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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Depletion-Driven Assembly of Polymer-Coated Nanocrystals

Depletion-driven assembly has been widely studied for micron-sized colloids, but questions remain at the nanoscale where the governing physics are impacted by stabilizing surface ligands or wrapping polymers, whose dimensions are similar to those of the colloidal core and the depletant. Here, we probe how wrapping colloidal tin-doped indium oxide nanocrystals with polymers affects their depletion-induced interactions and assembly in solutions of polyethylene glycol. Copolymers of polyacrylic acid grafted with polyethylene oxide provide nanocrystal wrappings with different effective polymer graft densities and molecular weights. (Ultra) small angle X-ray scattering, coarse-grained molecular dynamics simulation, and molecular thermodynamic theory were combined to analyze how depletant size and polymer wrapping characteristics affect depletion interactions, structure, and phase behavior. The results show how depletant molecular weight, as well as surface density and molecular weight of polymer grafts, set thresholds for assembly. These signatures are unique to depletion-driven assembly of nanoscale colloids and mirror phase behaviors of grafted nanoparticle–polymer composites. Optical and rheological responses of depletion-driven assemblies of nanocrystals with different polymer shell architectures were probed to learn how their structural differences impact properties. Finally, we discuss how these wrinkles for depletion-driven assembly at the nanoscale may provide fresh opportunities for designing responsive depletion interactions and dynamically reconfigurable materials.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗
DOE OSTI2024

Sinuosity‐Driven Hyporheic Exchange: Hydrodynamics and Biogeochemical Potentials

Abstract Hydrologic exchange processes are critical for ecosystem services along river corridors. Meandering contributes to this exchange by driving channel water, solutes, and energy through the surrounding alluvium, a process called sinuosity‐driven hyporheic exchange. This exchange is embedded within and modulated by the regional groundwater flow (RGF), which compresses the hyporheic zone and potentially diminishes its overall impact. Quantifying the role of sinuosity‐driven hyporheic exchange at the reach‐to‐watershed scale requires a mechanistic understanding of the interplay between drivers (meander planform) and modulators (RGF) and its implications for biogeochemical transformations. Here, we use a 2D, vertically integrated numerical model for flow, transport, and reaction to analyze sinuosity‐driven hyporheic exchange systematically. Using this model, we propose a dimensionless framework to explore the role of meander planform and RGF in hydrodynamics and how they constrain nitrogen cycling. Our results highlight the importance of meander topology for water flow and age. We demonstrate how the meander neck induces a shielding effect that protects the hyporheic zone against RGF, imposing a physical constraint on biogeochemical transformations. Furthermore, we explore the conditions when a meander acts as a net nitrogen source or sink. This transition in the net biogeochemical potential is described by a handful of dimensionless physical and biogeochemical parameters that can be measured or constrained from literature and remote sensing. This work provides a new physically based model that quantifies sinuosity‐driven hyporheic exchange and biogeochemical reactions, a critical step toward their representation in water quality models and the design and assessment of river restoration strategies.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Synthetic data-driven deep learning for label-free autonomous atomic force microscopy

Atomic force microscopy (AFM) is a widely used tool for nanoscale characterization across materials science, energy research, and biology. However, its adoption in high-throughput materials discovery and statistically driven studies remains limited by a strong dependence on expert operator input and by the scarcity of annotated experimental AFM datasets needed to enable data-driven automation. Here, we introduce SimuScan, a synthetic-data–driven framework that enables reliable AFM feature identification, segmentation, and targeted imaging without requiring large manually labeled experimental datasets. SimuScan generates tunable, high-fidelity synthetic AFM images of defined morphologies while incorporating realistic experimental artifacts, including tip–sample convolution, noise, flattening distortions, and surface debris. These datasets are shown to support scalable, label-free training of modern deep learning models for AFM analysis. When integrated into data-driven AFM workflows, SimuScan-trained models can locate and analyze nanoscale structures across large datasets and guide targeted follow-up imaging. We validate this approach on nanostructured surfaces, DNA assemblies, and bacterial cells, demonstrating robust generalization across diverse sample types with minimal operator intervention. More broadly, this work establishes a general strategy for generating explicitly conditioned, task-relevant synthetic data to improve the reliability of downstream models in autonomous microscopy.

Millan-Solsona, Ruben [Oak Ridge National Laborato↗
DOE OSTI2022

Entropy-driven order in an array of nanomagnets

Long-range ordering is typically associated with a decrease in entropy. Yet, it can also be driven by increasing entropy in certain special cases. Here we demonstrate that artificial spin-ice arrays of single-domain nanomagnets can be designed to produce such entropy-driven order. We focus on the tetris artificial spin-ice structure, a highly frustrated array geometry with a zero-point Pauling entropy, which is formed by selectively creating regular vacancies on the canonical square ice lattice. We probe thermally active tetris artificial spin ice both experimentally and through simulations, measuring the magnetic moments of the individual nanomagnets. Furthermore, we find two-dimensional magnetic ordering in one subset of these moments, which we demonstrate to be induced by disorder (that is, increased entropy) in another subset of the moments. In contrast with other entropy-driven systems, the discrete degrees of freedom in tetris artificial spin ice are binary and are both designable and directly observable at the microscale, and the entropy of the system is precisely calculable in simulations. This example, in which the system’s interactions and ground-state entropy are well defined, expands the experimental landscape for the study of entropy-driven ordering.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Data-driven analysis of neutron diffraction line profiles: application to plastically deformed Ta

Abstract Non-destructive evaluation of plastically deformed metals, particularly diffraction line profile analysis (DLPA), is valuable both to estimate dislocation densities and arrangements and to validate microstructure-aware constitutive models. To date, the interpretation of whole line diffraction profiles relies on the use of semi-analytical models such as the extended convolutional multiple whole profile (eCMWP) method. This study introduces and validates two data-driven DLPA models to extract dislocation densities from experimentally gathered whole line diffraction profiles. Using two distinct virtual diffraction models accounting for both strain and instrument induced broadening, a database of virtual diffraction whole line profiles of Ta single crystals is generated using discrete dislocation dynamics. The databases are mined to create Gaussian process regression-based surrogate models, allowing dislocation densities to be extracted from experimental profiles. The method is validated against 11 experimentally gathered whole line diffraction profiles from plastically deformed Ta polycrystals. The newly proposed model predicts dislocation densities consistent with estimates from eCMWP. Advantageously, this data driven LPA model can distinguish broadening originating from the instrument and from the dislocation content even at low dislocation densities. Finally, the data-driven model is used to explore the effect of heterogeneous dislocation densities in microstructures containing grains, which may lead to more accurate data-driven predictions of dislocation density in plastically deformed polycrystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Nonlinear excitation of energetic particle driven geodesic acoustic mode by resonance overlap with Alfvén instability in ASDEX Upgrade

The Alfvén instability nonlinearly excited the energetic-particle-driven geodesic acoustic mode on the ASDEX-Upgrade tokamak, as demonstrated experimentally. The mechanism of the energetic-particle-driven geodesic acoustic mode excitation and the mode nonlinear evolution is not yet fully understood. In the present work, a first-principles simulation using the MEGA code investigated the mode properties in both the linear growth and nonlinear saturated phases. Here we show that the simulation successfully reproduced the excitation and coexistence of these two modes, and agreed with the experimental results well. Conclusive evidence showed that the resonance overlap is the excitation mechanism of the energetic-particle-driven geodesic acoustic mode. In the linear growth phase, energetic particles that satisfied different resonance conditions excited the Alfvén instability, which then caused energetic particle redistribution in phase space. These redistributed energetic particles caused resonance overlap, exciting the energetic-particle-driven geodesic acoustic mode in the nonlinear phase.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Online charge measurement for petawatt laser-driven ion acceleration

Laser-driven ion beams have gained considerable attention for their potential use in multidisciplinary research and technology. Preclinical studies into their radiobiological effectiveness have established the prospect of using laser-driven ion beams for radiotherapy. In particular, research into the beneficial effects of ultrahigh instantaneous dose rates is enabled by the high ion bunch charge and uniquely short bunch lengths present for laser-driven ion beams. Such studies require reliable, online dosimetry methods to monitor the bunch charge for every laser shot to ensure that the prescribed dose is accurately applied to the biological sample. In this paper, we present the first successful use of an Integrating Current Transformer (ICT) for laser-driven ion accelerators. This is a noninvasive diagnostic to measure the charge of the accelerated ion bunch. It enables online estimates of the applied dose in radiobiological experiments and facilitates ion beam tuning, in particular, optimization of the laser ion source, and alignment of the proton transport beamline. We present the ICT implementation and the correlation with other diagnostics, such as radiochromic films, a Thomson parabola spectrometer, and a scintillator.

47 OTHER INSTRUMENTATION↗
DOE OSTI2021

Knowledge extraction and transfer in data-driven fracture mechanics

Significance Data-driven approaches have launched a new paradigm in scientific research that is bound to have an impact on all disciplines of science and engineering. However, at this juncture, the exploration of data-driven techniques in the century-old field of fracture mechanics is highly limited, and there are key challenges including accurate and intelligent knowledge extraction and transfer in a data-limited regime. Here, we propose a framework for data-driven knowledge extraction in fracture mechanics with rigorous accuracy assessment which employs active learning for optimizing data usage and for data-driven knowledge transfer that allows efficient treatment of three-dimensional fracture problems based on two-dimensional solutions.

Liu, Xing↗
DOE OSTI2025

Increased flood exposure in the Pacific Northwest following earthquake-driven subsidence and sea-level rise

Climate-driven sea-level rise is increasing the frequency of coastal flooding worldwide, exacerbated locally by factors like land subsidence from groundwater and resource extraction. However, a process rarely considered in future sea-level rise scenarios is sudden (over minutes) land subsidence associated with great (>M8) earthquakes, which can exceed 1 m. Along the Washington, Oregon, and northern California coasts, the next great Cascadia subduction zone earthquake could cause up to 2 m of sudden coastal subsidence, dramatically raising sea level, expanding floodplains, and increasing the flood risk to local communities. Here, we quantify the potential expansion of the 1% floodplain (i.e., the area with an annual flood risk of 1%) under low (~0.5 m), medium (~1 m), and high (~2 m) earthquake-driven subsidence scenarios at 24 Cascadia estuaries. If a great earthquake occurred today, floodplains could expand by 90 km 2 (low), 160 km 2 (medium), or 300 km 2 (high subsidence), more than doubling the flooding exposure of residents, structures, and roads under the high subsidence scenario. By 2100, when climate-driven sea-level rise will compound the hazard, a great earthquake could expand floodplains by 170 km 2 (low), 240 km 2 (medium), or 370 km 2 (high subsidence), more than tripling the flooding exposure of residents, structures, and roads under the high subsidence scenario compared to the 2023 floodplain. Our findings can support decision-makers and coastal communities along the Cascadia subduction zone as they prepare for compound hazards from the earthquake cycle and climate-driven sea-level rise and provide critical insights for tectonically active coastlines globally.

Science & Technology - Other Topics↗
DOE OSTI2021

Pressure-driven stochastization of flux surfaces in stellarator equilibria: a review of the experimental observations and their analysis

Stellarator vacuum fields are designed to have a dense set of nested flux surfaces, with only small islands and stochastic regions in the interior of the confinement region. As the plasma pressure is increased, pressure driven currents appear, and they can significantly modify the vacuum field. The question arises whether pressure driven currents can cause significant flux surface breakage. Both the Wendelstein 7-AS and large helical device stellarators had dedicated experimental campaigns to study the β limit in those devices. In this paper, we review the evidence that the flux surfaces in a significant fraction of the plasma volume were stochastized by the pressure-driven currents in the highest β experiments. That phenomenon appears to have been the dominant source of pressure-driven flux surface loss in these experiments.

stellarator equilibrium↗
DOE OSTI2025

Data-driven results for light-quark connected and strange-plus-disconnected hadronic g − 2 short- and long-distance windows

A key issue affecting the attempt to reduce the uncertainty on the Standard Model prediction for the muon anomalous magnetic moment is the current discrepancy between lattice-QCD and data-driven results for the hadronic vacuum polarization. Progress on this issue benefits from precise data-driven determinations of the isospin-limit light-quark-connected (lqc) and strange-plus-light-quark-disconnected ( s + lqd ) components of the related RBC/UKQCD windows. In this paper, using a strategy employed previously for the intermediate window, we provide data-driven results for the lqc and s + lqd components of the short- and long-distance RBC/UKQCD windows. Comparing these results with those from the lattice, we find significant discrepancies in the lqc parts but good agreement for the s + lqd components. We also explore the impact of recent CMD- 3 e + e − → π + π − cross section results, demonstrating that an upward shift in the ρ -peak region of the type seen in the CMD-3 data serves to eliminate the discrepancies for the lqc components without compromising the good agreement between lattice and data-driven s + lqd results. Published by the American Physical Society 2025

Benton, Genessa (ORCID:0009000515763654)↗
DOE OSTI2021

Physics-constrained, low-dimensional models for magnetohydrodynamics: First-principles and data-driven approaches

Plasmas are highly nonlinear and multiscale, motivating a hierarchy of models to understand and describe their behavior. However, there is a scarcity of plasma models of lower fidelity than magnetohydrodynamics (MHD), although these reduced models hold promise for understanding key physical mechanisms, efficient computation, and real-time optimization and control. Galerkin models, obtained by projection of the MHD equations onto a truncated modal basis, and data-driven models, obtained by modern machine learning and system identification, can furnish this gap in the lower levels of the model hierarchy. This work develops a reduced-order modeling framework for compressible plasmas, leveraging decades of progress in projection-based and data-driven modeling of fluids. We begin by formalizing projection-based model reduction for nonlinear MHD systems. To avoid separate modal decompositions for the magnetic, velocity, and pressure fields, we introduce an energy inner product to synthesize all of the fields into a dimensionally consistent, reduced-order basis. Next, we obtain an analytic model by Galerkin projection of the Hall-MHD equations onto these modes. We illustrate how global conservation laws constrain the model parameters, revealing symmetries that can be enforced in data-driven models, directly connecting these models to the underlying physics. We demonstrate the effectiveness of this approach on data from high-fidelity numerical simulations of a three-dimensional spheromak experiment. Finally, this manuscript builds a bridge to the extensive Galerkin literature in fluid mechanics and facilitates future principled development of projection-based and data-driven models for plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Data-Conforming Data-Driven Control: Avoiding Premature Generalizations Beyond Data

Data-driven and adaptive control approaches face the problem of introducing sudden distributional shifts beyond the distribution of data encountered during learning. Therefore, they are prone to invalidating the very assumptions used in their own construction. This is due to the linearity of the underlying system, inherently assumed and formulated in most data-driven control approaches, which may falsely generalize the behavior of the system beyond the behavior experienced in the data. This article seeks to mitigate these problems by enforcing consistency of the newly designed closed-loop systems with data and slowing down any distributional shifts in the joint state-input space. This is achieved through incorporating affine regularization terms and linear matrix inequality constraints to data-driven approaches, resulting in convex semi-definite programs that can be efficiently solved by standard software packages. We discuss the optimality conditions of these programs and then conclude this article with a numerical example that further highlights the problem of premature generalization beyond data and shows the effectiveness of our proposed approaches in enhancing the safety of data-driven control methods.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Mixing Driven by Critical Reflection of Near-Inertial Waves over the Texas–Louisiana Shelf

Studies of internal wave-driven mixing in the coastal ocean have been focused on internal tides, while wind-driven near-inertial waves (NIWs) have received less attention in this regard. Here, this study demonstrates a scenario of NIW-driven mixing over the Texas–Louisiana shelf. Supported by a high-resolution simulation over the shelf, the NIWs driven by land–sea breeze radiate downward at a sharp front and enhance the mixing in the bottom boundary layer where the NIWs are focused because of slantwise critical reflection. The criterion for slantwise critical reflection of NIWs is ω=$\sqrt{f{^2}+s^{2}_{bot}N{^2}(1-s_{ρ}/s_{bot})}$ (where ω is the wave frequency, s bot is the bottom slope, and s ρ is the isopycnal slope), under the assumption that the mean flow is in a thermal wind balance and only varies in the slope-normal direction. The mechanism driving the enhanced mixing is explored in an idealized simulation. During slantwise critical reflection, NIWs are amplified with enhanced shear and periodically destratify a bottom boundary layer via differential buoyancy advection, leading to periodically enhanced mixing. Turbulent transport of tracers is also enhanced during slantwise critical reflection of NIWs, which has implications for bottom hypoxia over the Texas–Louisiana shelf.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Bridging Power System Protection Gaps with Data-driven Approaches

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this research, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A convolutional neural network (CNN) based fault detection approach is implemented to achieve data translation invariance of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN–based method avoids the complicated manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on four kinds of protection gaps: high impedance faults, transformer/generator inter-turn faults, distribution system PV circuit faults, and the mis-operation situations of Zone 3 line protection relays operating under system stress. Finally, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Exploration of Rayleigh-Taylor seeding mechanisms in laser- and pulsed-power-driven inertial fusion

The ubiquitous Rayleigh-Taylor instability (RTI) has been observed in fluids and plasmas in a wide range of parameter regimes from astrophysical to laboratory plasmas. In high-energy-density (HED) laboratory plasmas, such as laser-driven inertial confinement fusion (ICF) plasmas, the RTI can have a detrimental impact on achieving fusion ignition through the generation of hydrodynamic mix. Hence, an understanding of the seeding mechanisms that produce the RTI and identifying ways to mitigate hydrodynamic mix is of critical importance. Appropriately aligned magnetic fields have been known to stabilize short-wavelength RTI. A number of numerical and experimental studies have demonstrated the benefits of using imposed magnetic fields in laser-driven ICF to achieve higher ion temeratures, higher neutron yields, and a relaxation of the ignition criteria. This work addresses seeding mechanisms in laser-driven implosions to understand critical early-stage physics that ultimately leads to substantial growth of the RTI along with mechanisms for mitigation of this growth. Surface perturbations due to machining tolerances and single-feature seeds (for example due to fill tubes) can produce substantial RTI growth in the ignition-relevant high-convergence ratio targets for laser- driven ICF implosions. RTI growth from single-feature seeding in laser-based implosions has the potential to be mitigated through appropriately aligned externally applied magnetic fields and the goal of this work has been to quantify that numerically and experimentally. Resistive-magnetohydrodynamic (MHD) simu- lations are used to study the seeding and evolution of the RTI leveraging previous support from the DOE HEDLP program. Furthermore, experimental data has been obtained and applied towards code validation of unmagnetized and magnetized evolution of single-feature seeded RTI growth.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Simulating Turbulence-aided Neutrino-driven Core-collapse Supernova Explosions in One Dimension

The core-collapse supernova (CCSN) mechanism is fundamentally 3D, with instabilities, convection, and turbulence playing crucial roles in aiding neutrino-driven explosions. Simulations of CCNSe including accurate treatments of neutrino transport and sufficient resolution to capture key instabilities remain among the most expensive numerical simulations in astrophysics, prohibiting large parameter studies in 2D and 3D. Studies spanning a large swath of the incredibly varied initial conditions of CCSNe are possible in 1D, though such simulations must be artificially driven to explode. We present a new method for including the most important effects of convection and turbulence in 1D simulations of neutrino-driven CCSNe, called Supernova Turbulence In Reduced-dimensionality, or STIR. Our new approach includes crucial terms resulting from the turbulent and convective motions of the flow. We estimate the strength of convection and turbulence using a modified mixinglength theory approach, introducing a few free parameters to the model that are fit to the results of 3D simulations. For sufficiently large values of the mixing-length parameter, turbulence-aided neutrino-driven explosions are obtained. We compare the results of STIR to high-fidelity 3D simulations and perform a parameter study of CCSN explosion using 200 solar-metallicity progenitor models from 9 to 120 M ⊙ . We find that STIR is a better predictor of which models will explode in multidimensional simulations than other methods of driving explosions in 1D. We also present a preliminary investigation of predicted observable characteristics of the CCSN population from STIR, such as the distributions of explosion energies and remnant masses.

79 ASTRONOMY AND ASTROPHYSICS↗