Applicability of using machine learning to improve computational efficiency of combined pre-crash and crash simulations
Typ
Examensarbete för masterexamen
Program
Automotive engineering (MPAUT), MSc
Publicerad
2022
Författare
Wang, Peifeng
Xu, Qiang
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Vehicle occupant safety is commonly evaluated in dynamic finite element simulations.
Common finite element occupant models are human body models (HBM),
active human body models (AHBM), and dummy models in pre-crash and crash
simulations are time-consuming, and a simulation may cost dozens of hours or even
days, which will affect economic efficiency. Machine learning offers a potentially
time-efficient approach with high accuracy. This thesis project aimed to compare
finite element (FE) simulations and machine learning (ML) prediction results to
determine the applicability and efficiency of the ML method for pre-crash and crash
simulations. The Hybrid III fast finite element model was always used as an occupant
model in combined pre-crash (braking or steering) and crash simulations. Six
parameters related to occupant kinematics and safety system design were varied.
Pre-pretensioner force, activation time of pre-pretensioner, braking level, braking
duration, steering level, and steering duration in 200 FE simulations. From these
FE simulations head kinematics, rib strains and seatbelt forces were extracted as
output. Using the commercially available ML software, LUNAR, corresponding
curves were predicted with algorithms that LUNAR suggested. Then the prediction
accuracy was evaluated by calculating mean square error (MSE), root mean
square error (RMSE), mean absolute error (MAE) and mean absolute percentage
error (MAPE) between actual FE- simulation results and ML-model predictions.
The conclusion shows that ML can provide a method that saves more than 19% of
the time to obtain highly accurate results for head node displacement, rib strain and
belt force prediction in both braking and steering simulations, while for head node
acceleration especially in in-crash phase, it has low accuracy.
Beskrivning
Ämne/nyckelord
FE , ML , Hybrid III fast model , Pre-crash and crash simulations , DOE , LUNAR , Algorithms , Interpolation , Error evaluation , Time reduction