear was exposed to 3-nitropropionic acid at 0.5 mol/L (3-NP (H)) and 0.3 mol/L (3-NP (L)) through the round window membrane for 30 minutes in 50 male guinea pigs. Thresholds of auditory brainstem responses(ABR) were established before the treatment and retested at 4 hours, 1 day, 3 days and 6 days following 3-NP exposure. Control animals were treated with phosphate buffered saline (PBS) and their ABRs were retested at 4 hours and 1 day after the treatment. Animals were monitored for nystagmus and postural signs of vestibular dysfunction, using a digital video camera, following the treatment procedure. Specimens were taken at 12 hours, 1 day, 3 days and 7 days following 3-NP(H) exposure and embedded in JB4 for light microscopy observation. Results ABRs were lost in all animals tested at 4 hours following 3-NP (H) exposure. The rate of complete ABR loss decreased as post-treatment test time increased. ABRs were lost in 80% (4/5) of the animals at 1 day after exposure to 3-NP (L). Spontaneous horizontal nystagmus with a fast phase away from the treated ear developed in all 3-NP (H)-treated animals and in 20 % ( 1/5) of the animals exposed to 3-NP (L), except for the one treated bilaterally. Various degree of postural disturbances consistent with unilateral vestibular dysfunction, such as spontaneous barrel rolling towards the exposure side while walking, were seen in all animals exposed to 3-NP(H) and 40% (2/5) of animals exposed to 3-NP(L), except for the one animal treated bilaterally, which showed no signs of imbalance. Both nystagmus and postural disturbances resolved in 2 days following 3-NP exposure. Histological study showed temporary edema tin the organ or Corti, Claudius cells and the inner sulcus cells 3 days after 3-NP (H) treatment. Enlargement of intercellular space in the spiral prominence was first noticed at 12 hours post-3-NP (H) exposure, progressed at day 3 and recovered at day 7. Vacuoles in the cellular plasm and nucleus was seen as early as at 12 hours post-3-NP exposure in the spiral ganglion cells, and signs of degeneration were visible at day 7. Conclusion Inner ear exposure to 3-NP through the round window membrane appears to reproduce clinic manifestations and may serve as a legitimate animal model of SSNHL

Podocyte loss and albuminuria of KK-Ay mouse: A spontaneous animal model for human type 2 diabetic nephropathy

Podocyte loss was well known in type 2 diabetic nephropathy patients. The objective of the present study was to determine the number of podocytes and the degree of albuminuria in diabetic KK-Ay/Ta (KK-Ay) mice which had been reported as diabetic nephropathy model. Diabetic KK-Ay mice, diabetic KK/Ta mice and non-diabetic BALB/cA Jcl (BALB/cA) mice were studied. We analyzed glomerular lesions in all mice by morphometric analysis and immunofluorescence to determine the number of podocytes. Level of urinary albumin was also measured. Glomerular enlargement and mesangial expansion were observed in KK-Ay mice. Mean number of podocytes per glomerulus (NG pod) in diabetic KK-Ay mice was significantly lower than that in non-diabetic BALB/cA mice. Mean NG pod/glomerular area (GA) per glomerulus was also significantly decreased in diabetic KK-Ay mice. The level of urinary albumin/creatinine ratio (ACR) in diabetic KK-Ay mice was significantly higher than that in non-diabetic BALB/cA mice. These data suggest that podocyte loss might induce albuminuria in KK-Ay mice. This finding confirmed our previous report that KK-Ay mice, especially in terms of histological findings, are a suitable animal model for glomerular injury in type 2 diabetic nephropathy.

Study on Analysis Model of Millet Yield Loss Caused by Weeds in Summer Season Millet Field

Weed management in summer season foxtall millet field was studied by evaluating weed damage and exploring competition between weeds and foxtail millet, and a few fitting models were simulated and compared by employing field plot experiment and nonlinear regression analysis. The results showed that the millet yield losses and weed density were extremely significantly correlated. Among the tested models, the determination coefficient （ R2 ） of hyperbolic model was 0.997 12, and minimum residual sum of squares was 16.174, which was considered the optimal model to simulate the competition relation between weeds and millet. The predicted equation was Y = d/（ 1. 733 ＋ 0. 018d） ; the interspecific competitiveness of weeds was 0. 577 0 and the intraspecific competitiveness was 0.010 3 ; the maximum loss rate of millet yield was 55.56%. This study had established an analysis model with high gcodness-of-fit and practical prediction which could help weed management in summer season millet field.

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.

Model of Probabilistic Prediction for Life Loss

Based on discussion of the reasonableness of the seismic destruction described by using the death degree,the probabilistic curves of the different death degrees with different future times in several regions on the Chinese mainland have been obtained by applying the probabilistic model,which is in accord with present seismic destruction data and related data.The historical data of the life loss have been processed with the dynamic system model.Finally,the results from two kinds of data have been analyzed and discussed.