On the adaptive time-stepping for large-scale parabolic problems: computer simulation of heat and mass transfer in vacuum freeze-drying
|
|
- Neil Blake
- 5 years ago
- Views:
Transcription
1 On the adaptive time-stepping for large-scale parabolic problems: computer simulation of heat and mass transfer in vacuum freeze-drying Krassimir Georgiev, Nikola Kosturski, Svetozar Margenov Institute for Parallel Processing BAS Sofia Bulgaria Jiří Starý Institute of Geonics AS CR Ostrava Czech Republic SNA Liberec Page 1
2 Introduction The work is motivated by a process of the dehydrating frozen materials by sublimation under high vacuum. The apparatus consists of two containers, one (food camera) for the product to be dried and one (absorbent camera) filled by a material (zeolite granules) for the sorbtion of water molecules. Three phases: preparation of the source and activation (dehydration) of zeolites, self-freezing of the source, drying in conditions of an uniform sublimation of water steam. The main advantages for such kind of drying are a higher food quality of the dry due to the minimum loss of flavour and aroma, the minimal chances of the microbal to growth due to the water absence and no thermal and oxidizing processes. SNA Liberec Page 2
3 Heat and mass transfer problem Model of the absorbent camera cρ T t = LT + f(x, t, T) x Ω, t > LT = d i=1 ( k(x,t) T ) x i x i Notations: T(x, t) unknown distribution of the temperature, d dimension of the space (d = 2), Ω R d computational domain, k = k(x,t) > 0 heat conductivity, c = c(x, t) > 0 heat capacity, ρ > 0 material density, f(x, t, T) function responsible for the non-linear process of transfer of water molecules in the absorption container. Initial conditions: Boundary conditions: T(x, 0) = T 0 (x) x Ω T(x, t) = µ(x, t) x Γ Ω, t > 0 SNA Liberec Page 3
4 Numerical treatment of the problem M dt dt + KT = F Note: f(x, t, T) connects the system with heat sources in the food camera. The heat is supplied to the source material by radiation, conduction or combination of both types. Mass matrix: Stiffness matrix: Right hand side: [ ] N [ M = c ρ φ i φ j dx K = k φ i φ j dx F = f(x, t, T) φ i dx Ω i,j=1 Ω i,j=1 Ω ] N [ ] N i=1 The coarser finite element mesh Number of nodes: 6568 Number of elements: SNA Liberec Page 4
5 Solving the systems of linear equations After the variational formulation, the problem is discretized by the linear triangular finite elements in space (minimal angle of a triangle, maximal area of a single triangle) and the simplest finite differences in time. For the uniform discretization of the time interval (t j = t 0 + j τ, where τ is the time stepsize), we get the following simple algorithm: for j = 1,...,N: compute find T j : b = ( M τ (1 ϑ) K ) T j 1 + τ ϑ F j + τ (1 ϑ) F j 1 (M + τ ϑk) T j = b end for We restrict our attention to implicit methods with ϑ 1, 1. Particularly, we shall consider two 2 cases with ϑ= 1 and ϑ=1, which correspond to Crank-Nicholson (CN) and backward Euler (BE) 2 method, respectively. SNA Liberec Page 5
6 Adaptive time steps The time interval is divided by time steps t 0 < t 1 <... < t N 1 < t N and τ j = t j t j 1. for k = 1,2,... until stop: find T k+1 : ( M + τ k K ) T k = MT k 1 + τ k F k // for the BE steps is ϑ = 1 end for compute r = ( M τk K ) T k ( M 1 2 τk K ) T k τk F k 1 2 τk F k 1 compute η k = r / T k // we suppose T CN T BE r decide if η k <ε min then τ k+1 = 2τ k // in usual practice, we specify if η k >ε max then τ k+1 = τ k /2 if η k ε min, ε max or ( η k <ε min and η k 1 >ε max ) then τ k+1 = τ k and T k+1 = T k, stop if η k >ε max and η k 1 <ε min then τ k+1 = τ k /2 and T k+1 = T k 1, stop // ε min = 10 8, ε max = 10 7 SNA Liberec Page 6
7 How often to perform the ATS procedure? The adaptivity test depends on the input parameters ε min, ε max and is performed each #N NA time steps. The time interval is 5000s and the adaptive time stepping begins with τ = 5. The linear system is solved up to the relative residual accuracy The exact solution vector T ex means the solution of the linear system for the constant time stepsizes (no adaptivite time steps, τ = 5, Time = s, #It = 33067, #N TS = 999). ε min ε max #N NA Time [s] #It #It A #N TS τ T T ex 2 T 2 T T ex ScLion: Pentium 4 / 1.5GHz 256 L2 cache, 256 RAM, Scientific Linux 4.5, GNU C SNA Liberec Page 7
8 How to set the parameters ε min, ε max? No adaptivite time steps, τ = 5: Time = s, #It = 33067, #N TS = 999). ε min ε max #N NA Time [s] #It #It A #N TS τ T T ex 2 T 2 T T ex Impact for the practical usage For the given ε min, ε max : #N NA 3, 15 with the preference of smaller values. For the given ε PCG : ε min ε PCG, ε PCG 10 2 and then ε max = ε min 10. SNA Liberec Page 8
9 Numerical experiments in the full time interval The considered time interval is set to be s 8 days 19 hours 13 minutes. No adaptivite time steps, τ = 5: Time = s, #It = , #N TS = ). ε min ε max #N NA Time [s] #It #N TS τ T T ex 2 T 2 T T ex The temperature field at the end of time interval SNA Liberec Page 9
10 From the engineering point of view The water filling of the zeolites at the 1/3, 2/3 and 3/3 of the time interval. Red: 100 % Yellow: 75 % Green: 50 % Cyan: 25 % Blue: 0 % SNA Liberec Page 10
11 Conclusion The introduced work is devoted to mathematical modelling of the freeze-drying process under high vacuum. Especially, it concerns the computer simulation of heat and mass transfer in the absorbent camera. The origin of such simulation is in solving the time-dependent nonlinear partial differential equation of parabolic type and the core is the repeated solution of linear systems for different time steps. The existing program code used only the uniform discretization of the considered time interval, when the time stepsizes are constant. In an effort to optimize the whole simulation, we included the adaptive time stepping procedure in the code. This procedure is based on the local comparison of Crank-Nicholson and backward Euler time steps. Finally, we performed the numerical experiments to know the behaviour of the resulting program, to find the optimal (in some sense) parameters and to try it on a selected real-life problem. The tests confirmed its big usefulness for practice. SNA Liberec Page 11
12 Thank you for your attention SNA Liberec Page 12
The Evaluation of the Thermal Behaviour of an Underground Repository of the Spent Nuclear Fuel
The Evaluation of the Thermal Behaviour of an Underground Repository of the Spent Nuclear Fuel Roman Kohut, Jiří Starý, and Alexej Kolcun Institute of Geonics, Academy of Sciences of the Czech Republic,
More informationComparative Analysis of Mesh Generators and MIC(0) Preconditioning of FEM Elasticity Systems
Comparative Analysis of Mesh Generators and MIC(0) Preconditioning of FEM Elasticity Systems Nikola Kosturski and Svetozar Margenov Institute for Parallel Processing, Bulgarian Academy of Sciences Abstract.
More informationFUZZY TRANSFORM: Application to Reef Growth Problem. Irina Perfilieva
FUZZY TRANSFORM: Application to Reef Growth Problem Irina Perfilieva University of Ostrava Institute for research and applications of fuzzy modeling DAR Research Center Ostrava, Czech Republic OUTLINE
More informationAssignment on iterative solution methods and preconditioning
Division of Scientific Computing, Department of Information Technology, Uppsala University Numerical Linear Algebra October-November, 2018 Assignment on iterative solution methods and preconditioning 1.
More informationSchwarz-type methods and their application in geomechanics
Schwarz-type methods and their application in geomechanics R. Blaheta, O. Jakl, K. Krečmer, J. Starý Institute of Geonics AS CR, Ostrava, Czech Republic E-mail: stary@ugn.cas.cz PDEMAMIP, September 7-11,
More informationFINITE ELEMENT ANALYSIS USING THE TANGENT STIFFNESS MATRIX FOR TRANSIENT NON-LINEAR HEAT TRANSFER IN A BODY
Heat transfer coeff Temperature in Kelvin International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622 FINITE ELEMENT ANALYSIS USING THE TANGENT STIFFNESS MATRIX FOR TRANSIENT
More informationNumerical Solutions to Partial Differential Equations
Numerical Solutions to Partial Differential Equations Zhiping Li LMAM and School of Mathematical Sciences Peking University The Implicit Schemes for the Model Problem The Crank-Nicolson scheme and θ-scheme
More informationFirst Order Initial Value Problems
First Order Initial Value Problems A first order initial value problem is the problem of finding a function xt) which satisfies the conditions x = x,t) x ) = ξ 1) where the initial time,, is a given real
More informationFinite Difference Methods for
CE 601: Numerical Methods Lecture 33 Finite Difference Methods for PDEs Course Coordinator: Course Coordinator: Dr. Suresh A. Kartha, Associate Professor, Department of Civil Engineering, IIT Guwahati.
More informationLecture 42 Determining Internal Node Values
Lecture 42 Determining Internal Node Values As seen in the previous section, a finite element solution of a boundary value problem boils down to finding the best values of the constants {C j } n, which
More informationA Hybrid Method for the Wave Equation. beilina
A Hybrid Method for the Wave Equation http://www.math.unibas.ch/ beilina 1 The mathematical model The model problem is the wave equation 2 u t 2 = (a 2 u) + f, x Ω R 3, t > 0, (1) u(x, 0) = 0, x Ω, (2)
More informationComputation Fluid Dynamics
Computation Fluid Dynamics CFD I Jitesh Gajjar Maths Dept Manchester University Computation Fluid Dynamics p.1/189 Garbage In, Garbage Out We will begin with a discussion of errors. Useful to understand
More informationFinite difference method for heat equation
Finite difference method for heat equation Praveen. C praveen@math.tifrbng.res.in Tata Institute of Fundamental Research Center for Applicable Mathematics Bangalore 560065 http://math.tifrbng.res.in/~praveen
More informationMultilevel Preconditioning of Graph-Laplacians: Polynomial Approximation of the Pivot Blocks Inverses
Multilevel Preconditioning of Graph-Laplacians: Polynomial Approximation of the Pivot Blocks Inverses P. Boyanova 1, I. Georgiev 34, S. Margenov, L. Zikatanov 5 1 Uppsala University, Box 337, 751 05 Uppsala,
More informationPerformance comparison between hybridizable DG and classical DG methods for elastic waves simulation in harmonic domain
March 4-5, 2015 Performance comparison between hybridizable DG and classical DG methods for elastic waves simulation in harmonic domain M. Bonnasse-Gahot 1,2, H. Calandra 3, J. Diaz 1 and S. Lanteri 2
More informationMath 128A Spring 2003 Week 12 Solutions
Math 128A Spring 2003 Week 12 Solutions Burden & Faires 5.9: 1b, 2b, 3, 5, 6, 7 Burden & Faires 5.10: 4, 5, 8 Burden & Faires 5.11: 1c, 2, 5, 6, 8 Burden & Faires 5.9. Higher-Order Equations and Systems
More informationCOMPUTATIONAL MODELING OF SHAPE MEMORY MATERIALS
COMPUTATIONAL MODELING OF SHAPE MEMORY MATERIALS Jan Valdman Institute of Information Theory and Automation, Czech Academy of Sciences (Prague) based on joint works with Martin Kružík and Miroslav Frost
More informationFirst Order Linear DEs, Applications
Week #2 : First Order Linear DEs, Applications Goals: Classify first-order differential equations. Solve first-order linear differential equations. Use first-order linear DEs as models in application problems.
More informationHigh Performance Computing Applications
BULGARIAN ACADEMY OF SCIENCES CYBERNETICS AND INFORMATION TECHNOLOGIES Volume 17, No 5 Special issue with selected papers from the workshop Two Years Avitohol: Advanced High Performance Computing Applications
More informationData Structures and Algorithms CMPSC 465
Data Structures and Algorithms CMPSC 465 LECTURE 9 Solving recurrences Substitution method Adam Smith S. Raskhodnikova and A. Smith; based on slides by E. Demaine and C. Leiserson Review question Draw
More informationRicharson Extrapolation for Runge-Kutta Methods
Richarson Extrapolation for Runge-Kutta Methods Zahari Zlatevᵃ, Ivan Dimovᵇ and Krassimir Georgievᵇ ᵃ Department of Environmental Science, Aarhus University, Frederiksborgvej 399, P. O. 358, 4000 Roskilde,
More informationME FINITE ELEMENT ANALYSIS FORMULAS
ME 2353 - FINITE ELEMENT ANALYSIS FORMULAS UNIT I FINITE ELEMENT FORMULATION OF BOUNDARY VALUE PROBLEMS 01. Global Equation for Force Vector, {F} = [K] {u} {F} = Global Force Vector [K] = Global Stiffness
More informationLarge Steps in Cloth Simulation. Safeer C Sushil Kumar Meena Guide: Prof. Parag Chaudhuri
Large Steps in Cloth Simulation Safeer C Sushil Kumar Meena Guide: Prof. Parag Chaudhuri Introduction Cloth modeling a challenging problem. Phenomena to be considered Stretch, shear, bend of cloth Interaction
More informationNumerical Simulation for Freeze Drying of Skimmed Milk with Moving Sublimation Front using Tri- Diagonal Matrix Algorithm
Journal of Applied Fluid Mechanics, Vol. 10, No. 3, pp. 813-818, 2017. Available online at www.jafmonline.net, ISSN 1735-3572, EISSN 1735-3645. DOI: 10.18869/acadpub.jafm.73.240.27054 Numerical Simulation
More informationOptimal multilevel preconditioning of strongly anisotropic problems.part II: non-conforming FEM. p. 1/36
Optimal multilevel preconditioning of strongly anisotropic problems. Part II: non-conforming FEM. Svetozar Margenov margenov@parallel.bas.bg Institute for Parallel Processing, Bulgarian Academy of Sciences,
More informationCHAPTER 5: Linear Multistep Methods
CHAPTER 5: Linear Multistep Methods Multistep: use information from many steps Higher order possible with fewer function evaluations than with RK. Convenient error estimates. Changing stepsize or order
More informationModule 4: Numerical Methods for ODE. Michael Bader. Winter 2007/2008
Outlines Module 4: for ODE Part I: Basic Part II: Advanced Lehrstuhl Informatik V Winter 2007/2008 Part I: Basic 1 Direction Fields 2 Euler s Method Outlines Part I: Basic Part II: Advanced 3 Discretized
More informationFirst Order Linear DEs, Applications
Week #2 : First Order Linear DEs, Applications Goals: Classify first-order differential equations. Solve first-order linear differential equations. Use first-order linear DEs as models in application problems.
More informationChapter Two: Numerical Methods for Elliptic PDEs. 1 Finite Difference Methods for Elliptic PDEs
Chapter Two: Numerical Methods for Elliptic PDEs Finite Difference Methods for Elliptic PDEs.. Finite difference scheme. We consider a simple example u := subject to Dirichlet boundary conditions ( ) u
More information1 Conduction Heat Transfer
Eng6901 - Formula Sheet 3 (December 1, 2015) 1 1 Conduction Heat Transfer 1.1 Cartesian Co-ordinates q x = q xa x = ka x dt dx R th = L ka 2 T x 2 + 2 T y 2 + 2 T z 2 + q k = 1 T α t T (x) plane wall of
More informationLYO CALCULATOR. User manual
LYO CALCULATOR User manual Stanislav Karpuk 2015 Table of contents Introduction...2 Background 3 Methods.4 Procedure...6 Useful data.8 1 Introduction Lyo Calculator is a tool with a Graphical User Interface
More informationCOMBINING MAXIMAL REGULARITY AND ENERGY ESTIMATES FOR TIME DISCRETIZATIONS OF QUASILINEAR PARABOLIC EQUATIONS
COMBINING MAXIMAL REGULARITY AND ENERGY ESTIMATES FOR TIME DISCRETIZATIONS OF QUASILINEAR PARABOLIC EQUATIONS GEORGIOS AKRIVIS, BUYANG LI, AND CHRISTIAN LUBICH Abstract. We analyze fully implicit and linearly
More informationEcon 2148, fall 2017 Gaussian process priors, reproducing kernel Hilbert spaces, and Splines
Econ 2148, fall 2017 Gaussian process priors, reproducing kernel Hilbert spaces, and Splines Maximilian Kasy Department of Economics, Harvard University 1 / 37 Agenda 6 equivalent representations of the
More informationNonlinear Dynamical Systems Lecture - 01
Nonlinear Dynamical Systems Lecture - 01 Alexandre Nolasco de Carvalho August 08, 2017 Presentation Course contents Aims and purpose of the course Bibliography Motivation To explain what is a dynamical
More informationIterative Solution methods
p. 1/28 TDB NLA Parallel Algorithms for Scientific Computing Iterative Solution methods p. 2/28 TDB NLA Parallel Algorithms for Scientific Computing Basic Iterative Solution methods The ideas to use iterative
More informationIterative Solvers in the Finite Element Solution of Transient Heat Conduction
Iterative Solvers in the Finite Element Solution of Transient Heat Conduction Mile R. Vuji~i} PhD student Steve G.R. Brown Senior Lecturer Materials Research Centre School of Engineering University of
More informationThe method of lines (MOL) for the diffusion equation
Chapter 1 The method of lines (MOL) for the diffusion equation The method of lines refers to an approximation of one or more partial differential equations with ordinary differential equations in just
More informationTime-adaptive methods for the incompressible Navier-Stokes equations
Time-adaptive methods for the incompressible Navier-Stokes equations Joachim Rang, Thorsten Grahs, Justin Wiegmann, 29.09.2016 Contents Introduction Diagonally implicit Runge Kutta methods Results with
More informationExam in TMA4215 December 7th 2012
Norwegian University of Science and Technology Department of Mathematical Sciences Page of 9 Contact during the exam: Elena Celledoni, tlf. 7359354, cell phone 48238584 Exam in TMA425 December 7th 22 Allowed
More informationPartitioned Methods for Multifield Problems
Partitioned Methods for Multifield Problems Joachim Rang, 10.5.2017 10.5.2017 Joachim Rang Partitioned Methods for Multifield Problems Seite 1 Contents Blockform of linear iteration schemes Examples 10.5.2017
More informationTong Sun Department of Mathematics and Statistics Bowling Green State University, Bowling Green, OH
Consistency & Numerical Smoothing Error Estimation An Alternative of the Lax-Richtmyer Theorem Tong Sun Department of Mathematics and Statistics Bowling Green State University, Bowling Green, OH 43403
More informationOne-Dimensional Stefan Problem
One-Dimensional Stefan Problem Tracy Backes May 5, 2007 1 Introduction Working with systems that involve moving boundaries can be a very difficult task. Not only do we have to solve the equations describing
More informationPHYS 532 Lecture 10 Page Derivation of the Equations of Macroscopic Electromagnetism. Parameter Microscopic Macroscopic
PHYS 532 Lecture 10 Page 1 6.6 Derivation of the Equations of Macroscopic Electromagnetism Parameter Microscopic Macroscopic Electric Field e E = Magnetic Field b B = Charge Density η ρ = Current
More information154 Chapter 9 Hints, Answers, and Solutions The particular trajectories are highlighted in the phase portraits below.
54 Chapter 9 Hints, Answers, and Solutions 9. The Phase Plane 9.. 4. The particular trajectories are highlighted in the phase portraits below... 3. 4. 9..5. Shown below is one possibility with x(t) and
More informationFDM for parabolic equations
FDM for parabolic equations Consider the heat equation where Well-posed problem Existence & Uniqueness Mass & Energy decreasing FDM for parabolic equations CNFD Crank-Nicolson + 2 nd order finite difference
More informationBlock-Structured Adaptive Mesh Refinement
Block-Structured Adaptive Mesh Refinement Lecture 2 Incompressible Navier-Stokes Equations Fractional Step Scheme 1-D AMR for classical PDE s hyperbolic elliptic parabolic Accuracy considerations Bell
More informationOn the Discretization Time-Step in the Finite Element Theta-Method of the Discrete Heat Equation
On the Discretization Time-Step in the Finite Element Theta-Method of the Discrete Heat Equation Tamás Szabó Eötvös Loránd University, Institute of Mathematics 1117 Budapest, Pázmány P. S. 1/c, Hungary
More informationMath 7824 Spring 2010 Numerical solution of partial differential equations Classroom notes and homework
Math 7824 Spring 2010 Numerical solution of partial differential equations Classroom notes and homework Jan Mandel University of Colorado Denver May 12, 2010 1/20/09: Sec. 1.1, 1.2. Hw 1 due 1/27: problems
More informationNumerical Methods for Differential Equations
Numerical Methods for Differential Equations Chapter 2: Runge Kutta and Multistep Methods Gustaf Söderlind Numerical Analysis, Lund University Contents V4.16 1. Runge Kutta methods 2. Embedded RK methods
More informationUse of Differential Equations In Modeling and Simulation of CSTR
Use of Differential Equations In Modeling and Simulation of CSTR JIRI VOJTESEK, PETR DOSTAL Department of Process Control, Faculty of Applied Informatics Tomas Bata University in Zlin nám. T. G. Masaryka
More informationHOW TO SIMPLIFY RETURN-MAPPING ALGORITHMS IN COMPUTATIONAL PLASTICITY: PART 2 - IMPLEMENTATION DETAILS AND EXPERIMENTS
How to simplify return-mapping algorithms in computational plasticity: Part 2 Implementation details and experiments XIII International Conference on Computational Plasticity. Fundamentals and Applications
More informationOn fuzzy entropy and topological entropy of fuzzy extensions of dynamical systems
On fuzzy entropy and topological entropy of fuzzy extensions of dynamical systems Jose Cánovas, Jiří Kupka* *) Institute for Research and Applications of Fuzzy Modeling University of Ostrava Ostrava, Czech
More informationOn Multigrid for Phase Field
On Multigrid for Phase Field Carsten Gräser (FU Berlin), Ralf Kornhuber (FU Berlin), Rolf Krause (Uni Bonn), and Vanessa Styles (University of Sussex) Interphase 04 Rome, September, 13-16, 2004 Synopsis
More informationControl of Interface Evolution in Multi-Phase Fluid Flows
Control of Interface Evolution in Multi-Phase Fluid Flows Markus Klein Department of Mathematics University of Tübingen Workshop on Numerical Methods for Optimal Control and Inverse Problems Garching,
More informationNUMERICAL SIMULATION OF INTERACTION BETWEEN INCOMPRESSIBLE FLOW AND AN ELASTIC WALL
Proceedings of ALGORITMY 212 pp. 29 218 NUMERICAL SIMULATION OF INTERACTION BETWEEN INCOMPRESSIBLE FLOW AND AN ELASTIC WALL MARTIN HADRAVA, MILOSLAV FEISTAUER, AND PETR SVÁČEK Abstract. The present paper
More informationMathematical Nomenclature
Mathematical Nomenclature Miloslav Čapek Department of Electromagnetic Field Czech Technical University in Prague, Czech Republic miloslav.capek@fel.cvut.cz Prague, Czech Republic November 6, 2018 Čapek,
More informationc 2002 Society for Industrial and Applied Mathematics
SIAM J. SCI. COMPUT. Vol. 4 No. pp. 507 5 c 00 Society for Industrial and Applied Mathematics WEAK SECOND ORDER CONDITIONS FOR STOCHASTIC RUNGE KUTTA METHODS A. TOCINO AND J. VIGO-AGUIAR Abstract. A general
More informationContinuum Physics II Examination Assignment Finite difference simulation of the baking process
Continuum Physics II Examination Assignment Finite difference simulation of the baking process Andrew N. Jackson - 5th May 1996 - Continuum Physics II Assignment. Introduction The brief for this assignment
More informationA Three-Dimensional Anisotropic Viscoelastic Generalized Maxwell Model for Ageing Wood Composites
A Three-Dimensional Anisotropic Viscoelastic Generalized for Ageing Wood Composites J. Deteix, G. Djoumna, A. Fortin, A. Cloutier and P. Blanchet Society of Wood Science and Technology, 51st Annual Convention
More informationLevel-Set Minimization of Potential Controlled Hadwiger Valuations for Molecular Solvation
Level-Set Minimization of Potential Controlled Hadwiger Valuations for Molecular Solvation Bo Li Dept. of Math & NSF Center for Theoretical Biological Physics UC San Diego, USA Collaborators: Li-Tien Cheng
More informationRadiative transfer equation in spherically symmetric NLTE model stellar atmospheres
Radiative transfer equation in spherically symmetric NLTE model stellar atmospheres Jiří Kubát Astronomický ústav AV ČR Ondřejov Zářivě (magneto)hydrodynamický seminář Ondřejov 20.03.2008 p. Outline 1.
More informationComputer Animation. Animation Methods Keyframing Interpolation Kinematics Inverse Kinematics. Slides courtesy of Leonard McMillan and Jovan Popovic
Computer Animation Animation Methods Keyframing Interpolation Kinematics Inverse Kinematics Slides courtesy of Leonard McMillan and Jovan Popovic Lecture 13 6.837 Fall 2002 Office hours Administrative
More informationarxiv: v1 [math.na] 28 Feb 2008
BDDC by a frontal solver and the stress computation in a hip joint replacement arxiv:0802.4295v1 [math.na] 28 Feb 2008 Jakub Šístek a, Jaroslav Novotný b, Jan Mandel c, Marta Čertíková a, Pavel Burda a
More informationT. Liggett Mathematics 171 Final Exam June 8, 2011
T. Liggett Mathematics 171 Final Exam June 8, 2011 1. The continuous time renewal chain X t has state space S = {0, 1, 2,...} and transition rates (i.e., Q matrix) given by q(n, n 1) = δ n and q(0, n)
More informationThe second-order 1D wave equation
C The second-order D wave equation C. Homogeneous wave equation with constant speed The simplest form of the second-order wave equation is given by: x 2 = Like the first-order wave equation, it responds
More informationLecture 4: Numerical solution of ordinary differential equations
Lecture 4: Numerical solution of ordinary differential equations Department of Mathematics, ETH Zürich General explicit one-step method: Consistency; Stability; Convergence. High-order methods: Taylor
More informationChapter 4 Differential Equations and Derivatives
Chapter 4 Differential Equations and Derivatives This chapter uses the microscope approximation to view derivatives as time rates of change and describe some practical changes. The microscope approximation
More informationThe Finite Element Method for the Analysis of Non-Linear and Dynamic Systems: Thermomechanics
The Finite Element Method for the Analysis of Non-Linear and Dynamic Systems: Thermomechanics Prof. Dr. Eleni Chatzi Dr. Giuseppe Abbiati, Dr. Konstantinos Agathos Lecture 13-14 December, 2017 1 / 30 Forewords
More informationFinite Elements for Nonlinear Problems
Finite Elements for Nonlinear Problems Computer Lab 2 In this computer lab we apply finite element method to nonlinear model problems and study two of the most common techniques for solving the resulting
More informationNumerical Analysis Comprehensive Exam Questions
Numerical Analysis Comprehensive Exam Questions 1. Let f(x) = (x α) m g(x) where m is an integer and g(x) C (R), g(α). Write down the Newton s method for finding the root α of f(x), and study the order
More informationNumerical Methods for Differential Equations
Numerical Methods for Differential Equations Chapter 2: Runge Kutta and Linear Multistep methods Gustaf Söderlind and Carmen Arévalo Numerical Analysis, Lund University Textbooks: A First Course in the
More information3D HP Protein Folding Problem using Ant Algorithm
3D HP Protein Folding Problem using Ant Algorithm Fidanova S. Institute of Parallel Processing BAS 25A Acad. G. Bonchev Str., 1113 Sofia, Bulgaria Phone: +359 2 979 66 42 E-mail: stefka@parallel.bas.bg
More informationLast quiz Comments. ! F '(t) dt = F(b) " F(a) #1: State the fundamental theorem of calculus version I or II. Version I : Version II :
Last quiz Comments #1: State the fundamental theorem of calculus version I or II. Version I : b! F '(t) dt = F(b) " F(a) a Version II : x F( x) =! f ( t) dt F '( x) = f ( x) a Comments of last quiz #1:
More information(x x 0 )(x x 1 )... (x x n ) (x x 0 ) + y 0.
> 5. Numerical Integration Review of Interpolation Find p n (x) with p n (x j ) = y j, j = 0, 1,,..., n. Solution: p n (x) = y 0 l 0 (x) + y 1 l 1 (x) +... + y n l n (x), l k (x) = n j=1,j k Theorem Let
More informationHalf of Final Exam Name: Practice Problems October 28, 2014
Math 54. Treibergs Half of Final Exam Name: Practice Problems October 28, 24 Half of the final will be over material since the last midterm exam, such as the practice problems given here. The other half
More informationThe effect of natural convection on solidification in tall tapered feeders
ANZIAM J. 44 (E) ppc496 C511, 2003 C496 The effect of natural convection on solidification in tall tapered feeders C. H. Li D. R. Jenkins (Received 30 September 2002) Abstract Tall tapered feeders (ttfs)
More informationWhat we have learned What is algorithm Why study algorithm The time and space efficiency of algorithm The analysis framework of time efficiency Asympt
Lecture 3 The Analysis of Recursive Algorithm Efficiency What we have learned What is algorithm Why study algorithm The time and space efficiency of algorithm The analysis framework of time efficiency
More informationLast time: Diffusion - Numerical scheme (FD) Heat equation is dissipative, so why not try Forward Euler:
Lecture 7 18.086 Last time: Diffusion - Numerical scheme (FD) Heat equation is dissipative, so why not try Forward Euler: U j,n+1 t U j,n = U j+1,n 2U j,n + U j 1,n x 2 Expected accuracy: O(Δt) in time,
More informationThe Direct Transcription Method For Optimal Control. Part 2: Optimal Control
The Direct Transcription Method For Optimal Control Part 2: Optimal Control John T Betts Partner, Applied Mathematical Analysis, LLC 1 Fundamental Principle of Transcription Methods Transcription Method
More informationPart IB Numerical Analysis
Part IB Numerical Analysis Definitions Based on lectures by G. Moore Notes taken by Dexter Chua Lent 206 These notes are not endorsed by the lecturers, and I have modified them (often significantly) after
More informationMath 3313: Differential Equations Second-order ordinary differential equations
Math 3313: Differential Equations Second-order ordinary differential equations Thomas W. Carr Department of Mathematics Southern Methodist University Dallas, TX Outline Mass-spring & Newton s 2nd law Properties
More informationLogistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations
Logistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations S. Y. Ha and J. Park Department of Mathematical Sciences Seoul National University Sep 23, 2013 Contents 1 Logistic Map 2 Euler and
More informationIterative methods for positive definite linear systems with a complex shift
Iterative methods for positive definite linear systems with a complex shift William McLean, University of New South Wales Vidar Thomée, Chalmers University November 4, 2011 Outline 1. Numerical solution
More informationExperience in Factoring Large Integers Using Quadratic Sieve
Experience in Factoring Large Integers Using Quadratic Sieve D. J. Guan Department of Computer Science, National Sun Yat-Sen University, Kaohsiung, Taiwan 80424 guan@cse.nsysu.edu.tw April 19, 2005 Abstract
More informationOrdinary Differential Equations
Ordinary Differential Equations Michael H. F. Wilkinson Institute for Mathematics and Computing Science University of Groningen The Netherlands December 2005 Overview What are Ordinary Differential Equations
More informationInput-to-state stability and interconnected Systems
10th Elgersburg School Day 1 Input-to-state stability and interconnected Systems Sergey Dashkovskiy Universität Würzburg Elgersburg, March 5, 2018 1/20 Introduction Consider Solution: ẋ := dx dt = ax,
More informationChapter 5. Formulation of FEM for Unsteady Problems
Chapter 5 Formulation of FEM for Unsteady Problems Two alternatives for formulating time dependent problems are called coupled space-time formulation and semi-discrete formulation. The first one treats
More informationApplied Math for Engineers
Applied Math for Engineers Ming Zhong Lecture 15 March 28, 2018 Ming Zhong (JHU) AMS Spring 2018 1 / 28 Recap Table of Contents 1 Recap 2 Numerical ODEs: Single Step Methods 3 Multistep Methods 4 Method
More informationMultiview Geometry and Bundle Adjustment. CSE P576 David M. Rosen
Multiview Geometry and Bundle Adjustment CSE P576 David M. Rosen 1 Recap Previously: Image formation Feature extraction + matching Two-view (epipolar geometry) Today: Add some geometry, statistics, optimization
More informationEECS 700: Exam # 1 Tuesday, October 21, 2014
EECS 700: Exam # 1 Tuesday, October 21, 2014 Print Name and Signature The rules for this exam are as follows: Write your name on the front page of the exam booklet. Initial each of the remaining pages
More informationChemistry 24b Lecture 23 Spring Quarter 2004 Instructor: Richard Roberts. (1) It induces a dipole moment in the atom or molecule.
Chemistry 24b Lecture 23 Spring Quarter 2004 Instructor: Richard Roberts Absorption and Dispersion v E * of light waves has two effects on a molecule or atom. (1) It induces a dipole moment in the atom
More informationChapter 1: Matter, Energy, and the Origins of the Universe
Chapter 1: Matter, Energy, and the Origins of the Universe Problems: 1.1-1.40, 1.43-1.98 science: study of nature that results in a logical explanation of the observations chemistry: study of matter, its
More informationQUESTION 1: Find the derivatives of the following functions. DO NOT TRY TO SIMPLIFY.
QUESTION 1: Find the derivatives of the following functions. DO NOT TRY TO SIMPLIFY. (a) f(x) =tan(1/x) (b) f(x) =sin(e x ) (c) f(x) =(1+cos(x)) 1/3 (d) f(x) = ln x x +1 QUESTION 2: Using only the definition
More informationExperiences with Model Reduction and Interpolation
Experiences with Model Reduction and Interpolation Paul Constantine Stanford University, Sandia National Laboratories Qiqi Wang (MIT) David Gleich (Purdue) Emory University March 7, 2012 Joe Ruthruff (SNL)
More informationA Model of Human Capital Accumulation and Occupational Choices. A simplified version of Keane and Wolpin (JPE, 1997)
A Model of Human Capital Accumulation and Occupational Choices A simplified version of Keane and Wolpin (JPE, 1997) We have here three, mutually exclusive decisions in each period: 1. Attend school. 2.
More informationBACKGROUNDS. Two Models of Deformable Body. Distinct Element Method (DEM)
BACKGROUNDS Two Models of Deformable Body continuum rigid-body spring deformation expressed in terms of field variables assembly of rigid-bodies connected by spring Distinct Element Method (DEM) simple
More informationCOMBINING MAXIMAL REGULARITY AND ENERGY ESTIMATES FOR TIME DISCRETIZATIONS OF QUASILINEAR PARABOLIC EQUATIONS
COMBINING MAXIMAL REGULARITY AND ENERGY ESTIMATES FOR TIME DISCRETIZATIONS OF QUASILINEAR PARABOLIC EQUATIONS GEORGIOS AKRIVIS, BUYANG LI, AND CHRISTIAN LUBICH Abstract. We analyze fully implicit and linearly
More informationBoundary Value Problems and Iterative Methods for Linear Systems
Boundary Value Problems and Iterative Methods for Linear Systems 1. Equilibrium Problems 1.1. Abstract setting We want to find a displacement u V. Here V is a complete vector space with a norm v V. In
More informationRichardson Extrapolated Numerical Methods for Treatment of One-Dimensional Advection Equations
Richardson Extrapolated Numerical Methods for Treatment of One-Dimensional Advection Equations Zahari Zlatev 1, Ivan Dimov 2, István Faragó 3, Krassimir Georgiev 2, Ágnes Havasi 4, and Tzvetan Ostromsky
More informationEuler s Method applied to the control of switched systems
Euler s Method applied to the control of switched systems FORMATS 2017 - Berlin Laurent Fribourg 1 September 6, 2017 1 LSV - CNRS & ENS Cachan L. Fribourg Euler s method and switched systems September
More information