基于动态称重系统实测轴载分布特征研究

以济青高速改扩建工程为例,利用动态称重系统（Weight-in-Motion,WIM）实测数据分析了2019年至2020年的济青高速公路的交通轴载分布特征,获得了不同轴型车辆的轴载谱变化规律;通过对全年交通量的分析,得到了车道拓宽后的高速公路年平均日交通量调节系数及车辆横向分布系数的变化规律。研究结果表明：采用轴载谱的方法可以更加全面、精确地分析交通荷载对路面结构产生的破坏机理,可以为类似区域条件下道路设计分析提供基本的交通输入参数。     

Geometric calibration algorithm of polarization camera using planar patterns

Polarization pattern provides additional information besides spectral signatures. It can be used in many applications, such as navigation, defect detection and object identification. A novel polarization camera composed of four synchronized cameras is proposed, and it can realize real-time polarization measurement. This study particularly concentrates on the geometric calibration of the system. The projection model is analyzed and the multi-camera calibration algorithm is proposed. Firstly, each camera is calibrated separately using planar patterns, and then the geometric calibration algorithms are performed. Due to the geometrical constraint, a global optimization method results in smaller estimation uncertainties. A mean rotation error of 0.025° and a mean translation error of 0.26mm are achieved after geometric calibration. The images are rectified to establish a correspondence among cameras and are combined to acquire the polarization measurement. The polarization pattern of the skylight is measured by the system and the results are consistent with the previous studies.     

Research of legal affair simulation based on Petri net

Legal affair simulation is on the basis of information technology, and can simulate legal scene. College students do legal experiments with virtual reality scene. This helps to train college students. Petri net has not only strict math definition to analyze the dynamic behavior of models, but also intuitive graphic expression. Petri net is used to model for legal process. Reduction rules of Petri net are used to analyze the correctness of the system. At last Java Business Process Management (JBPM) technology is used to realize legal affair simulation models, models are transformed into flow charts. Simulation software deduces processes of legal affair. Many college students have improved their ability with the software, the fact proves that the method is effective.     

Magnetic field tuning characteristics of bimodal ultrasonic motor stator

The magnetic field tuning characteristics of an ultrasonic motor (USM) stator are discussed. The stator consists of two piezoelectric ceramic transducer (PZT) plates and one sandwiched-in Terfenol-D plate. The dimensions of the stator are carefully adjusted to specifically discuss the influence of the magnetic field on the frequency difference between the longitudinal and bending modes of the stator. The frequency difference discussed in this paper is usually small and mainly caused by uneven materials, machining errors and changes in external conditions (temperature, pre-stress or load). The longitudinal and bending modes of the stator are simultaneously excited by an external electric field to generate the elliptic motion trajectories of the driving points. A direct current (DC) magnetic field is applied to decrease the difference between the two mode frequencies of the fabricated stator. In experiments, the dependences of the two mode frequencies and their difference on DC magnetic fields are all investigated. The experimental results indicate that the difference between the longitudinal and bending mode frequencies of the PZT/Terfenol-D/PZT composite stator can be tuned by changing the intensity of the external DC magnetic field.     

Sensorimotor self-learning model based on operant conditioning for two-wheeled robot

Traditional control methods of two-wheeled robot are usually model-based and require the robot’s precise mathematic model which is hard to get. A sensorimotor self-learning model named SMM TWR is presented in this paper to handle these problems. The model consists of seven elements: the discrete learning time set, the sensory state set, the motion set, the sensorimotor mapping, the state orientation unit, the learning mechanism and the model’s entropy. The learning mechanism for SMM TWR is designed based on the theory of operant conditioning (OC), and it adjusts the sensorimotor mapping at every learning step. This helps the robot to choose motions. The leaning direction of the mechanism is decided by the state orientation unit. Simulation results show that with the sensorimotor model designed, the robot is endowed the abilities of self-learning and self-organizing, and it can learn the skills to keep itself balance through interacting with the environment.     

Compensation of pressure enthalpy effects on temperature fields for throttling of high-pressure real gas

For the pressure enthalpy of high pressure pneumatics, the computational fluid dynamics (CFD) simulation based on ideal gas assumption fails to obtain the real temperature information. Therefore, we propose a method to compensate the pressure enthalpy of throttling for CFD simulation based on ideal gas assumption. Firstly, the pressure enthalpy is calculated for the pressure range of 0.101 to 30 MPa and the temperature range of 190 to 298 K based on Soave-Redlich-Kwong (S-R-K) equation. Then, a polynomial fitting equation is applied to practical application in the above mentioned range. The basic idea of the compensation method is to convert the pressure enthalpy difference between inlet air and nodes into the compensation temperature. In the above temperature and pressure range, the compensated temperature is close to the real one, and the relative temperature drop error is below 10%. This error is mainly caused by the velocity difference of the orifice between the real and ideal gas models. Finally, this compensation method performs an icing analysis for practical high pressure slide pilot valve.     

Approximate method for reliability assessment of complex phased mission systems

Phased-mission systems (PMSs) have wide applications in engineering practices, such as manmade satellites. Certain critical parts in the system, such as cold standby, hot standby and functional standby, are designed in redundancy architecture to achieve high reliability performance. State-space models such as Markov process have been used extensively in previous studies for reliability evaluation of PMSs with dynamic behaviors. The most popular way to deal with the dynamic behaviors is Markov process, but it is well known that Markov process is limited to exponential distribution. In practice, however, the lifetime of most machinery products can follow non-exponential distributions like the Weibull distribution which cannot be handled by the Markov process. In order to solve this kind of problem, we present a semi-Markov model combined with an approximation algorithm to analyze PMS reliability subjected to non-exponential failures. Furthermore, the accuracy of the approximation algorithm is investigated by comparing to an accurate solution, and a typical PMS (attitude and orbit control system) is analyzed to demonstrate the implementation of the method.     

Design of car battery protection device based on torsion spring

In recent years, electric vehicles are developing rapidly in automotive industry. When involved in accidents, if the batteries of electric cars break, it is likely to cause a short circuit and start a fire. Aimed at this issue, a car battery protection device based on torsion spring has been designed. The car battery protection device can deform in a particular pattern in a collision accident. Impact energy of the accident is absorbed by the deformation, which can significantly reduce impact force on the batteries. Meanwhile, based on the principle of maximum energy absorption, some crucial parameters of the device can be determined. Furthermore, an impact simulation conducted on ANSYS software shows that maximum safety factors can be obtained when the material of car battery protection device is carbon steel. The analysis of “safe space” in the car battery protection device shows that the device can prevent battery damage effectively in general circumstances, which means the reliability of the device has been verified. Therefore, when applied to electric vehicles, the car battery protection device, which can prevent secondary accidents, significantly improves the vehicle security in accidents.     

Calculation of strain amplification matrix for strain invariant failure theory based on representative volume element models with periodical boundary condition

Strain invariant failure theory (SIFT) is a micro-mechanics-based failure theory for multi-scale failure analysis of composite materials originally proposed by Gosse and Christensen. In this paper, the approach for obtaining strain amplification matrix which is a key step for the execution of SIFT is improved by adopting representative volume element (RVE) finite element models considering periodical boundary condition, based on which more actual deformation mode is reflected. The deformation modes and strain data at the characteristic points of the centroid cell of multi-cell RVE model are analyzed and taken as a reference. It can be concluded that more reasonable deformation mode and relationship between the micro-mechanical and macro-mechanical strain states are obtained by employing the new model. Finally, numerical examples are provided to illustrate the determination of strain amplification factors within the RVEs considering periodical boundary condition at the characteristic points.     

Fault diagnosis for wind turbine based on improved extreme learning machine

A fault diagnosis method based on improved extreme learning machine (IELM) is proposed to solve the weakness (weak generalization ability, low diagnostic rate) of traditional fault diagnosis with feedforward neural network algorithm. This method fuses signal feature vectors, extracts six parameters as the principal component analysis (PCA) variables, and calculates correlation coefficient matrix among the variables. The weight values of control parameters in the extreme learning model are dynamically adjusted according to the test samples’ constantly changing. Consequently, the weight fixed drawback in the original model can be remedied. A fault simulation experiment platform for wind turbine drive system is built, eight kinds of fault modes are diagnosed by the improved extreme learning model, and the result is compared with that of other machine learning methods. The experiment indicates that the method can enhance the accuracy and generalization ability of diagnosis, and increase the computing speed. It is convenient for engineering application.