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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

Codebase release 2.0 for UFOManager

Research in the data-intensive discipline of high energy physics (HEP) often relies on domain-specific digital contents. Reproducibility of research relies on proper preservation of these digital objects. This paper reflects on the interpretation of principles of Findability, Accessibility, Interoperability, and Reusability (FAIR) in such context and demonstrates its implementation by describing the development of an end-to-end support infrastructure for preserving and accessing Universal FeynRules Output (UFO) models guided by the FAIR principles. UFO models are custom-made python libraries used by the HEP community for Monte Carlo simulation of collider physics events. Our framework provides simple but robust tools to preserve and access the UFO models and corresponding metadata in accordance with the FAIR principles.

Neubauer, Mark S.↗
DOE OSTI2020

The Nuclear Data Interface (NDI) [Slides]

The Nuclear Data Interface (NDI) can be described as: Standardized methodology for accessing and manipulating nuclear data; An API that accesses nuclear data while hiding underlying details from the user; Created to enable codes to access nuclear data without knowledge of a specific library format, content, or location; Both a code and ASCII data format (just to be confusing); Not continuous energy (that’s what NDATK is for).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2020

Project 57 Air Monitoring Report: (Jan 1 - Dec 31 2018)

During the late 1950s, the Atomic Energy Commission (AEC) (now the U.S. Department of Energy [DOE]) conducted a series of safety experiments to determine if a nuclear device subjected to a large conventional explosives detonation would result in a nuclear yield. The AEC obtained temporary use of a large portion of western Emigrant Valley from the U.S. Department of Defense (DOD) for one of these experiments: Project 57. Following the Project 57 safety experiment, the AEC fenced the contaminated area and returned control of the surrounding land to the DOD. Emigrant Valley is part of the Nevada Test and Training Range (NTTR). For safety and security reasons, access to the NTTR is controlled using both physical (i.e., fences) and administrative (e.g., signs and postings) controls. Therefore, the public cannot access the Project 57 site and there are no known human receptors that routinely access the site. Project 57 was detonated on April 24, 1957, in Emigrant Valley approximately 13 mi (21 km) northeast of the north end of Yucca Flat. This test was undertaken to develop: (1) a means of estimating immediate distribution and long-term redistribution of plutonium dispersed during a nonnuclear detonation; (2) biomedical evaluation techniques for use in likely plutonium-laden environments; (3) methods of decontaminating ground areas, pavements, and building materials; and (4) alpha survey instruments and field monitoring procedures to promptly estimate contaminant deposition. Although the test did not result in the fission of nuclear materials, it did disseminate plutonium across the ground surface. Various radiological surveys have been performed in the area since Project 57 was conducted. The AEC delineated the area containing radioactive material based on radioactivity surveys performed shortly after the test was conducted. A designated Contamination Area (CA), marked by a fence, was established later. The distribution of americium-241 (Am-241) in the area was determined again in a 1997 flyover, which showed Am-241 ranging from as much as 70,000 counts per second (cps) at ground zero to background (<70 cps). This survey also documented Am-241 radioactivity on the ground surface beyond the east side CA fence at levels of up to 150 cps. In 2007, the DOE expanded the CA by posting “Contamination Area” signs 200 ft to 400 ft (60 m to 120 m) beyond the original fence, which formed a new CA boundary.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Compiler and Runtime Approaches to Enable Large-Scale Irregular Programs. Final report, July 2013 - July 2019

While regular algorithms, characterized by operations on dense matrices and arrays, have long been the mainstay of scientific, high-performance computing, irregular algorithms, which feature unpredictable accesses to pointer-based data structures, are becoming increasingly common in high performance computing, arising in graph analysis, data mining and visualization, among other domains. Unfortunately, the defining characteristics of irregular applications, their dynamic, unpredictable, data-dependent access patterns and data layouts, make achieving high performance on large scale systems difficult. Scaling applications to peta- and exa-scale requires carefully controlling communication and data movement and placement, an inherently difficult task when access patterns and data layouts are unpredictable! Most irregular applications that attain high performance must be painstakingly hand-written and hand-tuned, with few common principles or paradigms uniting various implementations and easing future development. Despite the increasing importance of irregular applications, there is little programmer knowledge, and even less compiler ability, devoted to optimizing them. This project aims to solve these problems. By allowing programmers to write irregular applications in high level forms, with at most a few annotations highlighting key structural properties, programmers can focus on developing their algorithms and methods. The compiler and run-time system can take on the tedious task of optimizing the application for execution at large scales, and can automatically provide efficient implementations. This will provide portability and ease maintenance for existing irregular applications, but, more importantly, open up whole new domains of computational science to large-scale, high-performance simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM). Final Report

Distributed Resources for the Earth System Grid Federation (ESGF) Advanced Management (DREAM) is a proposed system that will enable data from an infinite number of diverse sources to be organized and accessed from anywhere using any handheld or other computer device. The approach offers a powerful roadmap for the creation and integration of a unified knowledge base of an entire ecosystem, including its many geophysical, geographical, social, political, agricultural, energy, transportation, and cyber aspects. The resulting aggregation of data has the potential to generate an informational universe of unprecedented size that has never before been possible due to the prohibitive costs, managerial complexity, and technical barriers associated with ever-changing exponential-growth data flows. We envision that DREAM will accelerate discovery by enabling climate researchers, among other types of researchers, to manage, analyze, and visualize data from earth-scale measurements and simulations. DREAM’s success will be built on proven components that leverage existing services and resources. A key building block for DREAM will be the ESGF, chaired by Dean N. Williams. Expanding on the existing ESGF, the project will ensure that the access, storage, movement, and analysis of the large quantities of data that are processed and produced by diverse science projects can be dynamically distributed with proper resource management. Much of the Office of Science data is currently generated by multiple stand-alone facilities. DREAM can collect data accumulated from these facilities and incorporate it into a fully integrated network accessible from anywhere in the world. The result is a completely new paradigm shift for data management, analysis, and visualization enabling researchers to: Manage their calculations, data, tools, and research results; Ensure that all data are sharable, reproducible and (re)usable—accompanied by appropriate metadata describing its provenance, syntax, and semantics at creation; Advance application performance by selectively adapting APIs and services in response to scientific requirements and architectural complexities; and Provide scalable interactive resource management—navigate data and metadata at multiple levels, provide architecture-aware data integration, analysis and visualization tools. We will engage closely with DOE, NASA, and NOAA science groups working at the leading edge of computing. These engagements—in domains such as biology, climate, and hydrology—will allow us to advance disciplinary science goals and inform our development of technologies that can accelerate discovery across DOE more broadly. We will advertise and promote our technologies via dedicated workshops, tutorials, and sessions at conferences, stand-alone events with broad inter-disciplinary invitation, and engagements with leadership facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Evaluation of the NSUF Reactor Activation and Damage (RAD) Calculator damage component

The Nuclear Science User Facilities (NSUF) provide researchers access to nuclear research facilities and expertise all over the United States and at the Belgian Center for Nuclear Research (SCK/CEN). One special capability of the NSUF is providing access to nuclear research and test reactors often used for conducting material testing experiments. These services are provided at no cost to the researcher through a competitive, peer-reviewed process. With so many proposals being submitted simultaneously, an NSUF information management resource called the Combined Materials Experiment Toolkit (CoMET) was implemented to speed up the review process and give users access to the information and tools necessary for designing meaningful experiments and writing the best proposals possible. One of CoMET’s tools is the Reactor Activation and Damage (RAD) Calculator. For those with little or no background in it, neutron irradiation in research and test reactors can be a challenging subject to include in a proposal. That is why the RAD Calculator was created to assist users with providing estimates of irradiation and post-irradiation conditions, enabling them to select the best neutron irradiation and post-irradiation examination facilities for reaching their experiment goals. The RAD Calculator is a general scoping tool that estimates radiation damage in terms of DPA, in addition to the resulting radioactivity of the material post-irradiation. This report evaluates the neutron damage component of the RAD Calculator. The evaluation of the activation component of the calculator is covered in report INL/EXT-20-58080.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Summary Report for the NEPA Impact Analysis. Revision 1

The Waste Isolation Pilot Plant (WIPP), located in southeastern New Mexico, has been developed by the U.S. Department of Energy (DOE) for the geologic (deep underground) disposal of defense-related transuranic (TRU) waste. Containment of TRU waste at the WIPP facility is derived from standards set forth in Title 40 of the Code of Federal Regulations (CFR), Part 191. The DOE assesses compliance with the containment standards according to the Certification Criteria in Title 40 CFR Part 194 by means of Performance Assessment (PA) calculations performed by Sandia National Laboratories (SNL). WIPP PA calculations estimate the probability of radionuclide releases from the repository to the accessible environment for a regulatory period of 10,000 years after facility closure. The DOE Carlsbad Field Office (CBFO) has initiated a National Environmental Policy Act (NEPA) action for a proposal to excavate and use additional transuranic (TRU) waste disposal panels at the WIPP facility. This report documents an analysis undertaken as part of an effort to evaluate the potential environmental consequences of the proposed action. Although not explicitly required for a NEPA analysis, evaluations of a dose indicator to hypothetical members of the public after final facility closure are presented in this report. The analysis is carried out in two stages: first, Performance Assessment (PA) calculations quantify the potential releases to the accessible environment over a 10,000-year post-closure period. Second, dose was evaluated for three hypothetical exposure pathways using the conservative radionuclide concentrations assumed to be released to the accessible environment.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Status and Availability of OPTD, the Out-of-Pile Transient Database

The DOE Advanced Reactor Technology program has supported knowledge preservation efforts to recover and preserve fuel data from the US sodium cooled fast reactor (SFR) program. Databases have been established containing experimental data generated during the Integral Fast Reactor program (from in-pile experiments at EBR-II, FFTF, and TREAT) and during related outof-pile examinations of EBR-II-irradiated fuels. The information in these databases is essential to support further development and licensing activities for advanced fast reactor designs. OPTD, the Out-of-Pile Transient Database, is an organized, searchable archive of records describing out-ofpile furnace transient tests conducted on metallic fuels. It includes records of over 150 tests on irradiated fuel pins conducted in furnace apparatuses constructed in the Alpha-Gamma Hot Cell Facility at ANL. This report presents the content, functions, and accessibility of the Out-of-Pile Transient Database (OPTD). It describes the information presently available in the database, the web interface created to provide access to this information, the activities undertaken to build OPTD, and the enabling of online access to the database for DOE users. Plans for FY2021 and beyond are also included.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

LaserNetUS. Final report

In this proposal we have formed a consortium in response to a request from the DoE Office of Fusion Energy Sciences to establish a national network of Petawatt-class laser systems to act as domestic user facilities that can enable a broad range of frontier scientific research. This new entity is called LaserNet US. The network formed here is directly responsive to recommendations made in a recently released National Academy of Sciences Report with regard to US strategy for high intensity laser research, “Opportunities in Intense Ultrafast Lasers: Reaching for the Brightest Light”. As detailed in this report, the research in this area has the potential to transform science in a number of research fields and to open up new areas of fundamental research. High field science was initiated in the US in the 1990’s, and subsequently Europe and Asia have embraced this research field, investing more than a billion dollars in this area over the past several years. Our network includes five academic and two national lab-based high intensity laser facilities. These facilities are distributed geographically throughout the US and the network provides access to complementary facilities which have unique world-class laser and experimental capabilities. LaserNet US will make these existing Petawatt-class laser facilities available to users from around the country who until now have not had regular access to such machines. Consequently, the network, which will develop over time, will also become a key element in driving forward national research in high field and high energy density plasma science. In particular, the University of Michigan provides access to the Hercules laser facility and the T-cubed laser facility for collaborative experiments as part of the first year operation. In the second year - to begin in summer 2019 - the experiments will be chosen via proposal submission and review by an outside panel formed by DOE.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

SIRIUS: Science-Driven Data Management for Multi-Tiered Storage

The data sets being generated by large applications on very large-scale systems are increasing in both size and complexity. At the same time, there are new ways available to store and access these data sets. The goal in this project is to develop software that applications can use to make use of new and existing storage technologies in more sophisticated ways. One challenge in scientific data management is handling ‘hot’ vs ‘cold’ data. Data that is hot is data that is needed (or will be needed soon) in order for the program to continue progressing, while cold data is either output (and so will not be need further during the life of the program) or will not be needed until significantly later in the program’s run. Hot data should be stored in a way that allows fast access. On most systems, economic factors lead to an inverse relationship between storage performance and storage capacity and so fast access storage is limited. This makes it important to correctly place hot and cold data and avoid cold data unnecessarily consuming precious resources. In this reporting period, we addressed this challenge in various ways and at various levels. Data management frameworks offer only limited control to applications in how data is stored. We have added software capabilities for seamlessly moving data between layers of the storage technology using promote and demote functions to existing software frameworks. This gives direct control to applications in deciding what priority data receives. Additionally, we integrated different storage layer management frameworks in order to allow data to be exchanged and moved between storage layers in a consistent way across the application. Further, applications are not always able to directly decide what storage level makes sense for a given piece of data without an understanding of the underlying storage technologies. Data storage frameworks are often positioned to make these sorts of decisions in service of the application. We have added machine-learning based capabilities to data staging frameworks in order to make intelligent decisions about where data should be stored given learning about patterns in previous usage of similar data.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Considerations for using Privacy Preserving Machine Learning Techniques for Safeguards

In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Data Repository for Power system Open models With Evolving Resources (DR POWER) (Final Scientific/Technical Report)

Pacific Northwest National Laboratory (PNNL) and the National Rural Electric Cooperative Association (NRECA) created a Data Repository for Power system Open models With Evolving Resources (DR POWER) to establish, curate, and evolve open-access power grid models and scenarios, and measurements and observations, collectively referred to as datasets. Existing open-access datasets are inadequate (too small and not representative of the complexity of the modern grid, and scattered all over the world wide web) for the investigation and benchmarking of the advanced optimization methods needed for next-generation grids. Open-access datasets are static and inadequate in other aspects: they do not provide format transformation tools; there is missing information needed for advanced applications; hard to cite; no model evolution history; difficult to update current collections; and little to no community involvement and feedback. Overall, the current landscape is hindering model evolution and benchmarking standards needed for transformational approaches to be developed, tested, and accepted by the power modeling community.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

A High-Throughput Computing Infrastructure to Generate Custom, Open Community Geothermal Datasets

The most significant challenge facing geothermal research, development, and deployment is a lack of comprehensive datasets describing the geological and economical properties of North America. Automated knowledge base construction, the process of designing algorithms to analyze text and images to programmatically build new datasets, is one possible solution to this problem. The xDD library of full-text scientific articles (https://xdd.wisc.edu) is one of the largest collections of open and controlled-access scientific documents available for knowledge base construction in the world, but it has been underutilized by experts in geothermal research. The xDD development team attributed the lack of engagement by software developers and geothermal researchers to two perceived shortcomings of the system. First, the workflow for obtaining data from xDD for local development and testing of data mining applications was unnecessarily abstruse and required significant manual intervention by xDD systems administrators. Second, although xDD already held articles from a broad cross-section of scientific literature with an emphasis on the geosciences, it did not have an explicit set of geothermal research documents that could serve as the nucleus of a geothermal data mining application. To address these issues, the Automated Data Extraction PlaTform (ADEPT) was proposed to extend the data distribution capabilities of the xDD system. The ADEPT extension added the following four key features to xDD: 1) integration of National Geothermal Data System (NGDS) documents into the xDD library to provide an explicitly geothermally-themed collection; 2) improved RESTful (i.e., https-protocol driven) web services for external partners to access xDD data for machine learning application development; 3) a web platform for end-users and xDD administrators to coordinate the development of data mining applications from the initial step of browsing available documents to the final stage of deploying a production-quality machine learning application on high-throughput computing infrastructure; and 4) the development of demonstration data mining applications to illustrate the new workflow to potential collaborators. A total of 21,674 geothermal documents from NGDS were fully ingested into the xDD library and the associated metadata is publicly available through the xDD web services; furthermore, the ADEPT web platform is now publicly accessible and fully live at https://xdd.wisc.edu/adept/.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Toward a Seamless Integration of Computing, Experimental, and Observational Science Facilities: A Blueprint to Accelerate Discovery

The Department of Energy, Office of Science operates world-leading facilities for experimental, observational, and computational science. DOE supercomputing facilities will reach performance at the scale of ExaFLOPs in the coming years, enabling new vistas of scale and precision for large scale simulations and data analysis. Experimental scientific facilities are undergoing similar upgrades that will lead to higher data rates and correspondingly larger computational demands, and will increase the need for near-real-time processing and resilient support for more complex workflows. A transformation of science is underway, with workloads at supercomputing facilities increasingly driven by this explosion of data from instruments and experimental facilities, as well as the accelerating use of Artificial Intelligence (AI) as a tool for scientific discovery. A seamless integration of computing, networking, instruments, and experimental facilities is required to support these emerging workloads and open up a new frontier of U.S. leadership in scientific discovery. We propose to accomplish this by providing frictionless access to the ASCR supercomputing facilities. We describe our vision of combining the power of ASCR supercomputers and networking infrastructure into an integrated scalable fabric, available to end user scientists via interfaces that aim to automate and simplify access to high performance computing systems. This will enable unprecedented computational science capabilities for experimental and observational facilities, and will create new opportunities to combine large simulations and modeling with experimental facility data analysis. This blueprint for creating an integrated network of computational and experimental facilities will provide an enriched discovery environment and open doors for new scientific communities to access the DOE’s world-leading computing and networking capabilities.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

High-Temperature Gas-Cooled Reactor Research Survey and Overview: Preliminary Data Platform Construction for the Nuclear Energy University Program

Since the U.S. Department of Energy Office of Nuclear Energy initiated the Nuclear Energy University Program (NEUP) in 2009, there are 29 NEUP projects focusing on high-temperature gas-cooled reactor (HTGR) research up to July 2022. The resultant research product, either experimental or computational, were published as final NEUP reports, journal articles and conference proceedings. However, these federally funded products have been scattered and sometimes cannot be easily accessed. To improve access to this valuable HTGR validation data and optimize the return on the significant investment made by the Department of Energy, the Advanced Reactor Technologies (ART) Gas-Cooled Reactor (GCR) program started a survey of completed and ongoing HTGR NEUP projects to develop a public-access database specific for HTGRs applications that can be used to retrieve computational fluid dynamics and system code validation data. This effort will help guide future NEUP-funded research, define new state of the ART Phenomena Identification and Ranking Table (PIRT), and promote the usage of this data in the codes validation matrices. This report provides an overview of the NEUP-funded HTGR-related research projects from Fiscal Year (FY) 2009–2021 and identifies validation knowledge gaps still existing in HTGR thermal-fluid research. A preliminary data platform has been developed for the 29 NEUP projects investigating HTGR thermal hydraulics, including their final reports as well as the available scientific publications. As an ultimate goal for this work, the ART-GCR program will create a central database at Idaho National Laboratory to identify, organize, and store these datasets generated by experimental investigations or computational models, experimental facility descriptions, and publicly-available academic products from the HTGR-related NEUP projects and provide future guidance for the storage and transmission of important project documentations for later NEUP projects as well.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
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 OSTI2023

Technology Maturation of Wireless Harsh Environment Sensors For Improved Condition Based Monitoring Of Coal Fired Power Generation

The overall goal of this project was to demonstrate and develop the usage of high-temperature (HT) harsh-environment (HE) wireless surface acoustic wave resonator (SAWR) sensor technology to promote reliable maintenance through condition-based maintenance (CBM) for field applications in harsh service conditions associated with power plant environments. The project aimed to advance the HT HE wireless SAWR sensor technology from TRL 5 to TRL 7. In addition to HT HE wireless temperature sensing, efforts were dedicated during this project to investigate, develop and increase the TRL from 3 to 5 for the following technologies: (a) HT HE strain sensors to address additional CBM monitoring needs, such as boiler tube mechanical / thermal stresses, which can provide early indications for boiler tube cracking and failure; and (b) HT aluminum nitride (AlN) and scandium aluminum nitride (ScAlN) based piezoelectric thin film fabrication and implementation of SAW sensors, with the goal of releasing the need to use single crystal piezoelectric materials for SAWRs and thus broaden possible technology applications to non-planar and harder to modify surfaces. To achieve the goals mentioned above, UMaine and its partner, Environetix Technologies Corporation, established partnerships with the following power plants: Longview Power (Maidsville, WV), a coal-fired power plant; Penobscot Energy Recovery Corp (PERC, Orrington, ME), a waste-to-energy power plant; and the UMaine Steam Plant (Orono, ME), an oil / natural gas power plant. To realize wireless HT HE SAWR sensor systems in these harsh service conditions, the University of Maine research team worked with Environetix and these power plants to define, design, fabricate, test and validate a mature prototype wireless temperature SAWR sensor system for boiler tube applications within the HT HE of the reheater pass damper chamber to directly and wirelessly monitor the temperature at eighteen independent boiler tube locations. The system included three levels, or “tiers”, of wireless communication to enable remote monitoring: Tier 1, the wireless link in the reheater pass damper chamber directly accessing the sensors on the boilers; Tier 2, the wireless local area network link, transmitting processed sensor information within the power plant to the Tier 3, a commercial wireless signal carrier company for secure remote data monitoring outside of the power plant. Regarding the wireless sensor system installed at Longview Power, temperature information from the boilers was continuously transmitted from the Longview boilers at Maidsville, WV, to Environetix headquarters, Orono, ME, over a 34 month period, when the system was finally decommissioned. Strain sensors and piezoelectric ScAlN thin film sensors were successfully installed on the exhaust duct at the UMaine Steam Power plant. The advances in wireless strain sensors and thin film piezoelectric film fabrication and testing were performed mostly in UMaine laboratories and field tested at the UMaine Steam Plant, due to its close proximity to UMaine/Environetix, access to the plant facility, and due to difficulties in accessing the other power plants during the COVID shut-down period. The project accomplished the TRL level increase of the targeted CBM technologies through the successful fabrication, installation, test, and validation of dedicated and commercial wireless sensor systems, utilizing the three different power plants. The outcomes of this project, including the wireless sensor data capability, are expected to yield an advance for CBM in harsh power plant environments. The reduction of maintenance costs, improved safety during plant operation, and increased power plant efficiency will lead to increased revenues (i.e., fewer forced outages) due to better process monitoring enabled by the wireless HT HE SAWR temperature sensor technology.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Fixing Amdahl's Law within the Limits of Accelerated Systems: FALLACY

Closeout report for FALLACY project. The performance of Data Model Convergence Initiative (DMC) applications on parallel machines is far below the limit set by Amdahl’s law. Whether the machine is based on many-core, GPUs, FPGAs, or a heterogeneous combination, usually the most significant bottleneck is accessing data from the memory system. Aligning with DMC’s HW/architecture thrust, this project developed a set of memory-centric tools called ‘MemGaze’ that inform the HW/SW stack about an application’s memory behavior, including data access latency and diagnosing poor data layout and data composition. Our approach uses architectural modeling and analysis of workload data accesses.

97 MATHEMATICS AND COMPUTING↗