An Essay on Lagrangian and Eulerian Kinematics of Fluid Flow
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Relativistic Dynamics
Chapter 4 Relativistic dynamics We have seen in the previous lectures that our relativity postulates suggest that the most efficient (lazy but smart) approach to relativistic physics is in terms of 4-vectors, and that velocities never exceed c in magnitude. In this chapter we will see how this 4-vector approach works for dynamics, i.e., for the interplay between motion and forces. A particle subject to forces will undergo non-inertial motion. According to Newton, there is a simple (3-vector) relation between force and acceleration, f~ = m~a; (4.0.1) where acceleration is the second time derivative of position, d~v d2~x ~a = = : (4.0.2) dt dt2 There is just one problem with these relations | they are wrong! Newtonian dynamics is a good approximation when velocities are very small compared to c, but outside of this regime the relation (4.0.1) is simply incorrect. In particular, these relations are inconsistent with our relativity postu- lates. To see this, it is sufficient to note that Newton's equations (4.0.1) and (4.0.2) predict that a particle subject to a constant force (and initially at rest) will acquire a velocity which can become arbitrarily large, Z t ~ d~v 0 f ~v(t) = 0 dt = t ! 1 as t ! 1 . (4.0.3) 0 dt m This flatly contradicts the prediction of special relativity (and causality) that no signal can propagate faster than c. Our task is to understand how to formulate the dynamics of non-inertial particles in a manner which is consistent with our relativity postulates (and then verify that it matches observation, including in the non-relativistic regime). -
OCC D 5 Gen5d Eee 1305 1A E
this cover and their final version of the extended essay to is are not is chose to write about applications of differential calculus because she found a great interest in it during her IB Math class. She wishes she had time to complete a deeper analysis of her topic; however, her busy schedule made it difficult so she is somewhat disappointed with the outcome of her essay. It was a pleasure meeting with when she was able to and her understanding of her topic was evident during our viva voce. I, too, wish she had more time to complete a more thorough investigation. Overall, however, I believe she did well and am satisfied with her essay. must not use Examiner 1 Examiner 2 Examiner 3 A research 2 2 D B introduction 2 2 c 4 4 D 4 4 E reasoned 4 4 D F and evaluation 4 4 G use of 4 4 D H conclusion 2 2 formal 4 4 abstract 2 2 holistic 4 4 Mathematics Extended Essay An Investigation of the Various Practical Uses of Differential Calculus in Geometry, Biology, Economics, and Physics Candidate Number: 2031 Words 1 Abstract Calculus is a field of math dedicated to analyzing and interpreting behavioral changes in terms of a dependent variable in respect to changes in an independent variable. The versatility of differential calculus and the derivative function is discussed and highlighted in regards to its applications to various other fields such as geometry, biology, economics, and physics. First, a background on derivatives is provided in regards to their origin and evolution, especially as apparent in the transformation of their notations so as to include various individuals and ways of denoting derivative properties. -
Time-Derivative Models of Pavlovian Reinforcement Richard S
Approximately as appeared in: Learning and Computational Neuroscience: Foundations of Adaptive Networks, M. Gabriel and J. Moore, Eds., pp. 497–537. MIT Press, 1990. Chapter 12 Time-Derivative Models of Pavlovian Reinforcement Richard S. Sutton Andrew G. Barto This chapter presents a model of classical conditioning called the temporal- difference (TD) model. The TD model was originally developed as a neuron- like unit for use in adaptive networks (Sutton and Barto 1987; Sutton 1984; Barto, Sutton and Anderson 1983). In this paper, however, we analyze it from the point of view of animal learning theory. Our intended audience is both animal learning researchers interested in computational theories of behavior and machine learning researchers interested in how their learning algorithms relate to, and may be constrained by, animal learning studies. For an exposition of the TD model from an engineering point of view, see Chapter 13 of this volume. We focus on what we see as the primary theoretical contribution to animal learning theory of the TD and related models: the hypothesis that reinforcement in classical conditioning is the time derivative of a compos- ite association combining innate (US) and acquired (CS) associations. We call models based on some variant of this hypothesis time-derivative mod- els, examples of which are the models by Klopf (1988), Sutton and Barto (1981a), Moore et al (1986), Hawkins and Kandel (1984), Gelperin, Hop- field and Tank (1985), Tesauro (1987), and Kosko (1986); we examine several of these models in relation to the TD model. We also briefly ex- plore relationships with animal learning theories of reinforcement, including Mowrer’s drive-induction theory (Mowrer 1960) and the Rescorla-Wagner model (Rescorla and Wagner 1972). -
Chapter 5 Constitutive Modelling
Chapter 5 Constitutive Modelling 5.1 Introduction Lecture 14 Thus far we have established four conservation (or balance) equations, an entropy inequality and a number of kinematic relationships. The overall set of governing equations are collected in Table 5.1. The Eulerian form has a one more independent equation because the density must be determined, 1 whereas in the Lagrangian form it is directly related to the prescribed initial densityρ 0. Physical Law Eulerian FormN Lagrangian Formn DR ∂r Kinematics: Dt =V , 3, ∂t =v, 3; Dρ ∇ · Mass: Dt +ρ R V=0, 1,ρ 0 =ρJ, 0; DV ∇ · ∂v ∇ · Linear momentum:ρ Dt = R T+ρF , 3,ρ 0 ∂t = r p+ρ 0f, 3; Angular momentum:T=T T , 3,s=s T , 3; DΦ ∂φ Energy:ρ =T:D+ρB ∇ ·Q, 1,ρ =s: e˙+ρ b ∇ r ·q, 1; Dt − R 0 ∂t 0 − Dη ∂η0 Entropy inequality:ρ ∇ ·(Q/Θ)+ρB/Θ, 0,ρ ∇r (q/θ)+ρ b/θ, 0. Dt ≥ − R 0 ∂t ≥ − 0 Independent Equations: 11 10 Table 5.1: The governing equations for continuum mechanics in Eulerian and Lagrangian form. N is the number of independent equations in Eulerian form andn is the number of independent equations in Lagrangian form. Examining the equations reveals that they involve more unknown variables than equations, listed in Table 5.2. Once again the fact that the density in the Lagrangian formulation is prescribed means that there is one fewer unknown compared to the Eulerian formulation. Thus, in either formulation and additional eleven equations are required to close the system. -
Correct Expression of Material Derivative and Application to the Navier-Stokes Equation —– the Solution Existence Condition of Navier-Stokes Equation
Preprints (www.preprints.org) | NOT PEER-REVIEWED | Posted: 15 June 2020 doi:10.20944/preprints202003.0030.v4 Correct Expression of Material Derivative and Application to the Navier-Stokes Equation |{ The solution existence condition of Navier-Stokes equation Bo-Hua Sun1 1 School of Civil Engineering & Institute of Mechanics and Technology Xi'an University of Architecture and Technology, Xi'an 710055, China http://imt.xauat.edu.cn email: [email protected] (Dated: June 15, 2020) The material derivative is important in continuum physics. This paper shows that the expression d @ dt = @t + (v · r), used in most literature and textbooks, is incorrect. This article presents correct d(:) @ expression of the material derivative, namely dt = @t (:) + v · [r(:)]. As an application, the form- solution of the Navier-Stokes equation is proposed. The form-solution reveals that the solution existence condition of the Navier-Stokes equation is that "The Navier-Stokes equation has a solution if and only if the determinant of flow velocity gradient is not zero, namely det(rv) 6= 0." Keywords: material derivative, velocity gradient, tensor calculus, tensor determinant, Navier-Stokes equa- tions, solution existence condition In continuum physics, there are two ways of describ- derivative as in Ref. 1. Therefore, to revive the great ing continuous media or flows, the Lagrangian descrip- influence of Landau in physics and fluid mechanics, we at- tion and the Eulerian description. In the Eulerian de- tempt to address this issue in this dedicated paper, where scription, the material derivative with respect to time we revisit the material derivative to show why the oper- d @ dv @v must be defined. -
Statics FE Review 032712.Pptx
FE Stacs Review h0p://www.coe.utah.edu/current-undergrad/fee.php Scroll down to: Stacs Review - Slides Torch Ellio [email protected] (801) 587-9016 MCE room 2016 (through 2000B door) Posi'on and Unit Vectors Y If you wanted to express a 100 lb force that was in the direction from A to B in (-2,6,3). A vector form, then you would find eAB and multiply it by 100 lb. rAB O x rAB = 7i - 7j – k (units of length) B .(5,-1,2) e = (7i-7j–k)/sqrt[99] (unitless) AB z Then: F = F eAB F = 100 lb {(7i-7j–k)/sqrt[99]} Another Way to Define a Unit Vectors Y Direction Cosines The θ values are the angles from the F coordinate axes to the vector F. The θY cosines of these θ values are the O θx x θz coefficients of the unit vector in the direction of F. z If cos(θx) = 0.500, cos(θy) = 0.643, and cos(θz) = - 0.580, then: F = F (0.500i + 0.643j – 0.580k) TrigonometrY hYpotenuse opposite right triangle θ adjacent • Sin θ = opposite/hYpotenuse • Cos θ = adjacent/hYpotenuse • Tan θ = opposite/adjacent TrigonometrY Con'nued A c B anY triangle b C a Σangles = a + b + c = 180o Law of sines: (sin a)/A = (sin b)/B = (sin c)/C Law of cosines: C2 = A2 + B2 – 2AB(cos c) Products of Vectors – Dot Product U . V = UVcos(θ) for 0 <= θ <= 180ο ( This is a scalar.) U In Cartesian coordinates: θ . -
Lie Time Derivative £V(F) of a Spatial Field F
Lie time derivative $v(f) of a spatial field f • A way to obtain an objective rate of a spatial tensor field • Can be used to derive objective Constitutive Equations on rate form D −1 Definition: $v(f) = χ? Dt (χ? (f)) Procedure in 3 steps: 1. Pull-back of the spatial tensor field,f, to the Reference configuration to obtain the corresponding material tensor field, F. 2. Take the material time derivative on the corresponding material tensor field, F, to obtain F_ . _ 3. Push-forward of F to the Current configuration to obtain $v(f). D −1 Important!|Note that the material time derivative, i.e. Dt (χ? (f)) is executed in the Reference configuration (rotation neutralized). Recall that D D χ−1 (f) = (F) = F_ = D F Dt ?(2) Dt v d D F = F(X + v) v d and hence, $v(f) = χ? (DvF) Thus, the Lie time derivative of a spatial tensor field is the push-forward of the directional derivative of the corresponding material tensor field in the direction of v (velocity vector). More comments on the Lie time derivative $v() • Rate constitutive equations must be formulated based on objective rates of stresses and strains to ensure material frame-indifference. • Rates of material tensor fields are by definition objective, since they are associated with a frame in a fixed linear space. • A spatial tensor field is said to transform objectively under superposed rigid body motions if it transforms according to standard rules of tensor analysis, e.g. A+ = QAQT (preserves distances under rigid body rotations). -
Lagrangian and Eulerian Representations of Fluid Flow: Part I, Kinematics and the Equations of Motion
Lagrangian and Eulerian Representations of Fluid Flow: Part I, Kinematics and the Equations of Motion James F. Price MS 29, Clark Laboratory Woods Hole Oceanographic Institution Woods Hole, MA, 02543 http://www.whoi.edu/science/PO/people/jprice [email protected] Version 7.4 September 13, 2005 Summary: This essay introduces the two methods that are commonly used to describe fluid flow, by observing the trajectories of parcels that are carried along with the flow or by observing the fluid velocity at fixed positions. These yield what are commonly termed Lagrangian and Eulerian descriptions. Lagrangian methods are often the most efficient way to sample a fluid domain and the physical conservation laws are inherently Lagrangian since they apply to specific material parcels rather than points in space. It happens, though, that the Lagrangian equations of motion applied to a continuum are quite difficult, and thus almost all of the theory (forward calculation) in fluid dynamics is developed within the Eulerian system. Eulerian solutions may be used to calculate Lagrangian properties, e.g., parcel trajectories, which is often a valuable step in the description of an Eulerian solution. Transformation to and from Lagrangian and Eulerian systems — the central theme of this essay — is thus the foundation of most theory in fluid dynamics and is a routine part of many investigations. The transformation of the Lagrangian conservation laws into the Eulerian equations of motion requires three key results. (1) The first is dubbed the Fundamental Principle of Kinematics; the velocity at a given position and time (the Eulerian velocity) is identically the velocity of the parcel (the Lagrangian velocity) that occupies that position at that time. -
Sensitivity Analysis of Non-Linear Steep Waves Using VOF Method
Tenth International Conference on ICCFD10-268 Computational Fluid Dynamics (ICCFD10), Barcelona, Spain, July 9-13, 2018 Sensitivity Analysis of Non-linear Steep Waves using VOF Method A. Khaware*, V. Gupta*, K. Srikanth *, and P. Sharkey ** Corresponding author: [email protected] * ANSYS Software Pvt Ltd, Pune, India. ** ANSYS UK Ltd, Milton Park, UK Abstract: The analysis and prediction of non-linear waves is a crucial part of ocean hydrodynamics. Sea waves are typically non-linear in nature, and whilst models exist to predict their behavior, limits exist in their applicability. In practice, as the waves become increasingly steeper, they approach a point beyond which the wave integrity cannot be maintained, and they 'break'. Understanding the limits of available models as waves approach these break conditions can significantly help to improve the accuracy of their potential impact in the field. Moreover, inaccurate modeling of wave kinematics can result in erroneous hydrodynamic forces being predicted. This paper investigates the sensitivity of non-linear wave modeling from both an analytical and a numerical perspective. Using a Volume of Fluid (VOF) method, coupled with the Open Channel Flow module in ANSYS Fluent, sensitivity studies are performed for a variety of non-linear wave scenarios with high steepness and high relative height. These scenarios are intended to mimic the near-break conditions of the wave. 5th order solitary wave models are applied to shallow wave scenarios with high relative heights, and 5th order Stokes wave models are applied to short gravity waves with high wave steepness. Stokes waves are further applied in the shallow regime at high wave steepness to examine the wave sensitivity under extreme conditions. -
Continuum Fluid Mechanics
CONTINUUM FLUID MECHANICS Motion A body is a collection of material particles. The point X is a material point and it is the position of the material particles at time zero. x3 Definition: A one-to-one one-parameter mapping X3 x(X, t ) x = x(X, t) X x X 2 x 2 is called motion. The inverse t =0 x1 X1 X = X(x, t) Figure 1. Schematic of motion. is the inverse motion. X k are referred to as the material coordinates of particle x , and x is the spatial point occupied by X at time t. Theorem: The inverse motion exists if the Jacobian, J of the transformation is nonzero. That is ∂x ∂x J = det = det k ≠ 0 . ∂X ∂X K This is the statement of the fundamental theorem of calculus. Definition: Streamlines are the family of curves tangent to the velocity vector field at time t . Given a velocity vector field v, streamlines are governed by the following equations: dx1 dx 2 dx 3 = = for a given t. v1 v 2 v3 Definition: The streak line of point x 0 at time t is a line, which is made up of material points, which have passed through point x 0 at different times τ ≤ t . Given a motion x i = x i (X, t) and its inverse X K = X K (x, t), it follows that the 0 0 material particle X k will pass through the spatial point x at time τ . i.e., ME639 1 G. Ahmadi 0 0 0 X k = X k (x ,τ). -
Waves and Structures
WAVES AND STRUCTURES By Dr M C Deo Professor of Civil Engineering Indian Institute of Technology Bombay Powai, Mumbai 400 076 Contact: [email protected]; (+91) 22 2572 2377 (Please refer as follows, if you use any part of this book: Deo M C (2013): Waves and Structures, http://www.civil.iitb.ac.in/~mcdeo/waves.html) (Suggestions to improve/modify contents are welcome) 1 Content Chapter 1: Introduction 4 Chapter 2: Wave Theories 18 Chapter 3: Random Waves 47 Chapter 4: Wave Propagation 80 Chapter 5: Numerical Modeling of Waves 110 Chapter 6: Design Water Depth 115 Chapter 7: Wave Forces on Shore-Based Structures 132 Chapter 8: Wave Force On Small Diameter Members 150 Chapter 9: Maximum Wave Force on the Entire Structure 173 Chapter 10: Wave Forces on Large Diameter Members 187 Chapter 11: Spectral and Statistical Analysis of Wave Forces 209 Chapter 12: Wave Run Up 221 Chapter 13: Pipeline Hydrodynamics 234 Chapter 14: Statics of Floating Bodies 241 Chapter 15: Vibrations 268 Chapter 16: Motions of Freely Floating Bodies 283 Chapter 17: Motion Response of Compliant Structures 315 2 Notations 338 References 342 3 CHAPTER 1 INTRODUCTION 1.1 Introduction The knowledge of magnitude and behavior of ocean waves at site is an essential prerequisite for almost all activities in the ocean including planning, design, construction and operation related to harbor, coastal and structures. The waves of major concern to a harbor engineer are generated by the action of wind. The wind creates a disturbance in the sea which is restored to its calm equilibrium position by the action of gravity and hence resulting waves are called wind generated gravity waves. -
Ch.1. Description of Motion
CH.1. DESCRIPTION OF MOTION Multimedia Course on Continuum Mechanics Overview 1.1. Definition of the Continuous Medium 1.1.1. Concept of Continuum Lecture 1 1.1.2. Continuous Medium or Continuum 1.2. Equations of Motion 1.2.1 Configurations of the Continuous Medium 1.2.2. Material and Spatial Coordinates Lecture 2 1.2.3. Equation of Motion and Inverse Equation of Motion 1.2.4. Mathematical Restrictions 1.2.5. Example Lecture 3 1.3. Descriptions of Motion 1.3.1. Material or Lagrangian Description Lecture 4 1.3.2. Spatial or Eulerian Description 1.3.3. Example Lecture 5 2 Overview (cont’d) 1.4. Time Derivatives Lecture 6 1.4.1. Material and Local Derivatives 1.4.2. Convective Rate of Change Lecture 7 1.4.3. Example Lecture 8 1.5. Velocity and Acceleration 1.5.1. Velocity Lecture 9 1.5.2. Acceleration 1.5.3. Example 1.6. Stationarity and Uniformity 1.6.1. Stationary Properties Lecture 10 1.6.2. Uniform Properties 3 Overview (cont’d) 1.7. Trajectory or Pathline 1.7.1. Equation of the Trajectories 1.7.2. Example 1.8. Streamlines Lecture 11 1.8.1. Equation of the Streamlines 1.8.2. Trajectories and Streamlines 1.8.3. Example 1.8.4. Streamtubes 1.9. Control and Material Surfaces 1.9.1. Control Surface 1.9.2. Material Surface Lecture 12 1.9.3. Control Volume 1.9.4. Material Volume 4 1.1 Definition of the Continuous Medium Ch.1. Description of Motion 5 The Concept of Continuum Microscopic scale: Matter is made of atoms which may be grouped in molecules.