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DOE OSTI2019

New Insights in the Quench Mechanisms in Nitrogen Doped Cavities

This paper will cover a systematic study of the quench in nitrogen doped cavities: three cavities were sequentially treated/reset with different doping recipes which are known to produce different levels of quench field. Analysis of mean free path and TMAP coupled with sample analysis reveals new insights on the physics of the premature quench in nitrogen doped cavities; new recipes demonstrate the possibility to increase quench fields well beyond 30 MV/m.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

Oxygen Vacancies in Niobium Pentoxide as a Source of Two-Level System Losses in Superconducting Niobium

We identify a major source of quantum decoherence in three-dimensional superconducting radio-frequency (SRF) resonators and two-dimensional transmon qubits composed of oxidized niobium: oxygen vacancies in the niobium pentoxide, which drive two-level system (TLS) losses. By probing the effect of sequential in situ vacuum-baking treatments on the rf performance of bulk Nb SRF resonators and on the oxide structure of a representative Nb sample using TOF SIMS, we find a nonmonotonic evolution of cavity quality factor Q0, which correlates with the interplay of Nb2O5 vacancy generation and oxide-thickness reduction. We localize this effect to the oxide itself and present the insignificant role of diffused interstitial oxygen in the underlying Nb by regrowing the oxide via wet oxidation, which reveals a mitigation of aggravated TLS losses. We hypothesize that such vacancies in the pentoxide serve as magnetic impurities and are a source of TLS-driven rf loss.

Bafia, Daniel P. [Fermilab]↗

DOE OSTI2022

Measuring Very Low Radiation Doses in PTFE for Nuclear Forensic Enrichment Reconstruction

Every country that has made nuclear weapons has used uranium enrichment. Despite the centrality of this technology to international security, there is still no reliable physical marker of past enrichment that can be used to perform forensic verification of historically produced weapons. We show that the extremely low radioactivity from uranium alpha emissions during enrichment leaves detectable and irreversible calorimetric signatures in the common enrichment gasket material PTFE, allowing for historical reconstruction of past enrichment activities at a sensitivity better than one weapon's quantity of highly enriched uranium. Fast scanning calorimetry also enables the measurement of recrystallization enthalpies of sequentially microtomed slices, confirming the magnitude and the type of radiation exposure while also providing a detection of tampering and a method for analyzing field samples useful for treaty verification. This work opens the door for common items to be turned into precise dosimeters to detect the past presence of radioactivity, nuclear materials, and related activities with high confidence.

Connick, Rachel↗

DOE OSTI2018

Quantitative Prediction of Uranium Speciation and Amidoxime Binding in Seawater from Advanced Simulation Techniques

The goal of this project was to quantitatively predict stability of uranium species and thermochemistry of uranium binding with amidoxime ligands in realistic seawater conditions. The specific aims of the project were: (i) determine the stable species of uranium and its solvation structure in realistic seawater conditions; also speciation and solvation of uranium's major competing species in seawater, vanadium; (ii) accurately predict the free energies (and thereby stability constants) of binding between uranium and amidoxime; (iii) accurately predict the reaction enthalpies and entropies of binding between uranium and amidoxime as well as between vanadium and amidoxime. Main results and conclusions are as follows. First-principles molecular dynamic simulations found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable and that one Ca ion binds to the center UO 2 (CO 3 ) 3 4- anion stronger than the other Ca ion. This finding suggests that using time-resolved EXAFS spectra may confirm the asymmetry in binding of the two Ca ions in the aqueous Ca 2 UO 2 (CO 3 ) 3 complex. To consider the common ions, solvation of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater was simulated by classical molecular dynamics simulation. It was found that the structure of the Ca 2 UO 2 (CO 3 ) 3 complex is very stable in the model seawater. A Na+ ion was found to be closely associated with the Ca 2 UO 2 (CO 3 ) 3 complex by indirectly interacting with one axial oxygen atom of the UO 2 group bridged by a water molecule. In addition, the Na + ion interacts closely with one Ca 2+ ion than the other. The present simulations revealed the key role of common ions such as Na + in impacting the solvation, structure, and apparent charge of the Ca 2 UO 2 (CO 3 ) 3 complex in seawater. The sequential processes of UO 2 2+ binding with the three CO 3 2- groups and the two Ca 2+ ions in pure water and in 0.1 M NaCl were simulated by classical molecular dynamics with both the non-constrained brute-force approach and umbrella sampling. The simulated free energies show excellent agreement with the experiment. The coordination structures of UO 2 (CO 3 ) 3 4- and CaUO 2 (CO 3 ) 3 2- were found to be significantly affected by the presence of Na + ions, leading to a monodentate binding of a carbonate group to U and a water molecule entering the first coordination shell of U. To shed light on the binding between uranyl and glutardiamidoxime (H 2 B), an important model and state-of-the-art ligand for seawater uranium extraction, a suite of computational methods was employed. From molecular dynamics (MD) simulations, it was found that the binding configurations of B 2- with uranyl favor the twofold distorted η 2 binding between the oximate ends (C=N-O-) and U, while HB - prefers the chelating mode for the oximate end with the neutral end being solvated by water. The free energies of sequential ligand binding to form UO 2 B, [UO 2 B 2 ] 2- , and [UO 2 (HB)B] - were simulated with umbrella sampling and very good agreement with the experimental values was achieved, which corroborates the structural insights into the binding mode. State-of-the-art polymeric sorbents employ both amidoximate and carboxylate groups on the side chains to achieve optimal U uptake and selectivity, so we simulated the binding of a model amidoximate–carboxylate bifunctional ligand with uranyl. Classical MD and free-energy simulations in 0.5 M NaCl showed that the carboxylate group binds first to uranyl, leading to a loose intermediate state, and then, the amidoximate group binds, resulting in a more stable and tight chelate state. Binding of the second bifunctional ligand follows a similar process, and the two ligands prefer a trans configuration around the uranyl group. The simulated free energies indicate that the two bifunctional ligands bind with uranyl 14 kcal/mol stronger than the two ligands with only amidoximate groups, confirming an important synergy between amidoximate and carboxylate groups in binding uranyl.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Anisotropic structure and optical engineering of strontium titanate and zirconia responding to sequential hydrogen and helium irradiation

Unraveling the correlation between strain engineering with anisotropic optical properties via nanoscale defects gradually evolved into a strategy for fundamental studies and technological applications, yet it remains understudied in functional complex oxides compared to metals and semiconductors. Here, the methodology of strain engineering for the determination of the lattice parameters, parallel/normal to the sample surface, in the individual layers of single-crystalline superlattices is derived, which is based on analysis of high-angle X-ray diffraction measurements in combination with diffraction reciprocal space mapping. With modeling that takes into account the effect of elastic properties, two elastically anisotropic materials, SrTiO 3 and ZrO 2 , have been compared in terms of defect-induced elastic strain caused by individual and sequential H + and He 2+ irradiation. The anisotropic lattice swelling with corresponding refractive index, and obstructive behavior of elastic strain recovery are demonstrated in SrTiO 3 , while approaching strain behaviors accompanied by isotropic refractive index distribution in both orientations are confirmed in ZrO 2 . Under sequential He 2+ /H + irradiation, pre-existing He-vacancy complexes acted as vacancy traps, preferentially capturing H + clusters to induce lattice distortion and enhance absorption. Nanohardness increments (ΔH) calculated by the DBH model matched nanoindentation results, confirming that sequential irradiation generated higher indentation yield stress than individual irradiations.

Anisotropic expansion↗

DOE OSTI2018

The F-t-Pj-RG method: An adjacent-rolling-windows based steady-state detection technique for application to kinetic Monte Carlo simulations

A window-based steady-state detection algorithm has been developed for application to kinetic Monte Carlo simulation data. The algorithm, termed F-t-Pj-RG sequentially applies an F-test, a t-test, and a projection test on adjacent windows of the data while rolling (or shifting) and growing the windows when any of the tests fail. In aggregate, the algorithm is able to (a) automatically reject the warm-up period as not being at steady-state, as well as (b) determine an appropriate window size for converged statistics when sampling the data, which is necessary for detection of steady-state, and (c) detect steady-state within a particular tolerance. The last step, the projection test, is actually an oscillating-slope projection test, and is performed on j sequential data windows (i.e., more than two adjacent windows). It requires more than simply being within the user defined tolerance: the oscillating-slope projection test includes a condition that the slope must oscillate around zero when 2, which is an additional indication of steady-state. When all three tests are passed, the F-t-Pj test is passed, indicating that the prerequisites of steady-state detection have been met and also that conditions consistent with the definition of steady-state have been realized. This algorithm is applied to a variety of data sets that correspond to the diverse type of data trends that can be produced by kinetic Monte Carlo simulations. The algorithm is shown to be robust in its ability to handle differing functional forms, and is able to detect steady-state with low computational cost. Finally, the low computational cost of this method and its robustness towards varied data trends make it suitable for on-the-fly use in kinetic Monte Carlo simulations.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

A fast particle-based approach for calibrating a 3-D model of the Antarctic ice sheet

We consider the scientifically challenging and policy-relevant task of understanding the past and projecting the future dynamics of the Antarctic ice sheet. The Antarctic ice sheet has shown a highly nonlinear threshold response to past climate forcings. Triggering such a threshold response through anthropogenic greenhouse gas emissions would drive drastic and potentially fast sea level rise with important implications for coastal flood risks. Previous studies have combined information from ice sheet models and observations to calibrate model parameters. These studies have broken important new ground but have either adopted simple ice sheet models or have limited the number of parameters to allow for the use of more complex models. These limitations are largely due to the computational challenges posed by calibration as models become more computationally intensive or when the number of parameters increases. Here, we propose a method to alleviate this problem: a fast sequential Monte Carlo method that takes advantage of the massive parallelization afforded by modern high-performance computing systems. We use simulated examples to demonstrate how our sample-based approach provides accurate approximations to the posterior distributions of the calibrated parameters. The drastic reduction in computational times enables us to provide new insights into important scientific questions, for example, the impact of Pliocene era data and prior parameter information on sea level projections. These studies would be computationally prohibitive with other computational approaches for calibration such as Markov chain Monte Carlo or emulation-based methods. We also find considerable differences in the distributions of sea level projections when we account for a larger number of uncertain parameters. For example, based on the same ice sheet model and data set, the 99th percentile of the Antarctic ice sheet contribution to sea level rise in 2300 increases from 6.5 m to 13.1 m when we increase the number of calibrated parameters from three to 11. With previous calibration methods, it would be challenging to go beyond five parameters. Here, this work provides an important next step toward improving the uncertainty quantification of complex, computationally intensive and decision-relevant models.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Atomistic thermodynamics and kinetics of dicalcium silicate dissolution

Low-temperature cement manufacturing has garnered academic and industrial attention for its low environmental footprints. However, the sluggish hydration kinetics of the resultant cement affects their early-age strength development. This motivates fundamental studies to unravel the mechanistic picture of the dissolution process and discover science-informed pathways to accelerate hydration. Standard atomistic simulations seldomly exceed a microsecond making them impractical to study slow dissolution processes. We are using rare event sampling techniques, we provide the mechanistic picture of Ca 2+ ion dissolution from a kink site on the dicalcium silicate surface. The Ca 2+ ion dissolution is comprised of two sequential stages: breaking restraints from the kink sites to form a ledge adatom and detaching from the ledge/terrace adatom sites into the solution. The first and second stages feature free energy barriers of ~63 kJ/mol and ~ 29 kJ/mol respectively, making the first stage the rate-limiting step of the entire Ca 2+ dissolution kinetics. Using the reactive flux method, the rate and equilibrium constants for each reaction step are calculated, which yield the Ca 2+ ion activity of ~1.03 × 10 -5 . The diffusion calculations indicate that the surface effects lower the self-diffusion coefficient of Ca 2+ ions at the solid-water interface.

36 MATERIALS SCIENCE↗

DOE OSTI2017

Anaerobic digestion of solid agroindustrial waste in semi-continuous mode: Evaluation of mono-digestion and co-digestion systems

Highlights: • Agroindustrial waste were co-digested with an artificial organic fraction. • Variations in organic loading rate and hydraulic retention time were studied. • Sequential feeding of the substrates based on their seasonality was investigated. • Co-digestion resulted in higher methane yields compared with mono-digestion. • Applying sequential feeding led to a more equilibrated system operation. - Abstract: The present study aimed at investigating the anaerobic digestion of four agroindustrial waste, namely cotton gin waste, winery waste, olive pomace and juice industry waste, in semi-continuous mode, conducting mono-digestion and co-digestion assays, using an artificial organic fraction sample as co-substrate. These assays were divided into two groups, in which different conditions were applied. Group I investigated the variation in two operational parameters, i.e. the organic loading rate (OLR) and the hydraulic retention time (HRT), while in Group II, the assays were fed with different substrates in a sequential order. Results showed more elevated specific methane yields for co-digestion assays compared with mono-digestion assays. Maximum yields were achieved at an OLR of 1.0 gVS/(L·d) and a HRT reduced to half of the initial. Further reduction of the HRT coupled to an increase of the OLR generally caused a significant decrease of specific methane yields, as well as one case of severe overloading, i.e. the mono-digestion of juice industry waste, which resulted in instability and ultimately system failure. Sequential feeding with different substrates led to a more equilibrated operation, especially for co-digestion systems, with higher specific methane yields being observed during the phases corresponding to winery waste and juice industry waste substrates. Overall more positive results were obtained in the cases in which the latter substrates were fed to the reactors at process startup.

09 BIOMASS FUELS↗

DOE OSTI2021

Pressure-Driven Sequential Lattice Collapse and Magnetic Collapse in Transition-Metal-Intercalated Compounds Fe x NbS 2

Volume collapse under high pressure is an intriguing phenomenon involving subtle interplay between lattice, spin, and charge. The two most important causes of volume collapse are lattice collapse (low-density to high-density) and magnetic collapse (high-spin to low-spin). Herein we report the pressure-driven sequential volume collapses in partially intercalated Fe x NbS 2 (x = 1/4, 1/3, 1/2, 2/3). Because of the distinct interlayer atomic occupancy, the low-iron-content samples exhibit both lattice and magnetic collapses under compression, whereas the high-iron-content samples exhibit only one magnetic collapse. Therefore, theoretical calculations indicate that the low-pressure volume collapses for x = 1/4 and x = 1/3 are lattice collapses, and the high-pressure volume collapses for all four samples are magnetic collapses. The magnetic collapse involving the high-spin to low-spin crossover of Fe2+ has also been verified by in situ X-ray emission measurements. Integrating two distinct volume collapses into one material provides a rare playground of lattice, spin, and charge.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Automated pipeline framework for processing of large-scale building energy time series data

Commercial buildings account for one third of the total electricity consumption in the United States and a significant amount of this energy is wasted. Therefore, there is a need for “virtual” energy audits, to identify energy inefficiencies and their associated savings opportunities using methods that can be non-intrusive and automated for application to large populations of buildings. Here we demonstrate virtual energy audits applied to large populations of buildings’ time-series smart-meter data using a systematic approach and a fully automated Building Energy Analytics (BEA) Pipeline that unifies, cleans, stores and analyzes building energy datasets in a non-relational data warehouse for efficient insights and results. This BEA pipeline is based on a custom compute job scheduler for a high performance computing cluster to enable parallel processing of Slurm jobs. Within the analytics pipeline, we introduced a data qualification tool that enhances data quality by fixing common errors, while also detecting abnormalities in a building’s daily operation using hierarchical clustering. We analyze the HVAC scheduling of a population of 816 buildings, using this analytics pipeline, as part of a cross-sectional study. With our approach, this sample of 816 buildings is improved in data quality and is efficiently analyzed in 34 minutes, which is 85 times faster than the time taken by a sequential processing. The analytical results for the HVAC operational hours of these buildings show that among 10 building use types, food sales buildings with 17.75 hours of daily HVAC cooling operation are decent targets for HVAC savings. Overall, this analytics pipeline enables the identification of statistically significant results from population based studies of large numbers of building energy time-series datasets with robust results. These types of BEA studies can explore numerous factors impacting building energy efficiency and virtual building energy audits. This approach enables a new generation of data-driven buildings energy analysis at scale.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Use of Stable Mercury Isotopes to Assess Mercury and Methylmercury Transformation and Transport across Critical Interfaces from the Molecular to the Watershed Scale (Final Report)

This project titled “Use of Stable Mercury Isotopes to Assess Mercury and Methylmercury Transformation and Transport across Critical Interfaces from the Molecular to the Watershed Scale” represents a collaborative effort between the University of Michigan (Jason Demers, PI) and Oak Ridge National Laboratory (Scott Brooks, co-I). Much has been learned about mercury (Hg) cycling in stream ecosystems, and East Fork Poplar Creek (EFPC) in particular, through decades of previous research. Nevertheless, some of the most fundamental questions regarding the sources of bioavailable Hg and its transformation to toxic methylmercury (MeHg) have remained unanswered. These fundamental questions include: (1) what are the sources and biogeochemical processes that lead to the input of dissolved Hg to stream water across critical subsurface interfaces within stream ecosystems, and EFPC in particular? and (2) what are the sources and biogeochemical processes that control the production and fate of bioaccumulative MeHg within stream ecosystems, and in EFPC in particular? To address these fundamental questions, our project aimed to couple laboratory experiments and field observations, both utilizing natural abundance Hg stable isotope techniques, to identify the processes responsible for generating mobile, bioavailable dissolved Hg from recalcitrant legacy sources within critical subsurface zones (e.g., streambed hyporheic zone, riparian floodplain subsurface). We used the isotopic signature of this bioavailable dissolved Hg to track its mobilization across these critical interfaces in order to link diffuse subsurface sources of dissolved Hg with increases in surface water dissolved Hg flux measured at the watershed scale. Additionally, our research aimed to determine the isotopic composition of MeHg within these same critical subsurface zones. We directly assessed the isotopic composition of MeHg within biota in order to gain insight into which subsurface sources of inorganic Hg and toxic MeHg are available for bioaccumulation within the EFPC ecosystem. Net fluxes of dissolved Hg along the flow path of EFPC were shown to vary spatially and temporally. In the Upper EFPC, within the Y12 boundary, stream water flux of dissolved Hg consistently decreased between the outfall and the downstream boundary of Y12 (57% ± 29%, 1SD). Within the Upper EFPC, an assessment of Hg isotopic composition suggested that losses were strongly reaction-driven, although isotopic diagnostics did not conform to any known processes. Downstream of Y12, in the upper reach of the Lower EFPC, dissolved Hg fluxes tended to increase during the dormant season (net gain of 11-120%), and decrease during the growing season (net loss of 23% +/- 18%, 1SD). In the downstream-most reach of Lower EFPC, dissolved Hg fluxes increased by 12-108% in 9 out of 10 monthly assessments. Overall, diffuse fluxes from the non-Y12 watershed accounted for 34% (+/- 17%, 1SD) of all dissolved Hg exported during base flow. Within Lower EFPC, an assessment of Hg isotopic composition was consistent with the contribution of diffuse Hg inputs from high-concentration hotspots within riparian floodplains and streambed hyporheic pore water. To investigate remobilization of recalcitrant Hg from legacy sediment sources, we developed procedures that coupled isotopic analysis with sequential extractions of streambed sediment. We found that the proportion of weakly-bound Hg within EFPC streambed sediment was relatively small, but could still account for a large proportion of the annual flux of dissolved Hg from EFPC. These sequential extractions also showed that this weakly-bound Hg fraction could be replenished from the much larger fraction of recalcitrant Hg in sediment. The isotopic composition of these weakly-bound and remobilized recalcitrant Hg fractions within the sediment was consistent with high-concentration dissolved Hg hotspots within hyporheic pore water. Thus, this research provided novel evidence that legacy mercury sources within streambed sediment could provide an ongoing contribution of dissolved Hg to surface waters. Finally, we developed new methods for the direct determination of the MeHg isotopic composition of organisms, which allowed a more direct evaluation of inorganic Hg and MeHg sources accumulating in the food web. We found that fish and aquatic invertebrates in both EFPC and a regional background site obtained inorganic Hg and MeHg from multiple isotopically distinct sources, including sediment, suspended particulates, and periphyton. Photodemethylation was found to be an important reaction influencing MeHg dynamics at both sites. However, the balance of microbial methylation and demethylation processes differed between the two streams, with fractionation resulting from methylation and demethylation processes being relatively in balance within the regional background site, whereas microbial methylation appeared to be dominant over microbial demethylation within the EFPC ecosystem. Broadly, the application of Hg isotopic analysis in this study led to numerous novel insights regarding the biogeochemical cycling of Hg in stream ecosystems. This research project promoted the development of two new approaches, including the coupling of sequential extractions with Hg isotopic analysis to assess remobilization of recalcitrant Hg within sediments, and a new method for the isotopic analysis of MeHg isolated from environmental samples. Both of these efforts represent advances in capacity for the field of mercury isotopic analysis and environmental assessment. Overall, this study demonstrates that the application of Hg stable isotope techniques continues to provide new insights into the biogeochemical cycling of Hg in complex aquatic environments, both within the EFPC and beyond.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2019

Automated thermal extraction-desorption gas chromatography mass spectrometry: A multifunctional tool for comprehensive characterization of polymers and their degradation products

The TED-GC–MS analysis is a two-step method. A sample is first decomposed in a thermogravimetric analyzer (TGA) and the gaseous decomposition products are then trapped on a solid-phase adsorber. Subsequently, the solid-phase adsorber is analyzed with thermal desorption gas chromatography mass spectrometry (TDU-GC–MS). This method is ideally suited for the analysis of polymers and their degradation processes. A new entirely automated system is introduced which enables high sample throughput and reproducible automated fractioned collection of decomposition products. The fractionated collection together with low temperatures reduces the risk of contamination, improves instrumental stability and minimizes maintenance efforts. Through variation of the two main parameters (purge gas flow and heating rate) it is shown how the extraction process can be optimized. By measuring the decomposition products of polyethylene it is demonstrated that compounds with masses of up to 434 Da can be detected. This is achieved despite the low temperature (˜40 °C) of the solid-phase adsorber and the low thermal desorption temperature of 200 °C in the TDU unit. It is now shown that automated TED-GC–MS represents a new flexible multi-functional method for comprehensive polymer analyses. Comparable polymer characterization was previously only achievable through a combination of multiple independent analytical methods. This is demonstrated by three examples focused on practical challenges in materials analysis and identification: The first one is the analysis of wood plastic composites for which the decomposition processes of the polymer and the bio polymer (wood) could be clearly distinguished by fractionated collection using sequential adsorbers. Secondly, a fast quantitative application is shown by determining the weight concentrations of an unknown polyolefin blend through comparison with a reference material. Additionally, the determination of microplastic concentrations in environmental samples is becoming an increasingly important analytical necessity. It is demonstrated that with TED-GC–MS calibration curves showing good linearity for the most important precursors for microplastic, even complex matrix materials (suspended particulate matter) can be successfully analyzed.

36 MATERIALS SCIENCE↗

DOE OSTI2015

Geant4 Computing Performance Benchmarking and Monitoring

Performance evaluation and analysis of large scale computing applications is essential for optimal use of resources. As detector simulation is one of the most compute intensive tasks and Geant4 is the simulation toolkit most widely used in contemporary high energy physics (HEP) experiments, it is important to monitor Geant4 through its development cycle for changes in computing performance and to identify problems and opportunities for code improvements. All Geant4 development and public releases are being profiled with a set of applications that utilize different input event samples, physics parameters, and detector configurations. Results from multiple benchmarking runs are compared to previous public and development reference releases to monitor CPU and memory usage. Observed changes are evaluated and correlated with code modifications. Besides the full summary of call stack and memory footprint, a detailed call graph analysis is available to Geant4 developers for further analysis. The set of software tools used in the performance evaluation procedure, both in sequential and multi-threaded modes, include FAST, IgProf and Open|Speedshop. In conclusion, the scalability of the CPU time and memory performance in multi-threaded application is evaluated by measuring event throughput and memory gain as a function of the number of threads for selected event samples.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Gaming the beamlines—employing reinforcement learning to maximize scientific outcomes at large-scale user facilities

Abstract Beamline experiments at central facilities are increasingly demanding of remote, high-throughput, and adaptive operation conditions. To accommodate such needs, new approaches must be developed that enable on-the-fly decision making for data intensive challenges. Reinforcement learning (RL) is a domain of AI that holds the potential to enable autonomous operations in a feedback loop between beamline experiments and trained agents. Here, we outline the advanced data acquisition and control software of the Bluesky suite, and demonstrate its functionality with a canonical RL problem: cartpole. We then extend these methods to efficient use of beamline resources by using RL to develop an optimal measurement strategy for samples with different scattering characteristics. The RL agents converge on the empirically optimal policy when under-constrained with time. When resource limited, the agents outperform a naive or sequential measurement strategy, often by a factor of 100%. We interface these methods directly with the data storage and provenance technologies at the National Synchrotron Light Source II, thus demonstrating the potential for RL to increase the scientific output of beamlines, and layout the framework for how to achieve this impact.

36 MATERIALS SCIENCE↗

DOE OSTI2019

A cloud computing approach to massively-parallel distributed stochastic optimization with application to geomechanical reservoir characterization

Many search strategies used to optimize parameters during hydrologic model calibration are inherently limited in that simulations must be performed sequentially in order for the algorithm to converge. As a result, the overall time required to reach a solution increases linearly with the number of steps necessary. In contrast, sampling strategies that attempt to approximate the posterior distribution of the parameters do not require the model simulations to be run in sequence and therefore invite the use of a massively parallel approach across heterogeneous computing environments. We apply a cloud computing methodology to this problem where a head node running on Amazon Web Services continuously produces candidate models that are distributed to arbitrary compute environments to run the simulation, in our case Clemson’s Palmetto cluster computer. Thus the time required to obtain a sufficient number of samples to produce an adequate approximation of the posterior distribution of the parameters is limited by the number of available compute sites. Monte Carlo sampling of the posterior space would be duplicative and inefficient, thus we combine Latin hypercube methods with genetic algorithms to ensure that no large regions of the parameter space are left unexplored, and that high probability regions are investigated thoroughly. We independently assess the fit to different types of data sets without the need to construct a single objective function. Sets of Pareto optimal models are particularly valuable when the quality and information content of data is poorly known or conceptual model errors exist in our simulation. We illustrate our approach for a problem where in situ strain data are used to calibrate the parameters of an oil reservoir in the Avant oil field in Oklahoma. We use well injection and shut-in to stress a deep (500m) confined formation, and observe the geomechanical response using borehole strainmeters installed in the shallow subsurface (30m). These data were then used to calibrate the poroelastic properties of the reservoir and caprock along with the geometry of an elliptic channel associated with the formation. Using our approach we are able to identify issues with model error as well as substantial tradeoffs that occur within the parameters.

inversion, stochastic, geomechanics, poroelastic, ↗

DOE OSTI2016

CHIRP-Like Signals: Estimation, Detection and Processing A Sequential Model-Based Approach

Chirp signals have evolved primarily from radar/sonar signal processing applications specifically attempting to estimate the location of a target in surveillance/tracking volume. The chirp, which is essentially a sinusoidal signal whose phase changes instantaneously at each time sample, has an interesting property in that its correlation approximates an impulse function. It is well-known that a matched-filter detector in radar/sonar estimates the target range by cross-correlating a replicant of the transmitted chirp with the measurement data reflected from the target back to the radar/sonar receiver yielding a maximum peak corresponding to the echo time and therefore enabling the desired range estimate. In this application, we perform the same operation as a radar or sonar system, that is, we transmit a “chirp-like pulse” into the target medium and attempt to first detect its presence and second estimate its location or range. Our problem is complicated by the presence of disturbance signals from surrounding broadcast stations as well as extraneous sources of interference in our frequency bands and of course the ever present random noise from instrumentation. First, we discuss the chirp signal itself and illustrate its inherent properties and then develop a model-based processing scheme enabling both the detection and estimation of the signal from noisy measurement data.

42 ENGINEERING↗

DOE OSTI2021

Improved Acquisition and Reconstruction for Wavelength-Resolved Neutron Tomography

Wavelength-resolved neutron tomography (WRNT) is an emerging technique for characterizing samples relevant to the materials sciences in 3D. WRNT studies can be carried out at beam lines in spallation neutron or reactor-based user facilities. Because of the limited availability of experimental time, potential imperfections in the neutron source, or constraints placed on the acquisition time by the type of sample, the data can be extremely noisy resulting in tomographic reconstructions with significant artifacts when standard reconstruction algorithms are used. Furthermore, making a full tomographic measurement even with a low signal-to-noise ratio can take several days, resulting in a long wait time before the user can receive feedback from the experiment when traditional acquisition protocols are used. In this paper, we propose an interlaced scanning technique and combine it with a model-based image reconstruction algorithm to produce high-quality WRNT reconstructions concurrent with the measurements being made. The interlaced scan is designed to acquire data so that successive measurements are more diverse in contrast to typical sequential scanning protocols. The model-based reconstruction algorithm combines a data-fidelity term with a regularization term to formulate the wavelength-resolved reconstruction as minimizing a high-dimensional cost-function. Using an experimental dataset of a magnetite sample acquired over a span of about two days, we demonstrate that our technique can produce high-quality reconstructions even during the experiment compared to traditional acquisition and reconstruction techniques. In summary, the combination of the proposed acquisition strategy with an advanced reconstruction algorithm provides a novel guideline for designing WRNT systems at user facilities.

47 OTHER INSTRUMENTATION↗