Course - Digital Twin Technology - IP500520
IP500520 - Digital Twin Technology
Examination arrangement: Assignment
Grade: Letter grades
|Evaluation||Weighting||Duration||Grade deviation||Examination aids|
The course will introduce the students to Structural Health Monitoring (SHM) supported by Digital Twins (DTs). The basic idea with DTs is to minimize the use of physical sensors on a real structure (asset). The concept is to create a simulation model (analytical, FE or big data model) that replicates the physical asset. The DT is then instrumented with virtual sensors (robust and free of charge) that provide additional information used in predictive maintenance and decision support. The DT can either be implemented as a (fast) local edge, a (slow) IoT cloud solution, or as a combination of both for maximum flexibility. After finishing the course, the students shall be able to develop and implement a Digital Twin (DT) for SHM of a Hardrocx bicycle frame during hard riding (student competition).
Course topics are:
- INTRODUCTION to Structural Health Monitoring by Digital Twins
- Why DT Modelling and simulation?
- A review of various DT models (static versus dynamic)
- The FMU/FMI concept (how to integrate solvers and DT models)
- How to implement a SHM solution with DTs (Python and low-code tools.)
- Common structural failure modes (fatigue, buckling, yield etc)
- How to monitor assets like cranes, bridges, windmills, machinery and vehicles
- A review of physical sensors used in structural monitoring
- How and why eliminate noise and drifting of sensor outputs
- Low-pass, high pass filtering and FFT analysis
- Python or Modellica programming
- Why and how to turn data > information > knowledge>actions (decision support)
- Machine learning versus analytics
- Inverse methods for load identification based on physical sensor outputs
- Why and when implement an EDGE solution
- Hardware solutions (Rasperry Pie, Arduino, Nordic, Intel Nuc etc)
- Why and when implement a CLOUD (IoT) solution
- A presentation of various IoT systems (MindSphere, SAP, Tellu and Jotne IoT)
- IoT communication (REST, MQTT)
Knowledge: The student will learn the main principles of Digital Twin modeling and simulation for Structural Health Modeling. This includes knowledge about sensors, data filtering, inverse methods for load identification and data processing for decision support. The student will get basic knowledge about edge and IoT solutions as well as DT modeling and simulation tools.
The student shall learn about current pitfalls due to sensor noise and drifting as well as limitations in DT modeling using inappropriate boundary conditions, incorrect loads, and simulation settings. The student shall be able to identify the best candidates for SHM.
Skills: The student shall be able to setup and run DTs and simple IoT solutions for SHM. Important skills are python programming, instrumentation, load identification, real time process modeling, simulation and visualization. The students will also be trained in low-code app (Mendix) and IoT dashboard development (MindSphere).
General Competence: The student shall be able to master multidiscipline modeling, instrumentation, data filtering, and simulation of Digital Twins. The student will have a general competence on various IoT software solutions for structural health monitoring and visualization.
Learning methods and activities
Lectures, videos, exercises and examples from real applications will be used. There will be individual mandatory multiple-choice assignments and one project. The project will be related to structural health monitoring of a mountain bike from Hardrocx or the Palfinger offshore crane on Gunnerus.
Assignments, project work and field exercises (Hardrocx bike competition).
Tj @ YouTube is my digital twin (teaching assistant)
- Mandatory assignments
Further on evaluation
Evaluation is done according to NTNU guidelines, with feedback from the class and meetings with the reference group. A course survey will be prepared for all students on Blackboard. A final evaluation report is done at the end of the course.
Recommended previous knowledge
The students should have some basic knowledge of mechatronics, programming, finite element analysis and physics.
Required previous knowledge
Suggested teaching material and project:
- Hand-out slides
- Papers on Structural Health Monitoring
- Training videos on YouTube
- Python software templates and Mendix tutorials (app and process design)
- Hardrocx bikes used in Structural health Monitoring (project)
Credits: 7.5 SP
Study level: Second degree level
Term no.: 1
Teaching semester: AUTUMN 2023
Language of instruction: English
- Marine Technology
Department with academic responsibility
Department of Ocean Operations and Civil Engineering
Examination arrangement: Assignment
- Term Status code Evaluation Weighting Examination aids Date Time Examination system Room *
Room Building Number of candidates
- * The location (room) for a written examination is published 3 days before examination date. If more than one room is listed, you will find your room at Studentweb.
For more information regarding registration for examination and examination procedures, see "Innsida - Exams"