Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “limiting techniques”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,189 records · Page 66
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Increased Interpretability for Model-Driven Deception: MARS LDRD Project

Machine learning has been proposed as a solution to several cybersecurity solutions and one of the most promising applications is for digital twins for intrusion detection and driving deceptive defense. However, machine learning techniques often result in a black-box function that is difficult for end users to interpret which for deception limits their ability to effectively define decoys. In this report, an approach to validate the equations learned are accurate is provided and demonstrated. Following, begins the process of addressing this issue for a model-driven deception technology that produces equations representing the physical process controlled by operation technology devices. This research was performed by applying subject matter expert context to machine learned models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Artificial Intelligence/Machine Learning Technology in Power System Applications

The primary purpose of this report is to provide an overview of the advancement in artificial intelligence and machine learning (AI/ML) technologies and their applications in power systems. It offers a foundation for understanding the transformative role of AI/ML in power systems and aims to stimulate further research and development in this area. This report begins with a historical perspective of AI/ML technologies, then explores their advancement to today’s prominence. The document highlights key contributors to the success of AI/ML technologies, including increased computational power, greater data availability, innovative algorithms, and advanced tools. It further introduces various AI/ML techniques, including supervised, unsupervised and reinforcement learning, graph neural networks, and generative AI. It also emphasizes the critical importance of ensuring the safety, security, and trustworthiness of these AI/ML techniques within this sector. The report reviews the recent representative advancements in various power system applications enhanced by AI/ML techniques, underscoring key developments and their transformative impact as evidenced by numerous studies. It also explores both the opportunities and challenges associated with the application of AI/ML technologies to improve power system applications. While the report extensively covers AI/ML applications in power systems, focusing primarily on the technical and operational aspects, it may not thoroughly explore the sociopolitical, economic, and broader regulatory implications of AI/ML integration in power systems. AI/ML techniques hold significant potential for enhancing power system applications; however, they are not omnipotent. It is crucial to acknowledge their limitations and understand that they may not be able to address all challenges in the power system domain. Various factors must be considered that influence the implementation, adoption, and effectiveness of AI/ML solutions, including but not limited to safety, security, transparency, and trustworthiness. Additionally, the incorporation of advanced human–machine interfaces is essential, as it enables humans to validate the effectiveness of AI/ML solutions while remaining actively engaged, fostering trust in AI/ML deployment. Finally, the report summarizes AI/ML research activities supported by the Department of Energy (DOE) Office of Electricity (OE) through the Advanced Grid Modeling (AGM) program. The work aligns with the interests and mission of DOE-OE AGM, with the report serving as a resource for identifying existing progress and for pinpointing future applications within AI/ML that need further exploration and support.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Developing Partnership between San Jose State University and DOE Lawrence Livermore National Laboratory to Enhance Climate Research Equity and Inclusion

One of the key objectives of this project was to develop partnership between San Jose State University (SJSU) and the Lawrence Livermore National Laboratory (LLNL), a US Department of Energy (DOE) funded national laboratory. Both institutions are closely located within the San Francisco Bay Area in California and their researchers share overlapping research interests and expertise related to Earth system sciences. By leveraging their connections with LLNL, faculty and students from SJSU gained exposure to state-of-the-art observations and simulations related to Earth system sciences, including but not limited to the usage of the facility data provided by the DOE Atmospheric Radiation Measurement (ARM) program and data analysis techniques for interpreting and analyzing the DOE Energy Exascale Earth System Model (E3SM) simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

First Very Long Baseline Interferometry Detections at 870μm

The first very long baseline interferometry (VLBI) detections at 870$\mu$m wavelength (345$\,$GHz frequency) are reported, achieving the highest diffraction-limited angular resolution yet obtained from the surface of the Earth, and the highest-frequency example of the VLBI technique to date. These include strong detections for multiple sources observed on inter-continental baselines between telescopes in Chile, Hawaii, and Spain, obtained during observations in October 2018. The longest-baseline detections approach 11$\,$G$\lambda$ corresponding to an angular resolution, or fringe spacing, of 19$\mu$as. The Allan deviation of the visibility phase at 870$\mu$m is comparable to that at 1.3$\,$mm on the relevant integration time scales between 2 and 100$\,$s. The detections confirm that the sensitivity and signal chain stability of stations in the Event Horizon Telescope (EHT) array are suitable for VLBI observations at 870$\mu$m. Operation at this short wavelength, combined with anticipated enhancements of the EHT, will lead to a unique high angular resolution instrument for black hole studies, capable of resolving the event horizons of supermassive black holes in both space and time.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Photometry on Structured Backgrounds: Local Pixel-wise Infilling by Regression

Photometric pipelines struggle to estimate both the flux and flux uncertainty for stars in the presence of structured backgrounds such as filaments or clouds. However, it is exactly stars in these complex regions that are critical to understanding star formation and the structure of the interstellar medium. We develop a method, similar to Gaussian process regression, which we term local pixel-wise infilling (LPI). Using a local covariance estimate, we predict the background behind each star and the uncertainty of that prediction in order to improve estimates of flux and flux uncertainty. We show the validity of our model on synthetic data and real dust fields. We further demonstrate that the method is stable even in the crowded field limit. While we focus on optical-IR photometry, this method is not restricted to those wavelengths. We apply this technique to the 34 billion detections in the second data release of the Dark Energy Camera Plane Survey. In addition to removing many >3σ outliers and improving uncertainty estimates by a factor of ~2–3 on nebulous fields, we also show that our method is well behaved on uncrowded fields. The entirely post-processing nature of our implementation of LPI photometry allows it to easily improve the flux and flux uncertainty estimates of past as well as future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

High-fidelity retrieval from instantaneous line-of-sight returns of nacelle-mounted lidar including supervised machine learning

Abstract. Wind turbine applications that leverage nacelle-mounted Doppler lidar are hampered by several sources of uncertainty in the lidar measurement, affecting both bias and random errors. Two problems encountered especially for nacelle-mounted lidar are solid interference due to intersection of the line of sight with solid objects behind, within, or in front of the measurement volume and spectral noise due primarily to limited photon capture. These two uncertainties, especially that due to solid interference, can be reduced with high-fidelity retrieval techniques (i.e., including both quality assurance/quality control and subsequent parameter estimation). Our work compares three such techniques, including conventional thresholding, advanced filtering, and a novel application of supervised machine learning with ensemble neural networks, based on their ability to reduce uncertainty introduced by the two observed nonideal spectral features while keeping data availability high. The approach leverages data from a field experiment involving a continuous-wave (CW) SpinnerLidar from the Technical University of Denmark (DTU) that provided scans of a wide range of flows both unwaked and waked by a field turbine. Independent measurements from an adjacent meteorological tower within the sampling volume permit experimental validation of the instantaneous velocity uncertainty remaining after retrieval that stems from solid interference and strong spectral noise, which is a validation that has not been performed previously. All three methods perform similarly for non-interfered returns, but the advanced filtering and machine learning techniques perform better when solid interference is present, which allows them to produce overall standard deviations of error between 0.2 and 0.3 m s−1, or a 1 %–22 % improvement versus the conventional thresholding technique, over the rotor height for the unwaked cases. Between the two improved techniques, the advanced filtering produces 3.5 % higher overall data availability, while the machine learning offers a faster runtime (i.e., ∼ 1 s to evaluate) that is therefore more commensurate with the requirements of real-time turbine control. The retrieval techniques are described in terms of application to CW lidar, though they are also relevant to pulsed lidar. Previous work by the authors (Brown and Herges, 2020) explored a novel attempt to quantify uncertainty in the output of a high-fidelity lidar retrieval technique using simulated lidar returns; this article provides true uncertainty quantification versus independent measurement and does so for three techniques rather than one.

47 OTHER INSTRUMENTATION↗
DOE OSTI2019

Overcoming Technical Challenges During Demolition of the West Valley Vitrification Facility - 19387

In February 2019, CH2M Hill BWXT West Valley, LLC will complete demolition and waste disposal of the first vitrification facility in the history of the United States. This project marked the largest and most complex radiological demolition project undertaken at the U.S. Department of Energy-led West Valley Demonstration Project (WVDP) to date. Provisions put in place during demolition monitored for and mitigated contamination migration during demolition. The work was carried out in a well-planned manner to address specific areas where higher levels of radiological activity were known to exist and where difficult and oversized equipment removal or sensitive items required special handling. Inaccessible contamination on ceiling mounted equipment, controlled removal of two massive steel shield doors, intact removal of lead glass shield windows and a confined demolition boundary area were some of the limiting factors that required special consideration during demolition. This paper contains a discussion of planning and implementation of special handling techniques utilized during some of the more difficult evolutions encountered during open-air demolition of the Vitrification Facility. The work techniques, provisions to mitigate inclement weather and lessons learned have been incorporated into the planning for follow-on radiological demolition work at the WVDP. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Confronting Dark Matter with the Multiwavelength Sky (Final Technical Report)

The existence of dark matter is a key clue to new fundamental physics, yet its nature and non-gravitational interactions remain mysterious. In the course of this five-year project (spanning July 2015 - July 2020), we have developed novel analysis techniques to separate dark matter signals from astrophysical backgrounds in the inner Galaxy, and measured hitherto-unknown properties of the Fermi Bubbles and the Galactic Center excess in gamma-rays from the inner Milky Way. We have improved predictions for the spatial distribution and energy spectra of photon signals from dark matter, and set new limits from astrophysical and cosmological datasets on dark matter inhabiting complex dark sectors, multi-particle dark matter annihilation, and tiny primordial black holes. We have successfully adapted effective field theory techniques from collider physics to make high-precision predictions for signals from heavy weakly-interacting DM, allowing us to essentially exclude the possibility that DM is a thermal wino, and have discovered novel theoretical and observational implications for dark matter bound states and self-interactions. We have developed a comprehensive public code package for modeling the effects of exotic energy injections, such as from dark matter annihilation and decay, on the ionization and temperature history of the early cosmos, and have used these tools to set robust, powerful and broadly-applicable constraints on models of light dark matter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Achieving a near-ideal silicon crystal neutron interferometer using submicrometer fabrication techniques

Perfect-crystal neutron interferometry, which is analogous to Mach-Zehnder interferometry, uses Bragg diffraction to form interfering neutron paths. The measured phase shifts can be used to probe many types of interactions whether it be nuclear, electromagnetic, gravitational, or topological in nature. For a perfect-crystal interferometer to preserve coherence, the crystal must possess a high degree of dimensional tolerance as well as being relatively defect-free with minimal internal stresses. In the past, perfect-crystal neutron interferometers have been produced by a two-step process. First, a resin diamond wheel would be used to remove excess material and shape the interferometer. Afterword, the crystal would be etched to remove surface defects and elevate strains. This process has had limitations in terms of repeatability and in maximizing the final contrast, or fringe visibility, of the interferometer. We have tested various fabrication and post-fabrication techniques on a single perfect-crystal neutron interferometer and measured the interferometer's performance at each step. Here we report a robust, nonetching fabrication process with high final contrast. For the interferometer used in this work, we achieved contrasts of greater than 90% several times and ultimately finished with an interferometer that has 92% contrast and a uniform phase distribution. Published by the American Physical Society 2024

Huber, M. G. (ORCID:0000000237958445)↗
DOE OSTI2024

Dynamic Control of Sodium Cold Trap Purification Temperature Using LSTM System Identification

This study investigates the dynamic regulation of the sodium cold trap purification temperature at Argonne National Laboratory’s liquid sodium test facility, employing long short-term memory (LSTM) system identification techniques. The investigation introduces an innovative hybrid approach by integrating model predictive control (MPC) based on first principles dynamic models with a multi-step time–frequency LSTM model in predicting the temperature profiles of a sodium cold trap purification system. The long short-term memory–model predictive controller (LSTM-MPC) model employs a sliding window scheme to gather training samples for multi-step prediction, leveraging historical data to construct predictive models that capture the non-linearities of the complex system dynamics without explicitly modeling the underlying physical processes. The performance of the LSTM-MPC and MPC were evaluated through simulation experiments, where both models were assessed on their capacity to maintain the cold trap temperature within predefined set-points while minimizing deviations and overshoots. Results obtained show how the data-driven LSTM-MPC model demonstrates stability and adaptability. In contrast, the traditional MPC model exhibits irregularities, particularly evident as overshoots around set-point limits, which can potentially compromise its effectiveness over long prediction time intervals. The findings obtained offer valuable insights into integrating data-driven techniques for enhancing real-time monitoring systems.

LSTM-MPC↗
DOE OSTI2023

Simultaneous stabilization and control of the n = 1 and n = 2 resistive wall mode

Abstract DIII-D experiments demonstrate simultaneous stability measurements and control of resistive wall modes (RWMs) with toroidal mode numbers n = 1 and n = 2. RWMs with n > 1 are sometimes observed on DIII-D following the successful feedback stabilization of the n = 1 mode, motivating the development of multi- n control. A new model-based multi-mode feedback algorithm based on the VALEN physics code has been implemented on the DIII-D tokamak using a real-time GPU installed directly into the DIII-D plasma control system. In addition to stabilizing RWMs, the feedback seeks to control the stable plasma error field response, enabling compensation of the typically unaddressed DIII-D n = 2 error field component. Experiments recently demonstrated this algorithm’s ability to simultaneously control n = 1 and n = 2 perturbed fields for the first time in a tokamak, using reactor relevant external coils. Control was maintained for hundreds of wall-times above the n = 1 no-wall pressure limit and approaching the n = 1 and n = 2 ideal-wall limits. Furthermore, a rotating non-zero target was set for the feedback, allowing stability to be assessed by monitoring the rotating plasma response (PR) while maintaining control. This novel technique can be viewed as a closed-loop extension of active MHD spectroscopy, which has been used to validate stability models through comparisons of the PR to applied, open-loop perturbations. The closed-loop response measurements are consistent with open-loop MHD spectroscopy data over a wide range of β N approaching the n = 1 ideal-wall limit. These PR measurements were then fit to produce both VALEN and single-mode stability models. These models allowed for important plasma stability information to be determined and have been shown to agree with experimentally observed RWM growth rates.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Structured illumination for surface-resolved grazing-incidence X-ray scattering

Grazing-incidence (GI) scattering techniques are widely used to characterize thin films, offering high surface sensitivity and insight into morphology and structure. However, these approaches typically provide statistical averaged information due to elongated footprint or limited spatial resolution due to beam size. Here we introduce a method that combines structured illumination with GI X-ray scattering and leverages our computational imaging approach to resolve local structural details. We demonstrate that our method captures local features of an organic semiconductor thin film without the need for sample rotation as in tomography. The method expands GI techniques from statistical averaging to high-resolution imaging, thereby providing the capability for detailed analysis of local material properties, such as domain shape, orientation and polymorphism, which are critical for advancing material design towards more efficient and tailored materials.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

A practical way to regularize unfolding of sharply varying spectra with low data statistics

Unfolding is a well-established tool in particle physics. However, a naive application of the standard regularization techniques to unfold the momentum spectrum of protons ejected in the process of negative muon nuclear capture led to a result exhibiting unphysical artifacts. A finite data sample limited the range in which unfolding can be performed, thus introducing a cutoff. A sharply falling “true” distribution led to low data statistics near the cutoff, which exacerbated the regularization bias and produced an unphysical spike in the resulting spectrum. An improved approach has been developed to address these issues and is illustrated using a toy model. The approach uses full Poisson likelihood of data, and produces a continuous, physically plausible, unfolded distribution. Here, the new technique has a broad applicability since sharply falling spectra are common.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Np-237 LoD Measurement by TIMS-Multi-Ion Counting Total Evaporation [Poster]

Requests for ultra-low-level 237 Np analyses highlight the importance of a robust chemical purification and measurement technique for Np. In FY2022, LANL is quantifying the instrumental 237 Np LoD. Need to incorporate a chemical purification procedure for TIMS-MICTE sample preparation. What is the recovery of the chemical purification procedure? What is the ultimate detection limit that can be achieved while incorporating a chemical purification procedure? We have adapted and refined the Pu TIMS-MICTE method for Np analyses. We have determined expected uncertainties for 237 Np TIMS-MICTE measurements. We have increased sensitivity through technique improvements. We have improved method reliability for Np analyses and increased throughput. Apply knowledge gained from progress made in FY2022 to further develop Np sample purification and analyses by TIMSMICTE.

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

Preventing Wetting Between Liquid Copper and Solid Steel: A Simple Extraction Technique

Copper contamination of end-of-life steel scrap is the main barrier to high-quality recycling. Preferential melting of copper from solid steel scrap is a potential extraction technique, which could be integrated into conventional scrap re-melting with little additional energy. However, previous investigations show removal of liquid copper is limited by its adherence to solid scrap. Preventing wetting between liquid copper and steel is essential to enable separation. The carbon content of steel, initial surface oxidation, and applied coatings effect wetting behavior, but have not been systematically studied. In this study, the individual and combined effects of these parameters on wetting behavior in an inert gaseous environment are observed with a heating microscope. Carbon content appears to be the most significant factor: blistering of the oxide scale on medium-carbon steels causes liquid copper to flow rapidly between the oxide and steel substrate. Liquid copper exhibited a stable droplet on low-carbon steel, regardless of the initial level of oxidation. The tested coatings did not consistently improve nonwetting behavior, but impaired the connection between the scale and steel substrate. This study confirms the potential of the preferential melting technique, but further investigation is needed to determine the most robust process conditions to handle diverse, fragmented scrap at an industrial scale.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Synchrotron-based Characterization Techniques for Radiation Detection Materials and Devices: An Overview

High-performance room-temperature radiation detectors (high energy resolution for spectrometers, high spatial resolution for imaging devices, and low defect-density for high flux applications) are needed for photon energies (>20 keV) that are not well suited for silicon detectors. Applications for such radiation detectors include nonproliferation, synchrotron, medical, astrophysics, and homeland security. Material- and device- characterization to understand and solve the limiting factors of radiation detection materials and devices is a core element of a radiation detector development R&D program. This presentation will give an overview on the two main synchrotron-based characterization techniques that have been employed by the authors in the last ~20 years: (1) White Beam X-ray Diffraction Topography and (2) Micron-scale detector mapping. A perfect (one domain) crystal (radiation detection material) is a requirement to achieve a highperformance radiation detector. White Beam X-ray Diffraction Topography (WBXDT) allows the rapid screening of the crystallinity of the detector material. With WBXDT we can quickly screen CZT and other crystals to make sure they have only one domain, and to see the presence of extended defects and strain fields.

Camarda, Giuseppe S.↗
DOE OSTI2019

Visual Detection with Context for Document Layout Analysis

We present 1) a work in progress method to visually segment key regions of scientific articles using an object detection technique augmented with contextual features, and 2) a novel dataset of region-labeled articles. A continuing challenge in scientific literature mining is the difficulty of consistently extracting high-quality text from formatted PDFs. To address this, we adapt the object-detection technique Faster R-CNN for document layout detection, and incorporate contextual information that leverages the inherently localized nature of article contents to improve the region detection performance. Due to the limited availability of region-labels for scientific articles, we also contribute a novel dataset of region annotations, the first version of which covers 9 region classes and 822 article pages. Initial experimental results demonstrate a 23.9% absolute improvement in mean average precision over the baseline by incorporating contextual features, and a processing speed 14x faster than a text-based technique. Ongoing work on further improvements is also discussed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Single- and two-particle finite size effects in interacting lattice systems

Simulations of extended quantum systems are typically performed by extrapolating results of a sequence of finite-system-size simulations to the thermodynamic limit. In the quantum Monte Carlo community, twist-averaging was pioneered as an efficient strategy to eliminate one-body finite size effects. In the dynamical mean field community, cluster generalizations of the dynamical mean field theory were formulated to study systems with nonlocal correlations. In this work, we put the twist-averaging and the dynamical cluster approximation variant of the dynamical mean field theory onto equal footing, discuss commonalities and differences, and compare results from both techniques to the standard periodic boundary technique. At the example of Hubbard-type models with local, short-range and Yukawa-like longer range interactions we show that all methods converge to the same limit, but that the convergence speed differs in practice. We show that embedding theories are an effective tool for managing both one-body and two-body finite size effects, in particular if interactions are averaged over twist angles.

36 MATERIALS SCIENCE↗