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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Organic Matter in Itokawa Particles

The first Hayabusa mission returned samples from the near-Earth S-type asteroid 25143 Itokawa to Earth in 2010[1]. Although Itokawa has a lithology related to ordinary chondrites(OCs) that typically have low organic contents, several Itokawa particles were found to contain organic matter (OM)[2-5]. However, there was not an explicit conclusion to the origin of the observed OMin these early studies. We have extended our search for OM into other Itokawa grains. Here, we report extraterrestrial OM (macromolecular carbon and amino acids) observed in six Itokawa particles(including a category1 particle: RA-QD02-0162[#62; also nicknamed “Amazon”],and five category3 carbon-rich particles:RA-QD02-0012[#12], RA-QD02-0078[#78], RB-CV-0029[#29], RB-CV-0080 [#80] and RB-QD04-0052[#52]).All allocated Itokawa samples were initially analysed by spot and point-by-point mapping Raman spectroscopic analysis at the Open University, UK. Amazon was then transferred and mounted into indium on an aluminium stub, which was studied with a NanoSIMS 50L ion microprobe for its H,C,N isotopic compositions. The rest of the samples were mounted in sterile gold foils, and the amino acid contents of their acid hydrolysed hot water extracts were obtained with a liquid chromatography with tandem fluorescence and accurate mass detection at NASA Goddard Space Flight Center, USA. Based on the observation of the Raman parameters(e.g. the peak locations and widths of the defect (D) and graphite (G) bands)[6], a significant variety of carbonaceous materials has been observed in Amazon. The carbonaceous materials include primitive and unaltered OM that shares similarity with the IOM in primitive (CI,CM,CR) carbonaceous chondrites, as well as OM that has been heavily graphitised. The organic structure of the heated material is best represented by nanocrystalline graphite, comparable to that observed for metamorphosed meteorites (e.g., L3–6 Inman, Tieschitz and New Concord,CV3 Allende, and EH4 Indarch),suggesting peak metamorphic temperatures (PMT) of >~600°C. The thermal history recorded in the graphitic OM agrees with PMT estimates for returned Itokawa regolith grains (600–800°C)[7].We have obtained the H,C,N isotopic compositions for the primitive OM in Amazon, which exhibits unambiguously extraterrestrial isotopic signatures (δD = +4868±2288‰; δ13C = −24±5‰; δ15N = +344±20‰), contrasting to the typically negative isotopic values obtained for terrestrial organic matter[8]. The δD and δ13C values of the organic material in Amazon are comparable to OCs, however, the δ15N value is higher than that typically observed for OCs (δ15N = −47 to +36‰), and is similar to that of CRs (δ15N = +153 to +309‰)[9].Our data suggest a genetic link between the primitive organic material observed in Itokawa to CRs and IDPs for they share similar D, 13C and 15N enrichments[10].The high carbon contents of the five category 3 Itokawa particles suggest potentially higher OM abundances, hence we extracted and analysed amino acids in these samples. Although terrestrial contamination was observed primarily as L-protein amino acids, several terrestrially uncommon non-protein amino acids were also observed at low abundances, such as β-aminoisobutyric acid (β-AIB),β-amino-n-butyric acid (β-ABA), and β-alanine. Itokawa amino acid content observed here was dissimilar to thermally altered OCs, but preliminarily analogous to more aqueously altered CR2s.Continued evolution of Itokawa is evident by the in fall of primitive organic material derived from CRs/IDPs, accounting for a complex interplay between the remnant Itokawa silicates with exogenous organics. The results reported here are the first evidence of extraterrestrial OM in asteroid material from a sample-return mission, showcasing a working protocol for analysing samples returned by the Hayabusa2 and OSIRIS-REx missions

Q. H. S. Chan↗
NASA NTRSDate not supplied

An exact solution of an augmented Burgers equation and amplitude-dependent acoustic propagation speed

Nonlinear sound propagation in the atmosphere is usually modeled using an augmented Burgers equation accounting for a weak nonlinearity and atmospheric absorption. Because the absorption includes the molecular vibrational relaxation, such a Burgers equation is more complex than the regular Burgers equation that only accounts for the thermoviscous dissipation in the absorption. Although an exact solution of the regular Burgers equation has long been derived using the Cole- Hopf transform, an exact solution of the augmented Burgers equation has not been derived previously. Thus, this paper presents an exact solution of the augmented Burgers equation. This novel solution is shown to be equivalent to the solution using the Cole-Hopf transform when the absorption only involves thermoviscous dissipation. It can also be reduced to the known solution of an N-wave when the absorption is ignored. The augmented Burgers equation is an approximation valid for weak nonlinearity. However, this assumption may not be accurate for acoustic signals propagating from the lower atmosphere and which are subsequently refracted downward from the upper atmosphere (e.g., stratosphere and thermosphere) due to the decreasing air density with increasing altitude [Lonzaga, et al., Geophysical Journal International, 200(3), pp.1347-1361]. Consequently, the current paper also discusses the effects of a strong nonlinearity that lead to an amplitude-dependent increase in signal propagation speed. For an impulsive signal such as a sonic boom, these effects cause a dispersion of the signal similar to the observed dispersion of acoustic signals from supersonic Concorde as well as from large explosions.

Joel B Lonzaga↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Scale Resolving Jet Simulations to Reduce Airport Noise

- An emerging potential market has generated renewed interest in civil supersonic aircraft - Evidenced by the appearance of several commercial programs despite lack of standards for en route noise or landing and takeoff noise - Overland Flight Restrictions based on unacceptable sonic boom noise are viewed as the main barrier to this vision - National Research and Policy agencies play a central role in developing the data needed for the regulation change that is essential to enabling this new market - The vision of the Supersonics Community is a future where fast air travel is available for a broad spectrum of the traveling public - Future supersonic aircraft will not only be able to fly overland without creating an “unacceptable situation” but compared to Concorde and SST will be efficient, affordable, and environmentally responsible

ARMD↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRS2023

Detect-and-Avoid in Off-Nominal Situations for Supersonic Transport Aircraft in Upper Class E Airspace

This thesis researches potential failure conditions within supersonic and potential hypersonic aircraft. The most critical off-nominal situations have been identified to be Engine Failures, Rapid Decompression and High Altitude Radiation. Having in mind, current regulatory requirements as well as past operations of Concorde, potential locations for different speeds have been modeled to identify safe operational volumes. With an emphasis on the engine failure case as part of the thesis requirement, a potential first order approach to future DAA systems is described in order to add these models on top of current conflict resolution models.

supersonic↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

NASA has employed high-throughput molecular assays to identify sub-cellular changes impacting human physiology during spaceflight. Machine learning (ML) methods hold the promise to improve our ability to identify important signals within highly dimensional molecular data. However, the inherent limitation of study subject numbers within a spaceflight mission minimizes the utility of ML approaches. To overcome the sample power limitations, data from multiple spaceflight missions must be aggregated while appropriately addressing intra- and inter-study variabilities. Here we describe an approach to log transform, scale and normalize data from six heterogeneous, mouse liver derived transcriptomics datasets (ntotal=137) which enabled ML-methods to perform well (AUC ≥ 0.87) in classifying spaceflown vs ground control animals rather than mission-of-origin. Concordance was found between liver-specific biological processes identified from harmonized ML-based analysis and study-by-study classical omics analysis. This work demonstrates the feasibility of applying ML methods on integrated, heterogeneous datasets of small sample size.

Machine Learning↗
NASA NTRSDate not supplied

Nonlinear Acoustic Propagation in Low-Density Media

Nonlinear acoustic propagation in the atmosphere is usually modeled using an augmented Burgers equation accounting for atmospheric absorption and weak nonlinearity. However, the weak-nonlinearity assumption may not apply to acoustic signals propagating from the lower atmosphere that are subsequently refracted downward from the upper atmosphere (e.g., stratosphere and thermosphere) due to the decreasing air density with increasing altitude [Lonzaga, et al., Geophysical Journal International, 200(3), pp.1347-1361]. Consequently, this paper discusses the effects of a strong nonlinearity that lead to an amplitude-dependent increase in signal propagation speed. This propagation speed is obtained using a perturbation expansion where the leading-order term is simply the small-amplitude sound speed while the first-order term is the existing expression that gives rise to waveform steepening and stretching. Furthermore, the second-order term is proportional to the spectral density of the acoustic signal and is inversely proportional to the local density of the medium. Consequently, for an impulsive signal such as a sonic boom or an infrasound, the second-order effect causes a dispersion of the signal similar to the observed dispersion of acoustic signals from supersonic Concorde as well as from large explosions. This paper also discusses the extension of these results in general low-density propagation media.

sonic booms↗
NASA NTRS2023

Overview of Commercial High-Speed Market Studies and NASA’s Hypersonic Technology Project

Presenting a 40-minute Throughout the history of aviation, there has been a continual desire to develop aircraft that can go higher, faster, and farther. Within each of these broad goals, significant achievements have been made in aeronautics over the decades. Specialized military aircraft can reach the edges of the atmosphere and regularly fly at supersonic speeds, with the Concorde bringing transatlantic supersonic flight to the public. In addition, commercial subsonic aircraft regularly fly nonstop flights of over 7000 nautical miles with flight times lasting over 15 hours. Each of these achievements have been enabled by research and development on the technologies required to overcome these barriers. As major technical challenges for high-speed/hypersonic flight have been overcome, the next questions that arise relate to what precisely is a viable commercial high-speed/hypersonic vehicle and what is the market for it. In 2020, NASA funded two studies to provide an independent assessment of the economic viability of high-speed/hypersonic transportation and some high level guidance on the design requirements of such a vehicle. Results from these market studies will be presented as well as insight into how these results are being used to inform ongoing strategic planning efforts in this research area within NASA’s Hypersonic Technology project.

High-Speed Independent Market Studies↗
NASA NTRS2023

4.46 Ga Zircons Anchor Chronology of Lunar Magma Ocean

The crystallisation ages of lunar samples provide critical constraints on the minimum formation age of the Moon and its early evolution. Zircon crystals from Apollo 17 lunar impact melt breccia 72255 preserve ancient domains with a concordant average uranium-lead radiometric date of 4460 ± 31 Ma (Zhang et al., 2021), the oldest lunar zircon yet reported. To assess the possible mobility of radiogenic lead in zircon, which may lead to redistribution and clustering of Pb atoms that may cause a U-Pb age bias (Valley et al., 2014), we investigated a zircon grain from Zhang et al. (2021) by atom probe tomography (APT). The atomic spatial resolution analysis of individual mineral grains demonstrates the absence of nanoscale clustering of lead, which supports a 4.46 Ga ancient formation age for lunar zircon in sample 72255. This age pushes back the age of the first preserved lunar crust by ∼40 Myr and provides a minimum formation age for the Moon within 110 Myr after the formation of the solar system.

atom probe tomography↗