Multidisciplinārā analīze un optimizācija(English)(1),23/24-P
Engineering systems modelling for design and optimization. Selection of design variables,
objective functions and constraints. Overview of principles, methods and tools in
multidisciplinary design optimization (MDO) for systems. Subsystem identification,
development and interface design. Review of linear and non-linear constrained optimization
formulations. Scalar versus vector optimization problems from systems engineering and
architecting of complex systems. Heuristic search methods: Tabu search, simulated annealing,
genetic algorithms. Sensitivity, tradeoff analysis and isoperformance. Multiobjective
optimization and Pareto optimality. System design for value. Specific applications from
aerospace, mechanical, civil engineering and system architecture.
Purpose and Target Audience (Who should take this course and why?)
This course is offered for doctoral students who are interested in the multidisciplinary design
aspects of complex systems. These aspects appear frequently during the conceptual and
preliminary design phases of complex new systems and products, where technical disciplines
(structures, propulsion, aerodynamics, controls, optics etc…) and non-technical disciplines
(lifecycle costing, environmental impact analysis, marketing, etc…) have to be tightly coupled in
order to arrive at a competitive solution. During the product development process both
quantitative and qualitative effort streams are present, where qualitative work gives rise to
quantitative questions and vice-versa.
The purpose of the course is to present tools and methodologies for performing system
optimization in a multidisciplinary design context. Three main aspects of the problem are: (i) the multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. The course content will be applicable to the design of a broad range of systems including space systems, aircraft and transportation systems as well as the energy, civil architecture and telecommunications sectors, among others. This subject is designed to be fundamentally different from a traditional university study course on optimization.
objective functions and constraints. Overview of principles, methods and tools in
multidisciplinary design optimization (MDO) for systems. Subsystem identification,
development and interface design. Review of linear and non-linear constrained optimization
formulations. Scalar versus vector optimization problems from systems engineering and
architecting of complex systems. Heuristic search methods: Tabu search, simulated annealing,
genetic algorithms. Sensitivity, tradeoff analysis and isoperformance. Multiobjective
optimization and Pareto optimality. System design for value. Specific applications from
aerospace, mechanical, civil engineering and system architecture.
Purpose and Target Audience (Who should take this course and why?)
This course is offered for doctoral students who are interested in the multidisciplinary design
aspects of complex systems. These aspects appear frequently during the conceptual and
preliminary design phases of complex new systems and products, where technical disciplines
(structures, propulsion, aerodynamics, controls, optics etc…) and non-technical disciplines
(lifecycle costing, environmental impact analysis, marketing, etc…) have to be tightly coupled in
order to arrive at a competitive solution. During the product development process both
quantitative and qualitative effort streams are present, where qualitative work gives rise to
quantitative questions and vice-versa.
The purpose of the course is to present tools and methodologies for performing system
optimization in a multidisciplinary design context. Three main aspects of the problem are: (i) the multidisciplinary character of engineering systems, (ii) design of these complex systems, and (iii) tools for optimization. The course content will be applicable to the design of a broad range of systems including space systems, aircraft and transportation systems as well as the energy, civil architecture and telecommunications sectors, among others. This subject is designed to be fundamentally different from a traditional university study course on optimization.