Damage of Concrete: Application of Network Simulations

2001 ◽  
pp. 85-97
Author(s):  
J. G. M. Mier
Keyword(s):  
Concrete Application ◽  
Network Simulations

scholarly journals Microfluidic Network Simulations Enable On-Demand Prediction of Control Parameters for Operating Lab-On-A-Chip-Devices

Processes ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. 1320
Author(s):  
Julia Sophie Böke ◽  
Daniel Kraus ◽  
Thomas Henkel
Keyword(s):  
Fluid Management ◽  
Lab On A Chip ◽  
Microfluidic System ◽  
Flow Rates ◽  
Demand Prediction ◽  
Fast Running ◽  
Microfluidic Network ◽  
Fluid Properties ◽  
Network Simulations ◽  
User Friendly

Reliable operation of lab-on-a-chip systems depends on user-friendly, precise, and predictable fluid management tailored to particular sub-tasks of the microfluidic process protocol and their required sample fluids. Pressure-driven flow control, where the sample fluids are delivered to the chip from pressurized feed vessels, simplifies the fluid management even for multiple fluids. The achieved flow rates depend on the pressure settings, fluid properties, and pressure-throughput characteristics of the complete microfluidic system composed of the chip and the interconnecting tubing. The prediction of the required pressure settings for achieving given flow rates simplifies the control tasks and enables opportunities for automation. In our work, we utilize a fast-running, Kirchhoff-based microfluidic network simulation that solves the complete microfluidic system for in-line prediction of the required pressure settings within less than 200 ms. The appropriateness of and benefits from this approach are demonstrated as exemplary for creating multi-component laminar co-flow and the creation of droplets with variable composition. Image-based methods were combined with chemometric approaches for the readout and correlation of the created multi-component flow patterns with the predictions obtained from the solver.

Large Neural Network Simulations on Multiple Hardware Platforms

1997 ◽  
pp. 919-923 ◽  
Author(s):  
Per Hammarlund ◽  
Örjan Ekeberg ◽  
Tomas Wilhelmsson ◽  
Anders Lansner
Keyword(s):  
Neural Network ◽  
Network Simulations ◽  
Hardware Platforms

Primitive chain network simulations of probe rheology

Soft Matter ◽  
2017 ◽  
Vol 13 (37) ◽  
pp. 6585-6593 ◽  
Author(s):  
Yuichi Masubuchi ◽  
Yoshifumi Amamoto ◽  
Ankita Pandey ◽  
Cheng-Yang Liu
Keyword(s):  
Relaxation Time ◽  
Viscoelastic Relaxation ◽  
Network Simulations ◽  
Cross Correlations

The dynamics of probe chains immersed in immobile matrices was examined via the multi-chain slip-link simulation. The viscoelastic relaxation time was fairly reproduced, whereas the relaxation intensity was underestimated, possibly due to flaws in the orientational cross-correlations between the probe and the matrices.

Consecutive Market and Network Simulations to Optimize Investment and Operational Decisions Under Different RES Penetration Scenarios

2019 ◽  
Vol 10 (4) ◽  
pp. 2152-2162 ◽  
Author(s):  
Mahmut Erkut Cebeci ◽  
Osman Bulent Tor ◽  
Simona Vasilica Oprea ◽  
Adela Bara
Keyword(s):  
Operational Decisions ◽  
Network Simulations
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