DOCSLIB.ORG

Design and Construction of Computer Experiments in Fluid Mechanics and Ship Stability

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Design and Construction of Computer Experiments in Fluid Mechanics and Ship Stability The 14th International Ship Stability Workshop (ISSW), 29th September- 1st October 2014, Kuala Lumpur, Malaysia Design and Construction of Computer Experiments in Fluid Mechanics and Ship Stability Alexander Degtyarev*, Vasily Khramushin and Vladimir Mareev Dept. of Computer Modelling and Multiprocessor Systems, Faculty of Applied Mathematics and Control Processes, St.Petersburg State University, Russia Abstract: The paper considers a generalized functional and algorithmic construction of direct computational experiments in fluid dynamics. Tensor mathematics naturally embedded in the finite- operation in the construction of numerical schemes. As an elementary computing object large fluid particle which has a finite size, its own weight, internal displacement and deformation is considered. The proposed approach focuses on the use of explicit numerical schemes. The numerical solution of the problem is divided into several stages that are a combination of Lagrange and Euler methods. Key words: Fluid mechanics, direct computational experiment, computational efficiency 1. Introduction infinitesimal elements [12], which basically do not allow direct control of internal state of measurable In the paper mathematical basis for direct fluid volumes. At the same time the proposed computational hydroaeromechanic experiment approach differs from the well known smooth particle formation is considered. In contrast to the traditional hydrodynamics (SPH) simulation [13-16], which is a approach of finite difference numerical schemes purely Lagrangian method. For better numerical construction that are output from analytical models in realization we combine Lagrangian and Eulear the form of partial differential equations [1], the approaches at different stages. proposed techniques are focused on the construction Strict and mutually reversible mathematical and use of direct computational experiments. For these definition of properties and description of mechanics purposes fundamental laws of motion [2] are applied of finite fluid volumes transformations are possible to large fluid particles [3], which have a finite size, using classical tools of tensor calculus. This their own weight, internal displacement and instrument sufficiently specifies transformation of deformation [4, 5]. Each particle is represented in complex fluid flows through first-order spatial world (global) and local coordinate systems [6, 7]. It approximations. gives opportunity to examine them as free particles It is shown in the paper that with the proposed with strictly defined laws of neighbor interaction and approach hydromechanics problems can be reduced to with alternation of modeling stages of independent the use of explicit numerical schemes. At the same internal transformation processes [8, 9]. Such time tensor form of state control of three-dimensional modeling is carried out in accordance with the basic computational objects and processes allows to tailor conservation laws of energy, mass and fluid continuity the solution to the real laws of motion or to the [10, 11]. With this approach mathematical description empirical and the asymptotic dependences. Apparatus of physical processes in aerohydrodynamics is greatly of three-dimensional tensor mathematics in a natural enhanced. It is the better than the traditional way is embedded in the finite-difference operations of mathematical models based on differential calculus of © Marine Technology Centre, UTM 187 The 14th International Ship Stability Workshop (ISSW), 29 September-1 October 2014, Kuala Lumpur, Malaysia large particles (final volume) method. This is done in Firstly, equation change-type at its finite-difference a strict and an unambiguous representation of the representation is possible [22, 23]. Secondly, the physical laws in the nearest vicinity of an elementary hydrodynamic nature of the studied phenomena is far particle continuum. In the paper carrying out of from concept of infinitesimals with which we work at numerical experiments in a natural way comes down consideration of any differential equations [12, 17]. In to three conditionally independent physical processes. contrast to the problems of strength and elasticity of This fact, combined with predominant use of explicit solid body, where deflections, shifts, turns may be numerical schemes, enables natural parallel computing considered in the finite-difference representation as with the ability to dynamically select appropriate smalls, shift of particles in continuous medium hydrodynamics laws. This choice is carried out hydrodynamic problems even with a small impact depending on the characteristics of transformation and may be finite. interaction of considered computational objects Thus, as a result we not only have fundamentally (particles). wrong equation as a model, but we often incorrectly In practice, the constructions of direct numerically solve it. Therefore our task, in essence, computational experiments are usually obtained from consists in tearing off calculations from representation close analogs of the numerical schemes from systems of model of physical system. For the solution of this of partial differential equations. However, these problem methods of the direct computing experiment analogs differ as short canonical result expressions in based on the modern computer architecture are the final difference form [17]. For them, the results of developed in the paper. the calculations are more appropriate for comparisons 2. Numerical construction of continuous with physical or full scale experiments than for medium objects analytically accurate but simplistic solutions of classical mathematical physics. Direct numerical experiments in continuum It should be noted that the above considerations are mechanics using digital discrete computers are based not new or unexpected. This work is focused on on a limited set of numeric objects which interpolate overcoming of two "eternal" questions in parameters of the state of the physical fields in time. computational fluid mechanics: Computational processes with such numerical objects 1. incomplete adequacy of the Navier-Stokes have to take place in accordance with physical laws in equations; the mesh areas (including nonregularized ones). At the 2. problems arising in discretization of the equation. same time each mesh cell is represented as The essence of the first question is that the independent corpuscle actively interacting with the Navier-Stokes equation is not closed [19]. Therefore surrounding cell particles [12]. at the solution of these equations in different cases Let us call one mesh cell as elementary various closing ratios are put into practice [1, 22]. computational object (large particle of continuous These ratios have character of conservation laws. medium of finite volume). All internal transformation Thus, the first problem of hydrodynamics is isolation of such particle within linear approximations is strictly of physical model of considered system from the and uniquely determined by the rules of tensor actual situation. arithmetics. This is a convenient tool for geometric The problems arising at discretization of the model and kinematic description of a large particle. Apart equations of hydrodynamics, are also quite serious. from its position in space, classical tensor calculus © Marine Technology Centre, UTM 188 The 14th International Ship Stability Workshop (ISSW), 29 September-1 October 2014, Kuala Lumpur, Malaysia describes more complex transformation: rotation, a – value measured in a local basis, it refers to small compression, elastic deformation etc. Its functional volume or contiguous particles only (differential apparatus is sufficient for development of strong differences, can be scalar, vector or tensor) forward and reverse mathematical description of R,r – values projected on global basis physical processes of fluid mechanics in the finite R,r – values projected on local basis ∧ mesh area. r – local tensor in projections of global system ∨ For description of large mobile elementary particles r – local tensor in projections of local system in a three-dimensional space we introduce two Detailed notation is in appendix 1. coordinate systems: absolute and mobile local With this alphabet, capital letters for values in (associated with the particle) (Fig. 1). global coordinate system are used. Lower letters are used for small quantities at local bases projections in Y r 2 ( y ) a spatial location and current time. Basic mathematical r 3 (z ) r operations are tensors products and products of j R tensors and vectors. They define the ratio of local r (x ) A 1 reactions of the fluid particles to external influences of k the environment. Formally possibility of rank Ω i X increasing of tensor-vector objects is excluded. They Z have not immediate physical interpretation. Fig. 1 – Local basis ri is formed by triad of basis vectors, ijk – Absolute or full velocity vector of a large particle is unit vectors of global coordinate system (XYZ); R – radius introduced as a shift of the center of mass in the global vector of the moving system; A - radius vector of the point in coordinate system: global coordinate system; a - the same point in local → → → → → → V⋅t = ∆ R = t R− 0 R = T −t R− T R (1) coordinate system Tensor of instantaneous velocities relative to the Let us initially restrict our consideration of distant conditional center of large particle in projections on mechanical interference. Then mechanical laws for absolute coordinate system is assembled by direct local
Load more

Recommended publications
  • 1.2 Topological Tensor Calculus
    PH211 Physical Mathematics Fall 2019 1.2 Topological tensor calculus 1.2.1 Tensor fields Finite displacements in Euclidean space can be represented by arrows and have a natural vector space structure, but finite displacements in more general curved spaces, such as on the surface of a sphere, do not. However, an infinitesimal neighborhood of a point in a smooth curved space1 looks like an infinitesimal neighborhood of Euclidean space, and infinitesimal displacements dx~ retain the vector space structure of displacements in Euclidean space. An infinitesimal neighborhood of a point can be infinitely rescaled to generate a finite vector space, called the tangent space, at the point. A vector lives in the tangent space of a point. Note that vectors do not stretch from one point to vector tangent space at p p space Figure 1.2.1: A vector in the tangent space of a point. another, and vectors at different points live in different tangent spaces and so cannot be added. For example, rescaling the infinitesimal displacement dx~ by dividing it by the in- finitesimal scalar dt gives the velocity dx~ ~v = (1.2.1) dt which is a vector. Similarly, we can picture the covector rφ as the infinitesimal contours of φ in a neighborhood of a point, infinitely rescaled to generate a finite covector in the point's cotangent space. More generally, infinitely rescaling the neighborhood of a point generates the tensor space and its algebra at the point. The tensor space contains the tangent and cotangent spaces as a vector subspaces. A tensor field is something that takes tensor values at every point in a space.
    [Show full text]
  • A Some Basic Rules of Tensor Calculus
    A Some Basic Rules of Tensor Calculus The tensor calculus is a powerful tool for the description of the fundamentals in con- tinuum mechanics and the derivation of the governing equations for applied prob- lems. In general, there are two possibilities for the representation of the tensors and the tensorial equations: – the direct (symbolic) notation and – the index (component) notation The direct notation operates with scalars, vectors and tensors as physical objects defined in the three dimensional space. A vector (first rank tensor) a is considered as a directed line segment rather than a triple of numbers (coordinates). A second rank tensor A is any finite sum of ordered vector pairs A = a b + ... +c d. The scalars, vectors and tensors are handled as invariant (independent⊗ from the choice⊗ of the coordinate system) objects. This is the reason for the use of the direct notation in the modern literature of mechanics and rheology, e.g. [29, 32, 49, 123, 131, 199, 246, 313, 334] among others. The index notation deals with components or coordinates of vectors and tensors. For a selected basis, e.g. gi, i = 1, 2, 3 one can write a = aig , A = aibj + ... + cidj g g i i ⊗ j Here the Einstein’s summation convention is used: in one expression the twice re- peated indices are summed up from 1 to 3, e.g. 3 3 k k ik ik a gk ∑ a gk, A bk ∑ A bk ≡ k=1 ≡ k=1 In the above examples k is a so-called dummy index. Within the index notation the basic operations with tensors are defined with respect to their coordinates, e.
    [Show full text]
  • Tensor Calculus and Differential Geometry
    Course Notes Tensor Calculus and Differential Geometry 2WAH0 Luc Florack March 10, 2021 Cover illustration: papyrus fragment from Euclid’s Elements of Geometry, Book II [8]. Contents Preface iii Notation 1 1 Prerequisites from Linear Algebra 3 2 Tensor Calculus 7 2.1 Vector Spaces and Bases . .7 2.2 Dual Vector Spaces and Dual Bases . .8 2.3 The Kronecker Tensor . 10 2.4 Inner Products . 11 2.5 Reciprocal Bases . 14 2.6 Bases, Dual Bases, Reciprocal Bases: Mutual Relations . 16 2.7 Examples of Vectors and Covectors . 17 2.8 Tensors . 18 2.8.1 Tensors in all Generality . 18 2.8.2 Tensors Subject to Symmetries . 22 2.8.3 Symmetry and Antisymmetry Preserving Product Operators . 24 2.8.4 Vector Spaces with an Oriented Volume . 31 2.8.5 Tensors on an Inner Product Space . 34 2.8.6 Tensor Transformations . 36 2.8.6.1 “Absolute Tensors” . 37 CONTENTS i 2.8.6.2 “Relative Tensors” . 38 2.8.6.3 “Pseudo Tensors” . 41 2.8.7 Contractions . 43 2.9 The Hodge Star Operator . 43 3 Differential Geometry 47 3.1 Euclidean Space: Cartesian and Curvilinear Coordinates . 47 3.2 Differentiable Manifolds . 48 3.3 Tangent Vectors . 49 3.4 Tangent and Cotangent Bundle . 50 3.5 Exterior Derivative . 51 3.6 Affine Connection . 52 3.7 Lie Derivative . 55 3.8 Torsion . 55 3.9 Levi-Civita Connection . 56 3.10 Geodesics . 57 3.11 Curvature . 58 3.12 Push-Forward and Pull-Back . 59 3.13 Examples . 60 3.13.1 Polar Coordinates in the Euclidean Plane .
    [Show full text]
  • Fast Higher-Order Functions for Tensor Calculus with Tensors and Subtensors
    Fast Higher-Order Functions for Tensor Calculus with Tensors and Subtensors Cem Bassoy and Volker Schatz Fraunhofer IOSB, Ettlingen 76275, Germany, [email protected] Abstract. Tensors analysis has become a popular tool for solving prob- lems in computational neuroscience, pattern recognition and signal pro- cessing. Similar to the two-dimensional case, algorithms for multidimen- sional data consist of basic operations accessing only a subset of tensor data. With multiple offsets and step sizes, basic operations for subtensors require sophisticated implementations even for entrywise operations. In this work, we discuss the design and implementation of optimized higher-order functions that operate entrywise on tensors and subtensors with any non-hierarchical storage format and arbitrary number of dimen- sions. We propose recursive multi-index algorithms with reduced index computations and additional optimization techniques such as function inlining with partial template specialization. We show that single-index implementations of higher-order functions with subtensors introduce a runtime penalty of an order of magnitude than the recursive and iterative multi-index versions. Including data- and thread-level parallelization, our optimized implementations reach 68% of the maximum throughput of an Intel Core i9-7900X. In comparison with other libraries, the aver- age speedup of our optimized implementations is up to 5x for map-like and more than 9x for reduce-like operations. For symmetric tensors we measured an average speedup of up to 4x. 1 Introduction Many problems in computational neuroscience, pattern recognition, signal pro- cessing and data mining generate massive amounts of multidimensional data with high dimensionality [13,12,9]. Tensors provide a natural representation for massive multidimensional data [10,7].
    [Show full text]
  • Part IA — Vector Calculus
    Part IA | Vector Calculus Based on lectures by B. Allanach Notes taken by Dexter Chua Lent 2015 These notes are not endorsed by the lecturers, and I have modified them (often significantly) after lectures. They are nowhere near accurate representations of what was actually lectured, and in particular, all errors are almost surely mine. 3 Curves in R 3 Parameterised curves and arc length, tangents and normals to curves in R , the radius of curvature. [1] 2 3 Integration in R and R Line integrals. Surface and volume integrals: definitions, examples using Cartesian, cylindrical and spherical coordinates; change of variables. [4] Vector operators Directional derivatives. The gradient of a real-valued function: definition; interpretation as normal to level surfaces; examples including the use of cylindrical, spherical *and general orthogonal curvilinear* coordinates. Divergence, curl and r2 in Cartesian coordinates, examples; formulae for these oper- ators (statement only) in cylindrical, spherical *and general orthogonal curvilinear* coordinates. Solenoidal fields, irrotational fields and conservative fields; scalar potentials. Vector derivative identities. [5] Integration theorems Divergence theorem, Green's theorem, Stokes's theorem, Green's second theorem: statements; informal proofs; examples; application to fluid dynamics, and to electro- magnetism including statement of Maxwell's equations. [5] Laplace's equation 2 3 Laplace's equation in R and R : uniqueness theorem and maximum principle. Solution of Poisson's equation by Gauss's method (for spherical and cylindrical symmetry) and as an integral. [4] 3 Cartesian tensors in R Tensor transformation laws, addition, multiplication, contraction, with emphasis on tensors of second rank. Isotropic second and third rank tensors.
    [Show full text]
  • An Introduction to Space–Time Exterior Calculus
    mathematics Article An Introduction to Space–Time Exterior Calculus Ivano Colombaro 1,* , Josep Font-Segura 1 and Alfonso Martinez 1 1 Department of Information and Communication Technologies, Universitat Pompeu Fabra, 08018 Barcelona, Spain; [email protected] (J.F.-S.); [email protected] (A.M.) * Correspondence: [email protected]; Tel.: +34-93-542-1496 Received: 21 May 2019; Accepted: 18 June 2019; Published: 21 June 2019 Abstract: The basic concepts of exterior calculus for space–time multivectors are presented: Interior and exterior products, interior and exterior derivatives, oriented integrals over hypersurfaces, circulation and flux of multivector fields. Two Stokes theorems relating the exterior and interior derivatives with circulation and flux, respectively, are derived. As an application, it is shown how the exterior-calculus space–time formulation of the electromagnetic Maxwell equations and Lorentz force recovers the standard vector-calculus formulations, in both differential and integral forms. Keywords: exterior calculus, exterior algebra, electromagnetism, Maxwell equations, differential forms, tensor calculus 1. Introduction Vector calculus has, since its introduction by J. W. Gibbs [1] and Heaviside, been the tool of choice to represent many physical phenomena. In mechanics, hydrodynamics and electromagnetism, quantities such as forces, velocities and currents are modeled as vector fields in space, while flux, circulation, divergence or curl describe operations on the vector fields themselves. With relativity theory, it was observed that space and time are not independent but just coordinates in space–time [2] (pp. 111–120). Tensors like the Faraday tensor in electromagnetism were quickly adopted as a natural representation of fields in space–time [3] (pp.
    [Show full text]
  • Fundamental Theorem of Calculus
    Fundamental Theorem of Calculus Garret Sobczyk Universidad de Las Am´ericas - Puebla, 72820 Cholula, Mexico, Omar Sanchez University of Waterloo, Ontario, N2L 3G1 Canada October 29, 2018 Abstract A simple but rigorous proof of the Fundamental Theorem of Calculus is given in geometric calculus, after the basis for this theory in geometric algebra has been explained. Various classical examples of this theorem, such as the Green’s and Stokes’ theorem are discussed, as well as the new theory of monogenic functions, which generalizes the concept of an analytic function of a complex variable to higher dimensions. Keywords: geometric algebra, geometric calculus, Green’s theorem, mono- genic functions, Stokes’ theorem. AMS 2000 Subject Classification : 32A26, 58A05, 58A10, 58A15. Introduction Every student of calculus knows that integration is the inverse operation to differentiation. Things get more complicated in going to the higher dimensional analogs, such as Green’s theorem in the plane, Stoke’s theorem, and Gauss’ theorem in vector analysis. But the end of the matter is nowhere in sight when we consider differential forms, tensor calculus, and differential geometry, where arXiv:0809.4526v1 [math.HO] 26 Sep 2008 important analogous theorems exist in much more general settings. The purpose of this article is to express the fundamental theorem of calculus in geometric algebra, showing that the multitude of different forms of this theorem fall out of a single theorem, and to give a simple and rigorous proof. An intuitive proof of the fundamental theorem in geometric algebra was first given in [1]. The basis of this theorem was further explored in [2], [3].
    [Show full text]
  • 1.14 Tensor Calculus I: Tensor Fields
    Section 1.14 1.14 Tensor Calculus I: Tensor Fields In this section, the concepts from the calculus of vectors are generalised to the calculus of higher-order tensors. 1.14.1 Tensor-valued Functions Tensor-valued functions of a scalar The most basic type of calculus is that of tensor-valued functions of a scalar, for example the time-dependent stress at a point, S S(t) . If a tensor T depends on a scalar t, then the derivative is defined in the usual way, dT T(t t) T(t) lim , dt t0 t which turns out to be dT dT ij e e (1.14.1) dt dt i j The derivative is also a tensor and the usual rules of differentiation apply, d dT dB T B dt dt dt d dT d (t)T T dt dt dt d da dT Ta T a dt dt dt d dB dT TB T B dt dt dt T d dT TT dt dt For example, consider the time derivative of QQ T , where Q is orthogonal. By the product rule, using QQ T I , T d dQ dQ T dQ dQ QQ T Q T Q Q T Q 0 dt dt dt dt dt Thus, using Eqn. 1.10.3e T Q QT QQ T Q QT (1.14.2) Solid Mechanics Part III 115 Kelly Section 1.14 which shows that Q Q T is a skew-symmetric tensor. 1.14.2 Vector Fields The gradient of a scalar field and the divergence and curl of vector fields have been seen in §1.6.
    [Show full text]
  • A Simple and Efficient Tensor Calculus
    The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) A Simple and Efficient Tensor Calculus Soren¨ Laue,1,2 Matthias Mitterreiter,1 Joachim Giesen1 1Friedrich-Schiller-Universitat¨ Jena Faculty of Mathematics and Computer Science Ernst-Abbe-Platz 2 07743 Jena, Germany Friedrich-Schiller-University Jena 2Data Assessment Solutions GmbH, Hannover Abstract and JAX. However, the improvements are rather small and the efficiency gap of up to three orders of magnitude still Computing derivatives of tensor expressions, also known as tensor calculus, is a fundamental task in machine learning. A persists. key concern is the efficiency of evaluating the expressions and On the other hand, the approach of (Laue, Mitterreiter, and their derivatives that hinges on the representation of these ex- Giesen 2018) relies crucially on Ricci notation and therefore pressions. Recently, an algorithm for computing higher order cannot be incorporated into standard deep learning frame- derivatives of tensor expressions like Jacobians or Hessians works that use the simpler Einstein notation. Here, we remove has been introduced that is a few orders of magnitude faster this obstacle and provide an efficient tensor calculus in Ein- than previous state-of-the-art approaches. Unfortunately, the stein notation. Already the simple version of our approach is approach is based on Ricci notation and hence cannot be as efficient as the approach by (Laue, Mitterreiter, and Giesen incorporated into automatic differentiation frameworks like TensorFlow, PyTorch, autograd, or JAX that use the simpler 2018). We provide further improvements that lead to an even Einstein notation. This leaves two options, to either change better efficiency.
    [Show full text]
  • Some Second-Order Tensor Calculus Identities and Applications in Continuum Mechanics
    Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 27 November 2018 doi:10.20944/preprints201811.0598.v1 The Sun's work 1 Some second-order tensor calculus identities and applications in continuum mechanics Bohua Sunyz, Cape Peninsula University of Technology, Cape Town, South Africa (Received 25 November 2018) To extend the calculation power of tensor analysis, we introduce four new definition of tensor calculations. Some useful tensor identities have been proved. We demonstrate the application of the tensor identities in continuum mechanics: momentum conservation law and deformation superposition. Key words: tensor, gradient, divergence, curl, elasto-plastic deformation Nomenclature a; b; c; d, vector in lower case; A; B, tensor in capital case; X; Y , position vector in reference (undeformed) state; x; y, position vector in current (deformed) state; v, velocity; ek, base vector; Gk, base vector in undeformed state; gk, base vector in deformed state; F , deformation gradient; F e, deformation gradient; F p, deformation gradient; r, gradient nabla; rX , gradient nabla respect to X; rx, gradient nabla respect to x; ", permutation tensor; δij , Kronecker delta; I = δij ei ⊗ ej , unit tensor; ·, dot product; ×, cross product; ⊗, tensor product; σ, Cauchy stress tensor defined in current configuration; τ , Kirchhoff stress tensor defined in current configuration; P , the first Piola-Kirchhoff stress tensor; S, the second Piola-Kirchhoff stress tensor; T , dislocation density tensor; J = det(F ), the Jacobian of deformation gradient F ; ρ, mass density in current configuration; ρR, mass density in reference configuration; f, body force in current configuration; fR, body force in reference configuration. y Currently in Cape Peninsula University of Technology z Email address for correspondence: [email protected] © 2018 by the author(s).
    [Show full text]
  • A Gentle Introduction to Tensors (2014)
    A Gentle Introduction to Tensors Boaz Porat Department of Electrical Engineering Technion – Israel Institute of Technology [email protected] May 27, 2014 Opening Remarks This document was written for the benefits of Engineering students, Elec- trical Engineering students in particular, who are curious about physics and would like to know more about it, whether from sheer intellectual desire or because one’s awareness that physics is the key to our understanding of the world around us. Of course, anybody who is interested and has some college background may find this material useful. In the future, I hope to write more documents of the same kind. I chose tensors as a first topic for two reasons. First, tensors appear everywhere in physics, including classi- cal mechanics, relativistic mechanics, electrodynamics, particle physics, and more. Second, tensor theory, at the most elementary level, requires only linear algebra and some calculus as prerequisites. Proceeding a small step further, tensor theory requires background in multivariate calculus. For a deeper understanding, knowledge of manifolds and some point-set topology is required. Accordingly, we divide the material into three chapters. The first chapter discusses constant tensors and constant linear transformations. Tensors and transformations are inseparable. To put it succinctly, tensors are geometrical objects over vector spaces, whose coordinates obey certain laws of transformation under change of basis. Vectors are simple and well-known examples of tensors, but there is much more to tensor theory than vectors. The second chapter discusses tensor fields and curvilinear coordinates. It is this chapter that provides the foundations for tensor applications in physics.
    [Show full text]
  • A Primer on Exterior Differential Calculus
    Theoret. Appl. Mech., Vol. 30, No. 2, pp. 85-162, Belgrade 2003 A primer on exterior differential calculus D.A. Burton¤ Abstract A pedagogical application-oriented introduction to the cal- culus of exterior differential forms on differential manifolds is presented. Stokes’ theorem, the Lie derivative, linear con- nections and their curvature, torsion and non-metricity are discussed. Numerous examples using differential calculus are given and some detailed comparisons are made with their tradi- tional vector counterparts. In particular, vector calculus on R3 is cast in terms of exterior calculus and the traditional Stokes’ and divergence theorems replaced by the more powerful exte- rior expression of Stokes’ theorem. Examples from classical continuum mechanics and spacetime physics are discussed and worked through using the language of exterior forms. The nu- merous advantages of this calculus, over more traditional ma- chinery, are stressed throughout the article. Keywords: manifolds, differential geometry, exterior cal- culus, differential forms, tensor calculus, linear connections ¤Department of Physics, Lancaster University, UK (e-mail: [email protected]) 85 86 David A. Burton Table of notation M a differential manifold F(M) set of smooth functions on M T M tangent bundle over M T ¤M cotangent bundle over M q TpM type (p; q) tensor bundle over M ΛpM differential p-form bundle over M ΓT M set of tangent vector fields on M ΓT ¤M set of cotangent vector fields on M q ΓTpM set of type (p; q) tensor fields on M ΓΛpM set of differential p-forms
    [Show full text]