468 research outputs found
A 2D hybrid method for interfacial transport of passive scalars
A hybrid Eulerian-Lagrangian method is proposed to simulate passive scalar
transport on arbitrary shape interface. In this method, interface deformation
is tracked by an Eulerian method while the transport of the passive scalar on
the material interface is solved by a single-layer Lagrangian particle method.
To avoid particle clustering, a novel remeshing approach is proposed. This
remeshing method can resample particles, adjust the position of particles by a
relaxation process, and transfer mass from pre-existing particles to resampled
particles via a redistribution process, which preserves mass both globally and
locally. Computational costs are controlled by an adaptive remeshing strategy.
Accuracy is assessed by a series of test cases.Comment: 32 pages 1nd 14 figure
Analysis of the particle relaxation method for generating uniform particle distributions in smoothed particle hydrodynamics
We establish a theoretical framework of the particle relaxation method for
uniform particle generation of Smoothed Particle Hydrodynamics. We achieve this
by reformulating the particle relaxation as an optimization problem. The
objective function is an integral difference between discrete particle-based
and smoothed-analytical volume fractions. The analysis demonstrates that the
particle relaxation method in the domain interior is essentially equivalent to
employing a gradient descent approach to solve this optimization problem, and
we can extend such an equivalence to the bounded domain by introducing a proper
boundary term. Additionally, each periodic particle distribution has a
spatially uniform particle volume, denoted as characteristic volume. The
relaxed particle distribution has the largest characteristic volume, and the
kernel cut-off radius determines this volume. This insight enables us to
control the relaxed particle distribution by selecting the target kernel
cut-off radius for a given kernel function
Perception Imitation: Towards Synthesis-free Simulator for Autonomous Vehicles
We propose a perception imitation method to simulate results of a certain
perception model, and discuss a new heuristic route of autonomous driving
simulator without data synthesis. The motivation is that original sensor data
is not always necessary for tasks such as planning and control when semantic
perception results are ready, so that simulating perception directly is more
economic and efficient. In this work, a series of evaluation methods such as
matching metric and performance of downstream task are exploited to examine the
simulation quality. Experiments show that our method is effective to model the
behavior of learning-based perception model, and can be further applied in the
proposed simulation route smoothly
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