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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 OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Multi-fidelity is the new annealing: Gradient-free learning of posterior densities via transport maps

To tackle concentrated, multi-modal Bayesian inference problems, we propose using an annealed importance sampling procedure. To do this, we form a sequence of annealed distributions and employ transport maps to act as a surrogate of each distribution. This process is demonstrated on a few examples, favorably showing its potential for efficiently parallelizing the process of PDE evaluations and allowing for surrogates that we can sample from exactly.

van Bloemen Waanders, Bart G [Sandia National Labo↗
DOE OSTI2026

Scalable foundation models for numerical simulations on HPC platforms

In recent years, foundation models (FMs) have begun to reshape numerical simulations on high-performance computing (HPC) platforms. These large, pre-trained AI models enable rapid predictions across a broad range of physical domains, including Earth system modeling, fluid dynamics, materials science, as well as complex multi-modal simulations in aerospace engineering and fusion research. By training on diverse datasets, FMs learn intricate relationships and underlying physical behavior while also enabling the quantification of uncertainty in their predictions. This capability allows simulations that once required days of numerical calculation to be completed in minutes (FM inference), supporting real-time design optimization, uncertainty-aware decision making, and more comprehensive exploration of complex scenarios.

AI↗
NASA NTRS1976

Diurnal measurements with prototype CMOS Omega receivers

Diurnal signals from eight omega channels have been monitored at 10.2 KHz for selected station pairs. All eight Omega stations have been received at least 50 percent of the time over a 24 hour period during the month of October 1976. The data presented confirm the expected performance of the CMOS omega sensor processor in being able to digsignals out of a noisy environment. Of particular interest are possibilities for use of antipodal reception phenomena and a need for some ways of correcting for multi-modal propagation effects.

Burhans, R. W.↗
NASA NTRS1979

Satellite pattern classification using charge transfer devices

The potential uses of Charge Transfer Devices (CTDs) in pattern classification operations are explored. The needs for a hardware-based pattern classifier are established, and a matrix multiplication subsystem based upon a sum-of-products CTD is presented. Applications of the subsystem to the classification of multi-modal Gaussian distributions in general and to LANDSAT data processing in particular are discussed. Finally, the potential impact of this technology on satellite data processing methodologies is discussed.

Snyder, W. E.↗
NASA NTRS1987

Task-level robot programming: Integral part of evolution from teleoperation to autonomy

An explanation is presented of task-level robot programming and of how it differs from the usual interpretation of task planning for robotics. Most importantly, it is argued that the physical and mathematical basis of task-level robot programming provides inherently greater reliability than efforts to apply better known concepts from artificial intelligence (AI) to autonomous robotics. Finally, an architecture is presented that allows the integration of task-level robot programming within an evolutionary, redundant, and multi-modal framework that spans teleoperation to autonomy.

Reynolds, James C.↗
NASA NTRS1988

Presentation planning using an integrated knowledge base

A description is given of user interface research aimed at bringing together multiple input and output modes in a way that handles mixed mode input (commands, menus, forms, natural language), interacts with a diverse collection of underlying software utilities in a uniform way, and presents the results through a combination of output modes including natural language text, maps, charts and graphs. The system, Integrated Interfaces, derives much of its ability to interact uniformly with the user and the underlying services and to build its presentations, from the information present in a central knowledge base. This knowledge base integrates models of the application domain (Navy ships in the Pacific region, in the current demonstration version); the structure of visual displays and their graphical features; the underlying services (data bases and expert systems); and interface functions. The emphasis is on a presentation planner that uses the knowledge base to produce multi-modal output. There has been a flurry of recent work in user interface management systems. (Several recent examples are listed in the references). Existing work is characterized by an attempt to relieve the software designer of the burden of handcrafting an interface for each application. The work has generally focused on intelligently handling input. This paper deals with the other end of the pipeline - presentations.

Arens, Yigal↗
NASA NTRS1989

A layered abduction model of perception: Integrating bottom-up and top-down processing in a multi-sense agent

A layered-abduction model of perception is presented which unifies bottom-up and top-down processing in a single logical and information-processing framework. The process of interpreting the input from each sense is broken down into discrete layers of interpretation, where at each layer a best explanation hypothesis is formed of the data presented by the layer or layers below, with the help of information available laterally and from above. The formation of this hypothesis is treated as a problem of abductive inference, similar to diagnosis and theory formation. Thus this model brings a knowledge-based problem-solving approach to the analysis of perception, treating perception as a kind of compiled cognition. The bottom-up passing of information from layer to layer defines channels of information flow, which separate and converge in a specific way for any specific sense modality. Multi-modal perception occurs where channels converge from more than one sense. This model has not yet been implemented, though it is based on systems which have been successful in medical and mechanical diagnosis and medical test interpretation.

Josephson, John R.↗
NASA NTRS1991

An experimental study of natural and forced modes in an axisymmetric jet

The experiment consisted of two parts. The first part was an effort to study naturally occurring instability modes in the axisymmetric shear layer of a high Reynolds number turbulent jet. Untripped (transitional) and tripped (turbulent) nozzle exit conditions, both with 0.1 percent core turbulence, were studied. For the turbulent nozzle exit boundary layer case, the core turbulence, was varied systematically from 0.1 to 5 percent of the jet exit velocity. The region up to the end of the potential core was dominated by the axisymmetric mode. The azimuthal modes grew rapidly but dominated only downstream of the potential core region. For the jet excited by natural disturbances, the energy content of the higher order modes (m is less than 1) was significantly lower than that of the axisymmetric and m = +/-1 modes. The initial boundary layer had a profound effect on the natural jet evolution and its excitability. The shorter potential core allowed the jet in the transitional case to support helical disturbances closer the nozzle exit than the turbulent case. The natural jet evolution was found to remain unaffected for varying initial core turbulence over the range of 0.15 to 5 percent of the jet velocity. Target modes for efficient excitation of the jet were determined from the results of the naturally occurring jet instability mode experiments. The second part of this work describes an effort to control the axisymmetric shear layer by artificially exciting target modes. Under optimum conditions, two-frequency excitation is indeed more effective than single frequency plane wave excitation as far as jet mixing is concerned. At high amplitudes of fundamental and subharmonic forcing, the subharmonic augmentation and the axial location of the peak were independent of the initial phase difference. Two-frequency excitation also has its limitations, since axisymmetric waves are damped beyond the potential core. Higher spreading rates are obtained when multi-modal forcing is applied.

Raman, Ganesh↗
DOE OSTI2025

Elucidating the corrosion mechanism of Ni-based superalloys in the presence of uranium-containing chloride molten salt

The United States Department of Energy (DOE) is committed to the advancement of nuclear reactor technology through initiatives such as the Advanced Reactor Development Program (ARDP), in an effort to diversify the United States energy portfolio towards more sustainable energy options. The ARDP includes demonstration by industry partners of molten chloride fast reactors (MCFRs). Construction of MCFRs requires qualified nuclear structural materials. Unfortunately, there are no current materials that are fully qualified by the Nuclear Regulatory Commission for the construction of molten salt reactors, including MCFRs. Adapting current structural material qualifications requires expansion of our current knowledgebase on the property-performance relationships regarding corrosion performance. In this investigation, we assess microstructural changes in a Ni-based superalloy after exposure to a UCl3¬-containing chloride salt eutectic mixture through a correlated multi-modal approach combining several advanced characterization techniques, including scanning electron microscopy/focused ion beam (SEM/FIB) and transmission electron microscopy (TEM). SEM/FIB analysis will illustrate changes in elemental composition, microstructure, and isotopic information acquired from energy x-ray dispersive spectroscopy (EDS), electron backscatter diffraction (EBSD), and secondary ion mass spectroscopy (SIMS), respectively. This information will then aid in identifying localized regions to elucidate the corrosion mechanism with TEM through a combination of electron diffraction, electron energy loss spectroscopy (EELS), and additional EDS. The findings from this investigation will further expand our assessment of the corrosion performance of structural materials in molten salt chloride systems, aiding to developing fully qualified materials for construction of MCFRs.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating the corrosion mechanism of commercial Ni-based Superalloys in UCl3 containing-chloride Molten Salt Systems

Molten salt reactors (MSRs) have gained renewed interest, providing several advantages over their predecessors, including the capability to consume spent fuels, enhancing the environmental sustainability of the uranium fuel cycle. For example, molten chloride fast reactors (MCFRs) can reach criticality with molten chloride spent fuel containing high concentrations of impurities, such as actinide products like uranium chloride (UCl3). However, the redox potential of chloride molten salt fuels may change in the presence of these impurities, dictating their corrosivity and in turn the corrosion performance of structural components, such as those constructed from nickel (Ni)-based alloys. The purpose of this investigation is to assess the extent of corrosion of Ni-based alloy, Inconel 617, when exposed to UCl3-LiCl-KCl eutectic salt. Inconel 617 one of only six structural materials that are fully qualified by the American Society for Mechanical Engineers (ASME) Boiler and Pressure Vessel Code for high-temperature nuclear reactor components, making it a technologically mature material to consider for constructing MCFRs. Inconel 617 specimens were submerged in a static LiCl-KCl-UCl3 eutectic salt mixture heated at 700 C for 1000 h under an inert atmosphere. Upon completion, the extent of corrosion was analyzed through a multi-modal characterization approach spanning the engineering to nanoscale, employing computed tomography, focused-ion beam, and transmission electron microscopy techniques. Results from this investigation will enhance our understanding of property-to-performance relationships of candidate structural materials for MSRs with respect to corrosion resistance and interactions between the salt and alloy interface.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Hiding-in-Plain-Sight (HiPS) Attack on CLIP for Targetted Object Removal from Images

Machine learning models are known to be vulnerable to adversarial attacks, but prior works have mostly focused on single-modalities. With the rise of large multi-modal models (LMMs) like CLIP, which combine vision and language capabilities, new vulnerabilities have emerged. However, these multimodal targeted attacks aim to completely change the model's output to what the adversary wants. In many realistic scenarios, an adversary might seek to make only subtle modifications to the output, so that the changes go unnoticed by downstream models or even by humans. We introduce Hiding-in-Plain-Sight (HiPS) attacks, a novel class of adversarial attacks that subtly modifies model predictions by selectively concealing target object(s), as if the target object was absent from the scene. We propose two HiPS attack variants, HiPS-cls and HiPS-cap, and demonstrate their effectiveness in transferring to downstream image captioning models, such as CLIP-Cap, for targeted object removal from image captions.

Daw, Arka [ORNL] (ORCID:0009000633191271)↗
DOE OSTI2025

Characterization of the degradation of gamma-irradiated elastomers using Raman spectroscopy

This report presents key findings from Raman spectroscopic analysis of gamma-irradiated rubber samples extracted from a laminated lead-damped rubber (LDR) seismic isolation device. The samples were exposed to gamma radiation from a 60Co source in a Foss Therapy Services gamma irradiator, reaching absorbed doses up to 1600 kGy. A distinct threshold near 400 kGy was identified, beyond which significant spectral changes were observed. Two Raman peaks - at approximately 425 cm-1 and 2440 cm-1 - were tracked as a function of dose using Gaussian fitting. The 425 cm-1 peak, attributed to sulfur–sulfur (S–S) bond stretching (resulting from vulcanization of the rubber), exhibited a dose-dependent upshift, indicating radiation-induced crosslinking within the sulfur-based polymer network. Conversely, the 2440 cm-1 peak, likely associated with vibrational modes of additives or impurities, showed a downward shift with increasing dose, suggesting chain scission and degradation of non-rubber constituents. These results provide first-of-a-kind insights into the microstructural evolution of elastomers under high-dose gamma irradiation and establish a preliminary dose threshold for significant degradation. Future work will incorporate multi-modal characterization—including Fourier Transform Infrared (FTIR) spectroscopy, scanning electrom microscopy (SEM) of the rubber surface morphology, thermogravimetric analysis (TGA) to determine changes in thermal stability, and mechanical testing—to correlate molecular-level changes with macroscopic performance of these elastomers as damping media in seismic isolation devices. These findings are expected to provide regulatory guidance and design criteria for qualifying low-damping rubber seismic isolators in advanced nuclear reactor applications.

36 - MATERIALS SCIENCE↗
DOE OSTI2024

Elucidating the corrosion mechanism of commercial Ni-based superalloys in UCl3 containing-chloride molten salt systems

Elucidating the role of UCl3 in the corrosion mechanism of Ni-based superalloys exposed to chloride molten salts Trishelle Copeland-Johnson1, Michael Woods1, Ruchi Gakhar1, Daniel J. Murray1, Guoping Cao1, Lingfeng He1 1Idaho National Laboratory, Idaho Falls, ID, United States The United States Department of Energy (DOE) aims to diversify the domestic energy portfolio towards more sustainable options, including implementation of molten salt reactor (MSR) technology. Chloride molten salts have been investigated as an appropriate MSR coolant and fuel because their relatively inexpensive, abundant, and exhibit favorable thermophysical properties. However, the corrosivity of chloride molten salts have not been extensively studied, especially with the inclusion of actinide products, such as UCl3. Accordingly, the development of nuclear structural materials with excellent corrosion performance is critical to the successful implementation of MSRs, particularly from a mechanistic perspective. In this investigation, we attempt to elucidate the interfacial corrosion mechanism between Ni-based structural materials, such as Inconel 617, and UCl3-containing salt systems through a multi-modal advanced characterization approach, including electron microscopy techniques. The findings from this investigation will expand the knowledgebase of chloride molten salt corrosion of MSR structural materials for strategic property-to-performance design.

36 - MATERIALS SCIENCE↗
DOE OSTI2026

LAF-Net: A Deep Residual and Cross-Attention Framework for Day-Ahead Load Forecasting: Preprint

Accurate day-ahead load forecasting is essential for reliable power system operations and market efficiency. System operators such as the Midcontinent Independent System Operator (MISO) rely on forecasts from multiple vendors, yet combining them effectively remains a persistent challenge due to vendor-specific biases. This paper presents a novel LSTM-Attention Fusion Network with Error Representation (LAF-Net) that enhances day-ahead hourly load forecasting through deep residual learning and multi-modal cross-attention. The proposed model builds a historical error memory from past vendor performance and dynamically queries it with future hour context to generate adaptive, hour-specific trust weights for each vendor. A bounded residual correction further refines forecasts by mitigating systematic and temporally localized errors. Tested on real MISO LBA data with multi-vendor forecasts, LAF-Net consistently outperforms the best vendor baseline across all 38 LBAs, achieving more than a 40% reduction in system-level mean absolute error (MAE) during peak load hours relative to the best vendor baseline.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Entropy-defect synergy for dual luminescence mechanism in spinel: Time-resolved anti-counterfeiting and fingerprint visualization

Multimodal luminescent materials, while promising for anti-counterfeiting, often lack dynamic time-dependent responses and controllable spatial distribution, limiting their encryption capabilities in the spatiotemporal dimension. Here, this work presents a coordinated control strategy based on entropy and defect engineering, and uses a backpropagation (BP) neural network for material screening to successfully prepare spinel Mg 0.8 (Fe 0.04 Co 0.04 Ni 0.04 Cu 0.04 Zn 0.04 )Cr 2 O 4 (MgA 5 CO) phosphors with time-dependent dynamic luminescence behavior. This phosphor simultaneously activated the d-d transition luminescence (∼618 nm) derived from Co 2+ /Cr 3+ and the defect luminescence (∼398 nm) related to zinc vacancies (V Zn ) in a single-phase solid solution. The phosphor exhibits a time-dependent color evolution from pink to purple under fixed-wavelength excitation, due to the different excited-state dynamics and decay lifetimes associated with the d-d transition and defect luminescence. Structural characterization and spectral analysis confirmed the existence of V Zn and its significant role in defect luminescence process. The fluorescent and dynamic luminescent properties of entropy-based spinel oxide enable its use in advanced anti-counterfeiting applications like fingerprint recognition and color-changing dedicated anti-counterfeiting mark, showing promise in high-end and time-dynamic anti-counterfeiting fields. This research not only developed a new type of fluorescent dynamic anti-counterfeiting material, but also provided a new idea for constructing advanced optical functional materials with multiple luminescence mechanisms.

Defect project↗
DOE OSTI2026

Ripening of Rh Nanoparticle Catalysts in Reverse Water–Gas Shift via a Data-Driven Model Combining Physics, Theory, and Experiment

Degradation via sintering is an ongoing challenge that impedes the broad commercial success of supported metallic nanoparticle catalysts. To mitigate degradation via informed catalyst design and process operations, here we aim to disambiguate the underlying mechanisms of sintering by combining theory and experiment in a quantitative framework. While mechanistic sintering models exist, they only model a single sintering pathway, even though multiple sintering mechanisms can occur simultaneously or dominate at different stages of the process. Data-driven machine learning models have emerged as a means to represent complex processes through data regression. However, machine learning models have very large data needs and lack mechanistic insights due to their black-box encoding. To develop an interpretive model of catalyst degradation via sintering, we constructed a hybrid model combining mechanistic “physics-based” models and data-driven methods to obtain both reliable predictions and mechanistic insights regarding experimentally observed sintering phenomena. Focusing on nanoparticle sintering in the Rh–TiO 2 catalyst for the reverse water–gas shift (RWGS) reaction, the hybrid model couples a mechanistic term for Ostwald ripening with energy values calculated via density functional theory (DFT) with a parametric, data-driven discrepancy function term for unmodeled mechanisms. The hybrid model is trained using Bayesian inference with data collected from small-angle X-ray scattering (SAXS) in situ experiments wherein average nanoparticle diameter versus time was measured at three relevant operating temperatures. The calibrated hybrid model results show that an Ostwald ripening-only model parameterized with fixed DFT energies does not fully capture the time and temperature dependence of the SAXS-observed sintering kinetics, and that an additional functional contribution, or DFT energy calibration, is required to reconcile simulation and experiment. Analysis of the hybrid-model error confirms that the hybrid model outperforms both the purely mechanistic and purely data-driven alternatives in terms of expected predictive accuracy for time-evolving average particle sizes. Furthermore, the results support the hypothesis that the Ostwald ripening mechanism is less important for explaining the sintering phenomena as operating temperature increases under an assumed fixed DFT parameterization. This could be explained in one of two ways: either latent, unmodeled sintering mechanisms dominate at higher temperatures, or the DFT uncertainty increases with temperature. The proposed modeling approach directly links theory to experiments and simulations via a statistical hybrid modeling framework and can be extended to other catalytic systems to improve predictive models and mechanistic understanding.

Bayesian hybrid modeling↗