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

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All sourcesNASA NTRSDOE OSTI
NASA NTRS2023

The Impact of Dimensionality Reduction of Ion Counts Distributions on Preserving Moments, With Applications to Data Compression

The field of space physics has a long history of utilizing dimensionality reduction methods to distill data, including but not limited to spherical harmonics, the Fourier Transform, and the wavelet transform. Here, we present a technique for performing dimensionality reduction on ion counts distributions from the Multiscale Mission/Fast Plasma Investigation (MMS/FPI) instrument using a data-adaptive method powered by neural networks. This has applications to both feeding low-dimensional parameterizations of the counts distributions into other machine learning algorithms, and the problem of data compression to reduce transmission volume for space missions. The algorithm presented here is lossy, and in this work, we present the technique of validating the reconstruction performance with calculated plasma moments under the argument that preserving the moments also preserves fluid-level physics, and in turn a degree of scientific validity. The method presented here is an improvement over other lossy compressions in loss-tolerant scenarios like the Multiscale Mission/Fast Plasma Investigation Fast Survey or in non-research space weather applications.

D. da Silva↗
DOE OSTI2025

Best Practices for Nuclear Experiment Data Preservation at Idaho National Laboratory: A Guide for Researchers and Reactor Operators

Preserving experimental data is essential for supporting advancements in nuclear science and ensuring the longevity of Idaho National Laboratory's contributions to reactor technology and safety. This report provides a comprehensive guide to best practices for experimental data management and preservation, focusing on standardized data formats, redundancy in storage, metadata documentation, and alignment with international standards. By following these recommendations, experimentalists and reactor operators can enhance the accessibility, reproducibility, and utility of critical datasets for regulatory review, validation computational methods, and future research.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2022

Structural Shape Sensing from Strain Data Using the Basis Function Method

An aircraft shape must be measured during flight to implement an active trim shape control, such as an active dihedral control for a highly flexible aircraft with a high-aspect-ratio wing, and an active sonic boom control for a supersonic transport aircraft. A basis function method for linear as well as geometrically nonlinear structural shape sensing is proposed in this study. Basis functions can be mode shapes, linear static deformation shapes, or geometrically nonlinear static deformation shapes. The proposed basis function method is validated using a high-aspect-ratio wing, a swept test plate, and the National Aeronautics and Space Administration Low Boom Flight Demonstration mission X-59 Quiet Supersonic Technology (QueSST) aircraft (Lockheed Martin, Bethesda, Maryland). A large structural deformation of a high-aspect-ratio wing is successfully captured using the proposed basis function method with less than 0.3-percentprediction error at the wing-tip section. The basis function method gives excellent deformation prediction, even with stress concentration. Performance of the basis functions is compared using the X-59 stabilator sample case. In most of the sample load cases, static deformations give a better correlation with the target deformation than do mode shapes. Wing shape sensing with sparse strain data is also demonstrated in this study using the X-59 QueSST aircraft. Predicted structural deformations match reasonably with the target deformations even without strain gauges on some structural components. The predicted deformations have a good match with the target deformations.

Chan-gi Pak↗
DOE OSTI2018

Downscaling-Based Segmentation for Unresolved Images of Highly Heterogeneous Granular Porous Samples

Numerical simulations of pore-scale flow and transport in natural sediments require the knowledge of pore-space topology. Limited resolution of X-ray tomography is often insufficient to fully characterize pore-space structure within fine-grained regions. Single and multilevel threshold-based segmentation approaches are customarily employed to identify solid, pore and porous-solid regions by means of grey intensity thresholds. While the choice of cutoff thresholds is often arbitrary, it dramatically affects the effective properties and the dynamical response of the reconstructed porous structure. As such, we propose an algorithm of downscaling, i.e., the process of increasing image resolution, followed by segmentation, i.e., the identification of different phases, to reconstruct the unresolved pore-space from XCT images of natural geological porous media. The method, applicable to moderately unresolved, chemically homogeneous granular media, is based on a map between local pixel porosity and pore size that does not rely on the definition of arbitrary thresholds and it allows to generate a high-resolution binary image of the porous medium from poorly resolved grey-scale images. First, we validate the method on synthetic unresolved images and compare their known pore-space distribution with the extracted one. Then, we consider a synthetic porous medium and compare the pore size distribution, conductivity, and tortuosity between the original and the reconstructed structures. Finally, we apply the method to extract the pore-space distribution from unresolved XCT images of two natural sediment columns and use it (i) to parametrize a capillary-bundle model and (ii) to estimate the hydraulic conductivity by matching breakthrough behavior of passive solute transport.

58 GEOSCIENCES↗
DOE OSTI2017

A Rapid Method for Quantification OF 242Pu in Urine Using Extraction Chromatography and ICP-MS

Occupational exposure to plutonium is generally monitored through analysis of urine samples. Typically, plutonium is separated from the sample and other actinides, and the concentration is determined using alpha spectroscopy. Current methods for separations and analysis are lengthy and require long count times. A new method for monitoring occupational exposure levels of plutonium has been developed, which requires fewer steps and overall less time than the alpha spectroscopy method. In this method, the urine is acidified, and a 239 Pu internal standard is added. The urine is digested in a microwave oven, and plutonium is separated using an Eichrom TRU Resin column. The plutonium is eluted, and the eluant is injected directly into the Inductively Coupled Plasma–Mass Spectrometer (ICP-MS). Compared to a direct “dilute and shoot” method, a 30-fold improvement in sensitivity is achieved. This method was validated by analyzing several batches of spiked samples. Based on these analyses, a combined standard uncertainty plot, which relates uncertainty to concentration, was produced. As a result, the MDA 95 was calculated to be 7.0 × 10 –7 μg L –1 , and the Lc95 was calculated to be 3.5 × 10 –7 μg L –1 for this method.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

An update to the Sandia method for creating Typical Meteorological Years from a limited pool of calendar years

Typical Meteorological Years (TMYs) are essential for the efficient evaluation of energy system performance. Ideally, 30 years of weather data are required to generate TMYs, but significantly fewer years are typically available due to practical limitations. To address this issue, an update to the Sandia method was developed, referred to as the Argonne method, to create TMYs from a limited number of years. Furthermore, this method enhances candidate diversity by systematically shifting original candidate months forward or backward by specific days, creating an expanded pool of candidates. The effectiveness of the Argonne method was validated through statistical testing, comparison of monthly average weather parameters, and numerical simulations. The results demonstrate a high probability of identifying at least one shifted month whose cumulative distribution functions of weather parameters closely align with long-term distributions. In 67 % of all comparisons, the monthly average weather parameters in TMYs generated using the Argonne method exhibit better agreement with long-term averages than TMY3. Moreover, in 74 % of the 318 building simulation cases, the Argonne method outperforms TMY3 in estimating long-term average building heating and cooling demands. Therefore, the Argonne method effectively diversifies the candidate pool and produces typical years that provide more accurate estimations of long-term averages compared to TMY3 when only a limited pool of calendar years (10 years or fewer) is available.

Building energy modeling↗
DOE OSTI2020

Identifying a regional aerosol baseline in the eastern North Atlantic using collocated measurements and a mathematical algorithm to mask high-submicron-number-concentration aerosol events

Abstract. High-time-resolution measurements of in situ aerosol and cloud properties provide the ability to study regional atmospheric processes that occur on timescales of minutes to hours. However, one limitation to this approach is that continuous measurements often include periods when the data collected are not representative of the regional aerosol. Even at remote locations, submicron aerosols are pervasive in the ambient atmosphere with many sources. Therefore, periods dominated by local aerosol should be identified before conducting subsequent analyses to understand aerosol regional processes and aerosol–cloud interactions. Here, we present a novel method to validate the identification of regional baseline aerosol data by applying a mathematical algorithm to the data collected at the U.S. Department of Energy's (DOE) Atmospheric Radiation Measurement (ARM) user facility in the eastern North Atlantic (ENA). The ENA central facility (C1) includes an aerosol observing system (AOS) for the measurement of aerosol physical, optical, and chemical properties at time resolutions from seconds to minutes. A second temporary supplementary facility (S1), located ∼0.75 km from C1, was deployed for ∼1 year during the Aerosol and Cloud Experiments (ACE-ENA) campaign in 2017. First, we investigate the local aerosol at both locations. We associate periods of high submicron number concentration (Ntot) in the fine-mode condensation particle counter (CPC) and size distributions from the Ultra-High Sensitivity Aerosol Spectrometer (UHSAS) as a function of wind direction using a meteorology sensor with local sources. Elevated concentrations of Aitken-mode (< 100 nm diameter) particles were observed in correspondence with the wind directions associated with airport operations. At ENA, the Graciosa Airport and its associated activities were found to be the main sources of high-concentration aerosol events at ENA, causing peaks in 1 min Ntot that exceeded 8000 and 10 000 cm−3 at C1, in summer and winter, respectively, and 5000 cm−3 at S1 in summer. Periods with high Ntot not associated with these wind directions were also observed. As a result, the diverse local sources at ENA yielded a poor relationship between Ntot measurements collected at C1 and S1 (R2=0.03 with a slope =0.05±0.001). As a first approach to mask these events, the time periods when the wind direction was associated with the airport operations (west to northwest and southeast to south at C1 and east to south at S1) were applied. The meteorological masks removed 38.9 % of the data at C1 and 43.4 % at S1, and they did not significantly improve the relationship between the two sites (R2=0.18 with a slope =0.06±0.001). Due to the complexity of high-Ntot events observed at ENA, we develop and validate a mathematical ENA Aerosol Mask (ENA-AM) to identify high-Ntot events using 1 min resolution data from the AOS CPC at C1 and S1. After its parameterization and application, ENA-AM generated a high correlation between Ntot in the summer at C1 and S1 (R2=0.87 with a slope =0.84±0.001). We identified the regional baseline at ENA to be 428±228 cm−3 in the summer and 346±223 cm−3 in the winter. Lastly, we compared masked measurements from the AOS with the ARM Aerial Facility (AAF) during flights over C1 in the summer to understand submicron aerosol vertical mixing over C1. The high correlation (R2=0.71 with a slope of 1.04±0.01) observed between C1 and the AAF Ntot collected within an area of 10 km surrounding ENA and at altitudes < 500 m indicated that the submicron aerosol at ENA was well mixed within the first 500 m of the marine boundary layer during the month of July during ACE-ENA. Our novel method for determining a regional aerosol baseline at ENA can be applied to other time periods and at other locations with validation by a secondary site or additional collocated measurements.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Learning-Based Load Control to Support Resilient Networked Microgrid Operations

Microgrids have proven to be an effective option for increasing the resiliency of critical end-use loads during extreme events. Building on past operational experiences, some microgrid operators are examining the potential to network microgrids to further improve resiliency. However, the frequency deviations experienced on isolated microgrids during transient events, such as switching operations, step increases in load, and loss of generation, are significantly larger than those typically seen on bulk transmission systems. The larger frequency deviations can cause a loss of inverter-connected assets, resulting in a loss of power to critical end-use loads. This paper presents a method of mitigating the impact of transient events by engaging end-use loads using Grid-Friendly Appliance TM (GFA) controllers. An online, i.e., real-time, device-level algorithm is presented, which adjusts individual GFA controller frequency set-points based on the operational characteristics of each end-use load, and on the changing grid dynamic characteristics. The presented method improves the dynamic stability of the networked microgrid operations while minimizing the interruptions to end-use loads. The presented work is validated with dynamic simulations using a modified version of the IEEE 123-node test system with three microgrids, using the GridLAB-D TM simulation environment.

Radhakrishnan, Nikitha↗
DOE OSTI2021

Condensed Phase Water Molecular Multipole Moments from Deep Neural Network Models Trained on Ab Initio Simulation Data

Ionic solvation phenomena in liquids involve intense interactions in the inner solvation shell. For interactions beyond the first shell, the ion–solvent interaction energies result from the sum of many smaller-magnitude contributions that can still include polarization effects. Deep neural network (DNN) methods have recently found wide application in developing efficient molecular models that maintain near-quantum accuracy. In this work, we extend the DeePMD-kit code to produce accurate molecular multipole moments in the bulk and near interfaces. The new method is validated by comparing the DNN moments with those generated by ab initio simulations. The moments are used to compute the electrostatic potential at the center of a molecular-sized hydrophobic cavity in water. The results show that the fields produced by the DNN models are in quantitative agreement with the AIMD-derived values. These efficient methods will open the door to more accurate solvation models for large solutes such as proteins.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Modeling the Nucleation of Weak Electrolytes via Hybrid GCMC/MD Simulation

We have developed a hybrid grand canonical Monte Carlo/molecular dynamics (GCMC/MD) method for simulating the nucleation of weak electrolytes in an explicit solvent. In contrast to brute-force MD simulation, the approach is capable of efficiently simulating the nucleation of dilute solutions while including the atomistic influence of the surrounding solvent and provides access to the full nucleation free energy surface and associated nucleation free energy barrier. After validating the method against a simple model system, we applied the approach to the nucleation of a low-solubility rock-salt structure in liquid water. We find that the calculated nucleation barriers, in conjunction with analytic rate theories, yield predicted nucleation rates that are in excellent agreement with brute-force MD simulations of the supersaturated solution. We anticipate possible applications of this approach to a wide variety of related weak electrolytes, including CaCO 3 , zeolites, and metal–organic frameworks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Pairing Directional Solar Inputs From Ray Tracing to Solar Receiver/Reactor Heat Transfer Models on Unstructured Meshes: Development and Case Studies

A novel method for pairing surface irradiation and volumetric absorption from Monte Carlo ray tracing to computational heat transfer models is presented. The method is well-suited to directionally and spatially complex concentrated radiative inputs (e.g., solar receivers and reactors). The method employs a generalized algorithm for directly mapping absorbed rays from a Monte Carlo ray tracing model to boundary or volumetric source terms in the computational mesh. The algorithm is compatible with unstructured, two and three-dimensional meshes with varying element shapes. Four case studies were performed on a directly irradiated, windowed solar thermochemical reactor model to validate the method. The method was shown to conserve energy and preserve spatial variation when mapping rays from a Monte Carlo ray tracing model to a computational heat transfer model in ansys fluent.

14 SOLAR ENERGY↗
DOE OSTI2019

Rapidly Measuring Loop Footprints

Knowing a loop’s footprint — the unique data items it accesses — enables important locality and capacity analysis. Unfortunately, current methods for computing footprint cause integer factors of slowdown and are therefore difficult to use with realistic inputs. Current methods are slow because they are based on software tracing of load instructions and data addresses. We present an approach that combines lightweight measurements and static binary analysis. We use static analysis to reason about each load’s expected reuse. We then calculate the footprint for each loop using inference rules informed by measurements from common performance counters. We validate our method on benchmarks that vary access patterns (strided vs. unpredictable), reuse (varying and repeated accesses per element), and sparsity (all words in a cache line vs. some). For strided patterns, errors are within 1%; for unpredictable ones, errors are 5–10%. Our overheads are under 10%. A tool based on software tracing has an error of less than 1%, but either introduces at least 130× overhead or hangs.

Kilic, Ozgur O.↗
DOE OSTI2020

Multi-Commodity Traffic Signal Control and Routing With Connected Vehicles

We report a real-time traffic management policy that integrates traffic signal control and multi-commodity routing of connected vehicles in networks with multiple destinations is developed. The proposed policy is based on a multi-commodity formulation of the store-and-forward model and assumes all vehicles are able to exchange information with the infrastructure. Vehicles share information about their current location and final destination. Based on this information, the strategy determines both optimized signal timings at every intersection and vehicle-specific routing information at every link of the network. The control actions, i.e., signal times and routing information, are updated at every cycle and delivered by a finite horizon optimal control problem cast into a rolling horizon framework. The underlying optimization problem is convex, and thus the method is suitable for real-time operation in large networks. The method is validated via a micro-simulation study in networks with up to twenty intersections and, in all simulations, outperforms a real-time traffic-responsive signal control strategy that is based on a single-commodity store-and-forward model. The scalable computation effort for increasing network sizes and prediction horizon confirms the computational efficiency of the method.

42 ENGINEERING↗
NASA NTRS2015

Comparison of Analysis with Test for Static Loading of Two Hypersonic Inflatable Aerodynamic Decelerator Concepts

Acceptance of new spacecraft structural architectures and concepts requires validated design methods to minimize the expense involved with technology demonstration via flight-testing. Hypersonic Inflatable Aerodynamic Decelerator (HIAD) architectures are attractive for spacecraft deceleration because they are lightweight, store compactly, and utilize the atmosphere to decelerate a spacecraft during entry. However, designers are hesitant to include these inflatable approaches for large payloads or spacecraft because of the lack of flight validation. This publication summarizes results comparing analytical results with test data for two concepts subjected to representative entry, static loading. The level of agreement and ability to predict the load distribution is considered sufficient to enable analytical predictions to be used in the design process.

Lyle, Karen H.↗
DOE OSTI2021

Optical tweezer measurements of asymptotic nonlinearities in complex fluids

This article presents micro-medium-amplitude oscillatory shear (μMAOS), a method to measure the frequency-dependent micromechanical properties of soft materials in the asymptotically nonlinear regime using optical tweezers. For this work, we have developed a theoretical framework to extract these nonlinear mechanical properties of the material from experimental measurements and also proposed a physical interpretation of the third-order nonlinearities measured in single-tone oscillatory tests. We validate the method using a well-characterized surfactant solution of wormlike micelles, and subsequently employ this technique to demonstrate that the cytoplasm of a living cell undergoes strain softening and shear thinning when locally subjected to weakly nonlinear oscillatory deformations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Estimation of process steady state with autoregressive models and Bayesian inference

To improve efficiency, separations engineers will typically design process circuits containing recirculating streams, which mix one or more of the process outputs with the feed material. Doing so can improve efficiency, but will cause a delay in the system reaching steady state conditions until the recirculating load mass flows stabilize. In testing separation circuits, engineers will often test a variety of factors and complete an analysis from sample results. Knowledge of if a process is at steady state, as well as the steady state conditions of a process, is essential for a valid techno-economic analysis. However, the definition of process steady state is often poorly defined, or does not include uncertainty quantification. If the performance of a process operating under two different sets of conditions are compared, an engineer who does not test for steady state or quantify steady state conditions risks producing a faulty analysis. In this work, a Bayesian statistical method for testing if all streams are at steady state is further motivated and then derived. Then after testing for steady state, the same model is used with a prior distribution that enforces a steady state assumption to estimate steady state conditions. Further, these methods were validated in a solvent extraction pilot plant where steady state conditions for all outflows were inferred with uncertainty quantification. Analysis is completed with functions available to the reader as part of the BayesMassBal (V 1.1.0) software package written in R.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Accessing tens-to-hundreds femtoseconds nuclear state lifetimes with low-energy binary heavy-ion reactions

A novel Monte Carlo technique has been developed to determine lifetimes of excited states in the tens-to-hundreds femtoseconds range in products of low-energy heavy-ion binary reactions, with complex velocity distributions. The method is based on a detailed study of Doppler-broadened γ-ray lineshapes. Its relevance is demonstrated in connection with the 18 O(7.0MeV/u)+ 181 Ta experiment, performed at GANIL with the AGATA+VAMOS+PARIS setup, to study neutron-rich O, C, N, ... nuclei. Excited states in 17 O and 19 O, with known lifetimes, are used to validate the method over the ~20-400fs lifetime-sensitivity range. Emphasis is given to the unprecedented position resolution provided by γ-tracking arrays, which turns out to be essential for reaching the required accuracy in Doppler-shift correction. The technique is anticipated to be an important tool for lifetime investigations in exotic neutron-rich nuclei, produced with intense ISOL-type beams.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗