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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine‐Learning‐Driven Exploration of Surface Reconstructions of Reduced Rutile TiO 2

Titanium dioxide (TiO 2 ) is widely used as a catalyst support due to its stability, tunable electronic properties, and surface oxygen vacancies, which are crucial for catalytic processes such as the reverse water-gas shift (RWGS) reaction. Reduced TiO 2 surfaces undergo complex surface reconstructions that endow unique properties but are computationally challenging to describe. In this study, we utilize machine-learning interatomic potentials (MLIPs) integrated with an active-learning workflow to efficiently explore reduced rutile TiO 2 surfaces. This approach enabled the prediction of a phase diagram as a function of oxygen chemical potential, revealing a variety of reconstructed phases, including a previously unreported subsurface shear plane structure. We further investigate the electronic properties of these surfaces and validate our results by comparing experimental and theoretical high-resolution transmission electron microscopy (HRTEM). Our findings provide new insights into how extreme surface reductions influence the structural and electronic properties of TiO 2 , with potential implications for catalyst design.

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

Quantification of Swelling in Hematite Pellets Reduced Using Hydrogen–Nitrogen Gas Mixture

Iron ore pellets are reduced in a 50%H 2 –50%N 2 1 atm gas mixture at 750, 800, 850, 900, and 950 °C while simultaneously documenting swelling (change in pellet radius) and weight change. Swelling increases with increasing temperature, with catastrophic swelling (>20% of reduction swelling index) observed at 850, 900, and 950 °C. As the pellet is reduced, the pellet radius increases until 40–50% reduction is achieved, followed by a decrease in diameter beyond 40–50% reduction at 750 and 850 °C. At 950 °C, the pellet radius continues to increase with additional pellet reduction without any subsequent decrease in diameter. Scanning electron microscopy (SEM) analysis shows that the neighboring grains inside the pellet sinter together at 750 and 850 °C, whereas the individual grains sinter internally at 950 °C. SEM analysis and observations suggest that the reduction process at 750 and 850 °C can be approximated as a topochemical reaction process, while the reduction process at 950 °C can no longer be approximated as a topochemical reaction process. In conclusion, an empirical equation for the radius of the pellet is derived with fitting parameters dependent on temperature and the degree of reduction of the pellet undergoing reduction based on the experimental data.

08 HYDROGEN↗
DOE OSTI2024

Tensor network representation of non-abelian gauge theory coupled to reduced staggered fermions

We show how to construct a tensor network representation of the path integral for reduced staggered fermions coupled to a non-abelian gauge field in two dimensions. The resulting formulation is both memory and computation efficient because reduced staggered fermions can be represented in terms of a minimal number of tensor indices while the gauge sector can be approximated using Gaussian quadrature with a truncation. Numerical results obtained using the Grassmann TRG algorithm are shown for the case of SU(2) lattice gauge theory and compared to Monte Carlo results.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2025

Effectiveness of nature-based solutions to reduce flooding in Quad Cities Metro Area (QCMA) using SWMM-HEC based flood model

Nature-based solutions (NbS) have gained significant attention as strategies for addressing urban environmental challenges, particularly since the establishment of the UN Sustainable Development Goals (SDGs) for 2030. However, the current research on NbS for urban flood management lacks comprehensive methodological approaches for identifying suitable areas and evaluating their effectiveness across different urban settings. Here, this study attempts to fill this gap by proposing a methodological framework integrating multi-criteria analysis with a SWMM-HEC-based hydrologic and hydraulic (HH) model to assess the suitability of NbS for the Quad Cities Metro Area (QCMA), consisting of Davenport, Bettendorf, Moline, and Rock Island. Eight NbS options-green roofs, rain gardens, infiltration trenches, permeable pavements, vegetative swales, dry detention basins, retention ponds, and rain barrels/cisterns - were considered based on volumetric efficiency and runoff reduction efficiency. The study reveals that implementing the proposed NbS could have substantially reduced flood depths in key historical flood events by 21% in 1993, 15% in 2008, 16% in 2011, 23% in 2014, 40% in 2019, and 10% in 2023. The findings highlight a critical trade-off between peak runoff and NbS implementation: while NbS effectively reduce flood impacts, they also enhance volumetric efficiency by approximately 43%. In high-density areas of the QCMA, flood depth reductions of around 20% suggest that NbS are a viable solution for dense urban environments with limited space. This shows the potential for integrating NbS into existing infrastructure, offering a promising approach for cities facing increasing flooding risks. The proposed methodology provides a practical framework for incorporating NbS into urban stormwater management, addressing gaps in optimizing NbS performance, and offering a pathway to scale their application in other urban areas with various environmental and social contexts.

CMIP6↗
DOE OSTI2025

Simulated annealing of reduced magnetohydrodynamic systems

Theory of simulated annealing (SA), a method for equilibrium and stability analyses for Hamiltonian systems, is reviewed. The SA explained in this review is based on a double bracket formulation that derives from Hamiltonian structure. In addition to general theoretical aspects, the explicit formulation as well as numerical applications are presented. Both finite and infinite degree-of-freedom systems are treated, in particular, the heavy top, a toy model mimicking low-beta reduced magnetohydrodynamics (MHD) and low- and high-beta reduced MHD. Furthermore, the numerical results successfully demonstrate the usefulness of SA for equilibrium and stability analyses. At the same time, the results raise some future issues that are discussed in the paper.

Poisson Bracket↗
DOE OSTI2024

Vacuum-assisted extrusion to reduce internal porosity in large-format additive manufacturing

Large-scale 3D printing of polymer composite structures has gained popularity and seen extensive use over the last decade. Much of the research related to improving the mechanical properties of 3D-printed parts has focused on exploring new materials and optimizing print parameters to improve geometric control and minimize voids between printed beads. However, porosity at the microstructural level (within the printed bead) has been much less studied although it is almost universally observed at levels of 4 %-10 % when using fiber reinforced materials. This study introduces a vacuum-assist approach that minimizes internal porosity by removing ambient air from the interstitial space between pellets in the hopper and acts as a negative pressure vent for gases that evolve during the initial stages of single-screw extrusion. Vacuum-assisted extrusion was able to reduce porosity below 2 % across a wide range of processing parameters, moisture content, fiber reinforcements, and printing platforms. Specifically, when printing on a large-format extruder (Strangpresse Model-30), the vacuum-assisted extrusion reduced internal porosity by 35–75 % compared to conventional non-vacuum extrusion, and only pores with length scale > 2 microns are affected. The success of this approach prompted the design of a patent-pending continuous vacuum hopper relevant for large-scale 3D printing on commercial systems.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Reduced soil diazotroph diversity decreases nitrogen fixation rates, but depends on land management

Soil diazotrophs convert atmospheric nitrogen into plant-available ammonium through free-living nitrogen fixation (FLNF). This sustainable nitrogen source can reduce our dependence on synthetic fertilizer inputs in conventional agricultural systems. However, we know little about the effect of diazotroph diversity on FLNF, especially given that FLNF is intermediate within the broad-narrow functional spectrum. Here, we determined how management-mediated shifts in diazotroph diversity would impact their ecosystem function (FLNF) by quantifying diazotroph diversity across a long-term management gradient during and after the growing season. In addition to field observations, we leveraged the same management gradient to manipulate diversity in soil microcosms via chloroform fumigation exposure. In the field, diazotroph diversity was significantly higher after the growing season, and the biologically-based annual cropping system harbored the highest diazotroph diversity. However, perennial cropping systems maintained the highest FLNF despite lower diazotroph diversity, and both soil moisture and temperature were stronger predictors of FLNF. Based on these results, integrating diverse perennial crops into agricultural landscape could result in greater N from FLNF, particularly at the end of the growing season. When we reduced biodiversity in a manipulation experiment, the diversity-FLNF association was stronger than in the field experiment, suggesting that FLNF communities are not as functionally redundant as taxonomically ‘broad’ ecosystem functions. Here, the strength of diversity-FLNF correlation varied by previous land management. Diazotroph diversity better predicted FLNF in annual and forest soil microcosms, and microbial biomass carbon better predicted FLNF in perennial soil microcosms. Taken together, our results show that while diazotroph diversity influences FLNF, especially under extreme environmental disturbances, abiotic factors like soil moisture and temperature are stronger constraints on FLNF in the field.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Speeding up and reducing memory usage for scientific machine learning via mixed precision

Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neural networks (PINNs) and deep operator networks (DeepONets) stand out as the leading techniques for solving partial differential equations by incorporating both physical equations and experimental data. However, training PINNs and DeepONets require significant computational resources, including long computational times and large amounts of memory. In search of computational efficiency, training neural networks using half precision (float16) rather than the conventional single (float32) or double (float64) precision has gained substantial interest, given the inherent benefits of reduced computational time and memory consumed. However, we find that float16 cannot be applied to SciML methods, because of gradient divergence at the start of training, weight updates going to zero, and the inability to converge to a local minima. To overcome these limitations, we explore mixed precision, which is an approach that combines the float16 and float32 numerical formats to reduce memory usage and increase computational speed. Our experiments showcase that mixed precision training not only substantially decreases training times and memory demands but also maintains model accuracy. Here, we also reinforce our empirical observations with a theoretical analysis. The research has broad implications for SciML in various computational applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations

We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions (KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space to the state variable latent space. An approximate Bayesian method is developed for uncertainty quantification (UQ) in the proposed KL-DNN surrogate model. The KL-DNN method is tested for the linear advection–diffusion and nonlinear diffusion equations, and the Bayesian approach for UQ is compared with the deep ensembling (DE) approach, commonly used for quantifying uncertainty in DNN models. It was found that the approximate Bayesian method provides a more informative distribution of the PDE solutions in terms of the coverage of the reference PDE solutions (the percentage of nodes where the reference solution is within the confidence interval predicted by the UQ methods) and log predictive probability. The DE method is found to underestimate uncertainty and introduce bias. For the nonlinear diffusion equation, we compare the KL-DNN method with the Fourier Neural Operator (FNO) method and find that KL-DNN is 10% more accurate and needs less training time than the FNO method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Nonintrusive projection-based reduced order modeling using stable learned differential operators

Nonintrusive projection-based reduced order models (ROMs) are essential for dynamics prediction in multi-query applications where underlying governing equations are known but the access to the source of the underlying full order model (FOM) is unavailable; that is, FOM is a glass-box. This article proposes a learn-then-project approach for nonintrusive model reduction. In the first step of this approach, high-dimensional stable sparse learned differential operators (S-LDOs) are determined using the generated data. In the second step, the ordinary differential equations, comprising these S-LDOs, are used with suitable dimensionality reduction and low-dimensional subspace projection methods to provide equations for the evolution of reduced states. This approach allows easy integration into the existing intrusive ROM framework to enable nonintrusive model reduction while allowing the use of Petrov–Galerkin projections. The applicability of the proposed approach is demonstrated for Galerkin and LSPG projection-based ROMs through four numerical experiments: 1-D scalar advection, 1-D Burgers, 2-D scalar advection and 1-D scalar advection–diffusion–reaction equations. In conclusion, the results indicate that the proposed nonintrusive ROM strategy provides accurate and stable dynamics prediction.

42 ENGINEERING↗
DOE OSTI2025

Data-driven reduced-order models for port-Hamiltonian systems with operator inference

Hamiltonian operator inference has been developed in Sharma et al. (2022) to learn structure-preserving reduced-order models (ROMs) for Hamiltonian systems. The method constructs a low-dimensional model using only data and knowledge of the functional form of the Hamiltonian. The resulting ROMs preserve the intrinsic structure of the system, ensuring that the mechanical and physical properties of the system are maintained. In this work, we extend this approach to port-Hamiltonian systems, which generalize Hamiltonian systems by including energy dissipation, external input, and output. Based on snapshots of the system’s state and output, together with the information about the functional form of the Hamiltonian, reduced operators are inferred through optimization and are then used to construct data-driven ROMs. To further alleviate the complexity of evaluating nonlinear terms in the ROMs, a hyper-reduction method via discrete empirical interpolation is applied. Accordingly, we derive error estimates for the ROM approximations of the state and output. Lastly, we demonstrate the structure preservation, as well as the accuracy of the proposed port-Hamiltonian operator inference framework, through numerical experiments on a linear mass–spring-damper problem and a nonlinear Toda lattice problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Non-intrusive reduced-order modeling for dynamical systems with spatially localized features

This work presents a non-intrusive reduced-order modeling framework for dynamical systems with spatially localized features characterized by slow singular value decay. The proposed approach builds upon two existing methodologies for reduced and full-order non-intrusive modeling, namely Operator Inference (OpInf) and sparse Full-Order Model (sFOM) inference. We decompose the domain into two complementary subdomains that exhibit fast and slow singular value decay. The dynamics of the subdomain exhibiting slow singular value decay are learned with sFOM while the dynamics with intrinsically low dimensionality on the complementary subdomain are learned with OpInf. The resulting, coupled OpInf-sFOM formulation leverages the computational efficiency of OpInf and the high resolution of sFOM, and thus enables fast non-intrusive predictions for conditions beyond those sampled in the training data set. A novel regularization technique with a closed-form solution based on the Gershgorin disk theorem is introduced to promote stable sFOM and OpInf models. We also provide a data-driven indicator for subdomain selection and ensure solution smoothness over the interface via a post-processing interpolation step. We evaluate the efficiency of the approach in terms of offline and online speedup through a quantitative, parametric computational cost analysis. We demonstrate the coupled OpInf-sFOM formulation for two test cases: a one-dimensional Burgers’ model for which accurate predictions beyond the span of the training snapshots are presented, and a two-dimensional parametric model for the Pine Island Glacier ice thickness dynamics, for which the OpInf-sFOM model achieves an average prediction error on the order of 1% with an online speedup factor of approximately 8$\times$ compared to the numerical simulation.

42 ENGINEERING↗
DOE OSTI2026

Reduced-order CFD modeling of cryogenic hydrogen isotope extrusion for pellet fueling

This study presents a reduced-order model (ROM) for computational fluid dynamics (CFD) simulations of cryogenic hydrogen isotope extrusions, focusing on protium (H₂) and deuterium (D₂) piston extruders. Using a 2D axisymmetric ROM in ANSYS-Polyflow, significant computational savings were achieved (runtime reduced from 9∼24 h to 3∼5 min), with extrusion force discrepancies between the 2D ROM and 3D models being on the order of 1%. Parametric studies identified optimal cutoff shear rates in the viscosity model (0.01/s for H₂ and 0.001/s for D₂), providing recommendations for future simulations. Finally, a comprehensive comparison of ROM results with experimental data was performed across varying geometries, cryogenic materials, temperatures, extrusion lengths, and piston velocities. Predictions at low extrusion temperatures met the objective of providing quick and efficient solutions with an acceptable extrusion force error of approximately 10% or less, validating the effectiveness of the 2D ROM approach. However, at high temperatures closer to the triple point, extrusion force error grows, which necessitates developing an improved model that accounts for temperature effects, e.g. melting. Nevertheless, the findings still represent a significant improvement in efficiency of CFD modeling of cryogenic hydrogenic extrusion. The ROM framework can also be extended to tritium (T2) and screw extruders, which will ultimately provide a fast and effective tool for optimizing pellet injector design for ITER and future reactor systems.

Fan, Joy [ORNL] (ORCID:0000000229751735)↗
DOE OSTI2025

Evaluating methods to reduce duration of near-threshold fatigue crack growth rate measurements for low-alloy steels in hydrogen gas

Measurement of the near-threshold fatigue crack growth rate (da/dN) vs. stress-intensity factor range (∆K) relationship in hydrogen gas is essential for maximizing the calculated design fatigue life of high-pressure hydrogen storage vessels. However, such measurements are rarely performed, since the low cyclic loading frequencies applied in standard practice lead to prohibitively protracted test durations. The objective of this study was to demonstrate two means for reducing test durations when measuring near-threshold da/dN vs. ΔK relationships under decreasing ΔK for low-alloy pressure vessel steels in hydrogen gas: 1) imposing steeper K-gradients relative to the recommended limits in standards such as ASTM E647, and 2) increasing cyclic loading frequency relative to typical values applied during fatigue crack growth testing of low-alloy steels in hydrogen gas. Recognizing that steeper K-gradients could amplify loading-history effects, test methods employing this approach were designed to mitigate such effects by either maintaining constant K max or gradually increasing the K-gradient as the threshold was approached. Although the varying K-gradient method was vulnerable to loading-history effects in the form of plasticity-induced crack closure, particularly at lower stress ratio (R) and higher starting K max values, these effects could be compensated by applying the adjusted compliance ratio (ACR) method. Here, it was demonstrated that steeper K-gradients in concert with increased cyclic loading frequency reduced the duration of near-threshold fatigue crack growth tests in hydrogen gas by more than 99% relative to standard practices.

Fatigue threshold↗
DOE OSTI2026

ECLEIRS: Exact conservation law embedded identification of reduced states for parameterized nonlinear conservation laws from sparse and noisy data

Multi-query applications such as parameter estimation, uncertainty quantification and design optimization for parameterized partial differential equation (PDE) systems are expensive. While reduced/latent state dynamics approaches for parameterized PDEs offer a viable alternative, these approaches rely on high-quality data and struggle with highly sparse spatiotemporal noisy measurements typically obtained from experiments. Furthermore, there is no guarantee that these models satisfy governing physical conservation laws. In this article, we propose a reduced state dynamics approach, referred to as ECLEIRS, that embeds exact conservation in the solution and flux representation by utilizing a space-time divergence-free neural network formulation. We compare ECLEIRS with other reduced state dynamics approaches, those that do not enforce any physical constraints and those with physics-informed loss functions, for three shock-propagation problems: 1-D advection, 1-D Burgers and 2-D Euler equations. In conclusion, the numerical experiments conducted in this study demonstrate that ECLEIRS provides the most accurate prediction of dynamics for unseen parameters even in the presence of highly sparse and noisy data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Efficient data-driven regression for reduced-order modeling of spatial pattern formation

We present an efficient data-driven regression approach for constructing reduced-order models (ROMs) of reaction-diffusion systems exhibiting pattern formation. The ROMs are learned non-intrusively from available training data of physically accurate numerical simulations. The method can be applied to general nonlinear systems through the use of polynomial model form, while not requiring knowledge of the underlying physical model, governing equations, or numerical solvers. The process of learning ROMs is posed as a low-cost least-squares problem in a reduced-order subspace identified via Proper Orthogonal Decomposition (POD). Numerical experiments on classical pattern-forming systems–including the Schnakenberg and Mimura–Tsujikawa models–demonstrate that higher-order surrogate models significantly improve prediction accuracy while maintaining low computational cost. The proposed method provides a flexible, non-intrusive model reduction framework, well suited for the analysis of complex spatio-temporal pattern formation phenomena.

Data-driven modeling↗
DOE OSTI2026

Neural network reconstruction of the DIII-D tokamak plasma boundary using a reduced set of diagnostics

This study investigates the feasibility of reconstructing the last closed flux surface in the DIII-D tokamak using neural network models trained on reduced input feature sets, addressing an ill-posed task. Two models are compared: one trained solely on coil currents and another incorporating coil currents, plasma current and loop voltage. The model trained exclusively on coil currents achieved a mean point displacement of $0.04$ m on a held-out test set, while the inclusion of plasma current and loop voltage reduced the error to $0.03$ m. This comparison highlights the trade-offs between input feature complexity and reconstruction accuracy, demonstrating the potential of machine learning algorithms to perform effectively in data-limited environments, such as those expected in fusion power plants due to diagnostic constraints imposed by the presence of blankets and shielding.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Reducing the Cost of Energy Differences in Variational Monte Carlo with Spotlight Sampling

Here, we investigate an approximate sampling scheme that can significantly reduce the cost scaling of variational Monte Carlo when it is employed to predict the energy differences associated with local chemical changes. Inspired by side-chaining and embedding methods, this spotlight sampling approach adopts an approximate fragmented Hamiltonian and correlated sampling to reduce cost scaling to the point that it is essentially linear with system size, with the potential to go sublinear if certain conditions are met. In tests on bond stretching energies in alcohols, hydrogen dimer chains, and molecules with various degrees of π-system delocalization, we observe the anticipated linear scaling and an explicit cost crossover with standard variational Monte Carlo.

Bumann, Sonja [University of California, Berkeley,↗