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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 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Cyber-Physical System: Design for Sustainability and Resilience

When considering the design tools needed in the transition from numeric models to pilot plant, cyber-physical systems (CPS) come to the forefront as a method to model complex integrated energy systems. CPS approach has proven to be valuable to identify opportunities for economically viable early adoption of integrated energy technologies. This tutorial will introduce the concepts and the roles of CPS in co-design to minimize risks for pilot plant and technology deployment. This tutorial will also layout basic requirements for the CPS development, which requires a highly interdisciplinary effort with expertise in sensors, hardware testing, real-time modeling, controls, and system integration.

Harun, Nor Farida↗
DOE OSTI2021

Local and global decision fusion for cyber-physical system abnormality detection

Monitoring nodes may generate a series of current monitoring node values over time representing current operation of a cyber-physical system. A decision fusion computer platform may receive, from a local status determination module, an indication of whether each node has an initial local status of “normal”/“abnormal” and a local certainty score (with higher values of the local certainty score representing greater likelihood of abnormality). The computer platform may also receive, from a global status determination module, an indication of whether the system has an initial global status of “normal”/“abnormal” and a global certainty score. The computer platform may output, for each node, a fused local status of “normal” or “abnormal,” at least one fused local status being based on the initial global status. The decision fusion computer platform may also output a fused global status of “normal” or “abnormal” based on at least one initial local status.

Abbaszadeh, Masoud↗
DOE OSTI2020

Situation awareness and dynamic ensemble forecasting of abnormal behavior in cyber-physical system

A plurality of monitoring nodes may each generate a time-series of current monitoring node values representing current operation of a cyber-physical system. A feature-based forecasting framework may receive the time-series of and generate a set of current feature vectors using feature discovery techniques. The feature behavior for each monitoring node may be characterized in the form of decision boundaries that separate normal and abnormal space based on operating data of the system. A set of ensemble state-space models may be constructed to represent feature evolution in the time-domain, wherein the forecasted outputs from the set of ensemble state-space models comprise anticipated time evolution of features. The framework may then obtain an overall features forecast through dynamic ensemble averaging and compare the overall features forecast to a threshold to generate an estimate associated with at least one feature vector crossing an associated decision boundary.

Abbaszadeh, Masoud↗
NASA NTRS1985

Problems experienced and envisioned for dynamical physical systems

The use of high performance systems, which is the trend of future space systems, naturally leads to lower margins and a higher sensitivity to parameter variations and, therefore, more problems of dynamical physical systems. To circumvent dynamic problems of these systems, appropriate design, verification analysis, and tests must be planned and conducted. The basic design goal is to define the problem before it occurs. The primary approach for meeting this goal is a good understanding and reviewing of the problems experienced in the past in terms of the system under design. This paper reviews many of the dynamic problems experienced in space systems design and operation, categorizes them as to causes, and envisions future program implications, developing recommendations for analysis and test approaches.

Ryan, R. S.↗
NASA NTRS1960

Analog Techniques for Measuring the Frequency Response of Linear Physical Systems Excited by Frequency-Sweep Inputs

Data-reduction methods using general-purpose analog computer equipment and compatible testing techniques for determining the frequency response of linear physical systems are examined. The techniques considered may be classed as steady state or transient depending on the method of excitation. The relative merits of periodic, slow sweep, and transient (rapid sweep) forcing functions are discussed and applications are given that relate to dynamic-response tests of aeroelastic systems. Two frequency-sweep-input methods are considered in detail. one case the sweep rate is sufficiently slow that the response is approximately the same as that for steady-state conditions. With this input the frequency response can be evaluated and displayed in real time while the test is in progress. Errors due to treating sweep data as steady state can be eliminated, when desired, by reanalyzing tape-recorded time histories of the input and output as transient rather than as periodic data. In the second method the frequency-response function is deter- mined from the system's transient response to a very rapid sweep input. The purpose of frequency sweep in this case is to provide sufficient harmonic content in the input to overcome noise while keeping the test time as short as possible. tests and limited flight-test data presented herein, it appears that a transient-type rapid-sweep forcing function offers a considerable saving in test time while preserving the accuracy possible with steady-state sinusoidal inputs.

Wilmer H. Reed III↗
DOE OSTI2020

Automated Adversary Emulation for Cyber-Physical Systems via Reinforcement Learning

Adversary emulation is an offensive exercise that provides a comprehensive assessment of a system’s resilience against cyber attacks. However, adversary emulation is typically a manual process, making it costly and hard to deploy in cyber-physical systems (CPS) with complex dynamics, vulnerabilities, and operational uncertainties. In this paper, we develop an automated, domain-aware approach to adversary emulation for CPS. We formulate a Markov Decision Process (MDP) model to determine an optimal attack sequence over a hybrid attack graph with cyber (discrete) and physical (continuous) components and related physical dynamics. We apply model-based and model-free reinforcement learning (RL) methods to solve the discrete-continuous MDP in a tractable fashion. As a baseline, we also develop a greedy attack algorithm and compare it with the RL procedures. We summarize our findings through a numerical study on sensor deception attacks in buildings to compare the performance and solution quality of the proposed algorithms.

Bhattacharya, Arnab↗
DOE OSTI2022

Expedited Real Time Processing for the NETL HYPER Cyber-Physical System (Final Report)

The primary objective of the proposed project was to provide the National Energy Technology Laboratory’s Hybrid Performance (Hyper) Facility the needed numerical methods algorithm(s), software development and implementation support to enact real time cyber-physical systems (CPSs) that simulate process dynamics on the order of five milliseconds or smaller. The proposed paths forward comprised three distinct approaches to hasten transient simulations. They fell under the numerical methods categorical options of: 1) optimizing key parameters within the facility’s pre-existing real time processing scheme; 2) introducing an “informed” processing approach wherein a priori computations expedite real time attempts; and 3) implementing alternatives to the employed explicit-implicit blended finite difference (spatio-temporal) approach. Each of these three classes were proposed independently as options for improvement, yet in some cases one class of approach could have complemented another.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters

The industrial sector consumes about one-third of global energy, making them a frequent target for energy use reduction. Variation in energy usage is observed with weather conditions, as space conditioning needs to change seasonally, and with production, energy-using equipment is directly tied to production rate. Previous models were based on engineering analyses of equipment and relied on site-specific details. Others consisted of single-variable regressors that did not capture all contributions to energy consumption. Further, new modeling techniques could be applied to rectify these weaknesses. Applying data from 45 different manufacturing plants obtained from industrial energy audits, a supervised machine-learning model is developed to create a general predictor for industrial building energy consumption. The model uses features of air enthalpy, solar radiation, and wind speed to predict weather-dependency; motor, steam, and compressed air system parameters to capture support equipment contributions; and operating schedule, production rate, number of employees, and floor area to determine production-dependency. Results showed that a model that used a linear regressor over a transformed feature space could outperform a support vector machine and utilize features more representative of physical systems. Using informed parameters to build a reliable predictor will more accurately characterize a manufacturing facility's energy savings opportunities.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Systems and Implementation: Integrating Unmanned Aircraft Systems into Physical Protection Systems at Fixed Sites and During Transportation

Physical protection systems, and response forces in particular, are designed to prevent an adversary from successfully completing a malevolent act against a facility or transport operations. Timely detection and assessment of any potential adversary action against a target is an essential element of materials security. The timely detection and assessment must then be followed-up by a capable and timely response that might be enhanced with the additional situational awareness provided by unmanned aircraft systems (UAS). The United States Department of Energy’s National Nuclear Security Administration Office of International Nuclear Security has been exploring capabilities provided by UAS to support response force operations within the physical protection system. UAS have the potential to provide response force commanders and operators with situational awareness in assessing adversary locations and actions as well as the locations of responders. UAS may be utilized for area searches ahead of responder pathways to identify potential threats and to provide situational awareness of areas not normally covered by cameras (such as areas outside the fence line outside at fixed facilities). In addition, UAS can provide real-time information to transportation convoy teams that pass through constantly changing public access environments. This paper will provide operational recommendations to be addressed when integrating UAS into existing physical protection systems at fixed sites and during transport. Recommendations will include aspects of the following: needs analysis; tactics and techniques to support detection and assessment as well as response force deployment; remote pilot selection, qualifications, training, and currency; UAS selection criteria; UAS laws and regulations; possible cost sharing with other facility operations; and on-scene emergency management.

Stockwell, Brandon↗
DOE OSTI2025

Entanglement Cost for Infinite-Dimensional Physical Systems

We prove that the entanglement cost equals the regularized entanglement of formation for any infinite-dimensional quantum state ρ ΑΒ with finite quantum entropy on at least one of the subsystems A or B. This generalizes a foundational result in quantum information theory that was previously formulated only for operations and states on finite-dimensional systems. The extension to infinite-dimensional systems is nontrivial because the conventional tools for establishing both the direct and converse bounds, i.e., strong typicality, monotonicity, and asymptotic continuity, are no longer directly applicable. To address this problem, we construct a new entanglement dilution protocol for infinite-dimensional states implementable by local operations and a finite amount of one-way classical communication (one-way LOCC), using weak and strong typicality multiple times. We also prove the optimality of this protocol among all protocols, even under infinite-dimensional separable operations, by developing an argument based on alternative forms of monotonicity and asymptotic continuity of the entanglement of formation for infinite-dimensional states. Along the way, we derive a new integral representation for the quantum entropy of infinite-dimensional states, which we believe to be of independent interest. Our results allow us to fully characterize an important operational entanglement measure—the entanglement cost—for all infinite-dimensional physical systems.

Complexity↗
DOE OSTI2020

PRIME: a real-time cyber-physical systems testbed: from wide-area monitoring, protection, and control prototyping to operator training and beyond

As the power grid continues to evolve with advanced wide-area monitoring, protection, and control (WAMPAC) algorithms, there is an increasing need for realistic testbed environments with industry-grade software and hardware-in-the-loop (HIL) to perform verification and validation studies. Such testbed environments serve as ideal platforms to perform WAMPAC prototyping, operator training, and also to study the impacts of different types of cyberattack scenarios on the operation of the grid. In this paper, we introduce PRIME (PNNL cybeR physIcal systeMs tEstbed): the testbed that integrates real-time transmission system simulator with commercial industry grade energy management system (EMS) software and remote hardware-in-the-loop (RHIL). PRIME is an end-to-end, modular testbed that allows high-fidelity RHIL experimentation of a power system. We present two detailed case studies (fault location and clearing in transmission system, and operator training) to showcase the capabilities of our PRIME testbed. Finally, we briefly discuss some of the potential limitations of our testbed in terms of scalability and flexibility to setup larger test systems and identify directions for future work to address those limitations.

42 ENGINEERING↗
NASA NTRS2020

Seasonality of Biological and Physical Systems as Indicators of Climate Variation and Change

Evidence-based responses to climate change by society require operational and sustained information including biophysical indicator systems that provide up-to-date measures of trends and patterns against historical baselines. Two key components linking anthropogenic climate change to impacts on socio-ecological systems are the periodic inter- and intra-annual variations in physical climate systems (seasonality) and in plant and animal life cycles (phenology). We describe a set of national indicators that reflect sub-seasonal to seasonal drivers and responses of terrestrial physical and biological systems to climate change and variability at the national scale. Proposed indicators and metrics include seasonality of surface climate conditions (e.g., frost and freeze dates and durations), seasonality of freeze/thaw in freshwater systems (e.g., timing of stream runoff and durations of lake/river ice), seasonality in ecosystem disturbances (e.g., wildfire season timing and duration), seasonality in vegetated land surfaces (e.g., green-up and brown-down of landscapes), and seasonality of organismal life-history stages (e.g., timings of bird migration). Recommended indicators have strong linkages to variable and changing climates, include abiotic and biotic responses and feedback mechanisms, and are sufficiently simple to facilitate communication to broad audiences and stakeholders interested in understanding and adapting to climate change.

Seasonality↗
NASA NTRS2009

Towards Co-Engineering Communicating Autonomous Cyber-Physical Systems

In this paper, we sketch a framework for interdisciplinary modeling of space systems, by proposing a holistic view. We consider different system dimensions and their interaction. Specifically, we study the interactions between computation, physics, communication, uncertainty and autonomy. The most comprehensive computational paradigm that supports a holistic perspective on autonomous space systems is given by cyber-physical systems. For these, the state of art consists of collaborating multi-engineering efforts that prompt for an adequate formal foundation. To achieve this, we propose a leveraging of the traditional content of formal modeling by a co-engineering process.

Bujorianu, Marius C.↗
NASA NTRS2000

On the Computational Capabilities of Physical Systems: The Impossibility of Infallible Computation - Part 1

In this first of two papers, strong limits on the accuracy of physical computation are established. First it is proven that there cannot be a physical computer C to which one can pose any and all computational tasks concerning the physical universe. Next it is proven that no physical computer C can correctly carry out any computational task in the subset of such tasks that can be posed to C. This result holds whether the computational tasks concern a system that is physically isolated from C, or instead concern a system that is coupled to C. As a particular example, this result means that there cannot be a physical computer that can, for any physical system external to that computer, take the specification of that external system's state as input and then correctly predict its future state before that future state actually occurs; one cannot build a physical computer that can be assured of correctly 'processing information faster than the universe does'. The results also mean that there cannot exist an infallible, general-purpose observation apparatus, and that there cannot be an infallible, general-purpose control apparatus. These results do not rely on systems that are infinite, and/or non-classical, and/or obey chaotic dynamics. They also hold even if one uses an infinitely fast, infinitely dense computer, with computational powers greater than that of a Turing Machine. This generality is a direct consequence of the fact that a novel definition of computation - a definition of 'physical computation' - is needed to address the issues considered in these papers. While this definition does not fit into the traditional Chomsky hierarchy, the mathematical structure and impossibility results associated with it have parallels in the mathematics of the Chomsky hierarchy. The second in this pair of papers presents a preliminary exploration of some of this mathematical structure, including in particular that of prediction complexity, which is a 'physical computation analogue' of algorithmic information complexity. It is proven in that second paper that either the Hamiltonian of our universe proscribes a certain type of computation, or prediction complexity is unique (unlike algorithmic information complexity), in that there is one and only version of it that can be applicable throughout our universe.

Wolpert, David H.↗
DOE OSTI2021

Variational Autoencoders for Learning Nonlinear Dynamics of Physical Systems

We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mechanics. Our approach is based on Variational Autoencoders (VAEs) for learning nonlinear state space models from observations. We develop ways to incorporate geometric and topological priors through general manifold latent space representations. We investigate the performance of our methods for learning low dimensional representations for the nonlinear Burgers equation and constrained mechanical systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Learning nonlinear operators in latent spaces for real-time predictions of complex dynamics in physical systems

Abstract Predicting complex dynamics in physical applications governed by partial differential equations in real-time is nearly impossible with traditional numerical simulations due to high computational cost. Neural operators offer a solution by approximating mappings between infinite-dimensional Banach spaces, yet their performance degrades with system size and complexity. We propose an approach for learning neural operators in latent spaces, facilitating real-time predictions for highly nonlinear and multiscale systems on high-dimensional domains. Our method utilizes the deep operator network architecture on a low-dimensional latent space to efficiently approximate underlying operators. Demonstrations on material fracture, fluid flow prediction, and climate modeling highlight superior prediction accuracy and computational efficiency compared to existing methods. Notably, our approach enables approximating large-scale atmospheric flows with millions of degrees, enhancing weather and climate forecasts. Here we show that the proposed approach enables real-time predictions that can facilitate decision-making for a wide range of applications in science and engineering.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2015

Analyzing Cyber Security Threats on Cyber-Physical Systems Using Model-Based Systems Engineering

The spectre of cyber attacks on aerospace systems can no longer be ignored given that many of the components and vulnerabilities that have been successfully exploited by the adversary on other infrastructures are the same as those deployed and used within the aerospace environment. An important consideration with respect to the mission/safety critical infrastructure supporting space operations is that an appropriate defensive response to an attack invariably involves the need for high precision and accuracy, because an incorrect response can trigger unacceptable losses involving lives and/or significant financial damage. A highly precise defensive response, considering the typical complexity of aerospace environments, requires a detailed and well-founded understanding of the underlying system where the goal of the defensive response is to preserve critical mission objectives in the presence of adversarial activity. In this paper, a structured approach for modeling aerospace systems is described. The approach includes physical elements, network topology, software applications, system functions, and usage scenarios. We leverage Model-Based Systems Engineering methodology by utilizing the Object Management Group's Systems Modeling Language to represent the system being analyzed and also utilize model transformations to change relevant aspects of the model into specialized analyses. A novel visualization approach is utilized to visualize the entire model as a three-dimensional graph, allowing easier interaction with subject matter experts. The model provides a unifying structure for analyzing the impact of a particular attack or a particular type of attack. Two different example analysis types are demonstrated in this paper: a graph-based propagation analysis based on edge labels, and a graph-based propagation analysis based on node labels.

MBSE↗
DOE OSTI2021

A Cyber-Physical System for Freeway Ramp Meter Signal Control Using Deep Reinforcement Learning in a Connected Environment

Freeway bottlenecks such as on-ramp merging areas account for about 40% of recurring freeway congestion. It is generally agreed that building more roads and adding more lanes to existing infrastructure does not solve the congestion problem, and so dynamic traffic control measures offer a more cost-effective alternative. Ramp meters, traffic signal devices that regulate traffic flow entering freeways, are among the most effective measures to mitigate congestion at on-ramp merging areas on freeways. The confluence of deep reinforcement learning (RL) and connectivity provides a possible solution to advance ramp meter signal control. Deep RL is a group of machine-learning methods that enables an agent learning from the environment to improve its performance. In this study, three deep RL methods-proximal policy optimization (PPO), Ape-X deep Q-network (DQN), and asynchronous advantage actor-critic agents (A3C)-are explored for ramp meter signal control to maximize vehicle speed and traffic throughput, as well as to minimize energy consumption and emissions at freeway on-ramp merging areas in a connected environment. The low computational requirement and scalability of deep RL for deployment make it a powerful optimization tool for time-sensitive applications such as ramp meter signal control. The results of this study show that deep RL methods yield superior performance to both a fixed-time controller and ALINE A, a state-of-the-art feedback controller.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗