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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 757 records · Page 42
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

Exceptional Thermal Properties of Polymer-Derived Ceramic Composites Reinforced with High Volume Fractions of Boron Nitride Nanotube at Elevated Temperature

We report for the first time the synthesis of boron nitride nanotube (BNNT) reinforced ceramic composites using the polymer derived ceramic (PDC) processing route. The nanocomposites had a BNNT loading of up to 35.4 vol.%. TGA results showed that the nanocomposites have good thermal stability up to 900 oC in air. BNNTs in the nanocomposites survived in an oxidizing environment up to 900 oC, revealing that the nanocomposites can be used for high temperature applications. Thermal conductivity of PDC reinforced with 35.4 vol.% BNNT was measured as 4.123 W/(m·K) at room temperature, which is a 2100 % increase compared to that of pristine PDC. The thermal conductivity value increases with the increase of BNNT content. A thermal conductivity percolation phenomenon appeared when the BNNT content increased to 36±5 vol.%. The results of this study showed that BNNTs could effectively improve the thermal conductivity of PDC materials. BNNT reinforced PDC could be used as thermal structural materials in a harsh environment at the temperature up to 900 deg C.

Yujun Jia↗
NASA NTRS2021

Water Heavily Fractionated as it Ascends on Mars as Revealed by ExoMars/NOMAD

Isotopic ratios, and in particular the water D/Hratio, are powerful tracers of the evolutionand transportof water on Mars. From measurements performed with ExoMars/NOMAD, we observe dramatic and rapid variability of the D/H along altitude on Mars and across the whole planet. The observations (from April 2018 to April 2019) sample a broad range of events on Mars, including aglobal dust storm, the evolution ofwater released from the southern polar cap during southern summer, the equinox phases, and a short but intense regional dust storm. In three instances, we observe water at very high altitudes (>80 km), the prime region where water is photo-dissociated andstarts its escape to space.Rayleigh distillation appears the be the driving force affecting the D/H in many cases, yet in someinstances,the exchange of water reservoirs with distinctive D/H could be responsible.

Geronimo L Villanueva↗
NASA NTRSDate not supplied

A Zero-order Hold Approach for Fractional-delay Interpolation in Auralization

During the signal processing chain of an auralization simulating the propagation of a sound from a moving source to a stationary receiver, it is often necessary to interpolate between the samples of the source signal in order to arrive at uniformly-spaced samples at the receiver. In some cases, this interpolation is done in the receiver time frame – where the “input” samples of the source have become irregularly spaced due to time dilation effects. Canonical band-limited interpolation methods (i.e., sinc and sinc-derived approaches) cannot be applied in this case as they rely on having a uniformly-spaced input. The use of geometric interpolation methods that can handle irregularly-spaced input may not be grounded in signal processing principles and may produce unwanted artifacts and noise. This presentation outlines the possibility of embedding an irregularly-spaced zero-order hold signal within a highly over-sampled uniformly-spaced signal, and then processing down to the desired sampling rate through successive decimations. Initial distortion and noise characteristics of the approach are shown for some basic propagation geometries. The possible benefits of using such an approach in an auralization scheme with a time-varying Doppler shift are discussed including: the prevention of aliasing, processing time advantages, and the possibility for asynchronous processing.

Auralization↗
NASA NTRSDate not supplied

Exploring Flooded Fraction Prediction through Machine Learning Models Focusing on Medical Infrastructure in the Southeast U.S. Coastal Areas

Rising sea levels due to climate change increasingly threaten medical infrastructure through flooding. This study develops machine learning models to predict flood exposure for 11,508 medical facilities in the southeastern coastal regions of the United States by integrating datasets including meteorological, hydrological, topographic, and geological data, the Natural Risk Index, and historical flood records from NASA, HIFLD, and FEMA. Six regression models, namely Linear Regression, Support Vector Regression, Random Forest, k-Nearest Neighbors, XGBoost, and Artificial Neural Networks, are trained using 16 explanatory variables identified through literature review and correlation analysis. Data preprocessing employs the SMOGN for class imbalance and Winsorization for outliers. Model performance is evaluated using MAE, MSE, and RMSE, with Random Forest and XGBoost models achieving the highest performance (MSE of 2.58e-5 and 3.69e-5, respectively). This multifactorial approach allows the models to capture complex flood-influencing relationships, enhancing adaptability and performance across geographic regions. Future work focuses on expanding across the U.S. and developing a near real-time flood monitoring system.

Jihoon Chung↗