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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Modeling NASA’s Procedural Requirement Processes - Implications for Digital Future

The National Aeronautics and Space Administration (NASA) has an ongoing Digital Transformation effort and to leverage and showcase the power of Digital Transformation, an effort is underway to develop an integrated, datacentric, model representing NASA’s key process requirements. The task was divided into three phases: As Is modeling, Analysis, and To Be Planning. As part of this effort, a team has completed the first Phase I of the modeling task and is nearing completion of the second phase. This effort will capture the key elements as requirements, responsibilities, allocations, roles, products, and associated lifecycle elements. The scope of modeling included NASA’s NPR 7120.5 (Project and Program Management), NPR 7123.1 (Systems Engineering) and NPRs 8705.2 (Risk classification for Robotic Missions) and 8705.4 (Human-Rating Requirements for Space Missions). This paper will summarize the approach, scope, parsing patterns applied, metamodel, and associated workflows for the As-Is modeling. It will also summarize the results and insights gleaned during that phase, including the review process. These insights have informed the analysis and will be discussed. The analysis modeling phase will also be summarized including how the stakeholders were engaged, how the common elements were handled and dispositioned, and will also describe some of the plans for the future of NASA NPDs and NPRs.

Systems Engineering↗
NASA NTRSDate not supplied

Modeling NASA’s Procedural Requirement Processes – Implications for a Digital Future

The National Aeronautics and Space Administration (NASA) has an ongoing Digital Transformation effort and to leverage and showcase the power of Digital Transformation, an effort is underway to develop an integrated, datacentric, model representing NASA’s key process requirements. The task was divided into three phases: As Is modeling, Analysis, and To Be Planning. As part of this effort, a team has completed the first Phase I of the modeling task and is nearing completion of the second phase. This effort will capture the key elements as requirements, responsibilities, allocations, roles, products, and associated lifecycle elements. The scope of modeling included NASA’s NPR 7120.5 (Project and Program Management), NPR 7123.1 (Systems Engineering) and NPRs 8705.2 (Risk classification for Robotic Missions) and 8705.4 (Human-Rating Requirements for Space Missions).

NPR↗
NASA NTRS2010

Identification and Classification of Common Risks in Space Science Missions

Due to the highly constrained schedules and budgets that NASA missions must contend with, the identification and management of cost, schedule and risks in the earliest stages of the lifecycle is critical. At the Jet Propulsion Laboratory (JPL) it is the concurrent engineering teams that first address these items in a systematic manner. Foremost of these concurrent engineering teams is Team X. Started in 1995, Team X has carried out over 1000 studies, dramatically reducing the time and cost involved, and has been the model for other concurrent engineering teams both within NASA and throughout the larger aerospace community. The ability to do integrated risk identification and assessment was first introduced into Team X in 2001. Since that time the mission risks identified in each study have been kept in a database. In this paper we will describe how the Team X risk process is evolving highlighting the strengths and weaknesses of the different approaches. The paper will especially focus on the identification and classification of common risks that have arisen during Team X studies of space based science missions.

Risk Identification↗
NASA NTRS2013

Considerations of Unmanned Aircraft Classification for Civil Airworthiness Standards

The use of unmanned aircraft in the National Airspace System (NAS) has been characterized as the next great step forward in the evolution of civil aviation. Although use of unmanned aircraft systems (UAS) in military and public service operations is proliferating, civil use of UAS remains limited in the United States today. This report focuses on one particular regulatory challenge: classifying UAS to assign airworthiness standards. Classification is useful for ensuring that meaningful differences in design are accommodated by certification to different standards, and that aircraft with similar risk profiles are held to similar standards. This paper provides observations related to how the current regulations for classifying manned aircraft, based on dimensions of aircraft class and operational aircraft categories, could apply to UAS. This report finds that existing aircraft classes are well aligned with the types of UAS that currently exist; however, the operational categories are more difficult to align to proposed UAS use in the NAS. Specifically, the factors used to group manned aircraft into similar risk profiles do not necessarily capture all relevant UAS risks. UAS classification is investigated through gathering approaches to classification from a broad spectrum of organizations, and then identifying and evaluating the classification factors from these approaches. This initial investigation concludes that factors in addition to those currently used today to group manned aircraft for the purpose of assigning airworthiness standards will be needed to adequately capture risks associated with UAS and their operations.

Maddalon, Jeffrey M.↗
NASA NTRS2016

Risk-based SMA for Cubesats

This presentation conveys an approach for risk-based safety and mission assurance applied to cubesats. This presentation accompanies a NASA Goddard standard in development that provides guidance for building a mission success plan for cubesats based on the risk tolerance and resources available.

Small Satellites↗
NASA NTRSDate not supplied

What Risks are Acceptable for a Class D Project?

This talk is intended to help small project proposers to navigate the process of deciding what risks would be acceptable in their Class D projects, as well as steering away from misinterpretations of risk to mean “doing something different”. This will be largely NASA centric, largely focused on science missions, but there will be more general takeaways.

risk↗
NASA NTRSDate not supplied

Writing Good Risk Statements and Critical Thinking in Risk

The term risk is used ubiquitously in the space community, but more often than not it is used in a counterproductive way. Furthermore, risks are often written to convey some type of concern, but the risk statements are often more emotional in nature than technically substantive. In this talk, we will provide a rigorous view and definition of risk, along with some structured guidelines for writing risk statements centered on critical thinking, which will combine to provide effective risk management tools when dealing with complex space systems.

risk↗
NASA NTRS2015

Risk-Aware Planetary Rover Operation: Autonomous Terrain Classification and Path Planning

Identifying and avoiding terrain hazards (e.g., soft soil and pointy embedded rocks) are crucial for the safety of planetary rovers. This paper presents a newly developed groundbased Mars rover operation tool that mitigates risks from terrain by automatically identifying hazards on the terrain, evaluating their risks, and suggesting operators safe paths options that avoids potential risks while achieving specified goals. The tool will bring benefits to rover operations by reducing operation cost, by reducing cognitive load of rover operators, by preventing human errors, and most importantly, by significantly reducing the risk of the loss of rovers.

Ono, Masahiro↗
NASA NTRS1980

Minimum Bayes risk image correlation

In this paper, the problem of designing a matched filter for image correlation will be treated as a statistical pattern recognition problem. It is shown that, by minimizing a suitable criterion, a matched filter can be estimated which approximates the optimum Bayes discriminant function in a least-squares sense. It is well known that the use of the Bayes discriminant function in target classification minimizes the Bayes risk, which in turn directly minimizes the probability of a false fix. A fast Fourier implementation of the minimum Bayes risk correlation procedure is described.

Minter, T. C., Jr.↗
NASA NTRS1971

Nonparametric Bayer-risk estimation

Nonparametric Bayes risk estimation for measurement classification, using nearest neighbor error rate and Parzen probability density function estimators

Fralick, S. C.↗
NASA NTRSDate not supplied

Role of NDE and In-Situ Process Monitoring in Managing Risk of AM Space Hardware

The recently published NASA-STD-6030 defines the Additive Manufacturing (AM) Requirements for Spaceflight Systems. Key aspects of the certification approach include the development of a qualified material process (QMP) and material characterization determined by part classification. Nondestructive evaluation (NDE) of the full surface and volume is required for all part classifications except those with negligible risk. NASA is exploring the use of in-process monitoring data to improve risk posture and supplement post-build inspection for complex parts. Currently, the most challenging obstacle to overcome is linking the indications in the monitoring data to the physics of the process and the final material state of the finished part. NASA is undertaking studies to understand and quantify this relationship for various monitoring methods. The desired goal is to develop a protocol to establish this correlation for any monitoring method. Once this correlation is known, the critical defect size can be linked to a representative indication in the monitoring data, and the capability of the monitoring system can be tested using the 90/95 probability of detection requirement for NDE methods. This would enable the use of in-process monitoring as a defect screening activity for AM part certification. Many high-criticality components built with AM have high complexity and therefore limited inspectability, so using in-process monitoring can help address this certification gap. The use of adaptive, closed-loop monitoring systems that alter the locked process will require a new approach to the QMP.

additive manufacturing↗
NASA NTRSDate not supplied

Role of NDE and In-Situ Process Monitoring in Managing Risk of AM Space Hardware

The recently published NASA-STD-6030 defines the Additive Manufacturing (AM) Requirements for Spaceflight Systems. Key aspects of the certification approach include the development of a qualified material process (QMP) and material characterization determined by part classification. Nondestructive evaluation (NDE) of the full surface and volume is required for all part classifications except those with negligible risk. NASA is exploring the use of in-process monitoring data to improve risk posture and supplement post-build inspection for complex parts. Currently, the most challenging obstacle to overcome is linking the indications in the monitoring data to the physics of the process and the final material state of the finished part. NASA is undertaking studies to understand and quantify this relationship for various monitoring methods. The desired goal is to develop a protocol to establish this correlation for any monitoring method. Once this correlation is known, the critical defect size can be linked to a representative indication in the monitoring data, and the capability of the monitoring system can be tested using the 90/95 probability of detection requirement for NDE methods. This would enable the use of in-process monitoring as a defect screening activity for AM part certification. Many high-criticality components built with AM have high complexity and therefore limited inspectability, so using in-process monitoring can help address this certification gap. The use of adaptive, closed-loop monitoring systems that alter the locked process will require a new approach to the QMP.

advanced manufacturing↗
NASA NTRS2015

Resource Prospector (RP): A Cost-Effective Lunar Resource Pathfinder

Resource Prospector (RP) is an in-situ resource utilization (ISRU) technology demonstration mission under study by the NASA Human Exploration and Operations Mission Directorates (HEOMD). This clever mission is currently planned to launch in 2020 and will demonstrate extraction of oxygen, water and other volatiles, as well measure mineralogical content such as silicon and light metals, like aluminum and titanium, from lunar regolith. Expanding human presence beyond low-Earth orbit to asteroids and Mars will require the maximum possible use of local materials, so-called in-situ resources, and the moon presents a unique destination to conduct robotic investigations that advance ISRU capabilities, as well as providing significant exploration and science value. This mission is equally important; however, for how it executes as a risk-tolerant, cost-effective mission. RP follows on the path-finding approaches of the Lunar Crater Observation and Sensing Satellite (LCROSS) mission. The LCROSS mission confirmed the presence of water-ice on the moon, but also established a new lightweight-approach to project and mission execution which was considerably cheaper and faster than traditional NASA missions. RP has been designated as a Class D mission, just as LCROSS. This mission classification is the most risk-tolerant class of mission within the NASA risk framework and as such, is given more latitude to accept higher-levels of residual risk. The intention is that by saving monies normally spent attempting to assure a single missions success, more missions can be funded. A well-designed portfolio can accept occasional mission failure, as it still gets more done for the same investment of resources. This classification enables tailoring the NASA Policy Requirements (NPRs) to lighter-weight approaches to mission management and execution. RP is also studying both international and commercial partnerships as a means to maximize return on the investment. International partnerships provide both capabilities synergies and cost-sharing opportunities, while the evolving new space commercial options are revealing new approaches to acquiring cost-effective services, including the benefits of bundling services. Even the world of launch vehicles is changing, offering much less expensive access to space, especially if NASA is able to be flexible in how it approaches mission assurance. Finally, leveraging investments being made elsewhere within a program portfolio, can enable cost-savings by enabling two applications with one investment. RP will be the next pathfinder mission to both enable exploration capabilities for future missions, and continue to evolve cost-effective approaches for NASA.

Lunar↗
NASA NTRS2019

Satellite-Based Assessment of Grassland Conversion and Related Fire Disturbance in the Kenai Peninsula, Alaska

Spruce beetle-induced (Dendroctonus rufipennis (Kirby)) mortality on the Kenai Peninsula has heightened local wildfire risk as canopy loss facilitates the conversion from bare to fire-prone grassland. We collected images from NASA satellite-based Earth observations to visualize land cover succession at roughly five-year intervals following a severe, mid-1990's beetle infestation to the present. We classified these data by vegetation cover type to quantify grassland encroachment patterns over time. Raster band math provided a change detection analysis on the land cover classifications. Results indicate the highest wildfire risk is linked to herbaceous and black spruce land cover types, The resulting land cover change image will give the Kenai National Wildlife Refuge (KENWR) ecologists a better understanding of where forests have converted to grassland since the 1990s. These classifications provided a foundation for us to integrate digital elevation models (DEMs), temperature, and historical fire data into a model using Python for assessing and mapping changes in wildfire risk. Spatial representations of this risk will contribute to a better understanding of ecological trajectories of beetle-affected landscapes, thereby informing management decisions at KENWR.

Risk modeling↗
NASA NTRS1990

Flight payloads environmental approach

The Earth Observing System (EOS) and Space Station (SS) attached payload instruments to be developed by JPL will have a consistent implied level of reliability confidence based on the application of product assurance requirements. An important subset of these requirements is a set of detailed environmental design and test requirements. These requirements have a sound technical defense, are unambiguous in their level of detail, provide rational risk management as a function of payload classification, and are rigorously enforced with a comprehensive waiver process imposed on planned deviations.

Schlue, John W.↗
NASA NTRS1986

Dealing with Shuttle payload classifications NMI 8010.1 - A user's interpretation

The Shuttle payload classifications in the NASA Management Instruction (NMI) 8010.1 are examined in terms of risk management. The four payload classes, minimum risk, risk/cost compromise, reflight or repeat flight payload, and minimum single attempt cost, are described. The effects of the classifications on payload product assurance provisions are discussed. Environmental design and test requirements are interpreted in terms of NMI 8010.1.

Gindorf, Tom↗
NASA NTRS2026

Proactive Wildfire Management: A Remote Sensing and Multimodal CNN-MLP Architecture for Ignition Risk Forecasting

As the frequency and intensity of wildfires increase, with fire seasons now starting earlier and ending later than they have over the past decades, current monitoring systems, such as lookout towers and satellites, are hindered by cloud cover, low-resolution imagery, and static data gaps that fail to track vegetation moisture levels fast enough to catch rapid pre-ignition changes. This report proposes a Machine Learning-enabled Wildfire Ignition Prediction framework that combines satellite monitoring with dynamic and high-resolution remote sensing from Unmanned Aerial Vehicle (UAV) swarms. The method would use multispectral and thermal data from the Landsat program to create a baseline for vegetation health, calculating a two-band Enhanced Vegetation Index (EVI2) and the moisture content of the vegetation. These inputs will later be fused with microscale UAV weather data, including thermal hotspots found through thick canopies, hyperspectral chemical signatures of pre-visual combustion, and local weather streams. The multispectral satellite, multispectral Light Detection and Ranging (LiDAR), and thermal data would then be processed through a Convolutional Neural Network (CNN), alongside a Multilayer Perceptron (MLP) for the micro-weather telemetry. The outputs of these networks would be fused into a single feature representation and passed through a final prediction network to generate real-time ignition risk scores and hotspot alerts. Model performance would be assessed using standard classification metrics, including a Receiver Operating Characteristic - Area Under the Curve (ROC AUC) and F1 score. This system would allow first responders to identify high-risk zones and intervene before ignition occurs, improving emergency response time compared to current approaches.

machine learning↗