Surrogate Modeling Approaches for Ground Vehicle Mobility Simulations

Author(s):  
Jeremy Mange ◽  
Sara Boyle
Author(s):  
Tamer Wasfy ◽  
Hatem Wasfy ◽  
Paramsothy Jayakumar ◽  
Srinivas Sanikommu

Abstract A finite element vegetation model is presented for predicting the dynamic interaction of ground vehicles with vegetation. The purpose of the model is to predict ground vehicle mobility over vegetation covered terrains. The types of vegetation can range from small diameter highly compliant stems to large stiff trees. Those include various types of vegetation such as grass, crops, shrubs/bushes, small trees, and large trees. Mobility measures which can be predicted include maximum safe vehicle speed along a specified path, tire slip, and fuel consumption. The ground vehicles are modeled using high-fidelity multibody dynamics models. The vegetation stems are modeled using an arrangement of thin and/or thick beam finite elements. The thin beam model uses the torsional spring beam formulation for small flexible vegetation and only includes the axial and bending beam responses. The thick beam model includes axial, bending, torsional, and shear beam responses and uses a lumped parameter beam element which connects two rigid body type nodes. The vegetation model includes the effects of normal contact and friction with the vehicle and between stems, stem breaking, and stem aerodynamic forces.


2021 ◽  
Author(s):  
Jason Olivier ◽  
Sally Shoop

Autonomous ground vehicle (AGV) research for military applications is important for developing ways to remove soldiers from harm’s way. Current AGV research tends toward operations in warm climates and this leaves the vehicle at risk of failing in cold climates. To ensure AGVs can fulfill a military vehicle’s role of being able to operate on- or off-road in all conditions, consideration needs to be given to terrain of all types to inform the on-board machine learning algorithms. This research aims to correlate real-time vehicle performance data with snow and ice surfaces derived from multispectral imagery with the goal of aiding in the development of a truly all-terrain AGV. Using the image data that correlated most closely to vehicle performance the images were classified into terrain units of most interest to mobility. The best image classification results were obtained when using Short Wave InfraRed (SWIR) band values and a supervised classification scheme, resulting in over 95% accuracy.


Author(s):  
Erdem Acar ◽  
Nahide Tüten ◽  
Mehmet Ali Güler

The design of lightweight automotive structures has become a prevalent practice in the automotive industry. This study focuses on design optimization of an automobile torque arm subjected to cyclic loading. Starting from an available initial design, the shape of the torque arm is optimized for minimum weight such that the fatigue life of the torque arm does not fall below that of the initial design and the maximum von Mises stress developed in the torque arm does not exceed that of the initial design. The stresses are computed using ANSYS finite element software, and the fatigue life is calculated using the Smith–Watson–Topper model. Surrogate-based optimization approach is used to reduce the computational cost. Optimization results based on global surrogate modeling and successive surrogate modeling approaches are compared. It is found that the successive surrogate modeling approach results in 28.7% weight reduction for the torque arm, whereas the global surrogate modeling approach results in 25.7% weight saving for the torque arm.


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