System Design Engineering

Computer Science and ICT, Data, AI

About this course

Course’s Objectives:

• Effectively understand and evaluate the significant aspects of System Engineering

• Understand and apply the Holistic System thinking approach

• Understand and Apply

• 1 System Engineering and Analysis Concepts

• 2 System Engineering and Development Practices

• Learn how problem-solving can be improved by

• Utilizing thinking algorithms

• Creativity methods

• Problem-solving heuristics

• Evaluate the problem-solving stage by identifying and understanding the problem as the key towards a creative solution

• Learn how to model complex systems using Systems Design and Engineering Patterns

• Apply analytical decision support practices

• System Modeling and Simulation System Modeling and Simulation

• System Performance Analysis, Budgets, and Safety Margins

• TOOLS for System Engineering

• Model Driven System Engineering

Course Structure: Peoples: Course Coordinator – dr. ir. Ion Barosan Teacher Assistants: Three students are available to provide assistance and act as the initial point of contact for help with the final project and course activities.

• Teaching Method:

Hybrid sessions: Lectures and Hands-On

Final Project: working in groups of 3 students

Final Exam: Feedback: Every week, students are required to individually upload a One Minute Paper (OMP) on Canvas. Every OMP is graded 0.05 points. Final Grade (FG): has the following components:

FG = 56% EG + 30% PG + 10% IG + 4%OMP; EG – the written exam grade, PG – the final project grade, IG – the individual grade, Peer Review Project, OMP – one minute paper

• Written exam for TUe students

• On-line exam for international students – Proctoring format

Expected learning outcomes

Learning Objectives: Apply the concepts of Model-Driven System Engineering Implement, execute and check complex SysML Models representing systems and artifacts using an MBSE methodology:

• Implement and create viewpoints for describing the architecture of the system

• Generate and organize requirements models of system architecture

• Specify and build the structure of system architecture

• Define and produce the behavior of system architecture

• Execute and test the executable system architecture

• Document, Publish, Test, Validate the models using SysML tools.

• Integrate different engineering domains using an MDSE approach and modeling the interfaces between them

• Apply thinking algorithms, as a central tool for generating innovations in many aspects of system engineering using model-driven approach:

• Understand and apply the methods used for solving engineering contradictions and improve the decision-making process in engineering activities

• Define the levels of innovation and explain their importance.

• Recognize and overcome psychological inertia using creative thinking.