ESCI 341 Atmospheric Thermodynamics Lesson 10 The Physical Meaning of Entropy

Size: px
Start display at page:

Download "ESCI 341 Atmospheric Thermodynamics Lesson 10 The Physical Meaning of Entropy"

Transcription

1 ESCI 341 Atmospherc Thermodynamcs Lesson 10 The Physcal Meanng of Entropy References: An Introducton to Statstcal Thermodynamcs, T.L. Hll An Introducton to Thermodynamcs and Thermostatstcs, H.B. Callen An Introducton to Informaton Theory: Symbols, Sgnals and ose, J.R. Perce STATISTICAL MECHAICS Unlke other thermodynamc varables (e.g. U, T, H, G, F), entropy seemngly lacks a physcal meanng. Entropy does have a concrete physcal meanng, but ts meanng s found va the feld of statstcal mechancs. Statstcal mechancs s also referred to at tmes as statstcal physcs, thermostatstcs, or statstcal thermodynamcs. Thermodynamcs apples to macroscopc (large) systems consstng of on the order of at least 10 0 molecules. Thermodynamcs looks at the large scale propertes. Statstcal mechancs looks at the molecular level. Statstcal mechancs and thermodynamcs are ntmately related. Thermodynamc concepts such as pressure and temperature are ted to processes occurrng on the molecular level, but averaged over a large number of molecules. Ar parcels, cloud droplets, etc. are all macroscopc systems, and so as meteorologsts we get along just fne usng thermodynamcs. However, n order to really understand entropy we have to resort to statstcal mechancs. Ths lesson s just a very, very bref overvew of the feld of statstcal mechancs, and s meant only to gve a flavor of how t relates to thermodynamcs. In partcular, the man objectve of ths lesson s to gve a better understandng of entropy. QUATUM STATES A system of matter exsts n dscrete quantum states. Each quantum state has a certan energy level assocated wth t. A certan energy level may be assocated wth two or more quantum states, n whch case we say that partcular energy level s degenerate.

2 If n quantum states have the same energy level, the degeneracy for that energy level s = n. The possble energy levels for the system are denoted as E where s a postve nteger (0, 1,, 3, ). refers to the avalable energy levels, not the quantum states. The degeneracy of energy level E s denoted as. For macroscopc objects the dfference between adjacent energy levels s nfntesmally small. For macroscopc systems we don t need to bother wth quantum states, as t appears that there s a contnuous spectrum of energy. For mcroscopc systems the energy spectrum s not contnuous, but s dscrete. QUATUM FLUCTUATIOS For smplcty, assume our mcroscopc system s composed of number of molecules of a sngle substance contaned n a volume of V. The system s mmersed n a macroscopc heat bath that has a constant temperature of T. Our mcroscopc system s a closed but not solated system. Our mcroscopc system can exst n an nfnte number of quantum states, each wth ts own energy level. Rather than remanng n a sngle quantum state, the system actually fluctuates through all avalable quantum states. Over a long perod of tme the system wll actually spend some fnte amount of tme n every avalable quantum state. The energy of our mcroscopc system wll therefore fluctuate as t moves from one quantum state to another. The probablty of fndng the system n a specfc quantum state s gven by P E e kt, (1) Q where the functon Q s called the partton functon and s gven by (k s the Boltzmann constant). Q E e kt ()

3 The probablty of the system beng n a certan energy level, E, s gven by E, V kt e P (, V, T), (3) Q(, V, T) Recall that s the degeneracy of energy level, and s the number of ndvdual quantum states that have an energy value of E. PHYSICAL MEAIG OF ETROPY We ve very brefly attempted to descrbe the relatonshp between thermodynamcs and statstcal mechancs. The man reason we ve done ths s so that we can try to gve entropy a physcal meanng. In statstcal mechancs entropy s gven by S k P ln P (4) where P s the probablty of the system beng n quantum state, gven by Eqn. (1). Some examples may help. Imagne that a hypothetcal system has three dfferent possble quantum states, = 1 to 3, wth probabltes gven by P 11/16 1 P 3 /16 P 1/ 8 then from equaton (1) the entropy equal to 3 S k ln /16 ln 3/16 (1/ 8)ln(1/ 8) 0.831k. If nstead all three quantum states had the same energy level, and were therefore all equally probable, the entropy would be S k 1 3 ln 1 3 1/ 3 ln 1/ 3 (1/ 3)ln(1/ 3) 1.099k. The second system has hgher entropy because there s less certanty as to whch quantum state the system s n. Entropy s a measure of the uncertanty of the quantum state of the system. 3

4 ETROPY, TEMPERATURE, AD ABSOLUTE ZERO At hgher temperatures the probabltes of the system occupyng a hgherenergy quantum state become larger. The probabltes are spread over more possble quantum states. Ths leads to more uncertanty as to the quantum state of the system, and s why hgher temperatures generally have hgher entropy. As a system s cooled, the lower-energy quantum states have the hghest probablty, and the hgher-energy quantum states become less probable. There s more certanty of the quantum states at low temperature, and s why lower temperatures are assocated wth lower entropy. As absolute zero s approached, the only quantum states avalable are those that have the mnmum amount of energy. The hgher-energy quantum states are so mprobable that t s nearly certan whch states the system are n, and the entropy approaches zero. If the lowest energy state s nondegenerate, then at absolute zero the system s certanly n ths sngle quantum state. From equaton (4) the entropy would be zero at absolute zero for a system havng a nondegenerate, lowest energy quantum state. If the system has a degenerate lowest-energy level (multple quantum states havng the lowest possble energy), then the entropy of the system wll be nonzero even at absolute zero. ETROPY, UCERTAITY, AD RADOMESS Because entropy s assocated wth the amount of uncertanty of the quantum states of a system, hgher entropy s often assocated wth randomness (hgh uncertanty) whle lower entropy s assocated wth order (low uncertanty). It s mportant to keep n mnd that entropy was defned n terms of quantum states n the context of statstcal mechancs and thermodynamcs. Many tmes we see entropy appled (or msappled) n other contexts. For example, magne a bn contanng both red beads and whte beads. If the red and whte beads are separated, they presumably have more order. If they are mxed together, then presumably they are more dsordered. Some 4

5 would say that the mxed beads have a hgher entropy than the separated beads. However, t s not approprate to apply the thermodynamc concept of entropy to ths stuaton. There s a type of entropy that s applcable to the bead example, but t s not the thermodynamc entropy. Instead, t s a mathematcal entropy that appears n the branch of appled mathematcs known as nformaton theory (see the Perce reference for a good, layman s descrpton of nformaton theory). Thermodynamc entropy and nformaton theory entropy are analogous, and behave n smlar ways, but are two dstnct concepts. When people speak of randomness n nature as havng hgher entropy than order n nature, t s the entropy from nformaton theory that they speak of, not thermodynamc entropy. The second law of thermodynamcs apples to thermodynamc entropy, not to the entropy from nformaton theory. However, there are scentsts and mathematcans who are explorng possble connectons between the two entropes. A FIAL THOUGHT O THE PARTITIO FUCTIO The partton functon s of fundamental mportance when relatng statstcal mechancs to thermodynamcs, because all macroscopc state varables can be found from the partton functon. For example: ln Q p kt V U T, ln Q kt T V, ln Q S kt k ln Q T V, 5

University of Washington Department of Chemistry Chemistry 453 Winter Quarter 2015

University of Washington Department of Chemistry Chemistry 453 Winter Quarter 2015 Lecture 2. 1/07/15-1/09/15 Unversty of Washngton Department of Chemstry Chemstry 453 Wnter Quarter 2015 We are not talkng about truth. We are talkng about somethng that seems lke truth. The truth we want

More information

Thermodynamics and statistical mechanics in materials modelling II

Thermodynamics and statistical mechanics in materials modelling II Course MP3 Lecture 8/11/006 (JAE) Course MP3 Lecture 8/11/006 Thermodynamcs and statstcal mechancs n materals modellng II A bref résumé of the physcal concepts used n materals modellng Dr James Ellott.1

More information

Chapter 1. Probability

Chapter 1. Probability Chapter. Probablty Mcroscopc propertes of matter: quantum mechancs, atomc and molecular propertes Macroscopc propertes of matter: thermodynamcs, E, H, C V, C p, S, A, G How do we relate these two propertes?

More information

Lecture 4. Macrostates and Microstates (Ch. 2 )

Lecture 4. Macrostates and Microstates (Ch. 2 ) Lecture 4. Macrostates and Mcrostates (Ch. ) The past three lectures: we have learned about thermal energy, how t s stored at the mcroscopc level, and how t can be transferred from one system to another.

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

STATISTICAL MECHANICS

STATISTICAL MECHANICS STATISTICAL MECHANICS Thermal Energy Recall that KE can always be separated nto 2 terms: KE system = 1 2 M 2 total v CM KE nternal Rgd-body rotaton and elastc / sound waves Use smplfyng assumptons KE of

More information

STATISTICAL MECHANICAL ENSEMBLES 1 MICROSCOPIC AND MACROSCOPIC VARIABLES PHASE SPACE ENSEMBLES. CHE 524 A. Panagiotopoulos 1

STATISTICAL MECHANICAL ENSEMBLES 1 MICROSCOPIC AND MACROSCOPIC VARIABLES PHASE SPACE ENSEMBLES. CHE 524 A. Panagiotopoulos 1 CHE 54 A. Panagotopoulos STATSTCAL MECHACAL ESEMBLES MCROSCOPC AD MACROSCOPC ARABLES The central queston n Statstcal Mechancs can be phrased as follows: f partcles (atoms, molecules, electrons, nucle,

More information

Density matrix. c α (t)φ α (q)

Density matrix. c α (t)φ α (q) Densty matrx Note: ths s supplementary materal. I strongly recommend that you read t for your own nterest. I beleve t wll help wth understandng the quantum ensembles, but t s not necessary to know t n

More information

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction

The Multiple Classical Linear Regression Model (CLRM): Specification and Assumptions. 1. Introduction ECONOMICS 5* -- NOTE (Summary) ECON 5* -- NOTE The Multple Classcal Lnear Regresson Model (CLRM): Specfcaton and Assumptons. Introducton CLRM stands for the Classcal Lnear Regresson Model. The CLRM s also

More information

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur

Module 3 LOSSY IMAGE COMPRESSION SYSTEMS. Version 2 ECE IIT, Kharagpur Module 3 LOSSY IMAGE COMPRESSION SYSTEMS Verson ECE IIT, Kharagpur Lesson 6 Theory of Quantzaton Verson ECE IIT, Kharagpur Instructonal Objectves At the end of ths lesson, the students should be able to:

More information

Physics 181. Particle Systems

Physics 181. Particle Systems Physcs 181 Partcle Systems Overvew In these notes we dscuss the varables approprate to the descrpton of systems of partcles, ther defntons, ther relatons, and ther conservatons laws. We consder a system

More information

THERMAL DISTRIBUTION IN THE HCL SPECTRUM OBJECTIVE

THERMAL DISTRIBUTION IN THE HCL SPECTRUM OBJECTIVE ame: THERMAL DISTRIBUTIO I THE HCL SPECTRUM OBJECTIVE To nvestgate a system s thermal dstrbuton n dscrete states; specfcally, determne HCl gas temperature from the relatve occupatons of ts rotatonal states.

More information

Grover s Algorithm + Quantum Zeno Effect + Vaidman

Grover s Algorithm + Quantum Zeno Effect + Vaidman Grover s Algorthm + Quantum Zeno Effect + Vadman CS 294-2 Bomb 10/12/04 Fall 2004 Lecture 11 Grover s algorthm Recall that Grover s algorthm for searchng over a space of sze wors as follows: consder the

More information

EGR 544 Communication Theory

EGR 544 Communication Theory EGR 544 Communcaton Theory. Informaton Sources Z. Alyazcoglu Electrcal and Computer Engneerng Department Cal Poly Pomona Introducton Informaton Source x n Informaton sources Analog sources Dscrete sources

More information

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur

Module 2. Random Processes. Version 2 ECE IIT, Kharagpur Module Random Processes Lesson 6 Functons of Random Varables After readng ths lesson, ou wll learn about cdf of functon of a random varable. Formula for determnng the pdf of a random varable. Let, X be

More information

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced,

FREQUENCY DISTRIBUTIONS Page 1 of The idea of a frequency distribution for sets of observations will be introduced, FREQUENCY DISTRIBUTIONS Page 1 of 6 I. Introducton 1. The dea of a frequency dstrbuton for sets of observatons wll be ntroduced, together wth some of the mechancs for constructng dstrbutons of data. Then

More information

Statistical mechanics handout 4

Statistical mechanics handout 4 Statstcal mechancs handout 4 Explan dfference between phase space and an. Ensembles As dscussed n handout three atoms n any physcal system can adopt any one of a large number of mcorstates. For a quantum

More information

= z 20 z n. (k 20) + 4 z k = 4

= z 20 z n. (k 20) + 4 z k = 4 Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5

More information

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential

Open Systems: Chemical Potential and Partial Molar Quantities Chemical Potential Open Systems: Chemcal Potental and Partal Molar Quanttes Chemcal Potental For closed systems, we have derved the followng relatonshps: du = TdS pdv dh = TdS + Vdp da = SdT pdv dg = VdP SdT For open systems,

More information

Lecture 7: Boltzmann distribution & Thermodynamics of mixing

Lecture 7: Boltzmann distribution & Thermodynamics of mixing Prof. Tbbtt Lecture 7 etworks & Gels Lecture 7: Boltzmann dstrbuton & Thermodynamcs of mxng 1 Suggested readng Prof. Mark W. Tbbtt ETH Zürch 13 März 018 Molecular Drvng Forces Dll and Bromberg: Chapters

More information

x = , so that calculated

x = , so that calculated Stat 4, secton Sngle Factor ANOVA notes by Tm Plachowsk n chapter 8 we conducted hypothess tests n whch we compared a sngle sample s mean or proporton to some hypotheszed value Chapter 9 expanded ths to

More information

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body

χ x B E (c) Figure 2.1.1: (a) a material particle in a body, (b) a place in space, (c) a configuration of the body Secton.. Moton.. The Materal Body and Moton hyscal materals n the real world are modeled usng an abstract mathematcal entty called a body. Ths body conssts of an nfnte number of materal partcles. Shown

More information

10.40 Appendix Connection to Thermodynamics and Derivation of Boltzmann Distribution

10.40 Appendix Connection to Thermodynamics and Derivation of Boltzmann Distribution 10.40 Appendx Connecton to Thermodynamcs Dervaton of Boltzmann Dstrbuton Bernhardt L. Trout Outlne Cannoncal ensemble Maxmumtermmethod Most probable dstrbuton Ensembles contnued: Canoncal, Mcrocanoncal,

More information

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1

j) = 1 (note sigma notation) ii. Continuous random variable (e.g. Normal distribution) 1. density function: f ( x) 0 and f ( x) dx = 1 Random varables Measure of central tendences and varablty (means and varances) Jont densty functons and ndependence Measures of assocaton (covarance and correlaton) Interestng result Condtonal dstrbutons

More information

Thermodynamics General

Thermodynamics General Thermodynamcs General Lecture 1 Lecture 1 s devoted to establshng buldng blocks for dscussng thermodynamcs. In addton, the equaton of state wll be establshed. I. Buldng blocks for thermodynamcs A. Dmensons,

More information

Einstein-Podolsky-Rosen Paradox

Einstein-Podolsky-Rosen Paradox H 45 Quantum Measurement and Spn Wnter 003 Ensten-odolsky-Rosen aradox The Ensten-odolsky-Rosen aradox s a gedanken experment desgned to show that quantum mechancs s an ncomplete descrpton of realty. The

More information

} Often, when learning, we deal with uncertainty:

} Often, when learning, we deal with uncertainty: Uncertanty and Learnng } Often, when learnng, we deal wth uncertanty: } Incomplete data sets, wth mssng nformaton } Nosy data sets, wth unrelable nformaton } Stochastcty: causes and effects related non-determnstcally

More information

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests

Simulated Power of the Discrete Cramér-von Mises Goodness-of-Fit Tests Smulated of the Cramér-von Mses Goodness-of-Ft Tests Steele, M., Chaselng, J. and 3 Hurst, C. School of Mathematcal and Physcal Scences, James Cook Unversty, Australan School of Envronmental Studes, Grffth

More information

A quote of the week (or camel of the week): There is no expedience to which a man will not go to avoid the labor of thinking. Thomas A.

A quote of the week (or camel of the week): There is no expedience to which a man will not go to avoid the labor of thinking. Thomas A. A quote of the week (or camel of the week): here s no expedence to whch a man wll not go to avod the labor of thnkng. homas A. Edson Hess law. Algorthm S Select a reacton, possbly contanng specfc compounds

More information

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations

Robert Eisberg Second edition CH 09 Multielectron atoms ground states and x-ray excitations Quantum Physcs 量 理 Robert Esberg Second edton CH 09 Multelectron atoms ground states and x-ray exctatons 9-01 By gong through the procedure ndcated n the text, develop the tme-ndependent Schroednger equaton

More information

and Statistical Mechanics Material Properties

and Statistical Mechanics Material Properties Statstcal Mechancs and Materal Propertes By Kuno TAKAHASHI Tokyo Insttute of Technology, Tokyo 15-855, JAPA Phone/Fax +81-3-5734-3915 takahak@de.ttech.ac.jp http://www.de.ttech.ac.jp/~kt-lab/ Only for

More information

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law:

Introduction to Vapor/Liquid Equilibrium, part 2. Raoult s Law: CE304, Sprng 2004 Lecture 4 Introducton to Vapor/Lqud Equlbrum, part 2 Raoult s Law: The smplest model that allows us do VLE calculatons s obtaned when we assume that the vapor phase s an deal gas, and

More information

Expected Value and Variance

Expected Value and Variance MATH 38 Expected Value and Varance Dr. Neal, WKU We now shall dscuss how to fnd the average and standard devaton of a random varable X. Expected Value Defnton. The expected value (or average value, or

More information

Problem Set 9 Solutions

Problem Set 9 Solutions Desgn and Analyss of Algorthms May 4, 2015 Massachusetts Insttute of Technology 6.046J/18.410J Profs. Erk Demane, Srn Devadas, and Nancy Lynch Problem Set 9 Solutons Problem Set 9 Solutons Ths problem

More information

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X

3.1 Expectation of Functions of Several Random Variables. )' be a k-dimensional discrete or continuous random vector, with joint PMF p (, E X E X1 E X Statstcs 1: Probablty Theory II 37 3 EPECTATION OF SEVERAL RANDOM VARIABLES As n Probablty Theory I, the nterest n most stuatons les not on the actual dstrbuton of a random vector, but rather on a number

More information

Chapter 13: Multiple Regression

Chapter 13: Multiple Regression Chapter 13: Multple Regresson 13.1 Developng the multple-regresson Model The general model can be descrbed as: It smplfes for two ndependent varables: The sample ft parameter b 0, b 1, and b are used to

More information

Quantum and Classical Information Theory with Disentropy

Quantum and Classical Information Theory with Disentropy Quantum and Classcal Informaton Theory wth Dsentropy R V Ramos rubensramos@ufcbr Lab of Quantum Informaton Technology, Department of Telenformatc Engneerng Federal Unversty of Ceara - DETI/UFC, CP 6007

More information

Lecture 3: Probability Distributions

Lecture 3: Probability Distributions Lecture 3: Probablty Dstrbutons Random Varables Let us begn by defnng a sample space as a set of outcomes from an experment. We denote ths by S. A random varable s a functon whch maps outcomes nto the

More information

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore

Predictive Analytics : QM901.1x Prof U Dinesh Kumar, IIMB. All Rights Reserved, Indian Institute of Management Bangalore Sesson Outlne Introducton to classfcaton problems and dscrete choce models. Introducton to Logstcs Regresson. Logstc functon and Logt functon. Maxmum Lkelhood Estmator (MLE) for estmaton of LR parameters.

More information

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP

FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP C O L L O Q U I U M M A T H E M A T I C U M VOL. 80 1999 NO. 1 FACTORIZATION IN KRULL MONOIDS WITH INFINITE CLASS GROUP BY FLORIAN K A I N R A T H (GRAZ) Abstract. Let H be a Krull monod wth nfnte class

More information

5.60 Thermodynamics & Kinetics Spring 2008

5.60 Thermodynamics & Kinetics Spring 2008 MIT OpenCourseWare http://ocw.mt.edu 5.60 Thermodynamcs & Knetcs Sprng 2008 For nformaton about ctng these materals or our Terms of Use, vst: http://ocw.mt.edu/terms. 5.60 Sprng 2008 Lecture #29 page 1

More information

3. Be able to derive the chemical equilibrium constants from statistical mechanics.

3. Be able to derive the chemical equilibrium constants from statistical mechanics. Lecture #17 1 Lecture 17 Objectves: 1. Notaton of chemcal reactons 2. General equlbrum 3. Be able to derve the chemcal equlbrum constants from statstcal mechancs. 4. Identfy how nondeal behavor can be

More information

Polynomials. 1 More properties of polynomials

Polynomials. 1 More properties of polynomials Polynomals 1 More propertes of polynomals Recall that, for R a commutatve rng wth unty (as wth all rngs n ths course unless otherwse noted), we defne R[x] to be the set of expressons n =0 a x, where a

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for U Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for U Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Adjusted Control Lmts for U Charts Copyrght 207 by Taylor Enterprses, Inc., All Rghts Reserved. Adjusted Control Lmts for U Charts Dr. Wayne A. Taylor Abstract: U charts are used

More information

THEOREMS OF QUANTUM MECHANICS

THEOREMS OF QUANTUM MECHANICS THEOREMS OF QUANTUM MECHANICS In order to develop methods to treat many-electron systems (atoms & molecules), many of the theorems of quantum mechancs are useful. Useful Notaton The matrx element A mn

More information

6. Stochastic processes (2)

6. Stochastic processes (2) Contents Markov processes Brth-death processes Lect6.ppt S-38.45 - Introducton to Teletraffc Theory Sprng 5 Markov process Consder a contnuous-tme and dscrete-state stochastc process X(t) wth state space

More information

Chapter 9: Statistical Inference and the Relationship between Two Variables

Chapter 9: Statistical Inference and the Relationship between Two Variables Chapter 9: Statstcal Inference and the Relatonshp between Two Varables Key Words The Regresson Model The Sample Regresson Equaton The Pearson Correlaton Coeffcent Learnng Outcomes After studyng ths chapter,

More information

6. Stochastic processes (2)

6. Stochastic processes (2) 6. Stochastc processes () Lect6.ppt S-38.45 - Introducton to Teletraffc Theory Sprng 5 6. Stochastc processes () Contents Markov processes Brth-death processes 6. Stochastc processes () Markov process

More information

Lecture 3: Boltzmann distribution

Lecture 3: Boltzmann distribution Lecture 3: Boltzmann dstrbuton Statstcal mechancs: concepts Ams: Dervaton of Boltzmann dstrbuton: Basc postulate of statstcal mechancs. All states equally lkely. Equal a-pror probablty: Statstcal vew of

More information

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity

LINEAR REGRESSION ANALYSIS. MODULE IX Lecture Multicollinearity LINEAR REGRESSION ANALYSIS MODULE IX Lecture - 30 Multcollnearty Dr. Shalabh Department of Mathematcs and Statstcs Indan Insttute of Technology Kanpur 2 Remedes for multcollnearty Varous technques have

More information

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient

Lab 2e Thermal System Response and Effective Heat Transfer Coefficient 58:080 Expermental Engneerng 1 OBJECTIVE Lab 2e Thermal System Response and Effectve Heat Transfer Coeffcent Warnng: though the experment has educatonal objectves (to learn about bolng heat transfer, etc.),

More information

Linear Approximation with Regularization and Moving Least Squares

Linear Approximation with Regularization and Moving Least Squares Lnear Approxmaton wth Regularzaton and Movng Least Squares Igor Grešovn May 007 Revson 4.6 (Revson : March 004). 5 4 3 0.5 3 3.5 4 Contents: Lnear Fttng...4. Weghted Least Squares n Functon Approxmaton...

More information

Review of Classical Thermodynamics

Review of Classical Thermodynamics Revew of Classcal hermodynamcs Physcs 4362, Lecture #1, 2 Syllabus What s hermodynamcs? 1 [A law] s more mpressve the greater the smplcty of ts premses, the more dfferent are the knds of thngs t relates,

More information

Entropy generation in a chemical reaction

Entropy generation in a chemical reaction Entropy generaton n a chemcal reacton E Mranda Área de Cencas Exactas COICET CCT Mendoza 5500 Mendoza, rgentna and Departamento de Físca Unversdad aconal de San Lus 5700 San Lus, rgentna bstract: Entropy

More information

Outline. Unit Eight Calculations with Entropy. The Second Law. Second Law Notes. Uses of Entropy. Entropy is a Property.

Outline. Unit Eight Calculations with Entropy. The Second Law. Second Law Notes. Uses of Entropy. Entropy is a Property. Unt Eght Calculatons wth Entropy Mechancal Engneerng 370 Thermodynamcs Larry Caretto October 6, 010 Outlne Quz Seven Solutons Second law revew Goals for unt eght Usng entropy to calculate the maxmum work

More information

Lecture 17 : Stochastic Processes II

Lecture 17 : Stochastic Processes II : Stochastc Processes II 1 Contnuous-tme stochastc process So far we have studed dscrete-tme stochastc processes. We studed the concept of Makov chans and martngales, tme seres analyss, and regresson analyss

More information

SIMULATION OF WAVE PROPAGATION IN AN HETEROGENEOUS ELASTIC ROD

SIMULATION OF WAVE PROPAGATION IN AN HETEROGENEOUS ELASTIC ROD SIMUATION OF WAVE POPAGATION IN AN HETEOGENEOUS EASTIC OD ogéro M Saldanha da Gama Unversdade do Estado do o de Janero ua Sào Francsco Xaver 54, sala 5 A 559-9, o de Janero, Brasl e-mal: rsgama@domancombr

More information

10.34 Fall 2015 Metropolis Monte Carlo Algorithm

10.34 Fall 2015 Metropolis Monte Carlo Algorithm 10.34 Fall 2015 Metropols Monte Carlo Algorthm The Metropols Monte Carlo method s very useful for calculatng manydmensonal ntegraton. For e.g. n statstcal mechancs n order to calculate the prospertes of

More information

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor

Copyright 2017 by Taylor Enterprises, Inc., All Rights Reserved. Adjusted Control Limits for P Charts. Dr. Wayne A. Taylor Taylor Enterprses, Inc. Control Lmts for P Charts Copyrght 2017 by Taylor Enterprses, Inc., All Rghts Reserved. Control Lmts for P Charts Dr. Wayne A. Taylor Abstract: P charts are used for count data

More information

Q e E i /k B. i i i i

Q e E i /k B. i i i i Water and Aqueous Solutons 3. Lattce Model of a Flud Lattce Models Lattce models provde a mnmalst, or coarse-graned, framework for descrbng the translatonal, rotatonal, and conformatonal degrees of freedom

More information

10.34 Numerical Methods Applied to Chemical Engineering Fall Homework #3: Systems of Nonlinear Equations and Optimization

10.34 Numerical Methods Applied to Chemical Engineering Fall Homework #3: Systems of Nonlinear Equations and Optimization 10.34 Numercal Methods Appled to Chemcal Engneerng Fall 2015 Homework #3: Systems of Nonlnear Equatons and Optmzaton Problem 1 (30 ponts). A (homogeneous) azeotrope s a composton of a multcomponent mxture

More information

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis

Statistical analysis using matlab. HY 439 Presented by: George Fortetsanakis Statstcal analyss usng matlab HY 439 Presented by: George Fortetsanaks Roadmap Probablty dstrbutons Statstcal estmaton Fttng data to probablty dstrbutons Contnuous dstrbutons Contnuous random varable X

More information

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH

Turbulence classification of load data by the frequency and severity of wind gusts. Oscar Moñux, DEWI GmbH Kevin Bleibler, DEWI GmbH Turbulence classfcaton of load data by the frequency and severty of wnd gusts Introducton Oscar Moñux, DEWI GmbH Kevn Blebler, DEWI GmbH Durng the wnd turbne developng process, one of the most mportant

More information

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2

Physics 607 Exam 1. ( ) = 1, Γ( z +1) = zγ( z) x n e x2 dx = 1. e x2 Physcs 607 Exam 1 Please be well-organzed, and show all sgnfcant steps clearly n all problems. You are graded on your wor, so please do not just wrte down answers wth no explanaton! Do all your wor on

More information

Statistical mechanics canonical ensemble

Statistical mechanics canonical ensemble canoncal ensemble system n thermal equlbrum wth bath of system probablty of mcro state T = k/ bath system p = 1 Z e E Z = e E average energy of system he = p E = what about? P E E e P e E = @ ln Z @ replcate

More information

Finding Dense Subgraphs in G(n, 1/2)

Finding Dense Subgraphs in G(n, 1/2) Fndng Dense Subgraphs n Gn, 1/ Atsh Das Sarma 1, Amt Deshpande, and Rav Kannan 1 Georga Insttute of Technology,atsh@cc.gatech.edu Mcrosoft Research-Bangalore,amtdesh,annan@mcrosoft.com Abstract. Fndng

More information

CS-433: Simulation and Modeling Modeling and Probability Review

CS-433: Simulation and Modeling Modeling and Probability Review CS-433: Smulaton and Modelng Modelng and Probablty Revew Exercse 1. (Probablty of Smple Events) Exercse 1.1 The owner of a camera shop receves a shpment of fve cameras from a camera manufacturer. Unknown

More information

Thermodynamics Second Law Entropy

Thermodynamics Second Law Entropy Thermodynamcs Second Law Entropy Lana Sherdan De Anza College May 8, 2018 Last tme the Boltzmann dstrbuton (dstrbuton of energes) the Maxwell-Boltzmann dstrbuton (dstrbuton of speeds) the Second Law of

More information

II.D Many Random Variables

II.D Many Random Variables II.D Many Random Varables Wth more than one random varable, the set of outcomes s an -dmensonal space, S x = { < x 1, x 2,, x < }. For example, descrbng the locaton and velocty of a gas partcle requres

More information

The Feynman path integral

The Feynman path integral The Feynman path ntegral Aprl 3, 205 Hesenberg and Schrödnger pctures The Schrödnger wave functon places the tme dependence of a physcal system n the state, ψ, t, where the state s a vector n Hlbert space

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

Comparison of Regression Lines

Comparison of Regression Lines STATGRAPHICS Rev. 9/13/2013 Comparson of Regresson Lnes Summary... 1 Data Input... 3 Analyss Summary... 4 Plot of Ftted Model... 6 Condtonal Sums of Squares... 6 Analyss Optons... 7 Forecasts... 8 Confdence

More information

Chapter 8 SCALAR QUANTIZATION

Chapter 8 SCALAR QUANTIZATION Outlne Chapter 8 SCALAR QUANTIZATION Yeuan-Kuen Lee [ CU, CSIE ] 8.1 Overvew 8. Introducton 8.4 Unform Quantzer 8.5 Adaptve Quantzaton 8.6 Nonunform Quantzaton 8.7 Entropy-Coded Quantzaton Ch 8 Scalar

More information

Section 8.3 Polar Form of Complex Numbers

Section 8.3 Polar Form of Complex Numbers 80 Chapter 8 Secton 8 Polar Form of Complex Numbers From prevous classes, you may have encountered magnary numbers the square roots of negatve numbers and, more generally, complex numbers whch are the

More information

Lecture 6: Introduction to Linear Regression

Lecture 6: Introduction to Linear Regression Lecture 6: Introducton to Lnear Regresson An Manchakul amancha@jhsph.edu 24 Aprl 27 Lnear regresson: man dea Lnear regresson can be used to study an outcome as a lnear functon of a predctor Example: 6

More information

CinChE Problem-Solving Strategy Chapter 4 Development of a Mathematical Model. formulation. procedure

CinChE Problem-Solving Strategy Chapter 4 Development of a Mathematical Model. formulation. procedure nhe roblem-solvng Strategy hapter 4 Transformaton rocess onceptual Model formulaton procedure Mathematcal Model The mathematcal model s an abstracton that represents the engneerng phenomena occurrng n

More information

PES 2130 Fall 2014, Spendier Lecture 7/Page 1

PES 2130 Fall 2014, Spendier Lecture 7/Page 1 PES 2130 Fall 2014, Spender Lecture 7/Page 1 Lecture today: Chapter 20 (ncluded n exam 1) 1) Entropy 2) Second Law o hermodynamcs 3) Statstcal Vew o Entropy Announcements: Next week Wednesday Exam 1! -

More information

Pulse Coded Modulation

Pulse Coded Modulation Pulse Coded Modulaton PCM (Pulse Coded Modulaton) s a voce codng technque defned by the ITU-T G.711 standard and t s used n dgtal telephony to encode the voce sgnal. The frst step n the analog to dgtal

More information

IRO0140 Advanced space time-frequency signal processing

IRO0140 Advanced space time-frequency signal processing IRO4 Advanced space tme-frequency sgnal processng Lecture Toomas Ruuben Takng nto account propertes of the sgnals, we can group these as followng: Regular and random sgnals (are all sgnal parameters determned

More information

Frequency dependence of the permittivity

Frequency dependence of the permittivity Frequency dependence of the permttvty February 7, 016 In materals, the delectrc constant and permeablty are actually frequency dependent. Ths does not affect our results for sngle frequency modes, but

More information

The non-negativity of probabilities and the collapse of state

The non-negativity of probabilities and the collapse of state The non-negatvty of probabltes and the collapse of state Slobodan Prvanovć Insttute of Physcs, P.O. Box 57, 11080 Belgrade, Serba Abstract The dynamcal equaton, beng the combnaton of Schrödnger and Louvlle

More information

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens

THE CHINESE REMAINDER THEOREM. We should thank the Chinese for their wonderful remainder theorem. Glenn Stevens THE CHINESE REMAINDER THEOREM KEITH CONRAD We should thank the Chnese for ther wonderful remander theorem. Glenn Stevens 1. Introducton The Chnese remander theorem says we can unquely solve any par of

More information

U-Pb Geochronology Practical: Background

U-Pb Geochronology Practical: Background U-Pb Geochronology Practcal: Background Basc Concepts: accuracy: measure of the dfference between an expermental measurement and the true value precson: measure of the reproducblty of the expermental result

More information

5.62 Physical Chemistry II Spring 2008

5.62 Physical Chemistry II Spring 2008 MIT OpenCourseWare http://ocw.mt.edu 5.62 Physcal Chemstry II Sprng 2008 For nformaton about ctng these materals or our Terms of Use, vst: http://ocw.mt.edu/terms. 5.62 Lecture #8: Boltzmann, Ferm-Drac,

More information

Global Sensitivity. Tuesday 20 th February, 2018

Global Sensitivity. Tuesday 20 th February, 2018 Global Senstvty Tuesday 2 th February, 28 ) Local Senstvty Most senstvty analyses [] are based on local estmates of senstvty, typcally by expandng the response n a Taylor seres about some specfc values

More information

Markov Chain Monte Carlo (MCMC), Gibbs Sampling, Metropolis Algorithms, and Simulated Annealing Bioinformatics Course Supplement

Markov Chain Monte Carlo (MCMC), Gibbs Sampling, Metropolis Algorithms, and Simulated Annealing Bioinformatics Course Supplement Markov Chan Monte Carlo MCMC, Gbbs Samplng, Metropols Algorthms, and Smulated Annealng 2001 Bonformatcs Course Supplement SNU Bontellgence Lab http://bsnuackr/ Outlne! Markov Chan Monte Carlo MCMC! Metropols-Hastngs

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 151 Lecture 3 Lagrange s Equatons (Goldsten Chapter 1) Hamlton s Prncple (Chapter 2) What We Dd Last Tme! Dscussed mult-partcle systems! Internal and external forces! Laws of acton and

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

Entropy of Markov Information Sources and Capacity of Discrete Input Constrained Channels (from Immink, Coding Techniques for Digital Recorders)

Entropy of Markov Information Sources and Capacity of Discrete Input Constrained Channels (from Immink, Coding Techniques for Digital Recorders) Entropy of Marov Informaton Sources and Capacty of Dscrete Input Constraned Channels (from Immn, Codng Technques for Dgtal Recorders). Entropy of Marov Chans We have already ntroduced the noton of entropy

More information

Linear Regression Analysis: Terminology and Notation

Linear Regression Analysis: Terminology and Notation ECON 35* -- Secton : Basc Concepts of Regresson Analyss (Page ) Lnear Regresson Analyss: Termnology and Notaton Consder the generc verson of the smple (two-varable) lnear regresson model. It s represented

More information

Numerical Solution of Ordinary Differential Equations

Numerical Solution of Ordinary Differential Equations Numercal Methods (CENG 00) CHAPTER-VI Numercal Soluton of Ordnar Dfferental Equatons 6 Introducton Dfferental equatons are equatons composed of an unknown functon and ts dervatves The followng are examples

More information

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method

Comparison of the Population Variance Estimators. of 2-Parameter Exponential Distribution Based on. Multiple Criteria Decision Making Method Appled Mathematcal Scences, Vol. 7, 0, no. 47, 07-0 HIARI Ltd, www.m-hkar.com Comparson of the Populaton Varance Estmators of -Parameter Exponental Dstrbuton Based on Multple Crtera Decson Makng Method

More information

CIE4801 Transportation and spatial modelling Trip distribution

CIE4801 Transportation and spatial modelling Trip distribution CIE4801 ransportaton and spatal modellng rp dstrbuton Rob van Nes, ransport & Plannng 17/4/13 Delft Unversty of echnology Challenge the future Content What s t about hree methods Wth specal attenton for

More information

Rate of Absorption and Stimulated Emission

Rate of Absorption and Stimulated Emission MIT Department of Chemstry 5.74, Sprng 005: Introductory Quantum Mechancs II Instructor: Professor Andre Tokmakoff p. 81 Rate of Absorpton and Stmulated Emsson The rate of absorpton nduced by the feld

More information

California State Science Fair

California State Science Fair Calforna State Scence Far Mathematcal Modelng of Real World Systems Part 1 -- Explorng on Chaos on Your Computer Edward Ruth drruth@x.netcom.com 1) Introducton Mathematcs s the language that scentsts use

More information

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition) Count Data Models See Book Chapter 11 2 nd Edton (Chapter 10 1 st Edton) Count data consst of non-negatve nteger values Examples: number of drver route changes per week, the number of trp departure changes

More information

Solving Nonlinear Differential Equations by a Neural Network Method

Solving Nonlinear Differential Equations by a Neural Network Method Solvng Nonlnear Dfferental Equatons by a Neural Network Method Luce P. Aarts and Peter Van der Veer Delft Unversty of Technology, Faculty of Cvlengneerng and Geoscences, Secton of Cvlengneerng Informatcs,

More information

Channel Encoder. Channel. Figure 7.1: Communication system

Channel Encoder. Channel. Figure 7.1: Communication system Chapter 7 Processes The model of a communcaton system that we have been developng s shown n Fgure 7.. Ths model s also useful for some computaton systems. The source s assumed to emt a stream of symbols.

More information

Supplementary Notes for Chapter 9 Mixture Thermodynamics

Supplementary Notes for Chapter 9 Mixture Thermodynamics Supplementary Notes for Chapter 9 Mxture Thermodynamcs Key ponts Nne major topcs of Chapter 9 are revewed below: 1. Notaton and operatonal equatons for mxtures 2. PVTN EOSs for mxtures 3. General effects

More information

Unit 5: Quadratic Equations & Functions

Unit 5: Quadratic Equations & Functions Date Perod Unt 5: Quadratc Equatons & Functons DAY TOPIC 1 Modelng Data wth Quadratc Functons Factorng Quadratc Epressons 3 Solvng Quadratc Equatons 4 Comple Numbers Smplfcaton, Addton/Subtracton & Multplcaton

More information