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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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46 records · Page 3

Survive the Dust: Dust Mitigation Technology to Enable Survive the Night Capabilities

Introduction: As we return to the Moon, the lunar regolith (i.e. lunar dust) covering the surface will be an obstacle to nominal operations. Accounts from Apollo astronauts and analysis of hardware returned from the surface illustrate just how deleterious the dust can be [1]. During Apollo missions, the lunar dust adhered to hardware mechanically and electrostatically [2]. Surviving the Night: Mitigating the lunar dust will be critical to surviving the night. Going hand-in-hand with other extreme environment considerations, dust mitigation is critical to mission success. Dust Impacts on Other Systems: The lunar dust can have negative implications for power, thermal, mechanisms, and several other systems or sub-systems. For example, Apollo encountered marked degradation of performance in heat rejection systems for the lunar roving vehicle, science packages, and other components because of the lunar dust [1]. For power alone, dust can cause internal clogging for power connectors, heat rejection issues, excessive dust on reflective surfaces, reduced power output for solar arrays, and so on. Dust Mitigation Strategy: In addition to considering technology solutions, it is important for hardware, systems, and or components to have a dust mitigation strategy. At a high level, hardware that will encounter the lunar dust should consider these things when defining a dust mitigation strategy: • Understand Natural Environment • Understand Induced Environment • Understand Tolerance to Dust • Write Dust Requirements • Select Dust Mitigation Solutions • Test Hardware in Dusty Environment More information on each of these can be provided to hardware owners. Dust Mitigation Technology Development: NASA has a series of technologies that may be available for hardware that needs to survive the lunar night. Many of these solutions are leveraging dust mitigation technology development efforts from NASA’s Space Technology Mission Directorate (STMD), as well as efforts from ESDMD programs, industry, and academia. Through a series of STMD programs (both internal to NASA and through partnerships), there are several technologies in development as considerations as dust mitigation solutions for hardware. Within STMD, the Game Changing Development Program (GCD) has funded several internal dust mitigation projects including low to mid TRL development, demonstrations on CLPS landers of high TRL solutions, and creating standards and best practices for dust mitigation. STMD dust mitigation efforts also include a series of partnerships for developing technologies and advancing the state of dust mitigation at NASA. This includes the Lunar Surface Innovation Consortium (LSIC), Small Business Innovation Research, Early Stage Innovations (ESI), Space Technology Research Grants (STRG), Announcement of Collaboration Opportunities (ACOs) and Tipping Points (TPs), and Challenges and Crowdsourcing, among others. There are also a series of dust mitigation solutions that have been widely used terrestrially, or during Apollo. In recent years, several studies have produced more data on the efficacy of these potential solutions in the lunar environment. Dust Mitigation Solutions: Dust mitigation solutions generally fall into four categories: • Dust Tolerant Mechanisms • Passive Dust Mitigation Capabilities • Active Dust Mitigation Capabilities • Dust Measurement Capabilities There are a series of solutions that may prove beneficial for hardware that needs to survive the lunar night, including new technology development as well as proven, terrestrial solutions. This presentation will discuss in more detail what some of these solutions are for payloads going to the surface. References: [1] J. R. Gaier, NASA/TM—2005-213610, The Effects of Lunar Dust on EVA Systems During the Apollo Missions [2] T. J. Stubbs, et al. Impact of Dust on Lunar Exploration, 2005

dust mitigation↗

Role of Antecedent Soil Moisture and Vegetation Stress in Lightning-Initiated Wildfires

Lightning-caused wildfires are a small percentage of all wildfire events within the Conterminous U.S. (CONUS), but they account for over 56% of the acreage burned. The atmospheric conditions favoring wildfire and rapid growth are well understood: large dewpoint depressions, unstable planetary boundary layer, strong winds, etc. However, antecedent land surface conditions affecting dead and live fuel moisture is more difficult to quantify. This study examines over 20 years of antecedent land surface, vegetation stress, and wildfire characteristic data associated with nearly 77,000 lightning-initiated wildfires from the U.S. Forest Service Wildfire Database. We will invoke two in-house databases generated by the NASA Short-term Prediction Research and Transition (SPoRT) Center: an observations-driven, climatological run of the Noah land surface model within the NASA Land Information System (i.e., SPoRT-LIS) to depict soil moisture deficits / anomalies, and a satellite-constrained Evaporative Stress Index (ESI) product to denote areas of stressed vegetation. We will mine these datasets associated with lightning-caused (and null) events to determine important relationships, distributions, and delineators that correspond to elevated threat areas for lightning-initiated wildfires.

Wildfire↗

Trade Study of DEM Software

Review of Open-source Discrete Element Method Softwares: Three open-source solutions have been investigated for integration into OceanWATERS: Yade, EsysParticle, and ProjectChrono.

DEM↗

Idaho Wildfires II: Assessing the Relationship Between Drought Indicators and Wildfire Risk to Enhance Hazard Modeling and Inform Mitigation Planning

The western United States has experienced twenty years of increased and prolonged drought which have exacerbated wildfire hazards. These jeopardize population centers through increased risks to ecosystem services, local economies, and livelihoods. The Idaho Office of Emergency Management, Water Resources, and Department of Lands are seeking methods to dynamically monitor these conditions and update models that inform hazard mitigation planning and resource allocation. Towards this, these agencies partnered with NASA DEVELOP to produce drought-enhanced wildfire hazard models. Part of a two-term project, the two teams revised the state’s static wildfire hazard model with refined data layers and remotely-sensed data to reflect dynamic ecosystem responses to drought conditions and wildfire potential. Our team distinguished between rangeland and forestland ecosystems, and investigated relationships between drought metrics and vegetation condition using TerrSet Earth Trends Modeler. This analysis determined that total precipitation at a 5-month lag interval (r 2 = 0.72) along with the Evaporative Stress Index (r 2 = 0.69); and precipitation at a 5-month interval (r 2 = 0.42) were important drivers in rangeland and forestland, respectively. These driver variables were incorporated into a temporally dynamic wildfire hazard map. Our team used linear regression to correlate hazard ratings with wildfire frequency. For the year 2020, neither the enhanced hazard model (p < 0.10, r 2 = 0.01) nor the state’s static model (p < 0.05, r 2 = 0.03) were strongly correlated with actual wildfire frequency as they expressed an inverse relationship between wildfire hazard and frequency. This suggests wildfire occurrence is complex and not necessarily driven by the variables used.

Wildfire↗

Low-Cost Sensor Performance Intercomparison, Correction Factor Development, and 2+ Years of Ambient PM2.5 Monitoring in Accra, Ghana

Particulate matter air pollution is a leading cause of global mortality, particularly in Asia and Africa. Addressing the high and wide-ranging air pollution levels requires ambient monitoring, but many low- and middle-income countries (LMICs) remain scarcely monitored. To address these data gaps, recent studies have utilized low-cost sensors. These sensors have varied performance, and little literature exists about sensor intercomparison in Africa. By colocating 2 QuantAQ Modulair-PM, 2 PurpleAir PA-II SD, and 16 Clarity Node-S Generation II monitors with a reference-grade Teledyne monitor in Accra, Ghana, we present the first intercomparisons of different brands of low-cost sensors in Africa, demonstrating that each type of low-cost sensor PM2.5 is strongly correlated with reference PM2.5, but biased high for ambient mixture of sources found in Accra. When compared to a reference monitor, the QuantAQ Modulair-PM has the lowest mean absolute error at 3.04 μg/m3, followed by PurpleAir PA-II (4.54 μg/m3) and Clarity Node-S (13.68 μg/m3). We also compare the usage of 4 statistical or machine learning models (Multiple Linear Regression, Random Forest, Gaussian Mixture Regression, and XGBoost) to correct low-cost sensors data, and find that XGBoost performs the best in testing (R2: 0.97, 0.94, 0.96; mean absolute error: 0.56, 0.80, and 0.68 μg/m3 for PurpleAir PA-II, Clarity Node-S, and Modulair-PM, respectively), but tree-based models do not perform well when correcting data outside the range of the colocation training. Therefore, we used Gaussian Mixture Regression to correct data from the network of 17 Clarity Node-S monitors deployed around Accra, Ghana, from 2018 to 2021. We find that the network daily average PM2.5 concentration in Accra is 23.4 μg/m3, which is 1.6 times the World Health Organization Daily PM2.5 guideline of 15 μg/m3. While this level is lower than those seen in some larger African cities (such as Kinshasa, Democratic Republic of the Congo), mitigation strategies should be developed soon to prevent further impairment to air quality as Accra, and Ghana as a whole, rapidly grow.

Humidity↗

Urban Air Quality Management at Low-Cost Using Micro Air Sensors: A Case Study From Accra, Ghana

Urban air quality management is dependent on the availability of local air pollution data. In many major urban centers of Africa, there is limited to non-existent information on air quality. This is gradually changing in part due to the increasing use of micro air sensors which have the potential to enable the generation of ground-based air quality data at fine scales for understanding local emission trends. Regional literature on the application of the high-resolution data for emission source identification in this region is limited. In this study a micro air sensor was co-located at the Physics Department, University of Ghana with a reference grade instrument to evaluate its performance for estimating PM2.5 pollution accurately at fine scales and the value of this data in identification of local sources and their behavior over time. For this study 15 weeks of data at hourly resolution with approximately 2500 data pairs are generated and analyzed (June 01, 2023, to September 15, 2023). For this time period a coefficient of determination (r 2 ) of 0.83 was generated with a mean absolute error (MAE) of 5.44 μgm -3 between the pre local calibration micro air sensor (i.e. out of box) and the reference-grade instrument. Following currently accepted best practice methods (see e.g., PAS4023) a domain specific (i.e. local) calibration factor was generated using a multi-linear regression model and when this factor is applied to the micro air sensor data, a reduction i.e., improvement in MAE to 1.43 μgm -3 was found. Daily variation was calculated, a receptor model was applied, and time series plots as a function of wind direction were generated, including PM2.5/PM10 ratio scatter and count plots to explore the utility of this observational approach for local source identification. The 3 data sets were compared (out of box, domain calibrated and reference-grade) and it was found that although there were variations in the data reported, source areas highlighted based on these data were similar, with input from local sources such as traffic emissions and biomass burning. As the temporal resolution of observational data associated with these micro air sensors is higher than for reference grade instruments (primarily due to costs and logistics limitations), they have the potential to provide insight into the complex, often hyper localized sources associated with urban areas, such as those found in major African cities.

Source apportionment↗