Updated frequency domain analysis in LS-DYNA
|
|
- Noel Gallagher
- 6 years ago
- Views:
Transcription
1 Updated frequecy doma aalyss LS-DYNA Yu Huag, Zhe Cu Lvermore Software Techology Corporato 1 Overvew A seres of frequecy doma features have bee mplemeted to LS-DYNA sce verso 971 R6. These features ca be used to perform vbrato, acoustc ad fatgue aalyss for users from varous dustres [1]. These features clude FRF (frequecy respose fuctos) Keyword: *FREQUENCY_DOMAIN_FRF SSD (steady state dyamcs) Keyword: *FREQUENCY_DOMAIN_SSD Radom vbrato (fatgue) Keyword: *FREQUENCY_DOMAIN_RANDOM_VIBRATION_{FATIGUE} Respose spectrum aalyss Keyword: *FREQUENCY_DOMAIN_RESPONSE_SPECTRUM BEM acoustcs Keyword: *FREQUENCY_DOMAIN_ACOUSTIC_BEM FEM acoustcs Keyword: *FREQUENCY_DOMAIN_ACOUSTIC_FEM These features ca fd applcato NVH of automotves ad ar plaes Acoustc desg ad aalyss of buldgs ad products Defese dustry Fatgue of maches ad eges Safety evaluato of cvl ad hydraulc structures Earthquake egeerg Offshore dustres May others Regardg post-processg of the frequecy doma aalyss, a seres of ASCII ad BINARY databases have bee mplemeted. The ASCII databases clude FRF_AMPLITUDE (for FRF aalyss) FRF_ANGLE (for FRF aalyss) Press_Pa (for acoustc aalyss) Press_dB (for acoustc aalyss) NODOUT_PSD (for radom vbrato) NODOUT_SSD (for steady state dyamcs) NODOUT_SPCM (for respose spectrum aalyss) ELOUT_PSD (for radom vbrato) ELOUT_SSD (for steady state dyamcs) ELOUT_SPCM (for respose spectrum aalyss) Partcularly the NODOUT (_PSD, _SSD, _SPCM) ad ELOUT (_PSD, _SSD, _SPCM) databases are dumped to bary fle BINOUT ad oe ca use LS-PrePost or l2a.exe to extract them from BINOUT. The Nodes whose odal results are output to NODOUT_ databases are specfed by card *DATABASE_HISTORY_NODE. The sold, beam, shell ad thck shell elemets whose elemetal results are output to ELOUT_ databases are specfed by the followg cards:
2 *DATABASE_HISTORY_SOLID_{OPTION} *DATABASE_HISTORY_BEAM_{OPTION} *DATABASE_HISTORY_SHELL_{OPTION} *DATABASE_HISTORY_TSHELL_{OPTION} For BINARY plot databases, they are actvated by keyword *DATABASE_FREQUENCY_BINARY_{OPTION}, where the {OPTION} ca be ay oe of the followg databases: Database Lspcode Used for D3SSD 21 Steady state dyamcs D3SPCM 22 Respose spectrum aalyss D3PSD 23 Radom vbrato PSD D3RMS 24 Radom vbrato RMS D3FTG 25 Radom vbrato fatgue D3ACS 26 FEM acoustcs D3ATV 27 BEM acoustc trasfer vector Table 1: New bary databases for frequecy doma aalyss. These databases are output the same format as D3PLOT, ad are accessble to LS-PrePost. Partcularly the parameter lspcode (saved the header fle of the database) s a flag to tell LS- PrePost or other post-processg softwares that what kd of database t s. As ca be see from Table 1, the cotet of these databases s dfferet from those D3PLOT. Thus some updates or revso from LS-PrePost or other post-processg softwares are eeded to get them workg approprately wth these ew databases. The updates or revso clude markg the x-axs of amato as frequecy stead of tme, ad correctg the ame of the varables Some of such updates or revsos have bee accomplshed LS-PrePost. Some updates have bee made to these frequecy doma aalyss features sce the last forum. These updates were made to exted the capabltes of frequecy doma aalyss of LS-DYNA, or to mprove the computatoal performace. A bref troducto of the updates, accompaed by several examples, s provded the followg sectos of the paper. 2 ATV ad MATV techques for BEM Acoustc solvers A buch of BEM Acoustc solvers (collocato BEM, varatoal drect BEM, dual BEM wth Burto- Mller formulato, Raylegh method ad Krchhoff method) have bee mplemeted to LS-DYNA [2]. They are used to predct the radated ose from a vbratg structure. To facltate the acoustc aalyss for structures whch are subjected to multple loadg cases, two ew techques ATV (Acoustc Trasfer Vector) ad MATV (Modal Acoustc Trasfer Vector) have bee mplemeted to the BEM acoustc solvers. 2.1 ATV ATV s defed as the trasfer fucto betwee the ormal odal (or elemetal) velocty ad the acoustc pressure at feld pots. For example, the acoustc pressure at feld pot due to ut ormal velocty at ode j o the structure surface ca be expressed as, j. Ths provdes Acoustc Trasfer Vector from structural ode j to feld pot acoustc volume. Oe should ote that ATV s a fucto of frequecy. It oly depeds o the propertes of acoustc medum (desty, soud speed), geometry of structures, ad locato of feld pots. It s ot depedet o the real loadg codto.
3 Fg. 1: Structural surface odes ad feld pots for acoustc computato. For the structure show Fgure 1, f there are odes o the surface ad m feld pots acoustc volume, the ATV matrx ca be expressed as [ ATV ] m = m 1,1 2,1,1,1 1,2 2,2,2 m,2 1, j 2, j, j m, j Oce the ATV matrx s obtaed, for gve vbrato codto o structure surface, the acoustc pressure at the m feld pots ca be computed by smple matrx-vector multplcato, as follows = ATV v (2) { P } m [ ] m { } Where { P } m s the acoustc pressure vector at the m feld pots; { } the surface odes (or elemets); 1, 2,, m, (1) v s the ormal velocty vector at The ATV ca be plotted o the structural surface for vsualzato. A ew database D3ATV has bee mplemeted LS-DYNA. The database plots the real part, the magary part ad the SPL (Soud Pressure Level, ut of db) of ATV, for each feld pot ad each frequecy. Fgure 2 shows real part of ATV for a smplfed ege model for the feld pot ad the frequecy 100 Hz. Fg. 2: D3ATV for a smplfed ege model. 2.2 MATV If the dyamc respose of structures ca be obtaed usg the modal superposto method (for example, usg keyword *FREQUENCY_DOMAIN_SSD LS-DYNA), the ormal velocty vector { v }
4 (2) ca be obtaed as a product of roud frequecy ω, modal shape matrx ad modal coordates vector. Accordgly the equato (2) ca be revsed as { P} m = [ ATV ] m { v} = [ ATV ] m ω{ u} T = [ ATV ] m ω[ φ] l { q} l = [ MATV ] m l { q} l Where s the magary ut ( = 1 modal shape matrx, provded by mplct modal aalyss. { } l ) ad { u } s the dsplacemet vector. The matrx [ φ] l (3) s the q s the modal coordates (assumg l modes are used); [ MATV ] m l s the MATV matrx, whch s costat for each gve frequecy. For each exctato frequecy f, LS-DYNA wll geerate the psedo-velocty boudary codto { φ} ( ω = 2π f ), j = 1, l j, ω ad ru BEM acoustc computato for the m feld pots to get the MATV matrx. For each load case, oly the modal coordates vector { } l q eed to be updated. Oce t s ready, a smple matrx-vector multplcato ca provde soluto for acoustc pressure at the m feld pots. For a real problem, the umber of ege modes volved modal superposto s usually much less tha the umber of odes (or elemets) the boudary elemets ( l << ). So the MATV approach represeted by equato (3) s more effcet tha the ATV approach represeted by equato (2), f the vbrato smulato ca be accomplshed by modal superposto. Ths s due to the fact that the effort to get MATV matrx s much less tha the effort to get ATV matrx. There are 2 steps volved usg the MATV techque BEM Acoustcs: Step 1: geeratg MATV matrces. The keywords *CONTROL_IMPLICIT_GENERAL, *CONTROL_IMPLICIT_EIGENVALUE are used to. The the keyword perform mplct modal aalyss, to get ege modes { } j *FREQUENCY_DOMAIN_ACOUSTIC_BEM_MATV s used wthout specfyg ay boudary codto, to ru acoustc computato for each psedo-velocty boudary codto for each φ ω { φ} j exctato frequecy. The MATV matrces are saved bary scratch fle b_bepressure. Step 2: acoustc computato for each load case. The keywords *FREQUENCY_DOMAIN_SSD, *FREQUENCY_DOMAIN_ACOUSTIC_BEM_MATV are used, both wth a restart opto. For SSD, t restarts wth exstg d3egv from step 1 (restmd=1); for MATV BEM, t restarts wth exstg MATV matrces, saved b_bepressure, gve by step 1 (restrt=1). Moreover, oe ca defe multple load cases oe put deck, by takg advatage of the coveet CASE scheme. For example, the frst load case (defed by *FREQUENCY_DOMAIN_SSD wth correspodg load codto) ca be put the secto betwee *CASE_BEGIN_1 ad *CASE_END_1. For the other load cases, they ca be smlarly put the CASE secto oe by oe. See Fgure 3 for a example.
5 Fg. 3: Defg multple load cases wth CASE for rug MATV BEM. The wth a sgle LS-DYNA ru (wth flag CASE the commad le), oe ca get the soluto of acoustc pressure ad SPL for all the load cases (e.g. case1.press_pa, case1.press_db, case2.press_pa, case2.press_db,...) Fg. 4: usg MATV for a door model. For a smplfed door model show Fgure 4, bechmark testgs are performed to check the accuracy ad effcecy of the MATV method, comparg wth the tradtoal BEM. Fgure 5 shows the SPL (db) value of the ose at a feld pot, for the case wth a harmoc odal force exctato appled o the door. The exctato s gve the frequecy rage of Hz, wth 101 equally spaced frequeces. For rug mplct modal aalyss, ormal modes up to 600 Hz s
6 used, whch s 20% hgher tha the maxmum exctato frequecy. The soluto of the problem s based o the combato of SSD ad BEM acoustcs. Two sets of results are gve Fgure 5: Oe s obtaed wth MATV BEM, ad the other s obtaed wth tradtoal BEM. The two sets of results are actually detcal, as llustrated by the Fgure. Fg. 5: SPL at feld pot. To study the effcecy of the MATV BEM, we cosder 1 load case ad 10 load cases, by three approaches. The computato s performed o Itel Xero CPU GHz (CPU MHz: cache sze 4096 KB). The three approaches are: 1) SSD + tradtoal BEM, whch meas that LS-DYNA goes through the whole procedure (modal aalyss, SSD ad tradtoal BEM) for each load case; 2) Restart SSD + tradtoal BEM, whch meas that startg from the 2d load case, the modal aalyss s skpped (sce the D3EIGV bary database has bee geerated durg the soluto for the frst load case) ad LS-DYNA rus a restart SSD ad the tradtoal BEM; 3) Restart SSD + MATV BEM, whch meas that LS-DYNA skps the modal aalyss part startg from the 2d load case, ad rus a restart SSD ad the uses the MATV based BEM to get the acoustc pressure for all the load cases. As show Table 2, the approach 3) Restart SSD + MATV BEM shows sgfcat savg CPU cost comparg wth the other two approaches, whe 10 load cases are cosdered. Whe there s oly 1 load case, the MATV BEM s slower tha the other two approaches, sce the computato ad the savg of MATV matrces take some extra CPU tme. However, oce the MATV matrces are ready the soluto for the addtoal load cases takes oly a ty CPU tme. Cases 1) SSD + tradtoal BEM 2) Restart SSD + tradtoal BEM 3) Restart SSD + MATV BEM 1 load case 2 h 39 m 50 s 2 h 39 m 50 s 4 h 40 m 56 s 10 load cases 26 h 38 m 18 s 25 h 53 m 13 s 4 h 41 m 10 s Table 2: CPU tme for acoustc computato of door model. 3 Icdet acoustc wave A ew keyword *FREQUENCY_DOMAIN_ACOUSTIC_INCIDENT_WAVE has bee troduced to cosder cdet acoustc waves. The cdet wave s useful modelg soar system a submare ad explosve waves. The keyword format s Card Varable TYPE MAG XC YC ZC Type I F F F F Default 1 oe oe oe oe
7 For plae wave (TYPE=1), the cdet wave s defed by p k ( α x + βy + γz ) = (4) Ae For sphercal wave (TYPE=2), the cdet wave s defed by p e A R kr = (5) I equatos (4) ad (5), A s magtude or stregth of cdet wave (parameter MAG the keyword); k s wave umber (= ω / c, where ω s roud frequecy ad c s wave speed); α, β ad γ are drectoal coses for plae wave (see equato (4)), ad are defed by (XC, YC, ZC) from the keyword. R s dstace betwee sphercal source ad feld pot for sphercal cdet wave; (XC, YC, ZC) defe the ceter of the sphercal wave or the source pot. Oe ca defe multple cdet waves oe model, by repeatg the keyword *FREQUENCY_DOMAIN_ACOUSTIC_INCIDENT_WAVE, or smply repeatg the Card 1 the keyword. A bechmark example of soud scatterg o rgd sphere s adopted to valdate the mplemetato. See Fgure 6 below. Fg. 6: Soud scatterg o rgd sphere. The sphercal source s located at 4r from the ceter of the rgd sphere (r s the radus of the rgd sphere). Two feld pots are located at 5r from the ceter of the rgd sphere. Oe s o the same sde of the source (Feld pot A), ad the other s located o the opposte sde (Feld pot B). We compare the real part ad magary part of the pressure, gve by LS-DYNA ad by aalytcal soluto (gve as fte seres expaso, trucated computato) [3]. LS-DYNA results match very well wth the aalytcal soluto, as show Fgure 7. Fg. 7: Acoustc pressure at the two feld pots. 4 Frequecy depedet complex soud speed To take to accout dampg the acoustc system, a ew keyword *FREQUENCY_DOMAIN_ACOUSTIC_SOUND_SPEED s troduced to LS-DYNA, to allow defg the frequecy depedet complex soud speed by two load curves.
8 = (6) c( f ) c ( f ) c ( f ) r + For a smple muffler model show Fgure 8, a ut ormal velocty boudary codto s prescrbed at oe ed. A mpedace boudary codto s gve at the other ed. The rest of the muffler surface s assumed to be rgd. The rage of frequecy uder study s Hz. The acoustc pressure at oe feld pot sde the muffler s computed by LS-DYNA. Oe ca see that the peak db values of the acoustc pressure are reduced by usg complex soud speed. Fg. 8: Acoustc pressure due to real ad complex soud speed. 5 Fatgue aalyss based o SSD (Steady state dyamc aalyss) Fatgue s the weakeg of materal due to repeated or cyclc loadg. Fatgue falure uder harmoc or steady state vbrato codto s very commo varous dustres, e.g. a se sweep test. Fg. 9: A sample se sweep test load curve. A fatgue aalyss method s mplemeted based o steady state vbrato codto. The correspodg keyword s *FREQUENCY_DOMAIN_SSD_{FATIGUE}. Ths feature s based o the raflow coutg algorthm ad the materal s S-N fatgue curve. The raflow coutg algorthm s used to get the umber of stress cycles for Vo-Mses stress for each exctato frequecy. Lear superposto s employed to get the total fatgue damage uder dfferet stress levels. The cumulatve damage rato R ca be expressed as = R = R (7) N Where R s the damage rato due to stress level, s the actural umber of cycles for stress level, ad N s the umber of cycles for fatgue falure for stress level (obtaed from materal s S-N curve). R s a real umber larger tha 0. If R s equal to or larger tha 1, t meas that the materal has faled due to fatgue. I hgh-cycle fatgue stuato, the materal s fatgue behavor s usually characterzed by a S-N curve, whch s also kow as a Wöhler curve. To defe the materal s S-N curve, a ew keyword
9 *MAT_ADD_FATIGUE s added to LS-DYNA. Three optos are avalable for defg the S-N fatgue curve. 1) By curve ID (see *DEFINE_CURVE) 2) By equato N S m = a 3) By equato log( S) = a b log( N) Whe the S-N fatgue curve s defed by optos 2) or 3), the parameters a ad m ( opto 2) or a ad b ( opto 3) are costats whch are depedet o materal model. For a model show Fgure 10, the cumulatve damage rato s computed ad plotted D3FTG, whch s accessble to LS-PrePost. The loadg codto s gve as base accelerato spectrum, see Table 3. To get the soluto for SSD, mplct modal aalyss s frst performed for the structure. The frst 300 Normal modes, whch provdes atural frequecy up to 2703Hz (35% hgher tha the maxmum frequecy 2000 Hz for exctato) are used SSD. Table 3: Loadg codto. Frequecy (Hz) Accelerato (g) Durato (mute) The materal s S-N fatgue curve s defed as Table 4: S-N fatgue curve. σ (MPa) N Fg. 10: Cumulatve damage rato for the beam uder SSD. As show Fgure 10, the two eds of the structure, whch are costraed to the shaker table, are characterzed wth hgher cumulatve damage ratos, whch suggests a more severe damage to the materal. But the peak value of the cumulatve damage rato s stll less tha 1. It meas that the structure s stll safe after the whole loadg process.
10 6 Summary A lst of updated frequecy doma features LS-DYNA are revewed the paper. They clude ATV ad MATV techques for BEM Acoustc solvers; cdet waves acoustc aalyss; usg frequecy depedet complex soud speed acoustc aalyss ad fatgue aalyss based o SSD (steady state dyamcs). Several examples are provded to demostrate the effectveess of the updated features. 7 Lterature [1] LS-DYNA Keyword User s Maual, Lvermore Software Techology Corporato, [2] Yu Huag, Mhamed Soul, Rogfeg Lu, BEM Methods for acoustc ad vbroacoustc problems LS-DYNA. Proceedgs of the 11 th Iteratoal LS-DYNA Users Coferece, Smulato (2), , [3] Yuepg Guo, Computato of Soud Propagato by Boudary Elemet Method. NASA Cotract Report, NAS A003, 2005.
Ahmed Elgamal. MDOF Systems & Modal Analysis
DOF Systems & odal Aalyss odal Aalyss (hese otes cover sectos from Ch. 0, Dyamcs of Structures, Al Chopra, Pretce Hall, 995). Refereces Dyamcs of Structures, Al K. Chopra, Pretce Hall, New Jersey, ISBN
More informationLecture 07: Poles and Zeros
Lecture 07: Poles ad Zeros Defto of poles ad zeros The trasfer fucto provdes a bass for determg mportat system respose characterstcs wthout solvg the complete dfferetal equato. As defed, the trasfer fucto
More informationIntroduction to local (nonparametric) density estimation. methods
Itroducto to local (oparametrc) desty estmato methods A slecture by Yu Lu for ECE 66 Sprg 014 1. Itroducto Ths slecture troduces two local desty estmato methods whch are Parze desty estmato ad k-earest
More informationFunctions of Random Variables
Fuctos of Radom Varables Chapter Fve Fuctos of Radom Varables 5. Itroducto A geeral egeerg aalyss model s show Fg. 5.. The model output (respose) cotas the performaces of a system or product, such as weght,
More informationChapter 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 informationBeam Warming Second-Order Upwind Method
Beam Warmg Secod-Order Upwd Method Petr Valeta Jauary 6, 015 Ths documet s a part of the assessmet work for the subject 1DRP Dfferetal Equatos o Computer lectured o FNSPE CTU Prague. Abstract Ths documet
More information( q Modal Analysis. Eigenvectors = Mode Shapes? Eigenproblem (cont) = x x 2 u 2. u 1. x 1 (4.55) vector and M and K are matrices.
4.3 - Modal Aalyss Physcal coordates are ot always the easest to work Egevectors provde a coveet trasformato to modal coordates Modal coordates are lear combato of physcal coordates Say we have physcal
More informationEstimation of Stress- Strength Reliability model using finite mixture of exponential distributions
Iteratoal Joural of Computatoal Egeerg Research Vol, 0 Issue, Estmato of Stress- Stregth Relablty model usg fte mxture of expoetal dstrbutos K.Sadhya, T.S.Umamaheswar Departmet of Mathematcs, Lal Bhadur
More informationA 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 informationDynamic 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 informationC-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 informationd dt d d dt dt Also recall that by Taylor series, / 2 (enables use of sin instead of cos-see p.27 of A&F) dsin
Learzato of the Swg Equato We wll cover sectos.5.-.6 ad begg of Secto 3.3 these otes. 1. Sgle mache-fte bus case Cosder a sgle mache coected to a fte bus, as show Fg. 1 below. E y1 V=1./_ Fg. 1 The admttace
More informationBlock-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 informationSolving 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 informationL5 Polynomial / Spline Curves
L5 Polyomal / Sple Curves Cotets Coc sectos Polyomal Curves Hermte Curves Bezer Curves B-Sples No-Uform Ratoal B-Sples (NURBS) Mapulato ad Represetato of Curves Types of Curve Equatos Implct: Descrbe a
More informationChapter 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 informationbest estimate (mean) for X uncertainty or error in the measurement (systematic, random or statistical) best
Error Aalyss Preamble Wheever a measuremet s made, the result followg from that measuremet s always subject to ucertaty The ucertaty ca be reduced by makg several measuremets of the same quatty or by mprovg
More informationECE 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 informationMA/CSSE 473 Day 27. Dynamic programming
MA/CSSE 473 Day 7 Dyamc Programmg Bomal Coeffcets Warshall's algorthm (Optmal BSTs) Studet questos? Dyamc programmg Used for problems wth recursve solutos ad overlappg subproblems Typcally, we save (memoze)
More information2C09 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 informationu 1 Figure 1 3D Solid Finite Elements
Sold Elemets he Fte Elemet Lbrary of the MIDAS Famly Programs cludes the follog Sold Elemets: - ode tetrahedro, -ode petahedro, ad -ode hexahedro sho Fg.. he fte elemet formulato of all elemet types s
More informationOrdinary Least Squares Regression. Simple Regression. Algebra and Assumptions.
Ordary Least Squares egresso. Smple egresso. Algebra ad Assumptos. I ths part of the course we are gog to study a techque for aalysg the lear relatoshp betwee two varables Y ad X. We have pars of observatos
More informationApplication of Calibration Approach for Regression Coefficient Estimation under Two-stage Sampling Design
Authors: Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud Applcato of Calbrato Approach for Regresso Coeffcet Estmato uder Two-stage Samplg Desg Pradp Basak, Kaustav Adtya, Hukum Chadra ad U.C. Sud
More informationComparison of Dual to Ratio-Cum-Product Estimators of Population Mean
Research Joural of Mathematcal ad Statstcal Sceces ISS 30 6047 Vol. 1(), 5-1, ovember (013) Res. J. Mathematcal ad Statstcal Sc. Comparso of Dual to Rato-Cum-Product Estmators of Populato Mea Abstract
More informationENGI 4421 Propagation of Error Page 8-01
ENGI 441 Propagato of Error Page 8-01 Propagato of Error [Navd Chapter 3; ot Devore] Ay realstc measuremet procedure cotas error. Ay calculatos based o that measuremet wll therefore also cota a error.
More informationMu Sequences/Series Solutions National Convention 2014
Mu Sequeces/Seres Solutos Natoal Coveto 04 C 6 E A 6C A 6 B B 7 A D 7 D C 7 A B 8 A B 8 A C 8 E 4 B 9 B 4 E 9 B 4 C 9 E C 0 A A 0 D B 0 C C Usg basc propertes of arthmetc sequeces, we fd a ad bm m We eed
More informationCHAPTER VI Statistical Analysis of Experimental Data
Chapter VI Statstcal Aalyss of Expermetal Data CHAPTER VI Statstcal Aalyss of Expermetal Data Measuremets do ot lead to a uque value. Ths s a result of the multtude of errors (maly radom errors) that ca
More informationFeature Selection: Part 2. 1 Greedy Algorithms (continued from the last lecture)
CSE 546: Mache Learg Lecture 6 Feature Selecto: Part 2 Istructor: Sham Kakade Greedy Algorthms (cotued from the last lecture) There are varety of greedy algorthms ad umerous amg covetos for these algorthms.
More informationEconometric Methods. Review of Estimation
Ecoometrc Methods Revew of Estmato Estmatg the populato mea Radom samplg Pot ad terval estmators Lear estmators Ubased estmators Lear Ubased Estmators (LUEs) Effcecy (mmum varace) ad Best Lear Ubased Estmators
More informationAnalysis 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 informationEECE 301 Signals & Systems
EECE 01 Sgals & Systems Prof. Mark Fowler Note Set #9 Computg D-T Covoluto Readg Assgmet: Secto. of Kame ad Heck 1/ Course Flow Dagram The arrows here show coceptual flow betwee deas. Note the parallel
More informationA New Family of Transformations for Lifetime Data
Proceedgs of the World Cogress o Egeerg 4 Vol I, WCE 4, July - 4, 4, Lodo, U.K. A New Famly of Trasformatos for Lfetme Data Lakhaa Watthaacheewakul Abstract A famly of trasformatos s the oe of several
More informationNewton 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 informationSimple Linear Regression
Statstcal Methods I (EST 75) Page 139 Smple Lear Regresso Smple regresso applcatos are used to ft a model descrbg a lear relatoshp betwee two varables. The aspects of least squares regresso ad correlato
More informationEngineering Vibration 1. Introduction
Egeerg Vbrato. Itroducto he study of the moto of physcal systems resultg from the appled forces s referred to as dyamcs. Oe type of dyamcs of physcal systems s vbrato, whch the system oscllates about certa
More informationMOLECULAR VIBRATIONS
MOLECULAR VIBRATIONS Here we wsh to vestgate molecular vbratos ad draw a smlarty betwee the theory of molecular vbratos ad Hückel theory. 1. Smple Harmoc Oscllator Recall that the eergy of a oe-dmesoal
More informationComparison of Analytical and Numerical Results in Modal Analysis of Multispan Continuous Beams with LS-DYNA
th Iteratoal S-N Users oferece Smulato Techology omparso of alytcal ad Numercal Results Modal alyss of Multspa otuous eams wth S-N bht Mahapatra ad vk hatteree etral Mechacal Egeerg Research Isttute, urgapur
More informationBounds on the expected entropy and KL-divergence of sampled multinomial distributions. Brandon C. Roy
Bouds o the expected etropy ad KL-dvergece of sampled multomal dstrbutos Brado C. Roy bcroy@meda.mt.edu Orgal: May 18, 2011 Revsed: Jue 6, 2011 Abstract Iformato theoretc quattes calculated from a sampled
More informationCH 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 informationInvestigating Cellular Automata
Researcher: Taylor Dupuy Advsor: Aaro Wootto Semester: Fall 4 Ivestgatg Cellular Automata A Overvew of Cellular Automata: Cellular Automata are smple computer programs that geerate rows of black ad whte
More informationThe Selection Problem - Variable Size Decrease/Conquer (Practice with algorithm analysis)
We have covered: Selecto, Iserto, Mergesort, Bubblesort, Heapsort Next: Selecto the Qucksort The Selecto Problem - Varable Sze Decrease/Coquer (Practce wth algorthm aalyss) Cosder the problem of fdg the
More informationChapter 9 Jordan Block Matrices
Chapter 9 Jorda Block atrces I ths chapter we wll solve the followg problem. Gve a lear operator T fd a bass R of F such that the matrx R (T) s as smple as possble. f course smple s a matter of taste.
More informationRademacher Complexity. Examples
Algorthmc Foudatos of Learg Lecture 3 Rademacher Complexty. Examples Lecturer: Patrck Rebesch Verso: October 16th 018 3.1 Itroducto I the last lecture we troduced the oto of Rademacher complexty ad showed
More information( ) = ( ) ( ) Chapter 13 Asymptotic Theory and Stochastic Regressors. Stochastic regressors model
Chapter 3 Asmptotc Theor ad Stochastc Regressors The ature of eplaator varable s assumed to be o-stochastc or fed repeated samples a regresso aalss Such a assumpto s approprate for those epermets whch
More informationDescriptive Statistics
Page Techcal Math II Descrptve Statstcs Descrptve Statstcs Descrptve statstcs s the body of methods used to represet ad summarze sets of data. A descrpto of how a set of measuremets (for eample, people
More informationX X X E[ ] E X E X. is the ()m n where the ( i,)th. j element is the mean of the ( i,)th., then
Secto 5 Vectors of Radom Varables Whe workg wth several radom varables,,..., to arrage them vector form x, t s ofte coveet We ca the make use of matrx algebra to help us orgaze ad mapulate large umbers
More informationA 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 informationENGI 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 informationFREQUENCY 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 informationPGE 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 informationFor combinatorial problems we might need to generate all permutations, combinations, or subsets of a set.
Addtoal Decrease ad Coquer Algorthms For combatoral problems we mght eed to geerate all permutatos, combatos, or subsets of a set. Geeratg Permutatos If we have a set f elemets: { a 1, a 2, a 3, a } the
More information{ }{ ( )} (, ) = ( ) ( ) ( ) Chapter 14 Exercises in Sampling Theory. Exercise 1 (Simple random sampling): Solution:
Chapter 4 Exercses Samplg Theory Exercse (Smple radom samplg: Let there be two correlated radom varables X ad A sample of sze s draw from a populato by smple radom samplg wthout replacemet The observed
More informationIntegral Equation Methods. Jacob White. Thanks to Deepak Ramaswamy, Michal Rewienski, Xin Wang and Karen Veroy
Itroducto to Smulato - Lecture 22 Itegral Equato ethods Jacob Whte Thaks to Deepak Ramaswamy, chal Rewesk, X Wag ad Kare Veroy Outle Itegral Equato ethods Exteror versus teror problems Start wth usg pot
More informationSimulation 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 informationSpecial 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 informationf f... f 1 n n (ii) Median : It is the value of the middle-most observation(s).
CHAPTER STATISTICS Pots to Remember :. Facts or fgures, collected wth a defte pupose, are called Data.. Statstcs s the area of study dealg wth the collecto, presetato, aalyss ad terpretato of data.. The
More informationUnimodality Tests for Global Optimization of Single Variable Functions Using Statistical Methods
Malaysa Umodalty Joural Tests of Mathematcal for Global Optmzato Sceces (): of 05 Sgle - 5 Varable (007) Fuctos Usg Statstcal Methods Umodalty Tests for Global Optmzato of Sgle Varable Fuctos Usg Statstcal
More informationComparing Different Estimators of three Parameters for Transmuted Weibull Distribution
Global Joural of Pure ad Appled Mathematcs. ISSN 0973-768 Volume 3, Number 9 (207), pp. 55-528 Research Ida Publcatos http://www.rpublcato.com Comparg Dfferet Estmators of three Parameters for Trasmuted
More informationBayes (Naïve or not) Classifiers: Generative Approach
Logstc regresso Bayes (Naïve or ot) Classfers: Geeratve Approach What do we mea by Geeratve approach: Lear p(y), p(x y) ad the apply bayes rule to compute p(y x) for makg predctos Ths s essetally makg
More informationModule 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 informationECONOMETRIC THEORY. MODULE VIII Lecture - 26 Heteroskedasticity
ECONOMETRIC THEORY MODULE VIII Lecture - 6 Heteroskedastcty Dr. Shalabh Departmet of Mathematcs ad Statstcs Ida Isttute of Techology Kapur . Breusch Paga test Ths test ca be appled whe the replcated data
More informationDerivation of 3-Point Block Method Formula for Solving First Order Stiff Ordinary Differential Equations
Dervato of -Pot Block Method Formula for Solvg Frst Order Stff Ordary Dfferetal Equatos Kharul Hamd Kharul Auar, Kharl Iskadar Othma, Zara Bb Ibrahm Abstract Dervato of pot block method formula wth costat
More information1. Overview of basic probability
13.42 Desg Prcples for Ocea Vehcles Prof. A.H. Techet Sprg 2005 1. Overvew of basc probablty Emprcally, probablty ca be defed as the umber of favorable outcomes dvded by the total umber of outcomes, other
More information2006 Jamie Trahan, Autar Kaw, Kevin Martin University of South Florida United States of America
SOLUTION OF SYSTEMS OF SIMULTANEOUS LINEAR EQUATIONS Gauss-Sedel Method 006 Jame Traha, Autar Kaw, Kev Mart Uversty of South Florda Uted States of Amerca kaw@eg.usf.edu Itroducto Ths worksheet demostrates
More information1 Solution to Problem 6.40
1 Soluto to Problem 6.40 (a We wll wrte T τ (X 1,...,X where the X s are..d. wth PDF f(x µ, σ 1 ( x µ σ g, σ where the locato parameter µ s ay real umber ad the scale parameter σ s > 0. Lettg Z X µ σ we
More informationIntroduction to Matrices and Matrix Approach to Simple Linear Regression
Itroducto to Matrces ad Matrx Approach to Smple Lear Regresso Matrces Defto: A matrx s a rectagular array of umbers or symbolc elemets I may applcatos, the rows of a matrx wll represet dvduals cases (people,
More informationLecture Note to Rice Chapter 8
ECON 430 HG revsed Nov 06 Lecture Note to Rce Chapter 8 Radom matrces Let Y, =,,, m, =,,, be radom varables (r.v. s). The matrx Y Y Y Y Y Y Y Y Y Y = m m m s called a radom matrx ( wth a ot m-dmesoal dstrbuto,
More informationMultiple Choice Test. Chapter Adequacy of Models for Regression
Multple Choce Test Chapter 06.0 Adequac of Models for Regresso. For a lear regresso model to be cosdered adequate, the percetage of scaled resduals that eed to be the rage [-,] s greater tha or equal to
More informationLecture 2 - What are component and system reliability and how it can be improved?
Lecture 2 - What are compoet ad system relablty ad how t ca be mproved? Relablty s a measure of the qualty of the product over the log ru. The cocept of relablty s a exteded tme perod over whch the expected
More informationBayes 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 informationAnalysis of von Kármán plates using a BEM formulation
Boudary Elemets ad Other Mesh Reducto Methods XXIX 213 Aalyss of vo Kármá plates usg a BEM formulato L. Wademam & W. S. Vetur São Carlos School of Egeerg, Uversty of São Paulo, Brazl Abstract Ths work
More informationPRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION
PRACTICAL CONSIDERATIONS IN HUMAN-INDUCED VIBRATION Bars Erkus, 4 March 007 Itroducto Ths docuet provdes a revew of fudaetal cocepts structural dyacs ad soe applcatos hua-duced vbrato aalyss ad tgato of
More informationPart 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 informationLecture 7. Confidence Intervals and Hypothesis Tests in the Simple CLR Model
Lecture 7. Cofdece Itervals ad Hypothess Tests the Smple CLR Model I lecture 6 we troduced the Classcal Lear Regresso (CLR) model that s the radom expermet of whch the data Y,,, K, are the outcomes. The
More informationChapter 13, Part A Analysis of Variance and Experimental Design. Introduction to Analysis of Variance. Introduction to Analysis of Variance
Chapter, Part A Aalyss of Varace ad Epermetal Desg Itroducto to Aalyss of Varace Aalyss of Varace: Testg for the Equalty of Populato Meas Multple Comparso Procedures Itroducto to Aalyss of Varace Aalyss
More informationMaximum 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 informationMEASURES 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 informationSummary of the lecture in Biostatistics
Summary of the lecture Bostatstcs Probablty Desty Fucto For a cotuos radom varable, a probablty desty fucto s a fucto such that: 0 dx a b) b a dx A probablty desty fucto provdes a smple descrpto of the
More informationLecture Notes Types of economic variables
Lecture Notes 3 1. Types of ecoomc varables () Cotuous varable takes o a cotuum the sample space, such as all pots o a le or all real umbers Example: GDP, Polluto cocetrato, etc. () Dscrete varables fte
More informationCS286.2 Lecture 4: Dinur s Proof of the PCP Theorem
CS86. Lecture 4: Dur s Proof of the PCP Theorem Scrbe: Thom Bohdaowcz Prevously, we have prove a weak verso of the PCP theorem: NP PCP 1,1/ (r = poly, q = O(1)). Wth ths result we have the desred costat
More informationG 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 informationRecent developments for frequency domain analysis in LS-DYNA
Recent developments for frequency domain analysis in LS-DYNA Yun Huang, Zhe Cui Livermore Software Technology Corporation 1 Introduction Since ls971 R6 version, a series of frequency domain features have
More informationModule 7. Lecture 7: Statistical parameter estimation
Lecture 7: Statstcal parameter estmato Parameter Estmato Methods of Parameter Estmato 1) Method of Matchg Pots ) Method of Momets 3) Mamum Lkelhood method Populato Parameter Sample Parameter Ubased estmato
More informationESS 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 informationSequential Approach to Covariance Correction for P-Field Simulation
Sequetal Approach to Covarace Correcto for P-Feld Smulato Chad Neufeld ad Clayto V. Deutsch Oe well kow artfact of the probablty feld (p-feld smulato algorthm s a too large covarace ear codtog data. Prevously,
More informationOutline. 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 informationChapter 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 informationMultiple Regression. More than 2 variables! Grade on Final. Multiple Regression 11/21/2012. Exam 2 Grades. Exam 2 Re-grades
STAT 101 Dr. Kar Lock Morga 11/20/12 Exam 2 Grades Multple Regresso SECTIONS 9.2, 10.1, 10.2 Multple explaatory varables (10.1) Parttog varablty R 2, ANOVA (9.2) Codtos resdual plot (10.2) Trasformatos
More informationClass 13,14 June 17, 19, 2015
Class 3,4 Jue 7, 9, 05 Pla for Class3,4:. Samplg dstrbuto of sample mea. The Cetral Lmt Theorem (CLT). Cofdece terval for ukow mea.. Samplg Dstrbuto for Sample mea. Methods used are based o CLT ( Cetral
More informationThird handout: On the Gini Index
Thrd hadout: O the dex Corrado, a tala statstca, proposed (, 9, 96) to measure absolute equalt va the mea dfferece whch s defed as ( / ) where refers to the total umber of dvduals socet. Assume that. The
More informationManipulator Dynamics. Amirkabir University of Technology Computer Engineering & Information Technology Department
Mapulator Dyamcs mrkabr Uversty of echology omputer Egeerg formato echology Departmet troducto obot arm dyamcs deals wth the mathematcal formulatos of the equatos of robot arm moto. hey are useful as:
More informationAnalysis of Variance with Weibull Data
Aalyss of Varace wth Webull Data Lahaa Watthaacheewaul Abstract I statstcal data aalyss by aalyss of varace, the usual basc assumptos are that the model s addtve ad the errors are radomly, depedetly, ad
More informationAn Introduction to. Support Vector Machine
A Itroducto to Support Vector Mache Support Vector Mache (SVM) A classfer derved from statstcal learg theory by Vapk, et al. 99 SVM became famous whe, usg mages as put, t gave accuracy comparable to eural-etwork
More informationCubic 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 informationLecture 4 Sep 9, 2015
CS 388R: Radomzed Algorthms Fall 205 Prof. Erc Prce Lecture 4 Sep 9, 205 Scrbe: Xagru Huag & Chad Voegele Overvew I prevous lectures, we troduced some basc probablty, the Cheroff boud, the coupo collector
More information1. A real number x is represented approximately by , and we are told that the relative error is 0.1 %. What is x? Note: There are two answers.
PROBLEMS A real umber s represeted appromately by 63, ad we are told that the relatve error s % What s? Note: There are two aswers Ht : Recall that % relatve error s What s the relatve error volved roudg
More informationA tighter lower bound on the circuit size of the hardest Boolean functions
Electroc Colloquum o Computatoal Complexty, Report No. 86 2011) A tghter lower boud o the crcut sze of the hardest Boolea fuctos Masak Yamamoto Abstract I [IPL2005], Fradse ad Mlterse mproved bouds o the
More informationCHAPTER 4 RADICAL EXPRESSIONS
6 CHAPTER RADICAL EXPRESSIONS. The th Root of a Real Number A real umber a s called the th root of a real umber b f Thus, for example: s a square root of sce. s also a square root of sce ( ). s a cube
More informationInvestigation of Partially Conditional RP Model with Response Error. Ed Stanek
Partally Codtoal Radom Permutato Model 7- vestgato of Partally Codtoal RP Model wth Respose Error TRODUCTO Ed Staek We explore the predctor that wll result a smple radom sample wth respose error whe a
More informationA 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