An Improved Model for the Droplet Size Distribution in Sprays Developed From the Principle of Entropy Generation maximization

Size: px
Start display at page:

Download "An Improved Model for the Droplet Size Distribution in Sprays Developed From the Principle of Entropy Generation maximization"

Transcription

1 ILASS Amercas, 9 th Annual Conference on Lqud Atomzaton and Spray Systems, oronto, Canada, May 6 An Improved Model for the Droplet Sze Dstrbuton n Sprays Developed From the Prncple of Entropy Generaton maxmzaton Xanguo L * and Meshen L Department of Mechancal Engneerng Unversty of Waterloo Waterloo, Ontaro NL G Canada Abstract he maxmum entropy prncple (MEP), whch has been popular n the modelng of droplet sze and velocty dstrbuton n sprays, s, strctly speakng, only applcable for solated systems n thermodynamc equlbrum; whereas the spray formaton processes are rreversble and non-solated wth nteracton between the atomzng lqud and ts surroundng gas medum. In ths study, a new model for the droplet sze dstrbuton has been developed based on the thermodynamcally consstent concept the maxmzaton of entropy generaton durng the lqud atomzaton process. he model predcton compares favorably wth the expermentally measured sze dstrbuton for droplets, near the lqud bulk breakup regon, produced by an ar-blast annular nozzle, a planar nozzle and a practcal gas turbne nozzle. herefore, the present model can be used to predct the ntal droplet sze dstrbuton n sprays. * Correspondng author

2 Introducton Snce the later 98s, Maxmum Entropy Prncple (MEP) method has been appled popularly n the spray feld to predct droplet sze and velocty dstrbuton and obtaned reasonable success. he MEP approach can predct the most lkely droplet sze and velocty dstrbutons under a set of constrants expressng the avalable nformaton related to the dstrbuton sought. he applcaton of MEP to spray modelng was poneered by Sellens and Brzustowsk [] and L and ankn []. When only frst prncples are used as constrants, the droplet sze pdf does not go to zero n the formulaton as the droplet sze approaches zero. An addtonal constrant, partton of surface energy, s added by Sellens. L solved ths problem by defnng a probablty dstrbuton as the number probablty of fndng a droplet n a spray wthn a volume bn. he number-based droplet sze dstrbuton n the resultant formulaton goes to zero as the droplet sze approaches zero. hs s consstent wth physcal ntuton and expermental observaton. Other nvestgators have also mplemented and developed ths method n the past decade. Ahmad and Sellens [] reached a concluson that predcton of the droplet sze dstrbuton s ndependent of the velocty dstrbuton and the constrants on momentum and knetc energy carry only velocty nformaton. However, MEP s not physcally consstent wth the real atomzaton process. Strctly speakng, MEP method s only applcable for solated systems n thermodynamc equlbrum, whereas the spray formaton process s nonsolated and rreversble. hs leads to the dffculty of agreement between MEP-based dstrbutons wth varous expermental data. he success of MEP method may be explaned as the devaton from equlbrum condton s small for the cases nvestgated. he major objectve of the current study s to formulate a new model on the predcton of droplet sze dstrbuton based on the thermodynamcally consstent concept the maxmzaton of entropy generaton (MEG) durng the spray formaton whch s actually a non-solated and rreversble process. he valdaton of the model s made by comparng the theoretcal predcton wth the expermental data measured for three types of nozzles, an ar-blast annular nozzle, a planar nozzle and a practcal gas turbne nozzle. he comparson ndcates good agreement between the model predcton and the measured data. Model Formulaton Consderng an atomzer, such as an ar-blast atomzer, that produces flat or concal lqud sheets at the atomzer ext, the atomzaton process starts at the atomzer ext and ends at some downstream locaton where droplets are formed. Here the spray s assumed steady and sothermal. he control volume, as shown n Fgure, s taken from the atomzer ext to the dropletformaton plane. Accordng to the second law n thermodynamcs, we have, ds dt cv m s m s S m s S source gen () For the steady state steady flow process, the rate of entropy ncrease wthn the control volume vanshes. Here, the mass flow rates may be dfferent between the two states (.e., the state at the nozzle ext and at the droplet formaton plane downstream) consderng the possble mass exchange between the system and the surroundng (condensaton or evaporaton). he dfference can be denoted as S m m m, whch s commonly referred to as the mass source term, and the correspondng specfc entropy s denoted as ssource. hen Eq. () can be recast as S gen S S S m s source m s m s S m s source () At the nozzle ext, the flow at state s n bulk lqud form wth free surface produced. he entropy at the nozzle ext s due to the lqud bulk and the surface tenson effect, hence, S S m s ( l) ( l) () In the breakup regon (state ), a multtude of droplets forms and the total surface area s ncreased dramatcally. Lqud phase exsts nsde each of the droplets. he total entropy s composed of two parts. One part s assocated wth the lqud bulk and s smlar to that at the state ; therefore may be denoted as S. he other part s due to the exstence of the numerous ndvdual droplets, represented as m s ( l ) ( l ) S ( d ), whch s drectly assocated wth the partcle nature of the droplets. Hence, S S S S m s ( d ) ( l ) ( d ) ( l) (4) he formaton of droplets s not determnstc but stochastc. he entropy S ( d ) at state should be assocated wth the probablty dstrbutons of the droplet szes. Dfferent dstrbuton results n dfferent amount of entropy. o evaluate entropy quanttatvely, an analogy between the present case and that of Gbbs ensem-

3 ble n statstcal thermodynamcs may be made here. he entropy due to the droplet nature can be derved as, wth detals gven elsewhere [4] S ( d ) k N B total P ln P (5) Accordng to the Gbbs equaton for a smple compressble substance wth free nterfaces, the lqud bulk entropy change can be expressed as ds( l ) du pdv da pvdp da (6) Snce the atomzaton process s assumed sothermal, the nternal energy, only a functon of temperature, s unchanged. Entropy s a thermodynamc property that depends only on the state of the system. Hence ntegratng Eq. (6) over the sothermal process results n, s ( l ) s ( l ) v p p a a (7) At the atomzer ext, a thn lqud sheet forms from the annular ar-blast atomzer, and the lqud pressure s almost the same as the surroundng ar pressure snce the curvature effect s small and neglgble. However, a crcular lqud jet forms for sold-cone sprays produced by small orfce atomzers, the lqud pressure s larger than the ambent ar pressure due to the surface tenson effect. However, the specfc value of p wll not affect the determnaton of the droplet sze dstrbuton as shown later. herefore, for smplcty, we assume p = p g. For lqud pressure at state, let s consder only one lqud droplet. For a lqud droplet n the ar, the pressure dfference across the free nterface s related to the surface tenson effect. herefore, the entropy generaton per unt mass flow rate n the atomzaton process s therefore s gen k B D 6 E D F D S m a P ln P P (8) where D D D, v E 8 D, and v F 6 4 p D. At state, the droplet sze dstrbuton s n realty the ntal dstrbuton for droplets just formed n a spray. here are nfnte sets of the probablty dstrbuton P that can satsfy the global constrants on the atomzaton process such as the conservaton laws. In accordance wth rreversble thermodynamcs, the least based (or the most realstc) dstrbuton s the one that maxmzes the amount of entropy generated for the naturally occurrng atomzaton process. For the present study the constrants mposed on the atomzaton process are the conservaton of lqud mass and the normalzaton condton. he normalzaton condton s P (9) he expresson for the mass conservaton under steady state can be wrtten n non-dmensonalzed form as: P D S m () where S m S m m denotes the dmensonless mass source term. o maxmze the entropy generaton s gen under the constrants of Eq. (9) and Eq. (), the Lagrange s method s adopted here. hat yelds, P exp D D D () o obtan the probablty of fndng the droplets whose volume s between V n and V n, we have to evaluate Vn Vn n V V n P exp D D D P V Vn Vn () It s generally regarded that the droplet sze and velocty n sprays vary contnuously rather than dscretely. herefore, the subscrpt can be dropped, and the summaton form of Eq. () becomes an ntegral over the droplet sze; that s Vn Vn f d V Dn Dn f d D Dn Dn D exp D D D d D () where D n and D n are the droplet dameters correspondng to the droplet volume V n and V n respectvely; and f s the contnuous droplet sze probablty densty functon (pdf). hus, f D exp D D D (4) he above formulaton s equvalent to the dstrbuton from the MEP usng an extended set of constrants. In addton to the normalzaton condton and the conservaton of mass, two other constrants are wrtten on the bass of the defnton of the mean dameters D and D. All the constrants that are needed to determne the unknown coeffcents s are lsted below.

4 max D Dmn Dmax Dmn Dmax Dmn Dmax Dmn D exp D D D d D (5) D D D d D D exp D (6) D D D d D 4 D exp D (7) D D D d D S m 5 D exp (8) Results and Dscusson A modfed Newton-Raphson method s used to solve the set of equatons to obtan the Lagrangan multplers. he expermental data are obtaned from the measurements on an annular ar-blast nozzle, a planar nozzle and a practcal gas turbne nozzle usng a commercal phase Doppler Partcle Analyzer (PDPA). he detals on measurement of the annular ar-blast nozzle can be found n [5] and on the planar nozzle and the gas turbne nozzle can be found n [6]. It s assumed that the surroundng gas medum s fully saturated and therefore no mass transfer occurs between the lqud and the ambent gas from the nozzle ext to the breakup regon. herefore the mass source term s set to zero. In the model, the control volume for the theoretcal formulaton s taken from the atomzer ext to the lqud sheet breakup regon. Due to the dffculty encountered n measurng exactly at the breakup regon, all the measurements are taken beyond that regon. It s found that the dscrepancy between the model predcton and measurement data decreases wth the reducton of the dstance from the measurement locaton to the atomzer ext. he comparsons between the predcted and measured sze probablty dstrbuton for the arblast annular nozzle and the planar nozzle are llustrated n Fg. and Fg., respectvely. he comparson for two cases for each nozzle s shown here. Another comparson s made for the actual gas turbne nozzle provded by Pratt & Whtney Canada (PWC) and lsted n Fg. 4. he feature of the former dstrbuton, Eq. (4), s that the mnmum dameter s always equal to zero. However, the measurement for the PWC nozzle ndcates that the mnmum dameter starts wth a non-zero value. By ntroducng the mnmum dameter D nto the equaton as shown below, the dstrbuton s modfed to take ths fact nto account f D D D D (9) exp D he mnmum dameter s taken from the expermental data and normalzed wth mass mean dameter. he unknown parameters, s, are stll determned as before from the constrants equatons (5) - (8). It s seen that the model predcton agrees very well wth the measured droplet sze probablty dstrbuton. he wdth of the dstrbutons matches each other well for the skewed mono-modal dstrbutons, so do the poston and magntude of the peak of the dstrbuton. Conclusons A new model for the droplet sze dstrbuton n sprays has been formulated based on the physcally consstent concept the maxmzaton of entropy generaton durng the non-solated and rreversble lqud atomzaton process. A comparson between the model predcton and expermentally measured droplet sze dstrbuton produced by an ar-blast annular nozzle, a planar nozzle and a practcal gas turbne nozzle ndcates that the model predcton s n satsfactory agreement wth the measurements. he present model can be used to predct the ntal droplet sze dstrbuton n sprays. Further work s under way to extend the present model to the jont droplet sze and velocty dstrbuton. Nomenclature a surface area per unt mass [m /kg] D arthmetc mean dameter [m] D surface area mean dameter [m] D mass or volume mean dameter [m] k B constant [kj/k] m mass flow rate [kg/s] n droplet number produced per unt tme N total total number of droplets produced n a spray per unt tme p pressure [N/m ] s specfc entropy [kg/(kkg)] S entropy flow rate [kj/(ks)] S gen entropy generaton per unt tme [kj/(ks)] temperature [K] u specfc nternal energy [kj/kg] v specfc volume [m /kg] V volume [m ] sothermal compressblty [m /N] surface tenson [N/m] Subscrpts state state c.v. control volume g gas d droplet

5 l lqud References. Sellens, R. W. and Brzustowsk,. A., A Predcton of the Drop Sze Dstrbuton n a Spray from Frst Prncples, Atomzaton Spray echnol., Vol., 89: (985). L, X. and ankn, R. S., Droplet Sze Dstrbuton: A Dervaton of a Nukyama-anasawa ype Dstrbuton Functon, Combust. Sc. and ech., Vol. 56, 65:76 (987). Ahmad, M. and Sellens, R.W. Atomzaton and Sprays, 9: (99) 4. L, X., L, M. and Fu, H, Atomzaton and Sprays, accepted for publcaton 5. Fu, H., Master s thess, Unversty of Waterloo () 6. Km, W.., Mtra, S.K., L, X., Procw, L.A., Hu,.C.J., Part. Part. Syst. Charact., 5:49 () Control Volume Fgure. Schematc of the control volume chosen (a) Fgure. Comparson between the model predcton and measured droplet sze probablty dstrbuton for the annular nozzle (a: est 6; b: est 6) [5], and data measured at the spray centre-lne at a dstance of mm from the nozzle ext (b)

6 (a) Fgure. Comparson between the model predcton and measured droplet sze probablty dstrbuton for the planar nozzle (a: case II; b: case IV) [6] (b) (a) (b) Fgure 4. Comparson between the model predcton and measured droplet sze probablty dstrbuton for the practcal gas turbne nozzle (a: case II; b: case IV) [6]

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

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

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

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

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

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

Lecture Note 3. Eshelby s Inclusion II

Lecture Note 3. Eshelby s Inclusion II ME340B Elastcty of Mcroscopc Structures Stanford Unversty Wnter 004 Lecture Note 3. Eshelby s Incluson II Chrs Wenberger and We Ca c All rghts reserved January 6, 004 Contents 1 Incluson energy n an nfnte

More information

Solution Thermodynamics

Solution Thermodynamics Soluton hermodynamcs usng Wagner Notaton by Stanley. Howard Department of aterals and etallurgcal Engneerng South Dakota School of nes and echnology Rapd Cty, SD 57701 January 7, 001 Soluton hermodynamcs

More information

Uncertainty and auto-correlation in. Measurement

Uncertainty and auto-correlation in. Measurement Uncertanty and auto-correlaton n arxv:1707.03276v2 [physcs.data-an] 30 Dec 2017 Measurement Markus Schebl Federal Offce of Metrology and Surveyng (BEV), 1160 Venna, Austra E-mal: markus.schebl@bev.gv.at

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

Physics 5153 Classical Mechanics. Principle of Virtual Work-1

Physics 5153 Classical Mechanics. Principle of Virtual Work-1 P. Guterrez 1 Introducton Physcs 5153 Classcal Mechancs Prncple of Vrtual Work The frst varatonal prncple we encounter n mechancs s the prncple of vrtual work. It establshes the equlbrum condton of a mechancal

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

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski

EPR Paradox and the Physical Meaning of an Experiment in Quantum Mechanics. Vesselin C. Noninski EPR Paradox and the Physcal Meanng of an Experment n Quantum Mechancs Vesseln C Nonnsk vesselnnonnsk@verzonnet Abstract It s shown that there s one purely determnstc outcome when measurement s made on

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

Energy, Entropy, and Availability Balances Phase Equilibria. Nonideal Thermodynamic Property Models. Selecting an Appropriate Model

Energy, Entropy, and Availability Balances Phase Equilibria. Nonideal Thermodynamic Property Models. Selecting an Appropriate Model Lecture 4. Thermodynamcs [Ch. 2] Energy, Entropy, and Avalablty Balances Phase Equlbra - Fugactes and actvty coeffcents -K-values Nondeal Thermodynamc Property Models - P-v-T equaton-of-state models -

More information

Introduction to Statistical Methods

Introduction to Statistical Methods Introducton to Statstcal Methods Physcs 4362, Lecture #3 hermodynamcs Classcal Statstcal Knetc heory Classcal hermodynamcs Macroscopc approach General propertes of the system Macroscopc varables 1 hermodynamc

More information

Numerical Heat and Mass Transfer

Numerical Heat and Mass Transfer Master degree n Mechancal Engneerng Numercal Heat and Mass Transfer 06-Fnte-Dfference Method (One-dmensonal, steady state heat conducton) Fausto Arpno f.arpno@uncas.t Introducton Why we use models and

More information

Electrical double layer: revisit based on boundary conditions

Electrical double layer: revisit based on boundary conditions Electrcal double layer: revst based on boundary condtons Jong U. Km Department of Electrcal and Computer Engneerng, Texas A&M Unversty College Staton, TX 77843-318, USA Abstract The electrcal double layer

More information

A Hybrid Variational Iteration Method for Blasius Equation

A Hybrid Variational Iteration Method for Blasius Equation Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 1932-9466 Vol. 10, Issue 1 (June 2015), pp. 223-229 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) A Hybrd Varatonal Iteraton Method

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

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

ESCI 341 Atmospheric Thermodynamics Lesson 10 The Physical Meaning of Entropy

ESCI 341 Atmospheric Thermodynamics Lesson 10 The Physical Meaning of Entropy 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

More information

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Maxmum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models

More information

Lagrange Multipliers. A Somewhat Silly Example. Monday, 25 September 2013

Lagrange Multipliers. A Somewhat Silly Example. Monday, 25 September 2013 Lagrange Multplers Monday, 5 September 013 Sometmes t s convenent to use redundant coordnates, and to effect the varaton of the acton consstent wth the constrants va the method of Lagrange undetermned

More information

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1

Physics 5153 Classical Mechanics. D Alembert s Principle and The Lagrangian-1 P. Guterrez Physcs 5153 Classcal Mechancs D Alembert s Prncple and The Lagrangan 1 Introducton The prncple of vrtual work provdes a method of solvng problems of statc equlbrum wthout havng to consder the

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

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

Calculating the Quasi-static Pressures of Confined Explosions Considering Chemical Reactions under the Constant Entropy Assumption

Calculating the Quasi-static Pressures of Confined Explosions Considering Chemical Reactions under the Constant Entropy Assumption Appled Mechancs and Materals Onlne: 202-04-20 ISS: 662-7482, ol. 64, pp 396-400 do:0.4028/www.scentfc.net/amm.64.396 202 Trans Tech Publcatons, Swtzerland Calculatng the Quas-statc Pressures of Confned

More information

Temperature. Chapter Heat Engine

Temperature. Chapter Heat Engine Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the

More information

Color Rendering Uncertainty

Color Rendering Uncertainty Australan Journal of Basc and Appled Scences 4(10): 4601-4608 010 ISSN 1991-8178 Color Renderng Uncertanty 1 A.el Bally M.M. El-Ganany 3 A. Al-amel 1 Physcs Department Photometry department- NIS Abstract:

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

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U)

ANSWERS. Problem 1. and the moment generating function (mgf) by. defined for any real t. Use this to show that E( U) var( U) Econ 413 Exam 13 H ANSWERS Settet er nndelt 9 deloppgaver, A,B,C, som alle anbefales å telle lkt for å gøre det ltt lettere å stå. Svar er gtt . Unfortunately, there s a prntng error n the hnt of

More information

CHAPTER 7 ENERGY BALANCES SYSTEM SYSTEM. * What is energy? * Forms of Energy. - Kinetic energy (KE) - Potential energy (PE) PE = mgz

CHAPTER 7 ENERGY BALANCES SYSTEM SYSTEM. * What is energy? * Forms of Energy. - Kinetic energy (KE) - Potential energy (PE) PE = mgz SYSTM CHAPTR 7 NRGY BALANCS 1 7.1-7. SYSTM nergy & 1st Law of Thermodynamcs * What s energy? * Forms of nergy - Knetc energy (K) K 1 mv - Potental energy (P) P mgz - Internal energy (U) * Total nergy,

More information

STUDY ON TWO PHASE FLOW IN MICRO CHANNEL BASED ON EXPERI- MENTS AND NUMERICAL EXAMINATIONS

STUDY ON TWO PHASE FLOW IN MICRO CHANNEL BASED ON EXPERI- MENTS AND NUMERICAL EXAMINATIONS Blucher Mechancal Engneerng Proceedngs May 0, vol., num. www.proceedngs.blucher.com.br/evento/0wccm STUDY ON TWO PHASE FLOW IN MICRO CHANNEL BASED ON EXPERI- MENTS AND NUMERICAL EXAMINATIONS Takahko Kurahash,

More information

Appendix II Summary of Important Equations

Appendix II Summary of Important Equations W. M. Whte Geochemstry Equatons of State: Ideal GasLaw: Coeffcent of Thermal Expanson: Compressblty: Van der Waals Equaton: The Laws of Thermdynamcs: Frst Law: Appendx II Summary of Important Equatons

More information

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers

Psychology 282 Lecture #24 Outline Regression Diagnostics: Outliers Psychology 282 Lecture #24 Outlne Regresson Dagnostcs: Outlers In an earler lecture we studed the statstcal assumptons underlyng the regresson model, ncludng the followng ponts: Formal statement of assumptons.

More information

I wish to publish my paper on The International Journal of Thermophysics. A Practical Method to Calculate Partial Properties from Equation of State

I wish to publish my paper on The International Journal of Thermophysics. A Practical Method to Calculate Partial Properties from Equation of State I wsh to publsh my paper on The Internatonal Journal of Thermophyscs. Ttle: A Practcal Method to Calculate Partal Propertes from Equaton of State Authors: Ryo Akasaka (correspondng author) 1 and Takehro

More information

between standard Gibbs free energies of formation for products and reactants, ΔG! R = ν i ΔG f,i, we

between standard Gibbs free energies of formation for products and reactants, ΔG! R = ν i ΔG f,i, we hermodynamcs, Statstcal hermodynamcs, and Knetcs 4 th Edton,. Engel & P. ed Ch. 6 Part Answers to Selected Problems Q6.. Q6.4. If ξ =0. mole at equlbrum, the reacton s not ery far along. hus, there would

More information

Investigation of a New Monte Carlo Method for the Transitional Gas Flow

Investigation of a New Monte Carlo Method for the Transitional Gas Flow Investgaton of a New Monte Carlo Method for the Transtonal Gas Flow X. Luo and Chr. Day Karlsruhe Insttute of Technology(KIT) Insttute for Techncal Physcs 7602 Karlsruhe Germany Abstract. The Drect Smulaton

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

Module 1 : The equation of continuity. Lecture 1: Equation of Continuity

Module 1 : The equation of continuity. Lecture 1: Equation of Continuity 1 Module 1 : The equaton of contnuty Lecture 1: Equaton of Contnuty 2 Advanced Heat and Mass Transfer: Modules 1. THE EQUATION OF CONTINUITY : Lectures 1-6 () () () (v) (v) Overall Mass Balance Momentum

More information

Uncertainty in measurements of power and energy on power networks

Uncertainty in measurements of power and energy on power networks Uncertanty n measurements of power and energy on power networks E. Manov, N. Kolev Department of Measurement and Instrumentaton, Techncal Unversty Sofa, bul. Klment Ohrdsk No8, bl., 000 Sofa, Bulgara Tel./fax:

More information

Adiabatic Sorption of Ammonia-Water System and Depicting in p-t-x Diagram

Adiabatic Sorption of Ammonia-Water System and Depicting in p-t-x Diagram Adabatc Sorpton of Ammona-Water System and Depctng n p-t-x Dagram J. POSPISIL, Z. SKALA Faculty of Mechancal Engneerng Brno Unversty of Technology Techncka 2, Brno 61669 CZECH REPUBLIC Abstract: - Absorpton

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

Lecture Notes on Linear Regression

Lecture Notes on Linear Regression Lecture Notes on Lnear Regresson Feng L fl@sdueducn Shandong Unversty, Chna Lnear Regresson Problem In regresson problem, we am at predct a contnuous target value gven an nput feature vector We assume

More information

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry

Workshop: Approximating energies and wave functions Quantum aspects of physical chemistry Workshop: Approxmatng energes and wave functons Quantum aspects of physcal chemstry http://quantum.bu.edu/pltl/6/6.pdf Last updated Thursday, November 7, 25 7:9:5-5: Copyrght 25 Dan Dll (dan@bu.edu) Department

More information

Lecture 12: Discrete Laplacian

Lecture 12: Discrete Laplacian Lecture 12: Dscrete Laplacan Scrbe: Tanye Lu Our goal s to come up wth a dscrete verson of Laplacan operator for trangulated surfaces, so that we can use t n practce to solve related problems We are mostly

More information

FUZZY FINITE ELEMENT METHOD

FUZZY FINITE ELEMENT METHOD FUZZY FINITE ELEMENT METHOD RELIABILITY TRUCTURE ANALYI UING PROBABILITY 3.. Maxmum Normal tress Internal force s the shear force, V has a magntude equal to the load P and bendng moment, M. Bendng moments

More information

Determination of Structure and Formation Conditions of Gas Hydrate by Using TPD Method and Flash Calculations

Determination of Structure and Formation Conditions of Gas Hydrate by Using TPD Method and Flash Calculations nd atonal Iranan Conference on Gas Hydrate (ICGH) Semnan Unersty Determnaton of Structure and Formaton Condtons of Gas Hydrate by Usng TPD Method and Flash Calculatons H. Behat Rad, F. Varamnan* Department

More information

On the correction of the h-index for career length

On the correction of the h-index for career length 1 On the correcton of the h-ndex for career length by L. Egghe Unverstet Hasselt (UHasselt), Campus Depenbeek, Agoralaan, B-3590 Depenbeek, Belgum 1 and Unverstet Antwerpen (UA), IBW, Stadscampus, Venusstraat

More information

A large scale tsunami run-up simulation and numerical evaluation of fluid force during tsunami by using a particle method

A large scale tsunami run-up simulation and numerical evaluation of fluid force during tsunami by using a particle method A large scale tsunam run-up smulaton and numercal evaluaton of flud force durng tsunam by usng a partcle method *Mtsuteru Asa 1), Shoch Tanabe 2) and Masaharu Isshk 3) 1), 2) Department of Cvl Engneerng,

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

PHYS 705: Classical Mechanics. Newtonian Mechanics

PHYS 705: Classical Mechanics. Newtonian Mechanics 1 PHYS 705: Classcal Mechancs Newtonan Mechancs Quck Revew of Newtonan Mechancs Basc Descrpton: -An dealzed pont partcle or a system of pont partcles n an nertal reference frame [Rgd bodes (ch. 5 later)]

More information

Fermi-Dirac statistics

Fermi-Dirac statistics UCC/Physcs/MK/EM/October 8, 205 Fer-Drac statstcs Fer-Drac dstrbuton Matter partcles that are eleentary ostly have a type of angular oentu called spn. hese partcles are known to have a agnetc oent whch

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

Homework Assignment 3 Due in class, Thursday October 15

Homework Assignment 3 Due in class, Thursday October 15 Homework Assgnment 3 Due n class, Thursday October 15 SDS 383C Statstcal Modelng I 1 Rdge regresson and Lasso 1. Get the Prostrate cancer data from http://statweb.stanford.edu/~tbs/elemstatlearn/ datasets/prostate.data.

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

APPENDIX F A DISPLACEMENT-BASED BEAM ELEMENT WITH SHEAR DEFORMATIONS. Never use a Cubic Function Approximation for a Non-Prismatic Beam

APPENDIX F A DISPLACEMENT-BASED BEAM ELEMENT WITH SHEAR DEFORMATIONS. Never use a Cubic Function Approximation for a Non-Prismatic Beam APPENDIX F A DISPACEMENT-BASED BEAM EEMENT WITH SHEAR DEFORMATIONS Never use a Cubc Functon Approxmaton for a Non-Prsmatc Beam F. INTRODUCTION { XE "Shearng Deformatons" }In ths appendx a unque development

More information

Probability Theory. The nth coefficient of the Taylor series of f(k), expanded around k = 0, gives the nth moment of x as ( ik) n n!

Probability Theory. The nth coefficient of the Taylor series of f(k), expanded around k = 0, gives the nth moment of x as ( ik) n n! 8333: Statstcal Mechancs I Problem Set # 3 Solutons Fall 3 Characterstc Functons: Probablty Theory The characterstc functon s defned by fk ep k = ep kpd The nth coeffcent of the Taylor seres of fk epanded

More information

ONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00

ONE DIMENSIONAL TRIANGULAR FIN EXPERIMENT. Technical Advisor: Dr. D.C. Look, Jr. Version: 11/03/00 ONE IMENSIONAL TRIANGULAR FIN EXPERIMENT Techncal Advsor: r..c. Look, Jr. Verson: /3/ 7. GENERAL OJECTIVES a) To understand a one-dmensonal epermental appromaton. b) To understand the art of epermental

More information

Chemical Equilibrium. Chapter 6 Spontaneity of Reactive Mixtures (gases) Taking into account there are many types of work that a sysem can perform

Chemical Equilibrium. Chapter 6 Spontaneity of Reactive Mixtures (gases) Taking into account there are many types of work that a sysem can perform Ths chapter deals wth chemcal reactons (system) wth lttle or no consderaton on the surroundngs. Chemcal Equlbrum Chapter 6 Spontanety of eactve Mxtures (gases) eactants generatng products would proceed

More information

PHYS 705: Classical Mechanics. Calculus of Variations II

PHYS 705: Classical Mechanics. Calculus of Variations II 1 PHYS 705: Classcal Mechancs Calculus of Varatons II 2 Calculus of Varatons: Generalzaton (no constrant yet) Suppose now that F depends on several dependent varables : We need to fnd such that has a statonary

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

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

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force.

A particle in a state of uniform motion remain in that state of motion unless acted upon by external force. The fundamental prncples of classcal mechancs were lad down by Galleo and Newton n the 16th and 17th centures. In 1686, Newton wrote the Prncpa where he gave us three laws of moton, one law of gravty,

More information

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM

DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM Ganj, Z. Z., et al.: Determnaton of Temperature Dstrbuton for S111 DETERMINATION OF TEMPERATURE DISTRIBUTION FOR ANNULAR FINS WITH TEMPERATURE DEPENDENT THERMAL CONDUCTIVITY BY HPM by Davood Domr GANJI

More information

Finite Element Modelling of truss/cable structures

Finite Element Modelling of truss/cable structures Pet Schreurs Endhoven Unversty of echnology Department of Mechancal Engneerng Materals echnology November 3, 214 Fnte Element Modellng of truss/cable structures 1 Fnte Element Analyss of prestressed structures

More information

Chapter 8. Potential Energy and Conservation of Energy

Chapter 8. Potential Energy and Conservation of Energy Chapter 8 Potental Energy and Conservaton of Energy In ths chapter we wll ntroduce the followng concepts: Potental Energy Conservatve and non-conservatve forces Mechancal Energy Conservaton of Mechancal

More information

Indeterminate pin-jointed frames (trusses)

Indeterminate pin-jointed frames (trusses) Indetermnate pn-jonted frames (trusses) Calculaton of member forces usng force method I. Statcal determnacy. The degree of freedom of any truss can be derved as: w= k d a =, where k s the number of all

More information

If two volatile and miscible liquids are combined to form a solution, Raoult s law is not obeyed. Use the experimental data in Table 9.

If two volatile and miscible liquids are combined to form a solution, Raoult s law is not obeyed. Use the experimental data in Table 9. 9.9 Real Solutons Exhbt Devatons from Raoult s Law If two volatle and mscble lquds are combned to form a soluton, Raoult s law s not obeyed. Use the expermental data n Table 9.3: Physcal Chemstry 00 Pearson

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

Week 9 Chapter 10 Section 1-5

Week 9 Chapter 10 Section 1-5 Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,

More information

Laboratory 1c: Method of Least Squares

Laboratory 1c: Method of Least Squares Lab 1c, Least Squares Laboratory 1c: Method of Least Squares Introducton Consder the graph of expermental data n Fgure 1. In ths experment x s the ndependent varable and y the dependent varable. Clearly

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

MATH 5630: Discrete Time-Space Model Hung Phan, UMass Lowell March 1, 2018

MATH 5630: Discrete Time-Space Model Hung Phan, UMass Lowell March 1, 2018 MATH 5630: Dscrete Tme-Space Model Hung Phan, UMass Lowell March, 08 Newton s Law of Coolng Consder the coolng of a well strred coffee so that the temperature does not depend on space Newton s law of collng

More information

Statistics for Economics & Business

Statistics for Economics & Business Statstcs for Economcs & Busness Smple Lnear Regresson Learnng Objectves In ths chapter, you learn: How to use regresson analyss to predct the value of a dependent varable based on an ndependent varable

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

Maximum entropy & maximum entropy production in biological systems: survival of the likeliest?

Maximum entropy & maximum entropy production in biological systems: survival of the likeliest? Maxmum entropy & maxmum entropy producton n bologcal systems: survval of the lkelest? Roderck Dewar Research School of Bology The Australan Natonal Unversty, Canberra Informaton and Entropy n Bologcal

More information

EN40: Dynamics and Vibrations. Homework 4: Work, Energy and Linear Momentum Due Friday March 1 st

EN40: Dynamics and Vibrations. Homework 4: Work, Energy and Linear Momentum Due Friday March 1 st EN40: Dynamcs and bratons Homework 4: Work, Energy and Lnear Momentum Due Frday March 1 st School of Engneerng Brown Unversty 1. The fgure (from ths publcaton) shows the energy per unt area requred to

More information

Lecture 4: November 17, Part 1 Single Buffer Management

Lecture 4: November 17, Part 1 Single Buffer Management Lecturer: Ad Rosén Algorthms for the anagement of Networs Fall 2003-2004 Lecture 4: November 7, 2003 Scrbe: Guy Grebla Part Sngle Buffer anagement In the prevous lecture we taled about the Combned Input

More information

Constitutive Modelling of Superplastic AA-5083

Constitutive Modelling of Superplastic AA-5083 TECHNISCHE MECHANIK, 3, -5, (01, 1-6 submtted: September 19, 011 Consttutve Modellng of Superplastc AA-5083 G. Gulano In ths study a fast procedure for determnng the constants of superplastc 5083 Al alloy

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

3.1 ML and Empirical Distribution

3.1 ML and Empirical Distribution 67577 Intro. to Machne Learnng Fall semester, 2008/9 Lecture 3: Maxmum Lkelhood/ Maxmum Entropy Dualty Lecturer: Amnon Shashua Scrbe: Amnon Shashua 1 In the prevous lecture we defned the prncple of Maxmum

More information

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models

Maximum Likelihood Estimation of Binary Dependent Variables Models: Probit and Logit. 1. General Formulation of Binary Dependent Variables Models ECO 452 -- OE 4: Probt and Logt Models ECO 452 -- OE 4 Mamum Lkelhood Estmaton of Bnary Dependent Varables Models: Probt and Logt hs note demonstrates how to formulate bnary dependent varables models for

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

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

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

A Self-Consistent Gibbs Excess Mixing Rule for Cubic Equations of State: derivation and fugacity coefficients

A Self-Consistent Gibbs Excess Mixing Rule for Cubic Equations of State: derivation and fugacity coefficients A Self-Consstent Gbbs Excess Mxng Rule for Cubc Equatons of State: dervaton and fugacty coeffcents Paula B. Staudt, Rafael de P. Soares Departamento de Engenhara Químca, Escola de Engenhara, Unversdade

More information

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications

Durban Watson for Testing the Lack-of-Fit of Polynomial Regression Models without Replications Durban Watson for Testng the Lack-of-Ft of Polynomal Regresson Models wthout Replcatons Ruba A. Alyaf, Maha A. Omar, Abdullah A. Al-Shha ralyaf@ksu.edu.sa, maomar@ksu.edu.sa, aalshha@ksu.edu.sa Department

More information

Statistical Energy Analysis for High Frequency Acoustic Analysis with LS-DYNA

Statistical Energy Analysis for High Frequency Acoustic Analysis with LS-DYNA 14 th Internatonal Users Conference Sesson: ALE-FSI Statstcal Energy Analyss for Hgh Frequency Acoustc Analyss wth Zhe Cu 1, Yun Huang 1, Mhamed Soul 2, Tayeb Zeguar 3 1 Lvermore Software Technology Corporaton

More information

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6

Department of Quantitative Methods & Information Systems. Time Series and Their Components QMIS 320. Chapter 6 Department of Quanttatve Methods & Informaton Systems Tme Seres and Ther Components QMIS 30 Chapter 6 Fall 00 Dr. Mohammad Zanal These sldes were modfed from ther orgnal source for educatonal purpose only.

More information

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography

CSci 6974 and ECSE 6966 Math. Tech. for Vision, Graphics and Robotics Lecture 21, April 17, 2006 Estimating A Plane Homography CSc 6974 and ECSE 6966 Math. Tech. for Vson, Graphcs and Robotcs Lecture 21, Aprl 17, 2006 Estmatng A Plane Homography Overvew We contnue wth a dscusson of the major ssues, usng estmaton of plane projectve

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

ONE-DIMENSIONAL COLLISIONS

ONE-DIMENSIONAL COLLISIONS Purpose Theory ONE-DIMENSIONAL COLLISIONS a. To very the law o conservaton o lnear momentum n one-dmensonal collsons. b. To study conservaton o energy and lnear momentum n both elastc and nelastc onedmensonal

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

Limited Dependent Variables

Limited Dependent Variables Lmted Dependent Varables. What f the left-hand sde varable s not a contnuous thng spread from mnus nfnty to plus nfnty? That s, gven a model = f (, β, ε, where a. s bounded below at zero, such as wages

More information

Electrostatic Potential from Transmembrane Currents

Electrostatic Potential from Transmembrane Currents Electrostatc Potental from Transmembrane Currents Let s assume that the current densty j(r, t) s ohmc;.e., lnearly proportonal to the electrc feld E(r, t): j = σ c (r)e (1) wth conductvty σ c = σ c (r).

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

Physics 207: Lecture 20. Today s Agenda Homework for Monday

Physics 207: Lecture 20. Today s Agenda Homework for Monday Physcs 207: Lecture 20 Today s Agenda Homework for Monday Recap: Systems of Partcles Center of mass Velocty and acceleraton of the center of mass Dynamcs of the center of mass Lnear Momentum Example problems

More information

Computational Fluid Dynamics. Smoothed Particle Hydrodynamics. Simulations. Smoothing Kernels and Basis of SPH

Computational Fluid Dynamics. Smoothed Particle Hydrodynamics. Simulations. Smoothing Kernels and Basis of SPH Computatonal Flud Dynamcs If you want to learn a bt more of the math behnd flud dynamcs, read my prevous post about the Naver- Stokes equatons and Newtonan fluds. The equatons derved n the post are the

More information