{'fraction_non_alphanumeric': 0.01821668264621285, 'fraction_numerical': 0.0, 'mean_word_length': 4.931818181818182, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Turbulence Optimized Park model with Gaussian wake profile

A new analytical wake model for wind farm design is presented. The new model, TurbOPark, is based on the Park model but features non-linear wake expansion and a Gaussian deficit profile. The modelled wake expansion depends on the intensity of the local turbulence, which is a combination of ambient atmospheric turbulence and wake-generated turbulence. Moving downstream from the rotor, the intensity of the wake-generated turbulence decreases, and the wake expansion slows down. This leads to more persistent wakes in wind farms and clusters of wind farms with large distances between turbines. In the present study, we focus on the London Array wind farm and show examples of how results from Park and TurbOPark compare to SCADA data. We look at the array efficiency of the wind farm, the production of individual turbines, and the impact of neighbouring wind farms. We find that predictions from TurbOPark generally are closer to observations than predictions from the Park model.