The Air Force Office of Scientific Research and the Air Force Research Laboratory (AFRL) funded the Boundary Layer Turbulence Flight Experiment in Memory of Mike Holden (BOLT II) to better understand and quantify natural and forced boundary-layer transition and turbulence in hypersonic flight regimes for a complex, three-dimensional geometry. Texas A&M University and CUBRC led the mission and vehicle design along with critical support from AFRL and NASA. The experimental flight launched from NASA Wallops on a two-stage suborbital sounding rocket on March 21st, 2022. A detailed overview of the vehicle design, over 400 instrumentation channels, and integration work is presented in this paper. Additionally, this is the first known time that a full-scale hypersonic flight vehicle has been tested in a wind tunnel before flight. The flight computers and key instruments worked and responded well during the short ground-test campaign and detailed results are reported here with comparisons to the flight data for both the natural transition experiment, side A, and the tripped experiment, side B.
Wall shear stress is a key parameter in many oil and gas flow processes, including corrosion, erosion, and flow assurance challenges. However, it is typically inferred from indirect methods—such as pressure gradients or velocity profiles—which introduce uncertainty, especially in complex or multiphase systems. This study presents a feasibility demonstration of direct wall shear stress sensors in a controlled liquid flow environment using a Taylor-Couette apparatus. A total of four contoured shear sensors were embedded flush with the surface of the rotating inner cylinder to enable real-time, direct shear measurement. Experiments were conducted using Clearco 1000 cSt dimethylsiloxane oil (dynamic viscosity ≈ 0.97 Pa·s), a Newtonian fluid selected to provide stable, high-viscosity conditions. The system was operated over a range of inner cylinder speeds from 100 to 1500 RPM, producing analytically predicted shear stress values from approximately 7 to 200 Pa. Sensor outputs were compared against analytical predictions for laminar Taylor-Couette flow and validated with computational fluid dynamics (CFD) simulations. Across all conditions, the sensors exhibited strong linear response and stable performance, with an average deviation of 8.89% from theoretical values. These results confirm that direct shear sensors can reliably resolve wall shear stress in high-viscosity, single-phase liquid flows. Their accuracy, repeatability, and real-time capability position them as a valuable diagnostic tool for oil and gas applications—including corrosion rate estimation, erosion monitoring, and the improvement of flow assurance models in laboratory and field-scale systems.
This work addresses the challenge of modeling tire behavior for virtual vehicle development, particularly in untripped rollover scenarios where a vehicle rolls over solely due to the tire-road friction interface. Despite the existence of various models, limited attention has been given to tire testing methods that accurately represent rollover conditions. We propose a revised lateral tire testing method that uses data from rollover-critical driving tests to derive conditions for a flat-track test bench and that focuses on operational and thermal conditions. These data are used to parameterize empirical Magic Formula Tire 6.2 models, emphasizing the importance of accurate measurements for model parameterization. Component-level validation through comparison of measured and simulated tire reaction forces and moments demonstrates improved estimates of lateral forces, hence lateral friction coefficient, and overturning moments compared with models based on outdoor tire test data with limited operating conditions. At the full vehicle level, the proposed method significantly reduces the error in rollover key performance indicators based on wheel lift-off during Fishhook maneuvers. These findings are particularly relevant for battery electric sport utility vehicles with increased vehicle mass and wheel loads.
Data redundancy in health records represents a significant challenge for healthcare organizations, leading to inefficiencies, increased storage costs, and potential errors in patient care. The proliferation of digital health information, including electronic health records (EHRs), necessitates the implementation of robust data deduplication algorithms to streamline data management processes and enhance data integrity. This study investigates various data deduplication algorithms, analyzing their effectiveness in reducing redundancy within health records while maintaining accuracy and accessibility.The research employs a comprehensive evaluation framework that assesses each algorithm based on criteria such as computational efficiency, scalability, and impact on data integrity. Various algorithms, including hash-based methods, content-based deduplication, and machine learning approaches, are examined to determine their suitability for health record management. Case studies from diverse healthcare settings illustrate the practical implications of deduplication strategies, highlighting the potential benefits of implementing these techniques. Preliminary findings indicate that while several algorithms demonstrate significant potential in reducing data redundancy, their effectiveness varies based on the nature of the health data and the specific context of implementation. For instance, hash-based deduplication methods are highly efficient but may struggle with complex data structures often encountered in healthcare. Conversely, machine learning algorithms show promise in adapting to evolving data patterns but may require substantial computational resources. This study not only contributes to the understanding of data deduplication techniques in healthcare but also offers a strategic framework for healthcare organizations to optimize their data management practices. By identifying best practices for implementing effective deduplication algorithms, this research ultimately aims to improve the quality of health records, enhance patient care, and support cost-effective data management solutions.
