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At least 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Keweenaw Bay Water Resources: Turbidity Data Decision Support for Shoreline Assessment and Management in Lake Superior's Keweenaw Bay

Community Concerns: Keweenaw Bay is vulnerable to flooding and erosion, which threaten wetland restoration projects that provide a critical habitat for wildlife and fish. Erosion and flooding threaten the loss of public beaches and shoreline, coastal infrastructure, and impacts to coastal highways, which has driven shoreline armoring projects. Our goals were to first, produce a seasonal turbidity analysis to understand the sediment redistribution patterns in the bay during snow melt and rainy seasons to provide insight into coastal sediment dynamics. KBIC has completed an Environmental Protection Agency (EPA) - funded project titled "Long-Term Coastal Dynamic Collaborative for Tribal Resiliency" which has thus far produced detailed shoreline maps of the area. The NASA DEVELOP project is taking place in between phase 1 and 2 of the EPA-funded project.

Khaim Syed-Raza↗
NASA NTRSDate not supplied

Utilizing Gaps and Key Performance Parameters to Inform NASA Environmental Control and Life Support Capability Technology Decisions

Human spaceflight is a complex endeavor requiring multiple capabilities for transportation, crew health, scientific goals, and safe return to Earth. The difference between spaceflight proven capabilities and those needed for future exploration architectures is defined as a capability gap. Capability gaps are not technology specific. Each capability gap may be closed with a wide array of technologies that have unique benefits and challenges. Determining what a capability’s relevant and distinguishing key performance parameters (KPPs) are for a mission is critical. Mass, power, and volume are always constrained and important, but defining these in a way normalized by performance is very important. KPP definition for reliability, dormancy, and integration needs are hard to define but critical. Outside of technical considerations, the programmatic factors of the estimated time to develop the technology vs when a mission element is also a strong consideration in which technologies should be pursued. The Environmental Control and Life Support (ECLSS) capability areas include: life support, environmental monitoring, fire safety, and logistics. These capability areas are decomposed to high level gaps. While KPPs should be technology agnostic, they can be used to both compare technologies and measure progress of technology development over time. KPPs help define when the gap is closed, and the core mission objectives can be accomplished. Proposed technology improvements to enhance a capability should balance improved KPPs and against investments in other capabilities that are not yet closed. A selection of ECLSS gaps and KPPs and their formulation, current state, and how they inform capability roadmap planning are discussed.

Life Support↗
NASA NTRS2022

Keweenaw Bay Water Resources: Turbidity Data Decision Support for Shoreline Assessment and Management in Lake Superior'S Keweenaw Bay

The Keweenaw Bay Indian Community (KBIC) has shoreline along the south of Lake Superior that is contaminated with copper stamp sands from legacy mining. The stamp sands have been capped with sandy-loam soils and restored native species, but erosion and flooding threaten to re-deposit these stamp sands onto wetlands and into the bay. Erosion and flooding also threaten loss of beaches and shoreline, infrastructure, wetland restoration projects, and coastal highways, which has driven shoreline armoring. However, while shoreline armoring can be effective in protecting the intended areas, it can also exacerbate erosion in nearby unarmored areas, so its net impact on the shoreline is yet to be quantified. The DEVELOP team partnered with KBIC and the Environmental Protection Agency (EPA) to utilize imagery from Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) in order to analyze turbidity proxies. The results from these analyses showed that seasonal variation in study area was greatest during the season dominated by rain, but spatial variability across our study period did not show a clear trend. These results will be used to better inform future shoreline management efforts and support resilience in the face of more coastal erosion.

remote sensing↗
NASA NTRSDate not supplied

IMPACT, a Tool Suite for Crew Health and Performance System Trade Analyses and Decision Support - Status of Development

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 tool suite as it nears its System Acceptance Review (SAR). It will review IMPACT’s constituent parts, briefly discuss typical outputs and outline the plans for transitioning to operations, currently scheduled for later in FY23. 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, 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) schedule.

IMPACT↗
NASA NTRSDate not supplied

Utilizing Gaps and Performance Measures to Inform NASA Environmental Control and Life Support Systems and Human Health and Performance Capability Technology Decisions

Human spaceflight is a complex endeavor requiring multiple capabilities for transportation, crew health, scientific goals, and safe return to Earth. The difference between spaceflight proven capabilities and those needed for future exploration architectures is defined as a capability gap. Capability gaps are not technology specific. Each capability gap may be closed with a wide array of technologies that have unique benefits and challenges. Determining what a capability’s relevant and distinguishing key performance parameters (KPPs) are for a mission is critical. Mass, power, and volume are always constrained and important, but defining these in a way normalized by performance is very important. KPP definition for reliability, dormancy, and integration needs are hard to define but critical. Outside of technical considerations, the programmatic factors of the estimated time to develop the technology and how the technology validation objectives are matured are strong considerations in which technologies should be pursued and how they should leverage earlier mission elements. The Environmental Control and Life Support (ECLSS) and Crew Health and Performance (CHP) capability areas are decomposed to high level gaps. While KPPs should be technology agnostic, they can be used to both compare technologies and measure progress of technology development over time. KPPs help define when the gap is closed, and the core mission objectives can be accomplished. Proposed technology improvements to enhance a capability should balance improved KPPs and against investments in other capabilities that are not yet closed. A selection of gaps, KPPs, and validation objectives and their formulation, current state, and how they inform capability roadmap planning are discussed.

Life Support↗
NASA NTRSDate not supplied

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wild fire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry, a Marin-based fire service work force development program, to develop new models to assist with fire management. Using data from Sentinel-2A, Planet Scope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), the team developed several input data layers to three models evaluating wild fire severity. One model performed a suitability analysis with weights based on scientific literature, another utilized machine learning based on past fires in Marin and neighboring Sonoma County to predict the difference normalized burn ratio, and the third inputted data layers into the Flam Map tool, which outputs risk categories. The team compared model outputs and, using the best-fit model, performed fuzzy logic analysis to identify specific locations where a fire break could be constructed to interrupt the progress of an active fire. These tools were proven useful and will assist partners in preparing for and managing an active wildfire event.

Suhani Dalal↗
NASA NTRSDate not supplied

On the Use of Resilience Models as Digital Twins for Operational Support and In time Decision Making

Human error is a major contributor to accidents and performance losses in complex engineered systems. If one examines these human error caused failures further, a specific cause, the lack of situation awareness, has dominated as a major cause of human errors that instigate latent or catastrophic failures in complex systems. Studies of aviation accidents involving major air carriers revealed that situation awareness was the root cause of around 90% of accidents involving pilot error. Another study explored offshore drilling accidents involving human error and found that 40% of accidents were directly attributed to the loss of situation awareness. Studies of human errors in other domains such as nuclear power, air traffic control, process industry, and advanced driving show that loss of SA was a root cause in a majority of the events. Situation awareness-related failures are not only common but also costly and fatal (e.g., Bhopal Gas Leak, Air France 447 Flight Crash). Thus, the concept of situation awareness has emerged as an important construct in human factors, resulting in numerous models and measurement methods to aid in promoting appropriate levels of situation awareness.

Lukman Irshad↗
NASA NTRS2023

Marin County Wildland Fires: Examining Fuel Load and Land Cover Change to Inform Fire Prevention and Suppression Decisions in Marin County, CA

Heightened occurrence of severe wildfires in the Western United States is increasing the need to better understand regions of high potential wildfire severity and develop methodologies for identifying the best locations for fuels reduction and active wildfire suppression, especially in populated regions such as Marin County, California. Marin County, located in the San Francisco Bay Area, has had significant development in the wildland-urban interface and periods of highly wildfire-prone conditions. The NASA DEVELOP team collaborated with Fire Foundry (a Marin-based fire service workforce development program) and the Marin County Fire Department to develop models to assist with fire management. Using data from Sentinel-2A, PlanetScope, ECOSTRESS, a county-wide LiDAR mapping effort, Landsat 7 Enhanced Thematic Mapper (ETM+), and Landsat 8 Operational Land Imager (OLI), our team developed a number of input data layers for three different models to evaluate wildfire severity. One model performed a suitability analysis with weights based on scientific literature; another model utilized a U-Net Convolutional Neural Network trained on previous fires in Marin and neighboring Sonoma County to predict the difference normalized burn severity; and the third inputted data layers into the FlamMap tool that outputs risk categories. We compared model outputs and performed a weighted overlay analysis to identify specific locations where a fireline could be constructed to interrupt the progress of an active fire. These tools will assist partners in preparing for and managing active wildfire situations.

Remote sensing↗
NASA NTRSDate not supplied

Model Based Approaches for Fault Detection, Prognostics, Decision Making in Complex Systems

The presentation discusses application of model based approaches to complex systems. The model is composed of physics-derived and empirical equations, integrated with connected networks that are strategically placed within the model to substitute equations that are subject to large uncertainty. Polynomial fit driven by heuristics or empirical observations can be substituted by more flexible networks that can minimize the error between model predictions and observations without being restricted to a predefined functional form. This modeling strategy allows training of networks deep inside the model and unknown parameters in a single learning stage.

Physics Informed↗
NASA NTRSDate not supplied

Utilizing Gaps and Performance Measures to Inform NASA Environmental Control and Life Support Systems and Crew Health and Performance Technology Decisions

Human spaceflight is a complex endeavor requiring multiple capabilities for transportation, crew health, scientific goals, and safe return to Earth. The difference between spaceflight proven capabilities and those needed for future exploration architectures is defined as a capability gap. Capability gaps are not technology specific. Each capability gap may be closed with a wide array of technologies that have unique benefits and challenges. Determining what a capability’s relevant and distinguishing key performance parameters (KPPs) are for a mission is critical. Mass, power, and volume are always constrained and important, but defining these in a way normalized by performance is very important. KPP definition for reliability, dormancy, and integration needs are hard to define but critical. Outside of technical considerations, the programmatic factors of the estimated time to develop the technology and how the technology validation objectives are matured are strong considerations in which technologies should be pursued and how they should leverage earlier mission elements. The Environmental Control and Life Support (ECLSS) and Crew Health and Performance (CHP) capability areas are decomposed to high level gaps. While KPPs should be technology agnostic, they can be used to both compare technologies and measure progress of technology development over time. KPPs help define when the gap is closed, and the core mission objectives can be accomplished. Proposed technology improvements to enhance a capability should balance improved KPPs and against investments in other capabilities that are not yet closed. A selection of gaps, KPPs, and validation objectives and their formulation, current state, and how they inform capability roadmap planning are discussed.

Life Support↗