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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 1,765 records · Page 98
All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

Marin County Wildfires Ii: Improving Fire Suppression Modeling to Inform Fire Prevention and Suppression Decisions in Marin County, Ca

A future of increased wildfires requires greater integration of spatial analysis and local knowledge of emergency responders. We examine the application of a Potential Operational Delineations (PODs) framework for strategic pre-fire planning in Marin County. PODs are spatial units for wildfire management that combine predictive modeling and local firefighter expertise to identify potential control locations as unit boundaries and assess the difficulty of suppression within units. Additionally, this project explores the integration of road networks and social vulnerability to assess environmental justice in evacuation safety. This project constitutes a novel application of the PODs framework as it integrates expertise from Marin County senior firefighters with a Fireline Location Model (FLM) to achieve POD definition and uses a Suppression Difficulty Score (SDS) to rank each POD. The FLM uses network analysis and hydrologic modeling to identify key roads and ridgelines as boundaries and combines them with expert knowledge, in the form of workshops, to construct PODs. Once identified, PODs are classified using SDS, which includes processed inputs such as LiDAR-derived aboveground biomass, ECOSTRESS Evaporative Stress Index, land use cover type from Sentinel-2 Imagery, and a digital elevation model. Environmental justice for evacuation safety incorporated three key road metrics such as connectivity, travel area, and exit capacity, the Social Vulnerability Index from the Center for Disease Control, and cell coverage to determine a final Evacuation Difficulty Score. Results indicate a strong link between road networks as primary POD boundaries, with ridgelines and waterways as secondary and tertiary locations. Specifically, we find 78.5% of expertise-identified POD boundaries align with FLM-determined boundaries. More validation is needed to support this process; however, initial results signal a feasible framework to integrate expertise and spatial analysis in local level strategic fire planning

Wildfire modeling↗
NASA NTRS2022

Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa - Summary for Decision Makers

Air pollution and climate change are a deadly duo for Africa, and must be tackled together. Air pollutants and greenhouse gases often share the same sources and can be even more dangerous when combined. Africa is particularly vulnerable to climate change, and currently, an estimated 1 million people per year die prematurely from air pollution on the continent. But there is a way to improve the situation: preventing emissions from short-lived climate pollutants, like methane and black carbon, is crucial for the world to stay below 1.5°C. Reducing SLCPs will help both save lives and protect the environment. Africa has a huge opportunity to continue developing sustainably, improve human well-being, and protect nature by investing in solutions to fight climate change and air pollution together. A new Integrated Assessment of Air Pollution and Climate Change for Sustainable Development in Africa from the African Union Commission, the Climate and Clean Air Coalition, and the UN Environment Programme, developed by African scientists in a process led by the Stockholm Environment Institute, shows how African leaders can act quickly across 5 key areas—transport, residential, energy, agriculture, and waste—to fight climate change, prevent air pollution, and protect human health.

Africa↗
NASA NTRSDate not supplied

Impact, A Tool Suite for Crew Health and Performance System Trade Analyses and Decision to Support - Transition to Operations

Mission planners, systems engineers, and clinicians that support crew health and performance face very difficult choices on upcoming exploration missions. Given that there will be a heavily constrained mass and volume allocation for a medical system on these missions, what medical capability should be manifested to minimize both medical risk and mission risk? Given that not all promising research and technology proposals can be funded, how can proposals be prioritized so that those funded research investments produce the maximum benefit in reducing overall medical risk? The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) project seeks to answer these kinds of questions and others to support upcoming exploration missions. IMPACT enables risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This presentation will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include an updated medical database, consisting of an Evidence Library for medical conditions and a medical item database (MedID) for medical resources, dynamic Probabilistic Risk Assessment (PRA) capabilities, System Modeling Language (SysML) models, and contextual data visualizations of output data. IMPACT is the result of a multi-center collaborative effort. The trade space analyses performed by IMPACT can directly inform mission, vehicle, and habitat development by quantifying medical risk, given a design reference mission, crew attributes and a set of medical capabilities. This presentation will update the audience on the development status of the IMPACT tool suite as it comes out of its System Acceptance Review (SAR) and nears Transition to Operations (TTO). It will review IMPACT’s constituent parts, briefly discuss typical outputs, and outline the plans for transitioning to operations, currently scheduled for later in FY24. Recent development successes on the IMPACT project include the integration of the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) v2.0 to accommodate segmented missions with multiple carriers and medical systems, full onboarding of the IMPACT Medical Database (IMPACT-MD), clustering medical resources and skills into medical capabilities and mutually-dependent bundles, verification of IMPACT-MD, and the ability to perform trade analyses on different medical sets, different design reference missions (DRM), with different crew complements and extra-vehicular activity (EVA) schedules.

IMPACT↗
NASA NTRSDate not supplied

Creating A Consistent Historical NASA POWER Solar Radiation Dataset to Support Renewable Energy, Building Energy Efficiency and Agro-Climatology Decisions

Prediction of Worldwide Energy Resources (POWER) project provides irradiance dataset to support renewable energy, building energy efficiency and agricultural needs. These datasets are derived from Global Energy and Water Cycle Experiment Surface Radiation Budget (GEWEX SRB) and Clouds and the Earth’s Radiant Energy System (CERES SYN1Deg). A systematic bias has been reported between these two datasets for the years with overlapping observations. For obtaining a consistent climate data record spanning the entire time record of observations, it is crucial to understand and remove the bias in the irradiance dataset. Inconsistency in solar radiation data can lead to inaccurate conclusions about solar energy potential and obscure real trends in solar radiation patterns that would impact energy availability assessments. In this study, we adapt quantile mapping approach to remove the systematic bias and to improve reliability of shortwave and longwave irradiance data. We present a validation of the bias corrected data against ground truth. For each 1° latitude and 1° longitude grid box across the globe, we match the CDFs of the reference dataset (CERES SYN1Deg) to that of the SRB dataset, thereby, adjusting the irradiance values to match the empirical distribution of two different measurements. The performance of quantile mapping is evaluated by using the metrics such as Mean Absolute Deviation (MAD). The results indicate that the quantile mapping significantly improves the accuracy and reliability of solar irradiance dataset especially for the weather conditions associated with high cloud cover and extreme irradiance values. The initial range of MAD for the studied sites for daily data was 4 to 19 Wm-2. After correction these reduced to 3 to 7 Wm-2. The findings from this study have important implications for solar energy system design, agricultural planning, and climate modeling community. Reducing the inconsistency and biases in solar irradiance dataset can enable better planning and operation of solar energy systems, leading to increased efficiency and cost-effectiveness. Additionally, this work also contributes to the statistical post-processing techniques in the renewable energy domain and highlights the potential of historical and near-real-time NASA POWER dataset as a valuable resource for solar energy research and applications.

POWER↗
NASA NTRSDate not supplied

IMPACT, A Tool Suite for Crew Health and Performance System Trade Analyses and Decision Support- Transition to Operations

Mission planners, systems engineers, and clinicians that support crew health and performance face very difficult choices on upcoming exploration missions. Given that there will be a heavily constrained mass and volume allocation for a medical system on these missions, what medical capability should be manifested to minimize both medical risk and mission risk? Given that not all promising research and technology proposals can be funded, how can proposals be prioritized so that those funded research investments produce the maximum benefit in reducing overall medical risk? The Informing Mission Planning via Analysis of Complex Tradespaces (IMPACT) project seeks to answer these kinds of questions and others to support upcoming exploration missions. IMPACT enables risk-informed and evidence-based trade space analysis for future space vehicles, missions, and systems. This presentation will discuss the long-term HRP and ExMC vision for the larger ecosystem of tools, which include an updated medical database, consisting of an Evidence Library for medical conditions and a medical item database (MedID) for medical resources, dynamic Probabilistic Risk Assessment (PRA) capabilities, System Modeling Language (SysML) models, and contextual data visualizations of output data. IMPACT is the result of a multi-center collaborative effort. The trade space analyses performed by IMPACT can directly inform mission, vehicle, and habitat development by quantifying medical risk, given a design reference mission, crew attributes and a set of medical capabilities. This presentation will update the audience on the development status of the IMPACT tool suite as it comes out of its System Acceptance Review (SAR) and nears Transition to Operations (TTO). It will review IMPACT’s constituent parts, briefly discuss typical outputs, and outline the plans for transitioning to operations, currently scheduled for later in FY24. Recent development successes on the IMPACT project include the integration of the Medical Extensible Dynamic Probabilistic Risk Assessment Tool (MEDPRAT) v2.0 to accommodate segmented missions with multiple carriers and medical systems, full onboarding of the IMPACT Medical Database (IMPACT-MD), clustering medical resources and skills into medical capabilities and mutually-dependent bundles, verification of IMPACT-MD, and the ability to perform trade analyses on different medical sets, different design reference missions (DRM), with different crew complements and extra-vehicular activity (EVA) schedules.

IMPACT↗
NASA NTRS2024

Big Bend Ecological Forecasting: Integrating Earth Observations Into Invasive Species Management Decisions in Big Bend National Park in Texas

Located in Brewster County Texas, U.S., along the Texas-Mexico border, Big Bend National Park is 3,243 square kilometers of desert, mountains, and rivers. NASA’s MSFC Spring 2024 DEVELOP Team partnered with the National Park Service (NPS), to address the environmental concern of perineal invasive grasses in Big Bend National Park. Buffelgrass (Cenchrus ciliaris), introduced to the park in the 1940’s, poses an ongoing threat, and causes habitat destruction for many of the park’s native ecosystems. Buffelgrass amplifies fire risk in the park, aids in the destruction of historic structures, and alters stream channels. To address the rising concern of Buffelgrass presence, unique advanced spatial technique applications were necessary to construct a habitat suitability model and perform a comprehensive fire risk assessment. A habitat suitability model was developed considering climate, vegetation, and phenological variables, in addition to physical and topographical variables. Subsequently, a fire risk model was developed, taking into account fire history data, accessibility factors, climate trends and predictions, along with developed areas. Multi-Source Land Surface Phenology (LSP) Sentinel-2 and Landsat 8 Operational Land Imager (OLI) imagery were used to predict Buffelgrass hotspot locations throughout the park. These analyses allowed the identification of optimal Buffelgrass habitat and hotspot locations, as well as park zones that reflect the greatest risk for future Buffelgrass invasion and fire risk. The collective results of the habitat suitability model, fire risk assessment, and Buffelgrass hot spot identification will allow the NPS to facilitate efficient mitigation measures and management strategies, and improved resource allocation where it’s most needed.

remote sensing↗