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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO)

Inherent in interplanetary space travel are unprecedented challenges that could threaten mission success and negatively impact crew health and performance. Return to definitive care is essentially untenable and resources will be constrained with practically no re-supply capability. Access to ground-based medical expertise will be significantly delayed under nominal conditions with exacerbation during conjunction or prolonged dust storms. Taken together, these challenges necessitate the development of a progressively autonomous medical operational support system to assist the crew medical officer (CMO). While support from ground based medical experts will remain indispensable for pre-mission planning, the approach to management of acute/emergent medical contingencies will require a gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. To progressively enable EIMO, a series of meetings were convened with subject matter experts from within NASA, academia and industry to facilitate mapping of the path to support autonomous medical operations. Topics explored in these meetings included the scope of data (storage capacity, usage, transmission rate and bandwidth, computing capacity), CMO training, supply and resource management and task load balance. Recommendations from these meetings will inform the EIMO Concept of Operations and definition of the associated requirements culminating in updates to the NASA 3001 standards. The EIMO project team will work with stakeholders to conceptualize a clinical decision support system (CDSS) to assist the CMO in response to medical contingencies when terrestrial support is delayed or otherwise unavailable. The CDSS will be a system of systems that will utilize data from numerous input vectors. Successful deployment of the CDSS will facilitate medical decision making while decreasing the cognitive load leading to an improvement in task load balancing. Additional benefits of the envisioned CDSS include assistance with inventory management, locating resources, storage/retrieval of medical records, highlighting trends in recorded data, in addition to providing a consult for diagnosis and treatment. More advanced features might include passive monitoring to identify early warning signs of behavioral or medical anomalies to possibly pre-empt onset of conditions that would compromise crew health and performance.

Benjamin Easter↗
NASA NTRS2019

Radar Cross Section of Orbital Debris Objects

This discussion is concerned with the radar-data analysis and usage involved in the building of model orbital debris (OD) populations in the near-Earth environment, focusing on radar cross section (RCS). While varying with radar wavelength, physical dimension, material composition, overall shape and structure, the RCS of an irregular object is also strongly dependent on its spatial orientation. The historical records of observed RCSs for cataloged OD objects in the Space Surveillance Network are usually distributed over an RCS range, forming respective characteristic patterns. The National Aeronautics and Space Administration (NASA) Size Estimation Model provides an empirical probability-density function of RCS as a function of “effective diameter” (or “characteristic length”), which makes it feasible to predict possible RCS distributions for a given model OD population and to link data with model from a statistical perspective. The discussion also includes application of the widely used method of moments (MoM) and the Generalized Multi-particle Mie-solution (GMM) in the prediction of the RCS of arbitrarily shaped objects. Theoretical calculation results for an aluminum cube are compared with corresponding experimental measurements.

Xu, Yu-Lin↗
NASA NTRS2012

Extended Operation of Stirling Convertors at NASA Glenn Research Center

NASA Glenn Research Center (GRC) has been supporting development of free-piston Stirling conversion technology for spaceflight electrical power generation since 1999. GRC has also been supporting the development of the Advanced Stirling Radioisotope Generator (ASRG) since 2006. A key element of the ASRG project is providing life, reliability, and performance data for the Advanced Stirling Convertor (ASC). The Thermal Energy Conversion branch at GRC is conducting extended operation of several free-piston Stirling convertors. The goal of this effort is to generate long-term performance data (tens of thousands of hours) on multiple units to build a life and reliability database. Currently, GRC is operating 18 convertors. This hardware set includes Technology Demonstration Convertors (TDCs) from Infinia Corporation, of which one pair (TDCs #13 and #14) has accumulated over 60,000 hr (6.8 years) of operation. Also under test are various Sunpower, Inc. convertors that were fabricated during the ASC development activity, including ASC-0, ASC-E (including those in the ASRG engineering unit), and ASC-E2. The ASC-E2s also completed, or are in progress of completing workmanship vibration testing, performance mapping, and extended operation. Two ASC-E2 units will also be used for durability testing, during which components will be stressed to levels above nominal mission usage. Extended operation data analyses from these tests are covered in this paper.

Oriti, Salvatore, M.↗
NASA NTRS1996

Feasibility Study of a Rotorcraft Health and Usage Monitoring System ( HUMS): Usage and Structural Life Monitoring Evaluation

The objective of this study was to evaluate two techniques, Flight Condition Recognition (FCR) and Flight Load Synthesis (FLS), for usage monitoring and assess the potential benefits of extending the retirement intervals of life-limited components, thus reducing the operator's maintenance and replacement costs. Both techniques involve indirect determination of loads using measured flight parameters and subsequent fatigue analysis to calculate the life expended on the life-limited components. To assess the potential benefit of usage monitoring, the two usage techniques were compared to current methods of component retirement. In addition, comparisons were made with direct load measurements to assess the accuracy of the two techniques. The data that was used for the evaluation of the usage monitoring techniques was collected under an independent HUMS Flight trial program, using a commercially available HUMS and data recording system. The usage data collect from the HUMS trial aircraft was analyzed off-line using PC-based software that included the FCR and FLS techniques. In the future, if the technique prove feasible, usage monitoring would be incorporated into the onboard HUMS.

Dickson, B.↗
NASA NTRS1990

Systems engineering and integration: Cost estimation and benefits analysis

Space Transportation Avionics hardware and software cost has traditionally been estimated in Phase A and B using cost techniques which predict cost as a function of various cost predictive variables such as weight, lines of code, functions to be performed, quantities of test hardware, quantities of flight hardware, design and development heritage, complexity, etc. The output of such analyses has been life cycle costs, economic benefits and related data. The major objectives of Cost Estimation and Benefits analysis are twofold: (1) to play a role in the evaluation of potential new space transportation avionics technologies, and (2) to benefit from emerging technological innovations. Both aspects of cost estimation and technology are discussed here. The role of cost analysis in the evaluation of potential technologies should be one of offering additional quantitative and qualitative information to aid decision-making. The cost analyses process needs to be fully integrated into the design process in such a way that cost trades, optimizations and sensitivities are understood. Current hardware cost models tend to primarily use weights, functional specifications, quantities, design heritage and complexity as metrics to predict cost. Software models mostly use functionality, volume of code, heritage and complexity as cost descriptive variables. Basic research needs to be initiated to develop metrics more responsive to the trades which are required for future launch vehicle avionics systems. These would include cost estimating capabilities that are sensitive to technological innovations such as improved materials and fabrication processes, computer aided design and manufacturing, self checkout and many others. In addition to basic cost estimating improvements, the process must be sensitive to the fact that no cost estimate can be quoted without also quoting a confidence associated with the estimate. In order to achieve this, better cost risk evaluation techniques are needed as well as improved usage of risk data by decision-makers. More and better ways to display and communicate cost and cost risk to management are required.

Dean, ED↗
DOE OSTI2024

Collection And Analysis Of Telemetry For The Cyote Heuristic

CATCH CLI focuses on gathering telemetry data, storing it in the Neo4j database, querying for Mitre ATT&CK patterns, and creating STIX 2.1 reports. Key Components: Analysis Modules: Analyze data to detect attack patterns. GoSTOTS Collection Engines: Collect telemetry data. These tools can be used together or individually. Analysis modules rely on data from specific engines to identify attack patterns. Source Code Organization: Engines: CATCH/catch/cmd/collection Modules: CATCH/catch/cmd/analysis CGUI Overview CATCH Graphical User Interface (CGUI) offers a graphical shell to execute CATCH CLI, allowing easy editing of: Analysis Modules Database configurations Profiles (collection and device settings) Neo4j Overview Neo4j is a graph database using the Cypher query language, storing data in JSON. It seamlessly integrates with STIX 2.1 data for: Data Submission: CATCH Collection Engines Data Querying: Analysis Modules CATCH modifies STIX 2.1 data for Neo4j submission and reverts it back during querying. STIG Overview Structured Threat Intelligence Graph (STIG) is a tool for creating, editing, querying, analyzing, and visualizing threat intelligence using STIX 2.1 and storing data in Neo4j. Usage Tools can be run: Manually (CLI): Refer to CATCH documentation User Interface: Run ./cgui/CGUI or go run ./cgui/ Additional Information Logging System: Detailed in the config documentation Further Documentation: Available for CATCH and CGUI

Madsen, MichaelJ. [Idaho National Laboratory (INL)↗
NASA NTRS1975

SINDA/SINFLO computer routine, volume 1, revision A

The SINFLO package was developed to modify the SINDA preprocessor to accept and store the input data for fluid flow systems analysis and adding the FLOSOL user subroutine to perform the flow solution. This reduced and simplified the user input required for analysis of flow problems. A temperature calculation method, the flow-hybrid method which was developed in previous VSD thermal simulator routines, was incorporated for calculating fluid temperatures. The calculation method accuracy was improved by using fluid enthalpy rather than specific heat for the convective term of the fluid temperature equation. Subroutines and data input requirements are described along with user subroutines, flow data storage, and usage of the plot program.

Oren, J. A.↗
NASA NTRS1976

Use of steepest descent and various approximations for efficient computation of minimum noise aircraft landing trajectories

The following areas related to landing trajectory optimization research were discussed: (1) programming and modifying the steepest descent optimization procedure, (2) successfully iterating toward the optimum for a four-mile trajectory, (3) beginning optimization runs for a twenty-mile trajectory, and (4) adapt wind tunnel data for computer usage. Other related areas were discussed in detail in the two previous annual reports.

Cook, G.↗
NASA NTRS1976

A real-time control system with video feedback for sounding rocket payloads

NASA/Ames has developed a command system to control the attitude of a rocket payload from a ground-based site. The SPARCS command system allows a ground-based operator to adjust the attitude of a solar experiment in real time. The attitude relative to the desired target features on the sun is monitored visually from the ground through a video link using an on-board TV camera. The system has demonstrated rapid acquisition of solar features, resulting in a more optimal usage of the data gathering time of a sounding rocket payload, and has located solar features which could not be precisely located from preflight data.

Matsumoto, T.↗
NASA NTRS2000

Analysis of the Diurnal Cycle of Precipitation and its Relation to Cloud Radiative Forcing using TRMM Products

By incorporating the Tropical Rain Measuring Mission (TRMM) satellite orbital information into the geodesic version of the Colorado State University General Circulation Model (CSU GCM), we are able to fly a satellite in the GCM, and sample the simulated atmosphere in the same way as the TRMM sensors sample the real atmosphere. The TRMM sampling statistics of precipitation and radiative fluxes at annual, intraseasonal, monthly-mean and composited diurnal time scales are evaluated by comparing the satellite-sampled against fully-sampled simulated atmospheres. This information provides a valuable guidance for efficient usage of TRMM data and future satellite mission planning.

Randall, David A.↗
NASA NTRS2008

Considerations for IC and Component Selection for Space Systems

This viewgraph presentation addresses the integrated cycling and component selection technologies for aerospace systems. The topics include: 1) Semiconductors: The Evolution of ICs - Availability and Technology; 2) IC Selection Requirements - three fields of thought, "The Good", "The Bad" and "The Ugly"; 3) Reliability and Radiation; 4) Radiation Perspective-Four methods of selecting ICs for space systems, Guaranteed hardness, historical ground-based radiation data, historical flight usage, and unknown assurance; 5) Understanding Risk, including risk trade space and ASICs and FPGA sample selection criteria.

LaBel, Kenneth A.↗
NASA NTRS2007

IC and Component Selection for Space Systems

This viewgraph presentation reviews the integrated circuit (IC) and selections of the IC components for space systems. Included in the discussion are a overview of semiconductors and the evolution of integrated circuit. It also reviews the three different viewpoints of the IC selection: technical, programmatic, and risk. From a radiation perspective there are four criteria for selecting ICs for space systems: guaranteed hardness, historical ground-based data, historical flight usage, and unknown assurance.

Label, Kenneth A.↗
NASA NTRS2009

Summary of the Synergistic Effects Obtained by the Present Study of Vortical Flows

This chapter identifies the benefits that occurred to the AVT-113 task group members and the resulting progress made to two separate vortical flow proposals for task group status being combined into one. Both of these proposals dealt with multiple-vortices, and though they shared different focuses, the general topic, as well as the specific features of this flow, made it of great interest to each sub-task or facet member. The joint meetings increased our overall understanding of vortical flow and the synergistic benefits are summarized in terms of experimental and computational data, virtual laboratory usage, dissemination of results, and career development.

Lamar, John E.↗
NASA NTRS2012

Planning Coverage Campaigns for Mission Design and Analysis: Clasp for the Proposed DESDynI Mission

Mission design and analysis present challenges in that almost all variables are in constant flux, yet the goal is to achieve an acceptable level of performance against a concept of operations, which might also be in flux. To increase responsiveness, our approach is to use automated planning tools that allow for the continual modification of spacecraft, ground system, staffing, and concept of operations while returning metrics that are important to mission evaluation, such as area covered, peak memory usage, and peak data throughput. We have applied this approach to DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice) mission design concept using the CLASP (Compressed Large-scale Activity Scheduler/Planner) planning system [7], but since this adaptation many techniques have changed under the hood for CLASP and the DESDynI mission concept has undergone drastic changes, including that it has been renamed the Earth Radar Mission. Over the past two years, we have run more than fifty simulations with the CLASP-DESDynI adaptation, simulating different mission scenarios with changing parameters including targets, swaths, instrument modes, and data and downlink rates. We describe the evolution of simulations through the DESDynI MCR (Mission Concept Review) and afterwards.

DESDynI (Deformation, Ecosystem Structure, and Dyn↗
NASA NTRS2013

The GLAS Standard Data Products Specification--Level 2, Version 9

The Geoscience Laser Altimeter System (GLAS) is the primary instrument for the ICESat (Ice, Cloud and Land Elevation Satellite) laser altimetry mission. ICESat was the benchmark Earth Observing System (EOS) mission for measuring ice sheet mass balance, cloud and aerosol heights, as well as land topography and vegetation characteristics. From 2003 to 2009, the ICESat mission provided multi-year elevation data needed to determine ice sheet mass balance as well as cloud property information, especially for stratospheric clouds common over polar areas. It also provided topography and vegetation data around the globe, in addition to the polar-specific coverage over the Greenland and Antarctic ice sheets.This document defines the Level-2 GLAS standard data products. This document addresses the data flow, interfaces, record and data formats associated with the GLAS Level 2 standard data products. The term standard data products refers to those EOS instrument data that are routinely generated for public distribution. The National Snow and Ice Data Center (NSDIC) distribute these products. Each data product has a unique Product Identification code assigned by the Senior Project Scientist. The Level 2 Standard Data Products specifically include those derived geophysical data values (i.e., ice sheet elevation, cloud height, vegetation height, etc.). Additionally, the appropriate correction elements used to transform the Level 1A and Level 1B Data Products into Level 2 Data Products are included. The data are packaged with time tags, precision orbit location coordinates, and data quality and usage flags.

Lee, Jeffrey E.↗