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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 1,297 records · Page 72
All sourcesNASA NTRSDOE OSTI
NASA NTRS2010

Automated Assume-Guarantee Reasoning for Omega-Regular Systems and Specifications

We develop a learning-based automated Assume-Guarantee (AG) reasoning framework for verifying omega-regular properties of concurrent systems. We study the applicability of non-circular (AGNC) and circular (AG-C) AG proof rules in the context of systems with infinite behaviors. In particular, we show that AG-NC is incomplete when assumptions are restricted to strictly infinite behaviors, while AG-C remains complete. We present a general formalization, called LAG, of the learning based automated AG paradigm. We show how existing approaches for automated AG reasoning are special instances of LAG.We develop two learning algorithms for a class of systems, called infinite regular systems, that combine finite and infinite behaviors. We show that for infinity-regular systems, both AG-NC and AG-C are sound and complete. Finally, we show how to instantiate LAG to do automated AG reasoning for infinite regular, and omega-regular, systems using both AG-NC and AG-C as proof rules

Chaki, Sagar↗
DOE OSTI2021

Artificial Diversity and Defense Security (ADDSec)

Artificial Diversity and Defense Security (ADDSec) machine learning algorithms are used to classify and cluster threats so that an appropriate response can be initiated as a mitigation strategy. The package includes an ensemble of machine learning algorithms such as Support Vector Machines, naïve bayes, logistic regression, and random forest that evolve with the data to recognize anomalous behavior at the host and network levels. Inputs into the machine learning algorithms include end host system calls, system utilization, packet captures, and syslog messages. The machine learning algorithms can be retrained based on user defined intervals or on the number of packets received. ADDSEC's threat responses include Internet Protocol (IP) Address randomization, application port number randomization, and application library randomization. The IP randomization implementation is built on top of a Software Defined Networking (SDN) framework. The SDN controller installs flows on each of the SDN switches with randomized source and destination IP addresses. The application port numbers are randomized using iptables. The application library randomization is created with a LLVM compiler. All randomization schemes are transparent to the endpoints on the network. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-3379 O

Cox, RebeccaE.↗
DOE OSTI2021

Verification and Validation of Systems in which AI is a Key Element

Many systems are being considered in which artificial intelligence (AI) will be a key element. Failure of an AI element can lead to system failure, hence the need for AI verification and validation (V&V). This article addresses V&V of such a system, focusing on the issues created by characteristics of AI that make V&V challenging. The element(s) containing AI capabilities is treated as a subsystem and V&V is conducted on that subsystem and its interfaces with other elements of the system under study, just as V&V would be conducted on other subsystems. That is, the high-level definitions of verification and of validation do not change for systems containing one or more AI elements. However, AI V&V challenges require approaches and solutions beyond those for conventional or traditional (those without AI elements) systems. This article provides an overview of how machine learning components/subsystems “fit” in the systems engineering framework (Section 1), identifies characteristics of AI subsystems that create challenges in their V&V (Section 2), describes those challenges (Section 3) and provides some potential solutions (Section 4).

Pullum, Laura↗
DOE OSTI2021

Using machine learning and artificial intelligence to improve model-data integrated earth system model predictions of water and carbon cycle extremes

The research proposed here focuses on improving the predictive power of the land component of earth system models (ESMs) using (1) model-data fusion enabled by machine learning (ML) and artificial intelligence (AI), (2) predictive modeling through the combination of ML, AI, and big-data (comprising both model output and observations), and (3) insight of ESM structure and process mechanisms gleaned from complex data using ML and AI.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Report for 2.2.1 Task 5: Develop and Document a State-Based Alarm System for a Nuclear Power Plant Control Room Using Machine Learning. Light Water Reactor Sustainability Program report

In legacy control rooms the operators might have 20 to 30 alarms to deal with on 12 control boards, during a reactor trip. The new digital alarm system in the upgraded control room will display between 150 and 200 alarm points on a single screen. This overload of information prevents the operators from identifying any abnormal alarms quickly without having to scroll through the alarm list looking for the abnormal alarms. One way to tackle this problem could be to attempt to create a state-based alarm system which would identify plant states and would identify which alarms are expected in those states and suppress them from the alarm screen so that any unexpected alarms would be displayed and easily identified to the operators. This can be done today but is very labor intensive in that an operator or training instructor would be required to run scenarios and log the alarms to be suppressed. The hope is that Machine Learning (ML) can provide an alternative solution with less manual effort required.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRS1988

Igneous activity in the early solar system

Although the main emphasis of this book is on what can be learned about the early solar system from material that has escaped secondary processing, the study of differentiated meteorites can provide unique insights into the processes of basalt generation and core formation. Such processes would have been of fundamental importance during the evolution of planetary objects, including the terrestrial planets, early in solar-system history. The properties of igneous meteorites are studied with attention given to the howardite-eucrite-diogenite (HED) suite. Geochemical and petrologic trends in those meteorites are discussed with the objective of defining the thermal and chemical evolution of the HED parent body. A major issue is the nature of the dominant source of heat in the early solar system.

Hewins, R. H.↗
DOE OSTI2023

An Introductory Guide to Generating, Using, and Storing Energy: Understanding electricity, energy storage, microgrids, and related technologies

This guide was created to empower Energy Storage for Social Equity (ES4SE) community members to learn more about power systems, energy distribution, renewable energy, and battery storage systems. It was developed in an accessible format for audiences of varied backgrounds and requires no previous experience in energy storage. It is intended to serve readers as both an educational and reference document as they consider, procure, and deploy power systems that include battery energy storage technologies and microgrids in their communities. By providing an introductory overview of the basics of electricity, power generation, and household energy consumption and loads, this guide prepares readers to learn even more about battery energy storage, microgrid systems, and the sizing of solar and battery systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
NASA NTRS1975

Design and evaluation of a sensor fail-operational control system for a digitally controlled turbofan engine

A self-learning, sensor fail-operational, control system for the TF30-P-3 afterburning turbofan engine was designed and evaluated. The sensor fail-operational control system includes a digital computer program designed to operate in conjunction with the standard TF30-P-3 bill-of-materials control. Four engine measurements and two compressor face measurements are tested. If any engine measurements are found to have failed, they are replaced by values synthesized from computer-stored information. The control system was evaluated by using a realtime, nonlinear, hybrid computer engine simulation at sea level static condition, at a typical cruise condition, and at several extreme flight conditions. Results indicate that the addition of such a system can improve the reliability of an engine digital control system.

Hrach, F. J.↗
NASA NTRS1977

Experimental program for the operational study of data collection platforms in Bolivia

The system developed for the SMS/GOES satellite while learning the limitations possessed by this system with regard to the LANDSAT 1 and 2 satellites with respect to the transmission distance and horizon angle is investigated. The advantages possessed by this system in comparison with conventional methods are evaluated so as to permit studying the feasibility of introducing it into this country in the near future.

Source record↗
NASA NTRSDate not supplied

Data-Centric Operational Design Domain Characterization for Machine Learning-Based Aeronautical Products

We give for Machine Learning (ML)-based aeronautical products, a first rigorous characterization of Operational Design Domains (ODDs). Unlike in other application sectors (such as self-driving road vehicles) where ODD development is scenario-based, our approach is data-centric: we propose the dimensions along which the parameters that define an ODD can be explicitly captured, using a top-down approach starting from system specifications, and a bottom-up approach starting from detailed ML Model (MLM) designs. Then we give a categorization of the data that ML-based applications can encounter in operation, identifying their system-level relevance and impact. Specifically, we discuss how those data categories are useful to determine: (1) the requirements necessary to drive the design of MLMs; (2) the potential effects on the MLM and higher levels of the system hierarchy; (3) the learning assurance processes that may be needed, and (4) system architectural considerations. We illustrate the underlying concepts with an example of an aircraft flight envelope. The approach in this paper is one of the cornerstones of a future process guidance for development and certification/approval of safety-related aeronautical products implementing Artificial Intelligence (AI), currently being developed through aviation industry-based consensus, jointly by the SAE G-34 Committee for AI in aviation, and EUROCAE WG-114 for AI.

Aeronautical products↗
NASA NTRS2013

Lessons Learned During TBCC Design for the NASA-AFRL Joint System Study

NASA and the Air Force Research Laboratory are involved in a Joint System Study (JSS) on Two-Stage-to-Orbit (TSTO) vehicles. The JSS will examine the performance, operability and analysis uncertainty of unmanned, fully reusable, TSTO launch vehicle concepts. NASA is providing a vehicle concept using turbine-based combined cycle (TBCC) propulsion on the booster stage and an all-rocket orbiter. The variation in vehicle and mission requirements for different potential customers, combined with analysis uncertainties, make it problematic to define optimum vehicle types or concepts, but the study is being used by NASA for tool assessment and development, and to identify technology gaps. Preliminary analyses were performed on the entire TBCC booster concept; then higher-fidelity analyses were performed for particular areas to verify results or reduce analysis uncertainties. Preliminary TBCC system analyses indicated that there would be sufficient thrust margin over its mission portion. The higher fidelity analyses, which included inlet and nozzle performance corrections for significant area mismatches between TBCC propulsion requirements versus the vehicle design, resulted in significant performance penalties from the preliminary results. TBCC system design and vehicle operation assumptions were reviewed to identify items to mitigate these performance penalties. The most promising items were then applied and analyses rerun to update performance predictions. A study overview is given to orient the reader, quickly focusing upon the NASA TBCC booster and low speed propulsion system. Details for the TBCC concept and the analyses performed are described. Finally, a summary of "Lessons Learned" are discussed with suggestions to improve future study efforts.

Snyder, Christopher A.↗
DOE OSTI2025

Decision and Control of Complex Systems – A Data-Drive Framework

During the project period, we have collaborated with other team members and developed novel algorithms for novelty detection, continual learning, and graph learning algorithms for dynamic systems. The results are documented in publications and meeting notes. Moreover, we leverage virtual collaboration tools (such as Basecamp, Microsoft Teams and Zoom) for technical exchanges. Our research on novelty detection was published at AAAI 2022 and Lecture Notes in Artificial Intelligence, Springer Nature. The newly developed algorithms were successfully applied to realistic cases, including thermal data from buildings at Pacific Northwest National Lab and microelectronic data provided by GlobalFoundries. Multiple publications have been produced from this project, in collaboration with other team members. Three PhD students were supported in this project to conduct their research.

42 ENGINEERING↗
DOE OSTI2019

Learning Deep Neural Network Representations for Koopman Operators of Nonlinear Dynamical Systems

The Koopman operator has recently garnered much attention for its value in dynamical systems analysis and data-driven model discovery. However, its application has been hindered by the computational complexity of extended dynamic mode decomposition; this requires a combinatorially large basis set to adequately describe many nonlinear systems of interest, e.g. cyber-physical infrastructure systems, biological networks, social systems, and fluid dynamics. Often the dictionaries generated for these problems are manually curated, requiring domain-specific knowledge and painstaking tuning. In this paper we introduce a deep learning framework for learning Koopman operators of nonlinear dynamical systems. We show that this novel method automatically selects efficient deep dictionaries, outperforming state-of-the-art methods. We benchmark this method on partially observed nonlinear systems, including the glycolytic oscillator and show it is able to predict quantitatively 100 steps into the future, using only a single timepoint, and qualitative oscillatory behavior 400 steps into the future.

koopman operators, machine learning, deep learning↗
DOE OSTI2021

Multiresolution classification of turbulence features in image data through machine learning

During large-scale simulations, intermediate data products such as image databases have become popular due to their low relative storage cost and fast in-situ analysis. Serving as a form of data reduction, these image databases have become more acceptable to perform data analysis on. In this work, we present an image-space detection and classification system for extracting vortices at multiple scales through wavelet-based filtering. A custom image-space descriptor is used to encode a large variety of vortex-types and a machine learning system is trained for fast classification of vortex regions. By combining a radial-based histogram descriptor, a bag of visual words feature descriptor, and a support vector machine, our results show that we are able to detect and classify vortex features at various sizes at multiple scales. Once trained, our framework enables the fast extraction of vortices on new, unknown image datasets for flow analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Advanced Semi-Supervised Learning with Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Advanced Semi-Supervised Learning With Uncertainty Estimation for Phase Identification in Distribution Systems

The integration of advanced metering infrastructure (AMI) into power distribution networks generates valuable data for tasks such as phase identification; however, the limited and unreliable availability of labeled data in the form of customer phase connectivity presents challenges. To address this issue, we propose a semi-supervised learning (SSL) framework that effectively leverages labeled and unlabeled data. Our approach incorporates self-training, label spreading, and Bayesian neural networks (BNNs) to enhance phase identification with AMI data. Our method uses an ensemble of multilayer perceptron classifiers in a self-training setup, iteratively adding high-confidence pseudo-labels to improve robustness. We also apply label spread to propagate labels based on data similarity, which enhances generalization across diverse distributions. In addition, we employ a BNNs with uncertainty estimation, boosting confidence in predictions and reducing phase identification errors. In our case study, we achieved approximately 98% +/- 0.08 accuracy with uncertainty using minimal and unreliable labeled data from a real U.S. utility, Duquesne Light Company. Our SSL approach, combined with uncertainty estimation, provides an efficient solution for phase identification in AMI data, ultimately improving the reliability of smart grid applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Continual Learning for Pattern Recognizers using Neurogenesis Deep Learning

Deep neural networks have emerged as a leading set of algorithms to infer information from a variety of data sources such as images and time series data. In their most basic form, neural networks lack the ability to adapt to new classes of information. Continual learning is a field of study attempting to give previously trained deep learning models the ability to adapt to a changing environment. Previous work developed a CL method called Neurogenesis for Deep Learning (NDL). Here, we combine NDL with a specific neural network architecture (the Ladder Network) to produce a system capable of automatically adapting a classification neural network to new classes of data. The NDL Ladder Network was evaluated against other leading CL methods. While the NDL and Ladder Network system did not match the cutting edge performance achieved by other CL methods, in most cases it performed comparably and is the only system evaluated that can learn new classes of information with no human intervention.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Deep anomaly detection for industrial systems: a case study

We explore the use of deep neural networks for anomaly detection of industrial systems where the data are multivariate time series measurements. We formulate the problem as a self-supervised learning where data under normal operation are used to train a deep neural network autoregressive model, i.e., use a window of time series data to predict future data values. The aim of such a model is to learn to represent the system dynamic behavior under normal conditions, while expect higher model vs. measurement discrepancies under faulty conditions. In real world applications, many control settings are discrete in nature. In this paper, vector embedding and joint losses are employed to deal with such situations. Both LSTM and CNN based deep neural network backbones are studied on the Secure Water Treatment (SWaT) testbed datasets. Also, Support Vector Data Description (SVDD) method is adapted to such anomaly detection settings with deep neural networks. Evaluation methods and results are discussed based on the SWaT dataset along with potential pitfalls.

anomaly detection, deep neural network↗