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This article presents nondestructive neutron computed tomography (nCT) measurements of Diesel Particulate Filters (DPFs) as a method to measure ash and soot loading in the filters. Uncatalyzed and unwashcoated 200cpsi cordierite DPFs exposed to 100% biodiesel (B100) exhaust and conventional ultra low sulfur 2007 certification diesel (ULSD) exhaust at one speed-load point (1500 rpm, 2.6 bar BMEP) are compared to a brand new (never exposed) filter. Precise structural information about the substrate as well as an attempt to quantify soot and ash loading in the channel of the DPF illustrates the potential strength of the neutron imaging technique.

A Soot-NOx Trap (SNT) is a combinatorial aftertreatment device intended to decrease both particulate and NOx emissions simultaneously. A system-level Soot-NOx Trap model was developed by adding Lean NOx Trap kinetics to a 1D Diesel Particulate Filter model. The hybrid model was validated against each parent model for the limiting cases, then exercised to investigate the interacting redox behavior. Modulations in temperature and exhaust air-fuel ratio were investigated for their ability to facilitate particulate oxidation and NOx reduction in the trap.

An overall diesel engine and aftertreatment system model has been created that integrates diesel engine, exhaust system, engine emissions, and diesel particulate filter (DPF) models using MATLAB Simulink. The 1-D engine and exhaust system models were developed using WAVE. The engine emissions model combines a phenomenological soot model with artificial neural networks to predict engine out soot emissions. Experimental data from a light-duty diesel engine was used to calibrate both the engine and engine emissions models. The DPF model predicts the behavior of a clean and particulate-loaded catalyzed wall-flow filter. Experimental data was used to validate this sub-model individually. Several model integration issues were identified and addressed. These included time-step selection, continuous vs. limited triggering of sub-models, and code structuring for simulation speed. Required time-steps for different sub models varied by orders of magnitude.

Gasoline Direct Injection (GDI) is known to produce lower concentrations of smaller particulate matter (PM) compared to diesel combustion [1]. The lower concentration results in the absence of soot-cake formation on the filter channel wall and therefore filtration behavior deviates from the expected diesel particulate filter (DPF) performance. Therefore, studies of cake-less filtration regimes for smaller sized particulates is of interest for GDI PM mitigation. This work investigates the filtration efficiency of laboratory-generated particulates, representative of GDI-sized PM, in uncoated, commercial DPF cordierite substrates of varying porosities. Size-dependent particulate concentrations were measured using a Scanning Mobility Particle Sizer (SMPS), both upstream and downstream of the filters. By comparing these measured concentrations, the particle size-dependent filtration efficiency of filter samples was calculated.

The increasing number of gasoline direct injection (GDI) vehicles on the roads has drawn attention to their particulate matter (PM) emissions, which are greater both in number and mass than port fuel injected (PFI) spark ignition (SI) engines [1]. Regulations have been proposed and implemented to reduce exposure to PM, which has been shown to have negative impacts on both human health and the environment [2, 3]. Currently, the gasoline particulate filter (GPF) is the proposed method of reducing the amount of PM from vehicle exhaust, but modifications to improve the filtration efficiency (FE) and reduce the pressure drop across the filter are yet needed for implementation of this solution in on-road vehicles. This work evaluates the impacts of wall thickness and cell density on filtration efficiency and backpressure using a benchtop filtration system.