Fault Diagnosis in DC Motor Using Bond Graph Approach

Size: px
Start display at page:

Download "Fault Diagnosis in DC Motor Using Bond Graph Approach"

Transcription

1 Fult Dignosis in DC Motor Using Bond Grph Approch González Contrers B. M. Theilliol D. VelVldés L. G. Universidd Autónom Tlxcl Université Henri Poincré CENIDET Tlxcl VndoeuvreLes Nncy Cuernvc, MOR. México Frnci México Abstrct. The Bond Grp (BG) shows direct correspondence between their component prmeters nd physicl phenomen to be modeled, not only system model is obtined, but lso component prmeter filure representtions, since the BG model obtined is useful like dignosis model too. Using like cse of study direct current (dc) motor to implement the ppliction, this pper shows how to obtin fult tree to define the first set of fult hypothesis, nd then, for using temporl grph to define the fult, i.e. in order to reduce the set of fult hypothesis to only one element. Key words. Fult dignosis, cuslity, dc motor, Bond Grph, quntittive nd qulittive modelbsed pproches. 1. Introduction Fult dignosis hs been presented in mny industril processes s n indispensble prt of the control systems to be ble to gurntee the relibility nd vilbility of the process (Isermn et l. 1996; vn Schrick Dirk 2000; Vergé 1994). It is stge of the supervisory system, which hs the objective of indicting undesired or forbidden process sttes nd to tke pproprite ctions in order to mintin the opertion nd to void dmges or ccidents. The supervisory system blocks re shown in figure 1. ws developed tht integrtes studies where the BG hs been used for fult dignosis, but here is presented new mnner to interpret the energy flux immeditely fter fult is presented. The pper is orgnized s follows. In section 2 the BG bsics re given nd then in section 3, the dc motor circuit nd BG model of it re presented. Section 4 shows the proposed method using BG elements nd its order to be pplied to the systems nd the proposed method is pplied to dc motor; fult in prmeter R is used s exmple. Then, in section 5, the proposed method is pplied to dc convertermotor set system. The conclusions re given in section Bond Grph Bsics Bond Grph (BG) pproch hs been used in mny senses: simultion, modeling nd control, show itself like very useful tool for ppliction in severl systems. This is good tool to representtion models bsed on physicl concepts using finite symbols for ppliction to ny systems; provides structured pproch to system dynmics modeling without loss of the physicl sense, tht tkes dvntge to dignosis. In tble 1 re shown the equivlences between elements from severl domins (Blundell 1982; Broenink 2001; Gwthrop et l. 1996; Krnopp 1990). Tble 1. Equivlences between domins Figure 1. Supervisory system The dignosis prt in fult dignosis systems shows hole between qulittive nd quntittive nlysis (vn Schrick Dirk 2000; Vergé 1994), it is necessry to pply, in mny cses, sttisticl, opertor experience, heuristic nd functionl informtion to complete the dignosis stge. In this pper method In BG common vribles effort nd flow re used in ech domin nd their product is power, s energy derivtive, is common concept relting these systems. From the viewpoint of energy flow, using BG, system components nd physicl phenomen re clssified into five bsic types: 1) energy conserving or storing elements (I nd C), 2) energy dissipting elements (R), 3) energy source elements (Se nd Sf), 4) energy 419

2 conversion elements (TF y GY), nd 5) junction elements (Blundell 1982; Broenink 2001; Gwthrop et l. 1996; Krnopp 1990). Ech element with chrcteristic reltionship re shown in tble 2. We cn think in junction s common effort (0) or common flow (1). Tble 2. Bond Grph elements flow integrl is displcement generlized (Blundell 1982; Broenink 2001; Gwthrop et l. 1996; Krnopp 1990). Tble 3. Generlized sttes 3. DC motor model A dc motor in independent excitement or permnents mgnets configurtion is presented in figure 2. Here, the rmture voltge, V, is constnt s well s the field current, i f. Figure 2. DC motor circuit It is necessry give to ech element cuslity, tht mens wht direction the effort tkes, nd therefore, the flow direction, due to in ech bond the conjugte floweffort re in opposite directions. To ssign the cuslity, cusl stroke indictes the effort direction, wht tell us which vrible is outside of the element (Blundell 1982; Broenink 2001; Gwthrop et l. 1996; Krnopp et l. 1990). Exist rules to ssign cuslity nd generte the equtions describing the system. It is preferred n integrl cuslity, tht it is determined for storing elements I nd C, by the reltionships: 1 t 1 t q = f ( τ ) dτ (1) p = C 0 e( τ ) dτ (2) I 0 clled integrl cuslity, nd mkes the equtions obtining n esy wy. From (1) nd (2) we cn obtin the generlized sttes combining the equtions obtined from the other elements, this vribles re the stte vribles from the BG model. Using the tble 3 we cn to obtin the more common vribles, for tht, it should be hs in mind tht n effort integrl is momentum generlized nd The mthemticl model of the system cn be described in dynmicl form strting from the bond grph model presented in figure 3. To obtin the equtions the generlized vribles re used: p 3 relted to electricl prt nd p 8 relted to mechnicl prt. Figure 3. BG of dc motor From figure 3 the equtions tht describes the cusl interctions re: p3 = e e e..(3) f3 =..(8) e f 1 = f 2 = f3 = f 4..(4) p8 f8 =..(9) J e8 = e5 e6 e7..(5) e6 = βf 6.(10) f 5 = f6 = f7 = f8..(6) e 4 = kf5.(11) e2 = R f 2..(7) e 5 = kf4...(12) L 420

3 Here, the stte vribles re electric momentum (mgnetic flux linkge) nd mechnicl momentum (ngulr momentum). The stte equtions from bove describing the system re: R k p3 p L J 1 0 V 3 = (13) k β p 0 1 p τ L 8 8 L J but we re interested in current nd velocity s the output vribles, which re the mesured vribles from dc motor, then: i 1 0 = L p 3 (14) ω 0 1 r p8 J Now, we use prllel model (Leitch 1993) to generte the residul tht shows the system chnges in the fult cse. Observing these residuls is possible to pply the dignosis method noting chnges in ech vrible mesured. We cn estblish threshold to decide when the residul is not null. This threshold depends on the vlue due to the 2% of stedy stte of the signl mesured, i.e. current nd velocity in this cse. If the vlue is 2% upper (or lower), then the residul is nonzero. 4. Fult dignosis method The fult dignosis method proposed here, is bsed using reserches tht hs been used BG to derive some results focused in dignosis. The stges of this method re shown in figure 4. Figure 4. Fult dignosis method The cusl grph is consequence of the cusl reltionships between elements, obtined in direct form from the BG of the system (Isermnn 1996; Krnopp et l. 1990; Mostermn et l. 1997; Mostermn et l. 1995). This grph is seemed to the sign flow grph used in clssic control. To construct the cusl grph reference nodes re necessry, these re selected from the junction 1 (or 0) nd using the eqution tht describes it. For the figure 3 model we use the equtions (3) nd (5), nd we relte ech component until fits the other equtions. Ech node constitutes blocks tht re the bses of the cusl grph. Arrows connect vribles nd prmeters, nd the reltion between vribles is indicted over the link (Mostermn et l. 1997; Mostermn et l. 1995). Figure 5. Nodes (blocks) to form cusl grph The cusl grph is done joining the reference nodes. Cusl grph for dc motor is shown in figure 6. Figure 6. Cusl grph of dc motor F rom this cusl grph we obtin the next elements of the fult dignosis method proposed. It follows the fult tree, which shows the qulittive vritions when n observed vrible hs chnged. A qulittive vrition refers to chnge from nominl vlue. In this work, the three symbolic terms low [], norml [0] nd high [] re used. Qulittive trnsitions occur t the boundry endpoints. Mthemticlly, the qulittive vlue of x, denoted [x], defined s the devition from reference point x 0 is given s [x]= sgn (x x 0 ). When the reference is rnge, the qulittive vlue of x is defined s [x] = when x < x0 [x] = 0 when x 0 x x 0 [x] = when x 0 < x where x 0 nd x 0 re the lower nd upper rnge endpoints, respectively. The fult tree derived from BG is obtined for ech vrible of interest nd cn be built using bck propgtion in the cusl grph (or ntecedents nd consequents tble), in the next form (Krnopp et l. 1990; Vergé et l. 1994): Strt from the vrible of interest with its qulittive vlue nd trck bck the sign through the pth ssigning the right qulittive vlue (from the vrible of interest, propgte the qulittive 421

4 vlue, tke this vrible s the consequent nd write the ntecedent s the next consequent). Join the sign by mens of rrow ( brnch) nd continue the trck bck for ll the signs cross the vribles (join the consequents to ntecedents by mens of rrows or brnches). Propgtion is terminted long pth when conflicting ssignment is reched. The fult tree cn be gotten from reltionships (3)( 12), more precisely from cusl reltionships given for the BG rules (Blundell 1982; Broenink 2001; Krnopp et l. 1990; Mostermn et l. 1997). In tble 4 this reltionships re presented, nmed ntecedents nd consequents tble. Tble 5. Antecedents nd consequents for dc motor cusl grph Tble 4. Antecedents nd consequents Using qulittive vlu es (, 0, ), strting from the vrible observed with its vlue obtined from residul, one propgtes this vlue through the brnches. With help of tble 4 is built the tble 5 for dc motor. The fult tree for the vribles of interest, e 3, relted to the current i, nd e 8, relted to the velocity ω r with qulittive vlue [], re shown in figure 7. In these trees, the qulittive vlue is propgted using logicl opertions with positive vlues. Figure 7. Fult trees for e 3 nd e 8 A suggested lgorithm to generte the first fult hypothesis set could be the following: 1. Construct n ntecedents nd consequents tble, or from cusl grph use bck propgtion. 2. Construct the fult tree for the vribles of interest. 3. Observe wht type of qulittive vrition presents the observed vribles through the residuls, nd which they re mtched with the qulittive chnges in the fult tree. 4. Form the hypothesis fult set with the prmeters tht mtch the sme qulittive comportment in fult trees. So fr, the blocks shown in figure 4 hs been developed by others reserchers, but the finl block bout using the temporl grph is suggested by Mostermn in ((Mostermn et l. 1997; Mostermn et l. 1995). One cn to predict how will be the comportment of the signl nd its derivtives before the system rech the new stedy stte fter the fult hs occur by mens 422

5 of the temporl grph ((Mostermn et l. 1997; Mostermn et l. 1995). This grph provides the signtures tht indicte the comportment before the new stedy stte is reched. The following steps to obtin the cusl grph re used: 1. Strt from one of the prmeters tht re included in fult hypothesis set nd propgte its qulittive vlue through the grph. 2. When the signl cross through link with differentil, then mrk this with or if qulittive vlue increse or decrese. This indictes the first derivtives nd the qulittive signture. 3. The propgtion continues until rech second derivtive nd ll the observed vribles hve been covered by the second derivtive. 4. Signtures ssocites to derivtives with smll time constnts re rejected, if there were two vribles with different signtures. discrded becuse is differentil relted, i.e. the fult comportment continue while time go on. Tble 6. Signtures for prmeters in hypothesis fult R,, 0,, e 3 e 8,,,, J e3 e 8 K 0,,,, e 3 e 8, discrded As it is necessry obtin derivtives, cn be used stte vrible filters, if the system is liner, like treted here, or use numericl methods to generte the derivtives. In figure 9 re shown the norml signl nd its derivtives for current nd velocity Derivtive Derivd (bove), (rrib), norml Corriente signl i (bjo) (below) e Derivtive Derivd (bove), (rrib), norml Velocidd signl ω r (bjo) (below) e5 Temporl grph for R is shown in figure A Time Tiempo(s) Time Tiempo (s) Figure 7. Temporl grph for R As exmple, it is presented the residul (figure 8) from R fu lt in the electric prt of dc motor. mgnitud A rd/s time (s) Figure 8. Residuls when R is fulty It is observed tht, current nd velocity decrese, then we only construct fult trees for e 3 nd e 8 with qulittive vlues (). These fult trees re shown in figure 7, nd cn be observed prmeters K, R, J hve the sme qulittive signture in both fult tree, then the first hypothesis fult set is formed by {R, J, K, }. The others prmeters re rejected becuse hve different qulittive vlue in ech tree. When use the temporl grph, it is obtined the signtures presented in tble 6. From figure 9, the norml signl nd first order derivtive tht mtch signtures ginst shown in tble 6 re R, J, but J is () Current i (b) Velocity ω r Figure 9. Derivtives nd norml signl of mesured vribles 5. DC convertermotor system set To model in BG dc converter it is introduced n element tht describes the discontinuous behvior elements like the switching devices included in the converter. First, is modeled reductor converter (buck) introducing the discontinuous element s TF bond grph element with trnsformer modulus m, which tkes 1 nd 0 vlues depending the conmuttions sttes of the switching device, nmely, duty cycle. The converter circuit is shown in the left side of figure 10. Figure 10. DC converter circuit nd BG model The BG model is shown in the right side of figure 10. When the full bridge dc converter work in one qudrnt, s shown in figure 11, the model cn be interpreted like the shown in figure 10, so the BG model is the sme shown in the right side of figure

6 Figure 11. Full bridge dc converter circuit Looking the figure 6, e 1 corresponds to dc converter output, nd it is possible to dd the cusl grph of dc converter lone, shown in figure 12 ) to obtin the csul grph of ll the system, s is presented in figure 12 b). ) for buck converter b) for convertermotor system Figure 12. Cusl grph of dc converter The next tble 7, summrize the simulted fults pplying the proposed method. Tble 7. Summry of fults Fult Dignosis Residul (minimum hypothesis set) comportment J {J } Trnsitory J {J } T rnsitory K {R, K, m } Permnent K {R, K, m } Permnent R {R, m } Permnent R {R, m } Permnent L {L } Trnsitory L {L } Trnsitory β {β } Permnent β {β } Permnent m {R, K, m } Permnent m {m } Permnent 6. Conclusion The fult dignosis method presented in this pper use the BG pproch. The finl stge of fult dignosis needs to nlyze the comportment over the time, nd mtch this comportment ginst the provided by the temporl grph to decide where is the fult exctly by mens of reduction of the fult hypothesis set. Although the method cnnot reduce the fult hypothesis set to only one element in some cses, shows it s new ttempt to unify the qulittive nd quntittive resoning in fult detection nd cn pply the integrtion of fult detection methods becuse the equtions llow it. The method is esy to pply nd sequentil to perform, does not require nother tool more thn only BG elements. However, the limits of this method re reched when the system is non liner. Some interesting questions remin open in the frmework of this cse, for exmple the selection between: ) Anlyticl redundncy reltions (ARR) with prity equtions for nonliner systems (Stroswiecki et. l 1991). b) Multiple models pproch (Chen t l. 1999, Boskovic et l. 2003). References Blundell A. J., Bond grphs for modeling engineering systems, John Wiley nd Sons, Gret Britin, Boskovic J. D., Mehr R. M., Filure detection, identifiction nd reconfigurtion in flight control, in: Fult Dignosis nd Fult Tolernce for Mechtronic Systems: Recents Advences, pp , SpringerVerlg, Berlin, Broenink J.F., Introduction to physicl systems modeling with Bond Grphs, 27/07/2001. Chen J., Ptton R. J., Robust modelbsed fult dignosis for dynmic systems, chpter 9, pp , Kluwer Acdemic Publishers, London, 1999 Gwthrop P., Smith L., Metmodelling: For Bond Grphs nd dynmic systems, Prentice Hll, E. U., Isermnn Rolf, Bllé Peter, Trends in the ppliction of model bsed fult detection nd dignosis of technicl process, in: IFAC SAFEPROCESS '96, vol. N, Sn Frncisco, U.S., June 1996, pp Krnopp D.C., Mrgolis D.L., Rosenberg R.C., Systems dynmics: unified pproch, John Wiley nd Sons, New York, 2nd ed., Leitch R., Engineering dignosis: Mtching problems to solutions, en: Interntionl Conference on Fult Dignosis, vol. 3, Tolouse, Frnce, April, 1993, pp Mostermn P.J., Bisws G., Monitoring, prediction, nd fult isoltion in dynmic physicl systems, in: Proceedings of AAAI97, Rhode Islnd, July 1997, pp Mostermn P.J., Kpdi R., Bisws G., Model Bsed Dignosis of Dynmic Systems, in: Proceedings of DX95, the Sixth Interntionl Workshop on Principles of Dignosis, Goslr, Germny, October 24, 1995, pp Stroswiecki M, CometVrg G. Anlyticl redundncy reltions for fult detection nd isoltion in lgebric dynmic systems, in Automtic, vol 37, pp , Pergmon, vn Schrick Dirk, Conceptul system drft for sfe process: reltions nd definitions, in: IFAC SAFEPROCESS 2000, vol. 1, Budpest, Hungry, June 2000, pp Vergé M., Jume D., Fult detection nd Bond Grph modeling, in: IFAC SAFEPROCESS '94, vol. 2, Espoo, Finlnd, June 1994, pp

Ordinary differential equations

Ordinary differential equations Ordinry differentil equtions Introduction to Synthetic Biology E Nvrro A Montgud P Fernndez de Cordob JF Urchueguí Overview Introduction-Modelling Bsic concepts to understnd n ODE. Description nd properties

More information

Fig. 1. Open-Loop and Closed-Loop Systems with Plant Variations

Fig. 1. Open-Loop and Closed-Loop Systems with Plant Variations ME 3600 Control ystems Chrcteristics of Open-Loop nd Closed-Loop ystems Importnt Control ystem Chrcteristics o ensitivity of system response to prmetric vritions cn be reduced o rnsient nd stedy-stte responses

More information

New Expansion and Infinite Series

New Expansion and Infinite Series Interntionl Mthemticl Forum, Vol. 9, 204, no. 22, 06-073 HIKARI Ltd, www.m-hikri.com http://dx.doi.org/0.2988/imf.204.4502 New Expnsion nd Infinite Series Diyun Zhng College of Computer Nnjing University

More information

1.9 C 2 inner variations

1.9 C 2 inner variations 46 CHAPTER 1. INDIRECT METHODS 1.9 C 2 inner vritions So fr, we hve restricted ttention to liner vritions. These re vritions of the form vx; ǫ = ux + ǫφx where φ is in some liner perturbtion clss P, for

More information

THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS.

THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS. THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS RADON ROSBOROUGH https://intuitiveexplntionscom/picrd-lindelof-theorem/ This document is proof of the existence-uniqueness theorem

More information

DIRECT CURRENT CIRCUITS

DIRECT CURRENT CIRCUITS DRECT CURRENT CUTS ELECTRC POWER Consider the circuit shown in the Figure where bttery is connected to resistor R. A positive chrge dq will gin potentil energy s it moves from point to point b through

More information

Chapter 4 Contravariance, Covariance, and Spacetime Diagrams

Chapter 4 Contravariance, Covariance, and Spacetime Diagrams Chpter 4 Contrvrince, Covrince, nd Spcetime Digrms 4. The Components of Vector in Skewed Coordintes We hve seen in Chpter 3; figure 3.9, tht in order to show inertil motion tht is consistent with the Lorentz

More information

4.4 Areas, Integrals and Antiderivatives

4.4 Areas, Integrals and Antiderivatives . res, integrls nd ntiderivtives 333. Ares, Integrls nd Antiderivtives This section explores properties of functions defined s res nd exmines some connections mong res, integrls nd ntiderivtives. In order

More information

Section 6.1 INTRO to LAPLACE TRANSFORMS

Section 6.1 INTRO to LAPLACE TRANSFORMS Section 6. INTRO to LAPLACE TRANSFORMS Key terms: Improper Integrl; diverge, converge A A f(t)dt lim f(t)dt Piecewise Continuous Function; jump discontinuity Function of Exponentil Order Lplce Trnsform

More information

Properties of Integrals, Indefinite Integrals. Goals: Definition of the Definite Integral Integral Calculations using Antiderivatives

Properties of Integrals, Indefinite Integrals. Goals: Definition of the Definite Integral Integral Calculations using Antiderivatives Block #6: Properties of Integrls, Indefinite Integrls Gols: Definition of the Definite Integrl Integrl Clcultions using Antiderivtives Properties of Integrls The Indefinite Integrl 1 Riemnn Sums - 1 Riemnn

More information

Math 8 Winter 2015 Applications of Integration

Math 8 Winter 2015 Applications of Integration Mth 8 Winter 205 Applictions of Integrtion Here re few importnt pplictions of integrtion. The pplictions you my see on n exm in this course include only the Net Chnge Theorem (which is relly just the Fundmentl

More information

1.2. Linear Variable Coefficient Equations. y + b "! = a y + b " Remark: The case b = 0 and a non-constant can be solved with the same idea as above.

1.2. Linear Variable Coefficient Equations. y + b ! = a y + b  Remark: The case b = 0 and a non-constant can be solved with the same idea as above. 1 12 Liner Vrible Coefficient Equtions Section Objective(s): Review: Constnt Coefficient Equtions Solving Vrible Coefficient Equtions The Integrting Fctor Method The Bernoulli Eqution 121 Review: Constnt

More information

Unit #9 : Definite Integral Properties; Fundamental Theorem of Calculus

Unit #9 : Definite Integral Properties; Fundamental Theorem of Calculus Unit #9 : Definite Integrl Properties; Fundmentl Theorem of Clculus Gols: Identify properties of definite integrls Define odd nd even functions, nd reltionship to integrl vlues Introduce the Fundmentl

More information

The Regulated and Riemann Integrals

The Regulated and Riemann Integrals Chpter 1 The Regulted nd Riemnn Integrls 1.1 Introduction We will consider severl different pproches to defining the definite integrl f(x) dx of function f(x). These definitions will ll ssign the sme vlue

More information

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies Stte spce systems nlysis (continued) Stbility A. Definitions A system is sid to be Asymptoticlly Stble (AS) when it stisfies ut () = 0, t > 0 lim xt () 0. t A system is AS if nd only if the impulse response

More information

Week 10: Line Integrals

Week 10: Line Integrals Week 10: Line Integrls Introduction In this finl week we return to prmetrised curves nd consider integrtion long such curves. We lredy sw this in Week 2 when we integrted long curve to find its length.

More information

Improper Integrals, and Differential Equations

Improper Integrals, and Differential Equations Improper Integrls, nd Differentil Equtions October 22, 204 5.3 Improper Integrls Previously, we discussed how integrls correspond to res. More specificlly, we sid tht for function f(x), the region creted

More information

Review of Calculus, cont d

Review of Calculus, cont d Jim Lmbers MAT 460 Fll Semester 2009-10 Lecture 3 Notes These notes correspond to Section 1.1 in the text. Review of Clculus, cont d Riemnn Sums nd the Definite Integrl There re mny cses in which some

More information

Review of basic calculus

Review of basic calculus Review of bsic clculus This brief review reclls some of the most importnt concepts, definitions, nd theorems from bsic clculus. It is not intended to tech bsic clculus from scrtch. If ny of the items below

More information

On the Uncertainty of Sensors Based on Magnetic Effects. E. Hristoforou, E. Kayafas, A. Ktena, DM Kepaptsoglou

On the Uncertainty of Sensors Based on Magnetic Effects. E. Hristoforou, E. Kayafas, A. Ktena, DM Kepaptsoglou On the Uncertinty of Sensors Bsed on Mgnetic Effects E. ristoforou, E. Kyfs, A. Kten, DM Kepptsoglou Ntionl Technicl University of Athens, Zogrfou Cmpus, Athens 1578, Greece Tel: +3177178, Fx: +3177119,

More information

Continuous Random Variables

Continuous Random Variables STAT/MATH 395 A - PROBABILITY II UW Winter Qurter 217 Néhémy Lim Continuous Rndom Vribles Nottion. The indictor function of set S is rel-vlued function defined by : { 1 if x S 1 S (x) if x S Suppose tht

More information

MAC-solutions of the nonexistent solutions of mathematical physics

MAC-solutions of the nonexistent solutions of mathematical physics Proceedings of the 4th WSEAS Interntionl Conference on Finite Differences - Finite Elements - Finite Volumes - Boundry Elements MAC-solutions of the nonexistent solutions of mthemticl physics IGO NEYGEBAUE

More information

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes Jim Lmbers MAT 169 Fll Semester 2009-10 Lecture 4 Notes These notes correspond to Section 8.2 in the text. Series Wht is Series? An infinte series, usully referred to simply s series, is n sum of ll of

More information

Fault Modeling. EE5375 ADD II Prof. MacDonald

Fault Modeling. EE5375 ADD II Prof. MacDonald Fult Modeling EE5375 ADD II Prof. McDonld Stuck At Fult Models l Modeling of physicl defects (fults) simplify to logicl fult l stuck high or low represents mny physicl defects esy to simulte technology

More information

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions Physics 6C Solution of inhomogeneous ordinry differentil equtions using Green s functions Peter Young November 5, 29 Homogeneous Equtions We hve studied, especilly in long HW problem, second order liner

More information

NUMERICAL INTEGRATION. The inverse process to differentiation in calculus is integration. Mathematically, integration is represented by.

NUMERICAL INTEGRATION. The inverse process to differentiation in calculus is integration. Mathematically, integration is represented by. NUMERICAL INTEGRATION 1 Introduction The inverse process to differentition in clculus is integrtion. Mthemticlly, integrtion is represented by f(x) dx which stnds for the integrl of the function f(x) with

More information

and that at t = 0 the object is at position 5. Find the position of the object at t = 2.

and that at t = 0 the object is at position 5. Find the position of the object at t = 2. 7.2 The Fundmentl Theorem of Clculus 49 re mny, mny problems tht pper much different on the surfce but tht turn out to be the sme s these problems, in the sense tht when we try to pproimte solutions we

More information

Advanced Calculus: MATH 410 Notes on Integrals and Integrability Professor David Levermore 17 October 2004

Advanced Calculus: MATH 410 Notes on Integrals and Integrability Professor David Levermore 17 October 2004 Advnced Clculus: MATH 410 Notes on Integrls nd Integrbility Professor Dvid Levermore 17 October 2004 1. Definite Integrls In this section we revisit the definite integrl tht you were introduced to when

More information

#6A&B Magnetic Field Mapping

#6A&B Magnetic Field Mapping #6A& Mgnetic Field Mpping Gol y performing this lb experiment, you will: 1. use mgnetic field mesurement technique bsed on Frdy s Lw (see the previous experiment),. study the mgnetic fields generted by

More information

7.2 The Definite Integral

7.2 The Definite Integral 7.2 The Definite Integrl the definite integrl In the previous section, it ws found tht if function f is continuous nd nonnegtive, then the re under the grph of f on [, b] is given by F (b) F (), where

More information

The First Fundamental Theorem of Calculus. If f(x) is continuous on [a, b] and F (x) is any antiderivative. f(x) dx = F (b) F (a).

The First Fundamental Theorem of Calculus. If f(x) is continuous on [a, b] and F (x) is any antiderivative. f(x) dx = F (b) F (a). The Fundmentl Theorems of Clculus Mth 4, Section 0, Spring 009 We now know enough bout definite integrls to give precise formultions of the Fundmentl Theorems of Clculus. We will lso look t some bsic emples

More information

Bisimulation. R.J. van Glabbeek

Bisimulation. R.J. van Glabbeek Bisimultion R.J. vn Glbbeek NICTA, Sydney, Austrli. School of Computer Science nd Engineering, The University of New South Wles, Sydney, Austrli. Computer Science Deprtment, Stnford University, CA 94305-9045,

More information

Strong Bisimulation. Overview. References. Actions Labeled transition system Transition semantics Simulation Bisimulation

Strong Bisimulation. Overview. References. Actions Labeled transition system Transition semantics Simulation Bisimulation Strong Bisimultion Overview Actions Lbeled trnsition system Trnsition semntics Simultion Bisimultion References Robin Milner, Communiction nd Concurrency Robin Milner, Communicting nd Mobil Systems 32

More information

20 MATHEMATICS POLYNOMIALS

20 MATHEMATICS POLYNOMIALS 0 MATHEMATICS POLYNOMIALS.1 Introduction In Clss IX, you hve studied polynomils in one vrible nd their degrees. Recll tht if p(x) is polynomil in x, the highest power of x in p(x) is clled the degree of

More information

We partition C into n small arcs by forming a partition of [a, b] by picking s i as follows: a = s 0 < s 1 < < s n = b.

We partition C into n small arcs by forming a partition of [a, b] by picking s i as follows: a = s 0 < s 1 < < s n = b. Mth 255 - Vector lculus II Notes 4.2 Pth nd Line Integrls We begin with discussion of pth integrls (the book clls them sclr line integrls). We will do this for function of two vribles, but these ides cn

More information

Calculus of Variations

Calculus of Variations Clculus of Vritions Com S 477/577 Notes) Yn-Bin Ji Dec 4, 2017 1 Introduction A functionl ssigns rel number to ech function or curve) in some clss. One might sy tht functionl is function of nother function

More information

How to simulate Turing machines by invertible one-dimensional cellular automata

How to simulate Turing machines by invertible one-dimensional cellular automata How to simulte Turing mchines by invertible one-dimensionl cellulr utomt Jen-Christophe Dubcq Déprtement de Mthémtiques et d Informtique, École Normle Supérieure de Lyon, 46, llée d Itlie, 69364 Lyon Cedex

More information

Chapter 5 : Continuous Random Variables

Chapter 5 : Continuous Random Variables STAT/MATH 395 A - PROBABILITY II UW Winter Qurter 216 Néhémy Lim Chpter 5 : Continuous Rndom Vribles Nottions. N {, 1, 2,...}, set of nturl numbers (i.e. ll nonnegtive integers); N {1, 2,...}, set of ll

More information

UNIT 1 FUNCTIONS AND THEIR INVERSES Lesson 1.4: Logarithmic Functions as Inverses Instruction

UNIT 1 FUNCTIONS AND THEIR INVERSES Lesson 1.4: Logarithmic Functions as Inverses Instruction Lesson : Logrithmic Functions s Inverses Prerequisite Skills This lesson requires the use of the following skills: determining the dependent nd independent vribles in n exponentil function bsed on dt from

More information

x = b a N. (13-1) The set of points used to subdivide the range [a, b] (see Fig. 13.1) is

x = b a N. (13-1) The set of points used to subdivide the range [a, b] (see Fig. 13.1) is Jnury 28, 2002 13. The Integrl The concept of integrtion, nd the motivtion for developing this concept, were described in the previous chpter. Now we must define the integrl, crefully nd completely. According

More information

Conservation Law. Chapter Goal. 5.2 Theory

Conservation Law. Chapter Goal. 5.2 Theory Chpter 5 Conservtion Lw 5.1 Gol Our long term gol is to understnd how mny mthemticl models re derived. We study how certin quntity chnges with time in given region (sptil domin). We first derive the very

More information

Chapter 0. What is the Lebesgue integral about?

Chapter 0. What is the Lebesgue integral about? Chpter 0. Wht is the Lebesgue integrl bout? The pln is to hve tutoril sheet ech week, most often on Fridy, (to be done during the clss) where you will try to get used to the ides introduced in the previous

More information

Acceptance Sampling by Attributes

Acceptance Sampling by Attributes Introduction Acceptnce Smpling by Attributes Acceptnce smpling is concerned with inspection nd decision mking regrding products. Three spects of smpling re importnt: o Involves rndom smpling of n entire

More information

Math 1B, lecture 4: Error bounds for numerical methods

Math 1B, lecture 4: Error bounds for numerical methods Mth B, lecture 4: Error bounds for numericl methods Nthn Pflueger 4 September 0 Introduction The five numericl methods descried in the previous lecture ll operte by the sme principle: they pproximte the

More information

The Wave Equation I. MA 436 Kurt Bryan

The Wave Equation I. MA 436 Kurt Bryan 1 Introduction The Wve Eqution I MA 436 Kurt Bryn Consider string stretching long the x xis, of indeterminte (or even infinite!) length. We wnt to derive n eqution which models the motion of the string

More information

MA 124 January 18, Derivatives are. Integrals are.

MA 124 January 18, Derivatives are. Integrals are. MA 124 Jnury 18, 2018 Prof PB s one-minute introduction to clculus Derivtives re. Integrls re. In Clculus 1, we lern limits, derivtives, some pplictions of derivtives, indefinite integrls, definite integrls,

More information

( dg. ) 2 dt. + dt. dt j + dh. + dt. r(t) dt. Comparing this equation with the one listed above for the length of see that

( dg. ) 2 dt. + dt. dt j + dh. + dt. r(t) dt. Comparing this equation with the one listed above for the length of see that Arc Length of Curves in Three Dimensionl Spce If the vector function r(t) f(t) i + g(t) j + h(t) k trces out the curve C s t vries, we cn mesure distnces long C using formul nerly identicl to one tht we

More information

CBE 291b - Computation And Optimization For Engineers

CBE 291b - Computation And Optimization For Engineers The University of Western Ontrio Fculty of Engineering Science Deprtment of Chemicl nd Biochemicl Engineering CBE 9b - Computtion And Optimiztion For Engineers Mtlb Project Introduction Prof. A. Jutn Jn

More information

DISTRIBUTION OF SUB AND SUPER HARMONIC SOLUTION OF MATHIEU EQUATION WITHIN STABLE ZONES

DISTRIBUTION OF SUB AND SUPER HARMONIC SOLUTION OF MATHIEU EQUATION WITHIN STABLE ZONES Fifth ASME Interntionl Conference on Multibody Systems, Nonliner Dynmics nd Control Symposium on Dynmics nd Control of Time-Vrying nd Time-Dely Systems nd Structures September 2-2, 05, Long Bech, Cliforni,

More information

In-Class Problems 2 and 3: Projectile Motion Solutions. In-Class Problem 2: Throwing a Stone Down a Hill

In-Class Problems 2 and 3: Projectile Motion Solutions. In-Class Problem 2: Throwing a Stone Down a Hill MASSACHUSETTS INSTITUTE OF TECHNOLOGY Deprtment of Physics Physics 8T Fll Term 4 In-Clss Problems nd 3: Projectile Motion Solutions We would like ech group to pply the problem solving strtegy with the

More information

Chapter 14. Matrix Representations of Linear Transformations

Chapter 14. Matrix Representations of Linear Transformations Chpter 4 Mtrix Representtions of Liner Trnsformtions When considering the Het Stte Evolution, we found tht we could describe this process using multipliction by mtrix. This ws nice becuse computers cn

More information

Numerical Integration

Numerical Integration Chpter 5 Numericl Integrtion Numericl integrtion is the study of how the numericl vlue of n integrl cn be found. Methods of function pproximtion discussed in Chpter??, i.e., function pproximtion vi the

More information

ADVANCEMENT OF THE CLOSELY COUPLED PROBES POTENTIAL DROP TECHNIQUE FOR NDE OF SURFACE CRACKS

ADVANCEMENT OF THE CLOSELY COUPLED PROBES POTENTIAL DROP TECHNIQUE FOR NDE OF SURFACE CRACKS ADVANCEMENT OF THE CLOSELY COUPLED PROBES POTENTIAL DROP TECHNIQUE FOR NDE OF SURFACE CRACKS F. Tkeo 1 nd M. Sk 1 Hchinohe Ntionl College of Technology, Hchinohe, Jpn; Tohoku University, Sendi, Jpn Abstrct:

More information

SUMMER KNOWHOW STUDY AND LEARNING CENTRE

SUMMER KNOWHOW STUDY AND LEARNING CENTRE SUMMER KNOWHOW STUDY AND LEARNING CENTRE Indices & Logrithms 2 Contents Indices.2 Frctionl Indices.4 Logrithms 6 Exponentil equtions. Simplifying Surds 13 Opertions on Surds..16 Scientific Nottion..18

More information

Predict Global Earth Temperature using Linier Regression

Predict Global Earth Temperature using Linier Regression Predict Globl Erth Temperture using Linier Regression Edwin Swndi Sijbt (23516012) Progrm Studi Mgister Informtik Sekolh Teknik Elektro dn Informtik ITB Jl. Gnesh 10 Bndung 40132, Indonesi 23516012@std.stei.itb.c.id

More information

AMATH 731: Applied Functional Analysis Fall Some basics of integral equations

AMATH 731: Applied Functional Analysis Fall Some basics of integral equations AMATH 731: Applied Functionl Anlysis Fll 2009 1 Introduction Some bsics of integrl equtions An integrl eqution is n eqution in which the unknown function u(t) ppers under n integrl sign, e.g., K(t, s)u(s)

More information

Chapters 4 & 5 Integrals & Applications

Chapters 4 & 5 Integrals & Applications Contents Chpters 4 & 5 Integrls & Applictions Motivtion to Chpters 4 & 5 2 Chpter 4 3 Ares nd Distnces 3. VIDEO - Ares Under Functions............................................ 3.2 VIDEO - Applictions

More information

Lecture 14: Quadrature

Lecture 14: Quadrature Lecture 14: Qudrture This lecture is concerned with the evlution of integrls fx)dx 1) over finite intervl [, b] The integrnd fx) is ssumed to be rel-vlues nd smooth The pproximtion of n integrl by numericl

More information

CS 275 Automata and Formal Language Theory

CS 275 Automata and Formal Language Theory CS 275 Automt nd Forml Lnguge Theory Course Notes Prt II: The Recognition Problem (II) Chpter II.5.: Properties of Context Free Grmmrs (14) Anton Setzer (Bsed on book drft by J. V. Tucker nd K. Stephenson)

More information

Section 14.3 Arc Length and Curvature

Section 14.3 Arc Length and Curvature Section 4.3 Arc Length nd Curvture Clculus on Curves in Spce In this section, we ly the foundtions for describing the movement of n object in spce.. Vector Function Bsics In Clc, formul for rc length in

More information

THREE-DIMENSIONAL KINEMATICS OF RIGID BODIES

THREE-DIMENSIONAL KINEMATICS OF RIGID BODIES THREE-DIMENSIONAL KINEMATICS OF RIGID BODIES 1. TRANSLATION Figure shows rigid body trnslting in three-dimensionl spce. Any two points in the body, such s A nd B, will move long prllel stright lines if

More information

Taylor Polynomial Inequalities

Taylor Polynomial Inequalities Tylor Polynomil Inequlities Ben Glin September 17, 24 Abstrct There re instnces where we my wish to pproximte the vlue of complicted function round given point by constructing simpler function such s polynomil

More information

Improper Integrals. Type I Improper Integrals How do we evaluate an integral such as

Improper Integrals. Type I Improper Integrals How do we evaluate an integral such as Improper Integrls Two different types of integrls cn qulify s improper. The first type of improper integrl (which we will refer to s Type I) involves evluting n integrl over n infinite region. In the grph

More information

approaches as n becomes larger and larger. Since e > 1, the graph of the natural exponential function is as below

approaches as n becomes larger and larger. Since e > 1, the graph of the natural exponential function is as below . Eponentil nd rithmic functions.1 Eponentil Functions A function of the form f() =, > 0, 1 is clled n eponentil function. Its domin is the set of ll rel f ( 1) numbers. For n eponentil function f we hve.

More information

ELE B7 Power Systems Engineering. Power System Components Modeling

ELE B7 Power Systems Engineering. Power System Components Modeling Power Systems Engineering Power System Components Modeling Section III : Trnsformer Model Power Trnsformers- CONSTRUCTION Primry windings, connected to the lternting voltge source; Secondry windings, connected

More information

Convert the NFA into DFA

Convert the NFA into DFA Convert the NF into F For ech NF we cn find F ccepting the sme lnguge. The numer of sttes of the F could e exponentil in the numer of sttes of the NF, ut in prctice this worst cse occurs rrely. lgorithm:

More information

Definite integral. Mathematics FRDIS MENDELU

Definite integral. Mathematics FRDIS MENDELU Definite integrl Mthemtics FRDIS MENDELU Simon Fišnrová Brno 1 Motivtion - re under curve Suppose, for simplicity, tht y = f(x) is nonnegtive nd continuous function defined on [, b]. Wht is the re of the

More information

Chaos in drive systems

Chaos in drive systems Applied nd Computtionl Mechnics 1 (2007) 121-126 Chos in drive systems Ctird Krtochvíl, Mrtin Houfek, Josef Koláčný b, Romn Kříž b, Lubomír Houfek,*, Jiří Krejs Institute of Thermomechnics, brnch Brno,

More information

12 TRANSFORMING BIVARIATE DENSITY FUNCTIONS

12 TRANSFORMING BIVARIATE DENSITY FUNCTIONS 1 TRANSFORMING BIVARIATE DENSITY FUNCTIONS Hving seen how to trnsform the probbility density functions ssocited with single rndom vrible, the next logicl step is to see how to trnsform bivrite probbility

More information

Riemann is the Mann! (But Lebesgue may besgue to differ.)

Riemann is the Mann! (But Lebesgue may besgue to differ.) Riemnn is the Mnn! (But Lebesgue my besgue to differ.) Leo Livshits My 2, 2008 1 For finite intervls in R We hve seen in clss tht every continuous function f : [, b] R hs the property tht for every ɛ >

More information

ODE: Existence and Uniqueness of a Solution

ODE: Existence and Uniqueness of a Solution Mth 22 Fll 213 Jerry Kzdn ODE: Existence nd Uniqueness of Solution The Fundmentl Theorem of Clculus tells us how to solve the ordinry differentil eqution (ODE) du = f(t) dt with initil condition u() =

More information

ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER /2019

ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS 1 MATH00030 SEMESTER /2019 ACCESS TO SCIENCE, ENGINEERING AND AGRICULTURE: MATHEMATICS MATH00030 SEMESTER 208/209 DR. ANTHONY BROWN 7.. Introduction to Integrtion. 7. Integrl Clculus As ws the cse with the chpter on differentil

More information

How can we approximate the area of a region in the plane? What is an interpretation of the area under the graph of a velocity function?

How can we approximate the area of a region in the plane? What is an interpretation of the area under the graph of a velocity function? Mth 125 Summry Here re some thoughts I ws hving while considering wht to put on the first midterm. The core of your studying should be the ssigned homework problems: mke sure you relly understnd those

More information

Chapter 3. Vector Spaces

Chapter 3. Vector Spaces 3.4 Liner Trnsformtions 1 Chpter 3. Vector Spces 3.4 Liner Trnsformtions Note. We hve lredy studied liner trnsformtions from R n into R m. Now we look t liner trnsformtions from one generl vector spce

More information

221B Lecture Notes WKB Method

221B Lecture Notes WKB Method Clssicl Limit B Lecture Notes WKB Method Hmilton Jcobi Eqution We strt from the Schrödinger eqution for single prticle in potentil i h t ψ x, t = [ ] h m + V x ψ x, t. We cn rewrite this eqution by using

More information

A. Limits - L Hopital s Rule ( ) How to find it: Try and find limits by traditional methods (plugging in). If you get 0 0 or!!, apply C.! 1 6 C.

A. Limits - L Hopital s Rule ( ) How to find it: Try and find limits by traditional methods (plugging in). If you get 0 0 or!!, apply C.! 1 6 C. A. Limits - L Hopitl s Rule Wht you re finding: L Hopitl s Rule is used to find limits of the form f ( x) lim where lim f x x! c g x ( ) = or lim f ( x) = limg( x) = ". ( ) x! c limg( x) = 0 x! c x! c

More information

1B40 Practical Skills

1B40 Practical Skills B40 Prcticl Skills Comining uncertinties from severl quntities error propgtion We usully encounter situtions where the result of n experiment is given in terms of two (or more) quntities. We then need

More information

Definite integral. Mathematics FRDIS MENDELU. Simona Fišnarová (Mendel University) Definite integral MENDELU 1 / 30

Definite integral. Mathematics FRDIS MENDELU. Simona Fišnarová (Mendel University) Definite integral MENDELU 1 / 30 Definite integrl Mthemtics FRDIS MENDELU Simon Fišnrová (Mendel University) Definite integrl MENDELU / Motivtion - re under curve Suppose, for simplicity, tht y = f(x) is nonnegtive nd continuous function

More information

APPROXIMATE INTEGRATION

APPROXIMATE INTEGRATION APPROXIMATE INTEGRATION. Introduction We hve seen tht there re functions whose nti-derivtives cnnot be expressed in closed form. For these resons ny definite integrl involving these integrnds cnnot be

More information

Indefinite Integral. Chapter Integration - reverse of differentiation

Indefinite Integral. Chapter Integration - reverse of differentiation Chpter Indefinite Integrl Most of the mthemticl opertions hve inverse opertions. The inverse opertion of differentition is clled integrtion. For exmple, describing process t the given moment knowing the

More information

A REVIEW OF CALCULUS CONCEPTS FOR JDEP 384H. Thomas Shores Department of Mathematics University of Nebraska Spring 2007

A REVIEW OF CALCULUS CONCEPTS FOR JDEP 384H. Thomas Shores Department of Mathematics University of Nebraska Spring 2007 A REVIEW OF CALCULUS CONCEPTS FOR JDEP 384H Thoms Shores Deprtment of Mthemtics University of Nebrsk Spring 2007 Contents Rtes of Chnge nd Derivtives 1 Dierentils 4 Are nd Integrls 5 Multivrite Clculus

More information

Jack Simons, Henry Eyring Scientist and Professor Chemistry Department University of Utah

Jack Simons, Henry Eyring Scientist and Professor Chemistry Department University of Utah 1. Born-Oppenheimer pprox.- energy surfces 2. Men-field (Hrtree-Fock) theory- orbitls 3. Pros nd cons of HF- RHF, UHF 4. Beyond HF- why? 5. First, one usully does HF-how? 6. Bsis sets nd nottions 7. MPn,

More information

Finite Automata. Informatics 2A: Lecture 3. John Longley. 22 September School of Informatics University of Edinburgh

Finite Automata. Informatics 2A: Lecture 3. John Longley. 22 September School of Informatics University of Edinburgh Lnguges nd Automt Finite Automt Informtics 2A: Lecture 3 John Longley School of Informtics University of Edinburgh jrl@inf.ed.c.uk 22 September 2017 1 / 30 Lnguges nd Automt 1 Lnguges nd Automt Wht is

More information

PART 1 MULTIPLE CHOICE Circle the appropriate response to each of the questions below. Each question has a value of 1 point.

PART 1 MULTIPLE CHOICE Circle the appropriate response to each of the questions below. Each question has a value of 1 point. PART MULTIPLE CHOICE Circle the pproprite response to ech of the questions below. Ech question hs vlue of point.. If in sequence the second level difference is constnt, thn the sequence is:. rithmetic

More information

New data structures to reduce data size and search time

New data structures to reduce data size and search time New dt structures to reduce dt size nd serch time Tsuneo Kuwbr Deprtment of Informtion Sciences, Fculty of Science, Kngw University, Hirtsuk-shi, Jpn FIT2018 1D-1, No2, pp1-4 Copyright (c)2018 by The Institute

More information

CHM Physical Chemistry I Chapter 1 - Supplementary Material

CHM Physical Chemistry I Chapter 1 - Supplementary Material CHM 3410 - Physicl Chemistry I Chpter 1 - Supplementry Mteril For review of some bsic concepts in mth, see Atkins "Mthemticl Bckground 1 (pp 59-6), nd "Mthemticl Bckground " (pp 109-111). 1. Derivtion

More information

Introduction to the Calculus of Variations

Introduction to the Calculus of Variations Introduction to the Clculus of Vritions Jim Fischer Mrch 20, 1999 Abstrct This is self-contined pper which introduces fundmentl problem in the clculus of vritions, the problem of finding extreme vlues

More information

Lecture 1. Functional series. Pointwise and uniform convergence.

Lecture 1. Functional series. Pointwise and uniform convergence. 1 Introduction. Lecture 1. Functionl series. Pointwise nd uniform convergence. In this course we study mongst other things Fourier series. The Fourier series for periodic function f(x) with period 2π is

More information

Duality # Second iteration for HW problem. Recall our LP example problem we have been working on, in equality form, is given below.

Duality # Second iteration for HW problem. Recall our LP example problem we have been working on, in equality form, is given below. Dulity #. Second itertion for HW problem Recll our LP emple problem we hve been working on, in equlity form, is given below.,,,, 8 m F which, when written in slightly different form, is 8 F Recll tht we

More information

Theoretical foundations of Gaussian quadrature

Theoretical foundations of Gaussian quadrature Theoreticl foundtions of Gussin qudrture 1 Inner product vector spce Definition 1. A vector spce (or liner spce) is set V = {u, v, w,...} in which the following two opertions re defined: (A) Addition of

More information

UNIFORM CONVERGENCE. Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3

UNIFORM CONVERGENCE. Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3 UNIFORM CONVERGENCE Contents 1. Uniform Convergence 1 2. Properties of uniform convergence 3 Suppose f n : Ω R or f n : Ω C is sequence of rel or complex functions, nd f n f s n in some sense. Furthermore,

More information

THE INTERVAL LATTICE BOLTZMANN METHOD FOR TRANSIENT HEAT TRANSFER IN A SILICON THIN FILM

THE INTERVAL LATTICE BOLTZMANN METHOD FOR TRANSIENT HEAT TRANSFER IN A SILICON THIN FILM ROMAI J., v.9, no.2(2013), 173 179 THE INTERVAL LATTICE BOLTZMANN METHOD FOR TRANSIENT HEAT TRANSFER IN A SILICON THIN FILM Alicj Piseck-Belkhyt, Ann Korczk Institute of Computtionl Mechnics nd Engineering,

More information

3.4 Numerical integration

3.4 Numerical integration 3.4. Numericl integrtion 63 3.4 Numericl integrtion In mny economic pplictions it is necessry to compute the definite integrl of relvlued function f with respect to "weight" function w over n intervl [,

More information

Bernoulli Numbers Jeff Morton

Bernoulli Numbers Jeff Morton Bernoulli Numbers Jeff Morton. We re interested in the opertor e t k d k t k, which is to sy k tk. Applying this to some function f E to get e t f d k k tk d k f f + d k k tk dk f, we note tht since f

More information

INTRODUCTION. The three general approaches to the solution of kinetics problems are:

INTRODUCTION. The three general approaches to the solution of kinetics problems are: INTRODUCTION According to Newton s lw, prticle will ccelerte when it is subjected to unblnced forces. Kinetics is the study of the reltions between unblnced forces nd the resulting chnges in motion. The

More information

13.4 Work done by Constant Forces

13.4 Work done by Constant Forces 13.4 Work done by Constnt Forces We will begin our discussion of the concept of work by nlyzing the motion of n object in one dimension cted on by constnt forces. Let s consider the following exmple: push

More information

STEP FUNCTIONS, DELTA FUNCTIONS, AND THE VARIATION OF PARAMETERS FORMULA. 0 if t < 0, 1 if t > 0.

STEP FUNCTIONS, DELTA FUNCTIONS, AND THE VARIATION OF PARAMETERS FORMULA. 0 if t < 0, 1 if t > 0. STEP FUNCTIONS, DELTA FUNCTIONS, AND THE VARIATION OF PARAMETERS FORMULA STEPHEN SCHECTER. The unit step function nd piecewise continuous functions The Heviside unit step function u(t) is given by if t

More information

The Riemann-Lebesgue Lemma

The Riemann-Lebesgue Lemma Physics 215 Winter 218 The Riemnn-Lebesgue Lemm The Riemnn Lebesgue Lemm is one of the most importnt results of Fourier nlysis nd symptotic nlysis. It hs mny physics pplictions, especilly in studies of

More information

The Basic Functional 2 1

The Basic Functional 2 1 2 The Bsic Functionl 2 1 Chpter 2: THE BASIC FUNCTIONAL TABLE OF CONTENTS Pge 2.1 Introduction..................... 2 3 2.2 The First Vrition.................. 2 3 2.3 The Euler Eqution..................

More information

INTRODUCTION TO INTEGRATION

INTRODUCTION TO INTEGRATION INTRODUCTION TO INTEGRATION 5.1 Ares nd Distnces Assume f(x) 0 on the intervl [, b]. Let A be the re under the grph of f(x). b We will obtin n pproximtion of A in the following three steps. STEP 1: Divide

More information