7,728 research outputs found
Fractal model and Lattice Boltzmann Method for Characterization of Non-Darcy Flow in Rough Fractures.
The irregular morphology of single rock fracture significantly influences subsurface fluid flow and gives rise to a complex and unsteady flow state that typically cannot be appropriately described using simple laws. Yet the fluid flow in rough fractures of underground rock is poorly understood. Here we present a numerical method and experimental measurements to probe the effect of fracture roughness on the properties of fluid flow in fractured rock. We develop a series of fracture models with various degrees of roughness characterized by fractal dimensions that are based on the Weierstrass-Mandelbrot fractal function. The Lattice Boltzmann Method (LBM), a discrete numerical algorithm, is employed for characterizing the complex unsteady non-Darcy flow through the single rough fractures and validated by experimental observations under the same conditions. Comparison indicates that the LBM effectively characterizes the unsteady non-Darcy flow in single rough fractures. Our LBM model predicts experimental measurements of unsteady fluid flow through single rough fractures with great satisfactory, but significant deviation is obtained from the conventional cubic law, showing the superiority of LBM models of single rough fractures
Interaction-aware Kalman Neural Networks for Trajectory Prediction
Forecasting the motion of surrounding obstacles (vehicles, bicycles,
pedestrians and etc.) benefits the on-road motion planning for intelligent and
autonomous vehicles. Complex scenes always yield great challenges in modeling
the patterns of surrounding traffic. For example, one main challenge comes from
the intractable interaction effects in a complex traffic system. In this paper,
we propose a multi-layer architecture Interaction-aware Kalman Neural Networks
(IaKNN) which involves an interaction layer for resolving high-dimensional
traffic environmental observations as interaction-aware accelerations, a motion
layer for transforming the accelerations to interaction aware trajectories, and
a filter layer for estimating future trajectories with a Kalman filter network.
Attributed to the multiple traffic data sources, our end-to-end trainable
approach technically fuses dynamic and interaction-aware trajectories boosting
the prediction performance. Experiments on the NGSIM dataset demonstrate that
IaKNN outperforms the state-of-the-art methods in terms of effectiveness for
traffic trajectory prediction.Comment: 8 pages, 4 figures, Accepted for IEEE Intelligent Vehicles Symposium
(IV) 202
KLoB: a Benchmark for Assessing Knowledge Locating Methods in Language Models
Recently, Locate-Then-Edit paradigm has emerged as one of the main approaches
in changing factual knowledge stored in the Language models. However, there is
a lack of research on whether present locating methods can pinpoint the exact
parameters embedding the desired knowledge. Moreover, although many researchers
have questioned the validity of locality hypothesis of factual knowledge, no
method is provided to test the a hypothesis for more in-depth discussion and
research. Therefore, we introduce KLoB, a benchmark examining three essential
properties that a reliable knowledge locating method should satisfy. KLoB can
serve as a benchmark for evaluating existing locating methods in language
models, and can contributes a method to reassessing the validity of locality
hypothesis of factual knowledge. Our is publicly available at
\url{https://github.com/juyiming/KLoB}
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