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Gotowe bibliografie tematyczne / Sideslipping

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Gotowa bibliografia na temat „Sideslipping”

Autor: Grafiati

Data publikacji: 2 czerwca 2025

Data aktualizacji: 22 czerwca 2025

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Artykuły w czasopismach na temat "Sideslipping"

1

Li, Zhen, Muneshi Mitsuoka, Eiji Inoue, Takashi Okayasu, Yasumaru Hirai, and Zhongxiang Zhu. "Prediction of Tractor Sideslipping Behavior Using a Quasi–static Model." Journal of the Faculty of Agriculture, Kyushu University 60, no. 1 (2015): 215–18. http://dx.doi.org/10.5109/1526314.

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2

Zang, Xiao Jie, Zhi Dong Zhang, and Chang Liu. "Mechanism Modeling and Analysis of a Small Unmanned Vehicles Using Vortex Lattice Method." Applied Mechanics and Materials 496-500 (January 2014): 1068–72. http://dx.doi.org/10.4028/www.scientific.net/amm.496-500.1068.

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Streszczenie:

For a certain type of small unmanned aerial vehicles (SUAV), using the vortex lattice method (VLM) estimated the aerodynamic derivatives, establishing linear small-perturbation equations based on level and non-sideslipping flight. Analyzed the static stability and mode characteristics based on the dimensionless derivative and matlab simulation. Early in the design, by mechanism modeling and performance analysis for the UAV through pneumatic calculations could get a lot of valuable reference data of the UAV. This has some practical significance for system performance analysis and flight control system design.

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3

Ou, Jian, Xiaolong Cheng, and Pengju Zhang. "Research on Trajectory Prediction Based on Front Vehicle Sideslip Recognition." World Electric Vehicle Journal 16, no. 4 (2025): 241. https://doi.org/10.3390/wevj16040241.

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In order to solve the problem of emergency collision avoidance of autonomous vehicles when the front vehicle is unstable and sliding under high-speed conditions, a research method for the state recognition of the front side-skid vehicle and the trajectory prediction of the front side-skid vehicle was proposed. By extracting the key features of the vehicle in front of the vehicle in danger of sliding to build a skidding recognition model of the vehicle in front, a skidding recognition strategy of the vehicle in front was designed based on the extracted skidding feature indexes to judge the skidding state of the vehicle in front. The state quantity of the sliding vehicle in front is selected, and the constant rotation rate and acceleration model (CTRA) is established to predict the trajectory of the sliding vehicle in front in a short time. Considering the simplified assumptions of the model and the noise in the process of sensor perception information, the Unscented Kalman Filter (UKF) is used to deal with the uncertainty in the trajectory prediction process, the possible position and covariance of the front sideslipping vehicle are calculated, and the possible future area of the front sideslipping vehicle is estimated under the condition of a probability of 0.9. Through the established Carsim and Simulink co-simulation platform, the effectiveness of the front vehicle skidding state recognition strategy and the accuracy of the trajectory prediction of the sliding vehicle are verified under the condition of high speed and low attachment.

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4

Verhaagen, Nick G., and Steven H. J. Naarding. "Experimental and numerical investigation of vortex flow over a sideslipping delta wing." Journal of Aircraft 26, no. 11 (1989): 971–78. http://dx.doi.org/10.2514/3.45869.

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5

Hebbar, Sheshagiri K., Max F. Platzer, and Chang Ho Kim. "Experimental investigation of vortex breakdown over a sideslipping canard-configured aircraft model." Journal of Aircraft 31, no. 4 (1994): 998–1001. http://dx.doi.org/10.2514/3.46598.

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6

Žuraulis, Vidas, and Edgar Sokolovskij. "Vehicle Velocity Relation to Slipping Trajectory Change: An Option for Traffic Accident Reconstruction." PROMET - Traffic&Transportation 30, no. 4 (2018): 395–406. http://dx.doi.org/10.7307/ptt.v30i4.2720.

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In this paper, the relation of the velocity of a vehicle in the slip mode to the parameters of the tire marks on the road surface is examined. During traffic accident reconstructions, the initial velocity of a sideslipping vehicle is established according to the tire mark trajectory radius, and calculations highly depend on the directly measured parameters of the tire marks, in particular cases known as yaw marks. In this work, a developed and experimentally validated 14-degree-of-freedom mathematical model of a vehicle is used for an investigation of the relation between velocity and trajectories. The dependence of initial vehicle velocity on tire yaw mark length and trajectory radius was found as a characteristic relation. Hence, after approximation of the permanent slipping part by a polynomial, the parameters of the latter were related to vehicle velocity. The dependences were established by specific experimental tests and computer-aided simulation of the developed model.

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7

Kumar Singh, Siddhanta, and Anand Sharma. "Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics." Bulletin of Electrical Engineering and Informatics 14, no. 3 (2025): 2188–97. https://doi.org/10.11591/eei.v14i3.9398.

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This research work uses machine learning (ML) approaches to classify on-board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g-force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swerving, and sideslipping). The RF classifier achieved the highest accuracy, which reinforces the strength of the chosen models. The inclusion of comparisons with other techniques supports arguments about the model's performance. The related works section connects the references to the central topic by highlighting prior approaches and research studies related to OBD II and driver behaviour analysis. The goals of this study are improving the accuracy of driving behaviour classification, with implications for traffic safety, driver education, and insurance sectors.

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8

Li, Ming, Haicheng Zhang, Zhongyu Lin, Yanguo Sun, and Mingshui Li. "Experimental and numerical study on the local crosswind environment of a wide streamlined bridge deck equipped with wind barriers at different angles of attack." Physics of Fluids 36, no. 5 (2024). http://dx.doi.org/10.1063/5.0208748.

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In this study, the effects of wind barriers on the crosswind environment of vehicle safety on a long-span bridge with a streamlined bridge deck were investigated at different angles of attack (AOA). The profiles of the mean and the root mean square (RMS) of the wind pressure coefficients above different traffic lanes were obtained through synchronous pressure measurements. The mean pressure fields around the bridge decks were investigated by computational fluid dynamics (CFD) simulations. The crosswind environment of vehicle safety between the deck with railings and the deck with wind barriers was compared and analyzed in detail at different AOA. The results showed that the wind barriers produce a thicker separation shear layer above the bridge deck and result in a larger negative pressure region in comparison with the case with railings. This leads to a notable reduction in the mean wind pressure coefficients above the deck. The increase in the AOA enhances the above-mentioned reduction effect. It was also found that the wind barriers significantly increase the RMS of the wind pressure coefficients above the deck compared with the railings. The increase in the AOA inhibits this enhancement effect. The results of the equivalent mean pressure coefficients show that the wind barriers reduce the vehicle sideslipping risk more effectively than the vehicle overturning risk. It was also found that the effect of the wind barriers on the reduction of the overturning risk is more sensitive to the change in AOA than the effect on the reduction of the vehicle sideslipping risk.

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9

Siddhanta, Kumar Singh, and Sharma Anand. "Revving up insights: machine learning-based classification of OBD II data and driving behavior analysis using g-force metrics." May 19, 2025. https://doi.org/10.11591/eei.v14i3.9398.

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Streszczenie:

This research work uses machine learning (ML) approaches to classify on board diagnostics II (OBD II) data and g-force measures to provide a thorough analysis of driving behavior. The research paper effectively demonstrates the classification of driving behaviours using OBD II and g force data. Driving behaviours are analyzed by using ML algorithms such as random forest (RF), AdaBoost, and K-nearest neighbors (KNN). The analysis goes beyond a summary by discussing how OBD II data, g-force metrics, and the algorithms interrelate to classify ten distinct driving behaviors (e.g., weaving, swerving, and sideslipping). The RF classifier achieved the highest accuracy, which reinforces the strength of the chosen models. The inclusion of comparisons with other techniques supports arguments about the model's performance. The related works section connects the references to the central topic by highlighting prior approaches and research studies related to OBD II and driver behaviour analysis. The goals of this study are improving the accuracy of driving behaviour classification, with implications for traffic safety, driver education, and insurance sectors.

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Rozprawy doktorskie na temat "Sideslipping"

1

Kim, Chang Ho. "Flow visualization studies of a sideslipping, canard-configured X-31A-like fighter aircraft model." Thesis, Monterey, California. Naval Postgraduate School, 1991. http://hdl.handle.net/10945/26553.

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Książki na temat "Sideslipping"

1

Kim, Chang Ho. Flow visualization studies of a sideslipping, canard-configured X-31A-like fighter aircraft model. Naval Postgraduate School, 1991.

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Streszczenia konferencji na temat "Sideslipping"

1

HSU, C. H., and C. LIU. "Numerical study of vortical flow over a sideslipping delta wing." In 8th Applied Aerodynamics Conference. American Institute of Aeronautics and Astronautics, 1990. http://dx.doi.org/10.2514/6.1990-3001.

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