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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Lowering Barriers to Science and Space Weather Research at the Community Coordinated Modeling Center (CCMC)

The Space Weather and Heliophysics research and modeling community has been pushing the limits of our ability to understand and predict space weather events. The Community Coordinated Modeling Center (CCMC, https://ccmc.gsfc.nasa.gov) supports the community by providing a convenient collaborative platform hosting space weather models, model simulation data, curated datasets of solar events, and associated value-added services. Using these services, researchers and other end-users may exercise, evaluate, and intercompare contributed models, triage designated R2O models, as well as collaborate on a continuously updated archive of model run results. We will focus on CCMC’s ongoing commitment to the principles and guidelines of the Open Science initiative. Particularly, we will discuss our work towards making our services more transparent and our library of model simulations more accessible, open, and reproducible. We will introduce our recent tools for data discovery and correlative analysis designed to further increase the value of the user-generated data and metadata. We will also present our recent work on making heliophysical models more accessible and open to the community, particularly through simplified user experience and expert domain support. We will report on our progress in establishing an inter-center infrastructure with the ESA Virtual Space Weather Modelling Centre (VSWMC), designed to cross organizational boundaries and provide streamlined access to a joint palette of the models.

space weather↗
NASA NTRS2001

Cost model validation: a technical and cultural approach

This paper summarizes how JPL's parametric mission cost model (PMCM) has been validated using both formal statistical methods and a variety of peer and management reviews in order to establish organizational acceptance of the cost model estimates.

cost model validation↗
DOE OSTI2024

A Generalized approach to the operationalization of Software Quality Models

Comprehensive measures of quality are a research imperative, yet the development of software quality models is a wicked problem. Definitive solutions do not exist and quality is subjective at its most abstract. Definitional measures of quality are contingent on a domain, and even within a domain, the choice of representative characteristics to decompose quality is subjective. Thus, the operationalization of quality models brings even more challenges. A promising approach to quality modeling is the use of hierarchies to represent characteristics, where lower levels of the hierarchy represent concepts closer to real-world observations. Building upon prior hierarchical modeling approaches, we developed the Platform for Investigative software Quality Understanding and Evaluation (PIQUE). PIQUE surmounts several quality modeling challenges because it allows modelers to instantiate abstract hierarchical models in any domain by leveraging organizational tools tailored to their specific contexts. Here, we introduce PIQUE; exemplify its utility with two practical use cases; address challenges associated with parameterizing a PIQUE model; and describe algorithmic techniques that tackle normalization, aggregation, and interpolation of measurements.

Data aggregation↗
NASA NTRSDate not supplied

Nasa Pilot Fatigue Research: Past, Present & Future

Pilot fatigue research at NASA Ames has a long history, initiated by a Congressional request to investigate the magnitude of fatigue, sleep loss and circadian disruption in flight operations, and how these factors impact crew performance. Research has been conducted in a broad range of aviation operations, including short-haul, long-haul, rotorcraft, overnight cargo, corporate/business, and regional airlines. Studies have been conducted in the field during actual flight operations, in simulated settings, focus group meetings, and with surveys. Other investigations have examined mitigation strategies including in-flight rest periods and lighting applications. Other activities have included education and training, publications, accident investigation support and the development of research methods and tools. Future research may include further lighting interventions, controlled rest strategies, use of automation, the effectiveness of modeling tools, and implementation of organizational fatigue management programs.

aviation↗
DOE OSTI2020

A Community Energy Operations and Planning System: Concept, Use cases, Metrics, and Benefits

Community and city leaders are interested in achieving sustainability goals, providing resilient energy infrastructure, and improving economic competitiveness. Community-level data acquisition and analysis can provide energy and associated benefits that are not possible at the single building level. However, there is a lack of organizational structure, common semantic data models, interoperable systems, and methods to support data-driven decision-making for community-scale energy supply and demand systems. We explored the need and opportunity for a Community Energy Operations and Planning System (Community EOPS), a potential data exchange platform. We conducted “customer discovery” interviews, and reviewed literature, public tools, and technology platforms to identify key energy data “users” and use cases in communities. The key users of the Community EOPS could be developers of mixed-use districts, corporate, defense and university campus energy managers, and city managers of cities that own their energy utility. The value could be for community planning and reporting (for energy data-integrated land use planning and community infrastructure investments in microgrids, storage, district heating and cooling), energy efficiency (leveraging optimizations for community scale energy supply and demand), flexible load management (grid-edge load management to offset, shift, and flatten loads for multiple buildings and EV fleets), cost savings and revenue generation (participating in grid services), and social benefits such as energy resilience, equity, and awareness. We developed a conceptual Community EOPS architecture with recommendations for streamlined and prioritized data acquisition, sharing, and integration driven by prioritized use cases, common metrics, and actionable visualizations that can provide value to a community’s users.

Singh, Reshma↗
DOE OSTI2022

Always look on the bright side of life: Taking a different perspective on cybersecurity performance

This presentation will introduce participants to key mental models on how to think about and understand safety from two of the leading voices in the discipline – Dr. Erik Hollnagel and Dr. Todd Conklin – and from there we ask the simple question of “how can we apply this to cybersecurity?” At the completion of this presentation attendees will be able to describe Dr. Hollnagel’s Safety-II approach and Dr. Conkiln’s definition of safety as “not the absence of accidents but the presence of effective defenses.” They will be able to explain how these ideas translate to the cybersecurity world broadly, and more specifically to operational technology cybersecurity where physical safety consequences are coupled to control system performance. Finally, participants will be able to identify how selected principles from Weick and Sutcliffe’s high reliability organization theory can help operationalize these key mental models around safety, for improved organizational cybersecurity over the long run.

99 GENERAL AND MISCELLANEOUS↗
NASA NTRS2008

The Integrated Medical Model: Statistical Forecasting of Risks to Crew Health and Mission Success

The Integrated Medical Model (IMM) helps capture and use organizational knowledge across the space medicine, training, operations, engineering, and research domains. The IMM uses this domain knowledge in the context of a mission and crew profile to forecast crew health and mission success risks. The IMM is most helpful in comparing the risk of two or more mission profiles, not as a tool for predicting absolute risk. The process of building the IMM adheres to Probability Risk Assessment (PRA) techniques described in NASA Procedural Requirement (NPR) 8705.5, and uses current evidence-based information to establish a defensible position for making decisions that help ensure crew health and mission success. The IMM quantitatively describes the following input parameters: 1) medical conditions and likelihood, 2) mission duration, 3) vehicle environment, 4) crew attributes (e.g. age, sex), 5) crew activities (e.g. EVA's, Lunar excursions), 6) diagnosis and treatment protocols (e.g. medical equipment, consumables pharmaceuticals), and 7) Crew Medical Officer (CMO) training effectiveness. It is worth reiterating that the IMM uses the data sets above as inputs. Many other risk management efforts stop at determining only likelihood. The IMM is unique in that it models not only likelihood, but risk mitigations, as well as subsequent clinical outcomes based on those mitigations. Once the mathematical relationships among the above parameters are established, the IMM uses a Monte Carlo simulation technique (a random sampling of the inputs as described by their statistical distribution) to determine the probable outcomes. Because the IMM is a stochastic model (i.e. the input parameters are represented by various statistical distributions depending on the data type), when the mission is simulated 10-50,000 times with a given set of medical capabilities (risk mitigations), a prediction of the most probable outcomes can be generated. For each mission, the IMM tracks which conditions occurred and decrements the pharmaceuticals and supplies required to diagnose and treat these medical conditions. If supplies are depleted, then the medical condition goes untreated, and crew and mission risk increase. The IMM currently models approximately 30 medical conditions. By the end of FY2008, the IMM will be modeling over 100 medical conditions, approximately 60 of which have been recorded to have occurred during short and long space missions.

Fitts, M. A.↗
NASA NTRS1991

Modeling Personnel Turnover in the Parametric Organization

A primary issue in organizing a new parametric cost analysis function is to determine the skill mix and number of personnel required. The skill mix can be obtained by a functional decomposition of the tasks required within the organization and a matrixed correlation with educational or experience backgrounds. The number of personnel is a function of the skills required to cover all tasks, personnel skill background and cross training, the intensity of the workload for each task, migration through various tasks by personnel along a career path, personnel hiring limitations imposed by management and the applicant marketplace, personnel training limitations imposed by management and personnel capability, and the rate at which personnel leave the organization for whatever reason. Faced with the task of relating all of these organizational facets in order to grow a parametric cost analysis (PCA) organization from scratch, it was decided that a dynamic model was required in order to account for the obvious dynamics of the forming organization. The challenge was to create such a simple model which would be credible during all phases of organizational development. The model development process was broken down into the activities of determining the tasks required for PCA, determining the skills required for each PCA task, determining the skills available in the applicant marketplace, determining the structure of the dynamic model, implementing the dynamic model, and testing the dynamic model.

Dean, Edwin B.↗
NASA NTRS2003

Archetypes for Organisational Safety

We propose a framework using system dynamics to model the dynamic behavior of organizations in accident analysis. Most current accident analysis techniques are event-based and do not adequately capture the dynamic complexity and non-linear interactions that characterize accidents in complex systems. In this paper we propose a set of system safety archetypes that model common safety culture flaws in organizations, i.e., the dynamic behaviour of organizations that often leads to accidents. As accident analysis and investigation tools, the archetypes can be used to develop dynamic models that describe the systemic and organizational factors contributing to the accident. The archetypes help clarify why safety-related decisions do not always result in the desired behavior, and how independent decisions in different parts of the organization can combine to impact safety.

Marais, Karen↗
NASA NTRS2012

Command Center Training Tool (C2T2)

This abstract presents the training approach taken to create a management-centered, experiential learning solution for the Virginia Port Authority's Port Command Center. The resultant tool, called the Command Center Training Tool (C2T2), follows a holistic approach integrated across the training management cycle and within a single environment. The approach allows a single training manager to progress from training design through execution and AAR. The approach starts with modeling the training organization, identifying the organizational elements and their individual and collective performance requirements, including organizational-specific performance scoring ontologies. Next, the developer specifies conditions, the problems, and constructs that compose exercises and drive experiential learning. These conditions are defined by incidents, which denote a single, multi-media datum, and scenarios, which are stories told by incidents. To these layered, modular components, previously developed meta-data is attached, including associated performance requirements. The components are then stored in a searchable library An event developer can create a training event by searching the library based on metadata and then selecting and loading the resultant modular pieces. This loading process brings into the training event all the previously associated task and teamwork material as well as AAR preparation materials. The approach includes tools within an integrated management environment that places these materials at the fingertips of the event facilitator such that, in real time, the facilitator can track training audience performance and resultantly modify the training event. The approach also supports the concentrated knowledge management requirements for rapid preparation of an extensive AAR. This approach supports the integrated training cycle and allows a management-based perspective and advanced tools, through which a complex, thorough training event can be developed.

Jones, Phillip↗
NASA NTRS1984

Issues in Developing a Normative Descriptive Model for Dyadic Decision Making

Most research in modelling human information processing and decision making has been devoted to the case of the single human operator. In the present effort, concepts from the fields of organizational behavior, engineering psychology, team theory and mathematical modelling are merged in an attempt to consider first the case of two cooperating decisionmakers (the Dyad) in a multi-task environment. Rooted in the well-known Dynamic Decision Model (DDM), the normative descriptive approach brings basic cognitive and psychophysical characteristics inherent to human behavior into a team theoretic analytic framework. An experimental paradigm, involving teams in dynamic decision making tasks, is designed to produce the data with which to build the theoretical model.

Serfaty, D.↗
DOE OSTI2025

From clutter to clarity: Emergent neural operators via questionnaire metrics

Real-world datasets in chemical engineering and bioengineering processes—such as those from catalytic reactors, multiphase flows, polymerization reactors, bioreactors, and clinical trials—can often be unlabeled or disorganized, rendering the training of existing supervised learning models ineffective at learning the underlying dynamics. To salvage these datasets for decision-making, we first seek to obtain clarity from the cluttered data. Here, we present a framework for developing “structural” generative models, discovering emergent equations, and constructing efficient emulators from scrambled datasets by integrating unsupervised organizational learning techniques (Questionnaires) with advanced deep learning architectures (Deep Hidden Physics Models and Deep Operator Networks). Our approach is demonstrated on two illustrative model systems: (a) a 1D advection–diffusion partial differential equation representing a winding underground pipe and (b) an ensemble of Stuart–Landau oscillators, an agent-based system of coupled ordinary differential equations. In both cases, we successfully reconstruct meaningful spatial, temporal, and parameter embeddings from scrambled data, enabling good predictions of system dynamics. As a result, we highlight the framework’s potential for broader applications, enabling data-driven system identification in fields with inherently disorganized or hidden parameter spaces.

42 ENGINEERING↗
NASA NTRS1989

Personality and organizational influences on aerospace human performance

Individual and organizational influences on performance in aerospace environments are discussed. A model of personality with demonstrated validity is described along with reasons why personality's effects on performance have been underestimated. Organizational forces including intergroup conflict and coercive pressures are also described. It is suggested that basic and applied research in analog situations is needed to provide necessary guidance for planning future space missions.

Helmreich, Robert L.↗
NASA NTRS2016

Ontologies for Aviation Data Management

Managing complex aviation data can be a significant challenge for any enterprise – whether a government agency, airline, airframe manufacturer, or aviation service provider. To handle this challenge, data models are typically developed to characterize and manage the data generated, used, and stored by a given enterprise. Unfortunately, different data providers employ qualitatively different data models, and this gives rise to problems exchanging data across organizational boundaries. Over the past decade, these problems have motivated data producers and consumers to look toward standardized data exchange models to address data interoperability. In this paper we examine some of these standardized data exchange models and compare them with a new type of data model based on ontologies. Ontology models have emerged in recent years from a confluence of research in the artificial intelligence, semantic web, and information science communities. This paper introduces ontology models, provides several use cases for ontologies relevant to aviation data management, and summarizes state of the art aviation prototype applications that utilize ontologies.

artificial intelligence↗
DOE OSTI2023

Embracing fine-root system complexity in terrestrial ecosystem modeling

Projecting the dynamics and functioning of the biosphere requires a holistic consideration of whole-ecosystem processes. However, biases toward leaf, canopy, and soil modeling since the 1970s have constantly left fine-root systems being rudimentarily treated. As accelerated empirical advances in the last two decades establish clearly functional differentiation conferred by the hierarchical structure of fine-root orders and associations with mycorrhizal fungi, a need emerges to embrace this complexity to bridge the data-model gap in still extremely uncertain models. In this work, we propose a three-pool structure comprising transport and absorptive fine roots with mycorrhizal fungi (TAM) to model vertically resolved fine-root systems across organizational and spatial–temporal scales. Emerging from a conceptual shift away from arbitrary homogenization, TAM builds upon theoretical and empirical foundations as an effective and efficient approximation that balances realism and simplicity. A proof-of-concept demonstration of TAM in a big-leaf model both conservatively and radically shows robust impacts of differentiation within fine-root systems on simulating carbon cycling in temperate forests. Theoretical and quantitative support warrants exploiting its rich potentials across ecosystems and models to confront uncertainties and challenges for a predictive understanding of the biosphere. Echoing a broad trend of embracing ecological complexity in integrative ecosystem modeling, TAM may offer a consistent framework where modelers and empiricists can work together toward this grand goal.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1987

Affirmative action as organization development at the Johnson Space Center

The role of affirmative actions is investigated as an interventionist Organization Development (OD) strategy for insuring equal opportunities at the NASA/Johnson Space Center. In doing so, an eclectic and holistic model is developed for the recruiting and hiring of minorities and females over the next five years. The strategy, approach, and assumptions for the model are quite different than those for JSC's five year plan. The study concludes that Organization development utilizing affirmative action is a valid means to bring about organizational change and renewal processes, and that an eclectic model of affirmative action is most suitable and rational in obtaining this end.

Tryman, Mfanya Donald L.↗
NASA NTRS2003

Technology Transfer Challenges for High-Assurance Software Engineering Tools

In this paper, we describe our experience with the challenges thar we are currently facing in our effort to develop advanced software verification and validation tools. We categorize these challenges into several areas: cost benefits modeling, tool usability, customer application domain, and organizational issues. We provide examples of challenges in each area and identrfj, open research issues in areas which limit our ability to transfer high-assurance software engineering tools into practice.

John Penix↗