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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 955 records · Page 53
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Validation of the Public Radiation Exposure Calculation for the Incident at the National Institute of Standards and Technology Center for Neutron Research on February 3, 2021

The Department of Energy/National Nuclear Security Administration Consequence Management Program was contacted by the Health Physics Chief of the National Institute of Standards and Technology Center for Neutron Research (NCNR) to review public radiation exposure calculations for an event that occurred on its Gaithersburg, Maryland, campus on February 3, 2021. Subject matter experts from the Nuclear Emergency Support Team (NEST) assets, specifically the Consequence Management Home Team (CMHT) and the National Atmospheric Release Advisory Center (NARAC), were selected to provide support. CMHT used three separate modeling codes to validate the results the scientists at NCNR calculated using the HotSpot model. The analyses were performed using NARAC’s in-house Lagrangian dispersion codes known as LODI and Aeolus, as well as the Turbo FRMAC software from Sandia National Laboratories. The team used parameters provided by the NCNR scientists regarding the site, applicable observable meteorological data, and environmental survey and sampling data to estimate public exposure. Each model estimated public dose at much less than 0.5 mrem. CMHT concurs with the NCNR public radiation exposure calculations which state that members of the public at the 400-meter boundary would have received a radiological dose of less than 0.5 mrem.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2025

Market Driven Residential Energy Codes: Comparing Performance in a Changing Technological Environment

The research project is undertaken to better understand the changing relationship between the two basic methods of building energy code compliance – prescriptive and performance – and how those methods relate to each other with respect to advancements in building energy computer simulation standards and capabilities. The International Energy Efficiency Code (IECC) is a model code adopted by many jurisdictions across the United States. Historically, the prescriptive compliance methodology has been preferred in most jurisdictions. The prescriptive methodology requires meeting or exceeding specific efficiency minimums for each envelope component. This tends to be a simple method to teach and verify. A more involved prescriptive alternative called the Total UA alternative is sometimes used. This method requires some multiplication, summing, and comparison to compute, so it is done with a fairly simple computer program. However, advances in computer and building energy simulation technology have resulted in increased use of more detailed performance compliance methods. The performance compliance method establishes the annual energy cost threshold via hourly simulation models. The compliance threshold is determined with a comparison building model simulation with geometry similar to the proposed home and with energy feature parameters and efficiencies as specified in the IECC. This project examines relationships between the two methods of building energy code compliance, including: • Overall annual energy use based on utility bill analysis by compliance method • Code official work processes with respect to compliance methods • Gaps and issues associated with building code compliance methods • Simulated energy use difference between compliance methods • Code compliance cost as a function of compliance method • Code compliance labeling effectiveness for high performance residences • Getting to net zero energy use and net zero greenhouse gas emissions through high performance code alternatives • Electronic code permitting and compliance alternatives

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

The Workforce Readiness Index: A Local and Regional Assessment Tool for Energy Sector Preparedness

Building up a workforce that is properly trained and adequately sized is essential to ensuring that the deployment of energy generation sources in the United States meets future energy demand. Workforce development to support supply chain or large infrastructure investments typically occurs at a local level. However, there is a critical gap in understanding where and to what extent regional workforces are equipped to meet the needs of an energy industry. The Workforce Readiness Index ("the index") was developed to assess and compare energy sector workforce readiness across the United States at the county level to provide granular information to various stakeholders such as industry members, state decision makers, and training program developers. Workforce readiness is defined as the ability to recruit from the general labor market, transition workers with comparable skill sets, and train a workforce using scalable or existing training programs near a specific location. Therefore, the index evaluates readiness levels based on the likelihood that a county possesses the workforce development infrastructure needed to support the occupations required by an energy-related industry or sector. Furthermore, the index offers a standardized yet flexible approach that captures regional variations and highlights local strengths and challenges.

17 WIND ENERGY↗
DOE OSTI2026

A Methodology to Evaluate the Grid Reliability Impact of Oscillations Induced by Large Loads

The rapid growth of hyperscale AI data centers is bringing renewed attention to the reliability risk that sustained forced oscillations pose to bulk power systems, with cyclic computational workloads emerging as a new forcing source. Unlike the broadband, stochastic disturbances from traditional industrial loads such as arc furnaces, AI training and inference facilities can inject large active power swings concentrated at specific frequencies over extended durations - characteristics that existing grid planning practices do not account for. While the North American Electric Reliability Corporation (NERC) has recognized this gap and called for system-level studies of large load interconnections, no standardized methodology exists to screen, simulate, and quantify these risks at the planning stage. This report presents the Risk Assessment Tool for Large Load-induced Events (RATLLE), a Python-based, publicly available script suite developed at the Pacific Northwest National Laboratory to evaluate bulk power system reliability risks from data center-induced oscillations. RATLLE implements a three-module workflow: a screening module that identifies vulnerable interconnection locations and excitable system modes; a simulation module that models cyclic data center load behavior using a commercial positive sequence simulation platform; and an analysis module that computes risk metrics and generates interactive visualization dashboards. The risk metrics, formulated around simulation observables, map oscillation impacts to a three-stage severity scale spanning latent equipment fatigue through imminent cascading failure. The methodology is demonstrated on two Western Electricity Coordinating Council (WECC) system models: a publicly available 240-bus reduced representation and a detailed 2031 Heavy Winter planning case. Case studies illustrate that even modest 50 MW forced oscillations at resonant frequencies can produce wide-area power swings, N-1 security constraint violations, and cascading generator trips through protection actions - outcomes that would not occur under normal operating conditions without oscillations present. The results underscore the need for standardized oscillation impact assessment in large load interconnection studies and provide a reproducible, extensible framework for utilities to adopt or customize within their existing planning workflows.

Biswas, Shuchismita↗
DOE OSTI2020

Los Alamos National Laboratory Hazardous Waste Facility Permit Community Relations Plan

The requirements of the Hazardous Waste Facility Permit are fulfilled through execution of LANL's fourth strategic goal: “Enabling mission delivery through next-generation facility, infrastructure, and operational excellence.” With input from our stakeholders, we will continue to fulfill those requirements and to improve our waste operations. In 2015, the Laboratory purpose statement focused waste management efforts by directing delivery of mission success through operational effectiveness and scientific excellence. This Plan advances that purpose through community involvement. In the process of achieving our national security mission, Los Alamos National Laboratory generates some hazardous and mixed waste. Hazardous waste is solid waste that is dangerous or potentially harmful to human health or the environment. Hazardous wastes can be liquids, solids, gases, or sludges. They can be discarded as commercial products, such as cleaning fluids or pesticides, or as the byproducts of operations. Specific substances are listed in 40 Code of Federal Regulations (CFR), Part 261: 40 CFR Part 261. Hazardous waste management activities are regulated by the U. S. Environmental Protection Agency and the New Mexico Environment Department (NMED) pursuant to New Mexico Hazardous Waste Act (HWA; Chapter 74, Article 4 NMSA 1978) and regulations under the Act. In 1989, NMED issued the Hazardous Waste Facility Permit (EPA ID Number NM0890010515-1) that established standards for the Laboratory to manage, store, and treat hazardous wastes on-site and to undertake the closure, post closure care, and cleanup as necessary, of permitted waste management units. On November 30, 2010, NMED renewed the Hazardous Waste Facility Permit (the Permit). In June 2020 the Permittees submitted a permit renewal application to the NMED for their review and approval. The Hazardous Waste Facility Permit Community Relations Plan (CRP) describes the scope of public involvement in the activities of the Permit. The CRP is specifically designed to facilitate the community outreach, engagement, and relations activities concerning the Laboratory’s Hazardous Waste Facility Permit and coordinates with but does not include public involvement for other Laboratory programs, initiatives, or environmental activities.

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

Prognosis of Wind Turbine Gearbox Bearing Failures Using SCADA and Modeled Data

Predictive maintenance and condition monitoring systems for wind turbines have seen increased adoption to minimize downtime, reducing operation and maintenance costs. On today’s wind power plants, the integrated supervisory control and data acquisition (SCADA) system provides low- frequency operational data that can be leveraged to quantify a wind turbine’s health. The aim of this study is to utilize machine-learning techniques to predict axial cracking failures in wind turbine gearbox bearings up to 1 month ahead of time. The failures are assumed to have occurred when the investigated bearing was replaced. While current SCADA systems show the overall condition of a wind turbine, often they do not allow for the investigation of specific gearbox bearings’ health. To enrich bearing fault signatures, additional data are computed through physics-based models using gearbox design information. Based on SCADA data, modeled data, and bearing failure log data from an actual wind plant, the performances of different machine-learning models on unseen data are then evaluated using industry-standard metrics such as precision, recall, and F1 score. Results show the overall system performance enhancement in predicting bearing failure when modeled data are included with SCADA data. The reduction in terms of false alarms is about 50%, and improvement in terms of precision and F1 score is about 33% and 12% respectively, based on the best modeling case in this study.

49 EE - Wind and Water Power Program - Wind (EE-4W↗
DOE OSTI2020

Interpreting the Operando XANES of Surface-Supported Subnanometer Clusters: When Fluxionality, Oxidation State, and Size Effect Fight.

X-ray absorption near edge structure (XANES) spectroscopy is widely used for operando catalyst characterization. We show that, for highly fluxional supported nanoclusters, the customary extraction of the oxidation state of the metal from the XANES data by fitting to the bulk standards is highly questionable. The XANES signatures as well as the apparent oxidation state for such clusters arise from a complex combination of many factors, and not only from the chemical composition in reaction conditions (e.g., oxygen content in oxidizing atmosphere). The thermally accessible isomerization and population of several structurally distinct cluster forms, cluster-support interaction, and intrinsic size effects all impact the metal oxidation state and XANES signal. We demonstrate this on copper oxide clusters with different compositions, Cu4Ox(x = 2-5) and Cu5Oy (y = 3, 5), deposited on amorphous alumina and ultrananocrystalline diamond, for which we computed the XANES spectra and compare the results to the experiment. We show in addition that fitting the experimental spectrum to calculated spectra of supported clusters can, in contrast, provide good agreement and insight into the spectrum-composition-structure relation. Experimental XANES interpreted using the proposed fitting scheme shows the partial reduction of Cu oxide clusters at rising temperatures, and pinpoints the specific stoichiometries that dominate in the ensemble of cluster states as the temperature changes.

Zandkarimi, Borna↗
DOE OSTI2024

Solid State Reduction Driven Synthesis of Mn Containing Multi-principal Component Alloys

In recent years, high entropy alloys (HEAs), also known as multi-principal component alloys (MPCAs) have emerged as a new and exciting class of materials. This paper reports on the solid state reduction synthesis of a series of CoFeNiMn-based MPCA compositions, starting from a mixture of the corresponding oxides. One of the aims of the study was to test whether the degree of reduction of MnO, a highly stable oxide, could be enhanced by tailoring the alloy composition. Specifically, the influence of Ni content was studied because Ni exhibits a significant negative enthalpy of mixing with Mn. High purity precursor powders of Co(OH) 2 , Fe 2 O 3 , MnO 2 , and NiO were milled and mixed using standard ceramic processing methods. Here, the nominal sample compositions (assuming complete oxide reduction) were (CoFeMn) x Ni (1-x) , for x = 0, 0.083, 0.166, and 0.25. The oxide samples were subjected to a series of isothermal reduction anneals in flowing 3 pct H 2 –Ar at 1100 °C. The resulting microstructures were characterized using scanning electron microscopy (SEM), X-ray energy dispersive spectroscopy (EDS) and X-ray diffraction (XRD). The composition of the resulting MPCAs was determined quantitatively using wavelength dispersive spectroscopy (WDS) in the electron microprobe. The study revealed that for each of the initial oxide compositions studied, it was possible to achieve an MPCA with ~ 25 at. pct Mn. These results were found to be consistent with the predictions of a thermodynamic model whereby a negative enthalpy of mixing (ΔH mix ), combined with a contribution from configurational entropy, can offset a positive free energy of reduction (ΔG red ). The incorporation of vibrational entropy into first principles calculations was found to have a significant effect on the predicted crystal structure of the MPCAs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Best practices for predictions of radionuclide activity concentrations and total absorbed dose rates to freshwater organisms exposed to uranium mining/milling

Predictions of radionuclide dose rates to freshwater organisms can be used to evaluate the radiological environmental impacts of releases from uranium mining and milling projects. These predictions help inform decisions on the implementation of mitigation measures. The objective of this study was to identify how dose rate modelling could be improved to reduce uncertainty in predictions to non-human biota. For this purpose, we modelled the activity concentrations of 210 Pb, 210 Po, 226 Ra, 230 Th, and 238 U downstream of uranium mines and mills in northern Saskatchewan, Canada, together with associated weighted absorbed dose rates for a freshwater food chain using measured activity concentrations in water and sediments. Differences in predictions of radionuclide activity concentrations occurred mainly from the different default partition coefficient and concentration ratio values from one model to another and including all or only some 238 U decay daughters in the dose rate assessments. Consequently, we recommend a standardized best-practice approach to calculate weighted absorbed dose rates to freshwater biota whether a facility is at the planning, operating or decommissioned stage. At the initial planning stage, the best-practice approach recommend using conservative site-specific baseline activity concentrations in water, sediments and organisms and predict conservative incremental activity concentrations in these media by selecting concentration ratios based on species similarity and similar water quality conditions to reduce the uncertainty in dose rate calculations. At the operating and decommissioned stages, the best-practice approach recommends relying on measured activity concentrations in water, sediment, fish tissue and wholebody of small organisms to further reduce uncertainty in dose rate estimates. This approach would allow for more realistic but still conservative dose assessments when evaluating impacts from uranium mining projects and making decision on adequate controls of releases.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2025

Evaluation of data driven low-rank matrix factorization for accelerated solutions of the Vlasov equation

Low-rank methods have shown success in accelerating simulations of a collisionless plasma described by the Vlasov equation, but still rely on computationally costly linear algebra every time step. We propose a data-driven factorization method using artificial neural networks, specifically with convolutional layer architecture, that trains on existing simulation data. At inference time, the model outputs a low-rank decomposition of the distribution field of the charged particles, and we demonstrate that this step is faster than the standard linear algebra technique. Numerical experiments show that the method achieves comparable reconstruction accuracy for interpolation tasks, generalizing to unseen test data in a manner beyond just memorizing training data; patterns in factorization also inherently followed the same numerical trend as those within algebraic methods (e.g., truncated singular-value decomposition). However, when training on the first 70% of a time-series data and testing on the remaining 30%, the method fails to meaningfully extrapolate. Despite this limiting result, the technique may have benefits for simulations in a statistical steady-state or otherwise showing temporal stability. These results suggest that while the model offers a computationally efficient alternative for datasets with temporal stability, its current formulation is best suited for interpolation rather than for predicting future states in time-evolving systems. This study thus lays the groundwork for further refinement of neural network-based approaches to low-rank matrix factorization in high-dimensional plasma simulations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

In situ monitoring of the stopped muon flux at Mu2e

The specific object of Mu2e search is the neutrino-less coherent $\mu^-\rightarrow e^-$ conversion in the field of an aluminum nucleus and the signal is a monoenergetic electron of energy $ \approx 104.97$ MeV. This process is forbidden in the Standard Model but allowed in many of its extensions: with minimal changes to include neutrino masses and oscillation, the Branching Ratio of this process, or the similarly interesting $\mu\rightarrow e \gamma$ decay, is expected to be of the order of $\mathcal{O}(10^{-54})$. Values like these are below any currently achievable experimental sensitivity, and the observation of a signal would be unambiguous evidence of New Physics. The upper limit on the muon conversion was set by SINDRUM II at $7\times10^{-13}$ (90\% C.L.) and the goal of the Mu2e collaboration is an improvement of 4 order of magnitudes.\\ The Mu2e experiment can be conceptually divided into three stages: interaction of the primary proton pulse with the tungsten target a nd production of $\pi$ and $\mu$; collection and transport of the produced particles down to the aluminum stopping target; interaction with the stopping target and measurement of the output particles. The measurement of the stopped muon flux is of cardinal importance for the normalization of the Mu2e results. The baseline design includes two detectors developed to measure this flux, counting $\gamma$ emitted by the muonic atoms: HPGe and LaBr$_3$(Ce). The studies performed by the Mu2e Collaboration show that these two systems can be reliably used to determine the overall normalization.\\ The number of stopped muons is proportional to the number of protons on target, which itself depends on the extraction system for the proton beam. The Mu2e beam delivery system will use a resonant extraction to create the proton pulses and this method is characterized by intensity fluctuations on the time scale of milliseconds. These fluctuations, aside from the proportional fluctuation of t he number of muons, have a non-trivial impact on the overall! performance of the apparatus. For example, higher intensity proton bunches translate into higher veto rates by the Cosmic Rays Veto system, which is triggered by secondary particles generated by the proton interaction. Another example is the effect of the instantaneous luminosity on the reconstruction efficiency: higher intensity translates into higher detector occupancy, which in turn reduces the reconstruction efficiency.\\ The two cited detectors encounter limitations when trying to monitor the flux at the millisecond timescales: HPGe is by construction a slow detector while the rate of emission of the $\gamma (1809\textrm{ keV})$ the LaBr$_3$(Ce) will measure is too low. As of today, no system allows to monitor these fluctuations and the goal of this Thesis is to try filling this gap. The method we developed to monitor the fluctuations of the stopped muon flux, and by extension, the fluctuation in the proton pulses intensity is based on counting the number of muons capt ured in the stopping target by counting the number of protons produced in the process of the nuclear muon capture. This is one of the possible processes a stopped muon can undergo and it can lead to the ejection of charged particles.\\ The reconstruction algorithms were developed by the Mu2e collaboration with the explicit (and sometimes implicit) aim to reconstruct tracks of electrons in an energy range of a conversion electron. The protons we are interested in behave quite differently from these electrons and the cardinal task of this study has been to tailor the reconstruction routines to these particles. This has been done by analyzing simulated events consisting of a single proton to optimize the procedure and understand the features of these tracks. A major difference with respect to the electrons is the non-relativistic nature of $100\div300$ MeV$/c$ protons: accounting for the low velocity is both a challenge and a unique signature of the tracks.\\ The use of a pulse d beam and the need to allow produced $\pi$ to decay and $\m! u$ to int! eract in the stopping target forces the definition of a Mu2e \textit{event}: an event is comprised by everything which happens in a $1.7\ \mu$s window between the incidence two consecutive proton pulses. This means that the reconstruction will be performed in an environment crowded by many different particles. The next step of this study has been to estimate the performance of the method in a fully-fledged Mu2e simulated event and to study what kind of selections would improve it.\\ Our results are satisfactory since the number of reconstructed protons is significant (few per event), and show that a monitor on the timescale of milliseconds is indeed possible.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Real-Time Protocol Engineering with the B Language

It is an invariant that critical cyberphysical systems should not fail. Mission assurance requires systems to behave with predictability, especially in their ability to satisfy real-time constraints. The NIST standard for Engineering Trustworthy Secure Systems, National Institute of Standards and Technology (NIST) Special Publication (SP) 800-160v1r1 states that formal methods are the highest level for meeting assurance requirements. There is a tension between the formal method software development process (Figure 1), which does not introduce time specificity until the latter stages of the development process (concrete model), and the need to gain confidence that time constraints will be satisfied. This paper formalizes the practical application of temporal entities e.g. Propositional Temporal Logic (PTL), Temporal Logic of Actions Plus (TLA+), etc. to critical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Next-Generation Marine Energy Software Needs Assessment

The U.S. Department of Energy (DOE) Water Power Technologies Office’s (WPTO) initial investment in marine energy software was driven by needs identified over a decade ago. WPTO-funded research was first launched because of a U.S. congressional mandate that called for the DOE to officially research marine energy technologies, which also established the DOE WPTO in 2008. A congressional mandate requested the WPTO to evaluate a variety of marine energy devices, establish baseline levelized cost of energy estimates, and provide an overall report to Congress. This congressional mandate led to the Reference Model Project (RMP), for which WPTO funded a national multi-laboratory team to develop these reference models, based on state-of-the-art designs of six marine energy converter archetypes that consisted of three current energy converters and three wave energy converters (WEC). Each device was designed to operate for a specific marine resource, thus allowing the devices to serve as reference models for future studies. The RMP congressional report cited the need for improved marine energy software to handle a variety of device designs, as well as a need to standardize performance outputs. Without validated software packages and established metrics, information presented to the WPTO by technology developers could be incorrect or inaccurate and result in misleading conclusions. The recommendation to coordinate WPTO investment in software for numerical modeling and analysis was given a high priority because it would directly fill needs at the time, and focused funding would amplify impact. By sponsoring software development, WPTO would provide industry developers, university researchers, and national laboratories software that could be used, customized, and advanced, thus supporting the overall advancement of marine energy.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

Design Philosophy for Accelerator Control Rooms

This report describes the foundational human factors principles needed to design user interfaces for accelerator control rooms. These principles are presented as recommendations for accelerator control system interface design and are illustrated with examples drawn from accelerator operations at Fermi National Accelerator Laboratory (Fermilab). These high-level principles are intended to serve as design objectives that apply generally across all control system interfaces developed for the accelerator complex. This design philosophy document is a high-level roadmap to approach the general interface design for the accelerator complex. This philosophy applies state-of-the-art human factors principles and evidence-based design standards to an accelerator environment, which will transform the next era of accelerator interfaces as outlined by the Accelerator Controls Operations Research Network (ACORN) Project. Further work will include mapping the general design principles included in this report to more specific guidance in the form of a user interface style guide.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Innovative SCR Materials and System for Low Temperature - CRADA 350 (Abstract)

The aim of this CRADA is focused on providing a new enabling SCR catalyst system that will function at very high efficiency to attain the most demanding emissions regulations and thereby facilitate the market introduction of advanced powertrains that will support domestic energy independence and security. Future powertrains, that will be significantly more efficient than currently available technologies, will be needed by automotive manufacturers to meet rapidly increasing CAFE and GHG standards. However, these powertrains cannot enter into the US light duty vehicle market unless they are coupled with an aftertreatment system that will sufficiently remediate tailpipe emissions to meet EPA Tier III and California SULEV emissions standards. The low temperature exhaust associated with these powertrains is especially challenging for any current aftertreatment technology to meet these standards. The key focus of this CRADA is to further develop newly invented materials for the selective catalytic reduction (SCR) of NOx by ammonia (NH3) that show promise for significantly reducing ‘light-off’ temperatures compared to current commercial catalysts. Specifically, the goal of the proposed work is to achieve ‘light-off’ of NH3 SCR at 150 ºC in order to realize conversion efficiencies of 90% at these low temperatures. This will enable deployment of lean combustion powertrains with significantly increased fuel efficiencies but lower exhaust temperatures. To accomplish this overall goal, it will be essential to also identify an appropriate NH3 supply strategy for the SCR aftertreatment device that can controllably deliver NH3 at these low temperatures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

AI-Driven Crack Detection for Remanufacturing Cylinder Heads Using Deep Learning and Engineering-Informed Data Augmentation

Detecting cracks in cylinder heads traditionally relies on manual inspection, which is time-consuming and susceptible to human error. As an alternative, automated object detection utilizing computer vision and machine learning models has been explored. However, these methods often face challenges due to a lack of sufficiently annotated training data, limited image diversity, and the inherently small size of cracks. Addressing these constraints, this paper introduces a novel automated crack-detection method that enhances data availability through a synthetic data generation technique. Unlike general data augmentation practices, our method involves copying cracks from one location to another, guided by both random and informed engineering decisions about likely crack formations due to cyclic thermomechanical loads. The innovative aspect of our approach lies in the integration of domain-specific engineering knowledge into the synthetic generation process, which substantially improves detection accuracy. We evaluate our method’s effectiveness using two metrics: the F2 score, which emphasizes recall to prioritize detecting all potential cracks, and mean average precision (MAP), a standard measure in object detection. Experimental results demonstrate that, without engineering insights, our method increases the F2 score from 0.40 to 0.65, while maintaining a stable MAP. Incorporating detailed engineering knowledge further enhances the F2 score to 0.70 and improves MAP to 0.57, representing increases of 63% and 43%, respectively. These results confirm that our approach not only mitigates the limitations of traditional data augmentation but also significantly advances the reliability and precision of crack detection in industrial settings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Understanding Biases in Sample Preparation Techniques for Coupled Scanning Electron Microscopy and MAMA PuO 2 Morphological Analysis

In this project, the scanning electron microscopy (SEM) sampling method used during the statistical design study (SDS) was investigated to determine if any sampling biases were present in the analyzed data. Using standard particle size distribution powders from the National Institute of Standards and Technology (NIST 1984 standard reference material) with the origin wet dispersion method, it was determined that a bias to smaller particles was present. This was supported by theoretical calculations using Stokes’ law to determine the settling rate of spherical particles of roughly the same size and mass as those found in the SDS. Based on the theoretical calculations, it was determined that the settling rate for each of the 76 powder sets in the SDS could be unique based on specific particle shape and mass distributions, making a universal correction factor/formula not applicable. Therefore, priority shifted to developing an improved wet dispersion method that significantly reduced the particle settling rate for all particle size and shapes. This was achieved by replacing the original solvent (isopropyl alcohol) with a heavy liquid (lithium heteropolytungstates), which dramatically slowed the settling rate and allowed for the capture of a suitable homogeneous aliquot. SEM imaging and Morphological Analysis for Material Attribution (MAMA) software analysis were conducted on the NIST standard, and the SEM/MAMA data were compared to data captured by a dynamic image analysis particle size analyzer. The resulting data confirmed that the new wet dispersion method does indeed deliver an improved representative aliquot to the SEM stub. For instance, in the NIST certificate, the average particle size is ~17.1 µm ± 2.2 µm with a normal distribution. The initial wet dispersion method resulted in a drastically reduced average particle size of 6.1 µm in addition to a non-representative heavy bi-modal distribution whereas the improved LST wet dispersion method resulting in an average particle size that was much closer to the NIST certificate (12.7 µm) with a similar normal distribution. Although the improved method was still short of the NIST certificate average, atomic force microscopy analysis determined that the resulting ~20-25% reduction in size was due to particles sinking into the carbon sticky tape used for SEM imaging. It is believed that that this bias can be calibrated in a much more predicable manner than the original settling rate bias. In addition, the matching normal distribution curves between the NIST certificate and the heavy liquid method indicate a much-improved representative aliquot has been sampled and imaged. A surrogate CeO 2 powder was used to reflect PuO 2 more accurately and to aid in implementing radiological controls and shielding. The resulting data sets from the SEM/MAMA method and the particle size analyzer give almost identical average particle sizes and particle distribution statistics. Future work will re-analyze several select runs from the SDS to determine if morphological signatures can be found with the improved sampling method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Optimizing energetic light dark matter searches in dark matter and neutrino experiments

Neutrino and dark matter experiments with large-volume ($ \underset{\sim }{>} $,2 1 ton) detectors can provide excellent sensitivity to signals induced by energetic light dark matter coming from the present universe. Taking boosted dark matter as a concrete example of energetic light dark matter, we scrutinize two representative search channels, electron scattering and proton scattering including deep inelastic scattering processes, in the context of elastic and inelastic boosted dark matter, in a completely detector-independent manner. In this work, a dark gauge boson is adopted as the particle to mediate the interactions between the Standard Model particles and boosted dark matter. We find that the signal sensitivity of the two channels highly depends on the (mass-)parameter region to probe, so search strategies and channels should be designed sensibly especially at the earlier stage of experiments. In particular, the contribution from the boosted-dark-matter-initiated deep inelastic scattering can be subleading (important) compared to the quasi-elastic proton scattering, if the mass of the mediator is below (above) $ \mathcal{O} $(GeV). We demonstrate how to practically perform searches and relevant analyses, employing example detectors such as DarkSide-20k, DUNE, Hyper-Kamiokande, and DeepCore, with their respective detector specifications taken into consideration. For other potential detectors we provide a summary table, collecting relevant information, from which similar studies can be fulfilled readily.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗