Basic IIR Digital Filter Structures

Size: px
Start display at page:

Download "Basic IIR Digital Filter Structures"

Transcription

1 Basic IIR Digital Filter Structures The causal IIR igital filters we are concerne with in this course are characterie by a real rational transfer function of or, equivalently by a constant coefficient ifference equation From the ifference equation representation, it can be seen that the realiation of the causal IIR igital filters requires some form of feeback

2 Basic IIR Digital Filter Structures An N-th orer IIR igital transfer function is characterie by N+ unique coefficients, an in general, requires N+ multipliers an N two-input aers for implementation Direct form IIR filters: Filter structures in which the multiplier coefficients are precisely the coefficients of the transfer function

3 3 Direct Form IIR Digital Filter Direct Form IIR Digital Filter Structures Structures Consier for simplicity a 3r-orer IIR filter with a transfer function We can implement H() as a cascae of two filter sections as shown on the next slie p p p p D P H ) ( ) ( ) (

4 4 Direct Form IIR Digital Filter Direct Form IIR Digital Filter Structures Structures where p p p p P X W H ) ( ) ( ) ( ) ( () H ) H ( W () X () Y () D W Y H ) ( ) ( ) ( ) (

5 Direct Form IIR Digital Filter Structures The filter section H () can be seen to be an FIR filter an can be realie as shown below w n] p x[ n] + p x[ n ] + p x[ n ] + p x[ n [ 0 3 3] 5

6 Direct Form IIR Digital Filter Structures 6 The time-omain representation of H () is given by y[ n] w[ n] y[ n ] y[ n ] 3y[ n 3] Realiation of H () follows from the above equation an is shown on the right

7 Direct Form IIR Digital Filter Structures A cascae of the two structures realiing H () an H () leas to the realiation of H() shown below an is known as the irect form I structure 7

8 Direct Form IIR Digital Filter Structures Note: The irect form I structure is noncanonic as it employs 6 elays to realie a 3r-orer transfer function A transpose of the irect form I structure is shown on the right an is calle the irect form I t structure 8

9 Direct Form IIR Digital Filter Structures Various other noncanonic irect form structures can be erive by simple block iagram manipulations as shown below 9

10 Direct Form IIR Digital Filter Structures Observe in the irect form structure shown below, the signal variable at noes an are the same, an hence the two top elays can be share ' 0

11 Direct Form IIR Digital Filter Structures Likewise, the signal variables at noes an ' are the same, permitting the sharing of the mile two elays Following the same argument, the bottom two elays can be share Sharing of all elays reuces the total number of elays to 3 resulting in a canonic realiation shown on the next slie along with its transpose structure

12 Direct Form IIR Digital Filter Structures Direct form realiations of an N-th orer IIR transfer function shoul be evient

13 Cascae Form IIR Digital Filter Structures 3 By expressing the numerator an the enominator polynomials of the transfer function as a prouct of polynomials of lower egree, a igital filter can be realie as a cascae of low-orer filter sections Consier, for example, H() P()/D() expresse as P( ) P ( ) ( ) ( ) ( ) P P3 H D( ) D ( ) D ( ) D ( ) 3

14 Cascae Form IIR Digital Filter Structures Examples of cascae realiations obtaine by ifferent pole-ero pairings are shown below 4

15 Cascae Form IIR Digital Filter Structures Examples of cascae realiations obtaine by ifferent orering of sections are shown below 5

16 Cascae Form IIR Digital Filter Structures There are altogether a total of 36 ifferent cascae realiations of P ( ) P ( ) P ( ) H( ) D ( ) D ( ) D3 ( ) base on pole-ero-pairings an orering Due to finite worlength effects, each such cascae realiation behaves ifferently from others 6

17 Cascae Form IIR Digital Filter Structures Usually, the polynomials are factore into a prouct of st-orer an n-orer polynomials: + β + k k H p β ( ) 0 k + αk + αk In the above, for a first-orer factor α β 0 k k 7

18 Cascae Form IIR Digital Filter Structures Consier the 3r-orer transfer function + β + β + β H( ) p0 + α + α + α One possible realiation is shown below 8

19 Cascae Form IIR Digital Filter Structures Example- Direct form II an cascae form realiations of H( ) are shown on the next slie

20 Cascae Form IIR Digital Filter Structures Direct form II Cascae form 0

21 Parallel Form IIR Digital Filter Structures A partial-fraction expansion of the transfer function in leas to the parallel form I structure Assuming simple poles, the transfer function H() can be expresse as γ 0k ( ) γ 0 + k + αk In the above for a real pole α + γk H + αk k γ k 0

22 Parallel Form IIR Digital Filter Structures A irect partial-fraction expansion of the transfer function in leas to the parallel form II structure Assuming simple poles, the transfer function H() can be expresse as In the above for a real pole α k δ + 0k + δ k H( ) δ0 + αk + αk k δ k 0

23 Parallel Form IIR Digital Filter Structures The two basic parallel realiations of a 3rorer IIR transfer function are shown below 3 Parallel form I Parallel form II

24 Parallel Form IIR Digital Filter Structures Example - A partial-fraction expansion of H( ) in yiels H( )

25 Parallel Form IIR Digital Filter Structures The corresponing parallel form I realiation is shown below 5

26 Parallel Form IIR Digital Filter Structures 6 Likewise, a partial-fraction expansion of H() in yiels H( ) The corresponing parallel form II realiation is shown on the right

27 Realiation Using MATLAB The cascae form requires the factoriation of the transfer function which can be evelope using the M-file psos The statement sos psos(,p,k) generates a matrix sos containing the coefficients of each n-orer section of the equivalent transfer function H() etermine from its pole-ero form 7

28 Realiation Using MATLAB 8 sos is an L 6 matrix of the form sos p p p 0 0 0L p p p L p p p L 0 0 0L L L whose i-th row contains the coefficients{ p il } an { il }, of the the numerator an enominator polynomials of the i-th norer section

29 9 Realiation Using MATLAB Realiation Using MATLAB L enotes the number of sections The form of the overall transfer function is given by Program 6_ can be use to factorie an FIR an an IIR transfer function L i i i i i i i L i i p p p H H 0 0 ) ( ) (

30 Realiation Using MATLAB Note: An FIR transfer function can be treate as an IIR transfer function with a constant numerator of unity an a enominator which is the polynomial escribing the FIR transfer function 30

31 Realiation Using MATLAB Parallel forms I an II can be evelope using the functions resiue an resiue, respectively Program 6_ uses these two functions 3

32 Realiation of Allpass Filters 3 An M-th orer real-coefficient allpass transfer function A M () is characterie by M unique coefficients as here the numerator is the mirror-image polynomial of the enominator A irect form realiation of A M () requires M multipliers Objective - Develop realiations of A M () requiring only M multipliers

33 Realiation Using Multiplier Extraction Approach Now, an arbitrary allpass transfer function can be expresse as a prouct of n-orer an/or st-orer allpass transfer functions We consier first the minimum multiplier realiation of a st-orer an a n-orer allpass transfer functions 33

34 First-Orer Allpass Structures Consier first the st-orer allpass transfer function given by + A ( ) + We shall realie the above transfer function in the form a structure containing a single multiplier as shown below X Y 34 Y X

35 First-Orer Allpass Structures 35 We express the transfer function A ( ) Y/ in terms of the transfer parameters of the two-pair as tt t ( tt tt) A ( ) t + t t A comparison of the above with A ( ) + yiels t, t, + tt tt X

36 First-Orer Allpass Structures 36 Substituting t an t in tt tt we get t t There are 4 possible solutions to the above equation: Type A: Type B: t t, t, t, t, t, t +, t

37 First-Orer Allpass Structures Type A t : Type B : t, t, t t t, t, t, t, t + We now evelop the two-pair structure for the Type A allpass transfer function 37

38 First-Orer Allpass Structures From the transfer parameters of this allpass we arrive at the input-output relations: Y X X Y X + ( ) X Y + X A realiation of the above two-pair is sketche below 38

39 First-Orer Allpass Structures X By constraining the, Y terminal-pair with the multiplier, we arrive at the Type A allpass filter structure shown below Type A 39

40 First-Orer Allpass Structures In a similar fashion, the other three singlemultiplier first-orer allpass filter structures can be evelope as shown below Type B Type A t 40 Type B t

41 Secon-Orer Allpass Structures A n-orer allpass transfer function is characterie by unique coefficients Hence, it can be realie using only multipliers Type allpass transfer function: A ( )

42 Type Allpass Structures 4

43 Type 3 Allpass Structures Type 3 allpass transfer function: + + A 3( )

44 Type 3 Allpass Structures 44

45 Realiation Using Multiplier Extraction Approach Example - Realie A ( ) ( ( 0. 4 A 3-multiplier cascae realiation of the above allpass transfer function is shown below + 3 )( )( ) ) 45

46 Realiation Using Two-Pair Extraction Approach The stability test algorithm escribe earlier in the course also leas to an elegant realiation of an Mth-orer allpass transfer function The algorithm is base on the evelopment of a series of ( m ) th-orer allpass transfer functions A m () from an mth-orer allpass transfer function A m () for m M, M,..., 46

47 Realiation Using Two-Pair Extraction Approach 47 Let A ( m) m + m + m + + ( ) ( m ) m + m m We use the recursion Am ( ) km A m m ( ) [ ], km Am ( ) where k m Am ( ) m It has been shown earlier that A M () is stable if an only if k for m M, M,..., < m... + m m M, M,...,

48 Realiation Using Two-Pair Extraction Approach 48 If the allpass transfer function A m () is expresse in the form... m A m ' ' ' ( m ) ( m) m+ m ( ) '... ' ( m ) ' ( ) m + m then the coefficients of A m () are simply relate to the coefficients of A m () through ' i m m i i, i m m

49 Realiation Using Two-Pair Extraction Approach To evelop the realiation metho we express A m () in terms of A m (): A m ( ) k m + + km Am ( ) ( ) We realie A m () in the form shown below A m 49 A m () X Y t t t t Y A m () X

50 Realiation Using Two-Pair Extraction Approach The transfer function A m ( ) Y / X of the constraine two-pair can be expresse as A m ( ) ( t t tam Comparing the above with t t t ) A ( ) m ( ) 50 A m ( ) k m + + km Am ( ) ( ) we arrive at the two-pair transfer parameters A m

51 Realiation Using Two-Pair Extraction Approach t km, t km tt tt Substituting t k m an t km in the equation above we get tt ( km) There are a number of solutions for an t t 5

52 t Realiation Using Two-Pair Extraction Approach Some possible solutions are given below: m m km), t t k, t k, t ( + t t km, t km, t, t km km, t km, t km, t km km, t km, t ( km), t k m 5

53 Realiation Using Two-Pair Extraction Approach 53 Consier the solution t km, t km, t ( km), t Corresponing input-output relations are Y kmx ( km) X Y X k m X A irect realiation of the above equations leas to the 3-multiplier two-pair shown on the next slie

54 Realiation Using Two-Pair Extraction Approach The transfer parameters t km, t km, t ( km), t + lea to the 4-multiplier two-pair structure shown below k m 54

55 t Realiation Using Two-Pair Extraction Approach Likewise, the transfer parameters km, t km, t km, t lea to the 4-multiplier two-pair structure shown below km 55

56 Realiation Using Two-Pair Extraction Approach A -multiplier realiation can be erive by manipulating the input-output relations: Y kmx ( km) X Y X k m X Making use of the secon equation, we can rewrite the first equation as Y k Y + m X 56

57 Realiation Using Two-Pair Extraction Approach A irect realiation of Y k Y + Y m X X km X lea to the -multiplier two-pair structure, known as the lattice structure, shown below 57

58 Realiation Using Two-Pair Extraction Approach Consier the two-pair escribe by t k, t k, t ( k ), t + m m Its input-output relations are given by Y kmx + ( km) X Y ( + km) X km X Define V km ( X ) X m k m 58

59 Realiation Using Two-Pair Extraction Approach We can then rewrite the input-output relations as Y V + X an Y X + The corresponing -multiplier realiation is shown below V V 59

60 Realiation Using Two-Pair Extraction Approach An mth-orer allpass transfer function A m () is then realie by constraining any one of the two-pairs evelope earlier by the ( m )th-orer allpass transfer function A m () 60

61 Realiation Using Two-Pair Extraction Approach The process is repeate until the constraining transfer function is A0 () The complete realiation of A M () base on the extraction of the two-pair lattice is shown below 6

62 Realiation Using Two-Pair Extraction Approach 6 It follows from our earlier iscussion that A M () is stable if the magnitues of all multiplier coefficients in the realiation are less than, i.e., k m < for m M, M,..., The cascae lattice allpass filter structure requires M multipliers A realiation with M multipliers is obtaine if instea the single multiplier two-pair is use

63 63 Realiation Using Two-Pair Realiation Using Two-Pair Extraction Approach Extraction Approach Example - Realie ) ( A

64 Realiation Using Two-Pair Extraction Approach We first realie A 3( ) in the form of a lattice two-pair characterie by the multiplier coefficient k an constraine by a n-orer allpass as inicate below A ( ) 64 k3 0.

65 Realiation Using Two-Pair Extraction Approach The allpass transfer function A ( ) is of the form ' ' + + A ( ) + ' Its coefficients are given by ' + ' 0.4 ( 0.)(0.8) 3 3 ( 0.) ' 0.8 ( 0.)(0.4) 3 3 ( 0.)

66 Realiation Using Two-Pair Extraction Approach Next, the allpass A ( ) is realie as a lattice two-pair characterie by the multiplier coefficient k ' an constraine by an allpass A ( ) as inicate below 66 k3 0., k

67 Realiation Using Two-Pair Extraction Approach The allpass transfer function A ( ) is of the form " + A ( ) " + It coefficient is given by ' ' ' ' " ( ' ) + '

68 Realiation Using Two-Pair Extraction Approach Finally, the allpass A ( ) is realie as a lattice two-pair characterie by the multiplier coefficient k " an constraine by an allpass A0 ( ) as inicate below A 3( ) 68 k3 0., k , A ( ) A ( ) k

69 Cascae Lattice Realiation Using MATLAB The M-file polyrc can be use to realie an allpass transfer function in the cascae lattice form To this en Program 6_3 can be employe 69

Consider for simplicity a 3rd-order IIR filter with a transfer function. where

Consider for simplicity a 3rd-order IIR filter with a transfer function. where Basic IIR Digital Filter The causal IIR igital filters we are concerne with in this course are characterie by a real rational transfer function of or, equivalently by a constant coefficient ifference equation

More information

Tunable Lowpass and Highpass Digital Filters. Tunable IIR Digital Filters. We have shown earlier that the 1st-order lowpass transfer function

Tunable Lowpass and Highpass Digital Filters. Tunable IIR Digital Filters. We have shown earlier that the 1st-order lowpass transfer function Tuable IIR Digital Filters Tuable Lowpass a We have escribe earlier two st-orer a two -orer IIR igital trasfer fuctios with tuable frequecy respose characteristics We shall show ow that these trasfer fuctios

More information

We have shown earlier that the 1st-order lowpass transfer function

We have shown earlier that the 1st-order lowpass transfer function Tunable IIR Digital Filters We have described earlier two st-order and two nd-order IIR digital transfer functions with tunable frequency response characteristics We shall show now that these transfer

More information

Tunable IIR Digital Filters

Tunable IIR Digital Filters Tunable IIR Digital Filters We have described earlier two st-order and two nd-order IIR digital transfer functions with tunable frequency response characteristics We shall show now that these transfer

More information

Digital Filter Structures. Basic IIR Digital Filter Structures. of an LTI digital filter is given by the convolution sum or, by the linear constant

Digital Filter Structures. Basic IIR Digital Filter Structures. of an LTI digital filter is given by the convolution sum or, by the linear constant Digital Filter Chapter 8 Digital Filter Block Diagram Representation Equivalent Basic FIR Digital Filter Basic IIR Digital Filter. Block Diagram Representation In the time domain, the input-output relations

More information

Digital Signal Processing II Lecture 2: FIR & IIR Filter Design

Digital Signal Processing II Lecture 2: FIR & IIR Filter Design Digital Signal Processing II Lecture : FIR & IIR Filter Design Marc Moonen Dept EE/ESAT, KULeuven marcmoonen@esatuleuvenbe wwwesatuleuvenbe/sc/ DSP-II p PART-I : Filter Design/Realiation Step- : efine

More information

Let H(z) = P(z)/Q(z) be the system function of a rational form. Let us represent both P(z) and Q(z) as polynomials of z (not z -1 )

Let H(z) = P(z)/Q(z) be the system function of a rational form. Let us represent both P(z) and Q(z) as polynomials of z (not z -1 ) Review: Poles and Zeros of Fractional Form Let H() = P()/Q() be the system function of a rational form. Let us represent both P() and Q() as polynomials of (not - ) Then Poles: the roots of Q()=0 Zeros:

More information

6.003 Homework #7 Solutions

6.003 Homework #7 Solutions 6.003 Homework #7 Solutions Problems. Secon-orer systems The impulse response of a secon-orer CT system has the form h(t) = e σt cos(ω t + φ)u(t) where the parameters σ, ω, an φ are relate to the parameters

More information

Optimum Ordering and Pole-Zero Pairing of the Cascade Form IIR. Digital Filter

Optimum Ordering and Pole-Zero Pairing of the Cascade Form IIR. Digital Filter Optimum Ordering and Pole-Zero Pairing of the Cascade Form IIR Digital Filter There are many possible cascade realiations of a higher order IIR transfer function obtained by different pole-ero pairings

More information

THE ACCURATE ELEMENT METHOD: A NEW PARADIGM FOR NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS

THE ACCURATE ELEMENT METHOD: A NEW PARADIGM FOR NUMERICAL SOLUTION OF ORDINARY DIFFERENTIAL EQUATIONS THE PUBISHING HOUSE PROCEEDINGS O THE ROMANIAN ACADEMY, Series A, O THE ROMANIAN ACADEMY Volume, Number /, pp. 6 THE ACCURATE EEMENT METHOD: A NEW PARADIGM OR NUMERICA SOUTION O ORDINARY DIERENTIA EQUATIONS

More information

23 Implicit differentiation

23 Implicit differentiation 23 Implicit ifferentiation 23.1 Statement The equation y = x 2 + 3x + 1 expresses a relationship between the quantities x an y. If a value of x is given, then a corresponing value of y is etermine. For

More information

Digital Filter Structures

Digital Filter Structures Chapter 8 Digital Filter Structures 清大電機系林嘉文 cwlin@ee.nthu.edu.tw 03-5731152 8-1 Block Diagram Representation The convolution sum description of an LTI discrete-time system can, in principle, be used to

More information

Optimum Ordering and Pole-Zero Pairing. Optimum Ordering and Pole-Zero Pairing Consider the scaled cascade structure shown below

Optimum Ordering and Pole-Zero Pairing. Optimum Ordering and Pole-Zero Pairing Consider the scaled cascade structure shown below Pole-Zero Pairing of the Cascade Form IIR Digital Filter There are many possible cascade realiations of a higher order IIR transfer function obtained by different pole-ero pairings and ordering Each one

More information

arxiv: v1 [physics.flu-dyn] 8 May 2014

arxiv: v1 [physics.flu-dyn] 8 May 2014 Energetics of a flui uner the Boussinesq approximation arxiv:1405.1921v1 [physics.flu-yn] 8 May 2014 Kiyoshi Maruyama Department of Earth an Ocean Sciences, National Defense Acaemy, Yokosuka, Kanagawa

More information

'HVLJQ &RQVLGHUDWLRQ LQ 0DWHULDO 6HOHFWLRQ 'HVLJQ 6HQVLWLYLW\,1752'8&7,21

'HVLJQ &RQVLGHUDWLRQ LQ 0DWHULDO 6HOHFWLRQ 'HVLJQ 6HQVLWLYLW\,1752'8&7,21 Large amping in a structural material may be either esirable or unesirable, epening on the engineering application at han. For example, amping is a esirable property to the esigner concerne with limiting

More information

Transmission Line Matrix (TLM) network analogues of reversible trapping processes Part B: scaling and consistency

Transmission Line Matrix (TLM) network analogues of reversible trapping processes Part B: scaling and consistency Transmission Line Matrix (TLM network analogues of reversible trapping processes Part B: scaling an consistency Donar e Cogan * ANC Eucation, 308-310.A. De Mel Mawatha, Colombo 3, Sri Lanka * onarecogan@gmail.com

More information

Math Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors

Math Notes on differentials, the Chain Rule, gradients, directional derivative, and normal vectors Math 18.02 Notes on ifferentials, the Chain Rule, graients, irectional erivative, an normal vectors Tangent plane an linear approximation We efine the partial erivatives of f( xy, ) as follows: f f( x+

More information

EE 370L Controls Laboratory. Laboratory Exercise #7 Root Locus. Department of Electrical and Computer Engineering University of Nevada, at Las Vegas

EE 370L Controls Laboratory. Laboratory Exercise #7 Root Locus. Department of Electrical and Computer Engineering University of Nevada, at Las Vegas EE 370L Controls Laboratory Laboratory Exercise #7 Root Locus Department of Electrical an Computer Engineering University of Nevaa, at Las Vegas 1. Learning Objectives To emonstrate the concept of error

More information

Math 1271 Solutions for Fall 2005 Final Exam

Math 1271 Solutions for Fall 2005 Final Exam Math 7 Solutions for Fall 5 Final Eam ) Since the equation + y = e y cannot be rearrange algebraically in orer to write y as an eplicit function of, we must instea ifferentiate this relation implicitly

More information

Homework 7 Due 18 November at 6:00 pm

Homework 7 Due 18 November at 6:00 pm Homework 7 Due 18 November at 6:00 pm 1. Maxwell s Equations Quasi-statics o a An air core, N turn, cylinrical solenoi of length an raius a, carries a current I Io cos t. a. Using Ampere s Law, etermine

More information

The Sokhotski-Plemelj Formula

The Sokhotski-Plemelj Formula hysics 24 Winter 207 The Sokhotski-lemelj Formula. The Sokhotski-lemelj formula The Sokhotski-lemelj formula is a relation between the following generalize functions (also calle istributions), ±iǫ = iπ(),

More information

G j dq i + G j. q i. = a jt. and

G j dq i + G j. q i. = a jt. and Lagrange Multipliers Wenesay, 8 September 011 Sometimes it is convenient to use reunant coorinates, an to effect the variation of the action consistent with the constraints via the metho of Lagrange unetermine

More information

Applications of the Wronskian to ordinary linear differential equations

Applications of the Wronskian to ordinary linear differential equations Physics 116C Fall 2011 Applications of the Wronskian to orinary linear ifferential equations Consier a of n continuous functions y i (x) [i = 1,2,3,...,n], each of which is ifferentiable at least n times.

More information

Adjustable Fractional-Delay Filters Utilizing the Farrow Structure and Multirate Techniques

Adjustable Fractional-Delay Filters Utilizing the Farrow Structure and Multirate Techniques Ajustable Fractional-Delay Filters Utilizing the Farrow Structure an Multirate Techniques Håkan Johansson (hakanj@isy.liu.se)* an Ewa Hermanowicz (hewa@eti.pg.ga.pl)** *Division of Electronics Systems,

More information

Linear First-Order Equations

Linear First-Order Equations 5 Linear First-Orer Equations Linear first-orer ifferential equations make up another important class of ifferential equations that commonly arise in applications an are relatively easy to solve (in theory)

More information

Implicit Differentiation

Implicit Differentiation Implicit Differentiation Thus far, the functions we have been concerne with have been efine explicitly. A function is efine explicitly if the output is given irectly in terms of the input. For instance,

More information

ELEC3114 Control Systems 1

ELEC3114 Control Systems 1 ELEC34 Control Systems Linear Systems - Moelling - Some Issues Session 2, 2007 Introuction Linear systems may be represente in a number of ifferent ways. Figure shows the relationship between various representations.

More information

Math 1B, lecture 8: Integration by parts

Math 1B, lecture 8: Integration by parts Math B, lecture 8: Integration by parts Nathan Pflueger 23 September 2 Introuction Integration by parts, similarly to integration by substitution, reverses a well-known technique of ifferentiation an explores

More information

Unit #6 - Families of Functions, Taylor Polynomials, l Hopital s Rule

Unit #6 - Families of Functions, Taylor Polynomials, l Hopital s Rule Unit # - Families of Functions, Taylor Polynomials, l Hopital s Rule Some problems an solutions selecte or aapte from Hughes-Hallett Calculus. Critical Points. Consier the function f) = 54 +. b) a) Fin

More information

Table of Common Derivatives By David Abraham

Table of Common Derivatives By David Abraham Prouct an Quotient Rules: Table of Common Derivatives By Davi Abraham [ f ( g( ] = [ f ( ] g( + f ( [ g( ] f ( = g( [ f ( ] g( g( f ( [ g( ] Trigonometric Functions: sin( = cos( cos( = sin( tan( = sec

More information

19 Eigenvalues, Eigenvectors, Ordinary Differential Equations, and Control

19 Eigenvalues, Eigenvectors, Ordinary Differential Equations, and Control 19 Eigenvalues, Eigenvectors, Orinary Differential Equations, an Control This section introuces eigenvalues an eigenvectors of a matrix, an iscusses the role of the eigenvalues in etermining the behavior

More information

2Algebraic ONLINE PAGE PROOFS. foundations

2Algebraic ONLINE PAGE PROOFS. foundations Algebraic founations. Kick off with CAS. Algebraic skills.3 Pascal s triangle an binomial expansions.4 The binomial theorem.5 Sets of real numbers.6 Surs.7 Review . Kick off with CAS Playing lotto Using

More information

θ x = f ( x,t) could be written as

θ x = f ( x,t) could be written as 9. Higher orer PDEs as systems of first-orer PDEs. Hyperbolic systems. For PDEs, as for ODEs, we may reuce the orer by efining new epenent variables. For example, in the case of the wave equation, (1)

More information

Rules of Differentiation

Rules of Differentiation LECTURE 2 Rules of Differentiation At te en of Capter 2, we finally arrive at te following efinition of te erivative of a function f f x + f x x := x 0 oing so only after an extene iscussion as wat te

More information

On classical orthogonal polynomials and differential operators

On classical orthogonal polynomials and differential operators INSTITUTE OF PHYSICS PUBLISHING JOURNAL OF PHYSICS A: MATHEMATICAL AND GENERAL J. Phys. A: Math. Gen. 38 2005) 6379 6383 oi:10.1088/0305-4470/38/28/010 On classical orthogonal polynomials an ifferential

More information

and from it produce the action integral whose variation we set to zero:

and from it produce the action integral whose variation we set to zero: Lagrange Multipliers Monay, 6 September 01 Sometimes it is convenient to use reunant coorinates, an to effect the variation of the action consistent with the constraints via the metho of Lagrange unetermine

More information

SYNCHRONOUS SEQUENTIAL CIRCUITS

SYNCHRONOUS SEQUENTIAL CIRCUITS CHAPTER SYNCHRONOUS SEUENTIAL CIRCUITS Registers an counters, two very common synchronous sequential circuits, are introuce in this chapter. Register is a igital circuit for storing information. Contents

More information

Quantum Mechanics in Three Dimensions

Quantum Mechanics in Three Dimensions Physics 342 Lecture 20 Quantum Mechanics in Three Dimensions Lecture 20 Physics 342 Quantum Mechanics I Monay, March 24th, 2008 We begin our spherical solutions with the simplest possible case zero potential.

More information

Math 180, Exam 2, Fall 2012 Problem 1 Solution. (a) The derivative is computed using the Chain Rule twice. 1 2 x x

Math 180, Exam 2, Fall 2012 Problem 1 Solution. (a) The derivative is computed using the Chain Rule twice. 1 2 x x . Fin erivatives of the following functions: (a) f() = tan ( 2 + ) ( ) 2 (b) f() = ln 2 + (c) f() = sin() Solution: Math 80, Eam 2, Fall 202 Problem Solution (a) The erivative is compute using the Chain

More information

Thermal conductivity of graded composites: Numerical simulations and an effective medium approximation

Thermal conductivity of graded composites: Numerical simulations and an effective medium approximation JOURNAL OF MATERIALS SCIENCE 34 (999)5497 5503 Thermal conuctivity of grae composites: Numerical simulations an an effective meium approximation P. M. HUI Department of Physics, The Chinese University

More information

The derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x)

The derivative of a function f(x) is another function, defined in terms of a limiting expression: f(x + δx) f(x) Y. D. Chong (2016) MH2801: Complex Methos for the Sciences 1. Derivatives The erivative of a function f(x) is another function, efine in terms of a limiting expression: f (x) f (x) lim x δx 0 f(x + δx)

More information

Final Exam Study Guide and Practice Problems Solutions

Final Exam Study Guide and Practice Problems Solutions Final Exam Stuy Guie an Practice Problems Solutions Note: These problems are just some of the types of problems that might appear on the exam. However, to fully prepare for the exam, in aition to making

More information

II. First variation of functionals

II. First variation of functionals II. First variation of functionals The erivative of a function being zero is a necessary conition for the etremum of that function in orinary calculus. Let us now tackle the question of the equivalent

More information

A Weak First Digit Law for a Class of Sequences

A Weak First Digit Law for a Class of Sequences International Mathematical Forum, Vol. 11, 2016, no. 15, 67-702 HIKARI Lt, www.m-hikari.com http://x.oi.org/10.1288/imf.2016.6562 A Weak First Digit Law for a Class of Sequences M. A. Nyblom School of

More information

Linear and quadratic approximation

Linear and quadratic approximation Linear an quaratic approximation November 11, 2013 Definition: Suppose f is a function that is ifferentiable on an interval I containing the point a. The linear approximation to f at a is the linear function

More information

THE VAN KAMPEN EXPANSION FOR LINKED DUFFING LINEAR OSCILLATORS EXCITED BY COLORED NOISE

THE VAN KAMPEN EXPANSION FOR LINKED DUFFING LINEAR OSCILLATORS EXCITED BY COLORED NOISE Journal of Soun an Vibration (1996) 191(3), 397 414 THE VAN KAMPEN EXPANSION FOR LINKED DUFFING LINEAR OSCILLATORS EXCITED BY COLORED NOISE E. M. WEINSTEIN Galaxy Scientific Corporation, 2500 English Creek

More information

d dx But have you ever seen a derivation of these results? We ll prove the first result below. cos h 1

d dx But have you ever seen a derivation of these results? We ll prove the first result below. cos h 1 Lecture 5 Some ifferentiation rules Trigonometric functions (Relevant section from Stewart, Seventh Eition: Section 3.3) You all know that sin = cos cos = sin. () But have you ever seen a erivation of

More information

Experimental Robustness Study of a Second-Order Sliding Mode Controller

Experimental Robustness Study of a Second-Order Sliding Mode Controller Experimental Robustness Stuy of a Secon-Orer Sliing Moe Controller Anré Blom, Bram e Jager Einhoven University of Technology Department of Mechanical Engineering P.O. Box 513, 5600 MB Einhoven, The Netherlans

More information

How to manipulate Frequencies in Discrete-time Domain? Two Main Approaches

How to manipulate Frequencies in Discrete-time Domain? Two Main Approaches How to manipulate Frequencies in Discrete-time Domain? Two Main Approaches Difference Equations (an LTI system) x[n]: input, y[n]: output That is, building a system that maes use of the current and previous

More information

Chapter 9 Method of Weighted Residuals

Chapter 9 Method of Weighted Residuals Chapter 9 Metho of Weighte Resiuals 9- Introuction Metho of Weighte Resiuals (MWR) is an approimate technique for solving bounary value problems. It utilizes a trial functions satisfying the prescribe

More information

TMA 4195 Matematisk modellering Exam Tuesday December 16, :00 13:00 Problems and solution with additional comments

TMA 4195 Matematisk modellering Exam Tuesday December 16, :00 13:00 Problems and solution with additional comments Problem F U L W D g m 3 2 s 2 0 0 0 0 2 kg 0 0 0 0 0 0 Table : Dimension matrix TMA 495 Matematisk moellering Exam Tuesay December 6, 2008 09:00 3:00 Problems an solution with aitional comments The necessary

More information

Center of Gravity and Center of Mass

Center of Gravity and Center of Mass Center of Gravity an Center of Mass 1 Introuction. Center of mass an center of gravity closely parallel each other: they both work the same way. Center of mass is the more important, but center of gravity

More information

Lie symmetry and Mei conservation law of continuum system

Lie symmetry and Mei conservation law of continuum system Chin. Phys. B Vol. 20, No. 2 20 020 Lie symmetry an Mei conservation law of continuum system Shi Shen-Yang an Fu Jing-Li Department of Physics, Zhejiang Sci-Tech University, Hangzhou 3008, China Receive

More information

Nested Saturation with Guaranteed Real Poles 1

Nested Saturation with Guaranteed Real Poles 1 Neste Saturation with Guarantee Real Poles Eric N Johnson 2 an Suresh K Kannan 3 School of Aerospace Engineering Georgia Institute of Technology, Atlanta, GA 3332 Abstract The global stabilization of asymptotically

More information

Conservation Laws. Chapter Conservation of Energy

Conservation Laws. Chapter Conservation of Energy 20 Chapter 3 Conservation Laws In orer to check the physical consistency of the above set of equations governing Maxwell-Lorentz electroynamics [(2.10) an (2.12) or (1.65) an (1.68)], we examine the action

More information

The Principle of Least Action

The Principle of Least Action Chapter 7. The Principle of Least Action 7.1 Force Methos vs. Energy Methos We have so far stuie two istinct ways of analyzing physics problems: force methos, basically consisting of the application of

More information

Math 342 Partial Differential Equations «Viktor Grigoryan

Math 342 Partial Differential Equations «Viktor Grigoryan Math 342 Partial Differential Equations «Viktor Grigoryan 6 Wave equation: solution In this lecture we will solve the wave equation on the entire real line x R. This correspons to a string of infinite

More information

Review of Differentiation and Integration for Ordinary Differential Equations

Review of Differentiation and Integration for Ordinary Differential Equations Schreyer Fall 208 Review of Differentiation an Integration for Orinary Differential Equations In this course you will be expecte to be able to ifferentiate an integrate quickly an accurately. Many stuents

More information

Q( t) = T C T =! " t 3,t 2,t,1# Q( t) T = C T T T. Announcements. Bezier Curves and Splines. Review: Third Order Curves. Review: Cubic Examples

Q( t) = T C T =!  t 3,t 2,t,1# Q( t) T = C T T T. Announcements. Bezier Curves and Splines. Review: Third Order Curves. Review: Cubic Examples Bezier Curves an Splines December 1, 2015 Announcements PA4 ue one week from toay Submit your most fun test cases, too! Infinitely thin planes with parallel sies μ oesn t matter Term Paper ue one week

More information

Calculus and optimization

Calculus and optimization Calculus an optimization These notes essentially correspon to mathematical appenix 2 in the text. 1 Functions of a single variable Now that we have e ne functions we turn our attention to calculus. A function

More information

Integration Review. May 11, 2013

Integration Review. May 11, 2013 Integration Review May 11, 2013 Goals: Review the funamental theorem of calculus. Review u-substitution. Review integration by parts. Do lots of integration eamples. 1 Funamental Theorem of Calculus In

More information

LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION

LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION The Annals of Statistics 1997, Vol. 25, No. 6, 2313 2327 LATTICE-BASED D-OPTIMUM DESIGN FOR FOURIER REGRESSION By Eva Riccomagno, 1 Rainer Schwabe 2 an Henry P. Wynn 1 University of Warwick, Technische

More information

Interconnected Systems of Fliess Operators

Interconnected Systems of Fliess Operators Interconnecte Systems of Fliess Operators W. Steven Gray Yaqin Li Department of Electrical an Computer Engineering Ol Dominion University Norfolk, Virginia 23529 USA Abstract Given two analytic nonlinear

More information

Robustness and Perturbations of Minimal Bases

Robustness and Perturbations of Minimal Bases Robustness an Perturbations of Minimal Bases Paul Van Dooren an Froilán M Dopico December 9, 2016 Abstract Polynomial minimal bases of rational vector subspaces are a classical concept that plays an important

More information

Designing Information Devices and Systems II Fall 2017 Note Theorem: Existence and Uniqueness of Solutions to Differential Equations

Designing Information Devices and Systems II Fall 2017 Note Theorem: Existence and Uniqueness of Solutions to Differential Equations EECS 6B Designing Information Devices an Systems II Fall 07 Note 3 Secon Orer Differential Equations Secon orer ifferential equations appear everywhere in the real worl. In this note, we will walk through

More information

Analytic Scaling Formulas for Crossed Laser Acceleration in Vacuum

Analytic Scaling Formulas for Crossed Laser Acceleration in Vacuum October 6, 4 ARDB Note Analytic Scaling Formulas for Crosse Laser Acceleration in Vacuum Robert J. Noble Stanfor Linear Accelerator Center, Stanfor University 575 San Hill Roa, Menlo Park, California 945

More information

THE USE OF KIRCHOFF S CURRENT LAW AND CUT-SET EQUATIONS IN THE ANALYSIS OF BRIDGES AND TRUSSES

THE USE OF KIRCHOFF S CURRENT LAW AND CUT-SET EQUATIONS IN THE ANALYSIS OF BRIDGES AND TRUSSES Session TH US O KIRCHO S CURRNT LAW AND CUT-ST QUATIONS IN TH ANALYSIS O BRIDGS AND TRUSSS Ravi P. Ramachanran an V. Ramachanran. Department of lectrical an Computer ngineering, Rowan University, Glassboro,

More information

Quantum Algorithms: Problem Set 1

Quantum Algorithms: Problem Set 1 Quantum Algorithms: Problem Set 1 1. The Bell basis is + = 1 p ( 00i + 11i) = 1 p ( 00i 11i) + = 1 p ( 01i + 10i) = 1 p ( 01i 10i). This is an orthonormal basis for the state space of two qubits. It is

More information

16.30/31, Fall 2010 Recitation # 1

16.30/31, Fall 2010 Recitation # 1 6./, Fall Recitation # September, In this recitation we consiere the following problem. Given a plant with open-loop transfer function.569s +.5 G p (s) = s +.7s +.97, esign a feeback control system such

More information

ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS

ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS ALGEBRAIC AND ANALYTIC PROPERTIES OF ARITHMETIC FUNCTIONS MARK SCHACHNER Abstract. When consiere as an algebraic space, the set of arithmetic functions equippe with the operations of pointwise aition an

More information

Lecture 2 Lagrangian formulation of classical mechanics Mechanics

Lecture 2 Lagrangian formulation of classical mechanics Mechanics Lecture Lagrangian formulation of classical mechanics 70.00 Mechanics Principle of stationary action MATH-GA To specify a motion uniquely in classical mechanics, it suffices to give, at some time t 0,

More information

there is no special reason why the value of y should be fixed at y = 0.3. Any y such that

there is no special reason why the value of y should be fixed at y = 0.3. Any y such that 25. More on bivariate functions: partial erivatives integrals Although we sai that the graph of photosynthesis versus temperature in Lecture 16 is like a hill, in the real worl hills are three-imensional

More information

11.7. Implicit Differentiation. Introduction. Prerequisites. Learning Outcomes

11.7. Implicit Differentiation. Introduction. Prerequisites. Learning Outcomes Implicit Differentiation 11.7 Introuction This Section introuces implicit ifferentiation which is use to ifferentiate functions expresse in implicit form (where the variables are foun together). Examples

More information

NOTES ON EULER-BOOLE SUMMATION (1) f (l 1) (n) f (l 1) (m) + ( 1)k 1 k! B k (y) f (k) (y) dy,

NOTES ON EULER-BOOLE SUMMATION (1) f (l 1) (n) f (l 1) (m) + ( 1)k 1 k! B k (y) f (k) (y) dy, NOTES ON EULER-BOOLE SUMMATION JONATHAN M BORWEIN, NEIL J CALKIN, AND DANTE MANNA Abstract We stuy a connection between Euler-MacLaurin Summation an Boole Summation suggeste in an AMM note from 196, which

More information

a 0.45, 0.6, 0.5, 0.4, 0.54 b 0.74, 0.4, 0.47, 0.7, 0.44 c 0.8, 0.18, 0.88, 0.81, 0.08, 0.1 d 5.63, 5.6, 5.3, 5.06, 5.36

a 0.45, 0.6, 0.5, 0.4, 0.54 b 0.74, 0.4, 0.47, 0.7, 0.44 c 0.8, 0.18, 0.88, 0.81, 0.08, 0.1 d 5.63, 5.6, 5.3, 5.06, 5.36 2 3.1 Conversion between fractions an ecimals Questions are targete at the graes inicate 1 Write each of the following sets of numbers in orer of size. Start with the smallest number each time. a 45, 6,

More information

Rank, Trace, Determinant, Transpose an Inverse of a Matrix Let A be an n n square matrix: A = a11 a1 a1n a1 a an a n1 a n a nn nn where is the jth col

Rank, Trace, Determinant, Transpose an Inverse of a Matrix Let A be an n n square matrix: A = a11 a1 a1n a1 a an a n1 a n a nn nn where is the jth col Review of Linear Algebra { E18 Hanout Vectors an Their Inner Proucts Let X an Y be two vectors: an Their inner prouct is ene as X =[x1; ;x n ] T Y =[y1; ;y n ] T (X; Y ) = X T Y = x k y k k=1 where T an

More information

Make graph of g by adding c to the y-values. on the graph of f by c. multiplying the y-values. even-degree polynomial. graph goes up on both sides

Make graph of g by adding c to the y-values. on the graph of f by c. multiplying the y-values. even-degree polynomial. graph goes up on both sides Reference 1: Transformations of Graphs an En Behavior of Polynomial Graphs Transformations of graphs aitive constant constant on the outsie g(x) = + c Make graph of g by aing c to the y-values on the graph

More information

UNDERSTANDING INTEGRATION

UNDERSTANDING INTEGRATION UNDERSTANDING INTEGRATION Dear Reaer The concept of Integration, mathematically speaking, is the "Inverse" of the concept of result, the integration of, woul give us back the function f(). This, in a way,

More information

Assignment 1. g i (x 1,..., x n ) dx i = 0. i=1

Assignment 1. g i (x 1,..., x n ) dx i = 0. i=1 Assignment 1 Golstein 1.4 The equations of motion for the rolling isk are special cases of general linear ifferential equations of constraint of the form g i (x 1,..., x n x i = 0. i=1 A constraint conition

More information

Similar Operators and a Functional Calculus for the First-Order Linear Differential Operator

Similar Operators and a Functional Calculus for the First-Order Linear Differential Operator Avances in Applie Mathematics, 9 47 999 Article ID aama.998.067, available online at http: www.iealibrary.com on Similar Operators an a Functional Calculus for the First-Orer Linear Differential Operator

More information

JUST THE MATHS UNIT NUMBER DIFFERENTIATION 2 (Rates of change) A.J.Hobson

JUST THE MATHS UNIT NUMBER DIFFERENTIATION 2 (Rates of change) A.J.Hobson JUST THE MATHS UNIT NUMBER 10.2 DIFFERENTIATION 2 (Rates of change) by A.J.Hobson 10.2.1 Introuction 10.2.2 Average rates of change 10.2.3 Instantaneous rates of change 10.2.4 Derivatives 10.2.5 Exercises

More information

The canonical controllers and regular interconnection

The canonical controllers and regular interconnection Systems & Control Letters ( www.elsevier.com/locate/sysconle The canonical controllers an regular interconnection A.A. Julius a,, J.C. Willems b, M.N. Belur c, H.L. Trentelman a Department of Applie Mathematics,

More information

DERIVATIVES: LAWS OF DIFFERENTIATION MR. VELAZQUEZ AP CALCULUS

DERIVATIVES: LAWS OF DIFFERENTIATION MR. VELAZQUEZ AP CALCULUS DERIVATIVES: LAWS OF DIFFERENTIATION MR. VELAZQUEZ AP CALCULUS THE DERIVATIVE AS A FUNCTION f x = lim h 0 f x + h f(x) h Last class we examine the limit of the ifference quotient at a specific x as h 0,

More information

A new approach to explicit MPC using self-optimizing control

A new approach to explicit MPC using self-optimizing control 28 American Control Conference Westin Seattle Hotel, Seattle, Washington, USA June -3, 28 WeA3.2 A new approach to explicit MPC using self-optimizing control Henrik Manum, Sriharakumar Narasimhan an Sigur

More information

Section 7.2. The Calculus of Complex Functions

Section 7.2. The Calculus of Complex Functions Section 7.2 The Calculus of Complex Functions In this section we will iscuss limits, continuity, ifferentiation, Taylor series in the context of functions which take on complex values. Moreover, we will

More information

Diagonalization of Matrices Dr. E. Jacobs

Diagonalization of Matrices Dr. E. Jacobs Diagonalization of Matrices Dr. E. Jacobs One of the very interesting lessons in this course is how certain algebraic techniques can be use to solve ifferential equations. The purpose of these notes is

More information

Placement and tuning of resonance dampers on footbridges

Placement and tuning of resonance dampers on footbridges Downloae from orbit.tu.k on: Jan 17, 19 Placement an tuning of resonance ampers on footbriges Krenk, Steen; Brønen, Aners; Kristensen, Aners Publishe in: Footbrige 5 Publication ate: 5 Document Version

More information

Introduction to the Vlasov-Poisson system

Introduction to the Vlasov-Poisson system Introuction to the Vlasov-Poisson system Simone Calogero 1 The Vlasov equation Consier a particle with mass m > 0. Let x(t) R 3 enote the position of the particle at time t R an v(t) = ẋ(t) = x(t)/t its

More information

Lecture XII. where Φ is called the potential function. Let us introduce spherical coordinates defined through the relations

Lecture XII. where Φ is called the potential function. Let us introduce spherical coordinates defined through the relations Lecture XII Abstract We introuce the Laplace equation in spherical coorinates an apply the metho of separation of variables to solve it. This will generate three linear orinary secon orer ifferential equations:

More information

The Sokhotski-Plemelj Formula

The Sokhotski-Plemelj Formula hysics 25 Winter 208 The Sokhotski-lemelj Formula. The Sokhotski-lemelj formula The Sokhotski-lemelj formula is a relation between the following generalize functions (also calle istributions), ±iǫ = iπ(),

More information

Learning in Monopolies with Delayed Price Information

Learning in Monopolies with Delayed Price Information Learning in Monopolies with Delaye Price Information Akio Matsumoto y Chuo University Ferenc Sziarovszky z University of Pécs February 28, 2013 Abstract We call the intercept of the price function with

More information

Equations of lines in

Equations of lines in Roberto s Notes on Linear Algebra Chapter 6: Lines, planes an other straight objects Section 1 Equations of lines in What ou nee to know alrea: The ot prouct. The corresponence between equations an graphs.

More information

Hyperbolic Systems of Equations Posed on Erroneous Curved Domains

Hyperbolic Systems of Equations Posed on Erroneous Curved Domains Hyperbolic Systems of Equations Pose on Erroneous Curve Domains Jan Norström a, Samira Nikkar b a Department of Mathematics, Computational Mathematics, Linköping University, SE-58 83 Linköping, Sween (

More information

Multirate Feedforward Control with State Trajectory Generation based on Time Axis Reversal for Plant with Continuous Time Unstable Zeros

Multirate Feedforward Control with State Trajectory Generation based on Time Axis Reversal for Plant with Continuous Time Unstable Zeros Multirate Feeforwar Control with State Trajectory Generation base on Time Axis Reversal for with Continuous Time Unstable Zeros Wataru Ohnishi, Hiroshi Fujimoto Abstract with unstable zeros is known as

More information

Torque OBJECTIVE INTRODUCTION APPARATUS THEORY

Torque OBJECTIVE INTRODUCTION APPARATUS THEORY Torque OBJECTIVE To verify the rotational an translational conitions for equilibrium. To etermine the center of ravity of a rii boy (meter stick). To apply the torque concept to the etermination of an

More information

Vectors in two dimensions

Vectors in two dimensions Vectors in two imensions Until now, we have been working in one imension only The main reason for this is to become familiar with the main physical ieas like Newton s secon law, without the aitional complication

More information

cosh x sinh x So writing t = tan(x/2) we have 6.4 Integration using tan(x/2) 2t 1 + t 2 cos x = 1 t2 sin x =

cosh x sinh x So writing t = tan(x/2) we have 6.4 Integration using tan(x/2) 2t 1 + t 2 cos x = 1 t2 sin x = 6.4 Integration using tan/ We will revisit the ouble angle ientities: sin = sin/ cos/ = tan/ sec / = tan/ + tan / cos = cos / sin / tan = = tan / sec / tan/ tan /. = tan / + tan / So writing t = tan/ we

More information

Average value of position for the anharmonic oscillator: Classical versus quantum results

Average value of position for the anharmonic oscillator: Classical versus quantum results verage value of position for the anharmonic oscillator: Classical versus quantum results R. W. Robinett Department of Physics, The Pennsylvania State University, University Park, Pennsylvania 682 Receive

More information

Polynomial Inclusion Functions

Polynomial Inclusion Functions Polynomial Inclusion Functions E. e Weert, E. van Kampen, Q. P. Chu, an J. A. Muler Delft University of Technology, Faculty of Aerospace Engineering, Control an Simulation Division E.eWeert@TUDelft.nl

More information

Chapter 2 Governing Equations

Chapter 2 Governing Equations Chapter 2 Governing Equations In the present an the subsequent chapters, we shall, either irectly or inirectly, be concerne with the bounary-layer flow of an incompressible viscous flui without any involvement

More information