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At least 19 records

ROSE

Developed at Lawrence Livermore National Laboratory (LLNL), ROSE is an open source compiler infrastructure to build source-to-source program transformation and analysis tools for large-scale C (C89 to C23), C++ (C++98 to C++23), UPC, Fortran (Fortran4, 66, 77, 95, 2003), OpenMP, Java, Python, and Binary applications. ROSE users range from experienced compiler researchers to library and tool developers who may have minimal compiler experience. ROSE is particularly well suited for building custom tools for static analysis, program optimization, arbitrary program transformation, domain-specific optimizations, complex loop optimizations, performance analysis, and cyber-security. ROSE is: A library (and set of associated tools) to quickly and easily apply compiler techniques to one's code in order to improve application performance and developer productivity. A research and development compiler infrastructure for for writing custom source-to-source translators to perform source code transformations, analysis, and optimizations. Is

Pinnow, NathanT [Lawrence Livermore National Labor

AstraAI v1

AstraAI is an open-source, structure-aware AI coding agent designed for large scientific and DOE-HPC codebases such as AMReX-based applications. Unlike general-purpose coding assistants, AstraAI combines retrieval-augmented generation (RAG) with compiler-level Abstract Syntax Tree (AST) analysis to perform precise, scope-constrained code modifications. It identifies exact function spans, enforces locality of edits, and maintains cross-file invariants, enabling deterministic and build-safe transformations in complex C++/GPU environments. AstraAI is intended for developers working on large, evolving HPC frameworks where correctness, reproducibility, and structural integrity are critical. Typical use cases include modifying physics kernels, updating GPU device lambdas, and performing multi-file refactors without breaking compilation or runtime semantics. Compared to conventional LLM-based coding agents - even those with repository access - AstraAI provides structural guarantees rather than free-form text patches. It minimizes unintended diffs, prevents scope drift, preserves formatting and build stability, and reduces structural hallucinations. By integrating compiler tooling directly into the generation loop, AstraAI transforms AI-assisted coding from probabilistic text editing into deterministic, structure-preserving program transformation suitable for mission-critical scientific software.

Natarajan, Mahesh [Lawrence Berkeley National Labo

Alignment switching in 3D-printed smectic liquid crystal elastomers

Extrusion-based additive manufacturing has emerged as a powerful platform for designing shape-morphing materials through controlled orientation. However, existing approaches primarily rely on a single mode of flow-induced alignment, limiting orientation programmability. Herein, we present a direct-ink-writing approach for smectic liquid crystal elastics that exploits two distinct alignment modes within a single ink. The smectic ink exhibits shear- and temperature-dependent orientation switching, enabling molecular alignment either perpendicular or parallel to the print direction. Combined rheological, X-ray, and molecular dynamics analyses reveal that this alignment inversion arises from the preservation or collapse of smectic layers under flow. This reversible switching encodes both contractile and elongational actuation within individual filaments, greatly expanding the design freedom of printed liquid crystal elastomers. We demonstrate 2D and 3D structures with diverse programmed shape transformations, highlighting the potential of this platform for adaptive soft actuators and architected functional materials.

Lee, Jin Hyeong [Pusan National University, Busan,

Postirradiation Examination of HFIR-Irradiated Yttrium Hydrides

The US Department of Energy Microreactor Program is developing compact, high-temperature microreactors that require robust neutron moderators. Known for its high hydrogen retention and structural stability, YH x is a leading candidate for this purpose. As part of an ongoing evaluation, Oak Ridge National Laboratory conducted post-irradiation examinations on YH x specimens irradiated in the High Flux Isotope Reactor under the legacy Transformational Challenge Reactor program, focusing on their structural integrity, hydrogen retention, and thermal properties. The post-irradiation examination campaign primarily examined specimens from two irradiation campaigns, covering a range of hydrogen-to-yttrium atomic ratios (H/Y), neutron damage levels (0.1–2 displacements per atom), and targeted irradiation temperature of 600°C. The investigations included electron microscopy, high-energy x-ray diffraction, laser flash analysis, differential scanning calorimetry, and SiC thermometry analysis.

22 GENERAL STUDIES OF NUCLEAR REACTORS

Transformational Nano-confined Ionic Liquid Membrane for Greater than or Equal to 97 Percent Carbon Dioxide Capture from Natural Gas Combined Cycle Flue Gas

A transformational process based on nano-confined ionic liquid (NCIL) membranes was developed for capturing ≥97% CO 2 from natural gas combined cycle (NCCC) flue gas. The NCIL membranes were prepared by loading amino acid ionic liquid into a framework composed of single-walled carbon nanotube mesh filled with graphene oxide quantum dots. The membranes exhibited CO 2 permeance as high as 2,000 GPU with a CO 2 /N2 selectivity of 2,300 for a typical NGCC flue gas composition. When H 2 O vapor sweep was applied in the permeate side, 96.6% CO2 dry-basis purity and 97.6% CO 2 capture rate were achieved for a simulated NGCC flue gas with single stage. In the process design, a highly H 2 O-selective membrane would be needed to recover majority of the H 2 O vapor, and the recovered H 2 O vapor could be recycled to the permeate side of the NCIL membrane. Sulfonated poly(ether ether ketone) membranes were successfully developed for this purpose. These membranes exhibited H 2 O permeance great than 11,000 GPU and H 2 O/CO 2 selectivity greater than 1,000 at 70ºC for a feed mixture consisting of 14.5 vol% H2O and balanced CO 2 . A standalone membrane model using MATLAB platform was developed for process simulation. The model was validated with experimental data. Techno-economic analysis based on the testing data collected during the current program suggests this transformational membrane process can achieve 97% CO 2 capture efficiency with a cost of $47.8/tonne of CO 2 , which is a 21% reduction versus DOE’s reference case B31B.97.

03 NATURAL GAS

FAIR Data and Interpretable AI Framework for Architectured Metamaterials (Final Report)

This research program established a transformative framework for the discovery and design of mechanical metamaterials, which are architected structures engineered to control physical phenomena like sound and vibration in ways natural materials cannot. To overcome the traditional reliance on trial-and-error, the project developed an interpretable Artificial Intelligence (AI) framework that moves beyond "black box" models to reveal the specific geometric patterns—such as "unit-cell templates"—that govern a material’s performance. A major breakthrough was the development of a hierarchical design method, which allows a single material to block vibrations across multiple frequency ranges simultaneously by layering patterns at different scales without them interfering with one another. This was further expanded to include irregular, graph-based designs that use spanning tree algorithms to ensure structural connectivity while allowing for customized, direction-dependent properties like stiffness and acoustic impedance. Beyond design, the project addressed the practicalities of real-world production by developing uncertainty quantification techniques that account for manufacturing defects and material variability, reducing the need for expensive physical testing by orders of magnitude. To speed up the discovery process, the team implemented Gaussian Process Regression and other surrogate models that provide accurate performance predictions at a fraction of the traditional computational cost. The AI-generated designs were successfully validated through fabrication of physical samples and wave propagation experiments, confirming their ability to accurately guide or reflect waves as predicted. By contributing these tools and high-quality FAIR benchmark datasets to the wider scientific community, this work provides a scalable foundation for advancing technologies in aerospace vibration control, medical imaging, and noise reduction.

36 MATERIALS SCIENCE

Empowering Scientific Innovation Through An Integrated Research Infrastructure: The Role of the Advanced Computing Ecosystem

As the landscape of computational science evolves, the Department of Energy (DOE) is reimagining the roles of its large-scale computing facilities to meet emerging research challenges. The Integrated Research Infrastructure (IRI) program aims to transform how experiments are designed, conducted, and shared, with significant impacts on all stakeholders. In response, the Oak Ridge Leadership Computing Facility (OLCF) has established the Advanced Computing Ecosystem (ACE), a strategic framework to prepare its hardware, software, and experimental capabilities for the IRI era. ACE focuses on integrating novel compute environments, orchestrating advanced workflows, and developing foundational technologies, ensuring a seamless transition to IRI while accelerating scientific discovery. This paper outlines ACE's role in advancing OLCF's mission and its impact on the future of computational science.

Widener, Patrick

A Fast Dynamic Internal Predictive Power Scheduling Approach for Power Management in Microgrids: Preprint

This paper presents a Dynamic Internal Predictive Power Scheduling (DIPPS) approach for optimizing power management in microgrids, particularly focusing on external power exchanges among diverse prosumers. DIPPS utilizes a dynamic objective function with a time-varying binary parameter to control the timing of power transfers to the external grid, facilitated by efficient usage of energy storage for surplus renewable power. The microgrid power scheduling problem is modeled as a mixed-integer nonlinear programming (MINLP-PS) and subsequently transformed into a mixed-integer linear programming (MILPPS) optimization through McCormick's relaxation to reduce computational complexity. A predictive window window with 6 data points is solved at an average of 0.92s, a 97.6% improvement over the 38.27s required for the MINLP-PS formulation, implying the numerical feasibility of the DIPPS approach for real-time implementation. Finally, the approach is validated against a static objective using real-world load data across three case studies with different time-varying parameters, demonstrating the ability of DIPPS to optimize power exchanges and efficiently utilize distributed resources while shifting the external power transfers to specified time durations.

24 POWER TRANSMISSION AND DISTRIBUTION

Adsorption of CO on gold: effect of coverage and surface deformations

Recent insights into the nature of catalysts under reactive conditions have motivated investigating heterogeneously catalyzed reactions with non-rigid surfaces. As an example system to revisit, CO interactions with gold have been a longstanding system of interest due to its structure sensitivity. In this work, we investigate coverage-dependent CO adsorption trends on terraces, steps, and kinks. Calculating differential binding energy and mean absolute displacement of the atomic structures as a function of CO coverage, we propose a deformation quenching mechanism that partially determines CO saturation coverage. Extending this analysis to consider CO partial pressure, we show a phase change in CO step occupation, which we highlight as a possible explanation for previously reported temperature-programmed desorption and Fourier-transform infrared reflection-absorption spectroscopy measurements. Furthermore, this work shows how adopting a non-rigid surface model can lead to additional insights which may be applied to other heterogeneously catalyzed systems.

Kavalsky, Lance [University of Wisconsin-Madison,

Virtual Inspection of Advanced Manufacturing via Process-Scale Digital Twins (Abbreviated Report)

Inspection and certification comprise the most significant bottlenecks in advanced manufacturing for NNSA applications, often requiring far more time and resources than the fabrication of the parts themselves. Traditional methods, such as manual review and X-ray computed tomography, are not only slow and costly, but also struggle to provide a clear connection between manufacturing instructions and the final performance of critical components. This gap limits both the agility and assurance needed to support the modernization and safety of the United States nuclear stockpile. In response, our Strategic Initiative established a digital twin framework that integrates realtime process monitoring, automated data analysis, and immersive virtual reality collaboration into a unified inspection pipeline. By leveraging data from sensors, machine instructions, and imaging, we created high-fidelity virtual models of manufactured parts that could be rapidly analyzed and certified. This approach was first demonstrated with Direct Ink Write, and then extended to other manufacturing settings, including conventional (or “subtractive”) manufacturing and to predict the end of life performance of parts per the aging and lifetimes programs. The result is a transformational capability: inspection times have been reduced by a factor of 120,000 without loss of accuracy and while simultaneously improving traceability and confidence in part quality. This framework not only streamlines certification for critical applications, but also positions the national security enterprise to respond more flexibly to emerging challenges, supporting agile manufacturing and digital engineering practices across a broad range of mission-relevant domains.

42 ENGINEERING

Yeast Transformation on Hamilton Vantage (YT Vantage) v1

Our software program is designed for the Hamilton Vantage liquid handling robot, automating the Build step in the Design-Build-Test-Learn (DBTL) cycle for Saccharomyces cerevisiae. This program minimizes human intervention, enabling rapid identification of pathway bottlenecks and genes that enhance verazine production. The program takes competent yeast and plasmid DNA as input and generates an output library of engineered strains compatible with automated colony picking, high-throughput culturing, and chemical extraction for downstream LC-MS analysis. A user-friendly interface, developed using the Hamilton Method Editor software, allows for on-demand parameter customization. By automating this process, our program streamlines the construction of Saccharomyces cerevisiae, reducing manual labor and increasing efficiency. While the manual process is well-documented, integration with robotic automation is less common, making our program a valuable tool for researchers. With this software, we achieved 2-5 fold increases in verazine production, demonstrating its potential to accelerate research in this field.

Louie, Randy [Lawrence Berkeley National Laborator

Transforming Energy Through Computational Excellence: NREL HPC Resources for High Performance Computing for Energy Innovation (HPC4EI) Program

NREL hosts computing facilities for the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy (EERE). In 2024, NREL introduced Kestrel, the 3rd generation, EERE-sponsored supercomputer dedicated to renewable energy and energy efficiency research. Kestrel has already been used for hundreds of research projects by NREL, other national laboratories, and university partners. This includes HPC4EI-sponsored industrial partnerships.

high-performance computing

Equipment List Comparing Balance of Plant Containing a Heat Pump against a Reference Electricity Generating Plant

Approximately two-thirds of U.S. energy consumption in the industrial and transportation sectors relies on fossil fuels. These sectors require high-quality heat, i.e., thermal energy at very high temperatures, for molecular transformation processes. The Integrated Energy Systems (IES) program aims to assess the economic potential of utilizing nuclear-grade heat from Advanced Reactors (ARs) to meet the high-quality heat demands. By having industrial processes (IPs) supplied with nuclear-generated heat, manufacturers could benefit from more stable and potentially lower energy costs, reducing reliance on volatile fossil fuel markets. The main outcome of the FY24 research was the thermodynamic assessment and gap analysis of steam generation for IP applications. The study completed in June 2024 demonstrated that the required steam temperatures could be achieved by integrating a heat pump into an AR power plant. After identifying the thermal demands of target IPs, multiple balance of plant configurations for the Xe-100 reactor by X-energy, incorporating a heat pump, were analyzed. Their technical feasibilities were assessed, including the design of suitable axial compressors for these applications. Comparative performance analysis showed that thermal efficiency alone is insufficient to evaluate system suitability. To address this, a new indicator (heat factor) was introduced in the report released in September 2024 to quantify the low-quality thermal power needed to produce one unit of high-quality heat for the IP. Results showed that integrating heat pumps into Rankine cycles enables higher steam temperatures, though at the expense of increased thermal energy input. This report builds upon and completes the foundational work previously undertaken. It focuses on the design of two Balance of Plant (BOP) configurations, both based on Rankine energy conversion cycles: “Case 1”, which involves electricity generation only, and “Case 2”, which combines electricity and high-temperature heat generation for industrial use. For each configuration, a comprehensive equipment list was developed, detailing all major components such as turbines, compressors, heat exchangers, pumps, and control systems. These lists will serve as the basis for future comparative cost analyses, with the goal of assessing the number and type of components required to integrate a heat pump into the Rankine cycle and to establish a heat transport system capable of delivering thermal energy from the nuclear plant to an industrial facility. Using the constitutive equations presented in the June 2024 milestone, the operating conditions of all BOP components for both “Case 1” and “Case 2” were evaluated. These parameters—such as temperature, pressure, mass flow rate, and steam quality—served as the basis for calculating the associated thermal and mechanical power flows. The net power required from the heat pump to raise the steam temperature to the target level was also determined. The thermodynamic performance of the configuration was then assessed using the heat factor metric. The key outcome of this analysis is a comparative table that presents the equipment that was used in the “Case 1” and “Case 2” configurations. This study offers a preliminary comparison of the two designs, providing insight into the impact of integrating a heat pump in terms of component requirements and thermal efficiency. This equipment list, together with the evaluated operating conditions, also serves as a foundation for the economic analyses scheduled for the current fiscal year.

22 GENERAL STUDIES OF NUCLEAR REACTORS

EVALS: Enhanced VALidation of Advanced Battery Supply Chains

This is an overview of EVALS (Enhanced VALidation of advanced battery Supply chains), funded by the U.S. Department of Energy Vehicle Technologies Office (VTO) and some initial data from a transformational laboratory directed research and development (LDRD) program project funded at NREL. EVALS aims to accelerate the process to bring domestic and allied primary sources of battery materials online, from production to deployment.

ADVANCED PROPULSION SYSTEMS

Scaling Demand Flexibility: Building on 30 Years of Energy Efficiency Success

With electricity consumption across the United States (US) and Canada anticipated to grow, energy efficiency program administrators have a key role to play in helping to ensure energy affordability and reliability in support of the broader economic systems utilities and grid support. Connected, demand side load balancing solutions, such as load shifting heating, ventilation and air conditioning (HVAC) systems and managed charging for electric vehicles (EVs), can dynamically manage energy, allowing for more volumetric electricity consumption without incurring the expense of upgraded transmission and distribution capabilities. When combined, or aggregated, many small loads can be managed to have meaningful impact on energy demand on the grid. Utilities and their partners have an opportunity to leverage decades of experience and the infrastructure needed to assess, design, implement, and measure programs to scale up the adoption of equipment with built-in load flexibility capabilities. Current efforts among a wide variety of electricity system service providers, utilities, standards agencies, regulators, national labs and private industry stakeholders aim to identify common standards, metrics, and methodologies for valuing grid services offered by demand side equipment. By combining those efforts with decades of proven energy efficiency resources, utilities are poised to effectuate a scaling up of equipment with energy management capabilities installed in homes and businesses across the US and Canada. This paper will provide an overview of how utilities are approaching this era of load growth and new peak demands across the United States and Canada. It will highlight the specific strategies that program administrators are employing to advance market transformation for grid-enabled products and devices that have the greatest potential to reduce energy use and increase load flexibility.

Grant, Peter

DEVELOPMENT OF INEXPENSIVE HIGH TEMPERATURE NITI-BASED SHAPE MEMORY ALLOYS FOR POWDER BED ADDITIVE MANUFACTURING

NiTi and NiTi-based Shape Memory Alloys (SMA) exhibit a reversible solid-state phase transformation from martensite to austenite driven by thermal energy. High temperature (Mf>100°C) SMAs are martensite at room temperature and can be fabricated into solid-state actuators that return to a pre-programmed shape against a designed load after heating to transformation threshold. Reactive as-fabricated additively manufactured parts (4-D printing) is the current state of the art in manufacturing of SMAs but requires compositions compliant to rapid solidification. Existing actuator designs are developed from commercially available, highly investigated material compositions. However, existing high temperature high performance (high actuation strain, low thermal hysteresis) shape memory alloys contain significant (>10% at.) portions of high-cost Platinum Group Metals (PGMs). It is of significant scientific interest to investigate material compositions that are peer performing or superior to PGMs whose constituent elements represent a significant cost savings. Shape memory alloy properties vary significantly with small (0.1% at.) compositional changes making robust investigative sample sets very large. Computational material design can be deployed to shrink the compositional space of possible alloy combinations and reduce the experimental load in material discovery. Investigating shape memory effect (SME) and validating process additive process parameters for a single novel composition is cost intensive in both time and consumed materials. Additionally, sub-optimal processing, oxygen, or solidification rate sensitivity could render additively manufacturing specimens without micro, macro cracks, or significant chemical variance impossible. Unfortunately, such failure susceptibility cannot be simulated. Therefore, a research pathway to validate novel shape memory alloy compositions for powder bed fusion additive manufacturing without the need for powdered feedstock is also proposed. This research investigates novel high temperature shape memory alloys for actuators without platinum group alloying elements to discover one that could be commercially viable as an additive manufacturing feedstock.

Sundermann, Tayler

CRADA Number NFE-22-09375 with Vitriform3D, Inc. (CRADA Final Report)

Each year, roughly 8 million tons of glass are landfilled in the United States. Vitriform3D, in collaboration with Oak Ridge National Laboratory through the Innovation Crossroads program, is developing an additive manufacturing approach to transform post-consumer glass into engineered stone building materials. A custom UV-curable binder jetting system was commissioned to enable recycled glass printing without energy-intensive oven curing. While this printer is still under development, the team pursued a parallel research track using ORNL equipment and a polyethyleneimine (PEI) binder. Printed parts were thermally post-cured and infiltrated with epoxy, with the resulting material exceeding fiber cement board performance. Silane treatment of the glass particles further improved glass–epoxy adhesion, yielding an additional 24% strength gain over the epoxy-glass composite, achieving nearly 3x the fiber cement benchmark. Ongoing research is focused on reducing epoxy content to improve cost and recycled content while retaining mechanical performance. This work demonstrates a scalable, low-carbon pathway to convert glass waste into high-value, locally produced construction materials.

36 MATERIALS SCIENCE