TRANSIENT HEAT TRANSFER FOR CIRCULAR JET IMPACTING A HORIZONTAL SURFACE: APPLICATION OF THE ITERATIVE REGULARIZATION METHOD

Size: px
Start display at page:

Download "TRANSIENT HEAT TRANSFER FOR CIRCULAR JET IMPACTING A HORIZONTAL SURFACE: APPLICATION OF THE ITERATIVE REGULARIZATION METHOD"

Transcription

1 B Proceedgs of the 5 th Iteratoal Coferece o Iverse Problems Egeerg: Theory ad Practce, Cambrdge, UK, -5 th July 5 TRANSIENT HEAT TRANSFER FOR CIRCULAR JET IMPACTING A HORIZONTAL SURFACE: APPLICATION OF THE ITERATIVE REGULARIZATION METHOD J. B. BAONGA, H. LOUAHLIA-GUALOUS, E. ARTIOUKHINE ad P. K. PANDAY FEMTO ST, CREST departmet, CNRS-UMR 674, aveue Jea Moul, 9 Belfort, Frace e-mal: hasa.gualous@utbm.fr Abstract - Ths paper presets the expermetal results of the local thermal boudary codos obtaed by solvg the traset verse heat coducto problem. The expermetal study cocers the heat trasfer of a lqud crcular jet mpgg a horzotal surface. The ater s used as the test flud h the uform temperature. A electrc resstace heater s placed sde the cylder ad 5 thermocouples are stalled order to measure the temperature the sold. The eratve regularzato method s used to solve the traset verse problem uder study. The uko fuctos are approxmated by cubc B-sples. The gradet s computed by usg the adjot equatos. NOMENCLATURE f temperature fucto, K h heat trasfer coeffcet, W/m K J resdual fuctoal, K m N agular odes umber Q heat flux, W/m r radus, m R radus of the heated surface, m r co-ordate of measuremet pot T calculated temperature, K T meas measured temperature, K z axal co-ordate, m co-ordate of measured pot, m z Greek symbols λ thermal coductvy, W/mK θ temperature varato Ψ Lagrage multpler γ descet parameter δ Drac fucto Subscrpts measured pots all erato umber r local value. INTRODUCTION Jet mpgemet heat trasfer has bee employed may practcal applcatos for coolg ad dryg because provded hgh heat trasfer coeffcet. Jet mpgemet coolg has bee used a may applcatos from the metal sheet dustry to coolg laser ad electroc equpmets. Extesve umercal ad expermetal studes o heat trasfer ad hydrodyamcs of lqud jet mpgemet have bee reported the lerature [-4]. The applcatos of lqud jet cluded coolg teral combusto eges, quechg of metals ad other materals maufacturg process, ad thermal cotrol of hgh performace computer compoets. Whe a crcular lqud jet strkes a flat plate, spreads radally very th flm alog the heated surface. Follog may authors [5,6], the jet mpgemet flo s dvded to four regos defed as: () the stagato zoe here the heat trasfer s maxmal, () the vscous boudary layer zoe here the dyamc boudary thckess s loer tha the flm thckess, () the thermal boudary layer here the flm thckess s hgher tha the thermal boudary layer, (v) the fully thermal ad vscous boudary layer rego ad (v) the hydraulc jump here the lqud sheet thckess s creased ad the jump s assocated h a Raylegh-Taylor stably. Numerous studes are coducted order to evaluate the average heat trasfer coeffcets, but the estmato of the traset-state local heat trasfer hout eglectg the heat coducto the sold has ot retaed much

2 B atteto. The aalyss of thermal performace the lqud jet mpgemet requres the koledge of the thermal boudary codos for the coolg surface. I ths case, the drect measuremets of the velocy ad temperature dstrbutos the flog flm are very dffcult to carry out because the flm thckess s geerally less tha 5 µm. Oly the let bulk temperature of the flm ad the all temperature sde the expermetal cylder are easly to measure hout perturbg the lqud jet flo. I ths ork, the procedure for solvg the to-dmesoal usteady lear verse heat coducto problem (IHCP) s preseted. It s appled to the problem of the lqud jet mpgg the horzotal surface order to estmate the thermal boudary codo of a hollo half-cylder. The eratve regularzato method s used to solve the verse problem uder aalyss. The method s based o the cojugate gradet method that uses to mmze the resdual fuctoal ad the resdual dscrepacy prcpal as the regularzg stoppg crero. Ths paper presets the results of the expermetal study of heat trasfer coeffcet for a free ater jet mpactg a horzotal plate. The measured average heat trasfer coeffcets are compared to the values estmated by solvg IHCP.. EXPERIMENTAL SET UP The test facly sho Fgure s composed of to crcus. The frst oe s a closed crcu here the test lqud s forced crculato usg the pump (3). The flud the reservor () s cooled trough the heat exchager (4). The secod crcu cossts of the feed to the test secto. The flud s crculated usg the pump () from the reservor () to the adjustable costat head tak (5) equpped h a dra ad a overflo. A flter (3) s used to elmate ay dust or partcles the orkg flud. A electrcal heater of 5W (7) assocated h a temperature system cotroller (8) are stalled the head tak (5) to cotrol the temperature of the test lqud. A Chromel-Alumel thermocouple (6) s placed the lqud bath to measure the temperature of the flud order to estmate the lqud propertes. The flud from the overhead tak (5) flos uder gravy through the regulatg valves to the heated surface. The temperature of the orkg flud the reservor () s cotrolled by usg the heater cartrdge of W () ad the regulatg system (9). - reservor - pump 3- flter 4- test secto 5- head tak 6- thermocouple 7- heater 8, 9- regulatg systems - heater - thermocouple 3- pump 4- heat exchager Fgure. Expermetal apparatus Fgure a shos the heater assembly used ths study. The expermetal cylder as made of a brass block ad as heated by usg oe cartrdge heater of W. The electrc heater has a mm of dameter ad 4 mm of legth. It s placed sde the expermetal cylder as sho Fgure. The electrc poer put s measured usg a ammeter ad a voltmeter. The test sample s oreted vertcally ad s of 5 mm of dameter ad 6 mm of legth. The coolg crcular plae surface s of 5 mm of dameter. The heatg cylder s thermally sulated h Teflo (thermal coducto of.3 W/mK) o all faces except the coolg face order to prevet the heat loss. The test sample ca be moved alog the vertcal axs order to study the fluece of the space betee the ozzle ad the heated surface. The temperatures sde the expermetal cylder are measured h 5 Chromel-Alumel thermocouples of dameter. mm (ucertaty of ±. C), placed to secto defed at.6 mm ad 8 mm from the coolg surface (Fgure b). For the secto S, 5 thermocouples are placed at.6 mm from the etted surface at tervals of 3.5 mm. For the secto S, 5 thermocouples are used at 8 mm from the outer surface at tervals of 3.5 mm. The thermocouples are located at 4 cocetrc crcles of rad 8mm,.5mm, 5 mm ad mm as sho Fgure b.

3 B 3 Thermocouples (a) (b) Fgure. Istrumetato of the test secto. Because of the ucertaty the heat flux dstrbuto alog the legth of the cylder, the all temperature dstrbuto as measured at to dfferet sectos as sho Fgure b. A data acquso system s used to record all temperature measuremets for each mposed electrc poer sde the expermetal cylder. The IHCP s solved order to determe the local heat flux ad the surface temperature of the heated dsk. The local heat trasfer coeffcet s determed at the exchage crcular surface for dfferet flo rates. The expermets are coducted for let lqud jet temperature of 37 C, ad the total electrc poer mposed sde the cylder of 4W ad 7W. For each flo rate, the measured temperature sde the all of the expermetal dsk s used for solvg the IHCP. 3. INVERSE HEAT CONDUCTION PROBLEM The physcal model cosders a vertcal cylder of heght H ad exteral radus R (Fgure 3). The heatg cylder s thermally sulated h Teflo o all faces except the horzotal cooled face order to prevet the heat loss. The soluto of the verse problem s carred out for half of the cylder. 5 thermocouples are stalled sde the expermetal cylder order to measure the expermetal all temperature for each test. Uko heat flux Wetted surface Isulated H meas z r H Expermetal cylder R Fgure 3. Physcal model. The geometrcal ad physcal propertes of the cylder are preseted Table. The mathematcal model of a heat coducto process a hollo cylder treats the traset-state heat trasfer problem. It s gve by the follog system of equatos: ρc p T( r, z, t ) T( r, z, t ) T( r, z, t ) T( r, z, t ) = + +, here < r < R, < z < H () λ t r z T (, z, t) T (R, z, t) =, here < t tf, z H () =, here < t tf, z H (3)

4 T (r, z,) = T, here : r R, z H (4) T λ (r, H, t) = Q(r, H, t), here : < t tf, r R z (5) T (r,, t) = f(r, t), here : < t tf, r R (6) The mathematcal model s supposed symmetrcal about the vertcal axs of symmetry. B 4 Table : Physcal propertes ad geometrcal characterstcs of the tested cylder. Physcal propertes of brass cylder: λ =6W/ mk, C = 385 J.kg. K, p ρ= 85kg.m 3 Physcal propertes of copper cylder: λ = 389 W / mk, C = 4 J.kg. K, p ρ= 896kg.m 3 Geometrcal dmesos of the brass cylder: Geometrcal dmesos of the copper cylder: R = 5 mm, H = 8 mm, H meas = 7.4 mm R = 5 mm, H = mm, H meas = 8mm I the verse problem, the heat flux Q (r, H, t) at the exteral surface of the cylder s uko. The temperatures measured at odes (r, z ) sde the sold are used as a addoal formato o the temperature dstrbuto the cylder to estmate the fucto Q (r, H, t) : T (r, z, t) = f, =,,..., (7) meas The IHCP cossts estmatg the fucto Q (r, H, t) usg the codos ()-(7). The soluto of the verse problem s based o the mmzato of the resdual fuctoal defed as: N meas N meas f J(Q) = = t [ T(r, z, t; Q ) f ] dt (8) here T(r, z; Q) are the temperatures at the sesor locatos computed from the drect problem ()-(6). Formally, the verse problem cossts mmzg the resdual fuctoal uder costras ()-(6). The uko heat flux s approxmated the form of a cubc B-sple as follos: Q mt mr = ( r, t ) p φ ( r ) φ (t ) (9) j= here m t x m r s the umber of approxmato parameters, p are the uko approxmato parameters ad φ are the gve bass cubc B-sple. Therefore, the IHCP s reduced to the estmato of a vector of parameters p = [ p, p,..., p m ]. The cojugate gradet method s used order to mmze the resdual fuctoal. The cojugate gradet algorhm s eratve. At each erato, the successve mprovemets of desred parameters are bult as follos: p + = p + γ = d,j j, =,,, mt x m r () here s a erato umber. follog expresso : γ s the descet parameter. d s the descet drecto determed by the d = g + β d, =,,, mt x m r () here the parameter β s computed as follos: g g β = () g, g

5 here, s the scalar product, s the orm ad β =. To mplemet the eratve mmzato procedure, the vector g s determed by the follog equato: G g = J' ( p) (3) here G s the Gram s matrx for bass fuctos. { G = φ, φ,, j,,..., m} G = (4), j j = φ, φ j s the scalar product the L space. The Gram s matrx s symmetrc ad posve. Let J (p) be the vector gradet of the fuctoal J(p) defed as : here ψ s the adjot problem soluto. tf R J' = ψ(r, H, t) φ dr dt, =,,, m (5) 3. Descet parameter To compute the descet parameter, the lear approxmato of the descet parameter s used [7]: here θ ( r, z, t; δq) γ = t f N meas [ T (r,z, t;q) Tmeas(r,z, t) ] = t N f meas = θ (r,z, t; δq ) θ (r,z, t; δq dt s computed from the problem varatos at the erato by usg the varato of the uko heat flux ( δ Q ). ( r,z,t; δq s defed at the sesor locatos from a cubc B-sple as follos: ) dt θ ) (, z ) δq mt mr (6) r. δq s approxmated ( r, t ) = δp φ ( r ) φ ( t ) (7) j= = The problem varatos s defed by the follog equatos: ρc p θ( r, z, t ) θ( r, z, t ) θ( r, z, t ) θ( r, z, t ) = + +, here r R, z H, < t t f (8) λ t r z θ (, z, t) θ (R, z, t) =,j j =, here < t tf, z H (9), here < t tf, z H () θ ( r, z,) =, here : r R, z H () θ λ (r,h,t) =δq(r,h,t), here : < t tf, r R z () θ ( r,, t) =, here : < t tf, r R (3) 3. Adjot problem The ecessary codos of optmzato [7] are defed the form of a adjot problem hch s represeted by the follog boudary-value problem: here: S(r, z, t) = { δ(r, r; z, z) = ad r, z H, < t R N meas ( r, z, t ) = ρ ψ C p ψ ψ ψ + ψ + + S(r, z, t λ t r r z ) ψ tf [ T(r, z, t; Q ) T (r, z, t) ]} (, z, t) ψ = r (, z, t) B 5 meas (4), here < t tf, z H (5)

6 ψ (R, z, t) ψ = (R, z, t), here < t tf, z H (6) r ψ ( r, z, tf ) =, here : r R, z H (7) ψ λ (r, H, t) =, here : < t tf, r R (8) z ψ ( r,, t) =, here : < t tf, r R (9) The gradet of the resdual fuctoal s determed by solvg the adjot problem. The symbol δ( r, r, z, z) represets the Drac fucto. The drect problem, the adjot problem ad the problem varatos are solved usg the cotrol volume method [8] ad the mplc fractoal-step tme scheme proposed by Bra [9]. 3.3 Regularzato The verse problem s ll-posed ad the umercal results depeds o the fluctuato occurrg at the measuremets. To obta a stable soluto of the IHCP the eratve regularzato method s used by stoppg eratos at the optmal value of the resdual fuctoal hch satsfes the follog codo: J(Q ) δ here δ s the tegrated error of the measured data as follos : t f N meas = (3) δ = σ (r,z,t)dt (3) σ ( r,z,t) s the stadard devato of measuremet errors for the temperatures measured at the poso ( r, z). I the absece of the measuremet errors, the geeral stoppg crero s defed as: here the small umber ε s used equal to 4. Q (r, t) Q (r, t) ε (3) Q (r, t) 3.4 Algorhm The verse heat coducto problem s solved usg the follog eratve procedure: The calculatos are started by usg the parameter β ad the uko heat flux Q (r, H, t) equal to zero. () soluto of the drect problem, () calculato of the resdual fuctoal, () soluto of the adjot problem, (v) calculato of the compoets of the fuctoal gradet, (v) calculato of the parameter β, (v) calculato of the compoet of descet drecto, (v) soluto of the problem varato to determe the parameter γ, (v) the e value of the heat flux desy s corrected. If the covergece crero s ot satsfed the eratve procedure s repeated utl the fuctoal s mmzed. 4. EXPERIMENTAL DATA PROCESSING The umercal procedure as valdated by usg the temperature data obtaed from the drect problem to smulate the measured temperatures sde the cylder. The local heat flux requred to solve the drect problem s ko ad s take varable spatally ad temporally. For each umercal applcato, the tme step sze s choose h respect the delta Fourer umber codo based o the sesor depth that s defed by the follog equato: λ t Fo =. (33) ρc H H p B 6 ( ) 4. Numercal verfcato of the soluto procedure We cosder a copper vertcal cylder havg 5 mm of dameter ad mm of legth. We have choose to verfy the umercal procedure by usg a ko heat flux varyg h the tme ad the radus of the cylder. The heat flux s mposed at the etted surface of the cylder (z=h) as sho Fgure 4 by the cotuous curve. The doard surface (z=) s assumed to be sotherm (T(r,,t)= 4 C). The uko traset heat flux s assumed to vary as follos: meas

7 B (.4r r 6463 r r r )(.t Q (r,t) =. + ) (34) Q [kw/m ] 6 t = 5s 4 t = s t = 5s t = 35s t = 4s r [m] Fgure 4. Comparso of the calculated ad exact heat fluxes. T meas - T [ C] t = 5s t = s t = 5s t = 35s -.3 t = 4s r [m] Fgure 5. Radal dstrbutos of the temperature resduals J(Q ) Fgure 6. Evoluto of resdual fuctoal as a fucto of the umber of eratos. I order to valdate the verse estmato procedure, e have assumed that the temperatures calculated from the drect problem soluto at the measuremet pots are the measured temperatures ( Tmeas (r, z, t) = f(r, z, t) ) for solvg ICHP. Fgure 4 shos that the estmated heat flux practcally cocdes h the exact heat flux for dfferet tmes. Ths valdato s carred out for the umber of approxmato parameters equal to 9x9. Fgure 5 shos the dfferece betee the computed temperatures ad the smulated measured temperatures. The maxmum devato s of ±.3 C. Fgure 6 shos the evoluto of the resdual fuctoal J(Q ) as a fucto of the umber of eratos. The eratos are cotued tll the covergece - crera J< s satsfed. 4. Expermetal determato of the local thermal boudary codos A vertcal brass cylder havg 5 mm of dameter s cosdered. The sesors locatos are shoed Fgures a ad b. A grd of odes alog the heght of the cylder ad odes the radal drecto have bee used. The local thermal characterstcs are computed by solvg IHCP here the temperatures measured at doard surface are used as the boudary codo to solve the drect problem. The temperatures measured at

8 heght z = 7.4 mm are used to solve the verse problem. The computato s coducted by usg the umber of measuremet pots equal to the odes umber. Durg expermets, the fxed flo rate for the study s frst adjusted uder gravy usg the regulatg valves. Ially the cylder dsk s covered order to prevet the lqud to et the exchage surface. For a fxed total heat flux, the expermetal dsk s heated. Whe the measured all temperature reached a steadystate, the exchage surface s rapdly ucovered. The lqud jet the hs the heat trasfer surface perpedcularly. The tme-depedet local all temperatures s recorded, utl the expermetal dsk reached a e steady-state. T [ C] Q [kw/m ] T s [ C] Heated Heatg the the surface Coolg the surface 3 4 t [s] Fgure 7. Measured temperature sde the sold. B Surface temperature Surface heat flux t [s] Fgure 8. Estmated heat flux ad surface temperature. Fgure 7 shos the evoluto of the measured temperature sde the sold by the sesor placed the vertcal axs at.6mm far from the exchage surface. It shos to dfferet zoes, the frst oe here the surface s ot etted by the ater jet ad the all temperature creased cotually alog the tme. The steady state s attaed for t approxmately equal to 8s (see Fgure 7). After ths tme, the temperature sde the sold becomes uform alog the tme. Fgure 7 shos a secod zoe here the all temperature decreased alog the tme because of the jet mpactg the heated surface s at the temperature (= 37 C) loer tha the all temperature. Whe the ater jet mpacted the heated surface, the all temperature decreased ad attaed the stable value durg a quck perod (s approxmately) from 73.6 C to 39.4 C. Fgure 8 shos the estmato of the surface temperature ad the surface heat flux the stagato pot (r=) for the phase of the coolg surface. The heat trasfer s hghest the zoe of the stagato ad the mpgemet lqud flo suated at the ceter of the heated surface here the flm thckess does approach fy. After ths zoe, the heat trasfer decreases because the th flm becomes relatvely uform o the etted surface. Fgures 9 ad are obtaed for the electrc poer put of 4 W ad 7W; ad, the flo rate of ater jet of 9 g.s -. The estmated stadard devato of temperature resduals of ths computato s of.4 C. It s shoed that he the jet of the ater mpacted the heated surface, the dsspated heat flux creased durg s, decreased durg 8s, ad fally reached a asymptotc value at the steady state. I ths zoe, the surface temperature reaches the mmum ad becomes uform alog the tme T [ C] Q [kw/m ] W W W W t [s] t [s] Fgure 9. Measured temperature sde the sold. Fgure. Estmated heat flux. 7

9 B 9 Fgures 9 ad sho the fluece of the electrc poer mposed sde the sold. For these fgures, expermets are coducted for the ater let temperature of 37 C. For the total electrc poer of 4W ad 7W, Fgure 9 shos the measured all temperature obtaed from the thermocouples located the axal drecto at.6mm far from the surface. It ca be see that the all temperature decreased for each electrc poer ad reached the same temperature value at the steady state. Fgure shos the dstrbuto of the heat flux after the ater jet mpactg the heated surface. For 4 W, the heat flux dsspated at the surface reached the e steady state durg a tme loer tha for 7W. Fgure shos the radal dstrbuto of the local Nusselt umber for to fxed flo rate of the mpgg jet (5 g/s ad 4.4 g/s). I ths expermet, the poer put s of 5W ad the let temperature of the ater (T ) s of 4 C Nu r 5 g/s 4.4 g/s r [mm] Fgure. Radal dstrbuto of th e local Nusselt umber. The local thermal characterstcs are computed by solvg the IHCP here the temperatures measured at z= are used as the boudary codo to solve the drect problem. The temperatures measured at z=7.4mm are used to solve the verse problem. The local heat trasfer coeffcet s calculated from the surface heat flux desy (Q,r ), the local surface temperature (T s,r ) as follos: Q,r h r = (35) T T s,r The local Nusselt umber s deduced from hr as: = h r Nu D r (36) λ here D s the dameter of the heated surface, λ s the thermal coductvy. Fgure shos that the radal Nusselt umber creases h the mpgg ater flo rate because at hgher flo rate the lqud flm carres more eergy from the heated surface. It s oted that the heat trasfer s hghest the stagato zoe here the flm thckess does approach fy. After ths zoe, the heat trasfer decreases the th lqud flm thckess. 5. CONCLUSIONS A traset measuremet procedure as developed to combe measuremets of temperature, IHCP method h jet characterstcs ad electrc poer put. Ths artcle presets the results obtaed by solvg the IHCP appled to the cyldrcal geometry. The local heat flux ad the surface temperature are determed by usg the measured temperatures sde the heated sold. Ths artcle shos the advatage of usg IHCP to determe the thermal boudary characterstcs. I ths ork, the verse aalyss s appled to a partcular case of jet mpactg the heated surface. I ths case, the measuremet of the local heat flux or surface temperature s very dffcult hout perturbg the flo. I more, the comparso of the calculated ad exact heat fl ux ad the evoluto of calculated ad measured temperature sho the precse character of the method. REFERENCES. B. Elso ad B.W. Webb, Local heat trasfer to mpgg lqud jets the ally lamar, trasoal, ad turbulet regmes. It. J. Heat Mass Trasfer (994) 37, 7-6.

10 . Y. Pa ad B.W. Webb, Heat trasfer characterstcs of array of free-surface lqud jets. Tras. ASME, J. Heat Trasfer (995) 7, L.M. Jj ad Z.Daga, Expermetal vestgato of sgle-phase multjet mpgemet coolg of a array of mcroelectroc heat sources, Coolg Techology for Electroc Equpmet. Iteratoal Symposum Hoolulu, HA, 988, pp Y. Pa ad B.W. Webb, Vsualzato of local heat trasfer uder arrays of free surface lqud jets. Istuto of Chemcal Egeers Symposum Seres (994) 35, E.J. Watso, The radal spread of a lqud over horzotal plae. J. Flud Mech. (964), X. Lu ad J.H. Lehard V, Lqud mpgemet heat trasfer o a uform flux surface, Natoal Heat Trasfer Coferece, 989, pp O.M. Alfaov, E.A. Artyukh ad S.V. Rumyatsev, Extreme Methods for Solvg Ill-Posed Problems h Applcatos to Iverse Heat Trasfer Problems, Begell House, Ne York, S.V. Patakar, Numercal Heat Trasfer ad Flud Flo, Mc-Gra Hll, Ne York, 98. B 9. P.L.T. Bra, A fe-dfferece method of hgher order accuracy for soluto of three dmesoal traset heat coducto. A.I.Ch.E. J. (96) 7,

best estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best

best 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 information

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits

Block-Based Compact Thermal Modeling of Semiconductor Integrated Circuits Block-Based Compact hermal Modelg of Semcoductor Itegrated Crcuts Master s hess Defese Caddate: Jg Ba Commttee Members: Dr. Mg-Cheg Cheg Dr. Daqg Hou Dr. Robert Schllg July 27, 2009 Outle Itroducto Backgroud

More information

Functions of Random Variables

Functions 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 information

Introduction to local (nonparametric) density estimation. methods

Introduction 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 information

Mathematical Modelling of Transient Response of Plate Fin and Tube Heat Exchanger

Mathematical Modelling of Transient Response of Plate Fin and Tube Heat Exchanger Proceedgs of e Iteratoal Coferece o Heat rasfer ad Flud Flo Prague Czech Republc August - 4 Paper o. 9 Maematcal Modellg of raset Respose of Plate F ad ube Heat Exchager Aa Korzeń Craco Uversty of echology

More information

Analysis of Lagrange Interpolation Formula

Analysis of Lagrange Interpolation Formula P IJISET - Iteratoal Joural of Iovatve Scece, Egeerg & Techology, Vol. Issue, December 4. www.jset.com ISS 348 7968 Aalyss of Lagrage Iterpolato Formula Vjay Dahya PDepartmet of MathematcsMaharaja Surajmal

More information

TESTS BASED ON MAXIMUM LIKELIHOOD

TESTS BASED ON MAXIMUM LIKELIHOOD ESE 5 Toy E. Smth. The Basc Example. TESTS BASED ON MAXIMUM LIKELIHOOD To llustrate the propertes of maxmum lkelhood estmates ad tests, we cosder the smplest possble case of estmatg the mea of the ormal

More information

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem

Cubic Nonpolynomial Spline Approach to the Solution of a Second Order Two-Point Boundary Value Problem Joural of Amerca Scece ;6( Cubc Nopolyomal Sple Approach to the Soluto of a Secod Order Two-Pot Boudary Value Problem W.K. Zahra, F.A. Abd El-Salam, A.A. El-Sabbagh ad Z.A. ZAk * Departmet of Egeerg athematcs

More information

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory

C-1: Aerodynamics of Airfoils 1 C-2: Aerodynamics of Airfoils 2 C-3: Panel Methods C-4: Thin Airfoil Theory ROAD MAP... AE301 Aerodyamcs I UNIT C: 2-D Arfols C-1: Aerodyamcs of Arfols 1 C-2: Aerodyamcs of Arfols 2 C-3: Pael Methods C-4: Th Arfol Theory AE301 Aerodyamcs I Ut C-3: Lst of Subects Problem Solutos?

More information

Estimation of Stress- Strength Reliability model using finite mixture of exponential distributions

Estimation 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 information

L5 Polynomial / Spline Curves

L5 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 information

ENGI 4421 Propagation of Error Page 8-01

ENGI 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 information

Econometric Methods. Review of Estimation

Econometric 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 information

Maximum Likelihood Estimation

Maximum Likelihood Estimation Marquette Uverst Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 08 b Marquette Uverst Maxmum Lkelhood Estmato We have bee sag that ~

More information

CS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x

CS 2750 Machine Learning. Lecture 8. Linear regression. CS 2750 Machine Learning. Linear regression. is a linear combination of input components x CS 75 Mache Learg Lecture 8 Lear regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 75 Mache Learg Lear regresso Fucto f : X Y s a lear combato of put compoets f + + + K d d K k - parameters

More information

CHAPTER VI Statistical Analysis of Experimental Data

CHAPTER 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 information

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation

PGE 310: Formulation and Solution in Geosystems Engineering. Dr. Balhoff. Interpolation PGE 30: Formulato ad Soluto Geosystems Egeerg Dr. Balhoff Iterpolato Numercal Methods wth MATLAB, Recktewald, Chapter 0 ad Numercal Methods for Egeers, Chapra ad Caale, 5 th Ed., Part Fve, Chapter 8 ad

More information

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013

ECE 595, Section 10 Numerical Simulations Lecture 19: FEM for Electronic Transport. Prof. Peter Bermel February 22, 2013 ECE 595, Secto 0 Numercal Smulatos Lecture 9: FEM for Electroc Trasport Prof. Peter Bermel February, 03 Outle Recap from Wedesday Physcs-based devce modelg Electroc trasport theory FEM electroc trasport

More information

A Method for Damping Estimation Based On Least Square Fit

A Method for Damping Estimation Based On Least Square Fit Amerca Joural of Egeerg Research (AJER) 5 Amerca Joural of Egeerg Research (AJER) e-issn: 3-847 p-issn : 3-936 Volume-4, Issue-7, pp-5-9 www.ajer.org Research Paper Ope Access A Method for Dampg Estmato

More information

Derivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations

Derivation 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 information

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES

Lecture 12 APPROXIMATION OF FIRST ORDER DERIVATIVES FDM: Appromato of Frst Order Dervatves Lecture APPROXIMATION OF FIRST ORDER DERIVATIVES. INTRODUCTION Covectve term coservato equatos volve frst order dervatves. The smplest possble approach for dscretzato

More information

Line Fitting and Regression

Line Fitting and Regression Marquette Uverst MSCS6 Le Fttg ad Regresso Dael B. Rowe, Ph.D. Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Coprght 8 b Marquette Uverst Least Squares Regresso MSCS6 For LSR we have pots

More information

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines

Solving Constrained Flow-Shop Scheduling. Problems with Three Machines It J Cotemp Math Sceces, Vol 5, 2010, o 19, 921-929 Solvg Costraed Flow-Shop Schedulg Problems wth Three Maches P Pada ad P Rajedra Departmet of Mathematcs, School of Advaced Sceces, VIT Uversty, Vellore-632

More information

X ε ) = 0, or equivalently, lim

X ε ) = 0, or equivalently, lim Revew for the prevous lecture Cocepts: order statstcs Theorems: Dstrbutos of order statstcs Examples: How to get the dstrbuto of order statstcs Chapter 5 Propertes of a Radom Sample Secto 55 Covergece

More information

Multivariate Transformation of Variables and Maximum Likelihood Estimation

Multivariate Transformation of Variables and Maximum Likelihood Estimation Marquette Uversty Multvarate Trasformato of Varables ad Maxmum Lkelhood Estmato Dael B. Rowe, Ph.D. Assocate Professor Departmet of Mathematcs, Statstcs, ad Computer Scece Copyrght 03 by Marquette Uversty

More information

Linear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab

Linear Regression Linear Regression with Shrinkage. Some slides are due to Tommi Jaakkola, MIT AI Lab Lear Regresso Lear Regresso th Shrkage Some sldes are due to Tomm Jaakkola, MIT AI Lab Itroducto The goal of regresso s to make quattatve real valued predctos o the bass of a vector of features or attrbutes.

More information

to the estimation of total sensitivity indices

to the estimation of total sensitivity indices Applcato of the cotrol o varate ate techque to the estmato of total sestvty dces S KUCHERENKO B DELPUECH Imperal College Lodo (UK) skuchereko@mperalacuk B IOOSS Electrcté de Frace (Frace) S TARANTOLA Jot

More information

UNIVERSITY OF OSLO DEPARTMENT OF ECONOMICS

UNIVERSITY 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 information

Simple Linear Regression

Simple 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 information

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler

BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS. Aysegul Akyuz Dascioglu and Nese Isler Mathematcal ad Computatoal Applcatos, Vol. 8, No. 3, pp. 293-300, 203 BERNSTEIN COLLOCATION METHOD FOR SOLVING NONLINEAR DIFFERENTIAL EQUATIONS Aysegul Ayuz Dascoglu ad Nese Isler Departmet of Mathematcs,

More information

OPTIMAL LAY-OUT OF NATURAL GAS PIPELINE NETWORK

OPTIMAL LAY-OUT OF NATURAL GAS PIPELINE NETWORK 23rd World Gas Coferece, Amsterdam 2006 OPTIMAL LAY-OUT OF NATURAL GAS PIPELINE NETWORK Ma author Tg-zhe, Ne CHINA ABSTRACT I cha, there are lots of gas ppele etwork eeded to be desged ad costructed owadays.

More information

Summary of the lecture in Biostatistics

Summary 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 information

ESS Line Fitting

ESS Line Fitting ESS 5 014 17. Le Fttg A very commo problem data aalyss s lookg for relatoshpetwee dfferet parameters ad fttg les or surfaces to data. The smplest example s fttg a straght le ad we wll dscuss that here

More information

{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:

{ }{ ( )} (, ) = ( ) ( ) ( ) 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 information

Lecture 07: Poles and Zeros

Lecture 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 information

Module 7: Probability and Statistics

Module 7: Probability and Statistics Lecture 4: Goodess of ft tests. Itroducto Module 7: Probablty ad Statstcs I the prevous two lectures, the cocepts, steps ad applcatos of Hypotheses testg were dscussed. Hypotheses testg may be used to

More information

KLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames

KLT Tracker. Alignment. 1. Detect Harris corners in the first frame. 2. For each Harris corner compute motion between consecutive frames KLT Tracker Tracker. Detect Harrs corers the frst frame 2. For each Harrs corer compute moto betwee cosecutve frames (Algmet). 3. Lk moto vectors successve frames to get a track 4. Itroduce ew Harrs pots

More information

ENGI 3423 Simple Linear Regression Page 12-01

ENGI 3423 Simple Linear Regression Page 12-01 ENGI 343 mple Lear Regresso Page - mple Lear Regresso ometmes a expermet s set up where the expermeter has cotrol over the values of oe or more varables X ad measures the resultg values of aother varable

More information

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines

CS 1675 Introduction to Machine Learning Lecture 12 Support vector machines CS 675 Itroducto to Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Mdterm eam October 9, 7 I-class eam Closed book Stud materal: Lecture otes Correspodg chapters

More information

Chapter 5 Properties of a Random Sample

Chapter 5 Properties of a Random Sample Lecture 6 o BST 63: Statstcal Theory I Ku Zhag, /0/008 Revew for the prevous lecture Cocepts: t-dstrbuto, F-dstrbuto Theorems: Dstrbutos of sample mea ad sample varace, relatoshp betwee sample mea ad sample

More information

Module 1 : The equation of continuity. Lecture 5: Conservation of Mass for each species. & Fick s Law

Module 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 information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information

Bayes Estimator for Exponential Distribution with Extension of Jeffery Prior Information Malaysa Joural of Mathematcal Sceces (): 97- (9) Bayes Estmator for Expoetal Dstrbuto wth Exteso of Jeffery Pror Iformato Hadeel Salm Al-Kutub ad Noor Akma Ibrahm Isttute for Mathematcal Research, Uverst

More information

INVESTIGATION OF THE INFLUENCE ON ADDITIONAL MOUNTED ON BARREL EDGE CONCENTRATED MASS OVER THE GROUPING OF GUNSHOT HITS IN SINGLE SHOOTING

INVESTIGATION OF THE INFLUENCE ON ADDITIONAL MOUNTED ON BARREL EDGE CONCENTRATED MASS OVER THE GROUPING OF GUNSHOT HITS IN SINGLE SHOOTING Iteratoal Coferece KNOWLEDGE-BASED ORGANIZATION Vol. XXI No 05 INVESTIGATION OF THE INFLUENCE ON ADDITIONAL MOUNTED ON BARREL EDGE CONCENTRATED MASS OVER THE GROUPING OF GUNSHOT HITS IN SINGLE SHOOTING

More information

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter

A Robust Total Least Mean Square Algorithm For Nonlinear Adaptive Filter A Robust otal east Mea Square Algorthm For Nolear Adaptve Flter Ruxua We School of Electroc ad Iformato Egeerg X'a Jaotog Uversty X'a 70049, P.R. Cha rxwe@chare.com Chogzhao Ha, azhe u School of Electroc

More information

STA302/1001-Fall 2008 Midterm Test October 21, 2008

STA302/1001-Fall 2008 Midterm Test October 21, 2008 STA3/-Fall 8 Mdterm Test October, 8 Last Name: Frst Name: Studet Number: Erolled (Crcle oe) STA3 STA INSTRUCTIONS Tme allowed: hour 45 mutes Ads allowed: A o-programmable calculator A table of values from

More information

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific

Taylor s Series and Interpolation. Interpolation & Curve-fitting. CIS Interpolation. Basic Scenario. Taylor Series interpolates at a specific CIS 54 - Iterpolato Roger Crawfs Basc Scearo We are able to prod some fucto, but do ot kow what t really s. Ths gves us a lst of data pots: [x,f ] f(x) f f + x x + August 2, 25 OSU/CIS 54 3 Taylor s Seres

More information

Special Instructions / Useful Data

Special Instructions / Useful Data JAM 6 Set of all real umbers P A..d. B, p Posso Specal Istructos / Useful Data x,, :,,, x x Probablty of a evet A Idepedetly ad detcally dstrbuted Bomal dstrbuto wth parameters ad p Posso dstrbuto wth

More information

Convergence of the Desroziers scheme and its relation to the lag innovation diagnostic

Convergence of the Desroziers scheme and its relation to the lag innovation diagnostic Covergece of the Desrozers scheme ad ts relato to the lag ovato dagostc chard Méard Evromet Caada, Ar Qualty esearch Dvso World Weather Ope Scece Coferece Motreal, August 9, 04 o t t O x x x y x y Oservato

More information

UNIT 2 SOLUTION OF ALGEBRAIC AND TRANSCENDENTAL EQUATIONS

UNIT 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 information

CH E 374 Computational Methods in Engineering Fall 2007

CH E 374 Computational Methods in Engineering Fall 2007 CH E 7 Computatoal Methods Egeerg Fall 007 Sample Soluto 5. The data o the varato of the rato of stagato pressure to statc pressure (r ) wth Mach umber ( M ) for the flow through a duct are as follows:

More information

Simulation Output Analysis

Simulation Output Analysis Smulato Output Aalyss Summary Examples Parameter Estmato Sample Mea ad Varace Pot ad Iterval Estmato ermatg ad o-ermatg Smulato Mea Square Errors Example: Sgle Server Queueg System x(t) S 4 S 4 S 3 S 5

More information

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements

Chapter 4 (Part 1): Non-Parametric Classification (Sections ) Pattern Classification 4.3) Announcements Aoucemets No-Parametrc Desty Estmato Techques HW assged Most of ths lecture was o the blacboard. These sldes cover the same materal as preseted DHS Bometrcs CSE 90-a Lecture 7 CSE90a Fall 06 CSE90a Fall

More information

is the score of the 1 st student, x

is the score of the 1 st student, x 8 Chapter Collectg, Dsplayg, ad Aalyzg your Data. Descrptve Statstcs Sectos explaed how to choose a sample, how to collect ad orgaze data from the sample, ad how to dsplay your data. I ths secto, you wll

More information

To use adaptive cluster sampling we must first make some definitions of the sampling universe:

To use adaptive cluster sampling we must first make some definitions of the sampling universe: 8.3 ADAPTIVE SAMPLING Most of the methods dscussed samplg theory are lmted to samplg desgs hch the selecto of the samples ca be doe before the survey, so that oe of the decsos about samplg deped ay ay

More information

INVERSE DETERMINATION OF TRANSIENT LOCAL HEAT TRANSFER IN A LIQUID MICROLAYER.

INVERSE DETERMINATION OF TRANSIENT LOCAL HEAT TRANSFER IN A LIQUID MICROLAYER. 8 ème Cogrès Fraçais de Mécaique Greoble, 7-3 août 7 INVERSE DETERMINATION OF TRANSIENT LOCAL HEAT TRANSFER IN A LIQUID MICROLAYER. A. Odaymet, J.B. Baoga, H. Louahlia-Gualous, M. De Labachelerie FEMTO

More information

Kernel-based Methods and Support Vector Machines

Kernel-based Methods and Support Vector Machines Kerel-based Methods ad Support Vector Maches Larr Holder CptS 570 Mache Learg School of Electrcal Egeerg ad Computer Scece Washgto State Uverst Refereces Muller et al. A Itroducto to Kerel-Based Learg

More information

Correlation and Regression Analysis

Correlation and Regression Analysis Chapter V Correlato ad Regresso Aalss R. 5.. So far we have cosdered ol uvarate dstrbutos. Ma a tme, however, we come across problems whch volve two or more varables. Ths wll be the subject matter of the

More information

Lecture 3. Sampling, sampling distributions, and parameter estimation

Lecture 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 information

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971))

Part 4b Asymptotic Results for MRR2 using PRESS. Recall that the PRESS statistic is a special type of cross validation procedure (see Allen (1971)) art 4b Asymptotc Results for MRR usg RESS Recall that the RESS statstc s a specal type of cross valdato procedure (see Alle (97)) partcular to the regresso problem ad volves fdg Y $,, the estmate at the

More information

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn:

Chapter Business Statistics: A First Course Fifth Edition. Learning Objectives. Correlation vs. Regression. In this chapter, you learn: Chapter 3 3- Busess Statstcs: A Frst Course Ffth Edto Chapter 2 Correlato ad Smple Lear Regresso Busess Statstcs: A Frst Course, 5e 29 Pretce-Hall, Ic. Chap 2- Learg Objectves I ths chapter, you lear:

More information

Lecture 8: Linear Regression

Lecture 8: Linear Regression Lecture 8: Lear egresso May 4, GENOME 56, Sprg Goals Develop basc cocepts of lear regresso from a probablstc framework Estmatg parameters ad hypothess testg wth lear models Lear regresso Su I Lee, CSE

More information

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1

STA 108 Applied Linear Models: Regression Analysis Spring Solution for Homework #1 STA 08 Appled Lear Models: Regresso Aalyss Sprg 0 Soluto for Homework #. Let Y the dollar cost per year, X the umber of vsts per year. The the mathematcal relato betwee X ad Y s: Y 300 + X. Ths s a fuctoal

More information

Supervised learning: Linear regression Logistic regression

Supervised learning: Linear regression Logistic regression CS 57 Itroducto to AI Lecture 4 Supervsed learg: Lear regresso Logstc regresso Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 57 Itro to AI Data: D { D D.. D D Supervsed learg d a set of eamples s

More information

Support vector machines

Support vector machines CS 75 Mache Learg Lecture Support vector maches Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square CS 75 Mache Learg Outle Outle: Algorthms for lear decso boudary Support vector maches Mamum marg hyperplae.

More information

Bootstrap Method for Testing of Equality of Several Coefficients of Variation

Bootstrap Method for Testing of Equality of Several Coefficients of Variation Cloud Publcatos Iteratoal Joural of Advaced Mathematcs ad Statstcs Volume, pp. -6, Artcle ID Sc- Research Artcle Ope Access Bootstrap Method for Testg of Equalty of Several Coeffcets of Varato Dr. Navee

More information

1 Lyapunov Stability Theory

1 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 information

4. Standard Regression Model and Spatial Dependence Tests

4. Standard Regression Model and Spatial Dependence Tests 4. Stadard Regresso Model ad Spatal Depedece Tests Stadard regresso aalss fals the presece of spatal effects. I case of spatal depedeces ad/or spatal heterogeet a stadard regresso model wll be msspecfed.

More information

Support vector machines II

Support vector machines II CS 75 Mache Learg Lecture Support vector maches II Mlos Hauskrecht mlos@cs.ptt.edu 539 Seott Square Learl separable classes Learl separable classes: here s a hperplae that separates trag staces th o error

More information

Beam Warming Second-Order Upwind Method

Beam 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 information

MEASURES OF DISPERSION

MEASURES OF DISPERSION MEASURES OF DISPERSION Measure of Cetral Tedecy: Measures of Cetral Tedecy ad Dsperso ) Mathematcal Average: a) Arthmetc mea (A.M.) b) Geometrc mea (G.M.) c) Harmoc mea (H.M.) ) Averages of Posto: a) Meda

More information

Generalized Minimum Perpendicular Distance Square Method of Estimation

Generalized Minimum Perpendicular Distance Square Method of Estimation Appled Mathematcs,, 3, 945-949 http://dx.do.org/.436/am..366 Publshed Ole December (http://.scrp.org/joural/am) Geeralzed Mmum Perpedcular Dstace Square Method of Estmato Rezaul Karm, Morshed Alam, M.

More information

AN ALGORITHM CONSTRUCTION METHOD FOR INVERSE PROBLEM SOLUTION STABLE TO THE DESCRIPTION ERRORS

AN ALGORITHM CONSTRUCTION METHOD FOR INVERSE PROBLEM SOLUTION STABLE TO THE DESCRIPTION ERRORS Proceedgs of 3rd Iteratoal Coferece o Iverse Problems geerg: Jue 3-8 999 AN ALGORIHM CONSRUCION MHOD FOR INVRS PROBLM SOLUION SABL O H DSCRIPION RRORS OMAlfaov ad IYuGejadze Aerospace College Moscow Avato

More information

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations

hp calculators HP 30S Statistics Averages and Standard Deviations Average and Standard Deviation Practice Finding Averages and Standard Deviations HP 30S Statstcs Averages ad Stadard Devatos Average ad Stadard Devato Practce Fdg Averages ad Stadard Devatos HP 30S Statstcs Averages ad Stadard Devatos Average ad stadard devato The HP 30S provdes several

More information

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model

12.2 Estimating Model parameters Assumptions: ox and y are related according to the simple linear regression model 1. Estmatg Model parameters Assumptos: ox ad y are related accordg to the smple lear regresso model (The lear regresso model s the model that says that x ad y are related a lear fasho, but the observed

More information

d 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

d 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 information

G S Power Flow Solution

G S Power Flow Solution G S Power Flow Soluto P Q I y y * 0 1, Y y Y 0 y Y Y 1, P Q ( k) ( k) * ( k 1) 1, Y Y PQ buses * 1 P Q Y ( k1) *( k) ( k) Q Im[ Y ] 1 P buses & Slack bus ( k 1) *( k) ( k) Y 1 P Re[ ] Slack bus 17 Calculato

More information

Chapter 14 Logistic Regression Models

Chapter 14 Logistic Regression Models Chapter 4 Logstc Regresso Models I the lear regresso model X β + ε, there are two types of varables explaatory varables X, X,, X k ad study varable y These varables ca be measured o a cotuous scale as

More information

Theoretical background and applications of DEM. Simon Lo

Theoretical background and applications of DEM. Simon Lo Theoretcal backgroud ad applcatos of DEM Smo Lo Cotets DEM equatos DEM teracto models Examples Partcle trasport horzotal ppes Blast furace Mxg drum Coveyer belt cases Partcle trasport gas-lqud flow ppes

More information

Numerical Analysis Formulae Booklet

Numerical Analysis Formulae Booklet Numercal Aalyss Formulae Booklet. Iteratve Scemes for Systems of Lear Algebrac Equatos:.... Taylor Seres... 3. Fte Dfferece Approxmatos... 3 4. Egevalues ad Egevectors of Matrces.... 3 5. Vector ad Matrx

More information

2C09 Design for seismic and climate changes

2C09 Design for seismic and climate changes 2C09 Desg for sesmc ad clmate chages Lecture 08: Sesmc aalyss of elastc MDOF systems Aurel Strata, Poltehca Uversty of Tmsoara 06/04/2017 Europea Erasmus Mudus Master Course Sustaable Costructos uder atural

More information

ECE606: Solid State Devices Lecture 13 Solutions of the Continuity Eqs. Analytical & Numerical

ECE606: Solid State Devices Lecture 13 Solutions of the Continuity Eqs. Analytical & Numerical ECE66: Sold State Devces Lecture 13 Solutos of the Cotuty Eqs. Aalytcal & Numercal Gerhard Klmeck gekco@purdue.edu Outle Aalytcal Solutos to the Cotuty Equatos 1) Example problems ) Summary Numercal Solutos

More information

Pinaki Mitra Dept. of CSE IIT Guwahati

Pinaki Mitra Dept. of CSE IIT Guwahati Pak Mtra Dept. of CSE IIT Guwahat Hero s Problem HIGHWAY FACILITY LOCATION Faclty Hgh Way Farm A Farm B Illustrato of the Proof of Hero s Theorem p q s r r l d(p,r) + d(q,r) = d(p,q) p d(p,r ) + d(q,r

More information

6.4.5 MOS capacitance-voltage analysis

6.4.5 MOS capacitance-voltage analysis 6.4.5 MOS capactace-voltage aalyss arous parameters of a MOS devce ca be determed from the - characterstcs.. Type of substrate dopg. Isulator capactace = /d sulator thckess d 3. The mmum depleto capactace

More information

ECE 421/599 Electric Energy Systems 7 Optimal Dispatch of Generation. Instructor: Kai Sun Fall 2014

ECE 421/599 Electric Energy Systems 7 Optimal Dispatch of Generation. Instructor: Kai Sun Fall 2014 ECE 4/599 Electrc Eergy Systems 7 Optmal Dspatch of Geerato Istructor: Ka Su Fall 04 Backgroud I a practcal power system, the costs of geeratg ad delverg electrcty from power plats are dfferet (due to

More information

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR

Outline. Point Pattern Analysis Part I. Revisit IRP/CSR Pot Patter Aalyss Part I Outle Revst IRP/CSR, frst- ad secod order effects What s pot patter aalyss (PPA)? Desty-based pot patter measures Dstace-based pot patter measures Revst IRP/CSR Equal probablty:

More information

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II

Chapter 2 - Free Vibration of Multi-Degree-of-Freedom Systems - II CEE49b Chapter - Free Vbrato of Mult-Degree-of-Freedom Systems - II We ca obta a approxmate soluto to the fudametal atural frequecy through a approxmate formula developed usg eergy prcples by Lord Raylegh

More information

Chapter 3 Sampling For Proportions and Percentages

Chapter 3 Sampling For Proportions and Percentages Chapter 3 Samplg For Proportos ad Percetages I may stuatos, the characterstc uder study o whch the observatos are collected are qualtatve ature For example, the resposes of customers may marketg surveys

More information

FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM

FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM Joural of Appled Matematcs ad Computatoal Mecacs 04, 3(4), 7-34 FREQUENCY ANALYSIS OF A DOUBLE-WALLED NANOTUBES SYSTEM Ata Cekot, Stasław Kukla Isttute of Matematcs, Czestocowa Uversty of Tecology Częstocowa,

More information

Objectives of Multiple Regression

Objectives of Multiple Regression Obectves of Multple Regresso Establsh the lear equato that best predcts values of a depedet varable Y usg more tha oe eplaator varable from a large set of potetal predctors {,,... k }. Fd that subset of

More information

Dynamic Analysis of Axially Beam on Visco - Elastic Foundation with Elastic Supports under Moving Load

Dynamic Analysis of Axially Beam on Visco - Elastic Foundation with Elastic Supports under Moving Load Dyamc Aalyss of Axally Beam o Vsco - Elastc Foudato wth Elastc Supports uder Movg oad Saeed Mohammadzadeh, Seyed Al Mosayeb * Abstract: For dyamc aalyses of ralway track structures, the algorthm of soluto

More information

A Helmholtz energy equation of state for calculating the thermodynamic properties of fluid mixtures

A Helmholtz energy equation of state for calculating the thermodynamic properties of fluid mixtures A Helmholtz eergy equato of state for calculatg the thermodyamc propertes of flud mxtures Erc W. Lemmo, Reer Tller-Roth Abstract New Approach based o hghly accurate EOS for the pure compoets combed at

More information

Newton s Power Flow algorithm

Newton s Power Flow algorithm Power Egeerg - Egll Beedt Hresso ewto s Power Flow algorthm Power Egeerg - Egll Beedt Hresso The ewto s Method of Power Flow 2 Calculatos. For the referece bus #, we set : V = p.u. ad δ = 0 For all other

More information

Simple Linear Regression

Simple Linear Regression Correlato ad Smple Lear Regresso Berl Che Departmet of Computer Scece & Iformato Egeerg Natoal Tawa Normal Uversty Referece:. W. Navd. Statstcs for Egeerg ad Scetsts. Chapter 7 (7.-7.3) & Teachg Materal

More information

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line

A Study of the Reproducibility of Measurements with HUR Leg Extension/Curl Research Line HUR Techcal Report 000--9 verso.05 / Frak Borg (borgbros@ett.f) A Study of the Reproducblty of Measuremets wth HUR Leg Eteso/Curl Research Le A mportat property of measuremets s that the results should

More information

Research on Moving Force Estimation of the Bridge Structure using the Adaptive Input Estimation Method

Research on Moving Force Estimation of the Bridge Structure using the Adaptive Input Estimation Method Electroc Joural of Structural Egeerg (8) 28 Research o Movg Force Estmato of the Brdge Structure usg the Adaptve Iput Estmato Method Tsug-Che Che Chug Cheg Isttute of Techology Natoal Defese Uversty Ta-Hs,

More information

Point Estimation: definition of estimators

Point Estimation: definition of estimators Pot Estmato: defto of estmators Pot estmator: ay fucto W (X,..., X ) of a data sample. The exercse of pot estmato s to use partcular fuctos of the data order to estmate certa ukow populato parameters.

More information

Mathematics HL and Further mathematics HL Formula booklet

Mathematics HL and Further mathematics HL Formula booklet Dploma Programme Mathematcs HL ad Further mathematcs HL Formula booklet For use durg the course ad the eamatos Frst eamatos 04 Mathematcal Iteratoal Baccalaureate studes SL: Formula Orgazato booklet 0

More information

15-381: Artificial Intelligence. Regression and neural networks (NN)

15-381: Artificial Intelligence. Regression and neural networks (NN) 5-38: Artfcal Itellece Reresso ad eural etorks NN) Mmck the bra I the earl das of AI there as a lot of terest develop models that ca mmc huma thk. Whle o oe ke eactl ho the bra orks ad, eve thouh there

More information

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3

1 0, x? x x. 1 Root finding. 1.1 Introduction. Solve[x^2-1 0,x] {{x -1},{x 1}} Plot[x^2-1,{x,-2,2}] 3 Adrew Powuk - http://www.powuk.com- Math 49 (Numercal Aalyss) Root fdg. Itroducto f ( ),?,? Solve[^-,] {{-},{}} Plot[^-,{,-,}] Cubc equato https://e.wkpeda.org/wk/cubc_fucto Quartc equato https://e.wkpeda.org/wk/quartc_fucto

More information

Integral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy

Integral 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 information