Journal of Applied Fluid Mechanics

Accurate prediction of wind pressures on buildings is crucial for designing safe and efficient structures. Existing computational methods, like Reynolds-averaged Navier-Stokes (RANS) simulations, often fail to predict pressures accurately in separation zones. This study proposes a novel deep-learning methodology to enhance the accuracy and performance of eddy viscosity modeling within  RANS turbulence closures, particularly improving predictions for bluff body aerodynamics. A deep learning model, trained on large eddy simulation (LES) data for various bluff body geometries, including a flat-roof building and forward/backward facing steps, was used to adjust eddy viscosity in RANS equations. The results show that incorporating the machine learning-predicted eddy viscosity significantly improves agreement with LES results and experimental data, particularly in the separation bubble and shear layer. The deep learning model employed a neural network architecture with four hidden layers, 32 neurons, and tanh activation functions, trained using the Adam optimizer with a learning rate of 0.001. The training data consisted of LES simulations for forward/backward facing steps with width-to-height ratios ranging from 0.2 to 6. The study reveals that the machine learning model achieves a balance in eddy viscosity that delays flow reattachment, leading to more accurate pressure and velocity predictions than traditional turbulence closures like k-ω SST and k-ε. A sensitivity analysis demonstrated the pivotal role of eddy viscosity in governing flow separation, reattachment, and pressure distributions. Additionally, the investigation underscores the disparity in eddy viscosity values between RANS and LES models, highlighting the need for enhanced turbulence modeling. The findings presented in this paper offer substantive insights that can inform the advancement of more dependable computational methodologies tailored for engineering applications, encompassing wind load considerations for structural design and the intricate dynamics of unsteady aerodynamic phenomena.

This research aims to investigate the impact of a split airfoil on noise emissions from a horizontal-axis wind turbine. The objective is to comprehensively understand the airflow patterns around the airfoil to reduce noise emissions. The study rigorously examines a range of angles of attack, from 0° to 25°, for both the original airfoil and the airfoil with a split, using advanced computational aerodynamics coupled with analog acoustic analysis. The methodology involves two-dimensional flow simulations with Delayed Detached Eddy Simulation based on the Spalart-Allmaras model, enabling precise near-field flow calculations around the airfoil. Additionally, far-field noise predictions, employing the Ffowcs Williams and Hawkings analogy based on simulated sources, reveal the efficacy of the split airfoil design. Results indicate that the split airfoil design effectively reduces noise emissions across various angles of attack. These reductions translate into a significant decrease in the Overall Sound Pressure Level, ranging from 14% to 19%, and remarkable Sound Pressure Level reductions between 12% and 60% across diverse frequencies, showcasing substantial noise improvements in various frequency ranges.

This study focuses on the TM-141 supersonic compressor blade as the subject of investigation. Utilizing the Reynolds-averaged numerical simulation approach, the study examines the impact of the leading-edge sawtooth structure on the blade grid, flow field characteristics, and flow losses. Comparative analysis is conducted between numerical results and experimental data to assess the influence of the leading-edge sawtooth structure under various conditions, including fixed Mach numbers and variable static pressure ratios, fixed static pressure ratios and variable Mach numbers, and fixed Mach numbers and fixed static pressure ratios. The findings reveal that the leading-edge sawtooth structure effectively alters the distribution of total pressure loss coefficients within the blade grid channels, mitigates leading-edge spikes, and improves trailing-edge blade morphology, consequently reducing trailing-edge losses and total pressure loss compared to benchmark blade grids. These results offer insights for mitigating losses and enhancing efficiency in the transonic region of compressor blade operation, thereby providing a foundation for further investigation into the effects of leading-edge sawtooth structures on flow fields.

To assess the impact of different heights of the viaduct on air pollutant dispersion and the prediction accuracy of pollutant concentration in urban street canyons, simulation results based on LES and RANS models are analyzed. The presence of a viaduct generated a poorly ventilated region underneath it, and RANS significantly underestimated the wind speed and grossly overestimated the pollutant concentration. LES gives better results for the flow pattern, distribution of turbulent kinetic energy and mean pollutant concentration. With a fluctuation of less than 15% of the pollutant concentration, both RANS and LES cases show that an increase in the viaduct height has a weak impact on the concentration of pollutants in most areas of the canyon except windward, and cases with a viaduct height of 0.75H had the lowest predicted pollutant concentration relative to other cases with a viaduct as a result of better ventilation. In addition, LES found a subregion of pollutant accumulation above the ground, but RANS did not.

Significant progress has been made in understanding the mechanisms and simulations of ice-induced shock vibrations due to continuous experimentation and simulation of vibrations induced by ocean platforms. However, the threat of such vibrations to bridges in cold regions with spring rivers remains significant. Currently, challenges persist in the numerical analysis methods applied to vibrations caused by collisions between ice and bridges in river channels. This difficulty primarily arises from the insufficient consideration of the impact of flow field coupling on ice-induced shock vibrations under various simulation conditions. This paper aims to analyze the influence of ice-induced shock vibrations arising from collisions involving bridges, ice, water, and air. It also compares the Semi-Arbitrary Lagrangian-Eulerian (S-ALE) and Arbitrary Lagrangian-Eulerian (ALE) methods, finding that the S-ALE method is better suited for complex flow-solid coupling analysis under the same model. Comparative analysis shows that the fluid effect period increased by approximately 30%, resulting in an 8% reduction in peak values. This confirms the applicability of the ice-induced shock vibration theory and demonstrates that factors such as velocity and thickness significantly impact these vibrations. The findings offer valuable insights for the numerical simulation of river ice-induced shock vibrations due to bridge-ice collisions in cold areas.