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At least 433 records · Page 24

Phase Field Modeling of Chemical Reaction Related Damage Evolution in Environmental Barrier Coatings

The advent of next-generation engines necessitates materials capable of withstanding temperatures beyond the reach of current superalloys. SiC-based ceramic matrix composites, augmented with environmental barrier coatings (EBCs), present a promising materials solution. Given the active search for effective and durable EBCs, there is a pressing need for modeling tools to understand and predict damage evolution in these materials to help accelerate their development. This study introduces a phase-field model (PFM) designed to simulate the thermally grown oxides (TGO) and phase transformation in the degradation and failure of EBCs. The model accounts for the severe volume expansion due to oxidation, alongside phase transformations and porosity evolution during thermal cycling, offering a comprehensive view of the damage processes. Simulation results are validated against experimental findings reported in the literature, establishing the model's potential as a significant tool for understanding and improving the resilience of EBCs in cyclic oxidative environments.

fast-diffusion path↗

Large-scale additive manufacturing of self-heating molds

Large-scale material extrusion additive manufacturing technology is becoming the new mainstream technology for scaled-up composite mold and die applications. This paradigm shift in composite processing technology is primarily driven by out-of-autoclave tooling applications, in which fiber reinforced composite molds with scaled-up sizes and embedded heating elements are attractive. The present research describes the design, manufacturing, and testing of self-heating composite molds fabricated via a large-scale pellet extrusion 3D printing machine with an integrated wire co-extrusion tool. Polycarbonate (PC) composites reinforced with carbon fiber (PC/CF; 20 wt.%) and glass fiber (PC/GF; 20 wt.%) were used to fabricate mold parts. Joule heating thermal test results showed that uniform temperatures (~100 °C) were achieved for both PC/CF and PC/GF mold surfaces, using a custom-made feedback control power supply and infrared thermography. Mechanical characterizations, including tensile and flexural testing were performed on the wire-embedded and un-wired PC/CF and PC/GF base specimens to investigate the impact of the fiber reinforcement as well as the embedded wires. In the direction of extrusion, the ultimate tensile stress of PC/CF was 105 MPa, and that of PC/GF was 73 MPa, while the neat PC value was 64 MPa. Inner-bead voids and interfacial gaps were observed and characterized via optical and scanning electron microscopy. The embedded wires and inner bead impacted the mechanical properties of the composites. Furthermore, the stiffness of the wire-embedded mold was still satisfactory, proving that the technology can be used to fabricate additively manufactured out-of-oven/autoclave molds.

36 MATERIALS SCIENCE↗

Aemulus ν: precision halo mass functions in wνCDM cosmologies

Precise and accurate predictions of the halo mass function for cluster mass scales in wνCDM cosmologies are crucial for extracting robust and unbiased cosmological information from upcoming galaxy cluster surveys. Here, we present a halo mass function emulator for cluster mass scales (≳ 1013 M ⊙/h) up to redshift z = 2 with comprehensive support for the parameter space of wνCDM cosmologies allowed by current data. Based on the Aemulus ν suite of simulations, the emulator marks a significant improvement in the precision of halo mass function predictions by incorporating both massive neutrinos and non-standard dark energy equation of state models. This allows for accurate modeling of the cosmology dependence in large-scale structure and galaxy cluster studies. We show that the emulator, designed using Gaussian Process Regression, has negligible theoretical uncertainties compared to dominant sources of error in future cluster abundance studies. Our emulator is publicly available (https://github.com/DelonShen/aemulusnu_hmf), providing the community with a crucial tool for upcoming cosmological surveys such as LSST and Euclid.

cluster counts↗

Maskman

SAND2025-04369O Maskman is a user-friendly tool designed to create hex masks, which are essential for optimizing application performance in high-performance computing environments. By converting a list of integers into binary and then hex masks, Maskman simplifies the process of setting application affinity. This ensures that software runs efficiently on specific nodes within a computing cluster. Ideal for researchers and developers, Maskman streamlines the preparation of inputs for HPC schedulers, enhancing resource management and improving overall system performance. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Pase, Douglas [Sandia National Lab. (SNL-CA), Live↗

EV-ELM (Electric Vehicle Policies with the Energy Language Model) [SWR-25-156]

Electric Vehicle Policies with the Energy Language Model (EV-ELM) leverages previous work using Large Language Models (LLMs) to find, download, and parse policy information related to energy infrastructure. In this application, we use LLMs to find policy documents related to the permitting and installation of electric vehicle charging infrastructure. This software contains the code to find, download, and parse these documents, while a related data record in the Open Energy Data Initiative (OEDI) will include the resulting output dataset that can be used for downstream analysis. The EV-ELM repository contains code for the EV-ELM project, which focuses on retrieving and processing EV permitting processes using large language models. The project is composed of two pipelines: (1) a web scraping pipeline for discovering and downloading EV permitting documents, and (2) a document parsing and extraction pipeline that processes the downloaded files to produce structured data. The web scraping pipeline is designed to extract relevant information from various websites, while the document parsing pipeline processes and analyzes the extracted documents to derive meaningful insights. Both pipelines depend on the NLR elm repository, which provides essential tools and functionalities for handling and processing the data. The web scraping pipeline is a modified version of the ordinance_gpt example within the elm repository. It has been adapted to fit the specific requirements of the EV-ELM project, ensuring that it effectively captures and processes the necessary information related to EV permitting.

Olson, Reid [National Laboratory of the Rockies (N↗

Physics of wurtzite ferroelectrics

Ferroelectricity was long considered incompatible with the wurtzite structure, but the recent discovery of switchable polarization in wurtzite alloys has renewed interest in these materials for integrated electronic and memory applications. The development of wurtzite ferroelectrics faces significant technological challenges, which can be addressed through a fundamental physical understanding of their dielectric and ferroelectric properties. This article focuses on the physics that govern the polarization switching behavior, emphasizing the atomic- and meso-scale (domain) mechanisms involved in the transition between polarization states. A distinguishing feature of this article is a deep dive into the role of intrinsic and extrinsic defects—an area that has received limited attention in prior reviews, but is increasingly recognized as central to polarization switching, coercive fields, leakage, and fatigue. We highlight how defect behavior evolves during processing and electrical cycling, often contributing to long-term degradation. We also introduce powerful first-principles defect calculations, common in semiconductors but not yet widespread in ferroelectrics, as tools to understand and design materials. By integrating recent theoretical and experimental insights, we aim to provide a framework for advancing wurtzite ferroelectrics.

36 MATERIALS SCIENCE↗

gaia: An R package to estimate crop yield responses to temperature and precipitation

gaia is an open-source R package designed to estimate crop yield shocks in response to annual weather variations and CO 2 concentrations at the country scale for 17 major crops. This innovative tool streamlines the workflow from raw climate data processing to projections of annual shocks to crop yields at the country level, using the response surfaces from an empirical econometric model developed and documented in Waldhoff et al. (2020), which leverages historical weather, CO 2 , and crop yield data for robust empirical fitting for 17 crops. gaia uses these response surfaces with monthly temperature and precipitation projections (e.g., from the Coupled Model Intercomparison Project Phase 6 (CMIP6) (O’Neill et al., 2016) climate data bias-adjusted and statistically downscaled by the ISIMIP3BASD approach (Lange, 2019) in the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) (Warszawski et al., 2014)) to project yield shocks that can be applied to agricultural productivity changes at the country level for use in multisectoral economic models. The historical and future projections use gridded, country-and-crop specific monthly growing season precipitation and temperature data, aggregated to the national level, and weighted by cropland area derived from the global Monthly Irrigated and Rainfed Crop Areas around the year 2000 (MIRCA2000) dataset (Portmann et al., 2010). These annual, country, and crop-specific yield shocks can be aggregated to different definitions of regions, crop commodities, and time periods, as needed by specific multisectoral economic models. gaia serves as a lightweight, powerful tool that can aid exploration of crop yield responses under a broad range of future climate projections, enhancing human-Earth system analysis capabilities.

60 APPLIED LIFE SCIENCES↗

Biofuel Air Emissions Analysis

U.S. has goals to produce 3 billion gallons of sustainable aviation fuel (SAF) annually by 2030, increasing to 35 billion gallons by 2050 to decarbonize the aviation sector. Meeting these production targets will require large number of biorefineries to be set up. However, ability to comply with federal air quality standards is prerequisite to being issued a construction permit. Negotiating the permitting process for a new biorefinery can be quite onerous and cost the investors significant time and money. Although there can be numerous reasons for delayed biorefinery construction, air permitting is fraught with pitfalls because the permitting process relies on precedence, which the future SAF biorefineries lack. NREL's Biofuel Air Emissions Analysis project is unique and innovative in terms of the tools, approaches, and analyses provided. NREL is the only national laboratory that is actively working at the intersection of Federal air quality regulations, emissions and air quality analysis across the supply chain, and process design. This project is focused on providing much needed data and analyses that address biorefinery air permitting. This project develops models and quantitative analyses and measures progress towards meeting air quality regulatory requirements. These models and methods are applied to analyze air permitting related to wastewater sludge to biofuel conversion pathways using hydrothermal liquefaction (HTL), impacts of HTL pathway on local and regional air quality including an assessment of health and equity impacts. In addition to filling research gaps, this project also disseminates the findings to the relevant stakeholders at BETO, other national labs, and regulatory agencies.

air quality↗

Is Genetic Engineering a Route to Enhance Microalgae-Mediated Bioremediation of Heavy Metal-Containing Effluents?

Contamination of the biosphere by heavy metals has been rising, due to accelerated anthropogenic activities, and is nowadays, a matter of serious global concern. Removal of such inorganic pollutants from aquatic environments via biological processes has earned great popularity, for its cost-effectiveness and high efficiency, compared to conventional physicochemical methods. Among candidate organisms, microalgae offer several competitive advantages; phycoremediation has even been claimed as the next generation of wastewater treatment technologies. Furthermore, integration of microalgae-mediated wastewater treatment and bioenergy production adds favorably to the economic feasibility of the former process—with energy security coming along with environmental sustainability. However, poor biomass productivity under abiotic stress conditions has hindered the large-scale deployment of microalgae. Recent advances encompassing molecular tools for genome editing, together with the advent of multiomics technologies and computational approaches, have permitted the design of tailor-made microalgal cell factories, which encompass multiple beneficial traits, while circumventing those associated with the bioaccumulation of unfavorable chemicals. Previous studies unfolded several routes through which genetic engineering-mediated improvements appear feasible (encompassing sequestration/uptake capacity and specificity for heavy metals); they can be categorized as metal transportation, chelation, or biotransformation, with regulation of metal- and oxidative stress response, as well as cell surface engineering playing a crucial role therein. This review covers the state-of-the-art metal stress mitigation mechanisms prevalent in microalgae, and discusses putative and tested metabolic engineering approaches, aimed at further improvement of those biological processes. Finally, current research gaps and future prospects arising from use of transgenic microalgae for heavy metal phycoremediation are reviewed.

Ranjbar, Saeed (ORCID:0000000256518605)↗

Risk-based System Upgrade Planning for AOT-IC Presentation [Slides]

The number of deployed systems and different HW/SW form factors makes it difficult to maintain a complete understanding of their health which leads to a reduced level of certainty when assessing risk and trying to prioritize system upgrade efforts. AOT-IC must constantly plan and execute system upgrade and replacement projects to ensure the longevity of the control system. There are many more projects needed than the group can realistically accomplish given schedule and resource constraints. The group must prioritize their upgrade projects to choose only the highest priority projects to execute each year. Currently plans upgrade and replacement projects based on alignment with mission needs, group vision, resource availability, and qualitative risk assessments based on SME estimates. The group maintains a risk register. This is useful as a tool for shaping strategic planning but currently lacks the quantitative data and process needed to generate accurate risk assessments.

43 PARTICLE ACCELERATORS↗

DIVA/DeviceEditor (DIVA) v6.0.0

The DIVA software interfaces a process in which researchers design their DNA with a web-based graphical user interface (DeviceEditor), submit their designs to a central queue, and a few weeks later receive their sequence-verified clonal constructs. Each researcher independently designs the DNA to be constructed with a web-based BioCAD tool, and presses a button to submit their designs to a central queue. Researchers have web-based access to their DNA design queues, and can track the progress of their submitted designs as they progress from "evaluation", to "waiting for reagents", to "in progress", to "complete". Researchers access their completed constructs through the central DNA repository. Along the way, all DNA construction success/failure rates are captured in a central database. Once a design has been submitted to the queue, a small number of dedicated staff evaluate the design for feasibility and provide feedback to the responsible researcher if the design is either unreasonable (e.g., encompasses a combinatorial library of a billion constructs) or small design changes could significantly facilitate the downstream implementation process. The dedicated staff then use DNA assembly design automation software to optimize the DNA construction process for the design, leveraging existing parts from the DNA repository where possible and ordering synthetic DNA where necessary. Once all requisite process inputs are available, the design progresses from "waiting for reagents" to "in progress" in the design queue. Human-readable and machine-parseable DNA construction protocols output by the DNA assembly design automation software are then executed by the dedicated staff exploiting lab automation devices wherever possible. Since the all employed DNA construction methods are sequence-agnostic, standardized (utilize the same enzymatic master mixes and reaction conditions), completely independent DNA construction tasks can be aggregated into the same multi-well plates and pursued in parallel. The resulting sets of cloned constructs can then be screened by high-throughput next-gen sequencing platforms for sequence correctness. A combination of long read-length (e.g., PacBio) and paired-end read platforms (e.g., Illumina) would be exploited depending the particular task at hand (e.g., PacBio might be sufficient to screen a set of pooled constructs with significant gene divergence). Post sequence verification, designs for which at least one correct clone was identified will progress to a "complete" status, while designs for which no correct clones were identified will progress to a "failure" status. Depending on the failure mode (e.g., no transformants), and how many prior attempts/variations of assembly protocol have been already made for a given design, subsequent attempts may be made or the design can progress to a "permanent failure" state. All success and failure rate information will be captured during the process, including at which stage a given clonal construction procedure failed (e.g., no PCR product) and what the exact failure was (e.g. assembly piece 2 missing). This success/failure rate data can be leveraged to refine the DNA assembly design process.

Plahar, Hector↗

Idaho National Laboratory Site Natural Resources: Wildland Fire Recovery Framework

As pressures from invasive species, climate change, and anthropogenic impacts increase across the landscape in the western U.S., managing wildland fire recovery to promote healthy sagebrush steppe becomes an increasingly important stewardship responsibility. The Idaho National Laboratory (INL) has developed and implemented wildland fire recovery plans to hasten desirable vegetation re-establishment on several individual fires, but lacks an overarching wildland fire recovery strategy, or framework. The intent of this document is to develop the technical approach and scientific basis for wildland fire recovery at the INL Site and to evaluate the tools available to support it in a comprehensive and broadly applicable format. This wildland fire recovery framework will outline the process of assessing the potential impacts of wildland fire on natural resources, present a range of post-fire recovery options, outline an approach for post-fire monitoring, and provide a template for post-fire recovery plans designed to addresses the specific conditions of each wildland fire. There are numerous benefits to developing a wildland fire recovery framework for the INL Site. The first is streamlining the development of post-fire recovery plans for individual fires. A second benefit is more closely aligning INL’s post-fire planning processes with those of other federal agencies. The development of an INL Site fire recovery framework will also allow resource professionals to consider a broader set of recovery tools than they have before because all proposed tools included in the framework were vetted through the process of scoping and stakeholder review. Finally, this framework is a publicly available document that can be used as a basis for communicating and discussing post-fire natural resource recovery objectives with agency collaborators, conservation partners, and other stakeholders. Through the proactive land stewardship principles outlined in this framework, current INL sustainability initiatives can be enhanced, and future INL mission flexibility will be maintained.

99 GENERAL AND MISCELLANEOUS↗

Uncertainty Propagation from Experiment Measurements to Modeling Approaches: A Case for SMR Steam Entrainment Testing

To license new and advanced reactor designs, regulators must be convinced that their unique safety cases—relative to existing large scale reactors—have been adequately addressed by the designed reactor protection systems. In water cooled small modular reactors (SMRs), droplet entrainment in steam flow has significant implications on the progression of accident scenarios due to its compact design features, which requires representative test data applicable to SMR designs. Computer code, modeling and simulation (M&S) tools and models require adequate verification, assessment, and qualification. This includes M&S results validation against scaled empirical data within allowable uncertainty bands to gain regulatory approvals during the various stages of reactor system design, demonstration, and commercialization. However, measurement uncertainty within the empirical datasets and test data applicability ranges requires careful consideration of M&S inputs (i.e., boundary conditions, and initial conditions), and verification and validation efforts. This study focuses on uncertainty quantification in designing scaled test facilities for SMR applications with appropriate measurements and a standard data-reduction method to estimate thermal hydraulics characteristics parameters that incorporate physics phenomena of interest. In addition, this study supports the evaluation model development and assessment process using M&S that interfaces with advanced computing tools and digital twin capabilities. This will allow synchronization between experiment and modeling approaches for droplet entrainment testing and analysis, improving diagnostics, prognostics, and decision-making to accelerate regulatory approval.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

Design and Operation of a Multi-Bed Catalytic Micro-Reactor for the Study of Co-Processing of Bio-Oils with VGO

An industry wide shift from fossil-based fuel to renewable fuel sources including biomass, municipal waste, and plastics will require new process monitoring methods to minimize transitional risks including off specification product formation and catalyst deactivation. This project aims to provide a machine learning based process monitoring tool composed of online, slipstream mass spectra for use in biomass refineries and co-processing in existing refineries allowing operators to monitor product qualities and adjust process conditions accordingly. In order to maximize the robustness of the tool, large volumes of data must be collected to fine tune model parameters which consists of both micro and pilot scale mass spectral data. Micro-scale data is collected with a multi-tube micro-reactor housing up to six catalysts in horizontal beds, coupled with a molecular beam mass spectrometer. A pyrolizer equipped with an auto-sampler streamlines the micro-scale data collection process. This type of pyrolizer/micro-reactor configuration does not exist on the market, and therefore had to be created for the purposes of this project. The design and commissioning of this reactor will be presented in detail. This reactor set-up is highly flexible and increases throughput of analysis. For catalyst testing, each bed can be individually selected simply by turning valves. For catalyst reduction and regeneration, simultaneous flow through all six beds is used. The reproducibility of the system was first assessed with whole biomass pyrolysis along with pyrolysis of calibration standards. Initial work on this system evaluated two FCC catalysts, equilibrium catalyst (E-cat), and a proprietary catalyst from Johnson Matthey specifically design for co-processing of bio-oil with vacuum gas oil (VGO). This work used model compounds and VGO which illuminated differences in products produced by the catalysts.

biomass↗

Potential Applications of Quantum Computing at Los Alamos National Laboratory, v0.3.0

Since the scientific revolution in the 16th and 17th centuries, the process of scientific discovery has followed an iterative feedback process of observation, hypothesis development and testing with physical experiments, which is widely referred to as the scientific method. This process remained largely unchanged until the middle of the 20th century, when the emergence of digital computers empowered scientist to build and inspect detailed simulations of physical phenomena. Over the last century, computational tools have transformed modern approaches to scientific discovery by enabling fast and affordable hypothesis testing before physical experiments are conducted, shown in Figure 1-1. Some notable examples include: global climate forecasts to understand how the environment may change over decades [130]; modeling the behavior of plasma to design fusion reactors [59]; and understanding the behavior of molecules in biological processes [161, 223].

36 MATERIALS SCIENCE↗

Interactive Exploration of High-Dimensional Phase Diagrams

High-dimensional thermodynamic phase stability databases are becoming increasingly common due to the convergence of three recent trends: (i) the widespread interest in so-called “high-entropy” alloys, (ii) the availability of high-throughput computational assessments of phase stability in broad composition spaces and (iii) the ongoing development of ever-increasingly broad, multicomponent, multiphase CALPHAD databases. Although automated computational tools can readily process such high-dimensional data, scientists are often unable to visualize the relevant phase relations, an ability that is crucial to gaining an intuitive understanding of the stability constraints governing materials design. The present work addresses this need by providing algorithms that enable the interactive exploration of phase equilibria in high-dimensional spaces. These algorithms concentrate the complex nonlinear nonsmooth optimization needed into a preprocessing step that generates a large number of high-dimensional yet elementary graphical primitives. Furthermore, these primitives can then be cross-sectioned to yield 3-dimensional views in a computationally efficient manner that enables an interactive exploration of high-dimensional spaces. All of these operations are highly parallelizable, thus facilitating scaling of this method to large data sets.

36 MATERIALS SCIENCE↗

Extending SLUSCHI for Automated Diffusion Calculations

We present an extension of the SLUSCHI package (Solid and Liquid in Ultra Small Coexistence with Hovering Interfaces) to enable automated diffusion calculations from first-principles molecular dynamics. While the original SLUSCHI workflow was designed for melting temperature estimation via solid-liquid coexistence, we adapt its input and output handling to isolate the volume search stage and generate one production trajectory suitable for diffusion analysis. Post-processing tools parse VASP outputs, compute mean-square displacements (MSD), and extract tracer diffusivities using the Einstein relation with robust error estimates through block averaging. Diagnostic plots, including MSD curves, running slopes, and velocity autocorrelations, are produced automatically to help identify diffusive regimes. The method has been validated through representative case studies: self-diffusion in Al-Cu liquid alloys, sublattice melting in Li7La3Zr2O12 and Er2O3, interstitial oxygen transport in bcc and fcc Fe, and oxygen diffusivity in Fe-O liquids with variable Si and Al contents. Viscosity and diffusivity are linked through the Stokes-Einstein relation, with composition dependence assessed via simple linear mixing. This capability broadens SLUSCHI from melting-point predictions to transport property evaluation, enabling high-throughput, fully first-principles datasets of diffusion coefficients and viscosities across metals and oxides.

36 MATERIALS SCIENCE↗

An Open-Source Python Package for CFD Solution Verification

Informed decision-making using computational fluid dynamics (CFD) results requires quantifying the errors and uncertainties of a simulation. Verification, validation, and uncertainty quantification (VVUQ) methods were developed to address this need and have matured. However, these VVUQ analyses are often non-trivial and require CFD analysts and practitioners to have specific skill sets. This has led to the uneven adoption of VVUQ analyses, in part, based on the availability of software tools to aid CFD analysts and practitioners. Solution verification, a procedure to evaluate the accuracy of a simulation by estimating potential errors arising from the computational model and computing the uncertainties without comparing to results from a physical system, is one of the lagging VVUQ analyses as the absence of software has forced CFD analysts and practitioners to develop their own codes or piece together incomplete software from across the internet. This work presents an opensource Python package, CFDverify, to lower the barrier of entry and fill in the technological gap in solution verification. CFDverify also provides a streamlined framework to remove some potential errors in post-processing CFD results. The hope is that CFDverify can improve the quality and quantity of CFD solution verification in scientific and research studies and attract interest in developing a communal tool. This paper describes the design, features, and an example use of CFDverify.

Weinmeister, Justin [ORNL] (ORCID:0000000160090237↗