121 research outputs found
MHD Simulations on Magnetic Compression of Field Reversed Configurations
The magnetic compression has long been proposed a promising method for the
plasma heating in a field reversed configuration (FRC), however, it remains a
challenge to fully understand the physical mechanisms underlying the
compression process, due to its highly dynamic nature beyond the
one-dimensional (1D) adiabatic theory model [R. L. Spencer et al., Phys. Fluids
26, 1564 (1983)]. In this work, magnetohydrodynamics (MHD) simulations on the
magnetic compression of FRCs using the NIMROD code [C. R. Sovinec et al., J.
Comput. Phys. 195, 355 (2004)] and their comparisons with the 1D theory have
been performed. The effects of the assumptions of the theory on the compression
process have been explored, and the detailed profiles of the FRC during
compression have been investigated. The pressure evolution agrees with the
theoretical prediction under various initial conditions. The axial contraction
of the FRC can be affected by the initial density profile and the ramping rate
of the compression magnetic field, but the theoretical predictions on the FRC's
length in general and the relation in particular hold
approximately well during the whole compression process, where is the
major radius of FRC separatrix and is that of the magnetic axis. The
evolutions of the density and temperature can be affected significantly by the
initial equilibrium profile and the ramping rate of the compression magnetic
field. During the compression, the major radius of the FRC is another parameter
that is susceptible to the ramping rate of the compression field. Basically,
for the same magnetic compression ratio, the peak density is higher and the
FRC's radius is smaller than the theoretical predictions.Comment: 23 pages, 10 figure
Effects of zero and reversed magnetic shear on resistive wall modes in a limiter tokamak plasma
Advanced tokamak scenarios often feature equilibriums with zero and reversed
magnetic shear. To isolate and investigate their impacts on the resistive wall
mode (RWM) instability analytically, we construct a series of cylindrical
limiter equilibriums with reversed magnetic shear in the core and zero magnetic
shear towards plasma edge, as a prototype of the configurations in advanced
tokamak scenarios. Uniform plasma pressure is assumed, so that we can focus our
analysis on the current-driven RWMs. Based on the reduced ideal MHD equations,
analytical solutions for the resistive wall mode are obtained, which
indicate that increasing the reversal of magnetic shear in the core region
enhances the RWM instability, whereas the widened region of zero shear near
edge leads to lower growth rate of RWM, except when the value with zero
magnetic shear approaches rational values. On the other hand, enhanced positive
shear at plasma edge is found to be stabilizing on RWM. NIMROD calculation
results confirm these analytical findings
Context-Conditional Navigation with a Learning-Based Terrain- and Robot-Aware Dynamics Model
In autonomous navigation settings, several quantities can be subject to
variations. Terrain properties such as friction coefficients may vary over time
depending on the location of the robot. Also, the dynamics of the robot may
change due to, e.g., different payloads, changing the system's mass, or wear
and tear, changing actuator gains or joint friction. An autonomous agent should
thus be able to adapt to such variations. In this paper, we develop a novel
probabilistic, terrain- and robot-aware forward dynamics model, termed TRADYN,
which is able to adapt to the above-mentioned variations. It builds on recent
advances in meta-learning forward dynamics models based on Neural Processes. We
evaluate our method in a simulated 2D navigation setting with a unicycle-like
robot and different terrain layouts with spatially varying friction
coefficients. In our experiments, the proposed model exhibits lower prediction
error for the task of long-horizon trajectory prediction, compared to
non-adaptive ablation models. We also evaluate our model on the downstream task
of navigation planning, which demonstrates improved performance in planning
control-efficient paths by taking robot and terrain properties into account.Comment: \copyright 2023 IEEE. Accepted for publication in European Conference
on Mobile Robots (ECMR), 2023. Updated copyright statemen
Roles of non-axisymmetric perturbations in free drift vertical displacement events on EAST
The safe operation of most tokamaks, especially the largen sized ones, rely
on the feedback control of the vertical displacement events (VDEs). However,
most these feedback control systems are based on the axisymmetric VDE models.
In this work, we use NIMROD simulations to study the roles of non-axisymmetric
perturbations in free drift vertical displacement events on EAST. The high-
modes in non-axisymmetric VDE grow first, which drive the formation of high-
magnetic island chains. Subsequently, the magnetic island chains grow and
overlap with each other, leading to the destruction of the magnetic flux
surface, which induces a minor disruption and accelerates the start of the
following major disruption. The magnetic island and the stochastic magnetic
field allow the toroidally asymmetric poloidal plasma current to jet towards
the hoop force direction, forming the finger and filamentary structures. Such a
plasma current asymmetry strongly depends on the anisotropy in thermal
transport coefficients
EPCFormer: Expression Prompt Collaboration Transformer for Universal Referring Video Object Segmentation
Audio-guided Video Object Segmentation (A-VOS) and Referring Video Object
Segmentation (R-VOS) are two highly-related tasks, which both aim to segment
specific objects from video sequences according to user-provided expression
prompts. However, due to the challenges in modeling representations for
different modalities, contemporary methods struggle to strike a balance between
interaction flexibility and high-precision localization and segmentation. In
this paper, we address this problem from two perspectives: the alignment
representation of audio and text and the deep interaction among audio, text,
and visual features. First, we propose a universal architecture, the Expression
Prompt Collaboration Transformer, herein EPCFormer. Next, we propose an
Expression Alignment (EA) mechanism for audio and text expressions. By
introducing contrastive learning for audio and text expressions, the proposed
EPCFormer realizes comprehension of the semantic equivalence between audio and
text expressions denoting the same objects. Then, to facilitate deep
interactions among audio, text, and video features, we introduce an
Expression-Visual Attention (EVA) mechanism. The knowledge of video object
segmentation in terms of the expression prompts can seamlessly transfer between
the two tasks by deeply exploring complementary cues between text and audio.
Experiments on well-recognized benchmarks demonstrate that our universal
EPCFormer attains state-of-the-art results on both tasks. The source code of
EPCFormer will be made publicly available at
https://github.com/lab206/EPCFormer.Comment: The source code will be made publicly available at
https://github.com/lab206/EPCForme
SSD-MonoDETR: Supervised Scale-aware Deformable Transformer for Monocular 3D Object Detection
Transformer-based methods have demonstrated superior performance for
monocular 3D object detection recently, which aims at predicting 3D attributes
from a single 2D image. Most existing transformer-based methods leverage both
visual and depth representations to explore valuable query points on objects,
and the quality of the learned query points has a great impact on detection
accuracy. Unfortunately, existing unsupervised attention mechanisms in
transformers are prone to generate low-quality query features due to inaccurate
receptive fields, especially on hard objects. To tackle this problem, this
paper proposes a novel Supervised Scale-aware Deformable Attention (SSDA) for
monocular 3D object detection. Specifically, SSDA presets several masks with
different scales and utilizes depth and visual features to adaptively learn a
scale-aware filter for object query augmentation. Imposing the scale awareness,
SSDA could well predict the accurate receptive field of an object query to
support robust query feature generation. Aside from this, SSDA is assigned with
a Weighted Scale Matching (WSM) loss to supervise scale prediction, which
presents more confident results as compared to the unsupervised attention
mechanisms. Extensive experiments on the KITTI benchmark demonstrate that SSDA
significantly improves the detection accuracy, especially on moderate and hard
objects, yielding state-of-the-art performance as compared to the existing
approaches. Our code will be made publicly available at
https://github.com/mikasa3lili/SSD-MonoDETR.Comment: Code will be made publicly available at
https://github.com/mikasa3lili/SSD-MonoDET
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