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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.

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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1992

Constant amplitude and post-overload fatigue crack growth behavior in PM aluminum alloy AA 8009

A recently developed, rapidly solidified, powder metallurgy, dispersion strengthened aluminum alloy, AA 8009, was fatigue tested at room temperature in lab air. Constant amplitude/constant delta kappa and single spike overload conditions were examined. High fatigue crack growth rates and low crack closure levels compared to typical ingot metallurgy aluminum alloys were observed. It was proposed that minimal crack roughness, crack path delection, and limited slip reversibility, resulting from ultra-fine microstructure, were responsible for the relatively poor da/dN-delta kappa performance of AA 8009 as compared to that of typical IM aluminum alloys.

Reynolds, A. P.↗
NASA NTRS2007

Measuring a Precise Ultra-Lightweight Spaceflight Mirror on Earth: The Analysis of the SHARPI PM Mirror Figure Data during Mirror Processing at GSFC

The SHARPI (Solar High Angular Resolution Photometric Imager) primary mirror is a 5kg, 0.5m paraboloid, diffraction limited at FUV wavelengths when placed in a 0-G environment. The ULE sandwich honeycomb mirror and the attached mount pads were delivered by ITT (then Kodak) in 2003 to NASA s Goddard Space Flight Center (GSFC). At GSFC, we accepted, coated, mounted, and vibration tested this mirror in preparation for flight on the PICTURES (Planet Imaging Concept Testbed Using a Rocket Experiment) mission. At each step, the integrated analysis of interferometer data and FEA models was essential to quantify the 0-G mirror figure. This task required separating nanometer sized variations from hundreds of nanometers of gravity induced distortion. The ability to isolate such features allowed in-situ monitoring of mirror figure, diagnosis of perturbations, and remediation of process errors. In this paper, we describe the technical approach used to achieve these measurements and overcome the various difficulties maintaining UV diffraction-limited performance with this aggressively lightweighted mirror.

Antonille, Scott↗
NASA NTRS2011

Integration of NASA-Developed Lifing Technology for PM Alloys into DARWIN (registered trademark)

In recent years, Southwest Research Institute (SwRI) and NASA Glenn Research Center (GRC) have worked independently on the development of probabilistic life prediction methods for materials used in gas turbine engine rotors. The two organizations have addressed different but complementary technical challenges. This report summarizes a brief investigation into the current status of the relevant technology at SwRI and GRC with a view towards a future integration of methods and models developed by GRC for probabilistic lifing of powder metallurgy (P/M) nickel turbine rotor alloys into the DARWIN (Darwin Corporation) software developed by SwRI.

McClung, R. Craig↗
NASA NTRS2014

A Survey of Jet Aircraft PM by TEM in APEX III

Based upon field testing during the NASA led APEX III campaign conducted in November 2005 at the NASA Glenn Research Center in coordination with Continental Airlines and Cleveland Hopkins International Airport. This paper reports observations of particulate emissions collected from a suite of jet engine aircraft to assess differences and similarities in soot macro- micro- and nanostructure using transmission electron microscopy (TEM). Aggregates are compact, primary particle sizes varied and nanostructure mixed. Comparisons are made to more familiar laboratory flame-generated soot as a well-studied point of reference. Results are interpreted in terms of turbulence interacting with the different stages of particle formation and growth.

soot↗
NASA NTRS2013

Effects of Long Term Exposures on PM Disk Superalloys

Turbine disks in some advanced engine applications may be exposed to temperatures above 700 C for extended periods of time, approaching 1,000 h. These exposures could affect the near-surface composition and microstructure through formation of damaged and often embrittled layers. The creation of such damaged layers could significantly affect local mechanical properties. Powder metal disk superalloys LSHR and ME3 were exposed at temperatures of 704, 760, and 815 C for times up to 2,020 h, and the types and depths of environmental attacked were measured. Fatigue tests were performed for selected cases at 704 and 760 C, to determine the impact of these exposures on fatigue life. Fatigue resistance was reduced up to 98% in both superalloys for some exposure conditions. Tensile tests were also performed to help understand fatigue responses, and showed corresponding reductions in ductility. The changes in surface composition and phases, depths of these changed layers, failure responses, and failure initiation modes were compared.

Heat resistant allos↗
NASA NTRS2020

The Relationship Between MAIAC Smoke Plume Heights and Surface PM

Biomass burning is a source of fine particulate matter (PM2.5) air pollution, which adversely impacts human health. However, quantifying the health effects from biomass burning PM2.5 is difficult. Monitoring networks generally lack the spatial density needed to capture the heterogeneity of biomass burning smoke. Satellite aerosol optical depth (AOD) can be used to fill spatial gaps but does not distinguish surface‐level aerosols. Plume height (PH) observations may provide constraints on the vertical distribution of smoke and its impact on surface concentrations. We assessed PH characteristics from Multi‐Angle Implementation of Atmospheric Correction (MAIAC) and evaluated its correlation with colocated PM2.5 and AOD measurements. PH is generally highest over the western United States. The ratio PM2.5:AOD generally decreases with increasing PH:PBLH (planetary boundary layer height), showing that PH has the potential to refine surface PM2.5 estimates for collections of smoke events.

satellite retrieved smoke plume heights↗
NASA NTRS2023

Reconstructing PM 2.5 Data Record for the Kathmandu Valley Using a Machine Learning Model

This paper presents a method for reconstructing the historical hourly concentrations of particulate matter 2.5 (PM2.5) over the Kathmandu Valley from 1980 to the present. The method uses a machine learning model that is trained using PM2.5 readings from US Embassy (Phora Durbar) as a ground truth, and the meteorological data from Modern-Era Retrospective Analysis for Research and Applications v2 (MERRA2) as input. The Extreme Gradient Boosting (XGBoost) model acquires a credible 10-fold cross-validation (CV) score of ~83.4%, an r2-score of ~84%, a Root Mean Square Error (RMSE) of ~15.82 µg/m3, and a Mean Absolute Error (MAE) of ~10.27 µg/m3. Further demonstrating the model's applicability to years other than those for which truth values are unavailable, the multiple cross-test with an unseen data set offered r2-scores for 2018, 2019, and 2020 ranging from 56% to 67%. The model-predicted data agrees with true values and indicates that MERRA2 underestimates PM2.5 over the region. It strongly agrees with ground-based evidence showing substantially higher mass concentrations in the dry pre- and post-monsoon seasons than in the monsoon months. It also shows a strong anti-correlation between PM2.5 concentration and humidity. The results also demonstrate that none of the years fulfilled the annual mean air quality index (AQI) standards set by the World Health Organization (WHO).

machine learning↗
NASA NTRSDate not supplied

Overview of Extractive and Optical PM Measurements from the NASA SE-5 High Pressure Flame Diagnostics Laboratory

Soot emissions measurements of multiphase non-premixed flames are sparse in the literature due to the complexities of sampling in such environments. We present an overview from on-going work at the NASA SE-5 High Pressure Flame Diagnostics laboratory focusing on soot and gaseous emissions at elevated pressures. SE-5 is a gaseous and liquid fueled high pressure flame tube facility capable of operating at pressures up to 880 psia, cooling capacity of 4,000,000 BTU/hr, liquid fuel flow rates up to 2 GPH, and an equivalence ratio range of 0.2 (fuel lean) to 4 (fuel rich). SE-5 provides optical access via four UV-grade fused silica optical windows of the primary reaction zone enabling non-intrusive optical diagnostics of the flame.

Soot↗
NASA NTRSDate not supplied

PM 2.5 Concentrations over Major Metropolitan Regions Inferred from Airborne High Spectral Resolution Lidar Measurements Using Machine Learning Regression

We use measurements of near-surface aerosol backscatter, extinction, and depolarization acquired by four NASA Langley Research Center airborne High Spectral Resolution Lidars (HSRLs) to develop a machine learning regression methodology to infer PM2.5 concentrations at the surface and aloft. These airborne HSRL measurements were acquired over major metropolitan regions in the United States and Asia during more than 170 flights since 2010. Hourly surface PM2.5 measurements from the EPA air quality system and similar networks in other countries acquired within 10 km and 15 minutes of these near-surface HSRL measurements are used to train models that compute PM2.5 concentrations from the HSRL measurements. We examine several regression methods and find that exponential Gaussian Process algorithms consistently give the best performance in terms of the lowest root-mean-square (RMS) errors and the highest correlations. Model performance varies significantly depending on various combinations of HSRL aerosol measurements (e.g., aerosol backscatter, extinction, depolarization, backscatter color ratios, lidar ratios, aerosol optical thickness) and retrievals (e.g., mixed layer height, aerosol type) used in the regressions. Models that use near-surface measurements of aerosol backscatter and aerosol intensive properties such as depolarization, backscatter color ratio, and lidar ratio typically give the best performance with RMS errors around 4 mg/m3 and correlation coefficients above 0.9. HSRL measurements were often acquired when the aircraft flew systematic “raster-scan” patterns for several hours over these cities. These flight patterns enabled measurements of the spatial, temporal, and vertical variabilities in the distributions of aerosol backscatter and aerosol intensive properties and allowed us to derive the corresponding variabilities in PM2.5 concentrations. We present examples of such variabilities over urban areas in the United States as well as Asia. We describe also how the distribution of surface PM2.5 varies with aerosol type and use these retrievals to examine model simulations of surface PM2.5 in these metropolitan regions. We also discuss how this methodology may be applied to measurements from satellite lidars such as CALIOP on CALIPSO and ATLID on EarthCARE.

lidar↗