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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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NASA NTRS2015

Underestimation of Project Costs

Large projects almost always exceed their budgets. Estimating cost is difficult and estimated costs are usually too low. Three different reasons are suggested: bad luck, overoptimism, and deliberate underestimation. Project management can usually point to project difficulty and complexity, technical uncertainty, stakeholder conflicts, scope changes, unforeseen events, and other not really unpredictable bad luck. Project planning is usually over-optimistic, so the likelihood and impact of bad luck is systematically underestimated. Project plans reflect optimism and hope for success in a supposedly unique new effort rather than rational expectations based on historical data. Past project problems are claimed to be irrelevant because "This time it's different." Some bad luck is inevitable and reasonable optimism is understandable, but deliberate deception must be condemned. In a competitive environment, project planners and advocates often deliberately underestimate costs to help gain project approval and funding. Project benefits, cost savings, and probability of success are exaggerated and key risks ignored. Project advocates have incentives to distort information and conceal difficulties from project approvers. One naively suggested cure is more openness, honesty, and group adherence to shared overall goals. A more realistic alternative is threatening overrun projects with cancellation. Neither approach seems to solve the problem. A better method to avoid the delusions of over-optimism and the deceptions of biased advocacy is to base the project cost estimate on the actual costs of a large group of similar projects. Over optimism and deception can continue beyond the planning phase and into project execution. Hard milestones based on verified tests and demonstrations can provide a reality check.

Cost estimation↗
NASA NTRSDate not supplied

Generation of Continental Scale Percent Tree Cover Product Using Deep-learning and Multi-scale Remote Sensing Data

Spatially explicit percent tree cover (TC) estimation is critical for mapping forest aboveground biomass and its dynamics. While various TC products have been developed, there has not been a generalized framework that can be applied to diverse terrestrial ecosystems due to underlain extreme complexities. Deep learning algorithms can learn a spatial pattern and radiometric characteristics of tree canopy as a robust approximation of physical or empirical models, and thus have emerged as promising and efficient tools for large-scale TC mapping. In this study, we synergistically use very high-resolution aerial imageries (National Agriculture Imagery Program, NAIP) and medium resolution Landsat data to map continental-scale TC (CONUS and Mexico) through a hierarchical deep learning approach (Convolutional Neural Network), i.e., NAIP TC generated from a NAIP model is utilized to train a Landsat model. The produced TC product (hereafter, NEX-TC) is able to capture the spatial pattern of TC distribution and its changes driven by natural disturbance and human land management. We further explore and analyze the reliability and potential uncertainty of the NEX-TC by comparing it to lidar- (lidar-TC), National Land Cover Database (NLCD-TC), and MODIS Vegetation Continuous Field (MODIS-TC). This evaluation practice reveals that TC products based on passive optical sensors tend to underestimate TC across all land cover types while Landsat-based TCs (i.e., NEX-TC & NLCD-TC) perform better than the coarser MODIS TC estimate. Our results show that the NEX-TC is generally comparable to NLCD-TC but it particularly outperforms NLCD-TC and MODIS-TC over the dense forests where lidar-TC indicates >80% TC. These results indicate that our hierarchical deep learning approach and TC product will be effective and useful for characterizing large-scale tree cover and possibly associated carbon dynamics.

Landsat↗
NASA NTRS2016

An Integrated Gate Turnaround Management Concept Leveraging Big Data Analytics for NAS Performance Improvements

"Gate Turnaround" plays a key role in the National Air Space (NAS) gate-to-gate performance by receiving aircraft when they reach their destination airport, and delivering aircraft into the NAS upon departing from the gate and subsequent takeoff. The time spent at the gate in meeting the planned departure time is influenced by many factors and often with considerable uncertainties. Uncertainties such as weather, early or late arrivals, disembarking and boarding passengers, unloading/reloading cargo, aircraft logistics/maintenance services and ground handling, traffic in ramp and movement areas for taxi-in and taxi-out, and departure queue management for takeoff are likely encountered on the daily basis. The Integrated Gate Turnaround Management (IGTM) concept is leveraging relevant historical data to support optimization of the gate operations, which include arrival, at the gate, departure based on constraints (e.g., available gates at the arrival, ground crew and equipment for the gate turnaround, and over capacity demand upon departure), and collaborative decision-making. The IGTM concept provides effective information services and decision tools to the stakeholders, such as airline dispatchers, gate agents, airport operators, ramp controllers, and air traffic control (ATC) traffic managers and ground controllers to mitigate uncertainties arising from both nominal and off-nominal airport gate operations. IGTM will provide NAS stakeholders customized decision making tools through a User Interface (UI) by leveraging historical data (Big Data), net-enabled Air Traffic Management (ATM) live data, and analytics according to dependencies among NAS parameters for the stakeholders to manage and optimize the NAS performance in the gate turnaround domain. The application will give stakeholders predictable results based on the past and current NAS performance according to selected decision trees through the UI. The predictable results are generated based on analysis of the unique airport attributes (e.g., runway, taxiway, terminal, and gate configurations and tenants), and combined statistics from past data and live data based on a specific set of ATM concept-of-operations (ConOps) and operational parameters via systems analysis using an analytic network learning model. The IGTM tool will then bound the uncertainties that arise from nominal and off-nominal operational conditions with direct assessment of the gate turnaround status and the impact of a certain operational decision on the NAS performance, and provide a set of recommended actions to optimize the NAS performance by allowing stakeholders to take mitigation actions to reduce uncertainty and time deviation of planned operational events. An IGTM prototype was developed at NASA Ames Simulation Laboratories (SimLabs) to demonstrate the benefits and applicability of the concept. A data network, using the System Wide Information Management (SWIM)-like messaging application using the ActiveMQ message service, was connected to the simulated data warehouse, scheduled flight plans, a fast-time airport simulator, and a graphic UI. A fast-time simulation was integrated with the data warehouse or Big Data/Analytics (BAI), scheduled flight plans from Aeronautical Operational Control AOC, IGTM Controller, and a UI via a SWIM-like data messaging network using the ActiveMQ message service, illustrated in Figure 1, to demonstrate selected use-cases showing the benefits of the IGTM concept on the NAS performance.

Efficent ATM systems↗
NASA NTRS2016

State of the Carbon Cycle - Consequences of Rising Atmospheric CO2

The rise of atmospheric CO2, largely attributable to human activity through fossil fuel emissions and land-use change, has been dampened by carbon uptake by the ocean and terrestrial biosphere. We outline the consequences of this carbon uptake as direct and indirect effects on terrestrial and oceanic systems and processes for different regions of North America and the globe. We assess the capacity of these systems to continue to act as carbon sinks. Rising CO2 has decreased seawater pH; this process of ocean acidification has impacted some marine species and altered fundamental ecosystem processes with further effects likely. In terrestrial ecosystems, increased atmospheric CO2 causes enhanced photosynthesis, net primary production, and increased water-use efficiency. Rising CO2 may change vegetation composition and carbon storage, and widespread increases in water use efficiency likely influence terrestrial hydrology and biogeochemical cycling. Consequences for human populations include changes to ecosystem services including cultural activities surrounding land use, agricultural or harvesting practices. Commercial fish stocks have been impacted and crop production yields have been changed as a result of rising CO2. Ocean and terrestrial effects are contingent on, and feedback to, global climate change. Warming and modified precipitation regimes impact a variety of ecosystem processes, and the combination of climate change and rising CO2 contributes considerable uncertainty to forecasting carbon sink capacity in the ocean and on land. Disturbance regime (fire and insects) are modified with increased temperatures. Fire frequency and intensity increase, and insect lifecycles are disrupted as temperatures move out of historical norms. Changes in disturbance patterns modulate the effects of rising CO2 depending on ecosystem type, disturbance frequency, and magnitude of events. We discuss management strategies designed to limit the rise of atmospheric CO2 and reduce uncertainty in forecasts of decadal and centennial feedbacks of rising atmospheric CO2 on carbon storage.

Carbon Cycle↗
NASA NTRS2014

Integrating Land Cover Modeling and Adaptive Management to Conserve Endangered Species and Reduce Catastrophic Fire Risk

Land cover modeling is used to inform land management, but most often via a two-step process where science informs how management alternatives can influence resources and then decision makers can use this to make decisions. A more efficient process is to directly integrate science and decision making, where science allows us to learn to better accomplish management objectives and is developed to address specific decisions. Co-development of management and science is especially productive when decisions are complicated by multiple objectives and impeded by uncertainty. Multiple objectives can be met by specification of tradeoffs, and relevant uncertainty can be addressed through targeted science (i.e., models and monitoring). We describe how to integrate habitat and fuels monitoring with decision making focused on dual objectives of managing for endangered species and minimizing catastrophic fire risk. Under certain conditions, both objectives might be achieved by a similar management policy, but habitat trajectories suggest tradeoffs. Knowledge about system responses to actions can be informed by applying competing management actions to different land units in the same system state and by ideas about fire behavior. Monitoring and management integration is important to optimize state-specific management decisions and increase knowledge about system responses. We believe this approach has broad utility for and cover modeling programs intended to inform decision making.

adaptive management↗
NASA NTRS2007

The Roles of Verification, Validation and Uncertainty Quantification in the NASA Standard for Models and Simulations

The National Aeronautics and Space Administration (NASA) recently issued an interim version of the Standard for Models and Simulations (M&S Standard) [1]. The action to develop the M&S Standard was identified in an internal assessment [2] of agency-wide changes needed in the wake of the Columbia Accident [3]. The primary goal of this standard is to ensure that the credibility of M&S results is properly conveyed to those making decisions affecting human safety or mission success criteria. The secondary goal is to assure that the credibility of the results from models and simulations meets the project requirements (for credibility). This presentation explains the motivation and key aspects of the M&S Standard, with a special focus on the requirements for verification, validation and uncertainty quantification. Some pilot applications of this standard to computational fluid dynamics applications will be provided as illustrations. The authors of this paper are the members of the team that developed the initial three drafts of the standard, the last of which benefited from extensive comments from most of the NASA Centers. The current version (number 4) incorporates modifications made by a team representing 9 of the 10 NASA Centers. A permanent version of the M&S Standard is expected by December 2007. The scope of the M&S Standard is confined to those uses of M&S that support program and project decisions that may affect human safety or mission success criteria. Such decisions occur, in decreasing order of importance, in the operations, the test & evaluation, and the design & analysis phases. Requirements are placed on (1) program and project management, (2) models, (3) simulations and analyses, (4) verification, validation and uncertainty quantification (VV&UQ), (5) recommended practices, (6) training, (7) credibility assessment, and (8) reporting results to decision makers. A key component of (7) and (8) is the use of a Credibility Assessment Scale, some of the details of which were developed in consultation with William Oberkampf, David Peercy and Timothy Trocano of Sandia National Laboratories. The focus of most of the requirements, including those for VV&UQ, is on the documentation of what was done and the reporting, using the Credibility Assessment Scale, of the level of rigor that was followed. The aspects of one option for the Credibilty Assessment Scale are (1) code verification, (2) solution verification, (3) validation, (4) predictive capability, (5) technical review, (6) process control, and (7) operator and analyst qualification.

Zang, Thomas A.↗
NASA NTRS2023

Analysis of Strategic Conflict Management Approaches as Applied to Simulated UAM Operations

This report presents the results of a comparison analysis of candidate Strategic Conflict Management (SCM) strategies as applied to Urban Air Mobility (UAM) operations. The SCM strategies investigated included: no SCM, resource scheduling (RS), resource flow rates (FR), area-based flow rates (AR), and conflict detection and resolution (CR). The study evaluated each strategy against the same 3 levels of flight demand in a representative airspace construct for the Dallas/Fort Worth region. The study also accounted for different levels of uncertainty in operational planning and equivalent levels of trajectory following error. In the analysis, we compared the SCM strategies’ effectiveness in reducing the need for the tactical conflict management layer to act and looked at the metrics of unmitigated losses-of-separation (LOS), flight delays imposed by strategic planning, and throughput of the overall airspace. The study results indicated that the CR strategy was the most effective at reducing LOS and percentage of flights with LOS, even in the presence of trajectory error when uncertainty is accounted for in planning. Thus, if the objective is to reduce the number of actions that the tactical conflict management layer will have to take, in terms of conflicts that may need to be resolved, CR is the strategy to use. The FR strategy was found to be the worst at reducing LOS and percentage of flights with LOS. Thus, it is not very effective at reducing the actions required by a tactical conflict management layer. In the airspace tested, the demand was high enough to produce unacceptable levels of flight delay and reductions of throughput when the SCM strategies were implemented. This was especially evident at the higher levels of trajectory uncertainty and error. The need to account for expected levels of uncertainty becomes more and more important as the demand level increases and as the level of trajectory error increases. This is because the likelihood of flights with LOS increases as the density of operations increases and as the level of trajectory error increases. Accounting for uncertainty in the SCM strategies improves the strategy effectiveness with respect to LOS but increases delays and reduces throughput. Thus, there is a tradeoff between scalability and allowable levels of uncertainty. That is, we can implement an air traffic management construct that allows high levels of uncertainty, but we can expect that same system to have limits on scalability that may be evident even at small demand levels, such as those used in this study. Therefore, the results in this study indicate that an air traffic system should attempt to implement mechanisms appropriate for reducing uncertainty where possible in order to increase the chances for scalability. And this increased level of predictability of operations needs to be balanced with mechanisms for ensuring flexibility when operational conditions and plans need to change, even though those types of changes should be the exception rather than the rule under normal conditions. The study also introduced a trade space that could serve as a mechanism for selecting the appropriate SCM strategy in a trade-off between uncertainty and error, mean flight delay, and the LOS metrics (which this study equates to the potential for tactical conflict management actions). The optimal solution for a given airspace, demand level, and other factors, could likely be a combination of SCM strategies, although this study only compared the use of a single SCM strategy at a time. The CR strategy appeared to have the best opportunity for scalability by limiting the number of potential tactical actions to nearly zero.

Strategic Conflict Management↗
NASA NTRS2010

Energy Management of the Multi-Mission Space Exploration Vehicle Using a Goal-Oriented Control System

Safe human exploration in space missions requires careful management of limited resources such as breathable air and stored electrical energy. Daily activities for astronauts must be carefully planned with respect to such resources, and usage must be monitored as activities proceed to ensure that they can be completed while maintaining safe resource margins. Such planning and monitoring can be complex because they depend on models of resource usage, the activities being planned, and uncertainties. This paper describes a system - and the technology behind it - for energy management of the NASA-Johnson Space Center's Multi-Mission Space Exploration Vehicles (SEV), that provides, in an onboard advisory mode, situational awareness to astronauts and real-time guidance to mission operators. This new capability was evaluated during this year's Desert RATS (Research and Technology Studies) planetary exploration analog test in Arizona. This software aided ground operators and crew members in modifying the day s activities based on the real-time execution of the plan and on energy data received from the rovers.

Braman, Julia M. B.↗
NASA NTRS1986

Atmospheric climate data: Problems and promises

The explosive growth in the quantity and diversity of weather and climate data, the growing handicap that the distinction between weather and climate in NOAA imposes on the efficient management and use of data is discussed. Also discussed is the uncertainty induced by the lack of clear commitment and consistent policies regarding federal roles and responsibilities in operating and maintaining the national weather and climate data system.

Source record↗
NASA NTRS2016

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

model structure uncertainty↗
NASA NTRS2018

Laboratory Evaluation of Dynamic Routing of Air Traffic in an En Route Arrival Metering Environment

Arrival air traffic operations in the presence of convective weather are subject to uncertainty in aircraft routing and subsequently in flight trajectory predictability. Current management of arrival operations in weather-impacted airspace results in significant flight delay and suspension of arrival metering operations. The Dynamic Routing for Arrivals in Weather (DRAW) concept provides flight route amendment advisories to Traffic Management Coordinators to mitigate the impacts of convective weather on arrival operations. DRAW provides both weather conflict and schedule information for proposed route amendments, allowing air traffic managers to simultaneously evaluate weather avoidance routing and potential schedule and delay impacts. Subject matter experts consisting of retired Traffic Management Coordinators and retired Sector Controllers with arrival metering experience participated in a simulation study of Fort Worth Air Route Traffic Control Center arrival operations. Data were collected for Traffic Management Coordinator and Sector Controller participants over three weeks of simulation activities in October, 2017. Traffic Management Coordinators reported acceptable workload levels, a positive impact on their ability to manage arrival traffic while using DRAW, and initiated weather mitigation reroutes earlier while using DRAW. Sector Controllers also reported acceptable workload levels while using DRAW.

Air Traffic Management↗
NASA NTRS2018

Laboratory Evaluation of Dynamic Routing of Air Traffic in an En Route Arrival Metering Environment

Arrival air traffic operations in the presence of convective weather are subject to uncertainty in aircraft routing and subsequently in flight trajectory predictability. Current management of arrival operations in weather-impacted airspace results in significant flight delay and suspension of arrival metering operations. The Dynamic Routing for Arrivals in Weather (DRAW) concept provides flight route amendment advisories to Traffic Management Coordinators to mitigate the impacts of convective weather on arrival operations. DRAW provides both weather conflict and schedule information for proposed route amendments, allowing air traffic managers to simultaneously evaluate weather avoidance routing and potential schedule and delay impacts. Subject matter experts consisting of retired Traffic Management Coordinators and retired Sector Controllers with arrival metering experience participated in a simulation study of Fort Worth Air Route Traffic Control Center arrival operations. Data were collected for Traffic Management Coordinator and Sector Controller participants over three weeks of simulation activities in October, 2017. Traffic Management Coordinators reported acceptable workload levels, a positive impact on their ability to manage arrival traffic while using DRAW, and initiated weather mitigation reroutes earlier while using DRAW. Sector Controllers also reported acceptable workload levels while using DRAW.

Air Traffic Management↗
NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, Ocean Ecosystem (PACE) Mission: an Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA's next investment in satellite ocean color and the study of Earth's ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, and ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, and improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument - the Ocean Color Instrument (OCI) - is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-kilometer at nadir. This includes continuous collection of spectra from 340 to 890 nanometers in 5-nanometer steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth's biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a detailed discussion of the capabilities and design concept of OCI.

Passive Remote Sensing↗
NASA NTRS2019

The NASA Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) Mission: An Emerging Era of Global, Hyperspectral Earth System Remote Sensing

The Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission represents NASA’s next investment in satellite ocean color and the study of Earth’s ocean-atmosphere system, enabling new insights into oceanographic and atmospheric responses to Earth's changing climate. PACE objectives include extending systematic cloud, aerosol, ocean biological and biogeochemical data records, making essential ocean color measurements to further understand marine carbon cycles and ecosystem responses to a changing climate, as well as improving knowledge of how aerosols influence ocean ecosystems and, conversely, how ocean ecosystems and photochemical processes affect the atmosphere. PACE objectives also encompass management of fisheries, large freshwater bodies, and water quality and reducing uncertainties in climate and radiative forcing models of the Earth system. PACE observations will also provide information on radiative properties of land surfaces and characterization of the vegetation and soils that dominate their reflectance. The primary PACE instrument – the Ocean Color Instrument (OCI) – is a hyperspectral imaging radiometer that spans the ultraviolet to shortwave infrared, with a ground sample distance of 1-km at nadir. This includes continuous collection of spectra from 340 nm to 890 nm in 5 nm steps. The PACE payload is complemented by two multi-angle polarimeters with spectral ranges that span the visible to near-infrared region. Scheduled for launch in late 2022-to-early 2023, the PACE observatory will enable significant advances in the study of Earth’s biogeochemistry, carbon cycle, clouds, hydrosols, and aerosols in the ocean-atmosphere system. We present a brief overview of the PACE mission, followed by a discussion of the capabilities and design concept of OCI.

hyperspectral radiometer↗
NASA NTRS2016

Analysis of Multi-Flight Common Routes for Traffic Flow Management

This paper presents an approach for creating common weather avoidance reroutes for multiple flights and the associated benefits analysis, which is an extension of the single flight advisories generated using the Dynamic Weather Routes (DWR) concept. These multiple flight advisories are implemented in the National Airspace System (NAS) Constraint Evaluation and Notification Tool (NASCENT), a nation-wide simulation environment to generate time- and fuel-saving alternate routes for flights during severe weather events. These single flight advisories are clustered together in the same Center by considering parameters such as a common return capture fix. The clustering helps propose routes called, Multi-Flight Common Routes (MFCR), that avoid weather and other airspace constraints, and save time and fuel. It is expected that these routes would also provide lower workload for traffic managers and controllers since a common route is found for several flights, and presumably the route clearances would be easier and faster. This study was based on 30-days in 2014 and 2015 each, which had most delays attributed to convective weather. The results indicate that many opportunities exist where individual flight routes can be clustered to fly along a common route to save a significant amount of time and fuel, and potentially reducing the amount of coordination needed.

weather uncertainty↗
NASA NTRS2007

Advanced NASA Earth Science Mission Concept for Vegetation 3D Structure, Biomass and Disturbance

Carbon in forest canopies represents about 85% of the total carbon in the Earth's aboveground biomass (Olson et al., 1983). A major source of uncertainty in global carbon budgets derives from large errors in the current estimates of these carbon stocks (IPCC, 2001). The magnitudes and distributions of terrestrial carbon storage along with changes in sources and sinks for atmospheric C02 due to land use change remain the most significant uncertainties in Earth's carbon budget. These uncertainties severely limit accurate terrestrial carbon accounting; our ability to evaluate terrestrial carbon management schemes; and the veracity of atmospheric C02 projections in response to further fossil fuel combustion and other human activities. Measurements of vegetation three-dimensional (3D) structural characteristics over the Earth's land surface are needed to estimate biomass and carbon stocks and to quantify biomass recovery following disturbance. These measurements include vegetation height, the vertical profile of canopy elements (i.e., leaves, stems, branches), andlor the volume scattering of canopy elements. They are critical for reducing uncertainties in the global carbon budget. Disturbance by natural phenomena, such as fire or wind, as well as by human activities, such as forest harvest, and subsequent recovery, complicate the quantification of carbon storage and release. The resulting spatial and temporal heterogeneity of terrestrial biomass and carbon in vegetation make it very difficult to estimate terrestrial carbon stocks and quantify their dynamics. Vegetation height profiles and disturbance recovery patterns are also required to assess ecosystem health and characterize habitat. The three-dimensional structure of vegetation provides habitats for many species and is a control on biodiversity. Canopy height and structure influence habitat use and specialization, two fundamental processes that modify species richness and abundance across ecosystems. Accurate and consistent 3D measurements of forest structure at the landscape scale are needed for assessing impacts to animal habitats and biodiversity following disturbance.

Ranson, K. Jon↗
NASA NTRS2010

2009 Space Shuttle Probabilistic Risk Assessment Overview

Loss of a Space Shuttle during flight has severe consequences, including loss of a significant national asset; loss of national confidence and pride; and, most importantly, loss of human life. The Shuttle Probabilistic Risk Assessment (SPRA) is used to identify risk contributors and their significance; thus, assisting management in determining how to reduce risk. In 2006, an overview of the SPRA Iteration 2.1 was presented at PSAM 8 [1]. Like all successful PRAs, the SPRA is a living PRA and has undergone revisions since PSAM 8. The latest revision to the SPRA is Iteration 3. 1, and it will not be the last as the Shuttle program progresses and more is learned. This paper discusses the SPRA scope, overall methodology, and results, as well as provides risk insights. The scope, assumptions, uncertainties, and limitations of this assessment provide risk-informed perspective to aid management s decision-making process. In addition, this paper compares the Iteration 3.1 analysis and results to the Iteration 2.1 analysis and results presented at PSAM 8.

Hamlin, Teri L.↗
NASA NTRS2012

Dust Hazard Management in the Outer Solar System

Most robotic missions to the outer solar system must grapple with the hazards posed by the dusty rings of the gas giants. Early assessments of these hazards led simply to ring avoidance due to insufficient data and high uncertainties on the dust population present in such rings. Recent approaches, principal among them the Cassini dust hazard management strategy, provide useful results from detailed modeling of spacecraft vulnerabilities and dust hazard regions, which along with the range of mission trajectories are used to to assess the risks posed by each passage through a zone of potential hazard. This paper shows the general approach used to implement the analysis for Cassini, with recommendations for future outer planet missions.

mission hazards↗