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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cybersecurity Platform and Certification Framework Development for Extreme Fast Charging (XFC)-Integrated Charging Ecosystem (Final Project Report)

This report summarizes a pioneering effort in Electric Vehicle charging infrastructure ecosystem cybersecurity requirements, assessment methodologies, functional verification, as well as embodiment of the key technologies in the form of hardware and software tools being made available to the public. EPRI led a team of experts, as well as a stakeholder coalition encompassing all key actors in the EV charging infrastructure ecosystem that includes eXtreme Fast Charging (XFC) equipment (defined as 200kW or above). EV charging infrastructure in the United States is a patchwork of networks that have continued to grow organically and have been designed to serve the charging needs of the EV owners, who are their customers. In doing so, each network provider, as well as their connected entities such as the cloud Electric Vehicle Service Providers or EVSPs, utility back office, utility AMI networks, payment networks, as well as Original Equipment Manufacturer (EV manufacturer) telematics networks, have designed systems that may work well individually, but no single entity is responsible for the entire ecosystem to be secure in terms of data exchange. Furthermore, there is no uniformity in how each actor has implemented the cybersecurity requirements since no system-wide cybersecurity requirements existed prior to this project. The final project report describes the technical approach guided by the EV charging infrastructure cybersecurity working group, convened specifically for this project. The technical approach included definition of requirements at the ecosystem level, treated as a ‘system of systems’, and then passed down to individual systems (EVSE, EV, cloud EVSP, utility, and the payment networks), followed by developing the cybersecurity risk and vulnerability assessment methods, that were later applied to real-world cyber-physical systems at EPRI, ANL, and NREL laboratories, to validate both the process and the results. Finally, in a spotlight over the most vulnerable equipment, which is the EV charge station (AC or DC), the team developed a multi-layer cybersecurity implementation in the embedded domain embodied by the open-source Secure Network Interface Card (SNIC) demonstrating the various ways in which the infrastructure can be secured protecting against the identified attack surfaces. Finally, the entire process of EV infrastructure cybersecurity assessment was encapsulated in the Electric Vehicle Charging Cybersecurity Management (EVC2M) online GUI-based tool, that is expected to be released to the public. The report presents the objectives, the technical approach, the key results, and recommendations for future work.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1973

Marshall system for aerospace simulation (MARSYAS)

System is simple flexible language which can be coded by users unfamiliar with computer programming. It is designed for engineers with little experience in simulation, who desire to simulate large physical systems. User has ability to mix differential equations with diagrams in his model. With few exceptions, there is no rigid statement-operator structure within given module.

Tranboth, H. H.↗
NASA NTRS1993

Thermally-sustained structure in convectively unstable systems

The complex Ginzburg-Landau equation with a thermal noise term is studied under conditions when the system is convectively unstable. Under these conditions, the noise is selectively and spatially amplified giving rise to a noise-sustained structure. Analytical results, applicable to a wide range of physical systems, are derived for the variance, and the coefficients and thermal noise term are determined for Taylor-Couette flow with an axial through-flow. Comparison is made to recent experiments.

Deissler, Robert J.↗
NASA NTRS2006

Flight-Tested Prototype of BEAM Software

Researchers at JPL have completed a software prototype of BEAM (Beacon-based Exception Analysis for Multi-missions) and successfully tested its operation in flight onboard a NASA research aircraft. BEAM (see NASA Tech Briefs, Vol. 26, No. 9; and Vol. 27, No. 3) is an ISHM (Integrated Systems Health Management) technology that automatically analyzes sensor data and classifies system behavior as either nominal or anomalous, and further characterizes anomalies according to strength, duration, and affected signals. BEAM (see figure) can be used to monitor a wide variety of physical systems and sensor types in real time. In this series of tests, BEAM monitored the engines of a Dryden Flight Research Center F-18 aircraft, and performed onboard, unattended analysis of 26 engine sensors from engine startup to shutdown. The BEAM algorithm can detect anomalies based solely on the sensor data, which includes but is not limited to sensor failure, performance degradation, incorrect operation such as unplanned engine shutdown or flameout in this example, and major system faults. BEAM was tested on an F-18 simulator, static engine tests, and 25 individual flights totaling approximately 60 hours of flight time. During these tests, BEAM successfully identified planned anomalies (in-flight shutdowns of one engine) as well as minor unplanned anomalies (e.g., transient oil- and fuel-pressure drops), with no false alarms or suspected false-negative results for the period tested. BEAM also detected previously unknown behavior in the F- 18 compressor section during several flights. This result, confirmed by direct analysis of the raw data, serves as a significant test of BEAM's capability.

Mackey, Ryan↗
NASA NTRS2005

The Integrated Safety-Critical Advanced Avionics Communication and Control (ISAACC) System Concept: Infrastructure for ISHM

Integrated System Health Management (ISHM) architectures for spacecraft will include hard real-time, critical subsystems and soft real-time monitoring subsystems. Interaction between these subsystems will be necessary and an architecture supporting multiple criticality levels will be required. Demonstration hardware for the Integrated Safety-Critical Advanced Avionics Communication & Control (ISAACC) system has been developed at NASA Marshall Space Flight Center. It is a modular system using a commercially available time-triggered protocol, ?Tp/C, that supports hard real-time distributed control systems independent of the data transmission medium. The protocol is implemented in hardware and provides guaranteed low-latency messaging with inherent fault-tolerance and fault-containment. Interoperability between modules and systems of modules using the TTP/C is guaranteed through definition of messages and the precise message schedule implemented by the master-less Time Division Multiple Access (TDMA) communications protocol. "Plug-and-play" capability for sensors and actuators provides automatically configurable modules supporting sensor recalibration and control algorithm re-tuning without software modification. Modular components of controlled physical system(s) critical to control algorithm tuning, such as pumps or valve components in an engine, can be replaced or upgraded as "plug and play" components without modification to the ISAACC module hardware or software. ISAACC modules can communicate with other vehicle subsystems through time-triggered protocols or other communications protocols implemented over Ethernet, MIL-STD- 1553 and RS-485/422. Other communication bus physical layers and protocols can be included as required. In this way, the ISAACC modules can be part of a system-of-systems in a vehicle with multi-tier subsystems of varying criticality. The goal of the ISAACC architecture development is control and monitoring of safety critical systems of a manned spacecraft. These systems include spacecraft navigation and attitude control, propulsion, automated docking, vehicle health management and life support. ISAACC can integrate local critical subsystem health management with subsystems performing long term health monitoring. The ISAACC system and its relationship to ISHM will be presented.

Gwaltney, David A.↗
DOE OSTI2020

Encoding an Oscillator into Many Oscillators

An outstanding challenge for quantum information processing using bosonic systems is Gaussian errors such as excitation loss and added thermal noise errors. Thus, bosonic quantum error correction is essential. Most bosonic quantum error correction schemes encode a finite-dimensional logical qubit or qudit into noisy bosonic oscillator modes. In this case, however, the infinite-dimensional bosonic nature of the physical system is lost at the error-corrected logical level. On the other hand, there are several proposals for encoding an oscillator mode into many noisy oscillator modes. However, these oscillator-into-oscillators encoding schemes are in the class of Gaussian quantum error correction. Therefore, these codes cannot correct practically relevant Gaussian errors due to the established no-go theorems that state that Gaussian errors cannot be corrected by using only Gaussian resources. Here, we circumvent these no-go results and show that it is possible to correct Gaussian errors by using Gottesman-Kitaev-Preskill (GKP) states as non-Gaussian resources. In particular, we propose a non-Gaussian oscillator-into-oscillators code, namely the GKP two-mode squeezing code, and demonstrate that it can quadratically suppress additive Gaussian noise errors in both the position and momentum quadratures except for a small sublogarithmic correction. Furthermore, we demonstrate that our GKP two-mode squeezing code is near optimal in the weak noise limit by proving via quantum information theoretic tools that quadratic noise suppression is optimal when we use two physical oscillator modes. Lastly, in this work we show that our non-Gaussian oscillator encoding scheme can also be used to correct excitation loss and thermal noise errors, which are dominant error sources in many realistic bosonic systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Probing Electron Correlations in 1D Electronic Materials Using Single Quantum Channels (Final Technical Report)

This research focuses on the investigation of strong electron correlations in quasi-one-dimensional oxide nanostructures. The archetypical system consisting of heterostructures formed from an ultrathin layer of LaAlO 3 and TiO 2 -terminated SrTiO 3 offers a unique laboratory for probing strong correlations due to intrinsic richness of the physical system and its ability to be modulated at extreme nanoscale dimensions. Under suitable conditions, SrTiO 3 is known to exhibit a variety of important properties associated with strong electronic correlations, especially superconductivity, magnetism. Understanding the nature of these electronic correlations at fundamental scales, and in reduced spatial dimensions, will provide the scientific basis for new energy technologies, information technologies, and materials. The research program is motivated by recent discoveries and technical advances in the PIs' lab, specifically, (1) pioneering work to create conductive nanostructures to be created in LaAlO 3 /SrTiO 3 heterointerfaces with 2 nm spatial resolution, (2) the discovery of a novel correlated electronic phases in which electrons pair without forming a superconductor, and (3) the demonstrated ability to form high-quality quantum point contacts (QPCs) and achieve ballistic electron transport over micrometer-scales in LaAlO 3 /SrTiO 3 nanowires. The program has three major components. First, we have press forward to develop a full microscopic understanding of the mechanism for electron pairing in LaAlO 3 /SrTiO 3 nanostructures. Understanding the “glue” that leads to pairing and superconductivity in a doped semiconductor will help to form a scientific basis for the correlated oxide nanoelectronics platform being developed. Second, we have used their ability to create well-defined quantum channels in nanowires allows to allow novel “scattering” experiments to be performed that probe fundamentally new quasiparticle excitations using an unprecedented combination of experimental methodologies. Experiments already underway with artificially defined superlattices (5 nm period) demonstrate the feasibility of this approach. Third, we have investigated 1D correlations in proximally coupled nanowires, which are predicted exhibit Majorana-like zero modes in the absence of superconductivity or spin-orbit coupling. These studies provide insight not only into new and emergent phenomena in the rich class of complex oxides, but also fundamental mechanisms of superconductivity and magnetism. Thematically, the research aims to combine some of the most important and interesting challenges in the physics of semiconductor nanostructures and correlated electronic materials. The increased understanding of electron correlations can pave the way to the development of new materials with outstanding or novel properties, which could be relevant for energy-related technologies such as low-power high performance computing, quantum computation, or the development of novel materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2021

Machine learning of Kondo physics using variational autoencoders and symbolic regression

In this work, we employ variational autoencoders to extract physical insight from a dataset of one-particle Anderson impurity model spectral functions. Autoencoders are trained to find a low-dimensional, latent space representation that faithfully characterizes each element of the training set, as measured by a reconstruction error. Variational autoencoders, a probabilistic generalization of standard autoencoders, further condition the learned latent space to promote highly interpretable features. In our study, we find that the learned latent variables strongly correlate with well known, but nontrivial, parameters that characterize emergent behaviors in the Anderson impurity model. In particular, one latent variable correlates with particle-hole asymmetry, while another is in near one-to-one correspondence with the Kondo temperature, a dynamically generated low-energy scale in the impurity model. Using symbolic regression, we model this variable as a function of the known bare physical input parameters and "rediscover" the nonperturbative formula for the Kondo temperature. The machine learning pipeline we develop suggests a general purpose approach, which opens opportunities to discover new domain knowledge in other physical systems.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Floquet engineering with spatially nonuniform driving fields

In Floquet engineering, we apply a time-periodic modulation to change the effective behavior of a wave system. In this work, we generalize Floquet engineering to more fully exploit spatial degrees of freedom, expanding the scope of effective behaviors we can access. We develop a perturbative procedure to engineer space-time dependent driving forces that effectively transform broad classes of tight-binding systems into one another. We demonstrate several applications, including removing disorder, undoing Anderson localization, and enhancing localization to an extreme in spatially modulated waveguides. This procedure straightforwardly extends to other types of physical systems and different Floquet driving field implementations.

42 ENGINEERING↗
DOE OSTI2024

Digital Twins for Data Centers

Fueled by an unprecedented adoption of AI (Artificial Intelligence), data centers are becoming the largest growing consumers of energy. Digital Twins provide living digital models of physical systems that enable data-driven analysis and application of AI to better manage selective aspects of the data center and drive efficiency for sustainability. Digital twins have emerged as a way to create virtual prototypes of physical artifacts, which may be used in a variety of contexts. Physical artifacts include airplanes, factories, or even static objects, such as bridges or dams. Digital twin helps monitor changes and assist in predicting planned or unplanned behaviors of physical objects. In this paper, we discuss digital twins for data centers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Interpretation of autoencoder-learned collective variables using Morse–Smale complex and sublevelset persistent homology: An application on molecular trajectories

Dimensionality reduction often serves as the first step toward a minimalist understanding of physical systems as well as the accelerated simulations of them. In particular, neural network-based nonlinear dimensionality reduction methods, such as autoencoders, have shown promising outcomes in uncovering collective variables (CVs). However, the physical meaning of these CVs remains largely elusive. In this work, we constructed a framework that (1) determines the optimal number of CVs needed to capture the essential molecular motions using an ensemble of hierarchical autoencoders and (2) provides topology-based interpretations to the autoencoder-learned CVs with Morse–Smale complex and sublevelset persistent homology. Furthermore, this approach was exemplified using a series of n-alkanes and can be regarded as a general, explainable nonlinear dimensionality reduction method.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Reflecting on Planning Models: A Challenge for Verification and Validation of Planning Systems

We discuss the opportunities for autonomous sys- tems to perform reflection on their planners by adapting the models used to build plans. We first describe model-based planning systems, a form of automated planning system driven by declarative models of the planning domain. These models include descriptions of the conditions and effects of actions on the state of the world. When planning the activities of cyber-physical systems, the command and data representation of the system must be formally abstracted to the actions and states described in the planning system model. When the execution of a plan either fails or produces unexpected outcomes, the execution trace can be abstracted and compared to the predicted state according to the planning model, producing a list of discrepancies; these discrepancies can then be used to fix the model. This provides part of a reflection capability, namely, a set of well-formed problems with the domain model, the abstractions, or both. The challenge lies in the rest of the reflection capability, namely, a set of techniques for changing the models or the abstractions. We discuss these challenges and describe some of the options for addressing them

Mission Planning↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
DOE OSTI2024

Radiation and heat transport in divergent shock–bubble interactions

Shock–bubble interactions (SBIs) are important across a wide range of physical systems. In inertial confinement fusion, interactions between laser-driven shocks and micro-voids in both ablators and foam targets generate instabilities that are a major obstacle in achieving ignition. Experiments imaging the collapse of such voids at high energy densities (HED) are constrained by spatial and temporal resolution, making simulations a vital tool in understanding these systems. In this study, we benchmark several radiation and thermal transport models in the xRAGE hydrodynamic code against experimental images of a collapsing mesoscale void during the passage of a 300 GPa shock. We also quantitatively examine the role of transport physics in the evolution of the SBI. This allows us to understand the dynamics of the interaction at timescales shorter than experimental imaging framerates. We find that all radiation models examined reproduce empirical shock velocities within experimental error. Radiation transport is found to reduce shock pressures by providing an additional energy pathway in the ablation region, but this effect is small (∼1% of total shock pressure). Employing a flux-limited Spitzer model for heat conduction, we find that flux limiters between 0.03 and 0.10 produce agreement with experimental velocities, suggesting that the system is well-within the Spitzer regime. Higher heat conduction is found to lower temperatures in the ablated plasma and to prevent secondary shocks at the ablation front, resulting in weaker primary shocks. Finally, we confirm that the SBI-driven instabilities observed in the HED regime are baroclinically driven, as in the low energy case.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Designing a decentralized fault-tolerant software framework for smart grids and its applications

The vision of the ‘Smart Grid’ anticipates a distributed real-time embedded system that implements various monitoring and control functions. As the reliability of the power grid is critical to modern society, the software supporting the grid must support fault tolerance and resilience of the resulting cyber-physical system. This paper describes the fault-tolerance features of a software framework called Resilient Information Architecture Platform for Smart Grid (RIAPS). The framework supports various mechanisms for fault detection and mitigation and works in concert with the applications that implement the grid-specific functions. The paper discusses the design philosophy for and the implementation of the fault tolerance features and presents an application example to show how it can be used to build highly resilient systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Predicting Catalyst Surface Stability Under Reaction Conditions Using Deep Reinforcement Learning and Machine Learning Potentials

Catalysts are critical for most large-scale energy intensive chemical transformation processes, such as energy storage, liquid fuel production, and the formation of chemical building blocks. The catalyst composition, structure, and morphology impact the performance under reaction conditions and influences properties like activity and selectivity. Many industrial catalysts offer imperfect activity/selectivity, or contain expensive metals. Further, catalysts can deactivate over time as the morphology changes or harsh reactive environments alter the surface structure. Methods to automatically model and predict the kinetics of how catalyst surfaces will restructure would enable engineers to design around these challenges, improve performance, increase longevity. This project investigated a specific type of machine learning model, deep reinforcement learning, machine learning models to act as surrogates for the physical system, and compared the results of these approaches with a specialized high-throughput experimental synthesis and measurement platform.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1974

Limiting performance of ground transportation vehicles subject to transient loading

A computational approach to determining the limiting performance of vehicles subject to transient disturbances based on response variable criteria is set forth. For the purposes of a limiting performance study, the transportation system dynamics are described using second- or first-order equations in which the sought for quantity is a vector of time-varying functions called control or isolator forces that have replaced portions of the physical system. Computations are performed as a linear programming problem. As an example of the limiting performance of vehicles in protecting passengers or cargo under crash conditions, the problem of lading damage of a rail vehicle that is struck by another vehicle is considered.

Pilkey, W. D.↗