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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

QBTNs - Quantum Boolean Tensor Networks

We develop algorithms and software that uses the D-Wave 2000Q quantum annealer to solve several types of Boolean tensor factorization problems. Boolean tensor factorization refers to the problem of representing a high-dimensional tensor filled with Boolean values as a product of smaller Boolean core tensors and Boolean matrices. We consider different tensor factorization models, including Boolean Tensor Train, Boolean Tucker, and Boolean Hierarchical Tucker. As an exact decomposition of a given type may not exist in the general case, the objective is to minimize the difference between the input high-dimensional tensor and the product of the lower-dimensional tensors of the proposed factorization, using a specified tensor norm. In our approach, we reduce the Boolean tensor factorization problem to a sequence of quadratic unconstrained binary optimization problems suitable for the D-Wave 2000Q quantum annealer. Although current quantum technology is still fairly restricted in the problems it can tackle, we show that complex tensor factorization problems as the ones addressed by us can be solved efficiently and accurately.

Alexandrov, Boian↗
DOE OSTI2025

Sylvester-preconditioned adaptive-rank implicit time integrators for advection-diffusion equations with variable coefficients

Here, we consider the adaptive-rank integration of multi-dimensional time-dependent advection-diffusion partial differential equations (PDEs) with variable coefficients. We employ a standard finite-difference method for spatial discretization coupled with high-order diagonally implicit Runge-Kutta temporal schemes. The discrete equation is a generalized Sylvester equation (GSE), which we solve with a projection-based adaptive-rank algorithm structured around two key strategies: (i) constructing dimension-wise subspaces using a novel atypical extended Krylov strategy, and (ii) efficiently solving the basis coefficient matrix with a preconditioned GMRES solver. The low-rank decomposition is performed in 2D using SVD and with high-order SVD (HOSVD) in 3D to represent the tensor in a compressed Tucker format. For d-dimensional problems (here, d = 2 or 3), the computational complexity and memory storage of the approach are found numerically to scale as and $\mathscr{O}(Nr^2) + \mathscr{O} (r^{d+1})$ and $\mathscr{O}(Nr) + \mathscr{O} (r^{d})$, respectively, with the one-dimensional resolution and the maximal rank during the Krylov iteration (which we find to be largely independent of on our numerical examples). We present numerical examples that illustrate the advertised properties of the algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

PyAlbany: A Python interface to the C++ multiphysics solver Albany

Albany is a parallel C++ finite element library for solving forward and inverse problems involving partial differential equations (PDEs). In this paper we introduce PyAlbany, a newly developed Python interface to the Albany library. PyAlbany can be used to effectively drive Albany enabling fast and easy analysis and post-processing of applications based on PDEs that are pre-implemented in Albany. PyAlbany relies on the library PyBind11 to bind Python with C++ Albany code. Here we detail the implementation of PyAlbany and showcase its capabilities through a number of examples targeting a heat-diffusion problem. In particular we consider the following: (1) the generation of samples for a Monte Carlo application, (2) a scalability study, (3) a study of parameters on the performance of a linear solver, and finally (4) a tool for performing eigenvalue decompositions of matrix-free operators for a Bayesian inference application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Parallel Algorithms for Computing the Tensor-Train Decomposition

The tensor-train (TT) decomposition expresses a tensor in a data-sparse format used in molecular simulations, high-order correlation functions, and optimization. In this paper, we propose four parallelizable algorithms that compute the TT format from various tensor inputs: (1) Parallel-TTSVD for traditional format, (2) PSTT and its variants for streaming data, (3) Tucker2TT for Tucker format, and (4) TT-fADI for solutions of Sylvester tensor equations. We provide theoretical guarantees of accuracy, parallelization methods, scaling analysis, and numerical results. For example, for a d-dimension tensor in $\mathbb{R}$ $n\times∙∙∙$$\times$$n$ a two-sided sketching algorithm PSTT2 is shown to have a memory complexity of $O(n^{[d/2]})$, improving upon $O(n^{d—1})$ from previous algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

mvBayesPy

SAND2025-11476O The mvBayesPy tool is a Python package that performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis and visualization. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tucker, James [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2021

Combined modeling and experiments to predict corrosion and embrittlement in dual-phase stainless steels within the MARMOT framework

Extending service of Light Water Reactors (LWRs) to beyond 60 years will demand high integrity of materials and components in the reactors. The accurate evaluation and prediction of materials performance under anticipated operating conditions are of particular importance for ensuring the safety of nuclear power plants over their extended lifetime. Stainless steels are extensively used as structural alloys in light water reactor (LWR) systems because of their excellent combination of mechanical properties and corrosion resistance. Austenitic stainless steel welds and cast austenitic stainless steels (CASS) contain significant amounts of ferrite ~5-30% and are ubiquitous in LWR piping (elbows, pump casings, valves) and internal structural components. During their lifetime, these components are subjected to thermal aging at the temperature range of 288-327°C for pressurized water reactors (PWR). A phase transformation within the ferrite phase of these materials leads to embrittlement and degradation in corrosion resistance, which undermines the materials selection criteria.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank Determination

Post-training model compression can reduce the inference costs of deep neural networks, but uncompressed training still consumes enormous hardware resources and energy. To enable low-energy training on edge devices, it is highly desirable to directly train a compact neural network from scratch with a low memory cost. Low-rank tensor decomposition is an effective approach to reduce the memory and computing costs of large neural networks. However, directly training low-rank tensorized neural networks is a very challenging task because it is hard to determine a proper tensor rank a priori, and the tensor rank controls both model complexity and accuracy. Here, this paper presents a novel end-to-end framework for low-rank tensorized training. We first develop a Bayesian model that supports various low-rank tensor formats (e.g., CANDECOMP/PARAFAC, Tucker, tensor-train, and tensor-train matrix) and reduces neural network parameters with automatic rank determination during training. Then we develop a customized Bayesian solver to train large-scale tensorized neural networks. Our training methods shows orders-of-magnitude parameter reduction and little accuracy loss (or even better accuracy) in the experiments. On a very large deep learning recommendation system with over 4.2 ×10 9 model parameters, our method can reduce the parameter number to 1.6 ×10 5 automatically in the training process (i.e., by 2.6 ×10 4 times) while achieving almost the same accuracy. Code is available at https://github.com/colehawkins/bayesian-tensor-rank-determination.

compact neural networks↗
NASA NTRS2020

Toward an In-Depth Material Model for Cermet Nuclear Thermal Rocket Fuel Elements

The development and qualification of nuclear thermal propulsion (NTP) fuel element technologies would be aided by an in-depth model of material response and failure modes at operating conditions. Integrated computational materials engineering techniques have the potential to provide such a model, as demonstrated here through three case studies focused on a tungsten-uranium mononitride cermet fuel. The first case focuses on the erosion of tungsten (W, also named wolfram), a nominal coating/cladding material, in hot hydrogen. Ab initio techniques are used to calculate erosion rates and thermal expansion at NTP operating conditions. The second focuses on the stability of uranium mononitride (UN) fuels at high temperature and in the presence of hydrogen. Phase diagram techniques reveal potential instabilities and decomposition pathways at high hydrogen concentrations. The third focuses on using microstructure information to predict high temperature mechanical response and failure of tungsten, used in refractory cermet materials. Combined finite element and discrete dislocation dynamics techniques provide mechanical properties in agreement with experimental methods. The integration of these techniques for an all-encompassing material model is discussed.

cermet↗
DOE OSTI2025

mvBayesR

SAND2025-11559O The mvBayesR tool performs multivariate Bayesian analysis on generic data. It includes tools for regression modeling, diagnosis, basis decomposition, sensitivity analysis, and visualization. The tool compiles state-of-the-art methodology into one easy-to-use package. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Tucker, James [Sandia National Lab. (SNL-CA), Live↗
DOE OSTI2025

Voltage cycling as a dynamic operation mode for high temperature electrolysis solid oxide cells

Solid Oxide Electrolysis Cells (SOECs) have emerged as a promising technology for the efficient production of H2 via high-temperature electrolysis. However, power input from dynamic energy sources remains a significant challenge for their long-term stability. It is important to analyze the tolerance of cells under dynamic operation conditions. This study focuses on evaluating the impact of voltage cycling on the performance and durability of electrode-supported SOECs. We explore the operational limits and degradation mechanisms of SOECs subjected to various voltage conditions and find that the cells have high tolerance for dynamic voltage. Voltage cycling between 1.3 V and 1.5 V for 9000 cycles does not damage the cell. Conversely, cycling to higher voltages (≥1.7 V) results in accelerated degradation. Advanced characterization is used to screen for various degradation modes post operation. Within the oxygen electrode, XRD and STEM EDS find compositional and phase evolution in all voltage cycled samples including increased decomposition of the air electrode resulting in cation migration. Microstructural analysis of the fuel electrode from nano-CT data shows minimal change throughout the sample set and no evidence of Ni migration, indicating the fuel electrode is stable and not impacted by cycling to higher voltages within the timeframe studied.

Zhu, Zhikuan↗
NASA NTRS2006

The NASA Constellation University Institutes Project: Thrust Chamber Assembly Virtual Institute

This paper documents key aspects of the Constellation University Institutes Project (CUIP) Thrust Chamber Assembly (TCA) Virtual Institute (VI). Specifically, the paper details the TCA VI organizational and functional aspects relative to providing support for Constellation Systems. The TCA VI vision is put forth and discussed in detail. The vision provides the objective and approach for improving thrust chamber assembly design methodologies by replacing the current empirical tools with verified and validated CFD codes. The vision also sets out ignition, performance, thermal environments and combustion stability as focus areas where application of these improved tools is required. Flow physics and a study of the Space Shuttle Main Engine development program are used to conclude that the injector is the key to robust TCA design. Requirements are set out in terms of fidelity, robustness and demonstrated accuracy of the design tool. Lack of demonstrated accuracy is noted as the most significant obstacle to realizing the potential of CFD to be widely used as an injector design tool. A hierarchical decomposition process is outlined to facilitate the validation process. A simulation readiness level tool used to gauge progress toward the goal is described. Finally, there is a description of the current efforts in each focus area. The background of each focus area is discussed. The state of the art in each focus area is noted along with the TCA VI research focus in the area. Brief highlights of work in the area are also included.

Tucker, P. Kevin↗
NASA NTRS1992

Contamination of optical surfaces in Earth orbit

Glass and glass ceramic samples exposed to the low earth orbit environment for approximately 5.5 years on the Long Duration Exposure Facility (LDEF) were found to display limited degradation in optical transmission. Commercial optical quality fused silica samples display decreases in transmission in the 200 to 400 nm wavelength region, and this degradation appears to be a consequence of surface contamination. The contamination, found only on internal surfaces of samples, was measured by medium energy backscattering spectrometry and found to be primarily carbon. Additional thin film contamination by a species with atomic mass near 64, which was present at the level of about 8 x 10 exp 14/sq. cm has not been identified. These observations are consistent with the interpretation that organic binders used in the black absorbing paint (Chem Glaze Z-306) inside the sample holding tray were concentrated in the vicinity of the samples and photolytically cracked by solar UV radiation. The resulting decomposition products were deposited on the interior sample surface and gave rise to the optical transmission loss. No detectable contamination was observed on the external or space exposed surface of the samples. No measurable damage was detected which could be attributed to the direct action of gamma or UV radiation on the glass samples. These results emphasize the need for special precautions in the preparation of spacecraft carrying precision optical components on long duration missions.

Donald L Kinser↗
DOE OSTI2020

Tucker-1 Boolean Tensor Factorization with Quantum Annealers

Quantum annealers are an emerging computational architecture that have the potential to address some challenging computational issues that will be left unresolved as we approach the end of the Moore's Law era of computing. D-Wave quantum annealers are designed to solve a challenging set of problems - quadratic unconstrained binary optimization problems. This makes them a natural fit for solving problems with binary or Boolean variables. Here, we explore the use of a quantum annealer to solve Boolean tensor factorization. The goal of Boolean tensor factorization is to represent a high-dimensional tensor filled with Boolean values as a product of Boolean matrices and a Boolean core tensor. We show that a particular Boolean tensor factorization problem (called Tucker-1 factorization) can be decomposed into a sequence of quadratic unconstrained binary optimization problems that can be solved with a D-Wave 2000Q quantum annealer. While quantum annealers specifically and quantum computers in general are at a fairly early stage in their development, they are currently capable of solving these Boolean tensor factorization problems. Importantly, our results show that for fairly small tensors, we are frequently able to obtain an accurate (sometimes exact) factorization using quantum annealing.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1990

Production of continuous mullite fiber via sol-gel processing

The development of a continuous ceramic fiber which could be used in rocket engine and rocket boosters applications was investigated at the Marshall Space Flight Center. Methods of ceramic fiber production such as melt spinning, chemical vapor deposition, and precursor polymeric fiber decomposition are discussed and compared with sol-gel processing. The production of ceramics via the sol-gel method consists of two steps, hydrolysis and polycondensation, to form the preceramic, followed by consolidation into the glass or ceramic structure. The advantages of the sol-gel method include better homogeneity and purity, lower preparation temperature, and the ability to form unique compositions. The disadvantages are the high cost of raw materials, large shrinkage during drying and firing which can lead to cracks, and long processing times. Preparation procedures for aluminosilicate sol-gel and for continuous mullite fibers are described.

Tucker, Dennis S.↗
DOE OSTI2023

Influence of Oxygen Uptake on Pitch Carbon Fiber

Carbon fiber precursor materials, such as polyacrylonitrile, pitch, and cellulose/rayon, require thermal stabilization to maintain structural integrity during conversion into carbon fiber. Thermal stabilization mitigates undesirable decomposition and liquification of the fibers during the carbonization process. Generally, the thermal stabilization of mesophase pitch consists of the attachment of oxygen-containing functional groups onto the polymeric structure. In this study, the oxidation of mesophase pitch precursor fibers at various weight percentage increases (1, 3.5, 5, 7.5 wt%) and temperatures (260, 280, 290 °C) using in situ differential scanning calorimetry and thermogravimetric analysis is investigated. The results are analyzed to determine the effect of temperature and weight percentage increase on the stabilization process of the fibers, and the fibers are subsequently carbonized and tested for tensile mechanical performance. Finally, the findings provide insight into the relationship between stabilization conditions, fiber microstructure, and mechanical properties of the resulting carbon fibers.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Ensemble Federated Machine Learning‐Based Cybersecurity Situational Awareness in Microgrid Network

Cyber-physical microgrids are vulnerable to stealthy cybersecurity threats that disguise their actions through the exploitation of system knowledge. Such actions can severely impacts microgrids deployed in defense bases, slowing the response time of military forces during national emergencies. Several machine-learning algorithms have been proposed to detect intrusions in the grid networks; however, these traditional machine-learning algorithms lack data privacy and are subject to several adversarial machine-learning threats. This paper proposes a novel federated machine learning (FML)-based three-model framework to detect and identify stealthy data-integrity attacks while ensuring data privacy in microgrid networks. The proposed architecture uses a variational mode decomposition technique to extract derived features from incoming measurement and control datasets. The extraction of these derived features allows FML models to learn minute variations in data patterns that allow them to perform significantly better than the models trained with generic datasets consisting of raw features. Our experimental results show the efficient performance of the proposed methodology against different types of data integrity attacks while considering primary and secondary controllers in microgrids. Further, the applied FML-integrated random forest ensemble algorithm outperforms the existing generic FML algorithms during noisy and noise-free datasets with prediction latencies of only 91–134 µs per sample within the 0.1 s sampling interval and requires communication bandwidth of around ∼8.25 KB/s at the control center and ∼2.7 KB/s per edge client for communication.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Triiodide Attacks the Organic Cation in Hybrid Lead Halide Perovskites: Mechanism and Suppression

Molecular I 2 can be produced from iodide-based lead perovskites under thermal stress; triiodide, I 3 − , is formed from this I 2 and I − . Triiodide attacks protic cation MA + - or FA + -based lead halide perovskites (MA + , methylammonium; FA + , formamidinium) as explicated through solution-based nuclear magnetic resonance (NMR) studies: triiodide has strong hydrogen-bonding affinity for MA + or FA + , which leads to their deprotonation and perovskite decomposition. Triiodide is a catalyst for this decomposition that can be obviated through perovskite surface treatment with thiol reducing agents. In contrast to methods using thiol incorporation into perovskite precursor solutions, no penetration of the thiol into the bulk perovskite is observed, yet its surface application stabilizes the perovskite against triiodide-mediated thermal stress. Thiol applied to the interface between FAPbI 3 and Spiro-OMeTAD (“Spiro”) prevents oxidized iodine species penetration into Spiro and thus preserves its hole-transport efficacy. Surface-applied thiol affects the perovskite work function; it ameliorates hole injection into the Spiro overlayer, thus improving device performance. It helps to increase interfacial adhesion (“wetting”): fewer voids are observed at the Spiro/perovskite interface if thiols are applied. Perovskite solar cells (PSCs) incorporating interfacial thiol treatment maintain over 80% of their initial power conversion efficiency (PCE) after 300 h of 85 °C thermal stress.

36 MATERIALS SCIENCE↗