Data-driven deep learning models are emerging as a promising method for characterizing pore-scale flow through complex porous media while requiring minimal computational power. However, previous models often require extensive computation to simulate flow through synthetic porous media for use as training data. We propose a convolutional neural network trained solely on periodic unit cells to predict pore-scale velocity fields of complex heterogeneous porous media from binary images without the need for further image processing. Our model is trained using a range of simple and complex unit cells that can be obtained analytically or numerically at a low computational cost. Our results show that the model accurately predicts the permeability and pore-scale flow characteristics of synthetic porous media and real reticulated foams. We significantly improve the convergence of numerical simulations by using the predictions from our model as initial guesses. Our approach addresses the limitations of previous models and improves computational efficiency, enabling the rigorous characterization of large batches of complex heterogeneous porous media for a variety of engineering applications.

Recent escalating interest in the development of highly maneuverable hypersonic vehicles demands sharp leading edges. However, sharp leading edges induce severe aerothermal conditions where conventional passive or ablative thermal protection systems fail to protect the leading edge. Here, we numerically demonstrate transpiration cooling employing oxide coolants as a new alternative system to thermally protect sharp leading edges. We parametrically characterize the performance of transpiration cooling for various coolant properties, flight conditions, and leading edge radii using a semi-analytic boundary-layer model validated with third-order direct numerical simulations. We further utilize direct numerical simulation to examine the impact of the thermochemical behavior of oxide vapors with the external hypersonic flow on transpiration cooling. Our findings do not readily align with an optimal set of material properties for transpiration cooling. Instead, certain coolant properties are more appropriate for various flight conditions and leading edge sizes. Our results also demonstrate that the thermochemical interactions between the oxide vapors and the external hypersonic flow have a negligible impact on the performance of transpiration cooling. Our study provides numerical frameworks to evaluate the performance of transpiration cooling and optimize the coolant properties for various flight conditions to protect sharp leading edges, which are paramount across hypersonic applications.

Wet electrostatic precipitators (WESP) have been widely studied for collecting fine and ultrafine particles, such as diesel particulate matter (DPM), which have deleterious effects on human health. Here, we report an experimental and numerical simulation study on a novel string-based two-stage WESP. Our new design incorporates grounded vertically aligned polymer strings, along which thin films of water flow down. The water beads, generated by intrinsic flow instability, travel down the strings and collect charged particles in the counterflowing gas stream. We performed experiments using two different geometric configurations of WESP: rectangular and cylindrical. We examined the effects of the WESP electrode bias voltage, air stream velocity, and water flow rate on the number-based fractional collection efficiency for particles of diameters ranging from 10 nm to 2.5 μm. The collection efficiency improves with increasing bias voltages or decreasing airflow rates. At liquid-to-gas (L/G) as low as approximately 0.0066, our design delivers a collection efficiency over 70% even for fine and ultrafine particles. The rectangular and cylindrical configurations exhibit similar collection efficiencies under nominally identical experimental conditions. We also compare the water-to-air mass flow rate ratio, air flow rate per unit collector volume, and collection efficiency of our string-based design with those of previously reported WESPs. The present work demonstrates a promising design for a highly efficient, compact, and scalable two-stage WESPs with minimal water consumption.Implications: Wet Electrostatic Precipitators (WESPs) are highly effective for collecting fine particles in exhaust air streams from various sources such as diesel engines, power plants, and oil refineries. However, their large-scale adoption has been limited by high water usage and reduced collection efficiencies for ultrafine particles. We perform experimental and numerical investigation to characterize the collection efficiency and water flow rate-dependence of a new design of WESP. The string-based counterflow WESP reported in this study offers number-based collection efficiencies >70% at air flow rates per collector volume as high as 4.36 (m³/s)/m³ for particles of diameters ranging from 10 nm - 2.5 μm, while significantly reducing water usage. Our work provides a basis for the design of more compact and water-efficient WESPs.