Search result: Catalogue data in Autumn Semester 2016
Mechanical Engineering Master | ||||||
Core Courses | ||||||
Design, Computation, Product Development & Manufacturing | ||||||
Number | Title | Type | ECTS | Hours | Lecturers | |
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151-0104-00L | Uncertainty Quantification for Engineering & Life Sciences Does not take place this semester. Number of participants limited to 60. | W | 4 credits | 3G | P. Koumoutsakos | |
Abstract | Quantification 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. | |||||
Objective | The 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. | |||||
Content | Topics 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 notes | The 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. | |||||
Literature | 1. Data Analysis: A Bayesian Tutorial by Devinderjit Sivia 2. Probability Theory: The Logic of Science by E. T. Jaynes 3. Class Notes | |||||
Prerequisites / Notice | Fundamentals of Probability, Fundamentals of Computational Modeling | |||||
151-0735-00L | Dynamic Behavior of Materials and Structures Does not take place this semester. | W | 4 credits | 2V + 2U | D. Mohr | |
Abstract | Lectures 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. | |||||
Objective | Students 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. | |||||
Content | Topics 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 notes | Slides of the lectures, relevant journal papers and users manuals will be provided. | |||||
Literature | Various books will be recommended covering the topics discussed in class | |||||
Prerequisites / Notice | Course in continuum mechanics (mandatory), finite element method (recommended) | |||||
151-3205-00L | Experimental Ergonomics Number of participants limited to 15. | W | 4 credits | 2V + 2A | J. Held | |
Abstract | You 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. | |||||
Objective | Knowledge 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 notes | Handout at the start of the course. | |||||
Literature | Ahlstrom, 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 / Notice | Max. number of participants is 15. Experiments and field studies in teams of 2-3 students are obligatory. | |||||
151-3209-00L | Engineering Design Optimization Number of participants limited to 35. | W | 4 credits | 4G | K. Shea, T. Stankovic | |
Abstract | The 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. | |||||
Objective | The 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. | |||||
Content | 1. 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 notes | available on Moodle | |||||
363-1065-00L | Design Thinking: Human-Centred Solutions to Real World Challenges 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. | W | 5 credits | 5G | A. Cabello Llamas, F. Rittiner, S. Brusoni, C. Hölscher, M. Meboldt | |
Abstract | The 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 | |||||
Objective | During 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. | |||||
Content | The 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 / Notice | Class 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. |
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