PENG9520 Endelig elementmodellering og simulering av strukturer Emneplan

Pass or fail.

Master's degree in engineering science or related fields.

One examiner. External examiner is used periodically.

Knowledge

On successful completion of the course, the student:

• knows how to formulate decision to be made or questions to be answered so that it can be subject to evidence-based engineering
• knows about relevant sources for collection of evidence
• knows about the most common empirical methods for creating empirical evidence
• knows about theories and models for evaluation of argumentation and empirical evidence
• knows about theories and models for synthesizing evidence, including that of systematic litterature reviews
• knows about theories and models for good judgment and decision-making

Skills

On successful completion of the course, the student can:

• formulate decisions and questions as basis for evidence-based engineering
• collect and evaluate the quality and relevance of evidence
• design empirical studies for the purpose of creating empirical evidence
• aggregate the evaluated evidence so that it can be used to support a decision or answer a question
• design proper processes for judgment and decision-making

General competence

On successful completion of the course, the student can:

• practice evidence-based engineering in work settings
• support and lead experimentation and other means of creating local evidence in work settings
• assess the need for, initiate and drive innovation in the use of evidence-based principles in engineering contexts.

The topics covered in this course aims at giving the students the necessary skills to be evidence-based and includes lectures on:

• The basics of evidence-based engineering
• Empirical methods and their use in engineering disciplines
• How to critically evaluate and give rational argumentations
• How to collect, evaluate and use practice-based experience
• How to collect, evaluate and use research-based evidence
• How to conduct empirical studies
• How to aggregate evidence
• How to design good judgment and decision-making processes

To illustrate the topics and exemplify the use of evidence-based decision processes, selected engineering topics, such as software development methods, cost estimation, project management and workplace organization, will be used as cases. The course contains mandatory deliveries, exercises and student presentations.

The course is divided into three modules.

The first module covers lectures on economic interactions for the energy market, focusing mainly on applications such as demand response management (DRM), and vehicle-to-grid (V2G), etc.

The second module consists of lectures on current and emerging approaches such as machine learning and blockchain for energy intelligence and network security.

The third module will be a seminar which will include a hands-on session on tools such as optimisation and machine learning for solving specific problems in future energy information networks, and will conclude with a project assignment to be submitted by a given deadline.

The following required coursework must be approved before the student can take the exam:

Prior to examination it is required that a compulsory assignment be completed, presented to the other students, and approved.

The exam itself is a report written on a self-selected estimation topic. The structure and content of the report should follow evidence-based principles and require searching for, evaluating and summarising practice and research-based evidence.

The exam can be appealed.

All aids are permitted.

Pass or fail.

Two examiners. External examiner is used periodically.