Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “precision timing applications”

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 451 records · Page 25
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

On the Feynman-alpha method for reflected fissile assemblies

The Feynman-alpha method is a neutron noise technique that is used to estimate the prompt neutron period of fissile assemblies. The method and quantity are of widespread interest including in applications such as nuclear criticality safety, safeguards and nonproliferation, and stockpile stewardship; the prompt neutron period may also be used to infer the k eff multiplication factor. The Feynman-alpha method is predicated on time-correlated neutron detections that deviate from a Poisson random variable due to multiplication. Traditionally, such measurements are diagnosed with one-region point kinetics, but two-region models are required when the fissile assembly is reflected. This paper presents a derivation of the two-region point kinetics Feynman-alpha equations based on a double integration of the Rossi-alpha equations, develops novel propagation of measurement uncertainty, and validates the theory. Validation is achieved with organic scintillator measurements of weapons-grade plutonium reflected by various amounts of copper to achieve k eff values of 0.83–0.94 and prompt periods of 5–75 ns. The results demonstrate that Feynman-alpha measurements should use the two-region model instead of the one-region model. The simplified one-region model deviates from the validated two-region models by as much as 10% in the estimate of the prompt neutron period, and the two-region model reduces to the one-region model for small amounts of reflector. The Feynman-alpha estimates of the prompt neutron period are compared to those of the Rossi-alpha approach. The comparative results demonstrate that the Feynman-alpha method is more precise than the Rossi-alpha method and more accurate for k eff < 0.92, whereas the Rossi-alpha method is generally more accurate for higher multiplications. Here, the uncertainty propagation developed in this work should be used for all Feynman-alpha measurements and will therein improve fitting accuracy and appropriate precision estimates.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗
DOE OSTI2025

Using Separation-Enhanced Isotope Ratio Mass Spectrometry to Enable Increased Renewable Carbon Content in Transportation Fuels (CRADA 525)

Stable isotope ratio measurements of carbon atoms using isotope ratio mass spectrometry (IRMS) can be an effective tool for quantifying biogenic carbon in co-processed fuels, with results approaching the precision and accuracy of accelerator mass spectrometry (AMS). The lower cost of an IRMS may enable deployment to refineries, improving access and analysis turnaround times (≤2 hours), and, by extension, provide data that can allow process optimization to maximize renewable carbon in desired refinery products. This project explored the integration of chemical separation with IRMS analyses to enable highly detailed tracking of biogenic carbon into fuel product streams separated by boiling point range, chemical class, or specific compound. Forty-nine fuels and fuel components of fossil and biogenic origin, spanning gasoline and diesel boiling point ranges, were received from three refiners and were analyzed for their δ 13 C values via IRMS. Results spanned a 13 C range from ca. 10‰ to 44‰ and reflect materials derived from sustainable sources (e.g., C4 or C3 plants, animal-based pathways, syngas) or from fossil-derived fuels. Common ranges are approximately 18‰ to 9‰ and approximately 30‰ to 20‰ for C4 and C3 plants, respectively, and approximately 34‰ to 24‰ and approximately 70‰ to 33‰ for petroleum-derived fuels and methane, respectively. Fuel-like standards were developed and tested using direct-injection elemental analyzer (EA) IRMS for liquid fuels. This method was compared with the published methods, yielding statistically similar results. Four blend curve sets were produced ranging from 0% to 100% of a fuel containing biogenic carbon, focusing on 0% to 10% biogenic carbon. Linear fits were the most applicable for two of the four blend curve sets; however, two sets were found to exhibit slightly quadratic behavior, which was more pronounced in low biogenic blend samples, necessitating second-order fits. The origin of the slight quadratic behavior remains unclear; however, the discussion points to possible interpretations. CanmetENERGY thoroughly characterized a majority of the samples using one- and two-dimensional gas chromatography (GC and GC×GC, respectively) and other analyses. Selected samples were subjected to solid phase extraction (SPE) for saturate, olefin, aromatic, and polar (SOAP) analysis, and the resulting solvent-diluted fractions containing saturates and aromatics were returned to Pacific Northwest National Laboratory (PNNL), where the solvent was removed via evaporation or physical separation using GC techniques. Characterization and separations provided an understanding of saturate and aromatic content, as well as boiling point ranges for each sample and sample fraction. Samples resulting from SPE were examined using EA-IRMS and gas chromatography combustion IRMS (GC-C-IRMS) analyses. Both approaches suggest that the range in values between end-members can be increased by selecting the paraffinic or aromatic fraction of the end-member or by selecting among individual compounds resulting from GC separation of the paraffinic fractions. Considerable work remains to put these approaches into practice and statistically validate the benefit for using a fraction or individual compound over bulk analysis of a sample. However, initial results suggest that separations provide advantages for samples having blend ratios of less than 10% biogenic blendstocks. 13 C results showed statistically similar biofuel blend results to those obtained at PNNL, although additional work is needed to obtain better reproducibility. Select samples were sent to Los Alamos National Laboratory (LANL) for IRMS measurements and Beta Analytics for AMS measurements. This work suggests that IRMS and AMS yield closely comparable results and in some circumstances, IRMS could serve as a surrogate for AMS. While additional work is needed to better resolve statistical advantages for separations and better show the comparable nature of IRMS and AMS in both the biogenic carbon analysis of bulk chemical classes, initial results from this study suggest that these should be pursued in order to proliferate this approach for quantifying biogenic carbon in transportation fuels to the refinery level, thereby potentially enabling process optimization in co-processing scenarios.

09 BIOMASS FUELS↗
DOE OSTI2020

Ab initio polaritonic potential-energy surfaces for excited-state nanophotonics and polaritonic chemistry

Advances in nanophotonics, quantum optics, and low-dimensional materials have enabled precise control of light–matter interactions down to the nanoscale. Combining concepts from each of these fields, there is now an opportunity to create and manipulate photonic matter via strong coupling of molecules to the electromagnetic field. Toward this goal, here we demonstrate a first principles framework to calculate polaritonic excited-state potential-energy surfaces, transition dipole moments, and transition densities for strongly coupled light–matter systems. In particular, we demonstrate the applicability of our methodology by calculating the polaritonic excited-state manifold of a formaldehyde molecule strongly coupled to an optical cavity. This proof-of-concept calculation shows how strong coupling can be exploited to alter photochemical reaction pathways by influencing avoided crossings with tuning of the cavity frequency and coupling strength. Therefore, by introducing an ab initio method to calculate excited-state potential-energy surfaces, our work opens a new avenue for the field of polaritonic chemistry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Additive manufacturing of high explosives with inert dilution for wave shaping and detonation velocity grading

The performance of a high explosive charge is highly influenced by the detonation velocity and any inherent internal structure. Additively manufactured (AM) high explosive charges allow for a precisely engineered internal structure and the selective placement of discrete or graded volumes of a tailored material, which are unachievable by conventional means. The dilution of a solid explosive by an inert additive for detonation velocity reduction is herein evaluated for a method of material extrusion called a direct-ink-write AM, where an 1,3,5,7-tetranitro-1,3,5,7-tetrazoctane-based ultraviolet -curable energetic-binder paste explosive was diluted by the inert density mock 5-iodo-2′-deoxyuridine for use in the manufacture of multi-material explosive charges. The explosive ink was characterized for printing by rheological and particle size measurements, and the detonation velocities for 0–40 wt. % inert diluent were found to have a continuous linear reduction in detonation velocity down to 14.3% difference. A two-component 3D plane wave explosive lens with 8.038 km/s “fast” and 7.491 km/s “slow” diluted ink in an internal cone demonstrated a reduced time of breakout at the output face from 361 to 53 ns. A linearly graded “fast–slow–fast” cylinder was successfully printed and fired, showing engineered control along the length of the charge as the detonation velocity dropped from 7.851 to 6.700 km/s and then recovered to 7.241 km/s. The extreme control over the internal composition of a high explosive charge via a dual-material extruder/mixer challenges conventional manufacturing approaches and opens new avenues for engineered detonation wavefronts, high explosive charge performance, and new applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Efficient laser-driven proton acceleration from a petawatt contrast-enhanced second harmonic mixed-glass laser system

Efficient laser-driven plasma acceleration of ion beams requires precision control of the target–plasma profile, which is crucial to optimize the laser energy transfer. Along the laser propagation direction, this can be achieved by tailoring the temporal structure of the laser pulse. We show for the first time that frequency-doubling of a short pulse (hundreds-femtosecond range) petawatt-class mixed-glass laser system, which results in temporal intensity contrast enhancement, enables surface and volumetric laser–energy coupling, and the acceleration of proton beams from few-nanometer-thick foil targets. Experimentally, maximum ion energies and laser-to-proton energy conversion efficiencies were found to be both maximized at optimum laser and target conditions manifested when the normalized target density nearly equalizes the normalized laser vector potential, which is in agreement with theory and simulations. These signatures are recognized as a unique indication of the interaction between ultra-intense laser pulses with high temporal intensity contrast and ultra-thin nanometer-scale targets. Transverse modulations of accelerated proton beams in the form of bubble- and ring-like structures measured in the thinnest targets provide additional evidence of volumetric laser-driven particle acceleration regimes and transitional features in ultra-thin foil targets specific to laser–plasma interactions characterized by a high temporal intensity contrast. These results open avenues in the generation of high contrast laser pulses from short-pulse-femtosecond petawatt mixed-glass laser systems and demonstrate the feasibility of this technique for applications requiring high laser intensity contrast with high efficiency.

Physics↗
DOE OSTI2022

Hole Trapping in Halide Perovskites Induces Phase Segregation

Metal halide perovskites have garnered a great deal of attention for their applications in photovoltaics, LEDs, and radiation detection. The ease of solution processing high-quality perovskite semiconductors with large absorption coefficients and tolerance to native defects is decidedly attractive. Additionally, the ability to precisely tune the band gap of halide perovskites through compositional alloying of the halide ion is of particular interest for a range of applications, especially for tandem solar cells. However, under steady state light irradiation, an initially homogeneous mixed halide perovskite (MHP) will form local domains that are rich in one halide ion (e.g., Br or I). This light-induced phase segregation in MHPs forms iodide-rich domains that act as charge carrier traps and lowers the efficiency of perovskite-based devices. Thus, phase segregation poses a serious challenge to the implementation of MHPs in real-world device settings. Interestingly, when a phase segregated MHP film is placed in the dark, entropic driving forces become dominant and the segregated perovskite remixes and returns to its initially homogeneous state. Several key mechanistic details of phase segregation have been elucidated over the years. However, there are still aspects of halide segregation that are not clear, and there is ongoing debate in the literature as to what are the key factors that contribute to the mechanism. This Account discusses recent results that point to the specific role of hole trapping in phase segregation. Interestingly, generation of holes through above-band-gap excitation or through electrochemical injection increases ion migration and leads to phase segregation. The thermodynamic and redox properties of halide perovskites provide a strong driving force for hole trapping and oxidation of iodide species in MHPs. However, mobile halide species within the perovskite lattice take time to migrate and generate halide-rich domains. When in contact with a nonpolar solvent, the migration of iodine species is further extended to expulsion of iodine from the perovskite film. Thus, the mobility of halides and their susceptibility to hole-induced oxidation play a crucial role in determining the long-term stability of metal halide perovskites. Strategies to gain kinetic control over ion migration to slow phase segregation are needed to overcome these hurdles and achieve stable mixed halide perovskites. Modification of the perovskite composition through introduction of different cations or halide ions, or introduction of low-dimensional perovskite phases may suppress phase segregation. Furthermore, in achieving stability and improving the efficiency of perovskite solar cells and light emitting devices with minimal impacts, suppression of segregation remains the key factor.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Autonomous Coupler Alignment Using Position-Based Visual Servoing in a ROS 2 Framework

As robotic arms are becoming increasingly common alongside humans as collaborative robots, their high precision in motion enables tasks to be performed at significantly higher speeds with reduced disruption in the environment. The Fermi National Accelerator Laboratory is exploring this application by incorporating a UR16e from Universal Robots in a cleanroom setting during assembly of couplers to superconducting radio frequency cavities as part of the PIP-II project. The goal of the robotic assembly process is to precisely position the UR16e robot so that the coupler flange, mounted on the robot’s end-effector, is accurately aligned with and pressed against the cavity flange, requiring only final fastening by a lab technician. This thesis builds upon an initial system in which the robotic process was limited to the alignment phase using position-based visual servoing with an eye-in-hand camera to only align the coupler to the cavity with an offset distance. The objective of this thesis is to further advance autonomous robotic assembly by extending the process. To this end, the entire software framework was reconstructed, as the previous development environment posed significant challenges in modifying the software and adapting to hardware changes. The main contributions of this thesis are as follows: (i) a modular and scalable software framework based on ROS~2 was developed to facilitate performance expansion and interchangeability of software and hardware components; (ii) the desired alignment position for position-based visual servoing was parameterized to enable flexible configuration; and (iii) a methodology was developed to close the offset distance between the coupler and cavity utilizing the internal force-torque sensing capability of the UR16e, as visual feedback is not available during the offset-closing phase. The proposed autonomous robotic assembly reduces assembly time and technician involvement, thereby minimizing the risk of airborne particulate contamination, which is essential in the cleanroom setting. Moreover, the ROS~2-based framework provides a foundation for further expansion and continued advancement of robotic automation in Fermilab.

Giffen, Nickolas [Northern Illinois U.]↗
DOE OSTI2024

Improving Efficiency of Off-Road Vehicles by Novel Integration of Electric Machines and Advanced Combustion Engines

Modern off-road equipment will increasingly rely on electrified implements that will deliver precision control with a smaller footprint than hydraulics. The primary energy converter, however, will be an onboard reciprocating internal combustion engine because of the power density of hydrocarbon fuels in comparison to electrical energy storage and the remote locations where much of this equipment is deployed. Adding energy storage and electric machines creates opportunities to improve efficiency while reducing emissions. This program investigated approaches to take advantage of the extra flexibility that an enhanced electrical system to enable high-efficiency, low-emissions combustion technologies. The current program evaluated hybridization of both the torque application and air-handling systems to maximize efficiency while minimizing cost. This program designed, analyzed and tested a hybrid off-road vehicle consisting of a series electric powertrain with energy storage, an electrified air system, and a 33% downsized diesel engine. Detailed comparisons were made between the base powertrain and the developed powertrain using powertrain simulations, engine testing, and vehicle testing. The key results of the study are: • The resulting vehicle reduces fuel consumption by 5% to 15% at equal productivity. The primary improvements were due to recovery of regenerative braking losses for cycles where large transients are encountered. • The electrified air system was an enabler to allow engine downsizing. This was most important for duty-cycles where the engine primarily operated at moderate loads and had few low speed high torque conditions. • Engine level improvements showed re-optimization of the powertrain is possible when electrified air handing is available. Increased exhaust gas recirculation and advanced injection timing allowed up to a 15% reduction in brake specific fuel consumption at equal NOx and transient response. Compared to the larger engine, the downsized engine achieves up to an 18% reduction in brake specific fuel consumption over the non-road transient cycle. • The life cycle analysis and total cost of ownership study showed that the hybrid powertrain has the potential to reduce 5-year CO2 and total cost of ownership by ~6% over the baseline vehicle. The results also indicated that a pure battery electric vehicle is not feasible in this application and is likely to increase the CO2 emissions due to the CO2 from battery production. A hybrid powertrain with low carbon fuels shows the potential to substantially reduce CO2 and total cost of ownership.

02 PETROLEUM↗
DOE OSTI2026

Upcycling waste PET into functional multiblock copolymers through controlled macromolecular design

Poly(ethylene terephthalate) (PET) oligomers derived from glycolysis depolymerization were converted into multiblock copolymers through diisocyanate-mediated coupling with dihydroxy-terminated oligomers, enabling precise control over copolymer sequence distribution, connectivity, and mechanical performance. Here, we demonstrate that telechelic PET oligomers isolated directly from depolymerized consumer waste can serve as reactive building blocks for the formation of segmented multiblock copolymers, eliminating the need to revert to monomeric feedstocks. Dihydroxy-terminated PET oligomers (Mw ≈ 8 kg mol−1) were coupled with poly(ethylene oxide) (PEO, Mw ≈ 4 kg mol−1) to form PET-PEO multiblock copolymers with high molar mass (Mw ≈ 160 kg mol−1). We further show that the timing of end-capping reactions provides a key control parameter that governs the competition between chain extension and termination, thereby dictating the resulting multiblock architecture and molecular-weight evolution. Evaluation of their mechanical properties reveals that virgin PET exhibits high modulus (∼3 GPa) and strength (43 MPa) but limited ductility (<10% elongation). In contrast, PET–PEO multiblock copolymers retain comparable tensile strength (44 MPa), albeit while exhibiting dramatically enhanced ductility (>90% elongation), forming tougher materials with efficient stress transfer between the rigid PET domains and the flexible PEO segments. When incorporated into PET/PEO blends at low loadings, the multiblock copolymers serve as effective compatibilizers, yielding materials with an intermediate modulus (1.1–1.2 GPa) and improved elongation compared to uncompatibilized blends. Furthermore, the presence of PEO blocks increases water uptake and gas permeability relative to virgin PET, reflecting the tunability of molecular transport through the copolymeric blocks. To our knowledge, this represents the first report of PET–PEO multiblock copolymers derived from post-consumer PET for gas transport applications. These results demonstrate that multiblock copolymer formation from telechelic PET oligomers provides a versatile platform for tailoring the mechanical and transport behavior of polyester-based materials through controlled macromolecular design and establishes a generalizable strategy for transforming consumer plastic waste into functional segmented polymers without requiring complete depolymerization to monomers.

Watson-Sanders, Shelby [Department of Chemistry, U↗
DOE OSTI2024

Localized Evaluation for Constructing Discrete Vector Fields

Topological abstractions offer a method to summarize the behavior of vector fields, but computing them robustly can be challenging due to numerical precision issues. One alternative is to represent the vector field using a discrete approach, which constructs a collection of pairs of simplices in the input mesh that satisfies criteria introduced by Forman's discrete Morse theory. While numerous approaches exist to compute pairs in the restricted case of the gradient of a scalar field, state-of-the-art algorithms for the general case of vector fields require expensive optimization procedures. This paper introduces a fast, novel approach for pairing simplices of two-dimensional, triangulated vector fields that do not vary in time. The key insight of our approach is that we can employ a local evaluation, inspired by the approach used to construct a discrete gradient field, where every simplex in a mesh is considered by no more than one of its vertices. Specifically, we observe that for any edge in the input mesh, we can uniquely assign an outward direction of flow. We can further expand this consistent notion of outward flow at each vertex, which corresponds to the concept of a downhill flow in the case of scalar fields. Working with outward flow enables a linear-time algorithm that processes the (outward) neighborhoods of each vertex one-by-one, similar to the approach used for scalar fields. Here, we couple our approach to constructing discrete vector fields with a method to extract, simplify, and visualize topological features. Empirical results on analytic and simulation data demonstrate drastic improvements in running time, produce features similar to the current state-of-the-art, and show the application of simplification to large, complex flows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Metal-embedded fiber optic sensor packaging and signal demodulation scheme towards high-frequency dynamic measurements in harsh environments

Optical fiber-based sensors are commonly used for industrial monitoring and control processes due to their small size, resistance to electromagnetic interference, and ability to perform a wide variety of high-precision measurements. However, implementing optical fiber sensors in harsh environments is challenging because they are small and fragile. Proper packaging of fiber-optic sensors could extend their use to harsh environments, including at high temperature and under high radiation. Furthermore, conventional fiber optic-based measurement systems often use computationally expensive signal processing algorithms which hinder their use in high-frequency dynamic applications. This work reports on the design of an optical fiber-pressure sensor system based on low-coherence interferometry that uses a metal-embedded optical fiber to provide a robust sensor package. Additionally, a novel phase demodulation scheme is proposed to extract length changes from the deformation of a thin diaphragm within a Fabry–Pérot cavity to measure pressure. The sensor has been tested to ±100 kPa, has a theoretical linear response over a 270 kPa dynamic range (24 dB maximum signal-to-noise ratio), and can resolve pressure transients up to 3910 kPa/s. Sampling at 100 kHz, the present sensor can resolve 2 kPa dynamic pressures at frequencies up to 2 kHz. Faster transients on the order of tens to hundreds of kHz can theoretically be resolved at the expense of decreasing the maximum resolvable amplitude. A methodology for designing a LCI pressure sensor for a given application is outlined based on the limitations imposed by the Nyquist criterion, the diaphragm's resonant frequency, the LCI optoelectronics, and the phase demodulation scheme. The sensor is the first to implement a low-coherence light source and a Fabry–Pérot interferometer designed to provide real-time high-fidelity pressure measurements using a metal-embedded optical fiber. The demonstrated sensor provides a platform for sensing in harsh conditions such as in nuclear and energy applications.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Real-Time Identification of Oxygen Vacancy Centers in LiNbO 3 and SrTiO 3 during Irradiation with High Energy Particles

Oxygen vacancies are known to play a central role in the optoelectronic properties of oxide perovskites. A detailed description of the exact mechanisms by which oxygen vacancies govern such properties, however, is still quite incomplete. The unambiguous identification of oxygen vacancies has been a subject of intense discussion. Interest in oxygen vacancies is not purely academic. Precise control of oxygen vacancies has potential technological benefits in optoelectronic devices. In this review paper, we focus our attention on the generation of oxygen vacancies by irradiation with high energy particles. Irradiation constitutes an efficient and reliable strategy to introduce, monitor, and characterize oxygen vacancies. Unfortunately, this technique has been underexploited despite its demonstrated advantages. This review revisits the main experimental results that have been obtained for oxygen vacancy centers (a) under high energy electron irradiation (100 keV–1 MeV) in LiNbO 3 , and (b) during irradiation with high-energy heavy (1–20 MeV) ions in SrTiO 3 . In both cases, the experiments have used real-time and in situ optical detection. Moreover, the present paper discusses the obtained results in relation to present knowledge from both the experimental and theoretical perspectives. Our view is that a consistent picture is now emerging on the structure and relevant optical features (absorption and emission spectra) of these centers. One key aspect of the topic pertains to the generation of self-trapped electrons as small polarons by irradiation of the crystal lattice and their stabilization by oxygen vacancies. What has been learned by observing the interplay between polarons and vacancies has inspired new models for color centers in dielectric crystals, models which represent an advancement from the early models of color centers in alkali halides and simple oxides. The topic discussed in this review is particularly useful to better understand the complex effects of different types of radiation on the defect structure of those materials, therefore providing relevant clues for nuclear engineering applications.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Search for 3D topological superconductors using laser-based spectroscopy (Final Technical Report)

The three-dimensional topological superconductor (3D TSC) is a novel quantum phase of matter that is predicted to exhibit exotic thermal and electrical properties, which may serve applications ranging from precision thermal management and sensing technologies to fault-tolerant quantum information processing. Over the past decade, there has been an intensive effort to realize 1D and 2D TSCs by artificially engineering them from combinations of already existing materials. For example, coupling conventional superconductors to spin-orbit coupled 1D nanowires or to 2D topological insulator surfaces by the proximity effect provides a route to realizing 1D and 2D TSCs respectively. However no artificial engineering strategy is available for a 3D TSC. Therefore, current searches are limited to intrinsically superconducting compounds whose topological properties are difficult to predict. The overarching goal of this project was to realize and to identify 3D TSCs in bulk single crystals using novel laser-based spectroscopic techniques. One major thrust was to develop ultralow temperature and ultra-high-resolution angle-resolved photoemission spectroscopy (ARPES) to directly measure the dispersion of Majorana excitations that are predicted to exist on the surfaces of 3D TSCs. Another major thrust was to develop a rational route to realize 3D TSCs by identifying their precursor phases. It is theoretically proposed that the critical fluctuations of certain ordered electronic phases can mediate Cooper pairing in odd-parity channels and lead to topological superconductivity. Therefore, 3D TSCs may potentially emerge upon suppressing these inversion symmetry broken precursor phases to a critical point with external perturbations such as pressure, strain or even light. To test this hypothesis, we studied candidate materials using a suite of symmetry-sensitive optical probes including rotational anisotropy optical second harmonic generation (RA-SHG) and time-resolved coherent phonon spectroscopy, all operable under high-pressure and low temperature environments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

A Data-Agnostic, Continuous Machine Learning Framework for Application in High Energy Physics and Beyond: Phase 1 Final Scientific/Technical Report

This Phase 1 effort has focused on the development of continual learning frameworks for use in machine learning, specifically in the applied context of High Energy Physics (HEP). Machine learning (ML) is a transformative technology by which computers, typically through the use of neural networks, are able to perform tasks with proficiency that rivals or surpasses that of human users. Model Degradation & Catastrophic Forgetting are two undesired phenomena which can occur in ML where the performance of a model degrades when either deployed on novel data streams, or trained on novel data which are sufficiently different than the data the models were initially trained on. A natural example where these sorts of effects can be observed is in the performance of detectors in harsh environments, where the detector signature may change over the lifetime of the detector as it ages and deteriorates — precisely what occurs in the experiments conducted in HEP. Real world HEP data is therefore an excellent test-ground and use-case for Continual Learning paradigms, which are techniques used in ML to counteract these problems. Ensemble learning is one such technique, where multiple smaller models are trained on subsets of the overall data and are ensembled together during inference. The intuition behind this technique is that, although there are shifts in the distributions which govern the incoming data streams, these shifts are not expected to be homogeneous or global. If a sufficient diversity in solutions within the various sub-models has been achieved, then at least one sub-model is expected to retain its performance within the overall ensemble. One further strength of this approach is that the architectures of the various models do not need to be identical, and in fact even different modalities of data can naturally be combined in this way. This work focused on applying ensemble learning techniques to derive results using two main datasets, anomaly detection in HEP data & time-series forecasting in semiconductor manufacturing data. Semiconductor manufacturing involves data with surprising similarity to that of HEP (e.g. wafer maps look very similar to digi-occupancy maps) and Cerium Lab’s prominence within the semiconductor industry makes semiconductor manufacturing a natural opportunity for commercialization of this work. Our efforts have led to two strong results. The first is that we evaluated the proposed ensembling techniques using previously proposed machine learning architectures for use in anomaly detection, namely AutoEncoder based models and their derivatives. We also developed new architectures which have not been evaluated in this context before. In fact, this work marks the first use of Vision Transformers for anomaly detection in HEP. Second, we demonstrated that ensemble learning significantly improves model performance in scenarios prone to degradation, validating its effectiveness across both HEP and semiconductor datasets. These results further support ensemble learning as a powerful strategy for mitigating catastrophic forgetting and maintaining robust performance in evolving data environments.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Time-of-Flight Estimation for Nondestructive Evaluation

Ultrasonic testing (UT) for nondestructive evaluation (NDE) is a critical entity necessary to resolve both the quality and precision questions of complex parts evolving from the classical approaches to the innovative additive manufacturing (AM) process. This modality provides the essential quantitative information for acceptance and potential flaw detectionof apart under investigation. A primary ingredient in UT besides the required precision robotic hardware for theacquisition of high-qualitymeasurement data is the underlying signal processing. It is here that much of the system performance capability resides. In this report,we discuss the basic steps in UT signal processing along with current capabilities that must be achieved in order to satisfy the criticaldemands of the various LLNL programs.In itsbasic foundations, signal processing is essentially the “extraction of critical information (signals) from noisy, uncertain data.” Clearly, in a perfect world the best signal processing is none---just make a reliable, noise-free, uncertainty-free, measurement. Unfortunately, even the best of systems is still straddled within the confines and limitations of physical instrumentation. With this in mind, we discuss the development of signal processing techniques toextract the desired UT information from noisy measurement data. We start with a discussion of a simple homogeneous representation of a “part” under investigation and its insonification by an ultrasonicexcitation from a physics-based perspective. Once developed, we briefly discussthe various choices of excitation signals and their tradeoffs.Next,we discuss a simulation approach employing simple models from the signal processing perspective and then move on to the development of the basic signal processing approach to ultrasonic signal processing all based on estimating the pulse arrival estimation. Starting with simple peak detection techniques, progressing to the processing workhorse so-called “matched-filter” and finally to the more sophisticated “model-based matched filter.”The underlying pre-and post-processing of noisy measurement data along with the application of these techniques are subsequently demonstrated on experimental ultrasonic data.Finally, we present some results for processing “weld” data when the ultrasonic excitation signal is not directly available.

42 ENGINEERING↗
DOE OSTI2023

HOLISMOKES IX. Neural network inference of strong-lens parameters and uncertainties from ground-based images

Modeling of strong gravitational lenses is a necessity for further applications in astrophysics and cosmology. With the large number of detections in current and upcoming surveys, such as the Rubin Legacy Survey of Space and Time (LSST), it is pertinent to investigate automated and fast analysis techniques beyond the traditional and time-consuming Markov chain Monte Carlo sampling methods. Building upon our (simple) convolutional neural network (CNN), we present here another CNN, specifically a residual neural network (ResNet), that predicts the five mass parameters of a singular isothermal ellipsoid (SIE) profile (lens center x and y, ellipticity e x and e y , Einstein radius θ E ) and the external shear (γ ext, 1 , γ ext, 2 ) from ground-based imaging data. In contrast to our previous CNN, this ResNet further predicts the 1σ uncertainty for each parameter. To train our network, we use our improved pipeline to simulate lens images using real images of galaxies from the Hyper Suprime-Cam Survey (HSC) and from the Hubble Ultra Deep Field as lens galaxies and background sources, respectively. We find very good recoveries overall for the SIE parameters, especially for the lens center in comparison to our previous CNN, while significant differences remain in predicting the external shear. From our multiple tests, it appears that most likely the low ground-based image resolution is the limiting factor in predicting the external shear. Given the run time of milli-seconds per system, our network is perfectly suited to quickly predict the next appearing image and time delays of lensed transients. Therefore, we use the network-predicted mass model to estimate these quantities and compare to those values obtained from our simulations. Unfortunately, the achieved precision allows only a first-order estimate of time delays on real lens systems and requires further refinement through follow-up modeling. Nonetheless, our ResNet is able to predict the SIE and shear parameter values in fractions of a second on a single CPU, meaning that we are able to efficiently process the huge amount of galaxy-scale lenses expected in the near future.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Recommendations to Improve the Nuclear Regulatory Commission Reactor Licensing and Approval Process

Due to the urgency around climate change and associated goals for clean energy transition, and Battelle Energy Alliance's (BEA) and Idaho National Laboratory's (INL) role as the nation's nuclear energy laboratory, numerous stakeholders have asked for BEA’s thoughts and recommendations to reduce the time and costs associated with licensing new reactors at the U.S. Nuclear Regulatory Commission (NRC). As an M&O contractor for an FFRDC, BEA is a long-term partner with the Government in seeking to achieve clean energy goals, yet has a level of independence needed to appropriately evaluate this topic. The views herein are informed by extensive BEA experience supporting nuclear energy endeavors including ongoing discussions with current and former regulators, applicants, and licensees. With this background in mind, the United States benefits from having an agency such as the NRC, which is viewed internationally as the leader in nuclear safety licensing and regulation. Nonetheless, while acknowledging the important nuclear safety role satisfied by the NRC, it is apparent that one of the most significant time and resource intensive activities for new reactor developers is the NRC licensing process. The time and cost to obtain NRC licenses add significant financial stress for new reactor projects and may result in abandonment of projects or failure to even begin new projects. The challenge is particularly acute for advanced reactors which may raise unique or new regulatory questions and may be smaller in size, resulting in a much higher proportional impact from regulatory and cost challenges. This situation presents a particularly troublesome risk for the nation given the urgency in which utilities are working to transition to clean, non-carbon-emitting energy sources like nuclear energy. Reforms to the NRC licensing process have the potential to greatly increase certainty and support the successful progress of new reactors. The NRC can retain its world-class nuclear safety reputation while becoming a world leader for regulatory efficiency and a critical enabler to the clean energy transition. This report describes potential NRC reforms, focusing on those with a statutory connection. Recognizing the potential tradeoffs with any proposed changes, the report attempts to highlight those considerations in the analysis of the reforms. The recommendations are presented as a set of options for consideration. Unless noted, they are independent options, offering stakeholders the option to select a subset for further consideration. Although difficult to calculate precise time improvements for some of the changes, the reforms have the potential for substantial improvements, perhaps even by a factor of two.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Multi-Scale Integrated Monitoring System for Enhancing Methane Emission Detection, Quantification & Prediction

This report details the progress and findings of a comprehensive study on reviewing existing solutions, identifying technology gaps, and formulating an “all-in-one” integrated strategy for developing the next-generation multiscale methane monitoring and modeling platform, conducted under grant number DE-FE0032292. Co-led by Dr. David Ebert, Dr. Binbin Weng, and Dr. Chenghao Wang at the University of Oklahoma, the project’s goal was to develop an integrated approach for building this engineering platform to detect, quantify, and mitigate methane emissions across various temporal scale, spatial scales, and sectors. The planning grant study began with an extensive review of various methane sensing and monitoring technologies and systems, surveying over 100 technology providers globally. This review revealed the prevalence of optical methods over chemical methods in commercially available sensors, with Non-Dispersive Infrared (NDIR), Tunable Diode Laser Absorption Spectroscopy (TDLAS), and Optical Gas Imaging (OGI) cameras being the most prevalent options. A trend towards more advanced optical techniques was observed, driven by increased regulatory focus and technological advancements. The technical evaluation of these sensing technologies provided crucial insights into their capabilities and limitations. The study examined emerging technologies such as Differential Absorption LiDAR (DIAL), which show promise for high-precision and long-range detection. The team then investigated the features and application bandwidth of various sensing platforms, including handheld, fixed/stationary, mobile, aerials, and spaceborne monitors. Pilot field studies were conducted to assess the capabilities of solutions for different emission scenarios. Field work with sensor deployments was conducted at three distinct site types: an oil & gas industry site, a cattle ranching operation, and a waste processing facility. The team also conducted a thorough review of methane flux inverse modeling approaches, focused on physically based methods. These approaches were categorized into simple, intermediate, and advanced methods. A realtime WRF-GHG (Weather Research and Forecasting-Greenhouse Gas) modeling system was developed and applied, incorporating multiple data sources to guide field experiments and inform methane plume detection. The project identified and analyzed numerous categories of methane data sources, including satellite measurements, ground-based sensors, and inventory databases. Key platforms examined include EDGAR, EPA GHGI, NASA TROPOMI, Carbon Mapper, and Climate TRACE, among others. The team proposed an architecture for a comprehensive methane monitoring platform. This system incorporates multi-source data acquisition, advanced data processing and assimilation, interactive visualization tools, and analytical capabilities for emissions forecasting and scenario analysis. The proposed platform aims to provide a user-friendly interface catering to various stakeholders, from researchers to policymakers. The architecture includes sophisticated data ingestion methods, a centralized data warehouse, and advanced analytical tools for data fusion and interpretation. To ensure the relevance and effectiveness of the proposed system, a comprehensive survey was conducted to gather stakeholder input on system requirements. Key findings include a strong need for integrating various data types and formats, a preference for real-time data updates and advanced visualization tools, and a demand for user-friendly interfaces catering to different expertise levels.

03 NATURAL GAS↗