De student die dit vak met goed gevolg heeft doorlopen zal in staat zijn om: (1) Op basis van eigenschappen en gedrag onder externe invloeden een klassificatie te maken van materialen en op basis daarvan een eerste indruk te krijgen van hun geschiktheid in bepaalde toepassingen. (2) Inzicht te verkrijgen in de rol van materialen, materiaalgebruik en materiaalontwikkeling in de ontwikkeling, kwaliteit, mogelijkheden en bedreigingen van de samenleving afhankelijk van tijd, plaats en cultuur. Dit inzicht is gebaseerd op objectieve data. (3) Vast te stellen welke materiaaleigenschappen van kritisch belang zijn in mechanische en andere werktuigbouwkundige ontwerpen, en met behulp van eenduidige criteria materiaalkeuzes in de ontwerpcriteria van constructies te optimaliseren. De belangrijkste eigenschappen die aan de orde komen zijn dichtheid, stijfheid, sterkte, plasticiteit, breuk, vermoeiing, wrijving, slijtage. (4) Mechanische eigenschappen van materialen te herleiden tot chemische bindingen, onderlinge krachten, ordeningspatronen, defecten, en relatieve bewegingsmogelijkheden van atomen. De verschillende lengteschalen die materiaaleigenschappen bepalen staan hierbij centraal. Hiermee zal tevens inzicht verkregen worden in de mogelijkheden en beperkingen van materialen onder extreme omstandigheden en in de strategieën die gevolgd kunnen worden om materialen te verbeteren. (5) Optimale keuzes te maken binnen het beschikbare spectrum van procestechnieken (productie, bewerking, vorming, verbinding, afwerking) om componenten en eindproducten te vervaardigen. (6) Software te gebruiken waarmee, gegeven een aantal vereisten van materiaaleigenschappen, het beste materiaal voor een ontwerp kan worden geselecteerd. Deze materiaaleigenschappen gaan verder dan mechanische eigenschappen alleen. Thermische, elektrische, ecologische, economische en recycling-eigenschappen zullen in voorkomende gevallen ook meegewogen worden.

Mathematics explained: Here you find videos on various math topics:

Pre-university Calculus (functions, equations, differentiation and integration)
Vector calculus (preparation for mechanics and dynamics courses)
Differential equations, Calculus
Functions of several variables, Calculus
Linear Algebra
Probability and Statistics

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

In Mechanics and Relativity, the reader is taken on a tour through time and space. Starting from the basic axioms formulated by Newton and Einstein, the theory of motion at both the everyday and the highly relativistic level is developed without the need of prior knowledge. The relevant mathematics is provided in an appendix. The text contains various worked examples and a large number of original problems to help the reader develop an intuition for the physics. Applications covered in the book span a wide range of physical phenomena, including rocket motion, spinning tennis rackets and high-energy particle collisions.

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.

Distributed systems are the backbone of modern society but entail challenges in areas such as complexity and energy-use. Discover distributed systems from first principles, understand the architectures and techniques derived from them and explore examples of current practical use.

This course will provide learners with a fundamental understanding (theoretical and practical foundations) of how cloud, edge, and big data processing systems work and how they address common challenges for distributed systems such as performance, resilience, and scalability.

Modern IT infrastructure is built as distributed systems, an exciting concept that started with the first computers and evolved rapidly into its present form. From online video meetings to internet services, from social media platforms to online games, we all use and interact with distributed systems on a daily basis and increasingly depend on them. Designing and operating such large-scale distributed systems, however, is complex and typically involves making reasonable compromises. There are fundamental technical barriers as well as economic arguments why we cannot make these systems behave as if they were running on a single, perfectly reliable machine.

In this course, learners will be introduced to the essential functional and non-functional concerns of distributed systems and the common problems encountered while designing them, such as consistency, availability, elasticity, and scalability. A variety of practical solutions that have been established in the leading tech industry in recent years will be reviewed. These provide re-usable building blocks to create new large-scale applications. These recent developments, especially around cloud computing, large-scale data processing, distributed machine learning, and other fields are often not reflected in textbooks and are absent from many traditional curricula but are at the heart of this course.

The learning progress is assessed through a variety of different activities including quizzes, design exercises, experiments, and open questions, with peer review of other students’ solutions. In the final project, learners will design a distributed system based on the learners’ own experience and interests and describe the functional and non-functional properties of the system.

What You'll Learn:
Describe the principles of distributed systems.
Contrast distributed systems with other forms of computation (e.g., single machine computation, parallel computing).
Identify applications of distributed systems in science, engineering, business, and home use, and in particular the use of cloud and serverless applications, big data and graph processing applications, interactive and online gaming, etc.
Analyze and design core architectures, components, and techniques in distributed systems.
Solve practical problems related to modern uses of distributed systems.

The use of data to understand phenomena and evaluate designs and interventions in different disciplines is increasingly evident. As a result, engineers and other applied scientists frequently find themselves needing to collaborate in multidisciplinary fields when carrying out research to remain innovative.

This course will help you to become a successful multidisciplinary researcher in industry, non-profit, or academia, and be more efficient and successful as you will know where the pitfalls are! This course explains the fundamentals on how to plan and carry out state-of-the-art qualitative and quantitative research in different phases of an innovation or research project.

The course has been designed by a team of experienced, multidisciplinary researchers in education, engineering and research methodologies and will also feature experts in the field of research methodologies as guest lecturers. In the course you will be working towards creating a project plan for your research, giving you a head-start in your research project.

The interuniversity, interdisciplinary Leiden-Delft-Erasmus Center for Education and Learning is a leader in multidisciplinary technological research and innovation projects. Learning from leading experts in the field you will learn to apply the best practices in your own context.

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.

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

In this book we discuss several numerical methods for solving ordinary differential equations. We emphasize the aspects that play an important role in practical problems. We confine ourselves to ordinary differential equations with the exception of the last chapter in which we discuss the heat equation, a parabolic partial differential equation. The techniques discussed in the intro-ductory chapters, for instance interpolation, numerical quadrature and the solution to nonlinear equations, may also be used outside the context of differential equations. They have been in-cluded to make the book self-contained as far as the numerical aspects are concerned. Chapters, sections and exercises marked with a * are not part of the Delft Institutional Package.
The numerical examples in this book were implemented in Matlab, but also Python or any other programming language could be used. A list of references to background knowledge and related literature can be found at the end of this book. Extra information about this course can be found at http://NMODE.ewi.tudelft.nl, among which old exams, answers to the exercises, and a link to an online education platform. We thank Matthias Moller for his thorough reading of the draft of this book and his helpful suggestions.

This is a book about numerically solving partial differential equations occurring in technical and physical contexts and the authors have set themselves a more ambitious target than to just talk about the numerics. Their aim is to show the place of numerical solutions in the general modeling process and this must inevitably lead to considerations about modeling itself. Partial differential equations usually are a consequence of applying first principles to a technical or physical problem at hand. That means, that most of the time the physics also have to be taken into account especially for validation of the numerical solution obtained. This book aims especially at engineers and scientists who have ’real world’ problems. It will concern itself less with pesky mathematical detail. For the interested reader though, we have included sections on mathematical theory to provide the necessary mathematical background. Since this treatment had to be on the superficial side we have provided further reference to the literature where necessary.

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.