High-speed Environment Research Articles

Experimental Investigation on Leakage and Frictional Heat Generation Characteristics of Low Hysteresis Brush Seals

Abstract Low hysteresis brush seals are frequently used in systems operating at a high speed in environments featuring a high temperature and pressure. The high-speed rotor comes into contact with the bottom end of the bristles of the brush seal in such scenarios to generate a significant amount of frictional heat, which directly affects its sealing performance and service life, while a high temperature can increase the magnitude of its friction-induced heat. In this study, we report the cyclical testing of a low hysteresis brush seal while increasing and decreasing the speed of the rotor under varying differences in the pressure and temperature. We focus on the characteristics of leakage, hysteresis effect, and temperature rise at the bottom end of the bristles of the brush seal owing to frictional heat. The results showed that the volume of leakage increased at a high temperature under a strong hysteresis effect and a small rise in the temperature. Moreover, as the speed of the rotor exceeded 6000 rpm, the temperature of the system increased rapidly owing to frictional heat. During the speed decrease stage, the temperature rise decreased sharply and then gradually until it became nearly constant. The hysteresis effect resulted in a lower temperature rise during the decrease in the speed of the rotor compared with that during an increase in its speed. While the low hysteresis structure can effectively reduce hysteresis effect compared with that of the conventional brush seal, it induced greater leakage. It is necessary to choose a pressure relief chamber of a suitable size to minimize leakage. Furthermore, the low hysteresis brush seal exhibited a smaller friction temperature rise than the conventional seal, where this is beneficial for its service life.

Unveiling gap acceptance behaviour during lane change with EDIV data: A deep dive into driving behaviour on expressway using a three level mixed effect linear regression approach

Lane change has a potential significance in road safety. Gap acceptance phenomena serves as a primary and critical phase in lane change maneuver. This study aims to investigate the gap acceptance behaviour of drivers during lane changes on expressways, with a focus on understanding how various factors influence drivers' decisions to change lanes. An extensive dataset collected through various sensors tailored for expressway driving, known as the ‘Expressway Drive: Instrumented Vehicle (EDIV) Dataset’ is utilized. Driving data from 59 drivers covering a distance of around 4000 km was used in the current study. Total 2578 lane changing events are identified through computing lateral deviations measured through 3D LiDAR sensor. Substantial differences are observed within the groups in primary analysis which suggest that lane-change direction significantly affect gap acceptance. To effectively manage both intra- and inter-cluster variances, this study employs two separate three levels mixed-effects linear models. These models account for the interdependence of gap acceptance characteristics within individual drivers and for different directions of lane changes by incorporating random effects. Furthermore, these models examine relationships between lead/ lag gap acceptance and the various influencing factors as fixed effects. It was found that factors such as speed of the subject vehicle, gap position, relative speeds, and surrounding vehicle types had influence on gap acceptance during lane changes on expressways. The insights gained from this study could inform the development of advanced driver assistance systems (ADAS) as well as development of autonomous vehicles, contributing to improved road safety and traffic flow management in high-speed environments.

A Noncontact Rotational Speed Sensor for Bearing Cages in a High-Temperature and High-Speed Environment

A Noncontact Rotational Speed Sensor for Bearing Cages in a High-Temperature and High-Speed Environment

Design and thermal protection performance analysis of insulated wing storage box for hypersonic variable-sweep aircraft

Utilizing variable-sweep wing technology can further enhance the flight performance of hypersonic aircraft in a wide range of high-altitude, high-speed environments. Existing research on hypersonic variable-sweep flying wings has mainly focused on aerodynamic shape design, with limited research on the internal thermal protection of the aircraft. This study firstly investigates the performance improvement of a hypersonic flying wing using variable-sweep technology through CFD simulations. Secondly, to address the high-temperature issues induced by variable-sweep wings, an insulated wing storage box with a corrugated structure is designed, incorporating a thermal insulation layer made of corrugated webs and insulating materials. Heat transfer simulations are conducted by applying thermal loads to the wing storage box to study the temperature distribution during the variable-sweep process. Finally, a comparison between the corrugated structure design and the non-corrugated structure design of the wing storage box is performed to analyze the thermal insulation performance of the insulation layer. The results show that the heated area of the wing storage box is primarily influenced by the wing’s sweep angle, and the corrugated thermal insulation layer can effectively reduce heat transfer efficiency, resulting in a 30% reduction in the external temperature of the wing storage box.

Advancing Ultrasonic Defect Detection in High-Speed Wheels via UT-YOLO.

In the context of defect detection in high-speed railway train wheels, particularly in ultrasonic-testing B-scan images characterized by their small size and complexity, the need for a robust solution is paramount. The proposed algorithm, UT-YOLO, was meticulously designed to address the specific challenges presented by these images. UT-YOLO enhances its learning capacity, accuracy in detecting small targets, and overall processing speed by adopting optimized convolutional layers, a special layer design, and an attention mechanism. This algorithm exhibits superior performance on high-speed railway wheel UT datasets, indicating its potential. Crucially, UT-YOLO meets real-time processing requirements, positioning it as a practical solution for the dynamic and high-speed environment of railway inspections. In experimental evaluations, UT-YOLO exhibited good performance in best recall, mAP@0.5 and mAP@0.5:0.95 increased by 37%, 36%, and 43%, respectively; and its speed also met the needs of real-time performance. Moreover, an ultrasonic defect detection data set based on real wheels was created, and this research has been applied in actual scenarios and has helped to greatly improve manual detection efficiency.

Role of cavity in a Mach 8 axisymmetric scramjet combustor: Flame stabilization vs combustion enhancement

The cavity-assisted scramjet has been proven to be the most promising propulsion system for air-breathing hypersonic vehicles. In this paper, numerical simulations of a Mach 8 axisymmetric scramjet combustor are conducted and validated to investigate the effect of the cavity. The results indicate that the combustion state undergoes significant changes as the combustion heat release increases. Detailed analysis reveals that the role of the cavity in flame stabilization and combustion enhancement also changes with combustion heat release. Under weak heat release conditions, the high-speed environment results in reduced combustion efficiency, and the primary role of the cavity is to stabilize the flame. Increasing the cavity size does not yield significant gains but could bring redundant mass. As heat release intensifies, the combustion enhancement effect of the cavity becomes more prominent. The presence of the cavity dramatically improves fuel combustion efficiency. The distribution of supersonic and subsonic combustion modes, as well as that of premixed and diffusion combustion modes, is also affected by cavity size and combustion heat release. In the engineering development of scramjets, it is suggested that the design of the cavity flameholder should involve careful consideration of combustion heat release.

Effect of Iron Powder Type on Friction and Wear Properties of Copper-Based Friction Powder Metallurgy Material

The sintered copper-based friction materials for high-speed train brake pads have a complex composition that should possess good physical, mechanical, and tribological properties in practical applications. For this article, four kinds of friction materials were prepared by powder metallurgy technology and the effects of iron powders on the microstructural and tribological properties of copper-based friction materials were characterized using a scanning electron microscope (SEM) and x-ray diffraction (XRD). The results showed that iron powder as an enhancing component could effectively change the friction and wear properties of the experimental materials. The copper-based friction materials containing hydroxy iron (1–5 μm) have a relatively higher coefficient of friction: The friction coefficient was significantly improved by 16.8% compared with reduced iron under a rotation speed of 4000 r/min. The friction coefficient of friction material containing water-atomized iron powder is relatively reliable with 3000–7000 r/min speed range. In a high-speed environment, the friction surface of material containing reduced iron or hydroxy iron (1–5 μm) mainly has cracks and brittle fractures, and the material containing hydroxy iron (10 μm) or water-atomized iron has an obvious layered structure and shows fatigue wear.

Do Dynamic speed feedback signs impact drivers differently based on speeding tendencies? Insights from applications at select critical roadway contexts

The effects of a traffic control devices on drivers at different speed percentiles, particularly on the faster drivers, are of specific interest due to their higher likelihood of crash involvement. A study was designed to evaluate how the dynamic speed feedback signs (DSFS) affect drivers while transitioning from a high-speed to a low-speed environment. A total of 5 sites were selected, which included a freeway exit ramp, two speed transition zones, and two high-speed rural highway curves. Vehicle speed profiles approaching and entering these critical locations were collected before and after installing the DSFS. Based on the tracked speed of the free-flowing vehicles before the influence area, drivers were categorized as slower drivers, average drivers, and faster drivers using the 15th and 85th percentile speed at each site. Overall, installing the DSFS resulted in a significant reduction in speed across all locations for all sites. Drivers were found to reduce their speed while approaching the curve, traversing through the speed transition zones, and entering the curves or reduced speed limit areas. Results showed that faster drivers tend to reduce speed more than the average or slower drivers at every location. The faster drivers also reacted to the sign earlier compared to the slower or average drivers. The overall results are encouraging as they indicate that DSFS impact faster drivers more, which can ultimately reduce speeding-related crashes when transitioning from a high-speed environment. Based on the findings, the continued use of DSFS as a speed reduction strategy is recommended.

Assessing the effect of air-throttling on ignition dynamics in a hydrogen-fueled scramjet under various fuel-injection strategies using LES

Air-throttling has been experimentally substantiated as a potent technique for augmenting the ignition of fuel jets in high-speed scramjet engines. Nonetheless, the elucidation of its combustion enhancement mechanism remains conspicuously scant in the extant literature. In this work, a comprehensive numerical investigation was conducted to assess the ignition dynamics of a hydrogen-fueled cavity-stabilized scramjet combustor under various fuel-injection and air-throttling strategies using large-eddy simulation. The results demonstrated that air-throttling could improve the ignition environment by augmenting backpressure, fuel residence time, and mixture homogeneity. Remarkably, the subsonic region was improved by over 30%, and the maximum Mach number was limited to no more than 2.25. Additionally, the enhanced ignition modes under two injection strategies were quite different. For the passive injection, the flame developed rapidly as a result of the deflagration of lean mixture in the interval of Zmix = [0.01, 0.02]. Nevertheless, the direct injection resulted in an additional deflagration-to-detonation transition phenomenon in the expansion section due to the ignition of rich mixture in the interval of Zmix = [0.0225, 0.03]. Both injection strategies exhibited severe flow separation and flame flashback. After removing air-throttling, the flame became dispersed with large-amplitude fluctuations, emphasizing the importance of precise control of air-throttling parameters and operating sequence. These findings advanced the current understanding of the intricate interactions between pilot-hydrogen and air-throttling, thereby elucidating their pivotal role in promoting ignition within high-speed environments.

Fault Diagnosis of Rotating Machinery Bearings Based on Improved DCNN and WOA-DELM

A bearing is a critical component in the transmission of rotating machinery. However, due to prolonged exposure to heavy loads and high-speed environments, rolling bearings are highly susceptible to faults, Hence, it is crucial to enhance bearing fault diagnosis to ensure safe and reliable operation of rotating machinery. In order to achieve this, a rotating machinery fault diagnosis method based on a deep convolutional neural network (DCNN) and Whale Optimization Algorithm (WOA) optimized Deep Extreme Learning Machine (DELM) is proposed in this paper. DCNN is a combination of the Efficient Channel Attention Net (ECA-Net) and Bi-directional Long Short-Term Memory (BiLSTM). In this method, firstly, a DCNN classification network is constructed. The ECA-Net and BiLSTM are brought into the deep convolutional neural network to extract critical features. Next, the WOA is used to optimize the weight of the initial input layer of DELM to build the WOA-DELM classifier model. Finally, the features extracted by the Improved DCNN (IDCNN) are sent to the WOA-DELM model for bearing fault diagnosis. The diagnostic capability of the proposed IDCNN-WOA-DELM method was evaluated through multiple-condition fault diagnosis experiments using the CWRU-bearing dataset with various settings, and comparative tests against other methods were conducted as well. The results indicate that the proposed method demonstrates good diagnostic performance.

Design, performance evaluation and calibration of an indirectly-excited piezoelectric wind energy harvester via a double-bluffbody exciter

Using piezoelectric wind energy harvester to harvest energy from fluids has been an effective way to power wireless sensing systems in the last decade. In order to solve the problems of low reliability, poor environmental adaptability and narrow operating bandwidth of existing piezoelectric wind energy harvesters, a piezoelectric wind energy harvester with a double-bluffbody exciter (DE-PWEH) is proposed in this research. By summarizing the disadvantages of traditional piezoelectric wind energy harvester with upwind structure, a downwind structure was adopted to improve the ability of PWEH to adapt to high wind speed environment. In order to further improve the reliability of the DE-PWEH, a wind isolation structure and an indirect excitation structure was adopted, which means the piezoelectric element was installed inside a cylindrical shell. Therefore, this 2-DOF indirectly excited DE-PWEH could possess both high reliability and broad working bandwidth. To verify the principle feasibility and design regarding the proposed DE-PWEH, a prototype of the DE-PWEH was fabricated and tested in terms of output and operating bandwidth. The results showed that the relative size and position of the adjustable plate and cylindrical shell brought significant effects on the output and operating bandwidth. When the distance-diameter ratio was 0.6, the width-diameter ratio was 1 and the height ratio was 1, the maximum output voltage was 90.35 V and the critical wind speed was 0.96 m/s. The maximum increase of the critical wind speed was 550%, and the minimum was −85.67%. With a wind speed of 15 m/s and the optimal load resistance of 2000 kΩ, a maximum power of 2.57 mW was attained. In order to quantitatively study the performance of DE-PWEH, a data fitting method based on polynomial was proposed for the first time to calibrate PWEH performance. The sixth order polynomial fitting equation can be used to characterize the output characteristics of DE-PWEH. And in practice, 80 LEDs in series were successfully driven by the DE-PWEH. The DE-PWEH could charge the 470 μF, 1000 μF, 2200 μF and 3000 μF commercial capacitors to 2 V in 60 s, 128 s, 340 s, and 400 s, respectively.

Rotordynamic analysis of a complex high-speed rotor

High-speed turbomachinery such as cryogenic turboexpander, turbocharger, turbo-compressor, and dental drill machines are prone to rigorous vibration and instability. The rotors of such high-speed turbomachinery cannot be analyzed by the fundamental Jeffcott rotor model. This is because, under the conditions of high-speed operation, the flexible rotor experiences many factors such as gyroscopic effect, shear deformation, axial torque, internal friction and viscous damping. These factors can influence catastrophic subsynchronous whirl in the system. Turbomachinery rotors have complex geometries and involve discs, turbine, compressor and bearings. In addition, complex turbomachinery rotors operate in high-speed conditions where tangential forces come in to picture, which further influences the behavior of a rotor and tends to destabilize the turbomachinery. These instabilities can be addressed by the design of a suitable rotor and its supporting bearings. In the present work, a complex turboexpander rotor is designed, which is supported on gas foil bearings, and its modal behavior is studied under high-speed environment. The rotor is small-sized and incorporates a disc, a turbine, a compressor and a pair of gas foil bearings with support structures. Finite element modeling is carried out, and the bearing dynamic coefficients are considered while modeling. The dynamics of the rotor is studied by estimating critical speeds, mode shapes and unbalance response.

Ultra-wideband wireless channel and environment characterization assisted by dual optical frequency comb

In this work, a photonic technique based on dual optical frequency comb is proposed to perform ultra-wideband channel sounding that delivers high-resolution channel characterization and high-speed environment monitoring. The proof-of-concept experiments demonstrate the continuous sounding of non-line-of-sight wireless channels with > TS/s sampling resolution and the revealing of an electric fan rotating at up to 1100 rpm. A wireless channel of 12 GHz bandwidth is covered using electronics with less than 60 MHz bandwidth, showcasing the high practicality of the proposed technique in assisting the evolution towards the integration of communication and sensing essential in the next generation of wireless communication.

Reconfigurable Intelligent Surface-Aided Orthogonal Time Frequency Space and Its Deep Learning-Based Signal Detection

Orthogonal time frequency space (OTFS) has emerged as a promising modulation for next-generation wireless systems. This two-dimensional (2D) modulation has the inherent capability of providing uninterrupted connectivity and improved performance in a high-mobility doubly-selective channel by mapping the information symbols to the delay-Doppler (DD) domain. In order to improve the directivity of OTFS signals, this paper considers a reconfigurable intelligent surface (RIS)-aided OTFS transmission for a high-speed environment. While OTFS mitigates the dispersion due to the highly mobile wireless channels by mapping the transmit symbols to the DD domain, RIS improves the directivity by appropriately shifting the phase of the signal in non-line-of-sight (NLOS) communication channels. We provide a concrete matrix-based mathematical model of the RIS-aided OTFS communication system. Capitalizing on the simple matrix multiplication-based model, a number of detectors can be used. These include the usual zero-forcing (ZF) and the minimum mean-squared error (MMSE) linear detectors, and a low-complexity message passing algorithm (MPA)-assisted detector. We evaluate the performance of these detectors for RIS-based OTFS systems. A deep learning (DL)-based signal detector is also proposed for the RIS-aided OTFS system. A significant improvement in bit-error-rate (BER) performance is achieved using the system considered, while the RIS-induced computational burden is not high. Furthermore, in NLOS communication, our system can ensure seamless connectivity with a reduced number of base stations (BS).

Parameter Optimization of Large-Size High-Speed Cam-Linkage Mechanism for Kinematic Performance

The cam-linkage mechanism is a typical transmission mechanism in mechanical science and is widely used in various automated production equipment. However, conventional modeling methods mainly focus on the design and dimensional synthesis of the cam-linkage mechanism in the slow-speed scenario. The influence of component dimensions is not taken into consideration. As a result, the model accuracy dramatically falls when analyzing large-size cam-linkage mechanisms, especially in high-speed environments. The kinematic aspects of cam design have been investigated, but there are few studies discussing the motion characteristic and accuracy analysis models of the large-size cam-linkage mechanism under high-speed scenarios. To handle such issues, this paper proposes a parameter optimization methodology for the design analysis of the large-size high-speed cam-linkage mechanism considering kinematic performance. Firstly, the mathematical model of the cam five-bar mechanism is presented. The cam curve and motion parameters are solved forward with linkage length and output speed. Then, a particle swarm-based multi-objective optimization method is developed to find the optimal structure parameters and output speed curve to minimize cam pressure angle and roller acceleration and maximize linkage mechanism drive angle. A Monte Carlo-based framework is put forward for the reliability and sensitivity analysis of kinematic accuracy. Finally, a transverse device of a sanitary product production line is provided to demonstrate the applicability of the proposed method. With the parameter optimization, the productivity of the transverse device is doubled, from 600 pieces per minute (PPM) to 1200 PPM.

Explosive dispersal of particles in high speed environments

In this paper, we present the results of the explosive dispersal of particles in high-speed environments. We carry out Euler–Lagrange numerical simulations of a source at quiescent ambient conditions as well as moving at Mach numbers of 3 and 6. Particle volume fractions of 0%, 1%, and 4.5% are presented. The detonation profile is computed with the Jones–Wilkins–Lee equation of state using a reactive burn model. Non-static cases provide a framework to consider the effect of a bow shock and pre-existing high-speed flow conditions on the dispersal process. We also compute averages of both static and dynamic pressures, as well as impulse density histories on virtual probe planes to characterize the momentum of the flow and particles that would deposit on a target. Results suggest that the presence of the particles can have a substantial effect on the pressure average of the virtual target planes.

Effects of Pedestrians’ Assertiveness on Drivers’ Yielding Behavior at Mid-Block Sections: An Application of Bayesian Structural Equation Modeling

Crossing roads at mid-block sections often creates ambiguity about priority between pedestrians and drivers, resulting in conflicts, road crashes, death, and human injury. To share the road space safely, they need to anticipate other user behaviors whilst maintaining and modifying their own habitual and desired maneuvers. This study investigates the effect of pedestrian assertive behaviors and vehicle user characteristics on driver yielding at mid-block sections. Road users’ interactions were observed in a dense mixed land use urban area of Central Auckland, New Zealand. Bayesian structural equation modeling is used to find interrelationships of multivariate data. The result shows that yielding levels decrease when the vehicle speed increases and they are not part of a platoon. Pedestrians’ direct signals (i.e., hand gestures) can increase drivers’ willingness to yield. Conversely, pedestrians, who tend to run or cross heedlessly through the traffic, are less likely to modify driver behavior in a high-speed environment. However, these factors are mediated through vehicle speed-related factors. Women are more likely to be given priority compared to men, especially when they have slow crossing speed. The study offers a better understanding of road users’ interactions outside controlled crossings. It provides evidence why it is important to reduce operating speeds in areas where there is a high demand for sharing between vehicles and vulnerable road users and mid-block crossings. Road users can be better informed to understand more gesture communication combined with appropriate engineering practice, such as traffic calming, and where appropriate re-prioritization of road space to influence drivers’ operating speeds.

Unsupervised Deep Event Stereo for Depth Estimation

Bio-inspired event cameras have been considered effective alternatives to traditional frame-based cameras for stereo depth estimation, especially in challenging conditions such as low-light or high-speed environments. Recently, deep learning-based supervised event stereo matching methods have achieved significant performance improvements over the traditional event stereo methods. However, the supervised methods depend on ground-truth disparity maps for training, and it is difficult to secure a large amount of ground-truth disparity maps. A feasible alternative is to devise an unsupervised event stereo method that can be trained without ground-truth disparity maps. To this end, we propose the first unsupervised event stereo matching method that can predict dense disparity maps, and is trained by transforming the depth estimation problem into a warping-based reconstruction problem. We propose a novel unsupervised loss function that enforces the network to minimize the feature-level epipolar correlation difference between the ground-truth intensity images and warped images. Moreover, we propose a novel event embedding mechanism that utilizes both temporal and spatial neighboring events to capture spatio-temporal relationships among the events for stereo matching. Experimental results reveal that the proposed method outperforms the baseline unsupervised methods by significant margins (e.g., up to 16.88% improvement) and achieves comparable results with the existing supervised methods. Extensive ablation studies validate the efficacy of the proposed modules and architectural choices.

How do the type and duration of distraction affect speed selection and crash risk? An evaluation using naturalistic driving data

Distracted driving is among the leading causes of roadway crashes worldwide. However, due to limitations of police-reported crash data, it is often challenging to understand the nature and magnitude of this problem. Distraction has also been shown to affect driver speed selection, which is important as both mean speed and speed variance have substantive impacts on crash risk. This study utilizes naturalistic driving data to investigate the relationship between the engagement in various secondary (non-driving) tasks and driver speed selection under different driving contexts. Separate analyses were conducted for low-speed and high-speed driving environments. Two-way random effects linear regression models were estimated for both speed regimes, while controlling for driver, roadway, and traffic characteristics. The differences were assessed based upon ten types of secondary tasks. In general, engagement in all tasks was found to decrease speeds in high-speed environments while the effects were mixed in low-speed settings. The changes in speeds were much pronounced for secondary tasks that include a combination of visual, manual, and cognitive distractions, such as cell phone use. Among all secondary tasks, an average episode of a driver talking on a handheld cellphone was associated with a 6-mph speed reduction in high-speed environments, but a 3.5-mph increase in low-speed settings. In addition to examining impacts on speed selection, the risk of involvement in crash and near-crash events was also evaluated in consideration of the type and duration of distraction. Interestingly, distractions tended to show similar relationships, in both direction and magnitude, with the risk of involvement in both crash and near-crash events. From a policy standpoint, this study provides further motivation for legislation and other programs aimed at curbing distracted driving.

Design of a Novel Network Framework for Traffic Identification by Using Deep Packet Inspection and Machine Learning

This paper presents an investigation, involving experiments, which shows that current network intrusion, detection, and prevention systems (NIDPSs) have several shortcomings in detecting or preventing rising unwanted traffic and have several threats in high-speed environments. Precise organization traffic recognizable proof is a significant reason for network traffic checking and information investigation, and is the way to work on the nature of client administration. In this paper, through the examination of two organization traffic ID strategies in light of machine learning and profound parcel review, an organization traffic distinguishing proof strategy in view of machine learning and profound bundle examination is proposed. This strategy utilizes profound parcel assessment innovation to distinguish most organization traffic, diminishes the responsibility that should be recognized by machine learning. This paper presents an investigation, involving experiments, which shows that current network intrusion, detection, and prevention systems (NIDPSs) have several shortcomings in detecting or preventing rising unwanted traffic and have several threats in high-speed environments. It shows that the NIDPS performance can be weak in the face of high-speed and high-load malicious traffic in terms of packet drops, outstanding packets without analysis, and failing to detect/prevent unwanted traffic. A novel quality of service (QoS) architecture has been designed to increase the intrusion detection and prevention performance. Our exploration has proposed and assessed an answer involving an original QoS setup in a multi-facet change to sort out parcels/traffic and equal procedures to build the bundle handling speed. The new engineering was tried under various traffic velocities, types, and errands. The trial results show that the design works on the organization and security execution which is can conceal to 8 Gb/s with 0 bundles dropped. This paper likewise shows that this number (8Gb/s) can be improved, yet it relies upon the framework limit which is constantly restricted.