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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 sources NASA NTRS DOE OSTI

NASA NTRS1986

Antenna arraying of Voyager telemetry signals by symbol stream combining

Telemetry signals received from the Voyager 2 spacecraft at Deep Space Stations at Parkes and Canberra, Australia, on February 6, 1986, were combined by the method of symbol stream combining. This second demonstration of symbol stream combining followed the International Cometary Explorer (ICE) demonstration at Giacobini-Zinner encounter in September 1985. The Voyager demonstration was at a symbol rate of 43.2 ksymb/s, compared to 2 ksymb/s for ICE. Recording, playback, and combining at this higher rate were demonstrated. The average symbol signal-to-noise ratio (SNR) of the combined data was 2.84 dB, or 0.23 dB less than the sum of the SNRs of the two imput symbol streams. This 0.23 loss from ideal combining was due to use of 4-bit quantization of the input symbol stream and imperfect scaling. A practical implementation with 8-bit quantization could achieve combining losses of under 0.05 dB over a wide dynamic range of input signal levels.

Hurd, W. J.↗

NASA NTRS1987

Development of a takeoff performance monitoring system

Discussed are the development and testing of a real-time takeoff performance monitoring algorithm. The algorithm is made up of two segments: a pretakeoff segment and a real-time segment. One-time imputs of ambient conditions and airplane configuration information are used in the pretakeoff segment to generate scheduled performance data for that takeoff. The real-time segment uses the scheduled performance data generated in the pretakeoff segment, runway length data, and measured parameters to monitor the performance of the airplane throughout the takeoff roll. Airplane and engine performance deficiencies are detected and annunciated. An important feature of this algorithm is the one-time estimation of the runway rolling friction coefficient. The algorithm was tested using a six-degree-of-freedom airplane model in a computer simulation. Results from a series of sensitivity analyses are also included.

Srivatsan, Raghavachari↗

NASA NTRS1979

An analysis of the human as a predictor model

A hybrid sampled data-continuous impulse response of a preview tracker using a fast time model predictor aid is derived. The model can accept transient and nontransient imputs and is suitable for studying a human preview tracker using an internalized or externalized predictor display. The model is shown to behave reasonably in a simple example and its implications for modeling studies are discussed.

Kreifeldt, J. G.↗

NASA NTRS1989

Analysis of reactively loaded microstrip disk antenna

The moment method solution to the problem of a reactively loaded circular patch is presented. Using the reaction integral equation in conjuction with the method of moments, parameters of the Thevenin's equivalent network for the loaded patch are obtained. From the equivalent network parameters an expression for the imput impedance of the loaded patch is derived. A design procedure for a circularly polarized disk antenna is presented. Computed results are compared with the experimental data.

Deshpande, M. D.↗

DOE OSTI2024

Predictive analytics of selections of russet potatoes

We explore the application of machine learning algorithms specifically to enhance the selection process of Russet potato (Solanum tuberosum L.) clones in breeding trials by predicting their suitability for advancement. This study addresses the challenge of efficiently identifying high-yield, disease-resistant, and climate-resilient potato varieties that meet processing industry standards. Leveraging manually collected data from trials in the state of Oregon, we investigate the potential of a wide variety of state-of-the-art binary classification models. The dataset includes 1086 clones, with data on 38 attributes recorded for each clone, focusing on yield, size, appearance, and frying characteristics, with several control varieties planted consistently across four Oregon regions from 2013 to 2021. We conduct a comprehensive analysis of the dataset that includes preprocessing, feature engineering, and imputation to address missing values. We focus on several key metrics such as accuracy, F1-score, and Matthews correlation coefficient (MCC) for model evaluation. The top-performing models, namely a feedforward neural network classifier (Neural Net), a histogram-based gradient boosting classifier (HGBC), and a support vector machine classifier (SVM), demonstrate consistent and significant results. To further validate our findings, we conducted a simulation study using the aims, data-generating mechanisms, estimands, methods, and performance measures (ADEMP) framework, simulating different data-generating scenarios to assess model robustness and performance through true positive, true negative, false positive, and false negative distributions, area under the receiver operating characteristic curve (AUC-ROC) and MCC. The simulation results highlight that non-linear models like SVM and HGBC consistently show higher AUC-ROC and MCC than logistic regression, thus outperforming the traditional linear model across various distributions, and emphasizing the importance of model selection and tuning in agricultural trials. Variable selection further enhances model performance and identifies influential features in predicting trial outcomes. The findings emphasize the potential of machine learning in streamlining the selection process for potato varieties, offering benefits such as increased efficiency, substantial cost savings, and judicious resource utilization. Our study contributes insights into precision agriculture and showcases the relevance of advanced technologies for informed decision-making in breeding programs.

60 APPLIED LIFE SCIENCES↗

NASA NTRS2019

Formulative Input into Future NASA Aeronautics Planning

This presentation covers industry input received for future work in NASA Aeronautics over the next 5 years. It is intended to present areas of significant imput and to stimulate further discussion.

future aeronautics planning↗

DOE OSTI2025

Integrating Intermediate Traits in Phylogenetic Genotype-to-Phenotype Studies

A major goal of research in evolution and genetics is linking genotype to phenotype. This work could be direct, such as determining the genetic basis of a phenotype by leveraging genetic variation or divergence in a developmental, physiological, or behavioral trait. The work could also involve studying the evolutionary phenomena (e.g., reproductive isolation, adaptation, sexual dimorphism, behavior) that reveal an indirect link between genotype and a trait of interest. When the phenotype diverges across evolutionarily distinct lineages, this genotype-to-phenotype problem can be addressed using phylogenetic genotype-to-phenotype (PhyloG2P) mapping, which uses genetic signatures and convergent phenotypes on a phylogeny to infer the genetic bases of traits. The PhyloG2P approach has proven powerful in revealing key genetic changes associated with diverse traits, including the mammalian transition to marine environments and transitions between major mechanisms of photosynthesis. However, there are several intermediate traits layered in between genotype and the phenotype of interest, including but not limited to transcriptional profiles, chromatin states, protein abundances, structures, modifications, metabolites, and physiological parameters. Each intermediate trait is interesting and informative in its own right, but synthesis across data types has great promise for providing a deep, integrated, and predictive understanding of how genotypes drive phenotypic differences and convergence. We argue that an expanded PhyloG2P framework (the PhyloG2P matrix) that explicitly considers intermediate traits, and imputes those that are prohibitive to obtain, will allow a better mechanistic understanding of any trait of interest. Furthermore, this approach provides a proxy for functional validation and mechanistic understanding in organisms where laboratory manipulation is impractical.

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2004

Making the most of missing values : object clustering with partial data in astronomy

We demonstrate a clustering analysis algorithm, KSC, that a) uses all observed values and b) does not discard the partially observed objects. KSC uses soft constraints defined by the fully observed objects to assist in the grouping of objects with missing values. We present an analysis of objects taken from the Sloan Digital Sky Survey to demonstrate how imputing the values can be misleading and why the KSC approach can produce more appropriate results.

clustering↗

DOE OSTI2026

Integration of ultra-low coverage whole-genome sequences for reconstructing the evolutionary history of Galapagos giant tortoises

Genomic data from contemporary and historical samples often need to be coupled for evolutionary reconstructions of multitaxon complexes. However, the genetic data recovered from historical samples may result only in ultra-low coverage whole-genome sequences (ulcWGS; <0.15× depth), leading to inaccurate evolutionary inferences given a preponderance of missing data. Using the Galapagos giant tortoise radiation as a study system (Chelonoidis spp., composed of 13 extant and four extinct lineages), we assembled a novel methodological pipeline that removes potential noise introduced by the missing data and enhances the evolutionary signal from ulcWGS samples. We leveraged existing tools for phylogenomic placement (EPA-ng), population genomic structure (smartsnp) and admixture (Admixfrog, NGSadmix) to demonstrate that the evolutionary history of samples can be uncovered with sequencing depths as low as 0.008–0.139×. Importantly, these approaches do not use genotype imputation of the ulcWGS samples, which would require extensive reference datasets. Our application to two cases of extinct lineages of Galapagos giant tortoises, with and without references from the same lineage, demonstrates the general value of the approach. We confirm where the extinct lineages from San Cristóbal and Santa Fe islands fit into the Galapagos giant tortoise radiation, and that these lineages were evolutionarily distinct entities.

ancient DNA↗

DOE OSTI2024

Automated Gold Nanorod Spectral Morphology Analysis Pipeline

The development of a colloidal synthesis procedure to produce nanomaterials with high shape and size purity is often a time-consuming, iterative process. This is often due to quantitative uncertainties in the required reaction conditions and the time, resources, and expertise intensive characterization methods required for quantitative determination of nanomaterial size and shape. Absorption spectroscopy is often the easiest method for colloidal nanomaterial characterization. However, due to the lack of a reliable method to extract nanoparticle shapes from absorption spectroscopy, it is generally treated as a more qualitative measure for metal nanoparticles. This work demonstrates a gold nanorod (AuNR) spectral morphology analysis tool, called AuNR-SMA, which is a fast and accurate method to extract quantitative structural information from colloidal AuNR absorption spectra. To demonstrate the practical utility of this model, we apply it to three distinct applications. First, we demonstrate this model's utility as an automated analysis tool in a high-throughput AuNR synthesis procedure by generating quantitative size information from optical spectra. Second, we use the predictions generated by this model to train a machine learning model to predict the resulting AuNR size distributions under specified reaction conditions. Third, we apply this model to spectra extracted from the literature where no size distributions are reported and impute unreported quantitative information on AuNR synthesis. This approach can potentially be extended to any other nanocrystal system where absorption spectra are size dependent, and accurate numerical simulation of absorption spectra is possible. In addition, this pipeline could be integrated into automated synthesis apparatuses to provide interpretable data from simple measurements, help explore the synthesis science of nanoparticles in a rational manner, or facilitate closed-loop workflows.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning

The movement of solutes in a watershed is a complex process with multiple interactions and feedbacks across spatial and temporal scales. Modeling the dynamics of solute transport along diverse hydrologic pathways within watersheds – from hillslopes to stream channels and in and out of the hyporheic zones – is challenging but critically important, as these processes integrate and contribute to the biogeochemical functioning of the river corridor up to the river network scale. Here we use results from a long-term network-scale tracer test at the H.J. Andrews experimental forest in western Cascade Mountains, Oregon, USA to inform a multiscale framework for transport in stream corridors. The framework uses a Lagrangian-based subgrid model to represent the effects of hyporheic exchange flow and advective transport at stream network scales. The spatially and temporally resolved stream discharge needed for the transport model is imputed across the river system by an entity-aware long short-term memory network. Modeled concentrations show good agreements with the observations and exhibit power scaling laws indicative of a very wide range of timescales over which hyporheic exchange flow occurs. Our results demonstrate a data-informed modeling framework that links dynamical processes occurring at small scales to a network context to help understand how changes at reach scale cascade into network-scale effects, providing a useful tool for sustainable river basin management.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2017

Full-Coverage High-Resolution Daily PM(sub 2.5) Estimation using MAIAC AOD in the Yangtze River Delta of China

Satellite aerosol optical depth (AOD) has been used to assess population exposure to fine particulate matter (PM (sub 2.5)). The emerging high-resolution satellite aerosol product, Multi-Angle Implementation of Atmospheric Correction(MAIAC), provides a valuable opportunity to characterize local-scale PM(sub 2.5) at 1-km resolution. However, non-random missing AOD due to cloud snow cover or high surface reflectance makes this task challenging. Previous studies filled the data gap by spatially interpolating neighboring PM(sub 2.5) measurements or predictions. This strategy ignored the effect of cloud cover on aerosol loadings and has been shown to exhibit poor performance when monitoring stations are sparse or when there is seasonal large-scale missngness. Using the Yangtze River Delta of China as an example, we present a Multiple Imputation (MI) method that combines the MAIAC high-resolution satellite retrievals with chemical transport model (CTM) simulations to fill missing AOD. A two-stage statistical model driven by gap-filled AOD, meteorology and land use information was then fitted to estimate daily ground PM(sub 2.5) concentrations in 2013 and 2014 at 1 km resolution with complete coverage in space and time. The daily MI models have an average R(exp 2) of 0.77, with an inter-quartile range of 0.71 to 0.82 across days. The overall Ml model 10-fold cross-validation R(exp 2) (root mean square error) were 0.81 (25 gm(exp 3)) and 0.73 (18 gm(exp 3)) for year 2013 and 2014, respectively. Predictions with only observational AOD or only imputed AOD showed similar accuracy.Comparing with previous gap-filling methods, our MI method presented in this study performed bette rwith higher coverage, higher accuracy, and the ability to fill missing PM(sub 2.5) predictions without ground PM(sub 2.5) measurements. This method can provide reliable PM(sub 2.5)predictions with complete coverage that can reduce biasin exposure assessment in air pollution and health studies.

PM2.5↗

DOE OSTI2025

Enhancing approximate modular Bayesian inference by emulating the conditional posterior

In modular Bayesian analyses, complex models are composed of distinct modules, each representing different aspects of the data or prior information. In this context, fully Bayesian approaches can sometimes lead to undesirable feedback between modules, compromising the integrity of the inference. The “cut-distribution” prevents unwanted influence between modules by “cutting” feedback. The direct sampling (DS) algorithm is standard practice for approximating the cut-distribution, but it can be computationally intensive, especially when the number of imputations required is large. An enhanced method is proposed, the Emulating the Conditional Posterior (ECP) algorithm, which leverages emulation to increase the number of imputations. Through numerical experiment it is demonstrated that the ECP algorithm outperforms the traditional DS approach in terms of accuracy and computational efficiency, particularly when resources are constrained. Here, it is also shown how the DS algorithm can be improved using ideas from design of experiments. Some practical recommendations are given for algorithm choice in modular Bayesian analyses.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1975

Experimental studies for determining human discomfort response to vertical sinusoidal vibration

A study was conducted to investigate several problems related to methodology and design of experiments to obtain human comfort response to vertical sinusoidal vibration. Specifically, the studies were directed to the determination of (1) the adequacy of frequency averaging of vibration data to obtain discomfort predictors, (2) the effect of practice on subject ratings, (3) the effect of the demographic factors of age, sex, and weight, and (4) the relative importance of seat and floor vibrations in the determination of measurement and criteria specification location. Results indicate that accurate prediction of discomfort requires knowledge of both the acceleration level and frequency content of the vibration stimuli. More importantly, the prediction of discomfort was shown to be equally good based upon either floor accelerations or seat accelerations. Furthermore, it was demonstrated that the discomfort levels in different seats resulting from similar vibratory imputs were equal. Therefore, it was recommended that criteria specifications and acceleration measurements be made at the floor location. The results also indicated that practice did not systematically influence discomfort responses nor did the demographic factors of age, weight, and sex contribute to the discomfort response variation.

Dempsey, T. K.↗

NASA NTRS1986

MR Cygni revisited

New analysis tools and additional unanalyzed observations justify a reanalysis of MR Cygni. The reanalysis applied successively more restrictive physical models, each with an optimization program. The final model assigned separate first and second order limb darkening coefficients, from model atmospheres, to individual grid points. Proper operation of the optimization procedure was tested on simulated observational data, produced by light synthesis with assigned system parameters, and modulated by simulated observational error. The iterative solution converged to a weakly-determined mass ratio of 0.75. Assuming the B3 primary component is on the main sequence, the HR diagram location of the secondary from the light ratio (ordinate) and adjusted T sub eff (abscissa) was calculated. The derived mass ratio, together with a main-sequence mass for the B3 component, implies a main-sequence secondary spectral type of B4. The photometrically-determined secondary radii agree with this spectral type, in marginal disagreement with the B7 type from the HR diagram analysis. The individual masses, derived from the radial velocity curve of the primary component, the photometrically-determined i, and alternative values of derived mass ratio are seriously discrepant with main sequence objects. The imputed physical status of the system is in disagreement with representations that have appeared in the literature.

Linnell, Albert P.↗