SUPE-Net: An Efficient Parallel Simulation Environment for Large-Scale Networked Social Dynamics

2010 ◽  
Author(s):  
Bonan Hou ◽  
Yiping Yao ◽  
Bing Wang ◽  
Dongsheng Liao
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Parallel Simulation ◽  
Simulation Environment

scholarly journals A Large-Scale COVID-19 Twitter Chatter Dataset for Open Scientific Research—An International Collaboration

Epidemiologia ◽  
2021 ◽  
Vol 2 (3) ◽  
pp. 315-324
Author(s):  
Juan M. Banda ◽  
Ramya Tekumalla ◽  
Guanyu Wang ◽  
Jingyuan Yu ◽  
Tuo Liu ◽  
...  
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Additional Data ◽  
Open Data ◽  
Data Sources ◽  
Research Projects ◽  
Research Groups ◽  
The World ◽  
Data Source

As the COVID-19 pandemic continues to spread worldwide, an unprecedented amount of open data is being generated for medical, genetics, and epidemiological research. The unparalleled rate at which many research groups around the world are releasing data and publications on the ongoing pandemic is allowing other scientists to learn from local experiences and data generated on the front lines of the COVID-19 pandemic. However, there is a need to integrate additional data sources that map and measure the role of social dynamics of such a unique worldwide event in biomedical, biological, and epidemiological analyses. For this purpose, we present a large-scale curated dataset of over 1.12 billion tweets, growing daily, related to COVID-19 chatter generated from 1 January 2020 to 27 June 2021 at the time of writing. This data source provides a freely available additional data source for researchers worldwide to conduct a wide and diverse number of research projects, such as epidemiological analyses, emotional and mental responses to social distancing measures, the identification of sources of misinformation, stratified measurement of sentiment towards the pandemic in near real time, among many others.

Quantification of methane hydrate formation in the Large-scale Reservoir Laboratory Simulator (LARS) by numerical simulations

2021 ◽  
Author(s):  
Zhen Li ◽  
Thomas Kempka ◽  
Erik Spangenberg ◽  
Judith Schicks
Keyword(s):  
Gas Hydrates ◽  
Methane Hydrate ◽  
Large Scale ◽  
Hydrate Formation ◽  
Simulation Environment ◽  
Transport Simulation ◽  
Chemistry Research ◽  
Research Activities ◽  
In Situ Conditions ◽  
Hydrate Bearing Sediments

<p>Natural gas hydrates are considered as one of the most promising alternatives to conventional fossil energy sources, and are thus subject to world-wide research activities for decades. Hydrate formation from methane dissolved in brine is a geogenic process, resulting in the accumulation of gas hydrates in sedimentary formations below the seabed or overlain by permafrost. The LArge scale Reservoir Simulator (LARS) has been developed (Schicks et al., 2011, 2013; Spangenberg et al., 2015) to investigate the formation and dissociation of gas hydrates under simulated in-situ conditions of hydrate deposits. Experimental measurements of the temperatures and bulk saturation of methane hydrates by electrical resistivity tomography have been used to determine the key parameters, describing and characterising methane hydrate formation dynamics in LARS. In the present study, a framework of equations of state to simulate equilibrium methane hydrate formation in LARS has been developed and coupled with the TRANsport Simulation Environment (Kempka, 2020) to study the dynamics of methane hydrate formation and quantify changes in the porous medium properties in LARS. We present our model implementation, its validation against TOUGH-HYDRATE (Gamwo & Liu, 2010) and the findings of the model comparison against the hydrate formation experiments undertaken by Priegnitz et al. (2015). The latter demonstrates that our numerical model implementation is capable of reproducing the main processes of hydrate formation in LARS, and thus may be applied for experiment design as well as to investigate the process of hydrate formation at specific geological settings.</p><p>Key words: dissolved methane; hydrate formation; hydration; python; permeability.</p><p>References</p><p>Schicks, J. M., Spangenberg, E., Giese, R., Steinhauer, B., Klump, J., & Luzi, M. (2011). New approaches for the production of hydrocarbons from hydrate bearing sediments. Energies, 4(1), 151-172, https://doi.org/10.3390/en4010151</p><p>Schicks, J. M., Spangenberg, E., Giese, R., Luzi-Helbing, M., Priegnitz, M., & Beeskow-Strauch, B. (2013). A counter-current heat-exchange reactor for the thermal stimulation of hydrate-bearing sediments. Energies, 6(6), 3002-3016, https://doi.org/10.3390/en6063002</p><p>Spangenberg, E., Priegnitz, M., Heeschen, K., & Schicks, J. M. (2015). Are laboratory-formed hydrate-bearing systems analogous to those in nature?. Journal of Chemical & Engineering Data, 60(2), 258-268, https://doi.org/10.1021/je5005609</p><p>Kempka, T. (2020) Verification of a Python-based TRANsport Simulation Environment for density-driven fluid flow and coupled transport of heat and chemical species. Adv. Geosci., 54, 67–77, https://doi.org/10.5194/adgeo-54-67-2020</p><p>Gamwo, I. K., & Liu, Y. (2010). Mathematical modeling and numerical simulation of methane production in a hydrate reservoir. Industrial & Engineering Chemistry Research, 49(11), 5231-5245, https://doi.org/10.1021/ie901452v</p><p>Priegnitz, M., Thaler, J., Spangenberg, E., Schicks, J. M., Schrötter, J., & Abendroth, S. (2015). Characterizing electrical properties and permeability changes of hydrate bearing sediments using ERT data. Geophysical Journal International, 202(3), 1599-1612, https://doi.org/10.1093/gji/ggv245</p>

scholarly journals GPU-accelerated hydraulic simulations of large-scale natural gas pipeline networks based on a two-level parallel process

2020 ◽  
Vol 75 ◽  
pp. 86
Author(s):  
Yue Xiang ◽  
Peng Wang ◽  
Bo Yu ◽  
Dongliang Sun
Keyword(s):  
Natural Gas ◽  
Large Scale ◽  
Parallel Simulation ◽  
Simulation Software ◽  
Gas Pipeline ◽  
Processing Unit ◽  
Pipeline Network ◽  
Natural Gas Pipeline ◽  
Pipeline Networks ◽  
Speedup Ratio

The numerical simulation efficiency of large-scale natural gas pipeline network is usually unsatisfactory. In this paper, Graphics Processing Unit (GPU)-accelerated hydraulic simulations for large-scale natural gas pipeline networks are presented. First, based on the Decoupled Implicit Method for Efficient Network Simulation (DIMENS) method, presented in our previous study, a novel two-level parallel simulation process and the corresponding parallel numerical method for hydraulic simulations of natural gas pipeline networks are proposed. Then, the implementation of the two-level parallel simulation in GPU is introduced in detail. Finally, some numerical experiments are provided to test the performance of the proposed method. The results show that the proposed method has notable speedup. For five large-scale pipe networks, compared with the well-known commercial simulation software SPS, the speedup ratio of the proposed method is up to 57.57 with comparable calculation accuracy. It is more inspiring that the proposed method has strong adaptability to the large pipeline networks, the larger the pipeline network is, the larger speedup ratio of the proposed method is. The speedup ratio of the GPU method approximately linearly depends on the total discrete points of the network.

Harnessing Collective Intelligence Through Pattern Mining in Social Computational Systems

2018 ◽  
pp. 91-110
Author(s):  
Gaganmeet Kaur Awal ◽  
K. K. Bharadwaj
Keyword(s):  
Large Scale ◽  
Social Dynamics ◽  
Pattern Mining ◽  
Collective Intelligence ◽  
Social Systems ◽  
Big Data Analytics ◽  
Diverse Range ◽  
Business Applications ◽  
Computational Systems ◽  
The Web

Due to the digital nature of the web, the social web mimics the real-world social dynamics that manifest themselves as data and can be easily mined as patterns, making the web a fertile ground for business and research-oriented analytical applications. Collective intelligence (CI) is a multifaceted field with roots in sociology, biology, and many other disciplines. Various manifestations of CI support the successful existence of large-scale social systems. This chapter gives an overview of the principles of CI and the concept of “wisdom of crowds” and highlights how to maximize the potential of big data analytics for CI. Also, various techniques and approaches have been described that leverage these CI concepts across a diverse range of ever-evolving social systems for commercial business applications like influence mining, expertise discovery, etc.

Motion Simulation Experiments for Driver Behavior and Road Vehicle Dynamics

2011 ◽  
Vol 11 (4) ◽  
Author(s):  
Kemper Lewis ◽  
Kevin Hulme ◽  
Edward Kasprzak ◽  
Deborah Moore-Russo ◽  
Gregory Fabiano
Keyword(s):  
Vehicle Dynamics ◽  
Large Scale ◽  
Driving Simulation ◽  
Vehicle Design ◽  
Simulation Environment ◽  
Learning Mechanisms ◽  
Road Vehicle ◽  
Wide Range ◽  
Traffic Systems ◽  
The Impact

This paper discusses the design and development of a motion-based driving simulation and its integration into driving simulation research. The integration of the simulation environment into a road vehicle dynamics curriculum is also presented. The simulation environment provides an immersive experience to conduct a wide range of research on driving behavior, vehicle design and intelligent traffic systems. From an education perspective, the environment is designed to promote hands-on student participation in real-world engineering experiences that enhance conventional learning mechanisms for road vehicle dynamics and engineering systems analysis. The paper assesses the impact of the environment on student learning objectives in an upper level vehicle dynamics course and presents results from research involving teenage drivers. The paper presents an integrated framework for the use of real-time simulation and large-scale visualization to both study driving behaviors and to discover the impact that design decisions have on vehicle design using a realistic simulated driving interface.

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