Simulation-Based Engineering Laboratory

Randomly distributed patches of ice arising naturally on roadways adversely effect a driver's ability to navigate a determined path; among the escalated risks in icy conditions are yaw instability (spinning out) and slippage from desired path.  The anomalous and often unpredictable distribution of ice makes predictive results from traditional modeling methods inaccurate.  In collaboration with  Dr. Mihai Anitescu (Argonne National Lab), we employ Gaussian processes to form high fidelity, interpolative models of spatial friction coefficients from a limited data set (achievable with satellite imaging, sensors, or inter-vehicle communication). We work with a non-linear vehicle model on the ice models to  a) quantify the effect of ice on a vehicle’s trajectory and b) to identify high risk speeds and turn radii on surveyed roadways. Simulation methods, first developed and verified in MATLAB, are implemented in ADAMS car and results are compared. Further investigation into this problem will develop control methods robust to the stochastic nature of the conditions.  Furthermore, the inverse problem of going from trajectory to friction values will be addressed.  This capability would allow for inter-vehicle communication of road friction data in the era of onboard sensing and in turn would make our methodology of data interpolation more relevant. Read More... or click here for the current version of the submitted conference paper.

Contributors: Kyle Schmitt, Justin Madsen, Dan Negrut, Mihai Anitescu

Multibody Dynamics with Frictional Contact

Investigating the kinematics and dynamics of rigid bodies with frictional contact is a CPU intensive task requiring new methods that can leverage commodity high performance computing resources. This project, a collaboration with Prof. Alessandro Tasora (University of Parma, Italy) and Dr. Mihai Anitescu (Argonne National Lab), investigates parallel algorithms for the simulation of multibody systems with frictional contact. A first objective of this project is to leverage parallel computers to investigate the dynamics of very large systems containing granular material such as sand. Currently, several scenarios are being investigated including tires moving over granular material and a tracked vehicle moving over gravel. Click here to see a video of sand flowing through an hourglass (generated using CHRONO::ENGINE, the simulation kernel that is parallelized under this project).

Contributors: Toby Heyn, Justin Madsen, Dan Negrut

A complete vehicle simulation involves three components: Vehicle, Powertrain Simulation and Tire-Terrain interactions. The concept of co-simulation looks at simulation of these components by using different dedicated CAE packages. Vehicle dynamics will be simulated in MSC.ADAMS/Car. Powertrain will be simulated in PSAT (Powertrain Systems Analysis Toolkit) or MSC.ADAMS/Car. For the Tire Component, nonlinear finite element model of tire will be simulated using either FTire, ABAQUS or FEAP (Finite Element Analysis Program).  In addition to using existing software packages, we are collaborating with Ilinca Stanciulescu at the University of Illinois at Urbana-Champaign who is developing a fully nonlinear finite element model of a tire. Click here to see a video of Professor Stanciulescu's model tire rolling on a rigid surface. Here are a few more Simulation Videos. Video1, Video2, Video3.  Here is are the clips of HMMWV on a four post test rig undergoing a pitch and roll motion.

Contributors: Makarand Datar, Dan Negrut

This project looks at several low order numerical integration methods: Newmark, Hilber-Hughes-Taylor (HHT), the second order BDF method of Gear , and three new stabilized numerical methods that draw on the HHT formulas and BDF method, in an effort to assess their behavior. The first objective is to briefly indicate the theoretical results available in the literature regarding the stability and convergence properties of these low order methods when applied in the context of multibody dynamics simulation (MBS). The second objective is to perform a set of numerical experiments to compare these integration formulas in terms of several metrics: (a) efficiency, (b) energy preservation, and (c) velocity/acceleration constraint drift. A set of simple mechanical systems are used to this end: a double pendulum, a slider crank with rigid bodies, a slider crank with a flexible body represented in the floating frame formulation, and a seven body mechanism. Read More...

Contributors: Naresh Khude, Toby Heyn, Dan Negrut

With the continuous improvement of computer processing speed comes the opportunity to model and simulate increasingly complex mechanical systems. This investigation uses a crawler track model to produce high-fidelity simulations of a crawler propulsion system under different scenarios. A few of the scenarios currently being investigated are the behavior of the model running over obstacles, being dropped (i.e., a parachute landing), and driving over flat surfaces. A seven second simulation of the model moving across a flat surface is shown here: video 1, video 2. The support provided by Holger Haut (http://www.multibodysimulation.com) is gratefully acknowledged.

The track model used in this project consists of 45 individual tracks, each of which are constrained by revolute joints and solid to solid contact forces. There are a total of eight idlers, two sprockets attached to a drive, and a return roller. Since many of the constraints in this model are contact forces, a ten second simulation using the HHT integrator method can take upwards of four hours. This large demand for computing power makes this track model an ideal candidate for use with parallel computing, a topic currently investigated in the Simulation-Based Engineering Lab. In the near future we plan on investigating different ways of interconnecting track elements (using bushings) as well as using rubber pads on the shoes to improve ride comfort and performance.

Contributors: Justin Madsen, Dan Negrut

Molecular dynamics (MD) is an atomistic simulation technique that can be used to calculate properties of a material by measuring them as the system evolves in time. The system is evolved in time by calculating the forces on individual atoms and solving Newton's equations of motion at each time step. The numerical methods currently employed by MD simulation are only stable under short time steps. As a result, the forces between atoms must be calculated many times even for short simulations, greatly increasing the CPU time required.

We are currently investigating the usefulness of implicit methods developed in the realm of classical mechanical system simulation for use in both biological and material science applications of MD. Our results as of 3/5/2008 are summarized here in for form of a presentation. A more detailed discusion is available in both pdf and html versions.

Contributors: Nick Schafer, Dan Negrut

In Materials Science we participated in the development of numerical methods that enable first-principles (ab-initio) computational investigation of nanostructures. Nanostructures have dimensions of the order of 10nm, with up to several thousands of atoms. The electronic structure of materials undergoes substantial qualitative changes when their dimensions are reduced to nanoscale, leading to new regimes of physical, mechanical, and chemical behavior not observed in bulk materials. The goal is to determine through simulation the properties of these nanostructures. The two research directions pursued are multi-scale methods for fine/coarse resolution and a model reduction approach based on Orbital-Free Density-Functional Method (OFDFT) for electronic structure computation. Read More...

Contributors: Toby Heyn, Dan Negrut