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Characterizing The Mechanical Properties of Polyurethane Foams

The Department of Energy has many radioactive facilities that are on the path of deactivation and decommissioning (D and D). These facilities can sit cold and dark for many years before final disposition, and must be maintained to ensure no radiological release occurs in the interim. Improvements and additions to D and D tool sets can greatly: Save time and money, Reduce worker risk. Conventional fixatives widely used often take the form of paints or films that are not readily applicable to 3-dimensional void spaces. Foams are one promising platform that may offer solutions to a number of contaminated problem sets such as: Gloveboxes, Pipes, Tanks. SRNL is working to characterize various commercial foams that would encapsulate the interior volume of a given space and are capable of immobilizing any remaining contamination. One key performance metric of these foams is how well they will perform in an accident scenario. To this end, SRNL is researching the mechanical properties of these foams to ensure that the material can withstand the environment of application while maintaining structural integrity. ASTM standard E3191-18 served as a guiding document for this project, outlining the requirements that foaming fixatives used for the mitigation of radioactive contamination need to meet before being implemented. Objective: Quantification of the mechanical properties of 6 commercial polyurethane foams was performed using multiple ASTM standards to record measurements for: Compression testing: Flexible Foams, Force required to produce 50% compression, Rigid Foams, Compressive and apparent modulus, Point of 10% core deformation, The 'Zero Deformation' point, Compressive strength, Yield point. Tensile testing: Tensile strength, Tensile stress, Percent elongation. Experiment 1: An electromechanical compressive tester (MTS Criterion Series 43) was used to evaluate 6 foams (4 flexible, 2 rigid). Per ASTM D1621, each rigid foam was compressed at 10% of the measured thickness per minute until the sample was 13% of it's original thickness. Per ASTM D35/4, each flexible foam was pre-flexed twice to 80% original thickness at a rate of 250 mm/min, then compressed to 80% original thickness at a rate of 50 mm/min. Experiment 2: An electromechanical tensile tester (MTS Criterion Series 43) was used to evaluate 6 foams (4 flexible, 2 rigid). Per ASTM D1623, rigid foams were pulled apart at a rate of 1.27 mm/min until the sample broke. Per ASTM D35/4, flexible foams were pulled apart at a rate of 500 mm/min until the sample broke. The strongest material in both compression and tensile testing scenarios was found to be the rigid intumescent polyurethane Hilti foam. The experiments revealed that the Hilti foam in a tensile scenario had a peak stress value that was larger than the closest competitor by a factor of 2.3 and a compressive yield point that was larger than the closest competitor by a factor of 1.4, indicating that the Hilti foam is the best choice for implementation in mechanically harsh environments. The performance metrics measured can serve as a basis for future mechanical tests that would help set relevant ASTM standards (E3191) for intumescent polyurethane foams in fixating applications. Tests like surface adhesion, impact, and flame tests would serve as a better indicator as to how this material would perform in environmentally harsh scenarios often found in decommissioned nuclear facilities. Further tests of the foams' intumescent properties would also be important should these foams be implemented in environmentally harsh scenarios.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

Safety-Related Instrumentation and Control Upgrade Pilot Project: NUREG- 0711 Process Development, Planning and Analysis Activities, and Lessons Learned

Constellation Energy and the United States Department of Energy (DOE) have established a public/private partnership to implement a pilot digital upgrade to replace legacy analog, safety-related reactor protection and emergency safety feature actuation systems (RPS/ESFAS) with modern digital systems. This effort is occurring at Constellation’s Limerick Generating Station. This project is being performed in accordance with industry processes that have been adapted to better support digital upgrades. These processes include IP-ENG-001, Standard Design Process, NISP-EN-04, Standard Digital Engineering Process and Electric Power Research Institute Report 3002011816, Digital Engineering Guide. The Light Water Reactor Sustainability (LWRS) Program at the Idaho National Laboratory (INL) has been supporting this effort. Latest INL HFE efforts in support of this project have focused on definition and implementation of the Human Factors Engineering (HFE) Program in support of Constellation design and related licensing efforts. This paper presents the development execution of the HFE Program through completion of the Planning and Analysis Phase as defined by NUREG-0711 as well as HFE lessons learned.

99 GENERAL AND MISCELLANEOUS↗

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗

Plant science decadal vision 2020–2030: Reimagining the potential of plants for a healthy and sustainable future

Abstract Plants, and the biological systems around them, are key to the future health of the planet and its inhabitants. The Plant Science Decadal Vision 2020–2030 frames our ability to perform vital and far‐reaching research in plant systems sciences, essential to how we value participants and apply emerging technologies. We outline a comprehensive vision for addressing some of our most pressing global problems through discovery, practical applications, and education. The Decadal Vision was developed by the participants at the Plant Summit 2019, a community event organized by the Plant Science Research Network. The Decadal Vision describes a holistic vision for the next decade of plant science that blends recommendations for research, people, and technology. Going beyond discoveries and applications, we, the plant science community, must implement bold, innovative changes to research cultures and training paradigms in this era of automation, virtualization, and the looming shadow of climate change. Our vision and hopes for the next decade are encapsulated in the phrase reimagining the potential of plants for a healthy and sustainable future. The Decadal Vision recognizes the vital intersection of human and scientific elements and demands an integrated implementation of strategies for research (Goals 1–4), people (Goals 5 and 6), and technology (Goals 7 and 8). This report is intended to help inspire and guide the research community, scientific societies, federal funding agencies, private philanthropies, corporations, educators, entrepreneurs, and early career researchers over the next 10 years. The research encompass experimental and computational approaches to understanding and predicting ecosystem behavior; novel production systems for food, feed, and fiber with greater crop diversity, efficiency, productivity, and resilience that improve ecosystem health; approaches to realize the potential for advances in nutrition, discovery and engineering of plant‐based medicines, and "green infrastructure." Launching the Transparent Plant will use experimental and computational approaches to break down the phytobiome into a "parts store" that supports tinkering and supports query, prediction, and rapid‐response problem solving. Equity, diversity, and inclusion are indispensable cornerstones of realizing our vision. We make recommendations around funding and systems that support customized professional development. Plant systems are frequently taken for granted therefore we make recommendations to improve plant awareness and community science programs to increase understanding of scientific research. We prioritize emerging technologies, focusing on non‐invasive imaging, sensors, and plug‐and‐play portable lab technologies, coupled with enabling computational advances. Plant systems science will benefit from data management and future advances in automation, machine learning, natural language processing, and artificial intelligence‐assisted data integration, pattern identification, and decision making. Implementation of this vision will transform plant systems science and ripple outwards through society and across the globe. Beyond deepening our biological understanding, we envision entirely new applications. We further anticipate a wave of diversification of plant systems practitioners while stimulating community engagement, underpinning increasing entrepreneurship. This surge of engagement and knowledge will help satisfy and stoke people's natural curiosity about the future, and their desire to prepare for it, as they seek fuller information about food, health, climate and ecological systems.

59 BASIC BIOLOGICAL SCIENCES↗

BeyondPlanck: I. Global Bayesian analysis of the Planck Low Frequency Instrument data

We describe the BEYONDPLANCK project in terms of our motivation, methodology, and main products, and provide a guide to a set of companion papers that describe each result in more detail. Building directly on experience from ESA’s Planck mission, we implemented a complete end-to-end Bayesian analysis framework for the Planck Low Frequency Instrument (LFI) observations. The primary product is a full joint posterior distribution P(ω | d), where ω represents the set of all free instrumental (gain, correlated noise, bandpass, etc.), astrophysical (synchrotron, free-free, thermal dust emission, etc.), and cosmological (cosmic microwave background – CMB – map, power spectrum, etc.) parameters. Some notable advantages of this approach compared to a traditional pipeline procedure are seamless end-to-end propagation of uncertainties; accurate modeling of both astrophysical and instrumental effects in the most natural basis for each uncertain quantity; optimized computational costs with little or no need for intermediate human interaction between various analysis steps; and a complete overview of the entire analysis process within one single framework. As a practical demonstration of this framework, we focus in particular on low-ℓ CMB polarization reconstruction with Planck LFI. In this process, we identify several important new effects that have not been accounted for in previous pipelines, including gain over-smoothing and time-variable and non-1/f correlated noise in the 30 and 44 GHz channels. Modeling and mitigating both previously known and newly discovered systematic effects, we find that all results are consistent with the ΛCDM model, and we constrained the reionization optical depth to τ = 0.066 ± 0.013, with a low-resolution CMB-based χ 2 probability to exceed of 32%. This uncertainty is about 30% larger than the official pipelines, arising from taking a more complete instrumental model into account. The marginal CMB solar dipole amplitude is 3362.7 ± 1.4 μK, where the error bar was derived directly from the posterior distribution without the need of any ad hoc instrumental corrections. We are currently not aware of any significant unmodeled systematic effects remaining in the Planck LFI data, and, for the first time, the 44 GHz channel is fully exploited in the current analysis. We argue that this framework can play a central role in the analysis of many current and future high-sensitivity CMB experiments, including LiteBIRD, and it will serve as the computational foundation of the emerging community-wide COSMOGLOBE effort, which aims to combine state-of-the-art radio, microwave, and submillimeter data sets into one global astrophysical model.

79 ASTRONOMY AND ASTROPHYSICS↗

Hot and dense matter equation of state probability distributions for astrophysical simulations

We add an ensemble of nuclei to the equation of state for homogeneous nucleonic matter to generate a new set of models suitable for astrophysical simulations of core-collapse supernovae and neutron star mergers. We implement empirical constraints from (i) nuclear mass measurements, (ii) proton-proton scattering phase shifts, and (iii) neutron star observations. Our model is also guided by microscopic many-body theory calculations based on realistic nuclear forces, including the zero-temperature neutron matter equation of state from quantum Monte Carlo simulations and thermal contributions to the free energy from finite-temperature many-body perturbation theory. We ensure that the parameters of our model can be varied while preserving thermodynamic consistency and the connection to experimental or observational data, thus providing a probability distribution of the astrophysical hot and dense matter equation of state. Furthermore, we compare our results with those obtained from other available equations of state. While our probability distributions indeed represent a large number of possible equations of state, we cannot yet claim to have fully explored all of the uncertainties, especially with regard to the structure of nuclei in the hot and dense medium.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

hectorui: A web-based interactive scenario builder and visualization application for the Hector climate model

In a world of increasingly online, distributed, and diverse interdisciplinary research, there is a need to provide accessible and user-friendly interactive visualization tools that elucidate complex models and their output products. Hector UI is an R Shiny web interface built to extend the simple global climate model Hector (Hartin et al. 2015), originally designed in C++ with an accompanying R interface. Traditionally, Hector has only had a command line interface available which requires fluency in C++ or R and a full understanding of Hector’s parameter space to run the model and create output. Hector UI provides a fast, efficient solution that makes the model more accessible to a broader user base. This implementation allows users that may not be fluent in R or C++ to interactively explore model scenarios and outputs in an easy to use, guided point and click interface.

97 MATHEMATICS AND COMPUTING↗

HWO Pupil Chopping Wavefront Sensing and Control FY24 Mini-Project Writeup

This document is a summary of work completed in June through July 2024 to assess the feasibility of implementing non-coronagraphic pupil chopping (PC) for Habitable Worlds Observatory (HWO) primary mirror segment stabilization with an on- and/or off-axis Natural Guide Star (NGS) or Laser Guide Star (LGS). This work considers three different PC configurations: (1) a non-common path (NCP) continuous low-order Deformable Mirror (DM), (2) using the telescope’s primary mirror (M1) segment(s), and (3) using a NCP low-order segmented DM. We show that option 2 likely needs a LGS for guiding, while options 1 and 3 may be able to guide on NGSs. We also separately evaluate spectral bandwidth limitations in the absence of photon noise, showing that the technique is limited to Δλ/λ ≲ 25% in a standard imaging configuration but Δλ/λ ≲ 150% when using a NCP Wynne corrector.

79 ASTRONOMY AND ASTROPHYSICS↗

BeyondPlanck I. Global Bayesian analysis of the Planck Low Frequency Instrument data

We describe the BeyondPlanck project in terms of motivation, methodology and main products, and provide a guide to a set of companion papers that describe each result in fuller detail. Building directly on experience from ESA's Planck mission, we implement a complete end-to-end Bayesian analysis framework for the Planck Low Frequency Instrument (LFI) observations. The primary product is a joint posterior distribution P(omega|d), where omega represents the set of all free instrumental (gain, correlated noise, bandpass etc.), astrophysical (synchrotron, free-free, thermal dust emission etc.), and cosmological (CMB map, power spectrum etc.) parameters. Some notable advantages of this approach are seamless end-to-end propagation of uncertainties; accurate modeling of both astrophysical and instrumental effects in the most natural basis for each uncertain quantity; optimized computational costs with little or no need for intermediate human interaction between various analysis steps; and a complete overview of the entire analysis process within one single framework. As a practical demonstration of this framework, we focus in particular on low-l CMB polarization reconstruction, paying special attention to the LFI 44 GHz channel. We find evidence of significant residual systematic effects that are still not accounted for in the current processing, but must be addressed in future work. These include a break-down of the 1/f correlated noise model at 30 and 44 GHz, and scan-aligned stripes in the Southern Galactic hemisphere at 44 GHz. On the Northern hemisphere, however, we find that all results are consistent with the LCDM model, and we constrain the reionization optical depth to tau = 0.067 +/- 0.016, with a low-resolution chi-squared probability-to-exceed of 16%. The marginal CMB dipole amplitude is 3359.5 +/- 1.9 uK. (Abridged.)

Andersen, KJ↗

Cyber Informed Engineering (CIE) Curriculum Guide

The Cyber-Informed Engineering (CIE) Curriculum Guide provides a framework, guidance, and resources for incorporating CIE into university-level engineering programs and related educational activities. A key goal of this guide is to help institutions deliver CIE focused education to produce future engineers and technicians who meet the nation’s infrastructure needs. To accommodate a broad range of educational goals and approaches, this guide outlines several practical integration strategies, links to resources that can accelerate CIE adoption, and offers perspectives from partner academic institutions on the various implementation strategies.

42 ENGINEERING↗

Transforming New Multifamily Construction to Zero: Strategies for Implementing Energy Targets and Design Pathways: Preprint

Creating zero energy (ZE) mid- to high-rise multifamily buildings represent significant challenges in the design world. Tenants often take responsibility for some of the utilities and are responsible for consumption of some of the largest end uses: hot water and plug loads. Furthermore, designers can experience risk in creating new sets of details and recommending new strategies to reach aggressive EUI targets. To assist in a more streamlined and successful design approach to ZE multifamily buildings, five major professional societies and experts from the multifamily space created a Zero Energy Advanced Energy Design Guide (AEDG) for multifamily buildings based on data driven results coupled with actual design experiences and case studies. Pathways to achieve ZE design were established including whole building energy use intensity targets coupled with details on how to achieve success in implementing individual efficiency measures. The guidance strives to overcome design barriers and this paper provides an overview of the recommended process to achieve ZE or ZE-ready status, along with details on selecting energy targets, and strategy pathways to achieve the targets.

advanced energy design guide↗

Decoding the protein–ligand interactions using parallel graph neural networks

Abstract Protein–ligand interactions (PLIs) are essential for biochemical functionality and their identification is crucial for estimating biophysical properties for rational therapeutic design. Currently, experimental characterization of these properties is the most accurate method, however, this is very time-consuming and labor-intensive. A number of computational methods have been developed in this context but most of the existing PLI prediction heavily depends on 2D protein sequence data. Here, we present a novel parallel graph neural network (GNN) to integrate knowledge representation and reasoning for PLI prediction to perform deep learning guided by expert knowledge and informed by 3D structural data. We develop two distinct GNN architectures: $$\hbox {GNN}_{\mathrm{F}}$$ GNN F is the base implementation that employs distinct featurization to enhance domain-awareness, while $$\hbox {GNN}_{\mathrm{P}}$$ GNN P is a novel implementation that can predict with no prior knowledge of the intermolecular interactions. The comprehensive evaluation demonstrated that GNN can successfully capture the binary interactions between ligand and protein’s 3D structure with 0.979 test accuracy for $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and 0.958 for $$\hbox {GNN}_{\mathrm{P}}$$ GNN P for predicting activity of a protein–ligand complex. These models are further adapted for regression tasks to predict experimental binding affinities and $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 crucial for compound’s potency and efficacy. We achieve a Pearson correlation coefficient of 0.66 and 0.65 on experimental affinity and 0.50 and 0.51 on $$\hbox {pIC}_{\mathrm{50}}$$ pIC 50 with $$\hbox {GNN}_{\mathrm{F}}$$ GNN F and $$\hbox {GNN}_{\mathrm{P}}$$ GNN P , respectively, outperforming similar 2D sequence based models. Our method can serve as an interpretable and explainable artificial intelligence (AI) tool for predicted activity, potency, and biophysical properties of lead candidates. To this end, we show the utility of $$\hbox {GNN}_{\mathrm{P}}$$ GNN P on SARS-Cov-2 protein targets by screening a large compound library and comparing the prediction with the experimentally measured data.

59 BASIC BIOLOGICAL SCIENCES↗

Renewable Energy Integration in Remote Alaska Communities

This guide was developed through the U.S. Department of Energy (DOE) Energy Transitions Initiative Partnership Project (ETIPP) technical assistance (TA) projects for Nikolski and St. George. Both communities had wind turbine projects that failed due to integration and maintenance issues. Due to these failures, Nikolski and St. George sought help to investigate renewable energy alternatives and associated integration strategies, with a focus on technologies that could be easily maintained within the community. The primary objective of this guide was to interview subject matter experts and document lessons learned to address potential renewable energy and storage integration issues in remote Alaskan communities. This guide is intended to provide information to rural Alaskan communities considering renewables with the purpose of sharing best practices and case studies to facilitate the successful implementation of future renewable energy projects.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

Representative Phenomena of Cyclic Turbulent Combustion in High-Pressure Fuel Sprays

Abstract Cyclic variations in conventional diesel combustion engines can lead to large differences in engine out emissions even at steady operation. This study uses an optically accessible constant-pressure flow chamber to acquire fuel injections in quick succession to analyze mixing, auto-ignition, and combustion of diesel-surrogate n-heptane using multiple high-speed optical diagnostics. Prior studies have utilized fewer injections and/or they rely on analysis of ensemble average behavior. These approaches do not yield information on injection-to-injection variation or provide confidence in utilizing individual injection measurements for high-fidelity computational fluid dynamics(CFD) model validation. In this study, a large set of 500 injections is used to obtain global parameters including liquid length, vapor penetration length, ignition delay time, and lift-off length. Results for multiple injections are presented to illustrate large injection to injection variations. Potential sources for these variations are analyzed to conclude localized, small scale turbulence and rate of injection variations as the likely sources. Then, a statistical method based on z-scores is proposed and implemented to identify instantaneous injections that best represent the bulk data-set of jet boundaries measured independently by three different diagnostics. This synthesis of statistics-guided screening of data set and ensemble-average analysis offers higher confidence for CFD model validation relying upon both a representative single and average injection results.

42 ENGINEERING↗

Environmental application of chlorine-doped graphitic carbon nitride: Continuous solar-driven photocatalytic production of hydrogen peroxide

Solar-driven photocatalytic generation of H 2 O 2 over metal-free catalysts is a sustainable approach for value-added chemical production. Here, we synthesized chlorine-doped graphitic carbon nitride (Cl-doped g-C 3 N 4 ) through a solvothermal method to effectively produce H 2 O 2 with a rate of 1.19 ± 0.06 µM min –1 under visible light irradiation, which was improved by 104 times compared to pristine g-C 3 N 4 . Continuous net production of H 2 O 2 was realized at a rate of 2.78 ± 0.10 µM min –1 up to 54 h with isopropanol as the hole scavenger, whereas H 2 O 2 production was only sustained for ~ 6 h without scavengers. Both molecular simulations and advanced spectroscopic characterizations elucidated that the Cl dopant increased the charge transfer rate, decreased the bandgap, and reduced the activation energy of the rate-limiting step of O 2 reduction, all of which favored H 2 O 2 production. Furthermore, this work implemented a novel metal-free photocatalyst for sustainable H 2 O 2 production and elucidated the mechanism for promoting H 2 O 2 production that can guide future photoreactive nanomaterial design.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Fast Charging of Li-Ion Cells: Part V. Design and Demonstration of Protocols to Avoid Li-Plating

Fast charging of Li-ion batteries would make “fueling” of electric vehicles comparable in time to fueling of gasoline-powered cars, increasing consumer appeal of the new technology. Taking the US Department of Energy goal of safe 6 C charging to 80% capacity as a guide, we describe approaches that can mitigate Li plating on the graphite anode. To make this possible, a variable-rate anode potential charging protocol has been implemented by using a microprobe reference electrode to continuously monitor and adjust the current, in this way avoiding low anode potentials that favor Li deposition. Various implementations of the anode potential control are considered using electrochemical modeling and compared with the experimental data. For charge to 80% capacity at 30 °C, an average C-rate of 4.97 C was obtained for an NCM523/graphite cell with 70 μ m thick graphite electrode and 7.40 C for a cell with 47 μ m thick graphite electrode. Our electrochemical model accounts for these observations and provides a means to extrapolate the approach to other cell designs and operation regimes, drawing the maximum average fast charging rates that can still avoid Li plating.

25 ENERGY STORAGE↗

Practical Multi-Cycle Refueling Simulation of Stable Salt Reactors Based on REBUS Depletion Calculation

A Python software tool ReloadPy has been developed to assistant in formulating online refueling strategies for a family of stable salt reactors. ReloadPy wraps over the legacy fast reactor analysis codes REBUS, DIF3D, and PERSENT. These codes are used to perform single cycle depletion analysis and calculate reactivity worth and power density. ReloadPy determines a refueling assembly position to achieve the user-specified primary objective while satisfying constraints on various performance parameters such as the maximum power density, the maximum reactivity worth of reloaded assembly, and the minimum cycle length. Various capabilities of prescribing refueling sequence or a range of candidate refueling positions and restarting a previous simulation with altered options are implemented to reduce the required user’s efforts to investigate different refueling strategies. This report presents the computational models and methods and also serves as a user guide. Long-term simulation capabilities with different refueling strategies are demonstrated by a series of sample SSR refueling calculations with two-dimensional surrogate core models.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗