Offshore Wind Farm Efficiency Through Wake Dynamics

Wake interactions in offshore wind farms, caused by aerodynamic disturbances between turbines, pose a significant challenge to energy efficiency. These interactions decrease power output and increase mechanical stress on downstream turbines, leading to performance losses. Understanding and mitigating these effects are critical for enhancing the sustainability of wind energy systems. This study conducted a sensitivity analysis of key operational and environmental parameters to explore their influence on wake dynamics. Factors such as turbulence intensity, yaw misalignment, and turbine operations were analyzed using advanced modeling techniques. Addressing inefficiencies from wake effects in clustered turbines is increasingly important as offshore wind farms grow. The study integrates experimental data with modeling frameworks to calibrate predictive tools that reflect real-world conditions. Seasonal variations in turbulence and their impact on wake recovery and power losses were also examined, highlighting the temporal dynamics of offshore wind farm performance. These findings offer insights into optimizing turbine layout and strategies to minimize energy losses, contributing to the efficiency and reliability of offshore wind farms while supporting global renewable energy goals. (Journal Paper)

Smart Mobility Sensing of Passenger Origin-Destination Tracking

Understanding passenger movement within public transportation systems is essential for efficient transit planning, especially in resource-limited communities. Traditional methods for measuring origin-destination (OD) flows, like manual driver logs, are labor-intensive and often inaccurate. This study proposes an automated, computer vision-based system for accurately detecting and tracking OD pairs. Combining advanced vision-based modules—such as passenger detection, tracking, and re-identification—with GPS data, this system offers a robust, privacy-preserving solution tailored to the unique needs of resource-constrained areas. It highlights how affordable, scalable technologies can address limitations and meet the growing demand for efficient transit solutions. The study aims to enhance transportation services in under-resourced communities where advanced sensors or smart card systems are not feasible. Using deep learning models and edge computing, the system achieves high accuracy in tracking passenger movements under real-world conditions, overcoming challenges like occlusion and pose variation. The approach adapts existing algorithms for localized conditions and deploys them effectively in resource-constrained transit systems. Results show that the system not only enhances OD data precision but also provides actionable insights to optimize routes and improve bus system efficiency. (Master Thesis)

Structural Monitoring Using Multi-Camera Photogrammetry

Structural monitoring relies on advanced technologies to measure displacements and vibrations accurately. Multi-camera photogrammetry has proven to be a dependable, non-contact method for dynamic displacement measurement, particularly during shake table tests of civil structures. This study examines two critical aspects of this technique: multi-vision triangulation for accurate 3D reconstruction and data synchronization to align camera perspectives temporally. The multi-vision triangulation method achieves precise 3D displacement measurement using synchronized images from multiple cameras. By intersecting viewing rays from various angles, this approach accurately determines spatial positions of structural features, even under complex motion. Using direct linear transformation (DLT) methods, it captures structural responses in three dimensions, offering valuable insights into structural behavior during dynamic tests. Data synchronization is equally vital. Time drift between camera clocks can cause frame mismatches, leading to measurement errors. To mitigate this, audio-based synchronization with cross-correlation techniques was used to align video frames precisely. This ensures that data from all cameras corresponds to the same time points, allowing integrated multi-vision analysis. (Conference Paper 1, Conference Paper 2)

Watershed Mapping in Dense Forests Using UAV-Based Lidar

Accurate mapping of water bodies and analyzing watershed dynamics are crucial for understanding hydrologic processes like baseflow recession and surface water-groundwater interactions. Traditional methods, such as satellite imagery, often lack the spatial resolution to capture small waterways in densely vegetated areas, driving the adoption of UAV-based lidar technology. UAV-based lidar offers high precision by collecting detailed 3D data in diverse terrains, including areas with dense tree canopies. It penetrates vegetation, isolates ground data, and provides accurate representations of water surfaces and topography, outperforming traditional methods in challenging environments like Florida’s forests. This research applies UAV-based lidar to estimate water surface areas with high accuracy, focusing on small creeks obscured by dense vegetation. By filtering non-relevant data and leveraging lidar ground returns, it generates robust models of water bodies, aiding dynamic hydrological modeling and seasonal hydrology monitoring. (Conference Presentation, Workshop Presentation)

Seismic Performance of Integral and Semi-Integral Bridges

Bridges are essential elements of transportation networks, but their performance under seismic and thermal conditions poses significant engineering challenges. This study examines the behavior of integral, semi-integral, and conventional bridges under such loads, focusing on their structural responses and soil-structure interactions. Integral bridges, with their continuous connections between the deck and abutments, enhance seismic resilience and lower maintenance by eliminating expansion joints and bearings. Semi-integral bridges, using limited bearings, provide a compromise between flexibility and durability. Conventional bridges, though common, often experience greater displacements and maintenance due to their segmented design. The research explores how soil-backfill interactions and connection types affect bridge performance under these conditions. Advanced finite element modeling and time-history analysis show that integral and semi-integral bridges perform better during seismic events due to their frame action and soil interaction. The study also examines thermal loads on abutment deformations and backfill pressure, highlighting the superior stress distribution of semi-integral systems. These findings aid in optimizing bridge designs for improved resilience and sustainability by addressing interactions between structural components and environmental forces. (Journal Paper, Conference Paper 1, Conference Paper 2)