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DOE OSTI2025

Simulating Muon Capture with MARLEY

Core-collapse supernovae are intense sources of tens-of-MeV neutrinos. Each explosion releases about 1058 neutrinos in a burst lasting tens of seconds. As neutrinos are weakly interacting, they escape more easily compared to photons, allowing them to be observed before the light signal. Understanding low-energy neutrino interactions with argon will be critical for future supernova measurements with the Deep Underground Neutrino Experiment (DUNE). Interpreting future DUNE supernova neutrino data will require a high-quality model of O(10 MeV) neutrino interactions, but the dominant charged-current cross section has never been measured at these low energies for an argon target. We exploit the theoretical similarities between muon capture, which has been measured, and antineutrino scattering to test MARLEY (Model of Argon Reaction Low Energy Yields), the simulation code used by DUNE to model supernova neutrino reactions. I implemented C++ code to add a muon capture mode to MARLEY and compared the predictions of the new simulation to measured isotope branching ratios of the residual nuclei. The preliminary branching ratios predicted by MARLEY running in muon capture mode demonstrate promising baseline performance when compared to experimental data and are expected to improve with further model adjustments.

Wong, Baker Louie↗

DOE OSTI2024

Bridging Hydrological Ensemble Simulation and Learning Using Deep Neural Operators

Ensemble-based simulation and learning (ESnL) has long been used in hydrology for parameter inference, but computational demands of process-based ESnL can be quite high. To address this issue, we propose a deep neural operator learning approach. Neural operators are generic machine learning algorithms that can learn functional mappings between infinite-dimensional spaces, providing a highly flexible tool for scientific machine learning. Our approach is built upon DeepONet, a specific deep neural operator, and is designed to address several common problems in hydrology, namely, model parameter estimation, prediction at ungaged locations, and uncertainty quantification. Here we demonstrate the effectiveness of our DeepONet-based workflow using an existing large model ensemble created for an eastern U.S. watershed that is instrumented with 10 streamflow gages. Results suggest DeepONet achieves high efficiency in learning an ML surrogate model from the model ensemble, with the modified Kling-Gupta Efficiency exceeding 0.9 on holdout test sets. Parameter inference, carried out using the trained DeepONet surrogate model and genetic algorithm, also yields robust results. Additionally, we formulate and train a separate DeepONet model for physics-informed, seq-to-seq streamflow forecasting, which further reduces biases in the pre-trained DeepONet surrogate model. While this study focuses primarily on a single watershed, our approach is general and may be extended to enable learning from model ensembles across multiple basins or models. Thus, this research represents a significant contribution to the application of hybrid machine learning in hydrology.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Milestone 1.2.10: Steady-state H 2 “roll over” point data for aluminum alloys 1100 and 6061

Extended (> 50 years) dry storage is being evaluated by the U.S. Department of Energy (DOE) for the disposition of ~ 18 metric tons of aluminum-clad spent nuclear fuel (ASNF). Transition of the current ASNF inventory into dry storage—using the standard DOE canister—necessitates a rigorous, predictive understanding of the long-term physical and chemical factors that may influence the integrity of the proposed storage canister, including radiolytic molecular hydrogen (H 2 ) generation. Current model predictions employ initial radiolytic yields of H 2 , the values of which change as the cladding’s H 2 -precursor inventory is depleted and H 2 itself becomes progressively more involved in radiolytic and surface dissociation processes. Consequently, the absorbed radiation dose that this steady-state H 2 yield corresponds to is essential for the evaluation and improvement of model predictions. Here, we report our findings on the long-term generation of H 2 from the gamma irradiation (≤ 36 MGy) of corroded AA1100 and AA6061 coupons in helium environments at ambient temperature and ~ 50% RH. Our findings show that AA1100 systems reached steady-state by ~ 36 MGy, while higher doses were necessary for AA6061 systems. This discrepancy was attributed to the AA6061 coupons developing a thicker corrosion layer that led to the trapping of H 2 and its precursors, and potentially additional chemistries, ultimately delaying the depletion of H 2 precursors and the system’s “roll over” point. Further, current model predictions—based on previous AA1100 data—do not show steady-state attainment until above 120 MGy, which is not the case for the AA1100 data collected here. Consequently, the new alloy dependent data presented here are important for the continued improvement of predictive computer models for evaluating the feasibility of extended storage of ASNF in helium backfilled canisters.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗

DOE OSTI2023

Cosmic Supernova Neutrino and Gamma-Ray Backgrounds in the MeV Regime

Cosmic multimessenger backgrounds include relic diffuse components created in the early universe and contributions from individual sources. Here, we study both type Ia supernovae (SNe Ia) and core-collapse supernovae (CCSNe) contributions to the diffuse neutrino and gamma-ray backgrounds in the MeV regime referred to as DSNB and DSGB respectively. We show that the diffuse SN Ia background is 10 6 times lower (for $\overline{v}_e$) than the CCSN background making it negligible. Our predicted DSNB $\overline{v}_e$ flux at earth in the 19.3–32 MeV regime is 0.36 ν cm -2 s -1 . We also find that the DSNB flux in the energy range from 11.3 to 32 MeV varies by +29% with a change in the SFRD model from Madau & Fragos which yielded a minimum predicted flux, to the extragalactic background light reconstruction model (maximum predicted flux). The diffuse SN Ia gamma-ray background and its dependence on the progenitor supernova delay time distribution are also evaluated. Furthermore, we address the origin of the CGB (Cosmic Gamma-ray Background) in the 0.1–7 MeV regime by adding contributions from sources such as SNe Ia, CCSNe, radio-quiet Active Galactic Nuclei, Flat spectrum radio quasars (FSRQs) and Neutron star—neutron star mergers. We find that our modeled background (including uncertainties) matches the observed CGB above 1.0 MeV, but is a factor ≈2 lower than the observed flux in the 0.1–1.0 MeV range, highlighting the need for future MeV missions to establish the CGB spectrum more reliably, and to possibly identify additional sources or even source classes in the underexplored MeV band.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

BETO 2021 Peer Review - 3.4.2.302 - Process Scale Up for Production Environments

This project supports the Department of Energy's Bioenergy Technology Office mission of transitioning bioenergy technologies to market by de-risking integration, developing scaling relations and modeling that de-risks scale up, and providing a feedback loop with lower TRL projects to understand process and technology fundamentals. Two objectives were pursued during this merit cycle. First, the Thermal and Catalytic Process Development Unit (TCPDU), a 0.5 ton/day pilot plant, was used to validate kinetic models developed by the Consortium for Computational Physics and Chemistry (CCPC). Validating the models accurately predict product yields and composition is a first step toward de-risking a common industry failure - taking too large of steps between scales. In addition, these models help with design and troubleshooting of new unit operations for the TCPDU. The second objective was to conduct the FY22 verification campaign around ex situ CFP. The primary challenge of this effort was the coordination of multiple national labs and projects. This was overcome by a dedicated leadership role and effective communication strategy between the projects. Currently the project is undergoing a pivot based on a stagegate decision to not conduct the verification campaign in the TCPDU and it is focused on closing out the CFP technology at a smaller scale by the end of the current fiscal year.

BIOMASS FUELS↗

DOE OSTI2020

Probabilistic Power Consumption Modeling for Commercial Buildings Using Logistic Regression Markov Chain

The total energy consumed by buildings takes up to 40% of U.S. energy use, in which a large portion is contributed by commercial buildings. Building performance optimization is desirable but requires accurate building models with uncertainties taken into account. This paper proposes a novel probabilistic modeling method using Logistic Regression Markov Chain (LRMC). The LRMC model enhances the performance of traditional Markov Chain (MC) models by adopting time-variant transition matrices calibrated using logistic regression with exogenous inputs. Compared with existing building models, the proposed model produces accurate multi-step modeling results with full probability distribution. The proposed probabilistic building model is tested using actual commercial building measurements and modeling performance is evaluated with two probabilisitc metrics. The results show that the LRMC model has higher accuracy than traditional MC model and Logistic Regression (LR) model in that it yields lower error scores under both evaluation metrics.

Building modeling↗

DOE OSTI2024

Different model assumptions about plant hydraulics and photosynthetic temperature acclimation yield diverging implications for tropical forest gross primary production under warming

Tropical forest photosynthesis can decline at high temperatures due to (1) biochemical responses to increasing temperature and (2) stomatal responses to increasing vapor pressure deficit (VPD), which is associated with increasing temperature. It is challenging to disentangle the influence of these two mechanisms on photosynthesis in observations, because temperature and VPD are tightly correlated in tropical forests. Nonetheless, quantifying the relative strength of these two mechanisms is essential for understanding how tropical gross primary production (GPP) will respond to climate change, because increasing atmospheric CO 2 concentration may partially offset VPD-driven stomatal responses, but is not expected to mitigate the effects of temperature-driven biochemical responses. We used two terrestrial biosphere models to quantify how physiological process assumptions (photosynthetic temperature acclimation and plant hydraulic stress) and functional traits (e.g., maximum xylem conductivity) influence the relative strength of modeled temperature versus VPD effects on light-saturated GPP at an Amazonian forest site, a seasonally dry tropical forest site, and an experimental tropical forest mesocosm. By simulating idealized climate change scenarios, we quantified the divergence in GPP predictions under model configurations with stronger VPD effects compared with stronger direct temperature effects. Assumptions consistent with stronger direct temperature effects resulted in larger GPP declines under warming, while assumptions consistent with stronger VPD effects resulted in more resilient GPP under warming. Furthermore, our findings underscore the importance of quantifying the role of direct temperature and indirect VPD effects for projecting the resilience of tropical forests in the future, and demonstrate that the relative strength of temperature versus VPD effects in models is highly sensitive to plant functional parameters and structural assumptions about photosynthetic temperature acclimation and plant hydraulics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

YIELD STRENGTH PREDICTION FOR RAPID AGE-HARDENING HEAT TREATMENT OF ALUMINUM ALLOYS

A constitutive model has been developed to predict the yield strength aging curves for aluminum casting alloys during non-isothermal age-hardening processes. The model provides the specific relationship between the process variables and yield strength. Several aging heat treatment scenarios have been investigated using the proposed model, including two-step aging recipes. Two-step aging heat treatments involve a low temperature regime to promote nucleation of secondary phases and a second step at higher temperature for the growth of the secondary phases. The predicted results show that yield strength of approximately 300MPa might be obtained in shorter aging time, of approximately 30 minutes. Thus, better mechanical properties can be obtained by optimizing the time-temperature schedules for the precipitation hardening process of heat treatable aluminum alloys.

Yin, Hebi↗

DOE OSTI2024

A non-isothermal breakage-damage model for plastic-bonded granular materials incorporating temperature, pressure, and rate dependencies

Plastic-bonded granular materials (PBM) are widely used in industrial sectors, including building construction, abrasive applications, and defense applications such as plastic-bonded explosives. The mechanical behavior of PBM is highly nonlinear, irreversible, rate dependent, and temperature sensitive governed by various micromechanical attributions such as grain crushing and binder damage. This paper presents a thermodynamically consistent, microstructure-informed constitutive model to capture these characteristic behaviors of PBM. Key features of the model include a breakage internal variable to upscale the grain-scale information to the continuum level and to predict grain size evolution under mechanical loading. In addition, a damage internal state variable is introduced to account for the damage, deterioration, and debonding of the binder matrix upon loading. Temperature is taken as a fundamental external state variable to handle non-isothermal loading paths. The proposed model is able to capture with good accuracy several important aspects of the mechanical properties of PBM, such as pressure-dependent elasticity, pressure-dependent yield strength, brittle-to-ductile transition, temperature dependency, and rate dependency in the post-yielding regime. Furthermore, the model is validated against multiple published datasets obtained from confined and unconfined compression tests, covering various PBM compositions, confining pressures, temperatures, and strain rates.

Breakage↗

DOE OSTI2024

Evaluating Machine Learning-Based MRI Reconstruction Using Digital Image Quality Phantoms

Quantitative and objective evaluation tools are essential for assessing the performance of machine learning (ML)-based magnetic resonance imaging (MRI) reconstruction methods. However, the commonly used fidelity metrics, such as mean squared error (MSE), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR), often fail to capture fundamental and clinically relevant MR image quality aspects. To address this, we propose evaluation of ML-based MRI reconstruction using digital image quality phantoms and automated evaluation methods. Our phantoms are based upon the American College of Radiology (ACR) large physical phantom but created in k-space to simulate their MR images, and they can vary in object size, signal-to-noise ratio, resolution, and image contrast. Our evaluation pipeline incorporates evaluation metrics of geometric accuracy, intensity uniformity, percentage ghosting, sharpness, signal-to-noise ratio, resolution, and low-contrast detectability. We demonstrate the utility of our proposed pipeline by assessing an example ML-based reconstruction model across various training and testing scenarios. The performance results indicate that training data acquired with a lower undersampling factor and coils of larger anatomical coverage yield a better performing model. The comprehensive and standardized pipeline introduced in this study can help to facilitate a better understanding of the performance and guide future development and advancement of ML-based reconstruction algorithms.

47 OTHER INSTRUMENTATION↗

DOE OSTI2023

On High-Fluence Irradiation Hardening of Nine RPV Surveillance Steels in the UCSB ATR-2 Experiment: Implications for Extended-Life Embrittlement Predictions

Nine archival reactor pressure vessel (RPV) surveillance steels from commercial nuclear power plants were irradiated in the University of California, Santa Barbara Advanced Test Reactor 2 (ATR-2) experiment to evaluate irradiation embrittlement under low-flux surveillance capsule versus higher flux test reactor (ATR-2) conditions. The postirradiation measurements of irradiation hardening, measured as increases in yield stress (Δσy), and corresponding conversions of Δσy to Charpy V-notch 41-J transition temperature shifts (ΔTc) are compared with various embrittlement trend curve (ETC) model predictions for the nine steels. Tensile and converted shear punch and microhardness measurements of Δσy generally show a continuing increase between intermediate and high ATR-2 fluences. The Eason-Odette-Nanstad-Yamamoto and ASTM E900 ETC models underpredict embrittlement at the ATR-2 irradiation condition: irradiation temperature (Ti) of 292°C, neutron fluence (ϕt) of 1.4 × 1020 n/cm2 (E > 1 MeV), and neutron flux (ϕ) of 3.68 × 1012 n/cm2-s. On average, the French FIS and Japanese JAEC ETCs slightly overpredict the ATR-2 data. The increase in Δσy with higher fluence is primarily due to Ni-Mn-Si precipitates, which slowly evolve in both nearly copper-free and copper-bearing steels. Finally, a new Odette-Wells-Almirall-Yamamoto embrittlement model is shown that yields good predictions for the nine steels at high fluences (ϕt > 5.5 × 1019 n/cm2).

Nanstad, Randy K.↗

DOE OSTI2014

Rapid Debris Analysis Project Task 3 Final Report - Sensitivity of Fallout to Source Parameters, Near-Detonation Environment Material Properties, Topography, and Meteorology

This report describes the sensitivity of predicted nuclear fallout to a variety of model input parameters, including yield, height of burst, particle and activity size distribution parameters, wind speed, wind direction, topography, and precipitation. We investigate sensitivity over a wide but plausible range of model input parameters. In addition, we investigate a specific example with a relatively narrow range to illustrate the potential for evaluating uncertainties in predictions when there are more precise constraints on model parameters.

58 GEOSCIENCES↗

DOE OSTI2023

On High-Fluence Irradiation Hardening of Nine RPV Surveillance Steels in the UCSB ATR-2 Experiment: Implications for Extended-Life Embrittlement Predictions

Nanstad, Randy↗

DOE OSTI2011

Model-independent Higgs coupling measurements at the LHC using the H{yields}ZZ{yields}4l lineshape

We show that combining a direct measurement of the Higgs total width from the H{yields}ZZ{yields}4l lineshape with Higgs signal rate measurements allows Higgs couplings to be extracted in a model-independent way from CERN LHC data. Using existing experimental studies with 30 fb{sup -1} at one detector of the 14 TeV LHC, we show that the couplings squared of a 190 GeV Higgs to WW, ZZ, and gg can be extracted with statistical precisions of about 10%, and a 95% confidence level upper limit on an unobserved component of the Higgs decay width of about 22% of the standard model Higgs width can be set. The method can also be applied for heavier Higgs masses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2014

X-ray relative intensities at incident photon energies across the L{sub i} (i=1–3) absorption edges of elements with 35≤Z≤92

The intensity ratios, I{sub Lk}/I{sub Lα1} (k=l,η,α{sub 2},β{sub 1},β{sub 2,15},β{sub 3},β{sub 4},β{sub 5,7},β{sub 6},β{sub 9,10},γ{sub 1,5},γ{sub 6,8},γ{sub 2,3},γ{sub 4}) and I{sub Lj}/I{sub Lα} (j=β,γ), have been evaluated at incident photon energies across the L{sub i} (i=1–3) absorption edge energies of all the elements with 35≤Z≤92. Use is made of what are currently considered to be more reliable theoretical data sets of different physical parameters, namely, the L{sub i} (i=1–3) sub-shell photoionization cross sections based on the relativistic Hartree–Fock–Slater (RHFS) model, the X-ray emission rates based on the Dirac–Fock model, and the fluorescence and Coster–Kronig yields based on the Dirac–Hartree–Slater model. In addition, the Lα{sub 1} X-ray production cross sections for different elements at various incident photon energies have been tabulated so as to facilitate the evaluation of production cross sections for different resolved L X-ray components from the tabulated intensity ratios. Further, to assist evaluation of the prominent (L{sub i}−S{sub j}) (S{sub j}=M{sub j}, N{sub j} and i=1–3, j=1–7) resonant Raman scattered (RRS) peak energies for an element at a given incident photon energy (below the L{sub i} sub-shell absorption edge), the neutral-atom electron binding energies based on the relaxed orbital RHFS calculations are also listed so as to enable identification of the RRS peaks, which can overlap with the fluorescent X-ray lines. -- Highlights: •The L X-ray relative intensities and Lα{sub 1} XRP cross sections are evaluated using physical parameters based on the IPA models. •Comparison of the intensity ratios evaluated using the DHS and DF models based photoionization cross sections is presented. •Importance of many body effects including electron exchange effects is highlighted.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Constructing Hubbard models for the hydrogen chain using sliced-basis density matrix renormalization group

Sliced-basis DMRG(sb-DMRG) is used to simulate a chain of hydrogen atoms and to construct low-energy effective Hubbard-like models. The downfolding procedure first involves a change of basis to a set of atom-centered Wannier functions constructed from the natural orbitals of the exact DMRG one-particle density matrix. The Wannier function model is then reduced to a fewer-parameter Hubbard-like model, whose parameters are determined by minimizing the expectation value of the Wannier Hamiltonian in the ground state of the Hubbard Hamiltonian. This indirect variational procedure not only yields compact and simple models for the hydrogen chain, but also allows us to explore the importance of constraints in the effective Hamiltonian, such as the restricting the range of the single-particle hopping and two-particle interactions, and to assess the reliability of more conventional downfolding. The entanglement entropy for a model's ground state, cut in the middle, is an important property determining the ability of DMRG and tensor networks to simulate the model, and here we study its variation with the range of the interactions. Counterintuitively, we find that shorter ranged interactions often have larger entanglement.

36 MATERIALS SCIENCE↗

DOE OSTI2013

Corn Storage Protein - A Molecular Genetic Model

Corn is the highest yielding crop on earth and probably the most valuable agricultural product of the United States. Because it converts sun energy through photosynthesis into starch and proteins, we addressed energy savings by focusing on protein quality. People and animals require essential amino acids derived from the digestion of proteins. If proteins are relatively low in certain essential amino acids, the crop becomes nutritionally defective and has to be supplemented. Such deficiency affects meat and fish production and countries where corn is a staple. Because corn seed proteins have relatively low levels of lysine and methionine, a diet has to be supplemented with soybeans for the missing lysine and with chemically synthesized methionine. We therefore have studied genes expressed during maize seed development and their chromosomal organization. A critical technical requirement for the understanding of the molecular structure of genes and their positional information was DNA sequencing. Because of the length of sequences, DNA sequencing methods themselves were insufficient for this type of analysis. We therefore developed the so-called “DNA shotgun sequencing” strategy, where overlapping DNA fragments were sequenced in parallel and used to reconstruct large DNA molecules via overlaps. Our publications became the most frequently cited ones during the decade of 1981-1990 and former Associate Director of Science for the Office of Basic Energy Sciences Patricia M. Dehmer presented our work as one of the great successes of this program. A major component of the sequencing strategy was the development of bacterial strains and vectors, which were also used to develop the first biotechnology crops. These crops possessed new traits thanks to the expression of foreign genes in plants. To enable such expression, chimeric genes had to be constructed using our materials and methods by the industry. Because we made our materials and methods freely available to academia and industry, progress in plant research and new crop development could accelerate and benefit the public.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2024

Bilocal Field Theory for Composite Scalar Bosons

We give a bilocal field theory description of a composite scalar with an extended binding potential that reduces to the Nambu–Jona-Lasinio (NJL) model in the pointlike limit. This provides a description of the internal dynamics of the bound state and features a static internal wave function, $\phi(\vec{r})$, in the center-of-mass frame that satisfies a Schrödinger–Klein–Gordon equation with eigenvalues m 2 . We analyze the “coloron” model (single perturbative massive gluon exchange) which yields a UV completion of the NJL model. This has a BCS-like enhancement of its interaction, ∝N c the number of colors, and is classically critical with g critical remarkably close to the NJL quantum critical coupling. Negative eigenvalues for m 2 lead to spontaneous symmetry breaking, and the Yukawa coupling of the bound state to constituent fermions is emergent.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗