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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting

Many existing object counting methods rely on density map estimation (DME) of the discrete grid representation by decoding extracted image semantic features from designed convolutional neural networks (CNNs). Relying on discrete density maps not only leads to information loss dependent on the original image resolution, but also has a scalability issue when analyzing high-resolution images with cubically increasing memory complexity. Furthermore, none of the existing methods can offer reliable uncertainty quantification (UQ) for the derived count estimates. To overcome these limitations, we design UNcertainty-aware, hypernetwork-based Implicit neural representations for Counting (UNIC) to assign probabilities and the corresponding counting confidence over continuous spatial coordinates. We derive a sampling-based Bayesian counting loss function and develop the corresponding model training algorithm. UNIC outperforms existing methods on the Remote Sensing Object Counting (RSOC) dataset with reliable UQ and improved interpretability of the derived count estimates. Our code is available at https://github.com/SiyuanXu-tamu/UNIC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Advancing wildlife connectivity in land use planning: a case study with four-toed salamanders

Stable habitat connections that wildlife can safely traverse are essential to biodiversity conservation and healthy ecosystems. We developed high-resolution landscape connectivity models to predict resistance to movement by a threatened wetland-obligate amphibian, the four-toed salamander ( Hemidactylium scutatum ), and identified priority management areas on the 13,000-ha Department of Energy Oak Ridge Reservation (ORR) from 2019 to 2022. We developed a resistance surface based on aerial light detection and ranging data (LiDAR), >30 years of field-based mapping of forest, hydrologic, and geologic features, and contemporary population surveys, alongside derived predictors at <1-m resolution. We then modeled predicted movement corridors using a circuit theory-based modeling approach. We worked closely with land management and natural resources personnel to integrate ecological modeling with broader land use priorities, monetary costs, and feasibility. We identified important terrestrial and aquatic areas on ORR and simulated management scenarios to promote stable connections for four-toed salamanders. This approach allowed us to narrow down a list of 438 potential habitat manipulation sites to 10 sites where open-bottomed culverts and buffers could be implemented. This smaller-scale restoration approach produced a similar increase in landscape connectivity while costing <20% of a larger-scale approach based on barrier removal. We successfully identified feasible, cost-effective management strategies that integrated knowledge from a variety of sources. In conclusion, we offer a strategy that permitted integration of wildlife management goals into infrastructure upgrades wherein wildlife was not an initial consideration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

De Sitter decays to infinity

Bubbles of nothing are a class of vacuum decay processes present in some theories with compactified extra dimensions. We investigate the existence and properties of bubbles of nothing in models where the scalar pseudomoduli controlling the size of the extra dimensions are stabilized at positive vacuum energy, which is a necessary feature of any realistic model. We map the construction of bubbles of nothing to a four-dimensional Coleman-De Luccia problem and establish necessary conditions on the asymptotic behavior of the scalar potential for the existence of suitable solutions. We perform detailed analyses in the context of five-dimensional theories with metastable dS 4 x S 1 vacua, using analytic approximations and numerical methods to calculate the decay rate. We find that bubbles of nothing sometimes exist in potentials with no ordinary Coleman-De Luccia decay process, and that in the examples we study, when both processes exist, the bubble of nothing decay rate is typically faster. Our methods can be generalized to other stabilizing potentials and internal manifolds.

classical theories of gravity↗
DOE OSTI2020

Types of spatter and their features and formation mechanisms in laser powder bed fusion additive manufacturing process

Spatter causes defect formation, powder redistribution and contamination in laser powder bed fusion (LPBF) additive manufacturing process. It is critical to distinguish different types of spatter and understand their features and formation mechanisms. This work reveals the features and formation mechanisms of five unique types of spatter during the LPBF process by in-situ high-speed, high-energy x-ray imaging. Spatters observed during LPBF testing are quantified by their speed, size, and direction. Distinct quantifiable characteristics for each type of spatter are identified. Finally, effects of the laser power, scan speed, and ambient pressure on spatter formation and features are unraveled. A spatter formation map for AlSi10Mg alloy is constructed.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Machine learning models of intermittent operation of RO wellhead water treatment for salinity reduction and nitrate removal

Machine learning models were developed for intermittent multi-mode operation of a wellhead reverse osmosis water purification and desalination system to predict salt passage, nitrate passage, and permeate flux. The models, based on long short-term memory (LSTM) recurrent neural network (RNN) architecture, included an attention mechanism to increase model performance in proximity of the regulatory limit for nitrate. Training and testing of the models for the Startup, Production, Shutdown and Flushing operational modes were based on operational data (consisting of 22 process variables per data sample) acquired every 2–5 s over a six-month period. The significant sets of model input attributes for the different operational modes were assessed via Spearman ranking correlation, Self-Organizing Map (SOM) analysis and feed forward feature selection (FFFS). Although the variability of nitrate passage, salt passage and permeate flux was significant over the four operational modes, prediction performance for the three outcomes were with R2 and Average Absolute Relative Error (AARE) of 0.78–0.95 and 2.96–6.16 %, respectively. Model updates post membrane elements replacement demonstrated similar levels of prediction accuracy. The study results suggest that there is merit in exploring the utility of multi-mode models for sensor fault detection, data imputation, and for potential use in model-predictive control.

Intermittent RO operation↗
DOE OSTI2023

Identifying common stored product insects using automated deep learning methods

Monitoring stored product insect pests is a common practice for post-harvest management of stored grain and grain-based commodities, which helps ensure product quality from harvest to final consumer. Current methods of sampling and monitoring can be time-consuming, labor-intensive, expensive and require expertise in insect identification. Therefore, this study aims to develop an image-based automated identification system for common stored product insect species using deep-learning methods. Top-down images of the common stored product adult insect species of Rhyzopertha dominica, Cryptolestes ferrugineus, Tribolium castaneum, Sitophilus oryzae, and Oryzaephilus surinamensis were acquired and analyzed. Deep learning-based, state-of-the-art Convolutional Neural Networks (CNN) models (ResNet-50, MobileNet-v2, DarkNet-53, and EfficientNet-b0) were fine-tuned with a transfer learning approach to classify the insect species. All models were able to correctly identify the insect species with at least 96% accuracy and with few misclassifications. One issue with trained CNNs is that they do not explain the reasoning for the classification and are often called a “black box”. Therefore, visualization methods called Gradient-weighted Class Activation Mapping (Grad-CAM) were implemented to explore the black box network. The Grad-CAM uses heat maps to highlight the major image features that the network focused on to make insect species predictions. The Grad-CAM verifies the network's prediction and also helps improve network performance. This study contributes to the overall goal of developing a camera-based system for monitoring stored grain insects. As a result, the developed system would empower warehouse, flour mills, and other food facilities with a tool to quickly and accurately identify insect species in stored product environments and could be implemented as part of a close to real-time monitoring system.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Chemometrics and visible diffuse reflectance spectroscopy to classify plutonium dioxide

Diffuse reflectance (DR) spectra in the Vis-NIR (∼380–1050 nm) region were acquired for a series of PuO 2 samples with a spot size of about 10 × 10 μm. Two batches of six PuO 2 samples, synthesized approximately 7.5 months apart, were prepared using both Pu(III) and Pu(IV) oxalate precursors at three distinct calcination temperatures (450, 650, and 950 °C). This yielded a total of 12 PuO 2 samples and 433 DR spectra. The DR spectrum of PuO 2 contained numerous peaks in the visible region, and characteristic features were identified with respect to calcination temperature and chemistry. A distinct peak multiplet near 615 nm was observed for samples prepared at low calcination temperatures, and a peak near 660 nm was observed for higher calcination temperatures. A multivariate classification strategy based on principal component analysis (PCA) was developed to distinguish PuO 2 calcination temperatures of 450, 650, and 950 °C with 100 % accuracy. Classification results also indicate the potential to distinguish chemical processing history (i.e., Pu(III) or Pu(IV)) based on the spectra with 72 % accuracy based on k-nearest neighbors applied to the PCA scores. Partial least squares discriminant analysis was used to identify variation among batches with 88 % accuracy and found that peaks near 669, 681, 811, and 970 nm were the most useful for predicting the batch identity. Here, this work demonstrates how micro-diffuse reflectance spectroscopy and chemometrics can be used to classify PuO 2 processing history based on Vis-NIR spectral features. Combining the chemometric approach with mapping sequences could provide a rapid, nondestructive approach to classify Pu oxide materials for environmental, forensics, and nonproliferation applications.

Actinide↗
DOE OSTI2022

A new μ -high energy resolution fluorescence detection microprobe imaging spectrometer at the Stanford Synchrotron Radiation Lightsource beamline 6-2

In this report we describe a new synchrotron X-ray Fluorescence (XRF) imaging instrument with an integrated High Energy Fluorescence Detection X-ray Absorption Spectroscopy (HERFD-XAS) spectrometer at the Stanford Synchrotron Radiation Lightsource at beamline 6-2. The X-ray beam size on the sample can be defined via a range of pinhole apertures or focusing optics. XRF imaging is performed using a continuous rapid scan system with sample stages covering a travel range of 250 × 200 mm 2 , allowing for multiple samples and/or large samples to be mounted. The HERFD spectrometer is a Johann-type with seven spherically bent 100 mm diameter crystals arranged on intersecting Rowland circles of 1 m diameter with a total solid angle of about 0.44% of 4π sr. A wide range of emission lines can be studied with the available Bragg angle range of ~64.5°–82.6°. With this instrument, elements in a sample can be rapidly mapped via XRF and then selected features targeted for HERFD-XAS analysis. Furthermore, utilizing the higher spectral resolution of HERFD for XRF imaging provides better separation of interfering emission lines, and it can be used to select a much narrower emission bandwidth, resulting in increased image contrast for imaging specific element species, i.e., sparse excitation energy XAS imaging. This combination of features and characteristics provides a highly adaptable and valuable tool in the study of a wide range of materials.

47 OTHER INSTRUMENTATION↗
DOE OSTI2022

Do chaotic field lines cause fast reconnection in coronal loops?

Over the past decade, Boozer has argued that three-dimensional (3D) magnetic reconnection fundamentally differs from two-dimensional reconnection due to the fact that the separation between any pair of neighboring field lines almost always increases exponentially over distance in a 3D magnetic field. According to Boozer, this feature makes 3D field-line mapping chaotic and exponentially sensitive to small non-ideal effects; consequently, 3D reconnection can occur without intense current sheets. We test Boozer's theory via ideal and resistive reduced magnetohydrodynamic simulations of the Boozer–Elder coronal loop model driven by sub-Alfvénic footpoint motions [A. H. Boozer and T. Elder, Phys. Plasmas 28, 062303 (2021)]. In this work, our simulation results significantly differ from their predictions. The ideal simulation shows that Boozer and Elder under-predict the intensity of current density due to missing terms in their reduced model equations. Furthermore, resistive simulations of varying Lundquist numbers show that the maximal current density scales linearly rather than logarithmically with the Lundquist number.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Kohn anomalies and phonon anharmonicity in iridium

Elemental iridium presents surprising challenges for both inelastic neutron scattering (INS) and theoretical thermal transport calculations due to its high neutron absorption cross-section and strong electron-phonon interactions, respectively. Here, in this study, we overcome these challenges to measure temperature-dependent phonon dispersion curves, compare these with calculations based on density functional theory (DFT), and ultimately examine the electron-phonon limited transport behaviors of this material. Our DFT calculations demonstrate Kohn anomalies, near the 𝐾 point of the iridium Brillouin zone, indicating coupling between electrons and phonons. Strong electron-phonon coupling can compete with anharmonic effects to determine electrical and thermal transport behaviors and make the Kohn anomalies challenging to observe. Nonetheless, our INS measurements map these anomalies and other dispersion features over the Brillouin zone from 100 to 700 K. These measurements also uncover unexpectedly large mode specific Grüneisen parameters obtained from the temperature-dependent phonon energies, highlighting strong anharmonicity in iridium. DFT-based Boltzmann transport calculations demonstrate how anharmonicity and electron-phonon couplings determine electronic and lattice transport behaviors. Furthermore, we correlate the Kohn anomalies with calculated electron-phonon nesting functions, Fermi surfaces, and DFT-derived coupling strengths. This study provides detailed insights into the temperature-dependent mode-resolved lattice dynamics and anharmonicity, transport behavior, and electron-phonon interactions.

DFT↗
DOE OSTI2020

XtalCAMP: a comprehensive program for the analysis and visualization of scanning Laue X-ray micro-/nanodiffraction data

XtalCAMP is a software package based on the MATLAB platform, which is suitable for, but not limited to, the analysis and visualization of scanning Laue X-ray micro-/nanodiffraction data. The main objective of the software is to provide complementary functionalities to the Laue indexing software packages used at several synchrotron beamlines. Here, the graphical user interfaces allow the easy analysis of characteristic microstructure features, including real-time intensity mapping for a quick examination of phase, grain and defect distribution, 2D color-coded mapping of microstructural properties from the output of other Laue indexing software, crystal orientation visualization, grain boundary characterization based on orientation/misorientation calculation, principal strain/stress analysis, and strain ellipsoid representation, as well as a series of additional toolkits. As an example, XtalCAMP is applied to the microstructural investigation of a solution-heat-treated Ni-based superalloy manufactured using a laser 3D-printing technique, and a deformed natural quartzite from Val Bregaglia in the Central Alps.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Leveraging gradient weighted class activation mapping to improve classification effectiveness: Case study in transportation infrastructure characterization

Roadway “corners†are common for pedestrian use, whether designated with markings or not. Different types of markings have been deployed, ranging from simple parallel lines to more complex designs. Understanding the impact of different types of crosswalks is important for public safety. In this work we explore methods to improve the logging of marked crosswalk types. We used the Roadway Information Database from the Second Strategic Highway Research Project and used active learning methods with transfer learning to identify the crosswalk types (marked or unmarked). Upon completion we found our classifiers were unable to perform above roughly 94% correct classifications. To improve their efficacy, we separated the crosswalks into their “fine grained†types and used Gradient-Weighted Class Activation Mapping to isolate and study the features that classified the crosswalks. We compared this with sampled manually marked crosswalks and present findings. We believe this use case can represent a process to improve the active learning method for some visual machine learning applications.

Karnowski, Thomas↗
DOE OSTI2020

Chaos, Percolation and the Coronavirus Spread

The dynamics of the spreading of the COVID-19 virus has similar features to the turbulent flow, chaotic maps and other non-linear systems: a small seed grows exponentially and eventually saturates. Like in the percolation model, the virus is mostly dangerous if the probability of transmission (or the bond probability p in the percolation model) is high. This suggests a relation with the population density, ρ s , which must be higher than a certain value (ρ s > 1000 persons/km 2 ). A ‘seed’ implanted in such towns grows vigorously and affects nearby places at distance x. Thus, the spreading is governed by the ratio ρ = ρ s /x. Assuming a power law dependence τ of the number of positives to the virus N + from ρ, we find τ = 0.55, 0.75 and 0.96 for S.Korea, Italy and China respectively.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Axisymmetric Radiative Transfer Models of Kilonovae

The detailed observations of GW170817 proved for the first time directly that neutron star mergers are a major production site of heavy elements. The observations could be fit by a number of simulations that qualitatively agree, but can quantitatively differ (e.g., in total r-process mass) by an order of magnitude. We categorize kilonova ejecta into several typical morphologies motivated by numerical simulations, and apply a radiative transfer Monte Carlo code to study how the geometric distribution of the ejecta shapes the emitted radiation. We find major impacts on both spectra and light curves. The peak bolometric luminosity can vary by two orders of magnitude and the timing of its peak by a factor of five. These findings provide the crucial implication that the ejecta masses inferred from observations around the peak brightness are uncertain by at least an order of magnitude. Mixed two-component models with lanthanide-rich ejecta are particularly sensitive to geometric distribution. A subset of mixed models shows very strong viewing angle dependence due to lanthanide "curtaining," which persists even if the relative mass of lanthanide-rich component is small. The angular dependence is weak in the rest of our models, but different geometric combinations of the two components lead to a highly diverse set of light curves. We identify geometry-dependent P Cygni features in late spectra that directly map out strong lines in the simulated opacity of neodymium, which can help to constrain the ejecta geometry and to directly probe the r-process abundances.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Reduced Order Modeling conditioned on monitored features for response and error bounds estimation in engineered systems

Reduced Order Models (ROMs) form essential tools across engineering domains by virtue of their function as surrogates for computationally intensive digital twinning simulators. Although purely data-driven methods are available for ROM construction, schemes that allow to retain a portion of the physics tend to enhance the interpretability and generalization of ROMs. However, physics-based techniques can adversely scale when dealing with nonlinear systems that feature parametric dependencies. This study introduces a generative physics-based ROM that is suited for nonlinear systems with parametric dependencies and is additionally able to provide numerical error bounds associated with the respective estimates. A main contribution of this work is the conditioning of these parametric ROMs to features that can be derived from monitoring measurements, feasibly in an online fashion. This is contrary to most existing ROM schemes, which remain restricted to the prescription of the physics-based, and usually a priori unknown, system parameters. Our work utilizes conditional Variational Autoencoders to continuously map the required reduction bases to a feature vector extracted from limited output measurements, while additionally allowing for a probabilistic assessment of the ROM-estimated Quantities of Interest. An auxiliary task using a neural network-based parametrization of suitable probability distributions is introduced to re-establish the link with physical model parameters. We verify the proposed scheme on a series of simulated case studies incorporating effects of geometric and material nonlinearity under parametric dependencies related to system properties and input load characteristics.

Conditional VAEs↗
DOE OSTI2020

Discovery of Extended Tidal Tails around the Globular Cluster Palomar 13

We use photometry from the DECam Legacy Survey to detect candidate tidal tails extending ~5° on either side of the Palomar 13 globular cluster. The tails are aligned with the proper motion of Palomar 13 and are consistent with its old, metal-poor stellar population. We identify three RR Lyrae stars (RRLs) that are plausibly associated with the tails, in addition to four previously known in the cluster. From these RRLs, we find that the mean distance to the cluster and tails is 23.6 ± 0.2 kpc and estimate the total (initial) luminosity of the cluster to be , consistent with previous claims that its initial luminosity was higher than its current luminosity. Combined with previously determined proper motion and radial velocity measurements of the cluster, we find that Palomar 13 is on a highly eccentric orbit (e ~ 0.8) with a pericenter of ~9 kpc and an apocenter of ~69 kpc, and a recent pericentric passage of the cluster ~75 Myr ago. We note a prominent linear structure in the interstellar dust map that runs parallel to the candidate tidal features, but conclude that reddening due to dust is unlikely to account for the structure that we observe. If confirmed, the Palomar 13 stellar stream would be one of very few streams with a known progenitor system, making it uniquely powerful for studying the disruption of globular clusters, the formation of the stellar halo, and the distribution of matter within our Galaxy.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

REVEAL

Reveal allows a user to input location dimensions of common features from 2 orthogonal xray images of a cylindrical object. The output of the program will give the user a 2-Dimensional map with cylindrical coordinates showing the location of the features measured from the xrays.SAND2019-13499 M Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Hobart, Clinton↗
DOE OSTI2023

Nanoscale mapping of point defect concentrations with $\mathrm{4D-STEM}$

Vacancies are missing atoms in a crystalline material, and occur both at equilibrium (varying with temperature) and out of equilibrium such as when crystalline materials are damaged with radiation or corrosion. While we know of their importance, particularly regarding diffusive mechanisms, it is not straightforward to experimentally measure their concentration or to visualize them directly. Traditionally, measurements such as positron annihilation spectroscopy and to some degree X-ray diffraction can measure average concentrations, but generally lack the ability to visualize or quantify a heterogenous concentration of vacancies that can occur at the level of individual defects and microstructural features. Here, we present a method to map vacancy concentrations and their distribution using local lattice parameter measurements with a high-resolution electron microscope. Our method utilizes a Au thin film as a model to demonstrate the method via four-dimensional scanning transmission electron microscopy (4D-STEM) by correlating the differences between changes in lattice parameter and the volumetric thermal expansion during in situ heating experiments. The vacancy mapping methodology is also applied to non-equilibrium defects accumulated in pure Al via knock-on electron beam irradiation. Our method demonstrates the ability to map point defect concentrations in heterogeneous systems in situ with nanometer spatial resolution. The result is a technique that can provide direct measurements of vacancy concentrations at the level of individual defects in studies of materials in and out of equilibrium.

36 MATERIALS SCIENCE↗