Motivation and aims The Belousov-Zhabotinsky system The Java applet References 1.Stochastic Modelling Web Module:

Slides:

Advertisements

Similar presentations

Some Interesting Facts about Mechanical Engineering

Advertisements

CADGME, Pécs, June Piroska B. Kis, Computer aided education of numerical methods 1 College of Dunaújváros

Professor Walter W. Olson Department of Mechanical, Industrial and Manufacturing Engineering University of Toledo Laplace Transforms.

Åbo Akademi University & TUCS, Turku, Finland Ion PETRE Andrzej MIZERA COPASI Complex Pathway Simulator.

AJGregory, JCRead - University of Central Lancashire 1 Using WebCT as a tool for Mathematical Discussion Peggy Gregory Janet Read Stewart Eyres UCLAN,

Markov Nets are systems in which: given an interaction initiated by an agent towards an agent, one can decide in advance the time at which the second agent.

PROCESS INTEGRATED DESIGN WITHIN A MODEL PREDICTIVE CONTROL FRAMEWORK Mario Francisco, Pastora Vega, Omar Pérez University of Salamanca – Spain University.

On Modeling Feedback Congestion Control Mechanism of TCP using Fluid Flow Approximation and Queuing Theory　 Hisamatu Hiroyuki Department of Infomatics.

Modeling of Coupled Non linear Reactor Separator Systems Prof S.Pushpavanam Chemical Engineering Department Indian Institute of Technology Madras Chennai.

1 Using Information Technology to Enhance Control Engineering Education: Some Experiences Aidan O’Dwyer School of Control Systems and Electrical Engineering,

INTEGRATED DESIGN OF WASTEWATER TREATMENT PROCESSES USING MODEL PREDICTIVE CONTROL Mario Francisco, Pastora Vega University of Salamanca – Spain European.

Virtual Workbenches Richard Anthony Dept. Computer Science University of Greenwich Distributed Systems Operating Systems Networking.

The development of lessons, case studies, scenarios and simulations in the Moodle Virtual Learning Environment for self directed learning (SDL) By Michael.

NORM BASED APPROACHES FOR AUTOMATIC TUNING OF MODEL BASED PREDICTIVE CONTROL Pastora Vega, Mario Francisco, Eladio Sanz University of Salamanca – Spain.

Modelling Flow Distributed Oscillations In The CDIMA Reaction Jonathan R Bamforth, Serafim Kalliadasis, John H Merkin, Stephen K Scott School of Chemistry,

Teleoperation and Teleparticipation of Instructional Shake Tables Using the NEES Cyberinfrastructure S.J. Dyke 1, Z. Jiang 2, R. Christenson 2, X. Gao.

__________________ Engineering Education Systems for Environmental Project Management - Example of the Amise Simulation Program François Baillon School.

Lecture 14 Chemical Reaction Engineering (CRE) is the field that studies the rates and mechanisms of chemical reactions and the design of the reactors.

Chemical Reaction Engineering (CRE) is the field that studies the rates and mechanisms of chemical reactions and the design of the reactors in which they.

Miguel Martins, Senior Lecturer – Marketing Department eLearning Celebration 22 July 2007 Improving performance on a Marketing module through the use of.

The Monte Carlo Method: an Introduction Detlev Reiter Research Centre Jülich (FZJ) D Jülich

Opportunities in Quantitative Finance in the Department of Mathematics.

CCLI PI 2008www.tech.uh.edu/rock COLLABORATIVE RESEARCH: REMOTE LABORATORY FOR OPTICAL CIRCUITS COURSES Driss Benhaddou and Deniz Gurkan University of.

Bochum Prag Prof. Dr.-Ing. Chr. Schmid Institute of Automation and Computer Control Department of Electrical Engineering and Information Sciences Ruhr-Universität.

Simulation of Biochemical Reactions for Modeling of Cell DNA Repair Systems Dr. Moustafa Mohamed Salama Laboratory of Radiation Biology, JINR Supervisor.

Lecture 11 Implementation Issues – Part 2. Monte Carlo Simulation An alternative approach to valuing embedded options is simulation Underlying model “simulates”

Dimitrios Konstantas, Evangelos Grigoroudis, Vassilis S. Kouikoglou and Stratos Ioannidis Department of Production Engineering and Management Technical.

FORS 8450 Advanced Forest Planning Lecture 19 Ant Colony Optimization.

Evaluation and analysis of the application of interactive digital resources in a blended-learning methodology for a computer networks subject F.A. Candelas,

Experimenting Across the Atlantic A. Selmer 1, M. Goodson 1, M. Kraft 1, S. Sen 2, F. McNeill 2, B. Johnston 2 and C. Colton 2 1 Chemical Engineering,

1 USING EXPERT SYSTEMS TECHNOLOGY FOR STUDENT EVALUATION IN A WEB BASED EDUCATIONAL SYSTEM Ioannis Hatzilygeroudis, Panagiotis Chountis, Christos Giannoulis.

An innovative learning model for computation in first year mathematics Birgit Loch Department of Mathematics and Computing, USQ Elliot Tonkes CS Energy,

1 Performance Evaluation of Computer Networks: Part II Objectives r Simulation Modeling r Classification of Simulation Modeling r Discrete-Event Simulation.

Directors   Mark H. Holmes, Mathematics Chair   Robert L. Spilker, Biomedical Engineering Chair   Kenneth S. Manning, Links Technical Manager Rensselaer.

ChE 452 Lecture 24 Reactions As Collisions 1. According To Collision Theory 2 (Equation 7.10)

Stochastic lattice models for predator-prey coexistence and host-pathogen competition Uwe C. Täuber, Virginia Tech, DMR Research: The classical.

BsysE595 Lecture Basic modeling approaches for engineering systems – Summary and Review Shulin Chen January 10, 2013.

Brian Renzenbrink Jeff Robble Object Tracking Using the Extended Kalman Particle Filter.

An On-Line Approach to Teaching Report-Writing in Chemical Engineering Tim Langrish Chemical Engineering Helen Drury, Peter O’Carroll Learning Centre.

Michal Biran R&D and Training Center - Ort Israel Web-based Simulations in Science Education Wingate Seminar London, May 2005.

Computational Study of Liquid-Liquid Dispersion in a Rotating Disc Contactor A. Vikhansky and M. Kraft Department of Chemical Engineering, University of.

Modelling Direct Injection Homogeneous Charge Compression Ignition Combustion - A Stochastic Approach A probability density function based stochastic reactor.

1 Project / Action Plan Project / Action Plan “ Application of Information and Communication Technology to Production and Dissemination of Official statistics”

Technology enabled students? Engineering students use of web- based resources Aidan O’ Dwyer, School of Electrical Engineering Systems, Dublin Institute.

Communication Networks (Kommunikationsnetværk) Specialisations: Distributed Application Engineering Network Planning & Management Ole Brun Madsen Professor.

Monte Carlo Methods in Statistical Mechanics Aziz Abdellahi CEDER group Materials Basics Lecture : 08/18/

Tempus CD-JEP Meeting, Belgrade, SCG, Apr , Curriculum Development: Specific undergraduate IT Curriculum at Faculty of Mechanical Engineering,

University of Catania Computer Engineering Department 1 Educational tools for complex topics: a case study for Network Based Control Systems Prof. Orazio.

© The McGraw-Hill Companies, 2005 CAPITAL ACCUMULATION AND GROWTH: THE BASIC SOLOW MODEL Chapter 3 – second lecture Introducing Advanced Macroeconomics:

ACS'08, November, Venice, ITALY Designing organic reaction simulation engine using qualitative reasoning approach Y.C. Alicia Tang Tenaga Nasional.

1 A New Method for Composite System Annualized Reliability Indices Based on Genetic Algorithms Nader Samaan, Student,IEEE Dr. C. Singh, Fellow, IEEE Department.

© 2014 Carl Lund, all rights reserved A First Course on Kinetics and Reaction Engineering Class 12.

ChE 553 Lecture 15 Catalytic Kinetics Continued 1.

Mathematical Modeling of Signal Transduction Pathways Biplab Bose IIT Guwahati.

Surface Ageing of Soot Particles Reactions on the surface of soot are the principal route by which mass is transformed from the gas to solid phase in laminar.

1 Structure of Aalborg University Welcome to Aalborg University.

Figure 3. Log-log plot of simulated oscillating phantom, assuming a Gaussian-shaped field. Field constants a 1 =a 2 =0.1. The data initially plateau, then.

Digital Learning India 2008 July , 2008 Mrs. C. Vijayalakshmi Department of Computer science and Engineering Indian Institute of Technology – IIT.

Simulation and Visualization Enhanced Engineering Education This work is supported by the Division of Engineering Education Department, National Science.

Betim ÇIÇO, South East European University (Republic of Macedonia) Marco University of Pavia (Italy)

TKP2 Heterogen katalyse VK Heterogeneous Catalysis Advanced Course Purpose: Specialization in Heterogeneous Catalysis Goal: Prepare the students to –Learn.

1 Hypermedia learning and prior knowledge: domain expertise vs. system expertise. Timothy J. F. Mitchell, Sherry Y. Chen & Robert D. Macredie. (2005) Hypermedia.

Lecture 40 Numerical Analysis. Chapter 7 Ordinary Differential Equations.

Lecture №4 METHODS OF RESEARCH. Method (Greek. methodos) - way of knowledge, the study of natural phenomena and social life. It is also a set of methods.

1 Tom Edgar’s Contribution to Model Reduction as an introduction to Global Sensitivity Analysis Procedure Accounting for Effect of Available Experimental.

Arthur Straube PATTERNS IN CHAOTICALLY MIXING FLUID FLOWS Department of Physics, University of Potsdam, Germany COLLABORATION: A. Pikovsky, M. Abel URL:

Modelling Complex Systems Video 4: A simple example in a complex way.

Cmpe 588- Modeling of Internet Emergence of Scale-Free Network with Chaotic Units Pulin Gong, Cees van Leeuwen by Oya Ünlü Instructor: Haluk Bingöl.

S5.40. Module Structure 30% practical tests / 70% written exam 3h lectures / week (except reading week) 3 x 2h of computer labs (solving problems practicing.

By Harsh Tiwari.

Presentation transcript:

Motivation and aims The Belousov-Zhabotinsky system The Java applet References 1.Stochastic Modelling Web Module: 2.Web site of the Computational Modelling Group: 3.M. Kraft, S. Mosbach, and W. Wagner. Teaching Stochastic Modeling to Chemical Engineers Using a Web Module, (to appear in Chemical Engineering Education) Conclusions The stochastic algorithm System of ordinary differential equations in dimensionless form: A Web Teaching Module for Stochastic Modelling in Chemical Engineering M. Kraft, S. Mosbach, A. Selmer, Department of Chemical Engineering, University of Cambridge, United Kingdom W. Wagner, Weierstrass Institute for Applied Analysis and Stochastics, Berlin, Germany Acknowledgement The authors gratefully acknowledge financial support from the EPSRC. Phase space evolution Time evolution of species concentrations In order to support the lecture course “Stochastic Modelling in Chemical Engineering” at Cambridge University, an interactive web module simulating chemical reactions has been developed. It aims to enable students without knowledge of a programming language to gain hands-on experience with a Monte Carlo method. Two sets of reactions in a batch reactor are studied, one of which is the Belousov-Zhabotinsky system, which shows oscillating behaviour. Nullclines Output window for results of computations Parameters for simulation control (physical and numerical) Field, Körös, Noyes mechanism: Initialize the number of particles for each species, i.e.,,, and, set the time, and fix the stopping time. Calculate the reaction rate functions, the waiting time parameter, and the reaction probabilities. Advance the current time to. If then stop. Generate an exponentially distributed waiting time, where the decay parameter of the exponential is given by, and a reaction index according to the probabilities. Perform the reaction chosen in the previous step, i.e.: ●If then increase by 1 and decrease by 1. ●If then decrease by 2. ●If then decrease and each by 1. ●If then increase by 1 and by 2. ●If then increase by f/2 and decrease by 1. X-Z-Phase space evolution: Oscillatory case:Non-oscillatory cases: The parameters ε, ε’, and q, which are functions of the rate constants and A and B, determine the qualitative dynamical behaviour of the system. Species of interest: X, Y, Z Abundant supply of species A and B Product: P Model parameter: f f=0.25 f=3 f=1 Nullclines are curves in phase space for steady state of species. 1.Introduction explaining the subject and the aims of the module 2.Theoretical background on the considered systems, local stability analysis, and dynamical systems in general 3.Direct Simulation Monte Carlo Algorithms 4.Numerical experiments involving a Java applet, and a set of problems 5.Videos of actual experiments 6.A questionnaire to provide feedback 7.A short survey of web-based teaching 8.References and suggested reading We regard this as a first step towards web-based teaching: We plan to increase the number of web modules. A virtual laboratory has been set up within the Cambridge-MIT Institute (CMI) The web module was well received by students: Most students enjoyed completing the module and the hands-on experience. The aspect of modelling real reactions as seen in an experiment was appreciated. Students welcomed the fact that they could concentrate on the material rather than technicalities of computer programming. However, there were complaints about the wealth of material. Structure of the web module © G. Dupuis, N. Berland, Lycée Faidherbe Lille After having completed the web module, students have to hand in an essay for marking, including answers to the set of problem given on the web pages. Rate constants:,,,, 1.To provide a numerical tool based on a Monte Carlo method simulating chemical reactions 2.To study a chemical system analytically using linear stability analysis 3.To present an example for oscillating reactions and chemical feedback