CE324 Numerical Solutions to CE Problems Module No. Topic Period 1 Introduction Week no.: __1__ Session: __1__ Date: ____July 3-8, 2023__ INTRODUCTION 1.1 1.2 1.3 Introduction to Numerical Solutions Accuracy and Error Taylor Series of Expansion Objective/Intended Learning Outcomes • • • • Explain the concept of numerical solution and its use in solving complex equations. Recall the concept of accuracy and error and identify the kinds of numbers and sources of error. Compute for different representation of error. Compute for linearized function using Taylor Series. Discussion/Content 1. INTRODUCTION Numerical methods are methods for solving problems on computers by numerical calculations, often giving a table of numbers and/or graphical representations or figures. Numerical methods tend to emphasize the implementation of algorithms. The aim of numerical methods is therefore to provide systematic methods for solving problems in a numerical form. The process of solving problems generally involves starting from an initial data, using high precision digital computers, following the steps in the algorithms, and finally obtaining the results. Often the numerical data and the methods used are approximate ones. Hence, the error in a computed result may be caused by the errors in the data, or the errors in the method or both. In this lesson, we will describe Numerical Solutions and the few basic ideas and concepts regarding numerical computations; Accuracy and Error such as absolute and relative errors, inherent errors, roundoff errors and truncation errors, general error formulae; and the Taylor series of Expansion. Created by: CE324 MIDYEAR USTP INSTRUCTORS 1.1 INTRODUCTION TO NUMERICAL SOLUTIONS What are numerical methods and why should you study them? Numerical methods are techniques by which mathematical problems are formulated so that they can be solved with arithmetic and logical operations. Because digital computers excel at performing such operations, numerical methods are sometimes referred to as computer mathematics. A numerical solution means making guesses at the solution and testing whether the problem is solved well enough to stop. Numerical methods are techniques by which mathematical problems are formulated so that they can be solved with arithmetic operations. Although there are many kinds of numerical methods, they have one common characteristic: they invariably involve large numbers of tedious arithmetic calculations. It is little wonder that with the development of fast, efficient digital computers, the role of numerical methods in engineering problem solving has increased dramatically in recent years. Noncomputer Methods In the precomputer era there were generally three different ways in which engineers approached problem solving: 1. Solutions were derived for some problems using analytical, or exact methods. These solutions were often useful and provided excellent insight into the behavior of some systems. However, analytical solutions can be derived for only a limited class of problems. These include those that can be approximated with linear models and those that have simple geometry and low dimensionality. Consequently, analytical solutions are of limited practical value because most real problems are nonlinear and involve complex shapes and processes. 2. Graphical solutions were used to characterize the behavior of systems. These graphical solutions usually took the form of plots or nomographs. Although graphical techniques can often be used to solve complex problems, the results are not very precise. Furthermore, graphical solutions (without the aid of computers) are extremely tedious and awkward to implement. Finally, graphical techniques are often limited to problems that can be described using three or fewer dimensions. 3. Calculators and slide rules were used to implement numerical methods manually. Although in theory such approaches should be perfectly adequate for solving complex problems, in actuality several difficulties are encountered. Manual calculations are slow and tedious. Furthermore, consistent results are elusive because of simple blunders that arise when numerous manual tasks are performed. During the precomputer era, significant amounts of energy were expended on the solution technique itself, rather than on problem definition and interpretation (Figure 1a). This unfortunate situation existed because so much time and drudgery were required to obtain numerical answers using precomputer techniques. Today, computers and numerical methods provide an alternative for such complicated calculations. Using computer power to obtain solutions directly, you can approach these calculations without recourse to simplifying assumptions or time-intensive techniques. Although analytical solutions are still extremely valuable both for problem solving and for providing insight, numerical methods represent alternatives that greatly enlarge your capabilities to confront and solve problems. As a result, more time is available for the use of your creative skills. Thus, more emphasis can be placed on problem formulation and solution interpretation and the incorporation of total system, or “holistic,” awareness (Figure 1b). Created by: CE324 MIDYEAR USTP INSTRUCTORS FIGURE 1 The three phases of engineering problem solving in (a) the precomputer and (b) the computer era. The sizes of the boxes indicate the level of emphasis directed toward each phase. Computers facilitate the implementation of solution techniques and thus allow more emphasis to be placed on the creative aspects of problem formulation and interpretation of results. Numerical Methods Since the late 1940s the widespread availability of digital computers has led to a veritable explosion in the use and development of numerical methods. The recent evolution of inexpensive personal computers has given us ready access to powerful computational capabilities. There are several additional reasons why you should study numerical methods: 1. Numerical methods are extremely powerful problem-solving tools. They are capable of handling large systems of equations, nonlinearities, and complicated geometries that are not uncommon in engineering practice and that are often impossible to solve analytically. As such, they greatly enhance your problem-solving skills. 2. During your careers, you may often have occasion to use commercially available prepackaged, or “canned,” computer programs that involve numerical methods. The intelligent use of these programs is often predicated on knowledge of the basic theory underlying the methods. 3. Many problems cannot be approached using canned programs. If you are conversant with numerical methods and are adept at computer programming, you can design your own programs to solve problems without having to buy or commission expensive software. 4. Numerical methods are an efficient vehicle for learning to use computers. It is well known that an effective way to learn programming is to actually write computer programs. Because numerical methods are for the most part designed for implementation on computers, they are ideal for this purpose. Further, they are especially well-suited to illustrate the power and the limitations of computers. When you successfully implement numerical methods on a computer and then apply them to solve otherwise intractable problems, you will be provided with a dramatic demonstration of how computers can serve your professional development. At the same time, you will also learn to acknowledge and control the errors of approximation that are part and parcel of large-scale numerical calculations. 5. Numerical methods provide a vehicle for you to reinforce your understanding of mathematics. Because one function of numerical methods is to reduce higher mathematics to basic arithmetic operations, they get at the “nuts and bolts” of some otherwise obscure topics. Enhanced understanding and insight can result from this alternative perspective. Created by: CE324 MIDYEAR USTP INSTRUCTORS MATHEMATICAL BACKGROUND 1. Roots of Equations (Figure 2a). These problems are concerned with the value of a variable or a parameter that satisfies a single nonlinear equation. These problems are especially valuable in engineering design contexts where it is often impossible to explicitly solve design equations for parameters. 2. Systems of Linear Algebraic Equations (Figure 2ab). These problems are similar in spirit to roots of equations in the sense that they are concerned with values that satisfy equations. However, in contrast to satisfying a single equation, a set of values is sought that simultaneously satisfies a set of linear algebraic equations. Such equations arise in a variety of problem contexts and in all disciplines of engineering. In particular, they originate in the mathematical modeling of large systems of interconnected elements such as structures, electric circuits, and fluid networks. However, they are also encountered in other areas of numerical methods such as curve fi ting and differential equations. 3. Optimization (Figure 2c). These problems involve determining a value or values of an independent variable that correspond to a “best” or optimal value of a function. Thus, as in figure 2c, optimization involves identifying maxima and minima. Such problems occur routinely in engineering design contexts. They also arise in a number of other numerical methods. We address both single- and multi-variable unconstrained optimization. We also describe constrained optimization with particular emphasis on linear programming. Created by: CE324 MIDYEAR USTP INSTRUCTORS 4. Curve Fitting (Figure 2d). You will often have occasion to fit curves to data points. The techniques developed for this purpose can be divided into two general categories: regression and interpolation. Regression is employed where there is a significant degree of error associated with the data. Experimental results are often of this kind. For these situations, the strategy is to derive a single curve that represents the general trend of the data without necessarily matching any individual points. In contrast, interpolation is used where the objective is to determine intermediate values between relatively error-free data points. Such is usually the case for tabulated information. For these situations, the strategy is to fi t a curve directly through the data points and use the curve to predict the intermediate values. 5. Integration (Figure 2e). As depicted, a physical interpretation of numerical integration is the determination of the area under a curve. Integration has many applications in engineering practice, ranging from the determination of the centroids of oddly shaped objects to the calculation of total quantities based on sets of discrete measurements. In addition, numerical integration formulas play an important role in the solution of differential equations. 6. Ordinary Differential Equations (Figure 2f). Ordinary differential equations are of great significance in engineering practice. This is because many physical laws are couched in terms of the rate of change of a quantity rather than the magnitude of the quantity itself. Examples range from population-forecasting models (rate of change of population) to the acceleration of a falling body (rate of change of velocity). Two types of problems are addressed: initial-value and boundaryvalue problems. In addition, the computation of eigenvalues is covered. Created by: CE324 MIDYEAR USTP INSTRUCTORS 7. Partial Differential Equations (Figure 2g). Partial differential equations are used to characterize engineering systems where the behavior of a physical quantity is couched in terms of its rate of change with respect to two or more independent variables. Examples include the steady-state distribution of temperature on a heated plate (two spatial dimensions) or the time-variable temperature of a heated rod (time and one spatial dimension). Two fundamentally different approaches are employed to solve partial differential equations numerically. In the present text, we will emphasize finite-difference methods that approximate the solution in a pointwise fashion (Figure 2g). However, we will also present an introduction to finite-element methods, which use a piecewise approach. 1.2 ACCURACY AND ERROR ACCURACY AND PRECISION The errors associated with both calculations and measurements can be characterized with regard to their accuracy and precision. Accuracy refers to how closely a computed or measured value agrees with the true value. Precision refers to how closely individual computed or measured values agree with each other. These concepts can be illustrated graphically using an analogy from target practice. The bullet holes on each target in Figure 3 can be thought of as the predictions of a numerical technique, whereas the bull’s-eye represents the truth. Inaccuracy (also called bias) is defined as systematic deviation from the truth. Thus, although the shots in Figure 3c are more tightly grouped than those in Figure 3a, the two cases are equally biased because they are both centered on the upper left quadrant of the target. Figure 3. An example from marksmanship illustrating the concepts of accuracy and precision: (a) inaccurate and imprecise, (b) accurate and imprecise, (c) inaccurate and precise, and (d) accurate and precise. Imprecision (also called uncertainty), on the other hand, refers to the magnitude of the scatter. Therefore, although Figure 3b and Figure 3d are equally accurate (that is, centered on the bull’s-eye), the latter is more precise because the shots are tightly grouped. Numerical methods should be sufficiently accurate or unbiased to meet the requirements of a particular engineering problem. They also should be precise enough for adequate engineering design. Created by: CE324 MIDYEAR USTP INSTRUCTORS ERROR Numerical errors arise from the use of approximations to represent exact mathematical operations and quantities. Numerical methods should be sufficiently accurate or unbiased to meet the requirements of a particular problem. The collective term error represents both the inaccuracy and imprecision of our predictions. The concept of a significant figure, or digit, has been developed to formally define the reliability of a numerical value. The significant digits of a number are those that can be used with confidence. Errors are introduced by the computational process itself. Computers perform mathematical operations with only a finite number of digits. If the number ππ is an approximation to the exact result ππ, then the difference ππ – ππ is called error. Hence: π¬ππππ πππππ = πππππππππππ πππππ + πππππ Errors in numerical solutions include inherent errors, round-off errors, truncation errors, absolute errors, relative errors, percentage errors, general formula for error. Inherent errors – Exist in the problem either due to approximate given data or limitations of computing aids. This can be minimized by taking better data and using high precision computing aids. Round-off errors – These errors are due to rounding off the numbers while process of computations. This error can be reduced by doing the computations to stop on the most significant digits of a number. EXAMPLE: Large number of digits such as 7.5846712 which can be rounded off to a significant figure 7.585. πΉππππ πππ πππππ = π. πππππππ – π. πππ = −π. πππππππ (πΉππππ − πππ π¬ππππ) Truncation errors – These errors arise due to use of approximate formula in computation or by truncating the infinite series to some approximate terms. The study of this type of error is usually associated with the problem of convergence of infinite series. Absolute errors, Relative errors, and Percentage errors If XE is the exact or true value of a quantity and XA is its approximate value, then |XE – XA| is called the absolute error Ea. π¬π = |πΏπ¬ – πΏπ¨| Therefore, absolute error and relative error is defined by π¬π = | πΏπ¬ – πΏπ¨ | πΏπ¬ provided XE ≠ 0 or XE is not too close to zero. The percentage error is π¬π = πππ × π¬π = πππ × | πΏπ¬ – πΏπ¨ | πΏπ¬ EXAMPLE: Let the exact or true value = The absolute error is 20 and 3 the approximate value = 6.666. 20 π π¬π = |ππΈ – ππ΄| = | 3 – 6.666| = 0.000666. . . = ππππ The relative error is π¬π = ππΈ – ππ΄ | ππΈ | = | 20 – 6.666 3 20 3 |=| The percentage error is π¬π = 100 × πΈπ = 100 × The number of significant digits is 4. Created by: CE324 MIDYEAR USTP INSTRUCTORS 2 3000 20 3 π | = 0.0001 = πππππ 1 10000 = π. ππ% 1.3 TAYLOR SERIES OF EXPANSION Taylor series allows us to represent, exactly, and fairly general functions in terms of polynomials with a known, specified, and boundable error. It is a representation of a function as an infinite sum of terms calculated from the values of its derivatives at a single point. Taylor series provides a means to predict a function value at one point in terms of the function value and its derivatives at another point. In particular, the theorem states that any smooth function can be approximated as a polynomial. A Maclaurin Polynomial is a special case of the Taylor Polynomial that uses zero as our single point. Formula for Taylor Series π(π) = π(π) + π′(π)(π − π) π′′(π)(π − π)π π′′′(π)(π − π)π π′′′′(π)(π − π)π π′′′′′(π)(π − π)π ππ (π)(π − π)π + + + + +β― π! π! π! π! π! π! ∞ π(π) = ∑ π=π π€βπππ: ππ (π)(π − π)π π! π = ππππ‘ππ ππ π‘ππ¦πππ π πππππ πππ£ππ ππ = ππ‘β πππππ£ππ‘ππ£π ππ π, if ππ = π π(π) = πππ£ππ ππ’πππ‘πππ EXAMPLE 1. Obtain the taylor series expansion of π(π) = π₯π§π centered at π = π. Solution: i. Get the series of derivatives for ππ (π) = π₯π§π by the use of the power rule: Zero derivative: 1st derivative: 2nd derivative: 3rd derivative: 4th derivative: π(π) = π₯π§π π π′(π) = = π−π π′′(π) = π −π π′′′(π) = ππ π π′′′′(π) = = −π−π = ππ−π ππ −π ππ = −ππ−π ii. Substitute ππ(π) with π = π to the series of derivatives, and ππ(π) to taylor series formula. Zero derivative: 1st derivative: 2nd derivative: 3rd derivative: 4th derivative: π(π) = π(π) + π(π) = π₯π§π π(π) = π₯π§(π) = π π π′(π) = = π π′′(π) = π −π π′′′(π) = ππ π π′′′′(π) = π −π = −π−π = ππ ππ −π ππ π π′(π) = = = π π′′(π) −π = −ππ π π −π −π = π = (π)π π π π π′′′(π) = −π π′′′′(π) = = ππ −π ππ = −π (π)π −π = =π (π)π = −π π′(π)(π − π) π′′(π)(π − π)π π′′′(π)(π − π)π π′′′′(π)(π − π)π ππ (π)(π − π)π + + + + β―+ π! π! π! π! π! π₯π§π = π + π(π − π) −π(π − π)π π(π − π)π −π(π − π)π ππ (π)(π − π)π + + + + β―+ π! π! π! π! π! iii. Simplify π₯π§π = (π − π)π (π − π)π (π − π)π (π − π)π − + − +β― π π π π ∞ π₯π§π = ∑ π=π (−π)π (π − π)π+π π+π This will be the taylor series for the π(π) = ππ (π) centered at π = π. Created by: CE324 MIDYEAR USTP INSTRUCTORS EXAMPLE 2. Write the taylor series for π(π) = ππ centered at π = π as ∑∞ π=0 ππ (π)(π−π)π π! . Find the 5 coefficients. Solution: i. Get the series of derivatives for π(π) = ππ by the use of the power rule: Zero derivative: 1st derivative: 2nd derivative: 3rd derivative: 4th derivative: π(π) = ππ π′(π) = πππ π′′(π) = ππ π′′′(π) = π π′′′′(π) = π ii. Substitute ππ(π) with π = 1 to the derivatives formula, and ππ(π) to taylor series formula. Zero derivative: 1st derivative: 2nd derivative: 3rd derivative: π(π) = ππ π′(π) = πππ π′′(π) = ππ π′′′(π) = π π(π) = π(π) + π(π) = ππ = (π)π = π π′(π) = πππ = π(π)π = π π′′(π) = ππ = π(π) = π π′′′(π) = π π′(π)(π − π) π′′(π)(π − π)π π′′′(π)(π − π)π + + π! π! π! ππ = π + π(π − π) π(π − π)π π (π − π)π + + π! π! π! ∞ π π =∑ ππ (π)(π − π)π π=0 π! iii. Simplify ππ = π + π(π − π) + π(π − π)π + (π − π)π Answer: 1, 3, 3, 1 These are the coefficients of the terms of the taylor series for ππ . References • • • Numerical Methods for Engineers, 7th Edition, by Chapra, S. & Canale, R., McGrawHill Education, New York, 2015 Applied Numerical Methods with MATLAB for Engineers and Scientists, 3rd Ed, by Chapra, S., McGraw-Hill Education, New York, 2012. Numerical Methods by Rao V. Dukkipati, New Age International Publishers, 2010 Created by: CE324 MIDYEAR USTP INSTRUCTORS
