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DOE OSTI2020

Modeling Time-Dependent Surrogates of Additive-Manufactured Nuclear Fuels Processes

Additive manufacturing (AM) technology is being increasingly adopted in a wide variety of application areas for its ability to rapidly produce, prototype, and customize designs. Recently, a hybrid AM technique was successfully developed at Idaho National Laboratory (INL) to manufacture nuclear fuels [1]. Despite the advantages, this AM technique needs optimization due to defects from a highly complex melting and sintering process. The complex metallurgical phenomena during AM processes are strongly related to parameters such as applied laser power, traveling speed, and scan style, which could lead to differences in density, residual stress, crystallographic texture, and mechanical properties. In addition, stochastic variations in laser energy interaction and associated multiscale/multiphysics phenomena cause variations in microstructure evolution and mechanical properties. Currently, researchers at INL are focusing on developing a comprehensive modeling framework, leveraging INL’s simulation tools MOOSE/MARMOT/BISON/RAVEN [2-4] to describe all steps of this AM process across multiple length scales. Although this advanced framework plays a critical role in enabling enhancements to traditional trial and error approaches for design and optimization of nuclear fuel materials, it remains computationally intense, limiting its use in sensitivity and optimization analysis. In this case, an accurate and inexpensive surrogate becomes an effective tool for providing a tractable approximation of the underlying underline physics. Surrogate models generally not based on the physics of a system are purely mathematical models used to capture the relationships between specific system inputs and outputs. Popular approaches, including neural networks [5], response surfaces [6], and subspace-based reduced order models [7], have been applied to a wide range of disciplines, such as nuclear reactor design, aerospace design and automotive design. In this summary, we employ advanced time-dependent surrogate models such as high-dimensional model representation (HDMR) [8] and physics-informed deep neural network (PINNs) [9] to accelerate the design and optimization of AM process.

42 ENGINEERING↗

DOE OSTI2024

Development of a novel bunch oscillation recorder with RFSoC technology

The SuperKEKB accelerator is designed to achieve unprecedented luminosity levels, but this goal is currently hindered by Sudden Beam Loss (SBL) events. These events not only obstruct luminosity improvement but also pose a significant risk to accelerator components, the Belle II detectors, and the superconducting focusing system, potentially leading to severe damage and quenching of the superconducting system. Here, to address this critical challenge, we have developed a novel Bunch Oscillation Recorder (BOR) based on RFSoC technology. The BOR has demonstrated high precision with a position resolution of 0.03 mm, making it a powerful tool for real-time beam monitoring. In its initial deployment, the BOR successfully recorded multiple SBL events, providing valuable data for further analysis. By strategically positioning BORs at the suspected points of SBL origin, we aim to directly identify sources of beam instability. We anticipate that this portable, high-speed BOR monitor will play a crucial role in resolving the SBL issue, ultimately helping achieve SuperKEKB's luminosity targets.

Beam diagnostics↗

DOE OSTI2025

A Novel Framework for Performance Evaluation and Design Optimization of PCM Embedded Heat Exchangers for the Built Environment

This research sheds light on the performance evaluation and design optimization of PCM-HXs for the built environment, addressing several barriers to practical issues to PCM-HX commercialization such as modeling aspects (i.e., modeling expertise and computational / time investment, etc.), manufacturing aspects (i.e., at-scale manufacturing, cost assessments, etc.) and experimental performance assessment (i.e., reliable experimental data, assessment of multiple PCM-working fluid combinations, etc.). We present a novel, comprehensive, and experimentally-validated design optimization framework for PCM-HXs capable of simulating any PCM-HX geometry with reasonable accuracy and significant computational time savings when compared to traditional CFD-based design practices. The framework was validated for a wide range of PCM-HX configurations, including a design optimization for a domestic hot water heater application where TES partially replaces electrical heating input. The resulting PCM-HXs were found to deliver 34-68% of the total daily hot water supply with only 5-10% package volume increase from the water heater, thus within U.S. DOE targets for TES systems. To identify the most promising HXs for PCM applications, first-order geometry and cost analyses were conducted based on off-the-shelf HX products. As part of this work, 9 PCM-HX prototypes were manufactured using additive and conventional manufacturing methods. Detailed economy-of-scale assessments were conducted for the most promising PCM-HXs and were found to have a good outlook for the next 5-10 years. The PCM-HX design optimization framework was validated through comprehensive in-house experimental testing using newly-developed PCM-to-fluid test facilities. In total,10 total in-house component-level experiments were conducted using these prototypes, including 9 with water and 1 with refrigerant (R410A) as the working fluid. It was found that the framework can successfully predict experimental thermal-hydraulic performance within ±10-20% the first time without manual design changes, eliminating the need for time-consuming and expensive prototyping efforts as part of the design process. As part of this work, a publicly-available PCM web tool was released which includes a PCM property database (531 PCMs) and PCM-HX modeling tool to assist the design community on common PCM-HX use-cases, e.g., single/multiple flow path(s) fluid-to-PCM and air-to-fluid-to-PCM configurations (https://ceeeweb.umd.edu/pcmapp/). This work will accelerate the design and time to market for next generation PCM-HXs.

25 ENERGY STORAGE↗

DOE OSTI2026

Study of a Fixed Field Accelerator as a Driver for Proton Driven Plasma Wakefield Acceleration

A fixed field accelerator (FFA) scheme is studied that is aimed to provide a continuous stream of high-energy proton bunches at a rate of up to 20 kHz. The bunches can be used to drive plasma wakefields capable of acceleration of leptons in a single stage for a lepton collider at the Higgs and the top-mass scales. The proposed FFA is assumed to have a circumference of 6900 m and is designed to accelerate proton bunches from 150 GeV to 500 GeV .

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Window Test Stand Status and Future Work [Slides]

Challenges to getting the window test stand running that were addressed. Initial design of half-anchor connector was insufficient. Accommodation of non-Rexolite windows required engineering.

42 ENGINEERING↗

DOE OSTI2022

Invertible Neural Networks for Airfoil Design

We report the airfoil design problem, in which an engineer seeks a shape with desired performance characteristics, is fundamental to aerodynamics. Design workflows traditionally rely on iterative optimization methods using low-fidelity integral boundary-layer methods as higher-fidelity adjoint-based computational fluid dynamics methods are computationally expensive. Surrogate-based approaches can accelerate the design process but still rely on some iterative inverse design procedure. In this work, we leverage emerging invertible neural network (INN) tools to enable the rapid inverse design of airfoil shapes for wind turbines. INNs are specialized deep-learning models with well-defined inverse mappings. When trained appropriately, INN surrogate models are capable of forward prediction of aerodynamic and structural quantities for a given airfoil shape as well as inverse recovery of airfoil shapes with specified aerodynamic and structural characteristics. The INN approach offers a roughly 100 times speed-up compared to adjoint-based methods for inverse design. We demonstrate the INN tool for inverse design on three test cases of 100 airfoils each that satisfy the performance characteristics close to those of airfoils used in wind-turbine blades. All generated shapes satisfy the desired aerodynamic characteristics, demonstrating the success of the INN approach for inverse design of airfoils.

17 WIND ENERGY↗

DOE OSTI2023

MCNP Neutronic Design Optimization of an Accelerator-Driven Subcritical Assembly for Mo-99 Production

Molybdenum-99 (99Mo) has been in short supply in the recent decades. The most common productionmechanism, irradiation in a reactor fueled by highly enriched uranium (HEU), raisesproliferation concerns. Novel efforts are being pursued to meet the demand for 99Mo withoutHEU irradiation, relying on the processing of low-enriched uranium or natural uranium. Onesuch concept is to use an electron linear accelerator to generate bremsstrahlung that, through aphotonuclear reaction, can help multiply neutrons in a subcritical assembly. The goal of this workwas to neutronically optimize the subcritical assembly for keff and 99Mo production. This optimizationis unique for The design was optimized in terms of reflector type, pitch, and LEU andNU mass. Results of the paper show the effects of fuel mass, pitch, and reflector material on keffand the NU fission fraction. keff was found to be mostly driven by LEU mass, and the NU fissionfraction was dependent on several parameters, primarily pitch and mass.

Conant, Andrew↗

DOE OSTI2024

LAMP RFQ vane tip macro-based 3D modeling

This report covers the workflow and the demonstration of modeling the radiofrequency quadrupole (RFQ) vane tip geometry in 3D, for the Front End Upgrade of the LANSCE Modernization Project (LAMP). The goal is to develop an automated procedure to generate the 3D model of the LAMP RFQ vane tip geometry, so as to eliminate the need to manually enter the design parameters for each RFQ cell, when building the 3D model. With the developed procedure, the LAMP RFQ design optimization work can be significantly more efficient.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Niowave: Flow measurement development - FY2022

It is critical to maintain proper flow rate of lead bismuth eutectic (LBE) for the operation of the Niowave converter. The high temperature of the LBE (> 200 °C) makes it difficult to purchase simple off the shelf flow meters. One method that can be used to measure the flow rate indirectly, is to use a venturi, with a known throat diameter and differential pressure measurement across the throat. LANL designed the venturi and after numerous iterations with vendors, narrowed down a differential pressure transducer to use with the venturi.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Context-Aware Learning for Inverse Design in Photovoltaics

This document describes progress in the ARPA-E DIFFERENTIATE project titled “Context-Aware Learning for Inverse Design in Photovoltaics” during the period of May 2019 to May 2022. This project is being performed at Iowa State University, New York University, Stanford University, and National Renewable Energy Laboratory. The project aims to develop a new machine learning (ML) framework to significantly accelerate the design of organic microstructures for improved organic photovoltaic performance. In this project, we had developed an inverse design framework using Deep Learning called InvNets for generating microstructures with desired physics-driven properties. As a preliminary product, in Milestone 3, we demonstrated how InvNets show 20% improvement in the performance of the microstructures and over 100X speedup in the performance compared to traditional processes for physics-driven inverse design. Later, in Milestone 6, we demonstrated that InvNets work for more complex physics properties, specifically, generating microstructures for organic photovoltaic cells with desired current-voltage characteristics. Further, in Milestone 4, we explored the idea of using physics-aware surrogates for obtaining solutions of partial differential equations(PDE) called as DiffNets(now called as NeuFENets to avoid ambiguity of names). The connection between both frameworks is that DiffNet surrogates form the physics-aware surrogate in the InvNet framework. Finally in Milestone 8, we extend our framework for other physics domains. Specifically, we explore building geometry-aware NeuFENets by developing physics surrogates that exploit ideas from traditional immersed boundary finite element methods. With these updates, we are able to achieve all the Milestones.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Conceptual Design of 20 T Hybrid Accelerator Dipole Magnets

Hybrid magnets are currently under consideration as an economically viable option towards 20 T dipole magnets for next generation of particle accelerators. In these magnets, High Temperature Superconducting (HTS) materials are used in the high field part of the coil with so-called insert coils, and Low Temperature Superconductors (LTS) like Nb 3 Sn and Nb-Ti superconductors are used in the lower field region with so-called outsert coils. The attractiveness of the hybrid option lays on the fact that, on the one hand, the 20 T field level is beyond the Nb 3 Sn practical limits of 15-16 T for accelerator magnets and can be achieved only via HTS materials; on the other hand, the high cost of HTS superconductors compared to LTS superconductors makes it advantageous exploring a hybrid approach, where the HTS portion of the coil is minimized. Here we present in this paper an overview of different design options aimed at generating 20 T field in a 50 mm clear aperture. The coil layouts investigated include the Cos-theta design (CT), with its variations to reduce the conductor peak stress, namely the Canted Cos-theta design (CCT) and the Stress Management Cos-theta design (SMCT), and, in addition, the Block-type design (BL) including a form of stress management and the Common-Coil design (CC). Results from a magnetic and mechanical analysis are discussed, with particular focus on the comparison between the different options regarding quantity of superconducting material, field quality, conductor peak stress, and quench protection.

43 PARTICLE ACCELERATORS↗

DOE OSTI2024

Rapid Design of Cost-Effective Refractory High Entropy Alloys Strengthened by Precipitation

Funded by the ARPA-E ULTIMATE program, this project aims to develop cost-effective refractory high entropy alloys (RHEAs) for gas turbine blades applications at 1300 degree Celsius and above. The alloy design focuses on carbides precipitation strengthening in a strong and ductile solid solution BCC matrix. The goal is to achieve balanced mechanical properties including room temperature ductility & fracture toughness and high temperature strength and creep resistance, while maintaining comparable oxidation resistance and densities to Ni-base superalloys. To accelerate alloy design, multiscale modeling including CALPHAD and first-principles density functional theory (DFT) calculations and machine learning are performed. Specifically, high throughput phase diagram calculations and screening are carried out using CALPHAD; DFT calculations are performed to predict intrinsic ductility, grain boundary strengthening potential, coefficient of thermal expansion (CTE) and temperature-dependent elastic constants. Temperature dependent yield stress, creep strain and CTE are also predicted using machine learning. Down-selected alloys are synthesized in small buttons of about 250g using arc melting for rapid evaluation on microstructure and mechanical properties before employing plasma arc melting and additive manufacturing for producing large ingots. Preliminary computational and experimental results of this project will be presented and future direction in RHEA development will be discussed.

high temperature materials↗

DOE OSTI2026

Elucidating molecular level interfacial interactions between a de novo protein and nucleated calcite with solid-state NMR

Biomineralization is the process by which organisms use biomolecules to produce hierarchically structured organic–inorganic composites. Using biology as inspiration, a protein construct (FD31) was previously designed to accelerate formation of nano-calcite with an unconventional {110} face. Here, to understand the molecular interactions essential for protein aided calcite nucleation, solid-state nuclear magnetic resonance (ssNMR) spectroscopy was used in this work to characterize the FD31–calcite interface at the atomic level. Glutamic acid side chains designed to interact directly with calcium ions on the surface were found to have dynamics on the sub-millisecond timescale, indicating possible interactions between the protein and surface waters that were not included in the original model. Dipolar ssNMR recoupling techniques also showed that the protein backbone is ∼2 Å closer to the surface than in the original docking model. Refined molecular simulations were done in the presence of explicit waters, which resulted in the protein backbone closer to the surface than in the original docking structure, providing better agreement with experiment and highlighting the important role played by water in FD31–calcite interactions. While this work provides the first experimental confirmation that FD31 interactions with calcite are localized to the surface of the protein designed to serve as a template, these studies do indicate a more dynamic binding and closer binding mode between FD31 and the nucleated surface than originally proposed. In all, this enhanced molecular insight into the FD31–calcite interface has advanced our fundamental understanding of the atomic interactions at the organic–inorganic interface and will aid in the design of biological templates for the nucleation of inorganic crystals.

Close, Emily G. S. [Pacific Northwest National Lab↗

DOE OSTI2025

DataSet for Elucidating molecular level interfacial interactions between a de novo protein and nucleated calcite with solid-state NMR

Biomineralization is the process by which organisms use biomolecules to produce hierarchically structured organic-inorganic composites. Using biology as inspiration, a protein construct (FD31) was previously designed to accelerate formation of nano-calcite with an unconventional {110} face. To understand the molecular interactions essential for protein aided calcite nucleation, solid-state nuclear magnetic resonance (ssNMR) spectroscopy was used in this work to characterize the FD31-calcite interface at the atomic level. Glutamic acid side chains designed to interact directly with calcium ions on the surface were found to have dynamics on the sub-millisecond timescale, indicating possible interactions between the protein and surface waters that were not included in the original model. Dipolar ssNMR recoupling techniques also showed that the protein backbone is ~2 Å closer to the surface than in the original docking model. Refined molecular simulations were done in the presence of explicit waters, which resulted in the protein backbone closer to the surface than in the original docking structure, providing better agreement with experiment and highlighting the important role played by water in FD31-calcite interactions. These studies provide the first experimental evidence to confirm that FD31 interactions with calcite are localized to the surface of the protein designed to serve as a template. However, these studies do indicate a more dynamic binding and closer binding mode between FD31 and the nucleated surface than originally proposed. In all, this enhanced molecular insight into the FD31-calcite interface has advanced our fundamental understanding of the atomic interactions at the organic-inorganic interface and will aid in the design of biological templates for the nucleation of inorganic crystals.

Saccuzzo Close, Emily Grace [Pacific Northwest Nat↗

DOE OSTI2022

Mo-100 Disk Structural Integrity Tests I

The NorthStar pressed and sintered 29 mm diameter, 0.5 mm thick Mo-100 disks will be subjected to large temperature gradients when in beam during production. The beam preferentially heats the disk center, while the aggressive cooling by the helium keeps areas outside the beam spot relatively cool. The result is a temperature gradient of up to 900°C over the radial distance of 14.5 mm. To test the disk integrity with this high temperature gradient, a special holder for a single disk was made such that proper cooling can be applied as in the design target and the disk heating can be replicated with induction heating. NorthStar press and sintered disks were used for these tests.

36 MATERIALS SCIENCE↗

DOE OSTI2024

MICROREACTOR APPLICATIONS, RESEARCH, VALIDATION, AND EVALUATION (MARVEL) REACTOR ? STATUS, CONSTRUCTION, AND TESTING

The paper presents the current status of the Microreactor Applications, Research, Validation, and Evaluation (MARVEL) microreactor design, qualification testing, fabrication, and high-level construction schedule. An overview of initial criticality, low power physics testing, and start-up testing is included, as well as an overview of the envisioned processes in which end-users can engage the project for access to operational data or specific demonstrations. Designed by the Idaho National Laboratory (INL) under the auspices of the US Department of Energy’s Microreactor Program for construction and operation at the INL, MARVEL is a small, fully functional advanced reactor with UZrH fuel and thermal output of 85 kW. It offers a unique opportunity for scaled demonstrations that can dramatically accelerate the design, licensing, and deployment of commercial microreactors for power production or process heat applications. MARVEL’s objective is to build a small liquid-metal thermal reactor at the INL to demonstrate design and operating processes for microreactors, microgrid integration, and process heat applications. MARVEL finished 90%-final-design in September 2023 and completed an independent project assessment in early 2024. Fabrication of long-lead components and fuel, safety analysis review, and procurement for construction are underway. MARVEL assembly and construction will start in 2025 and fuel loading is expected in mid- 2027. Initial criticality will be performed in a dry condition in late 2027, followed by loading of NaK coolant and start-up testing. Approximately six months later, release for unrestricted operations will enable subsequent testing of microreactor characteristics, microgrid integration and select heat extraction applications.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2021

Additive Manufacturing in the Nuclear Reactor Industry

This article discusses the application of additive manufacturing in the nuclear reactor industry.Additive manufacturing for nuclear applications increased significantly in the late 2010s due to rapid progress in technology, particularly with methods using metals and ceramics.Because these manufacturing technologies are uniquely data-rich, they allow for an advanced understanding of materials, and they offer the potential to predict part performance based on build data. The inherent characteristics of additive manufacturing technologies allow for rapid prototyping and geometric freedom, making it possible to implement an accelerated agile design process for advanced nuclear reactor applications. Predictive high-fidelity multiphysics simulations are crucial to fully leverage this geometric flexibility. Qualification and regulator acceptance ultimately determine the breadth and scope of applications for these technologies. The US Department of Energy Office of Nuclear Energy Transformational Challenge Reactor program integrates many of these elements to accelerate the deployment of additive manufacturing technologies to industry.

Betzler, Benjamin↗

DOE OSTI2025

Artificial intelligence–powered biofoundries for protein engineering and metabolic engineering

Synthetic biology is rapidly evolving through the integration of artificial intelligence (AI) and automated biofoundries. This convergence accelerates the design–build–test–learn cycle, shifting protein engineering and metabolic engineering from labor-intensive manual experimentation to autonomous experimentation. This review summarizes recent advances in workflow development, AI models, and their integration with biofoundries for automated or autonomous protein engineering and metabolic engineering. Particularly, we highlight the potential of AI-powered biofoundries for accelerated scientific discovery and innovation in synthetic biology.

Chen, Junyu [Univ. of Illinois at Urbana-Champaign↗