SEARCH · Engineering Papers

Results for “Particulates”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 415 records · Page 23

All sources NASA NTRS DOE OSTI

NASA NTRS2020

Evaluating Multi-Sensor Agreement of Satellite Particulate Backscatter Retrievals by Validatin Against In-Water Measurement

Biogeochemical-Argo profiling floats have increased in situ data density across multiple water types, creating new opportunities to evaluate satellite instrument-to-instrument differences in particulate back scattering coefficient(bbp). Retrievals of bbp from identical GIOP algorithm configurations differ between satellite instruments due to1)algorithm input differences and 2) radiometric differences. 3.Instrument-to-instrument differences must be considered before creating a merged timeseries of satellite ocean color products,in order to distinguish real, environmental contributions from spurious algorithmic or radiometricones

Scott, Joel P.↗

NASA NTRS2020

Biases in the In Situ Measurement of Particulate Organic Carbon and Its Effect on the Calibration and Validation of Ocean Color Sensors

Particulate organic carbon (POC) plays an oversize role, relative to its standing stock in the global carbon (C) cycle. Accurate measurement of POC is central to understanding the ocean C flux and its sensitivity to climate forcing. POC is a standard NASA ocean color data product, which lacks a consensus, quality-assured measurement protocol for satellite validation. Thus, algorithms based on field measurements lacking verified uncertainties have limited applicability towards climate data records. Different sampling and filtration protocols, and blank corrections, introduce biases in the magnitude of POC measured from the field. A significant filter blank attributable to dissolved organic C (DOC) adsorption that, until recently has been seldom corrected for, likely has introduced biases in POC global datasets.

Chaves, Joaquín E.↗

NASA NTRS2020

Particulate Sensor Technology Development at NASA Glenn Research Center

GRC has developed multiple innovative particulate sensor technologies for spacecraft cabin environmental monitoring, spacecraft fire safety, and lunar dust characterization. This has been driven by lack of commercial (COTS) devices to meet the volume, mass, power, reliability, and measurement requirements of any given application.

Fischer, David G.↗

NASA NTRS2020

Global Estimates and Long-Term Trends of Fine Particulate Matter Concentrations (1998-2018)

Exposure to outdoor fine particulate matter (PM2.5) is a leading risk factor for mortality. We develop global estimates of annual PM2.5 concentrations and trends for 1998–2018 using advances in satellite observations, chemical transport modeling, and ground-based monitoring. Aerosol optical depths (AODs) from advanced satellite products including finer resolution, increased global coverage, and improved long-term stability are combined and related to surface PM2.5 concentrations using geophysical relationships between surface PM2.5 and AOD simulated by the GEOS-Chem chemical transport model with updated algorithms. The resultant annual mean geophysical PM2.5 estimates are highly consistent with globally distributed ground monitors (R2 = 0.81; slope = 0.90). Geographically weighted regression is applied to the geophysical PM2.5 estimates to predict and account for the residual bias with PM2.5 monitors, yielding even higher cross validated agreement (R2 = 0.90–0.92; slope = 0.90–0.97) with ground monitors and improved agreement compared to all earlier global estimates. The consistent long-term satellite AOD and simulation enable trend assessment over a 21 year period, identifying significant trends for eastern North America (−0.28 ± 0.03 μg/m3/yr), Europe (−0.15 ± 0.03 μg/m3/yr), India (1.13 ± 0.15 μg/m3/yr), and globally (0.04 ± 0.02 μg/m3/yr). The positive trend (2.44 ± 0.44 μg/m3/yr) for India over 2005–2013 and the negative trend (−3.37 ± 0.38 μg/m3/yr) for China over 2011–2018 are remarkable, with implications for the health of billions of people.

Melanie S. Hammer↗

NASA NTRSDate not supplied

Isotopically Tracking Nitrogen Oxides, Nitrous Acid, And Nitric Acid, Particulate Nitrate from Wildfire During FIREX-AQ

Wildfires are an important source of reactive nitrogen species including nitrogen oxides (NOx = NO + NO2) and nitrous acid (HONO), as well as products such as nitric acid (HNO3) and particulate nitrate (p-NO3-). These species have strongly influence air quality and climate. In summer of 2019 (July 25 —August 21), as part of the FIREX-AQ ground mission, we drove ~3000 miles and sampled smoke of different ages in both daytime and nighttime from 5 different wildfires in the western US. Using recently developed methods that have been validated in both laboratory and field settings, we conducted high time resolution collection of NOx, NO2, HONO, HNO3, and p-NO3- for off-line characterization of isotopic composition δ15N, δ18O and Δ17O (=δ17O−0.52 δ18O). δ15N serves as a potential tracer for tracking sources and reactive nitrogen cycling pathways, δ18O and Δ17O provide key information about oxidation pathways (i.e. O3 versus RO2). Our measured δ15N for HONO ranges from -4.9‰ to +5.2‰ and -8‰ to +5.2‰ respectively, in the majority overlapping with our prior lab controlled burning results, which suggests the capability of δ15N for tracing the biomass burning emissions against other sources including vehicular emissions and soil emissions. In addition, our measurements show δ18O and Δ17O for NO2 and HONO vary significantly day vs night, as well as in young smoke vs aged smoke, likely reflecting different oxidizing mechanisms under different smoke conditions. Preliminary results of isotopic signatures of all the above reactive nitrogen species will be presented, and their relationships will be explored to identify wildfire derived reactive nitrogen source signatures and to constrain atmospheric processing pathways (e.g. secondary formation of HONO) for different fires under various smoke conditions depending on fuel types, fire conditions and meteorological conditions.

Jiajue Chai↗

NASA NTRS2020

Particulate backscattering in the global ocean: A comparison of independent assessements

How well do we know the particulate backscattering coefficient (bbp) in the global ocean? Satellite lidar bbp has never been validated globally and few studies have compared lidar bbp to bbp derived from reflectances (via ocean color) or in-situ observations. Here we validate lidar bbp with autonomous biogeochemical Argo floats using a decorrelation analysis to identify relevant spatiotemporal matchup scales inspired by geographical variability in the Rossby radius of deformation. We compare lidar, float, and ocean color bbp at the same locations and times to assess performance. Lidar bbp outperforms ocean color, with a median percent error of 18% compared to 24% in the best case and a relative bias of -11% compared to -21%, respectively. Phytoplankton carbon calculated from ocean color and lidar exhibits basin-scale differences that can reach ± 50%.

ocean color↗

NASA NTRS2019

Toward Improving Short-Term Predictions of Fine Particulate Matter Over the United States Via Assimilation of Satellite Aerosol Optical Depth Retrievals

This study develops a new approach to improve simulations of the particulate matter of aerodynamic diameter smaller than 2.5μm (PM2.5) in the Community Multiscale Air Quality (CMAQ) model via assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) aerosol optical depth(AOD) retrievals using the Grid point Statistical Interpolation (GSI) system. In contrast to previous studies that only consider errors due to transport, our computation of the background error covariance matrix incorporates uncertainties in anthropogenic emissions. To understand the impact of this approach, three experiments (one background and two assimilations) are performed over the contiguous United States(CONUS) from 15 July to 14 August 2014. The background CMAQ experiment significantly underestimates both the MODIS AOD and surface PM2.5levels. MODIS AOD assimilation pushes both the CMAQ AOD and surface PM2.5distributions toward the observed distributions, but CMAQ still underestimates the observations. Averaged over CONUS, the two assimilation experiments with and without including the anthropogenic emission uncertainties improve the correlation coefficient between the model and independent observations of PM2.5 by ~67% and ~48%, respectively, and reduces the mean bias by ~38% and~10%, respectively. The assimilation improves the model performance everywhere over CONUS, except the New York and Wisconsin, where CMAQ overestimates the observed PM2.5during nighttime after assimilation likely because of overcorrection of aerosol mass concentrations by the AOD assimilation. Future work should incorporate uncertainties in other processes (biomass burning and biogenic emissions, deposition, chemistry, transport, and boundary conditions) to further enhance the value of assimilating spaceborne AOD retrievals.

Assimilation↗

NASA NTRS2020

Advancing Methodologies for Applying Machine Learning and Evaluating Spatiotemporal Models of Fine Particulate Matter (PM 2.5 ) Using Satellite Data Over Large Regions

Reconstructing the distribution of fine particulate matter (PM 2.5 ) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM 2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM 2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM 2.5 on a 1 x 1 km 2 resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m 3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m 3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM 2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

air pollution↗

NASA NTRS2022

Retrieving Particulate Matter Concentrations over the Contiguous United States Using CALIOP Observations

Using twelve years (2007-2018) of NASA Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) near-surface 532 nm aerosol extinction retrievals, multi-year mean and trends of particulate matter (PM) concentrations are derived over the contiguous United States (CONUS). Different from past studies that use column integrated aerosol optical thickness, here only near-surface CALIOP aerosol extinction is used for deriving near-surface PM with aerodynamic diameters less than 2.5 µm (PM2.5) concentrations using an innovative, bulk-mass-modeling-based method. Compared against ground based PM2.5 measurements from the U.S. Environmental Protection Agency (EPA), an encouraging relationship between CALIOP-derived PM2.5 and EPA-observed PM2.5 (Deming slope = 0.89; RMSE = 3.42 µg/m3; mean bias = -1.00 µg/m3) is found using combined daytime/nighttime CALIOP data. Also, comparable trends in PM2.5 concentrations from the EPA and daytime and nighttime CALIOP data are found for most of the eastern CONUS and imply that air quality is generally improving over this region for the study period. Over the western CONUS, a seasonal analysis reveals that PM2.5 trends are positive during the more active wildfire season (June through November) but negative for other months. This study suggests that lidar data show promise in their use for obtaining PM2.5 estimates and provides motivation to further explore aerosol extinction-based PM concentration retrievals in anticipation of future space-based lidar missions.

CALIOP↗

NASA NTRS2021

Asthma and Particulate Matter Pollution: Insights from Health Survey and Air Quality Monitoring in the Buzzard Point, Washington, DC Neighborhood

Air pollution, climate change, and other environmental factors contribute to increasing asthma in manycities, including Washington, DC. This work provides a case study of how community input, neighborhood-level health surveys, and air quality monitoring can inform the understanding of asthma and air pollution.A partnership between residents, concerned citizens, scientists, and educators has been working for envi-ronmental health in a DC neighborhood located on a major roadway, next to concrete batch plants andclose to several construction projects. A 2016 Community Health and Safety Study by the DC Departmentof Health, Office of Health Equity, recognized this particular neighborhood as more vulnerable to healthimpacts from recent construction in the area, compared with the surrounding areas, due to lower averageincome and higher percentage of seniors and children. This work presents neighborhood health surveysand air quality monitoring data at a more granular, local level than available from DC governmentagencies. The health surveys documented residents’ experiences around air pollution, asthma,and other health concerns. A key finding was evidence that asthma might be undercounted in thisneighborhood; among residents who did not indicate a diagnosis of asthma, many discussed havingsymptoms that could reflect asthma. Air quality monitoring (particulate matter [PM]) did not indicate thatfederal air quality standards have been violated. Real-time PM data, however,illustrated how current PMstandards, such as 1- and 24-hour averages, may fail to capture shorter duration high PM events that areconsistent with resident concerns.

Shizuka Hsieh↗

NASA NTRSDate not supplied

Global Burden of Tropospheric Ozone: Effects of Particulate Nitrate Photolysis and Assimilation of NO2 Measurements

Tropospheric ozone is a major pollutant, a greenhouse gas, and the primary source of the hydroxyl radical. It is largely produced in situ from the oxidation of CO and volatile organic compounds in the presence of nitrogen oxides (NOx=NO+NO2). Global atmospheric chemistry models generally underestimate NOx and CO concentrations in the northern hemisphere away from source regions, which would lead to an underestimate in the tropospheric ozone burden. Here we evaluate the tropospheric ozone simulation in the GEOS-Chem model (version 14) using observations from satellite, aircraft, and ozonesondes. We include GEOS-Chem simulations conducted in the standard offline mode, as well as in the online mode within the NASA GEOS earth system model which forms the basis for the GEOS Composition Forecasts (GEOS-CF). In both configurations, GEOS-Chem underestimates ozone concentrations in the northern midlatitude free troposphere in spring and summer. We find that we can improve the ozone simulation by including an additional NOx source over the oceans from particulate nitrate photolysis on sea salt aerosols at rates inferred from field studies over the Atlantic Ocean. We investigate whether further improvements in the ozone simulation can be achieved by assimilating satellite measurements of NO2 from the Ozone Monitoring Instrument (OMI) and CO from the Measurement of Pollution in the Troposphere (MOPITT) instruments.

tropospheric ozone↗

NASA NTRS2023

Fine particulate concentrations over East Asia derived from aerosols measured by the Advanced Himawari Imager using machine learning

Fine particulate matter with a diameter below 2.5 μm (PM 2.5 ) is deleterious to the cardiovascular and respiratory systems. It is often difficult to assess the effects of PM 2.5 on human health over regions with limited ground monitoring sites, especially in East Asia. As an alternative, we estimated near-surface PM 2.5 concentrations by analyzing Advanced Himawari Imager (AHI) Yonsei Aerosol Retrieval (YAER) products. This study incorporates daytime data for East Asia covering the Korean Peninsula, China, Japan, Southeast Asia, and southern Mongolia. We collocated AHI YAER product pixels with meteorological, land-cover, and other ancillary data for the period from March 2018 to February 2019. To estimate PM 2.5 concentrations over wide areas spanning many countries displaying various relationships between aerosol optical depth and PM 2.5 , monthly models were developed by considering both the spatial and temporal characteristics of ground-based PM 2.5 measurements. Random forest machine learning model estimated ground-level mass concentrations of PM 2.5 ; subsequent 10-fold cross validation (CV) yielded a CV R 2 value of 0.81 and a CV root mean squared error (RMSE) of 12.3 μg m -3 . We investigated the spatial pattern of PM 2.5 concentrations over multiple countries and seasonal variation in PM 2.5 concentrations. Diurnal variation of a severe PM 2.5 event in the Korean Peninsula was investigated as a case study. The model captured the extremely heterogeneous spatial distribution of PM 2.5 concentrations peaked around local noon. To measure the capability of the developed model to estimate PM 2.5 concentrations in areas with few in-situ data, its predictive performance was evaluated using a dataset independent of the training process with an R 2 of 0.60 and RMSE of 8.18 μg m −3 . This study demonstrates the potential for satellite-based PM 2.5 estimation for areas with insufficient measuring stations.

Pm2.5↗

NASA NTRSDate not supplied

Investigating the Adhesion Force of Lunar Regolith Particulates on Air Plasma Sprayed Alumina Coatings

Ceramic materials, often used to protect components due to their high strength, and wear resistance, have added benefits of being lightweight and providing multifunctional properties. They are, therefore, significant to provide durability and support during long-duration missions to the moon’s surface for rovers, landers, robotic systems, habitats, and many other components. Materials such as aluminum oxide with high mechanical strength and hardness can help to improve the durability of structures used in space exploration. Devices used for space exploration require the use of materials with the ability to withstand exposure to extreme environments. Lunar regolith is a constituent with the ability to adhere electrostatically and damage the components used to perform these missions due to adhesion of lunar dust projectiles that can cause delamination on surfaces, which may not be physically visible. The particulates of lunar dust are classified as corrosive material, leading to the degradation of structures. Aluminum oxide presents excellent resistance to different types of wear due to its high strength and hardness. Air plasma sprayed (APS) aluminum oxide coatings have demonstrated the potential to protect the surface to which they are applied. However, the parameters of roughness and porosity of the coatings need to be considered to establish if they can protect the components from extreme environments. Considering the electrostatic forces that the components are exposed to, the adhesion between the lunar regolith and the surface of the components needs to be investigated. In this work, the adhesion forces of lunar dust simulants with an average size of 30 𝜇m are investigated considering the roughness of the surface of the APS aluminum oxide. The centrifugal technique utilized here offers the advantage of establishing the adhesion force between particles with different shapes on smooth or rough surfaces. The simulants were deposited on the surface of APS aluminum oxide using an aerosolization technique to achieve a monolayer coating in three different locations across the specimen to determine the increments in adhesion force at the centrifuge. The roughness of the APS aluminum oxide was determined to be 2.257 𝜇m. The specimen was tested under an incremental centrifugal speed from 100 rpm to 3000 rpm for 20 seconds. Low magnification microscopy images were collected to cover a larger surface area of the test coating. The adhesion force was measured considering the distance from the centrifugal axis and the rotational speed. Due to the coating roughness, and the distance from the centrifugal axis, the results demonstrated a larger adhesion force in locations closer to the centrifugal axis. Smaller particles were entrapped within the roughness of the coating, and a stronger adhesion force was measured. In order to continue designing wear-resistant coating for structural protection in space missions, press-on experiments using a centrifuge will be performed. Future experiments will allow us to determine the design parameters for APS aluminum oxide coating to protect the structures from harsh space environments.

Perla Latorre-Suarez↗

NASA NTRS2023

Radiation Interaction with Particulates in Planetary Entry Flows

A hypersonic particle phase solver based on a continuum model is implemented in the NASA HyperSolve CFD code and used to predict particle phase conditions in a dusty Mars entry scenario. Black-body radiative emission from high temperature dust particles is predicted for a Mars relevant entry problem, and the emission integrated to an entry vehicle using a ray tracing approach to estimate surface heat flux. The surface heating due to this mechanism is found to be small relative to convective and radiation from the gas phase emission. Additionally, the effect of atmospheric haze particulates vaporizing in the Titan atmosphere is studied. The increase in carbon availability due to the composition of the haze particles is found to increase the gas radiative heating to the vehicle by up to 3% on the forebody of a representative Titan entry vehicle with approximately 0.15% haze in the freestream by mass.

Andrew Hinkle↗

NASA NTRS2023

An Analysis of Degradation in Low-Cost Particulate Matter Sensors

Low-cost sensors (LCSs) are increasingly being used to measure fine particulate matter (PM 2.5 ) concentrations in cities around the world. One of the most commonly deployed LCSs is PurpleAir with ∼15 000 sensors deployed in the United States, alone. PurpleAir measurements are widely used by the public to evaluate PM 2.5 levels in their neighborhoods. PurpleAir measurements are also increasingly being integrated into models by researchers to develop large-scale estimates of PM 2.5 . However, the change in sensor performance over time has not been well studied. It is important to understand the lifespan of these sensors to determine when they should be serviced or replaced, and when measurements from these devices should or should not be used for various applications. This paper fills this gap by leveraging the fact that: (1) each PurpleAir sensor is composed of two identical sensors and the divergence between their measurements can be observed, and (2) there are numerous PurpleAir sensors within 50 meters of regulatory monitors allowing for the comparison of measurements between these instruments. We propose empirically-derived degradation outcomes for the PurpleAir sensors and evaluate how these outcomes change over time. On average, we find that the number of ‘flagged’ measurements, where the two sensors within each PurpleAir sensor disagree, increases with time to ∼4% after 4 years of operation. Approximately, 2 percent of all PurpleAir sensors were permanently degraded. The largest fraction of permanently degraded PurpleAir sensors appeared to be in the hot and humid climate zone, suggesting that sensors in these locations may need to be replaced more frequently. We also find that the bias of PurpleAir sensors, or the difference between corrected PM2.5 levels and the corresponding reference measurements, changed over time by −0.12 μg m −3 (95% CI: −0.13 μg m −3 , −0.10 μg m −3 ) per year. The average bias increases dramatically after 3.5 years. Further, climate zone is a significant modifier of the association between degradation outcomes and time.

Priyanka deSouza↗

NASA NTRS2022

Investigating Drivers of Particulate Matter Pollution Over India and the Implications for Radiative Forcing With GEOS-Chem-TOMAS15

Ambient fine particulate matter (PM 2.5 ) concentrations in India frequently exceed 100 μg/m 3 during fall and winter pollution episodes. We use the GEOS-Chem chemical transport model with the TwO-Moment Aerosol Sectional microphysics scheme with 15 size bins (TOMAS15) to assess PM 2.5 composition and impacts on radiation and cloud condensation nuclei (CCN) during pollution episodes as compared to the seasonal (October-December) average. We conduct high resolution (0.25° × 0.3125°) nested-domain simulations over India for short-duration, high-PM2.5 episodes in the fall of 2015 and 2017. The simulations capture the magnitude and spatial patterns of pollution episodes measured by surface monitors (r PM2.5 = 0.69) although aerosol optical depth is underestimated. During the episodes, near-surface organic matter (OM), black carbon (BC), and secondary inorganic aerosol concentrations increase from seasonal averages by up to 36, 7, and 7 μg/m 3 , respectively. Episodic aerosol increases enhance cooling by lowering the top-of-atmosphere clear-sky direct radiative effect (DRE TOA ) during the 2015 episode (−6 W/m 2 ), with a smaller impact during the 2017 episode (−1 W/m 2 ). Differences in DRE TOA reflect larger increases in scattering aerosols in the column during the 2015 episode (+17 mg/m 2 ) than in 2017 (+13 mg/m 2 ), while absorbing aerosol column enhancements are smaller (+3 mg/m2) in both years. Changes in shortwave radiation at the surface (SW sfc ) are spatially similar to DRE TOA and mostly negative during both episodes. CCN enhancements (0.2% supersaturation) during these episodes occur across the western Indo-Gangetic Plain, coincident with higher PM 2.5 concentrations. Changes in DRE TOA , SW sfc , and CCN during high-PM 2.5 episodes may have implications for crops, the hydrologic cycle, and surface temperature.

PM2.5↗

NASA NTRSDate not supplied

Contrails Measurement and Testing Capabilities in NASA's Particulate Aerosol Laboratory

The NASA Glenn Research Center Particulate Aerosol Laboratory (PAL) is a ground test facility for studying emissions and contrails at upper atmospheric conditions. The facility consists of a small-scale combustor that generates hot combustion gases and soot nanoparticles, which flow through a transition pipe into a jet nozzle, which exhausts into an altitude chamber. Thermodynamic conditions experienced in flight are matched in the altitude chamber, allowing the study of ice particle formation of simulated aircraft engine exhaust plumes. A non-intrusive optical diagnostic technique based on Mie scattering provides ice particle size, number density, and mass density. A suite of commercial extractive particle measurement instruments provides estimates of soot number density, size, and black carbon mass distributions. Measurements are presented for various fuels at chamber conditions covering an altitude range of 20,000 ft (6 km) to 45,000 ft (14 km) altitude and ambient temperatures from -48°C to -68°C. The data presented demonstrate the effect of soot number density, altitude, and fuel-to-air equivalence ratio on the contrail ice particle formation. Ice mass density measurements are compared to values predicted by a thermodynamic plume similarity solution.

Optical Instrumentation↗

NASA NTRSDate not supplied

A Dynamic Testing Approach for Particulate Erosion–Corrosion for Gas Turbine Coatings

Particle interactions in gas turbine engines can be multicomponent, complex phenomena leading to the degradation of thermal (TBCs) and environmental barrier coatings (EBCs) meant to protect engine components. Ingestion of particles into the engine can lead to recession of coatings due to particle erosion. Similarly, these same particles can become molten, adhere to coatings and result in thermochemical corrosion of coating materials. Particle erosion testing is often carried out where the particles are injected into a gas stream, accelerated within a nozzle, and impinge on sample. Conversely, most molten particle corrosion testing is often done in static laboratory furnaces, which does not capture the dynamic nature of deposition in application. Nevertheless, these damage mechanisms are often tested separately and no single standard exists to test both erosive and corrosive particle interactions with coating materials under relevant operating conditions for gas turbine engines. Understanding the synergies of particle interactions in engines is crucial in determining operating lifetimes of potential coating materials. Such considerations emphasize the need for realistic approaches in standardizing particle interaction testing in combustion environments. The current study outlines initial efforts at NASA Glenn’s Erosion Burner Rig Facility in improving dynamic erosion/corrosion testing methods by assessing the durability of state-of-the-art (SOA) TBC material 7 wt.% yttria stabilized zirconia (7YSZ) as a function of particle deposition rate, burner temperature, and particle size. Calibration data to determine particle deposition rate will be presented, and mass and optical profilometry measurements were utilized to estimate mass/volume loss versus deposition per increment of particulate used over time. Electron microscopy analyses were then carried out to assess coating damage after testing.

TBC↗