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Thermodynamic Systems01:06

Thermodynamic Systems

6.5K
A thermodynamic system is a set of objects whose thermodynamic properties are of interest. The system is considered to be embedded in its surroundings or the environment. The system and its environment can exchange heat and do work on each other through a boundary that separates them. However, the immediate surroundings of the system interact with it directly and therefore have a much stronger influence on its behavior and properties.
Consider an example of  tea boiling in a kettle. The...
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Path Between Thermodynamics States01:21

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Consider the two thermodynamic processes involving an ideal gas that are represented by paths AC and ABC in Figure 1:
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Second Law of Thermodynamics00:53

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The Second Law of Thermodynamics states that entropy, or the amount of disorder in a system, increases each time energy is transferred or transformed. Each energy transfer results in a certain amount of energy that is lost—usually in the form of heat—that increases the disorder of the surroundings. This can also be demonstrated in a classic food web. Herbivores harvest chemical energy from plants and release heat and carbon dioxide into the environment. Carnivores harvest the...
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Second Law of Thermodynamics02:49

Second Law of Thermodynamics

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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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Thermodynamics: Activity Coefficient01:24

Thermodynamics: Activity Coefficient

2.5K
Activity is the measure of the effective concentration of the species in solution. It can be expressed as the product of the molar concentration of the species and its activity coefficient. The activity coefficient is a dimensionless quantity and depends on the total ionic strength of the solution.
The activity coefficient is a measure of the deviation from ideal behavior. When the ionic strength of the solution is minimal, the activity coefficient of an ionic species is close to unity, making...
2.5K
The Carnot Cycle01:30

The Carnot Cycle

3.7K
Converting work to heat is an irreversible process, and the purpose of a heat engine is to reverse the effect partially. Heat engines aim to increase the efficiency of the reversal, that is, maximize the work retrieved from heat. If the efficiency of a heat engine were 100%, it would imply reversing the process completely without introducing any other effect. Thus, it would violate the second law of thermodynamics.
What could be the theoretical limit to the efficiency of a heat engine? The...
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Related Experiment Video

Updated: Nov 27, 2025

Author Spotlight: Optimization of Airflow Velocities in Battery Cooling Systems for Enhanced Thermal Performance and Reduced Energy Consumption
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Averaged Optimization and Finite-Time Thermodynamics.

Anatoly Tsirlin1, Ivan Sukin1

  • 1Ailamazyan Program Systems Institute of Russian Academy of Sciences, Petra Pervogo st., 4a, Veskovo, Yaroslavl oblast 152021, Russia.

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

This study explores extremum problems involving control variable means, linking them to cyclic dynamical systems and finite-time thermodynamics. Optimal conditions for these cyclic modes are identified and explained.

Keywords:
averagedcyclic modeheat transferoptimizationthermodynamics

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Area of Science:

  • Control theory
  • Dynamical systems
  • Thermodynamics

Background:

  • Extremum problems with mean values are common in control theory.
  • Understanding cyclic modes in dynamical systems is crucial for optimization.
  • Finite-time thermodynamics offers a framework for analyzing non-equilibrium processes.

Purpose of the Study:

  • To analyze extremum problems incorporating mean values of control variables.
  • To establish connections between these problems and cyclic modes of dynamical systems.
  • To derive optimality conditions for cyclic modes and link them to finite-time thermodynamics.

Main Methods:

  • Mathematical formulation of extremum problems with mean values.
  • Analysis of dynamical systems to identify and characterize cyclic modes.
  • Derivation of optimality conditions using calculus of variations or similar techniques.
  • Exploration of connections to finite-time thermodynamic principles.

Main Results:

  • Established relationships between extremum problems and cyclic dynamical system modes.
  • Derived specific optimality conditions for these cyclic modes.
  • Demonstrated the relevance of finite-time thermodynamics to these problems.

Conclusions:

  • Extremum problems with mean values are intrinsically linked to cyclic dynamical behaviors.
  • Optimality conditions for cyclic modes provide insights into system efficiency.
  • Finite-time thermodynamics provides a valuable perspective for analyzing such dynamic optimization problems.