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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Project Assessment Framework through Design (PAFTD) - A Project Assessment Framework in Support of Strategic Decision Making

Research and development organizations that push the innovation edge of technology frequently encounter challenges when attempting to identify an investment strategy and to accurately forecast the cost and schedule performance of selected projects. Fast moving and complex environments require managers to quickly analyze and diagnose the value of returns on investment versus allocated resources. Our Project Assessment Framework through Design (PAFTD) tool facilitates decision making for NASA senior leadership to enable more strategic and consistent technology development investment analysis, beginning at implementation and continuing through the project life cycle. The framework takes an integrated approach by leveraging design principles of useability, feasibility, and viability and aligns them with methods employed by NASA's Independent Program Assessment Office for project performance assessment. The need exists to periodically revisit the justification and prioritization of technology development investments as changes occur over project life cycles. The framework informs management rapidly and comprehensively about diagnosed internal and external root causes of project performance.

Depenbrock, Brett T.↗
NASA NTRS2002

The National Aeronautics and Space Administration's Earth Science Applications Program: Exploring Partnerships to Enhance Decision Making in Public Health Practice

The National Aeronautics and Space Administration (NASA), Earth Science Enterprise is engaged in applications of NASA Earth science and remote sensing technologies for public health. Efforts are focused on establishing partnerships with those agencies and organizations that have responsibility for protecting the Nation's Health. The program's goal is the integration of NASA's advanced data and technology for enhanced decision support in the areas of disease surveillance and environmental health. A focused applications program, based on understanding partner issues and requirements, has the potential to significantly contribute to more informed decision making in public health practice. This paper intends to provide background information on NASA's investment in public health and is a call for partnership with the larger practice community.

Vann, Timi S.↗
NASA NTRS1984

Multi-crew Model Analytic Assessment of Landing Performance and Decision-making Demands

Some of the relative merits of the PROCRU approach to modelling multi-crew flight deck activity during approach to landing are discussed. On the basis of two realistic flight scenarios, the ability of the model to simulate different vectored approaches is demonstrated. A secondary exemplary analysis of a nominal and an accelerated final approach is performed, illustrating the potential of the expected net gain (ENGP) functions as a measure of decision-making load.

Milgram, P.↗
NASA NTRS2003

A Subjective Assessment of Alternative Mission Architecture Operations Concepts for the Human Exploration of Mars at NASA Using a Three-Dimensional Multi-Criteria Decision Making Model

The primary driver for developing missions to send humans to other planets is to generate significant scientific return. NASA plans human planetary explorations with an acceptable level of risk consistent with other manned operations. Space exploration risks can not be completely eliminated. Therefore, an acceptable level of cost, technical, safety, schedule, and political risks and benefits must be established for exploratory missions. This study uses a three-dimensional multi-criteria decision making model to identify the risks and benefits associated with three alternative mission architecture operations concepts for the human exploration of Mars identified by the Mission Operations Directorate at Johnson Space Center. The three alternatives considered in this study include split, combo lander, and dual scenarios. The model considers the seven phases of the mission including: 1) Earth Vicinity/Departure; 2) Mars Transfer; 3) Mars Arrival; 4) Planetary Surface; 5) Mars Vicinity/Departure; 6) Earth Transfer; and 7) Earth Arrival. Analytic Hierarchy Process (AHP) and subjective probability estimation are used to captures the experts belief concerning the risks and benefits of the three alternative scenarios through a series of sequential, rational, and analytical processes.

Tavana, Madjid↗
NASA NTRS2010

The Utility of the OMI HCHO and NO2 Data Products in Air Quality Decision- Making Activities

We will present three related air quality applications of the OMI HCHO (formaldehyde) and NO2 (nitrogen dioxide) data products, which we us to support mission planning of an OMI-like instrument for the proposed GEO-CAPE satellite that has as one of its objectives to study air quality from space. First, we will discuss a novel and practical application of the data products to the "weight of evidence" in the air quality decision-making process (e.g., State Implementation Plan (SIP)) for a city, region, or state to demonstrate that it is making progress toward attainment of the National Ambient Air Quality Standard (NAAQS) for ozone. Any trend, or lack thereof, in the observed OMI HCHO/NO2, which we use as an air quality indicator, may support that an emission control strategy implemented to reduce ozone is or is not occurring for a metropolitan area. Second, we will discuss how we use variations in the OMI HCHO product as a proxy for variability in the biogenic hydrocarbon, isoprene, which is an important player for the formation of high levels of ozone and the dominant source of HCHO in the eastern U.S. Third, we will discuss the variability of NO2 in the U.S. as indicated by the OMI NO2 product. In addition, we will show the impact of the 2005 hurricanes on pollutant emissions, including those associated with the intensive oil extraction and refining activities, in the Gulf of Mexico region using the OMI NO2 product. The variability of HCHO and NO2 as indicated by OMI helps us to understand changes in the OMI HCHO/NO2 and the implications for ozone formation.

Duncan, Bryan N.↗
NASA NTRS2012

Space Launch System Complex Decision-Making Process

The Space Shuttle program has ended and elements of the Constellation Program have either been cancelled or transitioned to new NASA exploration endeavors. The National Aeronautics and Space Administration (NASA) has worked diligently to select an optimum configuration for the Space Launch System (SLS), a heavy lift vehicle that will provide the foundation for future beyond low earth orbit (LEO) large-scale missions for the next several decades. From Fall 2010 until Spring 2011, an SLS decision-making framework was formulated, tested, fully documented, and applied to multiple SLS vehicle concepts at NASA from previous exploration architecture studies. This was a multistep process that involved performing figure of merit (FOM)-based assessments, creating Pass/Fail gates based on draft threshold requirements, performing a margin-based assessment with supporting statistical analyses, and performing sensitivity analysis on each. This paper focuses on the various steps and methods of this process (rather than specific data) that allowed for competing concepts to be compared across a variety of launch vehicle metrics in support of the successful completion of the SLS Mission Concept Review (MCR) milestone.

Lyles, Garry↗
NASA NTRSDate not supplied

Physics-Guided Deep Learning for Complex System Health Management and Decision Making

The landscape of complex engineered systems is rapidly evolving, from smart manufacturing facilities to next-generation transportation vehicles. As these systems become increasingly sophisticated and interconnected, the need for advanced health management systems grows ever more critical. These systems must go beyond simple monitoring, actively predicting potential failures before they occur. This paradigm shift from fixed maintenance schedules to condition-based predictions is key to optimizing system performance, enhancing safety, and paving the way for autonomous decision-making across various industries. Whether in industrial processes, energy systems, or advanced transportation, the ability to anticipate and prevent failures is becoming a cornerstone of operational excellence. To accurately predict the future health of any complex system, knowledge of its current health state and future operational conditions is essential. Recent advancements in data-driven algorithms have generated growing interest in artificial intelligence for industrial applications. However, the limitations of pure data-driven methods, particularly in industries where data acquisition is costly and limited, have become apparent. This has led to a focus on blending physics with data-driven algorithms, mitigating the drawbacks of both approaches while emphasizing their respective advantages. This research proposes a novel framework for integrating physics-based performance models with deep learning algorithms for the prognostics of complex safety-critical systems. In this approach, physics-based models serve as a blueprint, capturing fundamental system behaviors, while deep learning algorithms, leveraging real-world sensor data, fill in gaps and identify subtle patterns indicative of potential problems. This hybrid methodology, utilizing techniques such as Physics-Informed Neural Networks (PINNs), offers a powerful solution for predicting system health. By fusing domain knowledge with data-driven insights, this approach promises more accurate, adaptable, and reliable models for health prediction. The resulting framework is versatile, applicable across various sectors including aerospace, manufacturing, and energy systems, ultimately contributing to safer, more efficient operations in our increasingly complex technological landscape.

Diagnostics↗
NASA NTRS2013

Decision Making Training in the Mission Operations Directorate

At JSC, we train our new flight controllers on a set of team skills that we call Space Flight Resource Management (SFRM). SFRM is akin to Crew Resource Management for the airlines and trains flight controllers to work as an effective team to reduce errors and improve safety. We have developed this training over the years with the assistance of Ames Research Center, Wyle Labs and University of Central Florida. One of the skills we teach is decision making/ problem solving (DM/PS). We teach DM/PS first in several classroom sessions, reinforce it in several part task training environments, and finally practice it in full-mission, full-team simulations. What I am proposing to talk about is this training flow: its content and how we teach it.

O'Keefe, William S.↗
NASA NTRS2010

Weighting Statistical Inputs for Data Used to Support Effective Decision Making During Severe Emergency Weather and Environmental Events

National Aeronautical and Space Administration (NASA) weather and atmospheric environmental organizations are insatiable consumers of geophysical, hydrometeorological and solar weather statistics. The expanding array of internet-worked sensors producing targeted physical measurements has generated an almost factorial explosion of near real-time inputs to topical statistical datasets. Normalizing and value-based parsing of such statistical datasets in support of time-constrained weather and environmental alerts and warnings is essential, even with dedicated high-performance computational capabilities. What are the optimal indicators for advanced decision making? How do we recognize the line between sufficient statistical sampling and excessive, mission destructive sampling ? How do we assure that the normalization and parsing process, when interpolated through numerical models, yields accurate and actionable alerts and warnings? This presentation will address the integrated means and methods to achieve desired outputs for NASA and consumers of its data.

Gardner, Adrian↗
NASA NTRS1992

Acquisition and production of skilled behavior in dynamic decision-making tasks

Detailed summaries of two NASA-funded research projects are provided. The first project was an ecological task analysis of the Star Cruiser model. Star Cruiser is a psychological model designed to test a subject's level of cognitive activity. Ecological task analysis is used as a framework to predict the types of cognitive activity required to achieve productive behavior and to suggest how interfaces can be manipulated to alleviate certain types of cognitive demands. The second project is presented in the form of a thesis for the Masters Degree. The thesis discusses the modeling of decision-making through the use of neural network and genetic-algorithm machine learning technologies.

Kirlik, Alex↗
NASA NTRS2003

Shared Knowledge for Decision-making on Environment and Health Issues in the Arctic

This paper will describe a remote sensing and GIs-based system to bring indigenous traditional knowledge together with contemporary scientific knowledge to address impacts resulting from changes in climate, environment, weather and pollution in the Arctic. As scientists and policy-makers from both indigenous and non-indigenous communities continue to build closer partnerships to address common sustainability issues such as the health impacts of climate change and anthropogenic activities, it becomes increasingly important to create shared information management systems which integrate all relevant factors for optimal information sharing and decision-making. This system is being designed to bring together remotely sensed, indigenous and other data and observations for analysis, measuring, and monitoring parameters of interest (e.g., snow cover, rainfall, temperature, ice conditions, vegetation, infrastructure, fires). A description of the system and its components as well as a preliminary application of the system in the Arctic will be presented.

Maynard, Nancy G.↗
NASA NTRS2012

Integrating NASA Satellite Data Into USDA World Agricultural Outlook Board Decision Making Environment To Improve Agricultural Estimates

The USDA World Agricultural Outlook Board (WAOB) is responsible for monitoring weather and climate impacts on domestic and foreign crop development. One of WAOB's primary goals is to determine the net cumulative effect of weather and climate anomalies on final crop yields. To this end, a broad array of information is consulted. The resulting agricultural weather assessments are published in the Weekly Weather and Crop Bulletin, to keep farmers, policy makers, and commercial agricultural interests informed of weather and climate impacts on agriculture. The goal of the current project is to improve WAOB estimates by integrating NASA satellite precipitation and soil moisture observations into WAOB's decision making environment. Precipitation (Level 3 gridded) is from the TRMM Multi-satellite Precipitation Analysis (TMPA). Soil moisture (Level 2 swath and Level 3 gridded) is generated by the Land Parameter Retrieval Model (LPRM) and operationally produced by the NASA Goddard Earth Sciences Data and Information Services Center (GBS DISC). A root zone soil moisture (RZSM) product is also generated, via assimilation of the Level 3 LPRM data by a land surface model (part of a related project). Data services to be available for these products include GeoTIFF, GDS (GrADS Data Server), WMS (Web Map Service), WCS (Web Coverage Service), and NASA Giovanni. Project benchmarking is based on retrospective analyses of WAOB analog year comparisons. The latter are between a given year and historical years with similar weather patterns and estimated crop yields. An analog index (AI) was developed to introduce a more rigorous, statistical approach for identifying analog years. Results thus far show that crop yield estimates derived from TMPA precipitation data are closer to measured yields than are estimates derived from surface-based precipitation measurements. Work is continuing to include LPRM surface soil moisture data and model-assimilated RZSM.

Teng, William↗
NASA NTRS2018

Airplane Capabilities: Translating Non-Normal Information for Operational Decision-Making

We consider how a jet transport airplane interface supports the flight crew in managing airplane system failures (or non-normals) for continued safe flight and landing. The existing state of the art starts with a list of airplane system component failures and asks the flight crew to determine, with the help of non-normal procedures, the operational consequences of those failures. As airplane systems become more complex and interconnected, the flight crew's ability to determine operational consequences will become inadequate. We describe an approach that attempts to translate airplane system failures directly into airplane "capabilities," which is a set of basic airplane functions, such as the ability to stop after landing. This paper describes the overall framework for supporting flight crews in operational decision making and the initial efforts to develop a language and display concepts.

managing airplane system non-normals↗
DOE OSTI2026

Evaluating Supply Prioritization Strategies for Risk-Informed Decision Making in an Arbitrary Gas Network

Supply disruptions and infrastructure failures in natural gas networks present critical challenges to energy reliability and risk-informed planning. This study evaluates two supply prioritization strategies, Maximum Delivery Prioritization (MDP) and Demand-Based Prioritization (DBP), within an arbitrary natural gas network under conditions of supply shortage. Model performance under both strategies is assessed in response to node and edge failure using demand satisfaction metrics, system-wide and localized dependency scores, and geographic information system (GIS)-based spatial analysis. Results show that DBP better preserves supply for high-demand nodes, while MDP offers broader coverage. The underlying network topology plays a critical role in shaping prioritization outcomes. Integrated GIS visualization enhances the interpretability of vulnerability assessments, revealing structurally critical components and localized vulnerabilities. The proposed framework supports scalable, data-driven decision-making for infrastructure planners and engineers, enabling improved disruption recovery and efficiency in constrained natural gas networks. These insights contribute to the development of more robust energy systems capable of withstanding stress and disruptions.

Peterson, Steven [ORNL] (ORCID:0000000287672998)↗
NASA NTRS2013

Improving Decision-Making Activities for Meningitis and Malaria

Public health professionals are increasingly concerned about the potential impact that climate variability and change can have on infectious disease. The International Research Institute for Climate and Society (IRI) is developing new products to increase the public health community's capacity to understand, use and demand the appropriate climate data and climate information to mitigate the public health impacts of climate on infectious disease, in particular meningitis and malaria. In this paper, we present the new and improved products that have been developed for: (i) estimating dust aerosol for forecasting risks of meningitis and (ii) for monitoring temperature and rainfall and integrating them into a vectorial capacity model for forecasting risks of malaria epidemics. We also present how the products have been integrated into a knowledge system (IRI Data Library Map Room, SERVIR) to support the use of climate and environmental information in climate-sensitive health decision-making.

parasitic diseases↗
NASA NTRS2012

Complex Decision-Making Applications for the NASA Space Launch System

The Space Shuttle program is ending and elements of the Constellation Program are either being cancelled or transitioned to new NASA exploration endeavors. The National Aeronautics and Space Administration (NASA) has worked diligently to select an optimum configuration for the Space Launch System (SLS), a heavy lift vehicle that will provide the foundation for future beyond low earth orbit (LEO) large-scale missions for the next several decades. Thus, multiple questions must be addressed: Which heavy lift vehicle will best allow the agency to achieve mission objectives in the most affordable and reliable manner? Which heavy lift vehicle will allow for a sufficiently flexible exploration campaign of the solar system? Which heavy lift vehicle configuration will allow for minimizing risk in design, test, build and operations? Which heavy lift vehicle configuration will be sustainable in changing political environments? Seeking to address these questions drove the development of an SLS decision-making framework. From Fall 2010 until Spring 2011, this framework was formulated, tested, fully documented, and applied to multiple SLS vehicle concepts at NASA from previous exploration architecture studies. This was a multistep process that involved performing figure of merit (FOM)-based assessments, creating Pass/Fail gates based on draft threshold requirements, performing a margin-based assessment with supporting statistical analyses, and performing sensitivity analysis on each. This paper discusses the various methods of this process that allowed for competing concepts to be compared across a variety of launch vehicle metrics. The end result was the identification of SLS launch vehicle candidates that could successfully meet the threshold requirements in support of the SLS Mission Concept Review (MCR) milestone.

Lyles, Garry↗