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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 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Ground Delay Program Analytics with Behavioral Cloning and Inverse Reinforcement Learning

We used historical data to build two types of model that predict Ground Delay Program implementation decisions and also produce insights into how and why those decisions are made. More specifically, we built behavioral cloning and inverse reinforcement learning models that predict hourly Ground Delay Program implementation at Newark Liberty International and San Francisco International airports. Data available to the models include actual and scheduled air traffic metrics and observed and forecasted weather conditions. We found that the random forest behavioral cloning models we developed are substantially better at predicting hourly Ground Delay Program implementation for these airports than the inverse reinforcement learning models we developed. However, all of the models struggle to predict the initialization and cancellation of Ground Delay Programs. We also investigated the structure of the models in order to gain insights into Ground Delay Program implementation decision making. Notably, characteristics of both types of model suggest that GDP implementation decisions are more tactical than strategic: they are made primarily based on conditions now or conditions anticipated in only the next couple of hours.

Bloem, Michael↗
NASA NTRS2013

Flight Deck Weather Avoidance Decision Support: Implementation and Evaluation

Weather related disruptions account for seventy percent of the delays in the National Airspace System (NAS). A key component in the weather plan of the Next Generation of Air Transportation System (NextGen) is to assimilate observed weather information and probabilistic forecasts into the decision process of flight crews and air traffic controllers. In this research we explore supporting flight crew weather decision making through the development of a flight deck predicted weather display system that utilizes weather predictions generated by ground-based radar. This system integrates and presents this weather information, together with in-flight trajectory modification tools, within a cockpit display of traffic information (CDTI) prototype. that the CDTI features 2D and perspective 3D visualization models of weather. The weather forecast products that we implemented were the Corridor Integrated Weather System (CIWS) and the Convective Weather Avoidance Model (CWAM), both developed by MIT Lincoln Lab. We evaluated the use of CIWS and CWAM for flight deck weather avoidance in two part-task experiments. Experiment 1 compared pilots' en route weather avoidance performance in four weather information conditions that differed in the type and amount of predicted forecast (CIWS current weather only, CIWS current and historical weather, CIWS current and forecast weather, CIWS current and forecast weather and CWAM predictions). Experiment 2 compared the use of perspective 3D and 21/2D presentations of weather for flight deck weather avoidance. Results showed that pilots could take advantage of longer range predicted weather forecasts in performing en route weather avoidance but more research will be needed to determine what combinations of information are optimal and how best to present them.

CWAM↗
DOE OSTI2022

Caffeine: CoArray Fortran Framework of Efficient Interfaces to Network Environments

This paper provides an introduction to the CoArray Fortran Framework of Efficient Interfaces to Network Environments (Caffeine), a parallel runtime library built atop the GASNet-EX exascale networking library. Caffeine leverages several non-parallel Fortran features to write type- and rank-agnostic interfaces and corresponding procedure definitions that support parallel Fortran 2018 features, including communication, collective operations, and related services. One major goal is to develop a runtime library that can eventually be considered for adoption by LLVM Flang, enabling that compiler to support the parallel features of Fortran. The paper describes the motivations behind Caffeine's design and implementation decisions, details the current state of Caffeine's development, and previews future work. We explain how the design and implementation offer benefits related to software sustainability by lowering the barrier to user contributions, reducing complexity through the use of Fortran 2018 C-interoperability features, and high performance through the use of a lightweight communication substrate.

Rouson, Damian↗
NASA NTRS1989

TAE Plus: Transportable Applications Environment Plus tools for building graphic-oriented applications

The Transportable Applications Environment Plus (TAE Plus), developed by NASA's Goddard Space Flight Center, is a portable User Interface Management System (UIMS), which provides an intuitive WYSIWYG WorkBench for prototyping and designing an application's user interface, integrated with tools for efficiently implementing the designed user interface and effective management of the user interface during an application's active domain. During the development of TAE Plus, many design and implementation decisions were based on the state-of-the-art within graphics workstations, windowing system and object-oriented programming languages. Some of the problems and issues experienced during implementation are discussed. A description of the next development steps planned for TAE Plus is also given.

Szczur, Martha R.↗
DOE OSTI2021

Development of heavy-duty vehicle representative driving cycles via decision tree regression

Previously, researchers who developed representative driving cycles mainly focused on light-duty vehicles and only considered vehicle speed and related derivations. In this paper, we propose a novel approach to develop representative cycles for heavy-duty vehicles. By implementing decision tree regression (DTR) to the Fleet DNA on-road vehicle data, a broader set of metrics, such as engine power and fuel consumption, can be used for more robust cycle development. Additionally, the influence of each metric on the regression target is also accounted for by a weighted number derived through the DTR to enhance the representativenss of the developed cycle. As case studies, we applied the proposed method to five heavy-duty vocations (drayage, long haul, regional haul, local delivery, and transit bus) and derived the most representative cycle, as well as four extreme cycles (maximal energy consumption, maximal power-weighted work, maximal fraction of high speed, and minimal fuel economy) to advance the related alternative powertrain design.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS1981

An investigation into pilot and system response to critical in-flight events. Volume 1: Executive summary

Critical in-flight events (CIFE) that threaten the aircraft were studied. The scope of the CIFE was described and defined with emphasis on characterizing event development, detection and assessment; pilot information requirements, sources, acquisition, and interpretation, pilot response options, decision processed, and decision implementation and event outcome. Detailed scenarios were developed for use in simulators and paper and pencil testing for developing relationships between pilot performance and background information as well as for an analysis of pilot reaction decision and feedback processes. Statistical relationships among pilot characteristics and observed responses to CIFE's were developed.

Rockwell, T. H.↗
NASA NTRS1990

The implementation of POSTGRES

The design and implementation decisions made for the three-dimensional data manager POSTGRES are discussed. Attention is restricted to the DBMS backend functions. The POSTGRES data model and query language, the rules system, the storage system, the POSTGRES implementation, and the current status and performance are discussed.

Stonebraker, Michael↗
NASA NTRS1987

Space station evolution: Planning for the future

The need for permanently manned presence in space has been recognized by the United States and its international partners for many years. The development of this capability was delayed due to the concurrent recognition that reusable earth-to-orbit transportation was also needed and should be developed first. While the decision to go ahead with a permanently manned Space Station was on hold, requirements for the use of the Station were accumulating as ground-based research and the data from unmanned spacecraft sparked the imagination of both scientists and entrepreneurs. Thus, by the time of the Space Station implementation decision in the early 1980's, a variety of disciplines, with a variety of requirements, needed to be accommodated on one Space Station. Additional future requirements could be forecast for advanced missions that were still in the early planning stages. The logical response was the development of a multi-purpose Space Station with the ability to evolve on-orbit to new capabilities as required by user needs and national or international decisions, i.e., to build an evolutionary Space Station. Planning for evolution is conducted in parallel with the design and development of the baseline Space Station. Evolution planning is a strategic management process to facilitate change and protect future decisions. The objective is not to forecast the future, but to understand the future options and the implications of these on today's decisions. The major actions required now are: (1) the incorporation of evolution provisions (hooks and scars) in the baseline Space Station; and (2) the initiation of an evolution advanced development program.

Diaz, Alphonso V.↗
DOE OSTI2020

Removing the Cloak of Secrecy from Legacy Cleanup at Los Alamos national Laboratory - 20395

With the establishment in 2018 of the Los Alamos Legacy Cleanup Contract, the Department of Energy's (DOE) Environmental Management Los Alamos Field Office (EM-LA) and its contractor Newport News Nuclear BWXT Los Alamos (N3B) embarked on an expansive program to engage a broad range of local and regional stakeholders in the decision-making process for cleanup of legacy wastes (pre-1999) at the Los Alamos National Laboratory (LANL). From the inception of LANL during the Manhattan Project until the issuance of the new cleanup contract, the site was managed by a single management and operations (M and O) contactor whose main focus was national security. Segmenting from laboratory operations a separate cleanup contract managed by DoE's Office of Environmental Management (EM) brought new opportunities for the cleanup of legacy waste. That change likewise has opened new opportunities for engaging the community on legacy cleanup decisions. The new legacy cleanup contract - an unclassified contract - emphasizes public outreach, marking a cultural shift in how stakeholders in Los Alamos and throughout northern New Mexico are engaged. Without the constraints of classified work, EM-LA and N3B have increased the level of transparency with the public regarding cleanup activities. EM-LA and N3B have crafted an approach that best meets the values, priorities and needs of the neighboring communities by adapting the model successfully employed at Rocky Flats. Through enhanced stakeholder involvement, EM-LA will implement decisions that have considered broad input and deliver a safe and efficient cleanup of the site. EM-LA and N3B began the expanded stakeholder outreach program in January 2019, leading up to four Environmental Management Cleanup Forums held in the summer of 2019. EM-LA and N3B are now in the process of initiating a series of meetings with various stakeholder groups to determine their 'values' as they relate to cleanup using a novel approach that elicits and records values. The entire process is documented, ensuring that should questions arise about how, when and why decisions were made, they can be easily addressed. In addition to other public meetings to present cleanup scenarios and solicit active public involvement in cleanup decisions, the EM-LA and N3B approach involves discussions with stakeholders who want to explore specific technical issues and cleanup discussions in depth. EM-LA and N3B are also evaluating and implementing a variety of communications tools to help ensure the engagement plan sets the gold standard for transparency in legacy cleanup and stakeholder involvement. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2016

Exploring the Science Trade Space with the JPL Innovation Foundry A-Team

The JPL Innovation Foundry has established a new approach for exploring, developing, and evaluating early concepts with a group called the Architecture Team (A-Team). The A-Team combines innovative collaborative methods and facilitated sessions with subject matter experts and analysis tools to help mature mission concepts. Science, implementation, and programmatic elements are all considered during an ATeam study. In these studies, Concept Maturity Levels (CML) are used to group methods. These levels include idea generation and capture (CML 1), initial feasibility assessment (CML 2), and trade space exploration (CML 3). Methods used for exploring the science objectives, feasibility, and scope will be described including use of a new technique for understanding the most compelling science, called a Science Return Diagram (SRD). In the process of developing the SRD, gradients in the science trade space are uncovered along with their implications for implementation and mission architecture. Special attention is paid towards developing complete investigations, establishing a series of logical claims that lead to the natural selection of a measurement approach. Over 20 science-focused A-Team studies have used these techniques to help science teams refine their mission objectives, make implementation decisions and reveal the mission concept’s most compelling science. This paper will describe the A-Team process for exploring the mission concept's science trade space and the Science Return Diagram technique.In June of 2011 a new collaborative engineering approach forearly concept formulation began in the JPL InnovationFoundry [1], six months later becoming the “A-Team” [2].Responding to a need for exploring mission architecturelevel trades [3], the A-Team precedes Team X [4,5] in asequence of concurrent engineering teams at JPL that can beused to mature a concept from a “cocktail napkin” level ideato a complete mission point design. The A-Team efficientlyexplores the science, implementation, and programmatictrade space in early concept formulation. Small, facilitatedgroups of experts generate innovative ideas, quantitativelyassess feasibility, and discover key sensitivities in the tradespace through collaborative analysis and use of advancedmethods and tools. The A-Team process builds off theexperience within JPL and other recent approaches to earlyconcept formulation [6] including best practices of the JPLInnovation Foundry, Project Systems Engineering &Formulation Section, Team Eureka and the Rapid MissionArchitecture Team[7].The A-Team is a focal point for innovative formulationapproaches and people within JPL. It relies on a largebackground of study resources, creative thinkers and “greybeard” scrutinizers, advanced tools, and subject matterexperts with both breadth and depth in experience andexpertise that are all available at JPL. The A-Team isdesigned to be a rapid and efficient process takingapproximately 6 weeks (the entire process can be as short asjust a few days or as long as up to three months) and costingthe equivalent of a work-month of a full-time employee orless. Studies begin with detailed planning and client reviewfollowed by study sessions, analysis work, and reporting.The staffing on each study is customized to the study goalsand objectives, and it is addressed early in the A-Teamprocess. Sessions are generally half-day or whole-day eventsand conducted over a series of days with focused agendas thatare moderated by a trained facilitator. Preliminary results andknowledge capture are available within hours of each session,and a final report is generally available two weeks later.One of the biggest challenges facing early conceptdevelopment is understanding the gradient in science returnversus various available mission scenarios and payload options. Often times, major areas of scientific inquiry havealready been prioritized by science groups, including throughthe National Research Council’s Decadal Studies inAstronomy, Planetary, and Earth Science. Yet science teamscontinue to struggle, especially in competitive missionsolicitations, to capture the right amount of scope that’sachievable within the cost constraints of the opportunity.Often the desire to completely and comprehensively study ascience area in just one mission (after all, true missionopportunities are rare) drives teams to take on too much,providing requirements that are unachievable within theresources of the opportunity without inducing unacceptableimplementation risk. Alternatively, science teams can seekto reduce risk by using an established instrument, but havenot thought through the traceability and key aspects of thescience question to justify its use. Both scenarios lead to badassumptions at the beginning of the concept development thatcan then ripple through implementation option choices,potentially preventing what would have been a good scienceinvestigation from being selected.The purpose of this paper is first to provide some additionalbackground and summary of the A-Team process, tools,people, and facilities. We then focus on the A-Teammethodology for overcoming the barriers of defining thescience scope well at the early concept development stage.This includes understanding the science story andtraceability, and then examining the gradient in science returnversus key characteristics of observables, developing theright payload and mission requirement specification throughexamining the science and implementation trade space.

Ziemer, John K.↗
DOE OSTI2021

EpiCast: Simulating Epidemics with Extreme Detail

In early 2020, COVID-19 swept the globe. Governments attempted to “flatten the curve” through business shutdowns and stay-at-home orders, but the United States was hit hard. By the end of March, mere months after the virus first emerged in humans 7,000 miles away, the U.S. had recorded 192,300 cases and 5,300 deaths. While this unprecedented disaster sent shockwaves through every level of society and clouded an uncertain future, state and local governments turned to computational and mathematical epidemiology researchers to help formulate intervention strategies to limit the spread of the disease. Traditional forecasting models provided a reasonable understanding of how the near future was likely to look, but local policy makers and public health communities still struggled to understand how potential mitigations ought to be implemented. Decision makers needed a way to measure the impact of their policy choices—they needed better technology. EpiCast answered the call, bringing urgently needed answers to policymakers grappling with how to adjust school and business schedules. EpiCast is modeling software that generates a synthetic, representative population to simulate infectious disease transmission in the United States with extreme detail and granularity. The software models human behavior combined with community-specific information to provide a fine-grained preview of the effect of potential mitigation strategies for decision makers.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

PR100 One-Year Progress Summary Report: Preliminary Modeling Results and High-Resolution Solar and Wind Data Sets

The Puerto Rico Grid Resilience and Transitions to 100% Renewable Energy Study (PR100) is a 2-year study by the U.S. Department of Energy's (DOE's) Grid Deployment Office and six national laboratories to comprehensively analyze stakeholder-driven pathways to Puerto Rico's clean energy future. In Year 1 of the study, the PR100 team rigorously modeled and analyzed scenarios that meet Puerto Rico's renewable energy targets and achieve short-term recovery goals and long-term energy resilience. This report, which summarizes PR100 progress in Year 1, provides considerations that can inform potential funding and implementation decisions by key federal and local agencies and stakeholders. The summary report follows the publication in July 2022 of a PR100 Six-Month Progress Update (in English and Spanish), as well as public webinars in February 2022 to kick off the study and in July 2022 to present the 6-month update. A final written report and web-based visuals will be published in late 2023. All publications and public events associated with the study will be available in Spanish and English. This report is also available in Spanish https://www.nrel.gov/docs/fy23osti/85144.pdf.

100%↗
DOE OSTI2025

Comparing computational times for simulations when using PBPK model template and stand-alone implementations of PBPK models

Introduction We previously developed a PBPK model template that consists of a single model “superstructure” with equations and logic found in many physiologically based pharmacokinetic (PBPK) models. Using the template, one can implement PBPK models with different combinations of structures and features. Methods To identify factors that influence computational time required for PBPK model simulations, we conducted timing experiments using various implementations of PBPK models for dichloromethane and chloroform, including template and stand-alone implementations, and simulating four different exposure scenarios. For each experiment, we measured the required computational time and evaluated the impacts of including various model features (e.g., number of output variables calculated) and incorporating various design choices (e.g., different methods for estimating blood concentrations). Results We observed that model implementations that treat body weight and dependent quantities as constant (fixed) parameters can result in a 30% time savings compared with options that treat body weight and dependent quantities as time-varying. We also observed that decreasing the number of state variables by 36% in our PBPK model template led to a decrease of 20–35% in computational time. Other factors, such as the number of output variables, the method for implementing conditional statements, and the method for estimating blood concentrations, did not have large impacts on simulation time. In general, simulations with PBPK model template implementations of models required more time than simulations with stand-alone implementations, but the flexibility and (human) time savings in preparing and reviewing a model implemented using the PBPK model template may justify the increases in computational time requirements. Conclusion Our findings concerning how PBPK model design and implementation decisions impact computational speed can benefit anyone seeking to develop, improve, or apply a PBPK model, with or without the PBPK model template.

Bernstein, Amanda S.↗
NASA NTRS1977

Fast arithmetic in MacLISP

MacLISP provides a compiler which produces numerical code competitive in speed with some FORTRAN implementations and yet compatible with the rest of the MacLISP system. All numerical programs can be run under the MacLISP interpreter. Additional declarations to the compiler specify type information which allows the generation of optimized numerical code which generally does not require the garbage collection of temporary numerical results. Array accesses are almost as fast as in FORTRAN, and permit the use of dynamically allocated arrays of varying dimensions. The implementation decisions regarding user interface, data representations, and interfacing conventions are discussed which allow the generation of fast numerical LISP code.

Steele, G. L., Jr.↗
NASA NTRS1977

Evaluating the benefits of government funded R & D aimed at the private sector

Federal funding of technological research and development is discussed with regard to the procedures for an economic analysis with the goals of (1) determining when the public sector should invest in a research and development program, (2) evaluating the likelihood of private sector participation in terms of public sector participation, and (3) considering the major factors in the formulation of a research and development program in terms of defining initiatives. Public sector investments are evaluated, noting procedures for determining whether benefits exceed costs. The role of the public sector research and development planning is described, considering the procedure for private sector implementation decisions and a methodology for evaluating the possibility of private sector commercialization. The economic value of the public sector research and development program is presented with attention given to a specific case of NASA-sponsored research and development aimed toward the commercialization of new public communications services.

Greenberg, J. S.↗
NASA NTRS1991

Parallel processing and expert systems

Whether it be monitoring the thermal subsystem of Space Station Freedom, or controlling the navigation of the autonomous rover on Mars, NASA missions in the 1990s cannot enjoy an increased level of autonomy without the efficient implementation of expert systems. Merely increasing the computational speed of uniprocessors may not be able to guarantee that real-time demands are met for larger systems. Speedup via parallel processing must be pursued alongside the optimization of sequential implementations. Prototypes of parallel expert systems have been built at universities and industrial laboratories in the U.S. and Japan. The state-of-the-art research in progress related to parallel execution of expert systems is surveyed. The survey discusses multiprocessors for expert systems, parallel languages for symbolic computations, and mapping expert systems to multiprocessors. Results to date indicate that the parallelism achieved for these systems is small. The main reasons are (1) the body of knowledge applicable in any given situation and the amount of computation executed by each rule firing are small, (2) dividing the problem solving process into relatively independent partitions is difficult, and (3) implementation decisions that enable expert systems to be incrementally refined hamper compile-time optimization. In order to obtain greater speedups, data parallelism and application parallelism must be exploited.

Lau, Sonie↗
NASA NTRS1993

Design of a cooperative problem-solving system for enroute flight planning: An empirical study of its use by airline dispatchers

In a previous report, an empirical study of 30 pilots using the Flight Planning Testbed was reported. An identical experiment using the Flight Planning Testbed (FPT), except that 27 airline dispatchers were studied, is described. Five general questions were addressed in this study: (1) under what circumstances do the introduction of computer-generated suggestions (flight plans) influence the planning behavior of dispatchers (either in a beneficial or adverse manner); (2) what is the nature of such influences (i.e., how are the person's cognitive processes changed); (3) how beneficial are the general design concepts underlying FPT (use of a graphical interface, embedding graphics in a spreadsheet, etc.); (4) how effective are the specific implementation decisions made in realizing these general design concepts; and (5) how effectively do dispatchers evaluate situations requiring replanning, and how effectively do they identify appropriate solutions to these situations.

Smith, Philip J.↗
NASA NTRS1993

Mass storage system reference model, Version 4

The high-level abstractions that underlie modern storage systems are identified. The information to generate the model was collected from major practitioners who have built and operated large storage facilities, and represents a distillation of the wisdom they have acquired over the years. The model provides a common terminology and set of concepts to allow existing systems to be examined and new systems to be discussed and built. It is intended that the model and the interfaces identified from it will allow and encourage vendors to develop mutually-compatible storage components that can be combined to form integrated storage systems and services. The reference model presents an abstract view of the concepts and organization of storage systems. From this abstraction will come the identification of the interfaces and modules that will be used in IEEE storage system standards. The model is not yet suitable as a standard; it does not contain implementation decisions, such as how abstract objects should be broken up into software modules or how software modules should be mapped to hosts; it does not give policy specifications, such as when files should be migrated; does not describe how the abstract objects should be used or connected; and does not refer to specific hardware components. In particular, it does not fully specify the interfaces.

Coleman, Sam↗