Search result: Catalogue data in Autumn Semester 2016

Mechanical Engineering Master Information
Core Courses
Design, Computation, Product Development & Manufacturing
NumberTitleTypeECTSHoursLecturers
151-0104-00LUncertainty Quantification for Engineering & Life Sciences Restricted registration - show details
Does not take place this semester.
Number of participants limited to 60.
W4 credits3GP. Koumoutsakos
AbstractQuantification of uncertainties in computational models pertaining to applications in engineering and life sciences. Exploitation of massively available data to develop computational models with quantifiable predictive capabilities. Applications of Uncertainty Quantification and Propagation to problems in mechanics, control, systems and cell biology.
ObjectiveThe course will teach fundamental concept of Uncertainty Quantification and Propagation (UQ+P) for computational models of systems in Engineering and Life Sciences. Emphasis will be placed on practical and computational aspects of UQ+P including the implementation of relevant algorithms in multicore architectures.
ContentTopics that will be covered include: Uncertainty quantification under
parametric and non-parametric modelling uncertainty, Bayesian inference with model class assessment, Markov Chain Monte Carlo simulation, prior and posterior reliability analysis.
Lecture notesThe class will be largely based on the book: Data Analysis: A Bayesian Tutorial by Devinderjit Sivia as well as on class notes and related literature that will be distributed in class.
Literature1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia
2. Probability Theory: The Logic of Science by E. T. Jaynes
3. Class Notes
Prerequisites / NoticeFundamentals of Probability, Fundamentals of Computational Modeling
151-0735-00LDynamic Behavior of Materials and Structures
Does not take place this semester.
W4 credits2V + 2UD. Mohr
AbstractLectures and computer labs concerned with the modeling of the deformation response and failure of engineering materials (metals, polymers and composites) subject to extreme loadings during manufacturing, crash, impact and blast events.
ObjectiveStudents will learn to apply, understand and develop computational models of a large spectrum of engineering materials to predict their dynamic deformation response and failure in finite element simulations. Students will become familiar with important dynamic testing techniques to identify material model parameters from experiments. The ultimate goal is to provide the students with the knowledge and skills required to engineer modern multi-material solutions for high performance structures in automotive, aerospace and navel engineering.
ContentTopics include viscoelasticity, temperature and rate dependent plasticity, dynamic brittle and ductile fracture; impulse transfer, impact and wave propagation in solids; computational aspects of material model implementation into hydrocodes; simulation of dynamic failure of structures;
Lecture notesSlides of the lectures, relevant journal papers and users manuals will be provided.
LiteratureVarious books will be recommended covering the topics discussed in class
Prerequisites / NoticeCourse in continuum mechanics (mandatory), finite element method (recommended)
151-3205-00LExperimental Ergonomics Restricted registration - show details
Number of participants limited to 15.
W4 credits2V + 2AJ. Held
AbstractYou will learn how to apply the scientific discipline of ergonomics for system analysis and product development "in order to optimise human well-being and overall system performance" (Link). The course offers the framework of models, concepts, methods and tools of applied ergonomics. Teaching is combined with learning-by-doing and research-based learning.
ObjectiveKnowledge of:
- Principles and rules of applied ergonomic system and product design.
- Methods and tools of ergonomic analysis and evaluation.
Practical experiences and hands-on skills in:
- Conducting a study in system and task analysis.
- Analysing human-product interactions.
- Applying ergonomic knowledge for product and system improvements.
Content- Definition and role of applied ergonomics in engineering and design.
- Framework of ergonomic analysis and design.
- Design principles and rules.
- Methods and tools for system and task analysis.
Hands-on experience in team work:
- Experimental study of human-product interaction and usability through eye-tracking
- Field study of system and task analysis, including on-site visits of complex work stations (Hospital OR/ICU or Air traffic/Railway Control Rooms).
Lecture notesHandout at the start of the course.
LiteratureAhlstrom, V. and Longo, V. (2003). Human Factors Design Standard (HFDS). Link
Wiklund M.E., Wilcox, S.B. (2005). Designing Usability into Medical Products. Taylor & Francis.
Rubin, J. and Chisnell, D. (2008). Handbook of Usability Testing: How to Plan, Design and Conduct Effective Tests. Wiley.
Hölscher, U., Laurig, W. & Müller-Arnecke, H.W. (2008). Prinziplösungen zur ergonomischen Gestaltung von Medizingeräten. BAUA Forschung Projekt F1902.
Link
Niku, S.B. (2009). Creative Design of Products and Systems (Chapter 8). Wiley.
Prerequisites / NoticeMax. number of participants is 15.
Experiments and field studies in teams of 2-3 students are obligatory.
151-3209-00LEngineering Design Optimization Restricted registration - show details
Number of participants limited to 35.
W4 credits4GK. Shea, T. Stankovic
AbstractThe course covers fundamentals of computational optimization methods in the context of engineering design. It develops skills to formally state and model engineering design tasks as optimization problems and select appropriate methods to solve them.
ObjectiveThe lecture and exercises teach the fundamentals of optimization methods in the context of engineering design. After taking the course students will be able to express engineering design problems as formal optimization problems. Students will also be able to select and apply a suitable optimization method given the nature of the optimization model. They will understand the links between optimization and engineering design in order to design more efficient and performance optimized technical products. The exercises are MATLAB based.
Content1. Optimization modeling and theory 2. Unconstrained optimization methods 2. Constrained optimization methods - linear and non-linear 4. Direct search methods 5. Stochastic and evolutionary search methods 6. Multi-objective optimization
Lecture notesavailable on Moodle
363-1065-00LDesign Thinking: Human-Centred Solutions to Real World Challenges Restricted registration - show details
Due to didactic reasons, the number of participants is limited to 30.

All interested students are invited to apply for this course by sending a one-page motivation letter until 14.9.16 to Florian Rittiner (Link).

Additionally please enroll via mystudies. Places will be assigned after the first lecture on the basis of your motivation letter and commitment for the class.
W5 credits5GA. Cabello Llamas, F. Rittiner, S. Brusoni, C. Hölscher, M. Meboldt
AbstractThe goal of this course is to engage students in a multidisciplinary collaboration to tackle real world problems. Following a design thinking approach, students will work in teams to solve a set of design challenges that are organized as a one-week, a three-week, and a final six-week project in collaboration with an external project partner.

Information and application: Link
ObjectiveDuring the course, students will learn about different design thinking methods and tools. This will enable them to:
- Generate deep insights through the systematic observation and interaction of key stakeholders.
- Engage in collaborative ideation with a multidisciplinary (student) team.
- Rapidly prototype and iteratively test ideas and concepts by using various materials and techniques.
ContentThe purpose of this course is to equip the students with methods and tools to tackle a broad range of problems. Following a Design Thinking approach, the students will learn how to observe and interact with key stakeholders in order to develop an in-depth understanding of what is truly important and emotionally meaningful to the people at the center of a problem. Based on these insights, the students ideate on possible solutions and immediately validated them through quick iterations of prototyping and testing using different tools and materials. The students will work in multidisciplinary teams on a set of challenges that are organized as a one-week, a three-week, and a final six-week project with an external project partner. In this course, the students will learn about the different Design Thinking methods and tools that are needed to generate deep insights, to engage in collaborative ideation, rapid prototyping and iterative testing.

Design Thinking is a deeply human process that taps into the creative abilities we all have, but that get often overlooked by more conventional problem solving practices. It relies on our ability to be intuitive, to recognize patterns, to construct ideas that are emotionally meaningful as well as functional, and to express ourselves through means beyond words or symbols. Design Thinking provides an integrated way by incorporating tools, processes and techniques from design, engineering, the humanities and social sciences to identify, define and address diverse challenges. This integration leads to a highly productive collaboration between different disciplines.

For more information and the application visit: Link
Prerequisites / NoticeClass attendance and active participation is crucial as much of the learning occurs through the work in teams during class. Therefore, attendance is obligatory for every session. Please also note that the group work outside class is an essential element of this course, so that students must expect an above-average workload.
  •  Page  1  of  1