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At least 217 records · Page 12

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DOE OSTI2025

Joint state-parameter estimation for the reduced fracture model via the united filter

Here, in this paper, we introduce an effective United Filter method for jointly estimating the solution state and physical parameters in flow and transport problems within fractured porous media. Fluid flow and transport in fractured porous media are critical in subsurface hydrology, geophysics, and reservoir geomechanics. Reduced fracture models, which represent fractures as lower-dimensional interfaces, enable efficient multi-scale simulations. However, reduced fracture models also face accuracy challenges due to modeling errors and uncertainties in physical parameters such as permeability and fracture geometry. To address these challenges, we propose a United Filter method, which integrates the Ensemble Score Filter (EnSF) for state estimation with the Direct Filter for parameter estimation. EnSF, based on a score-based diffusion model framework, produces ensemble representations of the state distribution without deep learning. Meanwhile, the Direct Filter, a recursive Bayesian inference method, estimates parameters directly from state observations. The United Filter combines these methods iteratively: EnSF estimates are used to refine parameter values, which are then fed back to improve state estimation. Numerical experiments demonstrate that the United Filter method surpasses the state-of-the-art Augmented Ensemble Kalman Filter, delivering more accurate state and parameter estimation for reduced fracture models. This framework also provides a robust and efficient solution for PDE-constrained inverse problems with uncertainties and sparse observations.

Bayesian inference↗

DOE OSTI2025

Multi-material ALE remap with interface sharpening using high-order matrix-free finite element methods

The arbitrary Lagrangian-Eulerian (ALE) technique involves remapping field quantities from a Lagrangian mesh to an optimized mesh in a conservative, accurate and bounds-preserving manner. For methods based on arbitrary order finite elements, as described in a reference, material volume fractions are advected in pseudo-time using flux-corrected transport (FCT) without any form of interface reconstruction. In practice, this can lead to excessive propagation of small volume fractions throughout the domain. In addition, this method requires assembly of a global advection matrix to compute the bounds-preserving low-order FCT solution. In this work, we introduce a new approach for ALE remap using a high-order matrix-free technique which incorporates a flux modification to sharpen material interfaces in a conservative manner. Our approach begins with computing a bounds-preserving low-order solution to the ALE remap equations at the element level. We then compute a sharp interface solution (not guaranteed to be bounds-preserving) which comes from solving an augmented version of the ALE remap equations with a conservative flux modification which acts to sharpen material volume fractions based on their gradients and transport directions. Using the sharp interface solution, we make global corrections to the bounds-preserving solution while maintaining preservation of bounds. By blending with the sharpened solution at the global level we are able to globally conserve mass without hindering the remap pseudo-time step. This new interface-aware ALE remap method is based entirely on partial assembly techniques where globally assembled matrix operators are no longer needed, resulting in a globally matrix-free FCT method for multi-material, multi-field ALE remap with high performance on GPU architectures. We present results of our new remap method on 1D, 2D and 3D benchmarks and describe the algorithmic tailoring for GPU architectures that was developed.

Vargas, Arturo [Lawrence Livermore National Labora↗

DOE OSTI2026

Hourglass control in staggered-grid hydrodynamics using virtual element stabilization techniques

Numerical simulations using the staggered-grid hydrodynamics (SGH) discretization suffer from hourglass instabilities. In this work, we develop a stabilization method to suppress the hourglass instabilities using techniques from the virtual element method (VEM). The stiffness matrix of the VEM consists of two terms: the consistency matrix which is rank deficient and the stability matrix. Here, we first show that in two dimensions and on general polygons, the stiffness matrix of the SGH is identical to the consistency matrix of the linear VEM for both the diffusion equation and the linear elasticity equation. These analyses explain the origin of the hourglass instabilities of the SGH discretization method, and establish a theoretical foundation for our proposed stabilization method by augmenting the stiffness matrix of the SGH discretization using the VEM stability matrix. Then, we present numerical examples using Lagrangian SGH simulations. The numerical experiments demonstrate that the proposed VEM stabilization method is effective at eliminating hourglass modes in the SGH discretization.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Regional surrogates for predictive control of digital twins

Digital twins of complex systems must involve a model that is fast, generalizable, and usable for real-time control. For example, high-fidelity nonlinear multiphysics simulations can capture laser-material interactions, but are too slow for optimization or model predictive control (MPC). Reduced-order models, used to accelerate such computation, frequently fail to generalize to unseen inputs or control states. We show theoretically that this failure is intrinsic, i.e., that a learned model is non-unique outside the sampled subspace when its low-rank structure arises from limited excitation and clustered eigenvalues, rather than from a user-imposed truncation alone. Motivated by this result, we propose a control-ready regional surrogate-construction framework for both autonomous and nonautonomous dynamics; it employs Koopman lifting to represent nonlinearities, while preserving spatial locality. We illustrate our approach by constructing a control-ready surrogate for the digital twin of a thermal component of additive-manufacturing process. Our surrogate, localized in space through a von Neumann stencil, is learned from noisy high-fidelity simulations that emulate thermal-camera images collected during the manufacturing. It is linear in thermo-physically augmented states so that MPC reduces to a convex quadratic program. The surrogate requires no online correction, generalizes to unseen scan paths and power profiles of the laser, and is more than three orders of magnitude faster than a finite-difference solver. Furthermore, when the MPC sequence computed on the digital twin is applied to this solver, closed-loop temperature regulation is recovered, showing that the surrogate preserves control-relevant input-output behavior.

Data-driven model↗

DOE OSTI2026

A variational mimetic finite difference method for elliptic interface problems on non-matching polytopal meshes with geometric interface inconsistencies

A new variational mimetic finite difference method for elliptic interface problems with perfect and imperfect thermal contacts on non-matching polytopal meshes with geometric interface inconsistencies is developed and analyzed theoretically and numerically. The method is defined on multiple non-matching submeshes with gaps and overlaps along their interfaces. The discrete equations are derived from a minimization problem for the augmented Dirichlet functional. For a perfect thermal contact, the functional uses a modified mimetic gradient with extended stencil which couples unknowns from both sides of an interface, as well as penalty terms to enforce weak continuity of temperature across the interface. The method leads to a symmetric positive definite matrix for any scaling of the penalty terms. For an imperfect thermal contact, the Dirichlet functional is supplemented with a quadratic jump term along the interface related to the interface thermal resistance. We prove that the method conserves the total heat flux across each interface. In conclusion, the obtained results are verified with numerical experiments showing convergence in the discrete L 2 and L ∞ norms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Polyconvex neural network models of thermoelasticity

Machine-learning function representations such as neural networks have proven to be excellent constructs for constitutive modeling due to their flexibility to represent highly nonlinear data and their ability to incorporate constitutive constraints, which also allows them to generalize well to unseen data. Here, in this work, we extend a polyconvex hyperelastic neural network framework to (isotropic) thermo-hyperelasticity by specifying the thermodynamic and material theoretic requirements for an expansion of the Helmholtz free energy expressed in terms of deformation invariants and temperature. Different formulations which a priori ensure polyconvexity with respect to deformation and concavity with respect to temperature are proposed and discussed. The physics-augmented neural networks are furthermore calibrated with a recently proposed sparsification algorithm that not only aims to fit the training data but also penalizes the number of active parameters, which prevents overfitting in the low data regime and promotes generalization. The performance of the proposed framework is demonstrated on synthetic data, which illustrate the expected thermomechanical phenomena, and existing temperature-dependent uniaxial tension and tension-torsion experimental datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2026

Optimizing Porous Transport Layer Porosity for Proton Exchange Membrane Water Electrolysis

An empirical model is presented that describes anode-side losses related to porous transport layer (PTL) morphology in proton exchange membrane water electrolysis (PEMWE). The model is based on an advanced voltage breakdown analysis that links various overpotentials to PTL morphology. Custom Ti PTLs, spanning uncommonly low porosities (22 - 31%), were fabricated and analyzed with X-ray CT to obtain pore and particle size distributions. Particle size distributions were consistent across samples with an average particle diameter of 12.0?..mu..m, whereas average pore diameters ranged from 6.0 to 7.0?..mu..m. The PTLs were tested in standard PEMWE cell assemblies with anode catalyst loadings of 0.1 mgIr cm-2 to obtain polarization curves, electrochemical impedance spectra, and augmented Tafel analysis. The PTL-dependent anode side losses were deconvoluted and assigned to excess utilization, concentration, ion transport resistance, and electrical contact resistance overpotentials. The data and model reveal an optimal 20 - 28% PTL porosity region where utilization and contact resistance overpotentials are minimized without triggering concentration and ion transport losses related to water deprivation. The optimal PTL porosity depends on the operating current density and is demonstrated at realistic PEMWE water flow rates to establish PTL design guidance for operation at scale.

08 HYDROGEN↗

DOE OSTI2025

Leveraging explainable AI to characterize floating-point exceptions in linear solvers

Linear solver packages are central to many scientific, engineering, and machine learning applications. When floating-point exceptions occur in these solvers, e.g., division by zero or overflow, numerical results are compromised and become unreliable. Existing static and dynamic analysis tools can detect such exceptions, but they do not explain why the exceptions occur in terms of the solver inputs. Here, we present a study to characterize the inputs that cause numerical exceptions in linear solver packages. Our approach uses explainable AI (XAI) to find the most relevant characteristics of input matrices that explain the occurrence of exceptions in the solvers. Since training data in this domain is scarce, we perform extensive data gathering and data augmentation to obtain exception-inducing inputs. Our approach uses a repair strategy on the features blamed by XAI to validate that such features indeed explain the exceptions. We compare the LIME and SHAP XAI techniques using a dozen matrix features with three classifiers. We evaluate the approach on three widely used linear solver packages and find that some input characteristics can explain the occurrence of exceptions 100% of the time, in specific solvers and preconditioners.

Explainable AI↗

DOE OSTI2025

Investigation of competitive sorption and plasticization of hyperaged CANAL ladder polymers for acid gas purification

Identifying membrane materials that have exceptional separation performance and stability to complex CO 2 -containing mixtures is a pressing topic in separation science. In this work, the membrane separation performance for a recently discovered class of contorted polymers synthesized via catalytic arene-norbornene annulation (CANAL) polymerization is presented. These CANAL polymers achieve high CO 2 /CH 4 selectivity of 68 after physical aging (up to ∼1 year), which significantly augments the size-sieving capabilities of the membranes. Binary CO 2 /CH 4 and ternary H 2 S/CO 2 /CH 4 testing result in a 41 % and 50 % enhancement in selectivities, respectively, for hyperaged (∼1 year) contorted CANAL polymers, highlighting their size-sieving capabilities. The remarkably high CO 2 /CH 4 mixed-gas and combined acid gas (CAG, (CO 2 +H 2 S)/CH 4 ) selectivities of 88 and 95, respectively, for CANAL-Me-S 5 F in particular surpass both the 2018 CO 2 /CH 4 mixed-gas and CAG upper bounds. Performance stability was also investigated for high concentrations of CO 2 , revealing a reduction in CO 2 /CH 4 mixed-gas selectivity without compromising CO 2 permeability, suggesting strong sorption of CO 2 but minimal plasticization effects for short testing periods. In addition, time-dependent plasticization shows negligible effects on CO 2 /CH 4 mixed-gas selectivity despite an increase in CO 2 permeability when exposed to high concentrations of plasticizing CO 2 over an extended period of 170 h. This study provides valuable insights into hyperaged CANAL polymers and their performance in practical industrial processes.

03 NATURAL GAS↗

DOE OSTI2024

Weakly solvating ester electrolyte for high voltage sodium-ion batteries

Ethyl acetate (EA) was identified as a promising electrolyte solvent for sodium-ion batteries (SIBs), exhibiting low viscosity, cost-effectiveness, and low toxicity. Despite a significant portion of aggregation being linked to the weak solvation of Na + /EA as revealed by molecular dynamics (MD) simulations, pulsed-field gradient nuclear magnetic resonance (pfg-NMR) analysis identified a noteworthy Na+ diffusion coefficient of 3.95×10 -10 m 2 s -1 at 25°C in the presence of 1 m NaPF 6 salt. Employing fluoroethylene carbonate (FEC) as a film-forming additive to create electrode-electrolyte interphase, this electrolyte surprisingly made ~210 mAh Na 0.97 Ca 0.03 [Mn 0.39 Fe 0.31 Ni 0.22 Zn 0.08 ]O 2 (NCMFNZO)/hard carbon (HC) pouch cells achieve a lengthy cycling lifetime of 250 cycles with ~80 % capacity retention, cycled up to 4.0 V at 40°C. X-ray photoelectron spectroscopy (XPS) revealed increasing interphasial organic species over cycling, augmenting charge transfer resistance on both cathode and anode, particularly during fast charging or low temperatures (<10°C), promoting Na plating. Finally, gas chromatography-mass spectrometry combined with density functional theory identified CO 2 as the major gas generated from charged cathode/electrolyte interactions, exhibiting temperature/voltage dependence.

25 ENERGY STORAGE↗

DOE OSTI2025

An end-to-end deep learning solution for automated LiDAR tree detection in the urban environment

Cataloging and classifying trees in the urban environment is a crucial step in urban and environmental planning; however, manual collection and maintenance of this data is expensive and time-consuming. Although algorithmic approaches that rely on remote sensing data have been developed for tree detection in forests, they generally struggle in the more varied urban environment. This work proposes a novel end-to-end deep learning method for the detection of trees in the urban environment from remote sensing data. Specifically, we develop and train a novel PointNet-based neural network architecture to predict tree locations directly from LiDAR data augmented with multi-spectral imagery. We compare this model to a number of high-performing baselines on a large and varied dataset in the Southern California region, and find that our method outperforms all baselines in terms of tree detection ability (75.5% F-score) and positional accuracy (2.28 meter root mean squared error), while being highly efficient. We then analyze and compare the sources of errors, and how these reveal the strengths and weaknesses of each approach. Our results highlight the importance of fusing spectral and structural information for remote sensing tasks in complex urban environments.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Power generation forecasting for solar plants based on Dynamic Bayesian networks by fusing multi-source information

A Dynamic Bayesian network (DBN) model for solar power generation forecasting in solar plants is proposed in this paper. The key idea is to fuse sensor data, operational indicators, meteorological data, lagged output power information, and model errors for more accurate short-term (e.g., hours) and mid-term (e.g., days to weeks) power generation forecasting. The proposed DBN augments automated data-driven structure learning with expert knowledge encoding using continuous and categorical data given constraints to represent causal relationships within a solar inverter system. Additionally, an error compensation mechanism is proposed to capture temporal fluctuation. The effectiveness of the DBN on solar power generation forecasting was evaluated by rolling window analysis with one-year testing data collected from a local solar plant. The proposed DBN is compared with four state-of-art methods including support-vector regression (SVR), k-nearest neighbors (kNN), artificial neural network (ANN), and long short-term memory (LSTM) models. The result show that the proposed DBN achieves better accuracy in general, and it is not as data-hungry as some neural network-based models. The proposed DBN is also shown to have robust and consistent forecasting power with different forecasting horizons. The accuracy is 92% - 95% from one hour to one week ahead forecasting.

14 SOLAR ENERGY↗

DOE OSTI2025

Advances in high-pressure and high-temperature heat exchangers: Innovations via additive manufacturing and their applications

This paper provides a comprehensive overview of technological developments and applications for high-pressure and high-temperature (HPHT) heat exchangers produced through additive manufacturing (AM) techniques. As the demand for high-efficiency energy systems has been rising, the role of heat exchangers for thermal management and transport is becoming increasingly crucial. In this context, AM has emerged as a promising manufacturing solution to address the challenges posed by higher operating temperatures and/or pressures. This paper delves into three primary areas: the exploration of HPHT regimes supported by conventional heat exchangers, the impact of AM processes on thermal transport devices, and the discussion focused on HPHT heat exchangers produced via AM. The study examines the traditional manufacturing processes, materials, structures and geometries, and limitations of the commonly used heat exchangers for HPHT applications. Furthermore, it highlights the potential of AM in optimizing HPHT heat exchanger design, by reviewing studies that enable uniform flow distribution, minimization of fouling, and thermo-hydraulic performance augmentation without compromising structural stability. While addressing the limitations of cost, size, and other constraints, this study offers insights into the possibilities of AM in revolutionizing HPHT heat exchanger technology. The goal has been to fill the gap in literature by summarizing key studies, findings, and limitations, presenting a detailed examination, and proposing new areas for research. In conclusion, this review contributes to a deeper understanding of AM’s role in advancing HPHT heat exchangers for future energy systems.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Liquid-like solid-state diffusion of lithium ions in super-halide-rich argyrodite

The development of solid electrolytes with high ionic conductivity is essential for advancing safer, high-energy-density solid-state batteries, where lithium site distribution in the sublattice strongly affects ion transport. Here, we report a super-halide-rich argyrodite, Li 5.3 PS 4.3 Cl 1.7 , with remarkable room-temperature ionic conductivity (11.4 ± 0.7 mS cm -1 ) due to population of two additional interstitial lithium sites induced by vacancy redistribution. Prominent lithium density between lithium sites and elevated atomic displacement parameters indicate liquid-like diffusive behavior resembling sublattice melting. Combining electrochemical impedance spectroscopy, pulsed-field gradient NMR, and T 1 relaxation methods, we demonstrate that the augmented conductivity partly arises from a low energy barrier (0.08 eV) at the local scale, attributed to a three-site lithium distribution that drives correlated lithium dynamics. This work advances our understanding of the structure-dynamics interplay in super-halide-rich argyrodites, and highlighting their potential as solid-state battery electrolytes in cells with a coated single-crystal NMC82 cathode that achieve 170 mAh/g capacity at a 0.2 C rate .

25 ENERGY STORAGE↗

DOE OSTI2024

Magneto-Stokes flow in a shallow free-surface annulus

In this study, we analyse ‘magneto-Stokes’ flow, a fundamental magnetohydrodynamic (MHD) flow that shares the cylindrical-annular geometry of the Taylor–Couette cell but uses applied electromagnetic forces to circulate a free-surface layer of electrolyte at low Reynolds numbers. The first complete, analytical solution for time-dependent magneto-Stokes flow is presented and validated with coupled laboratory and numerical experiments. Three regimes are distinguished (shallow-layer, transitional and deep-layer flow regimes), and their influence on the efficiency of microscale mixing is clarified. The solution in the shallow-layer limit belongs to a newly identified class of MHD potential flows, and thus induces mixing without the aid of axial vorticity. We show that these shallow-layer magneto-Stokes flows can still augment mixing in distinct Taylor dispersion and advection-dominated mixing regimes. The existence of enhanced mixing across all three distinguished flow regimes is predicted by asymptotic scaling laws and supported by three-dimensional numerical simulations. Mixing enhancement is initiated with the least electromagnetic forcing in channels with order-unity depth-to-gap-width ratios. If the strength of the electromagnetic forcing is not a constraint, then shallow-layer flows can still yield the shortest mixing times in the advection-dominated limit. Our robust description of momentum evolution and mixing of passive tracers makes the annular magneto-Stokes system fit for use as an MHD reference flow.

58 GEOSCIENCES↗

DOE OSTI2025

Molecular fluctuations inhibit intermittency in compressible turbulence

In the standard picture of fully developed turbulence, highly intermittent hydrodynamic fields are nonlinearly coupled across scales, where local energy cascades from large scales into dissipative vortices and large density gradients. Microscopically, however, constituent fluid molecules are in constant thermal (Brownian) motion, but the role of molecular fluctuations in large-scale turbulence is largely unknown, and with rare exceptions, it has historically been considered irrelevant at scales larger than the molecular mean free path. Recent theoretical and computational investigations have shown that molecular fluctuations can impact energy cascade at Kolmogorov length scales. Here, we show that molecular fluctuations not only modify energy spectrum at wavelengths larger than the Kolmogorov length in compressible turbulence, but also significantly inhibit spatio-temporal intermittency across the entire dissipation range. Using large-scale direct numerical simulations of computational fluctuating hydrodynamics, we demonstrate that the extreme intermittency characteristic of turbulence models is replaced by nearly Gaussian statistics in the dissipation range. These results demonstrate that the compressible Navier–Stokes equations should be augmented with molecular fluctuations to accurately predict turbulence statistics across the dissipation range. Our findings have significant consequences for turbulence modelling in applications such as astrophysics, reactive flows and hypersonic aerodynamics, where dissipation-range turbulence is approximated by closure models.

compressible turbulence↗

DOE OSTI2024

Characterization of MoS 2 films via simultaneous grazing incidence X-ray diffraction and grazing incidence X-ray fluorescence (GIXRD/GIXRF)

Physical vapor deposited (PVD) molybdenum disulfide (nominal composition MoS 2 ) is employed as a thin film solid lubricant for extreme environments where liquid lubricants are not viable. The tribological properties of MoS 2 are highly dependent on morphological attributes such as film thickness, orientation, crystallinity, film density, and stoichiometry. These structural characteristics are controlled by tuning the PVD process parameters, yet undesirable alterations in the structure often occur due to process variations between deposition runs. Nondestructive film diagnostics can enable improved yield and serve as a means of tuning a deposition process, thus enabling quality control and materials exploration. Grazing incidence X-ray diffraction (GIXRD) for MoS 2 film characterization provides valuable information about film density and grain orientation (texture). However, the determination of film stoichiometry can only be indirectly inferred via GIXRD. The combination of density and microstructure via GIXRD with chemical composition via grazing incidence X-ray fluorescence (GIXRF) enables the isolation and decoupling of film density, composition, and microstructure and their ultimate impact on film layer thickness, thereby improving coating thickness predictions via X-ray fluorescence. We have augmented an existing GIXRD instrument with an additional X-ray detector for the simultaneous measurement of energy-dispersive X-ray fluorescence spectra during the GIXRD analysis. This combined GIXRD/GIXRF analysis has proven synergetic for correlating chemical composition to the structural aspects of MoS 2 films provided by GIXRD. We present the usefulness of the combined diagnostic technique via exemplar MoS 2 film samples and provide a discussion regarding data extraction techniques of grazing angle series measurements.

MoS2↗

DOE OSTI2025

Assessing the Impact of Measurement Precision on Metabolite Identification Probability in Multidimensional Mass Spectrometry-Based, Reference-Free Metabolomics

Identification of compounds with minimal ambiguity remains a central challenge in mass spectrometry-based metabolomics. Conventional compound identification relies on comparing analytical signatures (e.g., mass-to-charge ratio, collision cross section, tandem mass spectra) against reference data obtained from measurements of authentic chemical standards. The breadth of annotatable compounds using this approach is necessarily limited by availability of authentic standards, analytical throughput, and resolving power of the separations that underly the measurements. The maturation of computational methods, both theory-driven and artificial intelligence/machine learning-based, for prediction of various molecular properties relevant to multidimensional mass spectrometry measurements has opened the door to a new “reference-free” paradigm of compound annotation. Through augmenting existing reference data for molecular properties with computational predictions, the universe of identifiable chemical species can be expanded significantly beyond its current limits. An unexplored aspect of this novel approach is understanding how to gauge confidence in resulting annotations, especially as the compound search space is expanded. Intuitively, the confidence of a compound annotation is related to the inherent discriminatory power of the molecular properties used for identification, as well as the precision with which the properties are measured or predicted. In this work, we characterize this relationship between measurement precision and identification probability in a systematic and quantitative fashion for a defined region of chemical space that includes organic small molecule metabolites. Importantly, this work establishes a framework for conducting metabolite identification probability analysis that enables others to quantify this relationship for their own compounds and properties of interest.

Metabolite Identification↗