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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Biogeochemical Protocols and Diagnostics for the CMIP6 Ocean Model Intercomparison Project (OMIP)

The Ocean Model Intercomparison Project (OMIP) focuses on the physics and biogeochemistry of the ocean component of Earth system models participating in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). OMIP aims to provide standard protocols and diagnostics for ocean models, while offering a forum to promote their common assessment and improvement. It also offers to compare solutions of the same ocean models when forced with reanalysis data (OMIP simulations) vs. when integrated within fully coupled Earth system models (CMIP6). Here we detail simulation protocols and diagnostics for OMIP's biogeochemical and inert chemical tracers. These passive-tracer simulations will be coupled to ocean circulation models, initialized with observational data or output from a model spin-up, and forced by repeating the 1948-2009 surface fluxes of heat, fresh water, and momentum. These so-called OMIP-BGC simulations include three inert chemical tracers (CFC-11, CFC-12, SF [subscript] 6) and biogeochemical tracers (e.g., dissolved inorganic carbon, carbon isotopes, alkalinity, nutrients, and oxygen). Modelers will use their preferred prognostic BGC model but should follow common guidelines for gas exchange and carbonate chemistry. Simulations include both natural and total carbon tracers. The required forced simulation (omip1) will be initialized with gridded observational climatologies. An optional forced simulation (omip1-spunup) will be initialized instead with BGC fields from a long model spin-up, preferably for 2000 years or more, and forced by repeating the same 62-year meteorological forcing. That optional run will also include abiotic tracers of total dissolved inorganic carbon and radiocarbon, CTabio and 14CTabio, to assess deep-ocean ventilation and distinguish the role of physics vs. biology. These simulations will be forced by observed atmospheric histories of the three inert gases and CO2 as well as carbon isotope ratios of CO2. OMIP-BGC simulation protocols are founded on those from previous phases of the Ocean Carbon-Cycle Model Intercomparison Project. They have been merged and updated to reflect improvements concerning gas exchange, carbonate chemistry, and new data for initial conditions and atmospheric gas histories. Code is provided to facilitate their implementation.

Carbon↗
NASA NTRS2015

Parallel Study of HEND, RAD, and DAN Instrument Response to Martian Radiation and Surface Conditions

Nuclear detection methods are being used to understand the radiation environment at Mars. JPL (Jet Propulsion Laboratory) assets on Mars include: Orbiter -2001 Mars Odyssey [High Energy Neutron Detector (HEND)]; Mars Science Laboratory Rover -Curiosity [(Radiation Assessment Detector (RAD); Dynamic Albedo Neutron (DAN))]. Spacecraft have instruments able to detect ionizing and non-ionizing radiation. Instrument response on orbit and on the surface of Mars to space weather and local conditions [is discussed] - Data available at NASA-PDS (Planetary Data System).

Mars↗
NASA NTRS2018

High Speed Bearing Wear Rate Measurements for Spacecraft Active Thermal Control Fluid Pumps with a Novel Pin on Disk Apparatus

A novel pin on disc tribometer was designed and constructed to generate a high-speed, wear coefficient database for hydrodynamic bearings that are typically used in canned motors found in the active thermal control circuits of robotic and inhabited spacecraft. The primary motivation for this work was the premature failure of the active external thermal control pump on the International Space Station in 2010. During the failure investigation of this incident, the root cause was postulated to be high speed wear of the bearings. Although a detailed forensic analysis gave credibility to this theory, the lack of wear coefficient data at relevant conditions prevented validation of this finding. The database generated from the new Extreme Environment Tribometer (EET) enabled a closure calculation within 5% of the observed wear from inspections of the failed hardware. Testing in anhydrous ammonia and surrogate fluid was performed to provide a means for simplified testing in the future and to populate a preliminary database for the design of future active thermal control systems on spacecraft. The EET and test techniques developed for the measurement of high-speed wear coefficients are available to future system designers.

thermal control↗
NASA NTRS2014

Afterbody Heat Flux Measurements in the NASA Ames HFFAF Ballistic Range

In order to measure afterbody heat fluxes over a model in the ballistic range, the required modifications to a proven technique for measuring forebody heat fluxes are described. This involves the use of an extended helium gas plume to remove the glowing wake and the use of special high conductivity, high temperature capable graphite-filled plastic for the afterbody. The models and test conditions are described. Data in the form of plots of the surface temperature of the models are presented. Finally, experimental and computational fluid dynamic (CFD) heat flux data for forebody and afterbody heat fluxes are presented and compared. Data are presented for a 45 degree sphere-cone (with a projecting rear stud) at 2.70 km/s and for a sphere at 4.76 km/s. Both models were launched into 76 Torr of CO2 gas. The experimental forebody heat fluxes were within 1.5% of the CFD values. The experimental afterbody heat fluxes were within 1% of the CFD values for the sphere, but only 51% of the CFD values for the sphere-cone.

Bogdanoff, David W.↗
NASA NTRS2019

Design and Acoustic Efficacy of a Broadband Liner for the Inlet of the DGEN Aero-Propulsion Research Turbofan

Advanced fan designs (including higher bypass ratios) and shorter engine nacelles have highlighted a need for increased fan noise reduction over a broader frequency range. With these observations in mind, an acoustic liner optimization process has been developed to achieve improved broadband liner designs. A series of advanced liner design studies at increasing technology readiness levels have been conducted as the overall optimization methodology has been enhanced. As part of the analysis, the overall design and evaluation capability was extended such that external observer locations may be included in the optimization process. This enhancement will provide a much wider design space in designing advanced broadband liners. In this work, further validation was pursued through the fabrication and testing of two liner designs for the NASA Glenn DGEN Aero-propulsion Research Turbofan (DART). The DART was used to document the efficacy of acoustic liners installed in the inlet of the DGEN380. An advanced Multi-Degree of Freedom Liner was designed, fabricated, and tested, along with a traditional Single-Degree-of-Freedom Liner, and those results compared to a hardwall baseline inlet. Farfield acoustic data were acquired from and external array, evaluated and reported herein terms of overall, broadband, and tonal components of the insertion loss. The predicted attenuation results generally matched expected trends of the measured data for the conditions considered.

Liners↗
NASA NTRS2019

Mars Phoenix EDL Trajectory and Atmosphere Reconstruction Using NewSTEP

This document describes the trajectory and atmosphere reconstruction of the Mars Phoenix Entry, Descent, and Landing using the New Statistical Trajectory Estimation Program. The approach utilizes a Kalman filter to blend inertial measurement unit data with initial conditions and radar altimetry to obtain the inertial trajectory of the entry vehicle. The nominal aerodynamic database is then used in combination with the sensed accelerations to obtain estimates of the atmosphere-relative state. The reconstructed atmosphere pro le is then blended with pre-flight models to construct an estimate of the as-flown atmosphere.

Karlgaard, Christopher D.↗
NASA NTRS2020

Real Time Optical Receiver Project

The goals for the Real Time Optical Receiver project are to Infuse Consultative Committee for Space Data Systems High Photon Efficiency standard into missions, such as Artemis and to provide a real time ground receiver solution (fiber device, detector, real time FPGA-based receiver) that is: (1) Scalable: data rate, atmosphere conditions, telescope aperture, etc., and (2) Uses COTS components when available and work with companies to move custom parts to COTS.

Katz, Evan J.↗
NASA NTRSDate not supplied

Vanadium Valence in MgAl2O4 Spinels at Reducing Conditions (IW to IW-5)

Vanadium is commonly a major or minor element in spinel structured oxides from a wide range of planetary materials [1-3]. Vanadium is stable in multiple valence states of 5+, 4+, 3+, and 2+ in natural systems, and spinel-structured oxides are known to host vanadium in 4+, 3+, and 2+ [4-6]. However, the understanding of 2+ stability at lower fO2 conditions has been hindered by lack of experiments at or below IW-1 conditions [5]. Insufficient experimental data is available due to the difficulty in controlling fO2 at low conditions in general, and the lack of appropriate standards for comparison to natural materials. Our progress on controlling fO2 in high pressure experimental samples has allowed us to create reducing conditions that are appropriate to studying V valence in spinels at fO2 relevant to natural reducing systems [7]. Here we extend this approach to study V in reduced conditions, at high temperatures. After at-tempting shorter durations and lower temperatures, we demonstrated the need for equilibration times > 6 hours at 1600 °C in order to approach equilibrium. Only then can we produce highly equilibrated samples that provide new insights into V valence at low fO2.

oxygen fugacity↗
NASA NTRS2021

Mars Exploration Rovers EDL Trajectory and Atmosphere Reconstruction using NewSTEP

This document describes the trajectory and atmosphere reconstruction of the Mars Exploration Rovers (Spirit and Opportunity) Entry, Descent, and Landing using the New Statistical Trajectory Estimation Program. The approach utilizes a Kalman filter to blend inertial measurement unit data with initial conditions and radar altimetry to obtain the inertial trajectory of the entry vehicle. The nominal aerodynamic database is then used in combination with the sensed accelerations to obtain estimates of the atmosphere-relative state. The reconstructed atmosphere profile is then blended with pre-flight models to construct an estimate of the as-flown atmosphere.

EDL↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗
NASA NTRSDate not supplied

1d-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D CNN architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-Segmentation Tasks

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization are described as well as the results of application to three separate arc jet data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation.

Machine learning↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗
NASA NTRSDate not supplied

1D-Convolutional Neural Network Architecture for Generalized Time-series Segmentation

Time segmentation of experimental data is a common and often difficult task. Consequently, it is of interest to automate this type of segmentation to reduce manual inputs, which are labor intensive and less consistent. However, simple thresholding algorithms are often insufficiently robust due either to noise or inconsistent data. This paper proposes a simple 1D convolutional neural net (CNN) architecture as a generalized solution for typical time segmentation tasks. The layer architecture, training methods, and methods for simple customization will be described as well as the results of application to three separate data streams: facility condition segmentation, video highlight segmentation, and calorimeter time-series segmentation. In all three test cases the 1D-CNN performs better than tailored integral/derivative/thresholding algorithms across a range of signal-to-noise levels.

CNN↗
NASA NTRS2023

Multi-Campaign Ship and Aircraft Observations of Marine Cloud Condensation Nuclei and Droplet Concentrations

In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns: ACTIVATE; NAAMES; CAMP2EX; ORACLES; SOCRATES; MARCUS; and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, represent a range of clean to polluted conditions in various climate regimes. With the extensive range of environmental conditions sampled, this data collection is ideal for testing satellite remote detection methods of CDNC and CCN in marine environments. Remote measurement methods are vital to expanding the available data in these difficult-to-reach regions of the Earth and improving our understanding of aerosolcloud interactions. The data collection includes particle composition and continental tracers to identify potential contributing CCN sources. Several of these campaigns include High Spectral Resolution Lidar (HSRL) and polarimetric imaging measurements and retrievals that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.

Atmospheric chemistry↗
NASA NTRS2024

Comparison of OVERFLOW Computational and Experimental Results of the CobraMRV Mars Entry Vehicle Concept during Supersonic Retropropulsion

The CFD solver OVERFLOW was used to simulate the CobraMRV undergoing supersonic retropropulsion (SRP) in the Langley Unitary Plan Wind Tunnel as part of a CFD and wind-tunnel integration effort. The aerodynamics of the CobraMRV are summarized and comparisons to experimental data at flight conditions of interest are presented. The sensitivity of vehicle loads and flow field unsteadiness to CFD parameters at a subset of operating conditions are investigated.

CFD↗
NASA NTRSDate not supplied

Reach Performance Using Touchscreens Under G and Vibration Conditions

This presentation summarizes prior results from human-in-the-loop experiments that measured the accuracy and precision of human reaches to a touchscreen under a range of altered gravitational and vibrational conditions. From the data, this presentation proposes a method for estimating the worst-case reach accuracy and precision during lunar landings with an eye towards supporting engineering design decisions about human control interfaces and crew operations for lunar missions.

human performance↗
NASA NTRSDate not supplied

GT2024-128885: Flow Reconstruction in a Transonic Turbine Cascade using Physics-Informed Neural Networks (PINNs)

This presentation investigates the application of Physics-Informed Neural Networks (PINNs) for the analysis of turbine blades in a transonic cascade. PINNs are a machine learning method trained on losses calculated from reconstructed governing equations, assigned boundary/initial conditions, and measured data. We reconstruct the 2-D flow field in a transonic turbine cascade in two ways: the traditional forward approach (without training/experimental data) and by training the PINN using experimental data. We then compare the PINN solutions to measured data. This is repeated for three different turbine blades with distinct loading characteristics. The experimental data used for training is the static pressure measurements along the suction and pressure sides of each blade. The PINN is trained utilizing all available data, half the available data, data from only the leading edge region, and data from only the trailing edge region. It's shown that the PINN can reconstruct the flow field in all cases with acceptable errors. Cases where the PINN is trained on all the data, and even half the data, resulted in the lowest errors. The exit Mach number is inferred for each case and compared to the experimentally calculated value.

Machine Learning↗