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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

On tests for baby universes in AdS/CFT

To address a puzzle by Antonini and Rath — where a single CFT state has two bulk duals, one with a baby universe and one without — Engelhardt and Gesteau recently devised a test for baby universes in AdS/CFT. Using the extrapolate dictionary, they showed that the boundary dual of a bulk swap test favored bulk spacetimes without a baby universe, providing evidence against their semiclassical validity. However, recent work suggests that holographic maps should post-select on such closed universes, and we argue that this is consistent with the extrapolate dictionary. We therefore construct a new holographic map for bulk states with baby universes and use this to show that the swap test cannot distinguish between Antonini and Rath’s two candidate bulk duals. This not only allows for a valid semiclassical description of the baby universe, but also enables the application of recent techniques for including observers in holographic maps.

AdS-CFT correspondence↗
DOE OSTI2024

Efficient continuous Energy-Multigroup hybrid depletion scheme using the Shift Monte Carlo code. Part I: Energy condensation sensitivity analysis

Monte Carlo (MC) codes coupled to depletion solvers are increasingly used to provide high fidelity fuel cycle modeling capabilities. Here, these coupled depletion-MC tools produce accurate results in general but can experience nonphysical spatial oscillations when time steps are large or when a system’s dominance ratio approaches unity. Two substepping techniques have been developed previously to remedy and dampen these spatial oscillations without needing to reduce step sizes. The first approach relied on higher-order techniques to account for spectral changes within steps (extrapolation and interpolation techniques). The second approach used the first order perturbation (FOP) theory to account for the change in the one-group spatial flux distribution within steps. This paper develops a hybrid depletion methodology which, in a way, combines how the flux is handled in both substepping techniques. Specifically, the multigroup (MG) MC Shift code is used to update the flux distribution within steps rather than a one-group FOP solver. A fully reflected pincell is investigated, which is not spatially dependent in the MG representation. Thus, the analysis in this paper is an initial demonstration of hybrid depletion. An upcoming companion paper will focus on how the hybrid depletion dampens spatial oscillations. The hybrid depletion approach is verified to be consistent with previous constant extrapolation depletion (CED) methods. This paper finds that the hybrid CED exhibits some error in the eigenvalue and one group constants within macro steps. To address this discrepancy, a simple interpolation scheme (CELI) is investigated. This work found that CELI sufficiently addresses the discrepancy in spectrum for macro steps up to 100 days. Overall, this work demonstrates that the hybrid depletion method can significantly reduce the number of high fidelity MC executions in a MC-coupled depletion with an acceptable eigenvalue error.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

On the applicability of the Redlich-Kister framework for viscosity estimation of molten halide salt mixtures

For molten halide salt mixtures already being utilized or under consideration for carbon-free energy production systems, it is crucial that their viscosity is well understood so that system thermal hydraulics can be reliably assessed. Because of the difficulty in accurately measuring molten halide viscosity and the sheer size of the matrix of possible higher order salt mixtures that may be of interest to the energy industry, there are several gaps in the quantified understanding of molten halide viscosity across this matrix. As such, both first-principles and semi-empirical modeling techniques may be crucial for rapidly assessing this broad, complex compositional domain. Herein, the Redlich-Kister framework is applied to assess the feasibility of broadly interpolating and estimating the viscosity of several pseudobinary and pseudoternary molten halide salt systems that may be of key interest to the energy industry. The framework is based on the assumption that an ideal component and a nonideal component collectively describe the viscosity as a function of composition and temperature for a given molten halide system. Three different ideal models were considered for the ideal component, including Grunburg-Nissan, Katti-Chaudhri, and Gambill methods. Regarding the pseudobinary interpolations, the Redlich-Kister models with either the Grunburg-Nissan or Katti-Chaudhri models as the ideal component resulted in either highly (average error less than 5%) or reasonably (average error less than 15%) accurate interpolations of pseudobinary halide viscosity; BeF 2 - or UF 4 -bearing salts tended to result in reasonably accurate interpolations, whereas other pseudobinary mixtures tended to show high accuracy. Regarding the pseudoternary extrapolations, the Redlich-Kister framework shows reasonable success at estimating the extent to which a pseudoternary system may indicate deviations from ideal Grunburg-Nissan mixing, where discrepancies with comparative experimental data generally stay within 30%. Finally, the primary reasons identified for such discrepancies are (1) inaccuracy in the underlying experimental data, (2) different complexation behavior in the higher order systems compared to the pseudobinary subsystems, and (3) extrapolation into temperatures too far out of the domain, which is valid for the underlying experimental data feeding the Redlich-Kister model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Spectrophotometric determination of the stability of La hydroxyl complexes at near neutral to alkaline pH from 25 to 75 °C

The hydrolysis of rare earth elements (REE) potentially controls their mobility during fluid-rock interaction in a broad range of pH and temperature conditions. However, there is still a lack of thermodynamic data for modeling accurately the stability of REE hydroxyl complexes in hydrothermal aqueous fluids. Here, in this study, UV–Vis spectrophotometric experiments were conducted from 25 to 75 °C in near-neutral to alkaline NaOH-bearing aqueous solutions with varying lanthanum (La) concentrations (0 to ∼0.23 mmol/kg). The color indicator m-cresol purple was used to determine in situ pH and derive the average OH− ligand number ($\overrightarrow{n}$) and formation constants for the La hydroxyl complexes (LaOH 2+ , La(OH) 2 + , and La(OH) 3 0 ). From 25 to 50 °C, $\overrightarrow{n}$ ranges between ∼1 and 2 at pH from 7.0 to 9.3. At 75 °C, $\overrightarrow{n}$ ranges between ∼1.5 and 3 at pH from 6.3 to 8.8. These results suggest the predominance of LaOH 2+ and La(OH) 2 + complexes from 25 to 50 °C, and an increased predominance of La(OH) 3 0 at 75 °C. The cumulative formation constants (β n °, n = 1 to 3) are derived for the reaction La 3+ + nOH − = La(OH) n 3-n , and fitted between 25 and 250 °C by combining the UV–Vis and literature solubility data. The resulting logβ n ° are expressed as function of temperature (T in Kelvin): logβ 1 ° = −1.786 + 0.0133 T + 1.049·10 3 /T; logβ 2 ° = −5.797 + 0.0267 T + 2.713·10 3 /T; logβ 3 ° = 6.435 + 0.0223 T + 512.7/T. A comparison between these new fits and existing extrapolations using the Helgeson-Kirkham-Flowers equation of state indicates significant differences in the predicted hydrolysis of La. The latter extrapolations should therefore be updated for the hydrolysis of REE.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Rapid assessment of the creep rupture life of metals: A model enabling experimental design

Prediction of the creep rupture life of engineering metals is critical for qualification and design of new materials. The use of long-term creep tests and the need to quantify the performance variability in a priori similar systems hinder the rapid creep assessment of a given material. Therefore, it is essential to develop methods that can extrapolate the long-term performance of alloys and the associated variability from short-term experiments. To this end, this study introduces a new model which enables the estimation of the rupture life of a material for a given stress and temperature. This model relies on two components. First, a new relation for the minimum creep rate (MCR) of materials is introduced. It includes a stress dependent stress exponent allowing the model to capture the variation of MCR across a wide range of temperatures and stresses. Second, employing the Monkman-Grant (MG) law, we establish a relation between stress, temperature and creep rupture life. Together, these two elements yield a new closed-form mathematical expression for the Larson Miller parameter as a function of stress and temperature. This expression captures the creep rupture time for many metals (Gr91, Copper, Gr122 and 347H) and compares favorably with alternate empirical approaches. The model is then used to assess the minimum duration of creep rates necessary to qualify the material up to 100000h. Furthermore, it is found that depending on the material system, creep tests as few as five limited to 5000 h for steels (Gr91, Gr122, 347H) and 100 h for copper are sufficient to model creep lifetimes. Finally, using a Bayesian inference-based approach to calibrate the model, we demonstrate that variability in rupture life can be captured via the quantification of the uncertainty in the model parameters and extrapolated from a limited number of short to moderately short creep tests; thereby paving the way for accelerated creep testing.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Building a predictive model for polycyclic aromatic hydrocarbon dosimetry in organotypically cultured human bronchial epithelial cells using benzo[ a ]pyrene

The airway epithelium is a primary route of exposure for inhaled toxicants, and organotypic culture models represent an important advancement for toxicity testing compared to simple in vitro models that may lack metabolic capability and multicellular structure/communication associated with the bronchial epithelium in vivo. A quantitative understanding of chemical dosimetry is key for interpreting and extrapolating study results; however, dosimetry is understudied in organotypic models limiting ability to predict toxicity. We developed a dosimetry model for primary human bronchial epithelial cells (HBECs) cultured at the air-liquid interface (ALI) using benzo[a]pyrene (BAP), a representative polycyclic aromatic hydrocarbon. Dose and time course evaluation of metabolite formation and enzyme activity and expression were utilized to parameterize a cellular dosimetry model to improve the utility of ALI-HBECs for assessing chemical risk. Dosimetry analysis demonstrated absorption of BAP into cells and an increase in Phase 1 and 2 metabolites over time that correlated with regulation of metabolizing enzymes. BAP was cleared from cells by 48 h after exposure, and the primary metabolites generated in ALI-HBECs were BAP-3-phenol, BAP-4,5-dihydrodiol, BAP-7,8-dihydrodiol, BAP-9,10-dihydrodiol, BAP-7,8,9,10-tetrol, BAP-3-phenol-glucuronide, BAP-4,5-dihydrodiol-glucuronide, and BAP-9,10-dihydrodiol-glucuronide. The resulting dosimetry model described BAP and 7,8-dihydrodiol toxicokinetics in ALI-HBECs and suggested active excretion of 7,8-dihydrodiol. Overall, this study demonstrates metabolic competency of ALI-HBECs for BAP metabolism, demonstrates the usefulness of complex in vitro systems for human-relevant toxicity data, and exhibits how in silico models can be utilized for understanding the dosimetry of test compounds to aid in in vitro to human extrapolation of toxicity data for risk assessments.

Benzo[a]pyrene↗
DOE OSTI2026

Knowledge-guided graph machine learning for spatially distributed prediction of daily discharge and nitrogen export dynamics

Spatially distributed prediction of streamflow and nitrogen export dynamics is essential for precision management of agricultural watersheds. While temporal deep learning models such as Long Short-Term Memory (LSTM) have shown strong performance at basin scales, their ability to generalize spatially is limited by insufficient representation of spatial dependencies and flow paths, particularly under data-scarce conditions. To address this gap, we propose HydroGraphNet, a knowledge-guided graph machine learning framework that integrates process-based knowledge and explicit spatial learning into temporal modeling. This framework incorporates directed graph topology to encode watershed connectivity and upstream inflows, with mass balance constraints to improve physical consistency. To enhance generalization in sparsely monitored regions, HydroGraphNet is pretrained on synthetic data generated by the SWAT+ (Soil and Water Assessment Tool Plus) model. We evaluated HydroGraphNet in the Upper Sangamon River Basin (44 HUC-12 subwatersheds, 2001–2020) against two LSTM baselines: a lumped basin-level model and a distributed variant. When benchmarked on SWAT+ simulations in pretraining, HydroGraphNet improved test NSEs by 8.9% (discharge) and 13.7% (NO₃–N load) in temporal extrapolation, and by 27.1% and 34.7% in spatial extrapolation, relative to the Lumped LSTM baseline. After fine-tuning with USGS monitoring data, the model achieved mean test NSE (KGE) scores of 0.768 (0.861) for discharge and 0.626 (0.664) for NO₃–N load, substantially outperforming baselines. Attribution analysis further highlighted the importance of upstream inflow representation and graph-based spatial learning in capturing cross-subwatershed dependencies. The model also reproduced seasonal hydrological and biogeochemical patterns consistent with known processes, demonstrating its robustness and process fidelity for spatially distributed prediction. Altogether, HydroGraphNet advances the integration of physical knowledge and spatially explicit learning in hydrological modeling, offering a generalizable framework for distributed modeling to support spatially targeted water quality management in data-scarce watersheds.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Structural and Spectroscopic Characterization of Plutonium and Other Tetravalent Metals Complexed to a Keggin Ion

Here, we report the isolation of the first plutonium(IV) complex with a Keggin ion chelator: Cs 20 [Pu(PW 11 O 39 ) 2 ] 2 ·13H 2 O. Single crystal XRD and solid-state UV–vis absorbance analysis demonstrate the stabilization of Pu 4+ by the Keggin ligand. The unit cell contains two [Pu(PW 11 O 39 ) 2 ] 10– complexes (Pu(PW 11 ) 2 ) bridged by Cs + . Raman and 31 P NMR spectra of Pu(PW 11 ) 2 are consistent with the analogous Zr 4+ , Hf 4+ , Ce 4+ , and Th 4+ complexes. The Pu–O bond distances at the two Pu sites are 2.35(3) and 2.34(3) Å, matching the value extrapolated from the bonding trend built with the other 8-coordinated tetravalent cations. However, the long-range arrangement of the Pu(PW 11 ) 2 complexes within the lattice is unique in the series of M IV (PW 11 ) 2 compounds: pairs of Pu(PW 11 ) 2 are organized perpendicular to each other. Based on solution-state UV–visible absorbance, small-angle X-ray scattering (SAXS), and 31 P NMR, the tetravalent cations quantitatively form the 1:2 species in solution ([Pu(PW 11 O 39 ) 2 ] 10– (aq) ) and no 1:1 species ([Pu(PW 11 O 39 )(H 2 O) x ] 3– (aq) ). Finally, a linear correlation exists between the metal–oxygen distances in the M IV (PW 11 ) 2 compounds and the corresponding metal dioxides, allowing for extrapolation for Pa 4+ , Am 4+ , and Bk 4+ . The results indicate that our microscale POM approach represents a viable pathway to probe properties of rare actinide ions in discrete molecules, beyond the traditional oxide extended solids.

and nuclear chemistry↗
DOE OSTI2025

Structure and Superconductivity of Hydrogenated TiZr Alloys Under High Pressure

The effects of pressure on the structure and electrical transport properties, including electrical resistance and critical magnetic field values for superconductivity in pristine and hydrogenated TiZr alloys, were investigated. As pressure increased from ambient to 55 GPa, the structure of the pristine TiZr alloys transformed from hcp to bcc via an intermediate ω phase. The superconducting transition temperature (T c ) significantly increased from 1.76 K at ambient pressure to 15.5 K at 52.2 GPa, accompanied by an extrapolated upper critical field of 24.5 T at T = 0 K. In contrast, the hydrogenated samples exhibited substantially lower T c values compared to the pristine ones, with a T c of 4.6 K at 50.5 GPa and an extrapolated upper critical field of 1.8 T at T = 0 K. Upon decompression, the superconductivity in the hydrogenated sample persisted down to 25 GPa, disappearing when the pressure was reduced to 19 GPa. Synchrotron X-ray diffractions (XRD) analysis revealed the formation of three hydrides: I4/mmm-(Ti, Zr)H 2 , P6 3 /mmc-(Ti, Zr)H 3 , and I4̅3d-(Ti,Zr) 4 H 15 . Finally, by combining XRD and electrical resistance data, we tentatively propose that the P6 3 /mmc-(Ti, Zr)H 3 predominantly induces superconductivity. In contrast to superhydrides, where electron–phonon interactions increase the superconducting temperature at high pressure, hydrogen atoms in hcp-(Ti, Zr)H 3 reduce T c .

36 MATERIALS SCIENCE↗
DOE OSTI2024

Shallow Rate-Redox Potential Scaling in Aqueous Molecular Oxygen Reduction Electrocatalysis Across a Family of Iron Macrocycles

Rate-overpotential scaling relationships have been employed widely to understand trends in oxygen reduction reaction (ORR) electrocatalysis by dissolved metal macrocycles in organic electrolytes. Similar scaling relationships remain unknown for surface-adsorbed ORR electrocatalysts in the acidic aqueous environments germane to proton-exchange membrane (PEM) fuel cells. Herein, we examine ORR catalysis in aqueous perchloric acid media for a structurally diverse array of iron macrocycle complexes adsorbed on Vulcan carbon black. The macrocycles encompass Fe– N 4 , Fe–N 2 N' 2 and Fe–N x C 4-x motifs bearing pyrrolic, pyridinic, and N-heterocyclic carbene (NHC) moieties in the primary ligation sphere, giving rise to a 670 mV range in Fe(III/II) redox potentials, E Fe(III/II) . Experimental Tafel data in the micropolarization regime were extrapolated to the E Fe(III/II) to furnish estimated per-site-normalized current density (j per-site ) values that span ~4.6 orders of magnitude across the family of compounds. Despite the structural diversity of this family of compounds, extrapolated j per-site values correlate with the Fe(III/II) redox potentials in a roughly log-linear fashion with a shallow scaling factor of approximately 145 mV/decade. Further, these findings highlight that negative shifts in E Fe(III/II) lead to diminishing returns in catalytic rate promotion and suggest that changes to the primary ligating environment in a macrocycle are insufficient to break fundamental rate-potential scaling relationships in aqueous ORR catalysis. Together these studies motivate the further development of higher-potential iron complexes that employ motifs beyond the equatorial ligation plane to enhance ORR catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

On the Relationship Between Methane Production in Anaerobic Incubations of Peat Material and In Situ Methane Emissions

Anaerobic incubations of peat have been widely used to explore soil processes, but this in vitro technique raises many questions as to how well it reproduces in situ conditions. To investigate this, we conducted 60–100 days (+25 days pre‐incubation) anaerobic, temperature‐controlled incubation experiments across a temperature range of 1–26°C on samples from bog and fen habitats, at two different depths (9–19 and 25–35 cm). Here, we observed exponential increases in CO 2 and methane production with temperature in all conditions. We then compared field‐based measurements of methane emission with modeled expectations by extrapolating incubation‐determined methane production rates based on (a) soil temperature profiles, (b) the observed incubation temperature‐methane production relationship, and (c) seasonal thaw depth from each site. The resulting incubation‐extrapolated methane production agreed with measured emission rates within a factor of two at both sites and corresponded to 182 ± 54% and 59 ± 14% of the measured average yearly fluxes from the field for the bog and fen, respectively. The underestimation of fen methane fluxes may be due to the lack of living plant root‐derived dissolved organic carbon inputs in incubations, a key process in fens. Conversely, the overestimation in bogs could be attributed to methane oxidation in the field, which is absent in anaerobic incubation conditions. Nonetheless incubations predicted greenhouse gas emissions from a northern peatland within a factor of two.

Biological and medical sciences,Geosciences,Enviro↗
DOE OSTI2025

The Role of Dislocations in the Anelasticity of the Upper Mantle

Dislocation‐based dissipation mechanisms potentially control the viscoelastic response of Earth's upper mantle across a variety of geodynamic contexts, including glacial isostatic adjustment, postseismic creep, and seismic‐wave attenuation. However, there is no consensus on which dislocation‐based, microphysical process controls the viscoelastic behavior of the upper mantle. Although both intergranular (plastic anisotropy) and intragranular (backstress) mechanisms have been proposed, there is currently insufficient laboratory data to discriminate between those mechanisms. Here, we present the results of forced‐oscillation experiments in a deformation‐DIA apparatus at confining pressures of 3–7 GPa and temperatures of 298–1370 K. Our experiments tested the viscoelastic response of polycrystalline olivine—the main constituent of the upper mantle—at stress amplitudes from 70 to 2,800 MPa. Mechanical data are complemented by microstructural analyses of grain size, crystallographic preferred orientation, and dislocation density. We observe amplitude‐ and frequency‐dependent attenuation and modulus relaxation and find that numerical solutions of the backstress model match our results well. Therefore, we argue that interactions among dislocations, rather than intergranular processes (e.g., plastic anisotropy or grain boundary sliding), control the viscoelastic behavior of polycrystalline olivine in our experiments. In addition, we present a linearized version of the constitutive equations of the backstress model and extrapolate it to conditions typical of seismic‐wave propagation in the upper mantle. Our extrapolation demonstrates that the backstress model can explain the magnitude of seismic‐wave attenuation in the upper mantle, although some modification is required to explain the weak frequency dependence of attenuation observed in nature and in previous experimental work.

Hein, Diede [Univ. of Minnesota, Minneapolis, MN (↗
DOE OSTI2024

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE↗
DOE OSTI2025

On Alfvénic turbulence of solar wind streams observed by Solar Orbiter during March 2022 perihelion and their source regions

It has been recently accepted that the standard classification of the solar wind solely according to flow speed is outdated, and particular interest has been devoted to the study of the origin and evolution of so-called Alfvénic slow solar wind streams and to what extent such streams resemble or differ from fast wind. In March 2022, Solar Orbiter completed its first nominal phase perihelion passage. During this interval, it observed several Alfvénic streams, allowing for characterization of fluctuations in three slow wind intervals (AS1-AS3) and comparison with a fast wind stream (F) at almost the same heliocentric distance. This work makes use of Solar Orbiter plasma parameters from the Solar Wind Analyzer (SWA) and magnetic field measurements from the magnetometer (MAG). The magnetic connectivity to the solar sources of selected solar wind intervals was reconstructed using a ballistic extrapolation based on measured solar wind speed down to the (spherical) source surface at 2.5 R s below which a potential field extrapolation was used to map back to the Sun. The source regions were identified using SDO/AIA observations. A spectral analysis of in situ measured magnetic field and velocity fluctuations was performed to characterize correlations, Alfvénicity, normalized cross-helicity, and residual energy in the frequency domain as well as intermittency of the fluctuations and spectral energy transfer rate estimated via mixed third-order moments. A machine learning technique was used to separate proton core, proton beam, and alpha particles and to study v − b correlations for the different ion populations in order to evaluate the role played by each population in determining the Alfvénic content of solar wind fluctuations. The comparison between fast wind and Alfvénic slow wind intervals highlights the differences between the two solar wind regimes: The fast wind is characterized by larger amplitude fluctuations, and magnetic and velocity fluctuations are closer to equipartition of energy. In fact the Alfvénic slow wind streams appear to be on a spectrum of wind types, with AS1, originating from open field lines neighboring active regions and displaying similarities with the fast wind in terms of fluctuation amplitude and turbulence characteristics, but not with respect to the alpha particles and proton beams. The other two slow streams differed both in their sources as well as plasma characteristics, with AS2 coming from the expansion of a narrow coronal hole corridor and AS3 from a region straddling a pseudostreamer. The latter displayed the coldest and highest density but the slowest stream with the smallest fluctuation amplitude and greatest magnetic energy excess. It also showed the largest scatter in proton beam speeds and the greatest difference in speed between proton beam and alpha particles. This study shows how the old fast–slow solar wind dichotomy, already called into question by the observations of slower Alfvénic solar wind streams, should further be refined, as the Alfvénic slow wind, originating in different solar wind regions, show significant differences in density, temperature, and proton and alpha-particle properties in the inner heliosphere. The observations presented here provide the starting point for a better understanding of the origin and evolution of different solar wind streams as well as the evolving turbulence contained within.

magnetohydrodynamics (MHD)↗
DOE OSTI2024

Physical discovery in representation learning via conditioning on prior knowledge

Recent advances in electron, scanning probe, optical, and chemical imaging and spectroscopy yield bespoke data sets containing the information of structure and functionality of complex systems. In many cases, the resulting data sets are underpinned by low-dimensional simple representations encoding the factors of variability within the data. The representation learning methods seek to discover these factors of variability, ideally further connecting them with relevant physical mechanisms. However, generally, the task of identifying the latent variables corresponding to actual physical mechanisms is extremely complex. Here, we present an empirical study of an approach based on conditioning the data on the known (continuous) physical parameters and systematically compare it with the previously introduced approach based on the invariant variational autoencoders. The conditional variational autoencoder (cVAE) approach does not rely on the existence of the invariant transforms and hence allows for much greater flexibility and applicability. Interestingly, cVAE allows for limited extrapolation outside of the original domain of the conditional variable. However, this extrapolation is limited compared to the cases when true physical mechanisms are known, and the physical factor of variability can be disentangled in full. We further show that introducing the known conditioning results in the simplification of the latent distribution if the conditioning vector is correlated with the factor of variability in the data, thus allowing us to separate relevant physical factors. We initially demonstrate this approach using 1D and 2D examples on a synthetic data set and then extend it to the analysis of experimental data on ferroelectric domain dynamics visualized via piezoresponse force microscopy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Incorporating the molecular-scale into a hydrodynamic description of confined aqueous systems

Hydrodynamics provides a continuum-level description of fluid motion, but its applicability at the nanoscale becomes uncertain due to the emerging importance of molecular-level effects such as spatial heterogeneity. Hydrodynamic boundary conditions that incorporate molecular details allow us to partition the system into a near-wall region and a bulk fluid region. We identify a hydrodynamic wall located inside the fluid that determines where slip begins. By extending the hydrodynamic wall with the slip length, the position of the extrapolated wall is established. This offers a unified description of both slip and stagnant flow behaviors, with wall hydrophobicity characterized by the relative location of the extrapolated wall with respect to the physical wall. Employing this concept in analyses of equilibrium molecular dynamics (MD) and non-equilibrium MD simulations of Couette and Poiseuille flows, our results demonstrate consistency between equilibrium and non-equilibrium approaches across different flow types and confinement levels. This demonstrates the robust nature of linear response theory. We then explore the effects of fluid-wall and bulk fluid interactions on the hydrodynamic properties. These findings enhance the effectiveness of molecular-based simulations for investigating complex confined systems in nanofluidics, biology, and colloidal science, offering a complementary molecular-scale perspective to traditional continuum approaches.

flow simulations↗
DOE OSTI2026

Size scaling of acceleration phase energetics and its effects on direct-drive DT-layered implosions

A fundamental question in inertial confinement fusion is how implosion performance, and therefore ignition thresholds and fusion gain, evolve with target size. In laser-driven direct drive fusion, the scaling of laser-drive performance with size is critical to this evolution and to extrapolating results from the 30-kJ OMEGA laser-fusion experiments to ignition-class facilities such as the National Ignition Facility. Beyond the well-known adverse effects of cross-beam energy transfer (CBET) on drive performance, here we demonstrate that effects related to the non-scaling physics of thermal conduction and electron–ion energy equilibration exert an influence on drive behavior with scale that equals or surpasses that of CBET. We find that a significant portion of the lost implosion performance with increasing scale is due to the loss of shell implosion velocity. Here, we show that while modest modifications to hydro-scaled designs can recover most of the lost implosion velocity, a full hydro-equivalent performance extrapolation is difficult to achieve without CBET mitigation or subcooling below the triple point of DT.

Patel, D. [University of Rochester, NY (United Sta↗
DOE OSTI2024

Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration

Geological carbon sequestration (GCS) involves injecting CO2 into subsurface geological formationsfor permanent storage. Numerical simulations could guide decisions in GCS projects by predictingCO 2 migration pathways and the pressure distribution in storage formation. However, these simula-tions are often computationally expensive due to highly coupled physics and large spatial-temporalsimulation domains. Surrogate modelling with data-driven machine learning has become a promis-ing alternative to accelerate physics-based simulations. Among these, the Fourier neural operator(FNO) has been applied to three-dimensional synthetic subsurface models. Despite its good accuracyin simulating CO 2 plume migration, it requires large computational resources in training and alsolacks generalizability. Here, to further improve performance, we have developed a nested Fourier-DeepONet by combining the expressiveness of the FNO with the modularity of a deep operatornetwork (DeepONet). This new framework is twice as efficient as a nested FNO for training and has atleast 80% lower GPU memory requirement due to its flexibility to treat temporal coordinates sepa-rately. These performance improvements are achieved without compromising prediction accuracy.In addition, the generalization and extrapolation ability of nested Fourier-DeepONet beyond thetraining range has been thoroughly evaluated. Nested Fourier-DeepONet outperformed the nestedFNO for extrapolation in time with more than 50% reduced error. It also exhibited good extrapolationaccuracy beyond the training range in terms of reservoir properties, number of wells, and injectionrate.

Lee, Jonathan E. [Department of Chemical and Envir↗