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Types of Potential Accidents and Reactor Response Codes and Tools Experimental Validation Safety Analysis Approach Licensing Modernization Project Use of PRA in LMP, ASME/ANS, Non-LWR PRA Standard Methods for Incorporating Passive System Reliability into a PRA
Real-time and adaptive plasma control is crucial for robust tokamak operation, requiring sensitivity and tolerance measurements of the plasma state. This paper presents the implementation of an integrated real-time plasma monitoring framework on the DIII-D tokamak to support advanced control approaches, including machine-learning (ML) methods. The system is built on the SHIELD framework, a high-performance modular architecture that provides a unified pipeline for integrating diverse diagnostics. The framework leverages high-bandwidth digitizers, fast numerical processing, and deterministic, low-latency interconnects to stream high-fidelity data from diagnostics such as electron cyclotron emission (ECE), beam emission spectroscopy (BES), CO interferometers, and a visible tangential divertor camera (TangTV). The system’s validity is demonstrated through direct comparisons of real-time and offline data. Furthermore, we present two key applications of the developed plasma monitoring system with ML-based plasma control strategies, including real-time divertor detachment and active Alfvén Eigenmode control. As a result, this work presents a robust and scalable approach for integrating high-frequency, multidimensional diagnostics into advanced control algorithms for future fusion devices.
Recently, computational modeling has shifted towards the use of statistical inference, deep learning, and other data-driven modeling frameworks. Although this shift in modeling holds promise in many applications like design optimization and real-time control by lowering the computational burden, training deep learning models needs a huge amount of data. This big data is not always available for scientific problems and leads to poorly generalizable data-driven models. This gap can be furnished by leveraging information from physics-based models. Exploiting prior knowledge about the problem at hand, this study puts forth a physics-guided machine learning (PGML) approach to build more tailored, effective, and efficient surrogate models. For our analysis, without losing its generalizability and modularity, we focus on the development of predictive models for laminar and turbulent boundary layer flows. In particular, we combine the self-similarity solution and power-law velocity profile (low-fidelity models) with the noisy data obtained either from experiments or computational fluid dynamics simulations (high-fidelity models) through a concatenated neural network. We illustrate how the knowledge from these simplified models results in reducing uncertainties associated with deep learning models applied to boundary layer flow prediction problems. The proposed multi-fidelity information fusion framework produces physically consistent models that attempt to achieve better generalization than data-driven models obtained purely based on data. While we demonstrate our framework for a problem relevant to fluid mechanics, its workflow and principles can be adopted for many scientific problems where empirical, analytical, or simplified models are prevalent. In line with grand demands in novel PGML principles, this work builds a bridge between extensive physics-based theories and data-driven modeling paradigms and paves the way for using hybrid physics and machine learning modeling approaches for next-generation digital twin technologies.
Part of the Presidential Policy Directive 21 (PPD-21) (PPD 2013) mandate includes evaluating safety, security, and safeguards (or nonproliferation) mechanisms traditionally implemented within the nuclear reactors, materials, and waste sector of critical infrastructure—including a complex, dynamic set of risks and threats within an all-hazards approach. In response, research out of Sandia National Laboratories (Sandia) explores the ability of systems theory principles (hierarchy and emergence) and complex systems engineering concepts (multidomain interdependence) to better understand and address these risks and threats. Herein, this Sandia research explores the safety, safeguards, and security risks of three different nuclear sector-related activities—spent nuclear fuel transportation, small modular reactors, and portable nuclear power reactors—to investigate the complex and dynamic risk related to the PPD-21-mandated all-hazards approach. This research showed that a systems-theoretic approach can better identify inter-dependencies, conflicts, gaps, and leverage points across traditional safety, security, and safeguards hazard mitigation strategies in the nuclear reactors, materials, and waste sector. Resulting from this, mitigation strategies from applying systems theoretic principles and complex systems engineering concepts can be (1) designed to better capture interdependencies, (2) implemented to better align with real-world operational uncertainties, and (3) evaluated as a systems-level whole to better identify, characterize, and manage PPD-21's all hazards strategies.
We propose a sequential Monte Carlo (SMC) method to efficiently and accurately compute cut-Bayesian posterior quantities of interest, variations of standard Bayesian approaches constructed primarily to account for model misspecification. We prove finite sample concentration bounds for estimators derived from the proposed method along with a linear tempering extension and apply these results to a realistic setting where a computer model is misspecified. We then illustrate the SMC method for inference in a modular chemical reactor example that includes submodels for reaction kinetics, turbulence, mass transfer, and diffusion. The samples obtained are commensurate with a direct-sampling approach that consists of running multiple Markov chains, with computational efficiency gains using the SMC method. Overall, the SMC method presented yields a novel, rigorous approach to computing with cut-Bayesian posterior distributions.
In this work, we develop an envelope of design solutions for combined intensified reaction/separation systems. The attainable region-based continuous flow stirred tank reactor equivalence principle is applied to characterize design boundaries for a given chemistry independent of process design. The thermodynamic-based Generalized Modular Representation Framework is then employed to generate candidate process alternatives along the design boundaries. The proposed approach is showcased via a case study on olefin metathesis. As a result, we also highlight the need to incorporate the attainable region-based constraints into synthesis strategies to assist effective bounding of design/intensification space.
With increasing interest in using biomass as a raw material for fuel, the development of biorefineries is an active area of research. However, increasing market competition, uncertainty, and environmental concerns are a few of the challenges that need to be addressed. In this work, mathematical optimization formulations are proposed to address these challenges simultaneously. A multiobjective deterministic optimization framework is proposed to address economic and environmental objectives. A two-stage stochastic optimization framework is proposed to account for uncertainties in material flow and product yields. To model the environmental objective, an environmental risk metric is proposed. Finally, as modularization is gaining popularity in the process industry due to its ability to save capital cost because of standardization and reduce time to market, a formulation for simultaneously achieving modular process design and biorefinery configuration is proposed. Furthermore, the results demonstrate the efficacy of the proposed approach.
A pebble burnup monitoring system is a required component for domestic reactor safety and safeguards applications associated with pebble bed reactors (PBRs). One of the main requirements of a PBR burnup monitoring system is that it needs to be capable of rapid measurements to assess the burnup of each individual pebble to determine whether to recirculate it in the reactor or discard it as spent fuel. This report considers three different approaches for a burnup monitoring system for pebbles discharged from the reactor core in a pebble bed modular reactor-400: • passive gamma spectrometry measurement, • passive neutron coincidence measurement, and • active neutron counter based on the differential die-away technique. Conceptual designs have been created for each of these detectors, and preliminary analysis has been performed using Monte Carlo N-Particle and Oak Ridge Isotope Generation code simulations. The advantages and practical limitations (e.g., high radiation background) of each system were identified. Simulations suggest that each of the three measurement techniques can be successfully employed to distinguish between pebbles based on their number of passes through the core and to quantify the burnup of pebbles.
In 2020, the U.S. Department of Energy's Building Technologies Office (BTO) launched the Advanced Building Construction (ABC) Initiative. This initiative aimed to integrate energy efficiency into high-production construction practices. One focus area of the initiative is off-site construction, an approach that may achieve scalable, efficient, and high-performing construction buildings through process standardization. This report presents the findings and recommendations from our ABC Initiative project: A New State of the Art: Zero-Energy Modular Multifamily Construction System. The objectives of the BTO-funded project are to: 1) Achieve energy performance 50% better than the 2018 International Energy Conservation Code (IECC). 2) Achieve this level of energy performance at no additional cost VEIC worked with Louisiana State University (LSU), National Renewal Energy Lab (NREL), [the project team], and industry partners at the Mod Coach, KBS, and Solar Home Factory to identify and analyze multifamily business designs and modular construction practices. These designs and processes hold the promise of achieving the goals set out by the DOE.
Data collection at X-ray free electron lasers has particular experimental challenges, such as continuous sample delivery or the use of novel ultrafast high-dynamic-range gain-switching X-ray detectors. This can result in a multitude of data artefacts, which can be detrimental to accurately determining structure-factor amplitudes for serial crystallography or single-particle imaging experiments. Here, a new data-classification tool is reported that offers a variety of machine-learning algorithms to sort data trained either on manual data sorting by the user or by profile fitting the intensity distribution on the detector based on the experiment. This is integrated into an easy-to-use graphical user interface, specifically designed to support the detectors, file formats and software available at most X-ray free electron laser facilities. The highly modular design makes the tool easily expandable to comply with other X-ray sources and detectors, and the supervised learning approach enables even the novice user to sort data containing unwanted artefacts or perform routine data-analysis tasks such as hit finding during an experiment, without needing to write code.
Growing shale gas extraction in recent years has triggered wide discussions on the associated freshwater requirement and wastewater management. Many optimization approaches have been developed for shale gas water management; however, most of the studies assumed permanent utilization of wastewater treatment facilities with fixed capacities. Considering the rapidly declining characteristics of shale gas wastewater production, these treatment facilities could remain largely underutilized after the first few months/years of production, making them less economically attractive. To maximize the capacity utilization of treatment facilities and further improve the economic performance of shale gas development, this study develops a systematic optimization framework, where the capacity strategy of conventional treatment facilities and utilization of the recent concept of modular manufacturing are both considered for flexible shale gas water management. The proposed mixed-integer linear programming (MILP) model simultaneously optimizes the design and planning of integrated shale gas and water supply chain, with a focus on capacity planning for both large-scale conventional treatment facilities and small-scale modular devices. A series of Marcellus-based case studies are performed to illustrate the applicability of the proposed model and provide general insights into the trade-offs between the multiple types of treatment facilities. The optimization results reveal that the combinatorial utilization of conventional facilities and modular devices for wastewater treatment (66% by conventional facilities and 34% by modular devices) brings 9.3% more reused water for other well development and 6.2% savings in water-related costs, compared to flexible management of only conventional facilities. Furthermore, this work suggests that taking modular device as auxiliary equipment for shale gas water management is most beneficial to increase the capacity utilization of treatment facilities and achieve a more economic and sustainable shale gas production system.
The tearing parameter criterion and material softening failure method currently used in the multilinear elastic-plastic constitutive model was added as an option to modular failure capabilities. The modular failure implementation was integrated with the multilevel solver for multi-element simulations. Currently, this implementation is only available to the J 2 plasticity model due to the formulation of the material softening approach. The implementation compared well with multilinear elastic-plastic model results for a uniaxial tension test, a simple shear test, and a representative structural problem. Necessary generalizations of the failure method to extend it as a modular option for all plasticity models are highlighted.
Data centers (DCs) serve as critical infrastructure for powering the growth and evolution of AI. Next-generation AI DCs present unique challenges in thermal management driven by unprecedented computational demands. This paper provides a comprehensive summary of key stakeholder perspectives on technology gaps, infrastructure requirements, test bed needs, emerging opportunities, and preliminary solutions related to thermal management for AI DCs. It establishes six strategic pillars of thermal management for next generation AI DC: reliability, deployability, efficiency, resilience, measurability, and valorization. The discussion spans a range of critical topics, including advanced cooling technologies, thermal strategies for emerging modular and edge DCs, system-level optimization and control frameworks, infrastructure planning and grid integration designs, benchmarking approaches, and pathways for waste heat recovery and reuse. The proposed research, development, and demonstration efforts are aimed at accelerating the deployment of AI DCs while ensuring energy efficiency, reliability, safety, and regulatory compliance.
Hydrological models and geophysical measurements are widely used tools for understanding subsurface hydrological processes relevant to water resource management, yet they typically remain disconnected due to technical barriers. We present PyHydroGeophysX, an open-source Python platform bridging this gap by providing standardized interfaces between hydrological modeling software (MODFLOW, ParFlow) and geophysical simulation tools (PyGIMLi, SimPEG). The platform implements bidirectional workflows: translating hydrological outputs into simulated geophysical responses through petrophysical models, and extracting hydrological information from geophysical inversions. Key features include bidirectional workflow modules, configurable petrophysical models, time-lapse inversion with temporal regularization, parallel computing, and mesh utilities for property transfer between geophysical and hydrological grids. The modular architecture of PyHydroGeophysX enables researchers to incorporate additional models and methods, fostering broader adoption of integrated hydrogeophysical approaches. The software is freely available on GitHub and is intended for researchers and practitioners working at the intersection of hydrology and geophysics.
In this report a rapid and facile design strategy to create a highly complex optical tag with programmable, multimodal photoluminescent properties is described. This was achieved via intrinsic and DNA-fluorophore hidden signatures. As a first covert feature of the tag, an intricate novel heterometallic near-infrared (NIR)-emitting mesoporous metal–organic framework (MOF) was designed and synthesized. The material is constructed from two chemically distinct, homometallic hexanuclear clusters based on Nd and Yb. Uniquely, the Nd-based cluster is observed here for the first time in a MOF and consists of two staggered Nd μ3-oxo trimers. To generate controlled, multimodal, and tailorable emission with difficult to counterfeit features, the NIR-emissive MOF was post-synthetically modified via a fluorescent DNA oligo labeling design strategy. The surface attachment of several distinct fluorophores, including the simultaneous attachment of up to three distinct fluorescently labeled oligos was achieved, with excitation and emission properties across the visible spectrum (480-800 nm). The DNA inclusion as a secondary covert element in the tag was demonstrated via the detection of SYBR Gold dye association. Importantly, the approach implemented here serves as a rapid and tailorable way to encrypt distinct information in a facile and modular fashion and provides an innovative technology in the quest toward complex optical tags.
This final technical report tracks the accomplishments of the Tempus Project to the statement of project objectives and resulting deliverables. The objective of the Tempus project was to develop and demonstrate the capabilities of a secure, modular, and customizable time synchronization platform that provides layers of protection from GPS spoofing attacks and other vulnerabilities. These vulnerabilities present a challenge to power system operators utilizing GPS-based time synchronization, as time synchronization increasingly plays a critical role in the efficient and reliable operation of power systems. The Tempus project addressed these concerns through the development of a secure, customizable time synchronization platform. The Tempus platform applies a layered approach to time security that protects from manipulation of timing systems used by power utilities. The Tempus platform consists of a component framework utilizing modularized time sources and customizable manipulation detection algorithms. With multiple time sources, and comparisons between sources and tracking sudden or subtle changes to the timing phase and time information, the Tempus system can detect and mitigate compromised timing sources.
A critical barrier to the deployment of MSRs is the absence of a well-defined nuclear material control and accounting (MC&A) approach, a vital prerequisite to meet NRC licensing requirements as well as facilitating future international exports. Liquid-fueled MSR variants (especially those incorporating online refueling or reprocessing) present a unique set of challenges to traditional nuclear material control and accountancy. Unlike solid-fueled light-water reactors, traditional item-counting methods cannot be applied as an accountancy strategy. Rather, MC&A approaches to MSR variants (including both uranium and thorium fueled designs) are more analogous to bulk material handling facilities (e.g., enrichment and reprocessing); yet further complicating matters is the fact that fuel medium is also highly radioactive. Meanwhile, the space of MSRs covers a broad range of design parameters, including thermal and fast spectra designs; operation in actinide breeder or burner modes, choice of actinide fuel used (e.g., 235 U, 232 Th / 233 U, denatured 233 U, etc.), pool or loop-type configuration, and even different salt chemistry. Each of these design choices introduce significant challenges to MC&A approaches within MSR facilities. We propose to bridge this gap for liquid-fueled MSRs by developing a modular, component-based test framework for evaluating viable process monitoring and MC&A techniques specifically suited to liquid-fueled MSR system variants employing online refueling or reprocessing. This test platform will consist of a toolbox of independent process modules representing discrete physical units (such as the reactor core, off-gas processing, decay tanks, and actinide separation units), each with its own self-contained physics responsive to the input mass flow, along with appropriate measurement models that can be coupled to key flow points. These dynamic physical signatures thus afford the ability to test the viability and efficacy of potential accountancy techniques under the full range of reactor operating conditions. As process modules are connected via mass flows, the result is a reconfigurable, generic MSR mass flow model capable of serving as an MC&A test platform for a broad spectrum of possible MSR configurations. The resulting MSR MC&A toolbox will enable robust assessment of accountancy strategies for this unique facility type, including analysis of physical feedbacks arising both from depletion of the fuel over time as well as from potential off-normal events, including those introduced by equipment failures (e.g., a pump failure) as well as by malicious action (i.e., attempts to divert material). The proposed toolbox both addresses a critical needs area for the MPACT analysis toolkit while leveraging existing MPACT-sponsored tools, especially with respect to simulation of measurement and accountancy techniques for advanced fuel cycle facilities. Beyond enabling new analysis capabilities for MSR systems, the design of this toolbox will be to such to maximize compatibility with existing MPACT tools, such to enhance existing facility MC&A analysis capabilities.
This article presents the development of the Control-oriented, Reconfigurable, and Acausal Floating Turbine Simulator (CRAFTS). CRAFTS has a modular, hierarchical model architecture that enables rapid and accurate simulation of wind turbines. The architecture facilitates the incorporation of model variants, and its system reconfiguration features help simulate multiple design variants. CRAFTS also supports the integration of models developed on a causality-free platform (e.g., Modelica®) with existing causal models. This article focuses on the validation of a land-based wind turbine models against OpenFAST, an industry-standard platform, for several test cases. Closed-loop scenarios are also tested using the standard ROSCO controller and compared against OpenFAST. In addition, a nonlinear controller developed in our prior work is also evaluated. The test cases demonstrate the user-friendly and computationally efficient capabilities of CRAFTS to facilitate control co-design, assist in incorporating multiphysics models, be adaptable to design variants, and allow for rapid simulations to validate models and evaluate controllers.