CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Development Of

Total Page:16

File Type:pdf, Size:1020Kb

CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Development Of CALIFORNIA STATE UNIVERSITY, NORTHRIDGE Development of Fast Methods for Evaluating the Boltzmann Collision Operator Based on Discontinuous Galerkin Discretizations in the Velocity Variable, Convolution Formulation, and Fast Fourier Transform A thesis submitted in partial fulfillment of the requirements for the degree of Master of Science in Applied Mathematics By Jeffrey Limbacher August 2018 The thesis of Jeffrey Limbacher is approved: Ali Zakeri , Ph.D. Date Vladislav Panferov , Ph.D. Date Alexander Alekseenko , Ph.D., Chair Date California State University, Northridge ii Acknowledgements My deepest gratitude goes to my advisor, Dr. Alekseenko. Working with him has been an amazing experience. Dr. Alekseenko has given me amazing opportunities, guidance, and support. I would like to thank the CSUN Math department, which has a superb focus on its students. I would like to thank my friends and family for their support throughout my time at CSUN. They have supported my decision to pursue a Master’s degree without hesitation. Lastly, thanks to my girlfriend, Pauline, who has been a constant source of happiness and motivation for me. iii Table of Contents Signature page ii Acknowledgements iii List of Tables vi List of Figures vii Abstract viii 1 Introduction 1 2 The Boltzmann Equation 3 2.1 Binary Collisions of Particles . .4 2.2 The Collision Operator . .5 2.3 Moments of the Distribution Function . .7 2.4 The Maxwellian Distribution . .7 2.5 Dimensionless Reduction . .9 3 Discontinuous Galerkin Discretization in the Velocity Variable 13 3.1 DG Discretization in Velocity Space . 13 3.2 Nodal-DG Velocity Discretization of the Boltzmann Equation . 14 3.3 Reformulation of the Galerkin Projection of the Collision Operator . 15 3.4 Properties of the Kernel A(~v,~v1; φi;j).................. 17 3.5 Rewriting the Collision Operator in the Form of a Convolution . 18 3.6 Discretization of the Collision Integral . 19 3.7 The Micro-Macro Decomposition . 21 4 The Discrete Fourier Transform 23 4.1 The One-Dimensional Discrete Fourier Transform and its Properties . 23 4.1.1 Properties of the DFT . 24 4.1.2 The Convolution Theorem . 25 4.2 Circular Convolution as Linear Convolution . 27 4.2.1 Linear Convolutions and Circular Convolutions . 27 4.2.2 Linear Convolution of Two Finite-Length sequences . 27 4.2.3 Linear Convolution as Circular Convolution . 28 4.2.4 Circular Convolution as Linear Convolution with Aliasing. 30 4.3 The One-Dimensional Fast Fourier Transform . 31 4.3.1 Radix-2 FFT Algorithm . 33 5 Discretization of the Collision Integral and Fast Evaluation of Discrete Con- volution 35 5.1 Formulas for Computing the DFT of the Collision Integral . 35 iv 5.2 The Algorithm and its Complexity . 42 5.3 Periodic Continuation of f and A .................... 44 6 Numerical Results 47 6.1 Reduction in Computational Complexity . 47 6.2 Numerical Results of the Split Form of the Operator . 48 6.3 0d Homogeneuous Relaxation . 52 6.4 Zero-Padding . 54 7 The Model Kinetic Equations and the Rel-ES Method 58 7.1 The BGK Model . 58 7.2 The ES-BGK Model . 59 7.3 Rel-ES . 62 7.4 Experimental Results: 0d Homogeneous Relaxation . 63 8 Hierarchically Semi-Structured Compression of the Kernel A 65 9 Conclusion 69 References 70 v List of Tables 6.1 CPU times for evaluating the collision operator directly and using the Fourier transform. 48 6.2 Absolute errors in conservation of mass and temperature in the discrete collision integral computed using split and non-split formulations. 50 6.3 The Lmax and L1 errors as we increase npad............... 56 6.4 Performance of the method decreases as we increase npad........ 56 vi List of Figures 2.1 Kremer (2010, p. 27), Fig 1.6 . .4 2.2 A 1D Maxwellian distribution. .8 6.1 Evaluation of the collision operator using split and non-split forms: (a) and (d) the split form evaluated using the Fourier transform; (b) and (e) the split form evaluated directly; (c) and (f) the non-split form evaluated using the Fourier transform. 49 R p 6.2 Relaxation of moments fϕi,p = R3 (ui − u¯i) f(t, ~u) du, i = 1, 2, p = 2, 3, 4, 6 in a mix of Maxwellian streams corresponding to a shock wave with Mach number 3.0 obtained by solving the Boltzmann equation using Fourier and direct evaluations of the collision integral. In the case of p = 2, the relaxation of moments is also compared to moments of a DSMC solution [8]. 53 6.3 Relaxation of moments fϕi,p , i = 1, 2, p = 2, 3, 4, 6 in a mix of Maxwellian streams corresponding to a shock wave with Mach number 1.55 ob- tained by solving the Boltzmann equation using Fourier and direct evaluations of the collision integral. 54 7.1 Relaxation of moments fϕi,p , i = 1, 2, p = 2, 3, 4, 6 in a mix of Maxwellian streams corresponding to a shock wave with Mach number 1.55 ob- tained by solving the Boltzmann equation using Fourier and direct evaluations of the collision integral. 64 p 8.1 Color plots of magnitudes of values of A(~v,~v1; φi,jc ) on uniform rect- angle grid. (left) The case of simple numbering. (right) The case of Morton encoding. 66 p 8.2 HSS reduction of a matrix form of A(~v,~v1; φi,jc ) in the Morton encod- ing. Case of 16 × 16 × 16 grid. (left) Geometrical partitioning. (right) Algebraic partitioning. 67 p 8.3 HSS reduction of a matrix form of A(~v,~v1; φi,jc ) in the Morton encod- ing. Case of 32 × 32 × 32 grid. Algebraic partitioning. 67 vii ABSTRACT Development of Fast Methods for Evaluating the Boltzmann Collision Operator Based on Discontinuous Galerkin Discretizations in the Velocity Variable, Convolution Formulation, and Fast Fourier Transform By Jeffrey Limbacher Master of Science in Applied Mathematics Gas flows around high-speed high altitude aircraft and within rocket thrusters contain regions where the particle velocities deviates significantly from the Maxwellian distribution. The gas in these regions is said to be in non-continuum and is best described by kinetic equations. The most physically accurate model is given by the Boltzmann equation. Due to its high computational cost, there is great interest in reducing the computational costs associated with the evaluation of the collision integral. This thesis focuses on reducing the computational costs associated with the collision integral. The Discontinuous Galerkin (DG) method is used to discretize the equation. Discretization leads to a weighted convolution form which costs O(N 8) operations to calculate directly. A discussion of convolutions, their properties, and fast evaluation of convolutions is discussed. A method is introduced based on the Discrete Fourier Transform (DFT) to reduce the calculation of the convolution form down to O(N 6). Empirical results are shown demonstrating that it is conservative and error is minimal. A discussion of two different formulations of the collision integrals are discussed and numerical results are given. A brief discussion of a new approach based on model kinetic equations is also given. viii Chapter 1 Introduction This thesis concerns itself with the evolution of gas flows in low density regimes. This is of particular interest to the engineering community. One example of such a regime is gas flows around high-altitude high-velocity objects flying through the upper atmosphere such as spacecraft and aircraft. Under such regimes, the particles impart a large amount of kinetic energy on the object causing a large transfer of heat to the object. Preventing damage to the object under these circumstances is essential. It is often difficult to replicate these conditions within a laboratory setting. Consequently, there is hope in the development of high fidelity solvers that can simulate the high speed gas flows around these objects to help predict the correct heating patterns. In these gas regimes, the fluid mechanical laws of Navier-Stokes and Fourier break down. In contrast, kinetic theory provide an accurate description by describing particles at the microscopic level. Kinetic theory describes the non-equilibrium dynamics of a gas or any system comprised of a large number of particles. Kinetic equations have found applications in wide range of problems such as rarefied gas dynamics [16] [15], radiative transfer, and semiconductors modeling. The Boltzmann equation is a kinetic equation that describes gases at the molecular level at regimes where Navier-Stokes and Fourier methods fail. Analytic solutions to the Boltzmann equation have been constructed for simple geometries and special molecular potentials. However, the complexity of the equation, along with the the complexities of boundary conditions and gas-to-gas interactions that occur in engineering and physics applications, suggest that only numerical solutions are possible. At the same time, direct numerical computation of the Boltzmann equation remains elusive. The Boltzmann equation is composed of a 1 five-fold integral which must be evaluated at all points in time, space, and velocity resulting in O(n12) computational cost where n is the number of discretization points in space and velocity in each of the three dimensions. This thesis primarily concerns with evaluating the collision integral in velocity space which has of O(n9) operations. This is still computationally prohibitive, there is a strong interest to develop methods that lower the costs. In this thesis, we explore how to speed up evaluation of the collision operator within the Boltzmann equation by using a Discontinuous-Galerkin method based on the work of [17, 1, 2]. The DG method is advantagous for its ability to capture arbitrary surfaces and conserve mass, momentum, and temperature. However, the drawback of this method is its complexity.
Recommended publications
  • Convolution! (CDT-14) Luciano Da Fontoura Costa
    Convolution! (CDT-14) Luciano da Fontoura Costa To cite this version: Luciano da Fontoura Costa. Convolution! (CDT-14). 2019. hal-02334910 HAL Id: hal-02334910 https://hal.archives-ouvertes.fr/hal-02334910 Preprint submitted on 27 Oct 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Convolution! (CDT-14) Luciano da Fontoura Costa [email protected] S~aoCarlos Institute of Physics { DFCM/USP October 22, 2019 Abstract The convolution between two functions, yielding a third function, is a particularly important concept in several areas including physics, engineering, statistics, and mathematics, to name but a few. Yet, it is not often so easy to be conceptually understood, as a consequence of its seemingly intricate definition. In this text, we develop a conceptual framework aimed at hopefully providing a more complete and integrated conceptual understanding of this important operation. In particular, we adopt an alternative graphical interpretation in the time domain, allowing the shift implied in the convolution to proceed over free variable instead of the additive inverse of this variable. In addition, we discuss two possible conceptual interpretations of the convolution of two functions as: (i) the `blending' of these functions, and (ii) as a quantification of `matching' between those functions.
    [Show full text]
  • The Convolution Theorem
    Module 2 : Signals in Frequency Domain Lecture 18 : The Convolution Theorem Objectives In this lecture you will learn the following We shall prove the most important theorem regarding the Fourier Transform- the Convolution Theorem We are going to learn about filters. Proof of 'the Convolution theorem for the Fourier Transform'. The Dual version of the Convolution Theorem Parseval's theorem The Convolution Theorem We shall in this lecture prove the most important theorem regarding the Fourier Transform- the Convolution Theorem. It is this theorem that links the Fourier Transform to LSI systems, and opens up a wide range of applications for the Fourier Transform. We shall inspire its importance with an application example. Modulation Modulation refers to the process of embedding an information-bearing signal into a second carrier signal. Extracting the information- bearing signal is called demodulation. Modulation allows us to transmit information signals efficiently. It also makes possible the simultaneous transmission of more than one signal with overlapping spectra over the same channel. That is why we can have so many channels being broadcast on radio at the same time which would have been impossible without modulation There are several ways in which modulation is done. One technique is amplitude modulation or AM in which the information signal is used to modulate the amplitude of the carrier signal. Another important technique is frequency modulation or FM, in which the information signal is used to vary the frequency of the carrier signal. Let us consider a very simple example of AM. Consider the signal x(t) which has the spectrum X(f) as shown : Why such a spectrum ? Because it's the simplest possible multi-valued function.
    [Show full text]
  • The Discrete-Time Fourier Transform and Convolution Theorems: a Brief Tutorial
    The Discrete-Time Fourier Transform and Convolution Theorems: A Brief Tutorial Yi-Wen Liu 25 Feb 2013 (Revised 21 Sep 2015) 1 Definitions and interpretation 1.1 Units Throughout this semester, we will use the integer-valued variable n as the time variable for discrete-time signal processing; that is, n = −∞, ..., −1, 0, 1, ..., ∞. In this convention, the unit of n is dimensionless, but be aware that in reality the sampling period is 1/fs, where fs denotes the sampling rate. In some text books, 1/fs is denoted as T , and its unit is second. In this course we do not do this, thereby favoring conciseness of notations. 1.2 Definition of DTFT The discrete-time Fourier transform (DTFT) of a discrete-time signal x[n] is a function of frequency ω defined as follows: ∞ ∆ X(ω) = x[n]e−jωn. (1) n=−∞ X Conceptually, the DTFT allows us to check how much of a tonal component at fre- quency ω is in x[n]. The DTFT of a signal is often also called a spectrum. Note that X(ω) is complex-valued. So, the absolute value |X(ω)| represents the tonal component’s magnitude, and the angle ∠X(ω) tells us the phase of the tonal component. Also note that Eq. (1) defines the DTFT as the inner product between the signal and the complex exponential tone e−jωn. Because complex exponentials {e−jωn,ω ∈ [−π, π]} form a complete family of orthogonal bases for (practically) all signals of interest, what Eq. (1) essentially does is to project x[n] onto the space spanned by all complex exponen- tials.
    [Show full text]
  • Fourier Transforms & the Convolution Theorem
    Convolution, Correlation, & Fourier Transforms James R. Graham 11/25/2009 Introduction • A large class of signal processing techniques fall under the category of Fourier transform methods – These methods fall into two broad categories • Efficient method for accomplishing common data manipulations • Problems related to the Fourier transform or the power spectrum Time & Frequency Domains • A physical process can be described in two ways – In the time domain, by h as a function of time t, that is h(t), -∞ < t < ∞ – In the frequency domain, by H that gives its amplitude and phase as a function of frequency f, that is H(f), with -∞ < f < ∞ • In general h and H are complex numbers • It is useful to think of h(t) and H(f) as two different representations of the same function – One goes back and forth between these two representations by Fourier transforms Fourier Transforms ∞ H( f )= ∫ h(t)e−2πift dt −∞ ∞ h(t)= ∫ H ( f )e2πift df −∞ • If t is measured in seconds, then f is in cycles per second or Hz • Other units – E.g, if h=h(x) and x is in meters, then H is a function of spatial frequency measured in cycles per meter Fourier Transforms • The Fourier transform is a linear operator – The transform of the sum of two functions is the sum of the transforms h12 = h1 + h2 ∞ H ( f ) h e−2πift dt 12 = ∫ 12 −∞ ∞ ∞ ∞ h h e−2πift dt h e−2πift dt h e−2πift dt = ∫ ( 1 + 2 ) = ∫ 1 + ∫ 2 −∞ −∞ −∞ = H1 + H 2 Fourier Transforms • h(t) may have some special properties – Real, imaginary – Even: h(t) = h(-t) – Odd: h(t) = -h(-t) • In the frequency domain these
    [Show full text]
  • Fourier Analysis
    Chapter 1 Fourier analysis In this chapter we review some basic results from signal analysis and processing. We shall not go into detail and assume the reader has some basic background in signal analysis and processing. As basis for signal analysis, we use the Fourier transform. We start with the continuous Fourier transformation. But in applications on the computer we deal with a discrete Fourier transformation, which introduces the special effect known as aliasing. We use the Fourier transformation for processes such as convolution, correlation and filtering. Some special attention is given to deconvolution, the inverse process of convolution, since it is needed in later chapters of these lecture notes. 1.1 Continuous Fourier Transform. The Fourier transformation is a special case of an integral transformation: the transforma- tion decomposes the signal in weigthed basis functions. In our case these basis functions are the cosine and sine (remember exp(iφ) = cos(φ) + i sin(φ)). The result will be the weight functions of each basis function. When we have a function which is a function of the independent variable t, then we can transform this independent variable to the independent variable frequency f via: +1 A(f) = a(t) exp( 2πift)dt (1.1) −∞ − Z In order to go back to the independent variable t, we define the inverse transform as: +1 a(t) = A(f) exp(2πift)df (1.2) Z−∞ Notice that for the function in the time domain, we use lower-case letters, while for the frequency-domain expression the corresponding uppercase letters are used. A(f) is called the spectrum of a(t).
    [Show full text]
  • Fourier Transform, Convolution Theorem, and Linear Dynamical Systems April 28, 2016
    Mathematical Tools for Neuroscience (NEU 314) Princeton University, Spring 2016 Jonathan Pillow Lecture 23: Fourier Transform, Convolution Theorem, and Linear Dynamical Systems April 28, 2016. Discrete Fourier Transform (DFT) We will focus on the discrete Fourier transform, which applies to discretely sampled signals (i.e., vectors). Linear algebra provides a simple way to think about the Fourier transform: it is simply a change of basis, specifically a mapping from the time domain to a representation in terms of a weighted combination of sinusoids of different frequencies. The discrete Fourier transform is therefore equiv- alent to multiplying by an orthogonal (or \unitary", which is the same concept when the entries are complex-valued) matrix1. For a vector of length N, the matrix that performs the DFT (i.e., that maps it to a basis of sinusoids) is an N × N matrix. The k'th row of this matrix is given by exp(−2πikt), for k 2 [0; :::; N − 1] (where we assume indexing starts at 0 instead of 1), and t is a row vector t=0:N-1;. Recall that exp(iθ) = cos(θ) + i sin(θ), so this gives us a compact way to represent the signal with a linear superposition of sines and cosines. The first row of the DFT matrix is all ones (since exp(0) = 1), and so the first element of the DFT corresponds to the sum of the elements of the signal. It is often known as the \DC component". The next row is a complex sinusoid that completes one cycle over the length of the signal, and each subsequent row has a frequency that is an integer multiple of this \fundamental" frequency.
    [Show full text]
  • Numerical Analysis of Elastic Contact Between Coated Bodies
    Hindawi Advances in Tribology Volume 2018, Article ID 6498503, 13 pages https://doi.org/10.1155/2018/6498503 Research Article Numerical Analysis of Elastic Contact between Coated Bodies Sergiu Spinu 1,2 1 Department of Mechanics and Technologies, Stefan cel Mare University of Suceava, 13th University Street, 720229, Romania 2Integrated Center for Research, Development and Innovation in Advanced Materials, Nanotechnologies, and Distributed Systems for Fabrication and Control (MANSiD), Stefan cel Mare University of Suceava, Romania Correspondence should be addressed to Sergiu Spinu; [email protected] Received 31 May 2018; Revised 28 September 2018; Accepted 11 October 2018; Published 1 November 2018 Academic Editor: Patrick De Baets Copyright © 2018 Sergiu Spinu. Tis is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Substrate protection by means of a hard coating is an efcient way of extending the service life of various mechanical, electrical, or biomedical elements. Te assessment of stresses induced in a layered body under contact load may advance the understanding of the mechanisms underlying coating performance and improve the design of coated systems. Te iterative derivation of contact area and contact tractions requires repeated displacement evaluation; therefore the robustness of a contact solver relies on the efciency of the algorithm for displacement calculation. Te fast Fourier transform coupled with the discrete convolution theorem has been widely used in the contact modelling of homogenous bodies, as an efcient computational tool for the rapid evaluation of convolution products that appear in displacements and stresses calculation.
    [Show full text]
  • Generalized Poisson Summation Formula for Tempered Distributions
    2015 International Conference on Sampling Theory and Applications (SampTA) Generalized Poisson Summation Formula for Tempered Distributions Ha Q. Nguyen and Michael Unser Biomedical Imaging Group, Ecole´ Polytechnique Fed´ erale´ de Lausanne (EPFL) Station 17, CH–1015, Lausanne, Switzerland fha.nguyen, michael.unserg@epfl.ch Abstract—The Poisson summation formula (PSF), which re- In the mathematics literature, the PSF is often stated in a lates the sampling of an analog signal with the periodization of dual form with the sampling occurring in the Fourier domain, its Fourier transform, plays a key role in the classical sampling and under strict conditions on the function f. Various versions theory. In its current forms, the formula is only applicable to a of (PSF) have been proven when both f and f^ are in appro- limited class of signals in L1. However, this assumption on the signals is too strict for many applications in signal processing priate subspaces of L1(R) \C(R). In its most general form, ^ that require sampling of non-decaying signals. In this paper when f; f 2 L1(R)\C(R), the RHS of (PSF) is a well-defined we generalize the PSF for functions living in weighted Sobolev periodic function in L1([0; 1]) whose (possibly divergent) spaces that do not impose any decay on the functions. The Fourier series is the LHS. If f and f^ additionally satisfy only requirement is that the signal to be sampled and its weak ^ −1−" derivatives up to order 1=2 + " for arbitrarily small " > 0, grow jf(x)j+jf(x)j ≤ C(1+jxj) ; 8x 2 R, for some C; " > 0, slower than a polynomial in the L2 sense.
    [Show full text]
  • Lecture 6 Basic Signal Processing
    Lecture 6 Basic Signal Processing Copyright c 1996, 1997 by Pat Hanrahan Motivation Many aspects of computer graphics and computer imagery differ from aspects of conven- tional graphics and imagery because computer representations are digital and discrete, whereas natural representations are continuous. In a previous lecture we discussed the implications of quantizing continuous or high precision intensity values to discrete or lower precision val- ues. In this sequence of lectures we discuss the implications of sampling a continuous image at a discrete set of locations (usually a regular lattice). The implications of the sampling pro- cess are quite subtle, and to understand them fully requires a basic understanding of signal processing. These notes are meant to serve as a concise summary of signal processing for computer graphics. Reconstruction Recall that a framebuffer holds a 2D array of numbers representing intensities. The display creates a continuous light image from these discrete digital values. We say that the discrete image is reconstructed to form a continuous image. Although it is often convenient to think of each 2D pixel as a little square that abuts its neighbors to fill the image plane, this view of reconstruction is not very general. Instead it is better to think of each pixel as a point sample. Imagine an image as a surface whose height at a point is equal to the intensity of the image at that point. A single sample is then a “spike;” the spike is located at the position of the sample and its height is equal to the intensity asso- ciated with that sample.
    [Show full text]
  • Convolution and Filtering
    Convolution and 7 Filtering Lab Objective: The Fourier transform reveals information in the frequency domain about signals and images that might not be apparent in the usual time (sound) or spatial (image) domain. In this lab, we use the discrete Fourier transform to eciently convolve sound signals and lter out some types of unwanted noise from both sounds and images. This lab is a continuation of the Discrete Fourier Transform lab and should be completed in the same Jupyter Notebook. Convolution Mixing two sounds signalsa common procedure in signal processing and analysisis usually done through a discrete convolution. Given two periodic sound sample vectors f and g of length n, the discrete convolution of f and g is a vector of length n where the kth component is given by n−1 X (f ∗ g)k = fk−jgj; k = 0; 1; 2; : : : ; n − 1: (7.1) j=0 Since audio needs to be sampled frequently to create smooth playback, a recording of a song can contain tens of millions of samples; even a one-minute signal has 2; 646; 000 samples if it is recorded at the standard rate of 44; 100 samples per second (44; 100 Hz). The naïve method of using the sum in (7.1) n times is O(n2), which is often too computationally expensive for convolutions of this size. Fortunately, the discrete Fourier transform (DFT) can be used compute convolutions eciently. The nite convolution theorem states that the Fourier transform of a convolution is the element-wise product of Fourier transforms: Fn(f ∗ g) = n(Fnf) (Fng): (7.2) In other words, convolution in the time domain is equivalent to component-wise multiplication in the frequency domain.
    [Show full text]
  • Review of Fourier Analysis
    Review of Fourier Analysis. A. The Fourier Transform. Definition 0.1 The normed linear space L1(R) consists of all functions f, Lebesgue mea- surable on R with the property that the norm Z 1 kfk1 = jf(x)j dx −∞ is finite. Remarks. (a) L1(R) is a linear space under the usual addition and scalar multiplication of functions. 2 1 (b) We saw before that L [0; 1] ⊆ L [0; 1] which follows from the inequality kfk1 ≤ kfk2 (where the norms are on the spaces Lr[0; 1], r = 1; 2). However this is false if [0; 1] is replaced by R. 1 1 (c) The spaces Cc(R) and Cc (R) are both dense in L (R). Definition 0.2 The Fourier transform of f 2 L1(R), denoted fb(γ), is given by, Z 1 fb(γ) = f(x) e−2πiγx dx: −∞ Remarks. (a) Note a superficial resemblance to the definition of the Fourier coefficients of a function on [0; 1], namely Z 1 fb(n) = f(x) e−2πinx dx: 0 We will explore this relationship later. (b) We assume that f 2 L1(R) in order to guarantee that the integral converges. This is only technical as the integral can be interpreted for a variety of function spaces. Definition 0.3 The Fourier inversion formula is the following. If fb(γ) is the Fourier trans- form of f(x) then, Z 1 f(x) = fb(γ) e2πiγx dγ: −∞ Remarks. (a) Fourier inversion is not always valid. Indeed a major focus of a rigorous course in Fourier analysis is to determine conditions under which the inversion formula holds pointwise and in other senses.
    [Show full text]
  • Fourier Transforms of a Few Functions
    – 1 – 6. FOURIER TRANSFORM, CONVOLUTION AND CORRELATION Reference: ”The Fourier Transform and Its Applications”, by R. N. Bracewell. 6.1 Fourier Transform A function f(t) can be expressed as Z ∞ f(t) = F(ν) e−i2πνt dν, (6.1) −∞ where F(ν) is the Fourier Transform of f(t). This expression can be understood to mean that f(t) can be decomposed into a sum of ”Fourier components” – e−i2πνt – over a continuous range of ν. In other words, f(t) is decomposed into its frequency components. To show the frequency decomposition more directly, one can express equation (6.1) as Z ∞ Z ∞ f(t) = F(ν) cos(2πνt) dν + i F(ν) sin(2πνt) dν. −∞ −∞ F(ν) then specifies how much each frequency component, e−i2πνt, is present in the sum. Equation (6.1) is analogous to the decomposition of a vector V into its basis vectors, xi, 3 X V = vixi, i=1 −i2πνt where vi and xi are analogs of F(ν) and e . Just as in the case of vectors where one can specify the vector V by its components vi, one can specify f(t) by its Fourier Transform F(ν). For instance, if t and ν are time and frequency respectively, instead of plotting the entire f(t) = 2cos(2πνot) over the infinite t-axis, one can simply specify the Fourier Transform which consists of two delta functions (see below) at frequencies: ±νo. Given f(t), its Fourier Transform can be found by Z ∞ F(ν) = f(t) e+i2πνt dt. −∞ Table 1 gives a list of useful functions and their Fourier Transform.
    [Show full text]