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At least 631 records · Page 35

Light Microscopy Module Imaging Tested and Demonstrated

The Fluids Integrated Rack (FIR), a facility-class payload, and the Light Microscopy Module (LMM), a subrack payload, are integrated research facilities that will fly in the U.S. Laboratory module, Destiny, aboard the International Space Station. Both facilities are being engineered, designed, and developed at the NASA Glenn Research Center by Northrop Grumman Information Technology. The FIR is a modular, multiuser scientific research facility that is one of two racks that make up the Fluids and Combustion Facility (the other being the Combustion Integrated Rack). The FIR has a large volume dedicated for experimental hardware; easily reconfigurable diagnostics, power, and data systems that allow for unique experiment configurations; and customizable software. The FIR will also provide imagers, light sources, power management and control, command and data handling for facility and experiment hardware, and data processing and storage. The first payload in the FIR will be the LMM. The LMM integrated with the FIR is a remotely controllable, automated, on-orbit microscope subrack facility, with key diagnostic capabilities for meeting science requirements--including video microscopy to observe microscopic phenonema and dynamic interactions, interferometry to make thin-film measurements with nanometer resolution, laser tweezers to manipulate micrometer-sized particles, confocal microscopy to provide enhanced three-dimensional visualization of structures, and spectrophotometry to measure the photonic properties of materials. Vibration disturbances were identified early in the LMM development phase as a high risk for contaminating the science microgravity environment. An integrated FIR-LMM test was conducted in Glenn's Acoustics Test Laboratory to assess mechanical sources of vibration and their impact to microscopic imaging. The primary purpose of the test was to characterize the LMM response at the sample location, the x-y stage within the microscope, to vibration emissions from the FIR and LMM support structures.

Gati, Frank↗

A New Heavy-Lift Capability for Space Exploration: NASA's Ares V Cargo Launch Vehicle

The National Aeronautics and Space Administration (NASA) is developing new launch systems and preparing to retire the Space Shuttle by 2010, as directed in the United States (U.S.) Vision for Space Exploration. The Ares I Crew Launch Vehicle (CLV) and the Ares V heavy-lift Cargo Launch Vehicle (CaLV) systems will build upon proven, reliable hardware derived from the Apollo-Saturn and Space Shuttle programs to deliver safe, reliable, affordable space transportation solutions. This approach leverages existing aerospace talent and a unique infrastructure, as well as legacy knowledge gained from nearly 50 years' experience developing space hardware. Early next decade, the Ares I will launch the new Orion Crew Exploration Vehicle (CEV) to the International Space Station (ISS) or to low-Earth orbit for trips to the Moon and, ultimately, Mars. Late next decade, the Ares V's Earth Departure Stage will carry larger payloads such as the lunar lander into orbit, and the Crew Exploration Vehicle will dock with it for missions to the Moon, where astronauts will explore new territories and conduct science and technology experiments. Both Ares I and Ares V are being designed to support longer future trips to Mars. The Exploration Launch Projects Office is designing, developing, testing, and evaluating both launch vehicle systems in partnership with other NASA Centers, Government agencies, and industry contractors. This paper provides top-level information regarding the genesis and evolution of the baseline configuration for the Ares V heavy-lift system. It also discusses riskbased, management strategies, such as building on powerful hardware and promoting common features between the Ares I and Ares V systems to reduce technical, schedule, and cost risks, as well as development and operations costs. Finally, it summarizes several notable accomplishments since October 2005, when the Exploration Launch Projects effort officially kicked off, and looks ahead at work planned for 2007 and beyond.

Sumrall, John P.↗

Development of a High-Fidelity Simulation Environment for Shadow-Mode Assessments of Air Traffic Concepts

This paper describes the Shadow-Mode Assessment Using Realistic Technologies for the National Airspace System (SMART-NAS) Test Bed. The SMART-NAS Test Bed is an air traffic simulation platform being developed by the National Aeronautics and Space Administration (NASA). The SMART-NAS Test Bed's core purpose is to conduct high-fidelity, real-time, human-in-the-loop and automation-in-the-loop simulations of current and proposed future air traffic concepts for the United States' Next Generation Air Transportation System called NextGen. The setup, configuration, coordination, and execution of realtime, human-in-the-loop air traffic management simulations are complex, tedious, time intensive, and expensive. The SMART-NAS Test Bed framework is an alternative to the current approach and will provide services throughout the simulation workflow pipeline to help alleviate these shortcomings. The principle concepts to be simulated include advanced gate-to-gate, trajectory-based operations, widespread integration of novel aircraft such as unmanned vehicles, and real-time safety assurance technologies to enable autonomous operations. To make this possible, SNTB will utilize Web-based technologies, cloud resources, and real-time, scalable, communication middleware. This paper describes the SMART-NAS Test Bed's vision, purpose, its concept of use, and the potential benefits, key capabilities, high-level requirements, architecture, software design, and usage.

human-in-the-loop↗

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

A Recursive Multi-step Machine Learning Approach for Airport Configuration Prediction

Airport configuration selection is a complex decision-making process that involves several operational and human factors. In this paper we propose a novel recursive multi-step machine learning (ML) approach to predict airport configuration. The multi-step approach guarantees stability of the predicted configuration by taking as input the configuration predicted at the previous time step. The features of the proposed model include weather data, future arrival and departure counts and current configuration. Due to the importance of arrival and departure counts in predicting the airport configuration, arrival counts are calculated using landing time predictions selected from physics-based landing time predictions available in FAA System Wide Information Management data feeds for each flight. The selection rules were developed and refined to select the most accurate time for different phases of flight. The proposed model predicts the airport configurations up to 6 hours ahead. In this paper we show the predictive performance of the proposed model for six major US airports, including Charlotte Douglas International Airport (CLT), Dallas/Fort Worth International Airport (DFW), John F. Kennedy International Airport (JFK), Newark Liberty International Airport (EWR), LaGuardia Airport (LGA) and Dallas Love Field Airport (DAL). We trained and evaluated models on 2019 and 2020 data in order to study the effect of the pandemic and how changes in traffic patterns affected the performance of the proposed model. Results are compared with a baseline assuming no airport configuration changes. In our results for DFW, we obtained a prediction accuracy of 89.3% for 3 hours ahead prediction, and 82.8% for 6 hours ahead when applied on 2019 data.

machine learning↗

Software life cycle dynamic simulation model: The organizational performance submodel

The submodel structure of a software life cycle dynamic simulation model is described. The software process is divided into seven phases, each with product, staff, and funding flows. The model is subdivided into an organizational response submodel, a management submodel, a management influence interface, and a model analyst interface. The concentration here is on the organizational response model, which simulates the performance characteristics of a software development subject to external and internal influences. These influences emanate from two sources: the model analyst interface, which configures the model to simulate the response of an implementing organization subject to its own internal influences, and the management submodel that exerts external dynamic control over the production process. A complete characterization is given of the organizational response submodel in the form of parameterized differential equations governing product, staffing, and funding levels. The parameter values and functions are allocated to the two interfaces.

Tausworthe, Robert C.↗

Air Traffic Management TestBed Traffic Viewer: Developer's Guide

The Air Traffic Management (ATM) TestBed is a Platform as a Service that is being developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The platform is designed to provide cloud services including back-end, big-data analytics tools, on-demand computing resource management, data storage, and communication middleware. The ATM TestBed reduces the time to test concepts and technologies, supports interactions among various methods such as human-in-the-loop and automation-in-the-loop simulations, and enables collaborative simulations by sharing technologies and tools in the ATM community. The Traffic Viewer application provides a graphical user interface tool for visualizing real and simulated air traffic as well as airspace definition in two-dimensional space. This guide describes a high-level design and implementation of Traffic Viewer and provides information for a new developer or a user to add new capabilities by following the software design and leveraging existing capabilities.

Lai, Chok Fung↗

Air Traffic Management TestBed Data Exchange Model

The Air Traffic Management (ATM) TestBed is a Platform as a Service that is being developed by the National Aeronautics and Space Administration (NASA) to help design, configure, integrate, run, and monitor air traffic simulations. The platform is designed to provide cloud services including back-end, big-data analytics tools, on-demand computing resource management, data storage, and communication middleware. The ATM TestBed reduces the time to test concepts and technologies, supports interactions among various methods such as human-in-the-loop and automation-in-the-loop simulations, and enables collaborative simulations by sharing technologies and tools in the ATM community. In order to allow easier access to simulation components, TestBed provides a messaging support layer for connectivity using a consistent set of input/output interfaces. In addition, a standard data format is introduced to facilitate communication between the components. The data exchange model, supported in the messaging support layer, standardizes the format of the information to be exchanged among the components. This document describes the messaging data model currently developed in TestBed and provides data dictionaries for references to component developers as well as simulation engineers.

air traffic simulation↗

Service Management Database for DSN Equipment

This data- and event-driven persistent storage system leverages the use of commercial software provided by Oracle for portability, ease of maintenance, scalability, and ease of integration with embedded, client-server, and multi-tiered applications. In this role, the Service Management Database (SMDB) is a key component of the overall end-to-end process involved in the scheduling, preparation, and configuration of the Deep Space Network (DSN) equipment needed to perform the various telecommunication services the DSN provides to its customers worldwide. SMDB makes efficient use of triggers, stored procedures, queuing functions, e-mail capabilities, data management, and Java integration features provided by the Oracle relational database management system. SMDB uses a third normal form schema design that allows for simple data maintenance procedures and thin layers of integration with client applications. The software provides an integrated event logging system with ability to publish events to a JMS messaging system for synchronous and asynchronous delivery to subscribed applications. It provides a structured classification of events and application-level messages stored in database tables that are accessible by monitoring applications for real-time monitoring or for troubleshooting and analysis over historical archives.

Zendejas, Silvino↗

A Data Science and Machine Learning Platform Supporting Large Particle Accelerator Control and Diagnostics Applications Final Report: SBIR Initial Phase II DE-SC0022583

The Machine Learning Data Platform (MLDP) is a product providing full-stack support for data science, Machine Learning, and Artificial Intelligence (ML/AI) applications at particle accelerator and large experimental physics facilities. It supports ML/AI applications from front-end, high-speed acquisition of heterogeneous, time-series data, through data archiving and management, to back-end analysis. The MLDP embodies a “data-science ready” platform for data analysis and ML/AI applications in diagnosis, modelling, control, and optimization of these facilities. It provides data scientists and applications a consistent, datacentric interface to archive data standardizing implementation and deployment of ML/AI algorithms to different operations configurations within the same facility, or between facilities. Being an open-source, public-domain project, the MLDP is intended for broadest possible impact by increasing accessibility and minimizing the required expertise for installation and operation. The MLDP can also be deployed at user facilities for experimental data collection, archiving, and analysis. It is capable of acquisition and archiving of heterogeneous data from experimental equipment (e.g., images, arrays, structures, etc.) along with system hardware configurations (e.g., scalars, tables), control system process variables, and any metadata required for provenance. Thus, the MLDP can manage experimental data through its entire lifecycle, from acquisition and archiving, through analysis and investigation, to release and final publication.

43 PARTICLE ACCELERATORS↗

Space Station Freedom - Technical and management challenges

The development of the Space Station is reviewed, focusing on the technical and managerial aspects of the program. The optimization of the Space Station configuration, utilization impacts on design, technical aspects of the distribution systems, and the problems of designing for a lifetime of 30 years or more are discussed. In addition, cost reduction studies, testing and verification, determining the assembly sequence, and operational communications and support systems are examined. Managerial aspects of the program include organization, program control, management tools and processes, and the integration of elements from the international partners.

Moser, Thomas L.↗

Bio-Medical Factors and External Hazards in Space Station Design

The design of space-station configurations is influenced by many factors. Probably the most demanding and critical are the biomedical and external hazards requirements imposed to provide the proper environment and supporting facilities for the crew and the adequate protective measures necessary to provide a configuration'in which the crew can live and work efficiently in relative comfort and safety. The major biomedical factors, such as physiology, psychology, nutrition, personal hygiene, waste management, and recreation, all impose their own peculiar requirements. The commonality and integration of these requirements demand the utmost ingenuity and inventiveness be exercised in order to achieve effective configuration compliance. The relationship of biomedical factors for the internal space-station environment will be explored with respect to internal atmospheric constituency, atmospheric pressure levels, oxygen positive pressure, temperature, humidity, CO2 concentration, and atmospheric contamination. The range of these various parameters and the recommended levels for design use will be analyzed. Requirements and criteria for specific problem areas such as zero and artificial gravity and crew private quarters will be reviewed and the impact on the design of representative solutions will be presented. In the areas of external hazards, the impact of factors such as meteoroids, radiation, vacuum, temperature extremes, and cycling on station design will be evaluated. Considerations with respect to operational effectiveness and crew safety will be discussed. The impact of such factors on spacecraft design to achieve acceptable launch and reentry g levels, crew rotation intervals, etc., will be reviewed.

Olling, E. H.↗

Bio-Medical Factors and External Hazards in Space Station Design

The design of space-station configurations is influenced by many factors, Probably the most demanding and critical are the biomedical and external hazards requirements imposed to provide the proper environment and supporting facilities for the crew and the adequate protective measures necessary to provide a configuration in which the crew can live and work efficiently in relative comfort and safety. The major biomedical factors, such as physiology, psychology, nutrition, personal hygiene, waste management, and recreation, all impose their own peculiar requirements. The commonality and integration of these requirements demand the utmost ingenuity and inventiveness be exercised in order to achieve effective configuration compliance. The relationship of biomedical factors for the internal space-station environment will be explored with respect to internal atmospheric constituency, atmospheric pressure levels, oxygen positive pressure, temperature, humidity, CO2 concentration, and atmospheric contamination. The range of these various parameters and the recommended levels for design use will be analyzed. Requirements and criteria for specific problem areas such as zero and artificial gravity and crew private quarters will be reviewed and the impact on the design of representative solutions will be presented. In the areas of external hazards, the impact of factors such as meteoroids, radiation, vacuum, temperature extremes, and cycling on station design will be evaluated. Considerations with respect to operational effectiveness and crew safety will be discussed. The impact of such factors on spacecraft design to achieve acceptable launch and reentry g levels, crew rotation intervals, etc., will be reviewed. Examples of configurations, subsystems, and internal a arrangement and installations to comply with such biomedical factor requirements will ber presented. The effects of solutions to certain biomedical factors on configuration weight, operational convenience, and program costs will be compared.

Olling, Edward H.↗

Model data for infrastructure-aware simulation of compound flooding at Alligator Bayou Watershed, southeast Texas

This dataset supports infrastructure-aware hydrologic modeling and flood scenario analysis for the Alligator Bayou Watershed, a highly managed urban watershed in Southeast Texas. It includes Jupyter notebooks for figure reproduction, model configuration files, simulation outputs, and derived products used to quantify the influence of engineered stormwater infrastructure on flood behavior across multiple spatial scales. The dataset was generated using the Watershed Workflow Python package and the Advanced Terrestrial Simulator (ATS), enabling integrated surface–subsurface hydrologic simulations on a channel-aligned mesh with explicit representations of pump stations, gate structures, detention basins, and impervious surfaces. Outputs include time series of gate and pump flows, stage observations, and water balance components, as well as spatially explicit fields of peak ponded depth and flood duration across multiple infrastructure scenarios spanning a single-location detention basin expansion, distributed drainage limitations, and compound coastal flooding. These data facilitate full reproducibility of the manuscript figures and support further research on urban flood dynamics and the role of stormwater infrastructure in shaping watershed-scale flood response.

EARTH SCIENCE > OCEANS > COASTAL PROCESSES↗

Bill Savings vs. Backup Power: Evaluating operational tradeoffs for home solar+storage systems [Slides]

Adoption of residential solar photovoltaic+energy storage systems (PVESS) is driven by both bill savings opportunities and customer demand for backup power. Prior work by this team (Gorman et al., 2022; Gorman et al., 2023) explored PVESS backup power capabilities during long-duration power interruptions (e.g., due to severe weather events), when customers are assumed to be able to anticipate the event and charge their batteries in advance. In many cases, however, power interruptions are unpredictable (and often relatively short); for those types of events, a customer will typically set its battery to maintain some minimum capacity in reserve in case of an interruption, which reduces the capacity available for managing utility bills. This study evaluates this operational tradeoff to help customers and installers configure backup reserve settings, and to inform decision-making more generally about the customer value of backup power services compared to utility bill savings. This study utilizes Berkeley Lab’s PRESTO tool to produce stochastic simulations of (predominantly short-duration) power interruption events, and builds on an earlier case-study demonstrating PVESS backup performance during short-duration interruptions (Baik et al., 2023).

14 SOLAR ENERGY↗

Biomedical and Human Factors Requirements for a Manned Earth Orbiting Station

The primary objective of this study is to determine which biomedical and human factors measurements must be made aboard a space station to assure adequate evaluation of the astronaut's health and performance during prolonged space flights. The study has employed, where possible, a medical and engineering systems analysis to define the pertinent life sciences and space station design parameters and their influence on a measurement program. The major areas requiring evaluation in meeting the study objectives include a definition of the space environment, man's response to the environment, selection of measurement and data management techniques, experimental program, space station design requirements, and a trade-off analysis with final recommendations. The space environment factors that are believed to have a significant effect on man were evaluated. This includes those factors characteristic of the space environment (e. g. weightlessness, radiation) as well as those created within the space station (e. g. toxic contaminants, capsule atmosphere). After establishing the general features of the environment, an appraisal was made of the anticipated response of the astronaut to each of these factors. For thoroughness, the major organ systems and functions of the body were delineated, and a determination was made of their anticipated response to each of the environmental categories. A judgment was then made on the medical significance or importance of each response, which enabled a determination of which physiological and psychological effects should be monitored. Concurrently, an extensive list of measurement techniques and methods of data management was evaluated for applicability to the space station program. The various space station configurations and design parameters were defined in terms of the biomedical and human factors requirements to provide the measurements program. Research design of experimental programs for various station configurations, mission durations, and crew sizes were prepared, and, finally, a trade-off analysis of the critical variables in the station planning was completed with recommendations to enhance the confidence in the measurement program.

EARTH ORBIT↗

Verification and validation of the NASA Terminal Configured Vehicle's /TCV/ Wind Analysis program using real-time digital simulation

NASA's Terminal Configured Vehicle (TCV) program has been established to develop and evaluate aircraft and flight management technology that will benefit conventional takeoff and landing operations in the terminal area. To this end, a process whereby estimates of longitudinal, lateral, and vertical winds are obtained using the present complement of sensors onboard the TCV aircraft (B737-100) is developed. The results of the simulation experiment to verify and validate the TCV B737 Data Merging and Wind Analysis program have shown that the wind analysis algorithms are correct and that very satisfactory estimates of all three wind components are obtained even in the presence of adverse atmospheric conditions and realistic measurement noise. In realistic worst case conditions, wind component errors of the order of + or - 2.0 knots longitudinally, + or - 0.5 knot laterally, and + or - 1.0 knot vertically are to be expected.

Dieudonne, J. E.↗

System control of an autonomous planetary mobile spacecraft

The goal is to suggest the scheduling and control functions necessary for accomplishing mission objectives of a fairly autonomous interplanetary mobile spacecraft, while maximizing reliability. Goals are to provide an extensible, reliable system conservative in its use of on-board resources, while getting full value from subsystem autonomy, and avoiding the lure of ground micromanagement. A functional layout consisting of four basic elements is proposed: GROUND and SYSTEM EXECUTIVE system functions and RESOURCE CONTROL and ACTIVITY MANAGER subsystem functions. The system executive includes six subfunctions: SYSTEM MANAGER, SYSTEM FAULT PROTECTION, PLANNER, SCHEDULE ADAPTER, EVENT MONITOR and RESOURCE MONITOR. The full configuration is needed for autonomous operation on Moon or Mars, whereas a reduced version without the planning, schedule adaption and event monitoring functions could be appropriate for lower-autonomy use on the Moon. An implementation concept is suggested which is conservative in use of system resources and consists of modules combined with a network communications fabric. A language concept termed a scheduling calculus for rapidly performing essential on-board schedule adaption functions is introduced.

Dias, William C.↗