The invention describes a laser projector assembly (1) for use in a wind turbine rotor blade manufacturing facility (3), comprising a number of laser projector units (10), wherein each laser projector unit (10) comprises a positioning means (13) for positioning the laser projector unit (10) above a selected rotor blade mould (2), and a laser projector (12) configured to project layup guides (12G) into that mould (2) during a manual layup procedure; an imaging arrangement (11) adapted to capture images (110) of that mould (2); a machine learning algorithm (18) trained to determine coordinates of a feature (2M, 12P) in an image (110); and a calibration module (16) configured to calibrate a laser projector (12) to that mould (2) prior to the manual layup procedure on the basis of an output (180) of the machine learning algorithm (18). The invention further describes a method of manufacturing a wind turbine rotor blade (4) using such a laser projector assembly (1), a machine-learning algorithm (18) for use in such a laser projector assembly (1), and a method of training such a machine-learning algorithm.

A method for evolving a simulation model for a turbine device includes: acquiring current operation data of the turbine device with an inlet parameter as a design parameter; pre-checking the simulation model based on the current operation data; preprocessing the current operation data to obtain a combined dataset when a result of the pre-check indicates that the simulation model requires evolution, the combined dataset including input data and output data corresponding to the input data; constructing a mapping model based on the combined dataset and outputting, using the mapping model, an updated performance curve of the turbine device with the inlet parameter as the design parameter; and replacing a previous performance curve of the simulation model with the updated performance curve to evolve the simulation model.

Techniques for conducting an air flow simulation for a wind turbine include importing a file containing a digitized representation of a three-dimensional blade geometry, extracting from the file, blade constructive parameters, and calculating a low-order air flow past a wind-turbine that includes the blade, based on a Blade Element Momentum Theory (BEMT) to determine sectional angle of attack and free-stream velocity, boundary layer transition, and acoustic noise results. The techniques also include performing air flow simulation for a given number of blade sections, and generating virtual microphone rings. The process also includes computing noise spectra at the virtual microphone rings and blending the noise spectra generated and generating synthetic noise signals from each section by inverse Fourier transform of the noise spectra and converting the noise spectra into an audio track.

The invention relates to a method for constructing a wind farm in a predetermined space, wherein at least the following successive steps are carried out: a) Forming (GR) various grids in the predetermined space, b) For each grid, determining the average annual energy production of a mini-farm (AEP-mf) consisting of wind turbines at the points of intersection of a unit cell, c) Choosing (Ch) a few grids that make it possible to maximize energy production, d) For each grid c in step c), determining a first layout (Alg1) of the predefined number of wind turbines on the grid, e) Modifying the position (Alg2) of the wind turbines on the grid, f) Determining a definitive layout (Disp_F) of the wind turbines in the predetermined space, and constructing (Const) the wind farm.

Correlated sets of historical meteorological data and terrain data are obtained for at least one geographical area. A data model is derived based on the basis of the correlated sets, by training the data model. The trained data model is adapted to identify coherence between meteorological data and terrain data relating to the same geographical area. Meteorological data and terrain data related to the renewable energy site are fed to the trained data model, the terrain data having a higher resolution than the meteorological data. Using the trained model, meteorological data related to the renewable energy site with increased resolution is estimated by downscaling the meteorological data. The estimated meteorological data with increased resolution for the renewable energy site is then used for planning a layout of the renewable energy site.

A digital twin service of a provider network allows a user to build a digital twin to simulate behavior and changes of a physical system at a client's site. The digital twin service may receive, from the user, a definition for each model to be used to simulate each component of the physical system. For example, the user may indicate various properties of the model, behaviors of the model, and relationships between the model and one or more other models (e.g., chaining via inputs/outputs). The digital twin service may then build and deploy the models to a runtime environment. The runtime environment executes the models and receives telemetry data from the physical system. The user may modify the simulation (e.g., provide new telemetry data or modify/add new models). The simulation generates one or more results based on the modification and sends the results to a destination.