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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 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 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 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 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↗

DOE OSTI2026

Presupernova O–C Shell Mergers Could Produce More 44 Ti than the Explosion

The formation of 44 Ti in massive stars is thought to occur during explosive nucleosynthesis; however, recent studies have shown it can be produced during O–C shell mergers prior to core collapse. We investigate how mixing according to 3D macrophysics derived from hydrodynamic simulations impacts presupernova O–C shell merger nucleosynthesis and if it can dominate explosive supernova production of 44 Ti and other radioactive isotopes. We compare a range of observations and models of explosive 44 Ti yields to preexplosive multizone mixing–burning nucleosynthesis simulations of an O–C shell merger in a 15 M ⊙ Z = 0.02 stellar model with mixing conditions corresponding to different 3D hydrodynamic mixing scenarios. Radioactive species produced in the O shell have a multidex spread in preexplosive yield predictions across different 3D mixing scenarios of 1.54 and 2.14 dex on average, depending on mass cut. 44 Ti has the largest spread of 4.78 and 4.81 dex depending on mass cut. Further, we show that the preexplosive production of 44 Ti can be larger than the explosive production of models and can match observations. Our results also show that 3D mixing physics enhances 44 Ti in 1D models without modifying 56 Ni yields. We conclude that quantitative predictions of 44 Ti and other radioactive species more broadly require an understanding of the 3D hydrodynamic mixing conditions present during the O–C shell merger.

Issa, Joshua [University of Victoria, BC (Canada);↗

DOE OSTI2021

Interplay Between Crustal‐Scale Thrusting, High Metamorphic Heating Rates, and the Development of Inverted Thermal‐Metamorphic Gradients: Numerical Models and Examples From the Caledonides of Northern Scotland

Abstract Understanding the processes that produce high prograde metamorphic heating rates and the development of inverted metamorphic sequences in collisional thrust belts remains a fundamental challenge for tectonics and metamorphic petrology. New 2D finite element models of crustal‐scale thrusts with variable slip rates (10, 20, 35, 50 km Myr −1 ) are used to examine how thrust sheet emplacement contributes to these processes. In the models, average prograde heating rates of 31–118 °C Myr −1 are observed in the footwall, with maximum transient heating rates of ∼167 °C Myr −1 occurring at the highest slip rate. Also, thrust sheet emplacement produces an inverted thermal gradient in the model footwall. At slip rates of 10 km Myr −1 , heating magnitudes >200 °C are observed >7 km structurally beneath the thrust plane. At slip rates of 20–50 km Myr −1 , models produce thermal penetration depths of 4–5 km for similar heating magnitudes. Petrologically determined heating rates for thrust footwalls in the Scandian orogenic wedge of northern Scotland mostly yield rates consistent with model results (10–230 °C Myr −1 ). The model‐derived magnitude of inverted thermal gradients is also similar to those indicated in texturally determined deformation temperature transects across the Scandian orogenic wedge (100 °C–180 °C). Combined, these results indicate that high heating rates can be produced by fault slip at typical plate velocities. Additionally, this implies that crustal‐scale thrusts are likely to produce inverted metamorphic sequences in most systems, provided that P‐T conditions during slip allow for metamorphic or recrystallization processes that would preserve evidence for such features.

Thigpen, J. Ryan↗

DOE OSTI2024

Trajectory Shaper: A Solution for Disrupted Cooperative Adaptive Cruise Control

Cooperative adaptive cruise control (CACC) can effectively reduce energy consumption, alleviate traffic congestion, and enhance safety. However, communication-related constraints and uncooperative vehicle users can disrupt CACC during real-world operations, significantly undermining the putative benefits of CACC. To alleviate the negative impacts of disrupted CACC, this study develops the trajectory shaper (TS) methods as backup solutions for two scenarios: (i) communication between vehicles is infeasible, and vehicles execute adaptive cruise control (ACC) using local sensor measurements; (ii) follower vehicles reject forming a cooperative platoon and execute their local distributed controllers using the information attained via communication. When communication is infeasible, a distributed TS is devised on each vehicle to modify the sensor measurements, enabling safe and efficient ACC operations. When communication is available but uncooperative agents are involved, the lead vehicle of the platoon executes a centralized TS to modify the information shared with uncooperative agents, achieving optimal platoon-level performance. The centralized and distributed TSs are implemented based on the model predictive control algorithms to yield optimal modifications on input information. Robustness is also factored to tackle model uncertainties during TS operations to ensure safety and efficiency. Numerical experiments validate the control performance of the proposed TSs.

Zhou, Anye [ORNL] (ORCID:0000000301455579)↗

DOE OSTI2021

Nonlinear Interface Reduction for Time-Domain Analysis of Hurty/Craig-Bampton Superelements with Frictional Contact

Virtual prototyping in engineering design relies today on modern numerical models of contacting structures with accurate resolution of interface mechanics, which strongly affect the system-level stiffness and energy dissipation due to frictional losses. High-fidelity modeling within the localized interfaces is required to resolve local quantities of interest that may drive design decisions. The high-resolution finite element meshes necessary to resolve inter-component stresses tend to be computationally expensive, particularly when the analyst is interested in response time histories. The Hurty/Craig-Bampton (HCB) transformation is a widely used method in structural dynamics for reducing the interior portion of a finite element model while having the ability to retain all nonlinear contact degrees of freedom (DOF) in physical coordinates. These models may still require many DOF to adequately resolve the kinematics of the interface, leading to inadequate reduction and computational savings. This study proposes a novel interface reduction method to overcome these challenges by means of system-level characteristic constraint (SCC) modes and properly orthogonal interface modal derivatives (POIMDs) for transient dynamic analyses. Both SCC modes and POIMDs are computed using the reduced HCB mass and stiffness matrices, which can be directly computed from many commercial finite element analysis software. Comparison of time history responses to an impulse-type load in a mechanical beam assembly indicate that the interface-reduced model correlates well with the HCB truth model. Localized features like slip and contact area are well-represented in the time domain when the beam assembly is loaded with a broadband excitation. The proposed method also yields reduced-order models with greater critical timestep lengths for explicit integration schemes.

42 ENGINEERING↗

DOE OSTI2023

Molecular-dynamics simulation methods for macromolecular crystallography

It is investigated whether molecular-dynamics (MD) simulations can be used to enhance macromolecular crystallography (MX) studies. Historically, protein crystal structures have been described using a single set of atomic coordinates. Because conformational variation is important for protein function, researchers now often build models that contain multiple structures. Methods for building such models can fail, however, in regions where the crystallographic density is difficult to interpret, for example at the protein–solvent interface. To address this limitation, a set of MD–MX methods that combine MD simulations of protein crystals with conventional modeling and refinement tools have been developed. In an application to a cyclic adenosine monophosphate-dependent protein kinase at room temperature, the procedure improved the interpretation of ambiguous density, yielding an alternative water model and a revised protein model including multiple conformations. The revised model provides mechanistic insights into the catalytic and regulatory interactions of the enzyme. The same methods may be used in other MX studies to seek mechanistic insights.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Multi-Fidelity Learning for Distribution System Voltage Probabilistic Analysis with High Penetration of PVs

This paper proposes a multi-fidelity learning approach for distribution voltage probabilistic analysis with high penetration of PVs. Unlike the existing machine learning-based approaches that require a large number of high fidelity data to achieve satisfactory results, our approach strategically leverage massive low fidelity data from inaccurate model simulations and limited high fidelity historical data. The key idea is to use low-fidelity data to establish an initial model and then the high-fidelity data to calibrate and correct the constructed low-fidelity model. This allows us to fuse low- and high-fidelity data, yielding a high fidelity prediction model. Results obtained from a realistic feeder in US with 80% penetration of PVs show that the proposed approach can achieve a similar accuracy to the one with a large number of high fidelity data. This significantly highlights the advantages of the proposed method as compared to existing data-hungry machine learning methods. Different levels of fidelity data and their impacts are also investigated.

distribution system↗

DOE OSTI2021

Multiscale Data-Driven Seismic Full-Waveform Inversion With Field Data Study

Seismic full-waveform inversion (FWI), which uses iterative methods to estimate high-resolution subsurface models from seismograms, is a powerful imaging technique in exploration geophysics. In recent years, the computational cost of FWI has grown exponentially due to the increasing size and resolution of seismic data. Moreover, it is a nonconvex problem and can encounter local minima due to the limited accuracy of the initial velocity models or the absence of low frequencies in the measurements. To overcome these computational issues, we develop a multiscale data-driven FWI method based on fully convolutional networks (FCNs). In preparing the training data, we first develop a real-time style transform method to create a large set of synthetic subsurface velocity models from natural images. We then develop two convolutional neural networks with encoder-decoder structures to reconstruct the low- and high-frequency components of the subsurface velocity models, separately. To validate the performance of our data-driven inversion method and the effectiveness of the synthesized training set, we compare it with conventional physics-based waveform inversion approaches using both synthetic and field data. Finally, these numerical results demonstrate that, once our model is fully trained, it can significantly reduce the computation time and yield more accurate subsurface velocity models in comparison with conventional FWI.

58 GEOSCIENCES↗