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DOE OSTI2024

Ground-motions site and event specificity: Insights from assessing a suite of simulated ground motions in the San Francisco Bay Area

This article presents the results of a research that is part of a larger collaborative effort between the Lawrence Berkeley National Laboratory and the Pacific Earthquake Engineering Research Center, funded by the US Department of Energy Office of Cybersecurity, Energy Security and Emergency Response. The main objective of this study is to assess a suite of near and far-field simulated ground motions obtained from 20 realizations of an M7 Hayward Fault earthquake in the San Francisco Bay Area, California USA, and inform the selection of rupture simulation parameters leading to strong motions. To this aim, comparisons are conducted with NGA-W2 and directivity ground-motion models and a selected population of records. An archetypal steel moment-resisting frame is utilized to assess infrastructure response distributions. The analyses carried out for each simulated event and subdomain with consistent properties in terms of shallow shear-wave velocity proved to be instrumental for better interpreting the differences between simulated motions and empirical models. The main reasons identified for variances between simulations and empirical relationships included (1) directivity effects fully captured by the simulations across the full breadth of rupture models; (2) site vicinity to ruptures that incorporate large-slip patches, particularly if these are in the forward-directivity direction; and (3) presence of geologic structures that can “trap” seismic waves and produce ground motions with large amplitude and long signal duration. The analyses carried out in this work provide a path for interpreting ground-motion site and event specificity obtained from a suite of physics-based simulations, differing only in the rupture model characterization, to inform the selection of simulation scenarios for site-specific engineering analyses under strong excitations. Evidence from this work points to the possibility that current hazard models may underestimate ground-motion intensities in areas where the combined effect of directivity and site conditions results in large ground-motion amplitudes.

58 GEOSCIENCES↗

DOE OSTI2020

Tidal Resource Data from Sequim Bay Inlet, WA, August 2020

Data from a Nortek Signature1000 deployed on a lander for 14 days in Aug 2020 in the entrance to Sequim Bay, WA. Raw data were processed using the DOLfYN python package and standardized using the ME Data Pipeline python package, tsdat version 0.2.12. Processed data were partitioned into 24 hour increments and saved in the NETCDF file format.

16 TIDAL AND WAVE POWER↗

DOE OSTI2020

Materials Data on BaY(CoO2)4 by Materials Project

BaY(CoO2)4 crystallizes in the orthorhombic Pca2_1 space group. The structure is three-dimensional. there are two inequivalent Ba2+ sites. In the first Ba2+ site, Ba2+ is bonded in a 11-coordinate geometry to eleven O2- atoms. There are a spread of Ba–O bond distances ranging from 2.77–3.37 Å. In the second Ba2+ site, Ba2+ is bonded in a 1-coordinate geometry to nine O2- atoms. There are a spread of Ba–O bond distances ranging from 2.63–3.35 Å. There are two inequivalent Y3+ sites. In the first Y3+ site, Y3+ is bonded to seven O2- atoms to form distorted YO7 pentagonal bipyramids that share corners with two CoO6 octahedra, corners with eight CoO4 tetrahedra, and edges with two CoO6 octahedra. The corner-sharing octahedra tilt angles range from 52–55°. There are a spread of Y–O bond distances ranging from 2.29–2.44 Å. In the second Y3+ site, Y3+ is bonded in a 7-coordinate geometry to seven O2- atoms. There are a spread of Y–O bond distances ranging from 2.22–2.53 Å. There are eight inequivalent Co+2.75+ sites. In the first Co+2.75+ site, Co+2.75+ is bonded to four O2- atoms to form CoO4 tetrahedra that share a cornercorner with one CoO6 octahedra, corners with two equivalent YO7 pentagonal bipyramids, and corners with five CoO4 tetrahedra. The corner-sharing octahedral tilt angles are 64°. There are a spread of Co–O bond distances ranging from 1.89–2.02 Å. In the second Co+2.75+ site, Co+2.75+ is bonded to four O2- atoms to form CoO4 tetrahedra that share corners with three CoO6 octahedra, a cornercorner with one YO7 pentagonal bipyramid, and corners with two CoO4 tetrahedra. The corner-sharing octahedra tilt angles range from 57–60°. There are a spread of Co–O bond distances ranging from 1.83–1.88 Å. In the third Co+2.75+ site, Co+2.75+ is bonded to four O2- atoms to form CoO4 tetrahedra that share corners with four CoO6 octahedra, corners with three equivalent YO7 pentagonal bipyramids, and a cornercorner with one CoO4 tetrahedra. The corner-sharing octahedra tilt angles range from 47–70°. There are a spread of Co–O bond distances ranging from 1.84–1.89 Å. In the fourth Co+2.75+ site, Co+2.75+ is bonded to four O2- atoms to form CoO4 tetrahedra that share a cornercorner with one YO7 pentagonal bipyramid and corners with six CoO4 tetrahedra. There are a spread of Co–O bond distances ranging from 1.96–1.98 Å. In the fifth Co+2.75+ site, Co+2.75+ is bonded to four O2- atoms to form corner-sharing CoO4 tetrahedra. There are a spread of Co–O bond distances ranging from 1.92–1.97 Å. In the sixth Co+2.75+ site, Co+2.75+ is bonded to six O2- atoms to form CoO6 octahedra that share a cornercorner with one YO7 pentagonal bipyramid, corners with four CoO4 tetrahedra, edges with two equivalent CoO6 octahedra, and an edgeedge with one YO7 pentagonal bipyramid. There are a spread of Co–O bond distances ranging from 1.92–2.04 Å. In the seventh Co+2.75+ site, Co+2.75+ is bonded to six O2- atoms to form CoO6 octahedra that share a cornercorner with one YO7 pentagonal bipyramid, corners with four CoO4 tetrahedra, edges with two equivalent CoO6 octahedra, and an edgeedge with one YO7 pentagonal bipyramid. There are a spread of Co–O bond distances ranging from 1.90–1.97 Å. In the eighth Co+2.75+ site, Co+2.75+ is bonded to four O2- atoms to form CoO4 tetrahedra that share a cornercorner with one YO7 pentagonal bipyramid and corners with six CoO4 tetrahedra. There are a spread of Co–O bond distances ranging from 1.87–1.93 Å. There are sixteen inequivalent O2- sites. In the first O2- site, O2- is bonded in a 3-coordinate geometry to two Ba2+, one Y3+, and two Co+2.75+ atoms. In the second O2- site, O2- is bonded in a 3-coordinate geometry to two Ba2+, one Y3+, and two Co+2.75+ atoms. In the third O2- site, O2- is bonded in a distorted tetrahedral geometry to one Ba2+, one Y3+, and two Co+2.75+ atoms. In the fourth O2- site, O2- is bonded in a tetrahedral geometry to four Co+2.75+ atoms. In the fifth O2- site, O2- is bonded in a 4-coordinate geometry to one Ba2+, one Y3+, and two Co+2.75+ atoms. In the sixth O2- site, O2- is bonded in a 4-coordinate geometry to one Ba2+, one Y3+, and two Co+2.75+ atoms. In the seventh O2- site, O2- is bonded in a 4-coordinate geometry to one Ba2+, one Y3+, and two Co+2.75+ atoms. In the eighth O2- site, O2- is bonded in a 3-coordinate geometry to one Ba2+ and three Co+2.75+ atoms. In the ninth O2- site, O2- is bonded in a 5-coordinate geometry to two equivalent Ba2+, one Y3+, and two Co+2.75+ atoms. In the tenth O2- site, O2- is bonded in a 3-coordinate geometry to two equivalent Ba2+, one Y3+, and two Co+2.75+ atoms. In the eleventh O2- site, O2- is bonded in a 3-coordinate geometry to two Ba2+, one Y3+, and two Co+2.75+ atoms. In the twelfth O2- site, O2- is bonded in a 3-coordinate geometry to one Y3+ and three Co+2.75+ atoms. In the thirteenth O2- site, O2- is bonded in a 3-coordinate geometry to two equivalent Ba2+, one Y3+, and two Co+2.75+ atoms. In the fourteenth O2- site, O2- is bonded in a 4-coordinate geometry to one Ba2+, one Y3+, and two Co+2.75+ atoms. In the fifteenth O2- site, O2- is bonded in a 4-coordinate geometry to one Ba2+, one Y3+, and two Co+2.75+ atoms. In the sixteenth O2- site, O2- is bonded in a 4-coordinate geometry to one Ba2+, one Y3+, and two Co+2.75+ atoms.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Application of Gaussian Bayes classifier to differentiate chlorine-based chemical agents

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical warfare agents (CWA) non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a mechanically-cooled high-purity germanium (HPGe) detector. One of the difficult technical challenges is to discriminate the chlorine-based chemical agents. Especially, CN, CNB, CNS and CG have similar chemical compositions to make it hard to discriminate them with a higher confidence. Current identification algorithms for PINS systems with 252-Cf sources have been improved and updated continuously as more field data became available, and new algorithms was studied to complement the current algorithms by adopting the Gaussian Bayes classifier. These new algorithms were intended to be applied to a subset of chlorine-based chemical agents, and their main goal is discriminate CN, CNB, CNS and CG with their ratios of the chlorine neutron inelastic 1763keV peak to the chlorine thermal neutron capture 1959keV peak, which is referred to as the “Cl i/c” or “clic” ratio in this study. The Cl i/c ratios were assumed to follow Gaussian distributions with the means and the standard deviations unique to their corresponding chemical agents. The prior probabilities of these four chemical agents were optimized with a collection of field data to achieve the best performance in terms of precision or positive predictive value (PPV). Finally, their posterior probabilities as functions of the Cl i/c ratio were implemented in the current version of PINS analysis software in order to be tested with more field data.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2020

Bayes Inference Engine (BIE) Test Plan: TiltConeBeamCombo

The Bayes Inference Engine (BIE) is a general software tool intended to be used primarily in the analysis of radiographic data for density. An analysis is set up in the BIE by representing the problem as a collection of modules called glyphs. All glyphs to be used in creating the forward model of the experiment will be tested separately for a range of inputs. The testing of the adjoint code is performed in a separate document.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2020

Bayes Inference Engine (BIE) Test Plan: TiltConeBeamComboDMLinear

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Bayes Inference Engine (BIE) Test Plan: TiltConeBeamComboDMGeometric

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Bayes Inference Engine (BIE) Test Plan: TiltConeBeamFVT

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Characterizing the Non-linear Interactions Between Tide, Storm Surge, and River Flow in the Delaware Bay Estuary, United States

Low-lying coastal areas in the mid-Atlantic region are prone to compound flooding resulting from the co-occurrence of river floods and coastal storm surges. To better understand the contribution of non-linear tide-surge-river interactions to compound flooding, the unstructured-grid Finite Volume Community Ocean Model was applied to simulate coastal storm surge and flooding in the Delaware Bay Estuary in the United States. The model was validated with tide gauge data in the estuary for selected hurricane events. Non-linear interactions between tide-surge-river were investigated using a non-stationary tidal analysis method, which decomposes the interactions’ components at the frequency domain. Model results indicated that tide-river interactions damped semidiurnal tides, while the tide-surge interactions mainly influenced diurnal tides. Tide-river interactions suppressed the water level upstream while tide-surge interaction increased the water level downstream, which resulted in a transition zone of damping and enhancing effects where the tide-surge-river interaction was prominent. Evident compound flooding was observed as a result of non-linear tide-surge-river interactions. Furthermore, sensitivity analysis was carried out to evaluate the effect of river flooding on the non-linear interactions. The transition zone of damping and enhancing effects shifted downstream as the river flow rate increased.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2025

Investigation of the genesis and intensification of simultaneous cyclones in the Arabian sea and Bay of Bengal in November 2020

The North Indian Ocean (NIO), which includes the Bay of Bengal (BoB) and the Arabian Sea (AS), is frequently impacted by tropical cyclones (TCs). Notably, in November 2020, the simultaneous occurrence of Cyclones Gati in the AS and Nivar in the BoB presented a rare and compelling phenomenon worthy of investigation. This study delves into the underlying mechanisms driving the genesis and intensification of these two Very Severe Cyclonic Storms (VSCS). Key contributing factors include elevated sea surface temperatures (SST) and Tropical Cyclone Heat Potential (TCHP), both crucial for the development and intensification of TCs. Notably, the BoB exhibits higher TCHP values compared to the AS, which facilitated greater intensity for Nivar over Gati. Despite their concurrent formation, the genesis processes of Gati and Nivar were distinct. The early November development of Gati in the AS was modulated by the Rossby wave and the Madden–Julian Oscillation (MJO). Subsequently, the eastward propagation of the MJO triggered the formation of Cyclone Nivar in the BoB, enhancing convective activity from the AS and coinciding with the arrival of a downward-propagating Rossby wave. This study provides a comprehensive analysis of the simultaneous formation of Gati and Nivar, contributing novel insights into the complex interactions between oceanic and atmospheric phenomena. In conclusion, such insights are vital for enhancing the predictive capabilities for destructive weather events in the NIO.

Climate Change↗

DOE OSTI2020

Evaluating Exposures of Bay Mussels (Mytilus trossulus) to Contaminants of Emerging Concern through Environmental Sampling and Hydrodynamic Modeling.

Bay mussels (Mytilus trossulus) were transplanted to 18 locations representing a range of potential exposures throughout Puget Sound, WA. Tissues were analyzed for over 200 organic contaminants. Results indicated the widespread exposure of marine organisms to trace levels of organic contaminants including the synthetic opioid oxycodone, present at three urban sites, and the chemotherapy drug melphalan, present at nine locations, at levels that may be of biological concern. Land-use and wastewater outfalls were evaluated as potential sources of CECs to the nearshore. Exposure to alkylphenol ethoxylates was associated with increased impervious surfaces in upland watersheds. A hydrodynamic simulation was performed using the Salish Sea Model to integrate inputs from 99 wastewater sources to Puget Sound. Predictions were consistent with concentrations of several wastewater-associated contaminants and d15N enrichment. These results support the notion that Puget Sound nearshore biota suffer chronic exposures to a suite of contaminants from multiple sources and provide critical to focus future monitoring and management.

toxic contamination, Salish Sea, Sish Sea Model, m↗

DOE OSTI2022

Sand Migration Simulation during Gas Production from Gas Hydrate Reservoir at Kuparuk 7–11–12 site in the Prodhoe Bay Unit, Alaska

Uncontrolled sand production impedes continuous gas production from a hydrate reservoir as observed in the past field-scale gas production tests. Sand mobilization is strongly linked with sediment deformation and high pressure gradient. Sand production occurs when the mobilized sands reach the well. Throughout gas production from a hydrate reservoir, because of hydrate dissociation, deformation and pressure gradient change both in time and space. Therefore, it is necessary to consider the entire process to identify where sand is likely mobilized and how much mobilized sand could reach the well. Here, this study utilizes a coupled thermal, hydrological, chemical, and mechanical (thermo-hydro-chemo-mechanical) sand migration model to simulate a year long gas production from the hydrate reservoir at the Kuparuk 7–11–12 site in the Prodhoe Bay Unit, Alaska. It is found that sand would mainly come from the lower portion of the production zone where faster hydrate dissociation occurs. The relatively faster hydrate dissociation coupled with the fact that hydrate-bearing sediments and hydrate-free sediments have similar stiffness but different strengths causes complex stress transfer, which results in excessive shear deformation when the upper portion of the production zone starts to dissociate. This is evident not only near the well but also away from the well, leading to a large amount of sand mobilization. Another focus of the modeling study is evaluation of the effect of sand migration on gas production. The comparison of two extreme cases–with sandscreen and without sandscreen–suggests that migrated and settled solids around a sand control device can prevent pressure drop across the near-wellbore zone and lead to reduction of the gas production rate.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

3-D Geological Modeling for Numerical Flow Simulation Studies of Gas Hydrate Reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope

Accurate reservoir evaluation requires reliable three-dimensional (3-D) geological models. Here, this study conducted 3-D geological modeling for numerical flow simulation of the B1 sand gas hydrate reservoir at the Kuparuk State 7-11-12 pad, Prudhoe Bay Unit, Alaska North Slope. The model integrates well logs, core, and seismic data to address spatial heterogeneity in geological structures and reservoir properties. Two modeling types were performed: structural framework modeling and petrophysical property modeling. For structural framework modeling, seismic data and well log markers were used to reproduce subsurface structures characterized by a normal fault system. A volume-based modeling algorithm and stair-stepping grid were applied. The resulting 3-D model comprised 2,640,000 grid cells across 264 layers, including seven fault grids. For petrophysical property modeling, total porosity was initially modeled using sequential Gaussian simulation with collocated cokriging. To reproduce the upward coarsening of the B1 sand, upscaled log-derived total porosity and a three-dimensional (3-D) trend depicting total porosity variation were used as primary and secondary data, respectively. Gas hydrate saturation distribution was modeled similarly, with secondary data from estimated porosity distribution and seismic-derived acoustic impedance map enhancing accuracy. Results indicate higher gas hydrate saturation in the upper part of the B1 sand and areas with higher acoustic impedance. Intrinsic permeability was modeled from the total porosity and clay-bound water volume, and effective permeability was derived from the gas hydrate saturation and intrinsic permeability distributions based on the “Tokyo model”. Effective permeability distributions were influenced by the total porosity, gas hydrate saturation, and intrinsic permeability. Within the same layer, higher gas hydrate saturation leads to decreased effective permeability. In total, 100 sets of multiple scenarios were prepared, providing input data for dynamic flow simulations to evaluate the effects of lateral heterogeneity in reservoir properties and the hydraulic characteristics of faults on production behavior for preassessment before the long-term production test.

58 GEOSCIENCES↗

DOE OSTI2023

Multi‐Scale Temporal Variability of Turbulent Mixing During a Monsoon Intra‐Seasonal Oscillation in the Bay of Bengal: An LES Study

A process study using large‐eddy simulations is carried out to explore the dominant 1‐D processes that affect mixed layer (ML) properties during an event of summer Monsoon Intra‐seasonal Oscillations (MISO) in the Bay of Bengal (BOB). These simulations use realistic air‐sea fluxes and initial conditions that were collected during the summer 2018 MISO‐BOB field experiment to explore the roles of thermal inversion layer (TIL) and Langmuir turbulence (LT) in modulating ML properties. The simulations span an active period with heavy rain and strong winds and a break period with strong solar heat flux and little rain. The mixed layer depth (MLD), sea surface temperature (SST) and sea surface salinity (SSS) are most affected by the presence of near‐inertial oscillations, solar heating and precipitation, all of which occur at different timescales. The subsurface warming induced by the TIL reduces the SST variability at the MISO timescale when compared with the simulation without TIL. Comparison of simulations with and without LT indicates that LT enhances subsurface warming during the active phase and reduces diurnal SST modulation during the break phase. Finally, simulations with 1‐D mixing models show a wide disparity in the evolution of MLD, SST, and SSS.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

Search For Electron-Antineutrinos Associated With 2 Gravitational-Wave Events GW150914, GW151012, GW151226, 3 GW170104, GW170608, GW170814, and GW170817 at Daya Bay

Providing a possible connection between neutrino emission and gravitational-wave (GW) bursts is important to our understanding of the physical processes that occur when black holes or neutron stars merge. In the Daya Bay experiment, using the data collected from December 2011 to August 2017, a search has been performed for electron-antineutrino signals coinciding with detected GW events, including GW150914, GW151012, GW151226, GW170104, GW170608, GW170814, and GW170817. We used three time windows of ±10 s, ±500 s, and ±1000 s relative to the occurrence of the GW events, and a neutrino energy range of 1.8 to 100 MeV to search for correlated neutrino candidates. The detected electron-antineutrino candidates are consistent with the expected background rates for all the three time windows. Assuming monochromatic spectra, we found upper limits (90% confidence level) on electron-antineutrino fluence of (1.13 - 2.44)×1011 cm-2 at 5 MeV to 8.0×107cm-2at 100 MeV for the three timewindows. Under the assumption of a Fermi-Dirac spectrum, the upper limits were found to be (5.4 - 7.0)×109 cm-2 for the three time windows.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2021

Factors associated with emerging multimodal transportation behavior in the San Francisco Bay Area

Abstract This paper identifies the influence of demographic, local transportation environment, and individual preferences for transportation attributes on multimodal transportation behavior in an urban environment with emergent transportation mode availability. Multimodality is the use of more than one mode of transportation during a given timeframe. Multimodality has been considered a key component of sustainable and efficient transportation systems, as this travel behavior can represent a shift away from personal vehicle use to more sustainable transportation modes, especially in urban environments with diverse transportation systems and emergent shared transportation alternatives (e.g., carsharing, ridehailing, bike sharing). However, it is unclear what factors contribute towards people being more likely to exhibit multimodal transportation behavior in modern urban environments. We assessed commuting behavior based on a survey administered in the San Francisco Bay Area according to whether residents commuted (i) exclusively by vehicle, (ii) by a mix of vehicle and non-vehicle modes, or (iii) exclusively by non-vehicle modes. A classification tree approach identified correlations between commuting classes and demographic variables, preferences for transportation attributes, and location-based information. The characterization of commuting styles could inform regional transportation policy and design that aims to reduce vehicle use by identifying the demographic, preference, and location-based considerations correlated with each commuting style.

McAuliffe Wells, Emily↗

DOE OSTI2024

First measurement of the yield of 8 He isotopes produced in liquid scintillator by cosmic-ray muons at Daya Bay

Here, Daya Bay presents the first measurement of cosmogenic 8 He isotope production in liquid scintillator, using an innovative method for identifying cascade decays of 8 He and its child isotope, 8 Li. We also measure the production yield of 9 Li isotopes using well-established methodology. The results, in units of 10 –8 μ –1 ⁢g –1 cm 2 , are 0.307 ± 0.042, 0.341 ± 0.040, and 0.546 ± 0.076 for 8 He, and 6.73 ± 0.73, 6.75 ± 0.70, and 13.74 ± 0.82 for 9 Li at average muon energies of 63.9 GeV, 64.7 GeV, and 143.0 GeV, respectively. The measured production rate of 8 He isotopes is more than an order of magnitude lower than any other measurement of cosmogenic isotope production. It replaces the results of previous attempts to determine the ratio of 8 He to 9 Li production that yielded a wide range of limits from 0% to 30%. The results provide future liquid-scintillator-based experiments with improved ability to predict cosmogenic backgrounds.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Joint Determination of Reactor Antineutrino Spectra from U 235 and Pu 239 Fission by Daya Bay and PROSPECT

A joint determination of the reactor antineutrino spectra resulting from the fission of 235U and 239Pu has been carried out by the Daya Bay and PROSPECT collaborations. This Letter reports the level of consistency of 235U spectrum measurements from the two experiments and presents new results from a joint analysis of both data sets. The measurements are found to be consistent. The combined analysis reduces the degeneracy between the dominant 235U and 239Pu isotopes and improves the uncertainty of the 235U spectral shape to about 3%. The 235U and 239Pu antineutrino energy spectra are unfolded from the jointly deconvolved reactor spectra using the Wiener-SVD unfolding method, providing a data-based reference for other reactor antineutrino experiments and other applications. This is the first measurement of the 235U and 239Pu spectra based on the combination of experiments at low- and highly enriched uranium reactors.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗