General Description:

This course introduces sensor/actuator numerical modeling using FEA software and applies established calculation, analysis, and simulation methods to real-world design problems. Students model electrical, magnetic, and structural phenomena; design and optimize sensors/actuators; and practice experimental measurement, coding, and signal interpretation. The emphasis is hands-on: students start from scratch to design, optimize, and prototype devices for practical applications and future research or industry use.

Learning Objectives:

• Understand sensor/actuator elements and measurement tools; recognize and design suitable solutions for instrumentation systems.
• Use simulation software (including parameterized models in COMSOL) and apply optimization & inverse-problem algorithms.
• Interpret the physics behind sensors/actuators for multiphysics modeling.
• Apply foundational signal processing (ADC, CLK, PWM, serial I/O, microcontroller basics) for sensor/actuator design.
• Professional competence (knowledge/skills): plan experiments, analyze data, translate physical phenomena into design parameters, integrate mechatronics/EE/physics, and report results scientifically.
• Personal competence: communicate results, collaborate in teams, and work independently with time management and self-assessment.

Syllabus:

• Prerequisites: Measurement technology; basics in structural mechanics, acoustics, magnetic & electrostatic field physics; general Arduino coding.
• Study program: Master of Mechatronics — Participants: min. 6 / max. 12.

• Intro & sensor/actuator characteristics
• FEM solvers (stationary/time/frequency), solving workflow; dynamic vibration (2D)
• Parametric evaluation (2D); modal analysis of an electro-mechanical transducer (3D)
• Multiphysics modeling of a piezoelectric actuator
• Multiphysics modeling of a magnetostrictive actuator.
• Multiphysics modeling of an IMU MEMS accelerometer.
• Ultrasonic car-parking sensor.
• Build a parameterized FEA application.
• Inverse problems for multiphysics model optimization.

Teaching Format:

• Mixed format: a combination of hands-on practice and project-based learning.

Lecturer:

Dr. Mohammad Sadeghi