Results for “Structural components” — Page 32

DOE OSTI2026

ZENN: A thermodynamics-inspired computational framework for heterogeneous data–driven modeling

Traditional entropy-based methods—such as cross-entropy loss in classification problems—have long been essential tools for representing the information uncertainty and physical disorder in data and for developing artificial intelligence algorithms. However, the rapid growth of data across various domains has introduced new challenges, particularly the integration of heterogeneous datasets with intrinsic disparities. To address this, we introduce a zentropy-enhanced neural network (ZENN), extending zentropy theory into the data science domain via intrinsic entropy, enabling more effective learning from heterogeneous data sources. ZENN simultaneously learns both energy and intrinsic entropy components, capturing the underlying structure of multisource data. To support this, we redesign the neural network architecture to better reflect the intrinsic properties and variability inherent in diverse datasets. We demonstrate the effectiveness of ZENN on classification tasks and energy landscape reconstructions, showing its superior generalization capabilities and robustness-particularly in predicting high-order derivatives. In image and text classification tasks, ZENN demonstrates superior generalization by introducing a learnable temperature variable that models latent multisource heterogeneity, allowing it to surpass state-of-the-art models on CIFAR-10/100, BBC News, and AG News. As a practical application in materials science, we employ ZENN to reconstruct the Helmholtz energy landscape of Fe3Pt using data generated from density functional theory and capture key material behaviors, including negative thermal expansion and the critical point in the temperature–pressure space. Overall, this work presents a zentropy-grounded framework for data-driven machine learning, positioning ZENN as a versatile and robust approach for scientific problems involving complex, heterogeneous datasets.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Formation of SMBH seeds in Population III star clusters through collisions: the importance of mass loss

ABSTRACT Runaway collisions in dense clusters may lead to the formation of supermassive black hole (SMBH) seeds, and this process can be further enhanced by accretion, as recent models of SMBH seed formation in Population III star clusters have shown. This may explain the presence of SMBHs already at high redshift, z > 6. However, in this context, mass loss during collisions was not considered and could play an important role for the formation of the SMBH seed. Here, we study the effect of mass loss, due to collisions of protostars, in the formation and evolution of a massive object in a dense primordial cluster. We consider both constant mass-loss fractions as well as analytic models based on the stellar structure of the collision components. Our calculations indicate that mass loss can significantly affect the final mass of the possible SMBH seed. Considering a constant mass loss of 5 per cent for every collision, we can lose between 60–80 per cent of the total mass that is obtained if mass loss were not considered. Using instead analytical prescriptions for mass loss, the mass of the final object is reduced by 15–40 per cent, depending on the accretion model for the cluster we study. Altogether, we obtain masses of the order of $10^4\, \mathrm{M}_{\odot }$, which are still massive enough to be SMBH seeds.

Alister Seguel, P. J.↗

DOE OSTI2021

Phase transitions of the ferroelectric (ND 4 ) 2 FeCl 5 ·D 2 O under magnetic field

Due to the strong coupling between magnetism and ferroelectricity, (ND 4 ) 2 FeCl 5 ·D 2 O exhibits several intriguing magnetic and electric phases. In this Letter, we include higher-order onsite anisotropic spin interactions to explain the ferroelectric phase transitions of (ND 4 ) 2 FeCl 5 ·D 2 O in a magnetic field and to produce the large weights of high-order harmonic components in the cycloid structure that are observed from neutron diffraction experiments. Moreover, we predict a new ferroelectric phase sandwiched between the FE II and FE III phases in a magnetic field. Furthermore, by emphasizing the importance of the higher-order spin anisotropic interactions, our work provides a framework to understand multiferroic materials with rich phase diagrams.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2020

Arginine off-kilter: guanidinium is not as planar as restraints denote

Crystallographic refinement of macromolecular structures relies on stereochemical restraints to mitigate the typically poor data-to-parameter ratio. For proteins, each amino acid has a unique set of geometry restraints which represent stereochemical information such as bond lengths, valence angles, torsion angles, dihedrals and planes. It has been shown that the geometry in refined structures can differ significantly from that present in libraries; for example, it was recently reported that the guanidinium moiety in arginine is not symmetric. In this work, the asymmetry of the N ∊ —C ζ —N η1 and N ∊ —C ζ —N η2 valence angles in the guanidinium moiety is confirmed. In addition, it was found that the C δ atom can deviate significantly (more than 20°) from the guanidinium plane. This requires the relaxation of the planar restraint for the C δ atom, as it otherwise causes the other atoms in the group to compensate by distorting the guanidinium core plane. A new set of restraints for the arginine side chain have therefore been formulated, and are available in the software package Phenix , that take into account the asymmetry of the group and the planar deviation of the C δ atom. This is an example of the need to regularly revisit the geometric restraint libraries used in macromolecular refinement so that they reflect the best knowledge of the structural chemistry of their components available at the time.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2021

Spectroscopy and Scattering Studies Using Interpolated Ab Initio Potentials

The Born–Oppenheimer potential energy surface (PES) has come a long way since its introduction in the 1920s, both conceptually and in predictive power for practical applications. Nevertheless, nearly 100 years later—despite astonishing advances in computational power—the state-of-the-art first-principles prediction of observables related to spectroscopy and scattering dynamics is surprisingly limited. For example, the water dimer, (H 2 O) 2 , with only six nuclei and 20 electrons, still presents a formidable challenge for full-dimensional variational calculations of bound states and is considered out of reach for rigorous scattering calculations. The extremely poor scaling of the most rigorous quantum methods is fundamental; however, recent progress in development of approximate methodologies has opened the door to fairly routine high-quality predictions, unthinkable 20 years ago. In this review, in relation to the workflow of spectroscopy and/or scattering studies, we summarize progress and challenges in the component areas of electronic structure calculations, PES fitting, and quantum dynamical calculations.

Chemistry↗

DOE OSTI2020

Transportation analysis and related design optimization of the Fermilab high-beta 650 MHz cryomodule

The Proton Improvement Plan-II (PIP-II) at Fermi National Accelerator Laboratory (FNAL) will create a new and vastly improved accelerator, which will be the source of high-energy particles for the experiments taking place at FNAL. The new linear accelerator (LINAC) contains several types of cryomodules, which are individual particle accelerators. The last cryomodule in the LINAC will be the High-Beta 650 (HB650), which will operate at 650 MHz. Each module is approximately 15 meters in length and 1.5 meters wide, weighs 13 tonnes, and shares many design features with three of the other cryomodules. The HB650 consists of two primary sections, the outer vacuum vessel and the components that reside within it, which will be cooled to cryogenic temperatures – the cold mass. Once assembly is complete at FNAL, it will be transported to one of three places: another location at FNAL, another national laboratory within the continental United States, or to a scientific partner in Europe. Any excitation the module experiences, such as going over rough roads when on a semitrailer, can create high stresses in components and cause failure if severe enough. Additionally, any delicate components with low resonant frequencies that match the excitation spectrum could achieve resonance, potentially causing a fatigue failure by repeated flexing. To ensure the successful transportation of the HB650, a transport analysis utilizing ANSYS simulation software has been performed on major subassemblies, as well as analysis of the subassemblies as a combined system. The optimization of components, design of support structures, and overall increasing of the lowest resonant frequencies have resulted in a cryomodule that will be better equipped to handle transportation and any large impact loads that come with it.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

VTO_2021_APR_LLNL_Ye

Traditional batteries are composed of two-dimensional films that are stacked and/or rolled. Thin film batteries display high power density while their thick film counterparts show good energy density, but it has proven difficult to concurrently achieve both within these planar form factors. In addition, conventional Li-ion batteries based on liquid organic electrolytes or gel polymer electrolytes have raised severe safety concerns due to the intrinsic flammable properties of the organic electrolytes. They are also not ideal for the use of high energy density metallic lithium (Li) anodes due to Li dendrite growth, or sulfur cathodes due to shuttling effects that result in fast capacity fade. There is an urgent need to develop safe, high-performance solid-state batteries (SSBs) with advanced electrolyte and separator technologies. Although in recent years a series of superionic conductors have been developed for electrolytes and separators, their performance does not satisfy demanding criteria due to large impedance from poor solid electrolyte-electrode contact and questionable electrochemical and mechanical stability. Unlike the well-established roll-to-roll fabrication of conventional Li-ion batteries, the processing of SSBs is unique due to the brittleness of solid-state electrolytes (SSEs). The commercially available or lab-developed SSE discs are usually very thick (hundreds of micrometers to millimeters) to overcome their brittle nature, which unfortunately increases the cell impedance and accounts for the majority of the overall cell weight and volume, leading to dramatically decreased power and energy densities. In this project, we will investigate 3D printing techniques to overcome safety, fabrication, mechanical, and electrochemical issues in SSBs. 3D printing builds complex structures in a layer-by-layer fashion, which allows rapid production of hierarchical architectures, gradient and multi-material structures, and multi-component assemblies. 3D printing is an emerging area that could fundamentally transform energy storage devices. For example, 3D printing can produce batteries with arbitrary form factors to fit a product’s specific volume requirements and can create interwoven electrode arrangements over a wide range of length scales to improve transport and increase power density for a given energy density. For SSBs, 3D printing may dramatically reduce the separator thickness from ~1 mm (by hydraulic pressing) to tens of micrometers or less. In addition, the interfacial contact area between the electrolyte and the electrode may be increased via 3D interdigitated designs. Hence, we expect a significant reduction of the overall cell impedance and enhancement of both energy and power densities of SSBs by harnessing an array of 3D printing technologies being developed at Lawrence Livermore National Laboratory (LLNL).

25 ENERGY STORAGE↗

DOE OSTI2022

Modeling of a Generic Pebble Bed High-temperature Gas-cooled Reactor (PB-HTGR) with SAM

This report presents the modeling of the core of a generic pebble-bed reactor (PBR) at the system level using the System Analysis Module (SAM) code. This work is an extension of a previous work by the authors (Ooi et al. (2021)) that used the so-called 2-D ring model approach to model the PBMR-400. With the new approach, the pebble bed of the reactor is modeled with multiple PBCoreChannel components with spherical heat structures which allows the code to calculate thermal fluid parameters with built-in closure relations. The new core-channel approach is an improvement to the 2-D ring model approach as it does not introduce geometric distortions to the model and thus reduces the uncertainties of the predictions. In addition to thermal fluid simulations, point kinetics (PKE) are included to the model. Simulations are performed under a steady-state normal operation condition and a load-following transient scenario. This particular transient scenario is chosen as it tests both the thermal fluid and neutronics aspects of the model. The predicted results from both the steady-state and transient scenarios are compared with the results by Stew- art et al. (2021) who performed similar simulations with a Griffin-Pronghorn coupled tool. Despite the differences between the codes, with SAM being a system-analysis code and Pronghorn being a porous-medium code, both sets of results compare favorably. The over- all profiles and trends of the predicted temperatures and reactivities from the SAM and Griffin/Pronghorn simulations are similar, with some differences in their predicted values. The first part of the report covers the significance of a relatively fast-running approach that is capable of modeling the pebble bed reactor at the system-level while simultaneously capturing the radial thermal behavior of the core. Then, the modeling approach used in this work is discussed in details. Lastly, the results and comparisons with the Griffin/Pronghorn simulation are presented.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2026

Knowledge Graph of RB-Tnseq Data from Fitness Browser (KP-DP1)

Motivation: Predicting microbial gene fitness across environmental conditions remains a central challenge for predictive phenomics and autonomous experimentation. Fitness assays generate large volumes of genotype–phenotype measurements difficult to integrate with experimental metadata and biological function in a form that supports mechanistic reasoning. Knowledge graphs offer a semantic framework for unifying modalities and enabling context-aware inference. Results: We build GIMME (Graph Inference for Microbial Metabolism Exploration), a semantically grounded knowledge graph that unifies gene fitness measurements spanning 10 Pseudomonas species with experimental metadata and biological context. Media are decomposed into chemical components and experiments carry structured links to natural-language descriptions. The resulting graph supports two inference modes: (1) symbolic graph traversal to surface candidate gene–environment and gene–chemical associations, and (2) learned inference using heterogeneous graph neural networks that propagate information across neighborhoods. We formulate link regression over (gene, media, experiment) triplets, combining learned gene embeddings with pretrained LLM sourced text embeddings of node descriptions to predict gene fitness. We then augment a baseline MLP with an auxiliary message-passing encoder (GraphSAGE/GAT) that propagates information over gene–protein–function and media–chemical subgraphs, and fuse the two pathways with a gated residual connection. This approach produces strong agreement with held-out fitness measurements (GraphSAGE Pearson r 0.74) while also highlighting inference challenges in extreme-fitness regimes. We aggregate GAT edge-attention weights by relation type and layer to estimate which biological and environmental relations most influence fitness predictions. Conclusion: This work explores using knowledge graphs as “context graphs” for microbial phenotype prediction. They provide a rich substrate which enables explainable retrieval of supporting evidence, and provides a natural bridge to autonomous workflows that prioritize the next experiment.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Applications of Decellularized Plant Tissues in Regenerative Medicine and Tissue Engineering

The development of biomaterials capable of supporting complex tissue growth remains a central challenge in regenerative medicine and tissue engineering, particularly in replicating the structural, mechanical, and transport functions of native extracellular matrices. While decellularized animal tissues have demonstrated significant success as scaffolds for tissue engineering, they are still constrained by cost, immunogenicity, and ethical concerns. In recent years, decellularized plant tissues have emerged as a compelling alternative scaffold platform due to their inherent vascular architectures, ethical sourcing, tunable mechanical properties, cytocompatibility, and sustainability. This review summarizes current strategies for the decellularization of plant tissues, including chemical, enzymatic, and physical approaches, and discusses how these methods preserve plant cell wall structure while removing immunogenic components. Advances in surface loading and functionalization, including protein coatings, oxidation, nanoparticle incorporation, peptide conjugation, and bioactive molecule loading, have further enhanced cell adhesion, differentiation, biodegradability, and immunomodulation. Recent applications of decellularized plant scaffolds in cardiac, skeletal muscle, bone, nerve, and wound healing contexts are reviewed, highlighting proof-of-concept successes and remaining challenges. Beyond therapeutic applications, plant-derived scaffolds have also enabled physiologically relevant in vitro models for vascular biology, mechanotransduction, cancer, metabolic tissues, and drug response studies. Collectively, these advances position decellularized plant tissues as versatile, low-cost, and ethically favorable biomaterials with growing relevance for both regenerative medicine and tissue modeling.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Comparison of Ion and Neutron Irradiations to 3 dpa at 500C in Ferritic-Martensitic Alloys

The growing global demand for energy will increasingly call upon advanced nuclear fission reactors to supply safe and reliable electricity. The structural and fuel cladding components of these reactors will be subject to extreme conditions of irradiation damage up to several hundred displacements per atom (dpa) at temperatures as high as 700°C. Ferritic-martensitic (F-M) steels are leading candidates for these challenging conditions due to their strength and dimensional stability under irradiation. In order to accelerate the process for evaluating F-M alloys, charged particles are increasingly being used to emulate neutron irradiations. Charged particle irradiations allow the possibility of conducting irradiation experiments within a shorter time period (i.e. at a rate up to 4 orders of magnitude faster) and with minimal radioactivation of the material, enabling lower cost and faster turnaround of post irradiation examination and analysis. However, the irradiation dose rate, damage cascade morphologies, and irradiation damage depth profiles all differ widely between protons, heavier ions, and neutrons. Currently, there is limited understanding of the significance of these physical differences and how they manifest in the irradiated microstructure and mechanical properties of F-M steels. The objective of this study is to evaluate charged particles as a surrogate for neutron irradiations in F-M alloys by assessing common irradiation conditions using Fe++ ions, protons, and neutrons. Keeping the temperature and dose consistent enables isolation of the effects of each irradiating particle and their respective dose rates and cascade morphologies.

Swenson, M.J.↗

DOE OSTI2023

Solid oxide metal-air redox batteries (SOMARB)

The solid oxide metal-air redox batteries (SOMARB), composed of reversible solid oxide fuel cell (RSOFC) and energy storage unit (ESU), is an emerging technology for large-scale energy storage owing to its advantages in high energy density, high rate capability, decoupled energy and power components, all solid state structure, high safety and scalability. Starting with introducing the working principle of this battery, thermodynamics and kinetics, and battery chemistries based on Fe, Mo, and Wo of SOMARB are illustrated. The strategies to improve the performance of Fe-based SOMARB via optimizing RSOFC and ESU are discussed in detail. Finally, several high temperature metal-air batteries derived from SOMARB are introduced. The comparison with other batteries shows that SOMARB is expected to play an important role in future grid and renewable energy storage with concurrent advances in SOFC stack and system design.

Zhang, Cuijuan↗

DOE OSTI2025

Development of Surveillance Test Articles for Materials Degradation Management in MSR Environments

Materials in molten salt reactors (MSR) undergo accelerated degradation from corrosion, irradiation, and cyclic loads at elevated temperatures. Establishing a materials surveillance program to enable the assessment of material deterioration is critical to assure structural integrity of MSRs components. This presentation summarizes recent efforts towards the development of surveillance test articles for collecting various damages for monitoring materials degradation. Surveillance test articles with reduced dimensions were designed to capture creep-fatigue damage from cyclic loading at elevated temperatures. The strain evolution of test articles during thermal cycling was analyzed both numerically and experimentally. Out-of-reactor thermal cycling demonstrated successful capture of strain range for materials assessment. Moreover, test articles were subject to both mechanical loads and molten salt exposure, and the damage due to stress and corrosion was investigated. Additionally, damage inference models were developed to predict the remaining life of materials based on the accumulated damage in surveillance test articles.

36 - MATERIALS SCIENCE↗

DOE OSTI2026

Characterizing the GD-1 Stream with DESI DR2 Data: Thin Stream and Hot Cocoon

GD-1 is among the longest, coldest stellar streams in the Milky Way, making it an ideal target for probing dark matter substructure through dynamical heating. We present a catalog of 608 spectroscopically confirmed GD-1 members from the first three years of Dark Energy Spectroscopic Instrument (DESI) observations. This constitutes the largest homogeneous spectroscopic sample of GD-1, doubling the number of members previously available only through heterogeneous compilations combining multiple surveys with different systematics. Using these data, we derive updated stream tracks in sky position, proper motion, and radial velocity that extend over $100^\circ$ of the stream. We apply a Gaussian mixture model to decompose the stream into a dynamically cold thin component ($Ï_V = 2.49\pm 0.28$ km s$^{-1}$, width $= 0.23\pm0.01^\circ$) and a kinematically hot cocoon ($Ï_V = 6.13\pm0.75$ km s$^{-1}$, width $= 2.18\pm0.17^\circ$). The cocoon contains $\sim30\%$ of members and its velocity dispersion is consistent with $\sim11$ Gyr of heating by cold dark matter subhalos. We also detect a large proper motion dispersion ($41.36\pm4.98$ km s$^{-1}$) along the stream direction in the cocoon component. This feature indicates a significant line-of-sight distance spread in the cocoon, and its origin will be further explored in a forthcoming paper. These measurements demonstrate the power of DESI spectroscopy for characterizing the multi-component phase-space structure of stellar streams and constraining small-scale dark matter substructure.

Jarvis, Emma [Toronto U.] (ORCID:0009000656127336)↗

DOE OSTI2022

Graph-component approach to defect identification in large atomistic simulations

In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.

36 MATERIALS SCIENCE↗

DOE OSTI2020

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogeneities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogeneities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2020

ICME and In-Situ Process Monitoring for Rapid Qualification of Components Made by Laser-based Powder Bed Additive Manufacturing Processes for Nuclear Structural Applications

Additive manufacturing (AM) through selective powder bed melting of successive layers is being considered as disruptive technology for rapid production of low-cost nuclear reactor internal components with complex geometries. However, there is a potential for non-uniform distribution of physical features, such as porosity or microstructural differences, due to variations of temperature across a component build. These heterogeneities make the deployment of traditional material qualification and non-destructive evaluation of AM components difficult. This research project explored the feasibility of using in-situ process monitoring methods and integrated computational materials engineering (ICME) principles as an alternate qualification methodology and approach. This project included six individual tasks: (i) design of artifacts relevant to the nuclear power industry, (ii) evaluation of laser processing and in-situ measurements, (iii) computational modeling, (iv) ex-situ microstructural characterization, (v) evaluation of scaling the methodology for large-scale structures, and (vi) development of a data package to codes and standards organizations. As a part of this research, laser powder bed fusion of metals (316L, Alloy 718, and Ti6Al4V) was explored. This document provides an overview of the research performed over three years and serves as the final U.S. Department of Energy report for this project. In the third year, the project focused on the following activities: (a) in-situ infrared and optical imaging of each layer during processing of representative stainless steel geometries; (b) development of methodologies to analyze the data; and (c) application of ICME methodologies and other experiments to estimate the relevance of defects and microstructure to tensile properties.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗