68 research outputs found
Towards a Simplified Dynamic Wake Model using POD Analysis
We apply the proper orthogonal decomposition (POD) to large eddy simulation
data of a wind turbine wake in a turbulent atmospheric boundary layer. The
turbine is modeled as an actuator disk. Our analyis mainly focuses on the
question whether POD could be a useful tool to develop a simplified dynamic
wake model. The extracted POD modes are used to obtain approximate descriptions
of the velocity field. To assess the quality of these POD reconstructions, we
define simple measures which are believed to be relevant for a sequential
turbine in the wake such as the energy flux through a disk in the wake. It is
shown that only a few modes are necessary to capture basic dynamical aspects of
these measures even though only a small part of the turbulent kinetic energy is
restored. Furthermore, we show that the importance of the individual modes
depends on the measure chosen. Therefore, the optimal choice of modes for a
possible model could in principle depend on the application of interest. We
additionally present a possible interpretation of the POD modes relating them
to specific properties of the wake. For example the first mode is related to
the horizontal large scale movement. Besides yielding a deeper understanding,
this also enables us to view our results in comparison to existing dynamic wake
models
The Langevin Approach: An R Package for Modeling Markov Processes
We describe an R package developed by the research group Turbulence, Wind
energy and Stochastics (TWiSt) at the Carl von Ossietzky University of
Oldenburg, which extracts the (stochastic) evolution equation underlying a set
of data or measurements. The method can be directly applied to data sets with
one or two stochastic variables. Examples for the one-dimensional and
two-dimensional cases are provided. This framework is valid under a small set
of conditions which are explicitly presented and which imply simple preliminary
test procedures to the data. For Markovian processes involving Gaussian white
noise, a stochastic differential equation is derived straightforwardly from the
time series and captures the full dynamical properties of the underlying
process. Still, even in the case such conditions are not fulfilled, there are
alternative versions of this method which we discuss briefly and provide the
user with the necessary bibliography
The Iray Light Transport Simulation and Rendering System
While ray tracing has become increasingly common and path tracing is well
understood by now, a major challenge lies in crafting an easy-to-use and
efficient system implementing these technologies. Following a purely
physically-based paradigm while still allowing for artistic workflows, the Iray
light transport simulation and rendering system allows for rendering complex
scenes by the push of a button and thus makes accurate light transport
simulation widely available. In this document we discuss the challenges and
implementation choices that follow from our primary design decisions,
demonstrating that such a rendering system can be made a practical, scalable,
and efficient real-world application that has been adopted by various companies
across many fields and is in use by many industry professionals today
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