A hybrid method for hydrodynamic-kinetic flow - Part I -A particle-gridmethod for reducing stochastic noise in kinetic regimes

In this work we present a hybrid particle-grid Monte Carlo method for the Boltzmann equation, which is characterized by a significant reduction of the stochastic noise in the kinetic regime. The hybrid method is based on a first order splitting in time to separate the transport from the relaxation step. The transport step is solved by a deterministic scheme, while a hybrid DSMC-based method is used to solve the collision step. Such a hybrid scheme is based on splitting the solution in a collisional and a non-collisional part at the beginning of the collision step, and the DSMC method is used to solve the relaxation step for the collisional part of the solution only. This is accomplished by sampling only the fraction of particles candidate for collisions from the collisional part of the solution, performing collisions as in a standard DSMC method, and then projecting the particles back onto a velocity grid to compute a piecewise constant reconstruction for the collisional part of the solution. The latter is added to a piecewise constant reconstruction of the non-collisional part of the solution, which in fact remains unchanged during the relaxation step. Numerical results show that the stochastic noise is significantly reduced at large Knudsen numbers with respect to the standard DSMC method. Indeed in this algorithm, the particle scheme is applied only on the collisional part of the solution, so only this fraction of the solution is affected by stochastic fluctuations. But since the collisional part of the solution reduces as the Knudsen number increases, stochastic noise reduces as well at large Knudsen number

Reduced-order modeling of a sliding ring on an elastic rod with incremental potential formulation

Mechanical interactions between rigid rings and flexible cables are widespread in both daily life (hanging clothes) and engineering system (closing a tether net). A reduced-order method for the dynamic analysis of sliding rings on a deformable one-dimensional (1D) rod-like object is proposed. In contrast to discretize the joint rings into multiple nodes and edges for contact detection and numerical simulation, a single point is used to reduce the order of the numerical model. In order to achieve the non-deviation condition between sliding ring and flexible rod, a novel barrier functional is derived based on incremental potential theory, and the tangent frictional interplay is later procured by a lagged dissipative formulation. The proposed barrier functional and the associated frictional functional are $C^{2}$ continuous, hence the nonlinear elastodynamic system can be solved variationally by an implicit time-stepping scheme. The numerical framework is first applied to simple examples where the analytical solutions are available for validation. Then, multiple complex practical engineering examples are considered to showcase the effectiveness of the proposed method. The simplified ring-to-rod interaction model can provide lifelike visual effect for picture animations, and also can support the optimal design for space debris removal system.Comment: 15 pages, 9 figure

Behaviour of Augmented Lagrangian and Hamiltonian Methods for Multibody Dynamics in the Proximity of Singular Configurations

This is a post-peer-review, pre-copyedit version of an article published in Nonlinear Dynamics. The final authenticated version is available online at: http://dx.doi.org/10.1007/s11071-016-2774-5.[Abstract] Augmented Lagrangian methods represent an efficient way to carry out the forward-dynamics simulation of mechanical systems. These algorithms introduce the constraint forces in the dynamic equations of the system through a set of multipliers. While most of these formalisms were obtained using Lagrange's equations as starting point, a number of them have been derived from Hamilton's canonical equations. Besides being efficient, they are generally considered to be robust, which makes them especially suitable for the simulation of systems with discontinuities and impacts. In this work, we have focused on the simulation of mechanical assemblies that undergo singular configurations. First, some sources of numerical difficulties in the proximity of singular configurations were identified and discussed. Afterwards, several augmented Lagrangian and Hamiltonian formulations were compared in terms of their robustness during the forward-dynamics simulation of two benchmark problems. Newton-Raphson iterative schemes were developed for these formulations with the Newmark formula as numerical integrator. These outperformed fixed point iteration approaches in terms of robustness and efficiency. The effect of the formulation parameters on simulation performance was also assessed

Parallelism with limited nondeterminism

Computational complexity theory studies which computational problems can be solved with limited access to resources. The past fifty years have seen a focus on the relationship between intractable problems and efficient algorithms. However, the relationship between inherently sequential problems and highly parallel algorithms has not been as well studied. Are there efficient but inherently sequential problems that admit some relaxed form of highly parallel algorithm? In this dissertation, we develop the theory of structural complexity around this relationship for three common types of computational problems. Specifically, we show tradeoffs between time, nondeterminism, and parallelizability. By clearly defining the notions and complexity classes that capture our intuition for parallelizable and sequential problems, we create a comprehensive framework for rigorously proving parallelizability and non-parallelizability of computational problems. This framework provides the means to prove whether otherwise tractable problems can be effectively parallelized, a need highlighted by the current growth of multiprocessor systems. The views adopted by this dissertation—alternate approaches to solving sequential problems using approximation, limited nondeterminism, and parameterization—can be applied practically throughout computer science