MECH6661 lectures 10/1 Dr. M. Medraj Mech. Eng. Dept. - Concordia University
|
|
- Ashlee Greer
- 5 years ago
- Views:
Transcription
1 Outle Revew ublattce Mdel Example ublattce Mdel Thermdyamc Mdelg Itrduct Example lly Desg Terary Phase Dagrams Gbbs Phase Rule Thermdyamcs f Multcmpet ystems Mech. Eg. Dept. - crda Uversty Revew: ublattce Mdel Fr rdered termedate sld sluts, t s better t use a mdel that ca descrbe the structure mre specfcally. I the sublattce mdel the elemets f the phase are dvded t separate lattces accrdg t the crystallgraphc structure f the phase. Fr example, a smple structure stes at the bdy s cetre ad crer psts ca be descrbed as dfferet sublattces, as llustrated the accmpaed fgure. structure I the sublattce mdel, stead f the verall cmpst x, the ste-fract y s used as a ceffcet whe defg the Gbbs eergy f the phase. The ste-fract s defed as the fractal ste ccupat y f each f the cmpets the specfc sublattce. MEH666 lectures 0/ Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/ y Revew: ublattce Mdel where s the umber f atms f cmpet sublattce s perfrmed fr all cmpets sublattce If there are vacaces the sublattce they are tae t accut as cmpets. The verall cmpst gve mle fracts s drectly related t ste fracts by the fllwg relatshp: where N s the ttal umber f stes sublattce ad y Va s the umber f vacaces sublattce The deal etrpy f mxg s made up f the cfguratal ctrbuts by cmpets mxg each f the sublattces. The umber f permutats that are pssble, assumg deal terchages wth each sublattce, s gve by: Mech. Eg. Dept. - crda Uversty x W N P N y ( y Va N )!! Example: ublattce Mdel d the mlar Gbbs eergy f deal mxg s: d G T RT N The ed members geerated whe ly pure cmpets exst the sublattce, effectvely defe the Gbbs eergy referece state. Fr a sublattce phase wth the fllwg frmula (,) (,D), t s pssble fr fur pts f cmplete ccupat t exst where pure exsts sublattce ad ether pure r D sublattce r, cversely, pure exsts sublattce wth ether r D sublattce. MEH666 lectures 0/3 Mech. Eg. Dept. - crda Uversty d y l y MEH666 lectures 0/4
2 Example: ublattce Mdel - The cmpst space f the phase ca the be csdered the fgure abve as csstg f fur cmpuds, the s-called... (at the crers f the square). - The cmpst f the phase s the ecmpassed the space betwee the fur ed-member cmpuds ad the wll l as shw the abve fgure. Ths surface ca be represeted by: Why Thermdyamc Mdellg? s we all w s far, thermdyamcs prvde relats betwee may dfferet materals prpertes le amut f phases ad ther cmpsts, heat f trasfrmat, partal pressures etc. Expermetal thermdyamcs has prved ts value fr materals research fr mre tha 00 years. Mder multcmpet materals wuld requre very extesve expermetal wr rder t establsh the relevat data fr phase equlbra ad thermdyamc prpertes. Mdellg s the ly pssble techque t reduce expermetal wr ad t crease the value f each ew expermet by allwg accurate extraplat. Thermdyamcs prvde the mst mprtat frmat fr a phase trasfrmat: the fal equlbrum state Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/5 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/6 Example: lly Desg Usg Thermdyamc Mdelg lly Desg t d Z Z l l l-z exp. Phase dagram, after H. ag et. al Z9 (9 wt% l, wt% Z) qud wt% l T = 400 (heat treatmet ) Temperature Z9 (9 wt% l, wt% Z) q+ + qud q Mass fract l Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/7 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/8
3 - ystem supercductvty at 39 K (hghest T c fr -xde) Thermdyamcs prvde helpful sght t the apprprate prcessg cdts fr Temperature Example: Prcess Develpmet y Thermdyamc Mdelg Gas+ + Gas P= Trr Gas+ 7 Gas+ 4 ld (s) tmc Fract f r Temperature - calculated phase dagram, after X. X et. al. (00) Mech. Eg. Dept. - crda Uversty Gas+ Gas+ 7 Gas+ 4 + Gas ld+ P= mtrr (s) tmc Fract f r Isuffcet supply wll lead t 4, 7 r sld phases. Gas+ 4 - ystem The pressure temperature phase dagram fr the -, after Z.-K u et. al. (00) MEH666 lectures 0/9 Mech. Eg. Dept. - crda Uversty Prcess Develpmet t d uldg a thermdyamc database fr ths system helps t develp a prcess t depst the supercductg phase MEH666 lectures 0/0 Terary Phase Dagrams Terary PD ct d T:% =..%; % = %; % =..% R: % =..%; % =.%; % =..% :% =..%; % =. %; % =.% R T % % X +X + X = 00 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/ Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/
4 Terary PD ct d Terary PD ct d I rder t have the same frmat that we had fr bary dagrams (cmpst plus T), we wuld eed a 3D represetat wth the cmpst tragle as bass. Hwever, except fr relatvely smple cases, t s dffcult t depct clearly by meas f perspectve dagrams the cmplete terary space mdel wth ts csttuet spaces, curves, ad pts. Therefre, t s cveet t use: prected vews (e.g., a prect f the lqudus r the sldus surface t the base f the space mdel) plae sects, tae hrztally (.e. sthermally) r vertcally (smlar t the bary dagrams) thrugh the 3D space mdel. I ctrast t bares, t s mpssble t mae a cveet graphcal represetat f the cmplete terary phase dagram that ca be utlzed all practcally mprtat cases. lut: emplyg a cmputer prgram whch wll cmpute the equlbrum cmpst the terary system at ay gve temperature ad cmpst. Temperature T = value Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/3 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/4 Terary Isthermal ects Terary Eutectc qudus prect Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/5 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/6
5 Terary Eutectc Terary Eutectc sder the three bary systems..,, ad. We ca arrage the three dagrams three dmess as shw here. The space sde the tragular prsm represets systems wth all three cmpets preset. There wll be a feld where crystallzes frst, a feld where crystallzes frst, ad e where crystallzes frst. I realty, there s a lqudus surface cverg the tragle ad as the system cls, t wll slde dw the surface Ths dagram shws the relatshp betwee temperature the bary systems ad temperature the tragle dagram. Very fte, temperature cturs are mtted. If the system s smple, ths s t a prblem. Hwever, f there are maxma r mma the lqudus surface, the dagram becmes less useful wthut cturs Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/7 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/8 Phase Evlut f Terary Eutectcs Phase Evlut f Terary Eutectcs The frst thg that wll happe s that e cmpet wll beg t crystallze. I ths case, t s. s we remve frm the melt, the melt cmpst wll mgrate straght away frm the crer as shw. Evetually the melt wll ht a feld budary ad the a secd cmpet wll beg t frm. I ths case, t s. The cmpst f the melt wll mgrate away frm bth ad the geeral drect f. The path f the melt s shw mageta. ce s w frmg alg wth, the sld cmpst mgrates the drect f. The melt, system, ad sld cmpsts always le a straght le as shw. Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/9 Mech. Eg. Dept. - crda Uversty Fally the melt reaches the terary eutectc ad all three cmpets beg t frm. The eutectc s usually a temperature mmum s the melt des t mve ce t reaches the eutectc. ce all three cmpets are w frmg, the sld cmpst mves t the terr f the tragle. The melt, system, ad sld cmpsts always le a straght le as shw. ce the melt stays at the eutectc, the sld mgrates tward the system cmpst as shw gree. Oce t reaches the system cmpst, the etre system s sldfed. Gve the rules abve, we ca dvde the tragle t sx felds wth crystallzat rders as shw. Nte that ay rder s.. MEH666 lectures 0/0
6 Revew: Partal Mlar Free Eergy 0 mutes rea G G ls: G bslute Mlar Gbbs Free Eergy T, P, X Partal Gbbs eergy r.. X + X G mxg ad G X Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/ X chematc plt f Gbbs free eergy vs. mle fract fr a bary system Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/ Revew: Thermdyamcs wth mpstal hages The Master equats that we develped prevusly are: It ca als be shw that: du Td PdV dh Td VdP dg dt VdP d dt PdV U, V, T, V, d d d d H, P, Thermdyamcs f Multcmpet ystems sder a system wth cmpets,,, l, dstrbuted amg phases,,,, t equlbrum t must be true that: = = = = = = = =... = = = =... l = l = l = l =... etc. hemcal ptetals represet the slpe f the Gbbs free eergy surface cmpstal space. Thus, a cmpet wll mve frm a phase whch t has a. chemcal ptetal, t e whch t has a chemcal ptetal, utl ts chemcal ptetal all phases s the... pecfc example: sder a - l-a melt equlbrum wth tw slds ad t equlbrum: melt = = l melt = l = l a melt = a = a Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/3 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/4
7 Terary ystems Istead f curves fr a bary system, the case f a terary system, Gbbs free eergy surfaces ca be pltted fr all the pssble phases as fucts f temp ad cmpst. The chemcal ptetals f,, ad f ay phase ths system are gve by the pts where the tagetal plae t the free eergy surfaces tersects the,, ad axs. three-phase equlbrum the terary system fr a gve temperature ca be derved by meas f the tagetal plae cstruct. Terary ystems Eutectc pt: fur-phase equlbrum betwee α, β, γ, ad lqud Fr tw phases t be equlbrum, the chemcal ptetals shuld be.., that s the cmpsts f the tw phases equlbrum must be gve by pts cected by a cmm tagetal plae (e.g. l ad m). The relatve amuts f phases are gve by the. rule. three phase tragle ca result frm a cmm tagetal plae smultaeusly tuchg the Gbbs free eerges f three phases (e.g. pts x, y, ad z). Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/5 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/6 Thermdyamcs f Multcmpet ystems Thermdyamcs f Multcmpet ystems Fr bary systems: Partal excess Gbbs eergy Fr terary systems: x x G G 0 x G x G ( x x G ex ex x G x RT G RT l x G x G ) x x x l x x l x xx G 0 G RT l x G 0 0 Mech. Eg. Dept. - crda Uversty RT x ( x deal l x G x x Excess l x ) x x 0 ( x x ) x 0 ( x x ) l x ( x x ) ( x (( ) x x ( x ( ) x x ) where the parameters have the same values as each f the bary systems If ecessary, a terary term X X X G (T,X) ca be added rder t descrbe the ctrbut f three elemet teracts t the Gbbs eergy. ex MEH666 lectures 0/7 Mech. Eg. Dept. - crda Uversty m m G x x x x x Where = x 0 + x + x ad x + x + x = s a terary teract parameter Hgher rder teract parameters ca be usually mtted (a few excepts quaterary systems) MEH666 lectures 0/8
8 Example: Fe-N-r ystem The terary dagram f N-r-Fe cludes taless teel (wt.% f r >.5 %, wt.% f Fe > 50 %) ad Icel (Ncel based superallys). Mech. Eg. Dept. - crda Uversty The Gbbs Phase Rule: Example - Terary ystems t pt Ph = (sld & ) = 3 F = 3 Ph + as pressure s cstat F = 4 - Ph F =.. (T ad X, X r X shuld be specfed) t pt, Ph = 3 (sld, &) = 3 F = 4 - Ph = (T ly shuld be specfed) MEH666 lectures 0/9 Mech. Eg. Dept. - crda Uversty e + t E the eutectc where all three phase felds meet: P = 4 (sld,, ad ) = 3 F= 4 Ph = (T ad cmp are fxed) + E e + e 3 mple eutectc terary phase dagram at cstat pressure MEH666 lectures 0/30 Terary ystems Terary ystems wth ary gruet mpuds Nte that the eutectc pts each f the bary systems prect t the terary systems as les. These les are called budary curves, ad ay cmpst e f these curves wll crystallze the tw phases ether sde f the curve. Mech. Eg. Dept. - crda Uversty terary system that has a bary sub-system wth a cmpud that shws cgruet meltg (melts t a lqud f ts w cmpst). The result f the addt f ths termedate cmpud s essetally that the terary system XYZ s dvded t tw smaller terary systems represeted by tragles WYZ ad XWZ MEH666 lectures 0/3 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/3
9 Terary ystems wth ary gruet mpuds Terary ystems wth ary Icgruet mpuds e The termedate cmpuds are cgruetly meltg. It s characterstc f the cgruetly meltg bary cmpud that ts cmpst pt always falls ts prmary feld f stablty the terary system. I these tw cases, ths prmary phase feld s descrbed by the les E -E ad P-e. Mech. Eg. Dept. - crda Uversty terary system ad e f the bary systems that ctas a cmpud, D, that melts cgruetly. MEH666 lectures 0/33 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/34 Terary ystems wth ary Icgruet mpuds The termedate cmpud ths dagram s cgruetly meltg because t s t the prmary phase feld f the cmpud. gruet meltg s mprtat determg the lamade les ad the crystallzat path. Next tme: tue Terary ystems Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/35 Mech. Eg. Dept. - crda Uversty MEH666 lectures 0/36
Byeong-Joo Lee
yeg-j Lee OSECH - MSE calphad@pstech.ac.r yeg-j Lee www.pstech.ac.r/~calphad Fudametals Mcrscpc vs. Macrscpc Vew t State fuct vs. rcess varable Frst Law f hermdyamcs Specal prcesses 1. Cstat-Vlume rcess:
More informationThe Simple Linear Regression Model: Theory
Chapter 3 The mple Lear Regress Mdel: Ther 3. The mdel 3.. The data bservats respse varable eplaatr varable : : Plttg the data.. Fgure 3.: Dsplag the cable data csdered b Che at al (993). There are 79
More informationLecture 2. Basic Semiconductor Physics
Lecture Basc Semcductr Physcs I ths lecture yu wll lear: What are semcductrs? Basc crystal structure f semcductrs Electrs ad hles semcductrs Itrsc semcductrs Extrsc semcductrs -ded ad -ded semcductrs Semcductrs
More informationData Mining: Concepts and Techniques
Data Mg: cepts ad Techques 3 rd ed. hapter 10 1 Evaluat f lusterg lusterg evaluat assesses the feasblty f clusterg aalyss a data set ad the qualty f the results geerated by a clusterg methd. Three mar
More informationPY3101 Optics. Learning objectives. Wave propagation in anisotropic media Poynting walk-off The index ellipsoid Birefringence. The Index Ellipsoid
The Ide Ellpsd M.P. Vaugha Learg bjectves Wave prpagat astrpc meda Ptg walk-ff The de ellpsd Brefrgece 1 Wave prpagat astrpc meda The wave equat Relatve permttvt I E. Assumg free charges r currets E. Substtutg
More informationSolutions. Definitions pertaining to solutions
Slutis Defiitis pertaiig t slutis Slute is the substace that is disslved. It is usually preset i the smaller amut. Slvet is the substace that des the disslvig. It is usually preset i the larger amut. Slubility
More informationBasics of heteroskedasticity
Sect 8 Heterskedastcty ascs f heterskedastcty We have assumed up t w ( ur SR ad MR assumpts) that the varace f the errr term was cstat acrss bservats Ths s urealstc may r mst ecmetrc applcats, especally
More informationCHAPTER 5 ENTROPY GENERATION Instructor: Prof. Dr. Uğur Atikol
CAPER 5 ENROPY GENERAION Istructr: Pr. Dr. Uğur Atkl Chapter 5 Etrpy Geerat (Exergy Destruct Outle st Avalable rk Cycles eat ege cycles Rergerat cycles eat pump cycles Nlw Prcesses teady-flw Prcesses Exergy
More informationThe fuzzy decision of transformer economic operation
The fuzzy decs f trasfrmer ecmc perat WENJUN ZHNG, HOZHONG CHENG, HUGNG XIONG, DEXING JI Departmet f Electrcal Egeerg hagha Jatg Uversty 954 Huasha Rad, 3 hagha P. R. CHIN bstract: - Ths paper presets
More informationLecture 07: Poles and Zeros
Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto
More informationGoal of the Lecture. Lecture Structure. FWF 410: Analysis of Habitat Data I: Definitions and Descriptive Statistics
FWF : Aalyss f Habtat Data I: Defts ad Descrptve tatstcs Number f Cveys A A B Bur Dsk Bur/Dsk Habtat Treatmet Matthew J. Gray, Ph.D. Cllege f Agrcultural ceces ad Natural Resurces Uversty f Teessee-Kvlle
More informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationObjective of curve fitting is to represent a set of discrete data by a function (curve). Consider a set of discrete data as given in table.
CURVE FITTING Obectve curve ttg s t represet set dscrete dt b uct curve. Csder set dscrete dt s gve tble. 3 3 = T use the dt eectvel, curve epress s tted t the gve dt set, s = + = + + = e b ler uct plml
More informationWhat characteristics distinguish irreversible (real) transformations from reversible (idealized) transformations?
bbs Free Eergy: ptaety ad Equlbrum What haratersts dstgush rreversble real trasfrmats frm reversble dealzed trasfrmats? t every stage f a reversble trasfrmat the system departs frm ulbrum ly ftesmally.
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationON THE LAGRANGIAN RHEONOMIC MECHANICAL SYSTEMS
THE PUBISHING HOUSE PROCEEDINGS OF THE ROMANIAN ACADEMY, Seres A, OF THE ROMANIAN ACADEMY Vlume 1, Number 1/9, pp. ON THE AGRANGIAN RHEONOMIC MECHANICA SYSTEMS Radu MIRON*, Tmak KAWAGUCHI**, Hrak KAWAGUCHI***
More informationIntroduction to local (nonparametric) density estimation. methods
Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest
More informationMechanics of Materials CIVL 3322 / MECH 3322
Mechacs of Materals CVL / MECH Cetrods ad Momet of erta Calculatos Cetrods = A = = = A = = Cetrod ad Momet of erta Calculatos z= z A = = Parallel As Theorem f ou kow the momet of erta about a cetrodal
More informationWe have already referred to a certain reaction, which takes place at high temperature after rich combustion.
ME 41 Day 13 Topcs Chemcal Equlbrum - Theory Chemcal Equlbrum Example #1 Equlbrum Costats Chemcal Equlbrum Example #2 Chemcal Equlbrum of Hot Bured Gas 1. Chemcal Equlbrum We have already referred to a
More informationChemistry 163B Introduction to Multicomponent Systems and Partial Molar Quantities
Chemstry 163 Itroducto to Multcompoet Systems ad Partal Molar Quattes 1 the problem of partal mmolar quattes mx: 10 moles ethaol C H 5 OH (580 ml) wth 1 mole water H O (18 ml) get (580+18)=598 ml of soluto?
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationChemistry 163B Introduction to Multicomponent Systems and Partial Molar Quantities
Chemstry 163B Itroducto to Multcompoet Systems ad Partal Molar Quattes 1 the problem of partal mmolar quattes mx: 10 moles ethaol C H 5 OH (580 ml) wth 1 mole water H O (18 ml) get (580+18)=598 ml of soluto?
More informationApplying the condition for equilibrium to this equilibrium, we get (1) n i i =, r G and 5 i
CHEMICAL EQUILIBRIA The Thermodyamc Equlbrum Costat Cosder a reversble reacto of the type 1 A 1 + 2 A 2 + W m A m + m+1 A m+1 + Assgg postve values to the stochometrc coeffcets o the rght had sde ad egatve
More informationFuel & Advanced Combustion. Lecture Chemical Reaction
Fuel & Advaced Cmbust Lecture Cemcal eact Ideal Gas Mdel e deal gas equat f state s: V m m M were s te Uversal Gas Cstat (8.34 kj/kml K, M s te mlecular wegt ad s te umber f mles. Secfc teral eergy (uts:
More informationLecture 3. Sampling, sampling distributions, and parameter estimation
Lecture 3 Samplg, samplg dstrbutos, ad parameter estmato Samplg Defto Populato s defed as the collecto of all the possble observatos of terest. The collecto of observatos we take from the populato s called
More informationPTAS for Bin-Packing
CS 663: Patter Matchg Algorthms Scrbe: Che Jag /9/00. Itroducto PTAS for B-Packg The B-Packg problem s NP-hard. If we use approxmato algorthms, the B-Packg problem could be solved polyomal tme. For example,
More informationDerivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations
Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat
More information1 Onto functions and bijections Applications to Counting
1 Oto fuctos ad bectos Applcatos to Coutg Now we move o to a ew topc. Defto 1.1 (Surecto. A fucto f : A B s sad to be surectve or oto f for each b B there s some a A so that f(a B. What are examples of
More informationChapter 9 Jordan Block Matrices
Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.
More informationMA/CSSE 473 Day 27. Dynamic programming
MA/CSSE 473 Day 7 Dyamc Programmg Bomal Coeffcets Warshall's algorthm (Optmal BSTs) Studet questos? Dyamc programmg Used for problems wth recursve solutos ad overlappg subproblems Typcally, we save (memoze)
More informationGENERALIZATIONS OF CEVA S THEOREM AND APPLICATIONS
GENERLIZTIONS OF CEV S THEOREM ND PPLICTIONS Floret Smaradache Uversty of New Mexco 200 College Road Gallup, NM 87301, US E-mal: smarad@um.edu I these paragraphs oe presets three geeralzatos of the famous
More informationECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity
ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data
More informationThe E vs k diagrams are in general a function of the k -space direction in a crystal
vs dagram p m m he parameter s called the crystal mometum ad s a parameter that results from applyg Schrödger wave equato to a sgle-crystal lattce. lectros travelg dfferet drectos ecouter dfferet potetal
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationLecture 3 Probability review (cont d)
STATS 00: Itroducto to Statstcal Iferece Autum 06 Lecture 3 Probablty revew (cot d) 3. Jot dstrbutos If radom varables X,..., X k are depedet, the ther dstrbuto may be specfed by specfyg the dvdual dstrbuto
More informationUNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS
UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS Postpoed exam: ECON430 Statstcs Date of exam: Jauary 0, 0 Tme for exam: 09:00 a.m. :00 oo The problem set covers 5 pages Resources allowed: All wrtte ad prted
More informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationOrdinary Differential Equations. Orientation. Lesson Objectives. Ch. 25. ODE s. Runge-Kutta Methods. Motivation Mathematical Background
Ordar Deretal Equats C. 5 Oretat ODE s Mtvat Matematcal Bacgrud Ruge-Kutta Metds Euler s Metd Hue ad Mdpt metds Less Objectves Be able t class ODE s ad dstgus ODE s rm PDE s. Be able t reduce t rder ODE
More information1 Lyapunov Stability Theory
Lyapuov Stablty heory I ths secto we cosder proofs of stablty of equlbra of autoomous systems. hs s stadard theory for olear systems, ad oe of the most mportat tools the aalyss of olear systems. It may
More informationCh5 Appendix Q-factor and Smith Chart Matching
h5 Appedx -factr ad mth hart Matchg 5B-1 We-ha a udwg, F rcut Desg Thery ad Applcat, hapter 8 Frequecy espse f -type Matchg Netwrks 5B- Fg.8-8 Tw desg realzats f a -type matchg etwrk.65pf, 80 f 1 GHz Fg.8-9
More informationSection 3: Detailed Solutions of Word Problems Unit 1: Solving Word Problems by Modeling with Formulas
Sectn : Detaled Slutns f Wrd Prblems Unt : Slvng Wrd Prblems by Mdelng wth Frmulas Example : The factry nvce fr a mnvan shws that the dealer pad $,5 fr the vehcle. If the stcker prce f the van s $5,, hw
More informationDiscrete Mathematics and Probability Theory Fall 2016 Seshia and Walrand DIS 10b
CS 70 Dscrete Mathematcs ad Probablty Theory Fall 206 Sesha ad Walrad DIS 0b. Wll I Get My Package? Seaky delvery guy of some compay s out delverg packages to customers. Not oly does he had a radom package
More informationA Combination of Adaptive and Line Intercept Sampling Applicable in Agricultural and Environmental Studies
ISSN 1684-8403 Joural of Statstcs Volume 15, 008, pp. 44-53 Abstract A Combato of Adaptve ad Le Itercept Samplg Applcable Agrcultural ad Evrometal Studes Azmer Kha 1 A adaptve procedure s descrbed for
More informationModule 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law
Module : The equato of cotuty Lecture 5: Coservato of Mass for each speces & Fck s Law NPTEL, IIT Kharagpur, Prof. Sakat Chakraborty, Departmet of Chemcal Egeerg 2 Basc Deftos I Mass Trasfer, we usually
More informationSTRESS TRANSFER IN CARBON NANOTUBE REINFORCED POLYMER COMPOSITES
STESS TANSFE IN CABON NANOTUBE EINFOCED POLYME COMPOSITES A. Haque ad A. aasetty Departet f Aerspace Egeerg ad Mechacs, The Uversty f Alabaa, Tuscalsa, AL 35487, USA ABSTACT A aalytcal del has bee develped
More informationThe Mathematical Appendix
The Mathematcal Appedx Defto A: If ( Λ, Ω, where ( λ λ λ whch the probablty dstrbutos,,..., Defto A. uppose that ( Λ,,..., s a expermet type, the σ-algebra o λ λ λ are defed s deoted by ( (,,...,, σ Ω.
More informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationMA 524 Homework 6 Solutions
MA 524 Homework 6 Solutos. Sce S(, s the umber of ways to partto [] to k oempty blocks, ad c(, s the umber of ways to partto to k oempty blocks ad also the arrage each block to a cycle, we must have S(,
More informationIntermediate Division Solutions
Itermediate Divisi Slutis 1. Cmpute the largest 4-digit umber f the frm ABBA which is exactly divisible by 7. Sluti ABBA 1000A + 100B +10B+A 1001A + 110B 1001 is divisible by 7 (1001 7 143), s 1001A is
More informationNeville Robbins Mathematics Department, San Francisco State University, San Francisco, CA (Submitted August 2002-Final Revision December 2002)
Nevlle Robbs Mathematcs Departmet, Sa Fracsco State Uversty, Sa Fracsco, CA 943 (Submtted August -Fal Revso December ) INTRODUCTION The Lucas tragle s a fte tragular array of atural umbers that s a varat
More informationFeature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)
CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.
More informationv 1 -periodic 2-exponents of SU(2 e ) and SU(2 e + 1)
Joural of Pure ad Appled Algebra 216 (2012) 1268 1272 Cotets lsts avalable at ScVerse SceceDrect Joural of Pure ad Appled Algebra joural homepage: www.elsever.com/locate/jpaa v 1 -perodc 2-expoets of SU(2
More informationEECE 301 Signals & Systems
EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel
More informationmeans the first term, a2 means the term, etc. Infinite Sequences: follow the same pattern forever.
9.4 Sequeces ad Seres Pre Calculus 9.4 SEQUENCES AND SERIES Learg Targets:. Wrte the terms of a explctly defed sequece.. Wrte the terms of a recursvely defed sequece. 3. Determe whether a sequece s arthmetc,
More informationDepartment of Economics University of Toronto. ECO2408F M.A. Econometrics. Lecture Notes on Simple Regression Model
Deprtmet f Ecmc Uvert f Trt ECO48F M.A. Ecmetrc Lecture Nte Smple Regre Mdel Smple Regre Mdel I the frt lecture we lked t fttg le t ctter f pt. I th chpter we eme regre methd f eplrg the prbbltc tructure
More information18.413: Error Correcting Codes Lab March 2, Lecture 8
18.413: Error Correctg Codes Lab March 2, 2004 Lecturer: Dael A. Spelma Lecture 8 8.1 Vector Spaces A set C {0, 1} s a vector space f for x all C ad y C, x + y C, where we take addto to be compoet wse
More informationStatistics MINITAB - Lab 5
Statstcs 10010 MINITAB - Lab 5 PART I: The Correlato Coeffcet Qute ofte statstcs we are preseted wth data that suggests that a lear relatoshp exsts betwee two varables. For example the plot below s of
More informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationPeriodic Table of Elements. EE105 - Spring 2007 Microelectronic Devices and Circuits. The Diamond Structure. Electronic Properties of Silicon
EE105 - Srg 007 Mcroelectroc Devces ad Crcuts Perodc Table of Elemets Lecture Semcoductor Bascs Electroc Proertes of Slco Slco s Grou IV (atomc umber 14) Atom electroc structure: 1s s 6 3s 3 Crystal electroc
More information2. Independence and Bernoulli Trials
. Ideedece ad Beroull Trals Ideedece: Evets ad B are deedet f B B. - It s easy to show that, B deedet mles, B;, B are all deedet ars. For examle, ad so that B or B B B B B φ,.e., ad B are deedet evets.,
More information[1 & α(t & T 1. ' ρ 1
NAME 89.304 - IGNEOUS & METAMORPHIC PETROLOGY DENSITY & VISCOSITY OF MAGMAS I. Desity The desity (mass/vlume) f a magma is a imprtat parameter which plays a rle i a umber f aspects f magma behavir ad evluti.
More informationInvestigating Cellular Automata
Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte
More informationLecture Notes Types of economic variables
Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte
More informationOpen 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 informationIntegral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy
Itroducto to Smulato - Lecture 22 Itegral Equato ethods Jacob Whte Thaks to Deepak Ramaswamy, chal Rewesk, X Wag ad Kare Veroy Outle Itegral Equato ethods Exteror versus teror problems Start wth usg pot
More informationx 2 x 3 x b 0, then a, b, c log x 1 log z log x log y 1 logb log a dy 4. dx As tangent is perpendicular to the x axis, slope
The agle betwee the tagets draw t the parabla y = frm the pit (-,) 5 9 6 Here give pit lies the directri, hece the agle betwee the tagets frm that pit right agle Ratig :EASY The umber f values f c such
More informationChapter 3.1: Polynomial Functions
Ntes 3.1: Ply Fucs Chapter 3.1: Plymial Fuctis I Algebra I ad Algebra II, yu ecutered sme very famus plymial fuctis. I this secti, yu will meet may ther members f the plymial family, what sets them apart
More informationChapter 11 Systematic Sampling
Chapter stematc amplg The sstematc samplg techue s operatoall more coveet tha the smple radom samplg. It also esures at the same tme that each ut has eual probablt of cluso the sample. I ths method of
More informationWhat regression does. so β. β β β
Sect Smple Regress What regress des Relatshp Ofte ecmcs we beleve that there s a (perhaps causal) relatshp betwee tw varables Usually mre tha tw, but that s deferred t ather day Frm Is the relatshp lear?
More informationd dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin
Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace
More informationC.11 Bang-bang Control
Itroucto to Cotrol heory Iclug Optmal Cotrol Nguye a e -.5 C. Bag-bag Cotrol. Itroucto hs chapter eals wth the cotrol wth restrctos: s boue a mght well be possble to have scotutes. o llustrate some of
More informationThermodynamics of Materials
Thermdynamcs f Materals 14th Lecture 007. 4. 8 (Mnday) FUGACITY dg = Vd SdT dg = Vd at cnstant T Fr an deal gas dg = (RT/)d = RT dln Ths s true fr deal gases nly, but t wuld be nce t have a smlar frm fr
More informationUNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS
Numercal Computg -I UNIT SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS Structure Page Nos..0 Itroducto 6. Objectves 7. Ital Approxmato to a Root 7. Bsecto Method 8.. Error Aalyss 9.4 Regula Fals Method
More informationMATH 247/Winter Notes on the adjoint and on normal operators.
MATH 47/Wter 00 Notes o the adjot ad o ormal operators I these otes, V s a fte dmesoal er product space over, wth gve er * product uv, T, S, T, are lear operators o V U, W are subspaces of V Whe we say
More informationAppendix 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 informationMATH Midterm Examination Victor Matveev October 26, 2016
MATH 33- Midterm Examiati Victr Matveev Octber 6, 6. (5pts, mi) Suppse f(x) equals si x the iterval < x < (=), ad is a eve peridic extesi f this fucti t the rest f the real lie. Fid the csie series fr
More informationAn Introduction to. Support Vector Machine
A Itroducto to Support Vector Mache Support Vector Mache (SVM) A classfer derved from statstcal learg theory by Vapk, et al. 99 SVM became famous whe, usg mages as put, t gave accuracy comparable to eural-etwork
More informationCentroids & Moments of Inertia of Beam Sections
RCH 614 Note Set 8 S017ab Cetrods & Momets of erta of Beam Sectos Notato: b C d d d Fz h c Jo L O Q Q = ame for area = ame for a (base) wdth = desgato for chael secto = ame for cetrod = calculus smbol
More informationCentroids Method of Composite Areas
Cetrods Method of Composte reas small boy swallowed some cos ad was take to a hosptal. Whe hs gradmother telephoed to ask how he was a urse sad 'No chage yet'. Cetrods Prevously, we developed a geeral
More informationMATHEMATICS 9740/01 Paper 1 14 Sep hours
Cadidate Name: Class: JC PRELIMINARY EXAM Higher MATHEMATICS 9740/0 Paper 4 Sep 06 3 hurs Additial Materials: Cver page Aswer papers List f Frmulae (MF5) READ THESE INSTRUCTIONS FIRST Write yur full ame
More informationMaps on Triangular Matrix Algebras
Maps o ragular Matrx lgebras HMED RMZI SOUROUR Departmet of Mathematcs ad Statstcs Uversty of Vctora Vctora, BC V8W 3P4 CND sourour@mathuvcca bstract We surveys results about somorphsms, Jorda somorphsms,
More informationA tighter lower bound on the circuit size of the hardest Boolean functions
Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationUniversity of Washington Department of Chemistry Chemistry 452/456 Summer Quarter 2014
Lecture 12 7/25/14 ERD: 7.1-7.5 Devoe: 8.1.1-8.1.2, 8.2.1-8.2.3, 8.4.1-8.4.3 Unversty o Washngton Department o Chemstry Chemstry 452/456 Summer Quarter 2014 A. Free Energy and Changes n Composton: The
More informationPartial Molar Properties of solutions
Paral Molar Properes of soluos A soluo s a homogeeous mxure; ha s, a soluo s a oephase sysem wh more ha oe compoe. A homogeeous mxures of wo or more compoes he gas, lqud or sold phase The properes of a
More informationf f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).
CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The
More informationChapter 8: Statistical Analysis of Simulated Data
Marquette Uversty MSCS600 Chapter 8: Statstcal Aalyss of Smulated Data Dael B. Rowe, Ph.D. Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 08 by Marquette Uversty MSCS600 Ageda 8. The Sample
More information_J _J J J J J J J J _. 7 particles in the blue state; 3 particles in the red state: 720 configurations _J J J _J J J J J J J J _
Dsrder and Suppse I have 10 partcles that can be n ne f tw states ether the blue state r the red state. Hw many dfferent ways can we arrange thse partcles amng the states? All partcles n the blue state:
More informationBeam Warming Second-Order Upwind Method
Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet
More informationPREDICTION OF VAPOR-LIQUID EQUILIBRIA OF BINARY MIXTURES USING QUANTUM CALCULATIONS AND ACTIVITY COEFFICIENT MODELS
Joural of Chemstry, Vol. 47 (5), P. 547-55, 9 PREDICTIO OF VAPOR-LIQUID EQUILIBRIA OF BIARY MIXTURES USIG QUATUM CALCULATIOS AD ACTIVITY COEFFICIET MODELS Receved May 8 PHAM VA TAT Departmet of Chemstry,
More informationMATHEMATICAL LITERACY
MATBUS NOVEMBER 2013 EXAMINATION DATE: 8 NOVEMBER 2013 TIME: 14H00 16H00 TOTAL: 100 MARKS DURATION: 2 HOURS PASS MARK: 40% (UC-02) MATHEMATICAL LITERACY THIS EXAMINATION PAPER CONSISTS OF 11 QUESTIONS:
More informationStatistics Descriptive and Inferential Statistics. Instructor: Daisuke Nagakura
Statstcs Descrptve ad Iferetal Statstcs Istructor: Dasuke Nagakura (agakura@z7.keo.jp) 1 Today s topc Today, I talk about two categores of statstcal aalyses, descrptve statstcs ad feretal statstcs, ad
More informationLecture 9: Tolerant Testing
Lecture 9: Tolerat Testg Dael Kae Scrbe: Sakeerth Rao Aprl 4, 07 Abstract I ths lecture we prove a quas lear lower boud o the umber of samples eeded to do tolerat testg for L dstace. Tolerat Testg We have
More informationDATE: 21 September, 1999 TO: Jim Russell FROM: Peter Tkacik RE: Analysis of wide ply tube winding as compared to Konva Kore CC: Larry McMillan
M E M O R A N D U M DATE: 1 September, 1999 TO: Jm Russell FROM: Peter Tkack RE: Aalyss of wde ply tube wdg as compared to Kova Kore CC: Larry McMlla The goal of ths report s to aalyze the spral tube wdg
More informationRandom Variables and Probability Distributions
Radom Varables ad Probablty Dstrbutos * If X : S R s a dscrete radom varable wth rage {x, x, x 3,. } the r = P (X = xr ) = * Let X : S R be a dscrete radom varable wth rage {x, x, x 3,.}.If x r P(X = x
More informationChapter 11 The Analysis of Variance
Chapter The Aalyss of Varace. Oe Factor Aalyss of Varace. Radomzed Bloc Desgs (ot for ths course) NIPRL . Oe Factor Aalyss of Varace.. Oe Factor Layouts (/4) Suppose that a expermeter s terested populatos
More informationLecture 2 - What are component and system reliability and how it can be improved?
Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected
More information