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.

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.

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Data-driven deep learning models are emerging as a new method to predict the flow and transport through porous media with very little computational power required. Previous deep learning models, however, experience difficulty or require additional computations to predict the 3D velocity field which is essential to characterize porous media at the pore scale. We design a deep learning model and incorporate a physics-informed loss function that enforces the mass conservation for incompressible flows to relate the spatial information of the 3D binary image to the 3D velocity field of porous media. We demonstrate that our model, trained only with synthetic porous media as binary data without additional image processing, can predict the 3D velocity field of real reticulated foams which have microstructures different from porous media that were studied in previous works. Our study provides deep learning framework for predicting the velocity field of porous media and conducting subsequent transport analysis for various engineering applications. As an example, we conduct heat transfer analysis using the predicted velocity fields and demonstrate the accuracy and advantage of our deep learning model.