On the adaptive time-stepping for large-scale parabolic problems: computer simulation of heat and mass transfer in vacuum freeze-drying

Size: px
Start display at page:

Download "On the adaptive time-stepping for large-scale parabolic problems: computer simulation of heat and mass transfer in vacuum freeze-drying"

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 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 information

Comparative 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 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 information

FUZZY TRANSFORM: Application to Reef Growth Problem. Irina Perfilieva

FUZZY 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 information

Assignment on iterative solution methods and preconditioning

Assignment 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 information

Schwarz-type methods and their application in geomechanics

Schwarz-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 information

FINITE ELEMENT ANALYSIS USING THE TANGENT STIFFNESS MATRIX FOR TRANSIENT NON-LINEAR HEAT TRANSFER IN A BODY

FINITE 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 information

Numerical Solutions to Partial Differential Equations

Numerical 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 information

First Order Initial Value Problems

First 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 information

Finite Difference Methods for

Finite 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 information

Lecture 42 Determining Internal Node Values

Lecture 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 information

A Hybrid Method for the Wave Equation. beilina

A 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 information

Computation Fluid Dynamics

Computation 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 information

Finite difference method for heat equation

Finite 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 information

Multilevel Preconditioning of Graph-Laplacians: Polynomial Approximation of the Pivot Blocks Inverses

Multilevel 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 information

Performance comparison between hybridizable DG and classical DG methods for elastic waves simulation in harmonic domain

Performance 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 information

Math 128A Spring 2003 Week 12 Solutions

Math 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 information

COMPUTATIONAL MODELING OF SHAPE MEMORY MATERIALS

COMPUTATIONAL 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 information

First Order Linear DEs, Applications

First 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 information

High Performance Computing Applications

High 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 information

Data Structures and Algorithms CMPSC 465

Data 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 information

Richarson Extrapolation for Runge-Kutta Methods

Richarson 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 information

ME FINITE ELEMENT ANALYSIS FORMULAS

ME 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 information

Large 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 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 information

Numerical Simulation for Freeze Drying of Skimmed Milk with Moving Sublimation Front using Tri- Diagonal Matrix Algorithm

Numerical 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 information

Optimal 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. 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 information

CHAPTER 5: Linear Multistep Methods

CHAPTER 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 information

Module 4: Numerical Methods for ODE. Michael Bader. Winter 2007/2008

Module 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 information

First Order Linear DEs, Applications

First 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 information

Chapter Two: Numerical Methods for Elliptic PDEs. 1 Finite Difference Methods for Elliptic PDEs

Chapter 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 information

1 Conduction Heat Transfer

1 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 information

LYO CALCULATOR. User manual

LYO 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 information

COMBINING 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 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 information

Econ 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 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 information

Nonlinear Dynamical Systems Lecture - 01

Nonlinear 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 information

Iterative Solution methods

Iterative 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 information

Iterative Solvers in the Finite Element Solution of Transient Heat Conduction

Iterative 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 information

The method of lines (MOL) for the diffusion equation

The 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 information

Time-adaptive methods for the incompressible Navier-Stokes equations

Time-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 information

Exam in TMA4215 December 7th 2012

Exam 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 information

Partitioned Methods for Multifield Problems

Partitioned 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 information

Tong Sun Department of Mathematics and Statistics Bowling Green State University, Bowling Green, OH

Tong 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 information

One-Dimensional Stefan Problem

One-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 information

PHYS 532 Lecture 10 Page Derivation of the Equations of Macroscopic Electromagnetism. Parameter Microscopic Macroscopic

PHYS 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 information

154 Chapter 9 Hints, Answers, and Solutions The particular trajectories are highlighted in the phase portraits below.

154 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 information

FDM for parabolic equations

FDM 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 information

Block-Structured Adaptive Mesh Refinement

Block-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 information

On 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 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 information

Math 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 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 information

Numerical Methods for Differential Equations

Numerical 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 information

Use of Differential Equations In Modeling and Simulation of CSTR

Use 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 information

HOW 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 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 information

On fuzzy entropy and topological entropy of fuzzy extensions of dynamical systems

On 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 information

On Multigrid for Phase Field

On 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 information

Control of Interface Evolution in Multi-Phase Fluid Flows

Control 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 information

NUMERICAL SIMULATION OF INTERACTION BETWEEN INCOMPRESSIBLE FLOW AND AN ELASTIC WALL

NUMERICAL 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 information

Mathematical Nomenclature

Mathematical 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 information

c 2002 Society for Industrial and Applied Mathematics

c 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 information

Continuum Physics II Examination Assignment Finite difference simulation of the baking process

Continuum 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 information

A Three-Dimensional Anisotropic Viscoelastic Generalized Maxwell Model for Ageing Wood Composites

A 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 information

Level-Set Minimization of Potential Controlled Hadwiger Valuations for Molecular Solvation

Level-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 information

Radiative transfer equation in spherically symmetric NLTE model stellar atmospheres

Radiative 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 information

Computer 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 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 information

arxiv: v1 [math.na] 28 Feb 2008

arxiv: 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 information

T. Liggett Mathematics 171 Final Exam June 8, 2011

T. 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 information

The second-order 1D wave equation

The 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 information

Lecture 4: Numerical solution of ordinary differential equations

Lecture 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 information

Chapter 4 Differential Equations and Derivatives

Chapter 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 information

The 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 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 information

Finite Elements for Nonlinear Problems

Finite 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 information

Numerical Analysis Comprehensive Exam Questions

Numerical 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 information

Numerical Methods for Differential Equations

Numerical 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 information

3D HP Protein Folding Problem using Ant Algorithm

3D 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 information

Last 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. ! 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.

(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 information

Half of Final Exam Name: Practice Problems October 28, 2014

Half 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 information

The effect of natural convection on solidification in tall tapered feeders

The 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 information

What we have learned What is algorithm Why study algorithm The time and space efficiency of algorithm The analysis framework of time efficiency Asympt

What 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 information

Last time: Diffusion - Numerical scheme (FD) Heat equation is dissipative, so why not try Forward Euler:

Last 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 information

The Direct Transcription Method For Optimal Control. Part 2: Optimal Control

The 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 information

Part IB Numerical Analysis

Part 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 information

Math 3313: Differential Equations Second-order ordinary differential equations

Math 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 information

Logistic Map, Euler & Runge-Kutta Method and Lotka-Volterra Equations

Logistic 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 information

Iterative methods for positive definite linear systems with a complex shift

Iterative 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 information

Experience in Factoring Large Integers Using Quadratic Sieve

Experience 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 information

Ordinary Differential Equations

Ordinary 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 information

Input-to-state stability and interconnected Systems

Input-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 information

Chapter 5. Formulation of FEM for Unsteady Problems

Chapter 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 information

Applied Math for Engineers

Applied 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 information

Multiview Geometry and Bundle Adjustment. CSE P576 David M. Rosen

Multiview 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 information

EECS 700: Exam # 1 Tuesday, October 21, 2014

EECS 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 information

Chemistry 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. (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 information

Chapter 1: Matter, Energy, and the Origins of the Universe

Chapter 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 information

QUESTION 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. 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 information

Experiences with Model Reduction and Interpolation

Experiences 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 information

A 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) 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 information

BACKGROUNDS. Two Models of Deformable Body. Distinct Element Method (DEM)

BACKGROUNDS. 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 information

COMBINING 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 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 information

Boundary Value Problems and Iterative Methods for Linear Systems

Boundary 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 information

Richardson Extrapolated Numerical Methods for Treatment of One-Dimensional Advection Equations

Richardson 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 information

Euler s Method applied to the control of switched systems

Euler 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