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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
DOE OSTI2024

LPBF Processability of NiTiHf Alloys: Systematic Modeling and Single-Track Studies

Research into the processability of NiTiHf high-temperature shape memory alloys (HTSMAs) via laser powder bed fusion (LPBF) is limited; nevertheless, these alloys show promise for applications in extreme environments. This study aims to address this limitation by investigating the printability of four NiTiHf alloys with varying Hf content (1, 2, 15, and 20 at. %) to assess their suitability for LPBF applications. Solidification cracking is one of the main limiting factors in LPBF processes, which occurs during the final stage of solidification. To investigate the effect of alloy composition on printability, this study focuses on this defect via a combination of computational modeling and experimental validation. To this end, solidification cracking susceptibility is calculated as Kou’s index and Scheil–Gulliver model, implemented in Thermo-Calc/2022a software. An innovative powder-free experimental method through laser remelting was conducted on bare NiTiHf ingots to validate the parameter impacts of the LPBF process. The result is the processability window with no cracking likelihood under diverse LPBF conditions, including laser power and scan speed. This comprehensive investigation enhances our understanding of the processability challenges and opportunities for NiTiHf HTSMAs in advanced engineering applications.

36 MATERIALS SCIENCE↗
NASA NTRS1985

Models of the upper atmosphere

Several computer models of the thermosphere are discussed. J70MM, the Jacchia 1970 model with matrix and matrix mean output, and J703X, the Jacchia 1970, 1971, and 1977 models, are of primary interest. The subroutines in these programs were studied in detail, and several mistakes were found and corrected. It is proposed that a weighted average of the three-hour geomagnetic index be used in the models rather than a single index value. The densities (for a given date and time) generated by the J703X program (all three models), using both a single value of the index and weighted averages for three different lag times, are tabulated and discussed. Most of the equations used in the models are the results of empirical curve fits. An attempt was made to generate a theoretical prediction of the thermospheric temperature profile based on a simple physical model of atmospheric heat conduction in a spherically-symmetric shell. The exponential heating theory and the temperature dependence of k are discussed.

Davis, T. M.↗
DOE OSTI2024

Improving BC Mixing State and CCN Activity Representation With Machine Learning in the Community Atmosphere Model Version 6 (CAM6)

Abstract Representing mixing state of black carbon (BC) is challenging for global climate models (GCMs). The Community Atmosphere Model version 6 (CAM6) with the four‐mode version of the Modal Aerosol Module (MAM4) represents aerosols as fully internal mixtures with uniform composition within each aerosol mode, resulting in high degree of internal mixing of BC with non‐BC species and large mass ratio of coating to BC ( R BC , the mass ratio of non‐BC species to BC in BC‐containing particles). To improve BC mixing state representation, we coupled a machine learning (ML) model of BC mixing state index trained on particle‐resolved simulations to the CAM6 with MAM4 (MAM4‐ML). In MAM4‐ML, we use R BC to partition accumulation mode particles into two new modes, BC‐free particles and BC‐containing particles. We adjust R BC to make the modeled BC mixing state index ( χ mode ) match the one predicted by the ML model ( χ ML ). On a global average, the mass fraction of BC‐containing particles in accumulation mode decreases from 100% (MAM4‐default) to 48% (MAM4‐ML). The globally averaged χ mode decreases from 78% (MAM4‐default) to 63% (MAM4‐ML, 19% reduction) and agrees well with χ ML (66%). The R BC decreases by 52% for accumulation mode and better agrees with observations. The hygroscopicity drops by 9% for BC‐containing particles in accumulation mode, leading to a 20% reduction in the BC activation fraction. The surface BC concentration increases most (6.9%) in the Arctic, and the BC burden increases by 4%, globally. Our study highlights the application of the ML model for improving key aerosol processes in GCMs.

Meteorology & Atmospheric Sciences↗
NASA NTRS1974

Ionospheric irregularities causing scintillation of GHz frequency radio signals

Consideration of the recently observed phenomenon of scintillation of satellite signals at GHz frequency range. Based on the scintillation data and results from in situ measurements, several ionospheric irregularity models with different power spectra are studied. Scintillation index is computed for the various models and compared with observed results. Both magnitude and frequency dependence of the scintillation index are investigated. It is found that a thick irregularity slab of the order of 200 km with an electron density fluctuation of about 20 per cent of its background value and with a nonmonotonic power spectrum may account for the maximum observed values of the scintillation index as well as its frequency dependence. Some future observations and measurements are suggested.

Wernik, A. W.↗
NASA NTRS1975

The effects of stability augmentation on the gust response of a STOL aircraft during a curved manual approach

The task of investigating the effect of SAS was formulated as two optimal control problems for stochastic systems: (1) to compute SAS gains with a rate model in the performance index algorithm; and (2) to calculate the pilot gains and system gust response using an optimal pilot model. Both problems were solved to yield reasonable low gains for the pilot and SAS, and the lateral directional mode poles and the longitudinal short period poles could be placed accurately by the model matching algorithm. Of the longitudinal (SAS) poles achieved, the vertical rms path error was least for the unaugmented poles. The lateral rms path error was an order of magnitude larger than the vertical error and showed a plus or minus 50 percent variation with SAS. It increased with dutch roll frequency and damping, and it decreased most significantly with increased roll stability. The variation in lateral error with bank angle for curved flight was also a significant function of the augmented poles.

Porter, M. B., Jr.↗
DOE OSTI2022

Making Common Fund data more findable: catalyzing a data ecosystem

The Common Fund Data Ecosystem (CFDE) has created a flexible system of data federation that enables researchers to discover datasets from across the US National Institutes of Health Common Fund without requiring that data owners move, reformat, or rehost those data. This system is centered on a catalog that integrates detailed descriptions of biomedical datasets from individual Common Fund Programs’ Data Coordination Centers (DCCs) into a uniform metadata model that can then be indexed and searched from a centralized portal. This Crosscut Metadata Model (C2M2) supports the wide variety of data types and metadata terms used by individual DCCs and can readily describe nearly all forms of biomedical research data. We detail its use to ingest and index data from 11 DCCs.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

A Comparison of Machine Learning Methods to Forecast Tropospheric Ozone Levels in Delhi

Ground-level ozone is a pollutant that is harmful to urban populations, particularly in developing countries where it is present in significant quantities. It greatly increases the risk of heart and lung diseases and harms agricultural crops. This study hypothesized that, as a secondary pollutant, ground-level ozone is amenable to 24 h forecasting based on measurements of weather conditions and primary pollutants such as nitrogen oxides and volatile organic compounds. We developed software to analyze hourly records of 12 air pollutants and 5 weather variables over the course of one year in Delhi, India. To determine the best predictive model, eight machine learning algorithms were tuned, trained, tested, and compared using cross-validation with hourly data for a full year. The algorithms, ranked by R2 values, were XGBoost (0.61), Random Forest (0.61), K-Nearest Neighbor Regression (0.55), Support Vector Regression (0.48), Decision Trees (0.43), AdaBoost (0.39), and linear regression (0.39). When trained by separate seasons across five years, the predictive capabilities of all models increased, with a maximum R 2 of 0.75 during winter. Bidirectional Long Short-Term Memory was the least accurate model for annual training, but had some of the best predictions for seasonal training. Out of five air quality index categories, the XGBoost model was able to predict the correct category 24 h in advance 90% of the time when trained with full-year data. Separated by season, winter is considerably more predictable (97.3%), followed by post-monsoon (92.8%), monsoon (90.3%), and summer (88.9%). These results show the importance of training machine learning methods with season-specific data sets and comparing a large number of methods for specific applications.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1976

Remote probing of atmospheric particulates from radiation extinction experiments: A review of methods

The existing methodology for reconstructing the particle size distribution and inferring the refractive index of absorbing and scattering atmospheric particulates is critically reviewed. Emphasis is placed on method capabilities and shortcomings and, wherever possible, on achievable accuracy. The nature of the associated remote probing problem is analyzed with regard to the effects of the particulates on EM wave propagation in the atmosphere. The parameterization of size distribution is studied within the unifying framework of Pearson's distribution curves. The inversions of extinction measurements and their ratios are considered separately, and the potentialities of each type of measurement are identified. Work lacking in each of the methods reviewed is indicated. A method of determining both the effective complex refractive index and size distribution model parameters from the same data is also presented. Lastly, determination from extinction ratio data of the complex refractive index independent of size distribution is discussed and error analyzed.

Fymat, A. L.↗
NASA NTRS2013

Cryogenic Refractive Index and Coefficient of Thermal Expansion for the S-TIH1 Glass

Using the CHARMS facility at NASA GSFC, we have measured the cryogenic refractive index of the Ohara S-TIH1 glass from 0.40 to 2.53 micrometers and from 120 to 300 K. We have also examined the spectral dispersion and thermo-optic coefficients (dn/dT). We also derived temperature-dependent Sellmeier models from which refractive index may be calculated for any wavelength and temperature within the stated ranges of each model. The S-TIH1 glass we tested exhibited unusual behavior in the thermo-optic coefficient. We found that for delta < 0.5 micrometers, the index of refraction decrease with a decrease in temperature (positive dn/dT). However, the situation was reversed for delta larger than 0.63 micrometers, where the index will increase with a decrease in temperature (negative dn/dT). We also measured the coefficient of thermal expansion (CTE) for the similar batch of S-TIH1 glass in order to understand its thermal properties. The CTE showed a monotonic change with a decrease in temperature.

Quijada, Manuel A.↗
NASA NTRS1977

Using Landsat data to estimate evapotranspiration of winter wheat

Results obtained from an evapotranspiration model as applied to Kansas winter wheatfields were compared with results determined by a weighing lysimeter, and the standard deviation was found to be less than 0.5 mm/day (however, the 95% confidence interval was between plus and minus 0.2 mm/day). Model inputs are solar radiation, temperature, precipitation, and leaf area index; an equation was developed to estimate the leaf area index from Landsat data. The model provides estimates of transpiration, evaporation, and soil moisture.

Kanemasu, E. T.↗
NASA NTRS1994

Estimation of grain sizes and mixing ratios of fine powder mixtures of common geologic minerals

Two different approaches for modeling reflectance spectra of intimate mixtures, Hapke's model and the isograin model, are used to estimate grain sizes and mixing ratios of powder mixtures of three geologic minerals: olivine, orthopyroxene, and plagioclase. In Hapke's model, scattering and extinction effiencies are mixed separately, and both models employ semiempirical refractive index spectra for component minerals. Mixing ratios of mixtures of grain size 45-75 micrometers are well estimated by both models assuming a common grain size of 60 micrometers and optimizing the constants for the single-particle scattering. For each model, effective grain size ratios for mineral constituents in mixtures of grain size less than 25 micrometers, are derived successfully that allows mineral abundances to be accurately predicted within approximately 4 wt percent. On the other hand, neither model can accurately predict mineral reflectance spectra for its smaller grain sizes less than 25 and 25-45 micrometers using an absorption coefficient spectrum derived from a larger grain size (45-75 micrometers). The errors in both models are significantly reduced if surface roughness effects of the smaller grain-size fractions are modeled.

Hiroi, Takahiro↗
NASA NTRS2016

NuSTAR Hard X-Ray Survey of the Galactic Center Region. II. X-Ray Point Sources

We present the first survey results of hard X-ray point sources in the Galactic Center (GC) region by NuSTAR. We have discovered 70 hard (3-79 keV) X-ray point sources in a 0.6 deg(sup 2) region around SgrA* with a total exposure of 1.7 Ms, and 7 sources in the Sgr B2 field with 300 ks. We identify clear Chandra counterparts for 58 NuSTAR sources and assign candidate counterparts for the remaining 19. The NuSTAR survey reaches X-ray luminosities of approx. 4× and approx. 8 ×10(exp 32) erg/s at the GC (8 kpc) in the 3-10 and 10-40 keV bands, respectively. The source list includes three persistent luminous X-ray binaries (XBs) and the likely run-away pulsar called the Cannonball. New source-detection significance maps reveal a cluster of hard (>10 keV) X-ray sources near the Sgr A diffuse complex with no clear soft X-ray counterparts. The severe extinction observed in the Chandra spectra indicates that all the NuSTAR sources are in the central bulge or are of extragalactic origin. Spectral analysis of relatively bright NuSTAR sources suggests that magnetic cataclysmic variables constitute a large fraction (>40%-60%). Both spectral analysis and logN-logS distributions of the NuSTAR sources indicate that the X-ray spectra of the NuSTAR sources should have kT > 20 keV on average for a single temperature thermal plasma model or an average photon index of Lambda = 1.5-2 for a power-law model. These findings suggest that the GC X-ray source population may contain a larger fraction of XBs with high plasma temperatures than the field population.

center - X-rays: binaries - diffuse background↗
NASA NTRS2022

VESIcal: An Open-source Thermodynamic Model Engine for Mixed Volatile (H2O-CO2) Solubility in Silicate Melts

Modeling the solubility of volatiles in silicate melts is fundamental to the interpretation of volcanic systems and has implications for magma dynamics, eruption style, and material transport between the mantle, crust, and atmosphere. Recent advancements in computational capabilities and access to computing tools has outpaced the functionality and extensibility of previously available modeling platforms. Here we present VESIcal (Volatile Equilibria and Saturation Index calculator), the first comprehensive modeling tool for H2O, CO2, and mixed (H2O-CO2) solubility in silicate melts that: a) allows users access to seven popular models, with easy inter-comparison between models; b) provides universal functionality for all models (e.g., functions for calculating saturation pressures, degassing paths, etc.); c) can process large datasets (1,000’s of samples) automatically; d) can output computed data into an Excel spreadsheet or CSV file for post-modeling analysis; e) integrates plotting capabilities directly within the tool; and f) provides all of this within the framework of a python library, making the tool extensible by the user and allowing any of the model functions to be incorporated into any other code capable of calling python.Here we will provide a demonstration of VESIcal and its capabilities with applications to various volcanic processes affected by volatiles. VESIcal represents the first tool capable of directly comparing multiple solubility models and equations of state. We find that commonly used models predict surprisingly different volatile solubilities, particularly for pure CO2 or mixed CO2-H2O fluids. Even for melt compositions that are well represented in the calibration datasets of multiple models (e.g., MORBs), calculated solubilities for pure CO2 and pure H2O can deviate from one another by factors of >2 leading to 2x deviations in calculated saturation pressures (e.g., 5 to 10 kbar). The solubility of CO2 predicted by different rhyolitic models also differs substantially, overwhelming other sources of uncertainty such as analytical errors on measurements of volatile contents or uncertainties in crustal density profiles. This highlights the importance of model choice when drawing geological conclusions based on volatiles in magmas.

Kayla Iacovino↗
NASA NTRS2021

Precise Timing and Phase-resolved Spectroscopy of the Young Pulsar J1617-5055 with NuSTAR

We report on a Nuclear Spectroscopic Telescope Array (NuSTAR) observation of the young, energetic pulsar PSR J1617-5055. Parkes Observatory 3 GHz radio observations of the pulsar (taken about 7 yr before the NuSTAR observations) are also reported here. NuSTAR detected pulsations at a frequency of f ≍ 14.4 Hz (P ≍ 69.44 ms) and, in addition, the observation was long enough to measure the source's frequency derivative, ḟ ≈ -2.8 X10^(-11) Hz/s. We find that the pulsar shows one peak per period at both hard X-ray and radio wavelengths, but that the hard X-ray pulse is broader (having a duty cycle of ~0.7), than the radio pulse (having a duty cycle of ~0.08). Additionally, the radio pulse is strongly linearly polarized. J1617's phase-integrated hard X-ray spectrum is well fit by an absorbed power-law model, with a photon index Γ = 1.59 ± 0.02. The hard X-ray pulsations are well described by three Fourier harmonics, and have a pulsed fraction that increases with energy. We also fit the phase-resolved NuSTAR spectra with an absorbed power-law model in five phase bins and find that the photon index varies with phase from Γ = 1.52 ± 0.03 at phases around the flux maximum to Γ = 1.79 ± 0.06 around the flux minimum. Last, we compare our results with other pulsars whose magnetospheric emission is detected at hard X-ray energies and find that, similar to previous studies, J1617's hard X-ray properties are more similar to the MeV pulsars than the GeV pulsars.

Neutron Stars↗
NASA NTRS2018

Linking the Pilot Structural Model and Pilot Workload

Behavioral models are developed that closely reproduced pulsive control response of two pilots using markedly different control techniques while conducting a tracking task. An intriguing find was that the pilots appeared to: 1) produce a continuous, internally-generated stick signal that they integrated in time; 2) integrate the actual stick position; and 3) compare the two integrations to either issue or cease a pulse command. This suggests that the pilots utilized kinesthetic feedback in order to sense and integrate stick position, supporting the hypothesis that pilots can access and employ the proprioceptive inner feedback loop proposed by Hess's pilot Structural Model. A Pilot Cost Index was developed, whose elements include estimated workload, performance, and the degree to which the pilot employs kinesthetic feedback. Preliminary results suggest that a pilot's operating point (parameter values) may be based on control style and index minimization.

Pilot workload↗
DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗
NASA NTRS1992

The gravitational wave contribution to cosmic microwave background anisotropies and the amplitude of mass fluctuations from COBE results

A stochastic background of primordial gravitational waves may substantially contribute, via the Sachs-Wolfe effect, to the large-scale cosmic microwave background (CMB) anisotropies recently detected by COBE. This implies a bias in any resulting determination of the primordial amplitude of density fluctuations. We consider the constraints imposed on n is less than 1 ('tilted') power-law fluctuation spectra, taking into account the contribution from both scalar and tensor waves, as predicted by power-law inflation. The gravitational wave contribution to CMB anisotropies generally reduces the required rms level of mass fluctuation, thereby increasing the linear bias parameter, even in models where the spectral index is close to the Harrison-Zel'dovich value n = 1. This 'gravitational wave bias' helps to reconcile the predictions of CDM models with observations on pairwise galaxy velocity dispersion on small scales.

Lucchin, Francesco↗