SEARCH · Search NASA

Results for “performance based security”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 433 records · Page 24

All sources NASA NTRS DOE OSTI

DOE OSTI2018

Insider Threat Cybersecurity Framework Webtool & Methodology: Defending Against Complex Cyber-Physical Threats

The paper demonstrates how the Insider Threat Cybersecurity Framework (ITCF) web tool and methodology help provide a more dynamic, defense-in-depth security posture against insider cyber-physical threats. ITCF includes over 30 cyber-physical security best practices to help organizations identify, protect, detect, respond and recover to sophisticated insider threats and vulnerabilities. The following paper tests the efficacy of this approach and helps validate and verify ITCF’s capabilities and features through various insider attacks use-cases. Three different investment scenarios and explored to determine how an organization can most efficiently leverage ICTF to increase their overall security posture against insider attacks. ITCF also facilitates implementation of the goals outlined in Presidential Executive Order (EO) on Strengthening the Cybersecurity of Federal Networks and Critical Infrastructure (May 2017): To help owners and operators of critical infrastructure identify, protect, detect, respond, and recover cyber threats. ITCF helps organizations with the following features: provides an easy to use rapid assessment tool to perform an insider threat self-assessment; determines the current insider threat cybersecurity posture; defines investment based goals to achieve a target state; connects the cybersecurity posture with business processes, functions, and continuity; and finally, helps develop plans to answer critical organizational cybersecurity questions. In this paper, the webtool and its core capabilities are depicted by performing an extensive comparative assessment over two different high-profile insider threat incidents.

Mylrea, Michael E.↗

DOE OSTI2017

Enhancing Critical Infrastructure and Key Resources (CIKR) Level-0 Physical Process Security Using Field Device Distinct Native Attribute Features

Here, the need for improved Critical Infrastructure and Key Resource (CIKR) security is unquestioned and there has been minimal emphasis on Level-0 (PHY Process) improvements. Wired Signal Distinct Native Attribute (WS-DNA) Fingerprinting is investigated here as a non-intrusive PHY-based security augmentation to support an envisioned layered security strategy. Results are based on experimental response collections from Highway Addressable Remote Transducer (HART) Differential Pressure Transmitter (DPT) devices from three manufacturers (Yokogawa, Honeywell, Endress+Hauer) installed in an automated process control system. Device discrimination is assessed using Time Domain (TD) and Slope-Based FSK (SB-FSK) fingerprints input to Multiple Discriminant Analysis, Maximum Likelihood (MDA/ML) and Random Forest (RndF) classifiers. For 12 different classes (two devices per manufacturer at two distinct set points), both classifiers performed reliably and achieved an arbitrary performance benchmark of average cross-class percent correct of %C > 90%. The least challenging cross-manufacturer results included near-perfect %C ≈ 100%, while the more challenging like-model (serial number) discrimination results included 90%< %C < 100%, with TD Fingerprinting marginally outperforming SB-FSK Fingerprinting; SB-FSK benefits from having less stringent response alignment and registration requirements. The RndF classifier was most beneficial and enabled reliable selection of dimensionally reduced fingerprint subsets that minimize data storage and computational requirements. The RndF selected feature sets contained 15% of the full-dimensional feature sets and only suffered a worst case %CΔ = 3% to 4% performance degradation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

DECREASE: Development of a Commercial Ready Enzyme Application System for Ethanol (Final Technical Report)

Conversion of biomass to sugars plays a central in reducing our dependence on petroleum, as it allows production of a wide range of biobased fuels and chemicals, through fermentation of those sugars. The DECREASE project delivers an effective enzyme cocktail for this conversion, enabling reduced costs for producing advanced biofuels such as cellulosic ethanol. Benefits to the public contributed by growth of the advanced biofuels industry include job creation, economic growth, and energy security. The DECREASE primary project objective was to develop a two-fold improved enzyme cocktail, relative to an advanced cocktail (CZP00005) that had been developed previously (from 2000- 2007). While the final milestone was delivery of all enzyme components as an experimental mixture, a secondary objective was to deploy an improved cocktail within 3 years following the close of the project. In February 2012, Novozymes launched Cellic® CTec3, a multi-enzyme cocktail derived in part from components developed under DECREASE. The externally validated performance of CTec3 and an additional component under project benchmarking conditions indicated a 1.8-fold dose reduction in enzyme dose required for 90% conversion (based on all available glucose and xylose sources) of NREL dilute acid pretreated PCS, relative to the starting advanced enzyme cocktail. While the ability to achieve 90% conversion is impressive, targeting such high levels of biomass digestion is likely not the most cost effective strategy. Novozymes techno economic modeling showed that for NREL's dilute acid pretreated corn stover (PCS), 80% target conversion enables a lower total production cost for cellulosic ethanol than for 90% conversion, and this was also found to be the case when cost assumptions were based on the NREL 2002 Design Report. A 1.8X dose-reduction was observed for 80% conversion in the small scale (50 g) DECREASE benchmark assay for CTec3 and an additional component. An upscaled experiment (in 0.5 kg kettle reactors) was performed to compare the starting enzyme mixture CZP00005 with CTec3 alone; these results indicated a 1.9X dose- reduction for 80% conversion. The CTec3 composition does not include the best available enzyme components from the DECREASE effort. While these components are not yet available in a commercial product, experimental mixtures were assayed in a smaller scale assay using DECREASE PCS, at high solids loadings (21.5% TS). The results indicated that the newer mixtures required 2.9X-less enzyme for 90% conversion, and 3.2X-less enzyme for 80% conversion, relative to the starting enzyme cocktail. In conclusion, CTec3 delivers a 1.8-1.9X dose reduction on NREL PCS at high solids loadings, and the next generation enzyme from Novozymes will continue to show dramatically improved biochemical performance. CTec3 allows reduced costs today, and the experimental cocktails point to continued biotechnological improvements that will further drive down costs for biorefineries of tomorrow.

02 PETROLEUM↗

DOE OSTI2022

Autonomous System Subversion Tactics: Prototypes and Recommended Countermeasures

One of the fielding requirements for Advanced and Small Modular Reactors (AR/SMR) is the ability to support remote and autonomous operations. Autonomous Control Systems (ACS) are found on platforms such as Autonomous Space Vehicles, Cruise Missiles, and advanced driver-assistance systems. Each of these ACS implementations depends upon a set of decision support subsystems responsible for supporting Autonomous Mission Managers (names vary based upon field and author preferences). These Autonomous Mission Managers receive inputs from system sensors (e.g., LIDAR collection from an automobile travelling down a street; transients from a nuclear reactor), and perform a set of classifications (e.g., Red Traffic Light; Small Pedestrian at 10m; Load Rejection; Single Coolant Pump Trip), and then use these classifications in combination with recommendation algorithms to achieve platform goals (e.g., Stop the Vehicle at the Traffic Light, Avoid the Small Pedestrian; Trip the Reactor to prevent a Safety Event). The design, implementation, and fielding of an ACS capability will alter the cyber-attack surface such that existing risk management plans will need to be updated to include how to protect and defend against data-science and decision-support-system attack classes. These attack classes would include protection of the design and training environments where algorithm selection and testing and training data would be obvious attack vectors. These attack classes would also require an informed set of detection and response procedures to identify anomalous behaviors and document best practices for anomaly assessment and vulnerability mitigation and remediation. Last year we published a Cyber Threat Assessment Methodology for Autonomous and Remote Operations for AR/SMRs along with a companion publication on Cyber Attack and Defense Use Cases. The focus of the methodology was on describing and enumerating ACS processes, components, and functions such that security engineers could: evaluate subversion options against the target; identify threat actor attributes and capabilities derived from each subversion option; and identify security controls and response countermeasures. The Use Cases document offered detailed methodology examples including an assessment of a Military Base SMR, an Autonomous System Decision Loop, and implementation of AR/SMR Machine Learning algorithms. Our proposal at the end of last year was to focus on implementation of subversion prototypes related to the last Use Case area: AR/SMR Machine Learning (ML) Algorithms. We included six attack scenarios in our Use Cases paper: a Poisoning Attack against ML functions implemented using an FPGA; a Trojaning Attack against ML classifiers exploiting the excitability of Nuclear Engineers; a Backdooring Attack against ML Training environments to ensure persistence of an attack vector; a False Positive Evasion Attack against multi-factor Access Control Systems using clever inputs; an Inference Attack against ML models by an Insider with access to the Operational environment; and an Adversarial Reprogramming Attack against a Material Access Control Video Surveillance System. At the beginning of this year these six attack scenarios were provided to our research teams at Georgia Tech and Idaho State University and each team successfully implemented a subversion attack against a ML implementation to include transient misclassifications. While this is a notable outcome from this type of research, this paper offers the reader insight into not only how to structure and execute these types of attacks, but into the thought process behind how the researcher investigated the problem space, performed initial algorithm implementation, and the trial-and-error behind arriving at the successful subversion prototypes. We include in this paper a set of associated Scenarios on how these subversion prototypes could be implemented and an initial set of guidance for AR/SMR architects, Nuclear Regulators, and Cyber Defenders to implement awareness and defense capabilities into their current operational portfolios.

42 ENGINEERING↗

DOE OSTI2021

Evaluating Probability of Containment Effectiveness at a GCS Sites using integrated assessment modeling approach with Bayesian decision Networks

Improved scientific and engineering understanding of the behavior of geologic CO2 storage together with established regulatory framework and incentive structures raise the prospects for accelerated, large-scale deployment of this greenhouse gas emissions reduction approach. Incentive structures call for the establishment of appropriate verification and accounting approaches to support claims of the integrity of a geologic storage complex and to justify taking credit for long-term storage. In this study, we present a framework for assessing the probability of containment effectiveness over the lifetime of a geologic carbon storage site (e.g., after 70 years of injection and post-injection site performance) using forward stochastic model realizations based on site characterization data and using a monitoring-informed Bayesian network based on hypothetical detectability from surface seismic surveys over the site injection and post-injection phases. The National Risk Assessment Partnership’s open-source Integrated Assessment Model (NRAP-Open-IAM) was utilized to develop an ensemble of 10,000 a priori stochastic forecasts of CO2 containment. Those simulations were used to train the Bayesian network model to estimate the prior probabilities of the CO2 leakage mass into overlying, monitorable aquifers considering the uncertainties in the reservoir properties, permeability of potentially leaky wells and the overlying aquifers. The conditional probabilities in the Bayesian network were either learned from the NRAP-Open-IAM simulations or derived from the predefined detection thresholds for the monitoring method. Observations obtained from monitoring, over time during the site operation phases were then used to generate updated posterior probabilities of containment (and any loss from containment) in the Bayesian network by propagating the prior probabilities through the conditional probabilities. We demonstrate how to construct and use the Bayesian network for verifying the long-term storage complex effectiveness informed by monitoring based on the NRAP-Open-IAM simulations previously developed for the FutureGen 2.0 site. This approach may have relevance for stake holders to demonstrate secure geologic storage, provide a defensible, probabilistic approach to claim credit for geologic storage, and to estimate the likelihood that any fraction of the claimed credit may need to be refunded to the creditor based on available monitoring information.

Bayesian network, Risk assessment, Monitoring, car↗

DOE OSTI2013

Chemical Explosion Experiments to Improve Nuclear Test Monitoring [Developing a New Paradigm for Nuclear Test Monitoring with the Source Physics Experiments (SPE)]

A series of chemical explosions, called the Source Physics Experiments (SPE), is being conducted under the auspices of the U.S. Department of Energy’s National Nuclear Security Administration (NNSA) to develop a new more physics-based paradigm for nuclear test monitoring. Currently, monitoring relies on semi-empirical models to discriminate explosions from earthquakes and to estimate key parameters such as yield. While these models have been highly successful monitoring established test sites, there is concern that future tests could occur in media and at scale depths of burial outside of our empirical experience. This is highlighted by North Korean tests, which exhibit poor performance of a reliable discriminant, mb:Ms (Selby et al., 2012), possibly due to source emplacement and differences in seismic responses for nascent and established test sites. The goal of SPE is to replace these semi-empirical relationships with numerical techniques grounded in a physical basis and thus applicable to any geologic setting or depth.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2023

A comparison of machine learning methods to classify radioactive elements using prompt-gamma-ray neutron activation data

The detection of illicit radiological materials is critical to establishing a robust second line of defence in nuclear security. Neutron-capture prompt-gamma activation analysis (PGAA) can be used to detect multiple radioactive materials across the entire Periodic Table. However, long detection times and a high rate of false positives pose a significant hindrance in the deployment of PGAA-based systems to identify the presence of illicit substances in nuclear forensics. In the present work, six different machine-learning algorithms were developed to classify radioactive elements based on the PGAA energy spectra. The model performance was evaluated using standard classification metrics and trend curves with an emphasis on comparing the effectiveness of algorithms that are best suited for classifying imbalanced datasets. We analyse the classification performance based on Precision, Recall, F1-score, Specificity, Confusion matrix, ROC-AUC curves, and Geometric Mean Score (GMS) measures. The tree-based algorithms (Decision Trees, Random Forest and AdaBoost) have consistently outperformed Support Vector Machine and K-Nearest Neighbours. Based on the results presented, AdaBoost is the preferred classifier to analyse data containing PGAA spectral information due to the high recall and minimal false negatives reported in the minority class.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

MY EXES and EDM for Automated Remediation Data Quality Assessments - 19053

MY Electronic data exchange and Evaluation System (EXES) is a new web-based technology providing external admin access and full control to the Web-based EXES tool developed by GD for EPA for automated environmental remediation data quality assessments. EXES replaces most manual assessment processes, reducing remediation data review time and costs and ensuring that data are of known and documented quality. This tool is available from EPA to federal agencies and remediation firms and has successfully been used to support over 190 site investigations, 105 Remedial investigations and over 20 compliance monitoring efforts. The new MY EXES functionality has been developed for project managers and decision makers to setup projects, track receipt of environmental remediation monitoring data, assess data quality, and manage data. It is designed to be flexible and configurable to meet any government agency's data review and data management needs. It is accessible 24/7 through a secure portal. MY EXES efficiently evaluates analytical data regardless of the underlying method and data reporting requirements. It is a centralized project and data assessment tool that is scalable, flexible and configurable; improving data quality while reducing cost through the following: - Provides users with the ability to create a project and to specify the analytical methods corresponding to the data that will be submitted for that project. A project can be modified and managed anytime during the project's life cycle. - Allows users to manage a library of analytical methods that can be made available for association to their projects. - Users can specify the automated tests and assessment that needs to be performed on the analytical data deliverables based on specific requirements in the project's Quality Assurance Project Plan (QAPP). - Users specify: - The format of the incoming data deliverables from the laboratory; - The type and level of review services to be performed; - The assessment deliverables to be generated - output spreadsheets and reports; - Project owner sets up system access and permissions for members of the project team. - A faster (600%) and less expensive (85%) process than the traditional data usability/validation. The EXES Data Manager application (EDM) provides a centralized data repository to view and retrieve generated reports, spreadsheets, and other data deliverables as defined by the client, eliminating the need for data transfer between project team members. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2021

Creating heralded hyper-entangled photons using Rydberg atoms

Entangled photon pairs are a fundamental component for testing the foundations of quantum mechanics, and for modern quantum technologies such as teleportation and secured communication. Current state-of-the-art sources are based on nonlinear processes that are limited in their efficiency and wavelength tunability. This motivates the exploration of physical mechanisms for entangled photon generation, with a special interest in mechanisms that can be heralded, preferably at telecommunications wavelengths. Here we present a mechanism for the generation of heralded entangled photons from Rydberg atom cavity quantum electrodynamics (cavity QED). We propose a scheme to demonstrate the mechanism and quantify its expected performance. The heralding of the process enables non-destructive detection of the photon pairs. The entangled photons are produced by exciting a rubidium atom to a Rydberg state, from where the atom decays via two-photon emission (TPE). A Rydberg blockade helps to excite a single Rydberg excitation while the input light field is more efficiently collectively absorbed by all the atoms. The TPE rate is significantly enhanced by a designed photonic cavity, whose many resonances also translate into high-dimensional entanglement. The resulting high-dimensionally entangled photons are entangled in more than one degree of freedom: in all of their spectral components, in addition to the polarization—forming a hyper-entangled state, which is particularly interesting in high information capacity quantum communication. We characterize the photon comb states by analyzing the Hong-Ou-Mandel interference and propose proof-of-concept experiments.

74 ATOMIC AND MOLECULAR PHYSICS↗

DOE OSTI2012

Protecting Privacy of Shared Epidemiologic Data without Compromising Analysis Potential

Objective . Ensuring privacy of research subjects when epidemiologic data are shared with outside collaborators involves masking (modifying) the data, but overmasking can compromise utility (analysis potential). Methods of statistical disclosure control for protecting privacy may be impractical for individual researchers involved in small-scale collaborations. Methods . We investigated a simple approach based on measures of disclosure risk and analytical utility that are straightforward for epidemiologic researchers to derive. The method is illustrated using data from the Japanese Atomic-bomb Survivor population. Results . Masking by modest rounding did not adequately enhance security but rounding to remove several digits of relative accuracy effectively reduced the risk of identification without substantially reducing utility. Grouping or adding random noise led to noticeable bias. Conclusions . When sharing epidemiologic data, it is recommended that masking be performed using rounding. Specific treatment should be determined separately in individual situations after consideration of the disclosure risks and analysis needs.

Cologne, John↗

DOE OSTI2021

Universal Utility Data Exchange (UUDEX) Phase 4 Demonstration Environment and Results

This report provides an overview of the environment used by the Pacific Northwest National Laboratory (PNNL) in the Universal Utility Data Exchange (UUDEX) Phase 4 demonstration, and description of the demonstrations performed in the environment. The UUDX Phase 4 demonstration took place on March 31, 2021, with representatives from PNNL and the two project sub-contractors, OATI and MITRE, performing the demonstration for members of the UUDEX Industry Advisory Board (IAB). The demonstration tasks were based on the previous Phase 3 demonstration but conducted in a multi-site environment. Additional demonstrations showed features that were developed since the Phase 3 demonstration. The development environment was constructed by PNNL programming staff using the previously developed UUDEX Functional Design, Protocol Design, and Workflow Design documents as a starting point. The environment was augmented with subject creation management and access control security data structures and workflows, and data exchange structure documents developed as part of Phase 3 and Phase 4.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Scale up production of carbon fibers from petroleum mesophase pitch

This work investigates the scale-up of pitch precursors for the carbon fiber market using mesophase pitch formulated and produced by Advanced Carbon Products Technologies’ (ACPT’s) patented mesophase pitch processing technology. Through use of its patented process, ACPT developed strategic materials by converting petroleum-based pitch into a high-value, low-cost, carbon-rich feedstock for carbon fiber and other materials critical to our national security. The team successfully developed processing criteria for the tailored mesophase pitch material that can be processed further into precursor and carbon fiber. Processability of the mesophase pitch into precursor and carbon fiber was demonstrated at scale. The resulting materials sequester the carbon that would otherwise be burned and released into the atmosphere, making this an environmentally friendly way to produce these materials in the United States.Pitch-based carbon fibers offer a promising pathway toward cost-effective, high-performance materials for structural and high-modulus composite applications; however, adoption has been limited by challenges in precursor processability and scale-up. In this work, tailored isotropic and mesophase petroleum-derived pitch materials were developed and evaluated for precursor and carbon fiber production through a collaborative effort between Oak Ridge National Laboratory (ORNL) and ACPT. Processing conditions were established at ORNL’s Carbon Fiber Technology Facility to enable stable melt-blowing of pitch-based precursors under continuous operation. Mesophase pitch precursor fibers were produced following oxidation and carbonization, corresponding to a diameter shrinkage of approximately 12%–13% and an estimated mass yield of about 75%–80%. Continuous melt-blowing steady-state operation was demonstrated over time, indicating robust process stability. Melt-blowing was achieved at throughput rates of approximately 20 lb/h⁻¹, validating the commercial viability of petroleum-derived pitch feedstocks for fiber production. Further studies are required to tailor carbon fiber microstructure and properties for specific composite applications.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2017

The Waste Factory at the East Tennessee Technology Park - 17424

To successfully support the U.S. DOE Oak Ridge Office of Environmental Management (OREM) in its cleanup at the East Tennessee Technology Park (ETTP), URS - CH2M Oak Ridge LLC (UCOR) recognized the need for the timely disposition of large amounts of waste. To achieve cleanup goals, UCOR implemented the 'Waste Factory' concept. The Waste Factory requires a centralized waste management program, waste management-deployed staff, and numerous outlets for the various waste streams that move the waste from generation to disposition in a timely, compliant manner. Because no waste can be double handled or stored, characterization has to be sound, the disposition path selected based on the characterization, packaging determined, the Waste Acceptance Criteria (WAC) evaluated for the selected repository, and the waste shipped immediately upon generation. Since inception of the UCOR contract, a total of 509,703.24 m{sup 3} (18 million ft{sup 3}) have been dispositioned, totaling more than 60,000 waste loads and 6.5 million safe kilometers (4 million safe miles) driven. The generated waste has been dispositioned onsite and offsite based on the waste characterization and the UCOR waste management hierarchy. Without the timely disposition of the large amounts of waste generated from a massive cleanup project like ETTP, the project could not have achieved the aggressive schedule and performance that has been accomplished to date. The Waste Factory concept can serve as a model for waste management operations across the DOE complex and in other industries where significant quantities of waste must be disposed of. Without the availability of secure, onsite disposal, UCOR would have been forced to send hundreds of millions of pounds of waste by truck to repositories around the country, increasing risks and costs and slowing the overall progress of cleanup. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2023

Quantum Key Distribution for Critical Infrastructures: Towards Cyber-Physical Security for Hydropower and Dams

Hydropower facilities are often remotely monitored or controlled from a centralized remote control room. Additionally, major component manufacturers monitor the performance of installed components, increasingly via public communication infrastructures. While these communications enable efficiencies and increased reliability, they also expand the cyber-attack surface. Communications may use the internet to remote control a facility’s control systems, or it may involve sending control commands over a network from a control room to a machine. The content could be encrypted and decrypted using a public key to protect the communicated information. These cryptographic encoding and decoding schemes become vulnerable as more advances are made in computer technologies, such as quantum computing. In contrast, quantum key distribution (QKD) and other quantum cryptographic protocols are not based upon a computational problem, and offer an alternative to symmetric cryptography in some scenarios. Although the underlying mechanism of quantum cryptogrpahic protocols such as QKD ensure that any attempt by an adversary to observe the quantum part of the protocol will result in a detectable signature as an increased error rate, potentially even preventing key generation, it serves as a warning for further investigation. In QKD, when the error rate is low enough and enough photons have been detected, a shared private key can be generated known only to the sender and receiver. We describe how this novel technology and its several modalities could benefit the critical infrastructures of dams or hydropower facilities. The presented discussions may be viewed as a precursor to a quantum cybersecurity roadmap for the identification of relevant threats and mitigation.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2016

Bayesian analysis of rare events

In many areas of engineering and science there is an interest in predicting the probability of rare events, in particular in applications related to safety and security. Increasingly, such predictions are made through computer models of physical systems in an uncertainty quantification framework. Additionally, with advances in IT, monitoring and sensor technology, an increasing amount of data on the performance of the systems is collected. This data can be used to reduce uncertainty, improve the probability estimates and consequently enhance the management of rare events and associated risks. Bayesian analysis is the ideal method to include the data into the probabilistic model. It ensures a consistent probabilistic treatment of uncertainty, which is central in the prediction of rare events, where extrapolation from the domain of observation is common. We present a framework for performing Bayesian updating of rare event probabilities, termed BUS. It is based on a reinterpretation of the classical rejection-sampling approach to Bayesian analysis, which enables the use of established methods for estimating probabilities of rare events. By drawing upon these methods, the framework makes use of their computational efficiency. These methods include the First-Order Reliability Method (FORM), tailored importance sampling (IS) methods and Subset Simulation (SuS). In this contribution, we briefly review these methods in the context of the BUS framework and investigate their applicability to Bayesian analysis of rare events in different settings. We find that, for some applications, FORM can be highly efficient and is surprisingly accurate, enabling Bayesian analysis of rare events with just a few model evaluations. In a general setting, BUS implemented through IS and SuS is more robust and flexible.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2024

DIRAC current, upcoming and planned capabilities and technologies

DIRAC is the interware for building and operating large scale distributed computing systems. It is adopted by multiple collaborations from various scientific domains for implementing their computing models. DIRAC provides a framework and a rich set of ready-to-use services for Workload, Data and Production Management tasks of small, medium and large scientific communities having different computing requirements. The base functionality can be easily extended by custom components supporting community specific workflows. DIRAC is at the same time an aging project, and a new DiracX project is taking shape for replacing DIRAC in the long term. This contribution will highlight DIRAC’s current, upcoming and planned capabilities and technologies, and how the transition to DiracX will take place. Examples include, but are not limited to, adoption of security tokens and interactions with Identity Provider services, integration of Clouds and High Performance Computers, interface with Rucio, improved monitoring and deployment procedures.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Application of DLT cybersecurity stack to TES applications for a scalable, cybersecure, and interoperable future

Transactive Energy Systems (TES) are expected to improve upon existing grid operations and capabilities by enabling the integration of traditional grid resources with distributed energy resources (DER). Distributed Ledger Technology or DLT (e.g., blockchain) presents itself as a viable instrument to support decentralized, autonomous, and tamper-evident applications, which can be leveraged within TES's ecosystem. DLTs can provide pertinent security controls including access controls, data immutability, and traceability in addition to other well-known advantages such as decentralization and scalability. This work demonstrates the DLT Cybersecurity stack and its applicability to TES-based use-cases/applications. The seven-layer DLT cybersecurity stack is a DLT-agnostic framework that can quickly be used to classify and group the individual needs of an application into the different processing and cybersecurity layers offered by a DLT using a common taxonomy and an architectural mapping framework. This enables application engineers to demystify and strengthen the overall security aspects of their systems while maintaining an open perspective towards features and drawbacks that may hinder their performance in real-world scenarios. The paper leverages the work performed by the IEEE P2418.5 - Blockchain for Energy Standards working group.

Blockchain, blockchain interoperability, Cybersecu↗

DOE OSTI2012

Playas Grid Reliability and Distributed Energy Research

The future looks bright for solar and renewable energies in the United States. Recent studies claim that by 2050, solar power could supply a third of all electricity demand in the country’s western states. Technology advances, soft policy changes, and increased energy consciousness will all have to happen to achieve this goal. The New Mexico Institute of Mining and Technology (NMT) and the Department of Homeland Security (DHS) perform various training and research activities at this site. Given its unique nature, Playas was chosen to test Micro-Grids and other examples of renewable distributed energy resources (DER). Several proposed distributed energy sources (DERs) were not implemented as planned including the Micro-Grid. However, Micro-Grid design and computer modeling were completed and these results are included in this report. As part of this research, four PV (solar) generating systems were installed with remote Internet based communication and control capabilities. These systems have been integrated into and can interact with the local grid So that (for example) excess power produced by the solar arrays can be exported to the utility grid. Energy efficient LED lighting was installed in several buildings to further reduce consumption of utility-supplied power. By combining reduced lighting costs; lowering HVAC loads; and installing smart PV generating equipment with energy storage (battery banks) these systems can greatly reduce electrical usage drawn from an older rural electrical cooperative (Co-Op) while providing clean dependable power. Several additional tasks under this project involved conducting research to develop methods of producing electricity from organic materials (i.e. biofuels, biomass. etc.), the most successful being the biodiesel reactor. Improvements with Proton Exchange Membranes (PEM) for fuels cells were demonstrated and advances in Dye Sensitized Solar Cells (DSSC) were also shown. The specific goals of the project include: 1) Instrumentation of the power distribution system with distributed energy resources, demand-side control and intelligent homes within the town of Playas, NM, 2) Creation of models (power flow and dynamic) of the Playas power distribution system, 3) Validation of the models through comparison of predicted behavior to data collected from instrumentation, and 4) Utilization of the models and test grid to characterize the impact of new devices and approaches (e.g., distributed generation and load management) on the local distribution system as well as the grid at large. In addition to the above stated objectives, the research also focused on three critical challenges facing renewable distributed energy platforms: 1) hydrogen from biomass, 2) improved catalyst support systems for electrolysis membranes and fuel cell systems, and 3) improved manufacturing methodologies of low cost photovoltaics. The following sections describe activities performed during this project. The various tasks were focused on establishing Playas as a “…theoretical and experimental test bed…” through which components of a modern/smart grid could be characterized. On a broader scale, project efforts were aimed at development of tools and gathering of experience/expertise that would accelerate progress toward implementation of a modern grid.

09 BIOMASS FUELS↗