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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 739 records · Page 41
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Advanced control techniques for modern inertia based inverters

In this research three artificial intelligent (AI)-based techniques are proposed to regulate the voltage and frequency of a grid-connected inverter. The increase in the penetration of renewable energy sources (RESs) into the power grid has led to the increase in the penetration of fast-responding inertia-less power converters. The increase in the penetration of these power electronics converters changes the nature of the conventional grid, in which the existing kinetic inertia in the rotating parts of the enormous generators plays a vital role. The concept of virtual inertia control scheme is proposed to make the behavior of grid connected inverters more similar to the synchronous generators, by mimicking the mechanical behavior of a synchronous generator. Conventional control techniques lack to perform optimally in nonlinear, uncertain, inaccurate power grids. Besides, the decoupled control assumption in conventional VSGs makes them nonoptimal in resistive grids. The neural network predictive controller, the heuristic dynamic programming, and the dual heuristic dynamic programming techniques are presented in this research to overcome the draw backs of conventional VSGs. The nonlinear characteristics of neural networks, and the online training enable the proposed methods to perform as robust and optimal controllers. The simulation and the experimental laboratory prototype results are provided to demonstrate the effectiveness of the proposed techniques.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Development and transferability of neural-network models for plasma-surface interactions

Plasma-surface interactions are increasingly critical to modern technologies; yet, accurate molecular dynamics simulations remain limited by the capabilities of interatomic potentials. Deep Potentials (DPs) promise to revolutionize the field by providing a systematic method for producing accurate interatomic potentials. The primary challenge of DP development is selecting a dataset, which efficiently spans the set of atomic environments one expects to encounter in the subsequent molecular dynamics simulations. The computational cost of density functional theory calculations, which are the typical basis for DP development, makes it impossible to directly verify the quality of a given DP. To address this challenge, we explore the development of a deep-learned interatomic potential, “DeepREBO,” trained to reproduce the behavior of the REBO2 empirical potential, enabling direct validation of training methodology and transferability. Using an active learning framework, we begin with a minimal dataset and iteratively expand it to train a Deep Potential-Smooth Edition model that faithfully reproduces REBO2 results for 25 eV hydrogen bombardment of diamond (001), a particularly challenging case. We show that small, carefully curated datasets can outperform large, unguided ones, with effective models requiring fewer than 15 000 snapshots. Subsequent transferability tests demonstrate that while DeepREBO generalizes well to diamond (111) surfaces, performance degrades for amorphous carbon or higher-energy impacts, highlighting the need for use-case-specific training data. We also evaluate methods to improve short-range repulsion. This study outlines best practices for training robust deep potentials and underscores the importance of dataset design for predictive plasma simulations.

Ab-initio molecular dynamics↗
NASA NTRS2003

Toward a Dynamically Reconfigurable Computing and Communication System for Small Spacecraft

Future science missions will require the use of multiple spacecraft with multiple sensor nodes autonomously responding and adapting to a dynamically changing space environment. The acquisition of random scientific events will require rapidly changing network topologies, distributed processing power, and a dynamic resource management strategy. Optimum utilization and configuration of spacecraft communications and navigation resources will be critical in meeting the demand of these stringent mission requirements. There are two important trends to follow with respect to NASA's (National Aeronautics and Space Administration) future scientific missions: the use of multiple satellite systems and the development of an integrated space communications network. Reconfigurable computing and communication systems may enable versatile adaptation of a spacecraft system's resources by dynamic allocation of the processor hardware to perform new operations or to maintain functionality due to malfunctions or hardware faults. Advancements in FPGA (Field Programmable Gate Array) technology make it possible to incorporate major communication and network functionalities in FPGA chips and provide the basis for a dynamically reconfigurable communication system. Advantages of higher computation speeds and accuracy are envisioned with tremendous hardware flexibility to ensure maximum survivability of future science mission spacecraft. This paper discusses the requirements, enabling technologies, and challenges associated with dynamically reconfigurable space communications systems.

Kifle, Muli↗
DOE OSTI2024

Deep Koopman Neural Network for Analyzing High-Energy-Density Simulations of Electrical Wire Explosions

Megaampere-scale electrical wire experiments (EWEs) provide a platform for studying magnetohydrodynamic (MHD) instability growth in magneto-inertial fusion (MIF) devices. Even when nonlinear simulations of these experiments can digitally reproduce much of the experimentally observed instability growth, interpreting the results and understanding mode growth and evolution can be non-trivial. As a first step toward providing better interpretation of these simulation features, this work investigates the use of a deep neural network that uses Koopman operator theory to analyze the dynamics of pulsed-power-driven explosions of EWEs. This deep neural network is trained on 1-D resistive MHD simulations of EWEs. This neural network learns to transform the nonlinear data into a lower-dimensional representation where the time dynamics are linear. Layers of this neural network are shown to learn features of the simulations, including the locations of shock waves and different physical regimes of the simulation. Using the learned features, the network can compress a time state of the simulation consisting of 5120 data point into a 36-parameter lower-dimensional latent space embedding. Furthermore, these embeddings are shown to be clustered in the latent space by initial radius and time state.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

The matrix product approximation for the dynamic cavity method

Stochastic dynamics of classical degrees of freedom, defined on vertices of locally tree-like graphs, can be studied in the framework of the dynamic cavity method which is exact for tree graphs. Such models correspond for example to spin-glass systems, Boolean networks, neural networks, and other technical, biological, and social networks. The central objects in the cavity method are edge messages—conditional probabilities of two vertex variable trajectories. In this paper, we discuss a rather pedagogical derivation for the dynamic cavity method, give a detailed account of the novel matrix product edge message (MPEM) algorithm for the solution of the dynamic cavity equation as introduced, and present optimizations and extensions. Matrix product approximations of the edge messages are constructed recursively in an iteration over time. Computation costs and precision can be tuned by controlling the matrix dimensions of the MPEM in truncations. Without truncations, the dynamics is exact. Data for Glauber–Ising dynamics shows a linear growth of computation costs in time. In contrast to Monte Carlo simulations, the approach has a much better error scaling. Hence, it gives for example access to low probability events and decaying observables like temporal correlations. Here, we discuss optimized truncation schemes and an extension that allows to capture models which have a continuum time limit.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
NASA NTRS1995

Experimental evaluation of dynamic data allocation strategies in a distributed database with changing workloads

Traditionally, allocation of data in distributed database management systems has been determined by off-line analysis and optimization. This technique works well for static database access patterns, but is often inadequate for frequently changing workloads. In this paper we address how to dynamically reallocate data for partionable distributed databases with changing access patterns. Rather than complicated and expensive optimization algorithms, a simple heuristic is presented and shown, via an implementation study, to improve system throughput by 30 percent in a local area network based system. Based on artificial wide area network delays, we show that dynamic reallocation can improve system throughput by a factor of two and a half for wide area networks. We also show that individual site load must be taken into consideration when reallocating data, and provide a simple policy that incorporates load in the reallocation decision.

Brunstrom, Anna↗
DOE OSTI2024

Optimizing dynamic wireless charging for electric buses: A data-driven approach to infrastructure planning

The network configuration significantly impacts the performance of dynamic wireless charging (DWC) technology for electric buses. Here, this study presents a novel approach to planning charging infrastructure for public transit using data-driven nonconvex mixed-integer optimization. Integrating DWC and charging station technologies reveals a trade-off between enroute and stationary charging times. Our framework optimizes bus frequency settings and transmitter coil arrangements to minimize operational and infrastructure costs. A case study in Chattanooga, Tennessee, demonstrates the method's effectiveness in mitigating range anxiety and reducing charging expenses. This research implies that integrating DWC technology into public transit systems can enhance the feasibility and cost-effectiveness of electric bus operations, promoting sustainable urban mobility.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Insights into Rotational and Translational Dynamics in Mixtures of Ethylene Glycol and Choline Chloride Using Nuclear Magnetic Resonance Techniques

This work examines molecular dynamics and interactions in ethylene glycol–choline chloride (EG–ChCl) mixtures across 0–33 mol % ChCl, spanning the true eutectic region near 17–20 mol % and the commonly used 1:2 formulation. We combine pulsed-field-gradient (PFG) diffusion, fast-field-cycling (FFC) relaxometry, temperature-dependent 13 C T 1 , and nuclear Overhauser effect spectroscopy (NOESY) to disentangle local from macroscopic dynamics. PFG and FFC show that both translational and average rotational motions largely track the strong increase in viscosity with ChCl content, with ethylene glycol consistently diffusing faster than the choline cation and no global dynamical anomaly at the eutectic composition. More subtle, site-specific composition effects nevertheless emerge. The ratio of the diffusion coefficient of the hydroxyl group of choline to the diffusion coefficient of the methyl group of choline displays a shallow minimum in the 17–25 mol % region, indicating a modest change in how the hydroxyl-bearing end of choline samples the underlying translational motion relative to the methyl groups. 13 C T 1 analysis shows that rotational correlation times at 25 °C generally increase with ChCl, reflecting viscosity-coupled slowing, while the CH 2 –N α site exhibits a small but reproducible deviation from this monotonic trend near the eutectic. NOESY spectra at similar compositions reveal enhanced cross-relaxation between EG and choline protons, consistent with increased headgroup–solvent contact density rather than a wholesale structural rearrangement. Overall, our multitechnique study demonstrates that EG–ChCl dynamics are predominantly viscosity-dominated, with the eutectic region acting as a subtle dynamical crossover where specific choline segments become maximally coupled to the hydrogen-bond network. These insights refine the structure–dynamics picture of choline-chloride DESs and provide practical guidance for tuning composition in electrochemical, separation, and catalytic applications.

diffusion↗
DOE OSTI2025

Oscillation Risks of Grid-Following and Grid-Forming Inverter-Based Resources in Series-Compensated Networks

Here, this paper investigates the dynamic behavior of a grid-connected inverter-based resource (IBR) when connected radially to a series compensated line. Potential interactions between the series compensation and the IBR have been identified for both types: grid-following (GFL) or grid-forming (GFM). The study begins with electromagnetic transient (EMT) simulations to demonstrate stability issues. Subsequently, nonlinear analytical models are formulated in the dq frame, validated against the EMT simulation, and leveraged to assess eigenvalues and participation factors. Influencing factors of the dominant oscillation modes have been identified. The analysis results show that series compensation may make a mode associated with the synchronization unit unstable. Furthermore, customized feedback systems are built for the synchronizing loop. Series compensation can increase the sensitivity of the voltage phase angle towards the synchronizing angle, and introduce phase lag in the real power response towards the synchronizing angle. These factors may cause interactions with the phase-locked loop in GFL-IBR systems and with power-based synchronization in GFM-IBR systems, potentially leading to instability.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1996

Algorithm For A Self-Growing Neural Network

CID3 algorithm simulates self-growing neural network. Constructs decision trees equivalent to hidden layers of neural network. Based on ID3 algorithm, which dynamically generates decision tree while minimizing entropy of information. CID3 algorithm generates feedforward neural network by use of either crisp or fuzzy measure of entropy.

Cios, Krzysztof J.↗
DOE OSTI2025

Tree tensor network hierarchical equations of motion based on time-dependent variational principle for efficient open quantum dynamics in structured thermal environments

In this work, we introduce an efficient method, TTN-HEOM, for exactly calculating the open quantum dynamics for driven quantum systems interacting with highly structured bosonic baths by combining the tree tensor network (TTN) decomposition scheme with the bexcitonic generalization of the numerically exact hierarchical equations of motion (HEOM). The method yields a series of quantum master equations for all core tensors in the TTN that efficiently and accurately capture the open quantum dynamics for non-Markovian environments to all orders in the system–bath interaction. These master equations are constructed based on the time-dependent Dirac–Frenkel variational principle, which isolates the optimal dynamics for the core tensors given the TTN ansatz. The dynamics converges to the HEOM when increasing the rank of the core tensors, a limit in which the TTN ansatz becomes exact. We introduce TENSO, tensor equations for non-Markovian structured open systems, as a general-purpose Python code to propagate the TTN-HEOM dynamics. We implement three general propagators for the coupled master equations: two fixed-rank methods that require a constant memory footprint during the dynamics and one adaptive-rank method with a variable memory footprint controlled by the target level of computational error. We exemplify the utility of these methods by simulating a two-level system coupled to a structured bath containing one Drude–Lorentz component and eight Brownian oscillators, which is beyond what can presently be computed using the standard HEOM. Our results show that the TTN-HEOM is capable of simulating both dephasing and relaxation dynamics of driven quantum systems interacting with structured baths, even those of chemical complexity, with an affordable computational cost.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Shear-Induced Gelation of Self-Yielding Active Networks

An enticing feature of active materials is the possibility of controlling macroscale rheological properties through the activity of the microscopic constituents. Using a unique combination of microscopy and rheology we study three dimensional microtubule-based active materials whose autonomous flows are powered by a continually rearranging connected network. We quantify the relationship between the microscopic dynamics and the bulk mechanical properties of these nonequilibrium networks. Experiments reveal a surprising nonmonotonic viscosity that strongly depends on the relative magnitude of the rate of internally generated activity and the externally applied shear. Here, a simple two-state mechanical model that accounts for both the solidlike and yielded fluidlike elements of the network accurately describes the rheological measurements.

36 MATERIALS SCIENCE↗
NASA NTRS1998

Hidden Markov Models and Neural Networks for Fault Detection

Continuous online monitoring of complex dynamic systems is common in applications as diverse as industrial plant operations, telecommunications network, and biomedical health monitoring. For monitoring purposes, the exact nature of the system under observation is typically not relevant provided there exist some measurements or symptoms which provide diagnostics information regarding the underlying system state.

Markov Fault Detection↗
DOE OSTI2021

Distribution of blackouts in the power grid and the Motter and Lai model

Carreras, Dobson, and colleagues have studied empirical data on the sizes of the blackouts in real grids and modeled them with computer simulations using the direct current approximation. They have found that the resulting blackout sizes are distributed as a power law and suggested that this is because the grids are driven to the self-organized critical state. In contrast, more recent studies found that the distribution of cascades is bimodal resulting in either a very small blackout or a very large blackout, engulfing a finite fraction of the system. Here we reconcile the two approaches and investigate how the distribution of the blackouts changes with model parameters, including the tolerance criteria and the dynamic rules of failure of the overloaded lines during the cascade. Finally, we study the same problem for the Motter and Lai model and find similar results, suggesting that the physical laws of flow on the network are not as important as network topology, overload conditions, and dynamic rules of failure.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRSDate not supplied

Learning Based Edge Computing in Air-to-Air Communication Network

This paper studies learning-based edge computing and communication in a dynamic Air-to-Air Ad-hoc Network (AAAN). Due to spectrum scarcity, we assume the number of Air-to-Air (A2A) communication links is greater than that of the available frequency channels, such that some communication links have to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the aggregate spectrum utilization efficiency under resource and fairness constraints. A distributed deep Q learning-based edge computing and communication algorithm is proposed to find the optimal solution. In particular, we design two different neural network structures and each communication link can converge to the optimal operation by exploiting only the local information from its neighbors, making it scalable to large networks. Finally, experimental results demonstrate the effectiveness of the proposed solution in various AAAN scenarios.

Zhe Wang↗
DOE OSTI2020

Structural Dynamics in UV Curable Resins Resolved by In Situ 3D Printing X-ray Photon Correlation Spectroscopy

Additive manufacturing (AM) is a promising technique to rapidly produce polymeric materials into complex 3-dimensional (3D) geometries. While AM is widespread and relevant for a range of applications, implementation in industry has outpaced our fundamental understanding of polymer dynamics and structure development during the printing process. Characterization and quantification of such dynamics is necessary to optimize final material properties and design future materials and processes for 3D printing. Here, we utilize X-ray photon correlation spectroscopy (XPCS) to measure spatial and time-resolved, out-of-equilibrium dynamics during direct ink write (DIW) 3D printing. Specifically, we investigate the progression of structural dynamics in a dual cure (UV/thermal) nanocomposite during and directly after printing. As the filament is printed and cured in situ, the relaxation processes of the cross-linking network are measured through the dynamics of inorganic filler particles. The characteristic relaxation time of the dynamics is calculated through the intensity–intensity autocorrelation function g 2 and directly correlated to the printing process parameters, such as printhead velocity and UV light intensity. The time-resolved evolution of nanoscale dynamics follows a power-law dependence as the filament is cured. Additionally, bulk rheological characterizations reveal the macroscopic solidification of the resin, providing correlation of material properties across a wide range of length and time scales. Overall, the measurement of multiscale, out-of-equilibrium dynamics provides insight into the development of structure in polymer nanocomposite filaments during 3D printing and is used to further understand the influence of such parameters on the AM process.

36 MATERIALS SCIENCE↗
NASA NTRS2007

The Real Time Mission Monitor: A Situational Awareness Tool For Managing Experiment Assets

The NASA Real Time Mission Monitor (RTMM) is a situational awareness tool that integrates satellite, airborne and surface data sets; weather information; model and forecast outputs; and vehicle state data (e.g., aircraft navigation, satellite tracks and instrument field-of-views) for field experiment management RTMM optimizes science and logistic decision-making during field experiments by presenting timely data and graphics to the users to improve real time situational awareness of the experiment's assets. The RTMM is proven in the field as it supported program managers, scientists, and aircraft personnel during the NASA African Monsoon Multidisciplinary Analyses experiment during summer 2006 in Cape Verde, Africa. The integration and delivery of this information is made possible through data acquisition systems, network communication links and network server resources built and managed by collaborators at NASA Dryden Flight Research Center (DFRC) and Marshall Space Flight Center (MSFC). RTMM is evolving towards a more flexible and dynamic combination of sensor ingest, network computing, and decision-making activities through the use of a service oriented architecture based on community standards and protocols.

Blakeslee, Richard↗