bluuebunny/crossref_metadata_2025 huggingface dataset:
Floating Wind Farm Layout Optimization Considering Moorings and Seabed Variations
Authors: Hall Matthew , Biglu Michael , Housner Stein , Coughlan Katherine , Mahfouz Mohammad Youssef , Lozon Ericka
Published: June 2024

This paper presents a method for optimizing the layout of floating wind farms that accounts for realistic seabed variations and the consequent adjustments to the mooring systems required for different turbine positions. The mooring lines of floating wind farms create large spatial constraints that are depth-dependent, since mooring designs must adapt to variations in seabed conditions over the array area. We develop a layout optimization methodology that addresses this, adjusting mooring system designs based on the local seabed characteristics as the layout changes and using steady-state models for the wake effects and mooring lines. The approach includes design algorithms that adjust the anchor positions and line length to achieve the desired mooring line profile for different water depths, and a layout optimization framework that implements spatial constraints between the turbines, mooring lines, and lease area boundaries. Demonstrating the method on several cases shows the effect of the seabed and spatial-constraint factors, as well as their interactions, on the optimal array layout. This demonstration paves the way for scaling up the method, using more powerful optimization algorithms to handle larger farm sizes and situations with more intensely varied seabed conditions.
CFD analysis of local influences in offshore wind measurements employing a floating LiDAR system
Authors: Da Silva J E M , Fraga G C , Petry A P
Published: June 2024

In the present work, the CFD modeling of neutral and stable atmospheric boundary layers regarding the horizontal homogeneity condition is discussed to assess the impact of local influences in wind measurements made by a Light Detection and Ranging (LiDAR) remote sensor installed at an offshore site. The LiDAR will be mounted on a real floating, production, storage, and offloading (FPSO) vessel, 200 km far from the coast, and the simulations serve as preliminary study for the impact of the geometry presence, in the air flow and in the measurements taken by the LiDAR. The reproduction of the homogeneity condition is discussed and the impacts of the presence of the ship structure are quantified considering the scanning scheme of the LiDAR. Turbulence is modeled with the k-ε and k-ω models for the neutral case, and k-ε with a modification following the Monin-Obukhov Similarity Theory for the stable boundary laye. By carrying out simulations with empty and blocked (i.e., with the FPSO installed) domains, we show that the effect of the platform is local and only significant at small heights. The neutral and stable cases show similar deviations (≤ 2 %) between the velocity fields in the blocked and empty domains. The neutral case shows locally a little more impact than the stable case, and the reason for that is discussed. We also found that the LiDAR scanning scheme could attenuate the impacts of the flow distortion in comparison with direct punctual measurements.
Dragonfly-Inspired Blades for high Aerodynamic Performance small Horizontal Axis Wind Turbine
Authors: Khedr Amr , Castellani Francesco
Published: June 2024

Inspired from the fascinating realm of Dragonfly features, this study intricately investigates the aerodynamic performance of a distinctive 1 m diameter small-scale Horizontal Axis Wind Turbine characterized by a unique bio-inspired tandem-blades configuration. Based on experimental wind tunnel tests and computational fluid dynamics simulations, the turbine performance is explored at different Tip Speed Ratios (TSRs). Remarkably, the bio-inspired turbine exhibits exceptional traits, achieving a notable peak power coefficient of approximately 0.35 at a low TSR of 3. A detailed investigation includes thorough analyses of pressure contours, relative velocity distributions, and individual blade contributions, revealing unique dynamics within the tandem configuration. The fore blade notably prevails in extracting wind energy, showcasing superior performance. While the hind blade influence in enhancing the fore blade efficiency emerges, underscoring the role of blade interactions in this bio-inspired designs. This study contributes significantly to the comprehension of the new design dragonfly-inspired wind turbine aerodynamics.
Given a wingspan, which windplane design maximizes power?
Authors: Trevisi Filippo , Croce Alessandro
Published: June 2024

Windplanes (i.e. Fly-Gen airborne wind energy systems) harvest wind power via the turbines placed on the tethered wing, which flies crosswind trajectories. In this paper, the optimal design of windplanes is investigated with simplified models, enabling an intuitive understanding of their physical characteristics. The windplane is then idealized as a point mass flying circular fully crosswind trajectories. If the gravity is neglected, the dynamic problem is axial symmetric and the solution is steady. The generated power can be expressed in non-dimensional form by normalizing it with the wind power passing by a disk with radius the wingspan. Since the reference area is taken to be a function of just the wingspan, looking for the design which maximizes this power coefficient addresses the question ”Given a wingspan, which design maximizes power?”. This is different from the literature, where the design problem is formulated per wing area and not per wingspan. The optimal designs have a finite aspect ratio and operate at the maximum lift-to-drag ratio of the airfoil. Airfoils maximizing the lift-to-drag ratio are then optimal for windplanes. If gravity is included in the model, gravitational potential energy is being exchanged over one revolution. Since this exchange comes with an associated efficiency, the plane mass and the related trajectory radius are designed to reduce the potential energy fluctuating over the loop. However, for decreasing turning radii, the available wind power decreases because the windplane sweeps a lower area. For these two conflicting reasons, the optimal mass is finite.
On the underperformance of the full-scale Clarkson University 3 meter ducted turbine
Authors: Visser Kenneth D
Published: June 2024

The performance of the Clarkson University 3 meter wind turbine under ambient conditions was measured to be considerably below that observed from the initial wind tunnel data. Comparison is made of the tunnel results with data obtained at three different experimental test sites and two different electronic downstream configurations. It is suggested that the advantages of the duct are mitigated by the transient nature of the ambient wind velocity and a required minimum rotational speed is needed to develop a sufficient thrust coefficient at the rotor plane for the duct to operate effectively. In the absence of these conditions, the presence of the duct can be argued to diminish the turbine performance.
Machine learning to rapidly predict turbine yaw angles for wake steering
Authors: Stanley Andrew P. J. , Mulder Tim , Doekemeijer Bart , Kreeft Jasper
Published: June 2024

Wake steering is an important control strategy to boost power production of a wind farm. Because of computational expense and problem complexity, wind farm layouts are typically optimized assuming they will operate without wake steering. However, performance gains are possible by simultaneously optimizing wind farm layout and the yaw angles for wake steering. In this paper, we present a method to train a machine learning model to predict turbine yaw angles as a function of their position relative to other turbines in the wind farm and the inflow wind speed. This model is able to predict turbine yaw angles with an R2 value of 0.98. The model also produces turbine yaw angles with wind farm power production that is similar to yaw angles that have been directly optimized. This method to rapidly compute optimal turbine yaw angles for wake steering enables control co-design of wind farms and the associated performance increase.
Control Co-Design Studies for a 22 MW Semisubmersible Floating Wind Turbine Platform
Authors: Zalkind Daniel , Bortolotti Pietro
Published: June 2024

We present a control co-design software framework that can be used to optimize floating wind turbines and their controllers. Because this framework has many options for design variables, constraints, and merit figures, along with modeling fidelity levels, we seek to demonstrate best practices for using the tool while designing a floating platform for the new 22 MW offshore reference wind turbine developed within the International Energy Agency Wind Technology Commercialization Programme 55 on Reference Wind Turbines and Farms. During these studies, we evaluate the use of different simulation fidelity levels, the effect of using different load cases for controller tuning, and the difference between sequential and simultaneous control co-design solutions. Based on these efforts, we suggest using an algorithm that performs an initial search of the design space before optimization. We find that solving smaller optimization problems, in a sequential manner, leads to more reliable outcomes in fewer iterations than larger, simultaneous control co-design solutions. However a simultaneous CCD solution produces a platform with a 2% lower mass than the sequential CCD outcome.
Aerodynamic and production comparison of wind farms with downwind versus conventional upwind turbines
Authors: Frontin Cory , Vijayakumar Ganesh , Bortolotti Pietro
Published: June 2024

Ever-increasing turbine scales and their associated logistical challenges have reignited questions about the performance of downwind rotor configurations. A particular potential benefit of downwind rotor configurations is the farm-scale power increase that may be conferred by tilt-driven downward wake entrainment and associated wake recovery. In this work, a comprehensive aerodynamic analysis is carried out to understand the mechanisms for wake entrainment and recovery across a spectrum of velocity and inflow alignment conditions on a small, structured farm in order to understand the impact of downwind rotors on farm production. The results show that the benefits demonstrated previously in the literature for downwind-rotor farms in aligned flows are fragile, and, outside of strong farm/flow alignment conditions, power production benefits for small farms with downwind rotor configurations are significantly if not completely mitigated by misalignment effects. The work indicates that farm-scale benefits for downwind rotors must be realized either from large-scale entrainment benefits, with more exotic farm arrangements that can take advantage of the aerodynamic effects, or from beneficial fatigue impacts from entrainment of less turbulent outer boundary layer flows.
Wake Effect Quantification using SCADA Data and LES Modelling of an Operational Offshore Wind Farm
Authors: Chanprasert Warit , Sharma Rajnish N. , Cater John E. , Norris Stuart E.
Published: June 2024

Wake effects in the Anholt offshore wind farm have been investigated using both operational data and a Large Eddy Simulation (LES) model of a group of five turbines within the wind farm. Analysis of operational data showed that the variations of main shaft speeds of the downstream turbines were almost six times those of the upstream turbine at near-rated operation. The aim of the LES was to study the impact of atmospheric stability on the wind turbine array performance and compare this with the field data. An LES precursor method was used to model the near-neutral and unstably stratified atmospheric boundary layers that represent typical conditions in winter and summer, respectively, and the turbines in wind farm model were simulated using an actuator line method. It was found that LES with the actuator line method and generic turbine design data can generate a reasonable mean power generation trend for the Anholt wind farm under near-neutral and unstable conditions. The maximum difference in the mean power output between the LES and averaged operational data was approximately 20%.
Reconstruction of dynamic wind turbine wake flow fields from virtual Lidar measurements via physics-informed neural networks
Authors: Zhang Jincheng , Zhao Xiaowei
Published: June 2024

Accurate characterisation of wind turbine wakes is important for the optimal design and operation of wind farms. However, current techniques for full-scale wind measurements are still limited to point characterisation. To address the research challenge in obtaining field characterisation of real-world wind turbine wakes, this work investigates the reconstruction of the dynamic wake flow fields based on a virtual turbine-mounted Lidar and physics-informed neural networks. Specifically, the wake flow field is reconstructed by fusing the sparse measurements with the two-dimensional Navier-Stokes equations without imposing any models for the unsteady wake. Different from supervised machine learning approaches which need the measured values for the quantities of interest in the first place, the proposed method can achieve the prediction of the wind velocity at new locations where there is no measurement available. The reconstruction performance is evaluated via high-fidelity numerical experiments and it is shown that the dynamic wind turbine wake flow fields are predicted accurately, where the main wake features, including the downwind development and crosswind meandering of the wake, are both captured. This work thus paves the way for investigating full-scale in situ wake flow dynamics in real-world wind energy sites.
Control co-design of a large offshore wind farm considering the effect of wind extractability
Authors: Pahus M L , Nishino T , Kirby A , Vogel C R
Published: June 2024

We present a prototype of a novel control co-design (CCD) method for large offshore wind farms. A traditional wind farm internal flow model using a Gaussian wake model and a Boolean yaw optimisation method is coupled, via the two-scale momentum theory, with an analytical model of ‘wind extractability’, to account for the farm-scale blockage effect that depends on the farm size and atmospheric boundary layer height. We also introduce a ‘gridded’ turbine layout optimisation into the CCD, which allows us to quickly find optimal angles of the primary axes of a regular turbine array to maximise the annual energy production (AEP) of a given number of turbines at a given offshore farm site. The developed CCD method is used to demonstrate an important trade-off between the minimum number of turbines and minimum farm area required to achieve a certain AEP at the Seagreen offshore farm site as an example. Our results also suggest that the yaw optimisation could help reduce the number of turbines required, but only when we aim to achieve a high AEP with a small farm area. The developed model could be further coupled with a financial model of wind farms in future studies.
Turbulent flow field reconstruction in wind-farms using power measurements
Authors: Alreweny A , Vandewalle S , Meyers J
Published: June 2024

Knowledge of the turbulent wind field within the atmospheric boundary layer is of great importance in maximizing wind-farm power generation and reducing structural loading. In this context, we propose a 4D-Var algorithm that integrates wind-turbine power measurements, a large-eddy simulation model, and background information to reconstruct the wind-farm’s inflow field. Following the collection of measurements, the 4D-Var problem is efficiently solved using the L-BFGS optimization algorithm. The results show fast convergence and good tracking of both reference power and wind-speed measurements across the turbines.
RANS wake surrogate: Impact of Physics Information in Neural Networks
Authors: Schøler J. P. , Rosi N. , Quick J. , Riva R. , Andersen S. J. , Murcia Leon J. P. , Van Der Laan M. P. , Réthoré P.-E.
Published: June 2024

Artificial Neural Networks (ANNs) are being applied as a faster alternative to Computational Fluid Dynamics (CFD) for wind turbine engineering wake models. Unfortunately, ANNs can fail to generalize if the data is insufficient. Physics-Informed Neural Networks (PINNs) can improve convergence while lowering the required data amounts. This paper investigates the PINN methodology systematically by considering varying amounts of data and physics collocation points. This work considers the rotationally symmetric Reynolds Averaged Navier-Stokes (RANS) formulation. Initially, a baseline fully data-driven ANN is studied to determine a suitable network size. Then, multiple PINN-based wake surrogates are trained with continuity and momentum conservation knowledge, varying amounts of data, and physics collocation points. It was found that including physics information under the best circumstances could improve accuracy by 18% at the cost of increasing the training time by a factor of 116. The findings imply that physics information can improve neural network based wake surrogates.
Development and Verification of an Improved Wake-Added Turbulence Model in FAST.Farm
Authors: Branlard E. , Jonkman J. , Platt A. , Thedin R. , Martínez-Tossas L. A. , Kretschmer M.
Published: June 2024

We introduce a generalized wake-added turbulence (WAT) model in the multiphysics, multiturbine simulation tool FAST.Farm. The WAT model introduces additional small-scale turbulence that represents the breakdown of vortical structures and shear layers in the wake. The article describes the development, implementation, calibration, and verification of the model. The novelties of the model include support for wake asymmetry, buildup of WAT across the wind farm, and secondary effects of wake-induced turbulence (e.g., wake meandering) driven by smaller-scale turbulence structures that arise from wake breakdown. Large-eddy simulations were run to support the calibration of the WAT parameters and verification of the model. Previous studies hypothesized that the lack of WAT modeling was the source of underprediction of fatigue loads, in particular for cases at low turbulence intensities and/or stable atmospheric boundary layers. This study confirms that the newly implemented WAT model enhances the loads predictions in these cases.
Wind Farm Control Optimisation Under Load Constraints Via Surrogate Modelling
Authors: Liew Jaime , Riva Riccardo , Friis-Møller Mikkel , Göçmen Tuhfe
Published: June 2024

In the field of wind farm control, wake steering has shown the potential to increase the power output of a wind farm by deflecting wakes away from downstream turbines. However, in some wake steering scenarios, the fatigue damage experienced by the turbines can increase, particularly when the wakes partially overlap a downstream rotor. It is for this reason that fatigue load constraints should be introduced into the control optimisation process. Unfortunately, wind turbine loads are notoriously difficult to predict, requiring expensive aeroelastic simulations. In this study, we present a wind farm control optimisation with load constraints using surrogate models to estimate the fatigue damage of each turbine in a wind farm designed for maximum energy production. We use the state-of-the-art aeroelastic wind farm simulator, HAWC2Farm, to produce a comprehensive data set of fatigue loads, which is then used to train surrogate models for rapid execution during an optimisation loop. The inputs of the surrogate model are chosen using the most significant modes from a proper orthogonal decomposition. Artificial neural networks are used for the surrogate models, and the wind farm control optimisation is carried out using OpenMDAO. Finally, a wind farm control optimisation with load constraints using wake steering is performed. The presented methodology for surrogate modelling and control optimisation is significant to produce accurate set point optimisations for wind farms while recognising the implications to turbine fatigue loads.
Bias correction of wind power forecasts with SCADA data and continuous learning
Authors: Jonas S , Winter K , Brodbeck B , Meyer A
Published: June 2024

Wind energy plays a critical role in the transition towards renewable energy sources. However, the uncertainty and variability of wind can impede its full potential and the necessary growth of wind power capacity. To mitigate these challenges, wind power forecasting methods are employed for applications in power management, electricity trading, or maintenance scheduling. In this work, we present, evaluate, and compare four machine learning-based wind power forecasting models. Our models correct and improve 48-hour forecasts extracted from a numerical weather prediction (NWP) model. The models are evaluated on datasets from a wind park comprising 65 wind turbines. The best improvement in forecasting error and mean bias was achieved by a convolutional neural network, reducing the average NRMSE down to 22%, coupled with a significant reduction in mean bias, compared to a NRMSE of 35% from the strongly biased baseline model using uncorrected NWP forecasts. Our findings further indicate that changes to neural network architectures play a minor role in affecting the forecasting performance, and that future research should rather investigate changes in the model pipeline. Moreover, we introduce a continuous learning strategy, which is shown to achieve the highest forecasting performance improvements when new data is made available.
Mimicking the Helix with a porous disc for wind tunnel testing
Authors: De Vos Brian , Harder Bjorn M. , Huisman Thomas A. , Gutknecht Jonas , Van Wingerden Jan-Willem
Published: June 2024

A promising method to reduce wake effects in offshore wind farms is the Helix approach, which increases the mixing of the wake with the surrounding flow by exciting the individual blade pitch. This increases the wind speed in the wake, resulting in a higher power output at a downstream turbine. Wind tunnel testing is crucial to gather further understanding of the governing mechanisms behind the Helix and its efficiency in larger wind farm arrays. However, model turbines are expensive and complex. Porous Discs (PD) have proven to supply a less expensive and less complex alternative for wake-focused wind tunnel studies. In this study we present a novel PD model to mimic the Helix. The fundamental idea is to mimic the non-uniform, unsteady energy extraction over the rotor plane as observed at a Helix-controlled turbine. For this purpose, we derive a non-uniform porosity distribution over the PD, based on Large Eddy Simulations of a three-bladed turbine controlled with the Helix approach, and the actuator disc theory. The resulting non-uniform PD rotates at the excitation frequency described by the Strouhal number to mimic the Helix. We verified the novel experimental setup with smoke visualisation techniques and thrust measurements at a second PD in the wake and observed the typical characteristics of the Helix wake of a model turbine: First, the wake was deformed into a helical shape, and second, the wake velocity increased depending on the excitation Strouhal number.
Experimental comparison of induction control methods for wind farm power maximization on a scaled two-turbine setup
Authors: Van Der Hoek Daan , Ferreira Carlos Simão , Van Wingerden Jan-Willem
Published: June 2024

Induction control methods offer a potential solution to minimizing wake effects that occur in large wind farms. This paper presents an experimental study on multiple induction control methods for wind farm power maximization. Wind tunnel experiments were conducted on two aligned scaled wind turbines. The upstream turbine was operated with static induction control, periodic dynamic induction control with collective pitch actuation, and dynamic individual pitch control (the helix approach). All wind farm control implementations were compared to a baseline case, which optimized the individual power extraction of both turbines. Tomographic particle image velocimetry was used to measure the wake of the upstream turbine. Based on turbine measurements, grid searches were employed to discover the optimal frequency and amplitude of the pitch actuation in the dynamic induction control cases. While static induction control showed increased wake velocities in the near wake, it did not provide an overall increase in power production of the two-turbine array. Dynamic induction control methods, especially the helix approach in the counterclockwise direction, were seen to significantly increase the total power output compared to the baseline control case. However, this improvement came with a larger amount of pitch actuation and increased fatigue loading of structural components in the fore-aft direction.
Power performance of a model floating wind turbine subjected to cyclic pitch motion: A wind tunnel study
Authors: Duan Guiyue , Gattari Daniele , Porté-Agel Fernando
Published: June 2024

Wind tunnel experiments were performed with a miniature floating wind turbine model to study the effects of cyclic pitch motion on its power performance. The cyclic pitch motion was prescribed by two key parameters: pitch frequency and amplitude. The power performance of the turbine model was investigated at a frequency range of 0.1 − 5.0Hz and an amplitude range of 0 − 30°. Both the mean and time variation of the power production were analyzed, and the effects of the pitch parameters, i.e., the pitch amplitude and frequency, were investigated and discussed. The results show a clear periodicity of power variation and its dependence on pitch frequency and amplitude. For relatively small pitch frequencies (0.5 − 3.0Hz), the mean power and periodic power variation can be predicted based on the uniform and steady flow assumption. Compared to the power output in the baseline case of no pitch dynamics, cyclic pitch motions were found to cause higher power fluctuations, which were contributed by both the pitch motion and flow turbulence. Finally, the temporal variation of the free-rotation speed, used as an indicator of available aerodynamic power, is found to be periodic when the turbine is under cyclic pitch motion. This suggests the possibility of applying dynamic rotor control strategies to maximize power production.
A wind farm consisting of two turbines - The combined influence of turbine spacing and rotational direction
Authors: Englberger Antonia , Dörnbrack Andreas
Published: June 2024

Large-eddy simulations are conducted to investigate the impact of an actual decrease in turbine spacing on the wake structure for a small wind park consisting of two wind turbines in a row with a given rotor diameter D. A systematic variation of the inflow conditions (near-neutral and veering inflow) and the rotational direction of the rotors (clockwise and counterclockwise) provides an initial overview of the atmospheric flow fields in the wakes of both turbines. Whereas a smaller turbine spacing under near-neutral conditions results in a decrease of the streamwise flow component and an increase of turbulence, the situation is much more complicated under veering inflow. If a clockwise (counterclockwise) rotating turbine interacts with a Northern Hemispheric Ekman spiral, the combination of a rotor-induced v-component, which reduces (slightly amplifies) the meridional inflow velocity component, leads to a weakening (slight intensification) of this meridional wind in the vicinity of the downwind rotor.
Impact of inflow characteristics on the instantaneous power production using detrended fluctuation analysis
Authors: Vratsinis Konstantinos , Pauscher Lukas , Daems Pieter-Jan , Van Beeck Jeroen , Helsen Jan
Published: June 2024

This study investigates the influence of inflow characteristics on wind turbine power production using Detrended Fluctuation Analysis (DFA). The research focuses on extracting indicators for the energy of flow fluctuations at small timescales and their scaling properties. This methodology is demonstrated using one year of 1-second SCADA data from an offshore wind farm. These indicators are then compared with the commonly used Turbulence Intensity (TI). The findings demonstrate that DFA coefficients, specifically the DFA y-intercept and the DFA slope, account for a significantly larger portion of the variability in active power. This research enhances SCADA-based performance analysis.
Verification and Validation of Wind Farm Flow Models
Authors: Quick Julian , Mouradi Rem-Sophia , Devesse Koen , Mathieu Antoine , Paul Van Der Laan M. , Murcia Leon Juan Pablo , Schulte Jonas
Published: June 2024

Wind farm flow models allow for the analysis of wind power plant wake effects. There are several mathematical models available to describe this complex flow, and some of them are implemented across multiple code bases. This manuscript presents a framework for verification and validation of these models of different complexities. As part of this work, an API was developed to interface the different flow tools using the WindIO wind plant schema. Then, verification techniques are applied to the embedded tools as a test to make sure there are no mistakes in the codes, and across different flow models. This reveals similarities and differences in stochastic trends given realistic measurement uncertainty. Validation is performed using a database of large eddy simulations to quantify model agreement with high fidelity data and develop a predictor of what this implies for future simulations.
The use of LES to analyse higher-order moments of two-point statistics
Authors: Bock Marcel , Fuchs André , Friedrich Jan , Peinke Joachim
Published: June 2024

The models prescribed by the IEC standard for the generation of wind fields, as well as intermittent modifications of the original models, are used as inflows in LES. The behaviour of the wind fields is investigated with regard to higher-order moments. It is shown that all models reach an intermittent state in the LES. This intermittent state can be determined by the intermittency parameter and by higher orders of the moments. It can be seen that the Kaimal model generates significantly lower intermittency than the other models. The convergence length is significantly reduced with a correct intermittency description in the wind fields.
Observation and modelling of asymmetric loading on large offshore wind turbines in wake conditions
Authors: Bernard V , Andersen S J , Murcia Leon J P , Beaudet L , Verelst D , Iliopoulos A
Published: June 2024

We study the dynamic loading of a wind turbine in the presence of a single wake in the wind speed range of 8 to 12 m/s and a wind direction range of 40° around the wake alignment direction. Measurement data from an offshore wind farm in the North Sea is compared to simulations. Damage equivalent loads and order spectra are compared. Five wake models are used: Frandsen added turbulence model, three parametrizations of DWM, and LES wake simulations. A new parametrization of DWM with an added wake distortion model is introduced improving the wake effect on fatigue loading of tower top torsion and tilt bending moment. All DWM models compare more favourably to measurements than the added turbulence model, but only the wake distortion model allows for a conservative fatigue assessment on all channels. LES has the potential to be even more accurate in capturing the spectral response in wake, but results are preliminary as limited LES data was available.
Benchmarking engineering wake models for farm-to-farm interactions
Authors: Vollmer Lukas , Wurps Hauke , Dörenkämper Martin
Published: June 2024

Inter-farm wakes between offshore wind farms can cause a significant production loss at the downstream wind farm. We compare the power production at two wind farms that are separated by approximately 20 km to engineering model results provided by three different wind farm developers. Model inflow conditions were derived from the nacelle anemometry of the wake-creating wind farm and a mesoscale simulation. The comparison reveals that the model results show in general a good approximation of the wake losses, but underestimate the increase of losses in stable stability conditions. Here, specifically the measured wakes appear to be much wider than the modelled wakes.
A new parametrization of the Global Blockage Effect
Authors: Centurelli G , Vollmer L , Lukassen L J , Peinke J
Published: June 2024

An existing potential flow model for describing the interaction of complex structures and flow currents is adapted for the modeling of a wind farm. The aim is to investigate if such a simple formulation could improve the representation of the global blockage effect (GBE) in engineering models. The model is then coupled with a parametrization of GBE from LES in order to describe the power extraction redistribution GBE is normally associated with. Despite the original model formulation being found to not apply particularly well to the description of a wind farm, the further development introduces dependency on the atmospheric stratification above the wind farm, a feature of GBE observed in LES. Furthermore, it agrees with the trend of power extraction redistribution in a wind farm. However, more research is necessary to promote a better quantitative match between LES and the proposed model.
Nexus between the deficit in moisture transport and drought occurrence in regions with projected drought trends
Authors: Gimeno-Sotelo Luis , Stojanovic Milica , Sorí Rogert , Nieto Raquel , Vicente-Serrano Sergio M , Gimeno Luis
Published: June 2024

In this article, we focus on studying the nexus between moisture transport deficit and drought occurrence in nine key regions across the world where the magnitude of meteorological drought is projected to increase from 1850 to 2100 under a high anthropogenic emission scenario. These regions are central America, southwestern South America, northern Brazil, the Amazon, northeastern Brazil, the western Mediterranean, southern Africa, the eastern Mediterranean, and southwestern Australia. Using the Lagrangian particle dispersion model FLEXPART, we identify the specific moisture sources of the regions (the own region, the nearby continental source and the oceanic sources) and obtain their contributions to the precipitation in the regions for the period 1980–2018. For each region and specific moisture source, the conditional probability of meteorological drought occurrence given an equivalent contribution deficit from the source is estimated using copula models, a statistical methodology that allows us to capture complex relationships between variables. We identify the dominant moisture source in each region, which is the source for which the contribution deficit maximises drought probability. A variety of cases are found: in three regions, the dominant source is the region itself, in one region, it is the nearby terrestrial source, and in five regions, it is an oceanic source. In general, contribution deficits from specific moisture sources are associated with only slightly greater drought probabilities than those from major global moisture sources. We also reveal that the source that contributes the most to precipitation in a given region is not necessarily the dominant source of drought in the region. These results highlight the importance of understanding the role of dominant moisture sources and moisture transport deficits on meteorological drought occurrence at a regional scale.
Evaluating Performance and Grid Impacts of On-Grid Rooftop PV System: Case Study of A Mosque
Authors: Anugrah Pinto , Pratama Rizki Wahyu , Sholekan Muhammad , Fikriyadi Zakariya Arif
Published: June 2024

This study presents a load flow analysis of on-grid rooftop PV installed at Masjid Tablighiyah, Bukittinggi, Indonesia. The analysis was conducted using the Newton-Raphson method and simulated using ETAP software. Two scenarios was explored for the analysis: i) connecting the PV into 20 kV bus, and ii) connecting into 380 V bus. Results obtained showed that grid voltage are meeting the utility standards, with all bus voltages are within ±5% threshold. However, certain nodes have moderately low voltages, suggesting a potential need for pre-emptive measures during peak demand. The power factor performance falls within acceptable ranges (84-91%), indicating reasonable system efficiency. However, there is still needed for continuous monitoring to prevent degradation from harmonic-producing loads. In addition, substation transformers and distribution feeders show ample capacity for future expansions as electricity loads grow. The findings provide a robust baseline for additional grid integration studies, emphasizing the need for further enhancements at weaker nodes to ensure sustained stability and reliability in future demand conditions.
Enhancing Turbine Blade Manufacturing through MEMS-Based Milling Monitoring
Authors: Rahman Achmad Zaki , Jauhari Khairul , Huda Mahfudz Al , Sumantri Dede , Prahasto Toni , Rusnaldy  , Widodo Achmad
Published: June 2024

Manufacturing steam turbine blades with intricate shapes poses a substantial challenge. These blades significantly impact turbine efficiency, reliability, and productivity. Their precise formation through milling processes demands vigilant monitoring, especially concerning the cutting tool’s condition, which is responsible for shaping the blade profiles and ensuring high-quality outcomes. Direct tool monitoring, however, disrupts productivity, prompting the need for an indirect Tool Condition Monitoring (TCM) system. Such a system becomes essential for detecting tool wear and damage early, ensuring dimensional accuracy and surface smoothness. This experiment monitors tool conditions by analyzing vibrations generated by an endmill cutter while machining martensitic stainless steel (MSS) AISI 420. The setup employs a cost-effective MEMS vibration sensor, the ADXL 345, and Raspberry Pi for real-time online TCM functionality as the signal processor. This study delves into the sensor’s ability to capture vibrations representing actual tool conditions, focusing on the affordability and effectiveness of MEMS-based monitoring. The successful real-time implementation of MEMS-based monitoring could be an easily accessible gateway for manufacturing industries to embrace cloud-based monitoring systems, aligning with current technological trends. This research aims to underscore the viability and potential adoption of affordable MEMS sensors for comprehensive tool condition monitoring, offering a seamless transition toward cloud-integrated monitoring systems for manufacturing industries.
Two-step ALD process for non-oxide ceramic deposition: the example of boron nitride
Authors: Hossain Ali , Souvignet Thomas , Innis Neil R , Hao Wenjun , Boisron Olivier , Florea Ileana , Xiao Peng , Sledzinska Marianna , Journet Catherine , Marichy Catherine
Published: June 2024

Atomic layer deposition (ALD) based on polymer-derived ceramics (PDCs) chemistry is used for the fabrication of boron nitride thin films from reaction between trichloroborazine and hexamethyldisilazane. The transposition of the PDCs route to ALD is highly appealing for depositing ceramics, especially non-oxide ones, as it offers various molecular precursors. From a two-step approach composed of an ALD process forming a so-called preceramic film and its subsequent ceramization, conformal and homogenous BN layers are successfully synthesized on various inorganic substrates. In the first stage, smooth polyborazine coatings are obtained at a temperature as low as 90 °C. The saturation and self-limitation of the ALD gas-surface reactions are verified. Intriguingly, three ALD windows seem to exist and are attributed to change in ligand exchange. After the ceramization stage using a heat treatment, conformal near-stoichiometric BN layers are obtained. Their structure in terms of crystallinity can be adjusted from amorphous to well-crystalline sp2 phase by controlling the treatment temperature. In particular, a crystallization onset occurs at 1000 °C and well defined sp2 crystalline planes oriented parallel to the surface are noted after ceramization at 1350 °C. Finally, side-modification of the substrate surface induced by the thermal treatment appears to impact on the final BN topography and defect generation.
Production and mechanical characterization of Carbon fiber laminated composites modified by Graphene nano platelets (GnP) for high performance application
Authors: Srivastava Amit Kumar , Singh Abhishek , Verma Rajesh Kumar
Published: June 2024

Polymeric materials play a pivotal role in diverse high-performance engineering domains, including aerospace, marine, automotive, and defense sectors. Their applications span from essential protective gear to intricate components vital for aircraft missiles, showcasing their versatility and significance in modern technology. The Graphene nano platelets (GnP) have the exceptional properties of a high contact area with the reinforcement material and enhanced synergistic effect, which is highly desired to improve the material performance. The present work describes the production of Carbon fiber laminated composites enhanced by Graphene nano Platelets (GnP) using a cost-effective Hand layup method (HLM). Herein, three different concentrations of GnP at 0.25, 1.0, and 1.75 wt% were used to modify the CFRP laminates. This is primarily performed to examine the viscoelastic and mechanical properties of the proposed GnP/CFRP sample. The findings of mechanical testing reveal that GnP nanofiller addition of 1.00 wt% significantly enhances the tensile and flexural properties by 20.7% and 10.05% respectively in comparison to neat sample. Also, the composites show satisfactory improvement in impact strength by 31.60% and enhanced viscoelastic properties at a 0.25 wt% of GnP loading. The XRD and DMA findings support GnP loading for high performance applications.
Experimental investigations on coated copper wire and annealed copper wire electrodes in wire EDM machining through a comparative assessment employing the TOPSIS technique
Authors: Prathap Varma Uddarraju Dhana Satya , Harish Ippile , Battina N Malleswararao , Prasadaraju Kantheti , Chandu Prasad S
Published: June 2024

Metals that are electrically conductive but are difficult to work with are typically machined using WEDM (Wire Electric Discharge Machining). The production of tiny components, which are challenging to mill using earlier non-traditional machining techniques, has undergone a substantial transformation thanks to these machines. Two wire electrodes coated copper wire and annealed copper wire electrodes are used for the testing in this investigation, and the analysis is completed utilizing the tool material. The workpiece material, D2 Steel, was cut into cubes that were 10 mm thick for the experiment, and a wire electrode with a diameter of 0.25 mm was used. Using an L27 orthogonal array, six input variables were employed at three different levels in the testing. The TOPSIS technique is utilized to optimize the output variables, leading to the determination of various output parameters, including Material removal rate (MRR), Kerf width (KW), tool wear rate (TWR), and surface roughness (SR). The chosen input variables include wire tension (WT), a gap voltage (GV), input current (IP), wire feed rate (WF), pulse off time (Toff), and pulse on time (Ton). The 10 mm-wide D2 steel was milled using two wire electrodes, and an ANOVA table was made to identify the factors that had the greatest impact on the WEDM parameters. The rank and ideal Wire EDM parameter values must be calculated using main effect plots and tables of replies. From this investigation, we can also draw the conclusion that for machining D2 steel, annealed wire electrodes provide greater MRR than coated wire.
End-to-end simulation of particle physics events with flow matching and generator oversampling
Authors: Vaselli F , Cattafesta F , Asenov P , Rizzi A
Published: June 2024

The simulation of high-energy physics collision events is a key element for data analysis at present and future particle accelerators. The comparison of simulation predictions to data allows looking for rare deviations that can be due to new phenomena not previously observed. We show that novel machine learning algorithms, specifically Normalizing Flows and Flow Matching, can be used to replicate accurate simulations from traditional approaches with several orders of magnitude of speed-up. The classical simulation chain starts from a physics process of interest, computes energy deposits of particles and electronics response, and finally employs the same reconstruction algorithms used for data. Eventually, the data are reduced to some high-level analysis format. Instead, we propose an end-to-end approach, simulating the final data format directly from physical generator inputs, skipping any intermediate steps. We use particle jets simulation as a benchmark for comparing both discrete and continuous Normalizing Flows models. The models are validated across a variety of metrics to identify the most accurate. We discuss the scaling of performance with the increase in training data, as well as the generalization power of these models on physical processes different from the training one. We investigate sampling multiple times from the same physical generator inputs, a procedure we name oversampling, and we show that it can effectively reduce the statistical uncertainties of a dataset. This class of ML algorithms is found to be capable of learning the expected detector response independently of the physical input process. The speed and accuracy of the models, coupled with the stability of the training procedure, make them a compelling tool for the needs of current and future experiments.
The Genesis and Evolution of EU Food Law
Authors: Schebesta Hanna , Purnhagen Kai
Published: June 2024

This chapter sketches the history and emergence of EU food law in terms of politics and policy. It describes the EU Treaties as the political foundations of European integration and, at the same time, the cornerstone of EU food law (section 1.2). The chapter then discusses the main policy paradigms that have shaped EU food legislative harmonization (section 1.3), namely agriculture (section 1.3.1), the completion of the internal market (section 1.3.2), food safety (section 1.3.3), transparency (section 1.3.4), and most recently, sustainability (section 1.3.5). In addition, it sets out a major premise of the book, namely that the food domain has emerged as a distinctive regulatory domain within EU law.
Internal Market Law and Food
Authors: Schebesta Hanna , Purnhagen Kai
Published: June 2024

This chapter provides an overview of food in the internal market. In particular, this includes the free movement of goods and significant landmark case law related to Articles 34–36 TFEU, where the restrictions and justifications are discussed with examples from the food domain (sections 4.2. and 4.3). The principle of mutual recognition has been subsequently operationalized by legislation, namely Directive (EU) 2015/1535 on the duty to notify technical regulations and Regulation (EC) 764/2008 on mutual recognition for products not subject to harmonization (section 4.4). The chapter shows that the free movement Treaty articles have lost much of their significance due to both the legislative operationalization of the internal market and the extensive harmonization of EU food law. However, authorization procedures represent a notable exception in practical importance. The conclusions challenge the textbook narrative of the successful completion of the internal market, and instead highlight a reality where the free movement of foods is administratively managed (section 4.5).
Chemical Safety of Food Products
Authors: Schebesta Hanna , Purnhagen Kai
Published: June 2024

This chapter addresses EU law on the chemical safety of foodstuffs. The legal frameworks broadly distinguish unintentionally (section 11.2) from intentionally added substances (section 11.3). The chapter therefore examines EU legislation on unintentionally added contaminants and their maximum levels (MLs) (sections 11.2.1 to 11.2.3) as well as legislation on the use of avoidable substances and their maximum residue levels (MRLs), notably pesticides (section 11.3.1), veterinary medicinal products (section 11.3.2), biocides (section 11.3.3), and hormones in meat (section 11.3.4). It concludes with a discussion on the need for a regulatory overhaul of the current system to regulate chemical substances in foodstuffs (section 11.4).