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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Deciphering exopolysaccharide functional specificity via visualization of chemical motifs that mediate microbial-microenvironment interactions in soil micromodels

Exopolysaccharides (EPS) are carbohydrate-based matrices of microbial biofilms that microbes use to adapt to their surroundings. Microbial EPS also promotes soil aggregate stability and contributes toward soil health by reducing erosion caused by continued land use. The chemical structure of EPS, even within single species biofilms, can vary greatly: from repeating units and branching patterns to the non-carbohydrate decorations. Despite the wealth of knowledge on EPS chemistry, many unresolved questions regarding the biological implications of EPS chemical variations remain. Here, we develop a mass spectrometry imaging toolbox that reveal how EPS chemistry enables microbial adaptation to different microenvironments such as specific moisture, soil-biofilm interfaces, and food source proximity. First, we developed emulated soil micromodels (ESMs), microfluidic channels that simulate the physical properties of soil, for biofilm formation in different microenvironments. Then, in order to map chemical features of EPS in these micromodels, we develop strategies for in-situ releasing of oligosaccharides using specific enzymes. Lastly, we optimized matrix-assisted laser desorption ionization (MALDI) mass spectrometry imaging (MSI) to spatially analyze the released compounds in ESMs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Vitrification of Hanford Tank Wastes for Condensate Recycle and Feed Composition Changeover Testing (Rev. 1)

During the vitrification of Hanford Site nuclear waste at the Waste Treatment and Immobilization Plant (WTP), which is a part of the safe and efficient retrieval, treatment, and disposal mission of the U.S. Department of Energy Office of River Protection, the offgas condensate generated from the waste-to-glass conversion is currently planned to be concentrated by evaporation in the Effluent Management Facility (EMF). This concentrated condensate can then be recycled back to the incoming waste and vitrified. To test the recycle process, an apparatus was designed and built to mimic the EMF evaporator and was then used to concentrate a volume of condensate produced during the vitrification of a sample of Hanford tank 241-AP-107 (referred to herein as AP-107) waste in a continuous laboratory-scale melter (CLSM). The concentrated condensate was added to an additional sample of AP-107 waste, to mimic one round of the recycle process, and the combined solution was vitrified, producing a second round of recycle condensate. In the current study, the EMF test apparatus was used to concentrate the second-round recycle condensate under evaporation conditions (at 45 °C and 1.4 psia) designed to emulate EMF operation. The condensate was successfully concentrated by a factor of ~10 while retaining over 95 % of the technetium-99 (99Tc), Cs, and I inventories in the concentrate. Another portion of AP-107 waste was retrieved by Washington River Protection Solutions, LLC (WRPS) and transferred to Pacific Northwest National Laboratory (PNNL), where it was pretreated and then combined with the second-round recycle AP-107 condensate concentrate and glass-forming chemicals (GFCs) to form the two-time recycle AP-107 melter feed, approximating a second round to the recycling action to be performed at the WTP. A portion of AP-105 waste was also retrieved by WRPS and provided to PNNL for pretreatment and combining with GFCs to form AP-105 melter feed. The two-time recycle AP-107 and AP-105 melter feeds were processed consecutively in the CLSM. The CLSM run proceeded for 13.63 hours, producing 9.70 kg of glass for an average glass production rate of 1464 kg m 2 d -1 during the two-time recycle AP-107 feed charging and 1568 kg m 2 d -1 during the AP-105 feed charging. The rate during AP-107 charging was essentially equivalent to the rate when processing no-recycle AP-107 feed and lower than that achieved when processing one-time recycle AP-107 feed. However, all rates were within the potential range of variability when processing melter feeds with similar composition in the CLSM. Likewise, the rate during AP-105 charging was higher than the previous rate processing AP-105, but within the potential CLSM range. The cold-cap characteristics changed from the typically thin AP-107 cold cap to a foamy-edged cold cap as previously seen with AP-105 shortly after transitioning to the AP-105 melter feed. The glass produced during the CLSM run was within 10 % of its target composition for the primary glass components. The CaO and Li 2 O targets varied by more than 1 wt% between the two-time recycle AP-107 and AP-105 glass targets and it took about 2 turnovers of the CLSM glass inventory to reach a relative chemical steady state in the glass for CaO and Li 2 O after the melter feed inputs were switched.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

Fission with Exotic Nuclei (Full Technical Report)

Despite its importance for stockpile stewardship or nuclear forensics, data on fission is fragmentary: most experiments have only been performed on stable actinide nuclei and are often incomplete, and theoretical simulations often contain many parameters hard to constrain, which results in large uncertainties. Consequently, nuclear libraries have large gaps for important isotopes. The goal of this project was to prepare to take advantage of the unprecedented yields of radioactive isotopes at the upcoming DOE Facility for Rare Ion Beams and to leverage recent progress in the field of machine learning to develop a comprehensive program of fission studies that could address some of the most pressing problems in nuclear fission over the next several decades. Our project leveraged synergies between experimental nuclear physics, nuclear theory, and data science expertise at LLNL. On the experimental front, our main objective was to develop in-house expertise for inverse kinematics reactions with relativistic beams as well as to field and test new detectors to perform correlated measurements of fission properties. On the theoretical side, the objective was to develop a novel, high-fidelity and scalable approach of fissionfragment calculations based on emulating computationally expensive, quantum-mechanical calculations of nuclear properties with deep neural networks.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Fission with Exotic Nuclei (Abbreviated Report)

Nuclear fission is a key mechanism involved in the synthesis of heavy elements in the Cosmos and is the primary explanation for the stability of superheavy elements. Nevertheless, our knowledge of fission remains extremely fragmented. Most experiments have been conducted only on a tiny number of stable actinide nuclei and are often incomplete, leading to gaps in our basic understanding of the process. For many radioactive isotopes, basic fission data such as the charge or mass distribution of the fragments is unknown. These gaps cannot always be filled by simulation alone. Common fission models contain too many free parameters and lack predictive power. In contrast, the fundamental theory of fission under development at LLNL is much more predictive, but its current computational cost is too high to be used extensively for data evaluations. A unique window of opportunity to resolve these limitations has recently opened: the U.S. nuclear science community is ramping up major experimental programs at the Facility for Rare Isotope Beams (FRIB, the DOE flagship facility in low-energy nuclear science), and techniques from machine learning have shown great potential to simplify the use of a fundamental, quantum-mechanical theory of fission. This project has two components. On the experimental side, we acquired and deployed at the HIGS facility a new dual Frisch-Grid ionization chamber to measure correlated fragment-mass, kinetic energy, and angular distributions of fission fragments from induced fission. This new device was used to perform measurements of charge, mass and total kinetic energy of fission fragments in the photofission of 238 U and eight gamma-ray beam energies between 6.2 and 13 MeV, which allowed extracting high-precision independent yields for this reaction. The device was also used to perform measurements of the same quantities in the neutron-induced fission of 234 U with monoenergetic beams of energy between 5 and 8 MeV. In parallel, we collaborated with a team at Commissariat à l’énergie atomique et aux énergies alternatives (CEA) to perform a series of measurements of fission yields in inverse kinematics for the two isotopes of 236 U and 240 Pu. The experiment took place at the Grand Accélérateur National d’Ions Lourds in France in June 2023. The deployment of the VAMOS spectrometer with a new array called PISTA allowed determining the excitation energy of the fissioning system within 1 Mega-electronvolts. The second component of the project involved using deep neural networks to build fast and reliable emulators of our current fission models. In an invited paper published in Frontier in Physics, we showed that autoencoders could successfully compress nuclear wavefunctions in nuclear density functional theory. We achieved a dimensionality reduction of the order of two orders of magnitude while keeping the error in the total energy to less than 0.01%. In a second paper submitted to Physical Review Letters in June 2023 with our collaborators at CEA, we showed that variational autoencoders can learn the collective degrees of freedom driving the fission process.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2023

Rapidly Viable Sustained Grid

Rapid recovery of power flow, possibly after a blackout, is a crucial need arising in scenarios that are increasingly becoming more frequent; here, solutions for rapid viability of power while the grid is being restored are urgently needed to keep critical infrastructure (CI) online. Increasingly, after the initial recovery phase, sustenance of reliable power requires assistive services to the grid for long periods of time. Even though the need is urgent, there is only sparse effort present toward a comprehensive framework/strategy for making power rapidly viable with an emphasis on sustained grid ancillary services; which is the focus of this proposal. The proposed concept envisions four phases. In the first phase, when a large portion of power is disrupted (see Figure 1(a)), emphasis is on bringing CI online with the objective of maximizing the time horizon of power viability using resources available at the CI. In the second phase (Figure 1(b)), neighborhood resources are tightly coordinated that forms the CI’s central-core (CC) to provide guaranteed viability of CI over a longer horizon. In the third phase, self-organizing power networks are expanded in a distributed layer supporting the central-core (Figure 1(c)). In the fourth phase, separate CI-networks coalesce and are controlled in a coordinated fashion to provide grid ancillary services (AS), such as primary frequency control and enhancing grid resiliency. With time, system efficiencies and penetration of renewables increase, while the time-horizon of guaranteed sustenance of CI is maximized. Under proposed work, the concept will be instantiated with a focus on medical centers as CI. Comprehensive power hardware in the loop strategies and emulated field tests will guide and validate devised solutions. A strong T2M effort to commercialize resulting technology is outlined. The proposed technology will be transformative for the grid. It will fundamentally change the way large contingencies are managed where power systems and critical infrastructure transition from being fragile to being robust using intelligent, self-organizing control for coordinating resources, enhanced resiliency and use of sustainable energy sources.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

A self-certifying FPGA based pixel readout chip test system for CMS ETL upgrade

A flexible self-certifying pixel readout test system for testing the Endcap Timing Read-Out Chip (ETROC) has been developed for the CMS MIP Timing Detector (MTD), part of the upgrade for the HL-LHC. The system includes an FPGA-based (Xilinx KC705) test system that can take data from an emulator or up to four ETROC boards in telescope mode. A python based DAQ system simplifies configuration, calibration, and testing of the chips. The system provides an efficient & reliable solution for testing ETROC (ETROC2 since April 2023 with 16x16 pixels, full-size, full functionality prototype) and can be extended to other readout chips with similar architecture (AIDA integration is currently being explored).

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Studying light–matter interactions and energy transfer at the nanoscale with a trapped–ion quantum computer

A grand challenge in materials sciences is to control energy transduction, transfer and dissipation at the nanoscale. Meeting this challenge requires progress in nanoscale fabrication and synthesis, but equally importantly, also progress in our understanding of how nanoscale structure influences these phenomena. Although detailed quantum mechanical models exist that capture the structure and physics of materials and molecular species, solving these models to calculate the relevant emergent phenomena, which often occur over vastly different time and energy scales, has so far proven difficult. In this project, we studied how to model energy transfer and light-matter interactions using trapped-ion quantum computers. Our studies focused on emulation of critical processes in natural systems, such as the well-known photosynthetic pigment-protein complexes responsible for capturing and transducing light to electrons. In addition, we implemented proof-of-principle experiments demonstrating that such calculations are possible on trapped-ion quantum computing hardware. These studies and experiments pave the way to investigate energy transport and transduction in a detail not accessible with classical computing technologies. We expect that our studies will allow to extract important guiding principles on how to improve the efficiency of nano-scale light-harvesting and transduction devices.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Domain Adaptation for Measurements of Strong Gravitational Lenses

Upcoming surveys are predicted to discover galaxy-scale strong lenses on the order of 10\textsuperscript{5}, making deep learning methods necessary in lensing data analysis. Currently, there is insufficient real lensing data to train deep learning algorithms, but the alternative of training only on simulated data results in poor performance on real data. Domain Adaptation may be able to bridge the gap between simulated and real datasets. We utilize domain adaptation for the estimation of Einstein radius ($\Theta_E$) in simulated galaxy-scale gravitational lensing images with different levels of observational realism. We evaluate two domain adaptation techniques - Domain Adversarial Neural Networks (DANN) and Maximum Mean Discrepancy (MMD). We train on a source domain of simulated lenses and apply it to a target domain of lenses simulated to emulate noise conditions in the Dark Energy Survey (DES). We show that both domain adaptation techniques can significantly improve the model performance on the more complex target domain dataset. This work is the first application of domain adaptation for a regression task in strong lensing imaging analysis. Our results show the potential of using domain adaptation to perform analysis of future survey data with a deep neural network trained on simulated data.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

5G Open Core Hardware

This project aimed to develop an experimentation platform for 5G using commercially available software, firmware and hardware that is being used to standup experimental 5G Stand Alone (5G SA) networks. The commercially available systems are not natively set up for experimentation design and this work would develop a system capable of performing experimentation. This platform will need to be easily reconfigurable for each experiment, capture all configurations across key equipment before running an experiment, record and store the data during an experiment and be able to recall settings and data from prior experiments while being able to emulate and replay prior experiments. The project began in the process of creating this platform.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Advanced Reactors Integrated Energy System: Thermal Energy Storage Island Design

The main topic of this research is integrated energy systems (IES) designed for pairing industrial thermal energy loads with advanced reactors (ARs). The Idaho National Laboratory (INL) Crosscutting Technology Development IES program and the National Reactor Innovation Center (NRIC) are seeking to develop, design, and construct an AR-IES demonstration platform that couples the thermal output from an AR operating at the INL/NRIC Demonstration of Microreactor Experiments (DOME) test bed in the Experimental Breeder II dome to a variable capacity load emulator (i.e., air-cooled radiator) and sensible thermal energy storage (TES) via a molten salt thermal energy transfer fluid. In the rapidly evolving landscape of energy supply and distribution, flexibility has emerged as a prized attribute, surpassing the traditional notions of stability and baseload generation capability. This shift in priorities is particularly evident in the context of nuclear power plants (NPPs), where adaptability over constant output is becoming more important. As our energy infrastructure and resources embraces the rise of distributed energy generation, the inherent variability in net demand continues to grow. Moreover, the use of nuclear energy as a source of heat for decarbonizing the industrial sector is becoming a very pressing topic. In such environment, advanced NPPs are poised to enter a more competitive energy market, delivering both, flexible electricity and heat. This shift motivates the exploration of TES systems, designed to empower NPPs with nimble responsiveness to market fluctuations, flexible heat delivery capabilities, and redefine their role in the energy field. TES systems offer the unique advantage of storing nuclear energy in its original form as heat, thereby affording unparalleled flexibility in its subsequent utilization.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense (Final)

In the world of ever-advancing technology, Autonomous Systems (AS) find extensive application, bolstering functionalities of critical infrastructures such as nuclear power plants. These systems, however, are increasingly becoming a target for nefarious activities, namely through inference attacks, trojan attacks, and adversarial reprogramming. This paper delves into a comprehensive exploration of machine learning (ML)-driven autonomous control systems within advanced nuclear reactor designs, revealing the vulnerabilities and proposing strategies for defense against potential cyber-attacks. Advanced cyber-attacks against critical infrastructure and the energy sector are becoming more common. With the invention of autonomous control systems (ACS) within advanced nuclear reactor designs, system designers, reactor operators, and regulators must consider cybersecurity during the design and operational phases. This article provides a cyber threat assessment of machine learning (ML)- based digital twinning (DT) technologies in the context of advanced reactor ACS. A cyber-physical testbed was created to emulate nuclear reactor digital instrumentation and controls (I&C) and act as a basis for the ACS. The ACS was designed as two plant-level DTs predicting reactor malfunctions and determining control actions and two component-level DTs responsible for classifying component states and forecasting component inputs and outputs (I/O). Two duplicate ACS designs– one using a traditional ML framework and one using an automated ML (AutoML) framework– were created and tested against cyber-attacks on training data, real-time process data, and ML model architectures to determine their respective qualitative cyber-risk in terms of likelihood and impact. Both frameworks showed similar cyber-resilience against training, real-time, and ML architecture attacks, proving that neither is inherently more secure. Recommended safeguard and security measures are posed to system designers, reactor operators, and regulators to maintain the cybersecurity of ML-based DT technologies such as ACS, prompting a holistic view of shared responsibility for maintaining cyber-secure ML-based systems. As global reliance on generation III reactors begins to be critically assessed, the evolution towards advanced reactor systems utilizing digital instrumentation and controls (I&C) becomes not merely preferable, but essential. The integration of semi and fully autonomous control systems (ACS), powered by digital I&C and machine learning (ML)-based digital twinning (DT) technologies, emerges as a potent strategy to mitigate operations and maintenance costs, thereby enhancing the economic feasibility of novel reactor designs. However, with a staggering 500% and 380% increase in cyber-attacks reported against the energy sector by the United States Department of Energy (DoE) and the European Union respectively, a surge in cyber vulnerabilities specifically targeting the nuclear industry has been 2 markedly observed. Notable incidents, such as the W32.Ramnit spyware infiltration at the Gundremmingen nuclear power plant in Germany and the Dtrack spyware intrusion at the Kudankulam nuclear power plant in India, while not directly compromising core industrial control systems (ICS), underscore a compelling necessity to fortify cybersecurity protocols in safeguarding reactor systems against increasingly adept digital adversaries. In light of this, our investigation extends beyond conventional cybersecurity parameters, diving into the intricate web of potential vulnerabilities woven into ML-based DTs and ACS in advanced reactor systems. A crafted cyber-physical testbed and preliminary ACS were devised to act as a mirror, reflecting potential configurations of advanced reactor control designs. Moreover, this study is intertwined with a scrutinization of ML models, developed either through conventional, manually tuned methodologies or via automated means through AutoML, probing into their cyber-risk profiles within operational technology (OT) environments. Expanding on this, two distinct ACS blueprints were forged – one navigating through the corridors of traditional ML and the other traversing the path of AutoML – in an effort to holistically encapsulate the considerations pivotal to ML-based DT control system design. Employing the SANS Institute Industrial Control System (ICS) Kill Chain and the MITRE ATT&CK Tactics, Techniques, and Procedures (TTP) framework, a structured analysis was conducted, launching three targeted attacks against the training dataset, real-time dataset, and ML models, therein dissecting the potential cyber-attack implications against both ML frameworks within an ACS milieu. It is essential to note that three distinct categories of attacks were conducted against both ACS configurations, each encompassing three distinct ML-based DTs, cumulating in a total of 18 varied attacks. This exploration extends into the realms of Autonomous System Inference, Trojan, and Adversarial Reprogramming Attack and Defense, unraveling vulnerabilities, and opportunities for fortified defenses against such intrusions, particularly where ML-driven technologies, and by extension, ACS, are deployed. Final recommendations, articulated through a lens of security, safeguard, and implementation considerations, are presented for both traditional and AutoML models, anchoring upon the existing knowledge landscape and ML-based DT modeling for ACS, and are offered as a beacon to guide the nuclear industry through the intricate cybersecurity challenges that lie ahead.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Photovoltaic Analysis and Response Support (PARS) Platform for Solar Situational Awareness and Resiliency Services

The project's primary objective is to develop a digital-twin based Photovoltaic (PV) Analysis and Response Support (PARS) platform, which aims to provide real-time situational awareness and optimal response plans. This platform is designed to enhance the performance of hybrid PV systems, making them competitive with or even superior to conventional generation resources. The PARS platform enabled the project team to develop and evaluate an extensive suite of grid support functionalities for the hybrid PV systems to enhance grid performance, across key areas including visibility, dispatchability, security, resilience, and reliability. Given the global push toward achieving 100% clean energy by 2035, there is a significant increase in the integration of inverter-based resources (IBRs) throughout the energy grid. Effectively managing the inherent variability and uncertainty associated with IBRs is crucial for ensuring cost-effectiveness, reliability, and security in both the main grid and islanded microgrids. Constrained to a limited array of IEEE test systems or standard feeder models, traditional IBR modeling struggles to assimilate new field data, accurately reflect system dynamics, and adapt to the evolving energy landscape. In our project, we embraced a Digital Twin (DT) strategy for crafting the PARS platform. A digital twin acts as a precise virtual counterpart of a physical system, built on historical data and continuously honed with real-time insights. This enables the high-fidelity DT to accurately mirror current system operations and forecast future scenarios. Consequently, the PARS platform becomes an ideal environment for testing and refining monitoring, control, power, and energy management algorithms designed to boost hybrid PV system performance. The defining feature of the PARS platform, distinguishing it from other advanced simulation tools, is its exceptional adaptability. This is achieved by employing actual network topologies and utilizing real-time field data for fine-tuning and calibration, ensuring a close emulation of real-world conditions. The project deliverables include: 1) High-fidelity IBR models and tools for real-time parameterization, utilizing real-time field measurements to refine IBR models for enhanced accuracy and performance; 2) Grid-forming and Grid-following capabilities to deliver resilience services, including blackstart, voltage and frequency support, cold-load pick-up, power reserves, and three-phase load balancing across grid-connected and microgrid settings; 3) Machine learning-based forecasting tools and methods for generating synthetic data and topologies, creating diverse and realistic simulation environments for evaluating varied operational scenarios; 4) Advanced microgrid power and energy management algorithms for optimizing the integration and operation of PV, storage, and demand response resources within both feeder and community scales. The power grid data sets are provided by four utility companies in North Carolina and the New York Power Administration. Acting as industry advisors, our industry partners communicated stakeholder needs and regulatory standards to the research teams, aiding technology transfer by incorporating the developed methodologies into their daily operations. This collaboration ensures that the PARS platform, functioning as a power system digital twin, enhances our understanding of IBR dynamic behaviors and enables the development and evaluation of IBR control functions that match or exceed the capabilities of conventional synchronous generators.

14 SOLAR ENERGY↗
DOE OSTI2021

Experimental Capabilities to Support Design, Development and Demonstration of Microreactors

This work provides a summary of selected experimental capabilities being developed to support non-nuclear demonstration of microreactors under the U.S. Department of Energy’s (DOE’s) Microreactor Program. Major capabilities include the single primary heat extraction and removal emulator (SPHERE) and the microreactor agile non-nuclear experimental test bed (MAGNET). The SPHERE facility allows for controlled testing of the steady-state and transient heat rejection capabilities of a single heat pipe using electrical heaters that simulate nuclear heating. The facility is capable of monitoring axial temperature profiles along the heat pipe and surrounding test articles during startup, steady-state operation, and transients. Instrumentation includes non-contact infrared thermal imaging, surface thermocouples, spatially distributed fiber-optic temperature and strain sensors, electrical power meters, and a water-cooled, gas gap calorimeter for quantifying heat rejection from the heat pipe. The facility can be operated under both vacuum and inert-gas conditions.

42 ENGINEERING↗
DOE OSTI2024

Enabling a Physical Twin for Control Methods Evaluation

Advanced nuclear reactors play an important role in the energy future of the United States and the rest of the world. They are designed and operated based on a different model than that of the current operating fleet, thus enabling deployment in remote locations and allowing for safe semi-autonomous or autonomous operations. Such characteristics require the development of a new reactor control paradigm. A significant factor in the development of control technologies and methods is integration of the various technologies and methods with each other and with hardware (both reactor system hardware and control hardware). A recent workshop on control of advanced reactors identified the lack of a flexible, expandable software/hardware infrastructure to enable such integration as a key gap. A previous phase of the current effort involved developing and demonstrating the Control and Optimization Modular Modeling Application for Nuclear Deployment (COMMAND) platform, which is capable of integrating autonomous-control-enabling technologies and methods, without the constraints imposed by existing software solutions. This platform will enable advanced reactor developers to deploy and test any developed technologies and methods by employing a common framework, and to couple them with their own models and hardware. The present phase of this effort entails using the Microreactor Automated Control System (MACS) platform, which was developed by the U.S. Department of Energy (DOE) Microreactor Program, to serve as a control method testbed. MACS can be used by advanced reactor developers to integrate their control related research activities with any reactor system. For the present effort, MACS was customized to mirror Idaho National Laboratory (INL)’s Microreactor Applications Research Validation and Evaluation (MARVEL) microreactor, and COMMAND was leveraged to enable MACS to emulate the physics of MARVEL, thus positioning MACS as a physical twin of MARVEL.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Cognitive Grid Optimization

This project laid the foundation to include a security constrained economic dispatch (SCED) within one of the leading simulators which is used to train system operators who keep the lights on for over 150 million people in USA. The SCED is designed to handle very high penetrations of renewable generation as well as battery storage. As a follow on the this project, a Trusted Source Model of the North American Electric Interconnections will be built from public GIS data. The various North American Markets will be emulated. This Trusted Source Model will grow the software developers for the next generation of power applications that are needed to transition to all green generation while everything is electrified.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Bridging the gap between experiments and simulations using machine learning

The physics of inertial confinement fusion is rich and complex. Simulation codes that are used to design experiments are computationally expensive and lack the predictive capability required for extensive parameter exploration in search of a high-performing design for laser direct drive. In this work we use deep learning to build a fast emulator of experiments. To facilitate the development of the deep-learning model, an autoencoder is used to reduce the dimensionality of the input space. Two deep learning models are developed. One model is trained on a vast array of simulation data and is subsequently calibrated to expensive and limited experimental data using a technique known as “transfer learning.” The other model is trained on a statistical model and is subsequently calibrated using experimental data. A comparative study of the two predictive models is carried out. The models potentially reproduce key experimental observables with high accuracy and unprecedented inference times relative to those achieved with simulation codes. These models facilitate rapid exploration of a high dimensional input parameter space.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Data vs. MC Comparison of Light Signal from Cosmic Rays in the ICARUS Detectors

Currently, the ICARUS-T600 liquid argon TPC is collecting data exposed to Booster Neutrino and Numi off-axis beams within the SBN program at Fermilab. A light detection system, based on PMTs deployed behind the TPC wire chambers, is in place to detect vacuum ultraviolet photons produced by ionizing particles in LAr. This system is fundamental for the detector operation, providing an efficient trigger and contributing to the 3D reconstruction of events. Moreover, since the TPC is exposed to a huge flux of cosmic rays due to its operations at shallow depths, the light detection system allows for the time reconstruction of events, contributing to the identification and to the selection of neutrino interactions within the beam spill gates. This contribution will primarily focus on the comparative study (data vs. MC) of light signal of cosmic muons to validate the light emulation.

43 PARTICLE ACCELERATORS↗