Dynamic Systems
Dynamic Systems - Related Videos
16:06
Fabrication of Micropatterned Hydrogels for Neural Culture Systems using Dynamic Mask Projection Photolithography
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Cited by 46 •
2011
Simple techniques are described for the rapid production of microfabricated neural culture systems using a digital micromirror device for dynamic mask projection lithography on regular cell culture substrates. These culture systems may be more representative of natural biological architecture, and the techniques described could be adapted for numerous applications.
06:20
Transforming Static Barrier Tissue Models into Dynamic Microphysiological Systems
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Cited by 3 •
2024
This protocol describes a reconfigurable membrane-based cell culture platform that integrates the open-well format with fluid flow capabilities. This platform is compatible with standard protocols and allows for reversible transitions between open-well and microfluidic culture modes, accommodating the needs of both engineering and bioscience laboratories.
13:19
The Use of Chemostats in Microbial Systems Biology
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Cited by 67 •
2013
Cell growth rate is a regulated process and a primary determinant of cell physiology. Continuous culturing using chemostats enables extrinsic control of cell growth rate by nutrient limitation facilitating the study of molecular networks that control cell growth and how those networks evolve to optimize cell growth.
12:03
Dynamics of Structures
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2023
Source: Roberto Leon, Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA It is rare nowadays that a whole year goes by without a major earthquake event wreaking havoc somewhere around the world. In some cases, like the 2005 Banda Ache earthquake in Indonesia, the damage involved large geographic areas and casualties in the six figures. In general, the number and intensity of earthquakes is not increasing, however, the vulnerability of the built environment is...
02:20
Dynamic Equilibrium
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2020
A reversible chemical reaction represents a chemical process that proceeds in both forward (left to right) and reverse (right to left) directions. When the rates of the forward and reverse reactions are equal, the concentrations of the reactant and product species remain constant over time and the system is at equilibrium. A special double arrow is used to emphasize the reversible nature of the reaction. The relative concentrations of reactants and products in equilibrium systems vary greatly;...
FAQs
Dynamic systems models track state variables through differential equations for continuously changing behavior or difference equations for stepwise updates. These equations connect rates of change with current states, system inputs, and initial conditions. Solving them produces a time-dependent trajectory, allowing engineers to examine how a system moves from its starting condition toward later behavior under specified inputs.
Feedback links measured or inferred system behavior back to the system’s operation, so internal interactions and external inputs can be considered together. In a model, feedback helps explain why a disturbance may be reduced, amplified, or produce oscillations. Including initial conditions is equally important because identical inputs can lead to different trajectories when systems start in different states.
Stability analysis examines whether system behavior remains controlled as time evolves, while transient analysis focuses on the response during change rather than only its eventual condition. Steady-state analysis describes behavior after transient effects have subsided. Together with oscillation and nonlinear-behavior analysis, these perspectives help identify performance limitations and compare responses to inputs or disturbances.
Engineers first identify relevant state variables, inputs, interactions, and initial conditions, then express their relationships with differential or difference equations. They can simulate the resulting model under selected inputs or disturbances and examine time-dependent responses. The same model supports stability, transient, steady-state, oscillation, and nonlinear analyses before design decisions are made.
A model exposes how system states evolve in response to inputs and feedback, giving engineers a basis for predicting whether a control action improves or degrades behavior. By examining stability, transients, and oscillations in simulations, they can assess candidate control strategies before implementation. This provides evidence for selecting approaches that meet desired response and performance conditions.
They can represent mechanical, electrical, thermal, and biological systems, whose behaviors may differ but can be studied through the same time-evolution framework. Applications include simulation, controller design, fault diagnosis, and performance optimization. These uses help engineers predict responses to changing conditions, investigate disturbances, and improve operational reliability rather than assessing performance only at a single fixed state.