Dr. M. Perumal Professor & Head Department of Hydrology Indian Institute of Technology Roorkee INDIA Co-authors: Dr. B. Sahoo & Dr. C.M.

Size: px
Start display at page:

Download "Dr. M. Perumal Professor & Head Department of Hydrology Indian Institute of Technology Roorkee INDIA Co-authors: Dr. B. Sahoo & Dr. C.M."

Transcription

1 Dr.. Perumal Professor & Head Department of Hdrolog Indan Insttute of Tehnolog Roorkee INDIA o-authors: Dr. B. Sahoo & Dr... Rao Dr. Dr... Perumal, Professor & & Head, Dept. Dept. of of Hdrolog, I.I.T. I.I.T. Roorkee, Inda Inda

2 Lneart of hannel routng sheme n SWAT model usng lassal uskngum () method Frameworks of varable parameter uskngum-unge (VP) & uskngum-unge-todn (T) routng methods avalable n lterature Development of varable parameter arth- uskngum (VP) routng method Performane evaluaton rtera Numeral Applaton Trapezodal, Retangular & Trangular hannel setons Performane of, VP, T & VP methods onlusons Presentaton Outlne Dr. Dr... Perumal, Professor & & Head, Dept. Dept. of of Hdrolog, I.I.T. I.I.T. Roorkee, Inda Inda

3 lassfaton of Rver Flood Waves Old lassfaton b Ferrk (985) : Bulk waves Dffuson wave Dnam waves Knemat wave Gravt waves New suggested lassfaton : Bulk waves Dnam waves Gravt waves Dffuson wave AD wave Knemat wave (KW) s a speal ase of AD wave Works n transton range of DW & KW Dr. Dr... Perumal, Professor & & Head, Dept. Dept. of of Hdrolog, I.I.T. I.I.T. Roorkee, Inda Inda VP method 3

4 Development of VP ethod VP- dretl derved from Sant-Venant Equatons. an be used for routng flood waves n sem-nfnte rgd bed prsmat hannels havng an rosssetonal shape. Allows smultaneous omputatons of stage and dsharge hdrographs at an ungauged rver ste. Hpothess used: Durng stead flow n the hannel reah, there ests one-to-one dsharge - depth relatonshp; whh s not vald n ase of unstead flows. Durng unstead flow n the hannel reah, the dsharge observed at an seton has ts orrespondng normal depth at the upstream seton. 4

5 Defnton Sketh of VP ethod u 3 L 3 SB o o u 3 3 d d Routng parameters K V Δ/ Δ L o θ. 5 L Dr. Dr... Perumal, Professor & & Head, Dept. Dept. of of Hdrolog, I.I.T. I.I.T. Roorkee, Inda Inda 5

6 Framework of VP ethod (Perumal & Pone, ) K θ : 3 V o, ( ) S B.5 3, o o, o, θ I ( θ ) 3, Δt ( θ ) ( ) t. K. θ t. K. θ t. K. O I I O. ( )... ( ) t K. θ t. K. θ t. K. θ Δ Numeral sheme of VP method For dervaton detals of VP method, lk here 6

7 Framework of VP ethod (Pone and hagant, 994) O : Kθ O I I 3.5 t K( θ ).5 t Kθ.5 t K( θ ) K( θ ).5 t 3.5 t K( θ ).5 t K θ.5 ( S B ) o In method, the routng parameters reman onstant at ever routng tme step so that 3, makng t mass onservatve. 7

8 8 Framework of T ethod (Todn, 7) : O D D I D D I D D O ) / )( / ( t β ) / )( / ( t β v / β / v β ) /( BS D o β ) /( BS D o β

9 Performane Evaluaton omparatve evaluaton of, VP, T & VP methods wth respet to benhmark solutons of the Sant-Venant equatons. Evaluated these methods b routng a gven hpothetal flood wave n unform prsmat Trapezodal, Retangular and Trangular rossseton hannel reahes of dfferent onfguratons. Dr. Dr... Perumal, Professor & & Head, Dept. Dept. of of Hdrolog, I.I.T. I.I.T. Roorkee, Inda Inda 9

10 Performane Evaluaton rtera η q N ( ) ( ) o N o o Nash and Sutlffe (97) N N EVOL I q t per qper ( q q ) p po ( t t ) qp qpo Negatve ndates underestmaton; Postve ndates over estmaton Dr. Dr... Perumal, Professor & & Head, Dept. Dept. of of Hdrolog, I.I.T. I.I.T. Roorkee, Inda Inda

11 # Total reah length 4 km # km; 5 mn. # Total no. of runs 9847 (for eah of the Sant-Venant, VP & VP methods) Numeral Applaton Parameters Values Gamma, γ.5,.5,.5,.5 hannel bed slope, S o.,.,.8,.5,.4,.,. annng s roughness, n.,.,.3,.4,.5 Intal dsharge, b (m 3 /s). Peak stage, p (m) 5., 8.,.,., 5. Tme-to-peak stage, t p (h) 5.,., 5.,. hannel bottom wdth, b (m). hannel sde slope, z.,., 3., 5. Upstream boundar ondton: b p b t p /( γ ) t t γ t p (, t) ( ( t) ) ep Inflow dsharge hdrographs orrespondng to the nput stage hdrographs n the form of Pearson tpe III dstrbuton are used to generate random szes of dsharge hdrographs.

12 Evaluaton of, VP, T & VP ethods Nash-Sutlffe Effen η (%) (a) method (/S o )( / ) ma alwas performs wth η 5% for all the ases eept fve runs η 95% for all the runs eept 5 (.7%) ases

13 Evaluaton of, VP, T & VP ethods Nash-Sutlffe Effen η (%) (b) VP method (/S o )( / ) ma alwas performs wth η 9% for all the ases eept fve runs η 95% for all the runs eept 5 (.7%) ases 3

14 Evaluaton of, VP, T & VP ethods Nash-Sutlffe Effen η (%) T alwas performs wth η 8% for all the ases eept fve runs 6 () T method 5 (/S o )( / ) ma

15 Evaluaton of, VP, T & VP ethods Nash-Sutlffe Effen η (%) VP alwas performs wth η 9% for all the ases eept nne runs 6 (d) VP method 5 (/S o )( / ) ma

16 Evaluaton of, VP, T & VP ethods Error n Volume EVOL (%) (a) method (/S o )( / ) ma EVOL (%) (b) VP method (/S o )( / ) ma EVOL (%) - - EVOL (%) -3-4 () T method (/S o )( / ) ma -3-4 (d) VP method (/S o )( / ) ma

17 - Evaluaton of, VP, T & VP ethods Error n Peak Dsharge qper (%) - -3 qper (%) -4 (a) method -5 (/S o )( / ) ma (b) VP method -5 (/S o )( / ) ma qper (%) qper (%) -4-5 () T method (/S o )( / ) ma -4-5 (d) VP method (/S o )( / ) ma

18 Evaluaton of, VP, T & VP ethods Error n Tme-to-peak Dsharge tpqer (%) (a) method (/S o )( / ) ma tpqer (%) (b) VP method (/S o )( / ) ma tpqer (%) () T method (/S o )( / ) ma tpqer (%) (d) VP method (/S o )( / ) ma

19 onlusons VP method onsstentl performs better than the & VP method. VP, T & methods are full volume onservatve, whereas the VP method has the tenden to alwas loose mass. Due to the nonlneart dnams of rver flood onveton, the method should not be used n SWAT model. Sne the VP method has a sound phsal bass, t ma be preferred over the other estng methods for ts norporaton n the SWAT model for dealng wth varous feld problems. 9

20

21 athematal Formulaton of VP ethod Sant-Venant Equatons: ontnut equaton: omentum eqn. A t S f v v v S g g t () () S o bed slope; S f frton slope; / water surfae slope; (v/g)(v/) onvetve aeleraton; (/g)(v/t) loal aeleraton. agntudes of varous terms n eqn. () are usuall small n omparson wth S o [Henderson, 966; NER, 975]. Dr. Dr... Perumal, Professor & & Head, Dept. Dept. of of Hdrolog, I.I.T. I.I.T. Roorkee, Inda Inda Bak to Slde 6

22 Important steps of dervaton: Unstead flow relatonshp: Usng equatons (), () (4), (5), & (6) frton slope: athematal Formulaton of VP ethod (, ) ( ) S f / S ( ) Av onsderng / & / : B elert of the flood wave d PdR d v (Henderson,966; NER,975): da / da d 3 / Velot b the annng s frton law: (3) v R S f n 4 PdR d Sf So F So 9 da d (4) (5) /3 / (6) (7) Where Froude number: Bak to Slde 6 F v da d ga (8) To Eq. (4)

23 3 Usng Eq. (7) n (3), unstead dsharge: Smlarl, unstead velot: Hdraul ontnut Eq. (): Usng Eqs. (9) & () n (): Normal dsharge n Eq. (9) an be rewrtten as:.athematal Formulaton of VP ethod Bak to Slde 6 ( ) 9 4, d dr B P F S o (9) () ( ) 9 4, d dr B P F S v v v o v t () () v t ( ) 9 4 d dr B P F S o o (3)

24 4 Where: Whh s n the form of: Usng bnomal seres epanson n Eq. (3) wth (/S )(/) << & Eq. (4): elert an also be wrtten as: Usng Eqs. (9) & (7) n (5):.athematal Formulaton of VP ethod Bak to Slde 6 d dr B P F B S 9 4 a 9 4 d dr B P F B S a (4) (5) (6) (7) ( ) 9 4, d dr B P F S o a (8) 9 4 d dr B P F B S a Where: (9)

25 5 Usng Eq. (8) n ontnut Eq. (): Epressng Eq. (9) at the entre pont of the fnte dfferene grd:.athematal Formulaton of VP ethod Bak to Slde 6 (9) () v a t Fnte-dfferene grd representaton of the VP method omputatonal sheme Δ Δt,, v a v a t

26 6 Eq. () an be reorganzed as: Whh an be wrtten as:.athematal Formulaton of VP ethod Bak to Slde 6 () (),, t a a v a a v [ ] [ ] t K K ) ( ) ( θ θ θ θ

27 Where:.athematal Formulaton of VP ethod Eq. () an also be wrtten n the form of the lassal uskngum routng equaton as: (3) 3 Where: t. K. θ t. K. θ t. K t. K ( ). (4). θ ( θ ) t. K. ( 3 θ ) t. K. θ Bak to Slde 6 θ F K v, a B ga 3 S,, B 4 9 F P B dr d ( ) 7

28 8 The reah storage at an omputatonal tme level an be gven b: orrespondng downstream stage hdrograph an be omputed usng Eq. (4) as: Where:.athematal Formulaton of VP ethod Bak to Slde 6 [ ] ) ( K S θ K Prsm storage Wedge storage ) ( K θ ) ( B Ths proves that the lassal uskngum method advoated b arth (938) has the phsal bass

Charged Particle in a Magnetic Field

Charged Particle in a Magnetic Field Charged Partle n a Magnet Feld Mhael Fowler 1/16/08 Introduton Classall, the fore on a harged partle n eletr and magnet felds s gven b the Lorentz fore law: v B F = q E+ Ths velot-dependent fore s qute

More information

Dr. James C.Y. Guo, P.E., Professor and Director Civil Engineering, U. of Colorado Denver Pitot-Static Tube

Dr. James C.Y. Guo, P.E., Professor and Director Civil Engineering, U. of Colorado Denver Pitot-Static Tube Flow Measurement Dr. James C.Y. Guo, P.E., Professor and Dretor Cvl Engneerng, U. of Colorado Denver Ptot-Stat Tube 1 Ptot-tube for Veloty Measurement Operaton of Ptot-Tube Manometer and Pressure Dfferental

More information

EFFECT OF SPATIAL AND TEMPORAL DISCRETIZATIONS ON THE SIMULATIONS USING CONSTANT-PARAMETER AND VARIABLE-PARAMETER MUSKINGUM METHODS

EFFECT OF SPATIAL AND TEMPORAL DISCRETIZATIONS ON THE SIMULATIONS USING CONSTANT-PARAMETER AND VARIABLE-PARAMETER MUSKINGUM METHODS INDIAN INSTITUTE OF TECHNOLOGY ROORKEE EFFECT OF SPATIAL AND TEMPORAL DISCRETIZATIONS ON THE SIMULATIONS USING CONSTANT-PARAMETER AND VARIABLE-PARAMETER MUSKINGUM METHODS Muthiah Perumal and C. Madhusudana

More information

EVALUATION OF SEISMIC ACTIVE EARTH PRESSURE USING HORIZONTAL SLICE METHOD AND LOG-SPIRAL FAILURE SURFACE

EVALUATION OF SEISMIC ACTIVE EARTH PRESSURE USING HORIZONTAL SLICE METHOD AND LOG-SPIRAL FAILURE SURFACE EVALUATIO OF SEISMIC ACTIVE EARTH PRESSURE USIG HORIZOTAL SLICE METHOD AD LOG-SPIRAL FAILURE SURFACE S. BAISHYA orth Eastern Regonal Insttute of Sene and Tehnology (ERIST), Arunahal Pradesh, Inda A. SARKAR

More information

Lecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES

Lecture 13 APPROXIMATION OF SECOMD ORDER DERIVATIVES COMPUTATIONAL FLUID DYNAMICS: FDM: Appromaton of Second Order Dervatves Lecture APPROXIMATION OF SECOMD ORDER DERIVATIVES. APPROXIMATION OF SECOND ORDER DERIVATIVES Second order dervatves appear n dffusve

More information

Summary ELECTROMAGNETIC FIELDS AT THE WORKPLACES. System layout: exposure to magnetic field only. Quasi-static dosimetric analysis: system layout

Summary ELECTROMAGNETIC FIELDS AT THE WORKPLACES. System layout: exposure to magnetic field only. Quasi-static dosimetric analysis: system layout Internatonal Workshop on LCTROMGNTIC FILDS T TH WORKPLCS 5-7 September 5 Warszawa POLND 3d approah to numeral dosmetr n quas-stat ondtons: problems and eample of solutons Dr. Nola Zoppett - IFC-CNR, Florene,

More information

Numerical Solution of Ordinary Differential Equations

Numerical Solution of Ordinary Differential Equations Numercal Methods (CENG 00) CHAPTER-VI Numercal Soluton of Ordnar Dfferental Equatons 6 Introducton Dfferental equatons are equatons composed of an unknown functon and ts dervatves The followng are examples

More information

Key words: path synthesis, joint clearances, Lagrange s equation, Differential evaluation (DE), optimization.

Key words: path synthesis, joint clearances, Lagrange s equation, Differential evaluation (DE), optimization. Rub Mshra, T.K.Naskar, Sanb Ahara / nternatonal Journal of Engneerng Researh and Applatons (JERA) SSN: 8-96 www.era.om Vol., ssue, Januar -Februar 0, pp.9-99 Snthess of oupler urve of a Four Bar Lnkage

More information

Machine Learning: and 15781, 2003 Assignment 4

Machine Learning: and 15781, 2003 Assignment 4 ahne Learnng: 070 and 578, 003 Assgnment 4. VC Dmenson 30 onts Consder the spae of nstane X orrespondng to all ponts n the D x, plane. Gve the VC dmenson of the followng hpothess spaes. No explanaton requred.

More information

Complement of an Extended Fuzzy Set

Complement of an Extended Fuzzy Set Internatonal Journal of Computer pplatons (0975 8887) Complement of an Extended Fuzzy Set Trdv Jyot Neog Researh Sholar epartment of Mathemats CMJ Unversty, Shllong, Meghalaya usmanta Kumar Sut ssstant

More information

ME 501A Seminar in Engineering Analysis Page 1

ME 501A Seminar in Engineering Analysis Page 1 umercal Solutons of oundary-value Problems n Os ovember 7, 7 umercal Solutons of oundary- Value Problems n Os Larry aretto Mechancal ngneerng 5 Semnar n ngneerng nalyss ovember 7, 7 Outlne Revew stff equaton

More information

INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 3, No 3, 2013

INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 3, No 3, 2013 INTERNATIONAL JOURNAL OF CIVIL AND STRUCTURAL ENGINEERING Volume 3, No 3, 2013 Copyrght by the authors - Lcensee IPA- Under Creatve Commons lcense 3.0 Research artcle ISSN 0976 4399 Sudarshan patowary

More information

Appendix B. The Finite Difference Scheme

Appendix B. The Finite Difference Scheme 140 APPENDIXES Appendx B. The Fnte Dfference Scheme In ths appendx we present numercal technques whch are used to approxmate solutons of system 3.1 3.3. A comprehensve treatment of theoretcal and mplementaton

More information

Characteristic Analysis of Exponential Compact Higher Order Schemes for Convection-Diffusion Equations

Characteristic Analysis of Exponential Compact Higher Order Schemes for Convection-Diffusion Equations Ameran Journal of Computatonal Matemats 0 9- do:0./ajm.0.00 Publsed Onlne June 0 (ttp://www.srp.org/journal/ajm) 9 Caraterst Analss of Eponental Compat Hger Order emes for Conveton-Dffuson Equatons Abstrat

More information

Development of a computer model for long-throated flumes based on manning equation and different side slopes of trapezoidal channels

Development of a computer model for long-throated flumes based on manning equation and different side slopes of trapezoidal channels Iran Agrultural Researh (08) 37() Development of a omputer model for long-throated flumes based on mannng equaton and dfferent sde slopes of trapezodal hannels M. Mahbod * and Sh. Zand-Parsa Department

More information

Influence of Gravity on the Performance Index of Microchannel Heat Exchangers-Experimental Investigations

Influence of Gravity on the Performance Index of Microchannel Heat Exchangers-Experimental Investigations Proeedngs of the World Congress on Engneerng 011 Vol III WCE 011, July 6-8, 011, London, U.K. Influene of Gravty on the Performane Index of Mrohannel Heat Exhangers-Expermental Investgatons Thanhtrung

More information

Lecture 21: Numerical methods for pricing American type derivatives

Lecture 21: Numerical methods for pricing American type derivatives Lecture 21: Numercal methods for prcng Amercan type dervatves Xaoguang Wang STAT 598W Aprl 10th, 2014 (STAT 598W) Lecture 21 1 / 26 Outlne 1 Fnte Dfference Method Explct Method Penalty Method (STAT 598W)

More information

ALGEBRAIC SCHUR COMPLEMENT APPROACH FOR A NON LINEAR 2D ADVECTION DIFFUSION EQUATION

ALGEBRAIC SCHUR COMPLEMENT APPROACH FOR A NON LINEAR 2D ADVECTION DIFFUSION EQUATION st Annual Internatonal Interdsplnary Conferene AIIC 03 4-6 Aprl Azores Portugal - Proeedngs- ALGEBRAIC SCHUR COMPLEMENT APPROACH FOR A NON LINEAR D ADVECTION DIFFUSION EQUATION Hassan Belhad Professor

More information

Module 3: Element Properties Lecture 1: Natural Coordinates

Module 3: Element Properties Lecture 1: Natural Coordinates Module 3: Element Propertes Lecture : Natural Coordnates Natural coordnate system s bascally a local coordnate system whch allows the specfcaton of a pont wthn the element by a set of dmensonless numbers

More information

Finite Difference Method

Finite Difference Method 7/0/07 Instructor r. Ramond Rump (9) 747 698 rcrump@utep.edu EE 337 Computatonal Electromagnetcs (CEM) Lecture #0 Fnte erence Method Lecture 0 These notes ma contan coprghted materal obtaned under ar use

More information

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM

ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM ELASTIC WAVE PROPAGATION IN A CONTINUOUS MEDIUM An elastc wave s a deformaton of the body that travels throughout the body n all drectons. We can examne the deformaton over a perod of tme by fxng our look

More information

Journal of Engineering and Applied Sciences. Ultraspherical Integration Method for Solving Beam Bending Boundary Value Problem

Journal of Engineering and Applied Sciences. Ultraspherical Integration Method for Solving Beam Bending Boundary Value Problem Journal of Engneerng and Appled Senes Volue: Edton: Year: 4 Pages: 7 4 Ultraspheral Integraton Method for Solvng Bea Bendng Boundary Value Proble M El-Kady Matheats Departent Faulty of Sene Helwan UnverstyEgypt

More information

A REVIEW OF ERROR ANALYSIS

A REVIEW OF ERROR ANALYSIS A REVIEW OF ERROR AALYI EEP Laborator EVE-4860 / MAE-4370 Updated 006 Error Analss In the laborator we measure phscal uanttes. All measurements are subject to some uncertantes. Error analss s the stud

More information

ECE 6340 Intermediate EM Waves. Fall Prof. David R. Jackson Dept. of ECE. Notes 3

ECE 6340 Intermediate EM Waves. Fall Prof. David R. Jackson Dept. of ECE. Notes 3 C 634 Intermedate M Waves Fall 216 Prof. Davd R. akson Dept. of C Notes 3 1 Types of Current ρ v Note: The free-harge densty ρ v refers to those harge arrers (ether postve or negatve) that are free to

More information

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family

Using T.O.M to Estimate Parameter of distributions that have not Single Exponential Family IOSR Journal of Mathematcs IOSR-JM) ISSN: 2278-5728. Volume 3, Issue 3 Sep-Oct. 202), PP 44-48 www.osrjournals.org Usng T.O.M to Estmate Parameter of dstrbutons that have not Sngle Exponental Famly Jubran

More information

Notes prepared by Prof Mrs) M.J. Gholba Class M.Sc Part(I) Information Technology

Notes prepared by Prof Mrs) M.J. Gholba Class M.Sc Part(I) Information Technology Inverse transformatons Generaton of random observatons from gven dstrbutons Assume that random numbers,,, are readly avalable, where each tself s a random varable whch s unformly dstrbuted over the range(,).

More information

PART 8. Partial Differential Equations PDEs

PART 8. Partial Differential Equations PDEs he Islamc Unverst of Gaza Facult of Engneerng Cvl Engneerng Department Numercal Analss ECIV 3306 PAR 8 Partal Dfferental Equatons PDEs Chapter 9; Fnte Dfference: Ellptc Equatons Assocate Prof. Mazen Abualtaef

More information

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition)

See Book Chapter 11 2 nd Edition (Chapter 10 1 st Edition) Count Data Models See Book Chapter 11 2 nd Edton (Chapter 10 1 st Edton) Count data consst of non-negatve nteger values Examples: number of drver route changes per week, the number of trp departure changes

More information

Our focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e.

Our focus will be on linear systems. A system is linear if it obeys the principle of superposition and homogenity, i.e. SSTEM MODELLIN In order to solve a control syste proble, the descrptons of the syste and ts coponents ust be put nto a for sutable for analyss and evaluaton. The followng ethods can be used to odel physcal

More information

A solution to the Curse of Dimensionality Problem in Pairwise Scoring Techniques

A solution to the Curse of Dimensionality Problem in Pairwise Scoring Techniques A soluton to the Curse of Dmensonalty Problem n Parwse orng Tehnques Man Wa MAK Dept. of Eletron and Informaton Engneerng The Hong Kong Polytehn Unversty un Yuan KUNG Dept. of Eletral Engneerng Prneton

More information

Numerical Methods. ME Mechanical Lab I. Mechanical Engineering ME Lab I

Numerical Methods. ME Mechanical Lab I. Mechanical Engineering ME Lab I 5 9 Mechancal Engneerng -.30 ME Lab I ME.30 Mechancal Lab I Numercal Methods Volt Sne Seres.5 0.5 SIN(X) 0 3 7 5 9 33 37 4 45 49 53 57 6 65 69 73 77 8 85 89 93 97 0-0.5 Normalzed Squared Functon - 0.07

More information

Chapter 14 Simple Linear Regression

Chapter 14 Simple Linear Regression Chapter 4 Smple Lnear Regresson Chapter 4 - Smple Lnear Regresson Manageral decsons often are based on the relatonshp between two or more varables. Regresson analss can be used to develop an equaton showng

More information

= z 20 z n. (k 20) + 4 z k = 4

= z 20 z n. (k 20) + 4 z k = 4 Problem Set #7 solutons 7.2.. (a Fnd the coeffcent of z k n (z + z 5 + z 6 + z 7 + 5, k 20. We use the known seres expanson ( n+l ( z l l z n below: (z + z 5 + z 6 + z 7 + 5 (z 5 ( + z + z 2 + z + 5 5

More information

A computer-aided optimization method of bending beams

A computer-aided optimization method of bending beams WSES Internatonal Conferene on ENGINEERING MECHNICS, STRUCTURES, ENGINEERING GEOLOGY (EMESEG '8), Heraklon, Crete Island, Greee, July 22-24, 28 omputer-aded optmzaton method of bendng beams CRMEN E. SINGER-ORCI

More information

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system

Transfer Functions. Convenient representation of a linear, dynamic model. A transfer function (TF) relates one input and one output: ( ) system Transfer Functons Convenent representaton of a lnear, dynamc model. A transfer functon (TF) relates one nput and one output: x t X s y t system Y s The followng termnology s used: x y nput output forcng

More information

Finite Element Analysis of the Stability of Tunnel Surrounding Rock with Weak Rock Layer

Finite Element Analysis of the Stability of Tunnel Surrounding Rock with Weak Rock Layer Vol., No. 2 Modern Appled Sene Fnte Element Analyss of the Stablty of Tunnel Surroundng Rok wth Weak Rok Layer Yangsong Zhang Nanjng Unversty of Sene and Tehnology, Nanjng 294, Chna Tel: 86-25-84-577 E-mal:

More information

Rao IIT Academy/ SSC - Board Exam 2018 / Mathematics Code-A / QP + Solutions JEE MEDICAL-UG BOARDS KVPY NTSE OLYMPIADS SSC - BOARD

Rao IIT Academy/ SSC - Board Exam 2018 / Mathematics Code-A / QP + Solutions JEE MEDICAL-UG BOARDS KVPY NTSE OLYMPIADS SSC - BOARD Rao IIT Academy/ SSC - Board Exam 08 / Mathematcs Code-A / QP + Solutons JEE MEDICAL-UG BOARDS KVPY NTSE OLYMPIADS SSC - BOARD - 08 Date: 0.03.08 MATHEMATICS - PAPER- - SOLUTIONS Q. Attempt any FIVE of

More information

3D Numerical Analysis for Impedance Calculation and High Performance Consideration of Linear Induction Motor for Rail-guided Transportation

3D Numerical Analysis for Impedance Calculation and High Performance Consideration of Linear Induction Motor for Rail-guided Transportation ADVANCED ELECTROMAGNETICS SYMPOSIUM, AES 13, 19 MARCH 13, SHARJAH UNITED ARAB EMIRATES 3D Numeral Analss for Impedane Calulaton and Hgh Performane Consderaton of Lnear Induton Motor for Ral-guded Transportaton

More information

(Online First)A Lattice Boltzmann Scheme for Diffusion Equation in Spherical Coordinate

(Online First)A Lattice Boltzmann Scheme for Diffusion Equation in Spherical Coordinate Internatonal Journal of Mathematcs and Systems Scence (018) Volume 1 do:10.494/jmss.v1.815 (Onlne Frst)A Lattce Boltzmann Scheme for Dffuson Equaton n Sphercal Coordnate Debabrata Datta 1 *, T K Pal 1

More information

Lecture 2 Solution of Nonlinear Equations ( Root Finding Problems )

Lecture 2 Solution of Nonlinear Equations ( Root Finding Problems ) Lecture Soluton o Nonlnear Equatons Root Fndng Problems Dentons Classcaton o Methods Analytcal Solutons Graphcal Methods Numercal Methods Bracketng Methods Open Methods Convergence Notatons Root Fndng

More information

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal

Inner Product. Euclidean Space. Orthonormal Basis. Orthogonal Inner Product Defnton 1 () A Eucldean space s a fnte-dmensonal vector space over the reals R, wth an nner product,. Defnton 2 (Inner Product) An nner product, on a real vector space X s a symmetrc, blnear,

More information

Bootstrap AMG for Markov Chain Computations

Bootstrap AMG for Markov Chain Computations Bootstrap AMG for Markov Chan Computatons Karsten Kahl Bergsche Unverstät Wuppertal May 27, 200 Outlne Markov Chans Subspace Egenvalue Approxmaton Ingredents of Least Squares Interpolaton Egensolver Bootstrap

More information

Randomness and Computation

Randomness and Computation Randomness and Computaton or, Randomzed Algorthms Mary Cryan School of Informatcs Unversty of Ednburgh RC 208/9) Lecture 0 slde Balls n Bns m balls, n bns, and balls thrown unformly at random nto bns usually

More information

Physics 41 Chapter 22 HW Serway 7 th Edition

Physics 41 Chapter 22 HW Serway 7 th Edition yss 41 apter H Serway 7 t Edton oneptual uestons: 1,, 8, 1 roblems: 9, 1, 0,, 7, 9, 48, 54, 55 oneptual uestons: 1,, 8, 1 1 Frst, te effeny of te automoble engne annot exeed te arnot effeny: t s lmted

More information

Exercise 10: Theory of mass transfer coefficient at boundary

Exercise 10: Theory of mass transfer coefficient at boundary Partle Tehnology Laboratory Prof. Sotrs E. Pratsns Sonneggstrasse, ML F, ETH Zentrum Tel.: +--6 5 http://www.ptl.ethz.h 5-97- U Stoffaustaush HS 7 Exerse : Theory of mass transfer oeffent at boundary Chapter,

More information

FUZZY FINITE ELEMENT METHOD

FUZZY FINITE ELEMENT METHOD FUZZY FINITE ELEMENT METHOD RELIABILITY TRUCTURE ANALYI UING PROBABILITY 3.. Maxmum Normal tress Internal force s the shear force, V has a magntude equal to the load P and bendng moment, M. Bendng moments

More information

Classification learning II

Classification learning II Lecture 8 Classfcaton learnng II Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square Logstc regresson model Defnes a lnear decson boundar Dscrmnant functons: g g g g here g z / e z f, g g - s a logstc functon

More information

Lecture 2: Numerical Methods for Differentiations and Integrations

Lecture 2: Numerical Methods for Differentiations and Integrations Numercal Smulaton of Space Plasmas (I [AP-4036] Lecture 2 by Lng-Hsao Lyu March, 2018 Lecture 2: Numercal Methods for Dfferentatons and Integratons As we have dscussed n Lecture 1 that numercal smulaton

More information

PHYSICS 212 MIDTERM II 19 February 2003

PHYSICS 212 MIDTERM II 19 February 2003 PHYSICS 1 MIDERM II 19 Feruary 003 Exam s losed ook, losed notes. Use only your formula sheet. Wrte all work and answers n exam ooklets. he aks of pages wll not e graded unless you so request on the front

More information

Development of the Schrodinger equation for attosecond laser pulse interaction with Planck gas

Development of the Schrodinger equation for attosecond laser pulse interaction with Planck gas Develoment of the Shrodnger equaton for attoseond laser ulse nteraton wth Plank gas M. Kozlowsk 1 * J. Marak Kozlowska 1 Josef Plsudsk Warsaw Unversty, Insttute of Eletron Tehnology Abstrat The reaton

More information

Strong Markov property: Same assertion holds for stopping times τ.

Strong Markov property: Same assertion holds for stopping times τ. Brownan moton Let X ={X t : t R + } be a real-valued stochastc process: a famlty of real random varables all defned on the same probablty space. Defne F t = nformaton avalable by observng the process up

More information

Speech and Language Processing

Speech and Language Processing Speech and Language rocessng Lecture 3 ayesan network and ayesan nference Informaton and ommuncatons Engneerng ourse Takahro Shnozak 08//5 Lecture lan (Shnozak s part) I gves the frst 6 lectures about

More information

2.29 Numerical Fluid Mechanics

2.29 Numerical Fluid Mechanics REVIEW Lecture 10: Sprng 2015 Lecture 11 Classfcaton of Partal Dfferental Equatons PDEs) and eamples wth fnte dfference dscretzatons Parabolc PDEs Ellptc PDEs Hyperbolc PDEs Error Types and Dscretzaton

More information

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity

Week3, Chapter 4. Position and Displacement. Motion in Two Dimensions. Instantaneous Velocity. Average Velocity Week3, Chapter 4 Moton n Two Dmensons Lecture Quz A partcle confned to moton along the x axs moves wth constant acceleraton from x =.0 m to x = 8.0 m durng a 1-s tme nterval. The velocty of the partcle

More information

On Generalized Fractional Hankel Transform

On Generalized Fractional Hankel Transform Int. ournal o Math. nalss Vol. 6 no. 8 883-896 On Generaled Fratonal ankel Transorm R. D. Tawade Pro.Ram Meghe Insttute o Tehnolog & Researh Badnera Inda rajendratawade@redmal.om. S. Gudadhe Dept.o Mathemats

More information

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming

OPTIMISATION. Introduction Single Variable Unconstrained Optimisation Multivariable Unconstrained Optimisation Linear Programming OPTIMIATION Introducton ngle Varable Unconstraned Optmsaton Multvarable Unconstraned Optmsaton Lnear Programmng Chapter Optmsaton /. Introducton In an engneerng analss, sometmes etremtes, ether mnmum or

More information

NUMERICAL DIFFERENTIATION

NUMERICAL DIFFERENTIATION NUMERICAL DIFFERENTIATION 1 Introducton Dfferentaton s a method to compute the rate at whch a dependent output y changes wth respect to the change n the ndependent nput x. Ths rate of change s called the

More information

Example

Example Chapter Example.- ------------------------------------------------------------------------------ sold slab of 5.5 wt% agar gel at 78 o K s.6 mm thk and ontans a unform onentraton of urea of. kmol/m 3.

More information

Measuring the Strength of Association

Measuring the Strength of Association Stat 3000 Statstcs for Scentsts and Engneers Measurng the Strength of Assocaton Note that the slope s one measure of the lnear assocaton between two contnuous varables t tells ou how much the average of

More information

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE

CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE CHAPTER 5 NUMERICAL EVALUATION OF DYNAMIC RESPONSE Analytcal soluton s usually not possble when exctaton vares arbtrarly wth tme or f the system s nonlnear. Such problems can be solved by numercal tmesteppng

More information

Tenth Order Compact Finite Difference Method for Solving Singularly Perturbed 1D Reaction - Diffusion Equations

Tenth Order Compact Finite Difference Method for Solving Singularly Perturbed 1D Reaction - Diffusion Equations Internatonal Journal of Engneerng & Appled Senes (IJEAS) Vol.8, Issue (0)5- Tent Order Compat Fnte Dfferene Metod for Solvng Sngularl Perturbed D Reaton - Dffuson Equatons Faska Wondmu Galu, Gemes Fle

More information

arxiv:cond-mat/ v1 [cond-mat.stat-mech] 10 Jul 2001

arxiv:cond-mat/ v1 [cond-mat.stat-mech] 10 Jul 2001 Random-luster mult-hstogram samplng for the q-state Potts model arxv:ond-mat/010701v1 [ond-mat.stat-meh] 10 Jul 001 Martn Wegel and Wolfhard Janke Insttut für Theoretshe Physk, Unverstät Lepzg, Augustusplatz

More information

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1]

Outline. Communication. Bellman Ford Algorithm. Bellman Ford Example. Bellman Ford Shortest Path [1] DYNAMIC SHORTEST PATH SEARCH AND SYNCHRONIZED TASK SWITCHING Jay Wagenpfel, Adran Trachte 2 Outlne Shortest Communcaton Path Searchng Bellmann Ford algorthm Algorthm for dynamc case Modfcatons to our algorthm

More information

Voltammetry. Bulk electrolysis: relatively large electrodes (on the order of cm 2 ) Voltammetry:

Voltammetry. Bulk electrolysis: relatively large electrodes (on the order of cm 2 ) Voltammetry: Voltammetry varety of eletroanalytal methods rely on the applaton of a potental funton to an eletrode wth the measurement of the resultng urrent n the ell. In ontrast wth bul eletrolyss methods, the objetve

More information

Prediction of the reliability of genomic breeding values for crossbred performance

Prediction of the reliability of genomic breeding values for crossbred performance Vandenplas et al. Genet Sel Evol 217 49:43 DOI 1.1186/s12711-17-318-1 Genets Seleton Evoluton RESERCH RTICLE Open ess Predton of the relablty of genom breedng values for rossbred performane Jéréme Vandenplas

More information

Homework Key #7 - Phy 375R

Homework Key #7 - Phy 375R HMWK7-75R.nb Homewor Ke #7 - Ph 75R Problem #: See Ke for Homewor # Problem #: Transform the lne element... The nverse transformaton s t' = ÅÅÅÅ t tanh- ÅÅÅÅÅÅÅÅÅÅÅÅÅÅÅ + ÅÅÅÅÅ ' = $%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%

More information

Entropy generation in a chemical reaction

Entropy generation in a chemical reaction Entropy generaton n a chemcal reacton E Mranda Área de Cencas Exactas COICET CCT Mendoza 5500 Mendoza, rgentna and Departamento de Físca Unversdad aconal de San Lus 5700 San Lus, rgentna bstract: Entropy

More information

Week 9 Chapter 10 Section 1-5

Week 9 Chapter 10 Section 1-5 Week 9 Chapter 10 Secton 1-5 Rotaton Rgd Object A rgd object s one that s nondeformable The relatve locatons of all partcles makng up the object reman constant All real objects are deformable to some extent,

More information

Diffusion Mass Transfer

Diffusion Mass Transfer Dffuson Mass Transfer General onsderatons Mass transfer refers to mass n transt due to a speces concentraton gradent n a mture. Must have a mture of two or more speces for mass transfer to occur. The speces

More information

APPROXIMATE ANALYSIS OF RIGID PLATE LOADING ON ELASTIC MULTI-LAYERED SYSTEMS

APPROXIMATE ANALYSIS OF RIGID PLATE LOADING ON ELASTIC MULTI-LAYERED SYSTEMS 6th ICPT, Sapporo, Japan, July 008 APPROXIMATE ANALYSIS OF RIGID PLATE LOADING ON ELASTIC MULTI-LAYERED SYSTEMS James MAINA Prncpal Researcher, Transport and Infrastructure Engneerng, CSIR Bult Envronment

More information

Kinematics in 2-Dimensions. Projectile Motion

Kinematics in 2-Dimensions. Projectile Motion Knematcs n -Dmensons Projectle Moton A medeval trebuchet b Kolderer, c1507 http://members.net.net.au/~rmne/ht/ht0.html#5 Readng Assgnment: Chapter 4, Sectons -6 Introducton: In medeval das, people had

More information

Chapter 1. Probability

Chapter 1. Probability Chapter. Probablty Mcroscopc propertes of matter: quantum mechancs, atomc and molecular propertes Macroscopc propertes of matter: thermodynamcs, E, H, C V, C p, S, A, G How do we relate these two propertes?

More information

Exponential Type Product Estimator for Finite Population Mean with Information on Auxiliary Attribute

Exponential Type Product Estimator for Finite Population Mean with Information on Auxiliary Attribute Avalable at http://pvamu.edu/aam Appl. Appl. Math. ISSN: 193-9466 Vol. 10, Issue 1 (June 015), pp. 106-113 Applcatons and Appled Mathematcs: An Internatonal Journal (AAM) Exponental Tpe Product Estmator

More information

2.29 Numerical Fluid Mechanics Fall 2011 Lecture 12

2.29 Numerical Fluid Mechanics Fall 2011 Lecture 12 REVIEW Lecture 11: 2.29 Numercal Flud Mechancs Fall 2011 Lecture 12 End of (Lnear) Algebrac Systems Gradent Methods Krylov Subspace Methods Precondtonng of Ax=b FINITE DIFFERENCES Classfcaton of Partal

More information

3. Be able to derive the chemical equilibrium constants from statistical mechanics.

3. Be able to derive the chemical equilibrium constants from statistical mechanics. Lecture #17 1 Lecture 17 Objectves: 1. Notaton of chemcal reactons 2. General equlbrum 3. Be able to derve the chemcal equlbrum constants from statstcal mechancs. 4. Identfy how nondeal behavor can be

More information

Portfolios with Trading Constraints and Payout Restrictions

Portfolios with Trading Constraints and Payout Restrictions Portfolos wth Tradng Constrants and Payout Restrctons John R. Brge Northwestern Unversty (ont wor wth Chrs Donohue Xaodong Xu and Gongyun Zhao) 1 General Problem (Very) long-term nvestor (eample: unversty

More information

x yi In chapter 14, we want to perform inference (i.e. calculate confidence intervals and perform tests of significance) in this setting.

x yi In chapter 14, we want to perform inference (i.e. calculate confidence intervals and perform tests of significance) in this setting. The Practce of Statstcs, nd ed. Chapter 14 Inference for Regresson Introducton In chapter 3 we used a least-squares regresson lne (LSRL) to represent a lnear relatonshp etween two quanttatve explanator

More information

Interconnect Modeling

Interconnect Modeling Interconnect Modelng Modelng of Interconnects Interconnect R, C and computaton Interconnect models umped RC model Dstrbuted crcut models Hgher-order waveform n dstrbuted RC trees Accuracy and fdelty Prepared

More information

Instituto Tecnológico de Aeronáutica FINITE ELEMENTS I. Class notes AE-245

Instituto Tecnológico de Aeronáutica FINITE ELEMENTS I. Class notes AE-245 Insttuto Tecnológco de Aeronáutca FIITE ELEMETS I Class notes AE-5 Insttuto Tecnológco de Aeronáutca 5. Isoparametrc Elements AE-5 Insttuto Tecnológco de Aeronáutca ISOPARAMETRIC ELEMETS Introducton What

More information

Lecture 10 Support Vector Machines II

Lecture 10 Support Vector Machines II Lecture 10 Support Vector Machnes II 22 February 2016 Taylor B. Arnold Yale Statstcs STAT 365/665 1/28 Notes: Problem 3 s posted and due ths upcomng Frday There was an early bug n the fake-test data; fxed

More information

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems

Application of B-Spline to Numerical Solution of a System of Singularly Perturbed Problems Mathematca Aeterna, Vol. 1, 011, no. 06, 405 415 Applcaton of B-Splne to Numercal Soluton of a System of Sngularly Perturbed Problems Yogesh Gupta Department of Mathematcs Unted College of Engneerng &

More information

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur

Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur Analyss of Varance and Desgn of Exerments-I MODULE III LECTURE - 2 EXPERIMENTAL DESIGN MODELS Dr. Shalabh Deartment of Mathematcs and Statstcs Indan Insttute of Technology Kanur 2 We consder the models

More information

EC3075 Mathematical Approaches to Economics

EC3075 Mathematical Approaches to Economics EC3075 Mathematal Aroahes to Eonoms etures 7-8: Dualt and Constraned Otmsaton Pemberton & Rau haters 7-8 Dr Gaa Garno [Astle Clarke Room 4 emal: gg44] Dualt n onsumer theor We wll exose the rmal otmsaton

More information

Continuous Time Markov Chains

Continuous Time Markov Chains Contnuous Tme Markov Chans Brth and Death Processes,Transton Probablty Functon, Kolmogorov Equatons, Lmtng Probabltes, Unformzaton Chapter 6 1 Markovan Processes State Space Parameter Space (Tme) Dscrete

More information

Ionization fronts in HII regions

Ionization fronts in HII regions Ionzaton fronts n HII regons Intal expanson of HII onzaton front s supersonc, creatng a shock front. Statonary frame: front advances nto neutral materal In frame where shock front s statonary, neutral

More information

MULTICRITERION OPTIMIZATION OF LAMINATE STACKING SEQUENCE FOR MAXIMUM FAILURE MARGINS

MULTICRITERION OPTIMIZATION OF LAMINATE STACKING SEQUENCE FOR MAXIMUM FAILURE MARGINS MLTICRITERION OPTIMIZATION OF LAMINATE STACKING SEENCE FOR MAXIMM FAILRE MARGINS Petr Kere and Juhan Kos Shool of Engneerng, Natonal nversty of ruguay J. Herrera y Ressg 565, Montevdeo, ruguay Appled Mehans,

More information

HEAT TRANSFER THROUGH ANNULAR COMPOSITE FINS

HEAT TRANSFER THROUGH ANNULAR COMPOSITE FINS Journal of Mechancal Engneerng and Technology (JMET) Volume 4, Issue 1, Jan-June 2016, pp. 01-10, Artcle ID: JMET_04_01_001 Avalable onlne at http://www.aeme.com/jmet/ssues.asp?jtype=jmet&vtype=4&itype=1

More information

CS 2750 Machine Learning. Lecture 5. Density estimation. CS 2750 Machine Learning. Announcements

CS 2750 Machine Learning. Lecture 5. Density estimation. CS 2750 Machine Learning. Announcements CS 750 Machne Learnng Lecture 5 Densty estmaton Mlos Hauskrecht mlos@cs.ptt.edu 539 Sennott Square CS 750 Machne Learnng Announcements Homework Due on Wednesday before the class Reports: hand n before

More information

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9

Correlation and Regression. Correlation 9.1. Correlation. Chapter 9 Chapter 9 Correlaton and Regresson 9. Correlaton Correlaton A correlaton s a relatonshp between two varables. The data can be represented b the ordered pars (, ) where s the ndependent (or eplanator) varable,

More information

Chapter 2 Transformations and Expectations. , and define f

Chapter 2 Transformations and Expectations. , and define f Revew for the prevous lecture Defnton: support set of a ranom varable, the monotone functon; Theorem: How to obtan a cf, pf (or pmf) of functons of a ranom varable; Eamples: several eamples Chapter Transformatons

More information

On Adaptive Control of Simulated Moving Bed Plants. Plants Using Comsol s Simulink Interface. Speaker: Marco Fütterer

On Adaptive Control of Simulated Moving Bed Plants. Plants Using Comsol s Simulink Interface. Speaker: Marco Fütterer daptve Smulated Movng ed Plants Usng Comsol s Smulnk Interfae Speaker: Maro Fütterer Insttut für utomatserungstehnk Otto-von-Guerke Unverstät Unverstätsplatz, D-39106 Magdeburg Germany e-mal: maro.fuetterer@ovgu.de

More information

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS

NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS IJRRAS 8 (3 September 011 www.arpapress.com/volumes/vol8issue3/ijrras_8_3_08.pdf NON-CENTRAL 7-POINT FORMULA IN THE METHOD OF LINES FOR PARABOLIC AND BURGERS' EQUATIONS H.O. Bakodah Dept. of Mathematc

More information

JAB Chain. Long-tail claims development. ASTIN - September 2005 B.Verdier A. Klinger

JAB Chain. Long-tail claims development. ASTIN - September 2005 B.Verdier A. Klinger JAB Chan Long-tal clams development ASTIN - September 2005 B.Verder A. Klnger Outlne Chan Ladder : comments A frst soluton: Munch Chan Ladder JAB Chan Chan Ladder: Comments Black lne: average pad to ncurred

More information

Image classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing i them?

Image classification. Given the bag-of-features representations of images from different classes, how do we learn a model for distinguishing i them? Image classfcaton Gven te bag-of-features representatons of mages from dfferent classes ow do we learn a model for dstngusng tem? Classfers Learn a decson rule assgnng bag-offeatures representatons of

More information

CHALMERS, GÖTEBORGS UNIVERSITET. SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS. COURSE CODES: FFR 135, FIM 720 GU, PhD

CHALMERS, GÖTEBORGS UNIVERSITET. SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS. COURSE CODES: FFR 135, FIM 720 GU, PhD CHALMERS, GÖTEBORGS UNIVERSITET SOLUTIONS to RE-EXAM for ARTIFICIAL NEURAL NETWORKS COURSE CODES: FFR 35, FIM 72 GU, PhD Tme: Place: Teachers: Allowed materal: Not allowed: January 2, 28, at 8 3 2 3 SB

More information

Plate Theories for Classical and Laminated plates Weak Formulation and Element Calculations

Plate Theories for Classical and Laminated plates Weak Formulation and Element Calculations Plate heores for Classcal and Lamnated plates Weak Formulaton and Element Calculatons PM Mohte Department of Aerospace Engneerng Indan Insttute of echnolog Kanpur EQIP School on Computatonal Methods n

More information

Numerical Simulation of Wave Propagation Using the Shallow Water Equations

Numerical Simulation of Wave Propagation Using the Shallow Water Equations umercal Smulaton of Wave Propagaton Usng the Shallow Water Equatons Junbo Par Harve udd College 6th Aprl 007 Abstract The shallow water equatons SWE were used to model water wave propagaton n one dmenson

More information

Neuro-Adaptive Design - I:

Neuro-Adaptive Design - I: Lecture 36 Neuro-Adaptve Desgn - I: A Robustfyng ool for Dynamc Inverson Desgn Dr. Radhakant Padh Asst. Professor Dept. of Aerospace Engneerng Indan Insttute of Scence - Bangalore Motvaton Perfect system

More information

2. The Energy Principle in Open Channel Flows

2. The Energy Principle in Open Channel Flows . The Energy Priniple in Open Channel Flows. Basi Energy Equation In the one-dimensional analysis of steady open-hannel flow, the energy equation in the form of Bernoulli equation is used. Aording to this

More information

Temperature. Chapter Heat Engine

Temperature. Chapter Heat Engine Chapter 3 Temperature In prevous chapters of these notes we ntroduced the Prncple of Maxmum ntropy as a technque for estmatng probablty dstrbutons consstent wth constrants. In Chapter 9 we dscussed the

More information