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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 OSTI2023

Online real-time learning of dynamical systems from noisy streaming data

Abstract Recent advancements in sensing and communication facilitate obtaining high-frequency real-time data from various physical systems like power networks, climate systems, biological networks, etc. However, since the data are recorded by physical sensors, it is natural that the obtained data is corrupted by measurement noise. In this paper, we present a novel algorithm for online real-time learning of dynamical systems from noisy time-series data, which employs the Robust Koopman operator framework to mitigate the effect of measurement noise. The proposed algorithm has three main advantages: (a) it allows for online real-time monitoring of a dynamical system; (b) it obtains a linear representation of the underlying dynamical system, thus enabling the user to use linear systems theory for analysis and control of the system; (c) it is computationally fast and less intensive than the popular extended dynamic mode decomposition (EDMD) algorithm. We illustrate the efficiency of the proposed algorithm by applying it to identify the Van der Pol oscillator, the chaotic attractor of the Henon map, the IEEE 68 bus system, and a ring network of Van der Pol oscillators.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .

Marfo, William [Univ. of Texas, El Paso, TX (Unite↗
DOE OSTI2015

Superior Reliability Via Two‐Step Sintering: Barium Titanate Ceramics

Two‐step sintering ( TSS ) in a reducing atmosphere has been employed to obtain fine‐grain BaTiO 3 ceramics with a core‐shell microstructure, a more uniform grain‐size distribution, and superior reliability for multilayer ceramic capacitor applications. Compared to ceramics of the same composition conventionally sintered for about the same time, TSS ceramics feature a thinner shell thickness thus a stronger dopant localization, which leads to a lower concentration, higher internal resistance and more dopant‐ association. Improved reliability is manifest in a 50% higher breakdown strength at ambient temperature and a 400% longer endurance time to withstand DC stress at 185°C, in addition to a less field‐and‐temperature‐dependent capacitance. A scaling analysis of the redistribution and endurance dynamics identifies transmission across the shell‐grain‐boundary region as the critical element beneficially impacted by core‐shell structure and two‐step sintering.

Zhao, Qiancheng↗
DOE OSTI2018

Present Early Test Results at SECARB Industry Briefing [Slides]

Contents include the following presentations: 1) SECARB Phase III Early Test Cranfield, MS; 2) SECARB Phase III Early Test at Cranfield, March 9, 2010; 3) Update on Results of SECARB "Early" Test of Monitoring Large Volume Injection at Cranfield; 4) Progress Update SECARB "Early" Test Monitoring 3.5 Million Tons at Cranfield, March 7, 2012; 5) Cranfield Geophysical Monitoring Status and Results, March 8, 2012; 6) Large Volume CO 2 Injection at Cranfield, Early Field Test of the SECARB Phase III–Near-Surface Monitoring, March 7, 2012; 7) Shallow Groundwater Study at the Cranfield Site, March 12, 2013; 8) Subsurface Monitoring Studies–Results and Integration, March 12, 2013; 9) Review of the Petrophysics for the Cranfield Phase III Project, March 12, 2013; 10) Geomechanics at Cranfield, March 5, 2014; 11) Gulf Coast Carbon Center Near-Surface Update, March 4-5, 2014; 12) Monitoring Design Best Practices, March 5, 2014; 13) Assessing Impacts of CO 2 Leakage on Groundwater Quality and Monitoring Network Efficiency–Case Study at a CO 2 -EOR site, March 11, 2013; 14) Near-surface Stratigraphy and Geophysics, March 11, 2015; 15) Update on Reservoir Modeling and Final Testing and P&A Underway, March 11, 2013; 16) SECARB Early Test Retrospective; 17) Fluid Source Attribution at CCS and CO 2 -EOR Sites, March 8, 2016; 18) SECARB "Early Test" at Cranfield, March 8-9, 2017; 19) Analysis of Seismic Time-Lapse Observations at Cranfield; 20) SECARB Phase III Cranfield Project (Early Test), March 8, 2018; 21) Enhanced Analytical Simulation Tool (EASiTool) Dynamic Capacity Estimation; 22) Net Carbon Negative Oil Mass Accounting–Cranfield Case Study; 23) Knowledge Sharing; 24) Advancing Toward Commercialization Final Stages of the Early Test; 25) CO 2 EOR Feasibility in SECARB Region Limited by CH 4 ; 26) Understanding of CO2 Migration and Entrapment–Cranfield Site

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2024

Transforming Agricultural Productivity with AI-Driven Forecasting: Innovations in Food Security and Supply Chain Optimization

Global food security is under significant threat from climate change, population growth, and resource scarcity. This review examines how advanced AI-driven forecasting models, including machine learning (ML), deep learning (DL), and time-series forecasting models like SARIMA/ARIMA, are transforming regional agricultural practices and food supply chains. Through the integration of Internet of Things (IoT), remote sensing, and blockchain technologies, these models facilitate the real-time monitoring of crop growth, resource allocation, and market dynamics, enhancing decision making and sustainability. The study adopts a mixed-methods approach, including systematic literature analysis and regional case studies. Highlights include AI-driven yield forecasting in European hydroponic systems and resource optimization in southeast Asian aquaponics, showcasing localized efficiency gains. Furthermore, AI applications in food processing, such as plasma, ozone and Pulsed Electric Field (PEF) treatments, are shown to improve food preservation and reduce spoilage. Key challenges—such as data quality, model scalability, and prediction accuracy—are discussed, particularly in the context of data-poor environments, limiting broader model applicability. The paper concludes by outlining future directions, emphasizing context-specific AI implementations, the need for public–private collaboration, and policy interventions to enhance scalability and adoption in food security contexts.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Unsteady deflagration speed of an auto-ignitive dimethyl-ether (DME)/air mixture at stratified conditions

The propagation speed of an auto-ignitive dimethyl-ether (DME)/air mixture at elevated pressures and subjected to monochromatic temperature oscillations is numerically evaluated in a one-dimensional statistically stationary configuration using fully resolved numerical simulations with reduced kinetics and transport. Two sets of conditions with temperatures within and slightly above the negative temperature coefficient (NTC) regime are simulated to investigate the fundamental aspects of auto-ignition and flame propagation along with the transition from auto-ignitive deflagration to spontaneous propagation regimes under thermal stratification. Contrary to the standard laminar flame speed, the steady propagation speed of an auto-ignitive front is observed to scale proportionally to its level of upstream reactivity. It is shown that this interdependence is primarily influenced by the characteristic residence time and the homogeneous auto-ignition delay. Furthermore, the unsteady reaction front in either of the two cases responds distinctly to the imposed stratification. Specifically, the results in both cases show that the dynamic flame response depends on the mean temperature at the flame base Tb and the time-scale of thermal stratification. It is also found that, based on Tb and the propensity of the mixture to two-stage chemistry, the instantaneous peak propagation speed and the overall time taken to achieve that speed differs considerably. A displacement speed analysis is carried out to elucidate the underlying combustion modes that are responsible for such a variation in flame response.

Energy & Fuels↗
DOE OSTI2021

Dynamic infrared gas analysis from longleaf pine fuel beds burned in a wind tunnel: observation of phenol in pyrolysis and combustion phases

Pyrolysis is the first step in a series of chemical and physical processes that produce flammable organic gases from wildland fuels that can result in a wildland fire. We report results using a new time-resolved Fourier transform infrared (FTIR) method that correlates the measured FTIR spectrum with an infrared thermal image sequence, enabling the identification and quantification of gases within different phases of the fire process. The flame from burning fuel beds composed of pine needles (Pinus palustris) and mixtures of sparkleberry, fetterbush, and inkberry plants was the natural heat source for pyrolysis. Extractive gas samples were analyzed and identified in both static and dynamic modes synchronized to thermal infrared imaging: a total of 29 gases were identified including small alkanes, alkenes, aldehydes, nitrogen compounds, and aromatics, most previously measured by FTIR in wildland fires. This study presents one of the first identifications of phenol associated with both pre-combustion and combustion phases using ca. 1 Hz temporal resolution. Preliminary results indicate ~2.5× greater phenol emissions from sparkleberry and inkberry compared to fetterbush, with differing temporal profiles.

09 BIOMASS FUELS↗
DOE OSTI2017

Energy exchange dynamics across L–H transitions in NSTX

Here, we studied the energy exchange dynamics across the low-to-high-confinement (L–H) transition in NSTX discharges using the gas-puff imaging (GPI) diagnostic. The investigation focused on the energy exchange between flows and turbulence to help clarify the mechanism of the L–H transition. We applied this study to three types of heating schemes, including a total of 17 shots from the NSTX 2010 campaign run. Results show that the edge fluctuation characteristics (fluctuation levels, radial and poloidal correlation lengths) measured using GPI do not vary just prior to the H-mode transition, but change after the transition. Using a velocimetry approach (orthogonal-dynamics programming), velocity fields of a $24\times 30$ cm GPI view during the L–H transition were obtained with good spatial (~1 cm) and temporal (~2.5 μs) resolutions. Analysis using these velocity fields shows that the production term is systematically negative just prior to the L–H transition, indicating a transfer from mean flows to turbulence, which is inconsistent with the predator–prey paradigm. Moreover, the inferred absolute value of the production term is two orders of magnitude too small to explain the observed rapid L–H transition. These discrepancies are further reinforced by consideration of the ratio between the kinetic energy in the mean flow to the thermal free energy, which is estimated to be much less than 1, suggesting again that the turbulence depletion mechanism may not play an important role in the transition to the H-mode. Although the Reynolds work therefore appears to be too small to directly deplete the turbulent free energy reservoir, order-of-magnitude analysis shows that the Reynolds stress may still make a non-negligible contribution to the observed poloidal flows.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Variations of characteristic time scales in rotating stratified turbulence using a large parametric numerical study

In this work, we study rotating stratified turbulence (RST) making use of numerical data stemming from a large parametric study varying the Reynolds, Froude and Rossby numbers, Re, Fr and Ro in a broad range of values. The computations are performed using periodic boundary conditions on grids of 10243 points, with no modeling of the small scales, no forcing and with large-scale random initial conditions for the velocity field only, and there are altogether 65 runs analyzed in this paper. The buoyancy Reynolds number defined as R B = ReFr 2 varies from negligible values to ≈ 105, approaching atmospheric or oceanic regimes. This preliminary analysis deals with the variation of characteristic time scales of RST with dimensionless parameters, focusing on the role played by the partition of energy between the kinetic and potential modes, as a key ingredient for modeling the dynamics of such flows. We find that neither rotation nor the ratio of the Brunt-Väisälä frequency to the inertial frequency seem to play a major role in the absence of forcing in the global dynamics of the small-scale kinetic and potential modes. Specifically, in these computations, mostly in regimes of wave turbulence, characteristic times based on the ratio of energy to dissipation of the velocity and temperature fluctuations, T V and T P , vary substantially with parameters. Their ratio γ=T V /T P follows roughly a bell-shaped curve in terms of Richardson number Ri. It reaches a plateau --on which time scales become comparable, γ≈0.6 -- when the turbulence has significantly strengthened, leading to numerous destabilization events together with a tendency towards an isotropization of the flow.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Morphology and Dynamics of Bottlebrush Polymers

This thesis makes significant advances to the understanding of bottlebrush polymers. While bottlebrushes have received much attention due to the recent discovery of their unprecedented properties, including supersoftness, ultra-low viscosity, and hyperelasticity, this thesis is the first fundamental investigation at the molecular level that comprises structure and dynamics. Neutron scattering experiments, detailed within, reveal spherical or cylindrical shapes, instead of a random coil conformation. Another highlight is the analysis of the fast dynamics at the sub nm-length scale. The combination of three neutron spectrometers and the development of a new analysis technique enabled the calculation of the mean-square displacement over seven orders of magnitude in time scale. This unprecedented result can be applied to a broad class of samples, including polymers and other materials. The thesis is accessible to scientists from other fields, provides the reader with easily understandable guidelines for applying this analysis to other materials, and has the potential to make a significant impact on the analysis of neutron scattering data.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns, emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Modeling and Cost Benefit Analysis to Guide Deployment of POC Diagnostics for Non-typhoidal Salmonella Infections with Antimicrobial Resistance

Invasive non-typhoidal Salmonella (NTS) is among the leading causes of blood stream infections in sub-Saharan Africa and other developing regions, especially among pediatric populations. Invasive NTS can be difficult to treat and have high case-fatality rates, in part due to emergence of strains resistant to broad-spectrum antibiotics. Furthermore, improper treatment contributes to increased antibiotic resistance and death. Point of care (POC) diagnostic tests that rapidly identify invasive NTS infection, and differentiate between resistant and non-resistant strains, may greatly improve patient outcomes and decrease resistance at the community level. Here we present for the first time a model for NTS dynamics in high risk populations that can analyze the potential advantages and disadvantages of four strategies involving POC diagnostic deployment, and the resulting impact on antimicrobial treatment for patients. Our analysis strongly supports the use of POC diagnostics coupled with targeted antibiotic use for patients upon arrival in the clinic for optimal patient and public health outcomes. We show that even the use of imperfect POC diagnostics can significantly reduce total costs and number of deaths, provided that the diagnostic gives results quickly enough that patients are likely to return or stay to receive targeted treatment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Transient dynamics of terrestrial carbon storage: Mathematical foundation and its applications

Terrestrial ecosystems have absorbed roughly 30% of anthropogenic CO 2 emissions over the past decades, but it is unclear whether this carbon (C) sink will endure into the future. Despite extensive modeling and experimental and observational studies, what fundamentally determines transient dynamics of terrestrial C storage under global change is still not very clear. Here we develop a new framework for understanding transient dynamics of terrestrial C storage through mathematical analysis and numerical experiments. Our analysis indicates that the ultimate force driving ecosystem C storage change is the C storage capacity, which is jointly determined by ecosystem C input (e.g., net primary production, NPP) and residence time. Since both C input and residence time vary with time, the C storage capacity is time-dependent and acts as a moving attractor that actual C storage chases. The rate of change in C storage is proportional to the C storage potential, which is the difference between the current storage and the storage capacity. The C storage capacity represents instantaneous responses of the land C cycle to external forcing, whereas the C storage potential represents the internal capability of the land C cycle to influence the C change trajectory in the next time step. The influence happens through redistribution of net C pool changes in a network of pools with different residence times. Moreover, this and our other studies have demonstrated that one matrix equation can replicate simulations of most land C cycle models (i.e., physical emulators). As a result, simulation outputs of those models can be placed into a three-dimensional (3-D) parameter space to measure their differences. The latter can be decomposed into traceable components to track the origins of model uncertainty. In addition, the physical emulators make data assimilation computationally feasible so that both C flux- and pool-related datasets can be used to better constrain model predictions of land C sequestration. Altogether, this new mathematical framework offers new approaches to understanding, evaluating, diagnosing, and improving land C cycle models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Application of Detectability Analysis for Power System Dynamic State Estimation

To guide the measurement placement and selection for dynamic state estimation in power systems, it is important to determine the existence of an observer that can estimate the dynamic states. Past studies have been focused on observability analysis, which determines whether the initial states can be uniquely determined. In this paper, detectability analysis is proposed to determine whether the current states can be estimated as time involving. Through the proposed detectability analysis, the states of an unobservable system can be categorized into the detectable states and undetectable states. It is shown that if the eigenvalues corresponding to the unobservable states are stable, the states are detectable and their estimates will converge to the true ones asymptotically. The study can be used to guide measurement placement and selection.

Detectability, dynamic state estimation↗
DOE OSTI2019

Using Digital Trace Data to Identify Regions and Cities

A greater understanding of human dynamics as they play out in both physical space and through inter-personal communication is vital for the design and development of intelligent and resilient cities. Physical context provides insight in to the space-time distribution of population and their activity patterns, while inter-personal communication can, for the first time, be measured at scale through virtual interactions. The confluence of these two enables spatial social network analysis in the built environment for edge computing, knowledge discovery, and artificial intelligence activities. In this work, we propose a novel method to discover such dynamics. We use a dataset of 72 million tweets to develop a spatially embedded network of communication, and then use community detection algorithms to explore regional and urban delineation in the United States. We find that the broad spatial delineation of communities and community sub-structure is consistent with major United States socio-cultural regions, states, and major metropolitan areas. We describe how these methods could be extended to generate a measure of socially cohesive regions that can be consistently applied anywhere there is a sufficiently rich data source. We hope the proposed methods will generate new perspectives on smart and resilient cities.

Brelsford, Christa↗
DOE OSTI2020

Bidentate Lewis bases are preferred for passivation of MAPbI 3 surfaces: A time-domain ab initio analysis

We report chemical passivation is a predominant approach to inhibit intrinsic defects responsible for electron–hole recombination in perovskite solar cells. Using time-domain density functional theory combined with nonadiabatic molecular dynamics, we demonstrate defect passivation by separation of electrons and holes in metal halide perovskites (MHPs), and show that bidentate ligands exhibit the best performance. Defects in traditional semiconductors create deep midgap states, and passivation eliminates these states. In contrast, common defects produce no deep midgap states in MHPs. Instead, defects localize electrons and holes around defect sites, enhancing electron–hole interaction. Defect passivation in MHPs acts to separate charges, decreasing electron–hole and charge–phonon interactions, and increasing charge lifetimes. Bidentate ligands work best, because they can passivate both unsaturated chemical bonds created due to vacancy defects. Bidentate ligands with spatially separated binding sites are preferred, since they provide better match to the sparse inorganic lattice of MHPs. Similar to the Lewis base ligands, water also acts as a ligand and extends charge lifetimes, with the oxygen atom donating its lone electron pair to the defect site. However, water accelerates chemical degradation of MHPs. The binding energy of most ligands is larger than that of water, and therefore, the ligands displace water, increase MHP stability and prolong carrier lifetimes. The established physical mechanism of defect passivation, and the specific principles guiding the choice of passivating molecules advance our understanding of the exceptional properties of MHPs and suggest routes for further improvement of MHP performance in solar energy and optoelectronic applications.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Fluid dynamics analysis of a gas attenuator for X-ray FELs under high-repetition-rate operation

Newtonian fluid dynamics simulations were performed using the Navier–Stokes–Fourier formulations to elucidate the short time-scale (µs and longer) evolution of the density and temperature distributions in an argon-gas-filled attenuator for an X-ray free-electron laser under high-repetition-rate operation. Both hydrodynamic motions of the gas molecules and thermal conductions were included in a finite-volume calculation. It was found that the hydrodynamic wave motions play the primary role in creating a density depression (also known as a filament) by advectively transporting gas particles away from the X-ray laser–gas interaction region, where large pressure and temperature gradients have been built upon the initial energy deposition via X-ray photoelectric absorption and subsequent thermalization. Concurrent outward heat conduction tends to reduce the pressure in the filament core region, generating a counter gas flow to backfill the filament, but on an initially slower time scale. If the inter-pulse separation is sufficiently short so the filament cannot recover, the depth of the filament progressively increases as the trailing pulses remove additional gas particles. Since the rate of hydrodynamic removal decreases while the rate of heat conduction back flow increases as time elapses, the two competing mechanisms ultimately reach a dynamic balance, establishing a repeating pattern for each pulse cycle. Finally, by performing simulations at higher repetition rates but lower per pulse energies while maintaining a constant time-averaged power, the amplitude of the hydrodynamic motion per pulse becomes smaller, and the evolution of the temperature and density distributions approach asymptotically towards, as expected, those calculated for a continuous-wave input of the equivalent power.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Nonlinear dynamics and quantum chaos of a family of kicked p -spin models

Herein we introduce kicked p-spin models describing a family of transverse Ising-like models for an ensemble of spin-1/2 particles with all-to-all p-body interaction terms occurring periodically in time as delta-kicks. This is the natural generalization of the well-studied quantum kicked top (p = 2) [Haake, Kus', and Scharf, Z. Phys. B 65, 381 (1987)]. We fully characterize the classical nonlinear dynamics of these models, including the transition to global Hamiltonian chaos. The classical analysis allows us to build a classification for this family of models, distinguishing between p = 2 and p > 2, and between models with odd and even p's. Quantum chaos in these models is characterized in both kinematic and dynamic signatures. For the latter, we show numerically that the growth rate of the out-of-time-order correlator is dictated by the classical Lyapunov exponent. Finally, we argue that the classification of these models constructed in the classical system applies to the quantum system as well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗