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Cross-flow turbine performance and flow fields exhibit cycle-to-cycle variations, though this is often implicitly neglected through time- and phase-averaging. This variability could potentially arise from a variety of mechanisms—inflow fluctuations, the stochastic nature of dynamic stall, and cycle-to-cycle hysteresis—each of which have different implications for our understanding of cross-flow turbine dynamics. In this work, the extent and sources of cycle-to-cycle variability for both the flow fields and performance are explored experimentally under two, contrasting operational conditions. Flow fields, obtained through two-dimensional planar particle image velocimetry inside the turbine swept area, are examined in concert with simultaneously measured performance. Correlations between flow-field and performance variability are established by an unsupervised hierarchical flow-field clustering pipeline. This features a principal component analysis pre-processor that allows for clustering based on all the dynamics present in the high-dimensional flow-field data in an interpretable, low-dimensional subspace that is weighted by contribution to overall velocity variance. We find that the flow-field clusters and their associated performance are correlated primarily with inflow fluctuations, despite relatively low turbulence intensity, that drive variations in the timing of the dynamic stall process. Further, we find no evidence of substantial cycle-to-cycle hysteresis. Cycle-to-cycle performance variability occurs earlier in the cycle than flow-field variability, indicating the limits of co-temporal correlation between performance and flow fields, but clustering reveals persistent ties between performance and flow-field variability during the upstream portion of the turbine rotation. The approach employed here provides a more comprehensive picture of cross-flow turbine flow fields and performance than aggregate, statistical representations. [ABSTRACT FROM AUTHOR]

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In this study, the computational and experimental examination of the hydraulic cross-flow turbine (CFT) is presented. The experimental studies are conducted at the turbine laboratory of Gebze Technical University. The commercial Computational Fluid Dynamics (CFD) software, Star CCM+, is employed to calculate three-dimensional, incompressible, steady Reynolds-Averaged Navier–Stokes (RANS) equations for the flow inside the CFT. In the numerical model, the rotational motion of the blades is modeled by means of the moving reference frame approach. The correlation between the turbulent viscosity and the velocities is acquired using the standard k-ɛ turbulence model. The computational results are obtained in terms of the torque, power, and efficiency of the turbine. The outcome of this research reveals that the CFD software used in this study can reliably be used to predict experiments since the numerical model can accurately estimate the experimental values of the torque, power, and efficiency of the CFT. [ABSTRACT FROM AUTHOR]

Copyright of BHM Berg- und Hüttenmännische Monatshefte is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A cross-flow wind turbine has a high torque coefficient at a low tip speed ratio; therefore, it is a good candidate for a self-starting turbine. This study aims to investigate the best configuration between guide vanes and cross-flow vertical axis wind turbine. The experiment test was carried out to determine the turbine with the highest power coefficient. The cross-flow turbine has 14, 18, and 22 blades with using 6,10 and 14 blades of guide vane (GV) was developed in this study, employing 15°, 25°, 35°, 45°, 55°, 65°, and 75° of tilt angles in fifth different wind speed conditions 4 m/s, 6 m/s, 7.5 m/s, 9.20 m/s, and 11 m/s. The turbine has 22 blades with 14 GV blades at 55° of tilt angle blades producing more remarkable turbine performance improvement than other blades. The highest power coefficient (CP) of cross-flow using 14 GV blades at 55° was 0.0162 at 0.289 TSR, which increased around 59% of the turbine's performance using GV. [ABSTRACT FROM AUTHOR]

The urban landscape water areas are vulnerable to cyanobacterial blooms. However, current cyanobacteria removal equipment is unsuitable for urban landscape water due to its large size, high energy consumption, and complex operation. Therefore, the present study aims to design and optimize a novel cyanobacterial control system for urban water areas using FLUENT simulation software, as well as to construct the cyanobacterial removal module and assess its effectiveness based on the optimized parameters. The results showed increases in the velocity distribution uniformity index (γv) and the cyanobacterial concentration distribution uniformity index (γm) by 30% and 52.9%, respectively, following the addition of the water distributor. In addition, the γv and γm values were first decreased and then increased with increasing the diversion bin lengths, reaching an optimal value at a diversion bin length of 160 mm. On the other hand, the lowest distortion parameter (K) value was observed using the 300-mesh filter bag, indicating the best uniformity of the filter surface. The optimized parameter-based removal module can effectively remove different cyanobacteria bloom levels. Indeed, the removal efficiency was proportional to the severity of the cyanobacteria bloom. Furthermore, the removal rate was increased by increasing the rotation speed of the removal module, resulting in a shorter operation time. The cyanobacterial control system can be applied to control cyanobacterial blooms in urban landscape water areas. [ABSTRACT FROM AUTHOR]

Bangladesh has a huge potential for utilizing renewable energy with the availability of sources for solar, wind, hydro, and biogas. This paper provides a comprehensive analysis of the potential for renewable energy sources and predicts optimal sites for renewable plant implementation in Bangladesh. A country-wise mapping approach is followed by validation of prediction using multi-criteria decision analysis (MCDA) known as the analytical hierarchy process (AHP) which will aid in the planning and execution of Bangladesh's upcoming expansion of renewable farms and will also be a useful tool for policymakers. Moreover, an economic analysis for 50 MW solar and wind power plant has been presented to estimate the levelized cost of electricity (LCoE) of the system and society. This work predicts the enormous potential for the expansion of renewable energy by assessing the appropriate future sites, economic viability, and provides several suggestions on the basis of the analysis from Bangladesh's perspective. [ABSTRACT FROM AUTHOR]

Reconstruction of unsteady vortical flow fields from limited sensor measurements is challenging. We develop machine learning methods to reconstruct flow features from sparse sensor measurements during transient vortex–airfoil wake interaction using only a limited amount of training data. The present machine learning models accurately reconstruct the aerodynamic force coefficients, pressure distributions over airfoil surface, and two-dimensional vorticity field for a variety of untrained cases. Multi-layer perceptron is used for estimating aerodynamic forces and pressure profiles over the surface, establishing a nonlinear model between the pressure sensor measurements and the output variables. A combination of multi-layer perceptron with convolutional neural network is utilized to reconstruct the vortical wake. Furthermore, the use of transfer learning and long short-term memory algorithm combined in the training models greatly improves the reconstruction of transient wakes by embedding the dynamics. The present machine-learning methods are able to estimate the transient flow features while exhibiting robustness against noisy sensor measurements. Finally, appropriate sensor locations over different time periods are assessed for accurately estimating the wakes. The present study offers insights into the dynamics of vortex–airfoil interaction and the development of data-driven flow estimation. [ABSTRACT FROM AUTHOR]