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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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Washington, D.C. & Maryland Energy: Estimating Solar Potential Using NASA POWER Data to Inform Renewable Energy Policy for Washington, D.C.

In line with the Sustainable D.C. 2.0 plan to combat climate change, Washington D.C. aims to decrease its greenhouse gas emissions by 100% by 2050. As solar energy is a clean, renewable energy form, its integration into the region’s power grids lowers energy costs and incentivizes sustainable development. We partnered with the Washington D.C. Department of Energy & Environment (DOEE) to determine how urban areas surrounding D.C. can better be incorporated into decisions regarding renewable energy policy. The team used NASA’s Prediction of Worldwide Energy Resources (POWER) solar data and a Light Detection and Ranging (LiDAR) derived digital surface model, to estimate and visualize rooftop solar potential for Maryland’s Prince George’s and Montgomery counties. POWER provided solar irradiance data adjusted for tilt angle while the digital surface model contributed aspect and slope data. This methodology factored out areas that were unsuitable for solar panel installation while displaying areas that possess a high potential for energy return. The team found the total rooftop solar potential for the study area to be almost 32 million kW, which is equivalent to roughly 660 kW per building. The methodology used to generate the solar potential maps can be applied to other regions of the country seeking to efficiently utilize solar energy. The end users at the DOEE can use our resulting solar potential map and data table to effectively target buildings that have the highest potential to generate solar energy.

Edward Cronin↗

Analysis of Electrical Grid Capacity in Major U.S. Metropolitan Areas for Urban Air Mobility

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft as envisioned. This paper documents an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles (EVs) over time. Under the worst-case assumption that each U.S. Metropolitan Statistical Area (MSA) can only access and utilize the electricity generated by power plants within its boundaries, it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the 25 most populous MSAs will decrease by about 61% from about 129,000 in the year 2021 to about 51,000 in the year 2050, due to the projected increase in ground EVs. Also, it is estimated that the number of these MSAs that may lack any available electrical grid power capacity for UAM operations will increase from zero MSAs in 2021 to eleven MSAs in 2050—including three MSAs that prospective UAM operators have announced as target markets (Los Angeles, Miami, and Orlando)—unless electricity demand for other purposes is reduced.

electrical grid↗

Analysis of Electrical Grid Capacity in Major U.S. Metropolitan Areas for Urban Air Mobility

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft as envisioned. This paper documents an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles (EVs) over time. Under the worst-case assumption that each U.S. Metropolitan Statistical Area (MSA) can only access and utilize the electricity generated by power plants within its boundaries, it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the 25 most populous MSAs will decrease by about 61% from about 129,000 in the year 2021 to about 51,000 in the year 2050, due to the projected increase in ground EVs. Also, it is estimated that the number of these MSAs that may lack any available electrical grid power capacity for UAM operations will increase from zero MSAs in 2021 to eleven MSAs in 2050—including three MSAs that prospective UAM operators have announced as target markets (Los Angeles, Miami, and Orlando)—unless electricity demand for other purposes is reduced.

electrical grid↗

Rapid Aero Modeling of a Lift+Cruise UAM Configuration for Stability and Control Using Overset Grid CFD

This talk will address some aspects of an effort to develop both static and dynamic stability and control derivatives of a generic Lift+Cruise Urban Air Mobility vehicle. In particular, the use of NASA overset grid CFD packages OVERFLOW and Chimera Grid Tools allowed for rapid grid and solution generation while varying many database factors. Approach details include coarse gridding and control effectors, rotor disk modeling, convergence evaluation, and parallel execution on a midrange compute resource. A brief *description of the Rapid Aero Modeling (RAM) approach, including design of experiments and response surface modeling, will be given, and recent simulations of powered and unpowered dynamic multisine maneuvers will be presented.

Overset Grid CFD↗

Analysis of the Electrical Grid for UAM

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM) operations at scale with electric vertical takeoff and landing aircraft as envisioned. This presentation is on an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles over time. Under the best-case assumption that electricity can be transmitted and distributed as needed within each interconnection (i.e., electrical grid), it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the continental United States will decrease by 20% from about 595 thousand in the year 2021 to about 475 thousand in the year 2050 due to the projected increase in ground electric vehicles.

electrical grid; urban air mobility; electric vehi↗

Analysis of Electrical Grid Capacity by Interconnection for Urban Air Mobility

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM)operations at scale with electric vertical take-off and landing aircraft as envisioned. This paper documents an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles(EVs) over time. Under the best-case assumption that electricity can be transmitted and distributed as needed within each interconnection (i.e., electrical grid), it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the continental United States will decrease by 20% from about 595 thousand in the year 2021 to about 475 thousand in the year 2050due to the projected increase in ground EVs.

electrical grid↗

Analysis of Electrical Grid Capacity by Interconnection for Urban Air Mobility

A wide range of challenges must be overcome to conduct Urban Air Mobility (UAM)operations at scale with electric vertical take-off and landing aircraft as envisioned. This paper documents an analysis of one potential infrastructure challenge for UAM: available electrical grid power capacity. The success of UAM depends on the availability of electricity, which will be increasingly impacted by the proliferation of ground electric vehicles(EVs) over time. Under the best-case assumption that electricity can be transmitted and distributed as needed within each interconnection (i.e., electrical grid), it is estimated that the maximum number of UAM aircraft that can charge simultaneously in the continental United States will decrease by 20% from about 595 thousand in the year 2021 to about 475 thousand in the year 2050due to the projected increase in ground EVs.

electrical grid↗

Evaluation of VIIRS, GOCI, and MODIS Collection 6 AOD Retrievals Against Ground Sunphotometer Observations Over East Asia

Persistent high aerosol loadings together with extremely high population densities have raised serious air quality and public health concerns in many urban centers in East Asia. However, ground-based air quality monitoring is relatively limited in this area. Recently, satellite-retrieved Aerosol Optical Depth (AOD) at high resolution has become a powerful tool to characterize aerosol patterns in space and time. Using ground AOD observations from the Aerosol Robotic Network (AERONET) and the Distributed Regional Aerosol Gridded Observation Networks (DRAGON)-Asia Campaign, as well as from handheld sunphotometers, we evaluated emerging aerosol products from the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard the Suomi National Polar-orbiting Partnership (S-NPP), the Geostationary Ocean Color Imager (GOCI) aboard the Communication, Ocean, and Meteorology Satellite (COMS), and Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) (Collection 6) in East Asia in 2012 and 2013. In the case study in Beijing, when compared with AOD observations from handheld sunphotometers, 51% of VIIRS Environmental Data Record (EDR) AOD, 37% of GOCI AOD, 33% of VIIRS Intermediate Product (IP) AOD, 26% of Terra MODIS C6 3km AOD, and 16% of Aqua MODIS C6 3km AOD fell within the reference expected error (EE) envelope (+/-0.05/+/- 0.15 AOD). Comparing against AERONET AOD over the JapanSouth Korea region, 64% of EDR, 37% of IP, 61% of GOCI, 39% of Terra MODIS, and 56% of Aqua MODIS C6 3km AOD fell within the EE. In general, satellite aerosol products performed better in tracking the day-to-day variability than tracking the spatial variability at high resolutions. The VIIRS EDR and GOCI products provided the most accurate AOD retrievals, while VIIRS IP and MODIS C6 3km products had positive biases.

Xiao, Q.↗

Satellite-Based Characterization of Convection and Impacts from the Catastrophic 10 August 2020 Midwest U.S. Derecho

The catastrophic derecho that occurred on 10 August 2020 across the Midwest United States caused billions of dollars of damage to both urban and rural infrastructure as well as agricultural crops, most notably across the state of Iowa. This paper documents the complex evolution of the derecho through the use of low-Earth orbit passive-microwave imager and GOES-16satellite-derived products complemented by products derived from NEXRAD weather radar observations. Additional satellite sensors including optical imagers and synthetic aperture radar (SAR) were used to observe impacts to the power grid and agriculture in Iowa. SAR improved the identification and quantification of damaged corn and soybeans, as compared to true-color composites and Normalized Difference Vegetation Index (NDVI). A statistical approach to identify damaged corn and soybean crops from SAR was created with estimates of 1.97 million acres of damaged corn and 1.40 million acres of damaged soybeans in the state of Iowa. The damage estimates generated by this study were comparable to estimates produced by others after the derecho, including two commercial agricultural companies.

Derecho↗

The Use of Gridded Fossil Fuel CO2 Emissions (FFCO2) Inventory for Climate Mitigation Applications: Errors, Uncertainties, and Current and Future Challenges

Emission Inventory (EI) is a fundamental tool to monitor global compliance of greenhouse gases (GHGs) emissions reduction actions. Inventory guidelines provide a best practice to help EI compilers to make comparable national emission estimates, in spite of the differences in data availability across countries and regions. There are a variety of sources of errors and uncertainties, however, that originate beyond what the inventory guidelines define. For example, spatially-explicit EIs, which are a key product for atmospheric modeling applications, are often developed for research purposes, and there are no specific guidelines to disaggregate emission estimates from country scale. On top of that, EIs are fundamentally prone to systematic biases due to the simple calculation methodology and thus an objective evaluation (e.g. atmospheric top-down estimates) is needed to assure the accuracy of the estimates. ODIAC is a global high-resolution (1x1 km) fossil fuel carbon dioxide (CO2) gridded EI that is now often used in atmospheric CO2 modeling. ODIAC is based on disaggregation of national emission estimates made by CDIAC, which is the well accepted standard in the community. The ODIAC emission data product is updated on an annual basis using best available statistical data. Subnational spatial emission patterns are estimated using power plant profiles and satellite-observations of nighttime lights. In addition to the conventional CDIAC gridded data product, ODIAC carries international bunker emissions (shipping and aviation), which allows flux inversion modelers to accurately impose the global total fossil fuel emissions and their horizontal and vertical distribution. We have extensively evaluated ODIAC emissions using fine-grained EIs as well as a high-resolution atmospheric model simulation across different scales (national, subnational/regional, and urban policy relevant) with a focus on the uncertainties associated with the emission disaggregation. We have examined the use of NASA's Black Marble Suomi-NPP/VIIRS nightlight data.

Oda, Tomohiro↗

A New Hybrid Spatio-temporal Model for Estimating Daily Multi-year PM2.5 Concentrations Across Northeastern USA Using High Resolution Aerosol Optical Depth Data

The use of satellite-based aerosol optical depth (AOD) to estimate fine particulate matter PM(sub 2.5) for epidemiology studies has increased substantially over the past few years. These recent studies often report moderate predictive power, which can generate downward bias in effect estimates. In addition, AOD measurements have only moderate spatial resolution, and have substantial missing data. We make use of recent advances in MODIS satellite data processing algorithms (Multi-Angle Implementation of Atmospheric Correction (MAIAC), which allow us to use 1 km (versus currently available 10 km) resolution AOD data.We developed and cross validated models to predict daily PM(sub 2.5) at a 1X 1 km resolution across the northeastern USA (New England, New York and New Jersey) for the years 2003-2011, allowing us to better differentiate daily and long term exposure between urban, suburban, and rural areas. Additionally, we developed an approach that allows us to generate daily high-resolution 200 m localized predictions representing deviations from the area 1 X 1 km grid predictions. We used mixed models regressing PM(sub 2.5) measurements against day-specific random intercepts, and fixed and random AOD and temperature slopes. We then use generalized additive mixed models with spatial smoothing to generate grid cell predictions when AOD was missing. Finally, to get 200 m localized predictions, we regressed the residuals from the final model for each monitor against the local spatial and temporal variables at each monitoring site. Our model performance was excellent (mean out-of-sample R(sup 2) = 0.88). The spatial and temporal components of the out-of-sample results also presented very good fits to the withheld data (R(sup 2) = 0.87, R(sup)2 = 0.87). In addition, our results revealed very little bias in the predicted concentrations (Slope of predictions versus withheld observations = 0.99). Our daily model results show high predictive accuracy at high spatial resolutions and will be useful in reconstructing exposure histories for epidemiological studies across this region.

Air pollution↗

Quantifying Uncertainties in Nighttime Light Retrievals From Suomi-NPP and NOAA-20 VIIRS Day/Night Band Data

Satellite observations of nighttime lights (NTL) from Suomi-NPP and NOAA-20 VIIRS Day/Night Band data have been widely used to estimate human activities. Long-term changes such as urban development and abrupt short-term changes such as power outages have been monitored from temporal NTL acquired by satellites. While high temporal NTL variation has been found across NTL data of varying temporal scale (e.g., daily, monthly, and annual composites), the sources of measurement error and uncertainty are poorly understood. This paper quantifies the sources of VIIRS-derived NTL uncertainty due to view-illumination geometry, surface Bidirectional Reflectance Distribution Function (BRDF)/albedo, and the effects of snow cover, lunar irradiance, aerosol loading, cloud mask, vegetation, geometry, and ephemeral artifacts (e.g., the Aurora Borealis). Based on this current assessment of NASA Black Marble retrievals (VNP46, Collection V001), we found that angular and atmospheric effects dominate retrieval uncertainty. Errors introduced by upstream data inputs (e.g., a coarser nighttime snow cover flag and misclassification errors in the existing VIIRS nighttime cloud mask) were also found to impact retrieval quality. Despite these challenges, a consistent daily NTL time series record can be routinely generated from top-of-atmosphere VNP46 radiances. Key recommendations include: (1) the use of lunar-BRDF adjusted and atmospherically corrected NTL (i.e., as identified as high-quality retrievals in the VNP46 QA fields), (2) development and improvement to the VIIRS snow cover and cloud masks algorithms to accurately reflect NTL retrieval conditions, (3) characterizing seasonal variations in NTL due to vegetation and snow, (4) reducing geometric effects due to the spatial mismatch of gridded pixel and observation footprint, (5) employing angularly-consistent NTL observations from multiple VIIRS instruments (i.e., Suomi-NPP and NOAA-20) to reduce pixel-based uncertainties and address persistent data gaps, and (6) being mindful of surface-reflected radiance from aurora events at mid-to-high latitudes.

Zhuosen Wang↗

Urban Air Mobility Network and Vehicle Type - Modeling and Assessment

This paper describes exploratory modeling of an on-demand urban air mobility (UAM) network and sizing of vehicles to operate within that network. UAM seeks to improve the movement of goods and people around a metropolitan area by utilizing the airspace for transport. Aircraft sizing and overall network performance results are presented that include comparisons of battery-electric and various hybrid-electric vehicles that are fueled with diesel, jet fuel, compressed natural gas, and liquefied natural gas (LNG). Hybrid-electric propulsion systems consisting of internal combustion engine-generators, turbine-generators, and solid oxide fuel cells are explored. Ultimately, the "performance" of the UAM network over a day for each of the different vehicle types, propulsion systems, and stored energy sources is described in four parameters: 1) the average cost per seat-kilometer, which considers the costs of the energy/fuel, vehicle acquisition, insurance, maintenance, pilot, and battery replacement costs, 2) carbon dioxide emission rates associated with vehicle operations, 3) the average passenger wait time, and 4) the average load factor, i.e., the total number of seats filled with paying passengers divided by the total number of available seats. Results indicate that the "dispatch model," which determines when and where aircraft are flown around the UAM network, is critical in determining the overall network performance. This is due to the often-conflicting desires to allow passengers to depart with minimal wait time while still maintaining a high load factor to reduce operating costs. Additionally, regardless of the dispatch model, hybrid-electric aircraft powered by internal combustion engines fueled with diesel or LNG are consistently the lowest cost per seat-kilometer. Battery-electric and future technology LNG/solid oxide fuel cell aircraft produce the lowest emissions (assuming the California grid) with LNG-fueled internal combustion engine-powered hybrids producing only slightly more carbon dioxide.

Assessment↗

Airfoil Table Generation and Validation for the VR-12 and SSC-A09 Airfoils and Quadrotor Performance Prediction

As part of NASA’s Urban Air Mobility (UAM) mission, the Revolutionary Vertical Lift Technology (RVLT) project is creating rotorcraft reference designs that can be used by the rotorcraft community to develop quiet, efficient, and safer air vehicles. At NASA, this effort is made possible by the use of the RVLT toolchain software. Airfoil tables are often a critical part of the conceptual design process as they inform both comprehensive analysis and Computational Fluid Dynamics (CFD) codes of 2D aerodynamic coefficients, such as lift, drag, and moment coefficients. These airfoil tables, typically generated during experimental testing, are often proprietary and thus not widely distributable; further, there is not always clear or available reference documentation that provides information on the test conditions. The airfoil tables from these experiments are referred to in this work as Legacy airfoil tables. The scope of this study is to generate airfoil tables with the NASA OVERset grid CFD FLOW solver (OVERFLOW) coupled with the AirFoil Table Generator (AFTGen) software and validate these airfoil tables based on performance predictions calculated in Comprehensive Hierarchical Aeromechanics Rotorcraft Model (CHARM). The overall goal is to provide CFD generated airfoil tables, validate them through comprehensive analysis, and publish the airfoil tables for public distribution. The RVLT quadrotor reference model was selected as the basis for all analyses. The quadrotor uses the VR-12 and SSC-A09 airfoils for its rotor blades. Simulations were run in OVERFLOW for the VR-12 and SSC-A09 airfoils for ±20° angles of attack, Mach numbers from 0.3 to 1.0, and a Reynolds-Mach proportionality constant of 1.023x107. The C81 format airfoil tables from AFTGen were then blended with Legacy NACA 0012 airfoil table data for ±20° to ±180° angle of attack range. CHARM was used to compute thrust coefficient, power coefficient, and figure of merit for an isolated rotor in hover using proprietary legacy airfoil tables for the VR-12 and SSC-A09, as well as with the OVERFLOW generated airfoil tables for the VR-12 and SSC-A09. Overall, the OVERFLOW and Legacy performance predictions agreed well, with the best correlation between simulated and Legacy data observed with the VR-12 airfoil tables.

Airfoil↗

Characterization of Errors in Satellite-Based HCHO/NO 2 Tropospheric Column Ratios With Respect to Chemistry, Column-to-PBL Translation, Spatial Representation, and Retrieval Uncertainties

The availability of formaldehyde (HCHO) (a proxy for volatile organic compound reactivity) and nitrogen dioxide (NO 2 ) (a proxy for nitrogen oxides) tropospheric columns from ultraviolet–visible (UV–Vis) satellites has motivated many to use their ratios to gain some insights into the near-surface ozone sensitivity. Strong emphasis has been placed on the challenges that come with transforming what is being observed in the tropospheric column to what is actually in the planetary boundary layer (PBL) and near the surface; however, little attention has been paid to other sources of error such as chemistry, spatial representation, and retrieval uncertainties. Here we leverage a wide spectrum of tools and data to quantify those errors carefully. Concerning the chemistry error, a well-characterized box model constrained by more than 500 h of aircraft data from NASA's air quality campaigns is used to simulate the ratio of the chemical loss of HO 2 + RO 2 (LRO x ) to the chemical loss of NO x (LNO x ). Subsequently, we challenge the predictive power of HCHO/NO 2 ratios (FNRs), which are commonly applied in current research, in detecting the underlying ozone regimes by comparing them to LRO x /LNO x . FNRs show a strongly linear (R 2 =0.94) relationship with LRO x /LNO x , but only on the logarithmic scale. Following the baseline (i.e., ln(LRO x /LNO x ) = −1.0 ± 0.2) with the model and mechanism (CB06, r2) used for segregating NO x -sensitive from VOC-sensitive regimes, we observe a broad range of FNR thresholds ranging from 1 to 4. The transitioning ratios strictly follow a Gaussian distribution with a mean and standard deviation of 1.8 and 0.4, respectively. This implies that the FNR has an inherent 20 % standard error (1σ) resulting from not accurately describing the RO x –HO x cycle. We calculate high ozone production rates (PO 3 ) dominated by large HCHO × NO 2 concentration levels, a new proxy for the abundance of ozone precursors. The relationship between PO 3 and HCHO × NO 2 becomes more pronounced when moving towards NO x -sensitive regions due to nonlinear chemistry; our results indicate that there is fruitful information in the HCHO × NO 2 metric that has not been utilized in ozone studies. The vast amount of vertical information on HCHO and NO 2 concentrations from the air quality campaigns enables us to parameterize the vertical shapes of FNRs using a second-order rational function permitting an analytical solution for an altitude adjustment factor to partition the tropospheric columns into the PBL region. We propose a mathematical solution to the spatial representation error based on modeling isotropic semivariograms. Based on summertime-averaged data, the Ozone Monitoring Instrument (OMI) loses 12 % of its spatial information at its native resolution with respect to a high-resolution sensor like the TROPOspheric Monitoring Instrument (TROPOMI) (> 5.5 × 3.5 km 2 ). A pixel with a grid size of 216 km 2 fails at capturing ∼ 65 % of the spatial information in FNRs at a 50 km length scale comparable to the size of a large urban center (e.g., Los Angeles). We ultimately leverage a large suite of in situ and ground-based remote sensing measurements to draw the error distributions of daily TROPOMI and OMI tropospheric NO 2 and HCHO columns. At a 68 % confidence interval (1σ), errors pertaining to daily TROPOMI observations, either HCHO or tropospheric NO 2 columns, should be above 1.2–1.5 × 10 16 molec. cm −2 to attain a 20 %–30 % standard error in the ratio. This level of error is almost non-achievable with the OMI given its large error in HCHO. The satellite column retrieval error is the largest contributor to the total error (40 %–90 %) in the FNRs. Due to a stronger signal in cities, the total relative error (< 50 %) tends to be mild, whereas areas with low vegetation and anthropogenic sources (e.g., the Rocky Mountains) are markedly uncertain (> 100 %). Our study suggests that continuing development in the retrieval algorithm and sensor design and calibration is essential to be able to advance the application of FNRs beyond a qualitative metric.

Amir H. Souri↗

Smoke detection in low-G fires

Fires in spacecraft are considered a credible risk. To respond to this risk, NASA flew fire detectors on Skylab and the Space Shuttle (STS) and included them in the design for International Space Station Alpha (ISSA). In previous missions (Mercury, Gemini and Apollo), the crew quarters were so cramped that it was not considered credible that the astronauts could fail to observe a fire. The Skylab nodule included approximately 20 UV fire detectors. The space shuttle has 9 ionization detectors in the mid deck and flight deck and Spacelab has six additional ionization detectors. The planned detectors for ISSA are laser-diode, forward-scattering, smoke or particulate detectors. Current plans for the ISSA call for two detectors in the open area of the module and detectors in racks that have both cooling air flow and electrical power. Due to the complete absence of data concerning the nature of particulate and radiant emission from low-g fires, all three of these detector systems were designed based upon 1-g test data. As planned mission durations and complexity increase and the volume of spacecraft increases, the need for and importance of effective, crew independent, fire detection grows significantly. This requires more knowledge concerning low-gravity fires and how they might be detected. To date, no combustion-generated particulate samples have been collected for well-developed microgravity flames. All of the extant data come from drop tower tests and therefore only correspond to the early stages of a fire. The fuel sources were restricted to laminar gas-jet diffusion flames and rapidly overheated wire insulation. These gas-jet drop tower tests indicate, through thermophoretic sampling, that soot primaries and aggregates (groups of primary particles) in micro-g may be significantly larger than those in normal-g (ng). This raises new scientific questions about soot processes as well as practical issues for particulate detection/alarm threshold levels used in on-orbit smoke detectors. Furthermore, it is widely speculated but unverified that the aggregates will grow to very large scales in a microgravity fire of longer duration than available on the ground. Preliminary tests in the 2.2 second drop tower suggest that particulate generated by overheated wire insulation will also be larger in microgravity than in normal gravity. TEM grids downstream of the fire region in the WIF experiment as well as visual observation of long string-like aggregates, further confirm this suggestion. The combined impact of these limited results and theoretical predictions is that direct knowledge of low-g combustion particulate as opposed to extrapolation from 1-g data is needed for a more confident design of smoke detectors for spacecraft.

Urban, David L.↗

Comparative Soot Diagnostics: Preliminary Results

The motivation for the Comparative Soot Diagnostics (CSD) experiment lies in the broad practical importance of understanding combustion generated particulate. Depending upon the circumstances, particulate matter can affect the durability and performance of combustion equipment, can be a pollutant, can be used to detect fires and, in the form of soot, can be the dominant source of radiant energy from flames. The nonbuoyant structure of most flames of practical interest makes understanding of soot processes in low gravity flames important to our ability to predict fire behavior on earth. These studies also have direct applications to fire safety in human-crew spacecraft, since smoke is the indicator used for automated detection in current spacecraft. In the earliest missions (Mercury, Gemini and Apollo), the crew quarters were so cramped that it was considered reasonable that the astronauts would rapidly detect any fire. The Skylab module, however, included approximately 20 UV-sensing fire detectors. The Space Shuttle has 9 particle-ionization smoke detectors in the mid-deck and flight deck and Spacelab has six additional particle-ionization smoke detectors. The designated detectors for the ISS are laser-diode, forward-scattering, smoke or particulate detectors. Current plans for the ISS call for two detectors in the open area of the module, and detectors in racks that have both cooling air flow and electrical power. Due to the complete absence of data concerning the nature of particulate and radiant emission from incipient and fully developed low-g fires, all three of these detector systems were designed based upon l-g test data and experience. As planned mission durations and complexity increase and the volume of spacecraft increases, the need for and importance of effective, crew-independent, fire detection grows significantly. To provide this level of protection, more knowledge is needed concerning low-gravity fire phenomena and, in particular, how they might be detected and suppressed. Prior to CSD, no combustion-generated particulate samples had been collected near the flame zone for well-developed microgravity flames. All of the extant data either came from drop tower tests and therefore only corresponded to the early stages of a fire or were collected far from the flame zone. The fuel sources in the drop tower tests were restricted to laminar gas-jet diffusion flames and very rapidly overheated wire insulation. The gas-jet tests indicated, through thermophoretic sampling, (2) that soot primaries and aggregates (groups of primary particles) in low-gravity may be significantly larger than those in normal gravity (1-g). This raises new scientific questions about soot processes as well as practical issues for particulate size sensitivity and detection alarm threshold levels used in on-orbit smoke detectors. Preliminary tests in the 2.2 second drop tower suggest that particulate generated by overheated wire insulation may be larger in low-g than in 1-g. Transmission Electron Microscope (TEM) grids downstream of the fire region in the Wire Insulation Flammability experiment as well as visual observation of long string-like aggregates, further confirm this suggestion. The combined impact of these limited results and theoretical predictions is that, as opposed to extrapolation from l-g data, direct knowledge of low-g combustion particulate is needed for more confident design of smoke detectors for spacecraft. This paper describes the operation and preliminary results of the CSD, a project conceived and developed at NASA Lewis Research Center. The CSD flight experiment was conducted in the Middeck Glovebox Facility (MGBX) on USMP-3. The project is support by NASA Headquarters Microgravity Science and Applications Division and Code Q. The results presented here are from the microgravity portion of the experiment, including the temporal response of the detectors and average sizes of the primary and aggregate particles captured on the thermophoretic probes.

Urban, David L.↗