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Related Concept Videos

Entropy02:39

Entropy

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Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
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Entropy01:18

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The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
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Entropy Change in Reversible Processes01:10

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In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
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A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that...
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Entropy and Solvation02:05

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The process of surrounding a solute with solvent is called solvation. It involves evenly distributing the solute within the solvent. The rule of thumb for determining a solvent for a given compound is that like dissolves like. A good solvent has molecular characteristics similar to those of the compound to be dissolved. For example, polar solutions dissolve polar solutes, and apolar solvents dissolve apolar solutes. A polar solvent is a solvent that has a high dielectric constant (ϵ...
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Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
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Entropy and optimality in river deltas.

Alejandro Tejedor1, Anthony Longjas1, Douglas A Edmonds2,3

  • 1Department of Civil and Environmental Engineering, University of California, Irvine, CA 92697.

Proceedings of the National Academy of Sciences of the United States of America
|October 29, 2017
PubMed
Summary
This summary is machine-generated.

River deltas self-organize channel networks to maximize diverse water and sediment delivery to shorelines. This optimizes resilience but may lead to catastrophic events if perturbations exceed thresholds.

Keywords:
information theoryresilient deltasself-organizationspectral graph theory

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Area of Science:

  • Geomorphology
  • Hydrology
  • Complex Systems Science

Background:

  • River delta morphology and function depend on channel network structure, influencing sediment and nutrient delivery.
  • Understanding deltaic channel coevolution is vital for managing human-impacted, stressed delta systems.
  • A unified theory for self-organized water and sediment distribution in deltas is currently lacking.

Purpose of the Study:

  • To investigate the optimality principle governing self-organized flux partitioning in delta channel networks.
  • To propose a new metric, nonlocal entropy rate (nER), for analyzing delta network organization.
  • To explore the relationship between delta network configuration, flux delivery, and system resilience.

Main Methods:

  • Analysis of field data from natural deltas.
  • Simulation of deltaic processes using computational models.
  • Introduction and application of a nonlocal entropy rate (nER) metric.

Main Results:

  • Delta networks exhibit configurations that maximize the diversity of water and sediment flux delivery to the shoreline.
  • Prograding deltas achieve dynamically accessible optima in flux distribution across their channel network topologies.
  • High nonlocal entropy rate (nER) configurations correlate with increased delta resilience to perturbations.

Conclusions:

  • Delta channel networks self-organize based on an optimality principle maximizing flux diversity.
  • This self-organization decouples geomorphological and hydrological evolutionary timescales.
  • While enhancing resilience, the distributive mechanism may precipitate catastrophic events under extreme perturbation intensities.