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At least 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Evaluating the limitations of Bayesian metabolic control analysis

Bayesian Metabolic Control Analysis (BMCA) is a promising framework for inferring metabolic control coefficients in data-limited scenarios, combining Bayesian inference with linear-logarithmic (lin-log) rate laws. These metabolic control coefficients quantify how changes in enzyme activities affect steady-state fluxes and metabolite concentrations across a metabolic network. However, its predictive accuracy and limitations remain underexplored. This study systematically evaluates BMCA’s ability to infer elasticity values, flux control coefficients (FCC), and concentration control coefficients (CCC) under varying data availability conditions using three synthetic metabolic network models. We demonstrate that BMCA predictions are highly dependent on the inclusion of flux and enzyme concentration data, with the omission of these datasets leading to severe inaccuracies. In our synthetic, enzyme-perturbation datasets, external metabolite concentrations had minimal impact and, in some cases, their exclusion improved predictions; when external-nutrient perturbations were introduced and those concentrations were observed, gains were at most modest. Additionally, we find that posterior estimation with both ADVI and HMC can underestimate large-magnitude elasticities in our synthetic settings, with ADVI showing somewhat higher variance under strong up-regulation; thus, recovering |elasticity| ≳ 1.5 remains challenging regardless of the inference engine. ADVI also fails to accurately infer allosteric interactions, even when regulatory effects are strong. While BMCA maintains reasonable accuracy in partially recovering the rankings of the highest FCC values, its estimates of absolute values remain constrained by prior assumptions and data limitations. Our findings reveal the BMCA algorithm’s strengths and weaknesses, providing guidance on its application in metabolic engineering, and highlighting the need for methodological refinements to enhance its predictive capabilities.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Informing Plant Asset Reliability and Availability Through AI-Driven Analysis of Operator Logs

The availability and reliability of nuclear power plant (NPP) structures, systems, and components (SSCs) are critical parameters for NPP safety. Tracking these parameters is necessary but costly and labor-intensive, requiring the collection and evaluation of SSC event data such as shutdowns, startups, and failures. To show how these events are needed for the parameters an example is given: one measure of reliability is based on the number of equipment failure events and the number of run hours (i.e., the time from a startup event to a shutdown event). Here, this work investigates using artificial intelligence (AI) to mine NPP operator log entry texts for SSC event data. Four AI approaches were explored for identifying these events, including natural language processing (NLP) methods, generative AI, generative AI combined with NLP, and topic modeling. A key challenge addressed with all four approaches is the brevity of operator log entries. Among these four a neural network–based NLP method was shown to be the most promising for this application, achieving F1 scores of 86.0% for shutdowns, 92.2% for startups, and 80.4% for failures on a subject-matter-expert-curated dataset from NPP operator logs, compared to a baseline of 66.6% for a random classifier. This shows that NLP methods can perform better than generative AI. Additionally, the NLP methods combined with generative AI were shown to perform better than generative AI alone. Generative AI was most successful at providing the background information for the NLP methods to use. This work demonstrates the potential to use AI to automate parameter collection from NPP operator log entries and other records.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Electronically-coupled redox centers in trimetallic cobalt complexes

Synthesis and isolation of molecular building blocks of metal–organic frameworks (MOFs) can provide unique opportunities for characterization that would otherwise be inaccessible due to the heterogeneous nature of MOFs. Herein, we report a series of trinuclear cobalt complexes incorporating dithiolene ligands, triphenylene-2,3,6,7,10,11-hexathiolate (THT) (1 3+ ), and benzene hexathiolate (BHT) (2 3+ ), with 1,1,1,-tris(diphenylphosphinomethyl)ethane (triphos) employed as the capping ligand. Single crystal X-ray analyses of 1 3+ and 2 3+ display three five-coordinate cobalt centers bound to the triphos and dithiolene ligands in a distorted square pyramidal geometry. Cyclic voltammetry studies of 1 3+ and 2 3+ reveal three redox features associated with the formation of mixed valence states due to the sequential reduction of the redox-active metal centers (Co III/II ). Using this electrochemical data, the comproportionality values were determined for 1 and 2 (log K c = 1.4 and 1.5 for 1, and 4.7 and 5.8 for 2), suggesting strong resonance-stabilized coupling of the metal centers, with stronger electronic coupling observed for complex 2 compared to that for complex 1. Cyclic voltammetry studies were also performed in solvents of varying polarity, whereupon the difference in the standard potentials (ΔE 1/2 ) for 1 and 2 was found to shift as a function of the polarity of the solvent, indicating a negative correlation between the dielectric constant of the electrochemical medium and the stability of the mixed valence species. Spectroelectrochemical studies of in situ generated multi-valent (MV) states of complexes 1 and 2 display characteristic NIR intervalence charge transfer (IVCT) bands, and analysis of the IVCT transitions for complex 2 suggests a weakly coupled class II multi-valent species and relatively large electronic coupling factors (1700 cm –1 for the first multi-valent state of 2 2+ , and 1400 and 4000 cm –1 for the second multi-valent state of 2 + ). Here, density functional theory (DFT) calculations indicate a significant deviation in relative energies of the frontier orbitals of complexes 1 3+ , 2 3+ , and 3 + that contrasts those calculated for the analogous trinuclear cobalt dithiolene complexes employing pentamethylcyclopentadienyl (Cp*) as the capping ligand (Co 3 Cp* 3 THT and Co 3 Cp* 3 BHT, respectively), and may be a result of the cationic nature of complexes 1 3+ , 2 3+ , and 3 + .

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

Mask-side Hyper-NA EUV imaging on the SHARP microscope

Hyper-NA, the prospective successor to high-numerical aperture (NA) extreme ultraviolet lithography (EUVL) could be inserted soon after 2030. Hyper-NA poses a number of challenges, including reduced depth of focus, amplified mask three-dimensional effects, and increased mask-side angular range. A Hyper-NA capable extreme ultraviolet (EUV) mask-imaging tool can address these challenges and accelerate research and development toward Hyper-NA. The Sharp High-Numerical Aperture Actinic Reticle Review Project (SHARP) EUV mask microscope is supporting mask-side high-NA imaging since 2015. Implementing mask-side Hyper-NA imaging in 2024 enables research and development toward the corresponding nodes of EUVL. Hyper-NA zoneplates at 0.75 4x/8x NA with a 6.7-deg chief ray angle and 0.85 4x/8x NA with a 7.4-deg chief ray angle are added to the SHARP microscope. Imaging at mask-side Hyper-NA is demonstrated. Imaging of 5-nm half-pitch (wafer scale) horizontal lines and spaces is demonstrated using dipole illumination. Imaging of 5-nm half-pitch (wafer scale) vertical lines and spaces is demonstrated using frequency-doubled imaging of 40-nm hp (mask scale) lines and spaces. Normalized image log slope (NILS) and modulation of Hyper-NA image data match closely to simulations for horizontal lines and spaces. A reduction in NILS of 0.3 or less is observed for vertical lines and spaces in the two-beam imaging regime. Through-focus image data are discussed, comparing different dipole sources and mask-side NAs. Mask-side Hyper-NA photomask imaging has been implemented and demonstrated on the SHARP microscope and is now available to users of the instrument.

Benk, Markus↗
DOE OSTI2021

A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage (Task 5 Report Field Scale Stress Modeling)

This report describes research accomplishments achieved with funding provided through Department of Energy (DOE) Contract DE-FE0031686, for the project “A Non-Invasive Approach for Elucidating the Spatial Distribution of In Situ Stress in Deep Subsurface Geologic Formations Considered for CO 2 Storage.” The purpose of this DOE funding is to develop non-invasive methods to obtain important information about subsurface stresses. The overall research goal of the project was to develop and improve methods for determining the spatial distribution of stresses, including magnitude and orientation of the three principal stress components in the subsurface, based on new methods that extract stress information from seismic data combined with well measurements, extended with well tests and logs, and unified in a numerical model that permits computation of the full stress tensor throughout the domains under investigation.

58 GEOSCIENCES↗
DOE OSTI2022

The Open Cluster Chemical Abundances and Mapping Survey. VII. APOGEE DR17 [C/N]–Age Calibration

Large-scale surveys open the possibility to investigate Galactic evolution both chemically and kinematically; however, reliable stellar ages remain a major challenge. Detailed chemical information provided by high-resolution spectroscopic surveys of the stars in clusters can be used as a means to calibrate recently developed chemical tools for age-dating field stars. Using data from the Open Cluster Abundances and Mapping survey, based on the Sloan Digital Sky Survey/Apache Point Observatory Galactic Evolution Experiment 2 survey, we derive a new empirical relationship between open cluster stellar ages and the carbon-to-nitrogen ([C/N]) abundance ratios for evolved stars, primarily those on the red giant branch. With this calibration, [C/N] can be used as a chemical clock for evolved field stars to investigate the formation and evolution of different parts of our Galaxy. We explore how mixing effects at different stellar evolutionary phases, like the red clump, affect the derived calibration. We have established the [C/N]–age calibration for APOGEE Data Release 17 (DR17) giant star abundances to be $\mathrm{log}{[\mathrm{Age}(\mathrm{yr})]}_{\mathrm{DR}17}=10.14\,(\pm 0.08)+2.23(\pm 0.19)\,[{\rm{C}}/{\rm{N}}]$, usable for $8.62\leqslant \mathrm{log}(\mathrm{Age}[\mathrm{yr}])\leqslant 9.82$, derived from a uniform sample of 49 clusters observed as part of APOGEE DR17 applicable primarily to metal-rich, thin- and thick-disk giant stars. This measured [C/N]–age APOGEE DR17 calibration is also shown to be consistent with asteroseismic ages derived from Kepler photometry.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Subsurface Characterization of Hydraulic Fracture Test Site-2 (HFTS-2), Delaware Basin

Hydraulic Fracturing Test Site-2 (HFTS-2) is a field-based research experiment performed in the Wolfcamp Formation of the Permian (Delaware) Basin. This paper focuses on integration, advanced geological characterization, and 3D subsurface modeling of the comprehensive HFTS-2 dataset. The study showcases a multidisciplinary reservoir characterization approach that incorporates geology, petrophysics, geochemistry, geomechanics, microseismic, and subsurface engineering analysis. Subsurface characterization of organic-rich mudstone formations requires understanding complex hydraulic fracture network growth in relation to inherent lithology, geomechanical properties, and interaction with pre-existing natural fractures. This paper presents a characterization workflow incorporating pre- and post-stimulation subsurface data, unique to the HFTS-2 dataset. The study integrated: (1) rock properties from logs, cores, and thin sections; (2) natural and hydraulic fracture descriptions from cores and image logs; (3) local and regional stresses; (4) geomechanics; (5) microseismic; (6) fiber optic (FO) and bottomhole pressure gauge (BHPG) response; and (7) produced fluids analysis. During a stimulation treatment, creation of the stimulated rock volume (SRV) is influenced by several subsurface factors. Key contributing factors include structural context, stress conditions, lithology, facies architecture, pre-existing natural fractures, and geomechanical properties. The HFTS-2 subsurface data integration indicates that the SRV is comprised of a complex juxtaposition of hydraulic fracture swarms, as evidenced by image logs analysis, core description, and microseismic monitoring. The HFTS-2 microseismic event density was used to generate 3D heat maps that serve as a representative SRV footprint, corroborated by secondary datasets. These maps were further integrated with petrophysical and geomechanical characteristics, as well as responses from FO and BHPG, to estimate the lateral and vertical dimensions of the effective fractures. The geological characterization for the HFTS-2 dataset combined with 3D modeling for petrophysical and geomechanical properties provides a strong foundation for subsurface simulation and optimization studies. Downloaded from http://onepetro.org/URTECONF/proceedings-pdf/21URTC/1-21URTC/D011S005R001/2477501/urtec-2021-5243-ms.pdf/1 by Carol Worster on 28 February 2022 URTeC 5243 The workflow improved our understanding of HFTS-2 hydraulic fracture propagation and characteristics in relation to offset pressure depletion and interaction with pre-existing natural fractures. Analysis showed that fracture geometry varies by stage and by well, and a complex fracture network is generated with varying fracture density. The multidisciplinary workflow presented herein for integration and characterization serves as a foundation to evaluate completion efficiency and estimate areal and vertical stimulation and depletion extent for the project. Furthermore, the workflow and learnings can also be transferred to other unconventional plays.

58 GEOSCIENCES↗
DOE OSTI2020

Decreasing wind speed extrapolation error via domain-specific feature extraction and selection

Abstract. Model uncertainty is a significant challenge in the wind energy industry and can lead to mischaracterization of millions of dollars' worth of wind resources. Machine learning methods, notably deep artificial neural networks (ANNs), are capable of modeling turbulent and chaotic systems and offer a promising tool to produce high-accuracy wind speed forecasts and extrapolations. This paper uses data collected by profiling Doppler lidars over three field campaigns to investigate the efficacy of using ANNs for wind speed vertical extrapolation in a variety of terrains, and it quantifies the role of domain knowledge in ANN extrapolation accuracy. A series of 11 meteorological parameters (features) are used as ANN inputs, and the resulting output accuracy is compared with that of both standard log-law and power-law extrapolations. It is found that extracted nondimensional inputs, namely turbulence intensity, current wind speed, and previous wind speed, are the features that most reliably improve the ANN's accuracy, providing up to a 65 % and 52 % increase in extrapolation accuracy over log-law and power-law predictions, respectively. The volume of input data is also deemed important for achieving robust results. One test case is analyzed in depth using dimensional and nondimensional features, showing that the feature nondimensionalization drastically improves network accuracy and robustness for sparsely sampled atmospheric cases.

17 WIND ENERGY↗
DOE OSTI2022

Tuscaloosa Marine Shale Laboratory

The Tuscaloosa Marine Shale (TMS) in Louisiana and Mississippi is an Upper Cretaceous source rock formation sandwiched between the sands of the upper and lower Tuscaloosa sections. The TMS is believed to be the source rock for underlying prolific Tuscaloosa sand formation. The TMS has an unproven estimate of 7,000,000,000 bbls of recoverable oil while its current total average production is about 3,000 bbls of oil per day in 2017. In 2013 and 2014, more than 80 wells were drilled horizontally into the TMS that were fractured using multi-stage fracturing technology. The results from this have been mixed, but recent production for several wells show an appealing initial oil production rate of more than 1000 bbl/day. The preliminary core analysis by industry partners and a few literature studies shows that the TMS is one of the most clay-rich and sensitive shales to water. Due to these and other technical problems, there is high risk for the economic development of TMS compared to other shale plays. The experiences of major industrial players in the TMS show the necessity of open and collaborative efforts to better understand the critical gaps in the development of this challenging and potentially highly economic shale play to enable more cost-efficient and environmentally-sound recovery from this unconventional liquid-rich shale play. The overall objective of this project is to form a consortium of science and industry partners to address the following six major objectives using scientific and technical approaches: 1. To improve wellbore integrity by better understanding the sources of the wellbore instability issues, proposing innovative mud and cement design for the TMS. 2. To improve formation evaluation using laboratory techniques for the evaluation of mineralogical composition, organic content, and produced-water chemistry as well as well log and geophysical analysis. 3. To determine the role of geologic discontinuities on fracture growth and shale creep behavior using digital image correlation technique. 4. To investigate the application of stable CO 2 foam and super-hydrophobic proppants for improved reservoir stimulation. 5. To better understand the nature of water/hydrocarbon/CO 2 flow in clay and organic-rich formation and the role of water/fluid interaction on recovery. 6. To prepare better socio-economic environment for TMS development by community engagement. Subsequently, the TMS virtual laboratory conducted testing and analysis of various properties of rock and formation fluids from the TMS, including but not limited to the following: Analyzing reports and logs to better understand the source of wellbore instability in TMS wells; Experiments to design a customized cement based on TMS requirements; Experiments to obtain the mineralogical and geochemical composition of TMS samples; Seismic analysis of TMS geophysical data to better predict total organic carbon (TOC) content and brittleness in TMS; Well log analysis to better estimate the TOC and geo-mechanical properties of TMS; Experiments on formation water to understand the chemistry of produced water; Experiments to determine the role of lamination and natural fractures on fracture propagation or rock deformation using digital image correlation technique in in-direct tensile tests, semi-circular bend test and creep tests Experiments to determine the stability and rheological properties of nanoparticle-stabilized CO 2 foam in TMS rock samples; Experiments to determine fluid dynamics in un-propped TMS fractures and the role of nano-coating of proppants on fluid dynamics in fractures with proppants; Micro-fluidics experiments to enhance the understanding of fluid dynamics in tight liquidrich pores with high clay content; Socio-economic studies to better engage communities in TMS development.

58 GEOSCIENCES↗
DOE OSTI2024

Harmonized wood density data for Central Amazon species in the BIONTE experimental area in Manaus, Brazil

BIONTE (BIOmass and NuTrient Experiment) is a selective logging experiment established at the Experimental Station of Tropical Forestry (EEST, aka “ZF2”) field research station in the mid 1980s in the central Amazon (Higuchi et al. 1997, Amaral et al. 2019). The main data related to BIONTE are available as a separate dataset (Lima et al. 2022). Only species identified within the BIONTE plots were included in this dataset. Wood density, expressed in g cm-3, was collated from multiple sources that were integrated to compose the wood density for BIONTE. The starting point used was wood density data available from Chave et al. (2006), which was then adapted by Marra et al. (2016) and Marra et al. (2018). We also included data collected and synthesized by Ramírez-Méndez (2018) and Gimenez et al.(2021), who combined local estimates of wood density at the site with other sources collected in the Central Amazon. Data are included in .csv files, while BIONTE_WD_headers.txt provides descriptions of data file headers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

AT2017gfo: Bayesian inference and model selection of multicomponent kilonovae and constraints on the neutron star equation of state

The joint detection of the gravitational wave GW170817, of the short γ-ray burst GRB170817A and of the kilonova AT2017gfo, generated by the the binary neutron star (NS) merger observed on 2017 August 17, is a milestone in multimessenger astronomy and provides new constraints on the NS equation of state. We perform Bayesian inference and model selection on AT2017gfo using semi-analytical, multicomponents models that also account for non-spherical ejecta. Observational data favour anisotropic geometries to spherically symmetric profiles, with a log-Bayes’ factor of ~104, and favour multicomponent models against single-component ones. The best-fitting model is an anisotropic three-component composed of dynamical ejecta plus neutrino and viscous winds. Using the dynamical ejecta parameters inferred from the best-fitting model and numerical–relativity relations connecting the ejecta properties to the binary properties, we constrain the binary mass ratio to q < 1.54 and the reduced tidal parameter to $120\lt \tilde{\Lambda }\lt 1110$. Finally, we combine the predictions from AT2017gfo with those from GW170817, constraining the radius of a NS of 1.4 M ⊙ to 12.2 ± 0.5 km (1σ level). This prediction could be further strengthened by improving kilonova models with numerical-relativity information.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Archival, anonymization and presentation of HTCondor logs with GlideinMonitor

GlideinWMS is a pilot framework to provide uniform and reliable HTCondor clusters using heterogeneous and unreliable resources. The Glideins are pilot jobs that are sent to the selected nodes, test them, set them up as desired by the user jobs, and ultimately start an HTCondor schedd to join an elastic pool. These Glideins collect information that is very useful to evaluate the health and efficiency of the worker nodes and invaluable to troubleshoot when something goes wrong. This data, including local stats, the results of all the tests, and the HTCondor log files, is packed and sent to the GlideinWMS Factory. To access this information, developers and troubleshooters must exchange emails with Factory operators and dig manually into files. Furthermore, these files contain also information like email and IP addresses, and user IDs, that we want to protect and limit access to. GlideinMonitor is a Web application to make these logs more accessible and useful: it organizes the logs in an efficient compressed archive; it allows to search, unpack, and inspect them, all in a convenient and secure Web interface; via plugins like the log anonymizer, it can redact protected information preserving the parts useful for troubleshooting.

Mambelli, Marco↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
DOE OSTI2021

Chapmanite [Fe 2 Sb(Si 2 O 5 )O 3 (OH)]: thermodynamic properties and formation in low-temperature environments

Abstract. of synthetic Sb 2 O 5 , MgSb 2 O 6 (analogue of the mineral byströmite), Mg[Sb(OH) 6 ] 2 ∙6H 2 O (brandholzite), and natural chapmanite [(Fe 1.88 Al 0.12 )Sb(Si 2 O 5 )O 3 (OH)]. Enthalpies of reactions, including formation enthalpies, were evaluated using reference compounds Sb, Sb 2 O 3 , Sb 2 O 5 , and other phases, with high-temperature oxide melt solution calorimetry in lead borate and sodium molybdate solvents. Heat capacity and entropy were determined by relaxation and differential scanning calorimetry. The best set of Δ f H o (kJ mol -1 ) and S o (J mol -1 K -1 ) is byströmite -1733.0±3.6, 139.3±1.0; brandholzite -5243.1±3.6, 571.0±4.0; and chapmanite -3164.9±4.7, 305.1±2.1. The data for chapmanite give Δ f G o of -2973.6±4.7 kJ mol -1 and log K=-17.10 for the dissolution reaction (Fe 1.88 Al 0.12 )Sb(Si 2 O 5 )O 3 (OH) + 6H + → 1.88Fe 3+ + 0.12Al 3+ + 2SiO$_2^0$ + Sb(OH)$_3^0$ + 2H 2 O. Analysis of the data showed that chapmanite is finely balanced in terms of its stability with schafarzikite (FeSb 2 O 4 ) and tripuhyite (FeSbO 4 ) under a specific, narrow range of conditions when both aqueous Fe(III) and Sb(III) are abundant. In such a model, chapmanite is metastable by a narrow margin but could be stabilized by high SiO$_2^0$(aq) activities. Natural assemblages of chapmanite commonly contain abundant amorphous silica, suggesting that this mechanism may be indeed responsible for the formation of chapmanite. Chapmanite probably forms during low-temperature hydrothermal overprint of pre-existing Sb ores under moderately reducing conditions; the slightly elevated temperatures may help to overcome the kinetic barrier for its crystallization. During weathering, sheet silicates may adsorb Sb 3+ in tridentate hexanuclear fashion, thus exposing their chapmanite-like surfaces to the surrounding aqueous environment. Formation of chapmanite, as many other sheet silicates, under ambient conditions, is unlikely.

58 GEOSCIENCES↗
DOE OSTI2023

Piecewise linear approximation with minimum number of linear segments and minimum error: A fast approach to tighten and warm start the hierarchical mixed integer formulation

In several areas of economics and engineering, it is often necessary to fit discrete data points or approximate nonlinear functions with continuous functions. Piecewise linear (PWL) functions are a convenient way to achieve this. PWL functions can be modeled in mathematical problems using only linear and integer variables. Moreover, there is a computational benefit in using PWL functions that have the least possible number of segments. This work proposes a novel hierarchical mixed integer linear programming (MILP) formulation that identifies a continuous PWL approximation with minimum number of linear segments for a given target maximum error. The proposed MILP formulation also identifies the solution with the least maximum error among the solutions with minimum number of segments. Then, this work proposes a fast iterative algorithm that identifies non necessarily continuous PWL approximations by solving O(S log N) linear programming (LP) problems, where N is the number of data points and S is the minimum number of segments in the non necessarily continuous case. This work demonstrates that tight bounds for the MILP problem can be derived from these approximations. Next, a fast algorithm is introduced to transform a non necessarily continuous PWL approximation into a continuous one. Finally, the tight bounds and the continuous PWL approximations are used to tighten and warm start the MILP problem. The tightened formulation is shown in experimental results to be more efficient, especially for large data sets, with a solution time that is up to two orders of magnitude less than the existing literature.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Evaluation of a pair-based, joint-likelihood association approach for regional infrasound event identification

SUMMARY A Bayesian framework for the association of infrasonic detections is presented and evaluated for analysis at regional propagation scales. A pair-based, joint-likelihood association approach is developed that identifies events by computing the probability that individual detection pairs are attributable to a hypothetical common source and applying hierarchical clustering to identify events from the pair-based analysis. The framework is based on a Bayesian formulation introduced for infrasonic source localization and utilizes the propagation models developed for that application with modifications to improve the numerical efficiency of the analysis. Clustering analysis is completed using hierarchical analysis via weighted linkage for a non-Euclidean distance matrix defined by the negative log-joint-likelihood values. The method is evaluated using regional synthetic data with propagation distances of hundreds of kilometres in order to study the sensitivity of the method to uncertainties and errors in backazimuth and time of arrival. The method is found to be robust and stable for typical uncertainties, able to effectively distinguish noise detections within the data set from those in events, and can be made numerically efficient due to its ease of parallelization.

58 GEOSCIENCES↗
DOE OSTI2022

MARSAME Radiological Release Report for Metal Items from TA 53, Set 20

EPC-ES has evaluated the survey results for metal items from the Los Alamos Neutron Science Center (LANSCE) and found that the metal items described in Table 1 of this report (identified by RP Tracking Numbers) meet the criteria for unrestricted release under DOE Order 458.1 Radiation Protection of the Public and the Environment (DOE 2020) and can be recycled. This conclusion is based on the known history of the metal items and on radiation survey data (see the completed RP-Form-031 LANSCE Metals Clearance Log for each item). None of the items in this report are located within radiological areas. Therefore, the items are considered unencumbered and are not subject to the moratorium suspension on metal recycling from Department of Energy facilities. Additionally, LANL has determined that there is no practical opportunity for internal DOE reuse or recycling of this metal. Process knowledge indicates that these metal items were unlikely to ever be in direct contact with the beam and thus are unlikely to have become activated. Surface contamination measurements (both total and removable) showed either no detectable radioactivity or activity levels within the range of background. All measurements for volumetric contamination were indistinguishable from background based on calculated decision limits. Additionally, all gamma isotopic surveys conducted for defense-in-depth showed no identifiable gamma radiation from beam activation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

IER 268 CED-3b Report

The goal of IER 268 was to collect information from the operation of the Godiva IV super-prompt critical assembly to support multi-physics modeling efforts. There were two types of systems deployed. One was a Photo-Doppler Velocimetry (PDV) system which used the reflection of laser light on the surface of the Godiva IV core. The other consisted of an MHD 240 detector with two other detectors (an MHD-241 and an Eljen EJ-325A) used for reference as the MHD-240 detector was moved. The collection is referred to as the MHD Detector in the narrative log. There were two types of Godiva operations performed to gather data. A series of super-prompt critical bursts were performed and data collected on both systems, tied to a common trigger. Separately, a series of delayed critical operations at various power levels was performed for a combination of MHD detector distances from Godiva and the presence and absence of a shield in the line of sight between Godiva and the MHD detector. This data was collected to determine the contribution of room return during the burst measurements.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗