Crash Simulations of a Racing Kart's Structural Frame Colliding against a Rigid Wall (doi:10.18419/darus-3789)

View:

Part 1: Document Description
Part 2: Study Description
Part 5: Other Study-Related Materials
Entire Codebook

Crash Simulations of a Racing Kart's Structural Frame Colliding against a Rigid Wall

doi:10.18419/darus-3789

DaRUS

2023-11-17

1

Kneifl, Jonas; Fehr, Jörg, 2023, "Crash Simulations of a Racing Kart's Structural Frame Colliding against a Rigid Wall", https://doi.org/10.18419/darus-3789, DaRUS, V1

Crash Simulations of a Racing Kart's Structural Frame Colliding against a Rigid Wall

doi:10.18419/darus-3789

Kneifl, Jonas (University of Stuttgart)

Fehr, Jörg (University of Stuttgart)

EXC 2075 - 390740016

DaRUS

Kneifl, Jonas

Fehr, Jörg

Kneifl, Jonas

2023-11-15

https://doi.org/10.18419/darus-3789

Computer and Information Science, Engineering, Mathematical Sciences, Physics, Crash Simulation, Finite Element Method, Continuum Mechanics, Structural Dynamics

<b>Crash Simulations of a Racing Kart Frame Model</b> <br><br> This dataset contains results for several crash simulations of the frame of a racing kart colliding against a rigid wall. <br><br> The wall and the frame itself are modeled as finite element models, implemented in the commercial software tool LS-Dyna. The latter comprises 9314 nodes, each possessing 3 translational degrees of freedom. The simulated scenario involves the kart colliding against a rigid wall, with the impact speed varying between 5 and 30 m/s, the impact angle between -45 and 45 degrees and the yield stress between 168 and 758 MPa. <br><br> The impact angle is the angle between the wall normal and the orientation of the kart, while the yield stress influences the effective plastic stress-strain curve of the kart material. This curve matches that of typical steel, but is adjusted based on the unique yield stress of each simulation.<br> Each crash simulation covers a time span of 0.003 seconds with a sampling interval of 0.3 milliseconds, resulting in 101 samples per simulation. A total of 128 parameter combinations were generated with Halton sequences. <br><br> Moreover, the source code of the finite element model itself, written for the commercial simulation software LS-DYNA, is included as well. <br><br> <b>Content</b> <br> <ol> <li>Model<br> * input files for the FE simulation software LS-DYNA containing the model description<br></li> <li>Kart Dataset<br> * simulation results containing the node displacements and simulation parameters. The units are [N,m,s].<br></li> </ol>

Kneifl, J., Kutz, J. N., Brunton, S.L., Fehr, J.: Multi-Hierarchical Surrogate Learning of Structural Dynamical Systems Using Graph Convolutional Neural Networks. To be submitted (2023).

Kneifl, J., Kutz, J. N., Brunton, S.L., Fehr, J.: Multi-Hierarchical Surrogate Learning of Structural Dynamical Systems Using Graph Convolutional Neural Networks. To be submitted (2023).

Label:

kart_dataset.hdf5

Text:

Simulation Results

application/x-hdf5

Label:

load_data.py

Text:

script to load the simulation results in Python

text/x-python-script

Label:

README.md

text/markdown

Label:

Contacts.key

application/x-iwork-keynote-sffkey

Label:

ExplicitSolver.key

application/x-iwork-keynote-sffkey

Label:

kart.key

application/x-iwork-keynote-sffkey

Label:

kart_elements.key

application/x-iwork-keynote-sffkey

Label:

kart_explicit.key

application/x-iwork-keynote-sffkey

Label:

kart_nodes.key

application/x-iwork-keynote-sffkey

Label:

PARAMETERS.key

application/x-iwork-keynote-sffkey

Label:

REPORTING.key

application/x-iwork-keynote-sffkey

Label:

Wall.key

application/x-iwork-keynote-sffkey