Generalized coupled wake boundary layer model: applications and comparisons with field and LES data for two wind-farms

We describe a generalization of the Coupled Wake Boundary Layer (CWBL) model for wind-farms that can be used to evaluate the performance of wind-farms under arbitrary wind inflow directions whereas the original CWBL model (Stevens et al., J. Renewable and Sustainable Energy 7, 023115 (2015)) focused on aligned or staggered wind-farms. The generalized CWBL approach combines an analytical Jensen wake model with a "top-down" boundary layer model coupled through an iterative determination of the wake expansion coefficient and an effective wake coverage area for which the velocity at hub-height obtained using both models converges in the "deep-array" portion (fully developed region) of the wind-farm. The approach accounts for the effect of the wind direction by enforcing the coupling for each wind direction. Here we present detailed comparisons of model predictions with LES results and field measurements for the Horns Rev and Nysted wind-farms operating over a wide range of wind inflow directions. Our results demonstrate that two-way coupling between the Jensen wake model and a "top-down" model enables the generalized CWBL model to predict the "deep-array" performance of a wind-farm better than the Jensen wake model alone. The results also show that the new generalization allows us to study a much larger class of wind-farms than the original CWBL model, which increases the utility of the approach for wind-farm designers.Comment: 17 pages, 11 figure

Using the coupled wake boundary layer model to evaluate the effect of turbulence intensity on wind farm performance

We use the recently introduced coupled wake boundary layer (CWBL) model to predict the e ect of turbulence intensity on the performance of a wind farm. The CWBL model combines a standard wake model with a \top-down" approach to get improved predictions for the power output compared to a stand-alone wake model. Here we compare the CWBL model results for di erent turbulence intensities with the Horns Rev eld measurements by Hansen et al., Wind Energy 15, 183196 (2012). We show that the main trends as function of the turbulence intensity are captured very well by the model and discuss di erences between the eld measurements and model results based on comparisons with LES results from Wu and Port e-Agel, Renewable Energy 75, 945-955 (2015)

Effect of turbine alignment on the average power output of wind-farms

Using Large Eddy Simulation (LES), we investigate the influence of the alignment of successive turbine rows on the average power output of a finite length wind-farm with a stream-wise spacing between the turbines of Sx = 7:85D and a span-wise spacing of Sy = 5:23D, where D is the turbine diameter. Different turbine alignments affect the extent to which wakes from upstream turbines interact with downstream turbines. We consider 13 turbine rows in the stream-wise direction and change the layout of the wind-farm by adjusting the angle y = arctan Sdy Sx with respect to the incoming flow direction, where Sdy indicates the span-wise offset from one turbine row to the next. For the case considered here, y = 0 degrees corresponds to an aligned windfarm, while a perfectly staggered configuration occurs at y =arctan[(5:23D=2)=7:85D]=18:43 degrees. We simulate the interaction between each wind-farm and the atmospheric boundary layer using a LES that uses a newly developed concurrent-precursor inflow method. For an aligned configuration we observe a nearly constant average turbine power output for the second and subsequent turbine rows, which is about 60% of the average power produced by the turbines in the first row. With increasing y the power loss in subsequent turbine rows is more gradual. We find that the highest average power output is not obtained for a staggered wind-farm (y = 18:43 degrees), but for an intermediate alignment of around y = 12 degrees. Such an intermediate alignment allows more turbines to be outside the wake of upstream turbines than in the staggered configuration in which turbines are directly in the wake of turbines placed two rows upstream

Viscoelastic mechanics of tidally induced lake drainage in the Amery grounding zone

Drainage of supraglacial lakes through hydrofractures plays an important role in lubricating the ice--bedrock interface and causing ice-shelf collapse. For supraglacial lakes in Antarctic grounding zones, the effect of their drainage, which is complicated by the grounding line dynamics, is of great importance for understanding ice-sheet mass loss and ice-shelf vulnerability. Recently, a series of supraglacial lake drainage events through hydrofractures was observed in the Amery Ice Shelf grounding zone, East Antarctica, but the mechanism inducing hydrofracture was not determined. Here we explore the potential tidal contribution to hydrofracture propagation with a modelling approach. We model the viscoelastic tidal response of a marine ice sheet and hydrofracture propagation under tidal stress. Our results show that ocean tides and lake-water pressure together control supraglacial lake drainage through hydrofractures in the grounding zone. We give a model-based criterion that predicts supraglacial lake drainage based on observations of daily maximum tidal amplitude and lake depth. Our model-based criterion agrees with remotely sensed data, indicating the importance of tidal flexure to processes associated with hydrofracturing such as supraglacial lake drainage, rifting and calving.Comment: 23 pages, 8 figure

The role of Stewartson and Ekman layers in turbulent rotating Rayleigh-B\'enard convection

When the classical Rayleigh-B\'enard (RB) system is rotated about its vertical axis roughly three regimes can be identified. In regime I (weak rotation) the large scale circulation (LSC) is the dominant feature of the flow. In regime II (moderate rotation) the LSC is replaced by vertically aligned vortices. Regime III (strong rotation) is characterized by suppression of the vertical velocity fluctuations. Using results from experiments and direct numerical simulations of RB convection for a cell with a diameter-to-height aspect ratio equal to one at $Ra \sim 10^8-10^9$ ($Pr=4-6$) and $0 \lesssim 1/Ro \lesssim 25$ we identified the characteristics of the azimuthal temperature profiles at the sidewall in the different regimes. In regime I the azimuthal wall temperature profile shows a cosine shape and a vertical temperature gradient due to plumes that travel with the LSC close to the sidewall. In regime II and III this cosine profile disappears, but the vertical wall temperature gradient is still observed. It turns out that the vertical wall temperature gradient in regimes II and III has a different origin than that observed in regime I. It is caused by boundary layer dynamics characteristic for rotating flows, which drives a secondary flow that transports hot fluid up the sidewall in the lower part of the container and cold fluid downwards along the sidewall in the top part.Comment: 21 pages, 12 figure

Coupled wake boundary layer model of wind-farms

We present and test the coupled wake boundary layer (CWBL) model that describes the distribution of the power output in a wind-farm. The model couples the traditional, industry-standard wake model approach with a "top-down" model for the overall wind-farm boundary layer structure. This wake model captures the effect of turbine positioning, while the "top-down" portion of the model adds the interactions between the wind-turbine wakes and the atmospheric boundary layer. Each portion of the model requires specification of a parameter that is not known a-priori. For the wake model, the wake expansion coefficient is required, while the "top-down" model requires an effective spanwise turbine spacing within which the model's momentum balance is relevant. The wake expansion coefficient is obtained by matching the predicted mean velocity at the turbine from both approaches, while the effective spanwise turbine spacing depends on turbine positioning and thus can be determined from the wake model. Coupling of the constitutive components of the CWBL model is achieved by iterating these parameters until convergence is reached. We illustrate the performance of the model by applying it to both developing wind-farms including entrance effects and to fully developed (deep-array) conditions. Comparisons of the CWBL model predictions with results from a suite of large eddy simulations (LES) shows that the model closely represents the results obtained in these high-fidelity numerical simulations. A comparison with measured power degradation at the Horns Rev and Nysted wind-farms shows that the model can also be successfully applied to real wind-farms.Comment: 25 pages, 21 figures, submitted to Journal of Renewable and Sustainable Energy on July 18, 201

Tiresias: Predicting Security Events Through Deep Learning

With the increased complexity of modern computer attacks, there is a need for defenders not only to detect malicious activity as it happens, but also to predict the specific steps that will be taken by an adversary when performing an attack. However this is still an open research problem, and previous research in predicting malicious events only looked at binary outcomes (e.g., whether an attack would happen or not), but not at the specific steps that an attacker would undertake. To fill this gap we present Tiresias, a system that leverages Recurrent Neural Networks (RNNs) to predict future events on a machine, based on previous observations. We test Tiresias on a dataset of 3.4 billion security events collected from a commercial intrusion prevention system, and show that our approach is effective in predicting the next event that will occur on a machine with a precision of up to 0.93. We also show that the models learned by Tiresias are reasonably stable over time, and provide a mechanism that can identify sudden drops in precision and trigger a retraining of the system. Finally, we show that the long-term memory typical of RNNs is key in performing event prediction, rendering simpler methods not up to the task