In heat engine theory, the term ENTROY plays a very
vital role and leads to important results which by other methods can be
obtained much more laboriously.
It may be noted that, all the heat is equally not
available for converting into work. Heat that is supplied to a substance at
higher temperature has greater possibility of conversion into work rather than
heat supplied to a substance at lower temperature.
Meta Description: Confused by entropy? This guide breaks down the law of
entropy in simple terms. Learn how this fundamental concept of thermodynamics
affects everything from the cosmos to your daily life.
We’ve
all experienced it. An ice cube melts in your drink. Your once-organized desk
slowly descends into chaos. A hot cup of coffee gets colder, never hotter, on
its own. These everyday events seem unrelated, but they are all governed by a
single, powerful, and often misunderstood scientific law: the Second Law of Thermodynamics, often called the Law of Entropy.
In
simple terms:
·
Entropy
is a measure of how energy is distributed in a system.
·
It tells us how much of the system’s energy is unavailable to do
useful work.
·
It also represents the degree of disorder or randomness
of particles within the system.
Entropy is a function of quantity of heat which shows
the possibility of conversion of that heat into work. The increase in entropy
is small when heat is added at higher temperature and is greater when heat
addition is made at a lower temperature.
Thus, the for maximum ENTROPY there is minimum availability
for conversion into work and for minimum entropy there is maximum availability for
conversion into work.
As per the Third law of Thermodynamics: When a system
is at zero absolute temperature, the entropy of a system is zero. That is The
Entropy of all perfect crystalline solids is zero at absolute zero temperature.
Entropy may also be defined as
the thermal property of a substance which remains constant when substance is
expanded or compressed adiabatically in a cylinder.
In the simplest sense, entropy is a measure of disorder
or randomness in a system. In thermodynamics, however, it is
more precisely defined:
Entropy is
a measure of the amount of energy in a system that is unavailable to do useful
work.
This makes entropy a central
concept in the Second Law of Thermodynamics, which states that in any
natural process, the total entropy of an isolated system always increases or
remains constant.
In an isolated system, entropy never decreases.
Processes naturally move toward states of higher entropy.
Characteristics
of Entropy:
1-It increases when the heat is supplied irrespective
of the fact whether temperature changes or not.
2-Entropy decreases when heat is removed whether
temperature changes or not
3-It remains unchanged in all adiabatic frictionless processes
4-Entropy increases if temperature of heat is lowered
without work being done as in a throttling processes.
Entropy and Irreversibility:
One of the most practical consequences
of entropy is that it explains why certain processes are irreversible.
A cup of hot coffee placed on a table
will cool down, but the table will never spontaneously heat the coffee back up.
An inflated balloon can burst, but the
air inside will not naturally return into the balloon.
Entropy is
more than an abstract thermodynamic term — it is a guiding principle of nature.
It tells us why processes have direction, why machines have limits, and why
time seems to move forward. From a melting ice cube to the vast fate of the
universe, entropy governs the flow of energy and the evolution of systems.
Understanding entropy is not only essential for physicists and
engineers but also provides us with a deeper appreciation of the hidden order
behind everyday phenomena.
Two
Sides of the Same Coin: Thermodynamic vs. Informational Entropy
The concept of entropy has also revolutionized the field of information
theory, thanks to Claude Shannon.
·
Thermodynamic
Entropy: Deals with the physical dispersal of energy.
·
Informational
Entropy: Measures uncertainty or the surprise factor
in a message. A string of random letters has high informational entropy (it's
very surprising/unpredictable). A meaningful sentence in English has low
informational entropy (it's predictable and ordered).
