8. TURBULENCE MODELLING IN CFD SPRING 2007

Size: px
Start display at page:

Download "8. TURBULENCE MODELLING IN CFD SPRING 2007"

Transcription

1 8. TRBLENE MODELLING IN FD SRING 7 8. Trblence model for general-prpoe FD 8. Lnear edd-vco model 8. Non-lnear edd-vco model 8.4 Dfferenal re model 8.5 Implemenaon of rblence model n FD 8. Trblence Model For General-rpoe FD Trblence model for general-prpoe FD m be frame-nvaran.e. ndependen of an parclar coordnae em and hence m be expreed n enor form. Th rle o mpler model of bondar-laer pe e.g. mxng-lengh model. Trblen flo are comped eher b olvng he Renold-averaged Naver-Soe eqaon h able model for rblen flxe or b compng he flcang qane drecl. The man approache are mmared belo. Renold-Averaged Naver-Soe RANS Model Lnear edd-vco model EVM ame ha he devaorc rblen re proporonal o he mean ran; e an edd vco conrced from rblence calar all one oher, deermned b olvng ranpor eqaon. Non-lnear edd-vco model NLEVM ame ha he rblen re a non-lnear fncon of mean ran and vorc; e coeffcen conrced from rblence calar all one oher, deermned b olvng ranpor eqaon; mmc repone of rblence o ceran mporan pe of ran. Dfferenal re model DSM aa Renold-re ranpor model RSTM or econd-order clore SO; olve ranpor eqaon for all rblen flxe. ompaon of flcang qane Large-edd mlaon LES compe me-varng flo, b model b-grd-cale moon. Drec nmercal mlaon DNS no modellng; reolve he malle cale of he flo. FD 8- Davd Aple

2 8. Lnear Edd-Vco Model 8.. General Form Sre-ran conve relaon:, x x The edd vco derved from rblen qane ch a he rblen nec energ and dpaon rae. Thee qane are hemelve deermned b olvng calarranpor eqaon ee belo. A pcal hear re and normal re are gven b V v From hee he oher re componen are eal dedced b npecon/cclc permaon. General ommen a phcal proper of he fld and can be meared; a hpohecal proper of he flo and m be modelled. vare h poon. A hgh Renold nmber, hrogho mch of he flo. Advanage Ea o mplemen n vco olver. Exra vco ad abl. Some heorecal fondaon n mple hear flo. Dadvanage Lle rblence phc; n parclar, anorop and hor effec are negleced. Trblen ranpor of momenm deermned b a ngle calar, o a mo one Renold re v can be repreened accrael; ch model are qeonable n complex flo. Mo edd-vco model n general-prpoe FD code are of he -eqaon pe;.e. calar-ranpor eqaon are olved for rblen cale. The commone pe are - and - model, for hch pecfcaon are gven belo. FD 8- Davd Aple

3 8.. - Model Edd vco: Scalar-ranpor eqaon non-conervave form: D D D D rae of dffon prodcon dpaon change Dffve and are relaed o he edd vco va randl nmber :, and he rae of prodcon of rblen nec energ per n ma 4 In he andard - model Lander and Spaldng, 974 he coeffcen ae he vale.9,.9,.44,.,. 5 Oher mporan varan nclde RNG - Yaho e al., 99 and lo-re model ch a Lander and Sharma 974, Lam and Bremhor 98, and Len and Lechzner 99. Modfcaon are emploed n lo-re model o ncorporae effec of moleclar vco. Specfcall,, and are mlpled b vco-dependen facor f, f and f repecvel, and an addonal orce erm S ma be reqred n he eqaon. Some model noabl Lander and Sharma, 974 olve for he homogeneo dpaon rae ~ hch vanhe a old bondare and relaed o b ~ / D, D 6 Th conen h he heorecal near-all behavor, / Model nomnall eqal o dmenon of /me, or freqenc. omeme non a he pecfc dpaon rae and ha Edd vco: 7 FD 8- Davd Aple

4 Scalar-ranpor eqaon: D D D D * 8 The dffve of and are relaed o he edd-vco:, The orgnal - model a ha of Wlcox 988a h coeffcen ang he vale * 9 5,,,., The model a frher developed b Wlcox 998 n h boo, h he coeffcen becomng fncon of he rblen Renold nmber. Mener 994 deved a hear-re-ranpor SST model. The model, hch expreed n - form, blend he - model hch allegedl peror n he near-all regon, h he - model hch le enve o he level of rblence n he free ream. All model of - pe ffer from a problemac all bondar condon a Behavor of Lnear Edd-Vco Model n Smple Shear In mple hear flo he hear re v The hree normal ree are predced o be eqal: v herea, n pracce, here conderable anorop; e.g. n he log-la regon: : v :. :.4 :.6 v Acall, n mple hear flo, h no a problem, nce onl he graden of he hear re v pla a dnamcall-gnfcan role n he mean-momenm eqaon. Hoever, a arnng of more ero problem n complex flo. FD 8-4 Davd Aple

5 8. Non-Lnear Edd-Vco Model 8.. General Form The re-ran relaonhp for lnear edd-vco model gve for he devaorc Renold re.e. bracng he race: Dvdng b and rng / gve We defne he LHS of a he anorop enor a ; he dmenonle and racele form of he Renold re: a For he RHS of, he mmerc and anmmerc par of he mean-veloc graden are called he mean ran and mean vorc enor, repecvel: S, Thee can be made non-dmenonal ng he rblen mecale /. ng loer cae for he non-dmenonal form: S, Eqaon can hen be ren n he mpler form a or, a 4 Hence, he conve relaon for lnear edd-vco model mpl a: anorop enor proporonal o dmenonle mean ran The man dea of non-lnear edd-vco model o generale h o a non-lnear relaonhp beeen he anorop enor and he mean ran and vorc: a NL, 5 Addonal non-lnear componen canno be compleel arbrar, b m be mmerc and racele. For example a qadrac NLEVM m be of he form a 6 { } I { } I here {.} denoe a race and I he den marx: { M } race M M, I 7 We hall ee belo ha an approprae choce of he coeffcen, and allo he model o reprodce he correc anorop n mple hear. Theor baed on he ale-hamlon Theorem ho ha he mo general relaonhp FD 8-5 Davd Aple

6 nvolve en ndependen enor bae and nclde erm p o he 5 h poer n and : a T, 8 here all T are lnearl-ndependen, mmerc, racele, econd-ran enor prodc of and. One poble choce of bae b b no mean he onl one Lnear: Qadrac: bc: Qarc: Qnc: T T { } I T T { } I 4 5 { } 6 T { } I T T { } { } I { } I T T T { } { } Exerce. rove ha all hee bae are mmerc and racele. Sho ha bae T 5 T vanh n -d ncompreble flo. The fr bae correpond o a lnear edd-vco model and he nex hree o he qadrac exenon n eqaon 6. T 5, T 7, T 8, T 9 conan mlple of earler bae and hence cold be replaced b mpler form; hoever, he bae choen here enre ha he vanh n -d ncompreble flo. A nmber of roe have been aen n devng ch NLEVM, ncldng: amng he form of he ere expanon o qadrac or cbc order and mpl calbrang agan mporan flo e.g. Spezale, 987; raf, Lander and Sga, 996; mplfng a dfferenal re model b an explc olon e.g. Spezale and Ga, 99 or b cceve approxmaon e.g. Aple and Lechzner, 998; renormalaon grop mehod e.g. Rbnen and Baron, 99; drec neracon approxmaon e.g Yohzaa, 987. In devng ch NLEVM, model developer have ogh o ncorporae ch phcallgnfcan propere a realabl: pove normal ree ach Scharz neqal 9 FD 8-6 Davd Aple

7 8.. bc Edd-Vco Model The preferred level of modellng a he nver of Mancheer a cbc edd vco model, hch can be ren n he form a f { } I { } { } { } { } I 4 Noe he follong propere ome of hch ll be developed frher belo and on he example hee. A cbc re-ran relaonhp he mnmm order h a lea he ame nmber of ndependen coeffcen a he anorop enor.e. 5. In h cae ll be precel 5 f e ame ee v belo and noe ha he and erm are enorall mlar o he lnear erm ee v belo. The fr erm on he RHS correpond o a lnear edd-vco model. The varo non-lnear erm evoe enve o pecfc pe of ran: he qadrac,, erm evoe env o anorop; he cbc and erm evoe env o crvare; he cbc 4 erm evoe env o rl. v The and erm are enorall proporonal o he lnear erm; hoever he or raher her dfference provde a env o crvare, o have been ep dnc. v The and 4 erm vanh n -d ncompreble flo. v Theor and expermen ndcae ha pre roaon generae no rblence. Th mple ha ogh o be, a lea n he lm S. { } I A an example of ch a model e ce he raf e al. 996 model n hch coeffcen are fncon of he mean-ran nvaran and rblen Renold nmber:.75.[ exp.6e ] /.5 R / R f exp[ ], R 9 4 ~ here S S S,, ~ max S, The coeffcen of he non-lnear erm are n he preen noaon:,,.4,.4,.4 f,,, 4 4, 4,, 8 f Non-lnear bl no boh enor prodc and ran-dependen coeffcen noabl. The model compleed b ranpor eqaon for and ~. Mean ran and vorc are non-dmenonaled ng ~ raher han. FD 8-7 Davd Aple

8 FD 8-8 Davd Aple 8.. General ropere of Non-Lnear Edd-Vco Model -d Incompreble Flo The non-lnear combnaon of and have parclarl mple form n -d ncompreble flo. In ch a flo:, Incomprebl and he mmer and anmmer propere of and, redce hee o, From hee e fnd, 4 ROERTY In -d ncompreble flo: } { } { I I I I 5 here I dag,,. In parclar, ang enor prodc of or h marce hoe hrd ro and hrd colmn are all zero ha he ame effec a mlplcaon b he calar { } or } { repecvel. ROERTY } { a a 6 Moreover, n -d ncompreble flo he qadrac erm do no conrbe o he prodcon of rblen nec energ. roof. S a x

9 No a nce anmmerc, hl ncomprebl mple or S S. Hence, a S a { a} Th re for an ncompreble flo, b, n he -d cae, mlplng b, ang he race and ng he rel 5 fond ha he conrbon of he qadrac erm o {a}. ROERTY In -d ncompreble flo he - and 4-relaed erm of he non-lnear expanon vanh. roof. Sbe he rel 5 for and no. arclar Tpe of Sran The non-lnear conve relaonhp allo he model o mmc he repone of rblence o parclar mporan pe of ran. ROERTY 4 The qadrac erm eld rblence anorop n mple hear: v here 7 Th ma be dedced b bng he rel 4 no, nong ha, hl A an example he fgre rgh ho applcaon of he Aple and Lechzner 998 model o compng he Renold ree n channel flo vv -v FD 8-9 Davd Aple

10 ROERTY 5 The and -relaed cbc erm eld he correc env o crvare. V In crved hear flo,,, here R c rad of crvare. From 4, R R { } { } here, R Rc R Rc Hence, { } { } R Rc Inpecon of he prodcon erm n he reranpor eqaon Secon 7.4 ho ha crvare ablng redcng rblence f ncreae n he drecon aa from he cenre of crvare /R > and deablng ncreang rblence f decreae n he drecon aa from he cenre of crvare /R <. In he conve relaon he repone correc f and are boh pove. c 'able' crvare redcng rblence 'nable' crvare ncreang rblence ROERTY 6 In -d flo, he 4 -relaed erm evoe he correc env o rl. W FD 8- Davd Aple

11 8.4 Dfferenal Sre Modellng Dfferenal re model aa Renold-re ranpor model or econd-order clore olve a eparae calar-ranpor eqaon for each re componen : D d F 8 D For a dervaon ee he core noe for he Bondar Laer modle. Sch model, n prncple, conan mch more rblence phc becae he rae-of-change, advecon and prodcon erm are exac. The neare hng o a andard model a hgh-re clore baed on ha of Lander e al. 975 and Gbon and Lander 978. Term Name and role Model F D D RATE OF HANGE me dervave advecon Tranpor h he mean flo. RODTION mean ran Generaon of rblence energ from he mean flo. RODTION bod force Generaon of rblence energ b bod force. EXAT EXAT EXAT n prncple F f f d DIFFSION Spaal redrbon. d l l l Φ RESSRE-STRAIN Redrbon of rblence energ beeen componen. ~ n n ~ l l ~ n n, ~ n n f f / 4 / n / ε DISSIATION Removal of rblence energ b vco Tpcal vale of he conan are:.8,.6,.5,. 9 FD 8- Davd Aple

12 Energ n Trblen Flcaon In mple hear flo here / he onl non-zero mean-veloc graden he prodcon erm of he normal ree are: v, Hence, prodcon of rblence energ predomnanl feed he componen. Energ hen ranferred o flcaon n he cro-ream drecon b he redrbve effec of prere flcaon. A mall cale local graden are ffcenl large for vco o dpae rblen energ. There a connal energ cacade from he energ enerng he rblence a he large cale of he flo, hogh hear nable connall prodcng edde a maller cale, nl lmael energ removed b vco. RODTION ADVETION b mean flo v REDISTRIBTION b prere flcaon DISSIATION b vco FD 8- Davd Aple

13 The re-ranpor eqaon m be pplemened b a mean of pecfng b on ranpor eqaon, or one for a relaed qan ch a. pcall A ggeed b he able, he mo gnfcan erm reqrng modellng he prereran correlaon hch formed, n pracce, b he average prodc of prere flcaon and flcang veloc graden. Th erm racele.e. he m of he dagonal erm and acceped role o promoe orop hence he form of model for and neceang a all-correcon erm. Near all h oropng endenc m be over-rdden,. Where bod force are preen e.g. n boan or roang flo addonal prodcon erm m be nclded. General Aemen of DSM For: Inclde more rblence phc han edd-vco model. Advecon and prodcon erm energ-n erm are exac and do no need modellng. Agan: Model are ver complex and man mporan erm parclarl he redrbon and dpaon erm reqre modellng. Model are ver expenve compaonall 6 re-ranpor eqaon n dmenon and end o be nmercall nable onl he mall moleclar vco conrbe o an or of graden dffon erm. Oher DSM of Inere Spezale e al. 99 non-lnear formlaon, elmnang all-correcon erm; raf 998 lo-re DSM, aempng o elmnae all-dependen parameer; Jarl and Hanal 995 lo-re DSM admng anoropc dpaon; Wlcox 988b lo-re DSM, h raher han a addonal rblen calar. Excellen reference for developmen n Renold-re ranpor modellng can be fond n Lander 989 and Hanal 994. FD 8- Davd Aple

14 8.5 Implemenaon of Trblence Model n FD 8.5. Tranpor Eqaon The mplemenaon of a rblence model n FD reqre: a mean of pecfng he rblen ree, b eher: a conve relaon edd-vco model, or ndvdal ranpor eqaon dfferenal re model; he olon of addonal calar-ranpor eqaon. Specal onderaon for he Mean Flo Eqaon repreen a rblen flx of -momenm n he x drecon, b onl a par of h can be reaed mplcl a a dffon-le erm. e.g. for he eqaon hrogh a face normal o he drecon: V v non lnear erm dffve par ranferred o orce The non-dffve par of he flx ranferred o he orce erm and reaed explcl.e. held conan for ha eraon. Neverhele, ll reaed n a conervave fahon;.e. ored o on a cell face o ha he mean momenm lo b one cell eqal o ha ganed b neghbor. The lac of a rblen vco n dfferenal re model can lead o nmercal nabl. Th can be addreed b he e of effecve vcoe ee belo. Specal onderaon for he Trblence Eqaon The are all orce-domnaed;.e. he mo gnfcan erm are prodcon, redrbon and dpaon; h omeme ed a an exce for a lo-order advecon cheme. Varable ch a and m be non-negave. Th demand: care n dcreng he orce erm ee belo; e of an ncondonall-bonded advecon cheme. Sorce-Term Lnearaon For Non-Negave Qane The general dcreed calar-ranpor eqaon for a conrol volme cenred on node aφ afφ F b φ F For abl one reqre To enre non-negave φ one reqre, n addon, FD 8-4 Davd Aple

15 b Yo hold, b npecon of he and ranpor eqaon, be able o denf ho he orce erm lneared n h a, h one pove par and one negave par, he laer preferabl proporonal o he ranpored varable, or. If b < for a qan ch a or hch ala non-negave e.g. de o ranfer of non-lnear par of he advecon erm or non-dffve flxe o he orce erm hen, o enre ha he varable doen become negave, he orce erm hold be rearranged a b * φ φ b here * denoe he crren vale of a varable Wall Bondar ondon A all he no-lp bondar condon apple, o ha boh mean and flcang veloce vanh. A hgh Renold nmber h preen hree problem: here are ver large flo graden; all-normal flcaon are ppreed.e. elecvel damped; vco and rblen ree are of comparable magnde. There are o man a of handlng h n rblen flo: lo-renold-nmber rblence model reolve he flo rgh p o he all h a ver fne grd and vco modfcaon o he rblence eqaon o enre he correc near-all raher han log-laer behavor; all fncon e a coarer grd and ame heorecal profle beeen he near-all node and he bondar. Lo-Renold-Nmber Trblence Model Am o reolve he flo rgh p o he bondar. Have o nclde effec of moleclar vco n he coeffcen of he edd-vco formla and or ranpor eqaon. Tr o enre he heorecal near-all behavor:, ~ ~ conan, Fll reolon of he flo reqre he near-all node o af, here, / Th can be ver compaonall demandng, parclarl for hgh-peed flo. FD 8-5 Davd Aple

16 Hgh-Renold-Nmber Trblence Model Brdge he near-all regon h all fncon;.e. ame profle baed on bondar-laer heor beeen near-all node and bondar. OK f near eqlbrm e.g. lol-developng bondar laer, b dodg n hghl noneqlbrm regon parclarl near mpngemen, eparaon or reaachmen pon. conrol volme near-all node p τ p amed veloc profle The near-all node hold deall be placed n he regon < < 5 range 5-5 generall accepable. Th mean ha nmercal mehe canno be arbrarl refned cloe o old bondare. In he fne-volme mehod, varo qane are reqred from he all-fncon approach. Vale ma be fxed on he all elf or b forcng a vale a he near-all node. Varable Wall bondar condon Reqred from all fncon Mean veloc,v,w relave veloc a he all Wall hear re, a he all; zero flx fxed a near-all node The mean of dervng hee qane are e o belo. ell-averaged prodcon and dpaon Vale a he near-all node Mean-Veloc Eqaon: Wall Shear Sre The frcon veloc defned n erm of he all hear re: If he near-all node le n he logarhmc regon hen ln E, here bcrp denoe he near-all node. Gven he vale of h cold be olved eravel for and hence he all re. Hoever, a beer approach hen he rblence clearl far from eqlbrm e.g. near eparaon or reaachmen pon o emae an eqvalen frcon veloc from he rblen nec energ: / 4 / and negrae he mean-veloc profle amng an edd vco. If e adop he log-la veron: FD 8-6 Davd Aple

17 and olve for from e ge / 4 ln E If he rblence ere gennel n eqlbrm, hen old eqal and and 4 old be eqvalen. A beer approach o ame a oal vco moleclar edd hch mache boh he vco eff and log-laer eff lm: max{, } 5 eff here a machng hegh. Smlar negraon o before lead o boh vco blaer and log-la lm,, 6 ln{ }, here e noe ha baed on raher han he nnon. A mlar approach can be appled for rogh-all bondar laer Aple, 7, here a fncon of roghne. A pcal mooh-all vale of 7.7. A far a he compaonal mplemenaon concerned he reqred op for a fnevolme calclaon he all hear re n erm of he mean veloc a he near-all node, p, no vce vera. To h end, 6 convenenl rearranged n erm of an effecve all vco eff,all ch ha p eff, all 7 here p, eff, all, 8 ln{ } Eqaon: ell-averaged rodcon and Dpaon The orce erm of he ranpor eqaon reqre cell-averaged vale of prodcon and dpaon rae. Thee are derved b amng profle for hee qane: v here eff 9 > d > 4 FD 8-7 Davd Aple

18 FD 8-8 Davd Aple here, for mooh all, he machng hegh and offe d are gven n all n b ee Aple, 7: 7.4, 9 4. d Inegraon over a cell ee example hee hen lead o cell average ] ln[ / d av 4 ln d av 4 Eqaon: Bondar ondon on fxed from amed profle eqaon 4 a he near-all node. A parclar vale a a cell cenre can be forced n a fne-volme calclaon b modfng he orce coeffcen: b, here a large nmber e.g.. The marx eqaon for ha cell hen become F F a a φ φ or F F a a a φ φ Snce a large nmber h effecvel force φ o ae he vale. Renold-Sre Eqaon For he Renold ree, one mehod o fx he vale a he near-all node from he nearall vale of and he rcre fncon /, he laer beng derved from he dfferenal re-ranpor eqaon on he ampon of local eqlbrm. For he andard model h gve ee he example hee: v v v v v 4 Wh he vale for,, ec. from he andard model h gve

19 .98, v.48,.654, v When he near-all flo and all-normal drecon are no convenenl algned n he x and drecon repecvel, he acal rcre fncon can be obaned b roaon. Hoever, for -dmenonal and eparang/reaachng flo he flo-orened coordnae em no fxed a pror and can ng rond gnfcanl beeen eraon f he mean veloc mall, mang convergence dffcl o oban. A econd and no m preferred approach Aple, 7 o e cell-averaged prodcon and dpaon n he Renold-re eqaon n he ame manner a he -eqaon, nong ha, n mple hear and n floalgned coordnae: v, v v v, h he rao v / v deermned from 44 a.97. In he all-fncon formlaon, proporonal o he qare of he veloc a he near-all node, o roang from flo-algned coordnae o he acal arean coordnae em doe no cae dconne n he re prodcon here he veloc revere gn; e.g. near eparaon or reaachmen pon Effecve Vco for Dfferenal Sre Model DSM conan no rblen vco and have a repaon for nmercal nabl. An arfcal mean of promong abl o add and brac a graden-dffon erm o he rblen flx: 45 h he fr par averaged beeen nodal vale and he la par dcreed acro a cell face and reaed mplcl; ver mlar o he Rhe-ho algorhm for prere-veloc coplng n he momenm eqaon. The mple choce for he effecve vco 46 A beer choce o mae e of a naral lnage beeen ndvdal ree and he correpondng mean-veloc graden hch are from he acal re-ranpor eqaon. Amng ha he re-ranpor eqaon h no bod force are orce-domnaed hen or, h he bac DSM ho all-reflecon erm, FD 8-9 Davd Aple

20 FD 8- Davd Aple Expand h, denfng he erm hch conan onl or x a follo. For he normal ree : Hence, x Smlarl for he hear ree : hence x Hence, from he re-ranpor eqaon, x x 47 here he effecve vcoe boh for he α componen of momenm are:, 48 Noe ha he effecve vcoe are anoropc, beng lned o parclar normal ree. A more dealed anal can accommodae all-reflecon erm n he prere-ran model, b he exra complex no fed.

21 Some Reference for Indvdal Trblence Model Aple, D.D., 7, FD calclaon of rblen flo h arbrar all roghne, Flo, Trblence and ombon, 78, Aple, D.D. and Lechzner, M.A., 998, A ne lo-renold-nmber nonlnear oeqaon rblence model for complex flo, In. J. Hea Fld Flo, 9, 9-. raf, T.J., 998, Developmen n a lo-renold-nmber econd-momen clore and applcaon o eparang and reaachng flo, In. J. Hea Fld Flo, 9, raf, T.J., Lander, B.E. and Sga, K., 996, Developmen and applcaon of a cbc eddvco model of rblence, In. J. Hea Fld Flo, 7, 8-5. Ga, T.B. and Spezale,.G., 99, On explc algebrac re model for complex rblen flo, J. Fld Mech., 54, Gbon, M.M. and Lander, B.E., 978, Grond effec on prere flcaon n he amopherc bondar laer, J. Fld Mech., 86, Hanal, K., 994, Advanced rblence clore model: a ve of crren a and fre propec, In. J. Hea Fld Flo, 5, 78-. Jarl, S. and Hanal, K., 995, A econd-momen clore for non-eqlbrm and eparang hgh- and lo-re-nmber flo, roc. h Smp. Trblen Shear Flo, ennlvana Sae nver. Lam,.K.G. and Bremhor, K.A., 98, Modfed form of he -e model for predcng all rblence, Jornal of Fld Engneerng,, Lander, B.E., 989, Second-Momen lore and e n modellng rblen ndral flo, In. J. Nmer. Meh. Fld, 9, Lander, B.E., Reece, G.J. and Rod, W., 975, rogre n he developmen of a Renoldre rblence clore, J. Fld Mech., 68, Lander, B.E. and Sharma, B.I., 974, Applcaon of he energ-dpaon model of rblence o he calclaon of flo near a pnnng dc, Leer n Hea and Ma Tranfer,, -8. Lander, B.E. and Spaldng, D.B., 974, The nmercal compaon of rblen flo, omper Meh. Appl. Mech. Eng.,, Len, F-S. and Lechzner, M.A., 99, Second-momen modellng of recrclang flo h a non-orhogonal collocaed fne-volme algorhm, n Trblen Shear Flo 8 Mnch, 99, Sprnger-Verlag. Mener, F.R., 994, To-eqaon edd-vco rblence model for engneerng applcaon, AIAA J.,, Rbnen, R. and Baron, J.M., 99, Nonlnear Renold re model and he renormalaon grop, h. Fld A,, Spezale,.G., 987, On nonlnear K-l and K- model of rblence, J. Fld Mech., 78, Spezale,.G., Sarar, S. and Ga, T.B., 99, Modellng he prere-ran correlaon of rblence: an nvaran dnamcal em approach, J. Fld Mech., 7, Wlcox, D.., 988, Reaemen of he cale-deermnng eqaon for advanced rblence model, AIAA J., 6, 99-. Wlcox, D.., 988, Ml-cale model for rblen flo, AIAA Jornal, 6, -. Wlcox, D.., 998, Trblence modellng for FD, nd Edon, DW Indre. Yaho, V., Orzag, S.A., Thangam, S., Ga, T.B. and Spezale,.G., 99, Developmen of rblence model for hear flo b a doble expanon echnqe, h. Fld A, 7, 5. Yohzaa, A., 987, Sacal anal of he dervaon of he Renold re from edd-vco repreenaon, h. Fld, 7, FD 8- Davd Aple

Calculation of the Resistance of a Ship Mathematical Formulation. Calculation of the Resistance of a Ship Mathematical Formulation

Calculation of the Resistance of a Ship Mathematical Formulation. Calculation of the Resistance of a Ship Mathematical Formulation Ressance s obaned from he sm of he frcon and pressre ressance arables o deermne: - eloc ecor, (3) = (,, ) = (,, ) - Pressre, p () ( - Dens, ρ, s defned b he eqaon of sae Ressance and Proplson Lecre 0 4

More information

by Lauren DeDieu Advisor: George Chen

by Lauren DeDieu Advisor: George Chen b Laren DeDe Advsor: George Chen Are one of he mos powerfl mehods o nmercall solve me dependen paral dfferenal eqaons PDE wh some knd of snglar shock waves & blow-p problems. Fed nmber of mesh pons Moves

More information

CONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS

CONSISTENT EARTHQUAKE ACCELERATION AND DISPLACEMENT RECORDS APPENDX J CONSSTENT EARTHQUAKE ACCEERATON AND DSPACEMENT RECORDS Earhqake Acceleraons can be Measred. However, Srcres are Sbjeced o Earhqake Dsplacemens J. NTRODUCTON { XE "Acceleraon Records" }A he presen

More information

Outline. Review Solution Approaches. Review Basic Equations. Nature of Turbulence. Review Fluent Exercise. Turbulence Models

Outline. Review Solution Approaches. Review Basic Equations. Nature of Turbulence. Review Fluent Exercise. Turbulence Models Trblence Models Larry areo Mechancal Engneerng 69 ompaonal Fld Dynamcs Febrary, Olne Revew las lecre Nare of rblence Reynolds-average Naver-Soes (RNS) Mng lengh heory Models sng one dfferenal eqaon Two-eqaon

More information

Cooling of a hot metal forging. , dt dt

Cooling of a hot metal forging. , dt dt Tranen Conducon Uneady Analy - Lumped Thermal Capacy Model Performed when; Hea ranfer whn a yem produced a unform emperaure drbuon n he yem (mall emperaure graden). The emperaure change whn he yem condered

More information

Turbulence Modelling (CFD course)

Turbulence Modelling (CFD course) Trblence Modellng (CFD corse) Sławomr Kbac slawomr.bac@mel.pw.ed.pl 14.11.016 Copyrgh 016, Sławomr Kbac Trblence Modellng Sławomr Kbac Conens 1. Reynolds-averaged Naver-Soes eqaons... 3. Closre of he modelled

More information

Cartesian tensors. Order (rank) Scalar. Vector. 3x3 matrix

Cartesian tensors. Order (rank) Scalar. Vector. 3x3 matrix Caresan ensors Order (rank) 0 1 3 a b c d k Scalar ecor 33 mar Caresan ensors Kronecker dela δ = 1 f = 0 f Le- Ca epslon ε k = 1 f,, k are cclc 1 f,, k are ancclc 0 oherse Smmaon conenon (o eqal ncces

More information

VI. Computational Fluid Dynamics 1. Examples of numerical simulation

VI. Computational Fluid Dynamics 1. Examples of numerical simulation VI. Comaonal Fld Dnamcs 1. Eamles of nmercal smlaon Eermenal Fas Breeder Reacor, JOYO, wh rmar of coolan sodm. Uer nner srcre Uer lenm Flow aern and emerare feld n reacor essel n flow coas down Core Hh

More information

CHAPTER 10: LINEAR DISCRIMINATION

CHAPTER 10: LINEAR DISCRIMINATION CHAPER : LINEAR DISCRIMINAION Dscrmnan-based Classfcaon 3 In classfcaon h K classes (C,C,, C k ) We defned dscrmnan funcon g j (), j=,,,k hen gven an es eample, e chose (predced) s class label as C f g

More information

Reconstruction of Variational Iterative Method for Solving Fifth Order Caudrey-Dodd-Gibbon (CDG) Equation

Reconstruction of Variational Iterative Method for Solving Fifth Order Caudrey-Dodd-Gibbon (CDG) Equation Shraz Unvery of Technology From he SelecedWor of Habbolla Lafzadeh Reconrcon of Varaonal Ierave Mehod for Solvng Ffh Order Cadrey-Dodd-Gbbon (CDG Eqaon Habbolla Lafzadeh, Shraz Unvery of Technology Avalable

More information

H = d d q 1 d d q N d d p 1 d d p N exp

H = d d q 1 d d q N d d p 1 d d p N exp 8333: Sacal Mechanc I roblem Se # 7 Soluon Fall 3 Canoncal Enemble Non-harmonc Ga: The Hamlonan for a ga of N non neracng parcle n a d dmenonal box ha he form H A p a The paron funcon gven by ZN T d d

More information

Sound Transmission Throough Lined, Composite Panel Structures: Transversely Isotropic Poro- Elastic Model

Sound Transmission Throough Lined, Composite Panel Structures: Transversely Isotropic Poro- Elastic Model Prde nvery Prde e-pb Pblcaon of he Ray. Herrc aboraore School of Mechancal Engneerng 8-5 Sond Tranmon Throogh ned, Comoe Panel Srcre: Tranverely Ioroc Poro- Elac Model J Sar Bolon Prde nvery, bolon@rde.ed

More information

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!") i+1,q - [(!

In the complete model, these slopes are ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL. (! i+1 -! i ) + [(!) i+1,q - [(! ANALYSIS OF VARIANCE FOR THE COMPLETE TWO-WAY MODEL The frs hng o es n wo-way ANOVA: Is here neracon? "No neracon" means: The man effecs model would f. Ths n urn means: In he neracon plo (wh A on he horzonal

More information

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables

, t 1. Transitions - this one was easy, but in general the hardest part is choosing the which variables are state and control variables Opmal Conrol Why Use I - verss calcls of varaons, opmal conrol More generaly More convenen wh consrans (e.g., can p consrans on he dervaves More nsghs no problem (a leas more apparen han hrogh calcls of

More information

Improvement of Two-Equation Turbulence Model with Anisotropic Eddy-Viscosity for Hybrid Rocket Research

Improvement of Two-Equation Turbulence Model with Anisotropic Eddy-Viscosity for Hybrid Rocket Research evenh Inernaonal onference on ompaonal Fld Dynamcs (IFD7), Bg Island, awa, Jly 9-, IFD7-9 Improvemen of Two-Eqaon Trblence Model wh Ansoropc Eddy-Vscosy for ybrd oce esearch M. Mro * and T. hmada ** orrespondng

More information

The Elastic Wave Equation. The elastic wave equation

The Elastic Wave Equation. The elastic wave equation The Elasc Wave Eqaon Elasc waves n nfne homogeneos soropc meda Nmercal smlaons for smple sorces Plane wave propagaon n nfne meda Freqency, wavenmber, wavelengh Condons a maeral dsconnes nell s Law Reflecon

More information

Model-Based FDI : the control approach

Model-Based FDI : the control approach Model-Baed FDI : he conrol approach M. Saroweck LAIL-CNRS EUDIL, Unver Llle I Olne of he preenaon Par I : model Sem, normal and no normal condon, fal Par II : he decon problem problem eng noe, drbance,

More information

SMS-618, Particle Dynamics, Fall 2003 (E. Boss, last updated: 10/8/2003) Conservation equations in fluids

SMS-618, Particle Dynamics, Fall 2003 (E. Boss, last updated: 10/8/2003) Conservation equations in fluids SMS-68 Parcle Dnamcs Fall 3 (E. Boss las daed: /8/3) onseraon eqaons n flds onces e need: ensor (Sress) ecors (e.g. oson eloc) and scalars (e.g. S O). Prode means o descrbe conseraon las h comac noaon

More information

Real-Time Trajectory Generation and Tracking for Cooperative Control Systems

Real-Time Trajectory Generation and Tracking for Cooperative Control Systems Real-Tme Trajecor Generaon and Trackng for Cooperave Conrol Ssems Rchard Mrra Jason Hcke Calforna Inse of Technolog MURI Kckoff Meeng 14 Ma 2001 Olne I. Revew of prevos work n rajecor generaon and rackng

More information

Notes on the stability of dynamic systems and the use of Eigen Values.

Notes on the stability of dynamic systems and the use of Eigen Values. Noes on he sabl of dnamc ssems and he use of Egen Values. Source: Macro II course noes, Dr. Davd Bessler s Tme Seres course noes, zarads (999) Ineremporal Macroeconomcs chaper 4 & Techncal ppend, and Hamlon

More information

Solution of a diffusion problem in a non-homogeneous flow and diffusion field by the integral representation method (IRM)

Solution of a diffusion problem in a non-homogeneous flow and diffusion field by the integral representation method (IRM) Appled and ompaonal Mahemacs 4; 3: 5-6 Pblshed onlne Febrary 4 hp://www.scencepblshnggrop.com//acm do:.648/.acm.43.3 olon of a dffson problem n a non-homogeneos flow and dffson feld by he negral represenaon

More information

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4

CS434a/541a: Pattern Recognition Prof. Olga Veksler. Lecture 4 CS434a/54a: Paern Recognon Prof. Olga Veksler Lecure 4 Oulne Normal Random Varable Properes Dscrmnan funcons Why Normal Random Varables? Analycally racable Works well when observaon comes form a corruped

More information

Lecture 6: Learning for Control (Generalised Linear Regression)

Lecture 6: Learning for Control (Generalised Linear Regression) Lecure 6: Learnng for Conrol (Generalsed Lnear Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure 6: RLSC - Prof. Sehu Vjayakumar Lnear Regresson

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm H ( q, p, ) = q p L( q, q, ) H p = q H q = p H = L Equvalen o Lagrangan formalsm Smpler, bu

More information

Observer Design for Nonlinear Systems using Linear Approximations

Observer Design for Nonlinear Systems using Linear Approximations Observer Desgn for Nonlnear Ssems sng Lnear Appromaons C. Navarro Hernandez, S.P. Banks and M. Aldeen Deparmen of Aomac Conrol and Ssems Engneerng, Unvers of Sheffeld, Mappn Sree, Sheffeld S 3JD. e-mal:

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 9 Hamlonan Equaons of Moon (Chaper 8) Wha We Dd Las Tme Consruced Hamlonan formalsm Hqp (,,) = qp Lqq (,,) H p = q H q = p H L = Equvalen o Lagrangan formalsm Smpler, bu wce as

More information

Numerical simulation of flow reattachment length in a stilling basin with a step-down floor

Numerical simulation of flow reattachment length in a stilling basin with a step-down floor 5 h Inernaonal Symposm on Hydralc Srcres Brsbane, Asrala, 5-7 Jne 04 Hydralc Srcres and Socey: Engneerng hallenges and Eremes ISBN 97874756 - DOI: 0.464/ql.04.3 Nmercal smlaon of flow reaachmen lengh n

More information

A Demand System for Input Factors when there are Technological Changes in Production

A Demand System for Input Factors when there are Technological Changes in Production A Demand Syem for Inpu Facor when here are Technologcal Change n Producon Movaon Due o (e.g.) echnologcal change here mgh no be a aonary relaonhp for he co hare of each npu facor. When emang demand yem

More information

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data

Appendix H: Rarefaction and extrapolation of Hill numbers for incidence data Anne Chao Ncholas J Goell C seh lzabeh L ander K Ma Rober K Colwell and Aaron M llson 03 Rarefacon and erapolaon wh ll numbers: a framewor for samplng and esmaon n speces dversy sudes cology Monographs

More information

2.1 Constitutive Theory

2.1 Constitutive Theory Secon.. Consuve Theory.. Consuve Equaons Governng Equaons The equaons governng he behavour of maerals are (n he spaal form) dρ v & ρ + ρdv v = + ρ = Conservaon of Mass (..a) d x σ j dv dvσ + b = ρ v& +

More information

Control Systems. Mathematical Modeling of Control Systems.

Control Systems. Mathematical Modeling of Control Systems. Conrol Syem Mahemacal Modelng of Conrol Syem chbum@eoulech.ac.kr Oulne Mahemacal model and model ype. Tranfer funcon model Syem pole and zero Chbum Lee -Seoulech Conrol Syem Mahemacal Model Model are key

More information

Higher-order Graph Cuts

Higher-order Graph Cuts Example: Segmenaon Hgher-orer Graph Hroh Ihkawa 石川博 Deparmen of omper Scence & Engneerng Waea Unery 早稲田大学 Boyko&Jolly IV 3 Example: Segmenaon Local moel ex.: Moel of pxel ale for each kn of e Pror moel

More information

from normal distribution table It is interesting to notice in the above computation that the starting stock level each

from normal distribution table It is interesting to notice in the above computation that the starting stock level each Homeork Solon Par A. Ch a b 65 4 5 from normal dsrbon able Ths, order qany s 39-7 b o b5 from normal dsrbon able Ths, order qany s 9-7 I s neresng o noce n he above compaon ha he sarng sock level each

More information

Research Article Cubic B-spline for the Numerical Solution of Parabolic Integro-differential Equation with a Weakly Singular Kernel

Research Article Cubic B-spline for the Numerical Solution of Parabolic Integro-differential Equation with a Weakly Singular Kernel Researc Jornal of Appled Scences, Engneerng and Tecnology 7(): 65-7, 4 DOI:.96/afs.7.5 ISS: 4-7459; e-iss: 4-7467 4 Mawell Scenfc Pblcaon Corp. Sbmed: Jne 8, Acceped: Jly 9, Pblsed: Marc 5, 4 Researc Arcle

More information

Dynamic Model of the Axially Moving Viscoelastic Belt System with Tensioner Pulley Yanqi Liu1, a, Hongyu Wang2, b, Dongxing Cao3, c, Xiaoling Gai1, d

Dynamic Model of the Axially Moving Viscoelastic Belt System with Tensioner Pulley Yanqi Liu1, a, Hongyu Wang2, b, Dongxing Cao3, c, Xiaoling Gai1, d Inernaonal Indsral Informacs and Comper Engneerng Conference (IIICEC 5) Dynamc Model of he Aally Movng Vscoelasc Bel Sysem wh Tensoner Plley Yanq L, a, Hongy Wang, b, Dongng Cao, c, Xaolng Ga, d Bejng

More information

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes.

J i-1 i. J i i+1. Numerical integration of the diffusion equation (I) Finite difference method. Spatial Discretization. Internal nodes. umercal negraon of he dffuson equaon (I) Fne dfference mehod. Spaal screaon. Inernal nodes. R L V For hermal conducon le s dscree he spaal doman no small fne spans, =,,: Balance of parcles for an nernal

More information

1.B Appendix to Chapter 1

1.B Appendix to Chapter 1 Secon.B.B Append o Chper.B. The Ordnr Clcl Here re led ome mporn concep rom he ordnr clcl. The Dervve Conder ncon o one ndependen vrble. The dervve o dened b d d lm lm.b. where he ncremen n de o n ncremen

More information

( ) () we define the interaction representation by the unitary transformation () = ()

( ) () we define the interaction representation by the unitary transformation () = () Hgher Order Perurbaon Theory Mchael Fowler 3/7/6 The neracon Represenaon Recall ha n he frs par of hs course sequence, we dscussed he chrödnger and Hesenberg represenaons of quanum mechancs here n he chrödnger

More information

Lecture 11: Stereo and Surface Estimation

Lecture 11: Stereo and Surface Estimation Lecure : Sereo and Surface Emaon When camera poon have been deermned, ung rucure from moon, we would lke o compue a dene urface model of he cene. In h lecure we wll udy he o called Sereo Problem, where

More information

Is it necessary to seasonally adjust business and consumer surveys. Emmanuelle Guidetti

Is it necessary to seasonally adjust business and consumer surveys. Emmanuelle Guidetti Is necessar o seasonall adjs bsness and consmer srves Emmanelle Gde Olne 1 BTS feares 2 Smlaon eercse 3 Seasonal ARIMA modellng 4 Conclsons Jan-85 Jan-87 Jan-89 Jan-91 Jan-93 Jan-95 Jan-97 Jan-99 Jan-01

More information

Robustness Experiments with Two Variance Components

Robustness Experiments with Two Variance Components Naonal Insue of Sandards and Technology (NIST) Informaon Technology Laboraory (ITL) Sascal Engneerng Dvson (SED) Robusness Expermens wh Two Varance Componens by Ana Ivelsse Avlés avles@ns.gov Conference

More information

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS

V.Abramov - FURTHER ANALYSIS OF CONFIDENCE INTERVALS FOR LARGE CLIENT/SERVER COMPUTER NETWORKS R&RATA # Vol.) 8, March FURTHER AALYSIS OF COFIDECE ITERVALS FOR LARGE CLIET/SERVER COMPUTER ETWORKS Vyacheslav Abramov School of Mahemacal Scences, Monash Unversy, Buldng 8, Level 4, Clayon Campus, Wellngon

More information

TSS = SST + SSE An orthogonal partition of the total SS

TSS = SST + SSE An orthogonal partition of the total SS ANOVA: Topc 4. Orhogonal conrass [ST&D p. 183] H 0 : µ 1 = µ =... = µ H 1 : The mean of a leas one reamen group s dfferen To es hs hypohess, a basc ANOVA allocaes he varaon among reamen means (SST) equally

More information

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas)

Lecture 18: The Laplace Transform (See Sections and 14.7 in Boas) Lecure 8: The Lalace Transform (See Secons 88- and 47 n Boas) Recall ha our bg-cure goal s he analyss of he dfferenal equaon, ax bx cx F, where we emloy varous exansons for he drvng funcon F deendng on

More information

Solution in semi infinite diffusion couples (error function analysis)

Solution in semi infinite diffusion couples (error function analysis) Soluon n sem nfne dffuson couples (error funcon analyss) Le us consder now he sem nfne dffuson couple of wo blocks wh concenraon of and I means ha, n a A- bnary sysem, s bondng beween wo blocks made of

More information

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are

Chapter 6 DETECTION AND ESTIMATION: Model of digital communication system. Fundamental issues in digital communications are Chaper 6 DCIO AD IMAIO: Fndaenal sses n dgal concaons are. Deecon and. saon Deecon heory: I deals wh he desgn and evalaon of decson ang processor ha observes he receved sgnal and gesses whch parclar sybol

More information

Variants of Pegasos. December 11, 2009

Variants of Pegasos. December 11, 2009 Inroducon Varans of Pegasos SooWoong Ryu bshboy@sanford.edu December, 009 Youngsoo Cho yc344@sanford.edu Developng a new SVM algorhm s ongong research opc. Among many exng SVM algorhms, we wll focus on

More information

Computing Relevance, Similarity: The Vector Space Model

Computing Relevance, Similarity: The Vector Space Model Compung Relevance, Smlary: The Vecor Space Model Based on Larson and Hears s sldes a UC-Bereley hp://.sms.bereley.edu/courses/s0/f00/ aabase Managemen Sysems, R. Ramarshnan ocumen Vecors v ocumens are

More information

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function

(,,, ) (,,, ). In addition, there are three other consumers, -2, -1, and 0. Consumer -2 has the utility function MACROECONOMIC THEORY T J KEHOE ECON 87 SPRING 5 PROBLEM SET # Conder an overlappng generaon economy le ha n queon 5 on problem e n whch conumer lve for perod The uly funcon of he conumer born n perod,

More information

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s

Ordinary Differential Equations in Neuroscience with Matlab examples. Aim 1- Gain understanding of how to set up and solve ODE s Ordnary Dfferenal Equaons n Neuroscence wh Malab eamples. Am - Gan undersandng of how o se up and solve ODE s Am Undersand how o se up an solve a smple eample of he Hebb rule n D Our goal a end of class

More information

Stochastic Programming handling CVAR in objective and constraint

Stochastic Programming handling CVAR in objective and constraint Sochasc Programmng handlng CVAR n obecve and consran Leondas Sakalaskas VU Inse of Mahemacs and Informacs Lhana ICSP XIII Jly 8-2 23 Bergamo Ialy Olne Inrodcon Lagrangan & KKT condons Mone-Carlo samplng

More information

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD

HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Journal of Appled Mahemacs and Compuaonal Mechancs 3, (), 45-5 HEAT CONDUCTION PROBLEM IN A TWO-LAYERED HOLLOW CYLINDER BY USING THE GREEN S FUNCTION METHOD Sansław Kukla, Urszula Sedlecka Insue of Mahemacs,

More information

Rate Constitutive Theories of Orders n and 1 n for Internal Polar Non-Classical Thermofluids without Memory

Rate Constitutive Theories of Orders n and 1 n for Internal Polar Non-Classical Thermofluids without Memory Appled Maheac, 6, 7, 33-77 hp://www.crp.org/ournal/a ISSN Onlne: 5-7393 ISSN Prn: 5-7385 Rae Conuve heore of Order n and n for Inernal Polar Non-Clacal heroflud whou Meory Karan S. Surana, Sephen W. Long,

More information

Lecture VI Regression

Lecture VI Regression Lecure VI Regresson (Lnear Mehods for Regresson) Conens: Lnear Mehods for Regresson Leas Squares, Gauss Markov heorem Recursve Leas Squares Lecure VI: MLSC - Dr. Sehu Vjayakumar Lnear Regresson Model M

More information

On One Analytic Method of. Constructing Program Controls

On One Analytic Method of. Constructing Program Controls Appled Mahemacal Scences, Vol. 9, 05, no. 8, 409-407 HIKARI Ld, www.m-hkar.com hp://dx.do.org/0.988/ams.05.54349 On One Analyc Mehod of Consrucng Program Conrols A. N. Kvko, S. V. Chsyakov and Yu. E. Balyna

More information

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL

DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL DEEP UNFOLDING FOR MULTICHANNEL SOURCE SEPARATION SUPPLEMENTARY MATERIAL Sco Wsdom, John Hershey 2, Jonahan Le Roux 2, and Shnj Waanabe 2 Deparmen o Elecrcal Engneerng, Unversy o Washngon, Seale, WA, USA

More information

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC

CH.3. COMPATIBILITY EQUATIONS. Continuum Mechanics Course (MMC) - ETSECCPB - UPC CH.3. COMPATIBILITY EQUATIONS Connuum Mechancs Course (MMC) - ETSECCPB - UPC Overvew Compably Condons Compably Equaons of a Poenal Vecor Feld Compably Condons for Infnesmal Srans Inegraon of he Infnesmal

More information

FTCS Solution to the Heat Equation

FTCS Solution to the Heat Equation FTCS Soluon o he Hea Equaon ME 448/548 Noes Gerald Reckenwald Porland Sae Unversy Deparmen of Mechancal Engneerng gerry@pdxedu ME 448/548: FTCS Soluon o he Hea Equaon Overvew Use he forward fne d erence

More information

OPTIMIZATION OF A NONCONVENTIONAL ENGINE EVAPORATOR

OPTIMIZATION OF A NONCONVENTIONAL ENGINE EVAPORATOR Jornal of KONES Powerran and Transpor, Vol. 17, No. 010 OPTIMIZATION OF A NONCONVENTIONAL ENGINE EVAPORATOR Andre Kovalí, Eml Toporcer Unversy of Žlna, Facly of Mechancal Engneerng Deparmen of Aomove Technology

More information

The Distribution of Multiple Shot Noise Process and Its Integral

The Distribution of Multiple Shot Noise Process and Its Integral Appled Mahemac 4 5 478-489 Pblhed Onlne Febrary 4 (hp://www.crp.org/jornal/am hp://dx.do.org/.46/am.4.547 The Drbon of Mlple Sho Noe Proce and I Inegral Jwook Jang Deparmen of Appled Fnance & Acaral Sde

More information

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. number of linearly independent eigenvectors associated with this eigenvalue. Lnear Algebra Lecure # Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons

More information

HOMOGENIZATION METHOD TO PREDICT THREE-DIMENSIONAL PERMEABILITIES CONSIDERING MICRO-MACRO AND SOLID-FLUID INTERACTIONS

HOMOGENIZATION METHOD TO PREDICT THREE-DIMENSIONAL PERMEABILITIES CONSIDERING MICRO-MACRO AND SOLID-FLUID INTERACTIONS HOMOGNIZATION MTHOD TO PRDICT THR-DIMNSIONAL PRMABILITIS CONSIDRING MICRO-MACRO AND SOLID-FLUID INTRACTIONS Nao Taano Maar Zao Tomom ohoa 2 and Kenro Terada 3 Deparmen o Manacrng Scence Oaa Unver 2- amada-oa

More information

Scattering at an Interface: Oblique Incidence

Scattering at an Interface: Oblique Incidence Course Insrucor Dr. Raymond C. Rumpf Offce: A 337 Phone: (915) 747 6958 E Mal: rcrumpf@uep.edu EE 4347 Appled Elecromagnecs Topc 3g Scaerng a an Inerface: Oblque Incdence Scaerng These Oblque noes may

More information

Let s treat the problem of the response of a system to an applied external force. Again,

Let s treat the problem of the response of a system to an applied external force. Again, Page 33 QUANTUM LNEAR RESPONSE FUNCTON Le s rea he problem of he response of a sysem o an appled exernal force. Agan, H() H f () A H + V () Exernal agen acng on nernal varable Hamlonan for equlbrum sysem

More information

Simulation of Wind driven currents for continental shelf of Golestan Province (Iran)

Simulation of Wind driven currents for continental shelf of Golestan Province (Iran) Saeed Sharba / Inernaonal Jornal Of Compaonal Engneerng Reearch / ISSN: 5 35 Smlaon of Wnd drven crren for connenal helf of Golean Provnce Iran Saeed Sharba Facl Member Dep. of fher Grogan Unver of Agrclral

More information

CS286.2 Lecture 14: Quantum de Finetti Theorems II

CS286.2 Lecture 14: Quantum de Finetti Theorems II CS286.2 Lecure 14: Quanum de Fne Theorems II Scrbe: Mara Okounkova 1 Saemen of he heorem Recall he las saemen of he quanum de Fne heorem from he prevous lecure. Theorem 1 Quanum de Fne). Le ρ Dens C 2

More information

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours

NATIONAL UNIVERSITY OF SINGAPORE PC5202 ADVANCED STATISTICAL MECHANICS. (Semester II: AY ) Time Allowed: 2 Hours NATONAL UNVERSTY OF SNGAPORE PC5 ADVANCED STATSTCAL MECHANCS (Semeser : AY 1-13) Tme Allowed: Hours NSTRUCTONS TO CANDDATES 1. Ths examnaon paper conans 5 quesons and comprses 4 prned pages.. Answer all

More information

Block 5 Transport of solutes in rivers

Block 5 Transport of solutes in rivers Nmeral Hydrals Blok 5 Transpor of soles n rvers Marks Holzner Conens of he orse Blok 1 The eqaons Blok Compaon of pressre srges Blok 3 Open hannel flow flow n rvers Blok 4 Nmeral solon of open hannel flow

More information

Fundamentals of PLLs (I)

Fundamentals of PLLs (I) Phae-Locked Loop Fundamenal of PLL (I) Chng-Yuan Yang Naonal Chung-Hng Unvery Deparmen of Elecrcal Engneerng Why phae-lock? - Jer Supreon - Frequency Synhe T T + 1 - Skew Reducon T + 2 T + 3 PLL fou =

More information

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue.

. The geometric multiplicity is dim[ker( λi. A )], i.e. the number of linearly independent eigenvectors associated with this eigenvalue. Mah E-b Lecure #0 Noes We connue wh he dscusson of egenvalues, egenvecors, and dagonalzably of marces We wan o know, n parcular wha condons wll assure ha a marx can be dagonalzed and wha he obsrucons are

More information

Convection and conduction and lumped models

Convection and conduction and lumped models MIT Hea ranfer Dynamc mdel 4.3./SG nvecn and cndcn and lmped mdel. Hea cnvecn If we have a rface wh he emperare and a rrndng fld wh he emperare a where a hgher han we have a hea flw a Φ h [W] () where

More information

Graphene nanoplatelets induced heterogeneous bimodal structural magnesium matrix composites with enhanced mechanical properties

Graphene nanoplatelets induced heterogeneous bimodal structural magnesium matrix composites with enhanced mechanical properties raphene nanoplaele nce heerogeneo bmoal rcral magnem marx compoe wh enhance mechancal propere Shln Xang a, b, Xaojn Wang a, *, anoj pa b, Kn W a, Xaoh H a, ngy Zheng a a School of aeral Scence an ngneerng,

More information

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5

[ ] 2. [ ]3 + (Δx i + Δx i 1 ) / 2. Δx i-1 Δx i Δx i+1. TPG4160 Reservoir Simulation 2018 Lecture note 3. page 1 of 5 TPG460 Reservor Smulaon 08 page of 5 DISCRETIZATIO OF THE FOW EQUATIOS As we already have seen, fne dfference appromaons of he paral dervaves appearng n he flow equaons may be obaned from Taylor seres

More information

3.2 Models for technical systems

3.2 Models for technical systems onrol Laboraory 3. Mahemacal Moelng 3. Moels for echncal sysems 3.. Elecrcal sysems Fg. 3. shows hree basc componens of elecrcal crcs. Varables = me, = volage [V], = crren [A] omponen parameers R = ressance

More information

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS

THE PREDICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS THE PREICTION OF COMPETITIVE ENVIRONMENT IN BUSINESS INTROUCTION The wo dmensonal paral dfferenal equaons of second order can be used for he smulaon of compeve envronmen n busness The arcle presens he

More information

Multiple Regressions and Correlation Analysis

Multiple Regressions and Correlation Analysis Mulple Regreon and Correlaon Analy Chaper 4 McGraw-Hll/Irwn Copyrgh 2 y The McGraw-Hll Compane, Inc. All rgh reerved. GOALS. Decre he relaonhp eween everal ndependen varale and a dependen varale ung mulple

More information

On Line Supplement to Strategic Customers in a Transportation Station When is it Optimal to Wait? A. Manou, A. Economou, and F.

On Line Supplement to Strategic Customers in a Transportation Station When is it Optimal to Wait? A. Manou, A. Economou, and F. On Line Spplemen o Sraegic Comer in a Tranporaion Saion When i i Opimal o Wai? A. Mano, A. Economo, and F. Karaemen 11. Appendix In hi Appendix, we provide ome echnical analic proof for he main rel of

More information

The Finite Element Method for the Analysis of Non-Linear and Dynamic Systems

The Finite Element Method for the Analysis of Non-Linear and Dynamic Systems Swss Federal Insue of Page 1 The Fne Elemen Mehod for he Analyss of Non-Lnear and Dynamc Sysems Prof. Dr. Mchael Havbro Faber Dr. Nebojsa Mojslovc Swss Federal Insue of ETH Zurch, Swzerland Mehod of Fne

More information

Prediction of Wing Downwash Using CFD

Prediction of Wing Downwash Using CFD Predcon of Wng Downwash Usng CFD Mohammed MAHDI* *Correspondng ahor Aeronacal Research Cener-Sdan P.O. Bo 334 momahad7@homal.com DOI:.3/66-8.5.7.. 3 rd Inernaonal Worshop on Nmercal Modellng n Aerospace

More information

Method of Characteristics for Pure Advection By Gilberto E. Urroz, September 2004

Method of Characteristics for Pure Advection By Gilberto E. Urroz, September 2004 Mehod of Charaerss for Pre Adveon By Glbero E Urroz Sepember 004 Noe: The followng noes are based on lass noes for he lass COMPUTATIONAL HYDAULICS as agh by Dr Forres Holly n he Sprng Semeser 985 a he

More information

Solving Parabolic Partial Delay Differential. Equations Using The Explicit Method And Higher. Order Differences

Solving Parabolic Partial Delay Differential. Equations Using The Explicit Method And Higher. Order Differences Jornal of Kfa for Maemacs and Compe Vol. No.7 Dec pp 77-5 Solvng Parabolc Paral Delay Dfferenal Eqaons Usng e Eplc Meod And Hger Order Dfferences Asss. Prof. Amal Kalaf Haydar Kfa Unversy College of Edcaon

More information

CHAPTER 5: MULTIVARIATE METHODS

CHAPTER 5: MULTIVARIATE METHODS CHAPER 5: MULIVARIAE MEHODS Mulvarae Daa 3 Mulple measuremens (sensors) npus/feaures/arbues: -varae N nsances/observaons/eamples Each row s an eample Each column represens a feaure X a b correspons o he

More information

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany

John Geweke a and Gianni Amisano b a Departments of Economics and Statistics, University of Iowa, USA b European Central Bank, Frankfurt, Germany Herarchcal Markov Normal Mxure models wh Applcaons o Fnancal Asse Reurns Appendx: Proofs of Theorems and Condonal Poseror Dsrbuons John Geweke a and Gann Amsano b a Deparmens of Economcs and Sascs, Unversy

More information

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim

GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS. Youngwoo Ahn and Kitae Kim Korean J. Mah. 19 (2011), No. 3, pp. 263 272 GENERATING CERTAIN QUINTIC IRREDUCIBLE POLYNOMIALS OVER FINITE FIELDS Youngwoo Ahn and Kae Km Absrac. In he paper [1], an explc correspondence beween ceran

More information

Volatility Interpolation

Volatility Interpolation Volaly Inerpolaon Prelmnary Verson March 00 Jesper Andreasen and Bran Huge Danse Mares, Copenhagen wan.daddy@danseban.com brno@danseban.com Elecronc copy avalable a: hp://ssrn.com/absrac=69497 Inro Local

More information

Mechanics Physics 151

Mechanics Physics 151 Mechancs Physcs 5 Lecure 0 Canoncal Transformaons (Chaper 9) Wha We Dd Las Tme Hamlon s Prncple n he Hamlonan formalsm Dervaon was smple δi δ Addonal end-pon consrans pq H( q, p, ) d 0 δ q ( ) δq ( ) δ

More information

Comb Filters. Comb Filters

Comb Filters. Comb Filters The smple flers dscussed so far are characered eher by a sngle passband and/or a sngle sopband There are applcaons where flers wh mulple passbands and sopbands are requred Thecomb fler s an example of

More information

Gradient Flow Independent Component Analysis

Gradient Flow Independent Component Analysis Graden Fow Independen Componen Anay Mun Sanaćevć and Ger Cauwenbergh Adapve Mcroyem ab John Hopkn Unvery Oune Bnd Sgna Separaon and ocazaon Prncpe of Graden Fow : from deay o empora dervave Equvaen ac

More information

Advanced time-series analysis (University of Lund, Economic History Department)

Advanced time-series analysis (University of Lund, Economic History Department) Advanced me-seres analss (Unvers of Lund, Economc Hsor Dearmen) 3 Jan-3 Februar and 6-3 March Lecure 4 Economerc echnues for saonar seres : Unvarae sochasc models wh Box- Jenns mehodolog, smle forecasng

More information

Methods of Improving Constitutive Equations

Methods of Improving Constitutive Equations Mehods o mprovng Consuve Equaons Maxell Model e an mprove h ne me dervaves or ne sran measures. ³ ª º «e, d» ¼ e an also hange he bas equaon lnear modaons non-lnear modaons her Consuve Approahes Smple

More information

Chapter 1 Introduction of boundary layer phenomena

Chapter 1 Introduction of boundary layer phenomena Chaper 1 Inrodcon of bondary layer phenomena T-S Le Jan. 13, 018 Man Topcs Hsory of Fld Mechancs Developmen Idea of Bondary Layer Bondary Layer Eqaons 1 Fld Mechancs Developmen Hsory Ideal fld: Invscd

More information

Pacific Journal of Mathematics

Pacific Journal of Mathematics Pacfc Jornal of Mahemacs GRADIENT ESTIMATES FOR SOLUTIONS OF THE HEAT EQUATION UNDER RICCI FLOW SHIPING LIU Volme 43 No. 1 November 009 PACIFIC JOURNAL OF MATHEMATICS Vol. 43, No. 1, 009 GRADIENT ESTIMATES

More information

A. Inventory model. Why are we interested in it? What do we really study in such cases.

A. Inventory model. Why are we interested in it? What do we really study in such cases. Some general yem model.. Inenory model. Why are we nereed n? Wha do we really udy n uch cae. General raegy of machng wo dmlar procee, ay, machng a fa proce wh a low one. We need an nenory or a buffer or

More information

Chapter 6: AC Circuits

Chapter 6: AC Circuits Chaper 6: AC Crcus Chaper 6: Oulne Phasors and he AC Seady Sae AC Crcus A sable, lnear crcu operang n he seady sae wh snusodal excaon (.e., snusodal seady sae. Complee response forced response naural response.

More information

Density Matrix Description of NMR BCMB/CHEM 8190

Density Matrix Description of NMR BCMB/CHEM 8190 Densy Marx Descrpon of NMR BCMBCHEM 89 Operaors n Marx Noaon Alernae approach o second order specra: ask abou x magnezaon nsead of energes and ranson probables. If we say wh one bass se, properes vary

More information

XIII International PhD Workshop OWD 2011, October Three Phase DC/DC Boost Converter With High Energy Efficiency

XIII International PhD Workshop OWD 2011, October Three Phase DC/DC Boost Converter With High Energy Efficiency X nernaonal Ph Workshop OW, Ocober Three Phase C/C Boos Converer Wh Hgh Energy Effcency Ján Perdľak, Techncal nversy of Košce Absrac Ths paper presens a novel opology of mlphase boos converer wh hgh energy

More information

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy

Approximate Analytic Solution of (2+1) - Dimensional Zakharov-Kuznetsov(Zk) Equations Using Homotopy Arcle Inernaonal Journal of Modern Mahemacal Scences, 4, (): - Inernaonal Journal of Modern Mahemacal Scences Journal homepage: www.modernscenfcpress.com/journals/jmms.aspx ISSN: 66-86X Florda, USA Approxmae

More information

CFD Analysis of Aerodynamic Drag Effects on Vacuum Tube Trains

CFD Analysis of Aerodynamic Drag Effects on Vacuum Tube Trains Jornal of Appled Fld Mechancs, ol. 1, No. 1, pp. 303-309, 019. Aalable onlne a.afmonlne.ne, ISSN 1735-357, EISSN 1735-3645. DOI: 10.95/afm.75.53.9091 CFD Analss of Aerodnamc Drag Effecs on acm Tbe Trans

More information

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that

THEORETICAL AUTOCORRELATIONS. ) if often denoted by γ. Note that THEORETICAL AUTOCORRELATIONS Cov( y, y ) E( y E( y))( y E( y)) ρ = = Var( y) E( y E( y)) =,, L ρ = and Cov( y, y ) s ofen denoed by whle Var( y ) f ofen denoed by γ. Noe ha γ = γ and ρ = ρ and because

More information

Simulation of Contaminant Concentrations in Drinking-Water Distribution Systems

Simulation of Contaminant Concentrations in Drinking-Water Distribution Systems Maer Degree n hemcal Engneerng Smlaon of onamnan oncenraon n Drnkng-Waer Drbon Syem Maer The Developed n he amb of he bjec Developmen Projec Dogo Morera da oa Deparmen of hemcal Engneerng Spervor: Prof.

More information