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At least 631 records · Page 35
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Developing a simulator-based satellite dataset for using machine learning techniques to derive aerosol-cloud-precipitation interactions in models and observations in a consistent framework

Aerosol-cloud-precipitation interactions (ACPI) remain a major uncertainty in understanding the Earth’s radiation budget and water cycle (including extremes). After decades of active research, various observationally based metrics have been developed to constrain ACPI in Earth System Models (ESMs), but direct comparison of model and data estimates can confound scientific understanding because limitations and uncertainties in sampling and retrieval procedures may combine with model deficiencies in process representations of ACPI to obstruct understanding. Furthermore, conventional ACPI metrics often vary from one regime to another, and the ACPI process representation in ESMs is also typically derived based on only a limited area/regime (even though the parameterization applies globally). To bridge the gap between models and data and to correctly describe ACPI in all regimes, we propose to construct a new CALIPSO-CloudSat merged dataset that is produced by the same algorithms used in satellite simulators in ESMs, and to use machine learning techniques to derive new ACPI metrics that can be accurately estimated by satellites and can provide meaningful constraints on cloud microphysical process representations in ESMs. The dataset will include measured and retrieved variables for aerosol, cloud, and precipitation from CALIPSO and CloudSat, and environmental variables from meteorological reanalysis. The data will be used to train a neural network to construct the ACPI metrics as a function of environmental conditions. The new ACPI formula will be used to constrain the ACPI in the Energy Exascale Earth System Model (E3SM), and to augment/reformulate the ACPI process representation in the E3SM to improve the simulation of the evolution of the atmosphere under different environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Multi-fidelity information fusion with concatenated neural networks

Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.

42 ENGINEERING↗
NASA NTRS2025

Details of Adhering and Testing Artificial Ice Shapes to the High Lift Common Research Model in the National Transonic Facility

There is currently interest in conducting flight Reynolds number aerodynamic performance testing for iced airplane configurations. There has only been limited testing in the past, none of which has resulted in publicly available data. Consequently, there is virtually no documented knowledge for conducting icing related testing at flight Reynolds number where testing in cryogenic conditions is required. The National Transonic Facility (NTF) is a cryogenic wind tunnel at NASA Langley Research Center that is capable of achieving flight Reynolds numbers on subscale models. A large number of aerodynamic tests have been performed in this facility for uniced subscale model configurations. Through the collaborations associated with the High Lift Common Research Model, a test campaign was conducted to fill the existing research gaps with publicly available data. This resulted in a requirement that NASA develop a suitable level of competency for performing icing related tests in cryogenic environments. This document provides information about the challenges faced during the test campaign, how the issues were mitigated, and preserves the institutional knowledge gained by providing recommendations that will be used to reduce risk for future testing.

Iced Aerodynamics↗
NASA NTRS2016

Demonstration of Orbit Determination for the Lunar Reconnaissance Orbiter Using One-Way Laser Ranging Data

We used one-way laser ranging data from International Laser Ranging Service (ILRS) ground stations to NASA's Lunar Reconnaissance Orbiter (LRO) for a demonstration of orbit determination. In the one-way setup, the state of LRO and the parameters of the spacecraft and all involved ground station clocks must be estimated simultaneously. This setup introduces many correlated parameters that are resolved by using a priori constraints. More over the observation data coverage and errors accumulating from the dynamical and the clock modeling limit the maximum arc length. The objective of this paper is to investigate the effect of the arc length, the dynamical and modeling accuracy and the observation data coverage on the accuracy of the results. We analyzed multiple arcs using lengths of 2 and 7 days during a one-week period in Science Mission phase 02 (SM02,November2010) and compared the trajectories, the post-fit measurement residuals and the estimated clock parameters. We further incorporated simultaneous passes from multiple stations within the observation data to investigate the expected improvement in positioning. The estimated trajectories were compared to the nominal LRO trajectory and the clock parameters (offset, rate and aging) to the results found in the literature. Arcs estimated with one-way ranging data had differences of 5-30 m compared to the nominal LRO trajectory. While the estimated LRO clock rates agreed closely with the a priori constraints, the aging parameters absorbed clock modeling errors with increasing clock arc length. Because of high correlations between the different ground station clocks and due to limited clock modeling accuracy, their differences only agreed at the order of magnitude with the literature. We found that the incorporation of simultaneous passes requires improved modeling in particular to enable the expected improvement in positioning. We found that gaps in the observation data coverage over 12h (approximately equals 6 successive LRO orbits) prevented the successful estimation of arcs with lengths shorter or longer than 2 or 7 days with our given modeling.

Bauer, S.↗
NASA NTRS2000

Optical Characterization of Bulk ZnSeTe Solid Solutions

Optical characterization was performed on wafers sliced from crystals of ZnSe, ZnTe, and ZnSe(1-x)Te(x)(0 less than x less than 0.4) grown by physical vapor transport. Energy band gaps at room temperature were determined from optical transmission measurements on 11 wafers. A best fit curve to the band gap versus composition x data gives a bowing parameter of 1.45. This number lies between the value of 1.23 determined previously on ZnSeTe bulk crystals and the value of 1.621 reported on ZnSeTe epilayers. Low-temperature photoluminescence (PL) spectra were measured on six samples. The spectra of ZnSe and ZnTe were dominated by near band edge emissions and no deep donor-acceptor pairs were observed. The PL spectrum exhibited a broad emission for each of the ZnSe(1-x)Te(x) samples, 0.09 less than x less than 0.39. For x=0.09, this emission energy is about 0.2 eV lower than the band gap energy measured at low temperature. As x increases the energy discrepancy gradually decreases and reduces to almost zero at x=0.4. The single broad PL emission spectra and the spectra measured as a function of temperature were interpreted as being associated with the exciton bound to Te clusters because of the high Te content in these samples.

Su, Ching-Hua↗
NASA NTRS2000

Optical Characterization of Bulk ZnSeTe Solid Solutions

Optical characterization was performed on wafers sliced from crystals of ZnSe, ZnTe and ZnSe (sub 1-x) Te (sub x) (0 less than x less than 0.4) grown by physical vapor transport technique. The energy band gaps at room temperature were determined from optical transmission measurements on 11 wafers. The best fit to the band gap vs. composition, x, data gives a bowing parameter of 1.336 which is between the value of 1.23 determined previously on ZnSeTe bulk crystals by reflectivity and the value of 1.621 reported on epilayers by photoconductivity. Low-temperature photoluminescence (PL) spectra were measured on 6 samples. The spectra of ZnSe and ZnTe were dominated by near band edge emissions and no deep donor-acceptor pairs were observed. The PL spectrum exhibited a broad emission for each of the ZnSe (sub 1-x) Te (sub x) samples, 0.09 less than x less than 0.39. For x = 0.09, this emission energy is about 0.2eV lower than the band gap energy measured at low temperature. As x increases the energy discrepancy gradually decreases and reduces to almost zero at x = 0.4. The single broad PL emission spectra and the spectra measured as a function of temperature were interpreted to be associated with the exciton bound to Te clusters because of the high Te content in these samples.

Su, Ching-Hua↗
NASA NTRS1996

Calculation of tip clearance effects in a transonic compressor rotor

The flow through the tip clearance region of a transonic compressor rotor (NASA rotor 37) was computed and compared to aerodynamic probe and laser anemometer data. Tip clearance effects were modeled both by gridding the clearance gap and by using a simple periodicity model across the ungridded gap. The simple model was run with both the full gap height, and with half the gap height to simulate a vena-contracta effect. Comparisons between computed and measured performance maps and downstream profiles were used to validate the models and to assess the effects of gap height on the simple clearance model. Recommendations were made concerning the use of the simple clearance model. Detailed comparisons were made between the gridded clearance gap solution and the laser anemometer data near the tip at two operating points. The computer results agreed fairly well with the data but overpredicted the extent of the casing separation and underpredicted the wake decay rate. The computations were then used to describe the interaction of the tip vortex, the passage shock, and the casing boundary layer.

Chima, R. V.↗
NASA NTRS1998

Calculation of Tip Clearance Effects in a Transonic Compressor Rotor

The flow through the tip clearance region of a transonic compressor rotor (NASA rotor 37) was computed and compared to aerodynamic probe and laser anemometer data. Tip clearance effects were modeled both by gridding the clearance gap and by using a simple periodicity model across the ungridded gap. The simple model was run with both the full gap height, and with half the gap height to simulate a vena-contracta effect. Comparisons between computed and measured performance maps and downstream profiles were used to validate the models and to assess the effects of gap height on the simple clearance model. Recommendations were made concern- ing the use of the simple clearance model Detailed comparisons were made between the gridded clearance gap solution and the laser anemometer data near the tip at two operating points. The computed results agreed fairly well with the data but overpredicted the extent of the casing separation and underpredicted the wake decay rate. The computations were then used to describe the interaction of the tip vortex, the passage shock, and the casing boundary layer.

Chima, R. V.↗
DOE OSTI2025

Moltensaltpropnet

MoltenSaltPropnet is a physics-informed machine learning framework that aims to predict the thermophysical properties of molten fluoride and chloride salt mixtures, which are crucial for the design and safety of Generation IV molten salt reactors. The code processes data from the Molten-Salt Thermal Properties Database (MSTDB-TP) and the Janz compendium, converting critically evaluated correlations into fast, differentiable surrogate models for density, viscosity, thermal conductivity, and heat capacity across 448 distinct salt systems. The implementation consists of several key components: 1. Data Curation: The code parses and cleans the raw data, normalizing elemental mole fractions and extracting relevant regression coefficients for various thermophysical properties. 2. Feature Engineering: It generates fixed-length numerical descriptors that encapsulate the composition and temperature, incorporating polynomial interaction terms and dimensionality-reduction techniques to optimize model performance. 3. Coefficient Learning: Four different machine learning architectures are employed: a deep residual network (ResNet), a Kolmogorov–Arnold network (KAN), a sparsity-inducing neural network (SNN), and classical regression models. Each model learns to predict coefficients that define the temperature-dependent correlations for the thermophysical properties. 4. Property Reconstruction: The predicted coefficients are used to compute temperature-dependent property values, ensuring positivity and monotonic trends through a composite loss function that enforces physical constraints. 5. User Interface: An open-source web application enables users to filter the database, train task-specific models, and visualize the results, allowing for rapid exploration of candidate salt mixtures. MoltenSaltPropnet bridges the gap between limited experimental data and high-fidelity reactor simulations, providing a powerful tool for researchers in the field of molten salt reactors and advanced nuclear energy systems.

Retamales, Mauricio Eduardo Tano [Idaho National L↗
DOE OSTI2026

Plant Bioengineering Atlas: A Knowledge Graph of Genes, DNA Constructs, and Plant Traits.

Plant bioengineering has generated tens of thousands of genotype-to-phenotype relationships, but this knowledge remains fragmented across narrative literature and difficult to use computationally. Inconsistent descriptions of DNA constructs, host species, and traits, including variable species names, omitted regulatory elements, and inconsistent gene symbols, impede data reuse, comparative analysis, and design-build-test-learn cycles. Here, we present the Plant Bioengineering Atlas, a literature-mined, ontology-grounded knowledge base assembled using an artificial intelligence (AI)-aided extraction pipeline. A large language model parsed open-access primary research articles to generate structured, provenance-anchored records of engineered genes, modification types, promoter-gene-terminator constructs, host species, target traits, and reported phenotypes, with every record traceable to its source. The current release contains 14,358 curated records encompassing 6,998 distinct genes across 436 plant species from 6,452 papers published between 2000 and 2026. Corpus analysis reveals that experiments are concentrated in a small group of model and crop species, disease and pathogen resistance is the most frequently engineered trait class, and constitutive regulatory parts (particularly the CaMV 35S promoter and NOS terminator) remain pervasive. Two in five records omit one or both flanking regulatory elements (i.e., promoter and terminator), while only 23.4% describe cassettes in which both elements resolve to named part classes, exposing a systematic reproducibility gap. We organize these data into a knowledge graph linking genes, constructs, species, and traits; provide access through an interactive web portal; and propose an AI-compatible documentation standard for AI-ready reporting. The Plant Bioengineering Atlas provides a foundation for data-driven hypothesis generation and AI-aided plant biodesign.

, Genes, DNA Constructs↗
DOE OSTI2024

Elimination LArTPC Simulation Uncertainty

Liquid Argon Time Projection Chambers (LArTPC) are essential for detecting muons and neutrinos by capturing electrons released during particle collisions, which drift toward wire planes under an electric field and induce currents measured to reconstruct particle paths. However, LArTPCs face challenges from effects such as electron-ion recombination, electron diffusion, and electron attenuation, complicating data simulation. The Short Baseline Neutrino (SNB) detector aims to measure neutrinos before oscillation occurs. To bridge the gap between simulation and actual data, we propose modifying the amplitude and width of signals on the TPC wires, addressing uncertainties by adjusting signal characteristics to better match observed data. A Gaussian fit to current waveforms produces hits with associated charge and width, and by comparing data and simulated values, discrepancies highlight areas where the model fails. Initial results indicate the current modification algorithm may increase divergence between simulation and data, necessitating further refinement. A discovered bug in the WireModMakeHist_plug.cpp file, which incorrectly computed simulation and data ratios, underscores the need for precise algorithm adjustments. Future work involves correcting code errors, fine-tuning the model, and conducting multiple simulation runs to enhance statistical confidence and reduce uncertainties, ultimately aiming for accurate LArTPC operation and reliable neutrino detection.

Mkrtchyan, Ka'ren↗
DOE OSTI2021

Solar position confounds the relationship between ecosystem function and vegetation indices derived from solar and photosynthetically active radiation fluxes

Vegetation indices derived from solar and photosynthetically active radiation (PAR) sensors (i.e. radiation derived) have been under-utilized in inferring ecosystem function, despite measurement capability at hundreds of sites. This under-utilization may be attributed to reported mismatches among the seasonality of radiation- and satellite-derived vegetation indices and canopy photosynthesis; herein referred to as measurement biases. In this work, biases in radiation derived reflectance and vegetation indices were assessed using a decadal record of satellite and ground based spectroradiometer data, ecosystem phenology and CO 2 fluxes, and radiation derived vegetation indices (i.e. the Normalized Difference Vegetation Index [NDVI], the two band Enhanced Vegetation Index [EVI2]) from a high latitude tundra site (i.e. Imnaviat). At Imnaviat, we found poor correspondence between the three types of reflectance and vegetation indices, especially during the latter part of the growing season. Radiation derived vegetation indices resulted in incorrect estimates of phenological timing of up to a month and poor relationships with canopy photosynthesis (i.e. Gross Ecosystem Exchange (GEE)). These mismatches were attributed to solar position (i.e. solar zenith and azimuth angle) and a method, based on the diel visible and near-infrared albedo variation, was developed to improve the performance of the vegetation indices. The ability of radiation derived vegetation indices to infer GEE and phenological dates drastically improved once radiation derived vegetation indices were corrected for solar position associated biases at Imnaviat. Moreover, radiation derived vegetation indices became better aligned with MODerate resolution Imaging Spectroradiometer (MODIS) satellite estimates after solar position associated biases were corrected at Imnaviat and at 25 Fluxnet sites (~90 site years) across North America. Corrections developed here provide a way forward in understanding daily ecosystem function or filling large gaps in eddy covariance data at a significant number of Fluxnet sites.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bridging the Gap between Analytical and Microbial Sciences in Microbiome Research

Metabolites from the microbiome influence human, animal, and environmental health, but the diversity and functional roles of these compounds have only begun to be elucidated. Comprehensively characterizing these molecules are significant challenges, as it requires expertise in analytical methods, such as mass spectrometry and nuclear magnetic resonance spectroscopy, skills that not many traditional microbiologists or microbial ecologists possess. This creates a gap between microbiome scientists that want to understand the role of microbial metabolites in microbiome systems and the skills required to generate and interpret complex metabolomics data sets. To bridge this gap, microbiome scientists should engage analytical chemists to best understand the underlying chemical principles of the data. Conversely, analytical scientists are encouraged to engage with microbiome scientists to better understand the biological questions being asked with metabolomics and to best communicate its intricacies. Better communication across the chemistry/biology disciplines will further reveal the “dark matter” within microbiomes that maintain healthy humans and environments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Stability and Convergence of Solutions to Stochastic Inverse Problems Using Approximate Probability Densities

Data-consistent inversion is designed to solve a class of stochastic inverse problems where the solution is a pullback of a probability measure specified on the outputs of a quantities of interest (QoI) map. Here, this work presents stability and convergence results for the case where finite QoI data result in an approximation of the solution as a density. Given their popularity in the literature, separate results are proven for three different approaches to measuring discrepancies between probability measures: f-divergences, integral probability metrics, and L p metrics. In the context of integral probability metrics, we also introduce a pullback probability metric that is well-suited for data-consistent inversion. This fills a theoretical gap in the convergence and stability results for data-consistent inversion that have mostly focused on convergence of solutions associated with approximate maps. Numerical results are included to illustrate key theoretical results with intuitive and reproducible test problems that include a demonstration of convergence in the measure-theoretic "almost" sense.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1994

Spacecraft Data Simulator for the test of level zero processing systems

The Microelectronic Systems Branch (MSB) at Goddard Space Flight Center (GSFC) has developed a Spacecraft Data Simulator (SDS) to support the development, test, and verification of prototype and production Level Zero Processing (LZP) systems. Based on a disk array system, the SDS is capable of generating large test data sets up to 5 Gigabytes and outputting serial test data at rates up to 80 Mbps. The SDS supports data formats including NASA Communication (Nascom) blocks, Consultative Committee for Space Data System (CCSDS) Version 1 & 2 frames and packets, and all the Advanced Orbiting Systems (AOS) services. The capability to simulate both sequential and non-sequential time-ordered downlink data streams with errors and gaps is crucial to test LZP systems. This paper describes the system architecture, hardware and software designs, and test data designs. Examples of test data designs are included to illustrate the application of the SDS.

Shi, Jeff↗
DOE OSTI2021

Street context of various demographic groups in their daily mobility

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Salgado, Ariel (ORCID:0000000177015372)↗
DOE OSTI2020

Autoignition of CRC diesel surrogates at low temperature combustion conditions: Rapid compression machine experiments and modeling

As federal programs require increasingly stringent engine emissions and fuel economy standards, these ambitions can only be met if next-generation combustion technology is developed focusing on high-efficiency and low-emissions engines. Recent research has indicated the need to operate engines at higher compression ratios and with low temperature combustion (LTC) to achieve the needed gains in engine efficiency and reductions in emissions. Because there is a lack of understanding of the chemistry of diesel fuel components and their mixtures at these LTC conditions, this limits the ability to develop predictive chemical kinetic models that can be used to optimize engine combustion. The current study aims to fill in gaps in fundamental combustion data on surrogate fuel mixtures relevant to diesel fuels. Specifically, four multicomponent diesel surrogates formulated by the Coordinating Research Council (CRC) to emulate an ultra-low-sulfur research-grade #2 certification diesel fuel (CFA), namely V0a (4 components), V0b (5 components), V1 (8 components), and V2 (9 components), have been investigated in a rapid compression machine (RCM) through determination of total and first-stage ignition delay times. Autoignition characteristics of lean to rich fuel/O 2 /N 2 mixtures, for the four CRC surrogates and CFA, have been measured using an RCM at LTC relevant pressures and temperatures, in the ranges of 10–20 bar and 650–1000 K, respectively. The equivalence ratios have been varied by independently changing the oxygen mole fraction and the fuel mole fraction in the test mixtures, thereby illustrating the individual effects of oxygen concentration and fuel loading on diesel autoignition. Autoignition results of these four CRC surrogates are compared among them and with those of CFA. Some degree of agreement in autoignition response between each CRC surrogate and CFA is observed, while discrepancies are also identified and discussed. In addition, a detailed chemical kinetic model for diesel surrogates has been developed and validated against these newly-acquired RCM data. We find that this model shows reasonable agreement with the overall ignition delay time results of the current RCM experiments. Chemical kinetic analyses of the developed model were further conducted to help identify the reactions controlling the autoignition processes and the consumption of fuel components in CRC surrogates.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Water intensity of photovoltaic module manufacturing at the terawatt scale

As the U.S. ramps photovoltaic (PV) manufacturing to the terawatt scale and emphasizes re-shoring manufacturing, potential regional impacts on the U.S. water supply should be considered, particularly since many PV companies rely almost exclusively on public water supplies for manufacturing. This work surveys the academic literature and PV manufacturer reports to estimate the water intensity of monocrystalline silicon, multicrystalline silicon, and cadmium telluride modules manufactured at the terawatt scale, determining that on average, cadmium telluride manufacturing is less water intensive on a per megawatt scale – this is anticipated to be true for all thin film PV manufacturing. While much lower than the water intensity of thermoelectric (e.g., coal) energy generation, significant issues and gaps with PV manufacturing data quality in academic studies are identified which cause estimates to vary by over 1000x (0.04 – 49 trillion liters/terawatt). Data issues are discussed and the need for accurate accounting of water resources (e.g., via continuous, updated information during PV manufacturing) is highlighted. The opportunity to reconfigure decommissioned thermoelectric sites to PV manufacturing is also explored. Finally, factors that influence PV manufacturing water intensity, from individual manufacturing steps to trends across the PV industry, are examined and water conservation opportunities are presented.

14 SOLAR ENERGY↗