Computational fluid dynamics using the Reynolds-averaged Navier-Stokes (RANS) remains the most cost-effective approach to study wake flows and power losses in wind farms. The underlying assumptions associated with turbulence closures are the biggest sources of errors and uncertainties in the model predictions. This work aims to quantify model-form uncertainties in RANS simulations of wind farms at high Reynolds numbers under neutrally stratified conditions by perturbing the Reynolds stress tensor through a data-driven machine-learning technique. To this end, a two-step feature-selection method is applied to determine key features of the model. Then, the extreme gradient boosting algorithm is validated and employed to predict the perturbation amount and direction of the modeled Reynolds stress toward the limiting states of turbulence on the barycentric map. This procedure leads to a more accurate representation of the Reynolds stress anisotropy. The data-driven model is trained on high-fidelity data obtained from large-eddy simulation of a specific wind farm, and it is tested on two other (unseen) wind farms with distinct layouts to analyze its performance in cases with different turbine spacing and partial wake. The results indicate that, unlike the data-free approach in which a uniform and constant perturbation amount is applied to the entire computational domain, the proposed framework yields an optimal estimation of the uncertainty bounds for the RANS-predicted quantities of interest, including the wake velocity, turbulence intensity, and power losses in wind farms.

Automatic Dependent Surveillance - Broadcast (ADS-B) is a surveillance technology that is used extensively in Air Traffic Control (ATC) applications. Aircraft equipped with ADS-B transponders actively broadcast navigation information such as position, altitude, and velocity, and thus ATC is able to track aircraft continuously, even in regions not covered by traditional radars. However, raw ADS-B messages are typically contaminated with noise, which is typically mitigated using model-based tracking methods to predict the trajectories. In this work, we propose and evaluate the performance of several filtering strategies for trajectory prediction on an existing open source TrajAir aircraft data set and our own data set i.e., collected by Delft university of technology (TUD). In our evaluation, we observe the standard Kalman filter cannot accurately track the aircraft trajectory, especially for sharply maneuvering targets. A fading-memory filter tracks maneuvering targets but introduces delay in estimates, and requires a trade-off between responsiveness and smoothness by target-specific parameter tuning. The Kalman filter with augmented process noise also involves similar trade-off and parameter tuning. Finally, the particle filter performs the best during target maneuvers but admits more noise during steady-state and increases computational cost. In this paper, we present various filtering techniques, and study the performance of these algorithms on the TrajAir and TUD aircraft data sets.

In this paper, the comparative study carried out for focused wave interaction with a moving cylinder in ISOPE-2020 is reported. The fixed cylinder cases are reported in the companion paper as Part A (Sriram, Agarwal, Yan et al., 2021). The paper discusses qualitative and quantitative comparison between four different numerical solvers that participated in this comparative study. This is a challenging problem, as the cylinder moves over 40 m and interacts with the focusing waves. The performance of various solvers is compared for two different moving cylinder speeds. Both weakly coupled models and full Navier–Stokes (NS) solvers with different strategies for modeling the cylinder motion were adopted by the participants. In particular, different methods available for numerically simulating the forward speed problem emerge from this paper. The qualitative comparison based on the wave probe and pressure probe time histories between laminar and turbulent solvers is presented. Furthermore, the quantitative error analysis for individual solvers shows deviations up to 30% for moving wave probes and 50% for pressure time history. The reliability of each method is discussed based on all the wave probe and pressure probe discrepancies against experiments. The deviations for higher speed shown by all solvers indicate that further improvements in the modeling capabilities are required.