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At least 487 records · Page 27

Double-pion electroproduction off protons in deuterium: Quasifree cross sections and final-state interactions

Here, the single-differential and fully integrated cross sections for quasi-free $π^+π^-$ electroproduction off protons bound in deuterium have been extracted for the first time. The experimental data were collected at Jefferson Laboratory with the CLAS detector. The measurements were performed in the kinematic region of the invariant mass W from 1.3 GeV to 1.825 GeV and photon virtuality Q 2 from 0.4 GeV2 to 1.0 GeV 2 . Sufficient experimental statistics allow for narrow binning in all kinematic variables, while maintaining a small statistical uncertainty. The extracted cross sections were compared with the corresponding cross sections off free protons, which allowed us to obtain an estimate of the contribution from events in which interactions between the final-state hadrons and the spectator neutron took place.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Flexible and Accessible 4D Subsurface Visualization Using a Web-Based Platform

4D subsurface visualization using a web platform can provide improved communication, education and outreach to non-experts and stakeholders. It allows for the improved understanding of complex relationships and interactions that occur in inaccessible locations of which many experimental testbeds exist in. The use of a web-based visualization tool lessens the cognitive impact further by reducing the need of specialized software. Users can define their own exploration of a 3D or 4D scene, adding and removing data as needed and are able to make informed decisions based on accurate model visualizations, while providing a collaborative tool that can be accessed on any platform using a browser window. The tool is also able to point to real time streams to display up-to-the-second data as an experiment is ongoing. This level of latency can also provide operators with essential information that can direct an experiment’s progress. Additionally, this tool can leverage augmented and virtual reality (AR, VR) capabilities of certain mobile devices and head-mounted displays, providing further engaging visualization possibilities.

Pratt, Martin J.↗

Controls Status of Fermilab's PIP-II Project

The Fermilab Proton Improvement Project II (PIP-II) is building a new Super Conducting Linear Accelerator (SCL) accelerating protons to 800 MeV for injection into the rest of the FNAL beam complex. Key progress since the last status report given at ICALEPCS includes the adoption of modern DevOps practices with continuous integration and GitOps-based deployments, commissioning of EPICS-based systems at the Cryomodule Test Facility, and integration of a Virtual Accelerator framework for application development ahead of installation. In parallel, web-based applications using Dart and Flutter have matured, providing secure, unified access to both EPICS and legacy ACNET data. Data acquisition and timing systems have also evolved. This paper presents the current state of controls, emphasizing these recent developments and outlining upcoming milestones as PIP-II approaches commissioning of its cryoplant in 2026 and the Warm Front End in 2027.

Crisp, D. B. [Fermilab]↗

Improving and Automating Building Model Data Exchange

There are many instances throughout a project’s lifecycle where there arises a need for quick and accurate risk assessment of building designs. For example, an unexpected design change during construction may necessitate structural engineers to perform a seismic risk assessment on analytical models of the updated building design using high fidelity structural analysis software, such as ANSYS or Abaqus. However, the efficiency of such workflows often depends upon the interoperability of architectural design software and structural analysis software. When the quality of this interoperability is lacking or even non-existent, the efficiency of virtual engineering workflows is hampered, which increases project costs. A McGraw Hill industry survey of professional users of Building Information Modeling (BIM) technologies found that there is high demand for BIM interoperability for structural analysis, but that the value/difficulty ratio is currently too low for practical use. There have been efforts by the academic community to facilitate model data exchange between the architectural design and structural analysis domains, but such solutions have not been widely adopted by industry, face technical challenges, and oftentimes are limited in applicability for users of various BIM software. Therefore, INL is developing capabilities to improve, automate, and generalize model data exchange between architectural BIM software (e.g., Revit) and structural analysis software (e.g., SAP2000, ANSYS). The goal is to help expedite and automate as much of the pre-processing step for creating analytical models in finite element analysis software as reasonably as possible. Such a "BIM-to-FEA" conversion tool should provide direct benefit to end-users through accuracy, automation, quick turn-around, and wide applicability. To generalize the application of this BIM-to-FEA conversion tool and increase its useability among the many different commercial BIM software currently used by industry, the program is being developed with the concept of openBIM. OpenBIM is the application of non-proprietary, open data standards that allow for BIM model data exchange in a format that is accessible, retainable, and useable for all users. The most widely used open, non-proprietary data exchange format for BIM is the Industry Foundation Classes (IFC) schema. IFC is developed by buildingSMART international and is ISO certified (ISO 16739-1:2018). The BIM-to-FEA conversion tool is being developed for compatibility with typical commercial building designs of steel framed structures. The tool is currently capable of importing architectural BIM data of framed building structures, recognizing and extracting the aspects of the model that are required for structural analysis, adjusting the connectivity of frame members, and finally exporting to an analytical model stored in the IFC format. The exported IFC analytical model can then be imported into various openBIM compliant software, such as SAP2000. Such capabilities have already been tested on commercial software, as shown above, and continue to be improved. Work is underway to test the conversion on various commercial BIM software, develop a user-friendly interface, incorporate the program into the broader DeepLynx data warehouse project being developed by INL, and to eventually open-source the tool for the benefit of the community. Future development of the tool envisions the ability for efficient iterative risk assessment of generative building designs, all within a workflow utilizing open-source tools. One such open-source tool will be MOOSE, an advanced finite element analysis tool developed at INL. The conversion tool will also branch out from typical commercial building designs and will aim to incorporate nuclear construction. The aim will be to convert both structural and non-structural components of nuclear facilities, such as curved concrete containment structures and piping systems, respectively.

97 MATHEMATICS AND COMPUTING↗

GRUMDN: A Multi-Task Model for Predicting Human Patterns-of-Life from Stay Transition Data

Understanding human patterns-of-life (PoL) is essential towards ensuring safe and secure indoor facility environment as well as outdoor urban environment. Prediction of human movement in between places of interest is vital in understanding human PoL. Movement between spaces maybe represented and detected in one of the two forms: 1) trajectories: locations measured at regular time intervals by mobile sensors, bluetooth or GPS sensors; or 2) stay transitions: semantic PoI (points of interest) and stay duration data measurable by eventbased sensors that collect data when a check-in or check-out event is detected. Stay transition data provides a more compressed data format compared to trajectories data, especially in situations with longer stay durations, while preserving the information necessary for PoL analysis. Now as introduced briefly in the paper, our deployed end application (Digital Twin of a facility with non-player characters, besides the interactive user in virtual reality) needed a well-performing and validated AI/ML model for simulating high quality stay transitions behavior. In this study we thus primarily present our findings with developing and validating that model, which is a multi-task neural network for stay transition prediction. The neural network consists of two heads, for corresponding two tasks of stay category prediction and stay duration prediction. We evaluated gated recurrent units and multi-layer perceptrons of varying network sizes for stay category prediction; while mixture density networks, noisy generator-only networks, and generative adversarial networks of varying network sizes for stay duration prediction. We have then evaluated four multi-task models, constructed by combining these specialized models, on their ability to predict stay transition data. We tested our models on datasets from two different cases: 1) a simulation-generated dataset of indoor movement within the HFIR (high flux isotope reactor) nuclear reactor facility at Oak Ridge National Laboratory (ORNL); and 2) the GeoLife human mobility dataset of outdoor urban movement available in literature. Our results indicate that GRUMDN, which combines gated recurrent units (GRU) for stay category prediction task, and mixture density networks (MDN) for stay duration prediction task, did overall outperform other multitask models and the current state-of-the-art.

Gunaratne, Chathika [ORNL] (ORCID:0000000225088745↗

Immersive Analytics in Critical Spatial Domains: From Materials to Energy Systems

Immersive analytics (IA) leverages virtual reality, augmented reality, and mixed reality to transform how users interact with complex datasets across domains such as science, industry, and education. These immersive technologies offer spatial and multimodal environments that foster intuitive exploration, but they also introduce challenges related to cognitive load, interface design, and system performance. Here, this article presents a comprehensive review of visualization techniques, interaction models, and multimodal inputs utilized in IA. Drawing on case studies in scientific visualization, industrial training, and educational communication, we examine both the potential and limitations of current systems. Finally, we propose future research directions, focusing on real‐time collaboration, adaptive user interfaces, and scalable data exploration strategies to advance the field.

99 - GENERAL AND MISCELLANEOUS↗

Risk-informed Hierarchical Control of Behind-the-Meter DERs with AMI Data Integration (Final Technical Report)

This project addresses several key barriers to implement the next generation demand response applications and provides a clear understanding of implementing hierarchical and standalone control using AMI data. Through this program, Eaton has developed and tested a meter-as-a-controller prototype with the help of other partners--- National Renewable Energy Laboratory (NREL), Electric Power Research Institute (EPRI), Pecan St Inc. (PSI), and Delaware Electric Cooperative (DEC). The controller can utilize residential controllable loads such as heating, ventilation, and air conditioner (HVAC), electric water heater and distributed energy resources like solar PV and battery energy storage systems for off-setting the demand that is required from the grid, thus providing reliable grid-services for demand reduction or peak shaving. The controller is also capable of coordinating the resources of the premises for better management and energy efficiency while meeting the comfort bound of the premises owner as quality-of-service. The development has been demonstrated in a three virtual-home setup at system performance lab of NREL with real appliances (HVAC, electric water heater, solar PV, and battery). The technology has also been proved through laboratory and field demonstration with successful interconnectivity (e.g., end-to-end communication and data exchange) between the residential appliances and utility through the RF network at Delaware Electric Co-op (DEC) in Delaware.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

High Performance Computing Facility Operational Assessment 2022: Oak Ridge Leadership Computing Facility

The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2022, the OLCF supported 1,681 users and 570 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.6 out of 5 on the annual user survey, and 96% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,212 user tickets submitted in CY 2022, OLCF staff resolved 97% within 3 business days. The facility also introduced several new services for users this year, including weekly virtual office hours with subject matter experts from ORNL and vendor partners; new views in MyOLCF that allow users to analyze allocation and compute usage for a project; the ability to build and run containers on Summit; and improved data visualization support and training resources.

97 MATHEMATICS AND COMPUTING↗

Virtual Resource Management Framework (CRADA Final Report)

There is a need for advanced and widespread business automation within the nuclear industry to drive down operational costs while sustaining or improving safe operations. While there are many business process automation platforms commercially available, the difficulty is that business processes typically rely on a mixture of resource types to accomplish the desired activities and there is no universal software framework virtualizing diverse resource types for the purpose of automation. In this context, we are referring to any capability, physical or intangible, that can be used by an organization to achieve its objectives as a resource. To deploy business automation broadly and enable integrated operations for nuclear (ION), a framework is needed to represent all resource types and their associated disparate data within a plant and to enable seamless flow of resource information to the technologies used for process automation and resource optimization.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

First results on nucleon resonance electroexcitation amplitudes from ep → e'π + π – p' cross sections at W = 1.4–1.7 GeV and Q 2 = 2.0–5.0 GeV 2

The electroexcitation amplitudes or $\gamma_vpN^*$ electrocouplings of the $N(1440)1/2^+$, $N(1520)3/2^-$, and $\Delta(1600)3/2^+$ resonances were obtained for the first time from the $ep \to e'\pi^+\pi^-p'$ differential cross sections measured with the CLAS detector at Jefferson Lab within the range of invariant mass $W$ of the final state hadrons from 1.4–1.7~GeV for photon virtualities $Q^2$ from 2.0--5.0~GeV$^2$. A good description of the nine independent one-fold differential $\gamma_v p\to \pi^+\pi^-p'$ cross sections achieved within the data-driven Jefferson Lab-Moscow State University (JM) meson-baryon reaction model in each bin of ($W$,$Q^2$) allows for separation of the resonant and non-resonant contributions. The electrocouplings were determined in the fits of the $\pi^+\pi^-p$ cross sections within three overlapping $W$ intervals with a substantial contribution from each of the three resonances listed above. Consistent results on the electrocouplings extracted from the data in these $W$ intervals provide evidence for their reliable extraction. These studies extend information on the electrocouplings of the $N(1440)1/2^+$ and $N(1520)3/2^-$ available from this channel over a broader range of $Q^2$. The electrocouplings of the $\Delta(1600)3/2^+$, which decays preferentially into $\pi\pi N$ final states, have been determined for the first time. Here, the reliable extraction of the electrocouplings for these states is also supported by the description of the $\pi^+\pi^-p$ differential cross sections with $Q^2$-independent masses and total/partial hadronic decay widths into the $\pi\Delta$ and $\rho p$ final states.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Turbulence model reduction by deep learning

A defining problem of turbulence theory is to produce a predictive model for turbulent fluxes. These have profound implications for virtually all aspects of the turbulence dynamics. In magnetic confinement devices, drift-wave turbulence produces anomalous fluxes via cross-correlations between fluctuations. In this work, we introduce an alternative, data-driven method for parametrizing these fluxes. The method uses deep supervised learning to infer a reduced mean-field model from a set of numerical simulations. We apply the method to a simple drift-wave turbulence system and find a significant new effect which couples the particle flux to the local gradient of vorticity. Notably, here, this effect is much stronger than the oft-invoked shear suppression effect. We also recover the result via a simple calculation. The vorticity gradient effect tends to modulate the density profile. In addition, our method recovers a model for spontaneous zonal flow generation by negative viscosity, stabilized by nonlinear and hyperviscous terms. We highlight the important role of symmetry to implementation of the new method.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Implementation of Disruptive Designs for Gas Turbine Components Utilizing Additive Manufacturing

Electron beam melting (EBM) is an additive manufacturing process used to print metallic components with complex geometries for aerospace applications. A preheating characteristic enables this technology to avoid large accumulation of residual stress during printing. However, cracking mechanisms particularly in Ni-based superalloys are associated with not only residual stress but also with microstructure (i.e., solidification morphology, transient evolution of γ'). Siemens is committed to AM technology for gas turbine applications. The ability to novel predictive designs of turbine blades would reduce process development time and allow acquisition of key microstructure and performance data early in a design campaign. Siemens initiated a project to establish the design rules for the fabrication of non-printable Nibased superalloys. This phase 1 program demonstrates the feasibility for virtual fabrication of the actual blade geometries through EBM process.

42 ENGINEERING↗

Adaptive Discovery and Mixed-Variable Optimization of Next Generation Synthesizable Microelectronic Materials

Design of new microelectronic materials is characterized by several challenges such as high-dimensionality of the atomic structure-composition variable space, formidable cost of directly using high-fidelity simulations for design optimization, dispersity in literature-reported similar materials and synthesis methods, complex physical mechanisms, and mixed qualitative and quantitative design variables that lead to a disjointed design space. Even though machine learning (ML) techniques have been employed to expedite materials innovation, existing methods treat ML and design optimization as two separate processes, failing to resolve the fundamental challenges associated with high dimensionality and mixed-variable complexity. We have developed a ML enhanced mixed-variable material design optimization framework to efficiently extract useful information from existing data in literature and physics-based simulations to guide the autonomous search for optimal materials. Our proposed framework is composed of four computational modules: (1) a natural language processing (NLP) based virtual screening module, (2) classification based concept exploration module, (3) a density functional theory (DFT)-based high-fidelity evaluation model, and (4) a novel latent-variable Gaussian process (LVGP) ML model for mixed-variable problems with uncertainty quantification, which seamlessly integrates with Bayesian Optimization (BO) and achieves superb efficiency through embedded physics-based dimension reduction. Our approach is demonstrated and validated using the testbed of functional materials exhibiting metal-insulation transitions (MITs), with the targeted reversible resistivity changes (∼10^5) near room temperature. At the end of the 30-month project, we have developed a series of new ML techniques using NLP, conditional variational autoencoders, active learning, latent-variable Gaussian processes, integrated with Bayesian optimization. Our project has resulted in new predicted MITs compounds and improved understanding of MITs microscopic mechanisms, which in turn will revolutionize microelectronics science to provide energy-saving solutions. Our research has improved both creativity and efficiency in transforming rare-event discoveries of new functional materials to persistent innovations. In addition to open-sourcing the online MIT database and the classification model, the LVGP open source code has been downloaded more than 15,000 times within two years. More than 40 MIT compounds have been identified and many have been pursued experimentally via collaborators. The research results are published in close to 20 collaborative papers in high-impact journals, such as Chem. Mater., Appl. Phys. Rev., Sci. Rep., among others of design space.

36 MATERIALS SCIENCE↗

Hadronic contributions to (g - 2) µ

The Muon g-2 Experiment at Fermilab, which recently started running, plans to reduce the uncertain- ties on the already very precisely measured anomalous magnetic moment of the muon by a factor of four. The goal of this effort is to probe the observed difference of more than three standard deviations between Standard-Model theory and experiment, one of the few persistent hints for physics beyond the Standard Model. The Fermilab experiment collected data from their first run last year with statistics comparable to BNL E821. They expect to release the measurement result in 2019. On the theoretical side, because the muon g - 2 arises from quantum- mechanical loop contributions in the Standard Model, it is sensitive to virtual effects of new particles, and places important constraints on Standard-Model extensions. To leverage the anticipated reduction in experimental errors, and determine unambiguously whether or not new-physics effects contribute to this quantity, the theoretical errors must be made more reliable and reduced to a commensurate precision. The Muon g-2 Theory Initiative was created to facilitate this development. The dominant sources of uncertainty in the Standard-Model prediction of the muon g -2 are from the hadronic contributions. The hadronic vacuum polarization (HVP) provides the leading correction followed by hadronic light-by-light (HLbL) scattering. There are a number of complementary theoretical efforts underway to better understand and quantify these contributions, including dispersive and data driven methods, lattice QCD, and effective field theories. Given the precision goals and the phenomenological importance, it is important to have more than one independent method for each of the two hadronic corrections, each with fully quantified uncertainties. Fostering the development of such methods is a prime goal of the initiative, as this will enable critical cross checks, and, upon combination, may yield gains in precision, to maximize the impact of E989. An important aspect of the Muon g-2 Theory Initiative’s activities are providing platforms that facilitate interactions between the different groups, as well as between the theoretical and experimental g - 2 communities. To this end, several workshops were organized in 2017 and 2018. The first meeting, held at Fermilab (June 3–6, 2017, St. Charles, IL, USA), served to kick-off the Initiative’s activities. Two meetings in early 2018 were focused respectively on the HVP and HLbL corrections. The HVP meeting was held at KEK (February 12–14, 2018, Tsukuba, Japan) and the HLbL meeting at the University of Connecticut (March 12–14, 2018, Storrs, CT, USA). The most recent workshop, which served as the second plenary meeting of the Initiative, was held at the University of Mainz (June 18–22, 2018, Mainz, Germany). An important outcome of these meetings are concrete plans for a first white paper, which is currently being written. We aim to post the white paper just prior to the release of the first E989 measurement, to present a clean theoretical prediction. The first white paper is focused on assessing and improving the reliability of the SM prediction. The purpose of the INT workshop in September 2019 is to start the next stage of the Initiative, focusing on the development of strategies to improve the theory uncertainties beyond the current level towards the E989 precision goal. We aim to accelerate theoretical developments on the hadronic contributions to the muon g - 2 so that the Standard-Model theory error can be brought to the needed precision, again in advance of the next release from E989. Hence the workshop will provide crucial theory support for a US experiment with broad impact. The Muon g-2 Theory Initiative relies on input from representatives of all the different communities that are engaged in this effort. It is therefore important that all these areas are properly represented. The funds from this grant will be used to enable more people, especially early-career scientists, to participate and make essential contributions to the workshop discussions. Since the workshop’s main goal is to kick-off the next stage of the theory initiative’s activities, support for this workshop from the DOE will help the theory community provide crucial theoretical support to the Fermilab Muon g-2 Experiment.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Accelerating particle-in-cell kinetic plasma simulations via reduced-order modeling of space-charge dynamics using dynamic mode decomposition

We present a data-driven reduced-order modeling of the space-charge dynamics for electromagnetic particle-in-cell (EMPIC) plasma simulations based on dynamic mode decomposition (DMD). The dynamics of the charged particles in kinetic plasma simulations such as EMPIC is manifested through the plasma current density defined along the edges of the spatial mesh. We showcase the efficacy of DMD in modeling the time evolution of current density through a low-dimensional feature space. Not only do such DMD based predictive reduced-order models help accelerate EMPIC simulations, they also have the potential to facilitate investigative analysis and control applications. Here, we demonstrate the proposed DMD-EMPIC scheme for reduced-order modeling of current density and speedup in EMPIC simulations involving electron beam under the influence of magnetic field, virtual cathode oscillations, and backward wave oscillator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Improved Three-Dimensional Reconstructions in Electron Ptychography through Defocus Series Measurements

A detailed analysis of ptychography for three-dimensional (3D) phase reconstructions of thick specimens is performed. Here, we introduce multi-focus ptychography, which incorporates a 4D-STEM defocus series to enhance the quality of 3D reconstructions along the beam direction through a higher overdetermination ratio. This method is compared with established multi-slice ptychography techniques, such as conventional ptychography, regularized ptychography, and multi-mode ptychography. Additionally, we contrast multi-focus ptychography with an alternative method that uses virtual optical sectioning through a reconstructed scattering matrix (S-matrix), which offers more precise 3D structure information compared to conventional ptychography. Our findings from multiple 3D reconstructions based on simulated and experimental data demonstrate that multi-focus ptychography surpasses other techniques, particularly in accurately reconstructing the surfaces and interface regions of thick specimens.

3D reconstruction↗

Update on Radiochemical Assessment of High Burnup Commercially Irradiated Fuel

This work documents an effort to collect burnup measurements on a high burnup rod, designated 6XV, and first cycle accident tolerant fuel (ATF) rod, designated 47I, to enable benchmarking of fuel performance codes and neutronics codes. In addition to measurements, Virtual Environment for Reactor Applications (VERA) full-core-depletion analysis was also performed for the rods that were experimentally analyzed to provide an opportunity for code validation. This effort focuses on collecting data from rods irradiated at Byron Generating Station and shipped to the Oak Ridge National Laboratory (ORNL) hot-cells. This data will also anchor non-destructive examination evaluations of burnup of the various fuel rods undergoing postirradiation examination (PIE) at ORNL. Previous PIE of these fuel rods provides some guidance on the burnup trend across the fuel. Axial gamma spectroscopy scans provide a measure of relative changes in burnup across a fuel pin. Mass spectrometry based burnup measurements performed for this work at specific axial locations in the fuel are fully quantitative. By combining the mass spectrometry data with the gamma scans it is possible to more quantitatively evaluate axial variations in burnup across the entire fuel pin [1]. The combined set of burnup evaluations will be made available to other organizations that have an interest in high burnup radiochemistry data for validation of neutronic simulations and source term evaluation such as the Nuclear Regulatory Commission (NRC).

Harp, Jason [Oak Ridge National Laboratory (ORNL),↗

Rancor Integrated Procedure System (RIPS): A Computer-Based Procedure Platform for Advanced Reactor Research

The Rancor Microworld Simulator is a simplified, pressurized water, small modular reactor simulator that includes a multi-unit plant model server, an advanced digital human-machine control interface, and the Rancor Integrated Procedure System (RIPS). Rancor provides a research and development tool that can be used for collecting operator performance data and for prototyping concepts of operations (ConOps) for advanced reactor development. RIPS is meant as a research tool and includes many unique features: (1) RIPS has a robust procedure authoring system. (2) RIPS has the capability to run any of the three IEEE-Std-1786 computer-based procedure types. (3) RIPS can be configured to take on the look and feel of different vendors’ computer-based procedure systems for the purpose of developing and evaluating different ConOps for plant upgrades or new builds. (4) RIPS includes the capability for logging operator procedure use, including integrating procedure logs with Rancor simulator logs, thereby allowing automated data collection of operator scenario runs. (5) RIPS integrates with the Human Unimodel for Nuclear Technology to Enhance Reliability (HUNTER), a dynamic human reliability analysis environment that creates a digital human twin or virtual operator to mimic reactor operator performance. (6) RIPS includes support for automation of plant monitoring and control functions. While RIPS is explicitly built into Rancor, it may also be used with full-scope training simulators. This functionality allows RIPS to be used for existing plants and advanced reactors under development.

99 - GENERAL AND MISCELLANEOUS↗