Racing Car

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Anita Clayburn Cross - One of the best experts on this subject based on the ideXlab platform.

  • winning by design the methods of gordon murray Racing Car designer
    Design Studies, 1996
    Co-Authors: Nigel Cross, Anita Clayburn Cross
    Abstract:

    Abstract This is a case study of the working methods of one particularly successful designer in a highly competitive design domain, Formula One Racing Car design. Gordon Murray was chief designer for the very successful Brabham and McLaren Racing Car teams in the 1970s and 1980s. His record of success is characterized by innovative breakthroughs, often arising as sudden illuminations, based on considering the task from first principles and from a systemic viewpoint. His working methods are highly personal, and include intensive use of drawings. Personality factors and team management abilities also appear to be relevant. There are some evident similarities with some other successful, innovative designers.

Jun Ni - One of the best experts on this subject based on the ideXlab platform.

  • The Suspension Optimization of FSAE Racing Car Based on Virtual Prototyping Technology
    Lecture Notes in Electrical Engineering, 2012
    Co-Authors: Jun Ni, Sizhong Chen, Zhicheng Wu
    Abstract:

    Research and/or Engineering Questions/Objective: One important design goal of Racing Car suspension is to keep the tire perpendicular to the ground which needs an accurate kinematic design of suspension. This paper details the simulation method of FSAE Racing Car based on MSC.ADAMS and VI-Motorsport, then the optimization of suspension kinematic characteristic could be conducted. Meanwhile, the paper will show the effect of suspension kinematic characteristic on lap time. Then the problem that the developing period of FSAE Racing Car is not long enough to conduct sample prototype test can be solved by the performance prediction and optimization by virtual prototyping technology. Methodology: The virtual prototyping model of BIT FSAE Racing Car and a certain race track were built by multi-body dynamics simulation software MSC.ADAMS and professional Racing Car simulation software VI-Motorsport. During the modelling process, the non-linear mechanical characteristic of tires was taken into consideration by the tire data provided by FSAE TTC, as well as the aerodynamic characteristics. The correctness of the model was verified by the “g–g” diagram collected by data logger in competition, then the further analysis and optimization could be conducted based on these. The comparison of lap time between the original race Car and the race Car after optimization was also conducted by simulation. Results: The comparison of lap time simulation results shows that the grip of tire during turn is increased after optimization of suspension, and the lap time is reduced. Limitations of this study: The simulation is based on multi-body dynamics simulation which assumes the chassis and suspension as rigid body. It brings some errors because the compliance characteristic of chassis and suspension is ignored. What does the paper offer that is new in the field in comparison to other works of the author: In previous technical papers in FSAE Racing Car field, there is no precise comparison between simulation results and actual data. But in this paper, the correctness of the model was verified by the comparison between simulation results and actual data collected in competition. And in this paper, the “g–g” diagram of FSAE Racing Car was first presented and discussed which is vital important of Racing Car performance. Conclusion: The simulation of FSAE Racing Car lap time based on MSC.ADAMS and VI-Motorsport has a high accuracy which could provide a possibility of performance prediction. It can shorten the developing period of FSAE Racing Car and improve the performance of FSAE Racing Car. Furthermore, the designers can adjust the kinematic design of suspension to meet different requirements in different race tracks by the proposed method.

  • Research on inverse dynamics of FSAE Racing Car based on recurrent neural network
    International Conference on Automatic Control and Artificial Intelligence (ACAI 2012), 2012
    Co-Authors: Jun Ni, Ya-tai Ji
    Abstract:

    In order to research on inverse dynamics of FSAE Racing Car. The multi-body dynamic model of a certain FSAE Racing Car with 47 DOF was built, and its accuracy was verified by experiment data. Taken double lane change condition for example, the nonlinear mapping relation between lateral acceleration, velocity and steering angle was built by recurrent Elman neural network. The identification result shows, the method to study on automobile inverse handling dynamics by Elman neural network is feasible which has a rapid learning speed and high accuracy. The method can accurately identify the handling input of Racing Car when it has ideal performance.

  • Research on Crosswind Stability of FSAE Racing Car with Rear Wing at Different Attack Angles
    Applied Mechanics and Materials, 2012
    Co-Authors: Jun Ni, Sizhong Chen, Da Feng, Xujie Wang
    Abstract:

    In order to analyze the performance of a certain FSAE Racing Car with rear wing at different attack angles by virtual prototyping technology. The multi-body model of a FSAE Racing Car which takes non-linear factors into consideration was built by applying ADAMS/Car. The correctness of the model is verified by comparison with the actual experiment result. By the simulation of the air resistance and lift characteristics of the rear wing, a feasible method to building the aerodynamic characteristics of the rear wing in multi-body model was proposed. Based on these, the crosswind stability of FSAE Racing Car with rear wing at different attack angles was analyzed, the result shows that the effect of crosswind is reduced with the increase of the attack angle of the rear wing.

Sehat - One of the best experts on this subject based on the ideXlab platform.

  • UTeM FV Malaysia Racing Car Suspension Optimization Using Geometrical Optimization
    2020
    Co-Authors: Mohamad Hazerul, Sehat
    Abstract:

    The title of this project is “ UTeM FV Malaysia Racing Car suspension optimization using geometrical optimization ”. A suspension system for UTeM FV Malaysia Racing Car will be model by MotionView software using multibody dynamic method. In the vehicle suspension design the optimal suspension should fulfil the following basic requirements: the ride comfort, reduction of dynamic road-tyre forces, and reduction of relative motions between the vehicle bodies. In this project, push rod double wishbone suspension system is used and then analyse the suspension system using kinematic and compliance method. This analysis is done to study the movement of each components in UTeM FV Malaysia Racing Car suspension and optimize the suspension geometry (kinematic) and shock absorber characteristic (compliance) using geometrical optimization. MotionView analyze data for vehicle dynamics of resulting design and provide the information about the comparison of analyzing data for vehicle dynamics and suspension performance. The optimization method helps to find the optimum locations of the hard points efficiently. For each iteration in the process of optimization, prediction uncertainty is considered and the multi objective optimization method is applied to optimize all the performance indexes simultaneously. It is shown that the proposed optimization method is effective while being applied in the kinematic performance optimization of a push rod double wishbone suspension system. Several optimization techniques were made to optimize the parameters .The study will cover on both front and rear suspension system. As known Suspension system give our Car stability, steering control and improve comfort. The suspension allows the wheels to move up and down independently from the rest of the Car. That will keep the wheels on the road when hit bumps.

Nigel Cross - One of the best experts on this subject based on the ideXlab platform.

  • winning by design the methods of gordon murray Racing Car designer
    Design Studies, 1996
    Co-Authors: Nigel Cross, Anita Clayburn Cross
    Abstract:

    Abstract This is a case study of the working methods of one particularly successful designer in a highly competitive design domain, Formula One Racing Car design. Gordon Murray was chief designer for the very successful Brabham and McLaren Racing Car teams in the 1970s and 1980s. His record of success is characterized by innovative breakthroughs, often arising as sudden illuminations, based on considering the task from first principles and from a systemic viewpoint. His working methods are highly personal, and include intensive use of drawings. Personality factors and team management abilities also appear to be relevant. There are some evident similarities with some other successful, innovative designers.

Dongbin Zhao - One of the best experts on this subject based on the ideXlab platform.

  • vision based control in the open Racing Car simulator with deep and reinforcement learning
    Journal of Ambient Intelligence and Humanized Computing, 2019
    Co-Authors: Dongbin Zhao
    Abstract:

    With decades of development, computer intelligence has now reached a really high level. Especially deep learning (DL) and reinforcement learning (RL) endow computers the perception and decision abilities. This paper aims to design a vision-based system that is able to play The Open Racing Car Simulator (TORCS) like a human player that uses images. With the DL-trained perception module, useful and low-dimensional information is extracted from first-person images. Based on that, the RL-trained module further manipulates the simulated Car in the middle of the lane. The two modules are separately trained, and both DL and RL advantages are maximally utilized. Experiments on different tracks show the promising performance of the method.

  • driving control with deep and reinforcement learning in the open Racing Car simulator
    International Conference on Neural Information Processing, 2018
    Co-Authors: Dongbin Zhao
    Abstract:

    Vision-based control is a hot topic in the field of computational intelligence. Especially the development of deep learning (DL) and reinforcement learning (RL) provides effective tools to this field. DL is capable of extracting useful information from images, and RL can learn an optimal controller through interactions with environment. With the aid of these techniques, we consider to design a vision-based robot to play The Open Racing Car Simulator. The system uses DL to train a convolutional neural network to perceive driving data from images of first-person view. These perceived data, together with the Car’s speed, are input into a RL-learned controller to get driving commands. In the end, the system shows promising performance.