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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 1,513 records · Page 84
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Methodology to Generate the Decay Gamma Source for a Second Target Station Target Wedge

This report details the specifics of applying the Position-Averaged Method methodology for the calculation of the rotating lasagna-style target wedge activation and the resulting decay gamma source terms. The methodology uses MCNP® Code Version 6.2.0 with the RNUCS patch coupled with CINDER2008 from the AARE V1.0 package. The process begins with model preparations that include ensuring impurities are included in the materials in the regions of interest along with segmentation of the geometry in order to capture the decay gamma source gradients. This report also details the physics models and other MCNP®, CINDER2008, and ADVANTG 3.2.0 related options used to calculate the decay gamma source terms. ADVANTG 3.2.0 is used to generate a weight window set to aid in convergence of the neutron fluxes in the initial MCNP® protons-on-target particle transport calculation to calculate the neutron fluxes and spallation products in the regions of interest. The neutron fluxes and spallation products are then used in the CINDER2008 transmutation calculation, and the decay gamma sources are generated by the AARE_GAMMA_SOURCE_SCRIPT as a part of the AARE V1.0 package. These decay gamma sources include the energy distributions as well as the necessary distributions to sample the location of the source using the cell rejection method. The gamma sources that are produced using the methodology may be used for a wide variety of analysis involving the target wedge. This report includes the material specifications, physics options, proton source term description, geometrical configuration, and post-processing details.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Signal-Based Fast Tripping Protection Schemes for Electric Power Distribution System Resilience

This report is a summary of a 3-year LDRD project that developed novel methods to detect faults in the electric power grid dramatically faster than today’s protection systems. Accurately detecting and quickly removing electrical faults is imperative for power system resilience and national security to minimize impacts to defense critical infrastructure. The new protection schemes will improve grid stability during disturbances and allow additional integration of renewable energy technologies with low inertia and low fault currents. Signal-based fast tripping schemes were developed that use the physics of the grid and do not rely on communication to reduce cyber risks for safely removing faults.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Thermodynamically consistent incorporation of the Langmuir adsorption model into compressible fluctuating hydrodynamics

For a gas–solid interfacial system where chemical species undergo reversible adsorption, we develop a mesoscopic stochastic modeling method that simulates both gas-phase hydrodynamics and surface coverage dynamics by coupling the Langmuir adsorption model with compressible fluctuating hydrodynamics. To this end, we derive a thermodynamically consistent mass–energy update scheme that accounts for how the mass and energy variables in the gas and surface subsystems should be updated according to the changes in the number of molecules of each species in each subsystem due to adsorption and desorption events. By performing a stochastic analysis for the ideal Langmuir model and the full hydrodynamic system, we analytically confirm that our mass–energy update scheme captures thermodynamic equilibrium predicted by equilibrium statistical mechanics. We find that an internal energy correction term is needed, which is attributed to the difference in the mean kinetic energy of gas molecules colliding with the surface from that computed from the Maxwell–Boltzmann distribution. By performing an equilibrium simulation study for an ideal gas mixture of CO and Ar, with CO undergoing reversible adsorption, we validate our overall simulation method and implementation.

Adsorption↗
DOE OSTI2024

Development of a Full-Scale Connected U-Net for Reflectivity Inpainting in Spaceborne Radar Blind Zones

CloudSat’s Cloud Profiling Radar is a valuable tool for remotely monitoring high-latitude snowfall, but its ability to observe hydrometeor activity near the Earth’s surface is limited by a radar blind zone caused by ground clutter contamination. This study presents the development of a deeply supervised U-Net-style convolutional neural network to predict cold season reflectivity profiles within the blind zone at two Arctic locations. The network learns to predict the presence and intensity of near-surface hydrometeors by coupling latent features encoded in blind zone-aloft clouds with additional context from collocated atmospheric state variables (i.e., temperature, specific humidity, and wind speed). Results show that the U-Net predictions outperform traditional linear extrapolation methods, with low mean absolute error, a 38% higher Sørensen–Dice coefficient, and vertical reflectivity distributions 60% closer to observed values. The U-Net is also able to detect the presence of near-surface cloud with a critical success index (CSI) of 72% and cases of shallow cumuliform snowfall and virga with 18% higher CSI values compared to linear methods. An explainability analysis shows that reflectivity information throughout the scene, especially at cloud edges and at the 1.2-km blind zone threshold, along with atmospheric state variables near the tropopause, are the most significant contributors to model skill. This surface-trained generative inpainting technique has the potential to enhance current and future remote sensing precipitation missions by providing a better understanding of the nonlinear relationship between blind zone reflectivity values and the surrounding atmospheric state.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Adaptive methods of generating complex light arrays

Structured light arrays of various shapes have been a cornerstone in optical science, driven by the complexities of precise and adaptable generation. This study introduces an approach using a spatial light modulator (SLM) as a generator for these arrays. By projecting a holographic mask onto the SLM, it functions simultaneously as an optical convolution device, focusing mechanism, and structured light beam mask. Our approach offers unmatched versatility, allowing for the experimental fabrication of traditional beam arrays like azimuthal Laguerre–Gaussian (LG), Bessel–Gaussian (BG), and Hermite–Gauss (HG) in the far-field. Notably, it has enabled a method of generating Ince–Gauss (IG) and LG radial mode beam arrays using a convolution solution. Our system provides exceptional control over array periodicity and intensity distribution, bypassing the Talbot self-imaging phenomenon seen in traditional setups. We provide an in-depth theoretical discussion, supported by empirical evidence, of our far-field results. This method has vast potential for applications in optical communication, data processing, and multi-particle manipulation. It paves the way for rapid generation of structured light with high spatial frequencies and complex shapes, promising transformative advances in these domains.

Optics↗
DOE OSTI2020

A simple method for modelling fatigue spectra of small wind turbine blades

Small-scale wind turbines have market opportunities in distributed energy generation applications but face future challenges in remaining cost competitive compared with solar photovoltaic systems. High unit costs can be attributed to design conservatism when calculating fatigue loads of key structural components such as the blades. In this study, we use the aeroelastic software FAST to highlight limitations of the International Electrotechnical Commission 61400-2:2013 small wind turbine design standard for calculating fatigue life using the simplified load model. We present a modified method for calculating the fatigue spectra of small wind turbine blades. An advantage of this method is that it does not require complex aeroelastic simulations or field measurements. This modified method is intended to be implemented early in the blade design stage, such as during rotor optimization simulations, allowing for multiple rotor configurations to be rapidly compared.

17 WIND ENERGY↗
DOE OSTI2025

Strategic Placement and Sizing of Distributed Generation for Resilience Enhancement of Distribution Grids With Microgrid Formation

The rise in frequency and severity of extreme weather events highlights the need for resilient power distribution networks. Microgrids can help improve the resilience of distribution grids by providing continuous power supply using local distribution generation (DG) when the distribution grid fails. In this paper, we propose an approach for optimal placement and sizing of DG to form multiple microgrids throughout the distribution network by restoration actions such as switching operations in case of distribution grid outages caused by extreme weather events. Considering the randomness of damaged distribution lines, the DG placement and sizing problem is formulated as a two-stage stochastic mixed-integer program, with the first stage determining the placement and size of DG, and the second stage focusing on minimizing the amount of load shedding through network restoration and microgrid formations for each scenario. Due to the large number of scenarios, the sample average approximation (SAA) method is employed to solve the problem. The results of case studies on a modified IEEE 33 bus distribution grid demonstrate the effectiveness of the proposed DG placement and sizing strategy in improving the resilience of distribution grids by allowing the formation of multiple microgrids. In addition, the robustness and accuracy of the SAA method are validated through various case studies.

Distributed generation planning↗
DOE OSTI2024

Journey Mapping Distributed Wind Deployment: Installer Perspectives

This work uses journey maps to assess the deployment of distributed wind technologies through the perspective of installers. Journey mapping is a human-centered design method that chronologically traces processes from the perspective of those who participate in them. The journey map will be leveraged to identify deployment pain points (i.e., manifestations of generic deployment barriers) that the Strategize, Engage, Network and Deploy (SEND) Distributed Wind project team will seek to address in future work.

17 WIND ENERGY↗
DOE OSTI2020

In situ measurement of phase transformations and residual stress evolution during welding using spatially distributed fiber-optic strain sensors

Welding of high-strength steels can result in large tensile strains as the base metal and filler material cool from their molten state. To combat these large tensile strains, low-transformation-temperature (LTT) metal fillers have been proposed. These fillers undergo a martensitic phase transformation at a lower temperature which can ultimately reduce the tensile strain or can even introduce compressive strain adjacent to the weld metal. However, the process for optimizing the composition of the LTT material, as well as various weld parameters for each unique weld geometry, can be quite expensive, especially if the acceptance criterion requires using neutrons, x-ray beams, or destructive techniques to characterize residual stresses. This work describes a simple, low-cost method for quantifying residual stresses and phase transformations in situ during welding. Spatially distributed fiber-optic sensors were bonded to a cast iron plate, along with tack-welded thermocouples, to measure temperature and strain during multiple passes with LTT filler metals. Results show that the fiber-optic sensors can successfully resolve compressive strain adjacent to the weld region caused by the martensitic phase transformations in the LTT filler material.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Bridging Power System Protection Gaps with Data-driven Approaches

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this research, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A convolutional neural network (CNN) based fault detection approach is implemented to achieve data translation invariance of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN–based method avoids the complicated manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on four kinds of protection gaps: high impedance faults, transformer/generator inter-turn faults, distribution system PV circuit faults, and the mis-operation situations of Zone 3 line protection relays operating under system stress. Finally, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

SimLBR: Learning to Detect Fake Images by Learning to Detect Real Images

The rapid advancement of generative models has made the detection of AI-generated images a critical challenge for both research and society. Recent works have shown that most state-of-the-art fake image detection methods overfit to their training data and catastrophically fail when evaluated on curated hard test sets with strong distribution shifts. In this work, we argue that it is more principled to learn a tight decision boundary around the real image distribution and treat the fake category as a sink class. To this end, we propose SimLBR, a simple and efficient framework for fake image detection with Latent Blending Regularization (LBR). Our method significantly improves cross-generator generalization, achieving up to +24.85% accuracy and +69.62% recall on the challenging Chameleon benchmark. SimLBR is also highly efficient, training orders of magnitude faster than existing approaches. Furthermore, we emphasize the need for reliability-oriented evaluation in fake image detection, introducing risk-adjusted metrics and worst-case estimates to better assess model robustness. All the code and models are availabe at: https://github.com/mvrl/SimLBR

Dhakal, Aayush [Washington University, St. Louis]↗
DOE OSTI2021

Nonparametric, data-based kernel interpolation for particle-tracking simulations and kernel density estimation

Traditional interpolation techniques for particle tracking include binning and convolutional formulas that use pre-determined (i.e., closed-form, parameteric) kernels. In many instances, the particles are introduced as point sources in time and space, so the cloud of particles (either in space or time) is a discrete representation of the Green’s function of an underlying PDE. As such, each particle is a sample from the Green’s function; therefore, each particle should be distributed according to the Green’s function. In short, the kernel of a convolutional interpolation of the particle sample “cloud” should be a replica of the cloud itself. This idea gives rise to an iterative method by which the form of the kernel may be discerned in the process of interpolating the Green’s function. When the Green’s function is a density, this method is broadly applicable to interpolating a kernel density estimate based on random data drawn from a single distribution. We formulate and construct the algorithm and demonstrate its ability to perform kernel density estimation of skewed and/or heavy-tailed data including breakthrough curves.

42 ENGINEERING↗
DOE OSTI2021

Machine learning based inverse modeling of full-field strain distribution for mechanical characterization of a linear elastic and heterogeneous membrane

Heterogeneous membranes or films are thin and soft structures with spatial variations in material property and thickness. Mechanical behavior of heterogeneous membranes is not well understood, mainly due to the difficulty in obtaining accurate and reliable material property data. To understand the mechanical behavior of these materials, accurate and efficient characterization methods for heterogeneous membranes are needed. Here, in this paper, an inverse method based on machine learning is developed to efficiently extract mechanical properties from full-field strain distributions. This approach is demonstrated on a flat heterogeneous membrane with uniform thickness formed by up to four linear elastic synthetic materials in a grid arrangement, and deforming in a moderate strain range (true strain ~10%). The results show that the machine learning method achieves accuracy comparable to the traditional inverse finite element method, and is 6 orders of magnitude faster in the demonstrated case studies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Development of a Configurable DERMS Test System in GridAPPS-D

Distributed Energy Resources and their increased penetration within the power grid are a suitable method to provide essential reliability services. In order to do so, they must operate in aggregate. Aggregation of a wide variety of systems with varying standards and protocols is a complex task, which could benefit from rules and standards to support interoperability. To this end, the Portland State University Power Lab has developed the Energy Grid of Things project, which includes a Distributed Energy Resource Management System to provide aggregation and demonstrate the usage of an Energy Services Interface: a set of rules governing the interface between resource owners and aggregator businesses. This thesis presents a Modeling Environment that provides a simulation and test platform reflecting the complex needs of an aggregator. A grid simulator is required that is able to reflect the effects of thousands of Distributed Energy Resources operating in tandem; furthermore, these effects must be communicated to external actors such as the Grid Operator, which provides grid service requests to the aggregator and requires feedback. The Modeling Environment provides all of this functionality by leveraging the GridAPPS-D platform to develop an interoperable class-based system that provides configurable simulations with modular input and output APIs. The Modeling Environment uses abstractions to allow any feasible type of input to be reflected within the model as a Distributed Energy Resource, and serves as a proof-of-concept for the development of aggregation simulation and testing within GridAPPS-D.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Estimating biofuel contaminant concentration from 4D ERT with mixing models

Here, we present the results of a lab-scaled feasibility study to assess the performance of electrical resistivity tomography for detection, characterization, and monitoring of fuel grade ethanol releases to the subsurface. Further, we attempt to determine the concentration distribution of the ethanol from the electrical resistivity tomography data using mixing-models. Ethanol is a renewable fuel source as well as an oxygenate fuel additive currently used to replace the known carcinogen methyl tert-butyl ether; however, ethanol is preferentially biodegraded and a cosolvent. When introduced to areas previously impacted by nonethanol-based fuels, it will facilitate the persistence of carcinogenic fuel compounds like benzene and ethylbenzene, as well as remobilize them to the ground water. These compounds would otherwise be retained in the soil column undergoing active or passive remediation processes such as soil vapor extraction or natural attenuation. Here, we introduce ethanol to a saturated Ottawa sand in a tank instrumented for four-dimensional geoelectrical measurements. Forward model results suggest pure phase ethanol released into a water saturated silica sand should present a detectable target for electrical resistivity tomography relative to a saturated silica sand only. We observe the introduction of ethanol to the closed hydraulic system and subsequent migration over the duration of the experiment. One-dimensional and three–dimensional temporal data are assessed for the detection, characterization, and monitoring of the ethanol release. Results suggest one-dimensional geoelectrical measurements may be useful for monitoring a release, while three-dimensional geoelectrical field imaging would be useful to characterize, monitor, and design effective remediation approaches for an ethanol release, assuming field conditions do not preclude the application of geoelectrical methods. We then attempt to use predictive mixing models to calculate the distribution of ethanol concentration within the measurement domain. For this study we examine four different models: a nested parallel mixing model, a nested cubic mixing model, the complex refractive index model (CRIM), and the Lichtenecker-Rother (L-R) model. The L-R model, modified to include an electrical formation factor geometry term, provided the best agreement with expected EtOH concentrations.

09 BIOMASS FUELS↗
DOE OSTI2021

Spatially Resolved Potential and Li-Ion Distributions Reveal Performance-Limiting Regions in Solid-State Batteries

The performance of solid-state electrochemical systems is intimately tied to the potential and lithium distributions across electrolyte–electrode junctions that give rise to interface impedance. Here, we combine two operando methods, Kelvin probe force microscopy (KPFM) and neutron depth profiling (NDP), to identify the rate-limiting interface in operating Si-LiPON-LiCoO 2 solid-state batteries by mapping the contact potential difference (CPD) and the corresponding Li distributions. The contributions from ions, electrons, and interfaces are deconvolved by correlating the CPD profiles with Li-concentration profiles and by comparisons with first-principles-informed modeling. Furthermore, we find that the largest potential drop and variation in the Li concentration occur at the anode–electrolyte interface, with a smaller drop at the cathode–electrolyte interface and a shallow gradient within the bulk electrolyte. Correlating these results with electrochemical impedance spectroscopy following battery cycling at low and high rates confirms a long-standing conjecture linking large potential drops with a rate-limiting interfacial process.

25 ENERGY STORAGE↗
DOE OSTI2024

The photon content of the neutron

In this work, we complete our CT18qed study with the neutron’s photon parton distribution function (PDF), which is essential for the nucleus scattering phenomenology. Two methods, CT18lux and CT18qed, based on the LUXqed formalism and the DGLAP evolution, respectively, to determine the neutron’s photon PDF have been presented. Various low-Q 2 non-perturbative variations have been carefully examined, which are treated as additional uncertainties on top of those induced by quark and gluon PDFs. The impacts of the momentum sum rule as well as isospin symmetry violation have been explored and turned out to be negligible. A detailed comparison with other neutron’s photon PDF sets has been performed, which shows a great improvement in the precision and a reasonable uncertainty estimation. Finally, two phenomenological implications are demonstrated with photon-initiated processes: neutrino-nucleus W-boson production, which is important for the near-future TeV–PeV neutrino observations, and the axion-like particle production at a high-energy muon beam-dump experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗