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At least 613 records · Page 34

FY17 Status Report on the Initial Development of a Constitutive Model for Grade 91 Steel

Grade 91 is a candidate structural material for high temperature advanced reactor applications. Existing ASME Section III, Subsection HB, Subpart B simplified design rules based on elastic analysis are setup as conservative screening tools with the intent to supplement these screening rules with full inelastic analysis when required. The Code provides general guidelines for suitable inelastic models but does not provide constitutive model implementations. This report describes the development of an inelastic constitutive model for Gr. 91 steel aimed at fulfilling the ASME Code requirements and being included into a new Section III Code appendix, HBB-Z. A large database of over 300 experiments on Gr. 91 was collected and converted to a standard XML form. Five families of Gr. 91 material models were identified in the literature. Of these five, two are potentially suitable for use in the ASME code. These two models were implemented and evaluated against the experimental database. Both models have deficiencies so the report develops a framework for developing and calibrating an improved model. This required creating a new modeling method for representing changes in material rate sensitivity across the full ASME allowable temperature range for Gr. 91 structural components: room temperature to 650° C. On top of this framework for rate sensitivity the report describes calibrating a model for work hardening and softening in the material using genetic algorithm optimization. Future work will focus on improving this trial model by including tension/compression asymmetry observed in experiments and necessary to capture material ratcheting under zero mean stress and by improving the optimization and analysis framework.

36 MATERIALS SCIENCE↗

Ion bunch formation strategies for the JLEIC collider

An Electron-Ion Collider (EIC) has been proposed and supported as the next major nuclear physics facility in the United States, with very high design luminosities of 10^33 - 10^34 cm^-2 s^-1. The Jefferson Lab EIC concept, JLEIC, includes construction of a new ion beam accelerator complex. To achieve the required luminosity, the JLEIC ion collider requires unprecedentedly short ion bunches of 1 cm length at a high repetition frequency of 476 MHz. However, only much longer ion bunches are formed by the ion injector complex producing beams for the collider ring. Formation of short ion bunches from much longer ion bunches in this collider while limiting beam size growth is a fundamental challenge. This dissertation investigates several methods using longitudinal RF gymnastics to address this bunch formation challenge, including debunch/rebunch with barrier bucket manipulations and adiabatic bunch splitting. We discuss background of both methods, and show results of simulations that indicate that multiple adiabatic bunch splits are best suited to produce ion beam that satisfies the current JLEIC requirements. Adiabatic bunch splitting is also further optimized using genetic algorithms to find the optimum tradeoff between acceptable emittance growth and accelerator time necessary to perform the bunch formation process.

Gamage, Randika P.↗

Development of Grade 91 inelastic model for incorporation in ASME Division 5

This report describes a unified viscoplastic model for Grade 91 suitable for use with the Section III, Division 5 design by inelastic analysis procedures in the ASME Boiler and Pressure Vessel Code. The report identifies several key features a successful design inelastic model must capture along with critical aspects of the response of Grade 91 for elevated temperature loading conditions typical of expected reactor operating conditions. An inelastic model for Code design must capture the average response of a material including heat-to-heat and product form variations. This work develops a method for calibrating such a model to a large experimental database using genetic algorithm optimization. Finally, the report compares the final model to several specialized experimental tests to validate that it will be suitable for use in high temperature structural design. The techniques developed here can be applied to the remaining Section III, Division 5, Class A materials to develop a nonmandatory appendix to the Code describing inelastic material models suitable for use with the design by inelastic analysis procedures.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Neutronic Design and Analysis of the Holos-Quad Concept

HolosGen, LLC is proposing a highly innovative micro-reactor concept targeting both civilian and military applications. It consists of an advanced helium-cooled gas reactor using a turbojet-type turbine and compressor to achieve a highly condensed reactor that fits into a commercial ISO container. The Holos-Quad concept being considered for civilian applications will generate 22MWt, using four Subcritical Power Modules (SPMs) that fit into one 40-foot ISO container. It is a high-temperature gas-cooled reactor concept using TRISO fuel distributed in graphite hexagonal blocks, cooled with helium gas at 7MPa and a high outlet temperature. In FY2019, HolosGen, LLC received an ARPA-E MEITNER grant to team up with the Argonne National Laboratory (ANL) design team to demonstrate the feasibility of the Holos-Quad concept in terms of neutronics. In particular, the evaluation study of the Holos design features and alternative options for reactivity control was considered. Through these tasks, the ANL design team has been working on demonstrating the feasibility of the Holos-Quad concept in terms of neutronics to help advance the core design of the Holos-Quad. A rigorous design approach was employed for the Holos-Quad in order to ensure that the input space is fully investigated, and that the best solutions are considered. The first step of this approach consisted of properly defining the Holos-Quad design problem by identifying the competitive design objectives: its economic performance is maximized with longer lifetime and its transportability to remote locations is maximized with reduced weight. The design problem also required to identify the operational constraints (in terms of thermal hydraulics, reactivity feedback and shutdown margins) and the input parameters that could be varied. In the second step, a sensitivity analysis was performed to enable preliminary investigation of the input space to identify the correlations among input and output variables. It allowed, for instance, to find input parameters that have relatively poor impact on the core performance and that could be removed from the optimization problem to accelerate its convergence. This sensitivity analysis showed that the design problem defined is highly constrained since only 117 solutions out of 1,000 simulated cores met all the design constraints. The design optimization was performed in the third step employing a genetic algorithm to effectively explore this highly constrained input space and find global optimal solutions. More than 2,500 cores were fully evaluated to converge to a Pareto Frontier of optimum solutions providing best compromise between the life-time target and the total core weight, as shown in Figure 1. The main benefit of this approach was to reduce the human effort and to be able to automatically re-run the optimization problem when changing the design problem. The multi-objective optimization identified various core solutions that would be optimum solutions for different types of applications.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Co-Optimization of Boost Converter Reliability and Volumetric Power Density [Brief]

Optimized designs were achieved using a genetic algorithm to evaluate multi-objective trade space, including Mean-Time-Between-Failure (MTBF) and volumetric power density. This work provides a foundational platform that can be used to optimize additional power converters, such as an inverter for the EV traction drive system as well as trade-offs in thermal management due to the use of different device substrate materials.

33 ADVANCED PROPULSION SYSTEMS↗

Multi-Objective Optimization for Power Electronics used in Grid-Tied Energy Storage Systems

Multi-objective optimization is used to find a nondominated set of solutions for two conflicting performance metrics or objective functions. These functions are dependent variables in the system, controlled by a set of independent variables called decision variables. The decision variables represent the inputs to the problem, chosen by the system designer, and are values listed in the solution set. In this study, a multi-objective genetic algorithm compared insulated-gate bipolar transistor (IGBT) failure rate to filter and cooling system costs. This study demonstrated the use of multi-objective optimization for energy storage systems. IGBT failure rate was compared to its associated filter and cooling-system costs as part of the DC-AC inverter power electronics system in a battery energy storage system (BESS). The independent or decision variables were determined to be switching frequency and thermal resistance of a heat sink, R sink . The final results indicated that high values of switching frequency increased the effects of R sink . Future work will add additional objective functions and decision variables to the study to optimize additional components in the power electronics system and BESS.

25 ENERGY STORAGE↗

Deep learning techniques to disentangle water use efficiency, climate change, and carbon sequestration across ecosystem scales

Focal Areas: We plan to use machine learning (ML) to disentangle the impact of climate change on plant water use efficiency (WUE) across the leaf level, structured canopy, and community ecosystem scales. Due to the complex behavior of modeling plant WUE’s connection to carbon storage (and hidden co-varying interactions) we suggest that physics-based deep learning must be utilized. The second topic we propose is, based on a better understanding of WUE response to climate change, there is a need to predict how WUE will limit or enhance the recycling of water from transpiration by vegetation back to the land surface worldwide, and thus impact water storage capacity (i.e., natural and reservoirs), using machine learning optimization techniques such as genetic algorithms.

54 ENVIRONMENTAL SCIENCES↗

Estimating the Adequacy of a Multi-Objective Optimization

Multi-objective optimization methods can be criticized for lacking a statistically valid measure of the quality and representativeness of a solution. This stance is especially relevant to metaheuristic optimization approaches but can also apply to other methods that typically might only report a small representative subset of a Pareto frontier. Here we present a method to address this deficiency based on random sampling of a solution space to determine, with a specified level of confidence, the fraction of the solution space that is surpassed by an optimization. The Superiority of Multi-Objective Optimization to Random Sampling, or SMORS method, can evaluate quality and representativeness using dominance or other measures, e.g., a spacing measure for high-dimensional spaces. SMORS has been tested in a combinatorial optimization context using a genetic algorithm but could be useful for other optimization methods.

42 ENGINEERING↗

Rossi-alpha Analysis of CURIE Experiment [Slides]

Critical assembly measurement and operations are crucial to the development of benchmark data. Knowledge of the prompt neutron lifetime informs on the state of the system’s neutron spectrum. Rossi-alpha analysis can give us time-dependent information on the system. Teflon was selected, via genetic algorithm, as a moderator to target the URR in uranium for CURIE. Rossi-alpha measurements of the assembly's approach to critical using He-3 detectors estimated the prompt neutron decay constant (α) at delayed critical. α values of several subcritical measurements were then used to predict the α value at delayed critical.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

OTERR Theory Manual

OTERR is a python code designed to couple an external transport/depletion capability with an internal genetic algorithm for fuel reloading optimization. OTERR stores the state information required for creating neutronics code input, output from ARC codes, and data required for running optimization in HDF5 files.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

OTERR User Manual

OTERR (Optimization of TEst Reactor Reloading) is a software tool created to assist in the determination of optimal fuel reloading patterns for test reactors. The intended application is for the Versatile Test Reactor (VTR) program, but it provides functions that could be useful for analysis and optimization of many types of fast reactors. OTERR does not perform neutron/gamma transport, heat transfer, thermal hydraulics, or depletion calculations. Instead, it acts as a wrapper around codes that provide these capabilities, with a native genetic algorithm optimization capability. At this time, wrapping is only implemented for Argonne Reactor Computation (ARC) codes DIF3D, REBUS, and GAMSOR, and SE2-ANL. OTERR also has capabilities to facilitate input creation for DASSH, a thermal hydraulics code similar to SE2- ANL being developed for the VTR program.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Development and Demonstration of a Risk-Informed Approach to the Regulatory Required Fuel Reload Safety Analysis

The United States (U.S.) nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs) and can be achieved more economically by using a risk-informed ecosystem such as that being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development (R&D) activities with the goal to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal. The RISA Pathway has two main goals: (1) the deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety; and (2) the development of advanced applications that enable cost-effective plant operation. The plant reload optimization framework development project aims to build an artificial intelligence, i.e., Genetic Algorithm (GA), based reactor core designing tool taking into account reactor safety and fuel performance analyses.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Demonstration of the Plant Fuel Reload Process Optimization for an Operating PWR

The United States (U.S.) nuclear industry is facing a strong challenge to maintain regulatory-required levels of safety while ensuring economic competitiveness to stay in business. Safety remains a key parameter for all aspects related to the operation of light water reactor (LWR) nuclear power plants (NPPs) and can be achieved more economically by using a risk-informed ecosystem such as that being developed by the Risk-Informed Systems Analysis (RISA) Pathway under the U.S. Department of Energy (DOE) Light Water Reactor Sustainability (LWRS) Program. The LWRS Program is promoting a wide range of research and development (R&D) activities with the goal to maximize both the safety and economically efficient performance of NPPs through improved scientific understanding, especially given that many plants are considering second license renewal. The RISA Pathway has two main goals: (1) the deployment of methodologies and technologies that enable better representation of safety margins and the factors that contribute to cost and safety; and (2) the development of advanced applications that enable cost-effective plant operation. This report summarizes the research outcomes in FY-2021, which the project progressed from the planning and methodology development phase to the early demonstration phase. The highlights of these activities are: (1) the development of a multi-objective optimization process using Genetic Algorithms (GAs); (2) the development and test of an approach for optimization process acceleration using artificial intelligence (AI) that significantly reduces the computational burden; (3) the demonstration of the fuel reload optimization framework for a generic pressurized water reactor (PWR); and (4) the demonstration of limiting design basis accident (DBA) scenarios for evaluation of the transition from deterministic to risk-informed approach for fuel reload optimization.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Modeling and Optimization of Zeolites for Contaminant Removal from Coal Combustion Impoundment Leachates

Coal Combustion Residual (CCR) landfills in the U.S. can lead to elevated contaminant concentrations in groundwater and runoff, particularly of arsenic, boron, and selenium. Zeolites can be readily synthesized from materials contained within the coal combustion ash impoundments and can be cation-exchanged to enhance adsorption capacity, selectivity, and reactivity. However, the optimization of zeolites becomes a daunting task when accounting for the variety of Si:Al ratios, the species of extra-framework cations present, and the zeolite pore structure. Molecular simulations provide methods to study and guide the design of zeolites for the sorption of contaminants from aqueous solution. In this work, models that predict the sorption of arsenic, boron, and selenium from water using cation-exchanged zeolites. Because of the lack of experimental adsorption data for these species of contaminants, models were parametrized to reproduce the results of first-principles calculations and then used to predict sorption for zeolites for a dataset containing 6000 combinations of zeolites and sorption conditions. Machine learning was used to train a model to predict sorption for materials in this database based on the results of the molecular simulations. Next, a genetic algorithm was used to optimize zeolites for the removal of each contaminant from aqueous solution for individual impoundment sites based on contaminant concentrations reported by the Electric Power Research Institute.

Findley, John↗

Multi-physics Topology OPtimization and Additive Manufacturing for High-temperature Heat Exchangers

This research significantly advances the understanding of high-temperature heat exchanger design through an integrated approach that combines topology optimization (TO), triply periodic minimal surface (TPMS) structures, additive manufacturing (AM) and thermohydraulic testing. Each of these components contributes uniquely to a unified, high-performance design, fabrication and testing workflow. Topology optimization serves as the foundation of the design methodology by providing a systematic way to determine the most effective material layout for separating hot and cold fluids while maximizing thermal performance. The researchers introduced a novel three-material optimization framework using two density fields to represent hot fluid, cold fluid, and solid domains. This approach enables automated discovery of optimal shapes and flow paths that cannot be intuitively designed, especially under constraints imposed by manufacturing technologies. Furthermore, constraints such as minimal wall thickness and overhang angles were embedded into the optimization process, ensuring that resulting designs are not only thermally efficient but also manufacturable using modern additive techniques. In parallel, the study delves into the use of Gyroid-based TPMS geometries for constructing the core of the heat exchanger. TPMS structures are known for their high surface area, excellent fluid mixing capabilities, and minimal pressure drop characteristics. The researchers applied a data-driven modeling framework using Heteroscedastic Sparse Gaussian Process Regression (HSGPR) combined with genetic algorithms. This allowed for the rapid evaluation and optimization of key geometric parameters such as frequency, iso-value, and phase shift. The result was a set of Gyroid structures tailored for high heat transfer and low flow resistance, demonstrating clear improvements over conventional straight-channel designs. After the designing process, additive manufacturing played a critical role by turning these highly complex, optimized geometries into physical components. Utilizing Laser Powder Bed Fusion (LPBF) with Haynes 282, the study demonstrated the feasibility of fabricating these heat exchangers at high precision. Post-processing methods, including dilation-erosion operations, were applied to ensure local features adhered to self-supporting constraints. The fabricated structures were then subjected to thermohydraulic testing under conditions representative of supercritical CO 2 Brayton cycles, validating the predicted performance and confirming the viability of the full design-to-fabrication pipeline. Finally, thermohydraulic testing across the above studies served as a crucial experimental validation of advanced heat exchanger. Under consistent high-temperature and high-pressure conditions using supercritical CO 2 , the testing demonstrated that both TO and Gyroid-based TPMS designs significantly outperformed conventional straight-channel HXs. The TO design achieved a 115% increase in UA and NTU and a 27.6% boost in gravimetric power density, while the data-driven optimized Gyroid design delivered a 166% increase in UA and NTU and improved effectiveness from 68.7% to 86.1%. These results validate the simulation models, confirm the manufacturability of complex geometries under AM constraints, and provide key insights into design-performance trade-offs, thereby advancing the development of high-efficiency, compact heat exchangers for extreme environments.

36 MATERIALS SCIENCE↗

Web-Based Tools for Data-Informed Remedy Optimization: Software Theory and User Guide

This report documents the development and application of two web-based decision-support tools for pump-and-treat (P&T) groundwater remediation systems: PTOLEMY (Pump-and-Treat Optimized Location Evaluation to Maximize Yields) and OPTIMA (Optimization for Pump-and-Treat Implementation, Management, & Assessment). These tools enhance remedy design and management by leveraging advanced computational methods – specifically deep learning and multi-objective optimization – within a user-friendly platform. By integrating data-driven models with established hydrogeological knowledge, PTOLEMY and OPTIMA enable more efficient evaluation of well placement and operational strategies, helping site managers balance multiple remediation objectives under complex conditions. Both tools are implemented as modules within the SOCRATES (Suite Of Comprehensive Rapid Analysis Tools for Environmental Sites) web platform, which provides data access, visualization, and analytics to support remedy optimization across sites in the U.S. Department of Energy Office of Environmental Management complex. PTOLEMY is a rapid screening module designed to identify promising locations for new extraction wells. It employs a multi-channel three-dimensional convolutional neural network (MC3D-CNN) trained on high-fidelity simulation data to predict the relative performance (in terms of contaminant mass recovery) of potential well sites. Through an interactive web interface, PTOLEMY visualizes the probability of high performance across a site, highlighting areas where an extraction well is likely to yield above-threshold contaminant removal over a multi-year period. PTOLEMY’s map-based displays and exportable results support transparent communication of screening analyses. By focusing attention on the most favorable candidate locations, the tool augments traditional engineering judgment and physics-based modeling, providing a data informed basis for subsequent detailed evaluations. OPTIMA is a multi objective optimization module designed to find wellfield layouts and operating schedules that meet various cleanup goals. It quickly evaluates thousands of candidate setups – combinations of well locations, timing, and rates – and returns a small set of best trade-off options for comparison. At its core, OPTIMA uses a U-Net-based surrogate model – a deep-learning emulator of a groundwater flow and transport simulator – to dramatically accelerate scenario evaluations. Coupling this fast surrogate with the NSGA-II (Non-dominated Sorting Genetic Algorithm II) evolutionary algorithm, OPTIMA explores a wide decision space of well locations and schedules to identify Pareto-optimal solutions that trade off key objectives (e.g., minimizing cleanup time, maximizing contaminant mass removal, and minimizing plume extent). The tool outputs a family of optimal configurations and visualizes their trade-offs (Pareto frontiers of cleanup metrics and maps of optimized well placements). Site managers can use these results to understand the range of viable strategies and to select candidate designs for more detailed verification. OPTIMA is currently under active development and not yet fully released; this guide provides early documentation to support planning and gather user feedback.

54 ENVIRONMENTAL SCIENCES↗

Use of Digital Real-Time Simulation and Optimization to Identify Maximum Real Power Injection on Banshee Distribution Network

This study investigates the hosting capacity of the Banshee Distribution Network by optimizing the real power injection at carefully selected Distributed Energy Resource (DER) locations. The analysis is conducted within the framework of power system operational constraints, including bus voltage ranges, thermal line ratings, and transformer loading limits. A Python-based Genetic Algorithm (GA), implemented using the PyGAD library, is employed to iteratively identify the optimal power injection configuration that maximizes network utilization while preserving system reliability. The methodology integrates a real-time simulation environment using the Real-Time Digital Simulator (RTDS), allowing high-fidelity evaluation of power flow and voltage behavior under each proposed injection scenario. By coupling the optimization algorithm with real-time simulation feedback, this approach ensures that both static and dynamic constraints are enforced during the evaluation process. The GA leverages evolutionary operators such as selection, crossover, and mutation to navigate the nonlinear search space efficiently. The results of the study delineate the feasible hosting capacity at three targeted buses, reflecting maximum real power levels that can be injected without causing voltage violations, transformer overloading, or line congestion. These findings provide a decision- support tool for distribution planners and utilities aiming to integrate higher penetrations of DERs in existing infrastructure. Additionally, the work lays the foundation for extending such optimization techniques to multi-objective formulations, including economic dispatch and reactive power coordination, in future studies.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Design of a high power 8 MeV electron beam injector for the LERF

We present the beamline design for a high power 8 MeV electron injector to be installed at the Low Energy Recirculator Facility (LERF). The injector is designed to provide beam power of up to 80 kW for testing positron source target candidates. We discuss the results of beam dynamics simulations conducted using the General Particle Tracer (GPT) software, where electromagnetic beamline elements were optimized using multi-objective genetic algorithm tools. Additionally, we present preliminary studies on secondary particle generation from both rotating solid and free surface liquid metal jet targets described elsewhere in this workshop.

Lizarraga-Rubio, Victor [Thomas Jefferson National↗