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DOE OSTI2021

Enhancement of Industry Legacy Probabilistic Risk Assessment Methods and Tools

Probabilistic risk assessments (PRAs) are integral to nuclear power plant (NPP) operations, having tremendously benefitted the safety of the U.S. reactor fleet for decades. Insights obtained from the models have provided perspectives on a variety of applications, both at the plant and for the regulator. While these models are very useful, they are now being asked to represent and analyze aspects of the plant that were never envisioned by the initial PRA practitioners. Furthermore, heightened demands on the PRA models have led to increased computing power requirements. Additionally, as the complexity of the PRA models increased, the difficulty experienced by non-PRA experts in trying to understand these models, grasp the insights they provide, and effectively use that information has become problematic. The need for research to address key issues regarding PRA tools and methods has never been greater. Although the nuclear power industry has largely been well-served by these tools and methods, the underlying science is dated, remaining mostly unchanged for over two decades. Three areas were identified as most beneficial to address to maintain and improve the usefulness of the current practice legacy PRA tools: improvement in quantification speed, increased ability to efficiently model multi-hazard models, and improvement in modeling human action dependency in PRA. This report provides the outcomes from the FY2021 research into these three areas, identifying potential technical gaps and solutions, and charting the next steps forward to address current PRA challenges.

42 ENGINEERING↗

DOE OSTI2025

Feasibility of Metal Oxide Glasses and Polymer Membranes as Sorbents for Gaseous Oxidized Mercury

Mass spectrometry methods are currently under development by the atmospheric mercury (Hg) research community to elucidate the identity of atmospheric oxidized mercury (Hg II ) compounds. Due to high instrument detection limits, materials that can quantitatively preconcentrate atmospheric Hg II without facilitating compound-altering chemical reactions are needed to support these methods. Cation exchange membranes (CEM) and nylon membranes are currently used to preconcentrate ambient Hg II for concentration measurements and Hg II compound estimation, respectively. However, CEM and nylon membranes are poor candidates for observations by mass spectrometry methods due to release of interfering compounds upon heating; glasses do not have this problem. Here, three metal oxide glasses were explored as potential alternatives for Hg II preconcentration for future use with mass spectrometry methods: calcium phosphate (CaP), iron phosphate (FeP), and calcium aluminate (CaAl). The glasses demonstrated quantitative selective capture of HgBr 2 without capture of Hg 0 . Under ambient conditions, the CaP, FeP, and CaAl sorbed 36.4 ± 12.6% of the total HgII as the CEM. However, when Hg concentrations were normalized to surface area, CaP, FeP, and CaAl sorbed more HgBr 2 in the laboratory and ambient HgII compared to CEM. The CEM and CaP retained similar concentrations of HgBr 2 when preloaded samples were deployed in the field. Additionally, a permeation tube-based calibrator was used to load sorbents with HgBr 2 for investigation of HgII retention on CEM and thermal desorption profile changes on nylon membranes during active sampling. Nylon membranes were purchased from three vendors and used to compare HgBr 2 retention; a different HgBr 2 thermal desorption profile was achieved for each vendor’s nylon membrane.

Amides↗

DOE OSTI2024

The role of AI in detecting and mitigating human errors in safety-critical industries: A review

For safety-critical industries, human error (HE) presents continual risks to system productivity, reliability and safety. Artificial intelligence (AI) and machine learning (ML) methods have emerged as promising approaches to understand, categorize and mitigate the risk of HE in safety-critical industries. Furthermore, this review offers an examination of the current landscape regarding the utilization of AI/ML with regards to HE in safety-critical industries, categorizing literature into descriptive modeling, predictive modeling, prescriptive modeling, and generative modeling techniques. Additionally, the review aims to provide insights regarding themes in literature, challenges, and future research directions. Findings of the review suggest that AI/ML methods can prove useful in addressing the HE problem across safety-critical industries.

42 ENGINEERING↗

DOE OSTI2020

Topology optimization of permanent magnets for stellarators

In this work, we introduce a topology optimization method to design permanent magnets for advanced stellarators. Recent researches show that permanent magnets have great potentials to simplify stellarator coils. We adopt state-of-the-art numerical techniques to determine the presence of magnets in the entire designing space. The FAMUS code is developed and it can design engineering-feasible permanent magnets for general stellarators satisfying the constraints of the maximum material magnetization and explicit forbidden regions. FAMUS has been successfully verified against the previously proposed linear method. Three different permanent magnet designs together with planar TF coils for a half-Tesla NCSX configuration have been obtained for demonstrations. The designs have good accuracy in generating the desired equilibrium and offer considerably large plasma access on the outboard side. The results show that FAMUS is a flexible, advanced numerical tool for future permanent magnet stellarator designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

In Search of a Cost-Effective Approach for Estimating Dam Passage Survival

Abstract Passage of downstream-migrating fishes through hydropower dams poses a management issue in rivers throughout the world. As such, regulations have been enacted at many locales to understand and limit fish losses by requiring dam passage survival estimation. However, limiting estimates of survival to the dam and immediate tailrace presents a challenge to researchers. In this study, two alternative release–recapture methods were used to estimate dam passage survival of juvenile salmonids through Lower Granite Dam on the Snake River, Washington, in 2018. One approach, the virtual/paired-release (ViPRe) model, was extensively used in the Columbia–Snake River basin at federally operated hydroelectric dams during 2010–2014. This existing approach uses three releases of tagged smolts to isolate dam passage survival, defined as survival from the upstream dam face to the tailrace mixing zone 1–2 km downstream of the dam. An alternative approach, the virtual release/dead-fish correction (ViRDCt) model, uses one live-release group paired with a release of dead tagged fish at the dam to estimate the same survival parameter. The alternative estimation approaches were tested on two spring out-migrating stocks, yearling Chinook Salmon Oncorhynchus tshawytscha and steelhead Oncorhynchus mykiss, and on a summer out-migrating stock of subyearling Chinook Salmon. The alternative estimates for these stocks were all within 1 SE. However, the ViRDCt model produced survival estimates with SEs that were 59% smaller than those of the existing ViPRe model and did so using 42% fewer tagged fish and one less downstream acoustic detection array. Because of the reduced sample size, nearly US$540,000 less in tag costs and 4,000 fewer labor-hours were required to implement the ViRDCt model compared to the ViPRe model. As such, the ViRDCt model represents a cost-effective and precise approach to characterizing dam passage survival.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Chatter detection in simulated machining data: a simple refined approach to vibration data

Vibration monitoring is a critical aspect of assessing the health and performance of machinery and industrial processes. This study explores the application of machine learning techniques, specifically the Random Forest (RF) classification model, to predict and classify chatter—a detrimental self-excited vibration phenomenon—during machining operations. While sophisticated methods have been employed to address chatter, this research investigates the efficacy of a novel approach to an RF model. The study leverages simulated vibration data, bypassing resource-intensive real-world data collection, to develop a versatile chatter detection model applicable across diverse machining configurations. The feature extraction process combines time-series features and Fast Fourier Transform (FFT) data features, streamlining the model while addressing challenges posed by feature selection. By focusing on the RF model’s simplicity and efficiency, this research advances chatter detection techniques, offering a practical tool with improved generalizability, computational efficiency, and ease of interpretation. The study demonstrates that innovation can reside in simplicity, opening avenues for wider applicability and accelerated progress in the machining industry.

42 ENGINEERING↗

DOE OSTI2020

Tight-binding study of beryllium

In this work, we have applied the Naval Research Laboratory Tight-binding (NRL-TB) Method to the hcp alkaline-earth metal Beryllium. This approach consists of fitting to energy band and total energy data generated by the Linearized Augmented Plane Wave (LAPW) for the fcc, bcc, sc, and hcp structures as a function of volume. First, we found that using the Generalized Gradient Approximation (GGA) for the input data secures a better agreement to experimental volumes than the LDA. Second, we found that including small volumes to the fit is needed to get TB parameters that are transferable enough to successfully predict the energies of structures that were not fitted, and to perform molecular dynamics (MD) simulations. The non-orthogonal Hamiltonian obtained, is successful in correctly predicting the hcp lattice as the ground state of Be and finding eight other structures, that were not fitted to LAPW, positioned at higher energies. In addition, the NRL-TB produces accurate energy bands, densities of states, elastic constants, the Bain path and several quantities derived from MD simulations.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Perspectives on the integration between first-principles and data-driven modeling

Efficiently embedding and/or integrating mechanistic information with data-driven models is essential if it is desired to simultaneously take advantage of both engineering principles and data-science. Further the opportunity for hybridization occurs in many scenarios, such as the development of a faster model of an accurate high-fidelity computer model; the correction of a mechanistic model that does not fully-capture the physical phenomena of the system; or the integration of a data-driven component approximating an unknown correlation within a mechanistic model. At the same time, different techniques have been proposed and applied in different literatures to achieve this hybridization, such as hybrid modeling, physics-informed Machine Learning (ML) and model calibration. In this paper we review the methods, challenges, applications and algorithms of these three research areas and discuss them in the context of the different hybridization scenarios. Moreover, we provide a comprehensive comparison of the hybridization techniques with respect to their differences and similarities, as well as advantages and limitations and future perspectives. Finally, we apply and illustrate hybrid modeling, physics-informed ML and model calibration via a chemical reactor case study.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

SEM petrography of dispersed organic matter in black shales: A review

Organic matter (OM)-hosted pores are important constituents of the pore system of black shales and play a crucial role in determining their methane adsorption capacity and porosity. OM-hosted pores are generally observed and described with scanning electron microscope (SEM) on Ar ion-milled surfaces. However, SEM imaging is not able to reliably distinguish OM types and relate the observed pores to specific macerals. Partly because of this inability to relate organic pores to macerals, the evolution of organic porosity during thermal maturation remains poorly understood. In this paper, we review the petrographic characteristics of dispersed organic matter (DOM) in black shales under the SEM. Organic petrographic classification of DOM developed for reflected-light microscopy is so far the most practical method when describing DOM in black shales under the SEM because this classification has information on the origin of DOM. Therefore, correlative microscopy (combination of reflected-light and electron microscopy) is the most effective method to identify both OM types and OM-hosted pores. This method, however, is not readily available to most researchers. Although identifying OM on the basis of SEM observations is a challenging task, it is achievable provided there is a good understanding of the studied shales, especially their thermal maturity and original OM composition. Therefore, the overall objective of this paper is to review petrographic characteristics of DOM in black shales under the SEM to provide some guidelines for identifying DOM from SEM observations. We also review factors that control the formation and preservation of OM-hosted pores. OM-hosted pores consist of primary and secondary organic pores. Primary organic pores are pores inherited from the biological structure of the original OM. Secondary organic pores develop during hydrocarbon generation and expulsion from oil-prone OM and are hosted by solid bitumen or pyrobitumen. The development of secondary organic pores is controlled by thermal maturity and OM type, and their preservation is subject to thermal maturity, OM content, and mineralogical composition. The presented view of the evolution of micropore and mesopore characteristics of OM with thermal maturity is based on data from the literature. The specific surface area and pore volume of OM in black shales follow a parabolic pattern with increasing thermal maturity (quantified via vitrinite reflectance, R o ). The initial increase reflects development of OM-hosted pores, and the subsequent decrease is due to denser stacking of aromatic units in the macromolecular structure of OM, with maximum values (specific surface area ~ 300 m 2 /g and pore volume ~ 0.3 cm 3 /g) reached at R o values in the 2.5-3.5% range. The contribution of OM-hosted pores to the pore characteristics of black shales depends on OM content, OM type, and thermal maturity.

03 NATURAL GAS↗

DOE OSTI2025

A systematic review of machine learning in groundwater monitoring

With increasing concerns about water scarcity, groundwater has become crucial since this resource provides most of the freshwater needs. However, various human and natural activities often contaminate the groundwater, making it unsuitable for use. Over the years, scientists and engineers have used many methods to predict and track groundwater contamination as part of environmental monitoring. Consequently, there is an urgent need for improved methods, particularly in the face of increasing contamination. Machine learning has sometimes been used to monitor groundwater, air quality, and climate. Traditional methods must be improved due to the complexity and large amount of environmental data. This includes using hybrid models that combine traditional and new techniques. Despite the use of machine learning in many scientific areas, there is a lack of comprehensive reviews focusing on its use in environmental monitoring, especially groundwater monitoring. We aim to fill this gap by exploring machine-learning applications in groundwater monitoring. We discuss relevant methods, their limitations, and future potential. We summarize research on automating data processing and model training using groundwater sensor data. Our research underscores the transformative potential of machine learning to revolutionize long-term groundwater monitoring and contamination detection, providing valuable insights for future research and practical applications.

AI/ML↗

DOE OSTI2020

Reexamining supercritical gas adsorption theories in nano-porous shales under geological conditions

Adsorption is an important phenomenon in surface chemistry, especially for nano-porous shales. In the shale gas-in-place, adsorbed gas could contribute up to 85%. However, adsorption is hard to quantitatively characterize due to different adsorption mechanisms, patterns, surfaces, and pore sizes. Moreover, key thermodynamic parameters, such as the enthalpy of adsorption, are challenging to determine due to uncertainties in adsorbed gas densities used in constructing absolute isotherms. In this paper, we revisit the Brunauer, Emmett, and Teller (BET) model, analytically simplify Ono-Kondo (OK) models for subsurface shales, and compare commonly used mono- and multilayer adsorption models (e.g., Langmuir, supercritical Dubinin-Radushkevich (SDR), supercritical BET (SBET), and simplified OK (OKs) models) with recently proposed pressure-dependent adsorption densities to develop a practical and reliable methodology that can be used in the supercritical state, typical for subsurface black shale conditions.Three independent data sets were used for nitrogen and methane adsorption isotherms at different temperatures. Furthermore, we demonstrate that adsorption predicted by the SDR model is comparable or lower than that predicted by the SBET model, but higher than the amounts predicted by Langmuir and OK models. The nitrogen BET method tends to underestimate the accessible SSA for methane. Measurement of isosteric heat of adsorption is suggested instead of the experimental fitting method, due to the significant difference between the calculated results by two commonly used methods. Experimental fitting and simulation methods are also briefly reviewed to guide future research on shale gas adsorption.

58 GEOSCIENCES↗

DOE OSTI2024

Overcoming barriers to improved decision-making for battery deployment in the clean energy transition

Decarbonization plans depend on the rapid, large-scale deployment of batteries to sufficiently decarbonize the electricity system and on-road transport. This can take many forms, shaped by technology, materials, and supply chain selection, which will have local and global environmental and social impacts. Current knowledge gaps limit the ability of decision-makers to make choices in facilitating battery deployment that minimizes or avoids unintended environmental and social consequences. These gaps include a lack of harmonized, accessible, and up-to-date data on manufacturing and supply chains and shortcomings within sustainability and social impact assessment methods, resulting in uncertainty that limits incorporation of research into policy making. These gaps can lead to unintended detrimental effects of large-scale battery deployment. To support decarbonization goals while minimizing negative environmental and social impacts, we elucidate current barriers to tracking how decision-making for large-scale battery deployment translates to environmental and social impacts and recommend steps to overcome them.

25 ENERGY STORAGE↗

DOE OSTI2025

Multi-faceted framework for extrapolating early age flexural strength to facilitate rapid lifting/handling of high-volume fly ash precast members

Maintaining adequate early-age structural performance for precast concrete components has grown in importance as more sustainable mix designs become more widespread. Achieving high-early flexural strength is particularly crucial to facilitate rapid removal of hardened concrete components from formwork, often within 24 h after fresh concrete placement. Limited research has assessed the effectiveness of traditional design methods in correlating flexural strength with compressive strength for next-generation mix designs, or demonstrated extrapolation of such material performance to larger-scale structural tests. This paper presents a multi-faceted framework to reassess early-age flexural strength for concretes made with relatively high proportions of fly ash from both fresh and harvested sources. Here, the framework provides several pathways, from which the user can select based upon available resources and the specific application, to improve accuracy of early-age cracking moment calculations. Furthermore, the scope includes evaluation of strength performance under curing conditions emulative of those in a precast facility, recommending modulus of rupture equations which are more performance-driven than current design provisions, and experimental tests on prefabricated concrete beams to validate the proposed methodologies. Correlations of early-age strength with both concrete age and maturity measurements compare the effectiveness of utilizing in-situ data to further enhance the prediction methods. Ultimately, the proposed framework helped reduce errors when calculating cracking moment capacity at early ages by tailoring calculations to reflect mix-dependent behavior. Furthermore, most estimates of cracking moment were within 25 % of their corresponding experimental test results, thus promoting confidence for using these strategies with high-volume fly ash precast structures.

42 ENGINEERING↗

DOE OSTI2023

Electrospraying deposition and characterization of potassium chloride targets for nuclear science measurements

Natural and isotopically enriched KCl targets were prepared using specially formulated KCl solutions, deposited by electrospraying on thin (1.5 μm) gold backings, followed by a short annealing in a preheated (350-450 °C) furnace. Various techniques, such as X-ray fluorescence (XRF), scanning electron microscopy (SEM) imaging, and energy-dispersive X-ray spectroscopy (EDS), were used to characterize the produced targets. A white neutron beam at the Los Alamos Neutron Science Center (LANSCE) facility was also used to verify the estimated atomic percentages of the enriched 40 KCl targets. KCl was deposited relatively uniformly as large particles rather than films on the gold surface. As a result, this method can be broadly extended to make stable targets that are difficult to produce with other methods and more exotic radioactive targets required for research in fundamental sciences and applications.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2024

A Trapping-Micro-LC-FAIMS/dCV-MS Strategy for Ultrasensitive and Robust Targeted Quantification of Protein Drugs and Biomarkers

The sensitivity of LC-MS in quantifying target proteins in plasma/tissues is significantly hindered by co-eluted matrix interferences. While antibody-based immuno-enrichment effectively reduces interferences, developing and optimizing antibodies are often time-consuming and costly. Here, in this study, by leveraging the orthogonal separation capability of Field Asymmetric Ion Mobility Spectrometry (FAIMS), we developed a FAIMS/differential-compensation-voltage (FAIMS/dCV) method for antibody-free, robust, and ultra-sensitive quantification of target proteins directly from plasma/tissue digests. By comparing the intensity-CV profiles of the target vs. co-eluted endogenous interferences, the FAIMS/dCV approach identifies the optimal CV for quantification of each target protein, thus maximizing the signal-to-noise ratio (S/N). Compared to quantification without FAIMS, this technique dramatically reduces endogenous interferences, showing a median improvement of the S/N by 14.8-fold for the quantification of 17 representative protein drugs and biomarkers in plasma or tissues and a 5.2-fold median increase in S/N over conventional FAIMS approach, which uses the peak CV of each target. We also discovered that the established CV parameters remain consistent over months and are matrix-independent, affirming the robustness of the developed FAIMS/dCV method and the transferability of the method across matrices. The developed method was successfully demonstrated in three applications: the quantification of monoclonal antibodies with sub-ng/mL LOQ in plasma, an investigation of the time courses of evolocumab and its target PCSK9 in a preclinical setting, and a clinical investigation of low abundance obesity-related biomarkers. This innovative and easy-to-use method has extensive potential in clinical and pharmaceutical research, particularly where sensitive and high-throughput quantification of protein drugs and biomarkers is required.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Hydro-Mechanical Measurements of Sheared Crystalline Rock Fractures With Applications for EGS Collab Experiments 1 and 2

We present hydro-mechanical measurements that characterize shear on natural fractures in schist, amphibolite, and rhyolite specimens from the enhanced geothermal system (EGS) Collab Project's Experiment 1 and 2 sites (E1 and E2) at the Sanford Underground Research Facility. We employed a triaxial direct shear method augmented with X-ray imaging to perform hydroshearing (injection-induced shearing) and mechanical shearing on naturally fractured specimens at in situ stress conditions. Measurements included fracture permeability, strength, stress-dependent aperture, shear dilation, and frictional strength. Results reveal that in situ natural fractures must be permeable, weak, and shear-oriented to be hydrosheared, and only a subset of the observable in situ fractures were suitable. When sheared, the fracture permeability typically increased by a factor of 10 or more and this increase was retained over time. However, shear slip did not always result in permeability increase. High phyllosilicate content associated with exceptionally weak fractures exhibited poor or even decreased permeability after stimulation. These measurements in combination with site data were used to conduct a slip-tendency analysis for different fracture sets, and we selected the top candidate natural fractures for hydroshearing at the EGS Collab sites. We also found that the lower in situ shear stress and stronger fractures at the E2 site make hydroshearing more challenging than at the E1 site. Overall, the methods and analysis used in our work can be applied to any geothermal project to identify in situ joint sets that are best suited for hydroshearing, which in turn can help to optimize well placement and energy production.

58 GEOSCIENCES↗

DOE OSTI2022

Earthquake Phase Association Using a Bayesian Gaussian Mixture Model

Earthquake phase association algorithms aggregate picked seismic phases from a network of seismometers into individual seismic events and play an important role in earthquake monitoring and research. Dense seismic networks and improved phase picking methods produce massive seismic phase datasets, particularly for earthquake swarms and aftershocks occurring closely in time and space, making phase association a challenging problem. Here, we present a new association method, the Gaussian Mixture Model Association (GaMMA), that combines the Gaussian mixture model with earthquake location, origin time, and magnitude estimation. We treat earthquake phase association as an unsupervised clustering problem in a probabilistic framework, where each earthquake corresponds to a cluster of P and S phases with a hyperbolic moveout of arrival times and a decay of amplitude with distance. We use the multivariate Gaussian distribution to model the collection of phase picks of an event; and the mean of the multivariate Gaussian distribution is given by the predicted arrival time and amplitude from the causative event. We carry out the pick assignment to each earthquake and determine earthquake source parameters (i.e., earthquake location, origin time, and magnitude) under the maximum likelihood criterion using the Expectation-Maximization algorithm. The GaMMA method does not require typical association steps of other algorithms, such as grid-search or supervised training. The results for both synthetic tests and for the 2019 Ridgecrest earthquake sequence show that GaMMA effectively associates phases from a temporally and spatially dense earthquake sequence while producing useful estimates of earthquake location and magnitude.

58 GEOSCIENCES↗

DOE OSTI2024

Operational Analytics Studies for ATLAS Distributed Computing: Data Popularity Forecast and Utilization of the WLCG Centers

Operational analytics is the direction of research related to the analysis of the current state of computing processes and the prediction of future states in order to anticipate imbalances and take timely measures to stabilize a complex system. There are two relevant areas in ATLAS Distributed Computing that are currently the focus of studies: user physics analysis including the forecast of popularity of data samples among users, and evaluating WLCG centers for their readiness to process user analysis payloads. Studying these areas is challenging due to the complexity involved, as it requires a comprehensive understanding of numerous boundary conditions typically found in large-scale distributed computing infrastructures. Forecasts of data popularity are problematic without the categorization of user tasks by their types (data transformation or physics analysis), which do not always appear on the surface but may induce noise, which introduces significant distortions for predictive analysis. Evaluating the WLCG resources by their analysis workloads is also a challenging task as it is necessary to find a balance between the workload of the resource, its performance, the waiting time for jobs on it, as well as the volume of jobs that it processes. This is especially difficult in a heterogeneous computing environment, where legacy resources are used along with modern high-performance machines. We will look at these areas of research in detail and discuss what tools and methods are used in our work, demonstrating results already obtained.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗