scholarly journals On Dynamic Flow-Sensitive Floating-Label Systems

2014 ◽  
Author(s):  
Pablo Buiras ◽  
Deian Stefan ◽  
Alejandro Russo
Keyword(s):  
Dynamic Flow

Dynamic Flow Simulation to Study Flow Assurance Challenges in Water Alternating Gas WAG Wells

2016 ◽  
Author(s):  
Hassan Karimi ◽  
Erni Dharma Putra ◽  
Kapil Kumar Thakur ◽  
Rahel Yusuf ◽  
Azwan Shaharun ◽  
...  
Keyword(s):  
Flow Simulation ◽  
Flow Assurance ◽  
Dynamic Flow ◽  
Water Alternating Gas

Numerical study of fluid dynamic flow within an internal combustion engine cylinder

2017 ◽  
Author(s):  
Ana Marta Souza ◽  
Antônio César Valadares de Oliveira ◽  
Enrico Temporim Ribeiro ◽  
Francisco Souza ◽  
Marcelo Colombo Chiari
Keyword(s):  
Internal Combustion Engine ◽  
Combustion Engine ◽  
Numerical Study ◽  
Fluid Dynamic ◽  
Internal Combustion ◽  
Dynamic Flow ◽  
Engine Cylinder

A dynamic-flow carbon-cycle box model and high-latitude sensitivity

Tellus B ◽  
2006 ◽  
Vol 58 (4) ◽  
Author(s):  
Emily Lane ◽  
Synte Peacock ◽  
Juan M. Restrepo
Keyword(s):  
Carbon Cycle ◽  
High Latitude ◽  
Box Model ◽  
Dynamic Flow

scholarly journals Deep learning for vision‐based fall detection system: Enhanced optical dynamic flow

2020 ◽  
Author(s):  
Sagar Chhetri ◽  
Abeer Alsadoon ◽  
Thair Al‐Dala'in ◽  
P. W. C. Prasad ◽  
Tarik A. Rashid ◽  
...  
Keyword(s):  
Deep Learning ◽  
Detection System ◽  
Fall Detection ◽  
Dynamic Flow

scholarly journals Impact of Powder Properties on the Rheological Behavior of Excipients

Pharmaceutics ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1198
Author(s):  
Pauline H. M. Janssen ◽  
Sébastien Depaifve ◽  
Aurélien Neveu ◽  
Filip Francqui ◽  
Bastiaan H. J. Dickhoff
Keyword(s):  
Flow Field ◽  
Rheological Behavior ◽  
Quality By Design ◽  
Filling Process ◽  
Dynamic Flow ◽  
Die Filling ◽  
Flow Characterization ◽  
Wide Range ◽  
Powder Properties ◽  
The Impact

With the emergence of quality by design in the pharmaceutical industry, it becomes imperative to gain a deeper mechanistic understanding of factors impacting the flow of a formulation into tableting dies. Many flow characterization techniques are present, but so far only a few have shown to mimic the die filling process successfully. One of the challenges in mimicking the die filling process is the impact of rheological powder behavior as a result of differences in flow field in the feeding frame. In the current study, the rheological behavior was investigated for a wide range of excipients with a wide range of material properties. A new parameter for rheological behavior was introduced, which is a measure for the change in dynamic cohesive index upon changes in flow field. Particle size distribution was identified as a main contributing factor to the rheological behavior of powders. The presence of fines between larger particles turned out to reduce the rheological index, which the authors explain by improved particle separation at more dynamic flow fields. This study also revealed that obtained insights on rheological behavior can be used to optimize agitator settings in a tableting machine.

scholarly journals Research on the Method of Urban Jobs-Housing Space Recognition Combining Trajectory and POI Data

2021 ◽  
Vol 10 (2) ◽  
pp. 71
Author(s):  
Ya Zhang ◽  
Jiping Liu ◽  
Yong Wang ◽  
Yungang Cao ◽  
Youda Bai
Keyword(s):  
Flow Model ◽  
Trajectory Data ◽  
Dynamic Flow ◽  
Public Facilities ◽  
Point Of Interest ◽  
Recognition Result ◽  
Different Types ◽  
Core Issue ◽  
Space Recognition ◽  
New Framework

With the gradual emergence of the separation and dislocation of urban jobs-housing space, rational planning of urban jobs-housing space has become the core issue of national land-spatial planning. To study the existing relationship between workspaces and living spaces, a new method to identify jobs-housing space is proposed, which not only considers the static spatial distribution of urban public facilities but also identifies the jobs-housing space by analyzing the real mobility characteristics of people from a humanistic perspective. This method provides a new framework for the identification of urban jobs-housing space by integrating point-of-interest (POI) and trajectory data. The method involves three steps: Firstly, based on the trajectory data, we analyze the characteristics of the dynamic flow of passengers in the grid and construct the living factors and working factors to identify the distribution of jobs-housing space. Secondly, we reclassify the POIs to calculate the category ratios of different types of POIs in the grid to identify the jobs-housing space. Finally, an OR operation is performed on the results obtained by the two methods to obtain the final recognition result. We selected Haikou City as the experimental area to verify the method proposed in this paper. The experimental results show that the recognition accuracy of the travel flow model is 72.43%, the POI quantitative recognition method’s accuracy is 74.94%, and the accuracy of the method proposed in this paper is 85.90%, which is significantly higher than the accuracy of the previous two methods. Therefore, the method proposed here can serve as a reference for subsequent research on urban jobs-housing space.

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