Car Model Research Articles

NUMERICAL STUDY OF THE EFFECT OF TAPER ANGLES AND TAPER LENGTH ON THE REDUCTION OF AERODYNAMIC DRAG

Aerodynamic drag is still one of the main challenges facing the automotive industry. The design engineers are trying to develop novel techniques that could help to control the flow and optimize the shape of a vehicle to make it able to increase the base pressure drag at the wake region. The pressure drag is one of the main contributors to the aerodynamic drag. In our investigation, we have dealt with a benchmark car model (Windsor geometry) and visualized its effect on the characteristic parameters of the flow. In order to maximize the reduction of aerodynamic drag of vehicles, a numerical study has been undertaken based upon the CFD analysis with an active flow control technique including tapering of the rear lower and upper body along the length and height of the Windsor geometry. Taper angles from 12° to 20° in 4° steps have been implemented on the upper and lower part of the body while a taper length has been implemented along with the height of the body sides for the two cases. In comparison to other studies, we have come to sufficient results that gave us a significant reduction of aerodynamic drag for all the cases that we have tested. Thus, our study showed that the aerodynamic drag reductions are maximized, which indicates the efficiency of the technique we have applied.

Quantity Knowledge Extraction and Search

Vinh Thinh Ho is an applied scientist at Amazon Development Center, working in Alexa AI-NLU team. He completed his PhD at Max Planck Institute for Informatics, under the supervision of Prof. Gerhard Weikum. His research broadly covers the area of semantic web, with main focuses on knowledge bases, quantity search, information extraction and retrieval, rule mining and NLP. This research conducted by Vinh Thinh Ho developed new methods for the extraction and search of quantity knowledge over web contents. Quantities are more than mere numbers. They represent measurements of various entities in the world, such as the heights of buildings, the timings of athletes, the energy efficiency of car models, or the energy production of power plants, all expressed in numbers with associated units. While modern search engines effectively support entity-centric searches and question answering, they struggle when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. These systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases also fail as quantities are poorly covered by state-of-the-art KBs. We developed new methods to overcome these limitations and advance the state-of-the-art on quantity knowledge extraction and search.

Optimization of Car Use Time for Different Maintenance and Repair Scenarios Based on Life Cycle Assessment

New propulsion technologies and the development of electromobility increase the environmental efficiency of the transport sector. However, the current structure of car fleets with a large number of old cars with worse environmental parameters undermines the benefits of technological progress in the automotive industry. Older cars may require a more extensive service, involving a thorough overhaul, repair or even replacement of many parts. The result of using additional resources is a deterioration of environmental performance. This study considers various maintenance and repair scenarios and focuses on the use phase using the example of the popular Ford Focus passenger car model. The life cycle optimization model was used for environmental assessment, which determines the optimal duration of the car use phase, accounting for detailed service information and the type and environmental efficiency class of the different fuel versions. This model can be an effective tool for car fleet management and car replacement policy. Detailed life cycle inventories of 40 Ford Focus II diesel and petrol cars that represent all maintenance and repairs are the inputs to the life cycle optimization model. The inventory data were provided by the Ford Service Center in Szczecin (Poland), which regularly serviced the cars with a mileage of up to 200,000 km. Inventory at other stages of the cars’ life cycles was modeled using Ecoinvent data. The results show that by considering these vehicle servicing aspects on the basis of more detailed data, impact assessments are better represented compared to other studies. Depending on the replacement policy, the contribution of vehicle maintenance and repair can account for almost half of the cumulative life cycle burdens.

Establishing gaze markers of perceptual load during multi-target visual search

Highly-automated technologies are increasingly incorporated into existing systems, for instance in advanced car models. Although highly automated modes permit non-driving activities (e.g. internet browsing), drivers are expected to reassume control upon a ‘take over’ signal from the automation. To assess a person’s readiness for takeover, non-invasive eye tracking can indicate their attentive state based on properties of their gaze. Perceptual load is a well-established determinant of attention and perception, however, the effects of perceptual load on a person’s ability to respond to a takeover signal and the related gaze indicators are not yet known. Here we examined how load-induced attentional state affects detection of a takeover-signal proxy, as well as the gaze properties that change with attentional state, in an ongoing task with no overt behaviour beyond eye movements (responding by lingering the gaze). Participants performed a multi-target visual search of either low perceptual load (shape targets) or high perceptual load (targets were two separate conjunctions of colour and shape), while also detecting occasional auditory tones (the proxy takeover signal). Across two experiments, we found that high perceptual load was associated with poorer search performance, slower detection of cross-modal stimuli, and longer fixation durations, while saccade amplitude did not consistently change with load. Using machine learning, we were able to predict the load condition from fixation duration alone. These results suggest monitoring fixation duration may be useful in the design of systems to track users’ attentional states and predict impaired user responses to stimuli outside of the focus of attention.

Superposition of populations in multi-objective evolutionary optimization of car suspensions

The design of the suspension model of road cars is extremely important for both driver comfort and safety. Road traffic injuries are currently the eighth leading cause of death worldwide and are predicted to become the seventh leading cause of death by 2030, according to the World Health Organization (WHO). Among other factors, road infrastructure is one of the most important factors to consider when designing the suspension model, especially as autonomous driving becomes more popular. In addition, there is a growing need to test and study the impact of road roughness and pavement type on autonomous vehicles (AVs). This work provides the following contributions: a platform for suspension system optimization–flexible, highly parameterizable and rich in applications. Our solution implements, tests and optimises the 2, 3 and 4 degrees of freedom (DOF) quarter-car models (QCM) with linear seat suspension and the 5 DOF half-car model (HCM), respectively, based on random road profiles generated according to ISO 8608 standards. In addition, many multi-objective optimization (MOO) algorithms have been implemented and applied in isolation to see their comparative performance (in terms of Pareto fronts, hypervolume, coverage, spread, and epsilon), but most importantly we have also introduced a meta-optimization technique by super-positioning Non-dominated Sorting Genetic Algorithm II and Strength Pareto Evolutionary Algorithm 2, where each of the algorithms, running concurrently, produces a percentage of the total number of individuals in a generation, percentage that is calculated according to the solution quality of the individuals in the previous generation. The experimental results were obtained by simulating the optimal configurations generated by the MOO heuristic algorithms on different categories of cars (small/medium). Finally, the Taguchi method and regression analysis were used to evaluate the effects of specific parameters on system responses such as sprung mass acceleration and displacement. The Taguchi simulation was performed to experimentally validate the results obtained by metaheuristic optimization methods.

An Optimal BDCNN ML Architecture for Car Make Model Prediction

We take on the challenge of classifying car photos, from the most general car type to the precise make, model, and year of the vehicle for a given input. Analyzing pre-existing datasets, we find that the CompCars-SV are a great place to begin our classification project. We demonstrate that convolutional neural networks can obtain a classification accuracy of more than 90% on the most difficult task. Due to a skewed mix between training and testing, this impressive result isn't really typical of how people do in the actual world. Using an ML system for car detection, we automatically generate a vehicle-tight bounding box for each picture, which we disseminate to the full dataset together with the existing (but limited) type-level annotation. We have designed and implemented car classification algorithms to analyze this car dataset, two of which take advantage of the hierarchical nature of car annotations. According to our research, a more precise classification of car type at a finer resolution now achieves an accuracy of 99.25%. It serves as a baseline benchmark for future research. Focusing on "vehicle" tasks, this work intends to bring attention to the vision community's lack of attention to these tasks compared to other objects. The important reason getting higher accuracy is extraction of binary descriptor (BD) feature using edge detection before training the CNN. This step reduced the size of the car dataset; hence network took less time to get trained. From the result outcomes shown it is clear that the presented network architecture having 31 layers of 2d convolutional layer, batch normalization, maxpool, ReLU, fully connected layer and Softmax classifier layer, has given higher accuracy. Numerous relevant car-related issues and solutions have yet to be carefully examined and researched, according to our findings. Car model categorization, model verification, and attribute prognosis are just a few examples of how the dataset might be put to use.

Sensitivity Analysis of a Driver’s Lumped Parameter Model in the Evaluation of Ride Comfort

The ride comfort provided by a vehicle to the driver and the passengers is an important feature, directly correlated to the technical characteristics of the suspension system of the vehicle. In the literature, several lumped-parameter models simulating the vehicle and the driver are proposed for the computational evaluation of ride comfort. In order to quantify ride comfort, other than the values of acceleration, metrics such as seat effective amplitude transmissibility (SEAT) and seat-to-head transmissibility (STHT) are utilized. In this paper, a quarter car model is coupled with a six-degree-of-freedom lumped-parameter model, consisting of the driver’s seat and the driver. A sensitivity analysis is performed on the values of the lumped parameters of the seated human body with regard to ride comfort in order to evaluate the effect of their accuracy relative to the ride comfort evaluation. The results of the sensitivity analysis revealed that the values of the mass, the stiffness and the damping parameters of the seated human model influence the ride-comfort metrics to a different extent. Furthermore, it was depicted that ride-comfort metrics were affected in different manners depending on the characteristics of the excitation of the vehicle, yet less than 10% Finally, the importance of the consideration of single-disturbance excitations in such sensitivity studies emerged.

The impact of modal shift on passenger car CO2 emissions in Tampere region

Reducing emissions from transport is an important part of climate change mitigation. In Finland, Tampere region has set ambitious modal shift targets to reduce transport emissions. This research analyses how much CO2 emissions from passenger cars would decrease in Tampere region, if the modal split targets would be achieved in 2030. Finnish car fleet model SALAMA is used to model car fleet development and calculate changes in the car fleet CO2 emissions to 2030. In addition, Finnish National Travel Survey data is used to analyse current mobility habits and to calculate, how much could annual passenger car mileage decrease, if the modal split target would be achieved. The results show that even though electrification of car fleet has a major effect on the emissions of the car fleet, drastic emission reduction requires also that the total passenger car mileage starts to decrease.

Effect of liquid sloshing in lateral and longitudinal directions on railway tank cars based on an improved equivalent model

Additional slosh forces and moments are generated owing to the movement of the liquid inside a partly filled tank car. The loads may have a great influence on the structure reliability and dynamic performance of the vehicle. An improved equivalent model of liquid sloshing is put forward to obtain the forces and moments of sloshing liquid acting on the tank during the lateral and longitudinal sloshing processes. An improved method is proposed to obtain the equivalent masses and stiffness for the improved equivalent model. The equivalent model is integrated into the multi-body dynamic model of a railway tank car with 68 degrees of freedom. Furthermore, the equivalent liquid-vehicle model is used to investigate the effects of fluid sloshing on dynamic characteristics. The influence of the filling ratio on the dynamic response of the tank car is discussed based on the forces and the corresponding moments between the tank car body and the liquid in different layers. The railway tank car is obviously influenced by the liquid sloshing during passing the curved and sloped tracks. The influence of liquid sloshing on the pitching motion of the tank is heavy if the tank is about half filled. The derailment coefficient and the wheel unloading rate are slightly influenced by the filling ratio of tank cars on curved tracks while the indices of the tank cars on sloped tracks are the greatest if the filling ratio is 40%.

The Contact Phase in Vehicle–Pedestrian Accident Reconstruction

The need for continuous research to refine the models used in forensic accident reconstruction appears with the development of new car models that satisfy consumer complaints. This paper analyzed a sub-sequence of car and pedestrian accidents from the perspective of the distance traveled by them in the contact phase with the aim of improving the information regarding the reconstruction of road accidents. This research included the analysis of some real tests with pedestrian dummies, as well as simulations of the impact between different classes of vehicles and pedestrians in two different walking positions. Specialized software was used with complex multibody models of pedestrians, modifying the speed and deceleration parameters of the car at the time of the collision. For pedestrian characteristics, the friction coefficients of the ground, car and its mass were modified. The research results highlight the differences between the bilinear models used in accident reconstruction and the proposed study. They can also be used to determine the distance traveled by the vehicle in the first phase of a collision with pedestrians.

Automobile technology and tradeoffs: How does automobile footprint affect its fuel economy?

It is generally understood that automakers face a tradeoff between fuel economy and other automobile attributes, e.g., size and horsepower. Since the current fuel economy standards are set based on a vehicle’s footprint, we analyze the tradeoffs involved paying special attention to the relationship between fuel economy and footprint. In line with previous studies in the literature, we estimate the technology frontier using automobile specifications data for all car models sold in the U.S. market from 2005 to 2014. We show that while a tradeoff exists between miles per gallon (mpg) and other automobile attributes, automobile footprint has a positive impact on mpg controlling for other vehicle attributes. Taken together, our results imply that the current footprint-based CAFE standard treats larger cars more favorably, creating incentives for carmakers to substitute across models.

Analysis of Factors Associated with Highway Personal Car and Truck Run-Off-Road Crashes: Decision Tree and Mixed Logit Model with Heterogeneity in Means and Variances Approaches

Among several approaches to analyzing crash research, the use of machine learning and econometric analysis has found potential in the analysis. This study aims to empirically examine factors influencing the single-vehicle crash for personal cars and trucks using decision trees (DT) and mixed binary logit with heterogeneity in means and variances (RPBLHMV) and compare model accuracy. The data in this study were obtained from the Department of Highway during 2011–2017, and the results indicated that the RPBLHMV was superior due to its higher overall prediction accuracy, sensitivity, and specificity values when compared to the DT model. According to the RPBLHMV results, car models showed that injury severity was associated with driver gender, seat belt, mount the island, defect equipment, and safety equipment. For the truck model, it was found that crashes located at intersections or medians, mounts on the island, and safety equipment have a significant influence on injury severity. DT results also showed that running off-road and hitting safety equipment can reduce the risk of death for car and truck drivers. This finding can illustrate the difference causing the dependent variable in each model. The RPBLHMV showed the ability to capture random parameters and unobserved heterogeneity. But DT can be easily used to provide variable importance and show which factor has the most significance by sequencing. Each model has advantages and disadvantages. The study findings can give relevant authorities choices for measures and policy improvement based on two analysis methods in accordance with their policy design. Therefore, whether advocating road safety or improving policy measures, the use of appropriate methods can increase operational efficiency.

Round and round in circles—shifting relevance structures as students discuss acceleration and forces during circular motion in a vertical plane

Working out the relations between the forces involved in circular motion in a vertical plane can be challenging for first-year students, as illustrated in this analysis of a 30 min group discussion of a textbook problem where a remote-control model car moves with constant speed inside a cylinder. The analysis includes timelines of semiotic resources used, as well as of topics brought up by individual students. Questions from the students include: what is that force you drew on the paper? Does it act on the car or on the wall? What keeps the car from falling down? The normal force and the ‘centripetal force’ both point to the center—does it mean they are the same? Is it only a gravitational force at the top? Does the normal force at the bottom just cancel gravity or does it need to be larger? What is ‘normal’ about the normal force? Arriving at the correct numerical result is insufficient evidence for student understanding of forces in circular motion! Can students with fragmentary understanding bring their pieces together to solve the puzzle? From the timelines, we can identify a few critical moments where the discussion changes focus. This happens when one of the students in the group introduces a new dimension of variation, e.g. a reminder about the force of gravity, a free-body diagram drawn, as well as diagrams drawn in other parts of the circle than the top or bottom, where the centripetal and normal forces are no longer in the same direction. Embodied experiences are invoked, but only at a very late stage in the discussion. For teachers, an awareness of the different ways students use terms and think about the forces can be a guide to offering a larger variation in the interventions, as well as in problems assigned.

Holistic pedestrian safety assessment for average males and females.

An integrated assessment framework that enables holistic safety evaluations addressing vulnerable road users (VRU) is introduced and applied in the current study. The developed method enables consideration of both active and passive safety measures and distributions of real-world crash scenario parameters. The likelihood of a specific virtual testing scenario occurring in real life has been derived from accident databases scaled to European level. Based on pre-crash simulations, it is determined how likely it is that scenarios could be avoided by a specific Autonomous Emergency Braking (AEB) system. For the unavoidable cases, probabilities for specific collision scenarios are determined, and the injury risk for these is determined, subsequently, from in-crash simulations with the VIVA+ Human Body Models combined with the created metamodel for an average male and female model. The integrated assessment framework was applied for the holistic assessment of car-related pedestrian protection using a generic car model to assess the safety benefits of a generic AEB system combined with current passive safety structures. In total, 61,914 virtual testing scenarios have been derived from the different car-pedestrian cases based on real-world crash scenario parameters. Considering the occurrence probability of the virtual testing scenarios, by implementing an AEB, a total crash risk reduction of 81.70% was achieved based on pre-crash simulations. It was shown that 50 in-crash simulations per load case are sufficient to create a metamodel for injury prediction. For the in-crash simulations with the generic vehicle, it was also shown that the injury risk can be reduced by implementing an AEB, as compared to the baseline scenarios. Moreover, as seen in the unavoidable cases, the injury risk for the average male and female is the same for brain injuries and femoral shaft fractures. The average male has a higher risk of skull fractures and fractures of more than three ribs compared to the average female. The average female has a higher risk of proximal femoral fractures than the average male. A novel methodology was developed which allows for movement away from the exclusive use of standard-load case assessments, thus helping to bridge the gap between active and passive safety evaluations.

Numerical Simulation of Aerodynamic Characteristics of Electric Vehicles with Battery Packs Mounted on Chassis

Aerodynamic characteristics are of great significance to the fuel economy and handling the stability of electric vehicles. The battery pack of electric vehicles has a huge structure and is usually arranged in the chassis area of the vehicle, which inevitably occupies the space at the bottom of the vehicle and affects the aerodynamic characteristics of the vehicle. To study the effect of the power battery pack installed in the chassis on the aerodynamics characteristics of the electric vehicle, the Computational Fluid Dynamics (CFD) method is used to study the flow and pressure fields of the SAE (Society of Automotive Engineers) hierarchical car model with battery packs mounted on chassis. The influence of the structure parameters of the battery pack on the automobile’s aerodynamics are also analyzed in detail. Based on the simulation results, it can be seen that the battery pack installed on the chassis has a great impact on the flow and pressure field at the bottom and tail of the vehicle, causing the drag coefficient and lift coefficient to increase. The structural parameters of the battery pack have contradictory effects on the drag and lift coefficients. As the length of the battery pack increases, the drag coefficient decreases, and the lift coefficient increases. As the battery pack width and height increase, the drag coefficient increases, and the lift coefficient decreases. The research results provide a reference for the optimization of the aerodynamic characteristics of electric vehicles with battery packs mounted on chassis.

AUTOMATION OF CALCULATING THE POSITION OF THE BOUNDARY COLUMN BETWEEN ADJACENT TRACKS OF DIFFERENT DESIGNS

The purpose of the study is to develop appropriate algorithms and procedures used in simulating the movement of a model car along adjacent tracks. These algorithms will automate the process of calculating the position of the boundary column between adjacent tracks of different designs with a given accuracy and compliance with traffic safety requirements. Scientific methods. In the course of the study, simulation methods were used to model the movement of the design car along the adjacent tracks. Analytical geometry methods were used to determine the main geometric characteristics of the car in terms of its relative position relative to the track axis. Results. The following conclusions were obtained as a result of the research. The multivariability of the design of adjacent tracks in the plan does not make it possible to create a universal analytical expression for determining the position of the boundary column. Existing methods for calculating the position of the boundary post provide for the maximum spread of the dimension in curved sections, which results in an excessive distance between the boundary post and the switch and inefficient use of the useful track length. The developed algorithms and procedures for determining the position of the boundary column are universal and allow automating the process of calculating the position of the boundary column between adjacent tracks of different designs in the plan, even when technical standards or parameters of the design cars change. In this case, only the initial data for the calculation need to be changed. Scientific novelty. The scientific novelty lies in the development of algorithms and procedures for formalizing the design of adjacent tracks and simulating the movement of design rolling stock on them. Practical significance. The developed algorithms and procedures can be implemented in the form of a software package for automated calculation of the position of the boundary column with high accuracy. Also, the obtained results of calculations can be used in the analysis and creation of an analytical description of the position of the boundary column for complex structures of adjacent tracks.

Study on the Influence of Synthesized Nano Ferrite Powder and Micron Ferrite Powder on Damping of a Single Degree of Freedom System

Magneto Rheological Fluid (MRF) damper has been opted as a promising semi active element of advanced suspension system. This work focuses on the damping nature of magnetorheological fluid under the influence of a magnetic field strength, size and shape of the of the iron oxide particles. A reduced scale single degree of freedom quarter car model with two stage magnetorheological damper, viscosity measurement setup, synthesized and commercially available Fe2O3 powders are used for the experimental study. An air cooled electro-dynamic shaker, Data acquisition system, accelerometers and the Lab view software acquires and analyzes the response of the quarter car model. X-ray Diffraction and Scanning electron microscopy characterize the morphology of iron oxide particles. The vertical acceleration, the displacement, the transmissibility ratio and the damping characteristics are analyzed based on the response of the top plate at different frequencies of base excitation. The results show that the magnetorheological fluid made with nano iron oxide powder has better damping characteristics than that of micron sized iron oxide powder

Vehicle’s Turning Time Span Estimation Based on Linear and Non-linear Computational Turning Model

The time span of a vehicle making turns, which is from the time a turn starts to the time the turn ends, are the basis of determination of essential data of vehicle motion like slip angles, forces on tires, except the vehicle is in an ideal one-dimensional motion. When the road condition is sensed by sensors on vehicle’s driver assistance systems, how to take a turn is the next step that should be calculated. After the turn starts, the end time of the turn should be adjusted in real time to remedy the imperfection digital simulation of the road sensed by the sensors. This paper compares two turning models and proposes an algorithm to adjust the end time of the turning time span during the turning motion of the vehicle. Subsequently, the algorithm is applied to two classic models of car, BMW M3 and Chevrolet Cavalier in four common motions: U-turn, lane change, sharp U-turn and sharp lane change. The algorithm proposed can be applied to every scenario that should take use of the turning time length, including the mathematical model of crossings, traffic flow of rush hours, or simply estimate the time span in unmanned vehicle control systems.

РАСЧЕТНО-ЭКСПЕРИМЕНТАЛЬНАЯ ОЦЕНКА ПРОЧНОСТИ ПАССАЖИРСКОГО ЭКСКУРСИОННОГО ОБЗОРНОГО ВАГОНА МОДЕЛИ 61-941

Experiment-calculated evaluation of the strength of the load-bearing body metal structure of a passenger observation car model 61-941 is done. The car is designed for tourist and sightseeing passenger trips. The project is created on the basis of a serial passenger car model 61-4179 as part of the capital repairs by Tambov Railway Car Repair Plant VRM. In accordance with the design andengineering specification, the body of the base car was significantly modernized, new planning solutions were applied for the internal equipment of the car, the frame of the car body was strengthened. Some theoretical studies using the finite element method were carried out to assess the compliance of the observation car model 61-941 with regulatory requirements. The strength and stability of the body metal structure is evaluated. At the second stage, full-scale tests of the car were carried out in static and dynamic conditions with longitudinal collisions. Additional attention is paid to the attachment points of passenger seats to the elements of the car body. The analysis of the results of theoretical and experimental studies shows their satisfactory compliance with the requirements of regulatory documents required for passenger car bodies.

ОБОСНОВАНИЕ РАЦИОНАЛЬНОЙ КОНСТРУКЦИИ СКОЛЬЗУНОВ ТЕЛЕЖКИ ПАССАЖИРСКОГО ВАГОНА

At the first stage, a detailed dynamic solid-state model of a passenger car is developed which has a bolsterless truck. The dynamic model is a system of absolutely rigid bodies interconnected by elastically dissipative linear and nonlinear force elements, hinges and contact forces. The adequacy of the car dynamic model and the reliability of the results obtained are evaluated the results of running tests. Based on the developed solid-state dynamic model, a comprehensive study of the effect of the friction factor on the guide block support surfaces on the safety and dynamic performance of a passenger car under various modes of movement along the track, taking into account irregularities is carried out. As a result of modeling, a range of rational values of friction moments on the support guide blocks is obtained. The composition of the composite material for the support surfaces of the truck guide blocks is selected using neurocomputer modeling methods, the manufacture of the proposed composite material is tested, experimental studies of the characteristics of composite elements confirming their properties and safety were carried out.