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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2000

Langmuir Probe Measurements of Inductively Coupled CHF3/Ar and Ar/CHF3/O2 Plasmas

Plasma parameters, such as, electron number density (ne), electron temperature (Te), y electron energy distribution function (EEDF), mean electron energy (Ee), ion number density (ni), and plasma potential (Vp), have been measured by using Langmuir probe in low-pressure (10-50 mTorr) inductively coupled CHF3/Ar and CHF3/Ar/O2 plasmas generated in the GEC cell. The measurements were made at the center of the plasma, keeping the lower electrode grounded, for various CHF3/Ar and Ar/CHF3/O2 mixtures operating at 10-50 mTorr pressures and two input RF power levels, 200 and 300 W. EEDF data show a strong Druyvesteyn distribution with relatively lower number of low energy electrons as compared to a Maxwell distribution and a large electron population with energies higher than the plasma potential. The results further show that at low CHF3 concentrations (less than 50%) the electron number density remains nearly constant with increase in pressure. At higher CHF3 concentrations, however, it decreases with increase in pressure. Plasma potential and electron temperature increase with decrease in pressure and with increase in CHF3 concentration. An analysis of the above observations and mechanisms will be presented.

Sharma, S. P.↗
DOE OSTI2021

Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks: Preprint

Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classical state estimation algorithms. In this paper, a new method so-called pruned physics-aware neural network (P2N2) is developed to improve the voltage estimation accuracy in the distribution system. The method relies on the physical grid topology, which is used to design the connections between different hidden layers of a neural network model. To verify the proposed method, a numerical simulation based on one-year smart meter data of load consumptions for threephase power flow is developed to generate the measurement and voltage state data. The IEEE 123 node system is selected as the test network to benchmark the proposed algorithm against the classical weighted least squares (WLS). Numerical results show that P2N2 outperforms WLS, in terms of data redundancy and estimation accuracy.

distribution systems state estimation↗
DOE OSTI2024

Resolved resonance region evaluations of n+ 206,207,208 Pb for fast spectrum applications

Resolved resonance region evaluations of the major isotopes of natural lead, 206 Pb, 207 Pb, 208 Pb, have been per formed to support the development of Generation IV reactors. In this study, validation of nuclear data for lead fast reactors was performed with simulations of shielding benchmarks, integral critical benchmarks, and quasi-differential scattering measurements. Sensitivity analyses of these systems showed that elastic scattering reactions above 100 keV were the dominant reactions driving system performance. The resolved resonance regions (RRRs) of the lead isotopes extend past 100 keV, making the RRR an ideal starting point to evaluate lead cross sections. Since the R-matrix requires knowledge of bound, distant, and observed resonances, it was necessary to evaluate from 10 -5 eV up to the respective limit of the RRR. The 208 Pb RRR evaluation was extended to 1.5 MeV in order to obtain resonance parameters used to calculate new elastic scattering angular distributions up to 1.5 MeV. Resonance parameter uncertainties and covariance were generated using the R-matrix code SAMMY. The new RRR parameters show a direct improvement to the scattering kernel below 1.5 MeV which in turn greatly improves fast critical experiments over ENDF/B-VIII.0.

206Pb↗
NASA NTRS2002

Global Aerosol Remote Sensing from MODIS

The physical characteristics, composition, abundance, spatial distribution and dynamics of global aerosols are still very poorly known, and new data from satellite sensors have long been awaited to improve current understanding and to give a boost to the effort in future climate predictions. The derivation of aerosol parameters from the MODerate resolution Imaging Spectro-radiometer (MODIS) sensors aboard the Earth Observing System (EOS) Terra and Aqua polar-orbiting satellites ushers in a new era in aerosol remote sensing from space. Terra and Aqua were launched on December 18, 1999 and May 4, 2002 respectively, with daytime equator crossing times of approximately 10:30 am and 1:30 pm respectively. Several aerosol parameters are retrieved at 10-km spatial resolution (level 2) from MODIS daytime data. The MODIS aerosol algorithm employs different approaches to retrieve parameters over land and ocean surfaces, because of the inherent differences in the solar spectral radiance interaction with these surfaces. The parameters retrieved include: aerosol optical thickness (AOT) at 0.47, 0.55 and 0.66 micron wavelengths over land, and at 0.47, 0.55, 0.66, 0.87, 1.2, 1.6, and 2.1 micron over ocean; Angstrom exponent over land and ocean; and effective radii, and the proportion of AOT contributed by the small mode aerosols over ocean. To ensure the quality of these parameters, a substantial part of the Terra-MODIS aerosol products were validated globally and regionally, based on cross correlation with corresponding parameters derived from ground-based measurements from AERONET (AErosol RObotic NETwork) sun photometers. Similar validation efforts are planned for the Aqua-MODIS aerosol products. The MODIS level 2 aerosol products are operationally aggregated to generate global daily, eight-day (weekly), and monthly products at one-degree spatial resolution (level 3). MODIS aerosol data are used for the detailed study of local, regional, and global aerosol concentration, distribution, and temporal dynamics, as well as for radiative forcing calculations. We show several examples of these results and comparisons with model output.

Ichoku, Charles↗
NASA NTRS2000

Can Distributed Volunteers Accomplish Massive Data Analysis Tasks?

The science community is accustomed to interacting with the public for two main purposes: outreach to adults (since they are the patrons upon whose good will future funding will depend), and education for children (since they are the reservoir from which the next generation's talent must be drawn). We suggest that a third relationship can now be fruitful, one that has been little used until now - with the notable exception of the astronomy community. Astronomy has a long history of relying on non-professionals for some observations. Important contributions have been made by amateur astronomers in several areas of research including monitoring dust storms on Mars, timing asteroid occultations, and discovering comets. Note that three distinct contributions are being made by amateur astronomers: they supply their own instrumentation, provide access to observing sites around the Globe, and contribute their innate powers of perception.

Kanefsky, Bob↗
DOE OSTI2025

HPC Campaign Management: Remote data access with user-defined error bound using ADIOS and ZFP

Remote access to large-scale scientific datasets, like those generated by combustion simulations or other high-performance computing (HPC) applications, presents a significant challenge. Downloading entire datasets is often impractical due to their size and the bandwidth limitations of typical networks. To address this challenge, we propose a novel approach that enables efficient remote access to large datasets distributed across multiple facilities. Our method enables technologies to download only the data values of a select variable, in a select region of interest, to a user-defined accuracy. For this purpose, we extended the ADIOS IO library to provide read functions with user-defined accuracy, a remote data server that understands multidimensional selections of specific variables, steps and accuracy from an ADIOS dataset, and which uses lossy compression on the remote site to reduce the data to be transferred back to the client. In addition, our extension of the ADIOS library collects metadata from multiple datasets in small files called Campaign Archives, which can be shared among project participants on any HPC, cloud or laptop, and which can easily facilitate the discovery of content and pointers to the data location as well as remote access to the data by local tools as if data was local. This feature called Campaign Management, enables a group of scientists to manage related datasets stored in multiple files, across multiple facilities as if it was in a single file/database. We demonstrate the effectiveness of our approach using a 1.5 TB dataset from the S3D combustion simulation on Frontier at the Oak Ridge Leadership Facility. Even a single variable from this dataset, at 64 GB, is too large to be processed on a standard laptop. We show two different reading patterns for 2D plots and 3D visualization, with careful settings that a scientist studying combustion data would do and show that running the same Python scripts on Frontier directly takes comparable time than running them on the local laptop with remote access to the data on Frontier.

Podhorszki, Norbert [ORNL] (ORCID:000000019647542X↗
DOE OSTI2025

GenAI4UQ: A software for forward and inverse uncertainty quantification using conditional generative AI

We introduce GenAI4UQ, a software package for forward and inverse uncertainty quantification in model calibration, parameter estimation, and ensemble forecasting. GenAI4UQ leverages a generative AI-based conditional modeling framework to address limitations of traditional inverse modeling techniques, such as Markov Chain Monte Carlo (MCMC) methods. By replacing computationally intensive iterative processes with a direct, learned mapping, GenAI4UQ enables efficient calibration of input parameters and generation of predictions directly from observations. The software supports rapid ensemble forecasting with robust uncertainty quantification while maintaining computational and storage efficiency. Built-in auto-tuning of hyperparameters simplifies model training, ensuring accessibility for users with varying expertise. Its versatile conditional generative framework is applicable across diverse scientific domains. While GenAI4UQ offers significant advantages in flexibility and efficiency, users should interpret its uncertainty estimates with caution in data-sparse scenarios, as the model may overestimate uncertainty—an effect common to all surrogate-based approaches including MCMC with surrogate models. Despite this, GenAI4UQ transforms inverse modeling by providing a fast, reliable, and user-friendly solution. It empowers researchers and practitioners to quickly estimate parameter distributions and generate model predictions for new observations, facilitating efficient decision-making and advancing the state of uncertainty quantification in computational modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

MOVES Activity Updates Using Fleet DNA Data: Interim Report

The U.S. Environmental Protection Agency's Motor Vehicle Emissions Simulator (MOVES) is a publicly available tool used by researchers and policy makers to help understand motor vehicle emission sources at national, county, and project levels. However, estimates of heavy-duty activity (MOVES2014), have been identified as areas needing improvement. The start activities in MOVES2014 are calculated using a limited data set, prompting the concern that inventory values are not representative. In addition, MOVES2014 is believed to underestimate heavy-duty activity that is not captured in the current drive cycles used to represent on-network activity. For example, MOVES2014 does not account for work-day idling activity that takes place on off-network roads, such as at a distribution center while the truck is queuing or during loading and unloading. Under the guidance and expertise of the EPA, the National Renewable Energy Laboratory has leveraged its extensive Fleet DNA database of heavy-duty vehicles to reinforce the data set behind the next-generation MOVES model and enhance idle activity and start fractions using six heavy-duty vehicle classes. The data available in Fleet DNA from 420 conventional, diesel powered vehicles provided over 120,000 hours of operation. Start fraction, soak fraction, and idle fraction by hour of the day were derived for each source type, state, and vocation, and results were provided in the form of .CSV files representing MOVES table inputs. This midterm report details these results providing graphical analysis and context for the start, soak, and idle distributions.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRSDate not supplied

A Framework for Sense and Follow Convoys for Collective Autonomous Mobility

Convoys are collections of vehicles that share similar velocity profiles. In the sense-and follow convoy construct, a vehicle can follow a member of an existing convoy resulting in convoy chaining. We prove conditions under which this can occur such that the resulting convoy chain is bounded and the velocities of all members converge to the leader velocity through locally distributed control algorithms. To assess the approach, we utilize a six degree-of-freedom rigid body vehicle dynamics model for each agent. A dynamic inversion based control law is used to track the desired trajectory generated by the convoy algorithms. To simulate the system, we utilize Unity, a cross-platform game engine, where 3-dimensional terrain, building, and vehicle data can be visualized. Numerous simulations are presented to illustrate the concept.

convoys↗
NASA NTRS2011

Generation of the Ares I-X Flight Test Vehicle Aerodynamic Data Book and Comparison To Flight

A 3.5-year effort to characterize the aerodynamic behavior of the Ares I-X Flight Test Vehicle (AIX FTV) is described in this paper. The AIX FTV was designed to be representative of the Ares I Crew Launch Vehicle (CLV). While there are several differences in the outer mold line from the current revision of the CLV, the overall length, mass distribution, and flight systems of the two vehicles are very similar. This paper briefly touches on each of the aerodynamic databases developed in the program, describing the methodology employed, experimental and computational contributions to the generation of the databases, and how well the databases and underlying computations compare to actual flight test results.

Bauer, Steven X.↗
DOE OSTI2021

Investigating Wireless Quantum Key Distribution for Advanced Reactor Communications

Remote operation of small modular reactor (SMR) facilities is an appealing prospect for streamlined operation costs, safety concerns, and user convenience. The ability to guarantee security of communication channels between offsite users and nuclear reactor facilities is critical to enabling remote operations, given the sensitive nature of reactor state data. This report proposes a configuration implementing quantum key distribution (QKD) and advanced encryption standard (AES) protocols to establish secure reactor data feeds. The model outlined, which utilizes the Argonne-developed SeQUeNCe software package and custom MATLAB code, determines free-space channel losses for microwave-band communications in a variety of weather conditions and simulates QKD via ground-satellite links. This report demonstrates the feasibility of repeatable, real-time key generation and distribution for this configuration towards enabling secure wireless reactor communications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Predicting the propensity for thermally activated β events in metallic glasses via interpretable machine learning

Abstract The elementary excitations in metallic glasses (MGs), i.e., β processes that involve hopping between nearby sub-basins, underlie many unusual properties of the amorphous alloys. A high-efficacy prediction of the propensity for those activated processes from solely the atomic positions, however, has remained a daunting challenge. Recently, employing well-designed site environment descriptors and machine learning (ML), notable progress has been made in predicting the propensity for stress-activated β processes (i.e., shear transformations) from the static structure. However, the complex tensorial stress field and direction-dependent activation could induce non-trivial noises in the data, limiting the accuracy of the structure-property mapping learned. Here, we focus on the thermally activated elementary excitations and generate high-quality data in several Cu-Zr MGs, allowing quantitative mapping of the potential energy landscape. After fingerprinting the atomic environment with short- and medium-range interstice distribution, ML can identify the atoms with strong resistance or high compliance to thermal activation, at a high accuracy over ML models for stress-driven activation events. Interestingly, a quantitative “between-task” transferring test reveals that our learnt model can also generalize to predict the propensity of shear transformation. Our dataset is potentially useful for benchmarking future ML models on structure-property relationships in MGs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Coassembly and binning of a twenty-year metagenomic time-series from Lake Mendota

Abstract The North Temperate Lakes Long-Term Ecological Research (NTL-LTER) program has been extensively used to improve understanding of how aquatic ecosystems respond to environmental stressors, climate fluctuations, and human activities. Here, we report on the metagenomes of samples collected between 2000 and 2019 from Lake Mendota, a freshwater eutrophic lake within the NTL-LTER site. We utilized the distributed metagenome assembler MetaHipMer to coassemble over 10 terabases (Tbp) of data from 471 individual Illumina-sequenced metagenomes. A total of 95,523,664 contigs were assembled and binned to generate 1,894 non-redundant metagenome-assembled genomes (MAGs) with ≥50% completeness and ≤10% contamination. Phylogenomic analysis revealed that the MAGs were nearly exclusively bacterial, dominated by Pseudomonadota (Proteobacteria, N = 623) and Bacteroidota (N = 321). Nine eukaryotic MAGs were identified by eukCC with six assigned to the phylum Chlorophyta. Additionally, 6,350 high-quality viral sequences were identified by geNomad with the majority classified in the phylum Uroviricota. This expansive coassembled metagenomic dataset provides an unprecedented foundation to advance understanding of microbial communities in freshwater ecosystems and explore temporal ecosystem dynamics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Generative adversarial networks for scintillation signal simulation in EXO-200

Generative Adversarial Networks trained on samples of simulated or actual events have been proposed as a way of generating large simulated datasets at a reduced computational cost. In this work, a novel approach to perform the simulation of photodetector signals from the time projection chamber of the EXO-200 experiment is demonstrated. The method is based on a Wasserstein Generative Adversarial Network — a deep learning technique allowing for implicit non-parametric estimation of the population distribution for a given set of objects. Our network is trained on real calibration data using raw scintillation waveforms as input. We find that it is able to produce high-quality simulated waveforms an order of magnitude faster than the traditional simulation approach and, importantly, generalize from the training sample and discern salient high-level features of the data. In particular, the network correctly deduces position dependency of scintillation light response in the detector and correctly recognizes dead photodetector channels. Furthermore, the network output is then integrated into the EXO-200 analysis framework to show that the standard EXO-200 reconstruction routine processes the simulated waveforms to produce energy distributions comparable to that of real waveforms. Finally, the remaining discrepancies and potential ways to improve the approach further are highlighted.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Data efficiency and extrapolation trends in neural network interatomic potentials

Abstract Recently, key architectural advances have been proposed for neural network interatomic potentials (NNIPs), such as incorporating message-passing networks, equivariance, or many-body expansion terms. Although modern NNIP models exhibit small differences in test accuracy, this metric is still considered the main target when developing new NNIP architectures. In this work, we show how architectural and optimization choices influence the generalization of NNIPs, revealing trends in molecular dynamics (MD) stability, data efficiency, and loss landscapes. Using the 3BPA dataset, we uncover trends in NNIP errors and robustness to noise, showing these metrics are insufficient to predict MD stability in the high-accuracy regime. With a large-scale study on NequIP, MACE, and their optimizers, we show that our metric of loss entropy predicts out-of-distribution error and data efficiency despite being computed only on the training set. This work provides a deep learning justification for probing extrapolation and can inform the development of next-generation NNIPs.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Reconstructing small-scale lenses from the cosmic microwave background temperature fluctuations

Cosmic microwave background (CMB) lensing is a powerful probe of the matter distribution in the Universe. The standard quadratic estimator, which is typically used to measure the lensing signal, is known to be suboptimal for low-noise polarization data from next-generation experiments. In this paper, we explain why the quadratic estimator will also be suboptimal for measuring lensing on very small scales, even for measurements in temperature where this estimator typically performs well. Though maximum likelihood methods could be implemented to improve performance, we explore a much simpler solution, revisiting a previously proposed method to measure lensing that involves a direct inversion of the background gradient. An important application of this simple formalism is the measurement of cluster masses with CMB lensing. We find that directly applying a gradient inversion matched filter to simulated lensed images of the CMB can tighten constraints on cluster masses compared to the quadratic estimator. While the difference is not relevant for existing surveys, for future surveys it can translate to significant improvements in mass calibration for distant clusters, where galaxy lensing calibration is ineffective due to the lack of enough resolved background galaxies. Improvements can be as large as ~50 per cent for a cluster at z = 2 and a next-generation CMB experiment with 1 μK arcmin noise, and over an order of magnitude for lower noise levels. For future surveys, this simple matched filter or gradient inversion method approaches the performance of maximum likelihood methods, at a fraction of the computational cost.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

A Deep Generative Model for Non-Intrusive Identification of EV Charging Profiles

The proliferation of electric vehicles (EVs) brings environmental benefits and technical challenges to power grids. An identification algorithm which can accurately extract individual EV charging profiles out of widely available smart meter measurements has attracted great interests. This paper proposes a non-intrusive identification framework for EV charging profile extraction, which is driven by deep generative models (DGM). First, the proposed DGM is designed as a representation layer embedded into the Markov process and used to model the joint probability distribution of available time-series data. A novel contribution is to approximate posterior distributions by neural networks whose parameters are obtained by variational inference and supervised learning. Second, the EV charging status is inferred from the DGM via dynamic programming. Lastly, the desired EV charging profile can be reconstructed by the rated power of EV models and inferred status. Compared with the benchmark Hidden Markov Models, the proposed framework can better handle noise in data with less computational complexity and better overall accuracy performances with smaller recall. The proposed framework is validated by numerical experiments on the Pecan Street dataset.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

One-Step Ahead Prediction of Thermal Mixing Tee Sensors with Long Short Term Memory (LSTM) Neural Networks

High-temperature advanced reactors under development, such as sodium fast reactors (SFR) and molten salt cooled reactors (MSCR), are expected to offer lower levelized cost of energy (LCOE) compared to existing light water reactor (LWR’s). In the existing light water reactors (LWR’s), operation and maintenance (O&M) expenses constitute the largest fraction of the total operating cost. Some of the O&M costs are related maintenance of sensors which can fail due to exposure to harsh environment in a reactor. The O&M costs of Advanced Reactor (AR)’s are expected to constitute a significant fraction of the total cost as well, because of high temperature and radiation level in AR are likely to cause material fatigue and premature failure of sensors and components. The O&M costs in AR’s could be reduced through integration of advanced informatics of performance-related sensors into a digital twin designed for reactor monitoring. For example, machine learning (ML) could be employed for real-time validation and correction of performance-related sensors, and reducing the number of performance-related physical sensor units through virtual sensing. As part of the effort, we investigate real-time validation of thermal hydraulic sensors through one-step ahead forecasting of sensor values using long short-term memory (LSTM) recurrent neural networks (RNN). The sensors are installed in a flow loop containing a thermal mixing tee, which is a common experimental model to study thermal fatigue in a thermal hydraulic loop. In addition, nonlinear transients generated in a thermal mixing tee constitute a good challenge data set for training and validation of ML algorithms. Sensors in this study include thermocouples, flow meters, and optical fibers for distributed temperature sensing. In one experiment, measurement data sets were obtained for a loop was filled with water, and in another experiment, measurements were performed on a loop filled with liquid metal Galinstan. We have also conducted preliminary investigation of one-step ahead prediction of fiber optics-based distributed temperature sensing with LSTM networks. In predicting fiber-based temperature measurements, we treated each gauge pitch of the fiber as an independent sensor. Accuracy of one-step ahead forecasting was estimated by calculating root mean square error (RMSE) for the test segment of time series of each sensor. RMSE’s for temperature sensors in water loop were, for the most part, lower than for the same sensors in Galinstan loop. The RMSE’s for flow meters were similar for both loops. The RMSE’s for distributed temperature measured with the fiber optic sensor were similar to those of the point sensors. Results of this study demonstrated the capability of LSTM one-step ahead forecasting with RMSE comparable to uncertainty in sensor measurements.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗