Daniel Foti

On the use of spires for generating inflow conditions with energetic coherent structures in large eddy simulation

ABSTRACT While it has long been a practice to place spires near the inlet of a wind tunnel to quickly develop a turbulent boundary layer with similarities to an atmospheric boundary layer, this has not been the case for creating turbulent boundary layer inflow in large eddy simulations (LESs) of turbulent flows. We carry out LES with the curvilinear immersed boundary method to simulate the flow in a wind tunnel with a series of spires in order to investigate the feasibility of numerically developing inflow conditions from a precursory spire LES and assessing the similarities of the turbulence statistics to those of an atmospheric boundary layer. The simulated mean velocity field demonstrates that a turbulent boundary layer with height equal to the spire height develops very quickly, within five spire heights downstream. The major attribute of using spires for precursory simulations is the spatially evolving coherent structures that form downstream of the spires offering a range of length scales at both the vertical and streamwise directions allowing multiple turbulent inflow conditions to be extracted from a single simulation. While the distribution of length scales far from the spires resembles an atmospheric boundary layer, some turbulence statistics have some significant differences.

Wake meandering of a model wind turbine operating in two different regimes

The flows behind a model wind turbine under two turbine operating regimes are investigated using wind tunnel experiments and large-eddy simulations. Measurements from the model wind turbine experiment reveal that the power coefficient and turbine wake are affected by the operating regime. Simulations employing a new class of actuator surface methods which parameterize both the turbine blades and nacelle with and without a nacelle model are carried out for each operating condition to study the influence of the operating regime and nacelle. For simulations with a nacelle model, the mean flow field is composed of an outer wake, caused by energy extraction from the incoming wind by the turbine blades, and an inner wake directly behind the nacelle, while for the simulations without a nacelle model, the central region of the wake is occupied by a jet. The simulations with the nacelle model reveal an unstable helical hub vortex expanding outwards towards the outer wake; while the simulations without a nacelle model show a stable and columnar hub vortex. The hub vortex for the turbine operating in Region 3 remains in a tight helical spiral and intercepts the outer wake a few diameters further downstream than for the turbine operating in Region 2. Wake meandering commences in a region of high turbulence intensity for all simulations indicating that neither a nacelle model nor an unstable hub vortex is a necessary requirement for wake meandering. However, further analysis of the wake meandering using a filtering technique and dynamic mode decomposition show that the unstable hub vortex energizes the wake meandering. The turbine operating regime affects the shape and expansion of the hub vortex altering the location of the onset of the wake meandering and its oscillating intensity. The unstable hub vortex promotes a energetic meandering which cannot be predicted without a nacelle model.

Characterization of semi-synthetic dataset for big-data semantic analysis

Over the past decade, the use of semantic databases has served as the basis for storing and analyzing complex, heterogeneous, and irregular data. While there are similarities with traditional relational database systems, semantic data stores provide a rich platform for conducting non-traditional analyses of data. In support of new graph analytic algorithms and specialized graph analytic hardware, we have developed a large semi-synthetic, semantically rich dataset. The construction of this dataset mimics the real-world scenario of using relational databases as the basis for semantic data construction. In order to achieve real-world variable distributions and variable dependencies, data.gov data was used as the basis for developing an approach to build arbitrarily large semi-synthetic datasets. The intent of the semi-synthetic dataset is to serve as a testbed for new semantic graph analyses and computational software/hardware platforms. The construction process and basic data characterization is described. All code related to the data collection, consolidation, and augmentation are available for distribution.