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DOE OSTI2021

A coupled discontinuous Galerkin-Finite Volume framework for solving gas dynamics over embedded geometries

Herein, we present a computational framework for solving the equations of inviscid gas dynamics using structured grids with embedded geometries. The novelty of the proposed approach is the use of high-order discontinuous Galerkin (dG) schemes and a shock-capturing Finite Volume (FV) scheme coupled via an hp adaptive mesh refinement (hp-AMR) strategy that offers high-order accurate resolution of the embedded geometries. The hp-AMR strategy is based on a multi-level block-structured domain partition in which each level is represented by block-structured Cartesian grids and the embedded geometry is represented implicitly by a level set function. The intersection of the embedded geometry with the grids produces the implicitly-defined mesh that consists of a collection of regular rectangular cells plus a relatively small number of irregular curved elements in the vicinity of the embedded boundaries. High-order quadrature rules for implicitly-defined domains enable high-order accuracy resolution of the curved elements with a cell-merging strategy to address the small-cell problem. The hp-AMR algorithm treats the system with a second-order finite volume scheme at the finest level to dynamically track the evolution of solution discontinuities while using dG schemes at coarser levels to provide high-order accuracy in smooth regions of the flow. On the dG levels, the methodology supports different orders of basis functions on different levels. The space-discretized governing equations are then advanced explicitly in time using high-order Runge-Kutta algorithms. Numerical tests are presented for two-dimensional and three-dimensional problems involving an ideal gas. The results are compared with both analytical solutions and experimental observations and demonstrate that the framework provides high-order accuracy for smooth flows and accurately captures solution discontinuities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Microscopically based energy density functionals for nuclei using the density matrix expansion. II. Full optimization and validation

Background: Energy density functional methods provide a generic framework to compute properties of atomic nuclei starting from models of nuclear potentials and the rules of quantum mechanics. Until now, the overwhelming majority of functionals have been constructed either from empirical nuclear potentials such as the Skyrme or Gogny forces, or from systematic gradient-like expansions in the spirit of the density functional theory for atoms. Purpose: In this study, we seek to obtain a usable form of the nuclear energy density functional that is rooted in the modern theory of nuclear forces. We thus consider a functional obtained from the density matrix expansion of local nuclear potentials from chiral effective field theory. We propose a parametrization of this functional carefully calibrated and validated on selected ground-state properties that is suitable for large-scale calculations of nuclear properties. Methods: Our energy functional comprises two main components. The first component is a non-local functional of the density and corresponds to the direct part (Hartree term) of the expectation value of local chiral potentials on a Slater determinant. Contributions to the mean field and the energy of this term are computed by expanding the spatial, finite-range components of the chiral potential onto Gaussian functions. The second component is a local functional of the density and is obtained by applying the density matrix expansion to the exchange part (Fock term) of the expectation value of the local chiral potential. We apply the UNEDF2 optimization protocol to determine the coupling constants of this energy functional. Results: We obtain a set of microscopically constrained functionals for local chiral potentials from leading order up to next-to-next-to-leading order with and without three-body forces and contributions from Δ excitations. These functionals are validated on the calculation of nuclear and neutron matter, nuclear mass tables, single-particle shell structure in closed-shell nuclei, and the fission barrier of 240 Pu . Quantitatively, they perform noticeably better than the more phenomenological Skyrme functionals. Conclusions: The inclusion of higher-order terms in the chiral perturbation expansion seems to produce a systematic improvement in predicting nuclear binding energies while the impact on other observables is not really significant. In conclusion, this result is especially promising since all the fits have been performed at the single-reference level of the energy density functional approach, where important collective correlations such as center-of-mass correction, rotational correction, or zero-point vibrational energies have not been taken into account yet.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2017

Combinatorial Platform for Discovery of Nanocrystal-Ink Based Earth Abundant Element PV with Efficiency Greater than 20%

(1) We successfully developed an ultrasonic spray coating system that can be used to deposit thin chalcogenide films with composition gradients. 4 publications under the contract have been published with the instrument. The instrument was used to reveal the effects of intrinsic composition and examine the effects of 25 different dopant elements. Surprisingly, doping with most elements had little to no effect on the quasi-Fermi level splitting of bare films. Ge and Li were explored in depth, and our best devices utilize lithium doping. (2) We developed a new model of absorption coefficients, that when combined with absolute intensity photoluminescence, yield the steady-state quasi-Fermi level splitting and a way to quantify the sub-bandgap absorption. This has resulted in 2 publications on the method, with another in preparation. This is a significant development that should impact other PV technologies. (3) We found that lithium doping has several beneficial effects on CZTSSe. It improves the open-circuit voltage, short circuit current, fill factor, and shunt resistance. By using scanning Kelvin probe microscopy (SKPM) and conductive AFM (along with device measurements, DLPC, and XPS), we discovered that lithium acts to increase the p-type doping in both the grain and grain boundaries (GBs). The effect is stronger in the GBs and changes the direction of the electric field at the GB. In lithium doped devices, an electric field repels minority carrier electrons away from the GB. This resulted in a publication and the fabrication of 11.8% efficient devices from a DMSO-thiourea molecular ink. The mechanism of action is most likely due to the formation of LiCu, which inhibits the formation of the donor defect ZnCu. This reduces compensation and increases the net p-type doping. (4) By alloying with germanium, we have fabricated CZTGSSe devices with the best open-circuit voltage (relative the maximum theoretical open-circuit voltage for the bandgap) for any kesterite solar cell. The Voc/Voc,max is 63%, compared to 58% for the record efficiency cell from hydrazine. The origin of the increased voltage efficiency appears to be related to the conduction band off-set and the suppression of a deep defect (~0.8 eV), most likely due to CuSn, but SnZn or SnCu are also possible. All milestones and go/no-go metrics were met with exception of the device efficiency milestone (15% then 20%). However, under the contract, hydrazine-free CZTSSe device efficiencies increased from 7.2% at the start of the contract to 11.8% upon completion.

14 SOLAR ENERGY↗
DOE OSTI2022

Fluid-Kinetic Coupling: Advanced Discretizations for Simulations on Emerging Heterogeneous Architectures (LDRD FY20-0643)

Plasma physics simulations are vital for a host of Sandia mission concerns, for fundamental science, and for clean energy in the form of fusion power. Sandia's most mature plasma physics simulation capabilities come in the form of particle-in-cell (PIC) models and magnetohydrodynamics (MHD) models. MHD models for a plasma work well in denser plasma regimes when there is enough material that the plasma approximates a fluid. PIC models, on the other hand, work well in lower-density regimes, in which there is not too much to simulate; error in PIC scales as the square root of the number of particles, making high-accuracy simulations expensive. Real-world applications, however, almost always involve a transition region between the high-density regimes where MHD is appropriate, and the low-density regimes for PIC. In such a transition region, a direct discretization of Vlasov is appropriate. Such discretizations come with their own computational costs, however; the phase-space mesh for Vlasov can involve up to six dimensions (seven if time is included), and to apply appropriate homogeneous boundary conditions in velocity space requires meshing a substantial padding region to ensure that the distribution remains sufficiently close to zero at the velocity boundaries. Moreover, for collisional plasmas, the right-hand side of the Vlasov equation is a collision operator, which is non-local in velocity space, and which may dominate the cost of the Vlasov solver. The present LDRD project endeavors to develop modern, foundational tools for the development of continuum-kinetic Vlasov solvers, using the discontinuous Petrov-Galerkin (DPG) methodology, for discretization of Vlasov, and machine-learning (ML) models to enable efficient evaluation of collision operators. DPG affords several key advantages. First, it has a built-in, robust error indicator, allowing us to adapt the mesh in a very natural way, enabling a coarse velocity-space mesh near the homogeneous boundaries, and a fine mesh where the solution has fine features. Second, it is an inherently high-order, high-intensity method, requiring extra local computations to determine so-called optimal test functions, which makes it particularly suited to modern hardware in which floating-point throughput is increasing at a faster rate than memory bandwidth. Finally, DPG is a residual-minimizing method, which enables high-accuracy computation: in typical cases, the method delivers something very close to the $L^2$ projection of the exact solution. Meanwhile, the ML-based collision model we adopt affords a cost structure that scales as the square root of a standard direct evaluation. Moreover, we design our model to conserve mass, momentum, and energy by construction, and our approach to training is highly flexible, in that it can incorporate not only synthetic data from direct-simulation Monte Carlo (DSMC) codes, but also experimental data. We have developed two DPG formulations for Vlasov-Poisson: a time-marching, backward-Euler discretization and a space-time discretization. We have conducted a number of numerical experiments to verify the approach in a 1D1V setting. In this report, we detail these formulations and experiments. We also summarize some new theoretical results developed as part of this project (published as papers previously): some new analysis of DPG for the convection-reaction problem (of which the Vlasov equation is an instance), a new exponential integrator for DPG, and some numerical exploration of various DPG-based time-marching approaches to the heat equation. As part of this work, we have contributed extensively to the Camellia open-source library; we also describe the new capabilities and their usage. We have also developed a well-documented methodology for single-species collision operators, which we applied to argon and demonstrated with numerical experiments. We summarize those results here, as well as describing at a high level a design extending the methodology to multi-species operators. We have released a new open-source library, MLC, under a BSD license; we include a summary of its capabilities as well.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

DART-PFLOTRAN: An ensemble-based data assimilation system for estimating subsurface flow and transport model parameters

Ensemble-based Data Assimilation (EDA), based on the Monte Carlo approach, has been effectively applied to estimate model parameters through inverse modeling in subsurface flow and transport problems. However, implementation of EDA approach involves a complicated workflow that include setting up and executing ensemble forward model simulations, processing observations and model simulation results for parameter updates, and repeat for sequential or iterative EDA. To facilitate the management of such workflow and lower the barriers for adopting EDA-based parameter estimation in subsurface science, we develop a generic software frame-work linking the Data Assimilation Research Testbed (DART) with a massively parallel subsurface FLOw and TRANsport code PFLOTRAN. The new DART-PFLOTRAN leverages both the core data assimilation engines in DART and the computational power afforded by PFLOTRAN. In addition to the standard smoother and filtering options, DART-PFLOTRAN enables an iterative EDA workflow based on the Ensemble Smoother for Multiple Data Assimilation method (ES-MDA) to improve estimation accuracy for nonlinear forward problems. Here, we verify the implementation of ES-MDA in DART-PFLOTRAN using two synthetic cases designed to estimate static permeability and dynamic exchange fluxes across the riverbed, respectively, from continuous temperature measurements made across a depth profile. One-dimensional hydro-thermal simulations are performed in both cases to relate temperature responses with the parameters of interest. In the case of estimating dynamic parameters, we demonstrate the flexibility of DART-PFLOTRAN in automating sequential ES-MDA workflow, which will significantly reduce the time researchers spend on managing complex workflows in similar applications. Both studies yield accurate estimations of the parameters compared to their synthetic truth, while ES-MDA leads to more accurate estimation when a high level of nonlinearity exist between observed responses and unknown parameters. With a code base in Python and Fortran, DART-PFLOTRAN paves the way for applications in large-scale subsurface inverse modeling by automating the complex workflow of sequential ES-MDA that can be executed on various computing platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

High-level coupled-cluster energetics by merging moment expansions with selected configuration interaction

Inspired by our earlier semi-stochastic work aimed at converging high-level coupled-cluster (CC) energetics, we propose a novel form of the CC(P; Q) theory in which the stochastic Quantum Monte Carlo propagations, used to identify dominant higher-than-doubly excited determinants, are replaced by the selected configuration interaction (CI) approach using the perturbative selection made iteratively (CIPSI) algorithm. The advantages of the resulting CIPSI-driven CC(P; Q) methodology are illustrated by a few molecular examples, including the dissociation of F 2 and the automerization of cyclobutadiene, where we recover the electronic energies corresponding to the CC calculations with a full treatment of singles, doubles, and triples based on the information extracted from compact CI wave functions originating from relatively inexpensive Hamiltonian diagonalizations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2016

A Field Study of Wall Furnace Venting and Coincident Exhaust Fan Usage in 16 Northern California Apartments

To inform efforts to improve combustion appliance testing in residential energy efficiency programs, we studied the frequency of coincident fan use and depressurization-induced downdrafting and spillage from atmospherically vented (i.e., natural draft) wall furnaces in airtight apartments. Indoor environmental conditions, heating appliance operation, use of exhaust fans, and cooking with stovetop or oven were monitored for approximately three weeks each in 16 apartment units in two buildings in Northern California. Apartments also were assessed using standard combustion appliance safety test methods and enhanced protocols. Monitoring occurred in February and March of 2016, with heating demand corresponding to 7.3 ± 0.5 heating degree-days at a 65ºF reference temperature. Most of the furnaces spilled combustion products when the apartments were depressurized in the “worst-case” challenge condition of all exhaust fans operating at their highest settings and all windows closed. Many also spilled under less challenging conditions (e.g., with kitchen exhaust fan on low and bathroom fan operating). On average, bathroom exhaust fans were operated 3.9% of monitored minutes (13.5% max), and cooking (burner or kitchen fan operation) occurred 4.6% of minutes (max 13.3%). Event lengths averaged 17 minutes (max 540) and 34 minutes (max 324), respectively. Their coincident operation averaged 0.34% of minutes (max 2.0%), with average event length of 13 minutes (max 92 minutes). This suggests that the operation of apartment units at or near the currently used worst-case challenge condition is quite rare. Wall furnace burners operated an average of 2.8% of minutes (max of 8.9%), with average burner cycle length of 14 minutes (max 162). Coincident bath fan use, cooking and wall furnace operation was very rare, occurring only a handful of times across all apartments. The highest rate was 0.075% of monitored minutes in one apartment, and the longest event length was 12 minutes. Exhaust fan operation in this study may have been more frequent than typical as participants were asked to use an exhaust fan whenever cooking or bathing. Consistent with the low levels of coincident operation, unambiguous spillage occurred in only 4 apartments and the longest event was 5 minutes. The frequency of partial spillage is unknown, owing to a lack of a clear signal from monitored parameters. Downdrafting during exhaust fan use occurred in all 13 of the apartments with relevant data, and 9 of these units had 10 or more events. Exhaust fans also sometimes led to weakened draft, even if downdrafting did not occur. Each unambiguous spillage event identified in the study was immediately preceded by downdrafting. The observed occurrence of downdrafting and spillage may have been impacted in those apartments with the most severe drafting problems (i.e., appliances spilled combustion pollutants under ‘natural’ test conditions), because occupants in these units were instructed to open windows whenever using the kitchen exhaust fan.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

GeoThermalCloud: Cloud Fusion of Big Data and Multi-Physics Models using Machine Learning for Discovery, Exploration, and Development of Hidden Geothermal Resources

The primary goals of this project are exploring hidden geothermal resources in the U.S.A. and designing profitable enhanced geothermal systems (EGS). Many processes and parameters control geothermal exploration and energy production from geothermal fields. Diverse datasets (e.g., geology, geochemistry, geophysics, satellite, airborne geophysics) are available to help characterize subsurface geothermal conditions. Sparse and multi-scale characteristics of these datasets prohibit properly leveraging these datasets for geothermal exploration and profitable EGS design. Recent advancements in machine learning (ML) promise to resolve these issues. The tremendous challenges and risks of geothermal exploration and production bring the demand for novel ML methods and tools that can (1) analyze large field datasets, (2) assimilate model simulations (large inputs and outputs), (3) process sparse datasets, (4) perform transfer learning (between sites with different exploratory levels), (5) extract hidden geothermal signatures in the field and simulation data, (6) label geothermal resources and processes, (7) identify high-value data acquisition targets, and (8) guide geothermal exploration and production by selecting optimal exploration, production, and drilling strategies. To address these necessities, ML-based geothermal resources exploration and enhanced geothermal systems (EGS) design tools have been developed. The exploration tool is called GeoThermalCloud and EGS design tool is called GeoDT-ML. GeoThermalCloud (https://github.com/SmartTensors/GeoThermalCloud.jl) utilizes a LANL unsupervised ML platform called SmartTensors (https://tensors.lanl.gov/) to automate data analyses and interpretations by extracting hidden signatures to identify geothermal prospects. Also, it enables the identification of critical measurements needed to identify geothermal resource signatures. Alternatively, GeoDT-ML (https://github.com/SmartTensors/GeoThermalCloud.jl/tree/master/EGS) is an ML-based alternative to GeoDT (https://github.com/GeoDesignTool/GeoDT.git), a fast, simplified multi-physics solver to evaluate EGS project designs in uncertain geologic systems. GeoDT-ML leverages recent advances in deep learning and high-performance computing. It is a faster and simpler version of GeoDT. To make this project a success, we used capabilities of LANL, PNNL, Google, Stanford, and Julia Computing. We analyzed eight datasets of the U.S.A. using GeothermalCloud and demonstrated potential highly prospective geothermal resources and identified key factors defining highly prospective sites. The first data set includes 44 locations in southwest New Mexico and 18 geological, hydrogeological, geophysical, geothermal, geochemical attributes. We defined low- and medium-temperature hydrothermal systems and discovered a new highly prospective site. The second data set analyzed 18 shallow water chemistry attributes at 14,342 locations in the Great Basin. It demarcated modestly, moderately, and highly prospective sites including key attributes for each type of prospectivity. The third data set analyzed Utah FORGE data including satellite (InSAR), geophysical (gravity, seismic), geochemical, and geothermal attributes. Here, we performed prospectivity analysis to identify future drilling locations using geological, geochemical, and geophysical attributes. Maps of temperature at depth and heat flow are constructed based on the available data. Prospectivity maps were generated, and drilling locations were proposed for future geothermal field exploration. The fourth data set analyzed 21 attributes at 120 locations in Tularosa Basin, New Mexico; data comes from past play fairway analyses in this region. ML analyses identified geothermal signatures associated with modestly, moderately, and highly hydrothermal systems. We also defined dominant attributes and spatial distribution of the geothermal signatures. The fifth, sixth, seventh, and eighth datasets include Tohatchi Springs, New Mexico, Hawaii, Brady site, Nevada, and EGS Collab, respectively. Moreover, we coupled GeothermalCloud and magnetotellurics data to pinpoint drilling locations for developing geothermal projects in the Tularosa Basin, New Mexico. GeothermalCloud found potential prospective locations for geothermal resources near White Sands Missile Range and McGregor Range at Fort Bliss. Magnetotellurics data determined the potential depth (~1800m) of geothermal prospects at McGregor Range based on apparent resistivity structures/layers in the subsurface. The McGregor Range consists of three resistivity layers and two resistivity structures. Magnetotellurics data also helps identify that the western portion of the McGregor Range has thick and low-resistivity earth materials. The low resistivity to the west is most likely for a fault system. Assuming temperature is consistent with a geothermal reservoir, the west-central part of the McGregor Range has the highest geothermal potential because of the increase in porosity and associated permeability attributed to the interpreted fault system. Also, we devised a coupling strategy between a process model and GeothermalCloud to characterize hydrogeological conditions and geothermal conditions, respectively. The process model characterizes hydrogeological and geothermal conditions on highly prospective geothermal sites provided by GeothermalCloud. We developed a physics-informed neural network (PINN) version of the Burns equation that can be easily coupled with GeothermalCloud. Furthermore, we performed an optimal design decision maximizing the economic value of an EGS power plant. This study optimized the range of well spacing between injection and production wells maximizing net present value in dollars (NPV). For this task, we used the GeoDT to simulate the Utah FORGE EGS development cycle from the initial well design to the end of production. Next, we accomplished another crucial task, which is predicting permeability of geothermal reservoirs. Predicting permeability of geothermal reservoirs is a non-trivial task because of huge computational runtime of simulation and lack of measurements. To avoid these limitations, we used easy-to-measure chemical concentrations in the subsurface as measurement data and convolutional neural network based ML model of a high-fidelity model. Next, we predicted permeability using Markov chain Monte Carlo simulation. We found that Markov chain Monte Carlo simulation predicts permeability with a high certainty if the prediction zone in the simulation area has chemical concentration data. Finally, we analyzed the DOE funded INGENIOUS and GeoDAWN projects data. For discovering hidden geothermal systems in the Great Basin, the INGENIOUS project accumulated old data, collected new data, and released them in 2022. The dataset includes a total of 24 geological, geophysical, and geochemical attributes. Data resolution and scale significantly vary prohibiting an appropriate usage. To avoid such limitations, we brought all data in the same resolution and scale by applying the inverse distance weighting interpolation technique for predicting data in unsampled locations. Subsequently, we analyzed LiDAR data of the GeoDAWN project. We received data in tiles format. The DOE’s overarching goal is to use ML on LiDAR data for finding favorable geological structures (e.g., step up faults in Brady, Nevada). To serve the purpose, we need to label favorable geologic structures that correspond to LiDAR data. We wrote an algorithm to label the LiDAR data with the favorable geologic structures.

15 GEOTHERMAL ENERGY↗
DOE OSTI2024

Commissioning of the Mu2e tracker DAQ, planning for the Vertical Slice Test and pre-pattern recognition studies

The primary objective of the Mu2e experiment at Fermilab is to search for the neutrino-less coherent $\mu \rightarrow e$ conversion in the field of an aluminum nucleus ($\mu^- \text{Al} \rightarrow e^- \text{Al}$). The signature of this process is a monochromatic Conversion Electron (CE) with an energy of approximately 104.97 MeV \cite{bartoszek2015mu2e}. Within the Standard Model (SM), the branching ratio for this process, including neutrino masses and oscillation, is expected to be less than $\mathcal{O}(10^{-50})$. This value is far beyond current experimental capabilities. However, models of physics beyond the SM predict much higher relative rates, approaching an observable level. The SINDRUM II experiment set an upper limit on muon conversion at $7 \times 10^{-13}$ (90\% CL) on Au target \cite{SINDRUMII:2006dvw}, and the Mu2e collaboration aims to improve this limit by four orders of magnitude. Observing this process would provide a clear evidence of physics beyond the Standard Model. A brief discussion of the theoretical and experimental aspects is provided in Chapter \ref{intr}. Mu2e adopts a sophisticated experimental setup to achieve its goals, further described in Chapter \ref{mu2echapter}. The central part of the Mu2e detector is the tracker, that consists of 18 tracking stations. The tracker must provide excellent momentum resolution, approximately 1 MeV/c, to distinguish the monochromatic CE signal from the background. To minimize the energy losses, a straw tube tracker will be used \cite{bobbb}. Chapter \ref{chaptertrk} provides an overview of the straw tracker design and its working principles. This Thesis presents a comprehensive study of the Mu2e tracker, covering complementary aspects from initial commissioning to optimization and first steps of the calibration processes. My work at Fermilab has been focused on the complete Data Acquisition (DAQ) testing from both hardware and software perspectives. I was involved in the commissioning of the Mu2e DAQ system and the Vertical Slice Test (VST) of the tracker. The VST encompasses the entire testing chain, from the straws to the readout, and to processed data on disk. I was also focused on the offline analysis, especially on pre-pattern recognition studies, to explore the best methods for identifying $\delta$-electrons during the data taking. Chapter \ref{commissioning} details the commissioning of the tracker DAQ system, emphasizing the importance of understanding of the readout process before the data acquisition. This includes validating the readout logic and firmware through Monte Carlo simulations to confirm functionality and buffering, monitoring the quality of the data from the tracker preamplifiers and front-end electronics, and assessing overall DAQ performance to ensure reliability during future calibration and data-taking. Chapter \ref{planning} discusses the initial steps towards the tracker calibration. The ultimate goal is to perform a time calibration of the first assembled station of the tracker using cosmic muons, aiming for a longitudinal hit position resolution better than 4 cm. This involves determining the signal propagation times and channel-to-channel delays. I performed a Monte Carlo study to determine the impact of the station orientation on the quality of the calibration, in particular on the cosmic track reconstruction, focusing on potential biases that could arise. These studies provide essential insights into the operation, optimization, and calibration of the Mu2e tracker system. Given the high data volume expected during Mu2e operations, estimated at approximately 7 PBytes per year, optimizing memory usage and minimizing CPU consumption are critical. A significant challenge lies in effectively flagging $\delta$-electron hits, which are the primary source of hits in the tracker, without compromising the efficiency of CE hit detection and track reconstruction. A detailed study of pre-pattern recognition and a thorough comparison of two $\delta$-electron flagging algorithms is provided in Chapter \ref{delta}. In Chapter \ref{conclusions}, the findings are concisely summarized, offering a comprehensive synthesis of the research and emphasizing the key insights derived from this study.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

The role of international collaboration in the United States geologic disposal research program

Abstract. More than a decade ago, the United States disposal program discontinued all research activities focused on the unsaturated fractured tuff formation at Yucca Mountain as the geologic disposal site for spent fuel and high-level radioactive waste. A new research and development (R&D) program was initiated to provide a sound technical basis for alternative disposal options across clay, crystalline, and salt rocks. The goals of this broad program were (and still are) to (1) increase confidence in the robustness of generic disposal concepts, (2) develop the science and engineering tools needed to support disposal concept implementation, and (3) conduct R&D on the direct disposal of existing dual-purpose (storage and transportation) canisters. Recognizing the benefits of international collaboration toward the common goal of safely and efficiently managing the back end of the nuclear fuel cycle, the program emphasized international cooperation as an effective strategy for sharing information and knowledge. In a multi-laboratory effort coordinated by Lawrence Berkeley National Laboratory, the United States Department of Energy (DOE) program established formal and informal cooperation partnerships with several international initiatives and institutions and developed a number of collaborative R&D activities in important research areas, such as engineered barrier integrity, near-field perturbations, radionuclide transport, performance assessment, and methods for characterization and monitoring of engineered and natural barriers. This presentation gives an overview of these R&D activities, with a specific focus on activities that improve our current understanding of the coupled thermal–hydrological–mechanical–chemical (THMC) processes occurring in engineered and natural barriers. We start with a brief review of selected international collaboration initiatives and then describe a few specific collaboration projects. We focus specifically on such studies that use experimental data sets provided by international research cooperation for joint modeling work to increase confidence in performance-relevant predictions of coupled processes. Overall, the focus on international collaboration has allowed deep engagement of US researchers with the international waste management R&D community in terms of best practices, new scientific advances, state-of-the-art simulation tools, new monitoring and performance confirmation approaches, and lessons learned. The joint R&D with international researchers, worldwide sharing of knowledge and experience, and access to relevant data and experiments from a variety of host rocks have helped our researchers to significantly improve their understanding of the current technical basis for disposal in a range of potential host rock environments. International collaboration also provides ample opportunity for training and educating junior staff that are well suited to move the United States disposal research program forward into the next decades, a promising avenue for developing a next-generation workforce of disposal scientists.

Birkholzer, Jens T.↗
DOE OSTI2021

Weatherization Assistant NEAT/MHEA

The software provides a measure selection technique indicating cost effective retrofit activities that can be applied to a home using a standard Savings to Investment Ratio (SIR). Users must provide an input file describing the characteristics of the home to be evaluated. The software takes the input data provided and calculates energy savings and cost savings predicted for a standard set of measures given the input parameters. The Weatherization Assistant computes estimates of pre-retrofit whole building space heating and cooling energy consumptions based on the house description data supplied by the user. The consumptions are computed using a monthly heating and cooling variable base degree-day method by algorithms similar to those developed for the CIRA program [LBL, 1982]. The building consumptions are needed in computing the energy savings from measures affecting the efficiencies of the heating and cooling equipment. Weatherization Assistant then computes the energy savings and costs for each individual measure applicable to the building described as if it were the only measure installed in the house. From these energy savings, a discounted dollar savings over the life of each measure is computed. The ratio of this dollar savings to the cost of installing the measure, the "savings-to-investment ratio" (SIR), is used in an initial ranking of the measures' effectiveness. The "interacted" savings and SIR of measures are then determined assuming the measures are added to the house collectively, in order of their ranking, e.g., the second ranked measure is installed in the house initially described by the user after having been modified by the first ranked measure. If this second-ranked measure's updated SIR is greater than a user-defined limit, the measure is left implemented, else it is removed so that the next measure's effectiveness is not dependent on it. The choice between two mutually exclusive measures (such as different levels of insulation) is made on the basis of their "net present value" (NPV), the difference of life-time savings and installation cost, rather than their SIR. This has been shown to be the more correct criterion on which to base the selection between two measures, both of which cannot be installed. The audit computes and reports to the user the energy savings, discounted dollar savings, installation cost, and SIR for each measure considered cost-effective. For those with SIR greater than the user-designated cutoff, a materials list gives the material name, type, and quantity required for installation of the measure. Weatherization Assistant permits entry of pre-retrofit billing data for gas or electrically heated homes or homes with electric air-conditioning. The user may then make the decision to have the savings of the measures adjusted to reflect the difference in billed consumption and that predicted by the program.

Gettings, Michael↗
DOE OSTI2017

Relativistic distorted-wave collision strengths for Δn = 0 transitions in the 67 Li-like, F-like and Na-like ions with 26 ≤ Z ≤ 92

We calculated relativistic distorted-wave collision strength for all possible Δn=0 transitions, where n denotes the valence shell of the ground level, in the 67 Li-like, F-like and Na-like ions with Z in the range 26 ≤ Z ≤92. This choice produces 3 transitions with n=2 in the Li-like and F-like ions, and 10 transitions with n=3 in the Na-like ions. Moreover, for the Li-like and F-like ions, the calculations were made for the six final, or scattered, electron energies E'=0.008,0.04,0.10,0.21,0.41, and 0.75, where E' is in units of Z$2\atop{eff}$ Ry with Z eff = Z- 1.66 for Li-like ions and Z eff = Z- 6.667 for F-like ions. For the Na-like ions, the calculations were made for the six final electron energies E'=0.0025,0.015,0.04,0.10,0.21, and 0.40, with Z eff = Z- 8.34. In the present calculations, an improved “top-up” method, which employs relativistic plane waves, was used to obtain the high partial-wave contribution for each transition, in contrast to the partial-relativistic Coulomb–Bethe approximation used in previous works by Zhang, Sampson and Fontes [H.L. Zhang, D.H. Sampson, C.J. Fontes, At. Data Nucl. Data Tables 44 (1990) 31; H.L. Zhang, D.H. Sampson, C.J. Fontes, At. Data Nucl. Data Tables 48 (1991) 25; D.H. Sampson, H.L. Zhang, C.J. Fontes, At. Data Nucl. Data Tables 44 (1990) 209]. In those previous works, collision strengths were also provided for Li-, F- and Na-like ions, but for a more comprehensive set of transitions. Finally, the collision strengths covered in the present work should be more accurate than the corresponding data given in those previous works and are presented here to replace those earlier results.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2017

Identification of sperm mRNA biomarkers associated with testis injury during preclinical testing of pharmaceutical compounds

The human testis is sensitive to toxicant-induced injury but current methods for detecting adverse effects are limited, insensitive and unreliable. Animal studies use sensitive histopathological endpoints to assess toxicity, but require testicular tissue that is not available during human clinical trials. More sensitive and reliable molecular biomarkers of testicular injury are needed to better monitor testicular toxicity in both clinical and preclinical. Adult male Wistar Han rats were exposed for 4 weeks to compounds previously associated with testicular injury, including cisplatin (0, 0.2, 0.3, or 0.4 mg/kg/day), BI665915 (0, 20, 70, 100 mg/kg/d), BI665636 (0, 20, 100 mg/kg/d) or BI163538 (0, 70, 150, 300 mg/kg/d) to evaluate reproductive toxicity and assess changes in sperm mRNA levels. None of the compounds resulted in any significant changes in body, testis or epididymis weights, nor were there decreases in testicular homogenization resistant spermatid head counts. Histopathological evaluation found that only BI665915 treatment caused any testicular effects, including minor germ cell loss and disorganization of the seminiferous tubule epithelium, and an increase in the number of retained spermatid heads. A custom PCR-array panel was used to assess induced changes in sperm mRNA. BI665915 treatment resulted in a significant increase in clusterin (Clu) levels and decreases in GTPase, IMAP family member 4 (Gimap4), prostaglandin D2 synthase (Ptgds) and transmembrane protein with EGF like and two follistatin like domains 1 (Tmeff1) levels. Correlation analysis between transcript levels and quantitative histopathological endpoints found a modest association between Clu with retained spermatid heads. These results demonstrate that sperm mRNA levels are sensitive molecular indicators of testicular injury that can potentially be translated into a clinical setting. - Highlights: • Testing of pharmaceutical compounds identified altered sperm mRNA transcripts. • Altered sperm mRNAs correlated with changes in testicular histopathology. • Sperm mRNAs can potentially be used as surrogates to assess testis injury.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Low energy scattering cross section ratios of N 14 ( p , p ) N 14

Background: The slowest reaction in the first CNO cycle is 14 N(p, γ) 15 O, therefore its rate determines the overall energy production efficiency of the entire cycle. The cross section presents several strong resonance contributions, especially for the ground-state transition. Some of the properties of the corresponding levels in the 15 O compound nucleus remain uncertain, which affects the uncertainty in extrapolating the capture cross section to the low energy range of astrophysical interest. Purpose: The 14 N (p, γ) 15 O cross section can be described by using the phenomenological R matrix. Over the energy range of interest, only the proton and γ-ray channels are open. Since resonance capture makes significant contributions to the 14 N (p, γ) 15 O cross section, resonant proton scattering data can be used to provide additional constraints on the R-matrix fit of the capture data. Methods: A 4 MV KN Van de Graaff accelerator was used to bombard protons onto a windowless gas target containing enriched 14 N gas over the proton energy range from E p =1.0 to 3.0 MeV. Scattered protons were detected at θ lab = 90,120°,135 °,150°, and 160° using ruggedized silicon detectors. In addition, a 10 MV FN Tandem Van de Graaff accelerator was used to accelerate protons onto a solid Adenine ( C 5 H 5 N 5 ) target, of natural isotopic abundance, evaporated onto a thin self-supporting carbon backing, over the energy range from E p = 1.8 to 4.0 MeV. Scattered protons were detected at 28 angles between θ lab = 30. 4° and 167. 7° by using silicon photodiode detectors. Results: Relative cross sections were extracted from both measurements. While the relative cross sections do not provide as much constraint as absolute measurements, they greatly reduce the dependence of the data on otherwise significant systematic uncertainties, which are more difficult to quantify. The data are fit simultaneously using an R-matrix analysis and level energies and proton widths are extracted. Even with relative measurements, the statistics and large angular coverage of the measurements result in more confident values for the energies and proton widths of several levels; in particular, the broad resonance at E c.m. = 2.21 MeV, which corresponds to the 3/2 + level at E x = 9.51 MeV in 15 O. In particular, the s - and d -wave angular-momentum channels are separated. Conclusion: The relative cross sections provide a consistent set of data that can be used to better constrain a full multichannel R-matrix extrapolation of the capture data. It has been demonstrated how the scattering data reduce the uncertainty through a preliminary Monte Carlo uncertainty analysis, but several other issues remain that make large contributions to the uncertainty, which must be addressed by further capture and lifetime measurements.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

Digital Twin Model for Advanced Manufacture of a Rotating Detonation Engine Injector

A digital twin material model (DTMM) of an additive manufacturing (AM) process was created to advance the state of the art in rotating detonation engine (RDE) injector design. Current RDE injectors are designed with large pressure drops, enabling a stable and repeatable combustion process. However, this comes at the cost of system efficiency. For the technology to transition to commercial fossil-based power generation, it is important to develop injectors with reduced flow losses. Low-loss injectors are difficult to design and manufacture with conventional manufacturing techniques. AM enables new design options, but the AM manufacturing process must be thoroughly understood to result in a robust design. A DTMM provides the necessary insight by defining the cause-effect relationships between process parameters, microstructure features, and properties. Therefore, a DTMM to support the design and manufacturing process was developed and applied to the design of a new additively manufactured low-loss injector. The injector combustion behavior was characterized through hot-fire tests, and mechanical performance was compared to the DTMM predictions. The two project goals were the successful development of the DTMM and the demonstration of an improved RDE injector design. The RDE injector design and DTMM developments occurred on parallel but dependent paths. The injector was designed to reduce pressure drop by increasing the cross-sectional flow area ratio between the injector air passages and the combustor annulus. This resulted in less structural material, raising the concern that thin members would be susceptible to high-cycle fatigue (HCF) under the periodic loading inherent to an RDE. It was most important for the DTMM to predict behavior in these features; therefore, the injector design concept guided the material thicknesses used in fatigue tests. The DTMM development started by manufacturing a series of coupons over the range of possible AM process variations. A design-of-experiment approach was used to select which process variable combinations gave the most efficient coverage relevant to the injector design space. The microstructure in each of these coupons was characterized, and then computational methods were used to create a numerical model of the correlation between process variables and microstructure. Next, a set of HCF samples were tested to calibrate existing models that map microstructure to HCF performance. Together, these two links formed the DTMM that calculates HCF behavior from AM process variables. Two injector prototypes were additively manufactured. The first injector design strategy aggressively pursued low-loss performance by substantially increasing the oxidizer flow area. The combination of manufacturing lead times and the fatigue testing schedule meant that the DTMM was not available when building this first prototype. Therefore, its process parameters were chosen based on a manual review of the available coupon data. This prototype was built successfully and evaluated in 58 combustion tests. Sustained detonation was achieved with remarkably reduced pressure loss, and some tests even displayed pressure loss characteristics similar to conventional gas turbine combustors. This achieved the project goal of improving RDE injector design. The second injector was manufactured according to the optimized parameters predicted by the DTMM. The flow area modifications of this injector were less aggressive than the first injector since demonstrating low pressure loss was not an objective of the second hot-fire test series. Rather, the test objective was to cause high cycle fatigue failure in the part due to periodic loading from the rotating detonation wave. The observed number of cycles to failure was to be compared to the number predicted by the DTMM and thereby assess the utility of the DTMM in component design. However, the required level of vibration was not obtained during combustion. Therefore, high cycle fatigue was not experienced in the hot-fire tests of the second injector. Fatigue data was obtained by further testing the second injector in a conventional HCF test apparatus. The injector demonstrated HCF strength above the DTMM prediction. In fact, it did not fail and testing was only discontinued due to reaching the end of the period of performance. This points to some success in the project’s primary goal of successfully developing and applying the DTMM to a component design. Implementing the DTMM recommendations for optimal processing parameters led to a part with acceptable properties. The DTMM was also shown to be an efficient correlator of data and to provide insight into the relationship between process settings, microstructure, and property performance. However, the failure of the DTMM prediction to match the experimental result of the injector fatigue test also points to the need to include significantly more data in the model development. In this project, coupons made with identical processing parameters exhibited drastically different properties from each other and from the injector part, which clearly influences the accuracy of a model that predicts performance based on parameters. Uncertainties in the build process must be quantified to develop more robust models. A denser and broader matrix of coupon process and geometry variations, several repeated builds of every point, more in-situ build process measurements, and direct observation of tensile and HCF sample microstructure (as opposed to separate microstructure specimens) are recommendations to improve future AM modeling efforts.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2024

Interrelationships among methods of estimating microbial biomass across multiple soil orders and biomes: Supporting data

This dataset contains environmental and soil measurements from 18 different locations across the globe including the SPRUCE experiment site and multiple sampling depths, with 17 of these locations having samples processed between 2012-2013 and one location (SPRUCE) collected in 2021 and processed in 2022. Environmental measurements include: mean annual temperature, mean annual precipitation, and 30-day presampling temperature. Soil physicochemical measurements include: particle size analysis (PSA), pH, gravimetric moisture content (GMC), bulk soil carbon (C) and nitrogen (N), total organic C and N, C:N ratio, and dissolved organic carbon (DOC). Soil biological measurements include: microbial biomass carbon (MBC) measured through chloroform fumigation extraction (CFE), gene copy numbers (GCN) of bacteria, fungi, and archaea measured through quantitative polymerase chain reaction (qPCR), DNA yield measured through Nanodrop spectrophotometry, and phospholipid fatty acids (PLFA) of bacteria and fungi measured through PLFA analysis. This data set contains one file in comma separate (*.csv) format.

archaea gene copy number↗
DOE OSTI2021

An automated procedure built on MTEX for reconstructing deformation twin hierarchies from electron backscattered diffraction datasets of heavily twinned microstructures

Here we present a set of algorithms built on the MTEX and MATLAB graph toolboxes for automatic reconstruction of deformation twin hierarchies from Electron Backscatter Diffraction (EBSD) datasets with a focus on developing methods for heavily twinned microstructures (twin fractions >0.5). The algorithms address key issues arising at large strains, mainly: missing twin relationships, grouping of heavily deformed grain fragments into families of similar orientation originating from a single initial grain, identification of parent fragments for large twin volume fractions, and classification of families having twin relationships with multiple families. To facilitate the development of these algorithms, large-grained ultra-high purity α-Ti deformed in compression along two directions is investigated. Graphs are utilized to handle non-local geometric merging and to represent relationships throughout the reconstruction process. When determining if a grain fragment is from the undeformed microstructure, the combined metrics of the fragment's orientation volume fraction in the initial texture and the directed graph centrality measure of out-closeness (the number of nodes reached in a graph from a given node) are essential. To address automation in reconstructing the sequence of twinning and relating fragments originating from a single grain in the initial microstructure, the twin family tree is formulated as a minimum spanning tree emanating from the initial grain family. A scheme constructing the distances associated with twin relationship comprising the spanning tree is developed, and a novel quasi-directional Prim spanning tree algorithm is used to determine the twin family tree. The procedure is demonstrated to significantly improve the level of automation in reconstructing twin hierarchies in heavily twinned microstructure compared to other methodologies in literature. The procedure can readily be applied to analyses of twinning in metals, as well as provide an approach for routinely extracting twin statistics at larger deformation levels than previously possible. Significantly, the procedure is demonstrated to be capable of identifying third generation twinning in α-Ti microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Dynamically weighted multireference perturbation theory: Combining the advantages of multi-state and state-averaged methods

In this paper, we introduce two new approaches to compute near-degenerate electronic states based on the driven similarity renormalization group (DSRG) framework. The first approach is a unitary multi-state formalism based on the DSRG (MS-DSRG), whereby an effective Hamiltonian is built from a set of state-specific solutions. The second approach employs a dynamic weighting parameter to smoothly interpolate between the multi-state and the state-averaged DSRG schemes. The resulting dynamically weighted DSRG (DW-DSRG) theory incorporates the most desirable features of both multi-state approaches (ability to accurately treat many states) and state-averaged methods (correct description of avoided crossings and conical intersections). We formulate second-order perturbation theories (PT2) based on the MS- and DW-DSRG and study the potential energy curves of LiF, the conical intersection of the two lowest singlet states of NH 3 , and several low-lying excited states of benzene, naphthalene, and anthracene. The DW-DSRG-PT2 predicts the correct avoided crossing of LiF and avoids artifacts produced by the corresponding state-specific and multi-state theories. Excitation energies of the acenes computed with the DW-DSRG-PT2 are found to be more accurate than the corresponding state-averaged values, showing a small dependence on the number of states computed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗