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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Cyber-Physical Power Systems Protection: The Byzantine Cybersecurity Framework

Cybersecurity of smart grids have been topic of much interest in recent years. As this critical infrastructure operation increases dependency on automated processes and controls, exposure to cyber-physical threats become inevitable. Considering cyber-physical security of the grid, much focus of attention has been made towards smart grids real-time monitoring solutions, including the state estimation process. Analyzing the relevant literature, one can note though that seldom research has been done on cyber-physical security of smart grids protection systems. Protection systems have intangible value towards grid reliability. This paper presents a cybersecurity framework for smart grids protection systems. A physics-based inspired machine learning solution is at the core process of the framework. Processed relay inputs and outputs are used by a deep predictive coding network. Formal models, a quasi-static state estimator, provides an oracle when low confidence decision is reached. Evolving knowledge is derived through reinforcement learning. Implementation aspects considering the Pacific Northwest National Laboratory Electricity Infrastructure Operations Center are presented. Built as an extra control layer to protection systems, without hard-to-derive parameters, highlights potential aspects towards real-life applications.

Bretas, Arturo Suman↗
DOE OSTI2023

Risk-Constrained Reinforcement Learning for Inverter-Dominated Power System Controls

Here, this paper develops a risk-aware controller for grid-forming inverters (GFMs) to minimize large frequency oscillations in GFM inverter-dominated power systems. To tackle the high variability from loads/renewables, we incorporate a mean-variance risk constraint into the classical linear quadratic regulator (LQR) formulation for this problem. The risk constraint aims to bound the time-averaged cost of state variability and thus can improve the worst-case performance for large disturbances. The resulting risk-constrained LQR problem is solved through the dual reformulation to a minimax problem, by using a reinforcement learning (RL) method termed as stochastic gradient-descent with max-oracle (SGDmax). In particular, the zero-order policy gradient (ZOPG) approach is used to simplify the gradient estimation using simulated system trajectories. Numerical tests conducted on the IEEE 68-bus system have validated the convergence of our proposed SGDmax for GFM model and corroborate the effectiveness of the risk constraint in improving the worst-case performance while reducing the variability of the overall control cost.

Frequency control↗
DOE OSTI2018

An Equivalent Time-Variant Storage Model to Harness EV Flexibility: Forecast and Aggregation

The demand for vehicle charging will necessitate large investments in power distribution, transmission, and generation. However, this demand is often also flexible in time, and can be actively managed to reduce the needed investments, and to better integrate renewable electricity. Harnessing this flexibility requires forecasting and controlling electric vehicle (EV) charging at thousands of stations. In this work, we address the problem of forecasting and management of the aggregate flexible demand from tens to thousands of EV supply equipment (EVSEs). First, it presents an equivalent time-variant storage model for flexible demand at an aggregation of EVSEs. The proposed model is generalizable to different markets, and also to different flexible loads. Model parameters representing multiple EVSEs can be easily aggregated by summation, and forecasted using autoregressive models. The forecastability of uncontrolled demand and storage parameters is evaluated using data from 1341 nonresidential EVSEs located in Northern California. The median coefficient of variation is as low as 24% for the forecast of uncontrolled demand at the highest aggregation and 10-15% for the storage parameters. The benefits of aggregation and forecastability are demonstrated using an energy arbitrage scenario. Purchasing energy day ahead is less expensive than in the real-time market, but relies on a uncertain forecast of charging availability. The results show that the forecastability significantly improves for larger aggregations. This helps the aggregator make a better forecast, and decreases the cost of charging in comparison to an uncontrolled case by 60% with respect to an oracle scenario.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Two-Stage Reinforcement Learning Policy Search for Grid-Interactive Building Control

This paper develops an intelligent grid-interactive building controller, which optimizes building operation during both normal hours and demand response (DR) events. To avoid costly on-demand computation and to adapt to non-linear building models, the controller utilizes reinforcement learning (RL) and makes real-time decisions based on a near-optimal control policy. Learning such a policy typically amounts to solving a hard non-convex optimization problem. We propose to address this problem with a novel global-local policy search method. In the first stage, an RL algorithm based on zero-order gradient estimation is leveraged to search for the optimal policy globally, due to its scalability and the potential to escape some poor performing local optima. The obtained policy is then fine-tuned locally to bring the first-stage solution closer to that of the original unsmoothed problem. Experiments on a simulated five-zone commercial building demonstrate the advantages of the proposed method over existing learning approaches. They also show that the learned control policy outperforms a pragmatic linear model predictive controller (MPC) and approaches the performance of an oracle MPC in testing scenarios. Using a state-of-the-art advanced computing system, we demonstrate that the controller can be learned and deployed within hours of training.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2014

TH-A-18C-09: Ultra-Fast Monte Carlo Simulation for Cone Beam CT Imaging of Brain Trauma

Purpose: Application of cone-beam CT (CBCT) to low-contrast soft tissue imaging, such as in detection of traumatic brain injury, is challenged by high levels of scatter. A fast, accurate scatter correction method based on Monte Carlo (MC) estimation is developed for application in high-quality CBCT imaging of acute brain injury. Methods: The correction involves MC scatter estimation executed on an NVIDIA GTX 780 GPU (MC-GPU), with baseline simulation speed of ~1e7 photons/sec. MC-GPU is accelerated by a novel, GPU-optimized implementation of variance reduction (VR) techniques (forced detection and photon splitting). The number of simulated tracks and projections is reduced for additional speed-up. Residual noise is removed and the missing scatter projections are estimated via kernel smoothing (KS) in projection plane and across gantry angles. The method is assessed using CBCT images of a head phantom presenting a realistic simulation of fresh intracranial hemorrhage (100 kVp, 180 mAs, 720 projections, source-detector distance 700 mm, source-axis distance 480 mm). Results: For a fixed run-time of ~1 sec/projection, GPU-optimized VR reduces the noise in MC-GPU scatter estimates by a factor of 4. For scatter correction, MC-GPU with VR is executed with 4-fold angular downsampling and 1e5 photons/projection, yielding 3.5 minute run-time per scan, and de-noised with optimized KS. Corrected CBCT images demonstrate uniformity improvement of 18 HU and contrast improvement of 26 HU compared to no correction, and a 52% increase in contrast-tonoise ratio in simulated hemorrhage compared to “oracle” constant fraction correction. Conclusion: Acceleration of MC-GPU achieved through GPU-optimized variance reduction and kernel smoothing yields an efficient (<5 min/scan) and accurate scatter correction that does not rely on additional hardware or simplifying assumptions about the scatter distribution. The method is undergoing implementation in a novel CBCT dedicated to brain trauma imaging at the point of care in sports and military applications. Research grant from Carestream Health. JY is an employee of Carestream Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Recurrent Neural Network Attention Mechanisms for Interpretable System Log Anomaly Detection

Deep learning has recently demonstrated state-of-the art performance on key tasks related to the maintenance of computer systems, such as intrusion detection, denial of service attack detection, hardware and software system failures, and malware detection. In these contexts, model interpretability is vital for administrator and analyst to trust and act on the automated analysis of machine learning models. Deep learning methods have been criticized as black box oracles which allow limited insight into decision factors. In this work we seek to "bridge the gap'' between the impressive performance of deep learning models and the need for interpretable model introspection. To this end we present recurrent neural network (RNN) language models augmented with attention for anomaly detection in system logs. Our methods are generally applicable to any computer system and logging source. By incorporating attention variants into our RNN language models we create opportunities for model introspection and analysis without sacrificing state-of-the art performance. We demonstrate model performance and illustrate model interpretability on an intrusion detection task using the Los Alamos National Laboratory (LANL) cyber security dataset, reporting upward of 0.99 area under the receiver operator characteristic curve despite being trained only on a single day's worth of data.

Anomaly detection, Attention, Recurrent Neural Net↗
DOE OSTI2023

P6 Deeplynx Adapter

The purpose of this software is to pull data from the Oracle Primavera P6 scheduling tool and bring it into the DeepLynx data warehouse.

Brownlee, JarenM↗
DOE OSTI2019

Study of serious adverse drug reactions using FDA-approved drug labeling and MedDRA

Background Adverse Drug Reactions (ADRs) are of great public health concern. FDA-approved drug labeling summarizes ADRs of a drug product mainly in three sections, i.e., Boxed Warning (BW), Warnings and Precautions (WP), and Adverse Reactions (AR), where the severity of ADRs are intended to decrease in the order of BW > WP > AR. Several reported studies have extracted ADRs from labeling documents, but most, if not all, did not discriminate the severity of the ADRs by the different labeling sections. Such a practice could overstate or underestimate the impact of certain ADRs to the public health. In this study, we applied the Medical Dictionary for Regulatory Activities (MedDRA) to drug labeling and systematically analyzed and compared the ADRs from the three labeling sections with a specific emphasis on analyzing serious ADRs presented in BW, which is of most drug safety concern. Results This study investigated New Drug Application (NDA) labeling documents for 1164 single-ingredient drugs using Oracle Text search to extract MedDRA terms. We found that only a small portion of MedDRA Preferred Terms (PTs), 3819 out of 21,920 or 17.42%, were observed in a whole set of documents. In detail, 466/3819 (12.0%) PTs were in BW, 2023/3819 (53.0%) were in WP, and 2961/3819 (77.5%) were in AR sections. We also found a higher overlap of top 20 occurring BW PTs with WP sections compared to AR sections. Within the MedDRA System Organ Class levels, serious ADRs (sADRs) from BW were prevalent in Nervous System disorders and Vascular disorders. A Hierarchical Cluster Analysis (HCA) revealed that drugs within the same therapeutic category shared the same ADR patterns in BW (e.g., nervous system drug class is highly associated with drug abuse terms such as dependence, substance abuse, and respiratory depression). Conclusions This study demonstrated that combining MedDRA standard terminologies with data mining techniques facilitated computer-aided ADR analysis of drug labeling. We also highlighted the importance of labeling sections that differ in seriousness and application in drug safety. Using sADRs primarily related to BW sections, we illustrated a prototype approach for computer-aided ADR monitoring and studies which can be applied to other public health documents.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Emulating the Deutsch-Josza algorithm with an inverse-designed terahertz gradient-index lens

An all-dielectric photonic metastructure is investigated for application as a quantum algorithm emulator (QAE) in the terahertz frequency regime; specifically, we show implementation of the Deustsh-Josza algorithm. The design for the QAE consists of a gradient-index (GRIN) lens as the Fourier transform subblock and patterned silicon as the oracle subblock. First, we detail optimization of the GRIN lens through numerical analysis. Then, we employed inverse design through a machine learning approach to further optimize the structural geometry. Through this optimization, we enhance the interaction of the incident light with the metamaterial via spectral improvements of the outgoing wave.

Blackwell, Ashley N.↗
DOE OSTI2023

Gate-based quantum computing for protein design

Protein design is a technique to engineer proteins by permuting amino acids in the sequence to obtain novel functionalities. However, exploring all possible combinations of amino acids is generally impossible due to the exponential growth of possibilities with the number of designable sites. The present work introduces circuits implementing a pure quantum approach, Grover’s algorithm, to solve protein design problems. Our algorithms can adjust to implement any custom pair-wise energy tables and protein structure models. Moreover, the algorithm’s oracle is designed to consist of only adder functions. Quantum computer simulators validate the practicality of our circuits, containing up to 234 qubits. However, a smaller circuit is implemented on real quantum devices. Our results show that using iterations, the circuits find the correct results among all N possibilities, providing the expected quadratic speed up of Grover’s algorithm over classical methods (i.e.,).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2019

easyaccess: Enhanced SQL command line interpreter for astronomical surveys

easyaccess is an enhanced command line interpreter and Python package created to facilitate access to astronomical catalogs stored in SQL Databases. It provides a custom interface with custom commands and was specifically designed to access data from the Dark Energy Survey Oracle database, including autocompletion of tables, columns, users and commands, simple ways to upload and download tables using csv, fits and HDF5 formats, iterators, search and description of tables among others. It can easily be extended to another surveys or SQL databases. The package was completely written in Python and support customized addition of commands and functionalities. Visit https://github.com/mgckind/easyaccess to view installation instructions, tutorials, and the Python source code for easyaccess.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Schema for the LANL infrasound analysis tool, infrapy

The purpose of this document is to define the schema used for the operation of the infrasound analysis tool, infrapy. The tables described by this document extend the CSS3.0 or KB core schema to include information required for the operation of infrapy. This document is divided into three sections, the first being this introduction. Section two defines eight new, infrasonic data processing-specific database tables. Both internal (ORACLE) and external formats for the attributes are defined, along with a short description of each attribute. Section three of the document shows the relationships between the different tables by using entity-relationship diagrams.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Final Technical Report

Southern Africa is the world’s largest emitter of biomass burning aerosols. Their westward transport over the remote southeast Atlantic ocean colocates some of the largest atmospheric loadings of absorbing aerosol with the least examined of the Earth’s major subtropical stratocumulus decks. Global aerosol model results highlight that the largest positive top-ofatmosphere forcing in the world occurs in the southeast Atlantic, but this region exhibits large differences in magnitude and sign between reputable models, in part because of high variability in the underlying model cloud distributions. Many uncertainties contribute to the highly variable model radiation fields: the aging of the shortwave-absorbing aerosol during transport, how much of the aerosol mixes into the cloudy boundary layer, and how the low clouds adjust to smokeradiation and smoke-cloud interactions. In addition, the ability of the biomass burning aerosol to absorb shortwave radiation is known to vary seasonally as the fuel type on land changes. LASIC (Layered Atlantic Smoke Interactions with Clouds) is a strategy to improve our understanding of aged carbonaceous aerosol, its seasonal evolution, and the mechanisms by which clouds adjust to the presence of the aerosol. The observational strategy centers on deploying the AMF1 cloud, aerosol, and atmospheric profiling instrumentation to Ascension Island, located within the trade-wind shallow cumulus regime (14.50W, 80S) 3000 km offshore of continental Africa. The location is within the latitude zone of the maximum outflow of aerosol, with the deepening boundary layer known to entrain free-tropospheric smoke. The primary activities for LASIC are: 1) to improve current knowledge on aged biomass burning aerosol and its radiative properties as a function of the seasonal cycle; 2) to use surface-based remote sensing to sensitively interrogate the atmosphere for the relative vertical location of aerosol and clouds; 3) to improve our understanding of the cloud adjustments to the presence of shortwave-absorbing aerosol within the vertical column, both through aerosol-radiation and through aerosol-cloud interactions; 4) to aid low cloud parameterization efforts for climate models. The measurements span June 1, 2016 - October 31, 2017, encompassing two July-October biomass burning seasons. The August-September, 2016, months include an Intensive Observing Period (IOP) with 8x/daily radiosondes. In 2017, from 16 August through 7 September, the UK FAAM Bae- 146 plane was deployed from Ascension, providing complementary data on the atmosphere’s vertical structure as part of the CLARIFY project, with similar scientific goals. The NASA ORACLES aircraft campaign, sharing similar objectives to LASIC, deployed from Namibia in September, 2016, and Sao Tome in August, 2017. The latter included a suitcase flight to Ascension spanning August 18-21. In 2017 a CAPS-SSA instrument belonging to Aerodyne was brought to Ascension, gathering data primarily for August, towards providing a second, independent measurement of aerosol absorption. Ascension Island is also an AERONET site.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

SunSpec Open Solar Data Exchange (oSDX) (Final Report)

Orange Button is a data interchange standard for solar finance and operations practitioners designed to drive down soft costs. To achieve its goals, SunSpec convened 330+ companies to validate Orange Button functional requirements. Approximately 4,000 industry-expert hours were invested to dissect 25 priority use cases and develop 4,500 Orange Button taxonomy entries and accompanying APIs. Orange Button 1.0 is now available on GitHub royalty-free to the general public. Early adopters include highly influential companies such as Aurora Solar, BayWa r.e., Clean Power Research, DNV-GL, LG, Mercatus, Oracle, Salesforce, sPower, SunPower, and Wells Fargo. Finally, the project was completed on time and on budget, including $2M match funding provided by industry.

14 SOLAR ENERGY↗
DOE OSTI2020

Plant Summit 2019

The Plant Science Research Network organized the Plant Summit 2019, which took place February 10-13, 2019 at Biosphere 2 (Oracle, AZ). The Summit brought together plant scientists from many quarters, including stakeholders from academic institutions, government, nonprofits, and private industry with a broad range of experience in plant science research and education. Participants were guided through a facilitated workshop to review and assess the outcomes from previous PSRN workshops on cyberinfrastructure, postgraduate training, broadening participation, and future scenarios. An outcome of the Summit is the development of a report, “The Plant Science Decadal Vision (2020-2030): Reimagining the Potential of Plants for a Healthy Future.” The Decadal Vision frames societally vital, exciting and far-reaching research challenges as being necessary and deeply interwoven with human and technical resources. An Executive Summary of that report is provided.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Generic National Nuclear Forensics Library Implementation

This document provides instructions for the database implementation for national nuclear forensic library (NNFL) using a relational database. While there are many software options that can be used to implement an NNFL database, in this document we reference an Oracle database for the implementation. The principles related here can be applied to any relational database structure. The design revolves around two main types of data: Samples and Results. Samples are materials or descriptions of materials; results are the results of analyses of those samples. All additional tables exist as a result of the normalization process. The remaining tables/entities have keys that are referenced by the Result/Sample tables via foreign key constraints. This allows for the enforcement of different relationships between the tables such as one-to-one, one-to-many, many-to-many. This process helps to ensure that valid data is entered, optimizes database performance and avoids data redundancies.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

DFTT report for Signal Analysis (2023)

The Detection Framework Testbed and Toolkit (DFTT) is a database and associated java programs intended to facilitate the development and testing of algorithms for operating suites of correlation and subspace detectors. This framework is a generalization of the system described in Harris and Dodge (2011) and Dodge and Harris (2016). DFTT allows retrospective processing of sequences of data using various system configurations. Results are saved in a database, so it is easy to compare the results obtained using different configurations of the system. The principal software components of DFTT are the ConfigCreator, the framework_runner and the Builder program. ConfigCreator assembles the files necessary to define a particular configuration used for processing. The framework_runner operates suites of detectors and saves the results in an Oracle™ database. Builder allows visual examination of templates and detected signals and allows editing and creation of detectors. In addition, there are tools for importing continuous data into the database. DFTT is licensed under the MIT license and has LLNL release number LLNL-CODE-801881. In the current fiscal year DFTT has been used in two different projects. The first (NTL-funded) project investigates the feasibility of screening nuisance detections using correlation detectors. The second (GNEM-funded) project seeks to extend the results of Harris and Dodge (2021) to 3- D sources and multiple stations. Each of these efforts required that additional functionality be added to DFTT. In this report I will summarize the enhancements to DFTT in the context of the relevant research effort.

58 GEOSCIENCES↗
DOE OSTI2021

Evaluation of Apex Alpha with LabWare-LIMS Data Reduction for Alpha Analyses

Savannah River Site's Environmental Bioassay Laboratory is migrating its laboratory information management system (LIMS) from SQL-LIMS to Oracle Labware-LIMS (LW-LIMS) systems to align with the Department of Energy's cyber security policies. Concurrently with the LIMS upgrade, the current VMS based Alpha Measurement System (AMS) software is being replaced with Apex Alpha software to reduce other cyber vulnerabilities, streamline procedural production aspects of data handling, and improve outdated reduction processes to align the program with requirements outlined in ANSI N13.30. The primary technical changes which are being implemented are: - Reagent contributions are added in data reduction, - Moving average background equivalent activity are used for gross signal corrections, - Measured uncertainty of both are included in the decision level and propagated uncertainty, - Tracer levels are increased for precision and accuracy improvements, and - Reports are modified for compliance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗