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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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DOE OSTI2025

High-quality axions from higher-form symmetries in extra dimensions

The extradimensional axion solves the strong C P problem while largely circumventing the quality problem that plagues its four-dimensional counterparts. Such high quality can be clearly understood in terms of the generalized global symmetries of the higher-dimensional theory. We emphasize that an electric one-form symmetry is entirely responsible for protecting the potential of axions arising from 5D gauge theories and use this to systematically characterize the extradimensional axion quality problem. We identify three ways of breaking this one-form symmetry to generate an axion potential: adding electrically charged matter, gauging a magnetic higher-form symmetry, and turning on an Adler-Bell-Jackiw anomaly. In the latter case, we identify new ways of generating an axion potential via extradimensional magnetic monopoles. We also discuss how the axion is modified if the protective electric one-form symmetry is itself gauged. Finally, we relate these effects to gravitational expectations for the quality problem via generalized weak gravity conjectures. The clarity that generalized symmetries bring to the extradimensional axion quality problem highlights their relevance to particle phenomenology. Published by the American Physical Society 2025

Craig, Nathaniel↗

DOE OSTI2024

Protorheology in practice: Avoiding misinterpretation

Protorheology is the paradigm that any observed flow or deformation is a chance to infer quantitative rheological properties. While this creates many opportunities for insight, there is significant risk of misunderstanding the physics involved, e.g. misinterpreting a liquid as a solid or mistaking viscous flow time as viscoelastic relaxation time. We describe these and other potential mistakes, use case studies to show how serious the problems can be, and contrast misinterpretations with correct approaches and interpretations. Some issues are especially important with materials involving colloidal particles and flows involving surface tension. Whether the reader is making inference from a tilted vial, time-lapse gravity-driven flow, a bounce test, die swell, or any other protorheology observation, the examples here serve as a guide for avoiding bad data in protorheology.

42 ENGINEERING↗

DOE OSTI2026

Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develop two multi-modal Bayesian neural network surrogate models and leverage conditionally conjugate distributions in the last layer to estimate model parameters using stochastic variational inference (SVI). We provide a method to perform this conjugate SVI estimation in the presence of partially missing observations. Here, we demonstrate improved prediction accuracy and uncertainty quantification compared to unimodal surrogate models for both scalar and time series data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Study of Applications of Radiological Data Fusion for Nuclear Security and Non-proliferation Applications

This report describes the radiological Scene Data Fusion (SDF) technology and the potential to apply the SDF technology to a variety of problems related to nuclear non-proliferation, radiological security, and other missions related to the national nuclear security agency and the US government, more broadly. The application space was conceived by both the authors of this report and the many scientists and stakeholders they interviewed. In order to successfully apply the SDF technology to the various applications, further research and development is often necessary and those R&D steps are also outlined herein.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2026

Best Practices for Regulatory Engagement and Relationship Building – 26129

In the initiation of a new project or facility, the management team should begin to engage with appropriate regulators, early and often. However, if this did not occur and new issues arise, new challenges develop, or regulatory impacts become a concern, the regulatory engagement process should be reconsidered and reevaluated. Regulators are key in the siting, construction, and continued operations of a new facility. Municipalities often seek to attract new initiatives, projects, or facilities that will have a significant positive impact on the community. Even though this is typically the goal, there are many hurdles along the path that may cause negative impacts leading to regulatory concerns and hurdles that may delay or stop work. Additionally, early and frequent engagement with regulators can have a significant positive impact on the successful siting, permitting, and construction of a facility, including meeting needed schedules that are important to economic viability. Further, it is essential for ongoing operations for regulators to be effectively and meaningfully engaged throughout the life of the project or facility due to the potential of changing factors during the construction and/or operation. Regular and consistent engagement builds relationships and helps ensure that potential topics of concern are addressed early before they become problems. In this paper, we will discuss best practices in engaging with regulators, how to initiate effective regulatory interactions, and building lasting relationships.

Jacobs, Stephanie [Savannah River National Laborat↗

DOE OSTI2026

From the Great Wave of Translation to the Force between Quarks

Here, the chance observation of a novel traveling wave in a canal led over time to the formulation of a nonlinear wave equation—the Korteweg–de Vries equation—that describes strikingly robust disturbances now called solitons. The figure of an isolated soliton corresponds to a reflectionless potential that supports a single bound state in the one-dimensional Schrödinger equation. An appropriate combination of individual solitons yields a symmetric reflectionless potential that supports multiple bound states. Thus, the KdV equation opens the path to solving the inverse scattering problem for a collection of bound states. Applied to the quarkonium spectra, this formalism allows the construction of reflectionless approximations to the confining potentials that account for the force between quarks, and to tests of the flavor-independence of the interquark interaction.

Inverse Scattering↗

DOE OSTI2025

Fusion Intelligence: A Paradigm for Merging Natural and Artificial Intelligence

Here, this article presents fusion intelligence (FI), a bio-inspired paradigm that synergistically integrates the intrinsic capabilities of intelligent biological organisms with the advanced potential of artificial intelligence (AI)-driven systems. FI harnesses the unique intelligence, sensing, actuation, and mobility attributes of living organisms, such as honeybees, blending these with the sophisticated data-driven problem-solving functionalities of AI. By bridging the gap between natural intelligence (NI) and AI, FI can transform how humans interact with and harness the capabilities of both natural and artificial systems. This article presents the model of FI and its application to solve practical problems, discusses the challenges and future directions of FI research, emphasizing a generalized approach to solve complex problems, where AI can observe/control NI in a closed-loop system. We demonstrate the potential for FI to enhance the performance of an agricultural IoT system via a simulated case study, which achieves 50% improvement in the efficacy of insect pollination (entomophily).

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Two-Stage Distributionally Robust Conic Linear Programming over 1-Wasserstein Balls

Here, this paper studies two-stage distributionally robust conic linear programming under constraint uncertainty over type-1 Wasserstein balls. We present optimality conditions for the dual of the worst-case expectation problem, which characterizes worst-case uncertain parameters for its inner maximization problem. This condition offers an alternative proof, a counterexample, and an extension to previous works. Additionally, the condition highlights the potential advantage of a specific distance metric for out-of-sample performance, as exemplified in a numerical study on a facility location problem with demand uncertainty. Furthermore, cutting-plane-based algorithms, equipped with a unified scenario generation framework, are proposed for addressing both unbounded support and second-stage dual feasible regions, with a finite convergence proof under less stringent assumptions.

Wasserstein↗

DOE OSTI2025

Limitations of Fault-Tolerant Quantum Linear System Solvers for Quantum Power Flow

Quantum computers hold promise for solving problems intractable for classical computers, especially those with high time or space complexity. Practical quantum advantage can be said to exist for such problems when the end-to-end time for solving such a problem using a classical algorithm exceeds that required by a quantum algorithm. Reducing the power flow (PF) problem into a linear system of equations allows for the formulation of quantum PF (QPF) algorithms, which are based on solving methods for quantum linear systems such as the Harrow-Hassidim-Lloyd (HHL) algorithm. Speedup from using QPF algorithms is often claimed to be exponential when compared to classical PF solved by state-of-the-art algorithms. Here, we investigate the potential for practical quantum advantage in solving QPF compared to classical methods on gate-based quantum computers. Notably, this paper does not present a new QPF solving algorithm but scrutinizes the end-to-end complexity of the QPF approach, providing a nuanced evaluation of the purported quantum speedup in this problem. Our analysis establishes a best-case bound for the HHL-based quantum power flow complexity, conclusively demonstrating that the HHL-based method has higher runtime complexity compared to the classical algorithm for solving the direct current power flow (DCPF) and fast decoupled load flow (FDLF) problem. Notably, our analysis and conclusions can be extended to any quantum linear system solver with rigorous performance guarantees, based on the known complexity lower bounds for this problem. Additionally, we establish that for potential practical quantum advantage (PQA) to exist it is necessary to consider DCPF-type problems with a very narrow range of condition number values and readout requirements.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Fabrication and characterization of nanoscale magnesium diboride and tetraboride for propulsion and hydrogen storage applications

Abstract: Boron-loaded propellants have the potential to dramatically increase the performance of solid fuel ramjets, ducted rockets, and hybrid rocket engines. However, difficult ignition of boron decreases the combustion efficiency of these propellants. One approach to solving this problem involves the use of magnesium diboride, MgB2, which ignites easier than boron. Magnesium tetraboride, MgB4, potentially offers greater energetic performance as B has a higher energy density than Mg. However, the effect of the higher boron/metal ratio on the ignition and combustion is unclear. Nanoscale MgB2 particles and quasi 2D structures are promising propellant ingredients, but the oxidation and combustion properties of nanoscale MgB4 remain unknown. Nanoscale magnesium borides are also of interest as precursors for the synthesis of magnesium borohydride, Mg(BH4)2, a promising hydrogen storage material, but hydrogenation of MgB4 has not been studied yet. The objectives of the present work included synthesis, purification, and high-energy ball milling of MgB2 and MgB4 powders as well as investigation of their hydrogen uptake, thermal decomposition, oxidation, and combustion. The powders were fabricated by combustion synthesis and by heating in a tube furnace. The latter method was superior in the synthesis of MgB4. Oxide impurities in the synthesized powders were removed by acid leaching. Nanoscale powders were obtained by ball-mill exfoliation. The hydrogen intake of the obtained magnesium borides was examined at 700 bar and 300 ℃ and did not reveal any advantage of MgB4 over MgB2. Their thermal decomposition and oxidation were investigated with thermogravimetric analysis (TGA), while their combustion was studied using laser ignition and high-speed video recording. TGA has confirmed prior observations of multistep decomposition of magnesium borides, where each step involves formation of a boride with a higher B/Mg ratio and evaporation of formed magnesium. The oxidation rates of the borides are much higher than that of boron at temperatures over 1200 °C for MgB2 and over 900 °C for MgB4. The burning rates of non-milled MgB₂ and MgB₄ powders were much higher than for the used submicron boron. Milling the MgB₂ and MgB₄ powders further increased their burning rates. The milled MgB4 burned 7.5 times faster than submicron boron.

Combustion of metals, Solid fuels, Propellants, Hy↗

DOE OSTI2026

FIRM: federated image reconstruction using multimodal tomographic data

Here, we propose a federated algorithm for reconstructing images using multimodal tomographic data sourced from dispersed locations, addressing the challenges of traditional unimodal approaches that are prone to noise and reduced image quality, as well as the limitations of centralized multimodal approaches that require extensive data transfer, leading to significant communication overhead, storage demands, and potential data privacy concerns. Our approach formulates a joint inverse optimization problem incorporating multimodality constraints and solves it in a federated framework through local gradient computations complemented by lightweight central operations, thereby ensuring data decentralization. Leveraging the connection between our federated algorithm and the quadratic penalty method, we introduce an adaptive step-size rule with guaranteed sublinear convergence. Numerical results demonstrate superior computational efficiency and improved image reconstruction quality compared to existing approaches.

federated algorithm↗

DOE OSTI2026

Two-Level Sketching Alternating Anderson Acceleration for Complex Physics Applications

We present a novel two-level sketching extension of the Alternating Anderson–Picard (AAP) method for accelerating fixed-point iterations in challenging single- and multiphysics simulations governed by discretized PDEs. Our approach combines a static, physics-based projection that reduces the least-squares (LS) problem to the most informative field (e.g., via Schur-complement insight) with a dynamic, algebraic sketching stage driven by a backward stability analysis under Lipschitz continuity. We introduce inexpensive estimators for stability thresholds and cache-aware randomized selection strategies to balance computational cost against memory access overhead. The resulting algorithm solves reduced LS systems in place, minimizes memory footprints, and seamlessly alternates between low-cost Picard updates and Anderson mixing. Implemented in Julia, our two-level sketching AAP achieves up to 50% time-to-solution reductions compared to standard Anderson acceleration—without degrading convergence rates—on benchmark problems including Stokes, 𝑝-Laplacian, bidomain, and Navier–Stokes formulations at varying problem sizes. These results demonstrate the method’s robustness, scalability, and potential for integration into high-performance scientific computing frameworks. Our implementation is available open source in the AAP.jl library.

Barnafi, Nicolas [University of Chile, Santiago]↗

DOE OSTI2025

Model Development and Analysis of a High-Fidelity Neutron Transport Sensor: The Quadrupole Detector Concept for Measurement of the Neutron Flux Gradient

Accurate reconstruction of the neutron flux distribution within a reactor core is essential for safe and efficient reactor operation. Traditional power shape synthesis in Light Water Reactors relies on hundreds of in-core detectors. However, this approach becomes impractical for Advanced Reactors and Microreactors due to limited space and harsh environments. To address this challenge, we propose a data-driven methodology that combines high-fidelity modeling with real-time ex-core sensor measurements, enabling the reconstruction of core power distribution while minimizing the reliance on intrusive in-core instrumentation. This project began in FY24 and achieved two initial milestones: (1) the definition of a three-year development plan for a Digital Twin framework and (2) the development of high-fidelity neutronics models of the Purdue University Reactor One (PUR-1) using both MCNP6 and OpenMC. The PUR-1 reactor, a zero-power facility, was selected due to its suitability for neutronics-focused modeling and the availability of experimental data for validation. Both models were benchmarked using neutron flux measurements obtained from irradiated gold foils, which were strategically placed within the core during a dedicated campaign in July 2024. This report marks the continuation and completion of those foundational tasks. The OpenMC model has been refined (improved geometric accuracy, expanded cross-section libraries, and refined sampling) and validated using additional experimental data. An updated sensor design—based on quadrupole configuration—was designed to measure both ex-core flux and its spatial gradient. These measurements will serve as inputs to a neural network-based reconstruction algorithm. Finally, the methodology was demonstrated on a two-dimensional test case representative of the heterogeneous material composition of the PUR-1 reactor core. A neural network implementation of the Kirchhoff-Helmholtz integral equation was employed to solve the boundary value problem using peripheral sensor measurements. The preliminary results confirm the strong potential of the proposed approach for accurate and minimally invasive neutron flux reconstruction.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

ZEUS: An Efficient GPU Optimization Method Integrating PSO, BFGS, and Automatic Differentiation

We introduce a novel, efficient computational method, ZEUS, for numerical optimization, and provide an open-source implementation. It has four key ingredients: (1) particle swarm optimization (PSO), (2) the use of the Broyden-Fletcher-Goldfarb-Shanno (BFGS) method, (3) automatic differentiation (AD), and (4) GPUs. Our approach addresses the computational challenges inherent in high-dimensional, non-convex optimization problems. In the first phase of the algorithm, we get a potentially good set of starting points using PSO. Thereafter, we run BFGS independently in parallel from these starting points. BFGS is one of the best-performing algorithms for numerical optimization. However, it requires the gradient of the function being optimized. ZEUS integrates automatic differentiation into BFGS thus avoiding the need for the user to calculate derivatives explicitly. The use of GPUs allows ZEUS to speed up the calculations substantially. We carry out systematic studies to explore the trade-offs between the number of PSO iterations taken, starting points, and BFGS iteration depth. We show that a handful of iterations of PSO can improve global convergence when combined with BFGS. We also present performance studies using common test functions. The source code can be found at https://github.com/fnal-numerics/global-optimizer-gpu.

Soos, Dominik [Old Dominion U.]↗

DOE OSTI2025

Nonperturbative and perturbative dynamics of a light QCD axion: Dark matter and the strong 𝐶⁢𝑃 problem

Considerable theoretical efforts have gone into expanding the reach of the quantum chromodynamics (QCD) axion beyond its canonical mass–decay-constant relation. The 𝑍 𝒩 QCD axion model reduces the QCD axion mass naturally, by invoking a discrete 𝑍 𝒩 symmetry through which the axion field is coupled to 𝒩 copies of the Standard Model. Before the QCD phase transition at temperature 𝑇 QCD , the 𝑍 𝒩 potential has a minimum at misalignment angle 𝜃 = 𝜋. At 𝑇 QCD , 𝜃 = 𝜋 becomes a maximum; the axion potential becomes exponentially suppressed and develops 𝒩 minima—only one of which actually solves the strong 𝐶⁢𝑃 problem. Before 𝑇 QCD , 𝜃 relaxes toward 𝜋. After 𝑇 QCD , the axion field starts from around the hilltop and may have sufficient kinetic energy to overcome the newly suppressed potential barriers. Such a field evolution leads to nonperturbative effects via the self-interactions near the hilltop, which can cause the exponential growth of fluctuations and backreaction on the coherent motion. This behavior can influence the relic density of the field and the minimum in which it settles. We conduct the first lattice simulations of the 𝑍 𝒩 QCD axion using 𝒞osmoℒattice to accurately calculate dark matter abundances and find nonperturbative dynamics reduce the abundance by up to a factor of two. We furthermore find that the probability of solving the strong 𝐶⁢𝑃 problem tends to diverge considerably from the naïve expectation of 1/𝒩.

Axions↗

DOE OSTI2025

Quantum Simulation of Molecular Dynamics Processes─A Benchmark Study Using a Classical Simulator and Present-Day Quantum Hardware

Here, we explore how the fundamental problems in quantum molecular dynamics can be modeled using classical simulators (emulators) of quantum computers and the actual quantum hardware available to us today. The list of problems we tackle includes propagation of a free wave packet, vibration of a harmonic oscillator, and tunneling through a barrier. Each of these problems starts with the initial wave packet setup. Although Qiskit provides a general method for initializing wave functions, in most cases it generates deep quantum circuits. While these circuits perform well on noiseless simulators, they suffer from excessive noise on quantum hardware. To overcome this issue, we designed a shallower quantum circuit for preparing a Gaussian-like initial wave packet, which improves the performance of real hardware. Next, quantum circuits are implemented to apply the kinetic and potential energy operators for the evolution of a wave function over time. The results of our modeling on classical emulators of quantum hardware agree perfectly with the results obtained using the traditional (classical) methods. This serves as a benchmark and demonstrates that the quantum algorithms and Qiskit codes we developed are accurate. However, the results obtained on the actual quantum hardware available today, such as IBM’s superconducting qubits and IonQ’s trapped ions, indicate large discrepancies due to hardware limitations. This work highlights both the potential and challenges of using quantum computers to solve fundamental quantum molecular dynamics problems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Finite elements for Matérn-type random fields: Uncertainty in computational mechanics and design optimization

This work highlights an approach for incorporating realistic uncertainties into scientific computing workflows based on finite elements, focusing on prevalent applications in computational mechanics and design optimization. We leverage Matérn-type Gaussian random fields (GRFs) generated using the SPDE method to model aleatoric uncertainties, including environmental influences, variating material properties, and geometric ambiguities. Our focus lies on delivering practical GRF realizations that accurately capture imperfections and variations and understanding how they impact the predictions of computational models as well as the shape and topology of optimized designs. Here we describe a numerical algorithm based on solving a generalized SPDE to sample GRFs on arbitrary meshed domains. The algorithm leverages established techniques and integrates seamlessly with the open-source finite element library MFEM and associated scientific computing workflows, like those found in industrial and national laboratory settings. Our solver scales efficiently for large-scale problems and supports various domain types, including surfaces and embedded manifolds. We showcase its versatility through biomechanics and topology optimization applications, emphasizing the potential to influence these domains. The flexibility and efficiency of SPDE-based GRF generation empowers us to run large-scale optimization problems on 2D and 3D domains, including finding optimized designs on embedded surfaces, and to generate design features and topologies beyond the reach of conventional techniques. Moreover, these capabilities allow us to model and quantify geometric uncertainties on reconstructed submanifolds, such as the interpolated surfaces of cerebral aneurysms provided by postprocessing CT scans. In addition to offering benefits in these specific domains, the proposed techniques transcend specific applications and generalize to arbitrary forward and backward problems in uncertainty quantification involving finite elements.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Enhanced Interfacial Electron Transfer in Photocatalyst-Natural Enzyme Coupled Artificial Photosynthesis System: Tuning Strategies and Molecular Simulations

Laccase is capable of catalyzing a vast array of reactions, but its low redox potential limits its potential applications. The use of photocatalytic materials offers a solution to this problem by converting absorbed visible light into electrons to facilitate enzyme catalysis. Herein, MIL-53(Fe) and NH 2 -MIL-53(Fe) serve as both light absorbers and enzyme immobilization carriers, and laccase is employed for solar-driven chemical conversion. Electron spin resonance spectroscopy results confirm that visible light irradiation causes rapid transfer of photogenerated electrons from MOF excitation to T1 Cu(II) of laccase, significantly increasing the degradation rate constant of tetracycline (TC) from 0.0062 to 0.0127 min -1 . Conversely, there is only minimal or no electron transfer between MOF and laccase in the physical mixture state. Theoretical calculations demonstrate that the immobilization of laccase's active site and its covalent binding to the metal-organic framework surface augment the coupled system's activity, reducing the active site accessible from 27.8 to 18.1 Å. In conclusion, the constructed photo-enzyme coupled system successfully combines enzyme catalysis’ selectivity with photocatalysis's high reactivity, providing a promising solution for solar energy use.

36 MATERIALS SCIENCE↗