Optimisation 1 (Computer Aided Engineering)
Dr Mazdak Ghajari
+44 (0)20 7594 9236
Learning OutcomesOn successful completion of the module, students should be able to:
- Discuss system design and engineering optimisation problems in appropriate vocabulary;
- Formulate problems in systems engineering as requirements for the mathematical optimisation of the key performance parameters;
- Use tools such as Excel and Matlab to solve convex optimisation problems;
- Implement mathematical optimisation tools in engineering design.
Description of ContentIntroduction to System Design: Introduction, review of design, architecting, systems thinking, system life cycle models, functional allocation.
Systems Design and Optimisation I — Concepts: Optimisation formulations. Optimisation and QFD, Pareto optimality. Computer software introduction.
Basic Numerical Analysis: System modelling. State space models. Classification. Linear systems. Numerical integration. Numerical algorithms. Linear programming. Constrained and unconstrained problems. MatLab and Excel tools.
Problem Formulation: Optimality concepts. Convexity. Constrained problems. Examples: power systems planning, robotics, control systems, systems biology, signal processing.
Differential Theory and Bounded Optima: Local approximation, convergence, gradient methods, Newton optimisation, Lagrange multipliers.
Numerical Solutions: Implementation of algorithms. Convergence. Programming. Cost function.
System Design Optimisation II — Implementation: Practical modelling and implementation examples. Design optimisation and product development.