PPT-MODELLING, CONTROL

AND OPTIMISATION OF A DUAL CIRCUIT INDUCED DRAFT COOLING WATER SYSTEM February 2016 CJ Muller Sasol University of Pretoria Under supervision of Prof IK Craig University . 11 RECENT ADVANCES IN MODELLING FLOOD WATER LEVELS AT BRIDGES AND CULVERTS ROBERT LAMB PETER MANTZ SERTER ATABAY JEREMY BEN N JBA Consulting ANDREW PEPPER ATPEC Key Words Afflux bridges culverts Abstract Afflux Editor. Renji. . Remesan. School Of . Energy, Environment and . Agrifood. Cranfield. University. Bedfordshire. , UK. Biography. Renji. holds a basic degree in Agriculture Engineering from Kerala Agricultural University, India. . Pipeline . Qra. Seminar. Pipeline risk assessment . Software for consequence modelling. 2. What is consequence modelling?. Effects from accident (e.g. release or explosion). Strict physical effect (e.g. overpressure from explosion, flow from release and concentration at distance). Modelling for Engineering Processes. Peter Hale UWE. University of the West of England, Bristol. Abstract. Problem. -. Enable translation of human problems/representation to computer models and code.. Name: _______________________________. TG: _________. Class: ____________________. When you see the POP symbol it means you have a chance to get to the next NC level and the teacher will be checking your work for progress.. John Hughes. Software. Software = Programs + Data. Modelling Data. A big part of designing software is . modelling the data. in an appropriate way. Numbers are not good for this!. We model the data by defining . Dr Andy . Evans. I. ndividual level.. A. gents interact with environment and each other.. M. odel behaviour and decision making.. Agent-Based Models. Typical social science model:. Agents are burglars.. 3.5. Demonstrate understanding of how technological modelling supports technological development. Aims for this session. To share key messages for technological modelling level 3. T. o develop understanding of how . Predictions in ecology rely on models. . What is a model?. Matrix algebra. Linear regression models. More on regression. Variance analysis. Model selection techniques. Classification techniques. Eigenvector techniques. Tonight's agenda . Our focus is always somewhere else. A Secure Development Lifecycle?. Threat Modelling. Taking it in your STRIDE. How . to get everyone involved. How to win at Poker. Q & A. Fin. Dr Linda Bird. 2. nd. – 4. th. December 2012. Meeting Goals. Finalise draft CIMI Laboratory Results Report . mindmaps. Update CIMI Laboratory Results Report . ADL 1.5. Drafts prepared by Tom & Ian. Dr Linda Bird. 26. th. June 2013. Agenda. Background. CIMI . Modelling. Approach. CIMI . Modelling. . Foundations. CIMI . Modelling. Methodology. Future Work. Tomorrow. :. Terminology Binding. background. Barotse. floodplain, Zambia. . Tom Willis. 1. , Mark Smith. 1. , Donall Cross. 2. , Andrew Hardy. 3. , Georgina Ettritch. 3. , Happiness Malawo. 4. , . Mweemba. Sinkombo. 4. , Cosmas Chalo. 4. , Elizabeth Mroz. Issy . Codron. , University of Exeter. Stefan Kraus, Tyler Gardner, . Sorabh. Chhabra, Daniel Mortimer, Owain Snaith, Yi Lu. John Monnier, Antoine . Mérand. , and MIRC-X/MYSTIC Team. Disc Misalignments & Modelling the Inner AU of HD 143006.