A sensitivity study of the WRF model in offshore wind modeling over the Baltic Sea

Accurate wind modeling is important for wind resources assessment and wind power forecasting.To improve the WRF model configuration for the offshore wind modeling over the Baltic Sea,this study per-formed a sensitivity study of the WRF model to multiple model configurations,including domain setup,grid resolution,sea surface temperature,land surface data,and atmosphere-wave coupling.The simu-lated offshore wind was evaluated against LiDAR observations under different wind directions,atmo-spheric stabilities,and sea status.Generally,the simulated wind profiles matched observations,despite systematic underestimations.Strengthening the forcing from the reanalysis data through reducing the number of nested domains played the largest role in improving wind modeling.Atmosphere-wave cou-pling further improved the simulated wind,especially under the growing and mature sea conditions.Increasing the vertical resolution,and updating the sea surface temperature and the land surface infor-mation only had a slight impact,mainly visible during very stable conditions.Increasing the horizontal resolution also only had a slight impact,most visible during unstable conditions.Our study can help to improve the wind resources assessment and wind power forecasting over the Baltic Sea.     

Ultrasonic prediction of crack density using machine learning:A numerical investigation

Cracks are accounted as the most destructive discontinuity in rock, soil, and concrete. Enhancing our knowledge from their properties such as crack distribution, density, and/or aspect ratio is crucial in geo-systems. The most well-known mechanical parameter for such an evaluation is wave velocity through which one can qualitatively or quantitatively characterize the porous media. In small scales, such information is obtained using the ultrasonic pulse velocity(UPV) technique as a non-destructive test. In large-scale geo-systems, however, it is inverted from seismic data. In this paper, we take advantage of the recent advancements in machine learning(ML) for analyzing wave signals and predict rock properties such as crack density(CD) – the number of cracks per unit volume. To this end, we designed numerical models with different CDs and, using the rotated staggered finite-difference grid(RSG) technique, simulated wave propagation. Two ML networks, namely Convolutional Neural Networks(CNN) and Long Short-Term Memory(LSTM), are then used to predict CD values. Results show that, by selecting an optimum value for wavelength to crack length ratio, the accuracy of predictions of test data can reach R2> 96% with mean square error(MSE) < 25e-4(normalized values). Overall, we found that:(i) performance of both CNN and LSTM is highly promising,(ii) accuracy of the transmitted signals is slightly higher than the reflected signals,(iii) accuracy of 2D signals is marginally higher than 1D signals,(iv)accuracy of horizontal and vertical component signals are comparable,(v) accuracy of coda signals is less when the whole signals are used. Our results, thus, reveal that the ML methods can provide rapid solutions and estimations for crack density, without the necessity of further modeling.