Sudden Deceleration Research Articles

Data-Guided Low-Reynolds-Number Corrections for Two-Equation Models

Abstract The baseline Launder–Spalding k−ε model cannot be integrated to the wall. This paper seeks to incorporate the entire law of the wall into the model while preserving the original k−ε framework structure. Our approach involves modifying the unclosed dissipation terms in the k and ε equations specifically within the wall layer according to direct numerical simulation (DNS) data. The resulting model effectively captures the mean flow characteristics in both the buffer layer and the logarithmic layer, resulting in robust predictions of skin friction for zero-pressure-gradient (ZPG) flat-plate boundary layers and plane channels. To further validate our formulation, we apply our model to boundary layers under varying pressure gradients, channels experiencing sudden deceleration, and flow over periodic hills, with highly favorable results. Although not the focus of this study, the methodology here applies equally to the k–ω formulation and yields improved predictions of the mean flow in the viscous sublayer and buffer layer.

Multimodal assessment of mechanically induced transformation and damage in TRIP steels using X-ray nanotomography and pencil-beam diffraction tomography

A comprehensive multimodal measurement environment integrating X-ray computed tomography (CT) and special X-ray diffraction (XRD) using a collimated beam has been developed and was applied for the first time to investigate martensitic transformation and damage behaviour in transformation-induced plasticity (TRIP) steels. The transformation and damage behaviour of numerous γ grains were analysed individually to understand the behaviours and to identify the factors controlling these processes.A transition was observed from a stress-assisted transformation in the early loading phase, controlled by mechanical driving forces, to a strain-induced transformation controlled by dislocation density. A characteristic reversal in the transformation rate was observed whereby γ grains with a crystallographic orientation that initially undergo rapid transformation due to high mechanical driving force underwent a sudden deceleration of transformation. Conversely, γ grains with an orientation that initially underwent gradual transformation rapidly completed transformation. The preceding hard phase inhibited the transformation of neighbouring γ grains through the triple effects of reducing stress triaxiality, relaxing stresses and constraining plastic deformation.The growth of pre-existing voids was modest, and material damage was dominated by voids that were newly formed during deformation from α′ grains. The initiation and progress of damage were influenced by the combined effects of the γ grain morphology and their interaction with surrounding α′ grains. The complexity of the local morphology governed the martensitic transformation and damage behaviour, and geometrical parameters were identified as highly sensitive indicators for describing this phenomenon. Finally, industrial measures to design damage-resistant TRIP steels based on these research findings are discussed.

Adaptive Curve Passing Control in Autonomous Vehicles with Integrated Dynamics and Camera-Based Radius Estimation

Autonomous vehicles frequently encounter performance degradation during high-speed cornering due to excessive speed and lateral acceleration, potentially leading to collisions or rollovers. This paper proposes a novel curve-passing control approach that first estimates the curve radius and then controls the steer and speed for smooth and comfortable handling. In particular, the curve radius is innovatively estimated using a combination of a camera-based lane detection model and a steering wheel dynamic model. The curve-passing control approach is validated on high-speed ramps and curves, demonstrating its robustness and intelligence to adapt to dynamic changes in curve curvature. The model effectively prevents vehicles from entering curves at dangerously high speeds from straight roads and mitigates sudden accelerations or decelerations when entering curves. Experimental results indicate that the vehicle speed is reduced to around 50 km/h and the corresponding acceleration is −0.6 m/s2 upon entering curves with a minimum radius of 150 m. This demonstrates that the proposed control model can ensure a comfortable and safe driving experience by autonomously decelerating the vehicle before entering various types of curves.

Association between movement speed and instability catch kinematics and the differences between individuals with and without chronic low back pain

Studies reported the existence of instability catch (IC) during trunk flexion in patients with chronic low back pain (CLBP). However, different movement speeds can cause different neuromuscular demands resulting in altered kinematic patterns. In addition, kinematic characterization corresponding to clinical observation of IC is still limited. Therefore, this study aimed to determine (1) the association between movement speed and kinematic parameters representing IC during trunk flexion and (2) the differences in kinematic parameters between individuals with and without CLBP. Fifteen no low back pain (NoLBP) and 15 CLBP individuals were recruited. Inertial measurement units (IMU) were attached to T3, L1, and S2 spinous processes. Participants performed active trunk flexion while IMU data were simultaneously collected. Total trunk, lumbar, and pelvic mean angular velocity (T_MV, L_MV, and P_MV), as well as number of zero-crossings, peak-to-peak, and area of sudden deceleration and acceleration (Num, P2P, and Area), were derived. Pearson’s correlation tests were used to determine the association between T_MV and L_MV, P_MV, Num, P2P, and Area. An ANCOVA was performed to determine the difference in kinematic parameters between groups using movement speed as a covariate. Significant associations (P < 0.05) were found between movement speed and other kinematic parameters, except for Area. Results showed that L_MV significantly differed from the P_MV (P = 0.002) in the CLBP group, while a significant between-group difference (P = 0.037) was found in the P_MV. Additionally, significant between-group differences (P < 0.05) in P2P and Area were observed. The associations between movement speed and kinematic parameters suggest that movement speed changes can alter kinematic patterns. Therefore, clinicians may challenge lumbopelvic neuromuscular control by modifying movement speed to elicit greater change in kinematic patterns. In addition, the NoLBP group used shared lumbar and pelvic contributions, while the CLBP group used less pelvic contribution. Finally, P2P and Area appeared to offer the greatest sensitivity to differentiate between the groups. Overall, these findings may enhance the understanding of the mechanism underlying IC in CLBP.

The Effect of Repetitive Transcranial Magnetic Stimulation on Cognition in Diffuse Axonal Injury in a Rat Model.

Diffuse axonal injury (DAI) following sudden acceleration and deceleration can lead to cognitive function decline. Various treatments have been proposed. Repetitive transcranial magnetic stimulation (rTMS), a non-invasive stimulation technique, is a potential treatment for enhancing neuroplasticity in cases of brain injury. The therapeutic efficacy of rTMS on cognitive function remains unconfirmed. This study investigated the effects of rTMS and the underlying molecular biomechanisms using a rat model of DAI. Sprague-Dawley rats (n = 18) were randomly divided into two groups: one receiving rTMS after DAI and the other without brain stimulation. All rats were subjected to sudden acceleration and deceleration using a DAI modeling machine to induce damage. MRI was performed to confirm the DAI lesion. The experimental group received rTMS at a frequency of 1 Hz over the frontal cortex for 10 min daily for five days. To assess spatial memory, we conducted the Morris water maze (MWM) test one day post-brain damage and one day after the five-day intervention. A video tracking system recorded the escape latency. After post-MWM tests, all rats were euthanized, and their brain tissues, particularly from the hippocampus, were collected for immunohistochemistry and western blot analyses. The escape latency showed no difference on the MWM test after DAI, but a significant difference was observed after rTMS between the two groups. Immunohistochemistry and western blot analyses indicated increased expression of BDNF, VEGF, and MAP2 in the hippocampal brain tissue of the DAI-T group. In conclusion, rTMS improved cognitive function in the DAI rat model. The increased expression of BDNF, VEGF, and MAP2 in the DAI-T group supports the potential use of rTMS in treating cognitive impairments associated with DAI.

Ship Anomalous Behavior Detection in Port Waterways Based on Text Similarity and Kernel Density Estimation

The navigational safety of ships on waterways plays a crucial role in ensuring the operational efficiency of ports. Ship anomalous behavior detection is an important method of water traffic surveillance that can effectively identify abnormal ship behavior, such as sudden acceleration or deceleration. In order to detect potential anomalous ship behavior in real time, a method for ship anomalous behavior detection in waterways is proposed based on text similarity and kernel density estimation. Under the assumption of known traffic patterns entering and leaving the port, this method can identify ship behaviors that violate traffic patterns in real time. Firstly, kernel density estimation is applied to construct a traffic pattern density model for ship trajectories entering and leaving the port, used to estimate the density values of ship motion states. Simultaneously, a semantic transformation method is used to convert traffic pattern trajectory into pattern trajectory text, which is used to identify the ship’s traffic pattern. Subsequently, the historical trajectory data of the target ship are transformed into textual trajectories, and text similarity is used to identify ship inbound and outbound traffic patterns. Furthermore, the constructed traffic pattern density model is used to estimate real-time density values of the state of ship motion, and the trajectory points that exceed the threshold of the anomaly factor are marked as anomalies. Finally, the effectiveness of the proposed method is validated using simulation data, and the results indicate an accuracy of more than 90% for the comprehensive detection of anomalous behavior. This study, approaching the detection of potential ship anomalous behavior from the perspective of port traffic patterns, enriches the methods of ship anomalous behavior detection in port waterways.

Car Accident Detection and Alert System Leveraging Deep Learning

This paper presents a novel approach to address the critical issue of car accidents through an advanced Accident Detection and Alerting System (ADAS) empowered by cutting-edge technologies in deep learning, computer vision, and communication APIs. With road safety being a paramount concern worldwide, timely detection of car accidents followed by swift alerting mechanisms can significantly reduce the severity of accidents and save lives. The proposed system architecture incorporates the Ultralytics framework, Convolutional Neural Networks (CNN), You Only Look Once version 8 (YOLOv8), OpenCV, and Twilio API. Initially, YOLOv8 is employed to perform real-time detection of vehicles and potential accident scenarios from live video feeds obtained from roadside cameras or vehicle-mounted cameras. Subsequently, CNN models are utilized for comprehensive analysis of detected objects to discern potential accidents based on predetermined criteria, including sudden deceleration, collision patterns, and anomalous vehicle movements. Upon successful detection of an accident, the system triggers an alert mechanism via the Twilio API, which delivers instantaneous notifications to emergency services, nearby medical facilities, and designated contacts of the involved parties. These alerts are enriched with crucial details such as the accident location, vehicle types involved, and severity assessments derived from CNN analyses. Furthermore, the integration of OpenCV facilitates preprocessing tasks such as noise reduction, image enhancement, and object tracking, thereby enhancing the system's robustness and accuracy in diverse environmental conditions and lighting scenarios. The proposed ADAS offers numerous benefits, including real-time accident detection, automated alerting of emergency services, and minimized response times, ultimately contributing to mitigating the severity of accidents and preserving lives. By harnessing the power of deep learning algorithms, advanced computer vision techniques, and seamless communication APIs, this system represents a significant step towards fostering safer road environments within smart cities and transportation networks.

Pulsar glitches from quantum vortex networks

Neutron stars or pulsars are very rapidly rotating compact stars with extremely high density. One of the unsolved long-standing problems of these enigmatic celestial bodies is the origin of pulsars’ glitches, i.e., the sudden rapid deceleration in the rotation speed of neutron stars. Although many glitch events have been reported, there is no consensus on the microscopic mechanism responsible for them. One of the important characterizations of the glitches is the scaling law P(E)∼E-α\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$P(E) \\sim E^{-\\alpha }$$\\end{document} of the probability distribution for a glitch with energy E. Here, we reanalyse the accumulated up-to-date observation data to obtain the exponent α≈0.88\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\alpha \\approx 0.88$$\\end{document} for the scaling law, and propose a simple microscopic model that naturally deduces this scaling law without any free parameters. Our model explains the appearance of these glitches in terms of the presence of quantum vortex networks arising at the interface of two different kinds of superfluids in the core of neutron stars; a p-wave neutron superfluid in the inner core which interfaces with the s-wave neutron superfluid in the outer core, where each integer vortex in the s-wave superfluid connects to two half-quantized vortices in the p-wave superfluid through structures called “boojums”.

Military Injuries: Helocasting Accident.

Accidental injuries sustained during helocasting remain unexamined. Conditions prevalent during a helocasting exercise performed at a still water body and the resulting casualties were analyzed. Despatch from greater-than-ideal height (>7 m) and speed (>5 knots) causes a high-velocity impact of the body with water in a non-aerodynamic configuration, exposing maximal body area at penetration. The brunt is borne by the torso/back, specifically, the lungs, ribs, and posterior aspect of the spine. The injuries result from direct trauma, sudden deceleration, barotrauma, and hyperflexion. Computerized tomography (CT) is the imaging of choice in the assessment of these injuries. Prompt evacuation to an equipped center, whilst stabilizing the spine in the suspected, proves pivotal to the outcome. Adverse slamming dynamics cause accidental injuries in helocasting. Thorax and spine are predominantly traumatized, both directly and indirectly, and are assessed best using CT. Timely spine stabilization and evacuation prove vital. Accurate assessment of height/speed and adherence to their ideal limits, at despatch, may avert such injuries.

Experimental Transient Process Analysis of Micro-Turbojet Aviation Engines: Comparing the Effects of Diesel and Kerosene Fuels at Different Ambient Temperatures

In this paper, we investigate the impact of diesel and kerosene on the transient processes occurring in a micro-turbojet aviation engine. The experiments were conducted under two distinct ambient temperature conditions, 0 and 20 °C. Specifically, we analyzed the starting phase of the micro-engine while operating with kerosene and diesel at both ambient temperature settings. Comparative graphs were generated, and the starting time was meticulously examined. Subsequently, we constructed performance maps for the engine using both fuels and across the two ambient temperature scenarios. We then executed a transient process, comprising sudden acceleration and deceleration, under the aforementioned ambient temperature conditions and with both fuels. The fluctuations in temperature within the combustion chamber, thrust force, and fuel consumption are presented for both rapid acceleration and deceleration events. Furthermore, we conducted comparisons between the thrust force, fuel flow rate, combustion chamber temperature, and specific fuel consumption for the two fuels tested and under the two ambient temperature conditions, both during idle and at higher engine regimes. In the idle regime at 0 °C, the kerosene flow is about 0.78% higher than diesel, with the kerosene thrust approximately 1.92% greater. At 20 °C, the kerosene consumption rises by roughly 5.56% compared to diesel, while the thrust increases by about 1.38%. It was observed that at the maximum operating regime, at 0 °C, the kerosene flow exceeds diesel by around 6%, with the kerosene thrust slightly higher, by about 0.63%. At 20 °C, the kerosene consumption rises by roughly 13.19% compared to diesel, while the thrust increases by about 5.91%. In higher regimes, the kerosene consumption surpasses diesel, but the thrust increase is not significant. Thus, diesel’s use as a fuel for the microturbo engine is justified due to its lower consumption at both 0 °C and 20 °C.

Lab Scale Investigation of Gaseous Emissions, Performance and Stability of an Aviation Turbo-Engine While Running on Biodiesel Based Sustainable Aviation Fuel

The research experimentally examines the viability of biodiesel obtained from pork fat (BP) as a sustainable aviation fuel (SAF) when mixed with kerosene (Ke)—Jet-A aviation fuel + 5% Aeroshell 500 oil. Various blends of biodiesel and kerosene (10, 20, and 30% vol. of BP added in Ke) were subjected to testing in an aviation micro turbo-engine under different operational states: idle, cruise, and maximum power. During the tests, monitoring of engine parameters such as burning temperature, fuel consumption, and thrust force was conducted. The study also encompassed the calculation of crucial performance indicators like burning efficiency, thermal efficiency, and specific consumption for all fuel blends under maximum power conditions. Combustion temperatures ahead of the turbines rise with an increase in biodiesel concentration, particularly in the idle regime, without compromising engine integrity. However, for regimes 2 and 3, the temperature in front of the turbine decreases with rising biodiesel concentration, accompanied by an increase in fuel flow rate. This phenomenon is reflected in the elevated specific consumption. Notably, for regime 3, there is a noticeable rise in specific consumption, starting from S = 0.0264 kg/Nh when the turbo-engine operates solely with Ke, to S = 0.0266 kg/Nh for Ke + 10% BP, S = 0.0269 kg/Nh for Ke + 20% BP, and S = 0.0275 kg/Nh for Ke + 30% BP. Physical–chemical properties of the blends, encompassing density, viscosity, flash point, and calorific power, were determined. Furthermore, elemental analysis and FTIR were used for chemical composition determination. The amount of CO2 produced during the stoichiometric combustion reaction with air showed variations. Initially, when using only Ke, it amounted to 3.12 kg per kilogram of fuel. Upon adding 10% BP, this value decreased to 3.09 kg, further reducing to 3.05 kg with 20% BP. The lowest value was observed with 30% BP, reaching 3.04 kg. Experimental assessments were performed on the Jet Cat P80® micro-turbo-engine, covering aspects such as starting procedures, sudden acceleration, sudden deceleration, and emissions of pollutants (NOx, CO, and SO2) during several engine operational phases. The outcomes reveal that the examined fuel blends exhibited stable engine performance across all tested conditions. This indicates that these blends hold promise as sustainable aviation fuels for micro turbo-engines, presenting benefits in terms of diminished pollution and a more ecologically sound raw material base for fuel production.

A Manipulator Pose Planning Algorithm Based on Matrix Information Geometry

In an automatic ultrasonic testing system constituted by an ultrasonic probe and a six-axis manipulator, the manipulator needs to run from a static state to the target velocity. To prevent equipment damage caused by sudden acceleration or deceleration, it is necessary to plan the position and pose of the end effector of the manipulator at each detected point. In this manuscript, an algorithm for planning the position and pose of the manipulator is proposed based on the information geometry structure of special orthogonal groups. As the linear operation of the orthogonal matrix corresponding to the manipulator pose is not closed, the manipulator pose at each detected point was calculated using the straightness of the Lie algebra of the special orthogonal group. The matrix information geometry algorithm enabled not only the manipulator to accelerate and decelerate uniformly along the detection trajectory, but also the angular acceleration of the end effector to accelerate uniformly at first, then keep a uniform velocity, and finally decelerate uniformly. The platform motion experiments with the Turin TKB070S six-axis manipulator are carried out to verify the effectiveness of the matrix information geometry algorithm for planning the pose of the manipulator.

Characteristic stages of heat transfer for supercritical CO2 flowing downward in a vertical tube under cooling conditions

The present work investigates the streamwise development stages of supercritical carbon dioxide downward cooling flow and heat transfer in a vertical tube through direct numerical simulation. The tube has a diameter of 2 mm and an inlet Reynolds number of 5400. The formation mechanisms of flow stages are investigated by studying turbulence statistics and momentum balance analysis. The results indicate that there are three distinct development stages for both flow and heat transfer, including a deterioration stage, a recovery stage, and a re-deterioration stage. The last stage only occurs in some flows and is less well-understood than the first two stages. In the flow deterioration stage, both the weakening of the external buoyancy effect (due to mean flow modification) and the negative influence of the structural effect (direct interactions between the fluctuating density and the thermal field) contribute to flow laminarization. In the flow recovery stage, the increase in buoyancy further reduces the streamwise pressure gradient, increasing near-wall velocity. At this point, the velocity profile becomes M-shaped, accompanied by turbulence regeneration. As the bulk temperature drops around pseudo-critical temperature, a sudden global deceleration occurs due to the increased fluid density, which weakens both the external and structural effects and causes a decrease in turbulence, and hence the flow and heat transfer enter the re-deterioration stage. Moreover, the inlet temperature and wall heat flux have a pronounced effect on the development stages, which affects the duration distance of the deterioration stage and the corresponding enthalpy value at the beginning of the recovery stage.

A spatio-temporal deep learning approach to simulating conflict risk propagation on freeways with trajectory data

On freeways, sudden deceleration or lane-changing by vehicles can trigger conflict risk that propagates backward in a specific pattern. Simulating this pattern of conflict risk propagation can not only help prevent crashes but is also vital for the deployment of advanced vehicle technologies. However, conflict risk propagation simulation (CRPS) on freeways is challenging due to the nuanced nature of the pattern, intricate spatio-temporal interdependencies among sequences and the high-resolution requirements. In this work, we introduce a conflict risk index to delineate potential conflict risk by aggregating various surrogate safety measures (SSMs) over time and space, and then propose a Spatio-Temporal Transformer Network (STTN) to simulate its propagation patterns. Multi-head attention mechanism and stacking layers enable the transformer to learn dynamic and hierarchical features in conflict risk sequences globally and locally. Two components, spatial and temporal learning transformers, are innovatively incorporated to extract and fuse these features, culminating in a fine-grained conflict risk inference. Comprehensive tests in real-world datasets verified the effectiveness of the STTN. Specifically, we employ three widely-recognized SSMs: Modified Time-To-Collision (MTTC), Proportion of Stopping Distance (PSD), and Deceleration Rate to Avoid a Collision (DRAC). These SSMs, gleaned from vehicle trajectories, are employed to delineate the conflict risk. Then, we conduct three comparative simulation tasks: MTTC-based model, PSD-based model, and DRAC-based model. Experimental results show that the PSD-based model exhibits a robust performance on all tasks, and is minimally affected by the durations of the simulation time, while the DRAC-based model more distinctly delineates the spatio-temporal conflict risk heterogeneity. Furthermore, we benchmark the STTN against three common state-of-the-art machine learning models across all simulation tasks. Results reveal that the STTN consistently surpassed these benchmark models (LSTM, CNN and ConvLSTM), suggesting the potential of the attention mechanism on the CRPS tasks. Our investigation offers crucial insights beneficial for traffic safety warning, advanced freeway management systems, and driver assistance systems, among others.

Acoustic effects of drone flights during sudden decelerations

With introduction of U-Spaces into European urban airspaces, unmanned aircraft traffic is expected to increase in the coming years. The growing number of unmanned aircraft, common known as drones, raises the question of the noise impact of these vehicles. So far, this new noise source has only been investigated acoustically in a rudimentary way. The German Environment Agency has, therefore, launched a measurement campaign with various small drone models to investigate acoustic effects. Since drone flights must be designed to be safe, a measurement campaign was conducted in which the models performed a full braking maneuver just before the measurement microphone. This is the case for collision avoidance or when strong gusts of wind occur. In addition, binaural measurements were recorded to provide psychoacoustic findings, as these parameters may be related to the degree of annoyance perceived by the public. In this paper, the measurements and results are presented. Noise levels of the overflights arecompared with noise levels of the deceleration maneuvers. In addition, psychoacoustic quantities such as loudness or sharpness are used and compared for both maneuvers. Finally, conclusions are drawn for the evaluation of drone noise, which can be used for future noise regulations or standardization.

A robust MPC approach with controller tuning for close following operation of virtually coupled train set

Virtual coupling, as an emerging concept in railways, expects successive trains in a virtually coupled train set (VCTS) to maintain a short following distance. However, this close following operation is still difficult to be achieved given uncertain resistances and nonlinear safety constraints. To solve this problem, this paper proposes a robust model predictive control (RMPC) approach for the close following operation of VCTS while satisfying a nonlinear safety constraint with relative braking principle. First, we construct a robust positively invariant set that bounds the tracking errors caused by uncertain resistances. Further, a semi-definite program-based controller tuning algorithm is proposed to reduce the following distance in the premise of the tightened constraint for robustly satisfying the nonlinear safety constraint. Then, by mathematically examining the future trajectories of successive trains, we create a terminal constraint set to ensure the recursive feasibility of the proposed RMPC. This closed-loop property guarantees the satisfaction of the safety constraint in any situation, even in the case of sudden deceleration of VCTS. Finally, numerical experiments are conducted to evaluate the following distance with respect to heterogeneous trains and verify the effectiveness of our approach. Experimental results demonstrate that the expected close following operation can be achieved while robustly satisfying the nonlinear safety constraint with uncertain resistances. Moreover, our approach further reduces the following distance in a VCTS by over 5%, compared with existing research.

No Need for an Extreme Jet Energy in the Black Hole X-Ray Binary MAXI J1348–630

We model interaction with the surrounding medium of the main discrete jet ejection in the accreting black hole binary MAXI J1348–630. The kinetic energy in the ejection of that jet was estimated before to be >1046 erg. That energy requires that the jet power was about 2 orders of magnitude above the limit corresponding to a magnetically arrested accretion onto a maximally rotating black hole. That large estimate was obtained by considering the initial ballistic jet propagation in a surrounding cavity followed by a sudden deceleration in interstellar medium under the assumption of its standard density of ∼1 cm−3. Such densities are likely in the surrounding of this source given its location in the Galactic plane. Here, we show that the estimate of the kinetic energy can be reduced to realistic values of ∼1044 erg by considering the presence of a transition layer with an exponential density growth separating the cavity and the interstellar medium. In that case, the jet is found to decelerate mostly in the transition layer, in regions with densities ≪1 cm−3, which strongly reduces the energy requirement. Still, the required jet masses are large, ruling out the presence of a significant number of electron–positron pairs.

Modeling the impact of lane-changing’s anticipation on car-following behavior

Lane-changing (LC) in congested traffic has been identified as a trigger for the sudden deceleration behavior of the new follower in the target lane, leading to severe traffic disturbances. Thus, investigating the response of the new follower to an LC maneuver is an important research topic in the literature. To date, numerous efforts have been devoted to understanding the impact of the lane changer on the new follower after the insertion, while less attention has been given to this influence during the pre-insertion stage (anticipation). Therefore, this paper aims to establish a new car-following (CF) model to capture the new follower’s driving behavior during anticipation. Specifically, we introduce an attention mechanism deviating from Newell’s CF rules to quantify the impact of anticipation. Then, we apply a neural network with an attention layer to estimate the attention mechanism and incorporate it into the Newell CF model, which yields a new CF model, denoted as CF_Attention. Using real-world trajectory data, we design three experiments and select three representative CF models to validate the CF_Attention. The results indicate that the CF_Attention outperforms the other models in predicting the new follower’s trajectory, which is not affected by the heterogeneous behavior of the new follower and the anticipation duration. Additionally, the CF_Attention is proven effective in capturing the speed-space relationship and the formation of oscillation. Finally, our transferability test suggests that the CF_Attention is promising for different locations and times without requiring retraining. The results of this study could advance the integration of the LC impact and CF behavior, and could be implemented into commercial traffic simulation programs to describe vehicle movements in traffic flow more accurately.

An eco-aware framework for AI-based analysis of contextually enriched automotive trip data

ABSTRACT The transport sector is one of the main contributors to gas emissions and the greenhouse effect. Mobility as a Service seeks to combine different means of transport (public transport, bicycles, cars, etc.) in an integrated way to solve this problem. Raising drivers’ awareness and advising them to drive more efficiently and economically can help reduce environmental impact. Ultimately, eco-driving can help save fuel, money and provide higher passenger satisfaction during the trip. The research uses a contextually enriched automotive data set collected via OBD, during passenger transportation in private cars. A multilevel analysis was conducted using a variety of approaches. With AI-based analysis, using unsupervised learning, we identified five groups of similarities as a function of speed and acceleration. The efficiency of each driver was examined and speed and acceleration/deceleration were noted as the most influential factors. Also, passengers’ perception of driving quality is negatively affected by sudden acceleration and deceleration. Passengers have a poor perception of the driving efficiency and additional efforts should be made to educate them about the environmental aspect of driving. Finally, the main contribution is the eco-aware framework, which covers and describes the entire process from data collection to analysis. The collected data are publicly available and include 110 trips represented by more than 65,000 data points.

The Role of Vortex Preconditioning in Influencing the Occurrence of Strong and Weak Sudden Stratospheric Warmings in ERA5 Reanalysis

Abstract The anomalous stratospheric state favoring the occurrence of sudden stratospheric warmings (SSWs) is usually referred to as vortex preconditioning. This study investigates the role of vortex preconditioning in triggering strong and weak SSWs by using ERA5 reanalysis data. Strong and weak SSWs are distinguished by the amplitude of zonal wind deceleration of sudden stratospheric deceleration events. The robust stratospheric anomalies before strong SSWs last longer compared to weak SSWs, accompanied by stronger amplification of stratospheric wave activity near the warming. The stratospheric anomalies before weak SSWs have two significant enhancement stages, with two processes of stratospheric wave amplification. Robust stratospheric anomalies generally appear before tropospheric wave events (TWEs) followed by the strong and weak SSWs, which are absent before TWEs without SSWs. Stratospheric meridional potential vorticity gradient events (SPVEs) are defined to represent the anomalous stratospheric state during vortex preconditioning. The SPVEs can effectively modulate the stratospheric upward wave activity. No strong lower-tropospheric wave forcing is seen for the composites of both strong and weak SSWs preceded by SPVEs. These SSWs account for about 59% of the total SSWs. Furthermore, about 23% of strong SSWs and 36% of weak SSWs are only preceded by SPVEs without TWEs, indicating the major role of vortex preconditioning in triggering these SSWs. The SPVEs can be caused by wave breaking in the surfzone or the enhanced polar vortex, while the SPVEs preceded by clear wave breaking may be more favorable to the occurrence of SSWs.