General Physics I. New Lecture 27: Carnot Cycle, The 2nd Law, Entropy and Information. Prof. WAN, Xin

Similar documents
Lecture 27: Entropy and Information Prof. WAN, Xin

Lecture 27: Entropy and Information Prof. WAN, Xin

Lecture 27: Entropy and Information Prof. WAN, Xin

Physics 207 Lecture 23

Lecture 10: Carnot theorem

Chapters 19 & 20 Heat and the First Law of Thermodynamics

12 The Laws of Thermodynamics

Reversibility. Processes in nature are always irreversible: far from equilibrium

= T. (kj/k) (kj/k) 0 (kj/k) int rev. Chapter 6 SUMMARY

Chapter 20. Heat Engines, Entropy and the Second Law of Thermodynamics. Dr. Armen Kocharian

Chapter 12. The Laws of Thermodynamics. First Law of Thermodynamics

Irreversible Processes

Examples. Fire Piston (demo) Example (Comparison of processes)

Chapter 12. The Laws of Thermodynamics

Atkins / Paula Physical Chemistry, 8th Edition. Chapter 3. The Second Law

Thermodynamic Systems, States, and Processes

Chapter 20 Entropy and the 2nd Law of Thermodynamics

Phase space in classical physics

The Derivative as a Function

Lecture XVII. Abstract We introduce the concept of directional derivative of a scalar function and discuss its relation with the gradient operator.

3.1 Extreme Values of a Function

the first derivative with respect to time is obtained by carefully applying the chain rule ( surf init ) T Tinit

Derivation Of The Schwarzschild Radius Without General Relativity

Higher Derivatives. Differentiable Functions

Heat What is heat? Work = 2. PdV 1

Section 2.7 Derivatives and Rates of Change Part II Section 2.8 The Derivative as a Function. at the point a, to be. = at time t = a is

The derivative function

Click here to see an animation of the derivative

Chapter 12 Thermodynamics

Average Rate of Change

Continuity. Example 1

The Laws of Thermodynamics

Numerical Differentiation

Introduction to Derivatives

How to Find the Derivative of a Function: Calculus 1

Chapter 16 The Second Law of Thermodynamics

LIMITS AND DERIVATIVES CONDITIONS FOR THE EXISTENCE OF A LIMIT

Math 31A Discussion Notes Week 4 October 20 and October 22, 2015

158 Calculus and Structures

Adiabatic Expansion (DQ = 0)

Second Law of Thermodynamics

Reversible Processes. Furthermore, there must be no friction (i.e. mechanical energy loss) or turbulence i.e. it must be infinitely slow.

Entropy and the Second and Third Laws of Thermodynamics

INTRODUCTION TO CALCULUS LIMITS

Lecture Notes Set 4c: Heat engines and the Carnot cycle

Brazilian Journal of Physics, vol. 29, no. 1, March, Ensemble and their Parameter Dierentiation. A. K. Rajagopal. Naval Research Laboratory,

Thermodynamics Lecture Series

Heat Machines (Chapters 18.6, 19)

Physics 231 Lecture 35

Mathematics 5 Worksheet 11 Geometry, Tangency, and the Derivative

2.1 THE DEFINITION OF DERIVATIVE

1. Which one of the following expressions is not equal to all the others? 1 C. 1 D. 25x. 2. Simplify this expression as much as possible.

The Kelvin-Planck statement of the second law

On my honor as a student, I have neither given nor received unauthorized assistance on this exam.

First Law showed the equivalence of work and heat. Suggests engine can run in a cycle and convert heat into useful work.

Lecture 2 Entropy and Second Law

Polynomials 3: Powers of x 0 + h

THE LANDAUER LIMIT AND THERMODYNAMICS OF BIOLOGICAL SYSTEMS

REVIEW LAB ANSWER KEY

Combining functions: algebraic methods

Chapter 20 The Second Law of Thermodynamics

Exponentials and Logarithms Review Part 2: Exponentials

The Derivative The rate of change

2.11 That s So Derivative

Lesson 6: The Derivative

, meant to remind us of the definition of f (x) as the limit of difference quotients: = lim

Integral Calculus, dealing with areas and volumes, and approximate areas under and between curves.

Department of Mechanical Engineering ME 322 Mechanical Engineering Thermodynamics. Introduction to 2 nd Law and Entropy.

Recall from our discussion of continuity in lecture a function is continuous at a point x = a if and only if

PY2005: Thermodynamics

Chapter 2 Ising Model for Ferromagnetism

Even if you're not burning books, destroying information generates heat.

The total error in numerical differentiation

entropy Carnot-Like Heat Engines Versus Low-Dissipation Models Article

NUMERICAL DIFFERENTIATION. James T. Smith San Francisco State University. In calculus classes, you compute derivatives algebraically: for example,

Lecture 2 Entropy and Second Law

Practice Problem Solutions: Exam 1

1. Second Law of Thermodynamics

Derivatives of Exponentials

Notes: DERIVATIVES. Velocity and Other Rates of Change

University Mathematics 2

Continuity and Differentiability Worksheet

SECTION 3.2: DERIVATIVE FUNCTIONS and DIFFERENTIABILITY

1 The concept of limits (p.217 p.229, p.242 p.249, p.255 p.256) 1.1 Limits Consider the function determined by the formula 3. x since at this point

Why gravity is not an entropic force

The Story of Spontaneity and Energy Dispersal. You never get what you want: 100% return on investment

Conductance from Transmission Probability

Lecture 9. Heat engines. Pre-reading: 20.2

NAME and Section No. b). A refrigerator is a Carnot cycle run backwards. That is, heat is now withdrawn from the cold reservoir at T cold

Notes on wavefunctions II: momentum wavefunctions

The Electron in a Potential

WYSE Academic Challenge 2004 Sectional Mathematics Solution Set

1.72, Groundwater Hydrology Prof. Charles Harvey Lecture Packet #9: Numerical Modeling of Groundwater Flow

Chemistry 163B Refrigerators and Generalization of Ideal Gas Carnot (four steps to exactitude) E&R pp 86-91, Raff pp.

1 Limits and Continuity

Preface. Here are a couple of warnings to my students who may be here to get a copy of what happened on a day that you missed.

AMS 147 Computational Methods and Applications Lecture 09 Copyright by Hongyun Wang, UCSC. Exact value. Effect of round-off error.

S = S(f) S(i) dq rev /T. ds = dq rev /T

Math 212-Lecture 9. For a single-variable function z = f(x), the derivative is f (x) = lim h 0

Last lecture (#4): J vortex. J tr

Transcription:

General Pysics I New Lecture 27: Carnot Cycle, e 2nd Law, Entropy and Information Prof. AN, Xin xinwan@zju.edu.cn ttp://zimp.zju.edu.cn/~xinwan/

Carnot s Engine

Efficiency of a Carnot Engine isotermal process adiabatic process

Efficiency of a Carnot Engine

Efficiency of a Carnot Engine All Carnot engines operating between te same two temperatures ave te same efficiency.

Carnot Cycle e efficiency of te Carnot cycle only depends on te temperatures c &. e efficiency approaces 1 wen c / 0, or c 0. is te absolute energy scale, in units of K. Carnot cycle is a reversible cycle quasistatic wit no dissipation.

Reverse Carnot Cycle

Carnot s eorem No real eat engine operating between two energy reservoirs can be more efficient tan my engine operating between te same two reservoirs. -- Sadi Carnot

at if not? If we suppose carnot + =?

Matc ork First Matc ork First If we suppose + = 0 1 1 carnot carnot

Clausius Statement It is impossible to construct a cyclical macine wose sole effect is te continuous transfer of energy from one object to anoter object at a iger temperature witout te input of energy by work. No perfect refrigerator!

Matc Heat at te Cold End Matc Heat at te Cold End If we suppose + = 0 1 1 1 1 carnot c c c c carnot c c

Kelvin Statement It is impossible to construct a eat engine tat, operating in a cycle, produces no effect oter tan te absorption of energy from a reservoir and te performance of an equal amount of work. No perfect eat engine!

Laws in Plain Language e 1st and 2nd laws of termodynamics can be summarized as follows: e first law specifies tat we cannot get more energy out of a cyclic process by work tan te amount of energy we put in. e second law states tat we cannot break even because we must put more energy in, at te iger temperature, tan te net amount of energy we get out by work.

An Equality Now putting in te proper signs, positive c c Carnot Cycle 0 d negative 0

A Sum of Carnot Cycles P adiabats,i Any reversible process can be approximated by a sum of Carnot cycles, ence i, i, i c, i c, i 0 C d 0 c,i

Clausius Definition of Entropy Entropy is a state function, te cange in entropy during a process depends only on te end points and is independent of te actual pat followed. C 2 2 C ds ds 12 d C 1, 2 reversible ds,21 C ds 0 1 C 1 S 2 S 1 ds ds C1,1 2 C2,21 C 2 ds,1 2

Return to Inexact Differential Assume (2,1) (1,1) dg (2,2) (2,1) dx x y dx dy x y dy 1 2ln 2 (1,2) (1,1) (2,2) (1,2) dx x y dy ln 2 1 Note: df dg x dx x dy y is an exact differential. Integrating factor f ( x, y) ln x ln y f 0

e Second Law in terms of Entropy e total entropy of an isolated system tat undergoes a cange can never decrease. If te process is irreversible, ten te total entropy of an isolated system always increases. In a reversible process, te total entropy of an isolated system remains constant. e cange in entropy of te Universe must be greater tan zero for an irreversible process and equal to zero for a reversible process. S Universe 0

Example 1: Clausius Statement S S c c S S S c c 0 Irreversible!

Example 2: Kelvin Statement S 0 Irreversible!

Example 3: Free Expansion U? 0 S 0 e can only calculate S wit a reversible process! In tis case, we replace te free expansion by te isotermal process wit te same initial and final states. S i f d i f Pd i f nrd f nr ln 0 i Irreversible!

Entropy: A Measure of Disorder Entropy: A Measure of Disorder ln 2 ln B i f B Nk Nk S k S B ln N m f f N m i i N i f i f e assume tat eac molecule occupies some microscopic volume m. suggesting (Boltzmann)

Order versus Disorder Isolated systems tend toward disorder and tat entropy is a measure of tis disorder. Ordered: all molecules on te left side Disordered: molecules on te left and rigt

Landauer s Principle & erification Computation needs to involve eat dissipation only wen you do someting irreversible wit te information. Lutz group (2012) k B ln 2 0.693

Information and Entropy (1927) Bell Labs, Ralp Hartley Measure for information in a message Logaritm: 8 bit = 2 8 = 256 different numbers (1940) Bell Labs, Claude Sannon A matematical teory of communication Probability of a particular message But tere is no information. You are not winning te lottery.

Information and Entropy (1927) Bell Labs, Ralp Hartley Measure for information in a message Logaritm: 8 bit = 2 8 = 256 different numbers (1940) Bell Labs, Claude Sannon A matematical teory of communication Probability of a particular message Now tat s someting. Okay, you are going to win te lottery.

Information and Entropy (1927) Bell Labs, Ralp Hartley Measure for information in a message Logaritm: 8 bit = 2 8 = 256 different numbers (1940) Bell Labs, Claude Sannon A matematical teory of communication Probability of a particular message Information ~ - log (probability) ~ negative entropy S infomation i P i log P i

For ose o Are Interested Reading (downloadable from my website): Carles Bennett and Rolf Landauer, e fundamental pysical limits of computation. Antoine Bérut et al., Experimental verification of Landauer s principle linking information and termodynamics, Nature (2012). Set Lloyd, Ultimate pysical limits to computation, Nature (2000). Dare to adventure were you ave not been!