Deterministic and Monte Carlo Codes for Multiple Scattering Photon Transport

Size: px
Start display at page:

Download "Deterministic and Monte Carlo Codes for Multiple Scattering Photon Transport"

Transcription

1 Determnstc and Monte Carlo Codes for Multple Scatterng Photon Transport Jorge E. Fernández 1 1 Laboratory of Montecuccolno DIENCA Alma Mater Studorum Unversty of Bologna Italy Isttuto Nazonale d Fsca Nucleare INFN

2 Summary Introducton Unbased Monte Carlo smulaton of the Compton Profle Determnstc vs Monte Carlo codes for transport calculatons of lne wdth effects Electron contrbutons to photon transport Conclusons

3 Introducton Determnstc and Monte Carlo technques compete to provde the best descrpton of transport problems. However many tmes they demonstrate to be complementary. Ths tal offers three examples from our experence n photon transport whch llustrate the close cooperaton between these two approaches.

4 Summary Introducton Unbased Monte Carlo smulaton of the Compton Profle Determnstc vs Monte Carlo codes for transport calculatons of lne wdth effects Electron contrbutons to photon transport Conclusons

5 Unbased Monte Carlo smulaton of the Compton Profle Ths example shows how a determnstc calculaton has been used to correct a based Monte Carlo algorthm wdely adopted to smulate the Compton profle.

6 Compton profle It s the broadenng of the Compton pea It s produced by the momentum dstrbuton of the electrons n the atom t can be measured qute precsely n synchrotron facltes

7 Intensty [arb. unts] Based Algorthm analytcal profle based algorthm It was dscovered a based behavour n Compton profle MC smulaton at low energes when usng the standard algorthm by Namto 1 used by EGS and MCNP E [ev] The bas was responsble for: the creaton of a false pea n correspondence wth the low energy tal of the profle a wrong Compton profle at low energes 1 Y.Namto S.Ban H.HrayamaNIM A

8 Reason for the bas Wrong samplng of the atomc sub-shells: n shellnumber 1 sub-shells were assumed complete n.e. havng n electrons

9 Unbased samplng Correct samplng of the atomc sub-shells: n 1 Q shellnumber max n J Q sub-shells now are assumed ncomplete Q dq max J Q dq.e. havng n Q max J Q dq electrons

10 Intensty [arb. unts] Results of the unbased algorthm analytcal profle unbased algorthm ths wor The determnstc code was essental to dscover the wrong behavour of the based algorthm E [ev]

11 Summary Introducton Unbased Monte Carlo smulaton of the Compton Profle Determnstc vs Monte Carlo codes for transport calculatons of lne wdth effects Electron contrbutons to photon transport Conclusons

12 Condton to fulfll n order to produce photoelectrc effect Mechansm for producng XRF lnes E 0

13 The wdth of the atomc levels s responsble for the natural wdth of the lnes 1 The wdths of the atomc levels are the recommended values n Campbell and Papp At. Data and Nucl. Data Tables

14 Lorentzan shape of the lne sometmes s used the Half Wdth at Half Maxmum HWHM 1 1 ; E E E E E E E E E E E E E E

15 Emsson of a Lorentzan K-lne K edge E 0 K lne close to the edge K

16 Transport ernel for a Lorentzan lne wavelength regme U ] U [1 ; 4 1 e L P Q K E 0 edge Energy conservaton p g Q e s Emsson probablty Threshold for photoelectrc effect 1 ; Lorentzan dstrbuton

17 MC vs Determnstc descrpton of a Lorentzan lne In MC the energy of the characterstc photon s randomly sampled at every nteracton usng a Lorentzan dstrbuton centered at E 0. One nterestng effect appears when the Lorentzan tal crosses the edge.e. the energy of the emtted photon s hgh enough to produce another vacancy and therefore a self-enhancement effect. Snce the hgh energy tal has always a very low probablty ths case requres refned varance reducton technques n order to get sgnfcant results. The slow asymptotc decrease of the Lorentzan dstrbuton ntroduces a further complcaton to descrbe multple scatterng wth reasonable statstcs. Therefore we propose to use nstead a determnstc method based on the wavelength energy dscretzaton of the Lorentzan dstrbuton.

18 t t Dscretzaton of the Lorentzan dstrbuton wavelength regme 1 ; and use a dscrete δ-expanson for the Lorentzan t t p ; wth coeffcents t t d p.. 1 arctan 1 arctan 1 ; where We defne a new normalzed dstrbuton between the fnte lmts t t arctan ; 1 1 t d t t

19 Dscretzed ernel for the Lorentzan lne wavelength regme t e e t t L P p Q p Q mn ] U [1 4 1 ]U U [1 4 1 wth t max mn energy conservaton cut-off

20 Prmary XRF ntensty of a Lorentzan lne The prmary ntensty of the lne centered at the pea wavelength for an nfnte thcness specmen s computed wthn an nfntely large acquston wndow I 1 0 L I0 Q 4 [1 U ] 0 0 e t p where 0 0 max t 0 0 e photoelectrc collson 0

21 Secondary XRF ntensty of a Lorentzan lne The secondary ntensty of the lne centered at the pea wavelength for an nfnte thcness specmen s computed wthn an nfntely large acquston wndow. I 0 L I0 8 all lnes j t mn Q [1 U ] 0 p 0 0 js mn js max t Int j 0 smn t j 0 max Int j js j e j e j photoelectrc collson t ss mn j p s 0 Q 0 ln 1 js 0 j js 0 js [1 U js e js ] 0 ln 1 js 0 photoelectrc collson js e

22 Intensty a.u. Example of almost symmetrc Lorentzan lnes Sometmes the asymmetry s small E-3 1E-4 prmary Fe K total enh on Fe K 1E-5 1E-6 1E-7 1E-8 1E-9 1E-10 1E Energy ev

23 Intensty a. u. Secondary Lorentzan contrbutons can be very asymmetrc Sometmes the asymmetry s s large 1E-4 1E-5 1E-6 1E-7 1E-8 1E-9 a Cr K Cr K 1 Fe K 1 Cr K 1 N K 1 Cr K 1 1E-10 1E-11 1E-1 1E-13 1E-14 1E-15 1E-16 1E Energy ev

24 Summary Introducton Unbased Monte Carlo smulaton of the Compton Profle Determnstc vs Monte Carlo codes for transport calculatons of lne wdth effects Electron contrbutons to photon transport Conclusons

25 Electron contrbutons to photon transport The am s to evaluate the contrbuton due to electrons to be ncluded n photon transport codes wthout solvng the complete coupled problem. The code PENELOPE coupled electron-photon Monte Carlo was used to study the effect of secondary electrons nto the photon transport. The ad-hoc code KERNEL was developed to smulate a forced frst collson at the orgn of coordnates. We consdered a pont source of monochromatc photons. The physcs of the nteracton was descrbed usng the PENELOPE subroutne lbrary. All the secondary electrons were followed along ther multple-scatterng untl ther energy become lower of a predefnte threshold value. All photons produced by the electrons at every stage were accumulated. Polarzaton was not consdered.

26 Prevalng photon nteractons n the X-ray regme PRIMARY PHOTON Scattered photons COHERENT SCATTERING INCOHERENT SCATTERING PHOTOELECTRIC EFFECT RAYLEIGH PHOTON COMPTON PHOTON COMPTON ELECTRON CHAR. X-RAYS PHOTO ELECTRON Scattered electron

27 Electron-photon couplng SECONDARY ELECTRON Scattered electrons BREMMSTRAHLUNG INNER SHELL IMPACT IONIZATION PHOTON CONTINUOUS CHAR. X-RAYS PHOTO ELECTRON Scattered photon

28 Descrpton of the KERNEL code Prmary photon N h = N h +1 E=E0 r=x Y Z=0 d=u V W=001 Frst collson YES N h < N max NO Partcles n secondary stac? YES NO END Score of the dstrbutons SI NO Bremsstrahlung or relaxaton? NO Electrons? Smulaton of slowng-down untl E<E abs YES

29 Types of electron contrbuton to photon transport Bremmstrahlung: contrbutes a contnuous dstrbuton Inner shell mpact onzaton: modfes the ntensty of the characterstc lnes

30 Polar angular dstrbuton Inner-shell mpact onzaton Prmary photon source s 100 ev. Blue lnes denote computed values red symbols are error bars. The emsson s sotropc.

31 Spatal dstrbuton 1 Electron range vs photon MFP Parameter as a functon of energy. Bethe range of electrons Mean free-path of photons Value ranges eep always small order of 10-1.

32 Spatal dstrbuton Effectve electron range Effectve range ~ R/4 Au Bethe range

33 Photon emsson Inner shell mpact onzaton Radal dstrbuton Prmary photon source s 100 ev. Blue lnes denote computed values red symbols are error bars. X-axs s r/r All dstrbutons eep below R/3

34 Photon emsson Inner shell mpact onzaton Axal dstrbuton Prmary photon source s 100 ev. Blue lnes denote computed values red symbols are error bars. X-axs s z/r All dstrbutons eep below R/3

35 The correcton as a functon of energy Calculatons were performed for all the lnes of the elements Z=11-9 n the energy range ev. Snce the electrons loose ther energy more effcently n the low energy range the computed contrbuton s hgher for low energy lnes. To compute the correcton for a generc energy the whole nterval was dvded nto 5 energy regons. The best ft of the energy correcton at each energy nterval was computed usng 4 coeffcents.

36 Electron correcton on K-lnes Al Kα ev Low Z 11-0 Na - Ca f E E pe Best ft 3 fe E exp pe ln E 0

37 Electron correcton on L-lnes As Lα ev Medum Z f E E pe Zn - Sn Best ft 3 fe E exp pe ln E 0

38 Electron correcton on M-lnes W Mα ev Hgh Z 6-9 f E E pe Sm - U Best ft 3 fe E exp pe ln E 0

39 Kernel correcton due to nner shell mpact onzaton ] U [1 4 1 e electron electron P Q ] U [1 4 1 e pe electron P Q f Q Q f electron pe To avod data-base dfferences between PENELOPE and other transport codes the electron correcton s computed n unts of the photon contrbuton. pe f Q

40 Corrected ernel comprsng electron contrbutons ] U [ e pe P f Q p g Q e s pe E f ' ln exp 3 0 To compute the correcton for a generc energy the whole nterval s dvded nto 5 regons. The best ft of the energy correcton at each energy nterval requres 4 coeffcents. where

41 Summary Introducton Unbased Monte Carlo smulaton of the Compton Profle Determnstc vs Monte Carlo codes for transport calculatons of lne wdth effects Electron contrbutons to photon transport Conclusons

42 Conclusons Determnstc and Monte Carlo technques demonstrate to be complementary to provde the best descrpton of transport problems. We have shown three dfferent cases n photon transport to llustrate the symboss of MC and Determnstc approaches: 1 Determnstc calculatons were essental to dscover the wrong behavour of a based algorthm used to smulate the Compton profle n largely dffused MC codes. Determnstc calculatons provde a better framewor to descrbe the nfluence of the Lorentzan breath on multple scatterng contrbutons to XRF lnes. 3 Coupled photon-electron MC calculatons were essental to obtan a smple correcton of the photon ernel to nclude the effect of nner shell mpact onzaton from electrons.

43 Jorge Fernandez

Electron-Impact Double Ionization of the H 2

Electron-Impact Double Ionization of the H 2 I R A P 6(), Dec. 5, pp. 9- Electron-Impact Double Ionzaton of the H olecule Internatonal Scence Press ISSN: 9-59 Electron-Impact Double Ionzaton of the H olecule. S. PINDZOLA AND J. COLGAN Department

More information

Title: Radiative transitions and spectral broadening

Title: Radiative transitions and spectral broadening Lecture 6 Ttle: Radatve transtons and spectral broadenng Objectves The spectral lnes emtted by atomc vapors at moderate temperature and pressure show the wavelength spread around the central frequency.

More information

Amplification and Relaxation of Electron Spin Polarization in Semiconductor Devices

Amplification and Relaxation of Electron Spin Polarization in Semiconductor Devices Amplfcaton and Relaxaton of Electron Spn Polarzaton n Semconductor Devces Yury V. Pershn and Vladmr Prvman Center for Quantum Devce Technology, Clarkson Unversty, Potsdam, New York 13699-570, USA Spn Relaxaton

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

Course Electron Microprobe Analysis

Course Electron Microprobe Analysis Course 12.141 Electron Mcroprobe Analyss THE ELECTROMAGNETIC SPECTRUM Electron Probe X-ray Mcro-Analyss A) quanttatve chemcal analyss of solds: Be to U 1 mcrometer resoluton up to 10 ppm B) hgh-resoluton

More information

Uncertainty as the Overlap of Alternate Conditional Distributions

Uncertainty as the Overlap of Alternate Conditional Distributions Uncertanty as the Overlap of Alternate Condtonal Dstrbutons Olena Babak and Clayton V. Deutsch Centre for Computatonal Geostatstcs Department of Cvl & Envronmental Engneerng Unversty of Alberta An mportant

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

Parametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010

Parametric fractional imputation for missing data analysis. Jae Kwang Kim Survey Working Group Seminar March 29, 2010 Parametrc fractonal mputaton for mssng data analyss Jae Kwang Km Survey Workng Group Semnar March 29, 2010 1 Outlne Introducton Proposed method Fractonal mputaton Approxmaton Varance estmaton Multple mputaton

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

Lossy Compression. Compromise accuracy of reconstruction for increased compression.

Lossy Compression. Compromise accuracy of reconstruction for increased compression. Lossy Compresson Compromse accuracy of reconstructon for ncreased compresson. The reconstructon s usually vsbly ndstngushable from the orgnal mage. Typcally, one can get up to 0:1 compresson wth almost

More information

Supplemental Material: Causal Entropic Forces

Supplemental Material: Causal Entropic Forces Supplemental Materal: Causal Entropc Forces A. D. Wssner-Gross 1, 2, and C. E. Freer 3 1 Insttute for Appled Computatonal Scence, Harvard Unversty, Cambrdge, Massachusetts 02138, USA 2 The Meda Laboratory,

More information

Appendix B. The Finite Difference Scheme

Appendix B. The Finite Difference Scheme 140 APPENDIXES Appendx B. The Fnte Dfference Scheme In ths appendx we present numercal technques whch are used to approxmate solutons of system 3.1 3.3. A comprehensve treatment of theoretcal and mplementaton

More information

A random variable is a function which associates a real number to each element of the sample space

A random variable is a function which associates a real number to each element of the sample space Introducton to Random Varables Defnton of random varable Defnton of of random varable Dscrete and contnuous random varable Probablty blt functon Dstrbuton functon Densty functon Sometmes, t s not enough

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

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

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

SUPPLEMENTARY INFORMATION

SUPPLEMENTARY INFORMATION do: 0.08/nature09 I. Resonant absorpton of XUV pulses n Kr + usng the reduced densty matrx approach The quantum beats nvestgated n ths paper are the result of nterference between two exctaton paths of

More information

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k)

Hongyi Miao, College of Science, Nanjing Forestry University, Nanjing ,China. (Received 20 June 2013, accepted 11 March 2014) I)ϕ (k) ISSN 1749-3889 (prnt), 1749-3897 (onlne) Internatonal Journal of Nonlnear Scence Vol.17(2014) No.2,pp.188-192 Modfed Block Jacob-Davdson Method for Solvng Large Sparse Egenproblems Hongy Mao, College of

More information

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA

4 Analysis of Variance (ANOVA) 5 ANOVA. 5.1 Introduction. 5.2 Fixed Effects ANOVA 4 Analyss of Varance (ANOVA) 5 ANOVA 51 Introducton ANOVA ANOVA s a way to estmate and test the means of multple populatons We wll start wth one-way ANOVA If the populatons ncluded n the study are selected

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 3 Describing Data Using Numerical Measures

Chapter 3 Describing Data Using Numerical Measures Chapter 3 Student Lecture Notes 3-1 Chapter 3 Descrbng Data Usng Numercal Measures Fall 2006 Fundamentals of Busness Statstcs 1 Chapter Goals To establsh the usefulness of summary measures of data. The

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

Why Monte Carlo Integration? Introduction to Monte Carlo Method. Continuous Probability. Continuous Probability

Why Monte Carlo Integration? Introduction to Monte Carlo Method. Continuous Probability. Continuous Probability Introducton to Monte Carlo Method Kad Bouatouch IRISA Emal: kad@rsa.fr Wh Monte Carlo Integraton? To generate realstc lookng mages, we need to solve ntegrals of or hgher dmenson Pel flterng and lens smulaton

More information

(Online First)A Lattice Boltzmann Scheme for Diffusion Equation in Spherical Coordinate

(Online First)A Lattice Boltzmann Scheme for Diffusion Equation in Spherical Coordinate Internatonal Journal of Mathematcs and Systems Scence (018) Volume 1 do:10.494/jmss.v1.815 (Onlne Frst)A Lattce Boltzmann Scheme for Dffuson Equaton n Sphercal Coordnate Debabrata Datta 1 *, T K Pal 1

More information

Introduction to Random Variables

Introduction to Random Variables Introducton to Random Varables Defnton of random varable Defnton of random varable Dscrete and contnuous random varable Probablty functon Dstrbuton functon Densty functon Sometmes, t s not enough to descrbe

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

Kernel Methods and SVMs Extension

Kernel Methods and SVMs Extension Kernel Methods and SVMs Extenson The purpose of ths document s to revew materal covered n Machne Learnng 1 Supervsed Learnng regardng support vector machnes (SVMs). Ths document also provdes a general

More information

Neutral-Current Neutrino-Nucleus Inelastic Reactions for Core Collapse Supernovae

Neutral-Current Neutrino-Nucleus Inelastic Reactions for Core Collapse Supernovae Neutral-Current Neutrno-Nucleus Inelastc Reactons for Core Collapse Supernovae A. Juodagalvs Teornės Fzkos r Astronomjos Insttutas, Lthuana E-mal: andrusj@tpa.lt J. M. Sampao Centro de Físca Nuclear da

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

5.15 MICRO GAMMA SCANNING ON THE HIGH BURNUP PWR FUEL SAMPLES

5.15 MICRO GAMMA SCANNING ON THE HIGH BURNUP PWR FUEL SAMPLES 5.15 MICRO GAMMA SCANNING ON THE HIGH BURNUP PWR FUEL SAMPLES Hyoung-Mun Kwon, Hang-Seog Seo, Hyung-Kwon Lee, Duck-Kee Mn, Yong-Bum Chun Post Irradaton Examnaton Faclty, Korea Atomc Energy Research Insttute

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

Lecture 14: Forces and Stresses

Lecture 14: Forces and Stresses The Nuts and Bolts of Frst-Prncples Smulaton Lecture 14: Forces and Stresses Durham, 6th-13th December 2001 CASTEP Developers Group wth support from the ESF ψ k Network Overvew of Lecture Why bother? Theoretcal

More information

Note on the Electron EDM

Note on the Electron EDM Note on the Electron EDM W R Johnson October 25, 2002 Abstract Ths s a note on the setup of an electron EDM calculaton and Schff s Theorem 1 Basc Relatons The well-known relatvstc nteracton of the electron

More information

RELIABILITY ASSESSMENT

RELIABILITY ASSESSMENT CHAPTER Rsk Analyss n Engneerng and Economcs RELIABILITY ASSESSMENT A. J. Clark School of Engneerng Department of Cvl and Envronmental Engneerng 4a CHAPMAN HALL/CRC Rsk Analyss for Engneerng Department

More information

( ) ( ) ( ) ( ) STOCHASTIC SIMULATION FOR BLOCKED DATA. Monte Carlo simulation Rejection sampling Importance sampling Markov chain Monte Carlo

( ) ( ) ( ) ( ) STOCHASTIC SIMULATION FOR BLOCKED DATA. Monte Carlo simulation Rejection sampling Importance sampling Markov chain Monte Carlo SOCHASIC SIMULAIO FOR BLOCKED DAA Stochastc System Analyss and Bayesan Model Updatng Monte Carlo smulaton Rejecton samplng Importance samplng Markov chan Monte Carlo Monte Carlo smulaton Introducton: If

More information

b ), which stands for uniform distribution on the interval a x< b. = 0 elsewhere

b ), which stands for uniform distribution on the interval a x< b. = 0 elsewhere Fall Analyss of Epermental Measurements B. Esensten/rev. S. Errede Some mportant probablty dstrbutons: Unform Bnomal Posson Gaussan/ormal The Unform dstrbuton s often called U( a, b ), hch stands for unform

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

Negative Binomial Regression

Negative Binomial Regression STATGRAPHICS Rev. 9/16/2013 Negatve Bnomal Regresson Summary... 1 Data Input... 3 Statstcal Model... 3 Analyss Summary... 4 Analyss Optons... 7 Plot of Ftted Model... 8 Observed Versus Predcted... 10 Predctons...

More information

A Robust Method for Calculating the Correlation Coefficient

A Robust Method for Calculating the Correlation Coefficient A Robust Method for Calculatng the Correlaton Coeffcent E.B. Nven and C. V. Deutsch Relatonshps between prmary and secondary data are frequently quantfed usng the correlaton coeffcent; however, the tradtonal

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

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

Statistical Evaluation of WATFLOOD

Statistical Evaluation of WATFLOOD tatstcal Evaluaton of WATFLD By: Angela MacLean, Dept. of Cvl & Envronmental Engneerng, Unversty of Waterloo, n. ctober, 005 The statstcs program assocated wth WATFLD uses spl.csv fle that s produced wth

More information

Röntgen s experiment in X-ray Spectroscopy. Röntgen s experiment. Interaction of x-rays x. x-rays. with matter. Wavelength: m

Röntgen s experiment in X-ray Spectroscopy. Röntgen s experiment. Interaction of x-rays x. x-rays. with matter. Wavelength: m X-ray Spectroscopy Röntgen s experment n 1895 Lecture 1: Introducton & expermental aspects Lecture : Atomc Multplet Theory Crystal Feld Theory CTM4XAS program Lecture 3: Charge Transfer Multplet Theory

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

PES 1120 Spring 2014, Spendier Lecture 6/Page 1

PES 1120 Spring 2014, Spendier Lecture 6/Page 1 PES 110 Sprng 014, Spender Lecture 6/Page 1 Lecture today: Chapter 1) Electrc feld due to charge dstrbutons -> charged rod -> charged rng We ntroduced the electrc feld, E. I defned t as an nvsble aura

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

Applied Nuclear Physics (Fall 2004) Lecture 23 (12/3/04) Nuclear Reactions: Energetics and Compound Nucleus

Applied Nuclear Physics (Fall 2004) Lecture 23 (12/3/04) Nuclear Reactions: Energetics and Compound Nucleus .101 Appled Nuclear Physcs (Fall 004) Lecture 3 (1/3/04) Nuclear Reactons: Energetcs and Compound Nucleus References: W. E. Meyerhof, Elements of Nuclear Physcs (McGraw-Hll, New York, 1967), Chap 5. Among

More information

Experimental Studies of Quasi-fission Reactions. B.B.Back, B.G.Glagola, D.Henderson, S.B.Kaufman, J.G.Keller, S.J.Sanders, F.Videbaek, and B.D.

Experimental Studies of Quasi-fission Reactions. B.B.Back, B.G.Glagola, D.Henderson, S.B.Kaufman, J.G.Keller, S.J.Sanders, F.Videbaek, and B.D. Expermental Studes of Quas-fsson Reactons The submtted manuscrpt hs been authored by a contractor o the U. S. Government under contract No. W-31 19-ENG-3B. Accordmglv. the U. S- Government retans a nonexclusve,

More information

Aging model for a 40 V Nch MOS, based on an innovative approach F. Alagi, R. Stella, E. Viganò

Aging model for a 40 V Nch MOS, based on an innovative approach F. Alagi, R. Stella, E. Viganò Agng model for a 4 V Nch MOS, based on an nnovatve approach F. Alag, R. Stella, E. Vganò ST Mcroelectroncs Cornaredo (Mlan) - Italy Agng modelng WHAT IS AGING MODELING: Agng modelng s a tool to smulate

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

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0

n α j x j = 0 j=1 has a nontrivial solution. Here A is the n k matrix whose jth column is the vector for all t j=0 MODULE 2 Topcs: Lnear ndependence, bass and dmenson We have seen that f n a set of vectors one vector s a lnear combnaton of the remanng vectors n the set then the span of the set s unchanged f that vector

More information

Introduction to Regression

Introduction to Regression Introducton to Regresson Dr Tom Ilvento Department of Food and Resource Economcs Overvew The last part of the course wll focus on Regresson Analyss Ths s one of the more powerful statstcal technques Provdes

More information

Statistics for Business and Economics

Statistics for Business and Economics Statstcs for Busness and Economcs Chapter 11 Smple Regresson Copyrght 010 Pearson Educaton, Inc. Publshng as Prentce Hall Ch. 11-1 11.1 Overvew of Lnear Models n An equaton can be ft to show the best lnear

More information

Statistics Chapter 4

Statistics Chapter 4 Statstcs Chapter 4 "There are three knds of les: les, damned les, and statstcs." Benjamn Dsrael, 1895 (Brtsh statesman) Gaussan Dstrbuton, 4-1 If a measurement s repeated many tmes a statstcal treatment

More information

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands

1. Inference on Regression Parameters a. Finding Mean, s.d and covariance amongst estimates. 2. Confidence Intervals and Working Hotelling Bands Content. Inference on Regresson Parameters a. Fndng Mean, s.d and covarance amongst estmates.. Confdence Intervals and Workng Hotellng Bands 3. Cochran s Theorem 4. General Lnear Testng 5. Measures of

More information

Research Article Green s Theorem for Sign Data

Research Article Green s Theorem for Sign Data Internatonal Scholarly Research Network ISRN Appled Mathematcs Volume 2012, Artcle ID 539359, 10 pages do:10.5402/2012/539359 Research Artcle Green s Theorem for Sgn Data Lous M. Houston The Unversty of

More information

Statistics II Final Exam 26/6/18

Statistics II Final Exam 26/6/18 Statstcs II Fnal Exam 26/6/18 Academc Year 2017/18 Solutons Exam duraton: 2 h 30 mn 1. (3 ponts) A town hall s conductng a study to determne the amount of leftover food produced by the restaurants n the

More information

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc

More information

A Bound for the Relative Bias of the Design Effect

A Bound for the Relative Bias of the Design Effect A Bound for the Relatve Bas of the Desgn Effect Alberto Padlla Banco de Méxco Abstract Desgn effects are typcally used to compute sample szes or standard errors from complex surveys. In ths paper, we show

More information

CHAPTER IV RESEARCH FINDING AND DISCUSSIONS

CHAPTER IV RESEARCH FINDING AND DISCUSSIONS CHAPTER IV RESEARCH FINDING AND DISCUSSIONS A. Descrpton of Research Fndng. The Implementaton of Learnng Havng ganed the whole needed data, the researcher then dd analyss whch refers to the statstcal data

More information

Topic 23 - Randomized Complete Block Designs (RCBD)

Topic 23 - Randomized Complete Block Designs (RCBD) Topc 3 ANOVA (III) 3-1 Topc 3 - Randomzed Complete Block Desgns (RCBD) Defn: A Randomzed Complete Block Desgn s a varant of the completely randomzed desgn (CRD) that we recently learned. In ths desgn,

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

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

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

Assignment 5. Simulation for Logistics. Monti, N.E. Yunita, T.

Assignment 5. Simulation for Logistics. Monti, N.E. Yunita, T. Assgnment 5 Smulaton for Logstcs Mont, N.E. Yunta, T. November 26, 2007 1. Smulaton Desgn The frst objectve of ths assgnment s to derve a 90% two-sded Confdence Interval (CI) for the average watng tme

More information

MD. LUTFOR RAHMAN 1 AND KALIPADA SEN 2 Abstract

MD. LUTFOR RAHMAN 1 AND KALIPADA SEN 2 Abstract ISSN 058-71 Bangladesh J. Agrl. Res. 34(3) : 395-401, September 009 PROBLEMS OF USUAL EIGHTED ANALYSIS OF VARIANCE (ANOVA) IN RANDOMIZED BLOCK DESIGN (RBD) ITH MORE THAN ONE OBSERVATIONS PER CELL HEN ERROR

More information

Composite Hypotheses testing

Composite Hypotheses testing Composte ypotheses testng In many hypothess testng problems there are many possble dstrbutons that can occur under each of the hypotheses. The output of the source s a set of parameters (ponts n a parameter

More information

Distance-driven binning for proton CT filtered backprojection along most likely paths

Distance-driven binning for proton CT filtered backprojection along most likely paths Dstance-drven bnnng for proton CT fltered backprojecton along most lkely paths Smon Rt, Ncolas Freud, Davd Sarrut, Jean-Mchel Létang 12 1 CREATIS laboratory 2 Léon Bérard center May 25, 2012 Introducton

More information

Inductance Calculation for Conductors of Arbitrary Shape

Inductance Calculation for Conductors of Arbitrary Shape CRYO/02/028 Aprl 5, 2002 Inductance Calculaton for Conductors of Arbtrary Shape L. Bottura Dstrbuton: Internal Summary In ths note we descrbe a method for the numercal calculaton of nductances among conductors

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

5.04, Principles of Inorganic Chemistry II MIT Department of Chemistry Lecture 32: Vibrational Spectroscopy and the IR

5.04, Principles of Inorganic Chemistry II MIT Department of Chemistry Lecture 32: Vibrational Spectroscopy and the IR 5.0, Prncples of Inorganc Chemstry II MIT Department of Chemstry Lecture 3: Vbratonal Spectroscopy and the IR Vbratonal spectroscopy s confned to the 00-5000 cm - spectral regon. The absorpton of a photon

More information

Topic- 11 The Analysis of Variance

Topic- 11 The Analysis of Variance Topc- 11 The Analyss of Varance Expermental Desgn The samplng plan or expermental desgn determnes the way that a sample s selected. In an observatonal study, the expermenter observes data that already

More information

This chapter illustrates the idea that all properties of the homogeneous electron gas (HEG) can be calculated from electron density.

This chapter illustrates the idea that all properties of the homogeneous electron gas (HEG) can be calculated from electron density. 1 Unform Electron Gas Ths chapter llustrates the dea that all propertes of the homogeneous electron gas (HEG) can be calculated from electron densty. Intutve Representaton of Densty Electron densty n s

More information

Digital Signal Processing

Digital Signal Processing Dgtal Sgnal Processng Dscrete-tme System Analyss Manar Mohasen Offce: F8 Emal: manar.subh@ut.ac.r School of IT Engneerng Revew of Precedent Class Contnuous Sgnal The value of the sgnal s avalable over

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

Measurement of Radiation: Exposure. Purpose. Quantitative description of radiation

Measurement of Radiation: Exposure. Purpose. Quantitative description of radiation Measurement of Radaton: Exposure George Starkschall, Ph.D. Department of Radaton Physcs U.T. M.D. Anderson Cancer Center Purpose To ntroduce the concept of radaton exposure and to descrbe and evaluate

More information

Monte Carlo simulation study on magnetic hysteresis loop of Co nanowires

Monte Carlo simulation study on magnetic hysteresis loop of Co nanowires Monte Carlo smulaton study on magnetc hysteress loop of Co nanowres Ryang Se-Hun, O Pong-Sk, Sn Gum-Chol, Hwang Guk-Nam, Hong Yong-Son * Km Hyong Jk Normal Unversty, Pyongyang, D.P.R of Korea Abstract;

More information

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t

Snce h( q^; q) = hq ~ and h( p^ ; p) = hp, one can wrte ~ h hq hp = hq ~hp ~ (7) the uncertanty relaton for an arbtrary state. The states that mnmze t 8.5: Many-body phenomena n condensed matter and atomc physcs Last moded: September, 003 Lecture. Squeezed States In ths lecture we shall contnue the dscusson of coherent states, focusng on ther propertes

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

1 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations

1 Derivation of Rate Equations from Single-Cell Conductance (Hodgkin-Huxley-like) Equations Physcs 171/271 -Davd Klenfeld - Fall 2005 (revsed Wnter 2011) 1 Dervaton of Rate Equatons from Sngle-Cell Conductance (Hodgkn-Huxley-lke) Equatons We consder a network of many neurons, each of whch obeys

More information

/ n ) are compared. The logic is: if the two

/ n ) are compared. The logic is: if the two STAT C141, Sprng 2005 Lecture 13 Two sample tests One sample tests: examples of goodness of ft tests, where we are testng whether our data supports predctons. Two sample tests: called as tests of ndependence

More information

One-sided finite-difference approximations suitable for use with Richardson extrapolation

One-sided finite-difference approximations suitable for use with Richardson extrapolation Journal of Computatonal Physcs 219 (2006) 13 20 Short note One-sded fnte-dfference approxmatons sutable for use wth Rchardson extrapolaton Kumar Rahul, S.N. Bhattacharyya * Department of Mechancal Engneerng,

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

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1

C/CS/Phy191 Problem Set 3 Solutions Out: Oct 1, 2008., where ( 00. ), so the overall state of the system is ) ( ( ( ( 00 ± 11 ), Φ ± = 1 C/CS/Phy9 Problem Set 3 Solutons Out: Oct, 8 Suppose you have two qubts n some arbtrary entangled state ψ You apply the teleportaton protocol to each of the qubts separately What s the resultng state obtaned

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

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

Nice plotting of proteins II

Nice plotting of proteins II Nce plottng of protens II Fnal remark regardng effcency: It s possble to wrte the Newton representaton n a way that can be computed effcently, usng smlar bracketng that we made for the frst representaton

More information

Dose Calculation Algorithms and Commissioning. Particles contributing to dose. Dose Algorithms 7/24/2014. Primary protons. Secondary particles

Dose Calculation Algorithms and Commissioning. Particles contributing to dose. Dose Algorithms 7/24/2014. Primary protons. Secondary particles 7/4/014 Dose Calculaton lgorthms and Commssonng The Status of Intensty Modulated Proton and Ion Therapy PM Symposum 014 X. Ronald Zhu PhD July 4 014 Partcles contrbutng to dose Prmary protons lastc nteractons

More information

Numerical Transient Heat Conduction Experiment

Numerical Transient Heat Conduction Experiment Numercal ransent Heat Conducton Experment OBJECIVE 1. o demonstrate the basc prncples of conducton heat transfer.. o show how the thermal conductvty of a sold can be measured. 3. o demonstrate the use

More information

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin

LOW BIAS INTEGRATED PATH ESTIMATORS. James M. Calvin Proceedngs of the 007 Wnter Smulaton Conference S G Henderson, B Bller, M-H Hseh, J Shortle, J D Tew, and R R Barton, eds LOW BIAS INTEGRATED PATH ESTIMATORS James M Calvn Department of Computer Scence

More information

Simulation and Probability Distribution

Simulation and Probability Distribution CHAPTER Probablty, Statstcs, and Relablty for Engneers and Scentsts Second Edton PROBABILIT DISTRIBUTION FOR CONTINUOUS RANDOM VARIABLES A. J. Clark School of Engneerng Department of Cvl and Envronmental

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

A linear imaging system with white additive Gaussian noise on the observed data is modeled as follows:

A linear imaging system with white additive Gaussian noise on the observed data is modeled as follows: Supplementary Note Mathematcal bacground A lnear magng system wth whte addtve Gaussan nose on the observed data s modeled as follows: X = R ϕ V + G, () where X R are the expermental, two-dmensonal proecton

More information

International Journal of Pure and Applied Sciences and Technology

International Journal of Pure and Applied Sciences and Technology Int. J. Pure Appl. Sc. Technol., 4() (03), pp. 5-30 Internatonal Journal of Pure and Appled Scences and Technology ISSN 9-607 Avalable onlne at www.jopaasat.n Research Paper Schrödnger State Space Matrx

More information

The Study of Teaching-learning-based Optimization Algorithm

The Study of Teaching-learning-based Optimization Algorithm Advanced Scence and Technology Letters Vol. (AST 06), pp.05- http://dx.do.org/0.57/astl.06. The Study of Teachng-learnng-based Optmzaton Algorthm u Sun, Yan fu, Lele Kong, Haolang Q,, Helongang Insttute

More information

Tensor Smooth Length for SPH Modelling of High Speed Impact

Tensor Smooth Length for SPH Modelling of High Speed Impact Tensor Smooth Length for SPH Modellng of Hgh Speed Impact Roman Cherepanov and Alexander Gerasmov Insttute of Appled mathematcs and mechancs, Tomsk State Unversty 634050, Lenna av. 36, Tomsk, Russa RCherepanov82@gmal.com,Ger@npmm.tsu.ru

More information

Chapter 7 Channel Capacity and Coding

Chapter 7 Channel Capacity and Coding Chapter 7 Channel Capacty and Codng Contents 7. Channel models and channel capacty 7.. Channel models Bnary symmetrc channel Dscrete memoryless channels Dscrete-nput, contnuous-output channel Waveform

More information

Quantifying Uncertainty

Quantifying Uncertainty Partcle Flters Quantfyng Uncertanty Sa Ravela M. I. T Last Updated: Sprng 2013 1 Quantfyng Uncertanty Partcle Flters Partcle Flters Appled to Sequental flterng problems Can also be appled to smoothng problems

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

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