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In the realm of infectious disease control, accurate modeling of the transmission dynamics is pivotal. As human mobility and commuting patterns are key components of communicable disease spread, we introduce a novel travel time aware metapopulation model. Our model aims to enhance estimations of disease transmission. By providing more reliable assessments on the efficacy of interventions, curtailing personal rights or human mobility behavior through interventions can be minimized. The proposed model is an advancement over traditional compartmental models, integrating explicit transmission on travel and commute, a factor available in agent-based models but often neglected with metapopulation models. Our approach employs a multi-edge graph ODE-based (Graph-ODE) model, which represents the intricate interplay between mobility and disease spread. This granular modeling is particularly important when assessing the dynamics in densely connected urban areas or when heterogeneous structures across entire countries have to be assessed. The given approach can be coupled with any kind of ODE-based model. In addition, we propose a novel multi-layer waning immunity model that integrates waning of different paces for protection against mild and severe courses of the disease. As this is of particular interest for late-phase epidemic or endemic scenarios, we consider the late-phase of SARS-CoV-2 in Germany. The results of this work show that accounting for resolved mobility significantly influences the pattern of outbreaks. The improved model provides a refined tool for predicting outbreak trajectories and evaluating intervention strategies in relation to mobility by allowing us to assess the transmission that result on traveling. The insights derived from this model can serve as a basis for decisions on the implementation or suspension of interventions, such as mandatory masks on public transportation. Eventually, our model contributes to maintaining mobility as a social good while reducing exuberant disease dynamics potentially driven by travel activities. Author summary: As human contacts and contact networks are key to the development and prediction of infectious disease spread, travel and commuting activities are important components to be considered in mathematical-epidemiological modeling. Two, often contrasting modeling approaches, based on subpopulations and based on individuals can provide insights of different granularity but also come at different levels of complexity. With this article, we extend a recently introduced Graph-ODE-based model by the explicit introduction of mobility-based infection models in which we allow focused nonpharmaceutical interventions, like face mask mandates in public transport, and in which we can explicitly keep track of secondary cases induced by travel activities, a component mostly not available with equation-based models. In addition, we introduce a novel multi-layer waning immunity model particularly suitable for late-phase epidemic or endemic scenarios. On a daily level and geographically small scale, the newly proposed model often develops similarly, although our results show that complex mobility networks can lead to substantially different disease dynamics in the entirety of a federal state or country. The proposed model thus enables a better understanding of infectious disease dynamics through mobility. It allows for targeted numerical investigations and thus leads to more appropriate real-world interventions. [ABSTRACT FROM AUTHOR]

Field measurements of the flow interaction between the near wake of an upstream wind turbine and the induction zone of a downstream turbine are scarce. Measuring and characterising these flow features in wind farms under various operational states can be used to evaluate numerical flow models and design of control systems. In this paper, we present induction zone measurements of a utility-scale 3.5 MW turbine with a rotor diameter of 126 m in a two-turbine wind farm operating under waked and unwaked conditions. The measurements were acquired by two synchronised continuous-wave WindScanner lidars that could resolve longitudinal and lateral velocities by dual-Doppler reconstruction. An error analysis was performed to quantify the uncertainty in measuring complex flow situations with two WindScanners. This is done by performing a large-eddy simulation while using the same measurement layout, modelling the WindScanner sensing characteristics and simulating similar inflow conditions observed in the field. The flow evolution in the induction zone of the downstream turbine was characterised by performing horizontal-plane dual-Doppler scans at hub height. The measurements were conducted for undisturbed, fully waked and partially waked flows. Evaluation of the engineering models of the undisturbed induction zone showed good agreement along the rotor axis. In the full-wake case, the measurements indicated a deceleration of the upstream turbine wake due to the downstream turbine induction zone as a result of the very short turbine spacing. During a wake steering experiment, the interaction between the laterally deflected wake of the upstream turbine and the induction zone of the downstream turbine could be measured for the first time in the field. Additionally, the analyses highlight the affiliated challenges while conducting field measurements with synchronised lidars. [ABSTRACT FROM AUTHOR]

The accurate characterization of offshore wind resources is crucial for the efficient design and operation of wind energy projects. However, the scarcity of in situ observation in marine environments requires exploration of alternative approaches. For this reason, this study presents a comprehensive comparison between wind profiles derived from the Advanced Scatterometer ASCAT) satellite observations and the ERA5 reanalysis dataset against ship-based lidar measurements in the Northern Baltic Sea. In order to extrapolate ASCAT observations to wind turbine relevant heights, a long-term correction approach has been implemented. Due to the sensitivity of this method to the accurate characterization of the atmospheric stability, two different approaches were assessed to characterize the stability conditions, showing a great robustness of the methodology employed and leading to noticeable differences only in specific coastal locations. The comparison reveals a close agreement between ASCAT and ERA5 beyond 40 km distance from the coast. Specifically, ASCAT tends to overestimate the mean wind speed derived from lidar measurements, while ERA5 exhibits a consistent underestimation. In terms of vertical accuracy, ERA5 displays a consistent bias of approximately 0.5 m s-1 along the profile, whereas ASCAT exhibits a smaller bias within the lower 200 m of the profile. These findings underline the potential and limitations of ASCAT-derived wind profiles and ERA5 for offshore wind characterization. [ABSTRACT FROM AUTHOR]

We introduce an aerodynamic rotor concept for an offshore wind turbine which is tailored for an increased power feed-in at low wind speeds by a substantial increase in the rotor diameter while maintaining the rated power. The main objective of the conceptual design is to limit the steady-inflow loads (blade flapwise root bending moment (RBM) and thrust) to the maximum values of a reference turbine. The outer part of the blade (i.e. outer 30 % span) is designed for a higher design tip speed ratio (TSR) and a lower axial induction than the inner part. By operating at the high TSR in light winds, the slender outer part fully contributes to the increased power capture. In stronger winds the TSR is reduced and the torque generation is shifted to the inner section of the rotor. Moreover, the blade design efficiently reduces the power losses when the flapwise RBM is limited through peak shaving, below rated wind speed. This is of high importance, given the wind speed distribution at offshore sites. The characteristics of the rotor are first investigated with stationary blade element momentum simulations and further analysed with aeroelastic simulations, considering the flexibility of blades and tower to show that a structural design is feasible even for a blade of this size and complexity. The economic revenue and the cost of valued energy of the turbine are estimated and compared to the IEA 15 MW offshore reference turbine, considering a fictitious wind-speed-dependent feed-in price. Our results for the turbine concept with an increase in rotor diameter by 36 % show that the revenue can be increased by 30 % and the cost of valued energy can be reduced by 16 % compared to the reference turbine. [ABSTRACT FROM AUTHOR]

Data-driven wake models have recently shown a high accuracy in reproducing wake characteristics from numerical data sets. This study used wake measurements from a lidar-equipped commercial wind turbine and inflow measurements from a nearby meteorological mast to validate an interpretable data-driven surrogate wake model. The trained data-driven model was then compared to a state-of-the-art analytical wake model. A multi-plane lidar measurement strategy captured the occurrence of the wake curl during yaw misalignment, which had not yet conclusively been observed in the field. The comparison between the wake models showed that the available power estimations of a virtual turbine situated four rotor diameters downstream were significantly more accurate with the data-driven model than with the analytical model. The mean absolute percentage error was reduced by 19% to 36 %, depending on the input variables used. Especially under turbine yaw misalignment and high vertical shear, the data-driven model performed better. Further analysis suggested that the accuracy of the data-driven model is hardly affected when using only supervisory control and data acquisition (SCADA) data as input. Although the results are only obtained for a single turbine type, downstream distance and range of yaw misalignments, the outcome of this study is believed to demonstrate the potential of data-driven wake models. [ABSTRACT FROM AUTHOR]

In contrast to fixed measuring devices, ship-based lidar systems provide highly reliable wind observations within extensive regions. Therefore, this kind of reference dataset provides a great potential for evaluating the performance of mesoscale numerical models in resembling mesoscale flow phenomena such as low-level jets -- essential for an optimal development and operation of wind turbines. This paper presents a comparison between numerical output data from two state-of-the-art numerical datasets (ERA5 and NEWA) and the shipmounted lidar measurements from the NEWA Ferry Lidar Experiment. The comparison was performed along the route covered by the vessel, as well as in specific locations within this route, to better understand the capabilities and limitations of the numerical models to precisely resemble the occurrence and main properties of low-level jets (LLJs) in different locations. The findings of this study show that the non-stationary nature of ship-based lidar measurements allows evaluating the accuracy of the models when retrieving jets' characteristics and occurrence under different temporal and spatial effects. Numerical models underestimate the occurrence of LLJs, and they struggle to accurately describe their main characteristics, with a particularly large underestimation of the falloff. The found results are to be seen in relation to the characteristics of the observations, such as the data availability, the time--position relation of the selected vessel's route, or the profile height limitation, as well as the features of the jets, with a particular relevance of core height and falloff. Additionally, the results illustrate the temporal and spatial shift between the LLJ events detected by the measurements and the models and the potential benefit of considering such deviations when studying LLJs' climatology through numerical modes. [ABSTRACT FROM AUTHOR]

Due to the increasing share of wind energy in the power system, minute-scale wind power forecasts have gained importance. Remote-sensing-based approaches have proven to be a promising alternative to statistical methods and thus need to be further developed towards an operational use, aiming to increase their forecast availability and skill. Therefore, the contribution of this paper is to extend lidar-based forecasts to a methodology for observer-based probabilistic power forecasts of individual wind turbines and aggregated wind farm power. To do so, lidar-based forecasts are combined with supervisory control and data acquisition (SCADA)-based forecasts that advect wind vectors derived from wind turbine operational data. After a calibration, forecasts of individual turbines are aggregated to a probabilistic power forecast of turbine subsets by means of a copula approach. We found that combining the lidar- and SCADA-based forecasts significantly improved both forecast skill and forecast availability of a 5 min ahead probabilistic power forecast at an offshore wind farm. Calibration further increased the forecast skill. Calibrated observer-based forecasts outperformed the benchmark persistence for unstable atmospheric conditions. The aggregation of probabilistic forecasts of turbine subsets revealed the potential of the copula approach. We discuss the skill, robustness and dependency on atmospheric conditions of the individual forecasts, the value of the observer-based forecast, its calibration and aggregation, and more generally the value of minute-scale power forecasts of offshore wind. In conclusion, combining different data sources to an observer-based forecast is beneficial in all regarded cases. For an operational use one should distinguish between and adapt to atmospheric stability. [ABSTRACT FROM AUTHOR]

The dynamic inflow effect describes the unsteady aerodynamic response to fast changes in rotor loading due to the inertia of the wake. Fast changes in turbine loading due to pitch actuation or rotor speed transients lead to load overshoots. The phenomenon is suspected to be also relevant for gust situations; however, this was never shown, and thus the actual load response is also unknown. The paper's objectives are to prove and explain the dynamic inflow effect due to gusts, and compare and subsequently improve a typical dynamic inflow engineering model to the measurements. An active grid is used to impress a 1.8m diameter model turbine with rotor uniform gusts of the wind tunnel flow. The influence attributed to the dynamic inflow effect is isolated from the comparison of two experimental cases. Firstly, dynamic measurements of loads and radially resolved axial velocities in the rotor plane during a gust situation are performed. Secondly, corresponding quantities are linearly interpolated for the gust wind speed from lookup tables with steady operational points. Furthermore, simulations with a typical blade element momentum code and a higher-fidelity free-vortex wake model are performed. Both the experiment and higher-fidelity model show a dynamic inflow effect due to gusts in the loads and axial velocities. An amplification of induced velocities causes reduced load amplitudes. Consequently, fatigue loading would be lower. This amplification originates from wake inertia. It is influenced by the coherent gust pushed through the rotor like a turbulent box. The wake is superimposed on that coherent gust box, and thus the inertia of the wake and consequently also the flow in the rotor plane is affected. Contemporary dynamic inflow models inherently assume a constant wind velocity. They filter the induced velocity and thus cannot predict the observed amplification of the induced velocity. The commonly used Øye engineering model predicts increased gust load amplitudes and thus higher fatigue loads. With an extra filter term on the quasi-steady wind velocity, the qualitative behaviour observed experimentally and numerically can be caught. In conclusion, these new experimental findings on dynamic inflow due to gusts and improvements to the Øye model enable improvements in wind turbine design by less conservative fatigue loads. [ABSTRACT FROM AUTHOR]

The design of rotor blades is based on information about aerodynamic phenomena. An important one is fluid-structure interaction (FSI) which describes the interaction between a flexible object (rotor blade) and the surrounding fluid (wind). However, the acquisition of FSI is complex, and only a few practical concepts are known. This paper presents a measurement setup to acquire real information about the FSI of rotating wind turbines in wind tunnel experiments. The setup consists of two optical measurement systems to simultaneously record fluid (PIV system) and deformation (photogrammetry system) information in one global coordinate system. Techniques to combine both systems temporally and spatially are discussed in this paper. Furthermore, the successful application is shown by several experiments. Here, different wind conditions are applied. The experiments show that the new setup can acquire high-quality area-based information about fluid and deformation. [ABSTRACT FROM AUTHOR]

Wake steering models for control purposes are typically based on analytical wake descriptions tuned to match experimental or numerical data. This study explores whether a data-driven surrogate model with a high degree of physical interpretation can accurately describe the redirected wake. A linear model trained with largeeddy-simulation data estimates wake parameters such as deficit, center location and curliness from measurable inflow and turbine variables. These wake parameters are then used to generate vertical cross-sections of the wake at desired downstream locations. In a validation considering eight boundary layers ranging from neutral to stable conditions, the far wake's trajectory, curl and available power are accurately estimated. A significant improvement in accuracy is shown in a benchmark study against two analytical wake models, especially under derated operating conditions and stable atmospheric stratifications. Even though the results are not directly generalizable to all atmospheric conditions, locations or turbine types, the outcome of this study is encouraging. [ABSTRACT FROM AUTHOR]

Preview measurements of the inflow by turbine-mounted lidar systems can be used to optimise wind turbine performance or alleviate structural loads. However, nacelle-mounted lidars suffer data losses due to unfavourable environmental conditions and laser beam obstruction by the rotating blades. Here, we apply proper orthogonal decomposition (POD) to the simulated line-of-sight wind speed measurements of a turbine-mounted scanning lidar obtained from two large eddy simulations. This work aimed at identifying the dominant POD modes that can be used to subsequently derive a reduced-order representation of the turbine inflow. Secondly, we reconstructed the data points lost due to blade passage by using Gappy-POD. We found that only a few modes are required to capture the dynamics of the wind field parameters commonly used for lidar-assisted wind turbine control, such as the effective wind speed, vertical shear and directional misalignment. By evaluating turbine-relevant metrics in the time and frequency domain, we found that a ten-mode reconstruction could accurately describe most spatio-temporal variations in the inflow. Furthermore, a modal interpretation is presented by direct comparison with these wind field parameters. We found that the Gappy-POD method performs substantially better than spatial interpolation techniques, accurately reconstructing up to even 50% of missing data. A POD-based wind field reconstruction offers a trade-off between wind field reconstruction techniques requiring flow assumptions and more complex physics-based representations, offers dimensional reduction and can overcome the blade passage limitation of nacelle-mounted lidar systems. [ABSTRACT FROM AUTHOR]

We analyze the relaxation of non-pharmaceutical interventions (NPIs) under an increasing number of vaccinations in Germany. For the spread of SARS-CoV-2 we employ a SIR-type model that accounts for age-dependence and includes realistic contact patterns between age groups. The implementation of NPIs occurs on changed contact patterns, improved isolation, or reduced infectiousness when, e.g., wearing masks. We account for spatial heterogeneity and commuting activities in between regions in Germany, and the testing of commuters is considered as a further NPI. We include the ongoing vaccination process and analyze the effect of the B.1.617.2 (Delta) variant, which is considered to be 40%–60% more infectious then the currently dominant B.1.1.7 (Alpha) variant. We explore different opening scenarios under the ongoing vaccination process by assuming that local restrictions are either lifted in early July or August with or without continued wearing of masks and testing. Our results indicate that we can counteract the resurgence of SARS-CoV-2 despite the Delta variant with appropriate timing for the relaxation of NPIs. In all cases, however, school children are hit the hardest. Author summary: One of the greatest challenges within the Covid-19 pandemic is to identify the timing and amount of non-pharmaceutical interventions (face masks, travel bans, school closures, etc). In the year 2021 more and more people are getting vaccinated. When can we finally lift all restrictions and stop wearing masks? In order to provide more insights to this question, we use a mathematical model which is capable of simulating the effects of non-pharmaceutical interventions in Germany while accounting for age-dependent factors as well as commuting activities between regions. We include the vaccination process and analyze the much more infectious Delta coronavirus variant. We simulate scenarios that consider the timing of the return to pre-pandemic contacts as well as when to suspend wearing masks and testing. Our results show that a later opening by 1 August in combination with masks and testing reduces the chance of a further infection wave considerably. From the retrospective view of the revision, we see that the rise in infections at the end of summer could have been well predicted by our scenarios that considered lifting of NPIs in July as it happened in many places. In all of our scenarios, the infection manifests in the younger age groups. [ABSTRACT FROM AUTHOR]

This paper describes the development of a theoretical model for the turbulence spectrum measured by a short-range, continuous-wave lidar (light detection and ranging). The lidar performance was assessed by measurements conducted with two WindScanners in an open-jet wind tunnel equipped with an active grid, for a range of different turbulent wind conditions. A hot-wire anemometer is used as reference to assess the lidar's measured statistics, time series and spectra. In addition to evaluating the statistics, the correlation between the time series and the root-mean-square error (RMSE) on the wind speed, the turbulence spectrum measured by the lidar is compared with a modelled spectrum. The theoretical spectral model is applied in the frequency domain, using a Lorentzian filter in combination with Taylor's frozen turbulence hypothesis for the probe length averaging effect and an added white noise term, evaluated by qualitatively matching the lidar measurement spectrum. High goodness-of-fit coefficients and low RMSE values between the hot wire and WindScanner were observed for the measured time series. The correlation showed an inverse relationship with the prevalent turbulence intensity in the flow for cases with a comparable power spectrum shape. Larger flow structures can be captured more accurately by the lidar, whereas small-scale turbulent flow structures are partly filtered out as a result of the lidar's probe volume averaging effect. It is demonstrated that an accurate way to define the cut-off frequency at which the lidar's power spectrum starts to deviate from the hot-wire reference spectrum is the frequency at which the coherence drops below 0.5. This coherence-based cut-off frequency increases linearly with the mean wind speed and is generally an order of magnitude lower than the probe length equivalent cut-off frequency, estimated according to a simple model based on the full width at half maximum (FWHM) of the laser beam intensity along the line of sight and assuming Taylor's frozen turbulence hypothesis. A convincing match between the modelled and the measured WindScanner power spectrum was found for various different cases, which confirmed that the deviation of the lidar's measured power spectrum in the higher frequency range can be analytically explained and modelled as a combination of a Lorentzian-shaped intensity function and white noise in the lidar measurement. Although the models were developed on the basis of wind tunnel measurements, they should be applicable to atmospheric boundary layer field measurements as well. [ABSTRACT FROM AUTHOR]

Long-range Doppler wind lidars are applied more and more for high-resolution areal measurements in and around wind farms. Proper alignment, or at least knowledge on how the systems are aligned, is of great relevance here. The paper describes in detail two methods that allow a very accurate alignment of a long-range scanning lidar without the use of extra equipment or sensors. The well-known so-called hard targeting allows a very precise positioning and north alignment of the lidar using the known positions of the surrounding obstacles, e.g. wind turbine towers. Considering multiple hard targets instead of only one with a given position in an optimization algorithm allows us to increase the position information of the lidar device and minimizes the consequences of using erroneous input data. The method, referred to as sea surface levelling, determines the levelling of the device during offshore campaigns in terms of roll and pitch angles based on distance measurements to the water surface. This is particularly well-suited during the installation of the systems to minimize alignment error from the start, but it can also be used remotely during the measurement campaign for verification purposes. We applied and validated these methods to data of an offshore measurement campaign, where a commercial long-range scanning lidar was installed on the transition piece platform of a wind turbine. In addition, we present a model that estimates the quasi-static inclination of the device due to the thrust loading of the wind turbine at different operating conditions. The results show reliable outcomes with a very high accuracy in the range of 0.02 in determining the levelling. The importance of the exact alignment and the possible applications are discussed in this paper. In conclusion, these methods are useful tools that can be applied without extra effort and contribute significantly to the quality of successful measurement campaigns. [ABSTRACT FROM AUTHOR]