Numerical Prediction of Particulate Matter (PM) Collection Efficiency, Loading, and Flow Characteristics in Partially Damaged Particulate Filters with Different PM Size Classes

Chittipotula, Thirumalesha

doi:10.1007/s40825-021-00195-1

Cited by 2 publications

(1 citation statement)

References 28 publications

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“…A neural network is a machine-learning algorithm inspired by the human biological brain, which consists of interconnected nodes, or "neurons," that work together to process and interpret data (Hopfield, 1988;Hammerstrom, 1993;Canziani et al, 2016;Han et al, 2018;Montavon et al, 2018). There are many neural network architectures and the most widely used one for regression problems is the feed-forward ANN architecture consisting of multiple layers: an input layer, some hidden layers, and an output layer.…”

Section: Feed-forward Annmentioning

confidence: 99%

Modeling of diesel particulate filter temperature dynamics during exotherm using neural networks

Legala,

LakkiReddy,

Weber

et al. 2023

Front. Therm. Eng.

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Diesel Particulate Filter (DPF) in the diesel engine exhaust stream needs frequent regeneration (exotherm) to remove captured particulate matter (PM, or soot) without damaging to the porous DPF structure by controlling the peak temperatures and temperature gradients across the DPF. In this study, temperature distribution in a DPF is measured at 42 strategic locations in the test DPF under various regeneration conditions of exhaust flow rates, regeneration temperatures and soot loads. Then a data-based model with feed-forward neural network architecture is designed to model the thermal gradients and temperature dynamics of the DPF during the regeneration process. The neural network feature vector selection, network architecture, hyperparameter calibration process, measured data preprocessing, and experimental data acquisition procedure are evaluated. Over 7,400 experimental data points at various regeneration temperatures, flow rates and soot loads are used in training and validating the neural network model. It is found that the neural network model can accurately predict the 42 DPF bed temperatures simultaneously at different locations, and the time series analysis of both model-predicted and experimentally measured temperatures shows a good correlation. This indicates that the currently developed neural network model can provide spatial distribution of temperature in the DPF, and comprehend the nonlinearity of the temperature dynamics due to DPF soot load at exothermic conditions. These results demonstrate that the data-based model has capability in predicting thermal gradients within a DPF, aiding in determining a safer DPF regeneration strategy, onboard diagnostics and DPF development.

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