ASCI/Alliances Center for Astrophysical Thermonuclear Flashes An Interface Propagation Model for Reaction-Diffusion Advection Adam Oberman An Interface.

Slides:



Advertisements
Similar presentations
Lecture 20: Laminar Non-premixed Flames – Introduction, Non-reacting Jets, Simplified Description of Laminar Non- premixed Flames Yi versus f Experimental.
Advertisements

Flamelet-based combustion model for compressible flows
Laminar Premixed Flames and Diffusion Flames
Modelling - Module 1 Lecture 1 Modelling - Module 1 Lecture 1 David Godfrey.
Boundary Layer Flow Describes the transport phenomena near the surface for the case of fluid flowing past a solid object.
For a typical white dwarf density of 5  10 8 g cm -3 and a pure carbon environment, the flame thickness is 3.78  cm and the speed is 58 km s -1.
Luiza Bondar Jan ten Thije Boonkkamp Bob Matheij Combustion associated noise in central heating equipment Department of Mechanical Engineering, Combustion.
1cs533d-term Notes. 2 Fire  [Nguyen, Fedkiw, Jensen ‘02]  Gaseous fuel/air mix (from a burner, or a hot piece of wood, or …) heats up  When it.
The structure and evolution of stars
1 MECH 221 FLUID MECHANICS (Fall 06/07) Tutorial 7.
Adnan Khan Department of Mathematics Lahore University of Management Sciences.
AME 513 Principles of Combustion Lecture 10 Premixed flames III: Turbulence effects.
INTRODUCTION: Biological systems are characterized by significant heterogeneity at multiple scales. The fine scale (local scale) heterogeneity often has.
Computational Biology, Part 17 Biochemical Kinetics I Robert F. Murphy Copyright  1996, All rights reserved.
1 MECH 221 FLUID MECHANICS (Fall 06/07) Tutorial 6 FLUID KINETMATICS.
Two Approaches to Multiphysics Modeling Sun, Yongqi FAU Erlangen-Nürnberg.
1cs533d-winter-2005 Notes  I’m now in X663 Well, sort of…  Questions about assignment 3?
the equation of state of cold quark gluon plasmas
Turbopause and Gravity Waves Han-Li Liu HAO National Center for Atmospheric Research.
Temperature Gradient Limits for Liquid-Protected Divertors S. I. Abdel-Khalik, S. Shin, and M. Yoda ARIES Meeting (June 2004) G. W. Woodruff School of.
Euler’s Equation in Fluid Mechanics. What is Fluid Mechanics? Fluid mechanics is the study of the macroscopic physical behavior of fluids. Fluids are.
GENERAL PRINCIPLES OF BRANE KINEMATICS AND DYNAMICS Introduction Strings, branes, geometric principle, background independence Brane space M (brane kinematics)
AMBIENT AIR CONCENTRATION MODELING Types of Pollutant Sources Point Sources e.g., stacks or vents Area Sources e.g., landfills, ponds, storage piles Volume.
BsysE595 Lecture Basic modeling approaches for engineering systems – Summary and Review Shulin Chen January 10, 2013.
Emergent Universe Scenario
AMS 599 Special Topics in Applied Mathematics Lecture 5 James Glimm Department of Applied Mathematics and Statistics, Stony Brook University Brookhaven.
Faculty of Engineering, Kingston University London
Computational Biology, Part 15 Biochemical Kinetics I Robert F. Murphy Copyright  1996, 1999, 2000, All rights reserved.
Mathematical Equations of CFD
Materials Process Design and Control Laboratory MULTISCALE MODELING OF ALLOY SOLIDIFICATION LIJIAN TAN NICHOLAS ZABARAS Date: 24 July 2007 Sibley School.
Modeling of Materials Processes using Dimensional Analysis and Asymptotic Considerations Patricio Mendez, Tom Eagar Welding and Joining Group Massachusetts.
 Let’s take everything we have learned so far and now add in the two other processes discussed in the introduction chapter – advection and retardation.
Mass Transfer Coefficient
Xin Xi Feb. 28. Basics  Convective entrainment : The buoyant thermals from the surface layer rise through the mixed layer, and penetrate (with enough.
Distributed Flow Routing Surface Water Hydrology, Spring 2005 Reading: 9.1, 9.2, 10.1, 10.2 Venkatesh Merwade, Center for Research in Water Resources.
IIT-Madras, Momentum Transfer: July 2005-Dec 2005 Perturbation: Background n Algebraic n Differential Equations.
Order of Magnitude Scaling of Complex Engineering Problems Patricio F. Mendez Thomas W. Eagar May 14 th, 1999.
TURBULENT PREMIXED FLAMES AT HIGH KARLOVITZ NUMBERS UNDER OXY-FUEL CONDITIONS Yang Chen 1, K.H. Luo 1,2 1 Center for Combustion Energy, Tsinghua University,
Measurements in Fluid Mechanics 058:180:001 (ME:5180:0001) Time & Location: 2:30P - 3:20P MWF 218 MLH Office Hours: 4:00P – 5:00P MWF 223B-5 HL Instructor:
Title: SHAPE OPTIMIZATION OF AXISYMMETRIC CAVITATOR IN PARTIALY CAVITATING FLOW Department of Mechanical Engineering Ferdowsi University of Mashhad Presented.
Round Tables and Discussions Experiment + Theory + Numerical Simulations How to stay connected to reality?
ECE-7000: Nonlinear Dynamical Systems Overfitting and model costs Overfitting  The more free parameters a model has, the better it can be adapted.
1 Chapter 6 Flow Analysis Using Differential Methods ( Differential Analysis of Fluid Flow)
MA354 An Introduction to Math Models (more or less corresponding to 1.0 in your book)
Governing Equations Conservation of Mass Conservation of Momentum Velocity Stress tensor Force Pressure Surface normal Computation Flowsheet Grid values.
Ch 4 Fluids in Motion.
Types of Models Marti Blad Northern Arizona University College of Engineering & Technology.
Heat release modeling FPVA-based model V. Terrapon and H. Pitsch 1 Stanford PSAAP Center - Working draft.
Modelling and Simulation of Passive Optical Devices João Geraldo P. T. dos Reis and Henrique J. A. da Silva Introduction Integrated Optics is a field of.
MA354 Math Modeling Introduction. Outline A. Three Course Objectives 1. Model literacy: understanding a typical model description 2. Model Analysis 3.
Hirophysics.com Some Aspects of Numerical Solutions for Partial Differential Equations Austin Andries University of Southern Mississippi Dr. Hironori Shimoyama.
Center for Ocean-Atmospheric Prediction Studies
Electrical Wave Propagation in a Minimally Realistic Fiber Architecture Model of the Left Ventricle Xianfeng Song, Department of Physics, Indiana University.
Higher Order Runge-Kutta Methods for Fluid Mechanics Problems Abhishek Mishra Graduate Student, Aerospace Engineering Course Presentation MATH 6646.
Introduction to Modeling Technology Enhanced Inquiry Based Science Education.
University of Wisconsin -- Engine Research Center slide 1 Counter-flow diffusion flame ME Project Chi-wei Tsang Longxiang Liu Krishna P.
Advanced Numerical Techniques Mccormack Technique CFD Dr. Ugur GUVEN.
Droplet evaporation Liquid fuel combustion
General form of conservation equations
A.S.T.C #516, Yonsei University
OMA rational and results
ME 475/675 Introduction to Combustion
Distributed Flow Routing
Advection – Diffusion Equation
What is the future of applied mathematics? Chris Budd.
Computational Fluid Dynamics - Fall 2001
Experiments on Strained Premixed Flames in the Distributed Reaction Regime Alessandro Gomez, Department of Mechanical Engineering, Yale University, USA.
Anthony D. Fick & Dr. Ali Borhan Governing Equations
COMBUSTION ENGINEERING
The structure and evolution of stars
Presentation transcript:

ASCI/Alliances Center for Astrophysical Thermonuclear Flashes An Interface Propagation Model for Reaction-Diffusion Advection Adam Oberman An Interface Propagation Model for Reaction-Diffusion Advection Adam Oberman Discussion of current work Existing models represent flames as infinitely thin interfaces, which propagate according to a kinematical rule. The interface propagates in the normal direction according to Huygen’s Principle, (Huygen’s principle) which is often solved numerically as a partial differential equation, the G (geometric) equation (G-equation) Our current work is a generalization of the G-equation, which takes into account a small but finite flame thickness. (G-equation) where K is the mean curvature of the front, and D is the diffusivity of the progress variable. The new equation is derived as an asymptotic limit of the reaction diffusion equation, It reduces to the G-equation in the limit of infinitely thin flames. The derivation indicates that the model will break down when the scales of variation of the velocity field are on the order of the flame thickness. This is reasonable, since then the thin flame assumption breaks down, and we are in a different regime. Numerical tests were performed which compared the three models. The results show that the model is a significant improvement: in the limit of thin flames, the models agree, when the flame thickness approached the small scales of the velocity field, the G-equation breaks down, but the G-K equation agrees for a wide range parameters with the full reaction diffusion equations. Discussion of current work Existing models represent flames as infinitely thin interfaces, which propagate according to a kinematical rule. The interface propagates in the normal direction according to Huygen’s Principle, (Huygen’s principle) which is often solved numerically as a partial differential equation, the G (geometric) equation (G-equation) Our current work is a generalization of the G-equation, which takes into account a small but finite flame thickness. (G-equation) where K is the mean curvature of the front, and D is the diffusivity of the progress variable. The new equation is derived as an asymptotic limit of the reaction diffusion equation, It reduces to the G-equation in the limit of infinitely thin flames. The derivation indicates that the model will break down when the scales of variation of the velocity field are on the order of the flame thickness. This is reasonable, since then the thin flame assumption breaks down, and we are in a different regime. Numerical tests were performed which compared the three models. The results show that the model is a significant improvement: in the limit of thin flames, the models agree, when the flame thickness approached the small scales of the velocity field, the G-equation breaks down, but the G-K equation agrees for a wide range parameters with the full reaction diffusion equations. Introduction The problem of turbulent combustion is a big challenge. Flame chemistry, fluid turbulence, and the interaction of flames and the turbulence are all very hard problems, both scientifically and computationally. At any level, model simplifications must be made. The approach we have taken in previous work is to simplify the chemistry as much as possible: we represent the flame by a reacting and diffusing progress variable, which is advected by the fluid. We assume that we are given a proscribed velocity field, and make it our mission to determine important flame features (flame velocity, mass consumption) from the dynamics of the system. Previous work has been successful in determining analytically flame speeds as a function of fluid properties in the case of structurally simple, non-turbulent velocity fields. Even at this level of simplification, the wide range of spatial and temporal scales make it too costly to resolve the rich inner structure of the flame in a fully turbulent velocity field. Introduction The problem of turbulent combustion is a big challenge. Flame chemistry, fluid turbulence, and the interaction of flames and the turbulence are all very hard problems, both scientifically and computationally. At any level, model simplifications must be made. The approach we have taken in previous work is to simplify the chemistry as much as possible: we represent the flame by a reacting and diffusing progress variable, which is advected by the fluid. We assume that we are given a proscribed velocity field, and make it our mission to determine important flame features (flame velocity, mass consumption) from the dynamics of the system. Previous work has been successful in determining analytically flame speeds as a function of fluid properties in the case of structurally simple, non-turbulent velocity fields. Even at this level of simplification, the wide range of spatial and temporal scales make it too costly to resolve the rich inner structure of the flame in a fully turbulent velocity field. Discussion of Future Work A closer look at the relevance of spatial scales and diffusion is underway. Joint work with Alan Kerstein has produced a phase diagram which classifies burning regimes and classical predictions of flame speeds according to spatial scales and the Schmidt number. One dimensional turbulence models have been used as numerical validation of the results. A future project is to examine flame-fluid coupling. There are local effects are a result of vorticity production due to density differences across a flame front. Global coupling arises in the presence of gravity through buoyancy effects. Discussion of Future Work A closer look at the relevance of spatial scales and diffusion is underway. Joint work with Alan Kerstein has produced a phase diagram which classifies burning regimes and classical predictions of flame speeds according to spatial scales and the Schmidt number. One dimensional turbulence models have been used as numerical validation of the results. A future project is to examine flame-fluid coupling. There are local effects are a result of vorticity production due to density differences across a flame front. Global coupling arises in the presence of gravity through buoyancy effects.