Long Short-Term Memory Recurrent Neural Networks (LSTM) are used to build surrogate models to forecast time-series blade loads for both fixed and floating offshore wind turbines. In this paper, we train surrogate models on datasets generated with OpenFAST on the IEA-15MW-RWT under a range of metocean conditions. The aim of the surrogate models is to generate load forecasts inexpensively and accurately such that they can be used in a model predictive controller. Two cases are investigated with different model inputs: one with only measurements available to typical PI controllers and another one with additional wave elevation and deflection measurements (alongside the endogenous variable). The model performances are evaluated and compared. It was found that for the fixed turbine, the models predicted all three blade loads to a high degree of accuracy. The floating turbine surrogate models performed relatively worse, but edgewise and pitching moments are still reasonably accurate. The surrogate model forecasts the flapwise moment to a satisfactory accuracy only in 58% out of 400 test cases. The addition of wave elevation and blade deflection features did not significantly improve the prediction performance of the surrogate, demonstrating that just the information used by current PI controllers may be sufficient for forecasting blade loads.

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The use of load surrogates in offshore wind turbine site assessment has gained attention as a way to speed up the lengthy and costly siting process. We propose a novel probabilistic approach using mixture density networks to map 10 min average site conditions to the corresponding load statistics. The probabilistic framework allows for the modeling of the uncertainty in the loads as a response to the stochastic inflow conditions. We train the data-driven model on the OpenFAST simulations of the IEA 10 MW reference wind turbine (IEA-10MW-RWT) and compare the predictions to the widely used Gaussian process regression. We show that mixture density networks can recover the accurate mean response in all load channels with values for the coefficient of determination (R2) greater than 0.95 on the test dataset. Mixture density networks completely outperform Gaussian process regression in predicting the quantiles, showing an excellent agreement with the reference. We compare onshore and offshore sites for training to conclude the need for a more extensive training dataset in offshore cases due to the larger feature space and more noise in the data.@en

Floating offshore wind turbines can extract energy from deep offshore locations,
typically unfit for fixed bottom designs. The complex interaction between the structural
behavior of the floating offshore wind turbine and the stochastic site conditions, however,
is an active area of research. Characterizing the relationship between the environmental
conditions and loads may help design reduced-order models, surrogate models, and physicsbased engineering models for floating wind turbines. This study uses data from the TetraSpar prototype equipped with a 3.6 MW Siemens Gamesa wind turbine. One-to-one simulations performed using an aero-servo-hydro-elastic software are included for comparison. Various tools, including linear correlation, mutual information, feature ordering using conditional independence, and sensitivity analysis using a data-driven variogram fit, are used for the assessment. This study is also helpful in validating the engineering model for future global sensitivity analysis using elementary effects or Sobol indices that require a rigid sampling of features and can, therefore, only be calculated with simulation tools. We find a good agreement between the experiments and simulations. The 10-min. damage equivalent loads on the tower show a correlation, particularly with the wind speed statistics and the significant wave height.@en

Reliable prediction of aviation’s environmental impact, including the effect of nitrogen oxides on ozone, is vital for effective mitigation against its contribution to global warming. Estimating this climate impact however, in terms of the short-term ozone instantaneous radiative forcing, requires computationally-expensive chemistry-climate model simulations that limit practical applications such as climate-optimised planning. Existing surrogates neglect the large uncertainties in their predictions due to unknown environmental conditions and missing features. Relative to these surrogates, we propose a high-accuracy probabilistic surrogate that not only provides mean predictions but also quantifies heteroscedastic uncertainties in climate impact estimates. Our model is trained on one of the most comprehensive chemistry-climate model datasets for aviation-induced nitrogen oxide impacts on ozone. Leveraging feature selection techniques, we identify essential predictors that are readily available from weather forecasts to facilitate the implementation therein. We show that our surrogate model is more accurate than homoscedastic models and easily outperforms existing linear surrogates. We then predict the climate impact of a frequently-flown flight in the European Union, and discuss limitations of our approach.

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This paper presents a surrogate-assisted optimisation approach to speed up the substructure analysis in the preliminary design phase. The approach consists of replacing the radiation-diffraction analysis in a frequency domain analysis model for floating wind turbines with a data-driven surrogate model predicting the hydrodynamic coefficients for parameterised substructure geometries. This procedure is compared with the reference approach of estimating the hydrodynamic coefficients via radiation-diffraction analysis. A representative use case of assessing the trade-off between minimising the capital cost and reducing the wave-induced nacelle acceleration standard deviation for a semi-submersible substructure is presented. The accuracy of the surrogate model is found to increase significantly up to training datasets consisting of 400 designs and less noticeably afterwards. For a dataset consisting of 400 designs, the mean error on the prediction of the hydrodynamic coefficients and the error at one standard deviation from the mean are generally below 7% and 10%, respectively. For the same dataset size, the mean error on the most probable maximum wave-induced pitch over a 3h storm period is below 17%, while the error at one standard deviation from the mean is lower than 27%. The same values for the most probable maximum nacelle acceleration are under 7% and 12%, respectively. The surrogate model can capture the trade-off between the two objective functions, and the optimal designs identified with the surrogate model generally follow the same trend as those obtained with the reference model. However, relying on the surrogate model for performing the analysis of the substructure introduces local minima in the objective function that cause a discrepancy between the optimal designs identified with the surrogate model and those identified with the reference model.

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While efforts have been made to curb CO2 emissions from aviation, the more uncertain non-CO2 effects that contribute about two-thirds to the warming in terms of radiative forcing (RF), still require attention. The most important non-CO2 effects include persistent line-shaped contrails, contrail-induced cirrus clouds and nitrogen oxide (NOx) emissions that alter the ozone (O3) and methane (CH4) concentrations, both of which are greenhouse gases, and the emission of water vapour (H2O). The climate impact of these non-CO2 effects depends on emission location and prevailing weather situation; thus, it can potentially be reduced by advantageous re-routing of flights using Climate Change Functions (CCFs), which are a measure for the climate effect of a locally confined aviation emission. CCFs are calculated using a modelling chain starting from the instantaneous RF (iRF) measured at the tropopause that results from aviation emissions. However, the iRF is a product of computationally intensive chemistry-climate model (EMAC) simulations and is currently restricted to a limited number of days and only to the North Atlantic Flight Corridor. This makes it impossible to run EMAC on an operational basis for global flight planning. A step in this direction lead to a surrogate model called algorithmic Climate Change Functions (aCCFs), derived by regressing CCFs (training data) against 2 or 3 local atmospheric variables at the time of emission (features) with simple regression techniques and are applicable only in parts of the Northern hemisphere. It was found that in the specific case of O3 aCCFs, which provide a reasonable first estimate for the short-term impact of aviation NOx on O3 warming using temperature and geopotential as features, can be vastly improved [1]. There is aleatoric uncertainty in the full-order model (EMAC), stemming from unknown sources (missing features) and randomness in the known features, which can introduce heteroscedasticity in the data. Deterministic surrogates (e.g. aCCFs) only predict point estimates of the conditional average, thereby providing an incomplete picture of the stochastic response. Thus, the goal of this research is to build a new surrogate model for iRF, which is achieved by : 1. Expanding the geographical coverage of iRF (training data) by running EMAC simulations in more regions (North & South America, Eurasia, Africa and Australasia) at multiple cruise flight altitudes, 2. Following an objective approach to selecting atmospheric variables (feature selection) and considering the importance of local as well as non-local effects, 3. Regressing the iRF against selected atmospheric variables using supervised machine learning techniques such as homoscedastic and heteroscedastic Gaussian process regression. We present a new surrogate model that predicts iRF of aviation NOx-O3 effects on a regular basis with confidence levels, which not only improves our scientific understanding of NOx-O3 effects, but also increases the potential of global climate-optimised flight planning.@en

Heteroscedastic Gaussian process regression, based on the concept of chained Gaussian processes, is used to build surrogates to predict site-specific loads on an offshore wind turbine. Stochasticity in the inflow turbulence and irregular waves results in load responses that are best represented as random variables rather than deterministic values. Moreover, the effect of these stochastic sources on the loads depends strongly on the mean environmental conditions - for instance, at low mean wind speeds, inflow turbulence produces much less variability in loads than at high wind speeds. Statistically, this is known as heteroscedasticity. Deterministic and most stochastic surrogates do not account for the heteroscedastic noise, giving an incomplete and potentially misleading picture of the structural response. In this paper, we draw on the recent advancements in statistical inference to train a heteroscedastic surrogate model on a noisy database to predict the conditional pdf of the response. The model is informed via 10-minute load statistics of the IEA-10MW-RWT subject to both aero- and hydrodynamic loads, simulated with OpenFAST. Its performance is assessed against the standard Gaussian process regression. The predicted mean is similar in both models, but the heteroscedastic surrogate approximates the large-scale variance of the responses significantly better.@en

This paper presents the results of unsteady Lattice Boltzmann simulations of the JAXA high-lift configuration Standard Model (JSM) in the JAXA Low speed Wind Tunnel. The study is a part of a continuing collaboration between JAXA and Dassault Systèmes and focuses primarily on understanding the differences between free air and wind tunnel simulations of the JSM half-span model. First, the effect of simulating with a boundary layer transition model as opposed to having a fully turbulent boundary layer is presented. The influence of the standoff alone, followed by a detailed analysis of the entire wind tunnel to investigate wall interference effects is discussed. Finally, the impact of including the wind tunnel geometry on cases with and without the nacelle chine is presented. The paper also addresses some of the open challenges faced when simulating high-fidelity high-lift cases in wind tunnels. Overall, a significant reduction in drag, improvement in the stall mechanism and accuracy in capturing the chine effect is seen with the addition of the wind tunnel geometry. The results invariably highlight the sensitivity of a multi-element high-lift system to the geometric details on and around the model and the ability of the code to accurately capture it.@en

Unsteady transonic Lattice Boltzmann simulations of the NASA Common Research Model in the European Transonic Windtunnel are presented. Modelling of the slotted wall transonic test section is described and different simulation approaches are discussed. Quantitative comparisons are performed using data obtained for the NASA Common Research Model at its design point and at off-design conditions. Good qualitative and quantitative agreement between simulations and uncorrected experimental measurements are shown. A preliminary comparison between wind tunnel and free-air simulations is also presented and shows promising correlation to the wall interference corrections applied to the measurements.@en

This paper presents numerical investigations undertaken to analyze the turbulent flow produced in the wake of a micro air vehicle rotor interacting with the ground. Two configurations are investigated: a free rotor and a shrouded rotor. The Reynolds number based on the chord and tip speed is Retip = 0.86 × 105, which corresponds to a challenging flow where leading-edge separations are commonly observed. The numerical simulations are performed with a Reynolds-averaged Navier–Stokes approach and a large-eddy simulation (by means of a lattice-Boltzmann method), combined with an immersed boundary approach. The comparison of numerical data with measurements shows that the mean flow and the turbulent shear stresses are accurately predicted close to the ground and in the rotor wake. However, some discrepancies remain in the prediction of the rotor torque and thrust, mainly due to the difficulty to reproduce the flow near the rotor walls. An analysis is conducted to identify and understand the different sources of turbulent production. The numerical simulations show also that the presence of a shroud contributes, at a given thrust, to reduce the velocity and the turbulent intensity at the ground.@en

A Lattice-Boltzmann flow solver is used to simulate the unsteady flow around swept and unswept wings in the presence of ice shapes on the leading edge. The stall mechanism is studied for roughness ice, ice horns, and scallop ice. These types of flows are particularly challenging, due to the extremely complex geometries involved and the flow separations, which are inherently unsteady, and hence troublesome for traditional steady-state solvers. @en

Unsteady simulations using PowerFLOW, a Lattice-Boltzmann-method-based solver, of a rectangular deep (L/D= 4.5), open-flow cavity at subsonic and supersonic speeds (M= 0.6 to 1.5) are presented. Acoustic sound pressure levels and cavity modes are compared to experiments and analytical models. The simulations show a good quantitative and qualitative agreement with the experiments for the spectra at various probes located in and around the cavity. The shear layer mode is characterized by Rossiter modes and the frequencies are well predicted. The three-dimensionality in the flow is also identified by the presence of lateral pressure gradients, resulting in the interaction of lateral and longitudinal modes.@en

Transonic buffet simulations are carried out for the OAT15A airfoil and the NASA Common Research Model. Simulations are performed with a Lattice-Boltzmann based solver, which has been extended to transonic flow capabilities. The results are compared to data from the literature, including recent wind tunnel investigations, and further flow analysis is performed, focusing on the unsteady flow-field. Overall, good agreement is observed for the two-and three-dimensional simulations.@en

In the present study, the vortex dynamics associated with flapping wing motion is reviewed. Smoke flow visualization and phase-locked PIV measurement of velocity flow field generated by the simple flapping motion of the rigid wings of different planform shapes is reported in this paper. To understand the effect of flexibility on the strength of the leading edge vortex (LEV), one flexible wing of rectangular shape was also investigated. The LEV formed due to flapping motion is captured and quantified. The relative strength of the LEV in terms of circulation and averaged vorticity distribution is presented for different rigid wing planforms and one flexible rectangular wing. @en