Recommended prerequisite for participation in the module

The module is based on knowledge achieved in Probability Theory, Stochastic Processes and Applied Statistics on 1st semester of Master of Science study programme in Energy Engineering, Master of Science study programme in Sustainable Energy Engineering, or similar.

Content, progress and pedagogy of the module

Learning objectives

Knowledge

• Have comprehension of the fundamental concepts, terms and methods used within optimisation
• Have comprehension of the fundamental concepts, terms and typical methods used within numerical optimisation of linear and nonlinear optimisation problems
• Have gained an in‐depth understanding of important concepts and methods of optimisation for efficient solution of optimisation problems within different areas of engineering
• Have comprehension of how to apply reliability and robust design approach during product development
• Understand statistics that support robustness and reliability
• Have knowledge about cost of poor quality in a product lifetime
• Be able to establish mission profile for different applications and use it into the useful reliability context
• Understand difference between preventive scheduled maintenance or by degradation
• Have comprehension of stressor components like temperature, humidity, vibration and their impact
• Be able to model and determine life-time of components
• Understand physics of failure approach and also failure mechanism – both in normal operations and beyond
• Have knowledge about qualitative and quantitative test methods for reliability assessment
• Have knowledge about prognostic methods and real‐time monitoring in power electronic systems

Skills

• Be able to use optimisation concepts and topics
• Be able to use numerical methods of unconstrained optimisation
• Be able to use numerical (mathematical programming) methods for optimisation of multidimensional functions with constraints
• Be able to solve multiobjective optimisation problems
• Be able understand how designs fit into the robustness validation concept
• Be able to set up simple methods for reliability targets and field analysis
• Be able to set up lifetime requirement at function level or component level
• Have knowledge of how to use test methods for reliability and robustness assessment

Competences

• Be able to account for the considerations involved in the process of formulating and solving engineering optimisation problems, choosing an advantageous method of solution and implementing it in practice.
• Be able to build a system reliability model
• Set up design limits in respect to reliability
• Be able to specify test procedures for new product development

Type of instruction

The form(s) of teaching will be determined and described in connection with the planning of the semester. The description will account for the form(s) of teaching and maybe accompanied by an elaboration of the roles of the participants. The programme is based on a combination of academic, problem oriented and interdisciplinary approaches and organised based on the following types of instruction that combine skills and reflection:

• lectures
• project work
• workshops
• exercises (individually and in groups)
• e-learning in different ways such as flipped class-room, blended learning, game or quiz, etc.
• teacher feedback
• reflection
• portfolio work
• study circle
• self-study

Extent and expected workload

Since it is a 5 ECTS course module, the work load is expected to be 150 hours for the student.

Exam

Exams

Name of exam	Optimisation Theory and Reliability
Type of exam	Written or oral exam
ECTS	5
Assessment	7-point grading scale
Type of grading	Internal examination
Criteria of assessment	The criteria of assessment are stated in the Examination Policies and Procedures