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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 469 records · Page 26
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Middleware for a Heterogeneous CAV Fleet

This paper introduces CAN to ROS, a model-based code generation tool used in development, testing, and deployment of a heterogeneous fleet of vehicles with robotic sensing in ROS. Code generation supports two main features: (1) self-configuration for deployment in a heterogeneous vehicle fleet, and (2) quick iteration for testing and development of reading vehicle sensors and robotic control. This tool features the ability to detect the vehicle it is in and regenerate and rebuild itself at runtime to provide the proper two-way bridge between ROS and the sensed on-board vehicle sensor network. Code generation relies on a per-model defined JSON to map a CAN database (DBC) to the desired ROS topic names and message types. The live ROS publishing of CAN messages allows for instant feedback, and the code regeneration allows for adjustments in DBC or vehicle JSON to iteratively hone in on new vehicle signals. Generated ROS nodes are written in C++ for runtime use in lightweight embedded computers. This has been tested in vehicles from three different Original Equipment Manufacturers (OEMs), and can be extended to support a wide array of vehicles. By using a unifying ROS specification, a heterogeneous set of vehicles can be unified into a fleet with abstracted model-specific details; this opens the door for developing cross-model software applications for vehicle control, connected vehicle applications, or fleet monitoring systems.

42 ENGINEERING↗
DOE OSTI2026

Molecularly engineered ZnO–carbon nanosheets from fumaric acid precursors for efficient photocatalytic water purification

The photocatalytic breakdown of organic contaminants is crucial for the development of water purification technology. Zinc oxide (ZnO) is an extensively researched photocatalyst; however, its efficacy is hindered by rapid charge recombination and limited utilization of UV irradiation. Resolving these issues necessitates integrating ZnO with conductive carbon phases via scalable, low-temperature synthesis. We provide a molecularly designed sol–gel method that converts zinc–fumarate coordination networks into two-dimensional ZnO–carbon nanosheets utilizing solely aqueous precursors and mild annealing temperatures (400–600 °C). This method utilizes fumaric acid as a dicarboxylate linker and polyvinylpyrrolidone (PVP) as a structural carbon source to produce ultrathin wurtzite ZnO nanosheets embedded inside an amorphous carbon matrix. The resultant ZnO-C hybrid achieves nearly complete methylene blue degradation within 10 min under UV-A illumination, demonstrating first-order kinetics and outstanding recyclability. Compared with commercial ZnO, the ZnO–C nanosheets exhibit comparable rapid photocatalytic degradation, enhanced adsorption behavior, a porous nanosheet morphology, and an integrated ZnO–carbon interfacial structure. These findings provide a viable molecular-templating approach to fabricating various metal oxide–carbon photocatalysts and underscore substantial enhancements in semiconductor efficacy in eco-friendly water treatment systems.

Ozcan, Muca [ORNL] (ORCID:0000000320020474)↗
NASA NTRS1990

Fault-tolerant, embedded CLIPS applications

The enhancements to CLIPS4.3 presented in this paper provide an embedded CLIPS application with the ability to continue operation with minimal to no loss of information in the event of a hardware or a software failure. Given an arbitrary failure, the CLIPS application's environment (fact-list, agenda, and pattern matching network) will be reconstructed to the point at which the failure was experienced. The environment reconstruction is based on state files to which the application periodically checks environment information (fact-list and agenda). The routine for checkpointing the state of the application is as efficient as possible so that the overhead introduced to normal execution of the application is minimal. The only assumptions made by the CLIPS application are that it is running under an operating system that guarantees it access to uncorrupt state files and that the application will be automatically restarted should it terminate abnormally.

Hicks, Jaye↗
DOE OSTI2023

Revisiting Power Systems Time-Domain Simulation Methods and Models

The changing nature of power systems dynamics is challenging present practices related to modeling and study of system-level dynamic behavior. While developing new techniques and models to handle the new modeling requirements, it is also critical to review some of the terminology used to describe existing simulation approaches and the embedded assumptions. This article provides a first-principles review of the simplifications and transformations commonly used in the formulation of time-domain simulation models. It introduces a taxonomy and classification of time-domain simulation models depending on their frequency bandwidth, network representation, and software availability. Furthermore, it focuses on the fundamental aspects of averaging techniques, and model reduction approaches that result in modeling choices, and discusses the associated challenges and opportunities of applying these methods in systems with large shares of Inverter Based Resources (IBRs). The article concludes with an illustrative simulation that compares the trajectories of an IBR-dominated system.

behavioral sciences↗
NASA NTRS1990

A distributed agent architecture for real-time knowledge-based systems: Real-time expert systems project, phase 1

We propose a distributed agent architecture (DAA) that can support a variety of paradigms based on both traditional real-time computing and artificial intelligence. DAA consists of distributed agents that are classified into two categories: reactive and cognitive. Reactive agents can be implemented directly in Ada to meet hard real-time requirements and be deployed on on-board embedded processors. A traditional real-time computing methodology under consideration is the rate monotonic theory that can guarantee schedulability based on analytical methods. AI techniques under consideration for reactive agents are approximate or anytime reasoning that can be implemented using Bayesian belief networks as in Guardian. Cognitive agents are traditional expert systems that can be implemented in ART-Ada to meet soft real-time requirements. During the initial design of cognitive agents, it is critical to consider the migration path that would allow initial deployment on ground-based workstations with eventual deployment on on-board processors. ART-Ada technology enables this migration while Lisp-based technologies make it difficult if not impossible. In addition to reactive and cognitive agents, a meta-level agent would be needed to coordinate multiple agents and to provide meta-level control.

Lee, S. Daniel↗
NASA NTRS2005

Encryption for Remote Control via Internet or Intranet

A data-communication protocol has been devised to enable secure, reliable remote control of processes and equipment via a collision-based network, while using minimal bandwidth and computation. The network could be the Internet or an intranet. Control is made secure by use of both a password and a dynamic key, which is sent transparently to a remote user by the controlled computer (that is, the computer, located at the site of the equipment or process to be controlled, that exerts direct control over the process). The protocol functions in the presence of network latency, overcomes errors caused by missed dynamic keys, and defeats attempts by unauthorized remote users to gain control. The protocol is not suitable for real-time control, but is well suited for applications in which control latencies up to about 0.5 second are acceptable. The encryption scheme involves the use of both a dynamic and a private key, without any additional overhead that would degrade performance. The dynamic key is embedded in the equipment- or process-monitor data packets sent out by the controlled computer: in other words, the dynamic key is a subset of the data in each such data packet. The controlled computer maintains a history of the last 3 to 5 data packets for use in decrypting incoming control commands. In addition, the controlled computer records a private key (password) that is given to the remote computer. The encrypted incoming command is permuted by both the dynamic and private key. A person who records the command data in a given packet for hostile purposes cannot use that packet after the public key expires (typically within 3 seconds). Even a person in possession of an unauthorized copy of the command/remote-display software cannot use that software in the absence of the password. The use of a dynamic key embedded in the outgoing data makes the central-processing unit overhead very small. The use of a National Instruments DataSocket(TradeMark) (or equivalent) protocol or the User Datagram Protocol makes it possible to obtain reasonably short response times: Typical response times in event-driven control, using packets sized .300 bytes, are <0.2 second for commands issued from locations anywhere on Earth. The protocol requires that control commands represent absolute values of controlled parameters (e.g., a specified temperature), as distinguished from changes in values of controlled parameters (e.g., a specified increment of temperature). Each command is issued three or more times to ensure delivery in crowded networks. The use of absolute-value commands prevents additional (redundant) commands from causing trouble. Because a remote controlling computer receives "talkback" in the form of data packets from the controlled computer, typically within a time interval < or =1 s, the controlling computer can re-issue a command if network failure has occurred. The controlled computer, the process or equipment that it controls, and any human operator(s) at the site of the controlled equipment or process should be equipped with safety measures to prevent damage to equipment or injury to humans. These features could be a combination of software, external hardware, and intervention by the human operator(s). The protocol is not fail-safe, but by adopting these safety measures as part of the protocol, one makes the protocol a robust means of controlling remote processes and equipment by use of typical office computers via intranets and/or the Internet.

Lineberger, Lewis↗
DOE OSTI2026

Latent Catalysis as a Platform for Accessing Diverse Material Properties in Vat Photopolymerization 3D Printing

Vat photopolymerization (VP) 3D printing is an attractive strategy to manufacture customized polymer parts. The properties of printed materials are limited by the need to employ a low viscosity liquid resin and achieve rapid polymerization kinetics. To circumvent this limitation, dual‐cure methods have been developed using reagents embedded in the liquid resin formulation; however, the reagent‐based approach requires the discovery and optimization of new chemistry for each desired material. Here, in this work, we demonstrate a catalytic, dual‐cure platform that enables access to both Nylon‐6 and polyester interpenetrating networks through VP 3D printing under a universal approach. Structure–reactivity relationships of the latent NHC catalysts led to the identification of a magnesium chloride–NHC adduct as a latent catalyst that is orthogonal to radical polymerization and can be unmasked at elevated temperatures post‐printing to initiate ring‐opening polymerization of lactones and lactams. This strategy results in access to semicrystalline materials, which are a challenging morphology to access via VP 3D printing, that have attractive mechanical properties and can be printed at high resolution. This work represents the first photochemical‐based 3D printing of Nylon‐based materials and demonstrates the value of catalytic approaches to access new material properties in VP 3D printing.

Colliver, Cali N. [University of North Carolina, C↗
DOE OSTI2022

Towards computational polar-topotronics: Multiscale neural-network quantum molecular dynamics simulations of polar vortex states in SrTiO3/PbTiO3 nanowires

Recent discoveries of polar topological structures ( e.g ., skyrmions and merons) in ferroelectric/paraelectric heterostructures have opened a new field of polar topotronics. However, how complex interplay of photoexcitation, electric field and mechanical strain controls these topological structures remains elusive. To address this challenge, we have developed a computational approach at the nexus of machine learning and first-principles simulations. Our multiscale neural-network quantum molecular dynamics molecular mechanics approach achieves orders-of-magnitude faster computation, while maintaining quantum-mechanical accuracy for atoms within the region of interest. This approach has enabled us to investigate the dynamics of vortex states formed in PbTiO 3 nanowires embedded in SrTiO 3 . We find topological switching of these vortex states to topologically trivial, uniformly polarized states using electric field and trivial domain-wall states using shear strain. These results, along with our earlier results on optical control of polar topology, suggest an exciting new avenue toward opto-electro-mechanical control of ultrafast, ultralow-power polar topotronic devices.

Linker, Thomas↗
NASA NTRS2004

NASA Tech Briefs, May 2004

Topics include: Embedded Heaters for Joining or Separating Plastic Parts; Curing Composite Materials Using Lower-Energy Electron Beams; Aluminum-Alloy-Matrix/Alumina-Reinforcement Composites; Fibrous-Ceramic/Aerogel Composite Insulating Tiles; Urethane/Silicone Adhesives for Bonding Flexing Metal Parts; Scalable Architecture for Multihop Wireless ad Hoc Networks; Improved Thermoplastic/Iron-Particle Transformer Cores; Cooperative Lander-Surface/Aerial Microflyer Missions for Mars Exploration Dual-Frequency Airborne Scanning Rain Radar Antenna System Eight-Channel Continuous Timer Reduction of Phase Ambiguity in an Offset-QPSK Receiver Ambient-Light-Canceling Camera Using Subtraction of Frames Lightweight, Flexible, Thin, Integrated Solar-Power Packs Windows(Registered Trademark)-Based Software Models Cyclic Oxidation Behavior Software for Analyzing Sequences of Flow-Related Images Improved Ball-and-Socket Docking Mechanism Two-Stage Solenoid Ordered Nanostructures Made Using Chaperonin Polypeptides Low-Temperature Plasma Functionalization of Carbon Nanotubes Improved Cryostat for Cooling a Wide Panel Current Pulses Momentarily Enhance Thermoelectric Cooling Hand-Held Color Meters Based on Interference Filters Calculating Mass Diffusion in High-Pressure Binary Fluids Fresnel Lenses for Wide-Aperture Optical Receivers Increasing Accuracy in Computed Inviscid Boundary Conditions Higher-Order Finite Elements for Computing Thermal Radiation Radar for Monitoring Hurricanes from Geostationary Orbit Time-Transfer System for Two Orbiting Spacecraft

Source record↗
NASA NTRS2017

R2U2: Tool Overview

R2U2 (Realizable, Responsive, Unobtrusive Unit) is an extensible framework for runtime System HealthManagement (SHM) of cyber-physical systems. R2U2 can be run in hardware (e.g., FPGAs), or software; can monitorhardware, software, or a combination of the two; and can analyze a range of different types of system requirementsduring runtime. An R2U2 requirement is specified utilizing a hierarchical combination of building blocks: temporal formula runtime observers (in LTL or MTL), Bayesian networks, sensor filters, and Boolean testers. Importantly, the framework is extensible; it is designed to enable definitions of new building blocks in combination with the core structure. Originally deployed on Unmanned Aerial Systems (UAS), R2U2 is designed to run on a wide range of embedded platforms, from autonomous systems like rovers, satellites, and robots, to human-assistive ground systems and cockpits. R2U2 is named after the requirements it satisfies; while the exact requirements vary by platform and mission, the ability to formally reason about realizability, responsiveness, and unobtrusiveness is necessary for flight certifiability, safety-critical system assurance, and achievement of technology readiness levels for target systems. Realizability ensures that R2U2 is suficiently expressive to encapsulate meaningful runtime requirements while maintaining adaptability to run on different platforms, transition between different mission stages, and update quickly between missions. Responsiveness entails continuously monitoring the system under test, real-time reasoning, reporting intermediate status, and as-early-as-possible requirements evaluations. Unobtrusiveness ensures compliance with the crucial properties of the target architecture: functionality, certifiability, timing, tolerances, cost, or other constraints.

Rozier, Kristin Y.↗
DOE OSTI2024

QSpace - An open-source tensor library for Abelian and non-Abelian symmetries

This is the documentation for the tensor library QSpace (v4.0), a toolbox to exploit ‘quan tum symmetry spaces’ in tensor network states in the quantum many-body context. QSpace permits arbitrary combinations of symmetries including the abelian symmetries $\mathbb{Z}_n$ and U(1), as well as all non-abelian symmetries based on the semisimple classical Lie algebras: A n , B n , C n , and D n , or respectively, the special unitary group SU(n), the odd orthogonal group SO(2n+1), the symplectic group Sp(2n), and the even orthogonal group SO(2n). The code (C++ embedded via the MEX interface into Matlab) is available open source as of QSpace v4.0 on bitbucket under the Apache 2.0 license. QSpace is designed as a bottom-up approach for non-abelian symmetries. It starts from the defining representation and the respective Lie algebra. By explicitly comput ing and tabulating generalized Clebsch-Gordan coefficient tensors, QSpace is versatile in the type of operations that it can perform across all symmetries. At the level of an ap plication, much of the symmetry-related details are hidden within the QSpace C++ core libraries. Hence when developing tensor network algorithms with QSpace, these can be coded (nearly) as if there are no symmetries at all, despite being able to fully exploit general non-abelian symmetries.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Graph Embeddings for CEBAF Operations: Progress and Future Plans

We describe research towards leveraging deep learning on graph representations of the injector beamline at the Continuous Electron Beam Accelerator Facility (CEBAF) in order to create a tool for improving the efficiency of beam tuning tasks. Specifically, we use graphs to represent the injector beamline at any arbitrary date and time and invoke a graph neural network to extract a low-dimensional, informative representation that can be visualized in two-dimensions. By analyzing years of operational data from the CEBAF archiver, good and bad regions of parameter space can be identified. The goal is to exercise this framework as a real-time tool to aid beam tuning, which represents the dominant source of machine downtime.

Tennant, C.↗
DOE OSTI2022

A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems

Constructing surrogate models for uncertainty quantification (UQ) on complex partial differential equations (PDEs) having inherently high-dimensional O(10 n ), n ≥ 2, stochastic inputs (e.g., forcing terms, boundary conditions, initial conditions) poses tremendous challenges. The “curse of dimensionality” can be addressed with suitable unsupervised learning techniques used as a pre-processing tool to encode inputs onto lower-dimensional subspaces while retaining its structural information and meaningful properties. In this work, we review and investigate thirteen dimension reduction methods including linear and nonlinear, spectral, blind source separation, convex and non-convex methods and utilize the resulting embeddings to construct a mapping to quantities of interest via polynomial chaos expansions (PCE). Here, we refer to the general proposed approach as manifold PCE (m-PCE), where manifold corresponds to the latent space resulting from any of the studied dimension reduction methods. To investigate the capabilities and limitations of these methods we conduct numerical tests for three physics-based systems (treated as black-boxes) having high-dimensional stochastic inputs of varying complexity modeled as both Gaussian and non-Gaussian random fields to investigate the effect of the intrinsic dimensionality of input data. We demonstrate both the advantages and limitations of the unsupervised learning methods and we conclude that a suitable m-PCE model provides a cost-effective approach compared to alternative algorithms proposed in the literature, including recently proposed expensive deep neural network-based surrogates and can be readily applied for high-dimensional UQ in stochastic PDEs.

42 ENGINEERING↗
DOE OSTI2026

Geothermal-integrated thermally anisotropic building envelope for energy and peak-demand reduction

Buildings consume large amounts of energy for heating and cooling, while peak electricity demand places significant stress on the power grid. This paper presents a reduced-order co-simulation framework and load-oriented supervisory control strategy for a geothermal-integrated thermally anisotropic building envelope with a ground loop (TABE+GL). In TABE+GL, a hydronic loop embedded in the building envelope is directly coupled with a geothermal ground loop, allowing for bidirectional heat exchange between the envelope, the ground, and the indoor environment. A hybrid co-simulation framework was established by coupling a reduced-order resistor–capacitor (RC) thermal network model with EnergyPlus augmented with GHEDesigner modules. The RC model generated feasible heat flux options under three operating modes, and EnergyPlus predicted sensible loads, energy use, and pump energy demand. At each simulation step, a supervisory control algorithm selected the optimal loop configuration and duty factor that maximizes useful TABE geothermal utilization without exceeding the predicted sensible load, thereby avoiding overheating or cooling. Case studies were conducted for Los Angeles, California, Charleston, South Carolina, and Denver, Colorado. Results showed that the proposed framework reduced HVAC electricity consumption by 43%–67%, natural gas use for space heating by 11%–38%, and peak electricity demand by 43%–88%. These results highlight the potential of combining reduced-order envelope modeling, direct geothermal coupling, and load-oriented supervisory control to improve whole building energy performance and reduce peak demand across diverse weather conditions.

Howard, Daniel [Southern Adventist University]↗
DOE OSTI2026

Machine Learning-Enabled Wearable Piezoelectric Acoustic Sensor for Real-Time Breast Abnormality Detection

In contemporary society, breast health has become a significant public health concern, particularly among women. According to statistics from the World Health Organization, both the incidence and mortality rates of breast tumors have steadily increased in recent years. Therefore, effective early-stage screening and postoperative monitoring are essential for maintaining breast health. However, conventional clinical diagnostic modalities are typically bulky, operationally complex, and unsuitable for continuous real-time monitoring, which limits their use in portable and everyday health management applications. To address these limitations, this study proposes a machine learning-integrated wearable piezoelectric sensing platform as an auxiliary tool for breast health assessment. The device consists of a PDMS matching layer embedded with flexible silver nanowires, a P(VDF-TrFE) piezoelectric layer, and a multi-channel low-noise signal acquisition circuit. It is capable of acquiring acoustic echo signals from tissue-mimicking environments and automatically evaluating signal validity using a convolutional neural network (CNN). By integrating piezoelectric sensing with deep learning-based signal analysis, the proposed system achieves a signal-to-noise ratio exceeding 70 dB and a real-time classification accuracy above 96% under controlled conditions. These results demonstrate that the platform provides a compact, portable, and intelligent approach for wearable sensing of mechanical heterogeneity and highlight its potential for future development in continuous biomedical monitoring technologies.

He, Shuaitong↗
DOE OSTI2025

HydraGNN v4.0

The new version of HydraGNN v4.0 provides additional core capabilities, such as: Inclusion of multi-body atomistic cluster expansion MACE, polarizable atom interaction neural network PAINN, and equivariant principal neighborhood aggregation (PNAEq) among the message passing layers supported -Inclusion of graph transformers to directly model long-range interactions between nodes that are distant in the graph topology Integration of graph transformers with message passing layers by combining the graph embedding generated by the two mechanisms, which allows for an improved expressivity of the HydraGNN architecture Improved re-implementation of multi-task learning (MTL) to allow its use for stabilized training across imbalanced, multi-source, multi-fidelity data Introduction of multi-task parallelism, a newly proposed type of model parallelism specifically for MTL architectures, which allows to dispatch different output decoding heads to different GPU devices Integration of multi-task parallelism with pre-existing distributed data parallelism to enable a 2D parallelization for distributed training Improved portability of the distributed training across Intel GPUs, which has been testes on ALCF exascale supercomputer Aurora Inclusion of 2-level fine-grained energy profilers portable across NVIDIA, AMD, and Intel GPUs to monitor the power and energy consumption associated with different functions executed by the HydraGNN code during data pre-load and training Restructuring of previous examples and inclusion of new sets of examples to illustrate the download, preprocess, and training of HydraGNN models on new large-scale open-source datasets for atomistic materials modeling (e.g., Alexandria, Transition1x, OMat24, OMol25)

Lupo Pasini, Massimiliano [Oak Ridge National Labo↗
DOE OSTI2024

Temporal and spatial characterization of a thermogenic, fault-controlled gas hydrate system, Woolsey Mound, Gulf of Mexico

Woolsey Mound, located at Mississippi Canyon Lease Block 118 (MC118), is the site of the Gulf of Mexico hydrate research consortium’s seafloor observatory, where gas hydrates outcrop at the seafloor. The presence of gas hydrates in the mound is confirmed directly by coring and indirectly by 3D seismic reflection data. Craters, pockmarks, chemosynthetic communities, and authigenic carbonates populate the seafloor at Woolsey Mound. Each crater is characterized by a network of shallow crestal faults that connect the hydrate mound to the underlying allochthonous salt body. We characterize the temporal and spatial evolution of gas hydrates at Woolsey Mound under natural perturbations using four collocated 3D seismic reflection data sets that span over 14 years. Data acquisition differences embedded in the data sets arising from variation in geometry, sample rate, and phase are minimized using the “cross-equalization” method. Our results indicate that hydrate formation and dissociation vary temporally and spatially in close connection to the shallow crestal faults. Evidence of gas hydrate dissociation is observed over a period of three years (2000–2003), where major dissociation occurred along the southern portion of the crestal fault in the southeast crater. The dissociation is less prominent in the southwest crater. Evidence of methane venting is observed between 2000 and 2010, which is mostly concentrated in the southeast crater. The residual amplitude anomalies observed between 2000 and 2014 in the mound are mostly positive, implying that the methane venting had increased significantly. The positive anomalies are correlated with the methane seepage recorded in 2011. Our results indicate the evolution of a fault-controlled gas hydrate system in the northern Gulf of Mexico, which would aid in assessing its impact on the seafloor.

Geochemistry & Geophysics↗
DOE OSTI2025

Using "AI Poincare" to analyze non-linear integrable optics

This study dives into the applicability of using automated discovery of conserved quantities in dynamical systems relevant to accelerator physics. Specifically, we explore the performance of AI Poincaré in analyzing numerical trajectory data obtained using the McMillan system of non-linear integrable optics. A comprehensive evaluation of the algorithm's performance is conducted through diverse methodologies. These include the analysis of the estimated number of conserved quantities embedded in a dataset and the deviation of interpolated points on the inferred manifold with respect to points in actually in the dataset. the investigation identifies an optimal range of perturbation distances where the underlying manifold extraction algorithm inside AI Poincaré exhibits optimal performance. Additionally, an improved neural network architecture is proposed based on the observed results. Finally, we apply the algorithm to preliminary experimental data from the Integrable Optics Test Accelerator at Fermilab to successfully infer the number of conserved quantities even in the presence of fast decoherence of the measured signal.

Osmanov, Lazare [Free U. Tbilisi]↗