Introduction to reactor dynamics including subcritical multiplication, critical operation in absence of thermal feedback effects and effects of Xenon, fuel and moderator temperature, etc. Derivation of point kinetics and dynamic period equations. Techniques for reactor control including signal validation, supervisory algorithms, model-based trajectory tracking, and rule-based control. Overview of light-water reactor startup. Lectures and demonstrations with computer simulation and the use of the MIT Research Reactor.

" Problems in nuclear engineering often involve applying knowledge from many disciplines simultaneously in achieving satisfactory solutions. The course will focus on understanding the complete nuclear reactor system including the balance of plant, support systems and resulting interdependencies affecting the overall safety of the plant and regulatory oversight. Both the Seabrook and Pilgrim nuclear plant simulators will be used as part of the educational experience to provide as realistic as possible understanding of nuclear power systems short of being at the reactor."

This class introduces elementary programming concepts including variable types, data structures, and flow control. After an introduction to linear algebra and probability, it covers numerical methods relevant to mechanical engineering, including approximation (interpolation, least squares and statistical regression), integration, solution of linear and nonlinear equations, ordinary differential equations, and deterministic and probabilistic approaches. Examples are drawn from mechanical engineering disciplines, in particular from robotics, dynamics, and structural analysis. Assignments require MATLAB programming.

This course is an introduction to numerical methods and MATLAB®: Errors, condition numbers and roots of equations. Topics covered include Navier-Stokes; direct and iterative methods for linear systems; finite differences for elliptic, parabolic and hyperbolic equations; Fourier decomposition, error analysis and stability; high-order and compact finite-differences; finite volume methods; time marching methods; Navier-Stokes solvers; grid generation; finite volumes on complex geometries; finite element methods; spectral methods; boundary element and panel methods; turbulent flows; boundary layers; and Lagrangian coherent structures (LCSs).

This course examines how numerical methods are used by engineers to translate the language of mathematics and physics into information that may be used to make engineering decisions. Often, this translation is implemented so that calculations may be done by machines (computers). Upon successful completion of this course, the student will be able to: Quantify absolute and relative errors; Distinguish between round-off and truncation errors; Interconvert binary and base-10 number representations; Define and use floating-point representations; Quantify how errors propagate through arithmetic operations; Derive difference equations for first and second order derivatives; Evaluate first and second order derivatives from numerical evaluations of continuous functions or table lookup of discrete data; Describe situations in which numerical solutions to nonlinear equations are needed; Implement the bisection method for solving equations; List advantages and disadvantages of the bisection method; Implement both Newton-Raphson and secant methods; Describe the difference between Newton-Raphson and secant methods; Demonstrate the relative performance of bisection, Newton-Raphson, and secant methods; Define and identify special types of matrices; Perform basic matrix operations; Define and perform Gaussian elimination to solve a linear system; Identify pitfalls of Gaussian elimination; Define and perform Gauss-Seidel method for solving a linear system; Use LU decomposition to find the inverse of a matrix; Define and perform singular value decomposition; explain the significance of singular value decomposition; Define interpolation; Define and use direct interpolation to approximate data and find derivatives; Define and use NewtonĺÎĺĺÎĺs divided difference method of interpolation; Define and use Lagrange and spline interpolation; Define regression; Perform linear least-squares regression and nonlinear regression; Derive and apply the trapezoidal rule and Simpson's rule of integration; Distinguish Simpson's method from the trapezoidal rule; Estimate errors in trapezoidal and Simpson integration; Derive and apply Romberg and Gaussian quadrature for integration; Define and distinguish between ordinary and partial differential equations; Implement Euler's methods for solving ordinary differential equations; Investigate how step size affects accuracy in Euler's method; Implement and use the Runge-Kutta 2nd order method for solving ordinary differential equations; Apply the shooting method to solve boundary-value problems; Define Fourier series and the Fourier transform; Find Fourier coefficients for a given data set or function and domain; Describe the finite element method for one-dimensional problems. (Mechanical Engineering 205)

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.

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.

Open Signals and Systems Laboratory Exercises is a collection of lab assignments that have been used in EE 224: Signals and Systems I in the Department of Electrical and Computer Engineering at Iowa State University. These lab exercises have been curated, edited, and presented in a consistent format to improve student learning.

Table of Contents
MATLAB Basics
Tuning Forks
Impulse Response and Auralization
Frequency Response: Notch and Bandpass Filters
Fourier Series
Introduction to Digital Images
Filtering Digital Images

This text covers the theory and application of operational amplifiers and other linear integrated circuits. It is appropriate for Associate and Bachelors degrees programs in Electrical and Electronic Engineering Technology, Electrical Engineering and similar areas of study. Topics include negative feedback, comparators, voltage amplifiers, summing and differencing amplifiers, high speed and high power devices, non-linear circuit applications, regulators, oscillators, integrators and differentiators, active filters and AD/DA conversion. A companion laboratory manual is available.

The companion laboratory manual features 22 separate exercises. It covers the theory and application of operational amplifiers and other linear integrated circuits. Exercises include discrete differential amplifier analysis; inverting, non-inverting and differential configurations; frequency response; slew rate; DC offset; OTA; oscillators; linear regulator; function synthesis; active filters; and integrators and differentiators.

The goal of this text, as its name implies, is to allow the reader to become proficient in the analysis and design of circuits utilizing modern linear ICs. It progresses from the fundamental circuit building blocks through to analog/digital conversion systems. The text is intended for use in a second year Operational Amplifiers course at the Associate level, or for a junior level course at the Baccalaureate level. In order to make effective use of this text, students should have already taken a course in basic discrete transistor circuits, and have a solid background in algebra and trigonometry, along with exposure to phasors. Calculus is used in certain sections of the text, but for the most part, its use is kept to a minimum. For students without a calculus background, these sections may be skipped without a loss of continuity. (The sole exception to this being Chapter Ten, Integrators and Differentiators, which hinges upon knowledge of calculus.)

This course provides an introduction to optical science with elementary engineering applications. Topics covered in geometrical optics include: ray-tracing, aberrations, lens design, apertures and stops, radiometry and photometry. Topics covered in wave optics include: basic electrodynamics, polarization, interference, wave-guiding, Fresnel and Fraunhofer diffraction, image formation, resolution, space-bandwidth product. Analytical and numerical tools used in optical design are emphasized. Graduate students are required to complete assignments with stronger analytical content, and an advanced design project.

Deze cursus bestaat uit lesmodules te gebruiken in de bovenbouw van het Havo en het VWO met als onderwerp Optimaliseren in netwerken. Het materiaal is gemaakt door een kerngroep van vwo-docenten, aangevuld met universitaire medewerkers. Docenten kunnen er invulling mee geven aan het domein "Wiskunde in wetenschap" van het vak wiskunde D.

Modern computing platforms provide unprecedented amounts of raw computational power. But significant complexity comes along with this power, to the point that making useful computations exploit even a fraction of the potential of the computing platform is a substantial challenge. Indeed, obtaining good performance requires a comprehensive understanding of all layers of the underlying platform, deep insight into the computation at hand, and the ingenuity and creativity required to obtain an effective mapping of the computation onto the machine. The reward for mastering these sophisticated and challenging topics is the ability to make computations that can process large amount of data orders of magnitude more quickly and efficiently and to obtain results that are unavailable with standard practice. This class is a hands-on, project-based introduction to building scalable and high-performance software systems. Topics include performance analysis, algorithmic techniques for high performance, instruction-level optimizations, cache and memory hierarchy optimization, parallel programming, and building scalable distributed systems. The course also includes design reviews with industry mentors, as described in this MIT News article.

The purpose of this course is to discuss modern techniques of generation of x-ray photons and neutrons and then follow with selected applications of newly developed photon and neutron scattering spectroscopic techniques to investigations of properties of condensed matter which are of interest to nuclear engineers.

Optical and optoelectronic properties of semiconductors, ceramics, and polymers. Electronic structure, refractive index, electroluminescence, electro-optic and magneto-optic effects, and laser phenomena. Microphotonic materials and structures; photonic band gap materials. Materials design and processing for lasers, waveguides, modulators, switches, displays and optoelectronic integrated circuits. Alternate years. This course covers the theory, design, fabrication and applications of photonic materials and devices. After a survey of optical materials design for semiconductors, dielectrics and polymers, the course examines ray optics, electromagnetic optics and guided wave optics; physics of light-matter interactions; and device design principles of LEDs, lasers, photodetectors, modulators, fiber and waveguide interconnects, optical filters, and photonic crystals. Device processing topics include crystal growth, substrate engineering, thin film deposition, etching and process integration for dielectric, silicon and compound semiconductor materials. The course also covers microphotonic integrated circuits and applications in telecom/datacom systems. Course assignments include four design projects that emphasize materials, devices and systems applications.

This class will study the behavior of photovoltaic solar energy systems, focusing on the behavior of "stand-alone" systems. The design of stand-alone photovoltaic systems will be covered. This will include estimation of costs and benefits, taking into account any available government subsidies. Introduction to the hardware elements and their behavior will be included.

Overview of engineering analysis and design techniques for synthetic polymers. Treatment of materials properties selection, mechanical characterization, and processing in design of load-bearing and environment-compatible structures.