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DOE OSTI2016

Reactor Safety for Beyond Design Basis Events

In the aftermath of the March 2011 multi-unit accident at the Fukushima Daiichi nuclear power plant (Fukushima), the nuclear community has been reassessing certain safety assumptions about nuclear reactor plant design, operations and emergency actions, particularly with respect to extreme events that might occur and that are beyond each plant's current design basis. The DOE has played a major role in the U.S. response to the Fukushima accident. Initially, DOE worked with the Japanese and the international community to help develop a more complete understanding of the Fukushima accident progression and its consequences, and to respond to various safety concerns emerging from uncertainties about the nature of and the effects from the accident. DOE R and D activities are focused on providing scientific and technical insights, data, analyses methods that ultimately support industry efforts to enhance safety. These activities are expected to further enhance the safety performance of currently operating as well as better characterize the safety performance of future U.S. nuclear power plants. In pursuing this area of R and D, DOE recognizes that the commercial nuclear industry is ultimately responsible for the safe operation of licensed nuclear facilities. As such, industry is considered the primary end user of the results from this DOE-sponsored work. The purpose of the Reactor Safety Technology R and D is to improve understanding of beyond design basis events and reduce uncertainty in severe accident progression, phenomenology, and outcomes using existing analytical codes and information gleaned from severe accidents, in particular the Fukushima Daiichi events. This information will be used to aid in developing mitigating strategies and improving severe accident management guidelines for the current light water reactor fleet. The RST R and D activities have evolved from an initial coordinated international effort to assist in the analysis of the Fukushima accident progression and accident response into the following three areas of current work. Fukushima Forensics and Examination Plans: This R and D is focused on providing insights into the actual severe accident progression at Fukushima through planning and interpretation of visual examinations and data collection of in-situ conditions of the damaged units as well as collection and analysis of samples from the damaged reactors. Severe Accident Analyses: This R and D is focused on analyses using existing computer models and their ability to provide information and insights into severe accident progression that aid in the development of severe accident management guidelines (SAMG) and/or training operators on these SAMGs; an auxiliary benefit can be informing improvements in these models. Accident Tolerant Components: This R and D work is focused on analysis or experimental efforts for hardware-related issues, including systems, structures and components with the potential to prevent core degradation or mitigate the effects of beyond-design basis events. This presentation will review current and future R and D activities in this reactor safety area. (author)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2025

Evolving the SpaceFOM: Lessons Learned and Future Development

For over fifty years, simulation has been a cornerstone technology for space missions. In many cases, one single simulator, developed by one team, is not enough to meet the simulation objectives. Astronaut crew training, docking, vehicle testing and complex mission planning all require several simulations to be connected and to interoperate, in order to achieve the goal of the simulation. To facilitate the development of interoperable simulations, an open standard, the SISO SpaceFOM was developed in collaboration between government, industry and academia. The standard was released in 2020. The initial release of SpaceFOM focuses on the core interoperability topics for space simulation: (i) specifying spatial data using several, well defined reference frames, (ii) managing time (including real-time, hard real-time, and as-fast-as-possible execution), (iii) composition of systems with subsystems such as space vehicles, and (iv) execution control, such as initializing, running, pausing, and terminating simulations. The initial release of SpaceFOM focuses on the core interoperability topics for space simulation: (i) specifying spatial data using several, well defined reference frames, (ii) managing time (including real-time, hard real-time, and as-fast-as-possible execution), (iii) composition of systems with subsystems such as space vehicles, and (iv) execution control, such as initializing, running, pausing, and terminating simulations. A number of requirements for the next version of SpaceFOM have been identified and proposed solutions are already under development. One key area is the object classes used for data exchange, where a richer set of classes have been proposed, based on practical use cases. For time management, the use of variable time resolutions and variable time steps is also proposed. For execution control, save and restore of simulations is considered as well as improved robustness, to handle failing components. A number of clarifications are also under consideration. This paper provides both a forward-looking view of the technical developments underway and a retrospective on the standard's evolution. The authors emphasize that prioritizing core challenges and delivering a practical first version were crucial steps. While not every idea was implemented in the initial release, the timely delivery of a functional standard enabled real-world applications, the growth of a user community, and the collection of valuable insights to shape future versions. The authors look forward to working with the next version of SpaceFOM together with the growing user community.

Simulation Interoperability↗

NASA NTRSDate not supplied

A Decision Support System for Extravehicular Operations Under Significant Communication Latency

Humanity hopes to perform extravehicular activities (EVAs) on the surface of Mars; however, several technical and operational challenges must first be overcome. Foremost among these challenges is managing a significant communication latency between Earth and Mars. Current and historical paradigms of EVA operations have required near-real-time communication between the crewmember(s) and Earth-based mission control. Nextgeneration operational paradigms for supporting deep space exploration will necessitate a distributed decision authority system, including delayed Earth-based mission control, the onplanet extravehicular crewmember(s), and intermediate mission support from intravehicular (IV) crewmember(s) within real-time communication range. This latter group is of particular interest: they must provide operations support without the plentiful resources available to mission control on Earth. Thus, NASA is developing the Personalized EVA Informatics and Decision Support (PersEIDS) software platform. PersEIDS is designed to bolster operator situational awareness and offload operator workload by automatically tracking and projecting consumables usage over an EVA timeline, providing real-time probabilistic safety assessments and recommending alternative EVA timeline(s) when the active timeline is not expected to be completed under consumables limits. The PersEIDS concept of operations, use cases, and models will be presented. A limited version of PersEIDS was demonstrated during a three-day-long study where each day a roughly four-hour-long simulated Martian EVA was performed in virtual reality at the NASA Johnson Space Center. The first day was a control trial without PersEIDS support; the second and third days represented different levels of decision support provided by PersEIDS to the IV crewmember acting as mission control. With PersEIDS support, the IV crewmember was able to manage the mission to completion faster and with more remaining consumables; however, additional testing is required to understand confounding factors, e.g., training bias.

Mars↗

NASA NTRSDate not supplied

PersEIDS: A Biomedical Decision Support System for Extravehicular Operations Under Significant Communication Latency

Mars↗

NASA NTRS2003

A Fast Implementation of the ISOCLUS Algorithm

Unsupervised clustering is a fundamental tool in numerous image processing and remote sensing applications. For example, unsupervised clustering is often used to obtain vegetation maps of an area of interest. This approach is useful when reliable training data are either scarce or expensive, and when relatively little a priori information about the data is available. Unsupervised clustering methods play a significant role in the pursuit of unsupervised classification. One of the most popular and widely used clustering schemes for remote sensing applications is the ISOCLUS algorithm, which is based on the ISODATA method. The algorithm is given a set of n data points (or samples) in d-dimensional space, an integer k indicating the initial number of clusters, and a number of additional parameters. The general goal is to compute a set of cluster centers in d-space. Although there is no specific optimization criterion, the algorithm is similar in spirit to the well known k-means clustering method in which the objective is to minimize the average squared distance of each point to its nearest center, called the average distortion. One significant feature of ISOCLUS over k-means is that clusters may be merged or split, and so the final number of clusters may be different from the number k supplied as part of the input. This algorithm will be described in later in this paper. The ISOCLUS algorithm can run very slowly, particularly on large data sets. Given its wide use in remote sensing, its efficient computation is an important goal. We have developed a fast implementation of the ISOCLUS algorithm. Our improvement is based on a recent acceleration to the k-means algorithm, the filtering algorithm, by Kanungo et al.. They showed that, by storing the data in a kd-tree, it was possible to significantly reduce the running time of k-means. We have adapted this method for the ISOCLUS algorithm. For technical reasons, which are explained later, it is necessary to make a minor modification to the ISOCLUS specification. We provide empirical evidence, on both synthetic and Landsat image data sets, that our algorithm's performance is essentially the same as that of ISOCLUS, but with significantly lower running times. We show that our algorithm runs from 3 to 30 times faster than a straightforward implementation of ISOCLUS. Our adaptation of the filtering algorithm involves the efficient computation of a number of cluster statistics that are needed for ISOCLUS, but not for k-means.

Memarsadeghi, Nargess↗

DOE OSTI2019

Constraints in the Remediation of Uranium Former Uranium Mining Sites - 19586

Uranium mining and milling legacy sites still exist all over the world. Financial constraints play a key role in constraining the remediation of these sites, but a series of other factors must also be considered. The IAEA has been assisting its Member States in moving forward their remediation projects, and more specifically the Central Asian region has received dedicated attention in this aspect. In association with other international organisations such as the European Commission and European Bank of Reconstruction and Development among others in the scope of the CGULS initiative (Coordination Group of Uranium Legacy Sites), a series of activities have been put in place to assist the countries in that region. Through its Technical Cooperation Department, the IAEA has also supported other Member States to manage their legacies. The IAEA CIDER Project (Constraints in Decommissioning and Environmental Remediation) in its first phase investigated possible constraint to implementation of both decommissioning and environmental remediation projects, and collected successful experiences and lessons learned from remediation projects all over the world. It started with a survey in which almost 30 Member States participated. Constraints were examined in three main areas: i) Institutional Aspects (regulatory and policy); ii) Technological and iii) Social. These results have been published in a dedicated report available in the IAEA web page (also called the CIDER Baseline Report). Under the request of participating Member States, a second phase of CIDER has been initiated with a more focused (action oriented) character. In this regard experts developed a framework for helping countries in need under two major areas: Strategy Development and Implementation and Stakeholder Engagement Support. Two other initiatives under CIDER Phase II consisted of the Enhanced Capacity Building in Environmental Remediation (the so-called School of Environmental Remediation) and a database depicting the sites in and in need of remediation in a global perspective. This paper is intended to present and promote some aspects of the methodology conceived in CIDER Phase II to help countries establish remediation strategies that will facilitate overcoming some of the barriers mentioned above. Furthermore, it aims to discuss aspects that might be improved in the assistance provided by international organisations to countries in need of developing remediation projects, pointing out to some of the documented causes for not achieving more robust results. It is expected that the combination of different tools to share good practices e.g. web-based tools, e-Learning, webinars in addition to face-to-face training events, may enhance the chances of successful implementation of remediation of legacy sites, including former uranium production sites. This initiative opens a wide avenue for cooperation while providing a platform to bring together practitioners, regulators, contractors, funding organisations to enhance the support to be provided. By working together, more effective and sustainable results can be achieved and remediation of such sites implemented in a timely, safe and cost-effective way. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2018

IAEA Initiatives that Integrate Stakeholders, Models and Decision Making - 18646

The International Atomic Energy Agency (IAEA) has initiated an effort to provide stakeholder support services as part of Phase II of the Constraints to Implementing Decommissioning and Environmental Remediation (CIDER II) programs. The intent of this sub-group of CIDER II is to provide members states support in areas of stakeholder communication, engagement and participation, with the goal of building trust in decommissioning and environmental remediation (D and ER) decisions for radioactive contamination and facilities. IAEA has also initiated a follow-on effort to address Modeling and Data for Radiological Impact Assessments (MODARIA II), which is aimed at enhancing the capabilities of Member States to simulate radionuclide transfer in the environment, and to ensure appropriate levels of protection from ionizing radiation. Working Group 1 of MODARIA II is focused on the integration of modeling and decision analysis. These two groups have complementary and supporting objectives, which provides an opportunity for collaboration and mutual benefit. The technical integrator is the approach of structured decision making, which follows a five-step process that combines stakeholder values with models through a technically defensible decision analysis. Briefly, the steps are: 1. Provide the context and decision context for the problem; 2. Obtain costs, values and concerns from stakeholders, to develop objectives that need to be achieved (e.g., improve human health, minimize costs, increase jobs); 3. Identify options that might achieve the objectives (e.g., cleanup, institutional controls, education); 4. Develop probabilistic models that connect options to objectives so that the objectives can be evaluated collectively (e.g., a fate and transport and risk model to address the objective of reducing human health risk, a cost model to address costs of taking action); and, 5. Model evaluation, including sensitivity analysis, to determine if an action can be taken with sufficient certainty, or if collection of more data is needed to decrease uncertainty before a decision is made. This approach also supports adaptive management as new data are collected. This approach clarifies the roles of stakeholders and subject matter experts in the decision-making process, where stakeholders specify values, and subject matter experts focus on the models that are needed to evaluate the options. It also provides a technically defensible, reproducible, transparent and traceable approach to all of the components necessary to properly solve the types of complex decision problems that plague radioactive waste management, decommissioning and environmental remediation. This paper introduces the major challenges to implementing such a broad scale of decision analysis, and approaches to overcoming those challenges, which include not only the technical challenges, but also the need to develop capacity in a range of skills not usually accommodated in environmental modeling and includes the need for the IAEA to address decision making in different countries, and with different cultures, religions, political affiliations, and languages. This paper explores the potential for IAEA to benefit from both the CIDER II and MODARIA II programs, and hence to develop a fully integrated approach to effective decision making for radioactive waste management and environmental remediation. Note that there are many people involved in these efforts at the IAEA, and credit belongs to those people for the progress that has been made to date. Our intent here is simply to introduce these IAEA programs at the WM2018 Symposium to provide our perspective and some awareness of some of the directions in which IAEA appears to be headed regarding stakeholder engagement, environmental and risk modeling, data science, and decision analysis. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

NASA NTRSDate not supplied

A Decision Support System for Extravehicular Operations Under Significant Communication Latency

Within the next few decades, humanity hopes to perform extravehicular activities (EVAs) on the surface of Mars; however, several technical and operational challenges must first be overcome. Foremost among these challenges is managing a significant two-way communication latency between Earth and Mars. Current and historical paradigms of EVA operations have required near-real-time communication between the crewmember(s) performing an EVA and an Earth-based mission control. Next-generation operational paradigms for supporting deep space exploration will necessitate a distributed decision authority system, including delayed Earth-based mission control, the on-planet extravehicular crewmember(s), and intermediate mission support from intravehicular crewmember(s) within real-time communication range. This latter group is of particular interest: they must provide operations support without the plentiful resources available to mission control on Earth. For this purpose, NASA is developing the Personalized EVA Informatics and Decision Support (PersEIDS) software platform. PersEIDS is designed to bolster operator situational awareness and offload operator workload by automating the tracking and projection of consumables usage over an EVA timeline, providing real-time probabilistic safety assessments of an EVA timeline given consumables constraints, and recommending alternative EVA timeline(s) when the active timeline is not expected to be completed under consumables limits. The PersEIDS concept of operations, use cases, and models will be presented. A limited version of PersEIDS was demonstrated during a three-day-long study where each day a roughly four-hour-long simulated Martian EVA was performed in virtual reality at the NASA Johnson Space Center. The first day was a control trial without PersEIDS support; the second and third days represented different levels of decision support provided by PersEIDS to the intravehicular crewmember acting as mission control. With PersEIDS support, the IV crewmember was able to manage the mission to completion faster and with more remaining consumables; however, additional testing is required to understand confounding factors, e.g. training bias.

Mars↗

DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Study of Regional Climate Change Final Technical Report

Anthropogenic activity of fossil fuel and biomass burning emissions increase tropospheric aerosol concentrations causing a direct cooling effect by scattering solar radiation and thus increasing the Earth’s albedo. In addition, the aerosols can act as cloud condensation nuclei to regulate cloud droplet number and size, thereby changing both the cloud albedo, and the cloud lifetime and precipitation. Thus, the aerosol climate effect concerns intricate aerosol-cloud-radiation-precipitation interactions which are recognized to introduce the largest uncertainties in using GCMs to project future regional climate changes. The climate effects of aerosols are significant in the land regions of the Northern Hemisphere, in particular the sulfate aerosols over East Asia where significant anthropogenic SO 2 emissions have been observed and concentrations of other aerosol species are also high. The objective of the present project is to advance “modeling aerosol-cloud-climate interactions” in two areas: the aerosol-cloud microphysics interactions and model simulations of aerosol effects on regional climate with focus on East Asia. To achieve the objective, in addition to analyzing relevant observations, the project conducts and diagnoses regional and global climate model simulations. Specifically, two collaborative tasks were carried out: Task-1: Incorporating a physics-based, two-moments cloud scheme developed by J. P. Chen/National Taiwan University (NTU) into regional models (WRF and WRF-Chem) to study “aerosol-cloud microphysics interactions over eastern China and the southeast Pacific” ; for latter region, CMIP5 models exhibit distinct biases in (weaker) cloud radiative effects and (warmer) sea surface temperature. The task is conducted in collaboration with J.-P. Chen, H.-W. Xue/Peking University, and H. Liao/Institute of Atmospheric Physics; and Task-2: Diagnosis of NCAR-CESM and WRF model simulations of “aerosol climate effects” focusing on changes of circulation, precipitation and clouds over East Asia. The task is conducted in collaboration with J.-P. Chen, and I-Chun Tsai & H.-H. Hsu/Academia Sinica. For each task, several topical studies were carried out and documented in eleven (11) articles (five for Task-1 and six for Task-2), published in: J. Atmospheric Sciences (2); J. Climate (1); Climate Dynamics (3); J. Geophysical Research (2); Geophysical Research Letter (1); Int. J. Climatology (1); and Advances in Atmospheric Sciences (1). In addition, Wei-Chyung Wang (the PI) continues serving as the U. S. Chief Scientist for the joint agreement, “Climate Sciences”, between the U. S. DOE and China Ministry of Sciences and Technology (Riches et al., 2010). In this capacity, he works closely with Michael Riches/DOE (the U. S. Manager) to coordinate the scientific exchanges (visiting scientists) between the two countries. Educations (graduate students) and trainings (postdoctoral research associates) are key elements of the research project.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1998

1997 NASA Academy in Aeronautics

The NASA Academy in Aeronautics at the Dryden Flight Research Center (DFRC) was a ten-week summer leadership training program conducted for the first time in the summer of 1997. Funding was provided by a contract between DFRC and Purdue University. Mr. Lee Duke of DFRC was the contract monitor, and Professor Dominick Andrisani was the principal investigator. Five student research associates participated in the program. Biographies of the research associates are given in Appendix 1. Dominick Andrisani served as Dean of the NASA Academy in Aeronautics. NASA Academy in Aeronautics is a unique summer institute of higher learning that endeavors to provide insight into all of the elements that make NASA aeronautical research possible. At the same time the Academy assigns the research associate to be mentored by one of NASA!s best researchers so that they can contribute towards an active flight research program. Aeronautical research and development are an investment in the future, and NASA Academy is an investment in aeronautical leaders of the future. The Academy was run by the Indiana Space Grant Consortium at Purdue in strategic partnership with the National Space Grant College and Fellowship Program. Research associates at the Academy were selected with help from the Space Grant Consortium that sponsored the research associate. Research associate stipend and travel to DFRC were paid by the students' Space Grant Consortium. All other student expenses were paid by the Academy. Since the Academy at DFRC had only five students the opportunity for individual growth and attention was unique in the country. About 30% of the working time and most of the social time of the students were be spent as a "group" or "team." This time was devoted to exchange of ideas, on forays into the highest levels of decision making, and in executing aeronautical research. This was done by interviewing leaders throughout the aerospace industry, seminars, working dinners, and informal discussions. The other 70% of the working time was spent working on the technical research project with the engineering mentors. Abstracts of those projects are given in Appendix 4.

Andrisani, Dominick, II↗

NASA NTRS2020

Lower Omo Food Security & Agriculture: Mapping Land Cover Change in Unprotected and Protected Areas in the Lower Omo River Valley, Ethiopia

Ethiopia is home to unique wildlife, biodiversity, and ecosystem services and, like much of the world, is undergoing population growth, development, and land use change. As a result, some biodiverse regions may be at risk of being urbanized, cultivated as agricultural plots, or losing access to water bodies that are essential for maintaining both terrestrial and aquatic life. The DEVELOP team partnered with the Ethiopian Wildlife Conservation Authority to quantify the land cover change between the years 1994, 2010, and 2018. The team utilized Ethiopia’s dry season (January to May) for training point development which was crucial in differentiating the level of greenness between the four land cover classes: water, natural vegetation, cultivated land, and bare ground. The study area covered 62,000 km2 of the Lower Omo River Valley and includes eight protected areas. Data from Landsat 5 Thematic Mapper, Landsat 7 Enhanced Thematic Mapper Plus, Landsat 8 Operational Land Imager, and Shuttle Radar Topography Mission were used to employ a Random Forest classifier and identify the four classes within Google Earth Engine. For each of the supervised classifications, overall model accuracy was between 83% (2018) and 89% (1994). Between 1994 and 2018, the Lower Omo Valley experienced an overall increase of 258% (919 km2) in water and 291% (7,188 km2) in cultivated areas, while experiencing a 9% (2,761 km2) decrease in natural vegetation and 19% (5,346 km2) bare ground. There was an increase in water and cultivated land and a decrease in natural vegetation and bare ground in unprotected areas and all protected areas. However, protected areas maintained natural vegetation better than unprotected areas and only experienced a 1% (59 km2) loss compared to a 10% (2,701 km2) loss in unprotected areas.

Food Security & Agriculture↗

NASA NTRS2014

Creating a Prototype Web Application for Spacecraft Real-Time Data Visualization on Mobile Devices

Mobile devices (smart phones, tablets) have become commonplace among almost all sectors of the workforce, especially in the technical and scientific communities. These devices provide individuals the ability to be constantly connected to any area of interest they may have, whenever and wherever they are located. The Huntsville Operations Support Center (HOSC) is attempting to take advantage of this constant connectivity to extend the data visualization component of the Payload Operations and Integration Center (POIC) to a person's mobile device. POIC users currently have a rather unique capability to create custom user interfaces in order to view International Space Station (ISS) payload health and status telemetry. These displays are used at various console positions within the POIC. The Software Engineering team has created a Mobile Display capability that will allow authenticated users to view the same displays created for the console positions on the mobile device of their choice. Utilizing modern technologies including ASP.net, JavaScript, and HTML5, we have created a web application that renders the user's displays in any modern desktop or mobile web browser, regardless of the operating system on the device. Additionally, the application is device aware which enables it to render its configuration and selection menus with themes that correspond to the particular device. The Mobile Display application uses a communication mechanism known as signalR to push updates to the web client. This communication mechanism automatically detects the best communication protocol between the client and server and also manages disconnections and reconnections of the client to the server. One benefit of this application is that the user can monitor important telemetry even while away from their console position. If expanded to the scientific community, this application would allow a scientist to view a snapshot of the state of their particular experiment at any time or place. Because the web application renders the displays that can currently be created with the POIC ground system, the user can tailor their displays for a particular device using tools that they are already trained to use.

Lang, Jeremy S.↗

DOE OSTI2026

EXERGETIC: De-Risking Next-Generation Resilient Geothermal Hybrids via At-Scale Evaluation Using Virtual Emulation Digital Twin Environment for Efficient Operation

The DOE-GTO-funded project, award number 5.1.2.12, entitled "EXERGETIC - De-risking Next Generation Resilient Geothermal Hybrids via at-Scale Evaluation Using a Virtual Emulation Digital Twin Environment for Efficient Operation," advances the solution to these challenges by developing and validating a geothermal co-emulation environment implemented at the National Laboratory of the Rockies (NLR)'s Advanced Research on Integrated Energy Systems (ARIES) platform. This framework enables the de-risking of next-generation geothermal and geothermal hybrid systems through high-fidelity modeling, real-time digital emulation, advanced control strategies, and techno-economic assessment. The project focused on geothermal hybrid configurations that integrate geothermal power plants with concentrated solar power and underground thermal energy storage, enabling enhanced efficiency, flexibility, and grid support capabilities. The main goal of this project was the development of a geothermal digital co-emulation environment to demonstrate the technical and economic value of geothermal hybrid systems and their contribution to grid stability and flexibility. The EXERGETIC framework combined physics-based models, controls, and real assets at ARIES, including digital real-time simulators (DRTS), a 20-MW-scale controllable grid interface (CGI), and a 2-MW conventional generator. Detailed transient models were developed for the key subsystems of a hybrid geothermal plant, including parabolic trough solar collectors, reservoir thermal energy storage (RTES), and a binary Organic Rankine Cycle (ORC) power plant. The ORC model explicitly captured thermal inertia and off-design operation and integrated control strategies to dynamically respond to electric load profiles. The models were validated against published experimental and numerical studies, demonstrating strong agreement and confirming the accuracy and robustness of the modeling approach. The resulting digital twin represents geothermal-solar-storage systems at multiple scales (1 MW to 100 MW) and enables realistic emulation of grid-connected operation. The control architecture allows the geothermal resource to provide stable baseload generation, while solar and stored thermal energy supply flexible, dispatchable support during periods of high demand or variable grid conditions. A key contribution of the EXERGETIC project is the demonstration that geothermal hybrid systems can be designed to be active grid assets rather than passive baseload generators. Using the ARIES platform, the digital twin was evaluated under multiple grid scenarios, including load following, voltage support at the distribution level, and frequency response at the transmission level. Results show that hybrid geothermal systems can respond effectively to dynamic grid conditions, providing inertia-like behavior, primary frequency support, and voltage regulation through coordinated control. In addition to the performance and grid services capability analysis of geothermal and hybrid geothermal systems, the EXERGETIC project also focused on scalability and techno-economic analysis of geothermal hybrid plants. In particular, for the scalability analysis, machine-learning (ML)-based surrogate models were trained using data generated from the geothermal digital twin under different grid-connected scenarios and plant capacities. These ML models demonstrated strong interpolation and extrapolation capabilities across plant sizes, accurately reproducing both steady-state and transient responses with very low errors. Regarding the techno-economic analysis, plant performance results were integrated with cost models for hybrid geothermal systems, and the levelized cost of electricity (LCOE) was used as the main economic metric to evaluate system performance across a range of system capacities, solar shares, solar multiples, and storage durations. Results indicate that economies of scale significantly reduce geothermal LCOE as plant capacity increases, with large-scale systems (25-100 MW) achieving substantially lower costs than small plants. Hybridization with solar thermal energy and storage further improves economic performance by increasing capacity utilization and enabling flexible dispatch. In addition, thermal storage plays a critical role in reducing LCOE by maximizing geothermal, solar, and stored energy resources. In summary, the results from this project demonstrate that geothermal hybrid systems represent a promising alternative for increasing the energy conversion efficiency of geothermal technologies, contributing to the preservation of geothermal resources, and supporting the transition of geothermal plants from traditional baseload resources into flexible, resilient, and cost-competitive energy conversion technologies.

15 GEOTHERMAL ENERGY↗

NASA NTRS2014

Simulation Technology at NASA

NASA Ames Research Center is home to several high-fidelity research flight and air-traffic control simulation facilities which, together with an experienced workforce, produce high-quality research data and findings that have proven to be applicable in the real world. These assets include the Vertical Motion Simulator (VMS), Crew Vehicle Systems Research Facility (CVSRF), Future Flight Central (FFC) air traffic control tower simulator, and several air-traffic control (ATC) simulators. The VMS combines a high-fidelity simulation capability with an adaptable simulation environment, enabling customization for numerous human-in-the-loop research applications. The distinctive feature of the VMS is its unparalleled large amplitude, high-fidelity motion capability. In over 30 years of continuous operation, the VMS has contributed significantly to the body of knowledge in a range of disciplines directly benefiting several aerospace programs and flight safety, including the design and development of flight control systems for the Joint Strike Fighter, Space Shuttle Orbiter, and rotorcraft. It continues to be used for researching new vehicle configurations, vehicle control and safety, transfer-of-training, etc., by NASA, other government agencies, and Industry.The CVSRF includes two motion-based flight simulators: a Boeing 747-400 full flight simulator and the reconfigurable Advanced Concepts Flight Simulator (ACFS). These simulators are primarily used to research air-traffic management concepts and procedures, advanced navigation and avionics concepts, and cockpit human factors. FFC is a full-sized control tower simulator with a 360-degree external field-of-view display system and reconfigurable system architecture. FFC and the ATC simulators are used for testing air-traffic management automation and decision support tools and demonstrate their feasibility in a realistic environment prior to technology transfer for implementation in the National Airspace System (NAS).To support integrated simulations and flight-tests for NASA's Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Project, NASA developed a distributed test environment incorporating Live, Virtual, Constructive, (LVC) concepts. Development of the software enabling the LVC is conducted primarily at the Distributed Simulation Research Lab (DSRL) at NASA Ames. The LVC components provide the core infrastructure supporting simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This provides the ability to conduct tests more efficiently by promoting the use of existing distributed assets. The LVC infrastructure was used in several human-in-the-loop simulations to evaluate acceptance of Detect and Avoid (DAA) advisories used by UAS pilots to maintain well clear of other virtual traffic and to negotiate maneuvers with air traffic control. It is currently being used to support testing of self-separation algorithms between unmanned and manned aircraft in live flight. Further simulations with more comprehensive air traffic scenarios mixing live and virtual aircraft is planned.In the current fiscal environment, maintaining and upgrading these high-fidelity simulation assets and retaining the skilled workforce necessary to meet future research needs is the primary non-technical challenge.

CVSRF↗

DOE OSTI2024

2019 Budget Request for the DOE Computational Science Graduate Fellowship (CSGF) Grant

The Department of Energy Computational Science Graduate Fellowship (DOE CSGF) is necessary to meet the continual challenging national workforce needs that arise as computational science and engineering problems continue to grow in scope and complexity. Computational science and engineering (CSE) is a multidisciplinary approach that uses scientific computing to solve practical problems methods and to supply technical tools across the scientific discovery spectrum. In particular, the DOE CSGF emphasizes high-performance computing (HPC) that enables CSE that advances science and engineering in directions important to the DOE and the economy in general. Over the past half-century, HPC has been an essential tool for DOE’s success. During this period, important missions, such as nuclear stockpile stewardship, have turned to HPC as an essential technology. Entire science disciplines, such as biology and cosmology, have been transformed through the augmentation of scientific observation via HPC. At government laboratories and in industry, DOE CSGF alumni are helping push traditional HPC boundaries while contributing to discoveries in high-energy physics, renewable energy, fusion-reactor design, additive manufacturing, nanomaterials for next-generation batteries and transistors, and turbine and advanced nuclear reactor modeling. In addition, HPC is used to address national health needs that will eventually point to cures both by helping cancer researchers manage and analyze huge troves of data, by simulating biological mechanisms, and by accelerating drug development — including continuing to rise to the challenge of pandemic-related research. A 2023 report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR office, “Can the United States Maintain Its Leadership in High-Performance Computing?” says of the Program, “The CSGF program provides a barometer for disciplines that will be of interest to future DOE computing.” An explosion in scientific and technological data has driven the need for increasingly sophisticated HPC to transform those data into scientific understanding. With access to more and more data and the proliferation of HPC, Machine Learning and Artificial Intelligence are experiencing a renaissance, complementing the now well-established use of computational simulation. Indeed, in its September 2020 subcommittee report on “AI/ML, Data Intensive Science and High-Performance Computing”, the DOE Advanced Scientific Computing Advisory Committee (ASCAC) explicitly called for a fellowship program to train computational and data scientists to tackle exascale and data-intensive computing challenges. This collaboration of empirical and theory-based modeling will increasingly inform federal policymakers whose decisions affect American society and future generations, and it requires highly skilled and intellectually agile computational scientists who can support the fast-moving DOE National Laboratory research environment. In fact, the DOE CSGF program has explicitly and consistently addressed this need.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Reinforcement Learning-Based Oscillation Dampening: Scaling Up Single-Agent Reinforcement Learning Algorithms to a 100-Autonomous-Vehicle Highway Field Operational Test

In this article, we explore the technical details of the reinforcement learning (RL) algorithms that were deployed in the largest field test of automated vehicles designed to smooth traffic flow in history as of 2023, uncovering the challenges and breakthroughs that come with developing RL controllers for automated vehicles. We delve into the fundamental concepts behind RL algorithms and their application in the context of self-driving cars, discussing the developmental process from simulation to deployment in detail, from designing simulators to reward function shaping. We present the results in both simulation and deployment, discussing the flow-smoothing benefits of the RL controller. From understanding the basics of Markov decision processes to exploring advanced techniques such as deep RL, our article offers a comprehensive overview and deep dive of the theoretical foundations and practical implementations driving this rapidly evolving field. We also showcase real-world case studies and alternative research projects that highlight the impact of RL controllers in revolutionizing autonomous driving. From tackling complex urban environments to dealing with unpredictable traffic scenarios, these intelligent controllers are pushing the boundaries of what automated vehicles can achieve. Furthermore, we examine the safety considerations and hardware-focused technical details surrounding deployment of RL controllers into automated vehicles. As these algorithms learn and evolve through interactions with the environment, ensuring their behavior aligns with safety standards becomes crucial. Here, we explore the methodologies and frameworks being developed to address these challenges, emphasizing the importance of building reliable control systems for automated vehicles.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2016

Fourteenth Exotic Beam Summer School EBSS 2015

The Fourteenth Annual Exotic Beam Summer School EBSS 2015 was held August 2nd - August 7th, 2015, and belongs to the series of summer programs aimed at educating future workforce in nuclear physics-related areas, mostly about the challenges of radioactive ion beam physics. Through these schools the research community will be able to exploit fully the opportunities created by the exotic beam facilities. These facilities in the US include CARIBU at ANL, the NSCL and the future FRIB laboratory as well as smaller-scale university laboratories. The skill set needed by the future workforce is very diverse and a fundamental understanding of theoretical, technical, computational and applied fields are all important. Therefore, the Exotic Beam Summer Schools follow a unique approach, in which the students not only receive lectures but also participate in hands-on activities. The lectures covered broad topics in both the experimental and theoretical physics of nuclei far from stability as well as radioactive ions production and applications. The afternoons provided opportunities for "hands-on" projects with experimental equipment and techniques useful in FRIB research. Five activities were performed in groups of eight students, rotating through the activities over the five afternoons of the school. The center of the activities was an experiment at the FSU tandem accelerator, measuring the angular distribution and cross section of the 12 C(d,p) 13 C transfer reaction, measured with a silicon telescope in a scattering chamber. The experimental data were analyzed by performing a DWBA calculation with the program DWUCK, and the resulting spectroscopic factors were compared to a shell model calculation. The other activities included target preparation, digital gamma-spectroscopy and modern neutron detection methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗