Differential Equations
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Ordinary Differential Equations
Ordinary Differential Equations for Engineers and Scientists Gregg Waterman Oregon Institute of Technology c 2017 Gregg Waterman This work is licensed under the Creative Commons Attribution 4.0 International license. The essence of the license is that You are free to: Share copy and redistribute the material in any medium or format • Adapt remix, transform, and build upon the material for any purpose, even commercially. • The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution You must give appropriate credit, provide a link to the license, and indicate if changes • were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. No additional restrictions You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits. Notices: You do not have to comply with the license for elements of the material in the public domain or where your use is permitted by an applicable exception or limitation. No warranties are given. The license may not give you all of the permissions necessary for your intended use. For example, other rights such as publicity, privacy, or moral rights may limit how you use the material. For any reuse or distribution, you must make clear to others the license terms of this work. The best way to do this is with a link to the web page below. To view a full copy of this license, visit https://creativecommons.org/licenses/by/4.0/legalcode. -
Flow Routing Techniques for Environmental Modeling
EPA/600/B-18/256 | August 2018 | www.epa.gov/research Flow Routing Techniques for Environmental Modeling photo 0 EPA/600/B-18/256 August 2018 Flow Routing Techniques for Environmental Modeling by Jan Sitterson1 , Chris Knightes2 , Brian Avant3 1 Oak Ridge Associated Universities 2 U.S. Environmental Protection Agency, Office of Research and Development 3 Oak Ridge Institute for Science and Education Chris Knightes - Project Officer Office of Research and Development National Exposure Research Laboratory Athens, GA, 30605 1 Notice The U.S. Environmental Protection Agency (EPA) through its Office of Research and Development funded and managed the research described here. The research described herein was conducted at the Computational Exposure Division of the U.S. Environmental Protection Agency National Exposure Research Laboratory in Athens, GA. Any mention of trade names, products, or services does not imply an endorsement by the U.S. Government or the U.S. Environmental Protection Agency. The EPA does not endorse any commercial products, services, or enterprises. This document has been reviewed by the U.S. Environmental Protection Agency, Office of Research and Development, and approved for publication. 2 Abstract This report describes a few ways to simulate the movement of water through a network of streams. Flow routing connects excess water from precipitation and runoff to the stream to other surface water as part of the hydrological cycle. Simulating flow helps elucidate the transportation of nutrients through a stream system, predict flood events, inform decision makers, and regulate water quality and quantity issues. Three flow routing techniques are presented and discussed in this paper. -
New Dirac Delta Function Based Methods with Applications To
New Dirac Delta function based methods with applications to perturbative expansions in quantum field theory Achim Kempf1, David M. Jackson2, Alejandro H. Morales3 1Departments of Applied Mathematics and Physics 2Department of Combinatorics and Optimization University of Waterloo, Ontario N2L 3G1, Canada, 3Laboratoire de Combinatoire et d’Informatique Math´ematique (LaCIM) Universit´edu Qu´ebec `aMontr´eal, Canada Abstract. We derive new all-purpose methods that involve the Dirac Delta distribution. Some of the new methods use derivatives in the argument of the Dirac Delta. We highlight potential avenues for applications to quantum field theory and we also exhibit a connection to the problem of blurring/deblurring in signal processing. We find that blurring, which can be thought of as a result of multi-path evolution, is, in Euclidean quantum field theory without spontaneous symmetry breaking, the strong coupling dual of the usual small coupling expansion in terms of the sum over Feynman graphs. arXiv:1404.0747v3 [math-ph] 23 Sep 2014 2 1. A method for generating new representations of the Dirac Delta The Dirac Delta distribution, see e.g., [1, 2, 3], serves as a useful tool from physics to engineering. Our aim here is to develop new all-purpose methods involving the Dirac Delta distribution and to show possible avenues for applications, in particular, to quantum field theory. We begin by fixing the conventions for the Fourier transform: 1 1 g(y) := g(x) eixy dx, g(x)= g(y) e−ixy dy (1) √2π √2π Z Z To simplify the notation we denote integration over the real line by the absence of e e integration delimiters. -
Integration and Differential Equations
R.S. Johnson Integration and differential equations Download free eBooks at bookboon.com 2 Integration and differential equations © 2012 R.S. Johnson & Ventus Publishing ApS ISBN 978-87-7681-970-5 Download free eBooks at bookboon.com 3 Integration and differential equations Contents Contents Preface to these two texts 8 Part I An introduction to the standard methods of elementary integration 9 List of Integrals 10 Preface 11 1 Introduction and Background 12 1.1 The Riemann integral 12 1.2 The fundamental theorem of calculus 17 1.4 Theorems on integration 19 1.5 Standard integrals 23 1.6 Integration by parts 26 1.7 Improper integrals 32 1.8 Non-uniqueness of representation 35 Exercises 1 36 2 The integration of rational functions 38 2.1 Improper fractions 38 2.2 Linear denominator, Q(x) 39 The next step for top-performing graduates Masters in Management Designed for high-achieving graduates across all disciplines, London Business School’s Masters in Management provides specific and tangible foundations for a successful career in business. This 12-month, full-time programme is a business qualification with impact. In 2010, our MiM employment rate was 95% within 3 months of graduation*; the majority of graduates choosing to work in consulting or financial services. As well as a renowned qualification from a world-class business school, you also gain access to the School’s network of more than 34,000 global alumni – a community that offers support and opportunities throughout your career. For more information visit www.london.edu/mm, email [email protected] or give us a call on +44 (0)20 7000 7573. -
Second Order Linear Differential Equations Y
Second Order Linear Differential Equations Second order linear equations with constant coefficients; Fundamental solutions; Wronskian; Existence and Uniqueness of solutions; the characteristic equation; solutions of homogeneous linear equations; reduction of order; Euler equations In this chapter we will study ordinary differential equations of the standard form below, known as the second order linear equations : y″ + p(t) y′ + q(t) y = g(t). Homogeneous Equations : If g(t) = 0, then the equation above becomes y″ + p(t) y′ + q(t) y = 0. It is called a homogeneous equation. Otherwise, the equation is nonhomogeneous (or inhomogeneous ). Trivial Solution : For the homogeneous equation above, note that the function y(t) = 0 always satisfies the given equation, regardless what p(t) and q(t) are. This constant zero solution is called the trivial solution of such an equation. © 2008, 2016 Zachary S Tseng B-1 - 1 Second Order Linear Homogeneous Differential Equations with Constant Coefficients For the most part, we will only learn how to solve second order linear equation with constant coefficients (that is, when p(t) and q(t) are constants). Since a homogeneous equation is easier to solve compares to its nonhomogeneous counterpart, we start with second order linear homogeneous equations that contain constant coefficients only: a y″ + b y′ + c y = 0. Where a, b, and c are constants, a ≠ 0. A very simple instance of such type of equations is y″ − y = 0 . The equation’s solution is any function satisfying the equality t y″ = y. Obviously y1 = e is a solution, and so is any constant multiple t −t of it, C1 e . -
The University of Chicago Finite Element Method
THE UNIVERSITY OF CHICAGO FINITE ELEMENT METHOD AUTOMATION FOR NON-NEWTONIAN FLUID MODELS A DISSERTATION SUBMITTED TO THE FACULTY OF THE DIVISION OF THE PHYSICAL SCIENCES IN CANDIDACY FOR THE DEGREE OF DOCTOR OF PHILOSOPHY DEPARTMENT OF COMPUTER SCIENCE BY ANDY R. TERREL CHICAGO, ILLINOIS AUGUST 2010 Copyright c 2010 by Andy R. Terrel All rights reserved To my family, most especially: my wife, Cheryl, my mother, Judy, and my late grandfather, Virgil. To all the educators who have helped me along my path, most especially: my high school math teacher, Ms. Swauger, and chemistry teacher, Ms. Pirtle. TABLE OF CONTENTS LISTOFFIGURES .................................... vi LISTOFTABLES..................................... vii LIST OF ALGORITHMS . viii ACKNOWLEDGMENTS . ix ABSTRACT ........................................ x CHAPTER 1 INTRODUCTION ................................... 1 1.1 Finite Element Methods . 2 1.2 Fluid Models . 5 1.3 Algebraic Solvers . 5 2 AUTOMATED SCIENTIFIC COMPUTING . 7 2.1 Developing an automated system . 8 2.1.1 Defining the problem domain. 8 2.1.2 Building interfaces . 9 2.1.3 Optimizations and Code Generation . 10 2.2 Examples . 10 2.2.1 Linear Algebra . 11 2.2.2 Signal Processing . 12 3 AUTOMATING FINITE ELEMENT METHODS . 14 3.1 Automation of FEM Assembly . 15 3.1.1 Local assembly . 16 3.1.2 Global assembly . 22 3.2 Sieve........................................ 23 3.2.1 Reference Finite Element Method . 25 3.2.2 Functions over a Mesh . 26 3.2.3 Local Function Definition . 27 3.2.4 Global update . 28 3.2.5 Implementation Issues . 28 3.2.6 Examples . 31 iv 4 RHEOLOGY APPLICATION ENGINE: NEWTONIAN FLUIDS . 33 4.1 The Newtonian fluid model . -
5 Mar 2009 a Survey on the Inverse Integrating Factor
A survey on the inverse integrating factor.∗ Isaac A. Garc´ıa (1) & Maite Grau (1) Abstract The relation between limit cycles of planar differential systems and the inverse integrating factor was first shown in an article of Giacomini, Llibre and Viano appeared in 1996. From that moment on, many research articles are devoted to the study of the properties of the inverse integrating factor and its relation with limit cycles and their bifurcations. This paper is a summary of all the results about this topic. We include a list of references together with the corresponding related results aiming at being as much exhaustive as possible. The paper is, nonetheless, self-contained in such a way that all the main results on the inverse integrating factor are stated and a complete overview of the subject is given. Each section contains a different issue to which the inverse integrating factor plays a role: the integrability problem, relation with Lie symmetries, the center problem, vanishing set of an inverse integrating factor, bifurcation of limit cycles from either a period annulus or from a monodromic ω-limit set and some generalizations. 2000 AMS Subject Classification: 34C07, 37G15, 34-02. Key words and phrases: inverse integrating factor, bifurcation, Poincar´emap, limit cycle, Lie symmetry, integrability, monodromic graphic. arXiv:0903.0941v1 [math.DS] 5 Mar 2009 1 The Euler integrating factor The method of integrating factors is, in principle, a means for solving ordinary differential equations of first order and it is theoretically important. The use of integrating factors goes back to Leonhard Euler. Let us consider a first order differential equation and write the equation in the Pfaffian form ω = P (x, y) dy Q(x, y) dx =0 . -
Handbook of Mathematics, Physics and Astronomy Data
Handbook of Mathematics, Physics and Astronomy Data School of Chemical and Physical Sciences c 2017 Contents 1 Reference Data 1 1.1 PhysicalConstants ............................... .... 2 1.2 AstrophysicalQuantities. ....... 3 1.3 PeriodicTable ................................... 4 1.4 ElectronConfigurationsoftheElements . ......... 5 1.5 GreekAlphabetandSIPrefixes. ..... 6 2 Mathematics 7 2.1 MathematicalConstantsandNotation . ........ 8 2.2 Algebra ......................................... 9 2.3 TrigonometricalIdentities . ........ 10 2.4 HyperbolicFunctions. ..... 12 2.5 Differentiation .................................. 13 2.6 StandardDerivatives. ..... 14 2.7 Integration ..................................... 15 2.8 StandardIndefiniteIntegrals . ....... 16 2.9 DefiniteIntegrals ................................ 18 2.10 CurvilinearCoordinateSystems. ......... 19 2.11 VectorsandVectorAlgebra . ...... 22 2.12ComplexNumbers ................................. 25 2.13Series ......................................... 27 2.14 OrdinaryDifferentialEquations . ......... 30 2.15 PartialDifferentiation . ....... 33 2.16 PartialDifferentialEquations . ......... 35 2.17 DeterminantsandMatrices . ...... 36 2.18VectorCalculus................................. 39 2.19FourierSeries .................................. 42 2.20Statistics ..................................... 45 3 Selected Physics Formulae 47 3.1 EquationsofElectromagnetism . ....... 48 3.2 Equations of Relativistic Kinematics and Mechanics . ............. 49 3.3 Thermodynamics and Statistical Physics -
Pdemodelica a High-Level Language for Modeling with Partial Differential Equations
Linköping Studies in Science and Technology Dissertation No. 1016 PDEModelica A High-Level Language for Modeling with Partial Differential Equations by Levon Saldamli Department of Computer and Information Science Linköpings universitet SE-581 83 Linköping, Sweden Linköping 2006 “...toboldlygowhere no man has gone before.” James T. Kirk Abstract This thesis describes work on a new high-level mathematical modeling language and framework called PDEModelica for modeling with partial differential equa- tions. It is an extension to the current Modelica modeling language for object- oriented, equation-based modeling based on differential and algebraic equations. The language extensions and the framework presented in this thesis are consistent with the concepts of Modelica while adding support for partial differential equa- tions and space-distributed variables called fields. The specification of a partial differential equation problem consists of three parts: 1) the description of the definition domain, i.e., the geometric region where the equations are defined, 2) the initial and boundary conditions, and 3) the ac- tual equations. The known and unknown distributed variables in the equation are represented by field variables in PDEModelica. Domains are defined by a geo- metric description of their boundaries. Equations may use the Modelica derivative operator extended with support for partial derivatives, or vector differential opera- tors such as divergence and gradient, which can be defined for general curvilinear coordinates based on coordinate system definitions. The PDEModelica system also allows the partial differential equation models to be defined using a coefficient-based approach, where PDE models from a library are instantiated with different parameter values. Such a library contains both con- tinuous and discrete representations of the PDE model. -
FIRST-ORDER ORDINARY DIFFERENTIAL EQUATIONS III: Numerical and More Analytic Methods
FIRST-ORDER ORDINARY DIFFERENTIAL EQUATIONS III: Numerical and More Analytic Methods David Levermore Department of Mathematics University of Maryland 30 September 2012 Because the presentation of this material in lecture will differ from that in the book, I felt that notes that closely follow the lecture presentation might be appreciated. Contents 8. First-Order Equations: Numerical Methods 8.1. Numerical Approximations 2 8.2. Explicit and Implicit Euler Methods 3 8.3. Explicit One-Step Methods Based on Taylor Approximation 4 8.3.1. Explicit Euler Method Revisited 4 8.3.2. Local and Global Errors 4 8.3.3. Higher-Order Taylor-Based Methods (not covered) 5 8.4. Explicit One-Step Methods Based on Quadrature 6 8.4.1. Explicit Euler Method Revisited Again 6 8.4.2. Runge-Trapezoidal Method 7 8.4.3. Runge-Midpoint Method 9 8.4.4. Runge-Kutta Method 10 8.4.5. General Runge-Kutta Methods (not covered) 12 9. Exact Differential Forms and Integrating Factors 9.1. Implicit General Solutions 15 9.2. Exact Differential Forms 16 9.3. Integrating Factors 20 10. Special First-Order Equations and Substitution 10.1. Linear Argument Equations (not covered) 25 10.2. Dilation Invariant Equations (not covered) 26 10.3. Bernoulli Equations (not covered) 27 10.4. Substitution (not covered) 29 1 2 8. First-Order Equations: Numerical Methods 8.1. Numerical Approximations. Analytic methods are either difficult or impossible to apply to many first-order differential equations. In such cases direction fields might be the only graphical method that we have covered that can be applied. -
Phase-Space Matrix Representation of Differential Equations for Obtaining the Energy Spectrum of Model Quantum Systems
Phase-space matrix representation of differential equations for obtaining the energy spectrum of model quantum systems Juan C. Morales, Carlos A. Arango1, a) Department of Chemical Sciences, Universidad Icesi, Cali, Colombia (Dated: 27 August 2021) Employing the phase-space representation of second order ordinary differential equa- tions we developed a method to find the eigenvalues and eigenfunctions of the 1- dimensional time independent Schr¨odinger equation for quantum model systems. The method presented simplifies some approaches shown in textbooks, based on asymptotic analyses of the time-independent Schr¨odinger equation, and power series methods with recurrence relations. In addition, the method presented here facilitates the understanding of the relationship between the ordinary differential equations of the mathematical physics and the time independent Schr¨odinger equation of physical models as the harmonic oscillator, the rigid rotor, the Hydrogen atom, and the Morse oscillator. Keywords: phase-space, model quantum systems, energy spectrum arXiv:2108.11487v1 [quant-ph] 25 Aug 2021 a)Electronic mail: [email protected] 1 I. INTRODUCTION The 1-dimensional time independent Schr¨odinger equation (TISE) can be solved analyt- ically for few physical models. The harmonic oscillator, the rigid rotor, the Hydrogen atom, and the Morse oscillator are examples of physical models with known analytical solution of the TISE (1). The analytical solution of the TISE for a physical model is usually obtained by using the ansatz of a wavefunction as a product of two functions, one of these functions acts as an integrating factor (2), the other function produces a differential equation solv- able either by Frobenius series method or by directly comparing with a template ordinary differential equation (ODE) with known solution (3). -
By Gavin Lobo a Thesis Submitted in Conformity with the Requirements For
INVESTIGATION INTO SMOOTHED PARTICLE HYDRODYNAMICS FOR NON-NEWTONIAN DROPLET MODELLING by Gavin Lobo A thesis submitted in conformity with the requirements for the degree of Master of Science in The Faculty of Science Modelling and Computational Science University of Ontario Institute of Technology August 2011 Copyright c , Gavin Lobo, 2011 Abstract Droplet splatter dynamics is an important study in the field of forensics since a crime event can produce many blood stains. Understanding the origins of the blood stains from pure observations is very difficult because much of the information about the impact is lost. A theoretical model is therefore needed to better understand the dy- namics of droplet impact and splatter. We chose to explore a fluid modelling method known as Smoothed Particle Hydrodynamics (SPH) to determine whether it is capable of modelling droplet splatter accurately. Specifically, we chose to investigate an SPH version of a non-Newtonian pressure correction method with surface tension. Three experiments were performed to analyze the different aspects of SPH. From the results of the experiments, we concluded that this method can produce stable simulations if an artificial viscosity model is included, a third-order polynomial kernel is used and the pressure boundary condition on surface particles are non-zero. ii Dedication To my parents and my brother, Calvin. iii Contents 1 Introduction 1 1.1 Objective . .1 1.2 Related Work . .2 2 Introduction to Smoothed Particle Hydrodynamics 5 2.1 Formulation of Smoothed Particle Hydrodynamics . .5 2.1.1 Function Approximation . .5 2.1.2 Derivative Approximation . .8 2.2 Kernel Functions .