On the dependency of jumps on particle shape in bedload transport of monodisperse non-spherical particles

Author(s):  
Ramandeep Jain ◽  
Ricardo Rebel ◽  
Jochen Fröhlich

<p><span><span>Accurate prediction of sediment transport is highly desirable because of its </span><span>key</span><span> importance in many environmental and industrial applications. One way to approach this is to measure the length and height of the jump of a moving particle. This led to many studies dealing with the quantification of a particle jump. Nevertheless, few experiments have been performed to understand the effect of particle shape on its jump. A dataset of jumps of different</span><span>ly</span><span> shaped particles has been generated </span><span>by the authors</span><span> from direct numerical simulations of bedload transport in a turbulent open channel flow. A total of four simulations were performed with a large number of mobile single shaped, mono-disperse particles. Four ellipsoidal shapes were used in these simulations, i.e. oblate, prolate, sphere, and a generally shaped ellipsoid. In the present contribution, statistical properties of the jump trajector</span><span>ies</span><span> such as ejection and landing angles, flight length, height, and time, etc. </span><span>w</span><span>ill be reported</span><span>. </span><span>M</span><span>ean jump trajectories for different particle shapes were calculated using </span><span>the </span><span>Dynamic-Time-Warping algorithm. The analysis provides a quantification of the different behavior of the particles under the present conditions. For example, it is observed that oblate particles travel a maximum distance in a jump, while spherical particles take small jumps but more often. </span></span></p>

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jiangyuan Mei ◽  
Jian Hou ◽  
Hamid Reza Karimi ◽  
Jiarao Huang

Process monitoring and fault diagnosis (PM-FD) has been an active research field since it plays important roles in many industrial applications. In this paper, we present a novel data-driven fault diagnosis algorithm which is based on the multivariate dynamic time warping measure. First of all, we propose a Mahalanobis distance based dynamic time warping measure which can compute the similarity of multivariate time series (MTS) efficiently and accurately. Then, a PM-FD framework which consists of data preprocessing, metric learning, MTS pieces building, and MTS classification is presented. After that, we conduct experiments on industrial benchmark of Tennessee Eastman (TE) process. The experimental results demonstrate the improved performance of the proposed algorithm when compared with other classical PM-FD classical methods.


2021 ◽  
Author(s):  
Xiaowei Zhao ◽  
Shangxu Wang ◽  
Sanyi Yuan ◽  
Liang Cheng ◽  
Youjun Cai

Sign in / Sign up

Export Citation Format

Share Document