Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Structural Materials”

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

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

New class of tritium breeders for fusion applications: Metal-reinforced composite breeders

Commercial fusion reactors operating on a D-T fuel cycle will require a steady supply of tritium to maintain the burning plasma required for continuous power generation. Since tritium has a short half-life, there is negligible natural abundance which necessitates fusion reactors to produce their own source of tritium. Tritium is most easily produced by surrounding a fusion reactor core with lithium (Li), which reacts under the intense neutron flux leaving the reactor core to form tritium and helium. Due to the hazards and technical challenges associated with surrounding a fusion reactor core with many tons of molten Li, other Li-bearing tritium breeder materials have been pursued. Unfortunately, most of the liquid breeders historically examined are exceedingly corrosive to reactor structural materials while many solid breeders in the form of ceramics are forced to make tradeoffs between Li content and mechanical integrity. In this work, to break the historic limit between Li-density and mechanical integrity of traditional solid breeders, a new class of solid tritium breeders is developed: metal-reinforced composite (MERC) breeders. Specifically, the high Li-density of lithium oxide (Li 2 O) is exploited through the addition of a metal reinforcing phase, producing a composite breeder material exhibiting high splitting tensile strength and quasi-ductility with a Li-density greater than other leading solid breeder candidates, including lithium orthosilicate (Li 4 SiO 4 ) and lithium metatitanate (Li 2 TiO 3 ). Mechanical testing, microstructural characterization, and neutronic simulation results are presented and discussed in light of fusion reactor design considerations.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

MPEX AI Digital Twins

All magnetically confined plasma fusion power plant concepts (Tokamak, Spherical Tokamak, Stellarator, Mirror, ...) must exhaust the heat and plasma from the core confinement region to the material walls. The primary channel for this exhaust is through a plasma divertor which directs plasma along open magnetic field lines to a material target. The Material Plasma Exposure eXperiment (MPEX) illustrated in Figure 1, is a high-power, steady-state linear plasma device designed to produce the plasma material interaction (PMI) conditions of the divertor of future magnetic confinement fusion power plants: energy flux 20MW/m 2 , ion fluence 1031/m 2 , pulse duration 106 sec. These goals of plasma exposure in MPEX are well beyond those achieved in magnetic fusion experimental devices. Successfully achieving these high power steady state conditions for long pulses requires operational control of the heating and particle sources and the plasma flux to the walls and target. The MPEX AI Hot Spot Controller, proposed in this project, will help achieve the operational milestones of MPEX. The MPEX device will begin commissioning at the end of FY26. A smaller proto-MPEX was operated for 14,666 plasma discharges and will resume operation in September of 2025 as proto-MPEX-lite, with reduced capability, to test a new window for the Helicon plasma source. The proto-MPEX data has undergone surrogate modeling with machine learning methods (R. Archibald, 2022 IEEE International Conference on Big Data). This proto-MPEX data will be used to begin development of the AI digital twins described in this white paper. The scientific mission of MPEX is to qualify materials of different composition for use in the high energy and plasma flux conditions of a fusion power plant. The materials exposed in MPEX will in some cases be exposed to high neutron fluxes at other ORNL facilities to measure the changes to their PMI properties. The targets exposed in MPEX will be transported under vacuum to a Surface Analysis Station (SAS). The SAS will be equipped with the following diagnostics: Focused Ion Beam (FIB) for trench milling, 100-400 angstrom resolution scanning electron microscope (SEM), surface mapping x-ray spectrometer, high resolution camera, and a future upgrade to a laser induced breakdown spectroscopy quadruple mass spectrometer (LIBS-QMS). The MPEX experiments will generate diverse pre- and post-exposure measurement data of detailed material properties down to the crystal grain level in 3D for post-exposure assessment of PMI damage (e.g. cracking, melting, erosion and redeposition of the material). Physics models for the PMI, and how the material composition and manufacturing impact its performance under high energy plasma exposure, need to be validated with MPEX data to guide the selection of new candidate materials. Our vision for the MPEX AI Digital Twins project is to supply experimental and physics model simulation data to train Artificial Intelligence (AI) models for data processing, analysis, operational control, PMI and materials simulation to maximize the scientific output of the MPEX device. Ultimately, an AI digital twin of MPEX material assessment metrics for tested and synthetic material types with simulated PMI will be trained by the AI Modeling Teams on the experimental and physics simulation data submitted to the American Science Cloud by this project. A purely empirical search for the best material is inefficient given the finite number of samples that can be tested on MPEX. In order to expand the material properties database for training the MPEX Material Assessment AI Digital Twin, and to gain physics understanding of the PMI processes, physics models of the material properties and PMI processes are required. The physics simulations provide detailed simulation data, like impact angles for plasma ions, sputtering yields, transport of the ionized sputtered target material in the plasma, and redeposition locations. This simulation data expands the measurement data for deeper physics understanding. The experimental data is essential to validate the PMI and material structure simulation models. The validated models can then be used to generate new simulation data of MPEX material assessments for synthetic material compositions that have not been exposed in MPEX. These predictive simulations, plus the whole experimental dataset, will be used to train the MPEX Material Assessment AI Digital Twin allowing a rapid generative AI search for new materials with reduced PMI damage by interpolating the domain of the training set. These new optimum materials can be simulated with the physics codes and/or tested in MPEX. The ability of AI neural networks to interpolate multi-dimensional parameter spaces and generate virtual data is exploited for a more efficient search for optimum materials. The advent of the Transformational AI Models Consortium (TAIMC) is an opportunity to engage with state of the art private and public AI developers to achieve the goals of the AI digital twins and AI accelerated physics models proposed in this project. Our partners at ORNL from the Advance Scientific Computing Research (ASCR) organization will collaborate in accelerating the integrated plasma material interaction simulation framework. This simulation framework will provide a platform for generating simulation data across a range of physical fidelities, including hybrid methods that produce multi-fidelity results. This data will be leveraged for AI model development, both for generation of surrogates and the automation of simulation campaigns. A part of the research below will include collaborative efforts with the TAIMC to (i) adapt data storage approaches to ensure AI-readiness, (ii) provide a protypical exemplar to inform and exercise constructed workflows, and (iii) generate and share data, using the TAIMC unified AI data standard, for foundational models that will be trained from multiple sources across the DOE complex. We will also collaborate with the TAIMC, as well as the planned AI modeling teams, to develop approaches for reducing the cost of data generation. These include tailored multi-fidelity approaches as well as fine-tuning strategies to augment general, large-scale foundational models.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Dislocation-Grain Boundary Interaction Dataset for FCC Cu

Interactions between dislocations and grain boundaries play a major role in controlling the strength and ductility of structural materials. Experimentally, assessing and probing geometric and stress-based criteria at the local level for dislocation transmission through grain boundaries remains challenging. Therefore, there have been many efforts to systematically generate datasets of dislocation-grain boundary interactions (DGI) via computational models such as molecular dynamics simulations. So far, most DGI datasets have focused only on the subset of nominal minimum-energy grain boundary structures, which limits their applicability, especially to materials processed far from equilibrium. We present a comprehensive database of dislocation-grain boundary interactions for edge, screw, and 60° mixed dislocation with 330 <110> and 257 <112> symmetric tilt grain boundaries (total of 587) in FCC Cu consisting of 73 minimum-energy grain boundary structures and 514 metastable structures. The dataset contains the outcomes for 5234 unique interactions for various dislocation types, grain boundary structures, and applied shear stresses.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Molecular Scale Tuning of Covalent Organic Frameworks for Enhanced Properties

Covalent organic frameworks (COFs) are a type of porous, extended structure material which has demonstrated utility in numerous applications, including sensing and separations. Judicious choice of the linker and node not only fine tune the size of the framework, but also the functionality. We seek to tailor COF materials for applications in gas capture and electrochemical sensing. Herein, we demonstrate the material properties of COFs can be fine-tuned via post-synthetic metal cation doping of the parent structures. We have synthesized several COF structures with varying linkers which allow for the incorporation of metal cations. Structures incorporating Zn 2+ , Mn 2+ , and Cu 2+ have demonstrated utility in improving the CO 2 uptake properties of the COFs, while Ag + doped structures show potential as electrochemical sensors for chloride anions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Hierarchically ordered porous transition metal compounds from one-pot type 3D printing approaches

Solution-based soft matter self-assembly (SA) promises unique material structures and properties from approaches including additive manufacturing/three-dimensional (3D) printing. The 3D printing of periodically ordered porous functional inorganic materials through SA unfolding during printing remains a major challenge, however, due to the often vastly different ordering kinetics of separate processes at different length scales. Here, we report a “one-pot” direct ink writing process to produce hierarchically porous transition metal nitrides and precursor oxides from block copolymer (BCP) SA. Heat treatment protocols identified in various environments enable mesostructure retention in the final crystalline materials with periodic lattices on three distinct length scales. Moreover, embedded printing enables the first BCP directed mesoporous non-self-supporting helical oxides and nitrides. Resulting nitrides are superconducting, with record nanoconfinement-induced upper critical fields correlated with BCP molar mass and record surface areas for compound superconductors. Results suggest scalable porous functional inorganic material formation approaches for applications including catalysis, sensing, and microelectronics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Surrogate Model Integration with MOOSE XFEM for Creep Crack Growth

Ferritic-martensitic steels are key structural materials for advanced reactors but experience time-dependent deformation and damage under prolonged high temperature and irradiation, leading to creep-driven crack initiation and growth. High-fidelity models—crystal plasticity with irradiation mechanisms, phase-field for microstructural evolution, and continuum-damage viscoplasticity—capture the underlying physics but are too computationally intensive for broad design-space exploration and uncertainty quantification. This milestone advances a scalable alternative by integrating a microstructure-sensitive surrogate creep model into the Multiphysics Object-Oriented Simulation Environment (MOOSE) finite element framework and extending it to fracture via the extended finite element method (XFEM). The surrogate model, developed with collaborators at Sandia and Los Alamos National Laboratories, maps relevant microstructural descriptors to the viscoplastic response of HT9. We embed this surrogate within a coupled deformation-damage workflow in MOOSE/XFEM to simulate creep-driven crack initiation and propagation. Implementation enhancements include updates to the material interface, a plastic correction phase involving microstructure evolution, and fracture criteria to ensure numerical robustness and compatibility with the surrogate structure. Demonstrations on canonical creep benchmarks spanning uniaxial and multiaxial states show that the surrogate reproduces key trends of high-fidelity models while substantially reducing computational cost. The resulting capability bridges physics fidelity and performance, providing a practical path to a predictive, microstructure-aware assessment of creep and fracture in reactor materials.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Carbon nanotube coated metal mesh: Bridging nanoscale and macroscale

A novel hybrid structure has been developed by growing carbon nanotubes (CNTs) on a metal mesh that functions as a literally unlimitedly extendable backbone. This hybrid material structure provides a new approach of extending CNTs’ advantageous properties such as ultrahigh thermal and electrical conductivities, high sensitivity to gases, and distinctive wettability for different liquids with chemical inertness to macroscale—otherwise available only in nano- and microscales. In this feasibility work, CNTs were grown on a Type 316 stainless steel (SS) mesh by self-catalytical chemical vapor deposition (CVD) without the need of an externally added catalyst or catalyst support. For the radially aligned and entangled CNT forest on the SS mesh, the average CNT diameter is around 50 nm, while the length varies from 20 to 25 µm. High-resolution transmission electron microscopy analysis revealed the multiwall structure of the CNTs with >30 rolled-up graphitic sheets. Raman spectra of the CNTs showed a dominant G band, indicating a well-ordered graphitic nanostructure. Being highly hydrophobic with a water contact angle of ∼145° and oleophilic, the CNT-coated SS mesh could be used in fluid separation and organic contaminant removal from water. Moreover, CNTs are recognized for their exceptional thermal conductivity and the CNT-coated mesh offers a supportive structure with directly connected CNTs for efficient heat transfer. Proof of concept has been achieved for the CNT-coated mesh’s potentials as a liquid filter and an thermal interface material (TIM). Specifically, the CNT-coated mesh demonstrated the capability of capturing water from a water-organic mixture with a 100 % efficiency while allowing organic liquids to pass through the filter. Furthermore, when used as a TIM, the CNT-coated mesh reduced the interfacial thermal impedance by >30 %.

Carbon nanotubes (CNTS)↗
DOE OSTI2024

Down-selection of Innovative Fusion Materials

The goal of this proposal is to develop and fundamentally understand the microstructure of refractory multi-component alloys (MCA) as fusion-relevant plasma facing and structural materials, a step to down-select innovative fusion materials prior to the process of advancing their technology readiness level (TRL). Developing materials is paramount to enable fusion as energy source since no existing material is capable of coping with such extreme conditions. We will aim at understanding the role of chemistry and microstructure in these complex multi-component alloys systematically comparing their performance with pure tungsten materials in terms of mechanical properties and manufacturability. Such understanding will open opportunities to optimize MCAs for fusion applications and has a potential of attracting industry partnership.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Development, Verification, and Validation of an OpenFOAM-Based Solver for Modeling Inertial Fusion Energy Chambers

Our work seeks to introduce a computational tool tailored to the physics of inertial fusion energy chambers, in particular, those concepts based on thick liquid walls. In this approach, the structural materials are protected by several neutron mean-free-paths of renewable liquid and thus will be able to survive much longer than un-shielded walls, with virtually all structures lasting for the life of the plant and enabling the use of commercially available and qualified materials. The OpenFOAM-based solver named rhoCentralFoam has been used as a starting point. rhoCentralFoam belongs to the standard OpenFOAM solver toolset. It is a high-speed, explicit compressible flow solver with shock-capturing capability. While the main features have been retained, the solver had to be restructured to make use of tabular data for equations of states, a necessary addition to model the complex thermo-physical properties of ionized gasses. This entailed the need to change the independent state variables used by the solver, resulting in a new thermodynamic library and slightly different solution algorithm. Moreover, a radiation heat transfer model based on the P-1 approximation was added to the solver. The solver is verified against an analytical solution from the Sedov-Taylor-Neumann test problem to showcase the ability of the hydrodynamic solvers to handle strong shocks, whereas the P-1 model was verified using a simple one-dimensional problem with an analytical solution. Additionally, a validation case involving shock-wave propagation through jet array is presented, and the results are compared with experimental data from the open literature. Lastly, in order to showcase the utility of the solver for practical cases, we applied the refined solver to two representative scenarios: gas venting within the HYLIFE-II chamber and the compression of the gas following the partial ablation of the liquid wall.

Chamber dynamics↗
DOE OSTI2026

Andrew Dieringer Poster for review

Silicon carbide has been identified as a useful material for nuclear application due to its heat resistance, low neutron absorbing cross-section, chemical inertness, and its rigidity as a structural material. These properties make it very favorable for high temperature environments such as in TRISO fuel and advanced reactor designs. Treatments such as n-doping, where carrier atoms are added to increase the number of free electrons, are expected to further improve properties such as thermal conductivity and electrical resistivity. Due to the high amount of neutron interactions in a nuclear reactor, it is expected that silicon carbide used in a reactor will be passively n-doped via transmutation. Studying the effects of n-doping in silicon carbide can help us better understand how the material will operate under real world conditions. We have found that n-doping the SiC increases both thermal and electrical conductivity, providing positive feedback under reactor usage. Previously it had been thought that under reactor conditions material properties of SiC could only degrade due to defects caused by neutron interactions. This study shows that transmutation doping can help to counteract and slow this process. This research shows that SiC can be used in nuclear reactor components contrasting with more costly and complicated alternative materials.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Causal discovery from data assisted by large language models

Knowledge-driven discovery of novel materials necessitates the development of causal models for property emergence. While in the classical physical paradigm, the causal relationships are deduced based on physical principles or via experiment, the rapid accumulation of observational data necessitates learning causal relationships between dissimilar aspects of material structure and functionalities based on observations. For this, it is essential to integrate experimental data with prior domain knowledge. Here, we demonstrate this approach by combining high-resolution scanning transmission electron microscopy data with insights derived from large language models (LLMs). By applying ChatGPT to domain-specific literature, such as arXiv papers on ferroelectrics, and combining the obtained information with data-driven causal discovery, we construct adjacency matrices for directed acyclic graphs that map the causal relationships between structural, chemical, and polarization degrees of freedom in Sm-doped BiFeO 3 . This approach enables us to hypothesize how synthesis conditions influence material properties and guides experimental validation. Furthermore, the ultimate objective of this work is to develop a unified framework that integrates LLM-driven literature analysis with data-driven discovery, facilitating the precise engineering of ferroelectric materials by establishing clear connections between synthesis conditions and their resulting material properties.

Causal inference↗
DOE OSTI2026

Enabling In-Core Strain Measurement: Printed Sensors for Advanced Reactors

Advances in nuclear energy systems require an improved understanding of fuels, cladding, and structural materials; however, the extreme environmental conditions at irradiation test facilities often limit mechanical property evaluation to post-irradiation examination. In-situ monitoring techniques can accelerate materials qualification by providing direct insight into real-time material behavior during irradiation testing. Additively manufactured (AM) strain gauges offer a compact, material-compatible alternative to commercial sensors, particularly where space constraints, attachment limitations, or material compatibility requirements complicate conventional sensor use. This poster presents an overview of the development and testing of AM strain gauges in nuclear-relevant environments, demonstrating their potential to enhance structural health monitoring capabilities during irradiation experiments.

36 - MATERIALS SCIENCE↗
DOE OSTI2025

Reward Driven Workflows for Unsupervised Explainable Analysis of Phases and Ferroic Variants From Atomically Resolved Imaging Data

Rapid progress in aberration corrected electron microscopy necessitates development of robust methods for the identification of phases, ferroic variants, and other pertinent aspects of materials structure from imaging data. While unsupervised methods for clustering and classification are widely used for these tasks, their performance can be sensitive to hyperparameter selection in the analysis workflow. In this study, the effects of descriptors and hyperparameters are explored on the capability of unsupervised ML methods to distill local structural information, exemplified by the discovery of polarization and lattice distortion in Sm − dopped BiFeO 3 (BFO) thin films. It is demonstrated that a reward-driven approach can be used to optimize these key hyperparameters across the full workflow, where rewards are designed to reflect domain wall continuity and straightness, ensuring that the analysis aligns with the material's physical behavior. This approach allows the discovery of local descriptors that are best aligned with the specific physical behavior, providing insight into the fundamental physics of materials. The reward driven workflow is further extended to disentangle structural factors of variation via an optimized variational autoencoder (VAE). Lastly, the importance of well-defined rewards is explored as a quantifiable measure of the success of the workflow.

Barakati, Kamyar [University of Tennessee, Knoxvil↗
DOE OSTI2026

MatLib-1.2.2: Nuclear Material Properties Library

The U.S. Nuclear Regulatory Commission (NRC) uses the computer code Fuel Analysis under Steady-state and Transients (FAST) to model steady-state and transient fuel behavior to support regulatory decisions. FAST relies on a material properties library (MatLib) that contains the thermal and mechanical properties of the nuclear materials and coolants of interest to support the U.S. commercial nuclear industry. MatLib contains properties for a variety of nuclear fuels, cladding and other structural materials, gases, and coolants. In this document, material property correlations for the materials contained within MatLib are presented and discussed. When available, comparisons are made between the material property correlations and available data. Additionally, uncertainties are quantified on the material properties, which is then used by the NRC to support uncertainty quantification for best-estimate plus uncertainty safety evaluation reviews. This document describes MatLib-1.2.2, updated from MatLib-1.2.1 to include additional properties for metallic fuel. It is one of a series of documents on FAST; the other documents detail the models used by FAST as well as its integral assessment to experiments and commercial data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Integrated Approach to Post-Irradiation Examination of Nuclear Materials at Idaho National Laboratory

Idaho national Laboratory (INL) is the U.S. lead national laboratory for the Department of Energy’s Office of Nuclear Energy (DOE-NE), providing much of the nuclear research, development and demonstration capability needed to move nuclear innovation forward to deployment. INL’s Materials and Fuels Complex hosts a unique combination of personnel, facilities and infrastructure and offers the ability to perform post-irradiation examinations (PIE) of nuclear materials spanning multiple length scales. The ability to combine engineering-scale analysis and sub-microscopic characterization provides valuable insights into fuel and structural material behavior and degradation mechanisms. The holistic approach is used to accelerate these materials demonstration and deployment. Selected studies will be presented, highlighting the impact of these techniques on improving fuel reliability, safety, and efficiency, thereby advancing the development of sustainable and advanced nuclear energy technologies.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

ThermoPI—an Online Tool to Calculate Heat Transfer Through Foam Insulation

Thermal insulation materials with ultra-low effective thermal conductivity are crucial for a multitude of applications. Over the years, a significant amount of experimental work has been dedicated to creating new insulation materials with low thermal conductivity. Similarly, substantial efforts have been made to enhance the theoretical understanding of thermal transport mechanisms in thermal insulation materials and to push the boundaries of lower thermal conductivity. However, ample room remains for enhancing the thermal resistivity of closed-cell foam insulations. To aid in the development of ultra-low effective thermal conductivity foam insulations, Oak Ridge National Laboratory has introduced a unique online tool, ThermoPI. This tool calculates not only the effective thermal conductivity but also the thermal conductivity components for porous materials, including gases, solids, and radiative, based on the materials’ structural information (e.g., porosity, pore size, gas species, pressure, solid species, pore geometry, and temperature).This tool collected and improved the existing theoretical models for thermal insulation materials’ gas, solid, and radiative thermal conductivity. These improved models have been validated with experimental data. Effective medium theory and sound velocity softening effects are considered for solid thermal conductivity. The built-in solid materials include polystyrene, polyurethane, polyethylene, and silica. Users are allowed to input new solid materials that are not built inside the tool. For gas thermal conductivity, the subcontinuum Knudsen effect is considered. In addition to several built-in gases, users can input new gases. For solid and radiative thermal conductivity calculations, several models are available to select, and the tool can determine the best model to choose based on the materials that users input. Users are also allowed to change selections manually. In addition, the tool can also calculate the effect of interfacial resistance on the overall thermal conductivity of layered materials. This work will elaborate on the tool and discuss how it can guide the development of new insulation products.

Shrestha, Som [ORNL] (ORCID:0000000183993797)↗
DOE OSTI2025

Additive manufacturing of carbon fiber-reinforced thermoset composites via in-situ thermal curing

Fiber-reinforced polymer composites are lightweight structural materials widely used in the transportation and energy industries. Current approaches for the manufacture of composites require expensive tooling and long, energy-intensive processing, resulting in a high cost of manufacturing, limited design complexity, and low fabrication rates. Here, we report rapid, scalable, and energy-efficient additive manufacturing of fiber-reinforced thermoset composites, while eliminating the need for tooling or molds. Use of a thermoresponsive thermoset resin as the matrix of composites and localized, remote heating of carbon fiber reinforcements via photothermal conversion enables rapid, in-situ curing of composites without further post-processing. Rapid curing and phase transformation of the matrix thermoset, from a liquid or viscous resin to a rigid polymer, immediately upon deposition by a robotic platform, allows for the high-fidelity, freeform manufacturing of discontinuous and continuous fiber-reinforced composites without using sacrificial support materials. This method is applicable to a variety of industries and will enable rapid and scalable manufacture of composite parts and tooling as well as on-demand repair of composite structures.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Providing Experimental Infrastructure for Accelerating Advanced Reactor Demonstrations through the National Reactor Innovation Center

A suite of experimental infrastructure projects has been developed by the National Reactor Innovation Center to accelerate advanced reactor demonstrations and facilitate their development, addressing crucial gaps in data, materials characterization, and modeling. First, the Molten Salt Thermophysical Examination Capability (MSTEC) provides a specialized platform for post-irradiation characterization of molten salt reactor fuel, coolant salts, and structural materials, essential for supporting the design and operation of advanced reactors and future commercial molten salt reactor development and licensing. The Virtual Test Bed (VTB) complements these efforts by leveraging advanced modeling and simulation tools to evaluate reactor performance and safety. Serving as a library of reference models, the VTB offers a database of multiphysics reactor models, facilitating rapid safety evaluations and includes continuous software quality assurance, crucial for accelerating deployment while maintaining reliability. Additionally, the Helium Component Test Facility (HeCTF) addresses the need for high-temperature helium-cooled reactor component testing. As the first-of-its-kind facility in the United States, HeCTF emulates high-temperature gas reactor conditions, reducing time and cost associated with component validation, thereby accelerating reactor development. Finally, In-cell Thermal Creep Frames provide a unique solution for obtaining thermal creep data from irradiated materials, critical for materials qualification and licensing. Developed by the National Reactor Innovation Center, these compact frames enable the examination of previously irradiated materials, overcoming traditional limitations and enhancing the understanding of mechanical properties crucial for reactor development. Collectively, these experimental infrastructure projects form a comprehensive framework aimed at expediting advanced reactor demonstrations, fostering innovation, and ensuring the viability of next-generation nuclear energy solutions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗