The course will introduce the basic concepts of computation through modeling and simulation that are increasingly being used by architects, planners, and engineers to shorten design cycles, innovate new products, and evaluate designs and simulate the impacts of alternative approaches. Students will use MATLAB to explore a range of programming and modeling concepts while acquiring those skills. They will then undertake a final project that analyzes one of a variety of scientific problems by designing a representative model, implementing the model, completing a verification and validation process of the model, reporting on the model in oral and written form, and changing the model to reflect corrections, improvements and enhancements.
COURSE LEARNING OUTCOMES (CLO)
CLO: 1. Explain the model classification at different leve
CLO: 2. Analyse complex engineering systems and associated issues (using systems thinking and modelling techniques)
CLO: 3. Apply advanced theory-based understanding of engineering fundamentals and specialist bodies of knowledge in the selected discipline area to predict the effect of engineering activities.
CLO: 4. Analyse the simulation results of a medium sized engineering problem
• Introduction to system, modelling, simulation, and simulation models levels
• Classification of systems, & System theory basics, its relation to simulation
• Model classification at conceptual, abstract
• Methodology of model building & Simulation systems and languages,
• Introduction to MATLAB & SIMULINK, & Means for model and experiment description,
• Principles of simulation system design,
• Parallel process modeling using Petri nets and finite automata in simulation
• Models of queuing systems, & Discrete simulation models,
• Model time, & Simulation experiment control,
• Overview of numerical methods used for continuous simulation.
• System Dymola/ Modelica, Combined simulation
• Special model classes, & Models of heterogeneous systems,
• Cellular automata and simulation
• Checking model validity & Verification of models
• Analysis of simulation results, simulation results visualization, model optimization, generating, transformation, and testing of pseudorandom numbers with overview of commonly used simulation systems