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Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

3.0K
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.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
3.0K
Entropy02:39

Entropy

33.6K
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

Entropy

3.2K
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.
Consider an infinitesimal step in the expansion, which...
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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.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
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Second Law of Thermodynamics02:49

Second Law of Thermodynamics

26.0K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
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Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

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Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
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Related Experiment Video

Updated: Nov 27, 2025

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
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Entropy Analysis of a Flexible Markovian Queue with Server Breakdowns.

Messaoud Bounkhel1, Lotfi Tadj2, Ramdane Hedjar3

  • 1Department of Mathematics, King Saud University, Riyadh 11451, Saudi Arabia.

Entropy (Basel, Switzerland)
|December 8, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Markovian queueing system with a threshold-dependent batch service. The entropy principle accurately approximates queueing system performance, aligning well with analytical methods.

Keywords:
Markovian queueflexible servermaximum entropy principlesteady-state distributionunreliable server

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Area of Science:

  • Operations Research
  • Applied Probability
  • Queueing Theory

Background:

  • Queueing systems are fundamental in performance analysis.
  • Server failures and batch services introduce complexities.
  • Existing models may not capture threshold-dependent behaviors effectively.

Purpose of the Study:

  • To analyze a versatile Markovian queueing system with a threshold-dependent batch service.
  • To investigate the impact of server failures on system performance.
  • To apply the entropy principle for approximating queueing system probabilities.

Main Methods:

  • Modeling a Markovian queue with a threshold 'c' for single or batch service.
  • Incorporating server failures with state-dependent rates.
  • Utilizing analytical methods and the entropy principle to derive probability vectors.

Main Results:

  • The system exhibits distinct behaviors for queue lengths below and above the threshold 'c'.
  • Server failure and repair rates are dependent on service mode (single or batch).
  • Entropy principle provides accurate approximations for initial and tail probabilities, closely matching analytical results.

Conclusions:

  • The proposed queueing model effectively captures complex service dynamics.
  • The entropy principle offers a viable and accurate approximation method for such systems.
  • The findings are valuable for optimizing systems with threshold-based batching and server unreliability.