Fangjun Shu

Slug calorimeters are rugged and proven sensors for hypersonic flight and ground tests. Data reduction through inverse theory is employed to obtain the heat flux from the temperature time history. The unknown heat loss into the surrounding structure introduces inaccuracies. Other than through extensive calibration, the inaccuracies can be addressed by improved and new sensor designs and associated theory. In this paper, a novel design is proposed that addresses the unknown heat loss problem at the back face by adding a stainless steel part with a second thermocouple. This multilayered slug calorimeter design is being tested in a purpose-built laser heat flux facility. Measurements provide validation data for heat transfer simulations that are being employed for design parameter studies. The simulation data are used for the testing of novel inverse theory. The present results demonstrate that the proposed bimetal slug calorimeter, in combination with new inverse theory, can provide accurate heat flux measurements. To evaluate sensor performance in a representative environment, the Mach 18 airflow over a hemisphere was simulated, and the wall heat flux served as input for sensor heat transfer simulations. The analysis revealed a 20% variation in the heat flux prediction depending on the wall catalycity.

Off-body flow-field reconstruction from a limited number of surface sensor measurements is of significant practical value for applications where the flow field state is important for control but cannot be measured directly. In this work, we employ machine learning techniques to reconstruct the wake flow for a natural laminar flow airfoil NLF(1)-0115 at Re = 20, 000 and α = 5° from a limited number of pressure sensor measurements on the airfoil surface. The training data obtained from numerical simulations comprise the uncontrolled flow as well as controlled flows with steady and unsteady actuation. Two multilayer perceptron neural networks and a convolutional neural network were trained. A grid-search method was used to tune the hyperparameters of the neural networks. Hyperparameter tuning improved the convergence and reconstruction accuracy of the fully connected neural network models, which achieved excellent interpolation performance but showed limited cross-condition generalization. In contrast, the convolutional neural network demonstrated superior reconstruction performance and produced more accurate flow field reconstructions for cross-condition datasets. Among the test cases, all models achieved their best performance for the unsteady controlled flow. One possible explanation is that the controlled flow exhibits more organized and repeatable dynamics, which may facilitate reconstruction from sparse pressure measurements. These results suggest that incorporating both spatial and temporal flow information may improve cross-condition generalization and provide a promising direction for future flow-control applications.

Thin-film temperature gauges with nickel and silver sensing elements on a Macor ceramic substrate are evaluated for unsteady heat flux measurements in impulse facilities such as shock tunnels. Unsteady heat transfer simulations with known input heat flux provide resistance and temperature data. The heat flux is then recovered from inverse theory and compared with the known input heat flux. For the nickel-based sensor, the effect of the film thickness and film overlap with the insulator are investigated. Simulations with box and square function heat input demonstrate the dependence of the sensor frequency response on the film thickness. For the silver thin-film gauge, the effect of partial direct heat transfer into the surrounding insulator is investigated. The suitability of the silver thin film for time-resolved heat flux measurements in a turbulent high-speed boundary layer is evaluated. The unsteady wall heat flux is obtained from a large-eddy simulation of a high-enthalpy Mach 5 turbulent boundary layer in thermochemical nonequilibrium. Inverse analysis of the temperature data from the thin-film simulation provides the wall heat flux, which is in good agreement with the heat flux obtained directly from the large-eddy simulation, thus demonstrating that the thin-film gauge is well suited for impulse facility experiments.

This research investigates the fluid-structure interaction phenomenon of gravity-driven falling rigid plates through a combination of experimental and theoretical approaches. Plates of varying dimensions and densities are systematically examined to explore the influence of non-dimensional parameters, including the Reynolds number ( R e ) and dimensionless moment of inertia ( I * ), on the falling patterns. High-speed photography is employed to extract plate trajectories and posterior kinematics calculations. In the range of relatively high Reynolds numbers ( R e > 800 ), our study identifies three distinct falling modes: periodic fluttering, periodic tumbling, and marginal chaotic motion. The falling trajectories of the plates are analyzed and compared with their corresponding kinematic behaviors. By integrating theoretical analyses with experimental findings, we develop a semi-analytical model capable of calculating the real-time hydrodynamic forces and moments acting on falling plates. This model facilitates the prediction of falling trajectories for quasi-2-dimensional plates with arbitrary material and dimension combinations. Comparisons between model predictions and experimental results demonstrate good agreement in the fluttering and tumbling modes.

Aerosols such as dust, pollen, smoke, soot, or viruses in our environment can significantly affect air quality, impacting climate and health. For example, virus transmission through aerosols is an important mode of spread for certain respiratory viruses, including the SARS‐CoV‐2 virus responsible for the COVID‐19 pandemic. Numerous measures were proposed for mitigating the harmful effects of aerosol transmission on health. Though these measures are effective, they were found to have intrinsic disadvantages. Combined intervention is promising, with progress being made in combining different technologies to enhance aerosol removal and improve indoor air quality. While the combined or layered intervention was previously proposed as a rational measure to overcome some of the disadvantages associated with individual approaches, there is a lack of availability of quantitative experimental data for at least some of the many possible combinations from the viewpoint of the number of aerosol particles removed from the surrounding environment. This study assesses the efficacy of one possible layered approach on aerosol removal. Particle flow visualization was employed to quantitatively investigate the combined effect of two measures—filtration and air ionization—on aerosol removal. The results of particle counting showed that for the experimental configuration studied, the combined intervention had a synergistic effect and removed 15%–85% more aerosol particles from the environment compared to a single intervention (filtration) in the same duration. It is observed that combined approaches demonstrate the potential to address the disadvantages of the constituent approaches; for example, in this case, effective aerosol removal was shown to be a possibility even with a medium‐quality filter (which is economical and accessible) and a weak ionizer (with low ozone generation).