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Evaluation of Roadmap to Achieve Energy Delivery Systems Cybersecurity

The Department of Energy/Office of Electricity Delivery and Energy Reliability (DOE/OE) Cybersecurity for Energy Delivery Systems (CEDS) program is currently evaluating the Roadmap to Achieve Energy Delivery Systems Cybersecurity document that sets a vision and outlines a set of milestones. The milestones are divided into five strategic focus areas that include: 1. Build a Culture of Security; 2. Assess and Monitor Risk; 3. Develop and Implement New Protective Measures to Reduce Risk; 4. Manage Incidents; and 5. Sustain Security Improvements. The most current version of the roadmap was last updated in September of 2016. Sandia National Laboratories (SNL) has been tasked with revisiting the roadmap to update the current state of energy delivery systems cybersecurity protections. SNL is currently working with previous and current partners to provide feedback on which of the roadmap milestones have been met and to identify any preexisting or new gaps that are not addressed by the roadmap. The specific focus areas SNL was asked to evaluate are: 1. Develop and Implement New Protective Measures to Reduce Risk and 2. Sustain Security Improvements. SNL has formed an Industry Advisory Board (IAB) to assist in answering these questions. The IAB consists of previous partners on past CEDS funded efforts as well as new collaborators that have unique insights into the current state of cybersecurity within energy delivery systems. The IAB includes asset owners, utilities and vendors of control systems. SNL will continue to maintain regular communications with the IAB to provide various perspectives on potential future updates to further improve the breadth of cybersecurity coverage of the roadmap.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Earth Science Multimedia Theater

The presentation will begin with the latest 1998 NASA Earth Science Vision for the next 25 years. A compilation of the 10 days of animations of Hurricane Georges which were supplied daily on NASA to Network television will be shown. NASA's visualizations of Hurricane Bonnie which appeared in the Sept 7 1998 issue of TIME magazine. Highlights will be shown from the NASA hurricane visualization resource video tape that has been used repeatedly this season on network TV. Results will be presented from a new paper on automatic wind measurements in Hurricane Luis from 1 -min GOES images that will appear in the October BAMS. The visualizations are produced by the Goddard Visualization & Analysis Laboratory, and Scientific Visualization Studio, as well as other Goddard and NASA groups using NASA, NOAA, ESA, and NASDA Earth science datasets. Visualizations will be shown from the "Digital-HyperRes-Panorama" Earth Science ETheater'98 recently presented in Tokyo, Paris and Phoenix. The presentation in Paris used a SGI/CRAY Onyx Infinite Reality Super Graphics Workstation at 2560 X 1024 resolution with dual synchronized video Epson 71 00 projectors on a 20ft wide screen. Earth Science Electronic Theater '999 is being prepared for a December 1 st showing at NASA HQ in Washington and January presentation at the AMS meetings in Dallas. The 1999 version of the Etheater will be triple wide with at resolution of 3840 X 1024 on a 60 ft wide screen. Visualizations will also be featured from the new Earth Today Exhibit which was opened by Vice President Gore on July 2, 1998 at the Smithsonian Air & Space Museum in Washington, as well as those presented for possible use at the American Museum of Natural History (NYC), Disney EPCOT, and other venues. New methods are demonstrated for visualizing, interpreting, comparing, organizing and analyzing immense Hyperimage remote sensing datasets and three dimensional numerical model results. We call the data from many new Earth sensing satellites, Hyperimage datasets, because they have such high resolution in the spectral, temporal, spatial, and dynamic range domains. The traditional numerical spreadsheet paradigm has been extended to develop a scientific visualization approach for processing Hyperimage datasets and 3D model results interactively. The advantages of extending the powerful spreadsheet style of computation to multiple sets of images and organizing image processing were demonstrated using the Distributed Image SpreadSheet (DISS).

Hasler, A. F.↗

IER-539 CED-1: Preliminary Design of a New Horizontal Split Table

This report presents the preliminary design (CED-1) of IER-539, focused on a new Horizontal Split Table (HST) critical assembly machine. The Department of Energy (DOE) National Nuclear Criticality Safety Program (NCSP) funds critical experiment R&D and its Mission and Vision document identifies a long-standing technical gap of a general purpose HST. HSTs have historically been used for experiments that are impractical to assemble on a vertical lift machine (VLM), usually due to their large physical size or weight. While many HSTs were historically employed in the US (including at LLNL), there are currently no surviving HSTs in the US. While there are two critical experiments facilities remaining in the US, the National Critical Experiments Research Center (NCERC) operated by LANL and the Critical Experiments Facility (CX) operated by SNL, these facilities are not equipped with a general purpose HST. There are a number of experiment designs that require an HST, including needs for Defense Programs and Nuclear Energy. Lack of an HST constraints the national ability to perform large and heavy experiments. Large footprints are needed to successfully create mockup experiments for advanced reactors (mock-up cold critical reactor cores, which is a urgent need for new reactor designs using novel materials and longer cycle lengths), to create solution experiments in lattices, or to test nuclear data of heavy metals. A new HST is a vital piece of criticality experiment infrastructure that would allow for the conduct of integral critical experiments to address needs in the nuclear criticality safety, nuclear data, and other DOE stakeholder communities.

42 ENGINEERING↗

Learning Lessons from the X-37 Project

The X-37 was planned as an automated vehicle capable of flight-testing new aerospace technologies in combined environments that are beyond the capability of existing ground or flight platforms. Flight demonstration with the X-37 architecture and configuration in relevant environments was planned to reduce the risk of developing launch vehicle technologies for sustainable, affordable exploration and other aerospace applications. Current plans are for the X-37 Approach and Landing Test Vehicle (ALTV) to be atmospheric tested in 2005 from Scaled Composite s White Knight carrier aircraft at up to 40,000 feet over California's Mojave Spaceport, with landing and turnaround maintenance performed. The Flight Operations Control Center will conduct the mission, using a streamlined operations concept. Taxi-tow and captive-carry tests will be conducted prior to the atmospheric-test series. Sponsored by the Defense Advanced Research Projects Agency (DARPA) with NASA participation, technical objectives are to: (1) mature Computed Air Data System/Remote Pressure Sensor technology, (2) manage energy during Terminal Area Energy Management/Heading Alignment Cone maneuvers, and (3) validate the aerodatabase. The X-37 Project began in 1999 under a cooperative agreement as an element of NASA's Future X Program and transitioned to a NASA Research Announcement under the Space Launch Initiative. In mid-2004, NASA transferred ownership to DARPA, with its heritage of performing high-risk, high-payoff research and development (R&D). NASA contributes technical expertise, including risk analysis and system integration. The Boeing Company is the prime contractor, with nationwide suppliers. This partnership exemplifies the synergy attainable when NASA Centers, other Government agencies, and industry work together toward a common goal - contributing to the knowledge base for U.S. exploration and other aerospace endeavors. The X-37 team represents a range of space transportation disciplines - from engineering to management. Some members have been with the project since its inception. All have gained priceless experience during the design, manufacturing, and testing of the ALTV, as well as through developing advanced orbital flight technologies, such as state-of-the-art Thermal Protection Systems and hot structures. Throughout this process, the X-37 Project team captures lessons that are directly applicable to other such efforts. The upcoming ALTV flights offer another dimension of data and first-hand experience that will prove invaluable to those designing new generations of reusable spacecraft. And ongoing technology developments will expand the aerospace knowledge base. Delivering prototype hardware is always a risky proposition. During the course of this effort, the X-37 team has experienced many challenging opportunities, delivering significant accomplishments and learning numerous lessons in the process. The ability to manage the risk landscape is key to overcoming obstacles, especially technical hurdles that are encountered in progressing hardware from design to flight. The approach to managing risk under this partnership is evolving but, in general, the team allocates resources to reduce the likelihood of severe-consequence risks, thus maximizing mission success and ensuring that the X-37 Project delivers value to its stakeholders. As the team sharpens its focus on operations, it continues to contribute knowledge to those who would undertake high-risk, high-payoff R&D and provides valuable experience to implement the Vision for Space Exploration.

Turner, Susan↗

Learning Lessons from the X-37 Project

The X-37 was planned as an automated vehicle capable of flight-testing new aerospace technologies in combined environments that are beyond the capability of existing ground or flight platforms. Flight demonstration with the X-37 architecture and configuration in relevant environments was planned to reduce the risk of developing launch vehicle technologies for sustainable, affordable exploration and other aerospace applications. Current plans are for the X-37 Approach and Landing Test Vehicle (ALTV) to be atmospheric tested in 2005 from Scaled Composite's White Knight carrier aircraft at up to 40,000 feet over California's Mojave Spaceport, with and turnaround maintenance performed. The fight Operations Control Center will conduct the mission, using a streamlined operations concept. Taxi-tow and captive-carry tests will be conducted prior to the atmospheric-test series. Sponsored by the Defense Advanced Research Projects Agency (DARPA) with NASA participation, technical objectives are to: (1) mature Computed Air Data System/Remote Pressure Sensor technology, (2) manage energy during Terminal Area Energy Management/Heading Alignment Cone maneuvers, and (3) validate the aerodatabase. The X-37 Project began in 1999 under a cooperative agreement as an element of NASA's Future X Program and transitioned to a NASA Research Announcement under the Space Launch Initiative. In mid-2004, NASA transferred ownership to DARPA, with its heritage of performing high-risk, high-payoff research and development. NASA contributes technical expertise, including risk analysis and system integration. The Boeing Company is the prime contractor, with nationwide suppliers. This recent partnership exemplifies the synergy attainable when NASA Centers, other Government agencies, and industry work together toward a common goal - contributing to the knowledge base for U.S. exploration and other aerospace endeavors. The X-37 team represents a range of space transportation disciplines - from engineering to management. Some members have been with the project since its inception. All have gained priceless experience during the design, manufacturing, and testing of the ALTV, as well as through developing advanced orbital flight technologies, such as state-of-the-art Thermal Protection Systems and hot structures. Throughout this process, the X-37 Project team captures lessons that are directly applicable to other such efforts. The upcoming ALTV flights offer another dimension of data and first-hand experience that will prove invaluable to those designing new generations of reusable spacecraft. And ongoing technology developments will expand the aerospace knowledge base. Delivering prototype hardware is always a risky proposition. During the course of the X-37 effort, the team has experienced many challenging opportunities, delivering significant accomplishments and learning numerous lessons in the process. The ability to manage the risk landscape is key to overcoming obstacles, especially technical hurdles that are encountered in progressing hardware from design to flight. The approach to managing risk under this partnership is evolving but, in general, the team allocates resources to reduce the likelihood of severe-consequence risks, thus maximizing mission success and ensuring that the X-37 Project delivers value to its stakeholders. As the team sharpens its focus on operations, it continues to contribute knowledge to those who would undertake high-risk, high-payoff research and development and provides valuable experience to implement the exploration vision.

Turner, Susan↗

Fusion Energy Research at Idaho National Laboratory: Experimentation and Simulation to Support Safety and Rapid Technology Development

Research into fusion energy is growing rapidly, responding to a call for sustainable sources of energy to replace fossil fuels and mitigate climate change. Within the United States, at least, researchers are also responding to the “Bold Decadal Vision” proposed by the White House, seeking to have a commercially relevant fusion pilot plant deployed within a decade. Before this can become a reality, many Fusion Science & Technology (FS&T) gaps remain. For over 45 years, Idaho National Laboratory has been at the forefront of addressing these FS&T gaps in the context of fusion safety and technology via the operation of world-leading experimental facilities within the Safety and Tritium Applied Research (STAR) Facility. Here, INL focuses on the tritium fuel cycle, conceptual system design studies, risk assessment, waste management, and materials safety. Modeling and Simulation (M&S) has also been a component of this portfolio of research, but, early on, focused on individual systems. Since 2019, active development and research on integrated whole device modeling tools based on the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework has been undertaken. This has culminated in a MOOSE-based version of the Tritium Migration and Analysis Program (TMAP), an INL code historically focused on tritium permeation and trapping within fusion systems. More recently, INL Laboratory Directed Research and Development funds have been used to create the Fusion ENergy Integrated multiphys-X (FENIX) code focused on scrape-off layer plasma physics and the first wall of a magnetically confined fusion device. This talk will focus on an overview of INL activities in the FS&T research area, with a particular focus on recent M&S activities and results.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Vision 20/20: Magnetic resonance imaging-guided attenuation correction in PET/MRI: Challenges, solutions, and opportunities

Attenuation correction is an essential component of the long chain of data correction techniques required to achieve the full potential of quantitative positron emission tomography (PET) imaging. The development of combined PET/magnetic resonance imaging (MRI) systems mandated the widespread interest in developing novel strategies for deriving accurate attenuation maps with the aim to improve the quantitative accuracy of these emerging hybrid imaging systems. The attenuation map in PET/MRI should ideally be derived from anatomical MR images; however, MRI intensities reflect proton density and relaxation time properties of biological tissues rather than their electron density and photon attenuation properties. Therefore, in contrast to PET/computed tomography, there is a lack of standardized global mapping between the intensities of MRI signal and linear attenuation coefficients at 511 keV. Moreover, in standard MRI sequences, bones and lung tissues do not produce measurable signals owing to their low proton density and short transverse relaxation times. MR images are also inevitably subject to artifacts that degrade their quality, thus compromising their applicability for the task of attenuation correction in PET/MRI. MRI-guided attenuation correction strategies can be classified in three broad categories: (i) segmentation-based approaches, (ii) atlas-registration and machine learning methods, and (iii) emission/transmission-based approaches. This paper summarizes past and current state-of-the-art developments and latest advances in PET/MRI attenuation correction. The advantages and drawbacks of each approach for addressing the challenges of MR-based attenuation correction are comprehensively described. The opportunities brought by both MRI and PET imaging modalities for deriving accurate attenuation maps and improving PET quantification will be elaborated. Future prospects and potential clinical applications of these techniques and their integration in commercial systems will also be discussed.

61 RADIATION PROTECTION AND DOSIMETRY↗

Parallel algorithm for dominant points correspondences in robot binocular stereo vision

This paper presents an algorithm to find the correspondences of points representing dominant feature in robot stereo vision. The algorithm consists of two main steps: dominant point extraction and dominant point matching. In the feature extraction phase, the algorithm utilizes the widely used Moravec Interest Operator and two other operators: the Prewitt Operator and a new operator called Gradient Angle Variance Operator. The Interest Operator in the Moravec algorithm was used to exclude featureless areas and simple edges which are oriented in the vertical, horizontal, and two diagonals. It was incorrectly detecting points on edges which are not on the four main directions (vertical, horizontal, and two diagonals). The new algorithm uses the Prewitt operator to exclude featureless areas, so that the Interest Operator is applied only on the edges to exclude simple edges and to leave interesting points. This modification speeds-up the extraction process by approximately 5 times. The Gradient Angle Variance (GAV), an operator which calculates the variance of the gradient angle in a window around the point under concern, is then applied on the interesting points to exclude the redundant ones and leave the actual dominant ones. The matching phase is performed after the extraction of the dominant points in both stereo images. The matching starts with dominant points in the left image and does a local search, looking for corresponding dominant points in the right image. The search is geometrically constrained the epipolar line of the parallel-axes stereo geometry and the maximum disparity of the application environment. If one dominant point in the right image lies in the search areas, then it is the corresponding point of the reference dominant point in the left image. A parameter provided by the GAV is thresholded and used as a rough similarity measure to select the corresponding dominant point if there is more than one point the search area. The correlation is used as a final decision tool when there is still more than one point in the search area. If there is no dominant point in the search area of if the points in the search area are below a correlation threshold, then the dominant point in the reference image is occluded and can not be corresponded. The algorithm has been modeled, implemented and shown to be fast, robust and parallel. The parallelism is created from three main features: locality of the operators; a memory optimization scheme; and the ability to fully parallelize the extraction phase which is the most computational intensive task in the algorithm. The last feature is achieved by performing the extraction phase on the two images simultaneously.

Al-Tammami, A.↗

Vision 20/20: Positron emission tomography in radiation therapy planning, delivery, and monitoring

Positron emission tomography (PET) is increasingly considered as an effective imaging method to support several stages of radiation therapy. The combined usage of functional and morphological imaging in state-of-the-art PET/CT scanners is rapidly emerging to support the treatment planning process in terms of improved tumor delineation, and to assess the tumor response in follow-up investigations after or even during the course of fractionated therapy. Moreover, active research is being pursued on new tracers capable of providing different insights into tumor function, in order to identify areas of the planning volume which may require additional dosage for improved probability of tumor control. In this respect, major progresses in the next years will likely concern the development and clinical investigation of novel tracers and image processing techniques for reliable thresholding and segmentation, of treatment planning and beam delivery approaches integrating the PET imaging information, as well as improved multimodal clinical instrumentation such as PET/MR. But especially in the rapidly emerging case of ion beam therapy, the usage of PET is not only limited to the imaging of external tracers injected to the patient. In fact, a minor amount of positron emitters is formed in nuclear fragmentation reactions between the impinging ions and the tissue, bearing useful information for confirmation of the delivered treatment during or after therapeutic irradiation. Different implementations of unconventional PET imaging for therapy monitoring are currently being investigated clinically, and major ongoing research aims at new dedicated detector technologies and at challenging applications such as real-time imaging and time-resolved in vivo verification of motion compensated beam delivery. This paper provides an overview of the different areas of application of PET in radiation oncology and discusses the most promising perspectives in the years to come for radiation therapy planning, delivery, and monitoring.

60 APPLIED LIFE SCIENCES↗

The Role of Mobility Data Hubs in an Integrated Decarbonized Transportation Future

The landscape for connected mobility ecosystems is evolving rapidly as information and communication technologies lower the cost and complexity of connecting people to places, integrating transportation modes and collecting data regarding such movements. These developments have been key to unlocking new business opportunities, particularly through mobility services. While the mechanisms for data collection, processing, and transfer have made significant advances in the past decade, the broader landscape of mobility data architectures and data users remains largely unresolved. It is unclear as to whether the result will converge towards a framework that resembles a coherent quilt or a disjointed patchwork of competing visions. Initial approaches to mobility data collection and provisioning have been largely siloed - by mode or software - or held for exclusive use, however several key players are quickly realizing the need and opportunities enabled through integrated mobility data eco-systems, or mobility data hubs as referred to in this paper. As the business case for hosting mobility data hubs evolves, there is great uncertainty regarding their impact to either advance or exacerbate sustainable mobility (e.g., seamless connectivity across modes, decreased energy consumption and greenhouse gas emissions, etc.). Groups such as the United Nations and World Bank have identified data platforms as a key enabler of realizing environmental and social benefits. If designed with decarbonization in mind, we hypothesize that enhanced observability provided by these ever-expanding mobility data hubs can facilitate energy and emissions reductions that are otherwise limited by transactional barriers and knowledge asymmetry that is inherent to a more siloed approach. In this sense, integration of mobility data can help to create a competitive playing field where value is not determined by exclusivity of data, but rather the quality and uniqueness of a given service. The goals of this paper are to 1) identify key players and data architectures that are emerging in a service-based mobility market, 2) explore several use cases where mobility data hubs have enabled greater sustainability outcomes, and 3) discuss key issues that will need to be resolved to fully leverage emerging mobility data hubs towards a sustainable transportation future.

ADVANCED PROPULSION SYSTEMS,MATHEMATICS AND COMPUT↗

Institutional Transformation Version 2.5 Modeling and Planning.

Reducing the resource consumption and emissions of large institutions is an important step toward a sustainable future. Sandia National Laboratories' (SNL) Institutional Transformation (IX) project vision is to provide tools that enable planners to make well-informed decisions concerning sustainability, resource conservation, and emissions reduction across multiple sectors. The building sector has been the primary focus so far because it is the largest consumer of resources for SNL. The IX building module allows users to define the evolution of many buildings over time. The module has been created so that it can be generally applied to any set of DOE-2 ( http://doe2.com ) building models that have been altered to include parameters and expressions required by energy conservation measures (ECM). Once building models have been appropriately prepared, they are checked into a Microsoft Access (r) database. Each building can be represented by many models. This enables the capability to keep a continuous record of models in the past, which are replaced with different models as changes occur to the building. In addition to this, the building module has the capability to apply climate scenarios through applying different weather files to each simulation year. Once the database has been configured, a user interface in Microsoft Excel (r) is used to create scenarios with one or more ECMs. The capability to include central utility buildings (CUBs) that service more than one building with chilled water has been developed. A utility has been created that joins multiple building models into a single model. After using the utility, several manual steps are required to complete the process. Once this CUB model has been created, the individual contributions of each building are still tracked through meters. Currently, 120 building models from SNL's New Mexico and California campuses have been created. This includes all buildings at SNL greater than 10,000 sq. ft., representing 80% of the energy consumption at SNL. SNL has been able to leverage this model to estimate energy savings potential of many competing ECMs. The results helped high level decision makers to create energy reduction goals for SNL. These resources also have multiple applications for use of the models as individual buildings. In addition to the building module, a solar module built in Powersim Studio (r) allows planners to evaluate the potential photovoltaic (PV) energy generation potential for flat plate PV, concentrating solar PV, and concentration solar thermal technologies at multiple sites across SNL's New Mexico campus. Development of the IX modeling framework was a unique collaborative effort among planners and engineers in SNL's facilities division; scientists and computer modelers in SNL's research and development division; faculty from Arizona State University; and energy modelers from Bridger and Paxton Consulting Engineers Incorporated.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

The Center Master Plan For NASA Glenn Research Center

The Center Master Plan for NASA Glenn Research Center is a comprehensive survey of NASA Glenn's current facility assets and a vision of how we see the facilities will change over the next 20 years in order to support the changing NASA Mission. This Center Master Plan is a vital management tool used by all organizations for making near term decisions and in future planning. During the summer of 2004, I worked with Joseph Morris, the Chief Architect in the Facilities Division, on beginning this Center Master Planning Process. The previous Master Plan was completed by the Center in 1985 and contained general information on the background of the facility as well as maps detailing environmental and historic records, land use, utilities, etc. The new Master Plan is required for the Center by NASA headquarters and will include similar types of information as used in the past. The new study will provide additional features including showing how individual buildings are linked to the programs and missions that they serve. The Master Plan will show practical future options for the facility s assets with a twenty year look ahead. The Plan will be electronically retrievable so that it becomes a communications tool for Center personnel. A Center Master Plan, although required, is very beneficial to NASA Glenn Research Center in aiding management with the future direction of the campus. Keeping up-to-date information and future plans readily available to all of NASA Glenn will insure that future real property development efficiently and effectively supports the missions camed out and supported by the Center. A Center Master Plan will also facilitate coordination with Center supported programs, stakeholders, and customers. In addition, it will provide a basis for cooperative planning with local and other governmental organizations and ultimately ensure that future budgets include the Center program needs described in the plan. This will ensure that development plans are safe, practical, and cost effective. To properly formulate any future plans, background information and research are required. Before bringing in a consultant to tackle the three year process anticipated to prepare a Center Master Plan, my job was to gather information on different organizations contributions to the NASA mission, their facility needs, consistent trends they observe, etc. I conducted numerous interviews with personnel such as Directorate Representatives, Division Chiefs, Branch Chiefs, System Managers and Building Managers. I documented the information I received from them for future use. I used the information to create various color-coded maps layering the different data. This was done with the ending objective being to collect the information and place it in a database that will be linked with Aperture (a computer program that generates color-coded map layers from database information) and made electronically retrievable to the Planning Consultant, NASA personnel, program stakeholders, and other governmental agencies. My goal this summer was to gather information and ideas appropriate for use in NASA Glenn Research Center s Master Plan and organize them for future application by the Planning Consultant. By the end of the summer, after completion of my goal, I will utilize my knowledge and create an array of preliminary future plans for the facility that can be passed along as a guidance tool.

Bigach, Kristin M.↗

NASA Pilot-Engaged Expert Response Using IBM Watson Technology: Prototype Evaluation of Knowledge Retrieval System

NASA Langley Research Center and IBM have been investigating the use of IBM Watson technology in aerospace research and development. One application of Watson technology is the Pilot-Engaged Expert Response (PEER) use case. The PEER system is envisioned as an in-cockpit advisor that will act as a source of situationally-relevant information for pilots and other flight crew members to assist in decision making about real-time events and situations that arise in the course of aircraft operations. PEER will make available vast stores of knowledge and information quickly and directly, putting important informational resources where they are needed most. IBM has worked with NASA to develop an architecture and articulate a roadmap for the development of the PEER system. That vision is built around Watson Discovery Advisor (WDA) software solution, derived from IBM's Jeopardy!-winning automatic question answering system. PEER makes use of WDA's sophisticated question-answering capabilities as its core, adding important User Interface components and other customizations for the cockpit environment, including communication with flight systems and other external data sources. The development plan for PEER includes four development stages, with the current project constituting the first phase. In this project, a prototype instance of PEER was successfully adapted to the aviation domain, enabling users to ask questions about aviation topics and receive useful and accurate answers to these questions. Major tasks accomplished include the development of procedures for domain adaptation through automatic lexicon extraction from domain glossaries; generation of question-answer training data which was used to train the system; and assessment of the effectiveness of domain adaptation, which showed a dramatic improvement in the ability of the PEER system to answer domain-relevant questions. In addition, the vision for the PEER system was pushed forward by the articulation of a plan for the automatic enhancement of question-answering with contextual information. This initial phase focused on two main goals: 1) the targeted domain adaptation of the underlying WDA system to the aviation domain; and, 2) the design of the software systems needed to leverage flight-contextual data. Domain adaptation of the WDA system proceeds via three main activities: Domain data ingestion, lexical customization and model training. A textual corpus consisting of 1,147 individual documents with more than 7.5 million words of text was ingested into the system and this served as the basis of all further development. A domain lexicon of over 3,500 aviation-domain terms was semi-automatically generated from domain documents and used to train the system. In addition, a set of over 500 question-answer (QA) pairs relevant to the PEER use case was developed; these were used to train and assess the system. These important first steps established the basis for the PEER system. In addition, steps were taken towards the integration of the PEER system into the cockpit environment with the development of a functional design for the Contextual Data Augmentation (CDA) subsystem. This subsystem brings to bear contextual data to improve system responses. It has three main submodules: the Contextual Data Collection module, the Contextual Data Selection module, and the Contextual QA Augmentation module. These modules form a processing pipeline that addresses the problems associated with automatically integrating information from external resources into the knowledge-retrieval mechanism.

Machine learning↗

Artificial Intelligence, the Final Piece to the Counterforce Puzzle?

It is widely expected that artificial intelligence (AI) will disrupt and fundamentally transform many aspects of social, economic, and political life over the coming decades. As we are in the early stages of AI adoption in most fields, many discussions of its potential necessarily impact remain vague and superficial, including where national security is concerned. Nevertheless, some scholars have productively explored AI’s impact on the character of warfare, the balance of power among states, and human society itself. This article contributes to this growing body of literature by examining how this new technology might interact with one of the most prominent features of the post-World War II strategic environment: nuclear weapons. Specifically, it asks whether AI-driven improvements to intelligence, surveillance, and reconnaissance (ISR) capabilities, i.e., automated image recognition/computer vision, could enable an effective counterforce capability and thereby imperil nuclear deterrence and weaken first-strike stability. Since the advent of the nuclear age, analysts have been divided over the meaning of nuclear weapons for international politics. Proponents of the “nuclear revolution” argue that nuclear weapons, particularly a secure second-strike capability, which would allow a state to retaliate with nuclear force after suffering a first strike, satisfy a state’s security needs because they present a potential for unacceptable damage that would deter any potential challenger from even considering aggression. In their view, international politics in the shadow of nuclear weapons would be largely peaceful, competition would be relegated to the margins, and nuclear superiority would be meaningless. However, recent research suggests that this may not be the case. In fact, nuclear weapon states have sought to hold at risk the nuclear arsenals of their adversaries throughout the nuclear age. Still, no country has attacked another’s operational nuclear arsenal. That is because the requirements for a disarming strike are considerably higher than for damage limitation or retaliation against nuclear attack. A state trying to disarm its opponent would have to be certain to destroy, or at least disable, all of its adversary’s nuclear weapons. Even one operational nuclear warhead could inflict unacceptable damage in a retaliatory blow. The inability to be certain that a first strike could find and eliminate all enemy nuclear weapons has been the main roadblock for effective counterforce. As the number of battlefield sensors grows and ISR capabilities improve, however, this roadblock is crumbling. Some expect it to be fully overcome with the help of AI.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

Machine Learning for Predictive Performance Analysis in Charged Particle Beam Tools

Imaging methods driven by probes, electrons, and ions have played a dominant role in modern science and engineering. Opportunities for machine vision and AI that focus on consumer problems like driving and feature recognition, are now presenting themselves for automating aspects of the scientific processes. This proposal aims to enable and drive discovery in ultra-low energy implantation by taking advantage of faster processing, flexible control and detection methods, and architecture-agnostic workflows that will result in higher efficiency and shorter scientific development cycles. Custom microscope control, collection and analysis hardware will provide a framework for conducting novel in situ experiments revealing unprecedented insight into surface dynamics at the nanoscale. Ion implantation is a key capability for the semiconductor industry. As devices shrink, novel materials enter the manufacturing line, and quantum technologies transition to being more mainstream. Traditional implantation methods fall short in terms of energy, ion species, and positional precision. Here we demonstrate 1 keV focused ion beam Au implantation into Si and validate the results via atom probe tomography. We show the Au implant depth at 1 keV is 0.8 nm and that identical results for low energy ion implants can be achieved by either lowering the column voltage, or decelerating ions using bias – while maintaining a sub-micron beam focus. We compare our experimental results to static calculations using SRIM and dynamic calculations using binary collision approximation codes TRIDYN and IMSIL. A large discrepancy between the static and dynamic simulation is found that is due to lattice enrichment with high stopping power Au and surface sputtering. Additionally, we demonstrate how model details are particularly important to the simulation of these low-energy heavy-ion implantations. Finally, we discuss how our results pave a way to much lower implantation energies, while maintaining high spatial resolution.

47 OTHER INSTRUMENTATION↗

Machine Learning for Automated Metadata Assignment in Buildings: Cooperative Research and Development (Final Report, CRADA Number CRD-18-00767)

RealTerm Energy and NREL have identified a shared vision to evaluate opportunities to facilitate the organization and assignment of metadata to building control system (BCS) data via industry-informed machine learning (ML). Manual metadata assignment is labor intensive and costly, slowing down any Energy Management and Information System (EMIS) deployment in the building space. This project aims to develop methodologies to accurately assign this metadata and significantly decrease the level of effort associated with deploying EMIS. The objective of this project is to identify/design methodologies to assign metadata to HVAC control points automatically. The identified methodologies will be programmed in analytics algorithms so they can ingest a list of points and produce a detailed tagging following the Haystack classification nomenclature. To validate the efficacy of each methodology, tagging results will be compared utilizing a list of points extracted from RealTerm's building database-as well as data extracted from the NREL campus via the Intelligent Campus program-enabling testing against large datasets with real world challenges. The developed methodologies may leverage building manager/operator input on a limited basis to add context to the classifying algorithms. The partnership aims to advance global efforts in areas related to the DOE missions through improving operational performance of commercial buildings. It is well documented that buildings fall out of commission after they are occupied, wasting significant energy and incurring associated costs simply due to poor operational performance. Emerging EMIS technologies that perform continuous commissioning help to address this issue, yet integration of these systems can be labor intensive both for the technology vendor and the building owner/operator. This project will enable more efficient and cost-effective analytics for buildings, enabling improvement in building operations at lower cost points.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

A Machine Learning Approach to Quantitative Analysis of Enamel Microstructure from Scanning Electron Microscopy Images

Dental enamel, the outermost tissue of mammalian teeth, must withstand a lifetime of wear and cyclic contact. To meet this demand, enamel possesses a combination of high hardness and resistance to fracture, properties that are typically mutually exclusive. The impressive damage tolerance has been attributed largely to decussation of the enamel rods, the principal unit of its microstructure. As such, enamel is inspiring the design of next‐generation structural materials. However, quantitative descriptions of the decussated enamel rod microstructure remain limited due to challenges encountered in applying computed tomography and in acquiring quality images appropriate for traditional digital processing methods. Here, a machine learning segmentation method is applied to images of the enamel obtained using scanning electron microscopy to support quantitative analysis of the microstructure. A pretrained convolutional neural network is used to expand the input training image dataset to allow the training of a random forest classifier, which ultimately segments the image with a very small training set ( n = 3 images). A validation of this segmentation method is presented, in addition to its application to calculate relevant microstructural parameters for images of tooth enamel from selected mammalian species. The methodology applied here is equally applicable to other hard tissues.

36 MATERIALS SCIENCE↗

Project DarkStar: Vision for LLNL in 2030

DarkStar was a Strategic Initiative (FY2021-FY2024) to investigate applications of Artificial Intelligence (AI) and Machine Learning (ML) to scientific problems of complex hydrodynamics, shockwave physics and energetic materials. The research focused on physics and engineering design as a process that can be tremendously accelerated through merging AI with advanced physics simulation on exascale-class platforms, and to experimentally validate this revolutionary new approach through dynamic materials campaigns. A central thread of scientific inquiry was in the application of AI to enable human understanding of how to control hydrodynamic instability (which has impacts to areas such as inertial confinement fusion) via engineering features and time-dependent sources. Motivated by an unfinished line of research started by Dr. Johnny von Neumann, AI-enabled simulation approaches were developed that allowed DarkStar researchers to uncover several ground-breaking discoveries regarding hydrodynamic instability, including how to completely suppress Richtmyer-Meshkov instability (RMI). These S&T discoveries, along with other advances, have shown the way for an entirely new approach to time-dependent problems known as inverse design – the idea that complex systems can be developed directly from a final state that is to be achieved and resolve the initial design via satisfying several constraints simultaneously via AI/ML. Through experimental campaigns conducted across a wide range of facilities in the NNSA complex (the High Explosive Application Facility at LLNL, the Dynamic Compression Sector/Advanced Photon Source at Argonne National Lab, and Special Technologies Laboratory at MSTS) the radical new AI/ML approach to engineering complex material dynamics was verified, establishing a new field of study within the realm of shock physics. As advanced manufacturing capabilities continue to develop, the great importance of inverse design as a means to apply that technology effectively for NNSA missions will feature prominently over this decade. DarkStar has positioned NNSA as a world-leader in this newly emerging cross-disciplinary area of AI methods for advanced physics simulation and pioneered multiple novel approaches that have enabled the broader scientific community. By allowing us to see past the horizon, to 2030 and beyond, DarkStar has illuminated the vast potential of AI/ML to impact a wide range of new national security missions and, consequently, multiple areas of further research have already emerged across the NNSA and DOD complex.

42 ENGINEERING↗