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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

The NASA/GISS Mars general circulation model: Preliminary experiments

The NASA/GISS Mars General Circulation Model (GCM) is an adapted version of the GISS Global Climate/Middle Atmosphere Model, specifically developed for the diagnostic validation and objective analysis of measured atmospheric temperatures from the Mars Observer Pressure Modulator Infrared Radiometer (PMIRR) experiment. The GISS Mars GCM has 23 vertical layers extending from the surface to approximately 80 km altitude, representing a vertical resolution of about 0.3 scale heights. The primitive (vertically hydrostatic) equations are solved in finite difference form on the Krakawa B grid, with a horizontal resolution of 8 deg x 10 deg (latitude-longitude). The model includes a diurnal solar cycle, heat transport within a two-layer ground, and a high-order 'slopes-scheme' for the advection of heat in the upper atmosphere. The radiative transfer scheme is based on the correlated k distribution method for the treatment of nongray gaseous absorption thermal emission, and multiple scattering, including options for suspended dust. A special feature of the model of particular importance for Mars is a parameterization of gravity-wave-induced drag incorporating orographic forcing, wind shear, convection, and radiative damping. The implementation of the GISS Mars model includes global maps of topography, roughness, and albedo.

Allison, Michael↗
NASA NTRS1999

A Solar Radiation Parameterization for Atmospheric Studies

The solar radiation parameterization (CLIRAD-SW) developed at the Goddard Climate and Radiation Branch for application to atmospheric models are described. It includes the absorption by water vapor, O3, O2, CO2, clouds, and aerosols and the scattering by clouds, aerosols, and gases. Depending upon the nature of absorption, different approaches are applied to different absorbers. In the ultraviolet and visible regions, the spectrum is divided into 8 bands, and single O3 absorption coefficient and Rayleigh scattering coefficient are used for each band. In the infrared, the spectrum is divided into 3 bands, and the k-distribution method is applied for water vapor absorption. The flux reduction due to O2 is derived from a simple function, while the flux reduction due to CO2 is derived from precomputed tables. Cloud single-scattering properties are parameterized, separately for liquid drops and ice, as functions of water amount and effective particle size. A maximum-random approximation is adopted for the overlapping of clouds at different heights. Fluxes are computed using the Delta-Eddington approximation.

Chou, Ming-Dah↗
NASA NTRS2001

A Thermal Infrared Radiation Parameterization for Atmospheric Studies

This technical memorandum documents the longwave radiation parameterization developed at the Climate and Radiation Branch, NASA Goddard Space Flight Center, for a wide variety of weather and climate applications. Based on the 1996-version of the Air Force Geophysical Laboratory HITRAN data, the parameterization includes the absorption due to major gaseous absorption (water vapor, CO2, O3) and most of the minor trace gases (N2O, CH4, CFCs), as well as clouds and aerosols. The thermal infrared spectrum is divided into nine bands. To achieve a high degree of accuracy and speed, various approaches of computing the transmission function are applied to different spectral bands and gases. The gaseous transmission function is computed either using the k-distribution method or the table look-up method. To include the effect of scattering due to clouds and aerosols, the optical thickness is scaled by the single-scattering albedo and asymmetry factor. The parameterization can accurately compute fluxes to within 1% of the high spectral-resolution line-by-line calculations. The cooling rate can be accurately computed in the region extending from the surface to the 0.01-hPa level.

Chou, Ming-Dah↗
NASA NTRS2005

Next Generation Millimeter-Wave Radar for Safe Planetary Landing

Safe, precise landing on planetary bodies requires knowledge of altitude and velocity, and may require active detection and avoidance of hazardous terrain. Radar offers a superior solution to both problems due to its ability to operate at any time of day, through dust and engine plumes, and ability to detect velocity coherently. While previous efforts have focused on providing near term solutions to the safe landing problem, we are designing radar velocimeters and radar imagers for missions beyond the next decade. In this paper we identify the fundamental issues within each approach, at arrive at strawman sensor designs at a center frequency at or around 160 GHz (Gband). We find that a G-band radar velocimeter design is capable of sub-10 cm/s accuracy, and a G-band imager is capable of sub-0.5 degree resolution over a 28 degree field of view. From those designs, we arrive at the key technology requirements for the development of power and low noise amplifiers, signal distribution methods, and antenna arrays that enable the construction of these next generation sensors.

radar velocimetry↗
NASA NTRS2008

Intelligent Engine Systems: Thermal Management and Advanced Cooling

The objective is to provide turbine-cooling technologies to meet Propulsion 21 goals related to engine fuel burn, emissions, safety, and reliability. Specifically, the GE Aviation (GEA) Advanced Turbine Cooling and Thermal Management program seeks to develop advanced cooling and flow distribution methods for HP turbines, while achieving a substantial reduction in total cooling flow and assuring acceptable turbine component safety and reliability. Enhanced cooling techniques, such as fluidic devices, controlled-vortex cooling, and directed impingement jets, offer the opportunity to incorporate both active and passive schemes. Coolant heat transfer enhancement also can be achieved from advanced designs that incorporate multi-disciplinary optimization of external film and internal cooling passage geometry.

Bergholz, Robert↗
NASA NTRS2008

Intelligent Engine Systems: Thermal Management and Advanced Cooling

The objective of the Advanced Turbine Cooling and Thermal Management program is to develop intelligent control and distribution methods for turbine cooling, while achieving a reduction in total cooling flow and assuring acceptable turbine component safety and reliability. The program also will develop embedded sensor technologies and cooling system models for real-time engine diagnostics and health management. Both active and passive control strategies will be investigated that include the capability of intelligent modulation of flow quantities, pressures, and temperatures both within the supply system and at the turbine component level. Thermal management system concepts were studied, with a goal of reducing HPT blade cooling air supply temperature. An assessment will be made of the use of this air by the active clearance control system as well. Turbine component cooling designs incorporating advanced, high-effectiveness cooling features, will be evaluated. Turbine cooling flow control concepts will be studied at the cooling system level and the component level. Specific cooling features or sub-elements of an advanced HPT blade cooling design will be downselected for core fabrication and casting demonstrations.

Bergholz, Robert↗
NASA NTRS2012

3D Drop Size Distribution Extrapolation Algorithm Using a Single Disdrometer

Determining the Z-R relationship (where Z is the radar reflectivity factor and R is rainfall rate) from disdrometer data has been and is a common goal of cloud physicists and radar meteorology researchers. The usefulness of this quantity has traditionally been limited since radar represents a volume measurement, while a disdrometer corresponds to a point measurement. To solve that problem, a 3D-DSD (drop-size distribution) method of determining an equivalent 3D Z-R was developed at the University of Central Florida and tested at the Kennedy Space Center, FL. Unfortunately, that method required a minimum of three disdrometers clustered together within a microscale network (.1-km separation). Since most commercial disdrometers used by the radar meteorology/cloud physics community are high-cost instruments, three disdrometers located within a microscale area is generally not a practical strategy due to the limitations of these kinds of research budgets. A relatively simple modification to the 3D-DSD algorithm provides an estimate of the 3D-DSD and therefore, a 3D Z-R measurement using a single disdrometer. The basis of the horizontal extrapolation is mass conservation of a drop size increment, employing the mass conservation equation. For vertical extrapolation, convolution of a drop size increment using raindrop terminal velocity is used. Together, these two independent extrapolation techniques provide a complete 3DDSD estimate in a volume around and above a single disdrometer. The estimation error is lowest along a vertical plane intersecting the disdrometer position in the direction of wind advection. This work demonstrates that multiple sensors are not required for successful implementation of the 3D interpolation/extrapolation algorithm. This is a great benefit since it is seldom that multiple sensors in the required spatial arrangement are available for this type of analysis. The original software (developed at the University of Central Florida, 1998.- 2000) has also been modified to read standardized disdrometer data format (Joss-Waldvogel format). Other modifications to the software involve accounting for vertical ambient wind motion, as well as evaporation of the raindrop during its flight time.

Lane, John↗
NASA NTRS2011

Coarrars for Parallel Processing

The design of the Coarray feature of Fortran 2008 was guided by answering the question "What is the smallest change required to convert Fortran to a robust and efficient parallel language." Two fundamental issues that any parallel programming model must address are work distribution and data distribution. In order to coordinate work distribution and data distribution, methods for communication and synchronization must be provided. Although originally designed for Fortran, the Coarray paradigm has stimulated development in other languages. X10, Chapel, UPC, Titanium, and class libraries being developed for C++ have the same conceptual framework.

Fortran↗
NASA NTRS2017

Medical Data Architecture Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current International Space Station (ISS) medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable an increasingly autonomous crew than the current ISS paradigm. The MDA will develop capabilities that support automated data collection, and the necessary functionality and challenges in executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. To attain this goal, the first year of the MDA project focused on reducing technical risk, developing documentation and instituting iterative development processes that established the basis for the first version of MDA software (or Test Bed 1). Test Bed 1 is based on a nominal operations scenario authored by the ExMC Element Scientist. This narrative was decomposed into a Concept of Operations that formed the basis for Test Bed 1 requirements. These requirements were successfully vetted through the MDA Test Bed 1 System Requirements Review, which permitted the MDA project to begin software code development and component integration. This paper highlights the MDA objectives, development processes, and accomplishments, and identifies the fiscal year 2017 milestones and deliverables in the upcoming year.

Data management↗
NASA NTRS2018

Medical Data Architecture Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm. The medical system requirements are being developed in parallel with the exploration mission architecture and vehicle design. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products supported by current prototype development will directly inform exploration medical system requirements.

medical data architecture↗
NASA NTRS2018

Medical Data Architecture (MDA) Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm. The medical system requirements are being developed in parallel with the exploration mission architecture and vehicle design. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products supported by current prototype development will directly inform exploration medical system requirements.

medical data architecture↗
NASA NTRS2017

Medical Data Architecture Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current International Space Station (ISS) medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable an increasingly autonomous crew than the current ISS paradigm. The MDA will develop capabilities that support automated data collection, and the necessary functionality and challenges in executing a self-contained medical system that approaches crew health care delivery without assistance from ground support.To attain this goal, the first year of the MDA project focused on reducing technical risk, developing documentation and instituting iterative development processes that established the basis for the first version of MDA software (or Test Bed 1). Test Bed 1 is based on a nominal operations scenario authored by the ExMC Element Scientist. This narrative was decomposed into a Concept of Operations that formed the basis for Test Bed 1 requirements. These requirements were successfully vetted through the MDA Test Bed 1 System Requirements Review, which permitted the MDA project to begin software code development and component integration. This paper highlights the MDA objectives, development processes, and accomplishments, and identifies the fiscal year 2017 milestones and deliverables in the upcoming year.

Exploration medical system↗
NASA NTRS2019

Medical Data Architecture (MDA) Project Status

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm. The medical system requirements are being developed in parallel with the exploration mission architecture and vehicle design. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products supported by current prototype development will directly inform exploration medical system requirements.In fiscal year 2018, the MDA project developed Test Bed 2, the second iteration in a series of prototypes with functionality focused on data security through role-based access control and encryption, integration with One Portal exercise software and ingestion of an ultrasound Digital Imaging and Communications in Medicine (DICOM) file and image display. Test Bed 2 advances the medical data system architecture framework by providing these functionalities in a scalable system that maintained a layered, modular design. The architecture framework uses a data services approach with role-based access to data in a customized medical record system suitable for space exploration. These functionalities were demonstrated as part of the Next Space Technologies for Exploration Partnerships (NextSTEP) ground test demonstrated at the NASA Johnson Space Center Integrated Power, Avionics and Software (iPAS) facility. Interfacing to a Core Flight Software (CFS) system, the MDA system, using Consultative Committee for Space Data Systems (CCSDS) protocol, transferred an exercise file from the simulated flight MDA system to a mirrored MDA system on the ground through the CFS system. The selection of data sources and demonstrations enabled the team to address stakeholder concerns throughout the development process. In the next iteration, the MDA team will work with stakeholders to identify additional relevant functionalities to further advance system data models, standards and principles that will inform the medical system requirements development.

medical data architecture↗
NASA NTRS2020

Medical Data Architecture Platform and Recommended Requirements for a Medical Data System for Exploration Missions

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically- relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm of medical data management on the International Space Station. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products derived from the third MDA prototype development will directly inform exploration medical system requirements for Level of Care IV in Gateway missions. In fiscal year 2019, the MDA project developed Test Bed 3, the third iteration in a series of prototypes, that featured integrations with cognition tool data, ultrasound image analytics and core Flight Software (cFS). Maintaining a layered architecture design, the framework implemented a plug-in, modular approach in the integration of these external data sources. An early version of MDA Test Bed 3 software was deployed and operated in a simulated analog environment that was part of the Next Space Technologies for Exploration Partnerships (NextSTEP) Gateway tests of multiple habitat prototypes. In addition, the MDA team participated in the Gateway Test and Verification Demonstration, where the MDA cFS applications was integrated with Gateway-in-a-Box software to send and receive medically relevant data over a simulated vehicle network. This software demonstration was given to ExMC and Gateway Program stakeholders at the NASA Johnson Space Center Integrated Power, Avionics and Software (iPAS) facility. Also, the integrated prototypes served as a vehicle to provide Level 5 requirements for the Crew Health and Performance Habitat Data System for Gateway Missions (Medical Level of Care IV). In the upcoming fiscal year, the MDA project will continue to provide systems engineering and vertical prototypes to refine requirements for medical Level of Care IV and inform requirements for Level of Care V.

Krihak, M.↗
NASA NTRS2020

Medical Data Architecture Platform and Recommended Requirements for A Medical Data System for Exploration Missions

Minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically relevant information to support medical operations during exploration missions. This gap identifies that the current in-flight medical data management includes a combination of data collection and distribution methods that are minimally integrated with on-board medical devices and systems. Furthermore, there are a variety of data sources and methods of data collection. For an exploration mission, the seamless management of such data will enable a more medically autonomous crew than the current paradigm of medical data management on the International Space Station. ExMC has recognized that in order to make informed decisions about a medical data architecture framework, current methods for medical data management must not only be understood, but an architecture must also be identified that provides the crew with actionable insight to medical conditions. This medical data architecture will provide the necessary functionality to address the challenges of executing a self-contained medical system that approaches crew health care delivery without assistance from ground support. Hence, the products derived from the third MDA prototype development will directly inform exploration medical system requirements for Level of Care IV in Gateway missions.In fiscal year 2019, the MDA project developed Test Bed 3, the third iteration in a series of prototypes, that featured integrations with cognition tool data, ultrasound image analytics and core Flight Software (cFS). Maintaining a layered architecture design, the framework implemented a plug-in, modular approach in the integration of these external data sources. An early version of MDA Test Bed 3 software was deployed and operated in a simulated analog environment that was part of the Next Space Technologies for Exploration Partnerships (NextSTEP) Gateway tests of multiple habitat prototypes. In addition, the MDA team participated in the Gateway Test and Verification Demonstration, where the MDA cFS applications was integrated with Gateway-in-a-Box software to send and receive medically relevant data over a simulated vehicle network. This software demonstration was given to ExMC and Gateway Program stakeholders at the NASA Johnson Space Center Integrated Power, Avionics and Software (iPAS) facility. Also, the integrated prototypes served as a vehicle to provide Level 5 requirements for the Crew Health and Performance Habitat Data System for Gateway Missions (Medical Level of Care IV). In the upcoming fiscal year, the MDA project will continue to provide systems engineering and vertical prototypes to refine requirements for medical Level of Care IV and inform requirements for Level of Care V.

Krihak, M.↗
NASA NTRS2015

System and Method for Monitoring Distributed Asset Data

A computer-based monitoring system and monitoring method implemented in computer software for detecting, estimating, and reporting the condition states, their changes, and anomalies for many assets. The assets are of same type, are operated over a period of time, and outfitted with data collection systems. The proposed monitoring method accounts for variability of working conditions for each asset by using regression model that characterizes asset performance. The assets are of the same type but not identical. The proposed monitoring method accounts for asset-to-asset variability; it also accounts for drifts and trends in the asset condition and data. The proposed monitoring system can perform distributed processing of massive amounts of historical data without discarding any useful information where moving all the asset data into one central computing system might be infeasible. The overall processing is includes distributed preprocessing data records from each asset to produce compressed data.

Gorinevsky, Dimitry↗
NASA NTRS1940

A simple approximation method for obtaining the spanwise lift distribution

The approximation method described makes possible lift-distribution computations in a few minutes. Comparison with an exact method shows satisfactory agreement. The method is of greater applicability than the exact method and includes also the important case of the wing with end plates.

Schrenk, O↗