Effect of low-speed waterjet pressure on the rock-breaking performance of unsubmerged cavitating abrasive waterjet

Unsubmerged cavitating abrasive waterjet(UCAWJ)has been shown to artificially create a submerged environment that produces shear cavitation,which effectively enhances rock-breaking performance.The shear cavitation generation and collapse intensity depend on the pressure difference between the intermediate high-speed abrasive waterjet and the coaxial low-speed waterjet.However,the effect of the pressure of the coaxial low-speed waterjet is pending.For this purpose,the effect of low-speed waterjet pressure on rock-breaking performance at different standoff distances was experimentally investigated,and the effects of erosion time and ruby nozzle diameter on erosion performance were discussed.Finally,the micromorphology of the sandstone was observed at different locations.The results show that increased erosion time and ruby nozzle diameter can significantly improve the rock-breaking performance.At different standoff distances,the mass loss increases first and then decreases with the increase of low-speed waterjet pressure,the maximum mass loss is 10.4 g at a low-speed waterjet pressure of0.09 MPa.The surface morphology of cavitation erosion was measured using a 3D profiler,the increase in both erosion depth and surface roughness indicated a significant increase in the intensity of the shear cavitation collapse.At a low-speed waterjet pressure of 0.18 MPa,the cavitation erosion surface depth can reach 600μm with a roughness of 127μm.

Modeling differential car-following behavior under normal and rainy conditions: A memory-based deep learning method with attention mechanism

In order to analyze and learn the difference in car-following behavior between normal and rainy days, we first collect car-following trajectory data of an urban elevated road on normal and rainy days by microwave radar and analyze the differences in speed, relative speed, acceleration, space headway, and time headway among data through statistics. Secondly, owing to the time-series characteristics of car-following data, we use the long short-term memory(LSTM) neural network optimized by attention mechanism(AM) and sparrow search algorithm(SSA) to learn the different car-following behaviors under different weather conditions and build corresponding models(ASL-Normal, ASL-Rain, where ASL stands for AM-SSA-LSTM), respectively. Finally, the simulation test shows that the mean square error(MSE) and reciprocal of time-to-collision(RTTC) of the ASL model are better than those of LSTM and intelligent diver model(IDM), which is closer to the real data. The ASL model can better learn different driving behaviors on normal and rainy days. However,it has a higher sensitivity to weather conditions from cross test on normal and rainy data-sets which need classification training or sample diversification processing. In the car-following platoon simulation, the stability performances of two models are excellent, which can describe the basic characteristics of traffic flow on normal and rainy days. Comparing with ASL-Rain model, the convergence time of ASL-Normal is shorter, reflecting that cautious driving behavior on rainy days will reduce traffic efficiency to a certain extent. However, ASL-Normal model produces a more severe and frequent traffic oscillation within a shorter period because of aggressive driving behavior on normal days.

Research on Feature Extraction Method for Low-Speed Reciprocating Bearings Based on Segmented Short Signal Modulation Signal Bispectrum Slicing

Bearing condition monitoring and fault diagnosis (CMFD) can investigate bearing faults in the early stages, preventing the subsequent impacts of machine bearing failures effectively. CMFD for low-speed, non-continuous operation bearings, such as yaw bearings and pitch bearings in wind turbines, and rotating support bearings in space launch towers, presents more challenges compared to continuous rolling bearings. Firstly, these bearings have very slow speeds, resulting in weak collected fault signals that are heavily masked by severe noise interference. Secondly, their limited rotational angles during operation lead to a restricted number of fault signals. Lastly, the interference from deceleration and direction-changing impact signals significantly affects fault impact signals. To address these challenges, this paper proposes a method for extracting fault features in low-speed reciprocating bearings based on short signal segmentation and modulation signal bispectrum (MSB) slicing. This method initially separates short signals corresponding to individual cycles from the vibration signals based on encoder signals. Subsequently, MSB analysis is performed on each short signal to generate MSB carrier-slice spectra. The optimal carrier frequency and its corresponding modulation signal slice spectrum are determined based on the carrier-slice spectra. Finally, the MSB modulation signal slice spectra of the short signal set are averaged to obtain the overall average feature of the sliced spectra.

Car-following model featured with factors reflecting emergency evacuation situation

A new emergency evacuation car-following model （EECM） is proposed. The model aims to capture the main characteristics of traffic flow and driver behavior under an emergency evacuation, and it is developed on the basis of minimum safety distances with parts of the drivers＇ abnormal behavior in a panic emergency situation. A thorough questionnaire survey is undertaken among drivers of different ages. Based on the results from the survey, a safety-distance car-following model is formulated by taking into account two new parameters： a differential distributing coefficient and a driver＇ s experiential decision coefficient, which are used to reflect variations of driving behaviors under an emergency evacuation situation when compared with regular conditions. The formulation and derivation of the new model, as well as its properties and applicability are discussed. A case study is presented to compare the car-following trajectories using observed data under regular peak-hour traffic conditions and theoretical EECM results. The results indicate the consistency of the analysis of assumptions on the EECM and observations.

A new car-following model considering velocity anticipation

The full velocity difference model proposed by Jiang et al. [2001 Phys. Rev. E 64 017101] has been improved by introducing velocity anticipation. Velocity anticipation means the follower estimates the future velocity of the leader. The stability condition of the new model is obtained by using the linear stability theory. Theoretical results show that the stability region increases when we increase the anticipation time interval. The mKdV equation is derived to describe the kink-antikink soliton wave and obtain the coexisting stability line. The delay time of car motion and kinematic wave speed at jam density are obtained in this modeh Numerical simulations exhibit that when we increase the anticipation time interval enough, the new model could avoid accidents under urgent braking cases. Also, the traffic jam could be suppressed by considering the anticipation velocity. All results demonstrate that this model is an improvement on the full velocity difference model.

A velocity-difference-separation model for car-following theory

We introduce a velocity-difference-separation model that modifies the previous models in the literature. The improvement of this new model over the previous ones lies in that it not only theoretically retains many strong points of the previous ones, but also performs more realistically than others in the dynamical evolution of congestion. Furthermore, the proposed model is investigated with analytic and numerical methods, with the finding that it can demonstrate some complex physical features observed in real traffic such as the existence of three phases： free flow, synchronized flow, and wide moving jam; sudden flow drop in flow-density plane; and traffic hysteresis in transition between the free and the synchronized flow.

Car-following models of vehicular traffic

The Car-following models is a kind of microscopic simulation model for vehicular traffic, which describe the one-by-one following behaviors of vehicles in the same traffic lane. As a common traffic phenomenon, following behavior is of great importance in the micro-study of intelligent traffic control. Compared with other traffic-flow models, car-following model embodies the human factors and reflects the real traffic situation in a better way. This paper gives a systematic review of the development and actuality of car-following models by introducing and analyzing in detail the advantages and disadvantages of GHR model, OV model, CA model and fuzzy-logic model. In addition, local stability and asymptotic stability of car-following models are discussed in this paper.

A new car-following model with consideration of the traffic interruption probability

In this paper, we present a new car-following model by taking into account the effects of the traffic interruption probability on the car-following behaviour of the following vehicle. The stability condition of the model is obtained by using the linear stability theory. The modified Korteweg-de Vries （KdV） equation is constructed and solved, and three types of traffic flows in the headway sensitivity space-stable, metastable, and unstable--are classified. Both the analytical and simulation results show that the traffic interruption probability indeed has an influence on driving behaviour, and the consideration of traffic interruption probability in the car-following model could stabilize traffic flow.

Feedback control scheme of traffic jams based on the coupled map car-following model

Based on the pioneering work of Konishi et al. [Phys. Rev. E （1999） 60 4000], a new feedback control scheme is presented to suppress traffic jams based on the coupled map car-following model under the open boundary condition. The effect of the safe headway on the traffic system is considered. According to the control theory, the condition under which traffic jams can be suppressed is analyzed. The results are compared with the previous results concerning congestion control. The simulations show that the suppression performance of our scheme on traffic jams is better than those of the previous schemes, although all the schemes can suppress traffic jams. The simulation results are consistent with theoretical analyses.

An Improved Car-Following Model for Multiphase Vehicular Traffic Flow and Numerical Tests

This paper attempts to introduce an improved difference model that modifies a car-following model, which takes the next-nearest-neighbor interaction into account. The hnprovement of this model over the previous one lies in that it performs more realistically in the dynamical motion for small delay time. The traffic behavior of the improved model is investigated with analytic and numerical methods with the finding that the new consideration could further stabilize traffic flow. And some simulation tests verify that the proposed model can demonstrate some complex physical features observed recently in real traffic such as the existence of three phases： free flow, coexisting flow, and jam flow; spontaneous formation of density waves; sudden flow drop in flow-density plane; traffic hysteresis in transition between the free and the coexisting flow. Furthermore, th.e improved model also predicts that the stable state to relative density in the coexisting flow is insusceptible to noise.

A control method for congested traffic in the coupled map car-following model

Based on the pioneer work of Konishi et al, a new control method is presented to suppress the traffic congestion in the coupled map （CM） car-following model under an open boundary. A control signal concluding the velocity differences of the two vehicles in front is put forward. The condition under which the traffic jam can be contained is analyzed. The results axe compared with that presented by Konishi et al [Phys. Rev. 1999 E 60 4000-4007]. The simulation results show that the temporal behavior obtained by our method is better than that by the Konishi＇s et al. method, although both the methods could suppress the traffic jam. The simulation results are consistent with the theoretical analysis.

A new coupled map car-following model considering drivers' steady desired speed

Based on the pioneering work of Konishi et al., in consideration of the influence of drivers＇ steady desired speed ef/ect on the traffic flow, we develop a new coupled map car-following model in the real world. By use of the control theory, the stability condition of our model is derived. The validity of the present theoretical scheme is verified via numerical simulation, confirming the correctness of our theoretical analysis.

An improved car-following model with consideration of the lateral effect and its feedback control research

A car-following model is presented, in which the effects of non-motor vehicles on adjacent lanes are taken into ac- count. A control signal including the velocity differences between the following vehicle and the target vehicle is introduced according to the feedback control theory. The stability condition for the new model is derived. Numerical simulation is used to demonstrate the advantage of the new model including the control signal; the results are consistent with the analytical ones

A new coupled-map car-following model based on a transportation supernetwork framework

A new car-following model is proposed by considering information from a number of preceding vehicles with intervehicle communication. A supernetwork architecture is first described, which has two layers： a traffic network and a communication network. The two networks interact with and depend on each other. The error dynamic system around the steady state of the model is theoretically analyzed and some nonjam criteria are derived. A simple control signal is added to the model to analyze the criteria of suppressing traffic jams. The corresponding numerical simulations confirm the correctness of the theoretical analysis. Compared with previous studies concerning coupled map models, the controlled model proposed in this paper is more reasonable and also more effective in the sense that it takes into account the formation of traffic congestion.

A Model of Car-Following with Adaptive Headway

The performances of a well-known GHR car-following model was investigated by using numerical simulations in describing the acceleration and deceleration process induced by the motion of a leading car. It is shown that in GHR model vehicle is allowed to run arbitrarily close together if their speed are identical,and it waves aside even though the separation is larger than its desired distance. Based on these investigations, a modified GHR model which features a new nonlinear term which attempts to adjust the inter-vehicle spacing to a certain desired value was proposed accordingly to overcome these deficiencies. In addition, the analysis of the additive nonlinear term and steady-state flow of the new model were studied to prove its rationality.

SOME CHARACTERISTICS OF LOW-SPEED STREAKS UNDER SHEARED AIR-WATER INTERFACES

The characteristics of low-speed fluid streaks occurring under sheared air-water interfaces were examined by means of hydrogen bubble visualization technique. A critical shear condition under which the streaky structure first appears was determined to be u(tau) approximate to 0.19 cm/s. The mean spanwise streak spacing increases with distance from the water surface owing to merging and bursting processes, and a linear relationship describing variation of non-dimensional spacing <(<lambda>+)over bar> versus y(+) was found essentially independent of shear stress on the interface. Values of <(<lambda>+)over bar>, however, are remarkably smaller than their counterparts in the near-wall region of turbulent boundary layers. Though low-speed streaks occur randomly in time and space, the streak spacing exhibits a lognormal probability distribution behavior. A tentative explanation concerning the formation of streaky structure is suggested, and the fact that <(<lambda>+)over bar> takes rather smaller values than that in wall turbulence is briefly discussed.

POD analysis of the instability mode of a low-speed streak in a laminar boundary layer

The instability of one single low-speed streak in a zero-pressure-gradient laminar boundary layer is investigated experimentally via both hydrogen bubble visualization and planar particle image velocimetry(PIV) measurement. A single low-speed streak is generated and destabilized by the wake of an interference wire positioned normal to the wall and in the upstream. The downstream development of the streak includes secondary instability and self-reproduction process, which leads to the generation of two additional streaks appearing on either side of the primary one. A proper orthogonal decomposition(POD) analysis of PIV measured velocity field is used to identify the components of the streak instability in the POD mode space: for a sinuous/varicose type of POD mode, its basis functions present anti-symmetric/symmetric distributions about the streak centerline in the streamwise component, and the symmetry condition reverses in the spanwise component. It is further shown that sinuous mode dominates the turbulent kinematic energy(TKE) through the whole streak evolution process, the TKE content first increases along the streamwise direction to a saturation value and then decays slowly. In contrast, varicose mode exhibits a sustained growth of the TKE content,suggesting an increasing competition of varicose instability against sinuous instability.

A control method applied to mixed traffic flow for the coupled-map car-following model

In light of previous work [Phys. Rev. E 60 4000 （1999）], a modified coupled-map car-following model is proposed by considering the headways of two successive vehicles in front of a considered vehicle described by the optimal velocity function. The non-jam conditions are given on the basis of control theory. Through simulation, we find that our model can exhibit a better effect as p = 0.65, which is a parameter in the optimal velocity function. The control scheme, which was proposed by Zhao and Gao, is introduced into the modified model and the feedback gain range is determined. In addition, a modified control method is applied to a mixed traffic system that consists of two types of vehicle. The range of gains is also obtained by theoretical analysis. Comparisons between our method and that of Zhao and Gao are carried out, and the corresponding numerical simulation results demonstrate that the temporal behavior of traffic flow obtained using our method is better than that proposed by Zhao and Gao in mixed traffic systems.

Driving decision-making analysis of car-following for autonomous vehicle under complex urban environment

The decision-making under complex urban environment become one of the key issues that restricts the rapid development of the autonomous vehicles. The difficulty in making timely and accurate decisions like human beings under highly dynamic traffic environment is a major challenge for autonomous driving. Car-following has been regarded as the simplest but essential driving behavior among driving tasks and has received extensive attention from researchers around the world. This work addresses this problem and proposes a novel method RSAN(rough-set artificial neural network) to learn the decisions from excellent human drivers. A virtual urban traffic environment was built by Pre Scan and driving simulation was conducted to obtain a broad set of relevant data such as experienced drivers' behavior data and surrounding vehicles' motion data. Then, rough set was used to preprocess these data to extract the key influential factors on decision and reduce the impact of uncertain data and noise data. And the car-following decision was learned by neural network in which key factor was the input and acceleration was the output. The result shows the better convergence speed and the better decision accuracy of RSAN than ANN. Findings of this work contributes to the empirical understanding of driver's decision-making process and it provides a theoretical basis for the study of car-following decision-making under complex and dynamic environment.

Experimental investigation on vibration characteristics of the medium-low-speed maglev vehicle-turnout coupled system

The steel turnout is one of the key components in the medium–low-speed maglev line system.However,the vehicle under active control is prone to vehicle–turnout coupled vibration,and thus,it is necessary to identify the vibration characteristics of this coupled system through field tests.To this end,dynamic performance tests were conducted on a vehicle–turnout coupled system in a medium–low-speed maglev test line.Firstly,the dynamic response data of the coupled system under various operating conditions were obtained.Then,the natural vibration characteristics of the turnout were analysed using the free attenuation method and the finite element method,indicating a good agreement between the simulation results and the measured results;the acceleration response characteristics of the coupled system were analysed in detail,and the ride quality of the vehicle was assessed by Sperling index.Finally,the frequency distribution characteristics of the coupled system were discussed.All these test results could provide references for model validation and optimized design of medium–low-speed maglev transport systems.