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1 ΑΡΙΘΜΗΤΙΚΗ ΑΝΑΛΥΣΗ 1.Εισαγωγή 2 1. ΕΙΣΑΓΩΓΗ Η ΑΡΙΘΜΗΤΙΚΗ ΑΝΑΛΥΣΗ και ΤΑ ΥΠΟΛΟΓΙΣΤΙΚΑ ΜΑΘΗΜΑΤΙΚΑ είναι σχεδόν ταυτόσημες έννοιες. Αποτελούν τον κλάδο.

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Παρουσίαση με θέμα: "1 ΑΡΙΘΜΗΤΙΚΗ ΑΝΑΛΥΣΗ 1.Εισαγωγή 2 1. ΕΙΣΑΓΩΓΗ Η ΑΡΙΘΜΗΤΙΚΗ ΑΝΑΛΥΣΗ και ΤΑ ΥΠΟΛΟΓΙΣΤΙΚΑ ΜΑΘΗΜΑΤΙΚΑ είναι σχεδόν ταυτόσημες έννοιες. Αποτελούν τον κλάδο."— Μεταγράφημα παρουσίασης:

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2 1 ΑΡΙΘΜΗΤΙΚΗ ΑΝΑΛΥΣΗ 1.Εισαγωγή

3 2 1. ΕΙΣΑΓΩΓΗ Η ΑΡΙΘΜΗΤΙΚΗ ΑΝΑΛΥΣΗ και ΤΑ ΥΠΟΛΟΓΙΣΤΙΚΑ ΜΑΘΗΜΑΤΙΚΑ είναι σχεδόν ταυτόσημες έννοιες. Αποτελούν τον κλάδο των Εφαρμοσμένων Μαθηματικών που ασχολείται με τη «διακριτοποίηση» και την εύρεση προσεγγιστικών λύσεων Μαθηματικών προβλημάτων των οποίων η αναλυτική λύση είναι αδύνατον να βρεθεί αναλυτικά ή σχεδόν ακατόρθωτη. Το διακριτό πρόβλημα που προκύπτει ονομάζεται Αριθμητική Μέθοδος.

4 Τι είναι η Αριθμητική Ανάλυση; Είναι Επιστήμη: – Ασχολείται με μεθόδους επίλυσης μαθηματικών προβλημάτων με χρήση αριθμητικών πράξεων (με Η/Υ) καθώς και με την ανάλυση των σφαλμάτων στην προσέγγιση των λύσεων. Είναι Τέχνη: –Αφορά στην επιλογή εκείνης της μεθόδου που είναι πιο «κατάλληλη» για την επίλυση ενός συγκεκριμένου προβλήματος. 3

5 Το θεωρητικό μέρος της Αριθμητικής Ανάλυσης περιλαμβάνει την κατασκευή αλγορίθμων - ανάλυση, μελέτη της ακρίβειας και της ευστάθειας -, δηλαδή, την ανάλυση και εύρεση των πιθανών σφαλμάτων τους. Το εφαρμοσμένο μέρος αφορά τον προγραμματισμό των αλγορίθμων σε μια γλώσσα προγραμματισμού με το βέλτιστο τρόπο, δηλαδή, με όσο το δυνατό λιγότερο υπολογιστικό χρόνο (CPU) και απαιτούμενο χώρο μνήμης (RAM). Το θεωρητικό και το εφαρμοσμένο μέρος είναι, συνήθως, αλληλένδετα. Η ανάπτυξη των υπολογιστικών συστημάτων καθιστά απαραίτητη και επιτακτική την εκμάθηση αριθμητικών μεθόδων για την επίλυση προβλημάτων επιστημονικών εφαρμογών. 4

6 Συνεχείς διαδικασίες  Διακριτές διαδικασίες Άπειρες διαδικασίες  Πεπερασμένες διαδικασίες Στόχος : Η προσεγγιστική επίλυση προβλημάτων που συναντώνται στις επιστήμες και την τεχνολογία, σε εφικτό υπολογιστικό χρόνο και με το μικρότερο σφάλμα. 5

7 A Small Example The Difference in Numerical Computing is the numbers A computation of π 6

8 Simple iteration: 7

9 Result of 15 digit computation Red digits are correct Black and green digits are incorrect 8

10 Result of 15 digit computation Red digits are correct Black and green digits are incorrect π = 0 ? 9

11 Where’s the problem? is calculated as zero 10

12 Let’s replace with the algebraically identical expression 11

13 New iteration: results in … 12

14 π correct to all digits 13

15 The result of this computation affects The ability of the next plane you fly to stay in the air The integrity of the next bridge you cross The path of a missile that isn’t intended to strike you 14

16 Numerical Disasters Patriot system hit by SCUD missile –position predicted from time and velocity –the system up-time in 1/10 of a second was converted to seconds using 24bit precision (by multiplying with 1/10) –1/10 has non-terminating binary expansion –after 100h, the error accumulated to 0.34s –the SCUD travels 1600 m/s so it travels >500m in this time Ariane 5 –a 64bit FP number containing the horizontal velocity was converted to 16bit signed integer –range overflow followed

17 Application areas of numerical analysis Petroleum modeling Atomic energy – including weapons Weather modeling Other modeling such as aircraft and automobile Computer graphics & computer vision Simulation for prototyping – Circuit design – Mechanical design – CAD/CAM 16

18 Algorithm areas of numerical analysis Linear Equations Nonlinear equations - single and systems Optimization Data Fitting - interpolation and approximation Integration Differential Equations - ordinary and partial 17

19 Why You Need to Learn Numerical Methods? 1.Numerical methods are extremely powerful problem-solving tools. 2.During your career, you may often need to use commercial computer programs (canned programs) that involve numerical methods. You need to know the basic theory of numerical methods in order to be a better user. 3.You will often encounter problems that cannot be solved by existing canned programs; you must write your own program of numerical methods. 4.Numerical methods are an efficient vehicle for learning to use computers. 5.Numerical methods provide a good opportunity for you to reinforce your understanding of mathematics. You need that in your life as an engineer or a scientist. 18

20 Why use Numerical Methods? To solve problems that cannot be solved exactly 19

21 Example: seismic wave propagation Generally heterogeneous medium Seismometers explosion … we need numerical solutions! … we need grids! … And big computers … 20

22 Finite Elements – Examples 21 Virtual prototyping of engineering designs

23 Research Framework Motivation: Numerical investigation of cavitation phenomena occurring in turbopump inducers typical of liquid propellant rocket engines angular velocity INFLOW OUTFLO W Target : A 3D tool able to simulate complex cavitating flows in realistic geometries 22

24 Mathematical Modeling Mathematical modeling seeks to gain an understanding of science through the use of mathematical models on computers. Mathematical modeling involves teamwork 23

25 Mathematical Modeling Complements, but does not replace, theory and experimentation in scientific research. Experiment Computation Theory Is often used in place of experiments when experiments are too large, too expensive, too dangerous, or too time consuming. 24

26 25 Has emerged as a powerful, indispensable tool for studying a variety of problems in scientific research, product and process development, and manufacturing. Seismology Climate modeling Economics Environment Material research Drug design Manufacturing Medicine Biology Analyze - Predict

27 Example: Industry First jetliner to be digitally designed, "pre-assembled" on computer, eliminating need for costly, full-scale mockup. Computational modeling improved the quality of work and reduced changes, errors, and rework. 26

28 The Boeing 777 is the first jetliner to be 100 percent digitally designed using three-dimensional solids technology. Throughout the design process, the airplane was "preassembled" on the computer, eliminating the need for a costly, full-scale mock-up. The kg plane is the biggest twin-engine aircraft ever to fly-it can carry 375 passengers 7400 km-and from its first service flight in June 1995, has been certified for extended-range twin-engine operations. Boeing invested more than $4 billion (and insiders say much more) in CAD infrastructure for the design of the Boeing 777 and reaped huge benefits from design automation. The more than 3 million parts were represented in an integrated database that allowed designers to do a complete 3D virtual mock- up of the vehicle. 27

29 Boeing based its CAD system on CATIA (short for Computer-aided Three- dimensional Interactive Application) and ELFINI (Finite Element Analysis System), both developed by Dassault Systemes of France (Dassault systems acquired ABAQUS in 2005 and ABAQUS+CATIA is known as SIMULIA) and licensed in the United States through IBM. Designers also used EPIC (Electronic Preassembly Integration on CATIA) and other digital preassembly applications developed by Boeing. Much of the same technology was used on the B-2 program. To design the 777, Boeing organized its workers into 238 cross-functional "design build teams" responsible for specific products. The teams used 2200 terminals and the computer-aided three dimensional interactive application (CATIA) system to produce a "paperless" design that allowed engineers to simulate assembly of the 777. The system worked so well that only a nose mockup (to check critical wiring) was built before assembly of the first flight vehicle which was only 0.03 mm out of alignment when the port wing was attached. Boeing also included customers and operators, down to line mechanics, to help tell them how to design the plane. 28

30 Engine Thermal Analysis 29 Question –What is the temperature distribution in the engine block? Solve –Poisson Partial Differential Equation. Recent Developments –Fast Integral Equation Solvers, Monte-Carlo Methods

31 Mathematical Modeling Process 30

32 Engineering design Physical Problem Mathematical model Governed by differential equations Assumptions regarding Geometry Kinematics Material law Loading Boundary conditions Etc. Question regarding the problem...how large are the deformations?...how much is the heat transfer? 31

33 Engineering design Example: A bracket Physical problem Questions: 1.What is the bending moment at section AA? 2.What is the deflection at the pin? Finite Element Procedures, K J Bathe 32

34 Engineering design Example: A bracket Mathematical model 1: beam Moment at section AA Deflection at load How reliable is this model? How effective is this model? 33

35 Engineering design Example: A bracket Mathematical model 2: plane stress Difficult to solve by hand! 34

36 Engineering design Physical Problem Mathematical model Governed by differential equations..General scenario.. Numerical model e.g., finite element model 35

37 Engineering design..General scenario.. Finite element analysis Finite element modelSolid model PREPROCESSING 1. Create a geometric model 2. Develop the finite element model 36

38 Engineering design..General scenario.. Finite element analysis FEM analysis scheme Step 1: Divide the problem domain into non overlapping regions (“elements”) connected to each other through special points (“nodes”) Finite element model Element Node 37

39 Engineering design..General scenario.. Finite element analysis FEM analysis scheme Step 2: Describe the behavior of each element Step 3: Describe the behavior of the entire body by putting together the behavior of each of the elements (this is a process known as “assembly”) 38

40 Engineering design..General scenario.. Finite element analysis POSTPROCESSING Compute moment at section AA 39

41 Engineering design..General scenario.. Finite element analysis Preprocessing Analysis Postprocessing Step 1 Step 2 Step 3 40

42 Engineering design Example: A bracket Mathematical model 2: plane stress FEM solution to mathematical model 2 (plane stress) Moment at section AA Deflection at load Conclusion: With respect to the questions we posed, the beam model is reliable if the required bending moment is to be predicted within 1% and the deflection is to be predicted within 20%. The beam model is also highly effective since it can be solved easily (by hand). What if we asked: what is the maximum stress in the bracket? would the beam model be of any use? 41

43 Engineering design Example: A bracket Summary 1.The selection of the mathematical model depends on the response to be predicted. 2.The most effective mathematical model is the one that delivers the answers to the questions in reliable manner with least effort. 3.The numerical solution is only as accurate as the mathematical model. 42

44 Example: A bracket Modeling a physical problem...General scenario Physical Problem Mathematical Model Numerical model Does answer make sense? Refine analysis Happy YES! No! Improve mathematical model Design improvements Structural optimization Change physical problem 43

45 Example: A bracket Modeling a physical problem Verification and validation Physical Problem Mathematical Model Numerical model Verification Validation 44

46 Critical assessment of the FEM Reliability: For a well-posed mathematical problem the numerical technique should always, for a reasonable discretization, give a reasonable solution which must converge to the accurate solution as the discretization is refined. e.g., use of reduced integration in FEM results in an unreliable analysis procedure. Robustness: The performance of the numerical method should not be unduly sensitive to the material data, the boundary conditions, and the loading conditions used. e.g., displacement based formulation for incompressible problems in elasticity Efficiency: 45

47 Design of Yacht Appendages 46 Validation and calibration of the numerical model by comparisons with experimental results (wind tunnel and towing tank tests) Comparative analyses of design configurations: Different geometries Different sailing attitudes Different wind and boat speeds

48 Sail analysis 47

49 Flow around spinnaker and mainsail 48 streamlines velocity and flow separation

50 Computational Cost elements, unknowns Which cost to achieve the desired accuracy ? CPU Time RAM Memory Number of Processors  24 hours  30 gigabytes of RAM  32 processors (450 AMD Opteron processors, 900 Gb distributed RAM, Network)

51 Mathematical Background 50 Roots of equations: concerns with finding the value of a variable that satisfies a single nonlinear equation – especial valuable in engineering design where it is often impossible to explicitly solve design equations of parameters. Systems of linear equations: a set of values is sought that simultaneously satisfies a set of linear algebraic equations. They arise in all disciplines of engineering, e.g., structure, electric circuits, fluid networks; also in curve fitting and differential equations. Optimization: determine a value or values of an independent variable that correspond to a “best” or optimal value of a function. It occurs routinely in engineering contexts. Curve fitting: to fit curves to data points. Two types: regression and interpolation. Experimental results are often of the first type. Integration: determination of the area or volume under a curve or a surface. It has many applications in engineering practice, such as …

52 51 Ordinary differential equations: very important in engineering practice, because many physical laws are couched in terms of the rate of change of a quantity rather than the magnitude of the quantity itself, such as … Partial differential equations: used to characterize engineering systems where the behavior of a physical quantity is couched in terms of the rate of change with respect to two or more independent variables. Examples: steady- state distribution of temperature of a heated plate (two spatial dimensions) or the time-variable temperature of a heated rod (time and one spatial dimension).

53 Παράδειγμα: Επίλυση Γραμμικού Συστήματος Εξισώσεων (1) 52

54 Παράδειγμα: Επίλυση Γραμμικού Συστήματος Εξισώσεων (2) Μέθοδοι: –Τύπος του Cramer (  ) –x=A -1 b (  2 ) –Άμεσες μέθοδοι: Gauss ( ) Απλή Με μερική οδήγηση Με ολική οδήγηση –Επαναληπτικές μέθοδοι Jacobi Gauss-Seidel –κ.ά. 53

55 Παράδειγμα: Επίλυση Γραμμικού Συστήματος Εξισώσεων (3) Κριτήρια επιλογής μεθόδου: –Γενικά: Πολυπλοκότητα (υπολογιστική, μνήμης, υλοποίησης) Ταχύτητα σύγκλισης (για επαναληπτικές μεθόδους) Ακρίβεια Ανθεκτικότητα σε αριθμητικά σφάλματα (αναπαράστασης δεδομένων και πράξεων) –Εξαρτώμενα από το συγκεκριμένο πρόβλημα: Κατάσταση του πίνακα του συστήματος Πλήθος μηδενικών στοιχείων του πίνακα Διαστάσεις του προβλήματος Τυχόν συμμετρίες στον πίνακα, κ.ά. 54

56 Cramer’s Rule is Not Practical 55

57 Παράδειγμα: Επίλυση Γραμμικού Συστήματος Εξισώσεων (4) 56 Π.χ. Επίλυση του συστήματος με 4 σημαντικά ψηφία Ακριβής λύση (με 4 ψηφία): Απλή μέθοδος Gauss: Μέθοδος Gauss με μερική οδήγηση:


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