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At least 127 records · Page 7

ORNL Infrastructure: Status Update on BEA Research Reactor Cask Planning Activities Completed During FY 2025

This report documents Oak Ridge National Laboratory’s (ORNL’s) FY 2025 progress toward establishing readiness to use the Battelle Energy Alliance (BEA) Research Reactor (BRR) shipping cask in support of the Nuclear Science User Facilities (NSUF). The BRR cask will provide a new shared infrastructure capability for transporting irradiated fuels and materials between ORNL and Idaho National Laboratory (INL), thereby supporting NSUF’s mission of enabling user access to advanced nuclear research facilities. In FY 2025, ORNL advanced the regulatory, contractual, safety review, and planning activities necessary to qualify its facilities and staff for BRR cask handling. Although loaded shipments were delayed due to fabrication lead times for Orano Federal Services LLC’s internal basket hardware, the program progressed to the point where an empty cask dry run is scheduled for October 2025. This dry run represents a critical step in demonstrating ORNL’s ability to receive, handle, and return the BRR cask.

99 GENERAL AND MISCELLANEOUS↗

KBase Narrative - Complete genome sequence of a novel Microbacterium sp. strain Clip185.

We have isolated a new species of Microbacterium, an Actinobacterium. We have temporarily named this bacterium as Microbacterium sp. strain Clip185 (hereafter called strain Clip185) from a contaminated Tris-Acetate-Phosphate (TAP) medium culture plate of a green micro-alga Chlamydomonas reinhardtii strain LMJ.RY0402.185141 (a Chlamydomonas Library project CLiP strain). We sequenced the whole genome of strain Clip185 using the PacBio Sequel II Continuous Long Read technology and have submitted it to NCBI along with the SRA and PacBio methylation motif data. Additionally, we have submitted the PacBio methylome to REBASE, Ref#35996. We present the whole genome sequence of this new Microbacterium species that offers insights into its coding and non-coding genes and its nearest taxonomic neighbors.

Mitra, Mautusi↗

Complete genome sequence of Sphingobium yanoikuyae strain CC4533

We have isolated a new strain of Sphingobium yanoikuyae , which belongs to the class Alphaproteobacteria, order Sphingomonadales, and family Sphingomonadaceae. This carotenoid-producing strain is capable of degrading xenobiotics and is tolerant to toxic levels of six heavy metals. We have designated the newly isolated strain of S. yanoikuyae as S. yanoikuyae strain CC4533 (hereafter called strain CC4533) because it was isolated from a contaminated Tris-Acetate-Phosphate (TAP) medium culture plate of a green micro-alga Chlamydomonas reinhardtii wild type strain CC4533. We sequenced the whole genome of strain CC4533 using the PacBio Sequel II Continuous Long Read technology and have submitted it to NCBI along with the SRA and PacBio methylation motif data. Additionally, we have submitted the PacBio methylome to REBASE, Ref#35996. We present the whole genome sequence of S. yanoikuyae strain CC4533 that offers insights into its coding and non-coding genes and its nearest taxonomic neighbors.

59 BASIC BIOLOGICAL SCIENCES↗

Completing the Ba–As Compositional Space: Synthesis and Characterization of Three New Binary Zintl Arsenides, Ba3As4, Ba5As4, and Ba16As11

Three novel binary barium arsenides, Ba3As4, Ba5As4, and Ba16As11, were synthesized and their crystal and electronic structures were investigated. Structural data collected via the single-crystal X-ray diffraction method indicate that the anionic substructures of all three novel compounds are composed of structural motifs based on the homoatomic As–As contacts, with [As2]4− dimers found in Ba5As4 and Ba16As11, and an [As4]6− tetramer found in Ba3As4. Ba3As4 and Ba5As4 crystallize in the orthorhombic crystal system—with the non-centrosymmetric space group Fdd2 (a = 15.3680(20) Å, b = 18.7550(30) Å, c = 6.2816(10) Å) for the former, and the centrosymmetric space group Cmce (a = 16.8820(30) Å, b = 8.5391(16) Å, and c = 8.6127(16) Å) for the latter—adopting Eu3As4 and Eu5As4 structure types, respectively. The heavily disordered Ba16As11 structure was solved in the tetragonal crystal system with the space group P4¯21m (a = 12.8944(12) Å and c = 11.8141(17) Å). The Zintl concept can be applied to each of these materials as follows: Ba3As4 = (Ba2+)3[As4]6−, Ba5As4 = (Ba2+)5(As3−)2[As2]4−, and 2 × Ba16As11 = (Ba2+)32(As3−) ≈ 20[As2]4− ≈ 1, pointing to the charge-balanced nature of these compounds. Electronic structure calculations indicate narrow bandgap semiconducting behavior, with calculated bandgaps of 0.47 eV for Ba3As4, 0.34 eV for Ba5As4, and 0.33 eV for Ba16As11.

Crystallography↗

Complete the Planned FY24 Design Parameters Development at INL to Support the Drafting of the A709 Near-Term Code Case

This report presents the thermophysical properties measurements conducted in the Fiscal Year 2024 to extend the database up to 1000?. The new values are compared against properties of high Molybdenum steels in the ASME Section II Part D tables. This report also shows the comparison between new measurements against the previously measured values in literature and provides recommended thermophysical properties for A709 code case. These values will support the thermophysical property tables in near term code case of A709.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

Toward Complete Merger Identification at Cosmic Noon with Deep Learning

As we enter the era of large imaging surveys such as $\textit{Roman}$, Rubin, and $\textit{Euclid}$, a deeper understanding of potential biases and selection effects in optical astronomical catalogs created with the use of ML-based methods is paramount. This work focuses on a deeper understanding of the performance and limitations of deep learning-based classifiers as tools for galaxy merger identification. We train a ResNet18 model on mock Hubble Space Telescope CANDELS images from the IllustrisTNG50 simulation. Our focus is on a more challenging classification of galaxy mergers and nonmergers at higher redshifts $1

Schechter, Aimee [Colorado U.] (ORCID:000000017120↗

Combining genome-wide association studies and expression quantitative trait nucleotide mapping with molecular and genetic validations to identify transcriptional networks regulating drought tolerance in Populus

Objectives: (i). To deploy a large-scale experimental drought trial for up to 1000 unique genotypes of Populus equipping the sites with controlled irrigation and drought treatments that are fully automated and monitored. FULLY COMPLETED (ii) To test the hypothesis that a suite of traits identified for drought tolerance in P. nigra can be measured in drought and control treatments in the wide germplasm collection of P. trichocarpa. FULLY COMPLETED (iii) To use established and novel GWAS model approaches to identify gene loci linked to drought tolerance traits on interest in P. trichocarpa. FULLY COMPLETED (iv) To undertake comparative analysis of GWAS results for drought tolerance traits in P. nigra and P. trichocarpa. PARTIALLY COMPLETED – remains active (v) Using RNAseq in P. trichocarpa, in droughted and control treatments to identify cis- and trans-regulated eQTN. FULLY COMPLETED (vi) Validate up to 50 cis-QTNs, from network hubs using transient protoplast assays. FULLY COMPLETED (vii) To establish Agrobacterium-based gene editing protocols in Populus. FULLY COMPLETED (viii) To utilize early leads from previous research to investigate at least 6 candidate genes for drought tolerance in Populus. FULLY COMPLETED (ix) To validate up to 20 candidate genes for drought tolerance in P. trichocarpa refined from the long-list tested in the transient assays for cis-acting hub gene targets. PARTIALLY COMPLETED- remains active.

60 APPLIED LIFE SCIENCES↗

Multi-Source Machine Learning and Thermoplastics Enhanced Aerostructure Manufacturing (mTEAM)

RTX Technology Research Center (RTRC), together with Collins Aerospace (Collins) and Oak Ridge National Laboratory (ORNL) has developed an Artificial Intelligence (AI) / Machine Learning (ML) guided solution to advance the manufacturing and assembly of high performance and lightweight thermoplastic composite (TPC) aerospace products. The solution aims to lower risk, cost and lead time for induction heating based welding and consolidation processes for TPC structure. The cost and lead time of part and material specific process development for induction welding (IW) and induction consolidation will be reduced by replacing traditional empirical methods with optimization methods that merge AI/ML and physics-based process simulations and process experiments with sensing and controls. TPC-IW process development is empirical in nature, and uncertainties in material & process behavior exist near & far from the induction coil. Physics-based simulations can be leveraged directly for process optimization but can be too computationally expensive to run in high fidelity and real time to do robust process optimization. The key impact of successful TPC induction consolidation and welding is cost & lead time reduction for part & material specific consolidation and welding recipes. This is an enabler for more rapid deployment of TPC structures via joining assembly, which can reduce energy & cost intensive usage of autoclaves & ovens. The solution aimed to advance the U.S. Department of Energy’s interests in using thermoplastics and automation in composite manufacturing for improvement of products for existing markets via increased production speeds, reduced costs, and lowered use of energy. Welded TPC structures can offer significant weight & energy savings for high-value commercial aerospace & industrial applications compared to metal & thermoset composite structures assembled by mechanical fastening and/or adhesive bonding. The project was organized into two Budget Periods. Budget Period 1 (BP1) was 15 months and its goal was to perform ML process optimization framework development & deployment on lab-coupon aerostructure components. A Go/No-Go Review was performed at the end of BP1 to verify fulfilment of key tasks & milestones to justify a Go Decision to move into the next Budget Period. Budget Period 2 (BP2) was 12 months and its goal was the deployment of the ML framework for ML process optimization of pilot industrial scale aerostructure components. The overall project aim was to develop & demonstrate ML-enhanced modeling framework that learns process-property mapping from multiple data sources at different fidelities. During BP1, the team accomplished key tasks & milestones to demonstrate the concept of multi-source ML for TPC aerostructure consolidation and assembly. First, the team completed documentation of induction based TPC heating requirements including baseline metrics to compare measured results against. Next the team completed demonstration of data generation from physics-based simulations for ML surrogate model generation and demonstrated the integration of physics-based simulation data into multi-source AI/ML algorithms. In parallel, the team established the lab-coupon scale induction welding system and completed a process to label and reduce generated data from physics-based simulation and experiments for ML surrogate models to enable multi-source ML model training & testing. To complete BP1, the team integrated physics-based simulation data and experimental data into multi-source ML algorithms. This was based on the team completing ML deployment of the induction welding on a lab system at RTRC and AI/ML deployment on existing induction welding line at Collins. ORNL visited both Collins and RTRC sites to witness the TPC induction welding process. Then, ORNL designed and constructed a new version of their vision-based sensing system better adapted to acquire process signals of the TPC induction welding process for process anomaly and defect detection. In BP2, the team accomplished key tasks & milestones to scale up multi-source ML for TPC aerostructure consolidation and assembly from the lab-coupon scale to the pilot-industrial scale. In BP2, the team demonstrated real time anomaly & defect detection via experiments performed by ORNL & RTRC. The team completed ML-optimization heating trials for TPC induction consolidation at Collins, and the team confirmed pilot industrial scale experimental data from Collins was compatible with the developed ML pipeline from RTRC. The team completed sub-element scale ML process optimization demonstration at RTRC, where the team leveraged RTRC’s robotic TPC welding setup to de-risk the ML process optimization by performing ML analysis of recorded temperatures to account for complex part features. Then, the team applied its ML-derived control strategies and ML process optimization framework at Collins to the pilot-industrial scale on a demo skin-stiffener part representative of a nacelle aerostructure fan cowl section. The key innovation is the AI/ML framework enabling effective process development of high performance, lightweight, energy efficient TPCs for composite aircraft structures.

36 MATERIALS SCIENCE↗

Flexible Fuel Electric Hybrid Glass Furnace Demonstration

The furnace contractor completed General Arrangement (GA) layouts and Piping and Instrumentation Diagrams (P&ID) for both furnaces during this reporting period. Preliminary equipment lists accompanied this work. Control system architecture in progress. Key furnace design elements and deliverables on track to be complete by end of phase. The facilities design and integration contractor completed a 3D scan and modeling of the facility. The work during this period focused on preliminary design with a focus on overall project scope and feasibility. This key work supports the overall project deliverables for 30% preliminary engineering and Preliminary Design Report (PDR) which are on track to be complete by the end of the phase. These deliverables are approximately 50% complete through the end of this reporting period. The batch house operations design contractor continues development of site plan and General Arrangement (GA) layout to support the 30% preliminary engineering and design package. This work is 60% complete by the end of this reporting period and is on track to be complete by the end of the phase.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

Measuring Thread Timing to Assess the Feasibility of Early-Bird Message Delivery Across Systems and Scales

Early-bird communication is a communication/computation overlap technique that leverages fine-grained communication to improve application run-time. Communication is divided such that each individual thread can initiate transmission of its portion of the data upon completion rather than waiting for a dedicated communication phase. The benefit of early-bird communication depends on the completion timing of the individual threads: On the one hand, if all threads are complete at nearly the same time, the overheads of sending multiple messages will accumulate, leading to performance that is worse than if a single message had been sent. On the other hand, if thread completions are spread out in time, those that complete earlier can send data while others continue working, leading to performance that is better than if a single message had been sent. The challenge is that the completion times are currently unknown and can vary based on application, problem size, system software, and underlying hardware. In this paper, we address this lacuna by measuring and evaluating the potential overlap afforded by early-bird communication for a selection of proxy applications. These measurements help us understand whether a given application could benefit from early-bird communication. Here, we present our technique for gathering this data and evaluate data collected from three proxy applications: MiniFE, MiniMD, and MiniQMC. Each application is run on three systems with distinct CPU architectures and strong scales across three run sizes. To characterize the behavior of these workloads, we study the trends of thread timings at both a macro level, across all threads across all runs of an application, and a micro level, that is, within a single process of a single run. We observe that our tested applications exhibit significantly different thread arrival distributions. The machine used had a significant impact, with the window of potential overlap varying by as much as an order of magnitude.

97 MATHEMATICS AND COMPUTING↗

Decomposing a San Francisco estuary microbiome using long-read metagenomics reveals species- and strain-level dominance from picoeukaryotes to viruses

ABSTRACT Although long-read sequencing has enabled obtaining high-quality and complete genomes from metagenomes, many challenges still remain to completely decompose a metagenome into its constituent prokaryotic and viral genomes. This study focuses on decomposing an estuarine metagenome to obtain a more accurate estimate of microbial diversity. To achieve this, we developed a new bead-based DNA extraction method, a novel bin refinement method, and obtained 150 Gbp of Nanopore sequencing. We estimate that there are ~500 bacterial and archaeal species in our sample and obtained 68 high-quality bins (>90% complete, <5% contamination, ≤5 contigs, contig length of >100 kbp, and all ribosomal and tRNA genes). We also obtained many contigs of picoeukaryotes, environmental DNA of larger eukaryotes such as mammals, and complete mitochondrial and chloroplast genomes and detected ~40,000 viral populations. Our analysis indicates that there are only a few strains that comprise most of the species abundances. IMPORTANCE Ocean and estuarine microbiomes play critical roles in global element cycling and ecosystem function. Despite the importance of these microbial communities, many species still have not been cultured in the lab. Environmental sequencing is the primary way the function and population dynamics of these communities can be studied. Long-read sequencing provides an avenue to overcome limitations of short-read technologies to obtain complete microbial genomes but comes with its own technical challenges, such as needed sequencing depth and obtaining high-quality DNA. We present here new sampling and bioinformatics methods to attempt decomposing an estuarine microbiome into its constituent genomes. Our results suggest there are only a few strains that comprise most of the species abundances from viruses to picoeukaryotes, and to fully decompose a metagenome of this diversity requires 1 Tbp of long-read sequencing. We anticipate that as long-read sequencing technologies continue to improve, less sequencing will be needed.

Lui, Lauren M.↗

Low Purge and Mercury Recovery Testing with Sludge Batch 10 Tank 40 Simulant

Researchers at the Savannah River National Laboratory (SRNL) have completed testing as requested by Savannah River Mission Completion (SRMC) to perform experiments to determine the impact of using a lower or inert purge in Sludge Batch (SB) 10 processing under the Nitric-Glycolic Acid (NGA) flowsheet. A key objective of this testing was also to determine the mercury speciation and recovery during each experiment. The testing was performed as part of the SB10 Technical Task Request (TTR) and Task Technical and Quality Assurance Plan (TTQAP). Two sets of tests were performed, and a Run Plan was approved prior to each set of experiments to document the planned testing. Three initial experiments were completed to determine whether a low air purge would be beneficial to CPC processing at higher acid stoichiometry (110%) based on the Koopman minimum acid (KMA) equation (116% Hsu). One of the tests, an inert nitrogen purge experiment, was also completed to demonstrate that excluding oxygen did not introduce any new hazards. The experiments were designed to be identical except for the change in purge gas and purge flowrate from run to run. After reviewing the results from the initial three experiments, six additional tests were proposed by SRNL to support the lower purge study and to look for processing alternatives for improving mercury recovery. These additional six tests were all completed at a very low acid stoichiometry to mimic the pH experienced during processing in DWPF (~7). DWPF is processing SB10 sludge at an acid stoichiometry of 90% based on the Hsu equation. The additional SRNL experiments were performed at an acid stoichiometry of 62.5% KMA stoichiometry (66.3% Hsu) to produce a Sludge Receipt and Adjustment Tank (SRAT) product with a pH of about 7. All experiments used simulants of both SWPF streams, although no entrained solvent was added during any of the experiments

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Process–Property–Performance Mapping of Additively Manufactured 316H Stainless Steel Components

The Advanced Materials and Manufacturing Technologies Program is focused on accelerating the development of advanced materials and components fabricated via additive manufacturing, and is using laser powder bed fusion (LPBF) of 316H stainless steel as an initial case study. In the previous fiscal year, miniature high-throughput specimens were printed on multiple LPBF systems to provide initial processing windows to minimize porosity and limit epitaxial grain growth during prints. This fiscal year, scaled builds were completed on three different LPBF systems at ORNL: a GE Concept Laser M2, a Renishaw AM400, and an EOS M290. Builds on the Concept Laser were conducted on multiple powder lots and processing parameter ranges to provide microstructure effects on time-independent and time-dependent mechanical properties. Builds on the Renishaw were produced using Oak Ridge National Laboratory (ORNL)-optimized printing parameters and Argonne National Laboratory (ANL)-optimized printing parameters to compare outcomes of parallel process optimization efforts at different national laboratories on the same LPBF system. Similarly, the build completed on the EOS M290 replicated the processing parameters of builds completed at Los Alamos National Laboratory (LANL). Optical microscopy and electron backscatter diffraction characterization was completed on all builds. In addition to the general round robin characterization, this work-package generated time-independent data, including tensile and fracture toughness test data on scaled Concept Laser builds as a function of processing parameters and post-build heat treatment. This analysis is complimentary to work in parallel work packages aiming to establish heat treatment and processing effects on time-dependent properties. It was found that although the stress-relief heat treatment provides the highest strength at lower-temperatures, tensile strength begins to converge at higher temperatures regardless of heat treatment condition. In addition, the more rigorous solution annealing and hot-isostatic pressing post-build heat treatments result in higher fracture toughness than the stress-relieved condition. The root-causes of the lower fracture toughness of the stress-relieved LPBF 316H material was informed via a stress-relief optimization study on a scaled concept laser print, where it was found that although dislocation recovery was largely complete after only a couple hours at 650°C, the extended hold of the current 24h heat treatment employed on scaled builds likely caused increased carbide volume fractions along the LPBF 316H grain boundaries, thereby deteriorating crack propagation resistance. This trend was seen to become more deleterious with additional increases of stress-relief temperature to 750°C or 850°C. These results have helped inform a new optimal stress-relief annealing condition for LPBF 316H for future campaign testing (650°C for 2h).

36 MATERIALS SCIENCE↗

Fiscal Year 2025 Software Quality Assurance Activities for the ARC Software

The continued goal of the ARC SQA project in the Advanced Reactor Technologies program of DOE is to resolve the QA gaps for the ARC software that limit, or prevent, commercialization of the software for industry users. This project started in earnest in fiscal year 2023 which saw the entire code system moved from a SVN repository to a GitLab repository and an associated software quality assurance plan (SQAP) developed and ratified. Most of the QA gaps in the ARC software were identified in collaboration with industry partners and work begin in fiscal year 2023 and continued through 2024 and 2025. The continuous integration testing was extended to RCT, DASSH, and SE2ANL. Minor changes were required to the original continuous integration methodology to make this happen. When full confidence in the methodology is complete, a report will be created to detail the automated regression testing methodology and minor reports will be created to detail the tolerance settings that have been applied to the output for each ARC code. The primary documentation that is missing includes user manuals, user guides, software verification reports, and code coverage assessments. The DASSH, SE2ANL, and SE2RCT manuals were completed this fiscal year. A review of the SE2ANL software identified that it is unrealistic to include updated correlations or different geometry models and it was scheduled for deprecation in favor of DASSH. The SE2ANL manual is essential for SE2RCT as they are similar but quite different in purpose. The only piece of software missing a manual consistent with the source code is NUBOW-3D which is a focus of the coming year. The code coverage report for DIF3D was updated and code coverage reports were created for REBUS, RCT, PERSENT, GAMSRC, and DASSH. Minor coverage issues were identified for all of these pieces of software which did not prevent the work done to transition them to the OneAPI compiler. Because SE2ANL was scheduled for deprecation, it was not transitioned, but it was successfully tested with the OneAPI compiler. This leaves SE2RCT and NUBOW-3D as the only pieces of software not transitioned to OneAPI and further work is required to get SE2RCT to work properly. The SE2RCT software transition will begin early next year while the NUBOW-3D software requires a manual before it can begin. Software verification work has been completed for DIF3D, REBUS, GAMSOR, GAMSRC, VARPOW, EvaluateFlux, and SUMMAR. The PERSENT software verification work was completed this year which was somewhat delayed because of unexpected bugs in the software. The PERSENT manual was updated to detail some of the issues and discuss the bowing reactivity worth feature added in the previous fiscal year. The RCT, DASSH, SE2RCT, and NUBOW-3D software are the only maintained pieces of software without verification reports. The software verification work for DASSH will be a focus in the upcoming fiscal year and it is hoped that some of the test cases created can serve as verification tests for SE2RCT. The NUBOW-3D work will begin when the manual and requirements report are completed. Only minor industry partner software development funds were provided this year. The DASSH software was updated to handle general axial geometry for each assembly and the NUBOW-3D software was updated to incorporate a new input format and better output. Overall progress on resolving the QA gaps has been good this year.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗