Optimal Design of High-Speed Partial Flow Pumps using Orthogonal Tests and Numerical Simulations

To investigate the influence of structural parameters on the performances and internal flow characteristics of partial flow pumps at a low specific speed of 10000 rpm,special attention was paid to the first and second stage impeller guide vanes.Moreover,the impeller blade outlet width,impeller inlet diameter,blade inclination angle,and number of blades were considered for orthogonal tests.Accordingly,nine groups of design solutions were formed,and then used as a basis for the execution of numerical simulations(CFD)aimed at obtaining the efficiency values and heads for each design solution group.The influence of impeller geometric parameters on the efficiency and head was explored,and the“weight”of each factor was obtained via a range analysis.Optimal structural parameters were finally chosen on the basis of the numerical simulation results,and the performances of the optimized model were verified accordingly(yet by means of CFD).Evidence is provided that the increase in the efficiency and head of the optimized model was 12.11%and 23.5 m,respectively,compared with those of the original model.

Research on the strain gauge mounting scheme of track wheel force measurement system based on high-speed wheel/rail relationship test rig

Purpose-This paper aims to analyze the stress and strain distribution on the track wheel web surface and study the optimal strain gauge location for force measurement system of the track wheel.Design/methodology/approach-Finite element method was employed to analyze the stress and strain distribution on the track wheel web surface under varying wheel-rail forces.Locations with minimal coupling interference between vertical and lateral forces were identified as suitable for strain gauge installation.Findings-The results show that due to the track wheel web’s unique curved shape and wheel-rail force loading mechanism,both tensile and compressive states exit on the surface of the web.When vertical force is applied,Mises stress and strain are relatively high near the inner radius of 710 mm and the outer radius of 1110mmof the web.Under lateral force,high Mises stress and strain are observed near the radius of 670mmon the inner and outer sides of the web.As the wheel-rail force application point shifts laterally toward the outer side,the Mises stress and strain near the inner radius of 710 mm of the web gradually decrease under vertical force while gradually increasing near the outer radius of 1110 mm of the web.Under lateral force,the Mises stress and strain on the surface of the web remain relatively unchanged regardless of the wheel-rail force application point.Based on the analysis of stress and strain on the surface of the web under different wheel-rail forces,the inner radius of 870 mm is recommended as the optimal mounting location of strain gauges for measuring vertical force,while the inner radius of 1143 mm is suitable for measuring lateral force.Originality/value-The research findings provide valuable insights for determining optimal strain gauge locations and designing an effective track wheel force measurement system.

Improving Ultrasonic Testing by Using Machine Learning Framework Based on Model Interpretation Strategy

Ultrasonic testing(UT)is increasingly combined with machine learning(ML)techniques for intelligently identifying damage.Extracting signifcant features from UT data is essential for efcient defect characterization.Moreover,the hidden physics behind ML is unexplained,reducing the generalization capability and versatility of ML methods in UT.In this paper,a generally applicable ML framework based on the model interpretation strategy is proposed to improve the detection accuracy and computational efciency of UT.Firstly,multi-domain features are extracted from the UT signals with signal processing techniques to construct an initial feature space.Subsequently,a feature selection method based on model interpretable strategy(FS-MIS)is innovatively developed by integrating Shapley additive explanation(SHAP),flter method,embedded method and wrapper method.The most efective ML model and the optimal feature subset with better correlation to the target defects are determined self-adaptively.The proposed framework is validated by identifying and locating side-drilled holes(SDHs)with 0.5λcentral distance and different depths.An ultrasonic array probe is adopted to acquire FMC datasets from several aluminum alloy specimens containing two SDHs by experiments.The optimal feature subset selected by FS-MIS is set as the input of the chosen ML model to train and predict the times of arrival(ToAs)of the scattered waves emitted by adjacent SDHs.The experimental results demonstrate that the relative errors of the predicted ToAs are all below 3.67%with an average error of 0.25%,signifcantly improving the time resolution of UT signals.On this basis,the predicted ToAs are assigned to the corresponding original signals for decoupling overlapped pulse-echoes and reconstructing high-resolution FMC datasets.The imaging resolution is enhanced to 0.5λby implementing the total focusing method(TFM).The relative errors of hole depths and central distance are no more than 0.51%and 3.57%,respectively.Finally,the superior performance of the proposed FS-MIS is validated by comparing it with initial feature space and conventional dimensionality reduction techniques.

Predicting major adverse cardiovascular events after orthotopic liver transplantation using a supervised machine learning model:A cohort study

BACKGROUND Liver transplant(LT)patients have become older and sicker.The rate of post-LT major adverse cardiovascular events(MACE)has increased,and this in turn raises 30-d post-LT mortality.Noninvasive cardiac stress testing loses accuracy when applied to pre-LT cirrhotic patients.AIM To assess the feasibility and accuracy of a machine learning model used to predict post-LT MACE in a regional cohort.METHODS This retrospective cohort study involved 575 LT patients from a Southern Brazilian academic center.We developed a predictive model for post-LT MACE(defined as a composite outcome of stroke,new-onset heart failure,severe arrhythmia,and myocardial infarction)using the extreme gradient boosting(XGBoost)machine learning model.We addressed missing data(below 20%)for relevant variables using the k-nearest neighbor imputation method,calculating the mean from the ten nearest neighbors for each case.The modeling dataset included 83 features,encompassing patient and laboratory data,cirrhosis complications,and pre-LT cardiac assessments.Model performance was assessed using the area under the receiver operating characteristic curve(AUROC).We also employed Shapley additive explanations(SHAP)to interpret feature impacts.The dataset was split into training(75%)and testing(25%)sets.Calibration was evaluated using the Brier score.We followed Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis guidelines for reporting.Scikit-learn and SHAP in Python 3 were used for all analyses.The supplementary material includes code for model development and a user-friendly online MACE prediction calculator.RESULTS Of the 537 included patients,23(4.46%)developed in-hospital MACE,with a mean age at transplantation of 52.9 years.The majority,66.1%,were male.The XGBoost model achieved an impressive AUROC of 0.89 during the training stage.This model exhibited accuracy,precision,recall,and F1-score values of 0.84,0.85,0.80,and 0.79,respectively.Calibration,as assessed by the Brier score,indicated excellent model calibration with a score of 0.07.Furthermore,SHAP values highlighted the significance of certain variables in predicting postoperative MACE,with negative noninvasive cardiac stress testing,use of nonselective beta-blockers,direct bilirubin levels,blood type O,and dynamic alterations on myocardial perfusion scintigraphy being the most influential factors at the cohort-wide level.These results highlight the predictive capability of our XGBoost model in assessing the risk of post-LT MACE,making it a valuable tool for clinical practice.CONCLUSION Our study successfully assessed the feasibility and accuracy of the XGBoost machine learning model in predicting post-LT MACE,using both cardiovascular and hepatic variables.The model demonstrated impressive performance,aligning with literature findings,and exhibited excellent calibration.Notably,our cautious approach to prevent overfitting and data leakage suggests the stability of results when applied to prospective data,reinforcing the model’s value as a reliable tool for predicting post-LT MACE in clinical practice.

Torque effect on vibration behavior of high-speed train gearbox under internal and external excitations

The high-speed train transmission system,experiencing both the internal excitation originating from gear meshing and the external excitation originating from the wheel-rail interaction,exhibits complex dynamic behavior in the actual service environment.This paper focuses on the gearbox in the high-speed train to carry out the bench test,in which various operat-ing conditions(torques and rotation speeds)were set up and the excitation condition covering both internal and external was created.Acceleration responses on multiple positions of the gearbox were acquired in the test and the vibration behavior of the gearbox was studied.Meanwhile,a stochastic excitation modal test was also carried out on the test bench under different torques,and the modal parameter of the gearbox was identified.Finally,the sweep frequency response of the gearbox under gear meshing excitation was analyzed through dynamic modeling.The results showed that the torque has an attenuating effect on the amplitude of gear meshing frequency on the gearbox,and the effect of external excitation on the gearbox vibration cannot be ignored,especially under the rated operating condition.It was also found that the torque affects the modal param-eter of the gearbox significantly.The torque has a great effect on both the gear meshing stiffness and the bearing stiffness in the transmission system,which is the inherent reason for the changed modal characteristics observed in the modal test and affects the vibration behavior of the gearbox consequently.

Study of the Lift Force Induced by An Interceptor on A High-Speed Mono-Hull:The Affecting Factors and Estimation Formula

To find a better way to estimate the lift force induced by an interceptor on a high-speed mono-hull ship,a series of high-speed mono-hull ship models are designed and investigated under different conditions.Different lift forces are obtained by numerical calculations and validated by a model test in a towing tank.The factors that influence the force are the interceptor height,velocity,draft,and deadrise angle.The relationship between each factor and the induced lift force is investigated and obtained.We found that the induced lift mainly depends on the interceptor height and advancing velocity,and is proportional to the square of the interceptor height and velocity.The results also showed that the effects of the draft and deadrise angle are relatively less important,and the relationship between the induced lift and these two factors is generally linear.Based on the results,a formula including the combined effect of all factors used to estimate the lift force induced by the interceptor is developed based on systematic analysis.The proposed formula could be used to estimate the lift force induced by interceptors,especially under high-speed condition.

Investigation on influencing factors of wheel polygonization of a plateau high-speed EMU train

Purpose–This study aims to investigate the cause of high-order wheel polygonization in a plateau high-speed electric multiple unit(EMU)train.Design/methodology/approach–A series of field tests were conducted to measure the vibration accelerations of the axle box and bogie when the wheels of the EMU train passed through tracks with normal rail roughness after re-profiling.Additionally,the dynamic characteristics of the track,wheelset and bogie were also measured.These measurements provided insights into the mechanisms that lead to wheel polygonization.Findings–The results of the field tests indicate that wheel polygonal wear in theEMUtrain primarily exhibits 14–16 and 25–27 harmonic orders.The passing frequencies of wheel polygonization were approximately 283–323 Hz and 505–545 Hz,which closely match the dominated frequencies of axle box and bogie vibrations.These findings suggest that the fixed-frequency vibrations originate from the natural modes of the wheelset and bogie,which can be excited by wheel/rail irregularities.Originality/value–The study provides novel insights into the mechanisms of high-order wheel polygonization in plateau high-speed EMU trains.Futher,the results indicate that operating the EMU train on mixed lines at variable speeds could potentially mitigate high-order polygonal wear,providing practical value for improving the safety,performance and maintenance efficiency of high-speed EMU trains.

Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization

This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST(Internet of Sensing Things)device.Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning.Significant improvements were observed across various models,with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score,recall,and precision.The study underscores the critical role of tailored hyperparameter tuning in optimizing these models,revealing diverse outcomes among algorithms.Decision Trees and Random Forests exhibited stable performance throughout the evaluation.While enhancing accuracy,hyperparameter optimization also led to increased execution time.Visual representations and comprehensive results support the findings,confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease.This research contributes to advancing the understanding and application of machine learning in healthcare,particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies.

Real-time embedded software testing method based on extended finite state machine

The reliability of real-time embedded software directly determines the reliability of the whole real-time embedded sys- tem, and the effective software testing is an important way to ensure software quality and reliability. Based on the analysis of the characteristics of real-time embedded software, the formal method is introduced into the real-time embedded software testing field and the real-time extended finite state machine （RT-EFSM） model is studied firstly. Then, the time zone division method of real-time embedded system is presented and the definition and description methods of time-constrained transition equivalence class （timeCTEC） are presented. Furthermore, the approaches of the testing sequence and test case generation are put forward. Finally, the proposed method is applied to a typical avionics real- time embedded software testing practice and the examples of the timeCTEC, testing sequences and test cases are given. With the analysis of the testing result, the application verification shows that the proposed method can effectively describe the real-time embedded software state transition characteristics and real-time requirements and play the advantages of the formal methods in accuracy, effectiveness and the automation supporting. Combined with the testing platform, the real-time, closed loop and automated simulation testing for real-time embedded software can be realized effectively.

High-Speed Permanent Magnet Electrical Machines - Applications, Key Issues and Challenges

In this paper,application examples of high-speed electrical machines are presented,and the machine structures are categorized.Key issues of design and control for the high-speed permanent magnet machines are reviewed,including bearings selection,rotor dynamics analysis and design,rotor stress analysis and protection,thermal analysis and design,electromagnetic losses analysis and reduction,sensorless control strategies,as well as comparison and selection of sine-wave and square-wave drive modes.Some challenges are also discussed,so that future studies could be focused.

Calculation for Stator Loss of High-Speed Permanent Magnet Synchronous Machine in Torque-speed Envelope and Restraint Approach for Circulating Current in Windings

Iron loss and copper loss are the significant parts of electrical loss of machines,which are the major parts particularly under high frequency condition.High-speed permanent magnet synchronous machines(HS-PMSM)have the benefits of high power density,high efficiency and wide speed range.Which causes the calculation for iron loss and copper loss in whole operating range complex.By analyzing the components and influencing factors of iron loss and copper loss in stator,we have deduced the calculation formula of iron loss and copper loss in whole operating range based on the analytical solution and finite element approach(EFA)solution.According to the calculation solution,taking the influence of operating temperature on the iron loss and copper loss into account,we propose a temperature correction factor and establish the calculation method for the iron loss and copper loss with temperature influences.Finally,by the conductor transposition,we restrain the circulating current under high-frequency operating condition.

Software Development for Digital Control of WDW Series Testing Machine and Measurement of K_(IC)

Software has been developed for digital control of WDW series testing machine and the measurement of fracture toughness by modularized design. Development of the software makes use of multi-thread and serial communication techniques, which can accurately control the testing machine and measure the fracture toughness in real-time. Three-point bending specimens were used in the measurement. The software operates stably and reliably, expanding the function of WDW series testing machine.

Experimental Study on Thermal Stress of Concrete with Different Expansive Minerals Using a Temperature Stress Testing Machine

In order to compare the compensation effect of expansive materials with different mineral sources on the temperature stress of concrete,we investigated the temperature stress of concrete when adding calcium sulfoaluminate type expansive materials(CSA)or CaO and calcium sulfoaluminate mixed type expansive materials(HCSA)at different temperatures by temperature-stress testing machine(TSTM)considering the influence of temperature history on the expansion.The experimental results show that the expansion characteristics of the two kinds of expansive materials with different mineral sources significantly vary.When adding expansive materials,the growth rate of compressive stress during the heating stage increases obviously,the maximum compressive stress is higher,while the decline rate of tensile stress in the late cooling stage becomes slow,and finally cracking temperature decreases.It is proved that concrete with HCSA has lower cracking temperatures and better temperature shrinkage compensation effect.Therefore,it is rational to choose HCSA when preparing concrete with high expansion energy to reduce thermal cracking.

Machines and Testing Equipment

National Quality Supervision ＆ Inspection Center for Refractories Business scope： Selective examination for national quality supervision; Identification of production license; Arbitration inspection and technical achievements evaluation; Commodities inspection and otherquality inspections .

Estimation of Fatigue Strength of Reinforced Complete Upper Denture Using a Newly Designed Testing Machine: A Laboratory Research Project

In the present study, an aero pneumatic fatigue testing machine for complete dentures was designed, fabricated, and tested for the evaluation of the fatigue life of reinforced complete upper denture (CUD). On completion and testing, it was observed that the machine has the potential of generating reliable number of cyclic data. The machine’s performance was evaluated using test specimens of identical CUDs that were machined in conformity with standard procedures. The fatigue machine compressed the lower dental arch over the upper denture-specimen in centric occlusion, in the same way that the two masticatory muscles pull the lower jaw over the upper jaw during chewing. The incorporation of glass fibres into the CUD using a sandwich technique quadruples the lifespan of the denture (P = 0.004). The low standard deviation, along with the low coefficient of variation (CV) of the group of unreinforced dentures shows the repeatability of the results and the reliability of the machine. The high standard deviation and coefficient of variation of reinforced dentures was expected, since a high variation of results is usually recorded in fibre reinforcement cases. This research confirmed the view that the crack during denture fracture initiates in the anterior palatal area and propagates to the posterior.

Web Testing Generation: A Stream <i>X-Machine</i>Based Approach

To ensure the quality of Web applications, Web testing is one of the effective methods. The testing is a process of revealing errors that is used to give confidence that the implementation of a Web application meets its original specification. This work proposes a Web testing framework based on Stream X-Machines (SXMs), which provides a way to derive test cases for a Web application. It starts from constructing the SXM model, from which a test translator is employed to extract the test paths and then translates them into an XML-style test specification, which is the input of test engine. The test engine generates test cases and then executes them, and finally produces test report. This testing method is a significant contribution to informed research.

Development and Application of Complete Equipment for High-speed Tunnel Boring and Bolting Machines

With the improvement of coal mining speed and mechanization level in China,traditional tunnel boring methods can no longer meet the actual needs.In order to solve the problems of low efficiency,high labor intensity,slow tunnel boring speed,bad working environment and poor safety in traditional tunnel boring,on the basis of analyzing the development and application of coal roadway tunnel boring equipment at home and abroad,complete equipment for high-speed tunnel boring and bolting machines was developed by using the integrated technology of tunnel boring and bolting.The complete equipment for high-speed tunnel boring and bolting machines has the functions of tunnel boring and bolting synchronization,once-tunneling,negative pressure dust removal,digital guidance,independent cutting feed,digital cutting,safety monitoring and data interaction,which has the advantages of safety in use,reliability and efficiency.

Continuous prediction method of earthquake early warning magnitude for high-speed railway based on support vector machine

Purpose–Using the strong motion data ofK-net in Japan,the continuous magnitude prediction method based on support vector machine(SVM)was studied.Design/methodology/approach–In the range of 0.5–10.0 s after the P-wave arrival,the prediction time window was established at an interval of 0.5 s.12 P-wave characteristic parameters were selected as the model input parameters to construct the earthquake early warning(EEW)magnitude prediction model(SVM-HRM)for high-speed railway based on SVM.Findings–The magnitude prediction results of the SVM-HRM model were compared with the traditional magnitude prediction model and the high-speed railway EEW current norm.Results show that at the 3.0 s time window,themagnitude prediction error of the SVM-HRMmodel is obviously smaller than that of the traditionalτc method and Pd method.The overestimation of small earthquakes is obviously improved,and the construction of the model is not affected by epicenter distance,so it has generalization performance.For earthquake events with themagnitude range of 3–5,the single station realization rate of the SVM-HRMmodel reaches 95%at 0.5 s after the arrival of P-wave,which is better than the first alarm realization rate norm required by“The TestMethod of EEW andMonitoring Systemfor High-Speed Railway.”For earthquake eventswithmagnitudes ranging from3 to 5,5 to 7 and 7 to 8,the single station realization rate of the SVM-HRM model is at 0.5 s,1.5 s and 0.5 s after the P-wave arrival,respectively,which is better than the realization rate norm of multiple stations.Originality/value–At the latest,1.5 s after the P-wave arrival,the SVM-HRM model can issue the first earthquake alarm that meets the norm of magnitude prediction realization rate,which meets the accuracy and continuity requirements of high-speed railway EEW magnitude prediction.

An Optimal Feed Interpolator Based on G^2 Continuous Bézier Curves for High-Speed Machining of Linear Tool Path

A numerical control (NC) tool path of digital CAD model is widely generated as a set of short line segments in machining. However, there are three shortcomings in the linear tool path, such as discontinuities of tangency and curvature, huge number of line segments, and short lengths of line segments. These disadvantages hinder the development of high speed machining. To smooth the linear tool path and improve machining efficiency of short line segments, this paper presents an optimal feed interpolator based on G^2 continuous Bézier curves for the linear tool path. First, the areas suitable for fitting are screened out based on the geometric characteristics of continuous short segments (CSSs). CSSs in every area are compressed and fitted into a G^2 Continuous Bézier curve by using the least square method. Then a series of cubic Bézier curves are generated. However, the junction between adjacent Bézier curves is only G^0 continuous. By adjusting the control points and inserting Bézier transition curves between adjacent Bézier curves, the G^2 continuous tool path is constructed. The fitting error is estimated by the second-order Taylor formula. Without iteration, the fitting algorithm can be implemented in real-time environment. Second, the optimal feed interpolator considering the comprehensive constraints (such as the chord error constraint, the maximum normal acceleration, servo capacity of each axis, etc.) is proposed. Simulation and experiment are conducted. The results shows that the proposed method can generate smooth path, decrease the amount of segments and reduce machining time for machining of linear tool path. The proposed research provides an effective method for high-speed machining of complex 2-D/3-D profiles described by short line segments.

ANALYSIS AND SIMULATION ON THE MECHANISM OF A NOVEL DUAL-WAVE SHOCK TEST MACHINE

For qualifying the anti-shock performance of shipboard equipments and simulating actual underwater explosion environments, a novel dual-wave shock test machine is proposed to increase testing capability of shock test machines as well as to meet certain shock testing specification. The machine can generate a double-pulse acceleration shock for test articles according to specification defined in BV043/85. On the basis of the impact theory, a nonlinear dynamic model of the hydraulically-actuated test machine is established with thorough analysis on its mechanism which involves conversion of gas potential energy and dissipation of kinetic energy. Simulation results have demonstrated that the machine can produce a double-pulse acceleration shock in the time domain or a desired shock response spectrum in the frequency domain, which sets a theoretical base for the construction of the proposed machine.