bluuebunny/crossref_metadata_2025_split huggingface dataset:
Minimal sensor arrays for localizing objects using an electric sense
Authors: Pourziaei Babak , Lewis Gregory M , Lewis John E 
Published: June 2022

Abstract
Weakly electric fish encode perturbations in a self-generated electric field to sense their environment. Localizing objects using this electric sense requires that distance be decoded from a two-dimensional electric image of the field perturbations on their skin. Many studies of object localization by weakly electric fish, and by electric sensing in a generic context, have focused on extracting location information from different features of the electric image. Some of these studies have also considered the additional information gained from sampling the electric image at different times, and from different viewpoints. Here, we take a different perspective and instead consider the information available at a single point in space (i.e. a single sensor or receptor) at a single point in time (i.e. constant field). By combining the information from multiple receptors, we show that an object’s distance can be unambiguously encoded by as few as four receptors at specific locations on a sensing surface in a manner that is relatively robust to environmental noise. This provides a lower bound on the information (i.e. receptor array size) required to decode the three-dimensional location of an object using an electric sense.
Projective robustness for quantum channels and measurements and their operational significance
Authors: Ye Mingfei , Luo Yu , Li Zhihui , Li Yongming 
Published: June 2022

Abstract
Recently, the projective robustness of quantum states was introduced in (Regula 2022 Phys. Rev. Lett.
128 110505). This demonstrates that the projective robustness is a useful resource monotone and can comprehensively characterize the capabilities and limitations of probabilistic protocols that manipulate quantum resources deterministically. In this paper, we will extend the projective robustness to any convex resource theory of quantum channels and measurements. First, we introduce the projective robustness of quantum channels and prove that it satisfies some good properties, especially sub-multiplicativity under any free quantum process. Moreover, we show that the projective robustness of channels quantifies the maximal advantage that a given channel outperforms all free channels in the simultaneous discrimination and exclusion of a fixed-state ensemble. Second, we define the projective robustness of quantum measurements and prove that it exactly quantifies the maximal advantage that a given measurement outperforms all free measurements in the simultaneous discrimination and exclusion of two fixed-state ensembles. Finally, within a specific channel resource setting based on measurement incompatibility, we show that the projective robustness of quantum channels coincides with the projective robustness of measurement incompatibility.
Control of photon transport in an optical structure of four microresonators using spectral engineering
Authors: Tsukanov A V 
Published: June 2022

Abstract
A theoretical model of a photonic switch based on a T-shaped optical structure with combined switching control is considered. The transmission coefficient of photons through the structure varies between current and vacuum modes by several physical mechanisms. The main one is the resonant interaction between microresonators with adjustable photon tunneling energy. An alternative way is also pump compensation by switching on an additional laser field. The influence of the model parameters on the switching efficiency is studied. It is shown that this scheme can be implemented on the basis of already existing technologies.
Blockage effects in a single row of wind turbines
Authors: Bleeg J , Montavon C 
Published: June 2022

Abstract
Reynolds-averaged Navier-Stokes simulations are used to assess the magnitude and sign of turbine interaction loss in single-row wind farms with flow perpendicular to the row. Consistent with previous studies, the simulations show that lateral interactions between turbines can lead to a production gain when assuming idealised neutral conditions. These gains are amplified by using over-constraining boundary conditions. When more realistic conditions are simulated, the gains turn to losses, suggesting that losses are more likely to occur in actual single-row wind farms. The study results also indicate that commonly used assumptions in engineering models for blockage effects risk the underprediction of turbine interaction loss.
Wind farm response to mesoscale-driven coastal low level jets: a multiscale large eddy simulation study
Authors: Chatterjee Tanmoy , Li Jing , Yellapantula Shashank , Jayaraman Balaji , Quon Eliot 
Published: June 2022

Abstract
Realistic atmospheric turbulence–wind farm interactions during coastal low-level jet (LLJ) events are captured using high-fidelity, mesoscale-driven large eddy simulations (LES) to understand wind turbine loads, wakes and overall performance. The simulation has been carried out using the ExaWind aeroelastic solver, AMR-Wind. The simulations have been compared against a baseline unstable case matching the wind speed, wind direction and TI at hub-height location. Results indicate that the LLJ has negative impacts on the turbine hub and tower loads, and opens up potential avenues for design load mitigation strategies.
Sensitivity of Wake Modelling Setups
Authors: Kemme L , Strnad S , Vollmer L , Schmidt J , Lukassen L J 
Published: June 2022

Abstract
Engineering wake modelling with single turbine wake models and wake superposition models (SupModel) requires the choice of two crucial settings: the type of wake superposition and the wake expansion rate parameter. Based on offshore wind farm measurements, the appropriate tuning of such wake modelling setups with respect to wind farm efficiency and their sensitivity to this tuning is investigated. For this purpose, universal and inflow-individual wake expansion rate parameters are optimised for different SupModels via minimising the deviation between simulated and measured power and to compare the results to simulations using a literature value. The work reveals a logarithmic, SupModel-independent sensitivity of the farm efficiency to the wake expansion rate, sufficient to allow equally accurate modelling performance for all setups if a matching parameter is chosen. The choice of the SupModel only matters in case of an unadapted wake expansion rate parameter. Beyond that, the results indicate a systematic flaw of wake models in reproducing farm power measurements for both weak and full wake situations with the same model tuning. Inflow-individual parameters obtained by this work mitigate this with a systematic pattern over wind speed and wind direction and can significantly decrease the modelling uncertainty over varying inflow conditions.
Wind farm optimization with multiple hub heights using gradient-based methods
Authors: Wolf Ciavarra Andreas , Valotta Rodrigues Rafael , Dykes Katherine , Réthoré Pierre-Elouan 
Published: June 2022

Abstract
Optimization of the Levelized Cost of Energy (LCoE) in wind farms helps ensure profitability and competitiveness of the project. Recent work has explored driving down LCoE by allowing multiple wind turbines in a single wind farm - with different hub heights, rotor diameters, and rated powers. In this work, we performed optimization of the Lillgrund wind farm with continuously varying hub-heights to mitigate wake interference, improve annual energy production (AEP) and reduce LCoE. The optimization converged to a configuration where the turbines were vertically staggered, resulting in an improvement in both AEP and internal rate of return (IRR) - a financial metric related to LCoE. Reducing the number of turbines to a discrete set of 2 or 3 retained nearly all the benefits of staggering but is more aligned with limitations related to manufacturing and logistics.
Extension and validation of an operational dynamic wake model to yawed configurations
Authors: Lejeune M , Moens M , Chatelain P 
Published: June 2022

Abstract
This work extends the capabilities of an operational dynamic wake model to yawed cases. The proposed framework brings together flow sensing and Lagrangian flow modeling into a unified framework: both the freestream flow field and the wake one are discretized as series of information-carrying particles. A source condition for these particles is thus obtained from the wind turbine measurements through flow sensing techniques. The estimated flow field state across the wind farm is finally reconstructed by propagating these particles downstream at their own characteristic velocity. The resulting framework is first presented and its extension to yawed turbine is then discussed. Comparison against high-fidelity Large Eddy Simulations of yawed wind turbines confirms the good potential of the approach: different yaw angles are considered and the performance of the model are evaluated. This study indicates that the proposed framework captures the relevant large scale wake features caused by the combined effect of yawing and wake meandering at a low computational cost thereby making it suitable for online model-based control.
Further calibration and validation of FLORIS with wind tunnel data
Authors: Campagnolo Filippo , Imširović Lejla , Braunbehrens Robert , Bottasso Carlo L. 
Published: June 2022

Abstract
This paper compares the accuracy of four different FLORIS model implementations characterized by increasing levels of accounted physics. The predictions of each model were tested against experimental observations obtained from wind tunnel tests of a cluster of three scaled wind turbines. The experiments were conducted for a wide range of environmental conditions, and mainly with yawed turbines. The results showed a significant improvement in the accuracy of the base model by adding more details to the physical effects of the simulation, such as secondary steering and a heterogeneous flow field. It is also interesting to note that the FLORIS predictions become particularly accurate when considering inflow-dependent power losses in yaw-misaligned conditions.
The WRF model and the diurnal cycle of orographically-influenced flow: A case study
Authors: Sile T , Hahmann A N 
Published: June 2022

Abstract
The Weather Research and Forecasting (WRF) model is widely used in modelling for wind energy purposes. Previous research indicated that under some situations, the WRF model fails to properly represent the diurnal cycle in wind speed. Here we present a case study with a North Sea jet where the WRF-simulated wind diurnal cycle is compared with observations at the Østerild tall mast in Northern Jutland, Denmark. The diurnal cycle in wind speed has different shapes at different heights and the WRF model can replicate the diurnal cycle at higher levels (∼ 250 m a.g.l.) but fails to replicate it near surface. We investigate the effect of using different Planetary Boundary Layer (PBL) and Surface Layer (SL) schemes and compare stability data between model and observations. The results suggest a weak link between the model’s ability to replicate stability, probably linked to incorrect surface roughness length specification, and the model’s ability to replicate diurnal cycle at lower levels.
Dynamic wake tracking based on wind turbine rotor loads and Kalman filtering
Authors: Onnen D , Larsen G C , Lio W H , Liew J Y , Kühn M , Petrović V 
Published: June 2022

Abstract
In a wind farm setting, the location of the wake to which a downwind turbine is exposed is of high interest. It can be used for closed-loop active wake control, ultimately leading to fatigue load reduction and higher power generation. This work proposes a method for dynamic tracking of the meandering wake centre. The rotor takes the role of a sensor, with its blades sampling through the incoming wind field. The measurement of the flapwise blade root bending moment and its decomposition into the non-rotating yaw and tilt moments is used. The latter are linked to the lateral and vertical wake location via a parametric model, tuned with training data from aeroelastic simulations. The implementation of an Extended Kalman Filter (EKF) adds robustness to the tracking and allows to include physical knowledge of the wake meandering to the estimation. For this, the governing equations of the dynamic wake meandering model (DWM) are used to describe the meandering motion as a random walk process. The performance, possibilities and limitations of the tracking method in various inflow conditions are shown and discussed. Generally the wake tracking works satisfying, with estimation errors below 10% of the rotor diameter under moderate turbulence intensity. The Extended Kalman Filter formulation provides the confidence in the tracked wake position. The work shows how this can be effectively used for wake impingement detection.
Evaluation of a lattice Boltzmann-based wind-turbine actuator line model against a Navier-Stokes approach
Authors: Schottenhamml Helen , Anciaux-Sedrakian Ani , Blondel Frédéric , Borras-Nadal Adria , Joulin Pierre-Antoine , Rüde Ulrich 
Published: June 2022

Abstract
Due to the cost and difficulty to precisely measure aerodynamic quantities in onshore and offshore wind farms, researchers often rely on high-fidelity large eddy simulation, based on Navier-Stokes flow solvers. However, the cost of such simulation is very high and does not allow, in practice, extensive parametric studies for large wind farms. Among others, the lattice Boltzmann method is a good candidate for much faster, ExaScale wind farm flow simulations. The present paper aims to assess the validity of a lattice Boltzmann-based actuator line model and highlights its strengths and potential weaknesses. With this intent, comparisons against a Navier-Stokes approach commonly used in the wind energy community are performed. We assess the potential of the lattice Boltzmann method to reduce the computational cost of such simulations by analyzing the performance of the different solvers and their scalability. The lattice Boltzmann-based waLBerla solver reduces the computational costs significantly compared to SOWFA while maintaining the same accuracy as the Navier-Stokes-based method. Furthermore, we show that a multi-GPU implementation leads to an even more drastic reduction of the computational time, achieving faster-than-real-time simulations. This performance will allow extensive parametric studies over large wind farms in future studies.
Faster wind farm AEP calculations with CFD using a generalized wind turbine model
Authors: van der Laan M. P. , Andersen S. J. , Réthoré P.-E. , Baungaard M. , Sørensen J. N. , Troldborg N. 
Published: June 2022

Abstract
Wind farm Annual Energy Production (AEP) calculations are required to design energy efficient wind farm layouts. We investigate methods that can reduce the computational effort of AEP calculations using Reynolds-averaged Navier-Stokes simulations of an idealized atmospheric wind farm setup. In addition, we introduce a generalized wind turbine model that compares well with wind turbine aerodynamic data covering a large range of wind turbine sizes. We apply the general wind turbine model to reduce the computational effort of the AEP calculations by decreasing the number of independent wind speed flow cases. Furthermore, we apply Reynolds-number similarity to compute the wind speed flow cases faster and we show how wind farm layout mirror- and rotational-symmetry can reduce the number of independent wind direction flow cases.
Application of an open-loop dynamic wake model with high-frequency SCADA data
Authors: Braunbehrens R. , Schreiber J. , Bottasso C. L. 
Published: June 2022

Abstract
Future wind energy control applications require surrogate flow models that can capture processes on short time scales. Dynamic extensions of wake models might be a useful low cost solution. So far, studies have tested the proposals mostly in LES or LIDAR campaigns. At the same time, readily obtainable turbine SCADA measurements are increasingly being used directly for model input or validation. Newly available high resolution recordings allow for the study of processes on shorter time scales. The present study examines the modelling capabilities of a dynamic wake model by utilizing high frequency SCADA data. Therefore, the data is first analyzed for cross-correlation and time delays. Secondly, the dynamic wake model, initialized with SCADA measurements, is used to predict a two turbine wake interaction. An offline tuning algorithm is used to adapt the model for the shorter time scales.
Wind speed reconstruction from three synchronized short-range WindScanner lidars in a large wind turbine inflow field campaign and the associated uncertainties
Authors: Giyanani Ashim , Sjöholm Mikael , Rolighed Thorsen Gunhild , Schuhmacher Jakob , Gottschall Julia 
Published: June 2022

Abstract
Modern large wind turbines require high-resolution wind measurements as input to aerodynamic and aeroelastic simulations for modelling and validation purposes. Within the HighRe project, we aim at studying the aerodynamic effects at high Reynolds numbers by measuring four-dimensional wind fields (vx
,vy
,vz
,t) using three short-range WindScanners (SRWS). The systems were set up at the test site in Bremerhaven (Testfeld BHV) to perform an inflow wind field measurement campaign. In order to get a better understanding of the measurements, we describe the propagation of uncertainties in SRWS parameters to the measured wind field and propose an uncertainty model for a measurement setup with three SRWS lidars. In this study, we first evaluate the wind reconstruction and derive an uncertainty model for the wind components vx
, vy
, and vz
, which are mainly dependent on the input parameters, e.g., focus range, elevation angle, and azimuth angle. The effective intersection diameter at the intersection of three beams was found to be in the order of 2-5m. As expected, a high uncertainty was observed at lower heights in the vz
-component due to low elevation angles. This uncertainty evaluation forms the basis for comparing scanning patterns with regard to their accuracy in providing four-dimensional measurements.
Effects of the thrust force induced by wind turbine rotors on the incoming wind field: A wind LiDAR experiment
Authors: Letizia Stefano , Moss Coleman , Puccioni Matteo , Jacquet Clément , Apgar Dale , Valerio Iungo Giacomo 
Published: June 2022

Abstract
A field experiment was conducted to investigate the effects of the thrust force induced by utility-scale wind turbines on the incoming wind field. Five wind profiling LiDARs and a scanning Doppler pulsed wind LiDAR were deployed in the proximity of a row of four wind turbines located over relatively flat terrain, both before and after the construction of the wind farm. The analysis of the LiDAR data collected during the pre-construction phase enables quantifying the wind map of the site, which is then leveraged to correct the post-construction LiDAR data and isolate rotor-induced effects on the incoming wind field. The analysis of the profiling LiDAR data allows for the identification of the induction zone upstream of the turbine rotors, with an increasing velocity deficit moving from the top tip towards the bottom tip of the rotor. The largest wind speed reduction (about 5%) is observed for convective conditions and incoming hub-height wind speed between cut-in and rated wind speeds. The scanning LiDAR data indicate the presence of speedup regions within the gaps between adjacent turbine rotors. Speedup increases with reducing the transverse distance between the rotors, atmospheric instability (maximum 15%), while a longer streamwise extent of the speedup region is observed under stable atmospheric conditions.
Comparison of near wind farm wake measurements from scanning lidar with engineering models
Authors: Anantharaman A , Centurelli G , Schneemann J , Bot E , Kühn M 
Published: June 2022

Abstract
The presence of offshore wind farms causes downstream regions of reduced wind velocity, i.e. wind farm (cluster) wakes, which can affect the power of wind farms downstream. Engineering models are now being used to simulate the effects of these wakes, and an important requirement for model validation is a comparison with full-field measurements. Our objective in this paper is to parametrize and validate two engineering wake models with long-range lidar measurements. We use a long-range scanning Doppler lidar to scan the near wake region of a 400 MW offshore wind farm and compare the wind velocities in the wake to the outputs of two engineering models: FarmFlow and flappy. We adapt FarmFlow to solve the flow in highly unstable atmospheres by modifying the boundary conditions, which enables the comparison of velocity profiles behind the farm. The models perform qualitatively well in predicting the wake deficit and shape close to the farm and at lower heights. They predict higher wake losses within the farm when compared to production power data in a strongly unstable atmospheric case. However, the current analysis is limited due to the lack of inflow measurements for model initialization, compounded by limited data availability. We discuss the possibilities and limitations of long-range scanning lidar data for cluster wake model validation and the need for inflow measurements for model initialization. We conclude that with detailed inflow measurements, scanning long-range lidars could serve as a good tool for the validation of wind farm wake models.
Wind Park Power Prediction: Attention-Based Graph Networks and Deep Learning to Capture Wake Losses
Authors: Bentsen Lars Ødegaard , Dilp Warakagoda Narada , Stenbro Roy , Engelstad Paal 
Published: June 2022

Abstract
With the increased penetration of wind energy into the power grid, it has become increasingly important to be able to predict the expected power production for larger wind farms. Deep learning (DL) models can learn complex patterns in the data and have found wide success in predicting wake losses and expected power production. This paper proposes a modular framework for attention-based graph neural networks (GNN), where attention can be applied to any desired component of a graph block. The results show that the model significantly outperforms a multilayer perceptron (MLP) and a bidirectional LSTM (BLSTM) model, while delivering performance on-par with a vanilla GNN model. Moreover, we argue that the proposed graph attention architecture can easily adapt to different applications by offering flexibility into the desired attention operations to be used, which might depend on the specific application. Through analysis of the attention weights, it was showed that employing attention-based GNNs can provide insights into what the models learn. In particular, the attention networks seemed to realise turbine dependencies that aligned with some physical intuition about wake losses.