How do populations grow? How do viruses spread? What is the trajectory of a glider?

Many real-life problems can be described and solved by mathematical models. In this course, you will form a team with another student and work in a project to solve a real-life problem.

You will learn to analyze your chosen problem, formulate it as a mathematical model (containing ordinary differential equations), solve the equations in the model, and validate your results. You will learn how to implement Euler’s method in a Python program.

If needed, you can refine or improve your model, based on your first results. Finally, you will learn how to report your findings in a scientific way.

This course is mainly aimed at Bachelor students from Mathematics, Engineering and Science disciplines. However it will suit anyone who would like to learn how mathematical modeling can solve real-world problems.

This course is an introduction to measurement technology and describes the theoretical foundations and practical examples of measurement systems. The analyzing of measurements problems and specifying of measurements systems are the main subjects that are treated in this course, where the main focus will be on the different kind of measurement errors and the concept of uncertainty in measurement results. Electronic measurement instrumentation will be introduced; a number of conventional sensors for the measurement of non-electronic variables will be described, as well as electronic circuits for the reading of the sensors.-Analyzing of measurement problems-Describing of measurement problems -Analyzing the measurement quantity-Analyzing the measurement boundaries for a quantity to be measured in different circumstances-Professional use of the measurement system-Describing the operating principle of conventional instruments for electronic measurements.-Comparing the available measurement instruments on the basis of quality and accuracy.-Realization of simple measurement setups.-Using the electronic sensor for the measurement of non-electronic variables.-Using a simple signal processing circuits for the reading of the sensors.-Analyzing, presenting and interpreting of measurement results;-Recognizing and describing of error sources.

This course, Measurements for Water is in Dutch, but the following parts are in English:Lectures: Waterbalans Water balance)ReadingsDit vak gaat in op het hoe te doen van typische metingen op het vakgebied van gezondheidstechniek (waterkwaliteit), hydrologie, waterbeheer, waterbouw en vloeistofmechanica (waterkwantiteit).Onderdelen hierin zijn: het herkennen van de relevante parameters, leren over meetmethodes, meetapparatuur, nauwkeurigheid, opstellen van een meetplan, veiligheid, het zelf doen van metingen (laboratorium e/o in het veld) en bewerken en verwerken van gegevens.In een workshop wordt er geleerd met beschikbare electronica componenten een eigen meetsensor te bouwen.Leerdoelen- In staat zijn aan te geven welke parameters van belang zijn bij een bepaald proces- In staat zijn aan te geven hoe de parameters gemeten kunnen worden- Geschikte meetapparatuur kunnen kiezen- Een meetplan kunnen maken (uitvoering, tijd, duur, kosten, veiligheid)- Basis principes electronica in de meettechniek begrijpen en kunnen toepassen

Mechatronic system design deals with the design of controlled motion systems by the integration of functional elements from a multitude of disciplines. It starts with thinking how the required function can be realised by the combination of different subsystems according to a Systems Engineering approach (V-model).

Some supporting disciplines, like power-electronics and electromechanics, are not part of the BSc program of mechanical engineers. For this reason this course introduces these disciplines in connection with PID-motion control principles to realise an optimally designed motion system.
The target application for the lectures are motion systems that combine high speed movements with extreme precision.
The course covers the following four main subjects:

Dynamics of motion systems in the time and frequency domain, including analytical frequency transfer functions that are represented in Bode and Nyquist plots.
Motion control with PID-feedback and model-based feed forward control-principles that effectively deal with the mechanical dynamic anomalies of the plant.
Electromechanical actuators, mainly based on the electromagnetic Lorentz principle. Reluctance force and piezoelectric actuators will be shortly presented to complete the overview.
Power electronics that are used for driving electromagnetic actuators.
The fifth relevant discipline, position measurement systems is dealt with in another course: WB2303, Electronics and measurement.
The most important educational element that will be addressed is the necessary knowledge of the physical phenomena that act on motion systems, to be able to critically judge results obtained with simulation software.
The lectures challenge the capability of students to match simulation models with reality, to translate a real system into a sufficiently simplified dynamic model and use the derived dynamic properties to design a suitable, practically realiseable controller.
This course increases the understanding what a position control system does in reality in terms of virtual mechanical properties like stiffness and damping that are added to the mechanical plant by a closed loop feedback controller.

It is shown how a motion system can be analysed and modelled top-down with approximating (scalar) calculations by hand, giving a sufficient feel of the problem to make valuable concept design decisions in an early stage.
With this method students learn to work more efficiently by starting their design with a quick and dirty global analysis to prove feasibility or direct further detailed modelling in specific problem areas.

Mesoscopic physics is the area of Solid State physics that covers the transition regime between macroscopic objects and the microscopic, atomic world. The main goal of the course is to introduce the physical concepts underlying the phenomena in this field.

System design is the central topic of this course. We move beyond the methods developed in circuit design (although we shall have interest in those) and consider situations in which the functional behavior of a system is the first object under consideration.

Will robots take away our jobs? Will they have their own free will and – as some prophesy – have the power to take over the world? Or will robots offer a great contribution to our well-being and help us to achieve a sustainable future?

Many complex real-world problems can be solved using artificial intelligence. However, while combining artificial intelligence with robotics may offer positive implications for our society, it also raises ethical questions.

This course consists of four modules which can be followed in any order and accessed at any time. However, if you prefer to be part of a group with specific assignment deadlines and discussion forums, you can join and start the course on any of the following dates: June 25, September 3 or November 12, 2018.

In this course, you will be exposed to the potential societal and ethical impact of robots. We will touch upon the design principles that developers should adhere to and critically reflect on issues such as robot autonomy, consciousness, and intelligence.

At the end of the course, you will work with other participants to develop a groundbreaking solution that can be used by robot developers in the near future.

This course is a spin-off of the “Mind of the Universe” documentary series, created by the Dutch broadcasting company VPRO and professor Robbert Dijkgraaf, Princeton University. A number of universities in The Netherlands have used the open source material of the documentary series as a starting point to create similar learning experiences.

Modelling is about understanding the nature: our world, ourselves and our work. Everything that we observe has a cause (typically several) and has the effect thereof. The heart of modelling lies in identifying, understanding and quantifying these cause-and-effect relationships.

A model can be treated as a (selective) representation of a system. We create the model by defining a mapping from the system space to the model space, thus we can map system state and behaviour to model state and behaviour. By defining the inverse mapping, we may map results from the study of the model back to the system. In this course, using an overarching modelling paradigm, students will become familiar with several instances of modelling, e.g., mechanics, thermal dynamics, fluid mechanics, etc.

Physical and digital design skills are key to practitioners in art, design, and engineering, as well as many other creative professions. Models are essential in architecture. In design practice all kinds of physical scale models and digital models are used side by side.

In this architecture course, you will gain experience that will help and inspire you to advance in your personal and professional development. You will attain skills in a practical way. First, we will focus on sketch models for the early stages of a design process, then we will continue with virtual representations for design communication and finally more precise and detailed models will be used for further development of the ideas.

In the theoretical part of the course, you will learn about many different sorts of models: how architects use these and how they are essential in the design process.

The practical part of the course addresses a number of challenges. In small steps we will guide you through technical and creative difficulties in exciting, playful, and pleasant ways.

How can ecosystems contribute to quality of life and a more livable, healthier and more resilient urban environment?

Have you ever considered all the different benefits the ecosystem could potentially deliver to you and your surroundings? Unsustainable urbanization has resulted in the loss of biodiversity, the destruction of habitats and has therefore limited the ability of ecosystems to deliver the advantages they could confer.

This course establishes the priorities and highlights the direct values of including principles based on natural processes in urban planning and design. Take a sewage system or a public space for example. By integrating nature-based solutions they can deliver the exact same performance while also being beneficial for the environment, society and economy.

Increased connectivity between existing, modified and new ecosystems and restoring and rehabilitating them within cities through nature-based solutions provides greater resilience and the capacity to adapt more swiftly to cope with the effects of climate change and other global shifts.

This course will teach you about the design, construction, implementation and monitoring of nature-based solutions for urban ecosystems and the ecological coherence of sustainable cities. Constructing smart cities and metropolitan regions with nature-based ecosystems will secure a fair distribution of benefits from the renewed urban ecology.

This course forms a part of the educational programme of the AMS Amsterdam Institute for Advanced Metropolitan Solutions and will present the state-of-the-art theories and methods developed by the Delft University of Technology and Wageningen University & Research, two of the founding universities of the AMS Institute.

Instructors, with advanced expertise in Urban Ecology, Environmental Engineering, Urban Planning and Design, will equip designers and planners with the skills they need for the sustainable management of the built environment. The course will also benefit stakeholders from both private and public sectors who want to explore the multiple benefits of restored ecosystems in cities and metropolitan regions. They will gain the knowledge and skills required to make better informed and integrated decisions on city development and urban regeneration schemes.

Infrastructures for energy, water, transport, information and communications services create the conditions for livability and economic development. They are the backbone of our society. Similar to our arteries and neural systems that sustain our human bodies, most people however take infrastructures for granted. That is, until they break down or service levels go down.

In many countries around the globe infrastructures are ageing. They require substantial investments to meet the challenges of increasing population, urbanization, resource scarcity, congestion, pollution, and so on. Infrastructures are vulnerable to extreme weather events, and therewith to climate change.
Technological innovations, such as new technologies to harvest renewable energy, are one part of the solution. The other part comes from infrastructure restructuring. Market design and regulation, for example, have a high impact on the functioning and performance of infrastructures.

The course describes in a simple and practical way what non-equilibrium thermodynamics is and how it can contribute to engineering fields. It explains how to derive proper equations of transport from the second law of thermodynamics or the entropy production. The obtained equations are frequently more precise than used so far, and can be used to understand the waste of energy resources in central process units in the industry. The entropy balance is used to define the energy efficiency in energy conversion and create consistent thermodynamic models. It also provides a systematic method for minimizing energy losses that are connected with transport of heat, mass, charge and momentum. The entropy balance examines operation at the state of minimum entropy production and is used to propose some rules of design for energy efficient operation. For this course some knowledge of engineering thermodynamics is a prerequisite. The first and second law of thermodynamics and terms as entropy should be known before starting this course.

Non-Linear Structural Modeling covers the basics of non-linearities in the Finite Element Method (FEM), considering static and stability (buckling) analyses, and practical application thereof applied to both aerospace and non-aerospace examples. Special emphasis is put on the implementation of these non-linearities in a FEM model and any issues that might arise from incorporating these

Are you an engineer, scientist or technician? Are you dealing with measurements or big data, but are you unsure about how to proceed? This is the course that teaches you how to find the best estimates of the unknown parameters from noisy observations. You will also learn how to assess the quality of your results.

TU Delft’s approach to observation theory is world leading and based on decades of experience in research and teaching in geodesy and the wider geosciences. The theory, however, can be applied to all the engineering sciences where measurements are used to estimate unknown parameters.

The course introduces a standardized approach for parameter estimation, using a functional model (relating the observations to the unknown parameters) and a stochastic model (describing the quality of the observations). Using the concepts of least squares and best linear unbiased estimation (BLUE), parameters are estimated and analyzed in terms of precision and significance.

The course ends with the concept of overall model test, to check the validity of the parameter estimation results using hypothesis testing. Emphasis is given to develop a standardized way to deal with estimation problems. Most of the course effort will be on examples and exercises from different engineering disciplines, especially in the domain of Earth Sciences.

This course is aimed towards Engineering and Earth Sciences students at Bachelor’s, Master’s and postgraduate level.

Part 2 of offshore hydromechanics (OE4630) involves the linear theory of calculating 1st order motions of floating structures in waves and all relevant subjects such as the concept of RAOs, response spectra and downtime/workability analysis.

Offshore Hydromechanics includes the following modules:1. Hydrostatics, static floating stability, constant 2-D potential flow of ideal fluids, and flows in real fluids. Introduction to resistance and propulsion of ships. Review of linear regular and irregular wave theory. 2. Analytical and numerical means to determine the flow around, forces on, and motions of floating bodies in waves. 3. Higher order potential theory and inclusion of non-linear effects in ship motions. Applications to motion of moored ships and to the determination of workability. 4. Interaction between the sea and sea bottom as well as the hydrodynamic forces and especially survival loads on slender structures.

The course treats the design of offshore mooring systems literally from the ground up: Starting with the anchor and its soils mechanics in the sea bed, via the mechanics of a single mooring line and system of lines. The course concludes by touching on other mooring concepts and the dynamic behavior of the moored object as a non-linear mechanical system.

This course makes students familiar with the design of offshore wind farms in general and focuses on the foundation design in particular. The course is based on actual cases of real offshore wind farms that have been built recently or will be built in the near future.

How can governments become more open and transparent, while simultaneously dealing with various challenges, such as data sensitivity? How can open government data be used to improve policy making? Which technologies are available to make governments more open and to use open government data?

Governments all over the world aim to become more open and transparent in order to establish closer ties with their constituents. However, opening government involves complex challenges and poses two major areas of concerns. First, many different stakeholders are involved and there are various dependencies between them, and second, the technologies that support open government are fragmented. In addition, it is unclear how different contexts should alter the best practices for open government.

This course explores the foundations and objectives of Open Government and examines current developments, including the opening and reuse of governmental data such as the release of data by governments in America and Europe.

This course will empower you, by helping you grasp the key principles surrounding open government.

You can become a more visible, effective and impactful researcher by sharing your research data and publications openly. In this course, you will learn the objectives, main concepts, and benefits of Open Source principles along with practices for open data management and open data sharing.

You’ll learn to establish links between publications data and methods, how to attach a persistent identifier and metadata to your results, and methods for clarifying usage rights. You will also discover ways to apply these principles to your daily research and adapt existing routines. Finally, you’ll uncover potential barriers to sharing research and discuss possible solutions.

This course will help you grasp the key principles of Open Science, with answers to questions like:

How can researchers effectively store, manage, and share research data?
What kinds of open access publishing are most effective?
How can researchers increase the visibility and impact of their research?
How can the use of social media contribute to the visibility and impact of research?
You will apply the topics of the course to a variety of case studies on Open Science adoption, which you will then discuss among fellow students. You will also be presented with a hands-on guide to publishing your research with open access. This will help you to apply Open Science principles in your daily work. It will enable you to implement and benefit from the Open Science policies that are currently being developed by governments and research institutions.

This course is aimed at professionals. Those who will see the most benefit include academic researchers at different levels: PhD students, postdoctoral researchers, and professors; researchers working for governments; researchers working for commercial enterprises; MSc and BSc students interested to learn about the principles of Open Science.