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DOE OSTI2025

TCCSP Site Characterization and Feasibility Report

The Tulare County Carbon Storage Project (TCCSP) focused on establishing the geological and commercial viability of a 50 million metric ton (MMT) carbon storage complex located in Tulare County, California. The geologic scope of work included collecting new and existing subsurface data, such as two-dimensional (2D) seismic data, advanced geophysical logs, core and fluid samples, and specialized core-analysis datasets, to support the integration and interpretation of findings from earlier tasks. These efforts contributed to the comprehensive feasibility study for commercial carbon capture and storage (CCS) development at the TCCSP site and in the surrounding region. The TCCSP project team evaluated the feasibility of the Lower Monterey Group (Santa Margarita Sand Member or its equivalent), Temblor Formation sand members (Olcese, Jewett, Vedder), and Lodo-Martinez Zone (Domengine Sandstone, Walker Formation, Lodo Formation, and the Martinez Sand) to serve as commercial CCS reservoirs along with potential for the Upper Monterey Group (Reef Ridge, Antelope Members) and the Kreyenhagen Formation to vertically seal underlying reservoirs from shallow above-zones.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2026

EOS at the Fermilab LHC Physics Center(LPC)

The Fermilab CMS LPC center has operated an EOS instance since initial testing began in June 2012, transitioning to production storage in 2013 with an initial capacity of 600\,TB. Today, the system provides approximately 16\,PB of storage to support the 4{,}500-core LPC user analysis cluster, which serves several hundred active CMS users at any given time. In this talk, we will provide an update on our system setup and outline near-term plans for the CMS LPC production EOS system. We will share operational experiences from running our EOS system over the past year. Additionally, we will briefly discuss our recent efforts within the Fermilab Disk Evolution Project to consider the future architecture of HEP data storage at Fermilab for the HL-LHC era.

Wu, Yujun↗

DOE OSTI2026

EOS at the Fermilab LHC Physics Center(LPC)

Wu, Yujun↗

DOE OSTI2025

Overview of the Neutron Radiography Reactor (NRAD) for Neutron Imaging and In-Core Experiment Capabilities at Idaho National Laboratory

NRAD is a 250-kilowatt TRIGA research reactor that first went online at INL in 1977. (TRIGA stands for Training, Research, Isotopes, General Atomics.) Historically, NRAD was utilized as a neutron radiography reactor that provides comprehensive, non-destructive information about the internal condition of irradiated nuclear fuel. Idaho National Laboratory (INL) has multiple nuclear fuels research and development programs that routinely evaluate irradiated fuels using neutron radiography at NRAD. In recent years, NRAD has gone through a transformation from the single purpose radiography reactor for which it was designed into a multipurpose research reactor, and expanding its in-core irradiation capabilities to support a broader mission for the US Department of Energy (DOE) Nuclear Energy (NE) programs, Basic Energy Science (BES) Programs, as well as Fusion Energy programs. NRAD is a designated user facility under the DOE Nuclear Science User Facility (NSUF) program, and is available for access for general public via a competitive proposal process. More information about NSUF and NRAD are available from the website: https://nsuf.inl.gov/Home/Facility/654.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2026

LaSalle Leaking Pin PIE Proposal

Determine the most probable root cause of the LaSalle fuel rod failure, distinguish primary failure mechanisms from post-failure degradation, and assess whether the condition is isolated or indicative of a broader issue affecting similar rods or assemblies. Results will support decisions related to core operation, fuel management, and corrective actions.

LaSalle↗

DOE OSTI2021

Overview of Hydraulic Fracturing Test Site 2 in the Permian Delaware Basin (HFTS-2)

Here, the Hydraulic Fracturing Test Site 2 (HFTS-2) is a large collaborative field-based R&D program in the Permian Delaware Basin, funded by the US Department of Energy through the National Energy Technology Laboratory (NETL) and the E&P industry, with support from academia. The projects' main objective is to improve the understating of the hydraulic fracturing process through utilization of advanced diagnostics and collection of through-fracture cores to provide undisputable evidence and attributes of the created hydraulic fractures. At the HFTS-2, in excess of $30 million was used to perform hydraulic fracturing research focusing on the Wolfcamp formation at a field site hosted and operated by Occidental. In addition to the research data collected by the project, Occidental provided a significant amount of background data for about a dozen existing wells in the test area as well as access to previously collected core. Additional technical and laboratory support was provided by the program members. Building on learnings and unanswered questions from HFTS-1 in the Permian Midland basin, the HFTS-2 used eight new producing wells and two existing (parent) wells to perform hydraulic fracturing research. Multiple science wells were drilled to sample and characterize the subsurface, including the collection of 540 feet of core in a vertical pilot hole and 948 feet of high-angle through-fracture core. The project installed permanent fiber optic cables in 3 wells to monitor near wellbore signals during fracturing and to collect cross-well strain measurements. Additional advanced diagnostics included a significant formation evaluation program on the vertical whole core, multi array moment tensor inversion capable microseismic survey, multi-well time-lapse geochemistry analysis, analysis of proppant distribution in producing child and slant core well, and others. We will provide an overview of the HFTS-2 project, including list of the consortium members, details of the test site, experiments performed, and technologies tested.

58 GEOSCIENCES↗

DOE OSTI2023

Strong metal–support bonding enhanced thermal stability in Au–Al 2 O 3 core–shell nanowires characterized by in situ transmission electron microscopy

In this study, the thermal stability of Au–Al 2 O 3 core–shell and Au nanowires was investigated by in situ scanning transmission electron microscopy and other techniques. Here, the nanowires were synthesized by the helium droplets method and deposited on various substrates. The in situ characterization of Au–Al 2 O 3 thermal stability demonstrated a substantially enhanced stability as compared to that of pure Au nanowires, which can be a transformative approach to design more durable Au-based nanocatalysts. Our study also revealed the existence of strong metal–support bonding in the Au/Al 2 O 3 system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Refining HPCToolkit for application performance analysis at exascale

As part of the US Department of Energy’s Exascale Computing Project (ECP), Rice University has been refining its HPCToolkit performance tools to better support measurement and analysis of applications executing on exascale supercomputers. To efficiently collect performance measurements of GPU-accelerated applications, HPCToolkit employs novel non-blocking data structures to communicate performance measurements between tool threads and application threads. To attribute performance information in detail to source lines, loop nests, and inlined call chains, HPCToolkit performs parallel analysis of large CPU and GPU binaries involved in the execution of an exascale application to rapidly recover mappings between machine instructions and source code. To analyze terabytes of performance measurements gathered during executions at exascale, HPCToolkit employs distributed-memory parallelism, multithreading, sparse data structures, and out-of-core streaming analysis algorithms. To support interactive exploration of profiles up to terabytes in size, HPCToolkit’s hpcviewer graphical user interface uses out-of-core methods to visualize performance data. The result of these efforts is that HPCToolkit now supports collection, analysis, and presentation of profiles and traces of GPU-accelerated applications at exascale. These improvements have enabled HPCToolkit to efficiently measure, analyze and explore terabytes of performance data for executions using as many as 64K MPI ranks and 64K GPU tiles on ORNL’s Frontier supercomputer. HPCToolkit’s support for measurement and analysis of GPU-accelerated applications has been employed to study a collection of open-science applications developed as part of ECP. This paper reports on these experiences, which provided insight into opportunities for tuning applications, strengths and weaknesses of HPCToolkit itself, as well as unexpected behaviors in executions at exascale.

Adhianto, Laksono↗

DOE OSTI2023

FPGA Acceleration of GCN in Light of the Symmetry of Graph Adjacency Matrix

Graph Convolutional Neural Networks (GCNs) are widely used to process large-scale graph data. Different from deep neural networks (DNNs), GCNs are sparse, irregular, and unstructured, posing unique challenges to hardware acceleration with regular processing elements (PEs). In particular, the adjacency matrix of a GCN is extremely sparse, leading to frequent but irregular memory access, low spatial/temporal data locality and poor data reuse. Furthermore, a realistic graph usually consists of unstructured data (e.g., unbalanced distributions), creating significantly different processing times and imbalanced workload for each node in GCN acceleration. To overcome these challenges, we propose an end-to-end hardware-software co-design to accelerate GCNs on resource-constrained FPGAs with the features including: (1) A custom dataflow that leverages symmetry along the diagonal of the adjacency matrix to accelerate feature aggregation for undirected graphs. We utilize either the upper or the lower triangular matrix of the adjacency matrix to perform aggregation in GCN to improve data reuse. (2) Unified compute cores for both aggregation and transform phases, with full support to the symmetry-based dataflow. These cores can be dynamically reconfigured to the systolic mode for transformation or as individual accumulators for aggregation in GCN processing. (3) Preprocessing of the graph in software to rearrange the edges and features to match the custom dataflow. This step improves the regularity in memory access and data reuse in the aggregation phase. Moreover, we quantize the GCN precision from FP32 to INT8 to reduce the memory footprint without losing the inference accuracy. We implement our accelerator design in Intel Stratix10 MX FPGA board with HBM2, and demonstrate 1.3x-110.5x improvement in end-to-end GCN latency as compared to the state-of the-art FPGA implementations, on the graph datasets of Cora, Pubmed, Citeseer and Reddit.

Nair, Gopikrishnan R.↗

DOE OSTI2021

Synthesis of Core@Shell Cu-Ni@Pt-Cu Nano-Octahedra and Their Improved MOR Activity

Fabrication of 3d metal-based core@shell nanocatalysts with engineered Pt-surfaces provides an effective approach for improving the catalytic performance. The challenges in such preparation include shape control of the 3d metallic cores and thickness control of the Pt-based shells. Herein, we report a colloidal seed-mediated method to prepare octahedral CuNi@Pt-Cu core@shell nanocrystals using CuNi octahedral cores as the template. By precisely controlling the synthesis conditions including the deposition rate and diffusion rate of the shell-formation through tuning the capping ligand, reaction temperature, and heating rate, uniform Pt-based shells can be achieved with a thickness of < 1 nm. Furthermore, the resultant carbon-supported CuNi@Pt-Cu core@shell nano-octahedra showed superior activity in electrochemical methanol oxidation reaction (MOR) compared with the commercial Pt/C catalysts and carbon-supported CuNi@Pt-Cu nano-polyhedron counterparts, demonstrating that both the lattice strain and shape effects arising from the as-prepared CuNi@Pt-Cu octahedral nanocatalysts play a crucial role in MOR performance.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Wireless Sensing and Communication Capability from In-Core to a Monitoring Center

Significant cost savings can be made if electrical cables can be replaced by wireless technology in current Nuclear Power Plants (NPP) and in advance reactor designs. Wireless technology can also provide in-core opportunities by significantly reducing the number of penetrations in the pressure vessel, cost and complexity of sensor installation and by increasing the efficiency of current and advanced reactors. Unlike other deployment scenarios for an industrial environment, operators need to have centralized control over all the networks. Centralized control will reduce implementation costs, provide single point control and enable monitoring of network devices, improve security, and enhance connectivity. Micro-sensors that can simultaneously monitor temperature and pressure within a fuel rod inside nuclear reactors will enable preventative actions during abnormal operating conditions. This ability could avert accidents and enable the expedient development of accident tolerant fuels. A novel micro-sensor suite (~ mm) to simultaneously measure multiple parameters such as temperature, strain, pressure, and neutron/gamma flux inside a fuel rod is being developed for use in reactors. The necessary communication architecture is also being developed to transmit measurement signals from the core to the plant's data cloud or control room. A three three-tier strategy has been developed to support wireless transmission of in-core measurements to the control room or to a secure cloud platform for control, analytics, and decision-making purposes. 1. In-core: data signal from in-core to outside of the pressure vessel within the containment building 2. Containment building: data signal from inside to the outside of the containment building and into the balance of the plant network 3. Balance of the plant network: information transmitted to the data cloud and control room This plan presents a wireless sensing and communication system for use within a reactor core and elsewhere. The communication technology is advantageous to compensate for network equipment failures and adverse data transmission conditions. Wireless technology will significantly increase the resiliency of the plants network system. The wireless system naturally provides multiple transmission path capability and data redundancy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

ECAR-6589 Reflector Support Structure Analysis: Project #33526

The MARVEL microreactor consists of five main systems: MRS (MARVEL Reactor Structure), PGS (Power Generation System), FCS (Fuel Core System), RCS (Reactivity Control System), and ICS (Instrument Control System). The RSS (Reflector Control System) is a sub system of the MRS which contains the upper and lower reflector support plates which support and locate the stationary core reflector and provide vertical support for the control drums. They are suspended from the Primary Coolant System by bolted structural straps which utilize a turnbuckle to allow for adjustment and leveling of the support plates. The stainless-steel support plates are each formed from four quarter circle segments which are bolted together around the lower core barrel. Bearing plates of non-galling Nitronic 60 alloy are fitted to the inner diameter of the support plates to prevent adhesive wear between the plates and the core barrel as the barrel expands and contracts due to changes in the temperature of the reactor.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2023

IDAES-PSE 2.2.0 Release

The Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform is a versatile computational environment offering extensive process systems engineering (PSE) capabilities for optimizing the design and operation of complex, interacting technologies and systems. IDAES enables users to efficiently search vast, complex design spaces to discover the lowest cost, most environmentally sustainable solutions while supporting the full process modeling lifecycle, from conceptual design to dynamic optimization and control. The extensible, open platform empowers users to create models of novel processes and rapidly develop custom analyses, workflows, and end-user applications. New features in the 2.2 release include: DiagnosticsToolbox DiagnosticsToolbox (idaes.core.util.model_diagnostics. DiagnosticsToolbox is a new self-guiding toolbox for assisting with diagnosing modeling issues. The toolbox contains a wide range of tools for identifying common modeling issues and conveying these to the user, and more features will be added in the coming months. To try the new toolbox, use the following steps: from idaes.core.util import DiagnosticsToolbox Build an instance of your model Ensure your models has zero degrees of freedom (as best you can) dt = DiagnosticsToolbox(model) dt.report_structural_issues() Follow the prompts Deprecations DegeneracyHunter is being deprecated in favor of the new DiagnosticsToolbox. The features and capabilities of DegeneracyHunter will be moved over to the DiagnositcsToolbox in the coming months. Removal of usage of Pyomo's expr.current due to deprecation upstream. Removed support for radial basis functions in ALAMOpy as these did not work in the current implementation. Those wishing to use radial basis functions should use Pysmo instead. New Capabilities MSContactor model for unit operations involving mass transfer between 2 or more streams, such as membrane, solvent extraction and solids leaching operations. Other Improvements Improved formulation for solubility products with additional parameters for better scaling of terms Better backward compatibility, verification and robustness testing of core models and APIs Added support for Separator models without energy balances

DiagnosticsToolbox↗

DOE OSTI2021

Atomic Force Microscopy to Characterize Antimicrobial Peptide-Induced Defects in Model Supported Lipid Bilayers

Antimicrobial peptides (AMPs) interact with bacterial cell membranes through a variety of mechanisms, causing changes extending from nanopore formation to microscale membrane lysis, eventually leading to cell death. Several AMPs also disrupt mammalian cell membranes, despite their significantly different lipid composition and such collateral hemolytic damage hinders the potential therapeutic applicability of the AMP as an anti-microbial. Elucidating the mechanisms underlying the AMP–membrane interactions is challenging due to the variations in the chemical and structural features of the AMPs, the complex compositional variations of cell membranes and the inadequacy of any single experimental technique to comprehensively probe them. (1) Background: Atomic Force Microscopy (AFM) imaging can be used in combination with other techniques to help understand how AMPs alter the orientation and structural organization of the molecules within cell membranes exposed to AMPs. The structure, size, net charge, hydrophobicity and amphipathicity of the AMPs affect how they interact with cell membranes of differing lipid compositions. (2) Methods: Our study examined two different types of AMPs, a 20-amino acid, neutral, α-helical (amphipathic) peptide, alamethicin, and a 13-amino acid, non-α-helical cationic peptide, indolicidin (which intramolecularly folds, creating a hydrophobic core), for their interactions with supported lipid bilayers (SLBs). Robust SLB model membranes on quartz supports, incorporating predominantly anionic lipids representative of bacterial cells, are currently not available and remain to be developed. Therefore, the SLBs of zwitterionic egg phosphatidylcholine (PC), which represents the composition of a mammalian cell membrane, was utilized as the model membrane. This also allows for a comparison with the results obtained from the Quartz Crystal Microbalance with Dissipation (QCM-D) experiments conducted for these peptides interacting with the same zwitterionic SLBs. Further, in the case of alamethicin, because of its neutrality, the lipid charge may be less relevant for understanding its membrane interactions. (3) Results: Using AFM imaging and roughness analysis, we found that alamethicin produced large, unstable defects in the membrane at 5 µM concentrations, and completely removed the bilayer at 10 µM. Indolicidin produced smaller holes in the bilayer at 5 and 10 µM, although they were able to fill in over time. The root-mean-square (RMS) roughness values for the images showed that the surface roughness caused by visible defects peaked after peptide injection and gradually decreased over time. (4) Conclusions: AFM is useful for helping to uncover the dynamic interactions between different AMPs and cell membranes, which can facilitate the selection and design of more efficient AMPs for use in therapeutics and antimicrobial applications.

Swana, Kathleen W.↗

DOE OSTI2025

How Does the Rate of Chain Exchange Relate to Stress Relaxation in Triblock Copolymer Networks?

The relationship between macroscopic stress relaxation and molecular-level chain exchange in triblock copolymer micelles has been explored using rheology and time-resolved small-angle neutron scattering (TR-SANS), marking the first measurements of chain exchange in concentrated triblock networks. It has long been assumed in models of transient or thermoreversible networks that the time scales for these two processes are equal. Experimentally, we find that stress relaxation occurs many orders-of-magnitude faster than chain exchange. This difference is quantitatively explained by modest dispersity in the core block that results in a slight asymmetry within any given nominally symmetric triblock. For stress relaxation to occur, only the shorter chain must pull out, while chain exchange is slowed due to the requirement of the eventual pullout of the longer block. The pullout time is extremely sensitive to the length of the core block. This mechanism is supported by measurements with an intentionally asymmetric triblock copolymer, which displays an even larger difference between the stress relaxation and chain exchange rates. These results establish a quantitative molecular-level picture of the chain dynamics associated with stress relaxation in triblock copolymer networks.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

JGI-Trichoderma v1.0

There is a series of Python and bash scripts to parse genomics datasets used to evaluate the coevolution of gene families and the feature importance of gene families using an SVM classifier. - Cover analysis: takes a list of single-copy genes in a set of genomes, aligns and builds the gene trees to determine if two gene families have a signature of covariation with one another. It parses the files to run phykit cover script described here: https://jlsteenwyk.com/PhyKIT/usage/index.html - SVM-classifier: This Python script is an SVM-based genomic classifier designed for biological data analysis. It combines machine learning with feature selection to identify important genomic markers and classify biological samples. Core Functionality: The script uses Support Vector Machines from scikit-learn to classify genomic data, incorporating SelectKBest for automated feature selection and leave-one-out cross-validation for performance assessment. It operates in multiple modes: feature ranking, optimal combination discovery, and sample prediction. Primary Applications: Genomic sample classification and biomarker discovery Feature importance analysis in high-dimensional biological datasets Prediction of sample categories based on genomic profiles Research applications requiring robust classification of biological data Key Advantages: High-dimensional handling: SVMs excel with genomic data's typical high feature-to-sample ratios Integrated feature selection: Reduces noise and computational overhead while identifying key markers Probability estimation: Provides confidence scores essential for biological interpretation Validation robustness: Leave-one-out cross-validation ensures reliable performance metrics Operational flexibility: Multiple analysis modes support different research phases from exploration to prediction

Stecca Steindorff, Andrei [Lawrence Berkeley Natio↗

DOE OSTI2023

Orchestration of materials science workflows for heterogeneous resources at large scale

In the era of big data, materials science workflows need to handle large-scale data distribution, storage, and computation. Any of these areas can become a performance bottleneck. We present a framework for analyzing internal material structures (e.g., cracks) to mitigate these bottlenecks. We demonstrate the effectiveness of our framework for a workflow performing synchrotron X-ray computed tomography reconstruction and segmentation of a silica-based structure. Our framework provides a cloud-based, cutting-edge solution to challenges such as growing intermediate and output data and heavy resource demands during image reconstruction and segmentation. Specifically, our framework efficiently manages data storage, scaling up compute resources on the cloud. The multi-layer software structure of our framework includes three layers. A top layer uses Jupyter notebooks and serves as the user interface. A middle layer uses Ansible for resource deployment and managing the execution environment. A low layer is dedicated to resource management and provides resource management and job scheduling on heterogeneous nodes (i.e., GPU and CPU). At the core of this layer, Kubernetes supports resource management, and Dask enables large-scale job scheduling for heterogeneous resources. The broader impact of our work is four-fold: through our framework, we hide the complexity of the cloud’s software stack to the user who otherwise is required to have expertise in cloud technologies; we manage job scheduling efficiently and in a scalable manner; we enable resource elasticity and workflow orchestration at a large scale; and we facilitate moving the study of nonporous structures, which has wide applications in engineering and scientific fields, to the cloud. While we demonstrate the capability of our framework for a specific materials science application, it can be adapted for other applications and domains because of its modular, multi-layer architecture.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Neutronics Analysis of Cold Critical KRUSTY Experiments using MCNP and Serpent

Kilowatt Reactor Using Stirling Technology (KRUSTY) is a prototype designed as a proof of concept for NASA’s Kilo power program, which was funded in order to create a small reactor for various space applications such as providing power for a colony on Mars or the moon and eventually as a possible option for powering rockets on deep space missions. KRUSTY was tested at the National Criticality Experiments Research Center (NCERC) located at the Nevada National Security Site (NNSS) from November 2017, through March 2018. The experimental campaign was conducted in four phases: com ponent critical experiments, cold critical experiments, warm critical runs, and high temperature demonstration. The component critical and cold critical experiments were used to determine the worth of the beryllium oxide (BeO) reflector rings and the boron carbide (B4C) control rod disks under different conditions. These conditions were altered by the addition and subtraction of other components in the assembly. The component critical phase was performed at atmospheric pressure and had four solid support rods spanning the center core; whereas the cold critical phase had the center core placed in a vacuum chamber and eight sodium-filled heat pipes spanning the center core region. This summary discusses the static neutronic analysis completed on the cold critical phase of the campaign using MCNP ® and Serpent. These two codes were used to assess any potential differences or biases in the simulation results. Understanding these potential biases is a key step toward the final goal of being able to fully analyze the transient experiments performed during phases 3 and 4 of the campaign.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗