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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Machine learning informed rational design of high entropy double perovskite oxide universal air/steam electrodes for solid oxide electrochemical cells

Due to their high efficiency and versatility, solid oxide electrochemical cells (SOCs) are poised to play a significant role in future energy conversion and storage applications. In recent years, SOCs have bifurcated into two distinct categories: traditional oxygen-ion conducting SOCs that typically operate from ∼650—850 °C and the more recent proton-conducting ceramic (PCC) SOCs that typically operate from ∼400—650 °C. Current performance and lifetime of both oxygen-ion conducting SOCs and PCCs is primarily limited by the air/steam electrode, which facilitates the oxygen reduction reaction (ORR) during fuel cell operation and must also facilitate the oxygen evolution reaction (OER) during electrolysis operation. Here, we present a newly designed high-entropy double perovskite oxide suitable as a universal ORR/OER electrode for both oxygen-ion conducting SOCs and PCCs. Machine learning methods are applied to identify chemical descriptors for highly catalytic high-entropy double perovskite oxides (AA’B 2 O 6 ) across a large compositional space. Based on the machine-learning guidance, we ultimately converge on Ba 0.9 Cs 0.1 (Ca 0.2 Gd 0.2 La 0.2 Pr 0.2 Sr 0.2 )Co 1.5 Fe 0.5 O 6 (CsBaHEO) as a universal air/steam electrode. Structure stabilization is accomplished by an equimolar five-cation high-entropy composition on the A’-site, while cesium substitution on the A-site enhances the electrical conductivity and leads to a higher oxygen vacancy concentration. This material exhibits versatility and high performance in reversible oxygen-ion SOCs, reversible PCCs, and also large-scale tubular PCCs. For example, the CsBaHEO-based PCC reaches 1018 mW∙cm −2 at 600°C, while a large-scale tubular PCC using CsBaHEO for electrolysis achieves a hydrogen production rate of 21.314 ML∙min −1 at 600 °C.

Cell↗
DOE OSTI2025

Sharp front tracking with geometric interface reconstruction

Here, this paper presents a novel sharp front-tracking method designed to address limitations in classical front-tracking approaches, specifically their reliance on smooth interpolation kernels and extended stencils for coupling the front and fluid mesh. In contrast, the proposed method employs exclusively sharp, localized interpolation and spreading kernels, restricting the coupling to the interfacial fluid cells–those containing the interface/front. This localized coupling is achieved by integrating a divergence-preserving velocity interpolation method with a piecewise parabolic interface calculation (PPIC) and a polyhedron intersection algorithm to compute the indicator function and local interface curvature. Surface tension is computed using the Continuum Surface Force (CSF) method, maintaining consistency with the sharp representation. Additionally, we propose an efficient local roughness smoothing implementation to account for surface mesh undulations, which is easily applicable to any triangulated surface mesh. Building on our previous work, the primary innovation of this study lies in the localization of the coupling for both the indicator function and surface tension calculations. By reducing the interface thickness on the fluid mesh to a single cell, as opposed to the 4–5 cell spans typical in classical methods, the proposed sharp front-tracking method achieves a highly localized and accurate representation of the interface. This sharper representation mitigates parasitic currents and improves force balancing, making it particularly suitable for scenarios where the interface plays a critical role, such as microfluidics, fluid-fluid interactions, and fluid-structure interactions. The proposed method is comprehensively validated and tested on canonical interfacial flow problems, including stationary and translating Laplace equilibria, oscillating droplets, and rising bubbles. The presented results demonstrate that the sharp front-tracking method significantly outperforms the classical approach in terms of accuracy, stability, and computational efficiency. Notably, parasitic currents are reduced by approximately two orders of magnitude and stable results are obtained for parameter ranges where classical front tracking fails to converge.

42 ENGINEERING↗
DOE OSTI2026

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high-dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed-form, multidimensional distributions that preserve spatial correlation while remaining computationally tractable. In this work, we present a framework for training neural networks with a multidimensional Gaussian loss, generating a closed-form predictive distribution over outputs informed by non-identically distributed training data. Our approach captures aleatoric uncertainty by iteratively estimating the means and covariance matrices, and is demonstrated on a super-resolution example out-of-training-sample. We leverage a Fourier representation of the covariance matrix to stabilize network training and preserve spatial correlation. We introduce a novel regularization strategy—referred to as information sharing—that interpolates between image-specific and global covariance estimates, enabling convergence of the super-resolution downscaling network trained on image-specific distributional loss functions. This framework allows for efficient sampling, explicit correlation modeling, and extensions to more complex distribution families all without disrupting prediction performance. We demonstrate the method on a surface wind speed downscaling task and discuss its broader applicability to uncertainty-aware prediction in scientific models.

17 WIND ENERGY↗
DOE OSTI2024

Artificial correlation heating in PIC simulations

The Particle-in-Cell (PIC) method, a cornerstone in plasma modeling, is widely employed for its ability to simulate kinetic phenomena in device-scale domains. Part of what makes this possible is that computational macroparticles represent many physical particles. It converges under certain constraints, including a grid spacing that resolves the Debye length and a time step small enough to respect the Courant–Friedrichs–Lewy condition and plasma frequency stability limit. Here, we introduce a new constraint necessary to avoid Artificial Correlation Heating (ACH). This requires that the macroparticle coupling strength be smaller than one, Γ ω < 1, where Γ ω ≡ Γω 2/3 ⁠, Γ = Z 2 e 2 /(4πε 0 ak B T) is the physical coupling strength, and w is the macroparticle weight. This is particularly relevant to 3D simulations of dense plasmas, which are becoming common with modern computing power. If this condition is violated, the finite macroparticle weight artificially enhances the coupling strength and causes the plasma to heat until the macroparticle coupling strength is near unity, depending on the grid resolution. A comprehensive model of ACH is developed that incorporates electron density, temperature, macroparticle weight, and grid resolution. It is then tested using PIC simulations, delineating the boundaries of the method's applicability and offering a predictive framework for ACH. Moreover, the research explores a runaway heating process induced by ACH in the presence of ionization, which can lead to numerical instability. A conclusion of this study is that the onset of ACH can impose a more stringent constraint on the macroparticle weight and average number of macroparticles per cell than what is typically expected, particularly in 3D simulations of dense plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Towards Robustness Guarantees for Feedback-Based Optimization

Feedback-based online optimization algorithms have gained traction in recent years because of their simple implementation, their ability to reject disturbances in real time, and their increased robustness to model mismatch. While the robustness properties have been observed both in simulation and experimental results, the theoretical analysis in the literature is mostly limited to nominal conditions. In this work, we propose a framework to systematically assess the robust stability of feedback-based online optimization algorithms. We leverage tools from monotone operator theory, variational inequalities and classical robust control to obtain tractable numerical tests that guarantee robust convergence properties of online algorithms in feedback with a physical system, even in the presence of disturbances and model uncertainty. The results are illustrated via an academic example and a case study of a power distribution system.

approximation algorithms↗
DOE OSTI2026

From bulk to surface: Structure and dynamics of amorphous alumina from deep potential molecular dynamics

Understanding the atomic-scale structure and dynamics of amorphous oxide surfaces is essential for interpreting their chemical reactivity, mechanical stability, and interfacial behavior, yet direct experimental characterization remains challenging. We employ Deep Potential (DP) molecular dynamics to generate large-scale, ab initio -quality models of amorphous Al 2 O 3 bulk glasses and melt-quenched free surfaces, enabling a quantitative analysis of both structure and relaxation dynamics with statistical confidence inaccessible to direct ab initio simulation. The trained DP model reproduces experimental liquid and glass structure, captures the cooling-rate dependence of the bulk glass transition, and corrects systematic biases in the polyhedral populations predicted by widely used classical force fields. At the free surface, mass density recovers to bulk values over ~10 Å, while local coordination requires a slightly wider subsurface region to fully converge. The outermost layer is oxygen-enriched, exhibits altered polyhedral connectivity with contracted Al–O bonds, and hosts a broad population of under-coordinated motifs (notably AlO 3 and OAl 2 ) whose abundances are governed by glass stability. These under-coordinated surface motifs exhibit distinct vibrational signatures and occur as locally paired Lewis acid and Brønsted base sites consistent with bond-valence compensation, yet remain spatially dispersed rather than aggregating into extended clusters. Despite this pronounced structural heterogeneity, surface relaxation and the glass-transition temperature remain comparable to their bulk counterparts, suggesting that the disordered surface is kinetically stable once formed. Together, these results establish a molecular-level picture of amorphous alumina surfaces and demonstrate the capability of machine-learned potentials to resolve structure–property relationships in disordered oxide interfaces.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Structural Tolerance of Zirconium Diboride under Electron Irradiation through in-situ Convergent Beam Electron Diffraction and Energy-dispersive X-ray Spectroscopy

Zirconium diboride (ZrB 2 ) is a ultra-high temperature ceramic with high melting temperature (>3000 K), super strength and hardness, good thermal conductivity, and excellent resistance to plasma erosion and chemical erosion, being widely applied in hypersonic flight, arc-electrodes, refractory materials, and plasma-facing materials in fusion reactors. Therefore, the structural stability of the material is essential for the applications. Up to now, the material was investigated under neutron irradiation and high-energy ion irradiations. It was reported that the neutron irradiation caused swelling and catastrophic cracking. High-energy ions (30 keV He + ) changed the surface morphology and caused the bright blue and brown colors on ZrB 2 surfaces between 920 K and 1120 K. The electron irradiation tolerance of the material has not been reported yet. Here we used convergent beam electron diffraction (CBED) and energy-dispersive X-ray spectroscopy (EDS) to investigate structural tolerances of ZrB 2 crystals under high-energy electrons at room temperature under vacuum. Finally, the investigation would benefit the understanding of its structural tolerance at microscales.

42 ENGINEERING↗
DOE OSTI2022

Application of linear prolongation to coarse mesh finite difference acceleration in CASMO5

The Coarse-Mesh Finite Difference (CMFD) method has been used for over a decade to accelerate the convergence of the Method of Characteristics (MOC) solution to the two- dimensional particle transport equation in CASMO5. Numerical testing, along with widespread use in production-level calculations, have shown that the current CMFD implementation provides stability and robustness for a wide range of realistic reactor physics problems. However, the recent development of linear prolongation has attracted attention from the community as a way to further improve the performance and stability of CMFD. Two interpolation methods for linear prolongation are presented in this work and implemented into a test version of CASMO5. The performance of the proposed interpolations, referred to as the bilinear and linear directional schemes, is evaluated in terms of runtime relative to the default constant or uniform scaling update. Numerical results indicate that the use of linear prolongation can reduce the transport solver runtime on average by approximately 10% when tested with two hundred randomly selected cases. The new directional linear interpolation, combined with default constant boundary updates, is found to provide the highest reduction in runtime for the cases analyzed. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

A two-fluid single-column model of turbulent shallow convection. Part II: Single-column model formulation and numerics

The two-fluid single-column model of Thuburn et al. (Quart. J. R. Meteorol. Soc., 2019, 145, 1535–1550) is extended to include moisture and horizontal wind shear. Turbulent kinetic energy is introduced as a prognostic variable, dependence on a diagnosed boundary-layer height is removed, and subfilter fluxes are approximated using a two-fluid version of a Mellor–Yamada scheme. Three mechanisms for entrainment and detrainment processes are introduced, which represent entrainment of unstable air at the surface, forced detrainment of air at the top of the boundary/cloud layers, and turbulent mixing that relaxes the convective fluid to a reference profile. A semi-implicit Eulerian discretization replaces the semi-implicit semi-Lagrangian implementation of Thuburn et al. (Quart. J. R. Meteorol. Soc., 2019, 145, 1535–1550) to improve numerical stability and conservation. The equations for the implicit time step are solved using a quasi-Newton method, which is shown to perform well in numerical tests for conservation and convergence. The two-fluid single-column model presented in this article will be applied to simulations of shallow cumulus convection in Part III.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Dissecting Anvil Cloud Response to Sea Surface Warming

Abstract We derive an anvil cloud diagnostic from the continuity equation of cloud ice and apply it to the output of convection‐permitting Energy Exascale Earth System Model (E3SM) simulations run in radiative‐convective equilibrium mode. This diagnostic shows that anvil cloud fraction can be reliably diagnosed as a product of cloud detrainment and lifetime. Detrainment is found to be approximated well by a product of clear sky convergence and cloud ice mixing ratio, while cloud lifetime is dominated by sedimentation. Taken together, this diagnostic expresses anvil cloud fraction as a function of five physically measurable quantities. Of these, clear‐sky convergence changes drive the anvil cloud reduction with warming while an increase in cloud ice mixing ratio buffers the decrease. Accordingly, this study provides a theoretical foundation upon which the Stability‐Iris hypothesis can be tested.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Three-dimensional structure of hybrid magnetic skyrmions determined by neutron scattering

Magnetic skyrmions are topologically protected chiral spin textures which present opportunities for next-generation magnetic data storage and logic information technologies. The topology of these structures originates in the geometric configuration of the magnetic spins, more generally described as the structure. While the skyrmion structure is most often depicted using a two-dimensional projection of the three-dimensional (3D) structure, recent works have emphasized the role of all three dimensions in determining the topology and their response to external stimuli. Here, grazing-incidence small-angle neutron scattering and polarized neutron reflectometry are used to determine the 3D structure of hybrid skyrmions. The structure of the hybrid skyrmions, which includes a combination of Néel-like and Bloch-like components along their length, is expected to significantly contribute to their notable stability, which includes ambient conditions. To interpret the neutron scattering data, micromagnetic simulations of the hybrid skyrmions were performed, and the corresponding diffraction patterns were determined using a Born approximation transformation. The converged magnetic profile reveals the magnetic structure along with the skyrmion depth profile, including the thickness of the Bloch and Néel segments and the diameter of the core.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

AI-Assisted Conceptual Development of a Pre-Geometric Cosmological Model - An Exercise in AI-Assisted Conceptual Framework Generation, Paper II: Local Geometry and Metric Structure

This paper develops the geometric sector of the replication-driven cosmogenesis framework introduced in Paper I. Starting from a pre-geometric spectral substrate and a minimal set of replication axioms, we show how coherent self-replicating units generate a spatial adjacency graph whose continuum limit acquires an effective Riemannian structure. The replication dynamics determines a characteristic correlation length that seeds the local metric, while overlap relations among coherent units produce an isotropic neighborhood geometry with an emergent dimensionality $d_{\rm eff}\simeq 3$ across a broad range of replication factors. As replication slows and causal order stabilizes, a limiting signal speed $c_\ast$ appears, providing the basis for the Lorentzian structure of spacetime without assuming a pre-existing light cone. We derive conditions under which the adjacency graph converges to a smooth three-dimensional manifold, describe the transition from Euclidean to Lorentzian propagation, and identify geometric invariants controlled by the replication parameters. This work establishes the geometric and causal layer of the replication cosmogenesis program, bridging the spectral axioms of Paper I to the cosmological dynamics explored in Paper III.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Physics-based stabilized finite element approximations of the Poisson–Nernst–Planck equations

We present and analyze two stabilized finite element methods for solving numerically the Poisson–Nernst–Planck equations. The stabilization we consider is carried out by using a shock detector and a discrete graph Laplacian operator for the ion equations, whereas the discrete equation for the electric potential need not be stabilized. Discrete solutions stemmed from the first algorithm preserve both maximum and minimum discrete principles. For the second algorithm, its discrete solutions are conceived so that they hold discrete principles and obey an entropy law provided that an acuteness condition is imposed for meshes. Remarkably the latter is found to be unconditionally stable. We validate our methodology through transient numerical experiments that show convergence toward steady-state solutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

RESOLVING THE ELECTROCHEMICAL EQUATIONS OF A SOLID OXIDE FUEL CELL FOR USE IN TRANSIENT SIMULATION AND INTEGRATION INTO CYBER-PHYSICAL SYSTEMS

A major challenge with complex cyber-physical systems stems from long model computational time that creates a mismatch between the model system and the physical system. The numerical modeling of solid oxide fuel cells (SOFCs) presents particular challenges due to the highly coupled nature of the underlying equations and the multiphysics needed to fully resolve their behavior during a transient event. To this end current approaches revolve around splitting the computational efforts into resolving temperature effects and resolving electrochemical effects. Current methods employed for the transient simulation of an SOFC for implementation in the Hybrid Performance (HyPer) facility cyberphysical plant at the National Energy Technology Laboratory reveal a distinct need for accelerated results with a high degree of stability. To this aim, an investigation into the computational time for the code reveals that the underlying electrochemical algorithm takes an order of magnitude more time than its thermal counterpart and has a tendency to vary in terms of iteration time and as such a rework of the underlying system is proposed. The primary method for accelerated electrochemical algorithm solutions is to employ higher order root finding recipes for the resolution of the highly coupled electrochemical equations. This is done with the intention to reduce the overall number of subiterations necessary for resolving voltage, current density, and species concentration, properties of the fuel cell that are all directly coupled and require nested iterative approaches. The overall objective of this approach is an order of magnitude reduction in calculation time without sacrificing stability and increasing accuracy. Specific approaches involve using both bounded and unbounded techniques, such as the False Position method and the Secant method (or if applicable Newton-Raphson) respectively, the drawbacks being slower convergence for False Position and instability for the Secant or Newton-Raphson methods. Current preliminary results on simplified versions of the parent functions involved for electrochemical calculations indicate a reduction in computational steps by a factor of two for the secant method and a factor of three for Newton-Raphson. When implemented into new modified electrochemical algorithms, the results indicate a possible order of magnitude reduction in calculation time.

Arias, Jesus↗
DOE OSTI2026

A fast and robust computational modeling approach for density and shape predictions in powder metallurgy hot isostatic pressing

Powder metallurgy hot isostatic pressing (PM-HIP) is an advanced manufacturing process that produces near-net-shape parts with high material utilization and uniform microstructures. PM-HIP is frequently used for producing small-scale parts with complicated geometries and is potentially economical for producing large-scale parts. However, excessive post-HIP shape distortions can reduce its effectiveness and economic advantage, especially for larger parts. A PM-HIP computational model can predict and help mitigate these distortions. However, due to complex deformation mechanisms and thermo-mechanical coupling present in PM-HIP processes, these non-linear computational models sometimes become numerically unstable. The numerical instabilities in these models can lead to very slow convergence or no convergence at all, which often translates to slow and unreliable models. These limitations are more pronounced in large models with complicated geometries. Hence, in this work, an alternative modeling approach is presented that improves numerical stability and computational performance. The presented approach achieves these improvements through approximating the fully coupled thermo-mechanical PM-HIP model as a decoupled model and adding inertial damping to the model’s mechanical part. In conclusion, a comparison with the fully coupled model indicated a slight dip in prediction accuracy (<5% error) but significant improvements in numerical stability (>20 times larger time step size) and computational performance (5-10 times speed-up with less computational resource usage) when using the presented approach.

Hot isostatic pressing↗
DOE OSTI2021

Evolution of magnetic field induced ordering in the layered quantum Heisenberg triangular-lattice antiferromagnet Ba 3 CoSb 2 O 9

Quantum fluctuations in the effective spin-12 layered triangular-lattice quantum Heisenberg antiferromagnet Ba 3 CoSb 2 O 9 lift the classical degeneracy of the antiferromagnetic ground state in magnetic field, producing a series of novel spin structures for magnetic fields applied within the crystallographic ab plane, including a celebrated collinear “up-up-down” spin ordering with magnetization equal to 1/3 of the saturation magnetization over an extended field range. Theoretically unresolved, however, are the effects of interlayer antiferromagnetic coupling and transverse magnetic fields on the ground states of this system. Additional magnetic field induced phase transitions are theoretically expected and in some cases have been experimentally observed, but details regarding their number, location, and physical character appear inconsistent with the predictions of existing models. Conversely, an absence of experimental measurements as a function of magnetic-field orientation has left other key predictions of these models untested. To address these issues, in this study we have used specific heat, neutron diffraction, thermal conductivity, and magnetic torque measurements to map out the phase diagram as a function of magnetic field intensity and orientation relative to the crystallographic ab plane. For H||ab, we have discovered an additional magnetic field induced phase transition at low temperature and an unexpected tetracritical point in the high-field phase diagram, which coupled with the apparent second-order nature of the phase transitions eliminates several theoretically proposed spin structures for the high-field phases. Our calorimetric measurements as a function of magnetic field orientation are in general agreement with theory for field-orientation angles close to plane parallel (H||a) but diverge at angles near plane perpendicular; a predicted convergence of two phase boundaries at finite angle and a corresponding change in the order of the field induced phase transition are not observed experimentally. Our results emphasize the role of interlayer coupling in selecting and stabilizing field induced phases, provide guidance on the nature of the magnetic order in each phase, and reveal the need for new physics to account for the nature of magnetic ordering in this archetypal two-dimensional spin-12 triangular-lattice quantum Heisenberg antiferromagnet.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Numerical Experiments and Identification of Areas for Further Consideration (B639388 Subcontract Quarter II Report)

The second quarter of the project was spent carrying out numerical experiments on a variety of test matrices, using a combination of precisions, with the intent to study the numerical properties (namely, accuracy and convergence behavior) of the Conjugate Gradient (CG) method. We summarize our findings in the remainder of the document. Other activities include attending biweekly xSDK meetings. We are also currently collaborating with Steven Thomas (NREL) on the numerical stability analysis of “low-synch” Gram-Schmidt routines, for potential application within high-performance GMRES variants. This work is in progress. The subsequent quarter will be spent delving into the numerical analysis in order to provide theoretical explanation for the behavior observed in our experiments.

97 MATHEMATICS AND COMPUTING↗