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Related Concept Videos

Multimachine Stability01:25

Multimachine Stability

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Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
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Stability of structures01:14

Stability of structures

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In mechanical engineering, the stability of systems under various forces is critical for designing durable and efficient structures. One fundamental way to explore these concepts is by analyzing systems like two rods connected at a pivot point, O, with a torsional spring of spring constant k at the pivot point. This system is similar in appearance to a scissor jack used to change tires on a car. In this case, the arms of the linkage (equivalent to the rods in this system) are entirely vertical,...
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Microtubules are hollow cylindrical filaments having a diameter of approximately 25 nm and a length that varies from 200 nm to 25 μm. GTP-bound tubulin subunits form αβ-heterodimers for microtubule assembly. These core building blocks interact longitudinally, polymerizing into protofilaments. The protofilaments then interact with one another through lateral bonding forces to form stable cylindrical microtubules. These cylindrical filaments are dynamic as they undergo repeated...
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Cyclic Processes And Isolated Systems01:19

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A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
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Entropy Changes Accompanying Specific Processes01:21

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Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression...
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Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
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Related Experiment Video

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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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System crash as dynamics of complex networks.

Yi Yu1, Gaoxi Xiao2, Jie Zhou3

  • 1School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore 639798.

Proceedings of the National Academy of Sciences of the United States of America
|October 5, 2016
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Complex systems can crash unexpectedly. This study introduces a network model explaining how local actions within system structures can cause surprising crashes, even in robust systems.

Keywords:
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Area of Science:

  • Complex systems science
  • Network theory
  • System dynamics

Background:

  • Understanding system collapse is limited, despite extensive study of system growth and evolution.
  • Puzzling dynamics exist where seemingly doomed systems survive, while robust ones crash rapidly.

Purpose of the Study:

  • To propose a network-based system dynamics model to explain peculiar system crash dynamics.
  • To investigate how local information and system structures influence system collapse.

Main Methods:

  • Development of a network-based system dynamics model.
  • Extensive simulations on synthetic and real-life network data.

Main Results:

  • The model replicates "peculiar" system crash dynamics observed in complex systems.
  • Simulations reveal interesting system evolution patterns leading to final collapse.
  • Individual actions based on local information significantly impact system stability.

Conclusions:

  • The proposed model provides insights into the mechanisms driving complex system crashes.
  • Network structure and local interactions are critical factors in system resilience and failure.
  • The model has potential applications in various fields and suggests avenues for future research.