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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improvements in Reflected Target Non-intrusive Assessment (ReTNA)

Accurate optical surfaces are a primary driver of concentrated solar power plant performance. Errors in pointing and tracking mirrors, the canting of individual mirror facets, and the surface slope of the mirror itself can be caused by errors during assembly, transportation, wind loading, gravity, and many other sources. The tools that exist to measure these error sources today largely rely on fringe deflectometry (SOFAST, QDec, others), or photogrammetry with targets attached to the mirror surface. In 2022, NREL presented a new indoor optical measurement system to measure fully assembled heliostats during development and on the assembly line for quality assurance purposes, which has undergone significant development in the last year, including the installation of a new, larger laboratory setup. This system, called ReTNA (Reflected Target Non-intrusive Assessment), uses a printed pattern as a deflectometry target. Using printed pattern features as deflectometry targets instead of fringes allows the software to measure the surface slope in two dimensions, eliminating the need to for changing, projected targets. In the last year, NREL's ReTNA tool has been adapted to use a tall, thin target, that only spans one dimension of the mirror, and a camera moving on an automatic track system. We will present these new developments, including the new setup, target patterns, software methodology for fast, automatic optical analysis, and important lessons learned throughout the process. ReTNA results will be compared to established tools, to establish the uncertainty of this new method. ReTNA leverages recent improvements in computer vision and photogrammetry to remove as much careful calibration from the setup process as possible, making the tool low cost, simple, and portable. While it will not achieve the resolution of a fringe deflectometry system, its advantages make it well suited for CSP evaluation, and allow it to fill a different industry need.

concentrating solar power↗
DOE OSTI2025

A Climatology and Life‐Cycle Characteristics of Atmospheric Fronts and Their Associated Precipitation

Abstract Atmospheric fronts are one of the main sources of mid‐latitude variability. We employ a novel method for identifying and tracking fronts and frontal precipitation. Thermal and dynamical variables are used to identify fronts as areal objects in space, which are tracked in time using the open‐source TempestExtremes software package. Precipitation objects are co‐located to identify frontal precipitation. The method is subjected to validation and sensitivity tests using manually curated data from the National Weather Service. Climatologies of fronts and frontal precipitation are computed from reanalysis and observations; fronts are present upwards of 14% of the time in the storm tracks, and represent the majority (up to 90%) of total and extreme precipitation. Novel aspects of the method are showcased through the lifetime characteristics of fronts across North America. Three sets of warm and cold fronts were discovered, and their duration, distance‐traveled, and translation velocity are examined. Plain Language Summary Mid‐latitude low‐pressure systems and weather fronts are important for our day‐to‐day experience of weather events, particularly in the mid‐latitudes. This work makes use of standardized atmospheric data and creates a method of automatically tracking these important atmospheric features and their precipitation to quantify their relative role in global precipitation. Weather fronts are persistent in the mid‐latitudes and are associated with the majority of precipitation–particularly the most intense precipitation. Trajectories of fronts over North America are categorized to create a set of archetypal fronts that occur in that region. The differences between these types of fronts are characterized. Key Points An automated, efficient, and skillful frontal detection algorithm is developed and validated Fronts contribute a larger fraction of extreme precipitation than all precipitation in mid‐latitude storm tracks Fronts across North America have substantial variation in characteristics depending on their origin location

extratropical cyclone↗
DOE OSTI2022

High Velocity, Low‐Voltage Collective In‐Plane Switching in (100) BaTiO 3 Thin Films

Abstract Ferroelectrics are being increasingly called upon for electronic devices in extreme environments. Device performance and energy efficiency is highly correlated to clock frequency, operational voltage, and resistive loss. To increase performance it is common to engineer ferroelectric domain structure with highly‐correlated electrical and elastic coupling that elicit fast and efficient collective switching. Designing domain structures with advantageous properties is difficult because the mechanisms involved in collective switching are poorly understood and difficult to investigate. Collective switching is a hierarchical process where the nano‐ and mesoscale responses control the macroscopic properties. Using chemical solution synthesis, epitaxially nearly‐relaxed (100) BaTiO 3 films are synthesized. Thermal strain induces a strongly‐correlated domain structure with alternating domains of polarization along the [010] and [001] in‐plane axes and 90° domain walls along the [011] or [01] directions. Simultaneous capacitance–voltage measurements and band‐excitation piezoresponse force microscopy revealed strong collective switching behavior. Using a deep convolutional autoencoder, hierarchical switching is automatically tracked and the switching pathway is identified. The collective switching velocities are calculated to be ≈500 cm s −1 at 5 V (7 kV cm −1 ), orders‐of‐magnitude faster than expected. These combinations of properties are promising for high‐speed tunable dielectrics and low‐voltage ferroelectric memories and logic.

36 MATERIALS SCIENCE↗
DOE OSTI2021

An adaptive approach to machine learning for compact particle accelerators

Abstract Machine learning (ML) tools are able to learn relationships between the inputs and outputs of large complex systems directly from data. However, for time-varying systems, the predictive capabilities of ML tools degrade if the systems are no longer accurately represented by the data with which the ML models were trained. For complex systems, re-training is only possible if the changes are slow relative to the rate at which large numbers of new input-output training data can be non-invasively recorded. In this work, we present an approach to deep learning for time-varying systems that does not require re-training, but uses instead an adaptive feedback in the architecture of deep convolutional neural networks (CNN). The feedback is based only on available system output measurements and is applied in the encoded low-dimensional dense layers of the encoder-decoder CNNs. First, we develop an inverse model of a complex accelerator system to map output beam measurements to input beam distributions, while both the accelerator components and the unknown input beam distribution vary rapidly with time. We then demonstrate our method on experimental measurements of the input and output beam distributions of the HiRES ultra-fast electron diffraction (UED) beam line at Lawrence Berkeley National Laboratory, and showcase its ability for automatic tracking of the time varying photocathode quantum efficiency map. Our method can be successfully used to aid both physics and ML-based surrogate online models to provide non-invasive beam diagnostics.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

YOLO11 to SAM2 pipeline for feature extraction from nuclear test films

The response to the effects of nuclear detonations is supported by models that describe the evolution of the nuclear fireball and cloud and the associated transport of active debris. Validation of those descriptions relies on data from the nuclear test operations. Video records of those events offer a rich source of information that was exploited to a limited extent in historic analyses. Computer vision and machine learning techniques are powerful tools that can be used to increase the number of measurements that can be obtained from those films. In this work, we apply computer vision techniques to automatically track the temporal evolution of the nuclear fireball. In particular, we apply You Only Look Once 11 (YOLO11) and Segment Anything Model 2 (SAM2) in combination with minimal human intervention to digitized versions of the original nuclear test films. As part of the proposed workflow, the YOLO11 model is applied to films to determine bounding boxes for the fireball within each frame. These are then used as inputs to SAM2, which uses image segmentation to determine the fireball boundaries and their temporal evolution. We assess the accuracy of our approach by using it to determine the energy released during the Trinity nuclear test and comparing the results with previous analyses based on manual measurements.

Van Exel, Kimberly [ORNL] (ORCID:0009000877463894)↗
DOE OSTI2024

ScholarGuard

The ScholarGuard framework aims to address the gap in archiving and preservation efforts for scholarly artifacts beyond traditional research papers, such as software source code, datasets, presentation slides, workflows, protocols, videos, and more. It introduces a prototype system designed to automatically track researchers' outputs across various scholarly productivity portals on the open web, including platforms like GitHub, Slideshare, Figshare, and Wikipedia. The system detects the availability of new scholarly artifacts and applies modern web archiving technology to create a durable archival record, including high-level metadata for each artifact. This metadata is displayed within the system, linking both to the live version and the archived version of the resource, ensuring long-term accessibility and preservation of diverse research outputs. The software serves as a critical tool for preserving the broader spectrum of scholarly contributions, facilitating visibility, searchability, and long-term access to research artifacts beyond the traditional scope of journal publications.

Balakireva, Lyudmila↗
DOE OSTI2021

Using Machine Learning to Track Objects Across Cameras

Video surveillance is one of the most important technologies used by the International Atomic Energy Agency in international safeguards. At large, complicated facilities, multiple surveillance cameras are deployed to monitor the transfer of safeguards-relevant objects across the site. During inspections, all surveillance videos are reviewed to ensure the objects are not manipulated or diverted during transfer, a laborious, time-consuming task. This work describes using deep machine learning algorithms to track objects automatically across multiple cameras, greatly improving the efficiency of the review process. The fundamental problem in this object tracking task across multiple cameras is how to associate the same object, which may show extreme intra-class variations, such as viewpoints, occlusions, and various scales, in different and even non-overlapped cameras. Object re-identification (Re-ID) in nuclear facility video surveillance is even more challenging than classic person or vehicle Re-ID problems because different instances in the same category may display an identical appearance. One observation from nuclear facility surveillance videos is that all objects must be carted (e.g., via forklift) to move. Therefore, the spatial context information of an object, which provides the feature from the carrier, is critical for the object Re-ID task. This work proposes a two-stream convolutional neural networks model that takes features of objects and their surrounding regions into account. Moreover, the custom videos usually are gleaned from different scenes from the training data, which may have extreme variations in illumination changes and/or cluttered backgrounds. Directly applying the trained model to custom videos will dramatically decrease the performance. To tackle this problem, an advanced domain adaptation technique is proposed to mitigate the gap between the data taken from different scenes. The proposed framework will track objects of interest across a nuclear complex. The resulting tracks can be used in further analyses, such as event/activity recognition, anomaly detection, etc.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2024

Performance of the first mechanically-coupled twisting heliostat: encircled energy vs angle of incidence, and closed loop tracking

The first automatic twisting heliostat, with 8 m2 reflector, was completed and tested on-sun in January 2024. It was set up on a target-oriented dual-axis mount, with the target-axis aimed at a target 113 m to the west. The shape-twisting is purely automatic, made by a mechanical cam coupling to the cross-axis, which turns as the angle of incidence of the sun’s rays on the reflector. Three unsaturated images taken at different times of day were recorded of the sun on the target, reflected at angles of incidence of 5°, 49° and 68°. The measured FWHM of three images is very similar, about 1.09 m or 9.5 mrad, only slightly larger than an ideal solar disc, indicating that the different twisted reflector shapes are close to the ideal biconics needed to image the sun. The encircled energy measured for all three images was similar, 87% within approximately 1.30 m diameter. We report here also on a novel, tracking camera that employs a semitransparent beamsplitter fixed perpendicular to the mirror surface and to the plane of incidence. Over 2.5 hours of measurement of closed loop tracking, errors were ≤ 0.36 mrad rms, in both tracking axes.

14 SOLAR ENERGY↗
DOE OSTI2025

Automated nuclear cloud feature extraction from film

Chemical, biological, radiological, nuclear, and explosives incidents require rapid detection and characterization for appropriate response. For a nuclear detonation, visible-light cameras may be used to locate the cloud and characterize fallout deposition when coupled with numerical models. Films from the United States’ nuclear testing era compose the only sizeable collection of imagery depicting high-yield detonations. These films offer unique insights into characteristics of flows involving scales that are difficult to replicate experimentally, and they are a valuable source of data for the validation of models for nuclear fallout transport, either as part of emergency response or forensic activities. In this work, we implement modern computer vision and machine learning techniques to identify and track the cloud automatically and subsequently determine the time dependence of some of its features. We trained a ResNet-18 image classifier on hundreds of images to categorize nuclear cloud morphology. Each category or cloud regime is determined by early cloud evolution and is associated to constitutive properties of the flow, such as distribution of vorticity. Next, we identified keypoint features using the KAZE algorithm and tracked these keypoints in the images, allowing us to determine the dimensions and velocities of the cloud across film frames. These measurements converted to real-world units provide valuable experimental data that can be used in the development and validation of nuclear cloud models. We compared the results of this method against manual cloud rise measurements from two different films. In one, our automated method accelerated the feature extraction process without sacrificing measurement accuracy.

Khristy, Joel [ORNL] (ORCID:0000000209963060)↗
DOE OSTI2024

Automated Operational Forecasting of Monsoon Low Pressure Systems

Monsoon low pressure systems (LPSs) are the dominant rain-bearing weather system of South Asia, often producing extreme precipitation and hydrological disasters in a region inhabited by nearly two billion people. Despite the importance of these storms, no operational system has automatically identified and tracked LPS in real time in numerical weather prediction model output; many commonly used vortex-tracking algorithms are ill suited for monsoon LPS because of the weak winds and cold cores of these systems. Here, we describe a new system that uses optimized algorithms to identify monsoon LPS in short- to medium-range forecasts from the U.S. Global Ensemble Forecast System (GEFS) and a version of the deterministic Global Forecast System (GFS) adapted and used operationally by the Indian Institute of Tropical Meteorology (IITM). We also assess the historical performance of these models in forecasting South Asian monsoon LPS, comparing this with the performance of the Integrated Forecasting System of the ECMWF. We assess the accuracy of model predictions of LPS genesis, position, intensity, and precipitation rates for forecast lead times of 1–5 days, yielding quantitative information on model biases to guide operational forecasters and disaster managers. The system we introduce here could be extended to other low-latitude regions affected by dynamically weak, heavily precipitating atmospheric vortices that are often not included in tropical cyclone inventories.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

ThermalTracker 3D

ThermalTracker-3D is a stereo-vision solution for evaluating flight tracks of birds and bats around offshore wind turbines. Using a pair of thermal video cameras, the technology remotely senses movement of animals and objects, day and night, near critical assets. It generates motion tracks by collapsing a sequence of video frames from each camera into a single image that contains an entire flight track and then applies stereo-vision processing to transform the flight track into three dimensions. The approach allows tracking in near real time and automatically identifies moving objects based on features from the motion track and object size.

Matzner, Shari↗
DOE OSTI2024

Image registration for in situ X-ray nano-imaging of a composite battery cathode with deformation

The structural and chemical evolution of battery electrodes at the nanoscale plays an important role in affecting the cell performance. Nano-resolution X-ray microscopy has been demonstrated as a powerful technique for characterizing the evolution of battery electrodes under operating conditions with sensitivity to their morphology, compositional distribution and redox heterogeneity. In real-world batteries, the electrode could deform upon battery operation, causing challenges for the image registration which is necessary for several experimental modalities, e.g. XANES imaging. To address this challenge, this work develops a deep-learning-based method for automatic particle identification and tracking. This approach was not only able to facilitate image registration with good robustness but also allowed quantification of the degree of sample deformation. The effectiveness of the method was first demonstrated using synthetic datasets with known ground truth. The method was then applied to an experimental dataset collected on an operating lithium battery cell, revealing a high degree of intra- and interparticle chemical complexity in operating batteries.

25 ENERGY STORAGE↗
DOE OSTI2022

NucMesh: nuclear reactor geometry creation and mesh generation module in NEMoSys

NucMesh is a parameterized geometry and mesh generator for nuclear reactors developed within the Nuclear Energy Modeling System NEMoSys at Illinois Rocstar. NEMoSys is a platform developed for mesh generation, adaptive refinement, and solution verification. NucMesh is implemented to be generalized and extensible with a robust computer aided design engine and multiple mesh generation algorithms for unstructured triangular, quad-dominant, and structured quadrilateral meshing. In this paper, we present the geometric and meshing features of NucMesh. Geometrically objects are constructed bottom-to-top and overlaps are addressed automatically. A sophisticated object tracking algorithm prevents data from being lost for segmented objects. We discuss the primitive objects of circle and polygons that constitute the module and show how they are used with example inputs. Arrays of primitives and arrays of arrays are utilized to build large assemblies of objects. The concept of saved objects is discussed to demonstrate how repetitive objects can be reused easily and augmented in place. Three dimensional meshes can be obtained through mesh extrusion where all materials and side sets are extended to three dimensions. We show that side sets can be defined nearly anywhere within the geometry and can then be applied to the mesh. Finally, example reactor meshes are demonstrated for the Idaho National Laboratory Advanced Test Reactor and Los Alamos National Lab Empire reactor, both of which use control drums that NucMesh handles easily. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Automatic Classification of Biological Targets in a Tidal Channel Using a Multibeam Sonar

Multibeam sonars are widely used for environmental monitoring of fauna at marine renewable energy sites. However, they can rapidly accrue vast volumes of data, which poses a challenge for data processing. Here, using data from a deployment in a tidal channel with peak currents of 1–2 m s –1 , we demonstrate the data-reduction benefits of real-time automatic classification of targets detected and tracked in multibeam sonar data. First, we evaluate classification capabilities for three machine learning algorithms: random forests, support vector machines, and k-nearest neighbors. For each algorithm, a hill-climbing search optimizes a set of hand-engineered attributes that describe tracked targets. Here, the random forest algorithm is found to be most effective—in postprocessing, discriminating between biological and nonbiological targets with a recall rate of 0.97 and a precision of 0.60. In addition, 89% of biological targets are correctly classified as either seals, diving birds, fish schools, or small targets. Model dependence on the volume of training data is evaluated. Second, a real-time implementation of the model is shown to distinguish between biological targets and nonbiological targets with nearly the same performance as in postprocessing. From this, we make general recommendations for implementing real-time classification of biological targets in multibeam sonar data and the transferability of trained models.

16 TIDAL AND WAVE POWER↗
DOE OSTI2026

Study of fully coupled three-dimensional envelope instability using automatic differentiation

Automatic differentiation is a powerful tool for computing derivatives of simulation results with respect to given parameters. In this Letter, we have applied this tool to investigate the instability of a dynamical system governed by 21 ordinary differential equations. This second-order instability (named envelope instability) is driven by space-charge effects and has a significant impact on the operational regimes of particle accelerators. Our study delves into the three-dimensional envelope instability, incorporating both transverse and longitudinal coupling. Conventionally, analyzing this complex system would necessitate solving 441 ordinary differential equations, which is computationally intractable. However, by employing automatic differentiation, we were able to track only 21 equations. This approach allowed us to uncover an additional instability stopband, which arises from space-charge-induced coupling and has not been reported in previous studies. This research highlights the significant advantages of automatic differentiation in analyzing complicated dynamical systems involving a large number of ordinary differential equations.

Qiang, Ji [Lawrence Berkeley National Laboratory (↗
DOE OSTI2025

Extending JuTrack’s capabilities to the FRIB accelerator to enhance online modeling

JuTrack is a Julia-based accelerator modeling and tracking package that utilizes compiler-level automatic differentiation (AD) to enable fast and accurate derivative calculations. While JuTrack provides a solid foundation for beam dynamics simulations, its capabilities must be extended to support the Facility for Rare Isotopes (FRIB) linac. This includes modeling heavy-ion linac accelerator components such as the liquid-lithium charge stripper, which facilitates efficient acceleration by remove electrons from heavy isotopes, and incorporating multi-charge state acceleration tracking, which allows for charge-dependent beam dynamics. These extensions address challenges such as the beam matching and optimization of multi charge state through various accelerating structures and beam-material interaction modeling while maintaining the auto differentiation capability. This work focuses on adapting JuTrack to incorporate these elements, enhancing its online modeling abilities. We present modifications to JuTrack’s framework and demonstrate their performance in FRIB simulations.

Accelerator Physics↗
DOE OSTI2025

Analysis of the automatic reconstruction of protons from simulated neutrino interactions in the ICARUS detector: study of the performance of track vs shower discrimination algorithms

ICARUS, Imaging Cosmic and Rare Underground Signals, is the far detector of the Short Baseline Neutrino (SBN) Program at Fermi National Accelerator Laboratory (FNAL). This program was born with the purpose of definitely explaining some anomalies in the field of neutrino oscillation physics, that can suggest the existence of a 4-type of neutrino: the sterile neutrino. It doesn’t interact directly with ordinary matter through the weak interaction, but we can observed the effect of its oscillation. In this chapter I will briefly observe: • the physics goals of the SBN Program; • the role of ICARUS as SBN far detector; • the detection technology.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Automated analysis of unlabeled PV data with Solar Data Tools software: Overview and feature updates

Distributed rooftop PV systems: ubiquitous, yet commonly have unlabeled data Difficult or impossible to form a performance index We developed Solar Data Tools (SDT), an open-source Python library for analyzing PV power (and irradiance) time-series data SDT enables analysis of unlabeled PV data—no model, no meteorological data, no performance index required Takes a statistical signal processing approach Data processing steps are largely pre-defined and automatic regardless of system type—from utility tracking systems to multi-pitch rooftop systems

Meyers-Im, Bennet E↗