Chen, Weiqi2024-01-192024-01-192023-11https://hdl.handle.net/11299/260160University of Minnesota Ph.D. dissertation. November 2023. Major: Mechanical Engineering. Advisors: David Pui, David Kittelson. 1 computer file (PDF); xix, 265 pages.Particulate matter (PM) consists of minuscule solid or liquid airborne particles. Among these particles, fine particles like PM2.5 (particles with a diameter of 2.5 micrometers or smaller) are particularly concerning due to their ability to deeply penetrate the lungs and even enter the bloodstream and contribute to various adverse health effects, including an increased risk of heart disease, lung cancers, and other health issues. The engine combustion process of fossil fuels is a significant contributor to PM2.5 pollution in many urban areas. Furthermore, recent advancements in low-cost light-scattering PM sensors present a cost-effective solution for real-time spatiotemporal PM monitoring. Despite their benefits, these sensors have limitations in data quality due to relaxed quality control and their sensitivity to environmental conditions affecting light scattering. Therefore, this dissertation is driven by two primary objectives: Part 1 aims to mitigate Engine-out PM emissions by reducing their formation in the combustion chamber and improving filtration within the aftertreatment system. On the other hand, Part 2 is dedicated to evaluating and improving low-cost light-scattering PM sensor technology and exploring its diverse applications.In Part 1 Chapter 2, we investigated how combustion strategy and ethanol content affect particle properties in emissions from a lean burn gasoline direct injection (GDI) engine. GDI engines, increasingly used in light-duty vehicles for higher power and fuel efficiency, tend to produce elevated PM emissions. Different combustion strategies and fuel properties (like volatility and aromaticity) influence PM emission rates and characteristics. We measured particle number, size, mass concentration, effective density, and mass-mobility exponent for a GDI engine fueled with E10, E30, and E50, and operated in three combustion modes: stoichiometric, lean homogeneous, and lean stratified. Key findings include lower particle number and higher effective density with stoichiometric operation using E10, while lean homogeneous operation resulted in the smallest particles and lowest effective density. Lean stratified operation produced the highest particle concentrations and effective densities between lean homogeneous and stoichiometric conditions. Ethanol content changes influenced mass and number concentrations and effective density differently based on the combustion mode. In Part 1 Chapters 3-4, we focus on enhancing PM filtration control in gasoline particulate filters (GPFs) within aftertreatment systems, particularly for gasoline direct injection (GDI) engines that emit higher PM levels. GPFs use deep-bed filtration through ceramic walls in a honeycomb channel structure for effective PM emission control. The increasingly stringent global PM emissions regulations have amplified the demand for GPFs. Chapter 3 introduces a rapid screening method using wafer-based nanoscale membranes to improve GPF performance. It explores the use of artificial aerosols to synthesize nano-scale membranes resembling soot cake in diesel particulate filters (DPFs), enhancing GPF performance. The experiments show that membranes with larger aggregate sizes or under lower face velocity yield significantly improved performance. Chapter 4 delves into a theoretical study, while Chapter 3 focuses on experimental investigation. In Chapter 4, an analytical filter model is developed to understand efficiency and pressure drop under engine conditions, aiming to reduce development costs for meeting emission regulations and engine performance targets. The study treats the deep bed regime as two co-existing filters with different geometries based on natural morphologies, dynamically considering changes in pore size distribution and soot dendrite growth during loading. Model validation using 12 sets of experimental filter loading data demonstrates reasonable predictions for filter efficiency and pressure drop during deep-bed loading for various GPF wall-flow filters and operating conditions. The model was also used to examine the impact of porosity, mean pore size, and pore size distribution on filter loading performance, suggesting strategies for future filter optimization. Part 2 addresses the second objective of the dissertation, focusing on low-cost light-scattering PM sensors. In Chapter 6, the study describes the development of sensor test systems, including a lab-simulated environmental chamber, a field test sensor chamber, and a sensor data acquisition platform. The lab environmental chamber can simulate various environmental conditions for sensor performance assessment, including RH, temperature, aerosol types, and concentrations. Conversely, the field sensor chamber is designed to protect sensors in outdoor environments while ensuring precise sampling. The data acquisition platform involves the development of a custom printed circuit board (PCB) design and a dedicated sensor website, essential for extensive data collection, wireless transmission, and online storage capabilities. Chapter 7 begins by presenting a thorough assessment of sensor performance by conducting side-by-side experiments with gravimetric filter method (Federal Reference Methods (FRMs)) as reference to examine the dynamic response of more than 10 different popular low-cost light-scattering core sensors under diverse conditions in both controlled laboratory settings and the real-world outdoor environment. When measuring atmospheric salt particles, it was found that the low-cost sensors generally provide accurate readings within the 30% to 70% RH range but exhibit a significant spike after surpassing the deliquescence relative humidity (DRH). When environment shifts from wet to dry conditions, it was observed that sensor bias is more pronounced compared to the increasing RH conditions. Moreover, the behavior of the sensor was observed to demonstrate a strong dependency on both concentrations and particle composition. Based on the analysis, the performance ranking for all the tested sensors is provided. Following the performance assessment, Chapter 7 demonstrated various methods for improving sensor performance, including 1) Utilizing calibrations employing a multi-variable nonlinear model that incorporates RH level and differentiate the models for different concentration levels and RH hysteresis; 2) implementing sensor accessory/add-on, specifically, a silica dryer was affixed to the sensor inlet to mitigate the RH impact; 3) and exploring different sensing theories to enhance sensor lower detectable size limit. Moving to Chapter 8, the dissertation delved into exploring the various potential applications of low-cost light-scattering PM sensors. These applications included spatiotemporal measurements and filtration applications. By exploring these applications, the advantages of low-cost sensors compared to traditional reference instruments were highlighted, and the insights into the opportunities associated with the deployment of these sensors were provided.enEngine emission controlFiltrationGasoline particulate filterLight-scattering sensingMembrane filterParticulate matterThesis or Dissertation