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RESEARCH PRODUCT
Investigation of vehicle crash modeling techniques: theory and application
Kjell G. RobbersmyrWitold PawlusHamid Reza Karimisubject
VDP::Mathematics and natural science: 400::Mathematics: 410::Applied mathematics: 413Feedforward neural network; Lumped parameter models; Multiresolution analysis; Vehicle crash modeling; Control and Systems Engineering; Software; Mechanical Engineering; Computer Science Applications1707 Computer Vision and Pattern Recognition; Industrial and Manufacturing EngineeringEvent (computing)Computer scienceReliability (computer networking)Mechanical Engineeringvehicle crash modelingVDP::Technology: 500::Mechanical engineering: 570lumped parameter modelsCrashControl engineeringComputer Science Applications1707 Computer Vision and Pattern RecognitionCollisionIndustrial and Manufacturing EngineeringComputer Science Applicationsmultiresolution analysisAutoregressive modelControl and Systems Engineeringfeedforward neural networkRepresentation (mathematics)SimulationSoftwareMotor vehicle crashdescription
Published version of an article in the journal: The International Journal of Advanced Manufacturing Technology. Also available from the publisher at: http://dx.doi.org/10.1007/s00170-013-5320-3 Creating a mathematical model of a vehicle crash is a task which involves considerations and analysis of different areas which need to be addressed because of the mathematical complexity of a crash event representation. Therefore, to simplify the analysis and enhance the modeling process, in this work, a brief overview of different vehicle crash modeling methodologies is proposed. The acceleration of a colliding vehicle is measured in its center of gravity—this crash pulse contains detailed information about vehicle behavior throughout a collision. A virtual model of a collision scenario is established in order to provide an additional data set further used to evaluate a suggested approach. Three different approaches are discussed here: lumped parameter modeling of viscoelastic systems, data-based approach taking advantage of neural networks and autoregressive models and wavelet-based method of signal reconstruction. The comparative analysis between each method’s outcomes is performed and reliability of the proposed methodologies and tools is evaluated.
year | journal | country | edition | language |
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2013-10-06 |