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At least 163 records · Page 9

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DOE OSTI2025

A strategy for automated core design to increase economic viability and minimize fuel fragmentation, relocation, and dispersal susceptibility in high-burnup cores

The nuclear industry aims to increase the cycle length of pressurized water reactors from 18 to 24 months to increase power plant capacity factors and economic viability. These cycle length extensions will inherently require fuel rods to exceed the current peak rod average burnup limit of 62 GWd/MTU. A chief concern of operating beyond the current burnup limit is the fuel fragmentation, relocation, and dispersal (FFRD) phenomenon in which pulverized fuel fragments can axially relocate and escape through a burst in the cladding formed during a loss-of-coolant accident. In this work, we demonstrate an approach for automating core design employing an optimization tool based on a penalty-free, parallel simulated annealing algorithm to produce pressurized water reactor core designs with two different optimization objectives. The two objectives were to produce core designs with (1) mitigated FFRD susceptibility while achieving 24-month cycle lengths (2) maximum cycle length with no regard for the likelihood of FFRD. Batch size was considered in tandem with both cases to maximize economic viability. The PARCS nodal model was the primary reactor physics tool used in the optimizations and used nuclear cross sections calculated with 2D Polaris lattice physics models. Reactor performance and safety characteristics of the optimized cores were verified using high-fidelity Virtual Environment for Reactor Applications models. The core designs produced by the optimization tool are compared with each other and to a high-burnup core design produced and analyzed in previous works to highlight the fuel management strategies that may enhance high-burnup reactor safety and economic viability. The optimized cores satisfied their respective objective functions, producing a maximum cycle length of 720 effective full-power days in one core design and one that may reduce FFRD susceptibility by up to 50% based on the first-order approximation to FFRD risk formulated in this work. The optimized cores met most constraints but exceeded the hot channel factor limit, especially in FFRD cases where fresh fuel carried more power. Furthermore, this highlights the need for future lattice-level optimizations and broader assembly options.

Cycle length↗

DOE OSTI2023

Effect of Na Concentration on Cs Distribution with Crystalline Silicotitanate in Tank Waste Simulants

Crystalline silicotitanate (CST) ion exchange media is currently utilized in the tank side cesium removal (TSCR) system on the Hanford site to remove Cs-137 from the tank waste supernate. As the main dose contributor to the liquid supernate in Hanford tank waste, it is necessary to remove the Cs-137 to expedite low activity waste processing and immobilization. Ongoing batch contact experiments with CST have been done to tease out the impact of group II metals, anions, potassium, and temperature on Cs removal to better understand bounding conditions for operations as well as aid in development of a wholesome isotherm model to predict Cs behavior in the tank waste. This paper discusses the impact of Na concentration on Cs exchange and provides insight into the challenges associated with activity coefficient estimations for modeling Cs distribution in tank waste matrices.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Stabilizing NMC 811 Li-Ion Battery Cathode through a Rapid Coprecipitation Process

Improving the capacity of LiNi x Mn y Co 1-x-y O 2 (NMC) by increasing the Ni content results in materials with poor cycling performance and reduced thermal stability. These problems can be alleviated by creating concentration gradients in the materials, but this requires more expensive batch processing. Herein, a simple process is described that can create a coating that penetrates into the precursor particles, creating unique gradients in the precursor material. The coated material demonstrates improved cycling performance and thermal stability as compared to uncoated materials. Additionally, rapid heating to simulate a thermal runaway event while observing these materials with X-rays gives a deeper understanding of how the coating affects the decomposition process.

25 ENERGY STORAGE↗

DOE OSTI2023

Targeted electrochemical reduction of carcinogenic N -nitrosamines from emission control systems within CO 2 capture plants

N-Nitrosamines are one of the environmentally significant byproducts from aqueous amine-based post-combustion carbon capture systems (CCS) due to their potential risk to human health. Safely mitigating nitrosamines before they are emitted from these CO 2 capture systems is therefore a key concern before widescale deployment of CCS can be used to address worldwide decarbonization goals. Electrochemical decomposition is one viable route to neutralize these harmful compounds. The circulating emission control waterwash system, commonly installed at the end of the flue gas treatment trains to minimize amine solvent emissions, plays an important role to capture N-nitrosamines and control their emission into the environment. The waterwash solution is the last point where these compounds can be properly neutralized before becoming an environmental hazard. In this study, the decomposition mechanisms of N-nitrosamines in a simulated CCS waterwash with residual alkanolamines was investigated using several laboratory-scale electrolyzers utilizing carbon xerogel (CX) electrodes. Hcell experiments revealed that N-nitrosamines were decomposed through a reduction reaction and are converted into their corresponding secondary amines thereby neutralizing their environmental impact. Batch-cell experiments statistically examined the kinetic models of N-nitrosamine removal by a combined adsorption and decomposition processes. The cathodic reduction of the N-nitrosamines statistically obeyed the first-order reaction model. Finally, a prototype flow-through reactor using an authentic waterwash was used to successfully target and decompose N-nitrosamines to below the detectable level without degrading the amine solvent compounds allowing them to be return to the CCS and lower the system operating costs. Furthermore, the developed electrolyzer was able to efficiently remove greater than 98% of N-nitrosamines from the waterwash solution without producing any additional environmentally harmful compounds and offers an effective and safe route to mitigate these compounds from CO 2 capture systems.

20 FOSSIL-FUELED POWER PLANTS↗

DOE OSTI2023

Evaluation of High Level Waste Sludge Processing Behavior

The U.S. Department of Energy’s (DOE) Hanford Site has 177 underground storage tanks that contain wastes from past nuclear fuel reprocessing and waste-management operations. Over 20% of this waste is in the form of an insoluble sludge that will require slurry modification before its transfer to the Waste Treatment and Immobilization Plant (WTP). Specific WTP acceptance criteria for waste feed delivery describe the physical and chemical characteristics of the waste that must be met before the waste is transferred to the WTP. One challenging requirement relates to the undissolved solids (UDS) composition in a waste feed because the waste contains solid particles that settle, and their concentration and relative proportion can change during the transfer of the waste in individual batches. A key uncertainty is the ability to transfer and mix wastes with large variations in UDS concentrations and resulting settling rates. To address this uncertainty, a number of small scale mixing and settling tests have been conducted to determine the mobilization performance of variable chemistry simulants. Comparison of the size and density of the particulate for each simulant to that of southeast area Hanford sludge was made using metrics for particle mobilization, suspension, settling, and pipeline transfer where dependance on particle size and density may be different, including: 1. Settling velocity, 2. Critical shear stress for erosion, 3. Just-suspended impeller speed, and 4. Pipeline critical transport velocity. Existing high-level waste sludge data has shown the effect that increasing Al concentration has on resulting settled solids. This differential settling of particles in the sludge has the possibility of resulting in solids segregation during feed preparation and uneven particle distribution during pipeline transportation or mixer jet pump operations. Understanding the predictive capabilities of HLW solids settling and transport as well as potential remedies for addressing disparate sludge behaviors can help provide technical guidance during HLW flowsheet planning.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

A dictionary learning algorithm for compression and reconstruction of streaming data in preset order

There has been an emerging interest in developing and applying dictionary learning (DL) to process massive datasets in the last decade. Many of these efforts, however, focus on employing DL to compress and extract a set of important features from data, while considering restoring the original data from this set a secondary goal. On the other hand, although several methods are able to process streaming data by updating the dictionary incrementally as new snapshots pass by, most of those algorithms are designed for the setting where the snapshots are randomly drawn from a probability distribution. In this paper, we present a new DL approach to compress and denoise massive dataset in real time, in which the data are streamed through in a preset order (instances are videos and temporal experimental data), so at any time, we can only observe a biased sample set of the whole data. Here, our approach incrementally builds up the dictionary in a relatively simple manner: if the new snapshot is adequately explained by the current dictionary, we perform a sparse coding to find its sparse representation; otherwise, we add the new snapshot to the dictionary, with a Gram-Schmidt process to maintain the orthogonality. To compress and denoise noisy datasets, we apply the denoising to the snapshot directly before sparse coding, which deviates from traditional dictionary learning approach that achieves denoising via sparse coding. Compared to full-batch matrix decomposition methods, where the whole data is kept in memory, and other mini-batch approaches, where unbiased sampling is often assumed, our approach has minimal requirement in data sampling and storage: i) each snapshot is only seen once then discarded, and ii) the snapshots are drawn in a preset order, so can be highly biased. Through experiments on climate simulations and scanning transmission electron microscopy (STEM) data, we demonstrate that the proposed approach performs competitively to those methods in data reconstruction and denoising.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Cooperativity in the Aldol Condensation Using Bifunctional Mesoporous Silica–Poly(styrene) MCM-41 Organic/Inorganic Hybrid Catalysts

This work explores the efficacy of silica/organic hybrid catalysts, where the organic component is built from linear aminopolymers appended to the silica support within the support mesopores. Specifically, the role of molecular weight and polymer chain composition in amine-bearing atom transfer radical polymerization-synthesized poly(styrene-co-2-(4-vinylbenzyl)isoindoline-1,3-dione) copolymers is probed in the aldol condensation of 4-nitrobenzaldehyde and acetone. Controlled polymerization produces protected amine-containing poly(styrene) chains of controlled molecular weight and dispersity, and a grafting-to thiol–ene coupling approach followed by a phthalimide deprotection step are used to covalently tether and activate the polymer hybrid catalysts prior to the catalytic reactions. Site-normalized batch kinetics are used to assess the role of polymer molecular weight and chain composition in the cooperative catalysis. Lower-molecular-weight copolymers are demonstrated to be more active than catalysts built from only molecular organic components or from higher-molecular-weight chains. Molecular dynamics simulations are used to probe the role of polymer flexibility and morphology, whereby it is determined that higher-molecular-weight hybrid structures result in congested pores that inhibit active site cooperativity and the diffusivity of reagents, thus resulting in lower rates during the reaction.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Experiments and Simulations to Describe Alkalinity Release from Particle-Containing Oil-in-Water Emulsions and Particle Suspensions

Among the most common amendments added to groundwater during site remediation are compounds used to adjust or maintain the pH. This research describes an approach to encapsulate mineral particles (MgO and CaCO 3 ) within oil droplets suspended within an aqueous phase for the purpose of delivery to the subsurface environment. A series of batch experiments was combined with mathematical modeling to illustrate the encapsulation and understand the influence of particle encapsulation on rates and extents of alkalinity release. The encapsulation of the alkalinity-releasing particles results in slower rates of amendment release as compared to rates obtained using suspensions of bare mineral particles, allowing for the possibility of control as a function of the pH. The results indicate that the alkalinity release from particle suspensions followed a mineral dissolution mechanism that could not explain the rate of the alkalinity release of the encapsulated particles. The reduction in mineral dissolution rates observed with the encapsulated particles was found to result from a mass transfer limitation. This limitation was well described using a linear driving force expression to account for the resistance to mass transfer at the oil–water interface.

EVO↗

DOE OSTI2021

Effective kinetics driven by dynamic concentration gradients under coupled transport and reaction

Biogeochemical reaction kinetics are generally established from batch reactors where concentrations are uniform. In natural systems, many biogeochemical processes are characterized by spatially and temporally variable concentration gradients that often occur at scales which are not resolved by field measurements or biogeochemical and reactive transport models. Yet, it is not clear how these sub-scale chemical gradients affect reaction kinetics compared to batch kinetics. Here we investigate this question by studying the paradigmatic case of localized pulses of solute reacting with a solid or a dissolved species in excess. Additionally, we consider non-linear biogeochemical reactions, representative of mineral dissolution, adsorption and redox reactions, which we quantify using simplified power-law kinetics. The combined effect of diffusion and reaction leads to effective kinetics that differ quantitatively and qualitatively from the batch kinetics. Depending on the nonlinearity (reaction order) of the local kinetics, these effects lead to either enhancement or decrease of the overall reaction rate, and result in a rich variety of reaction dynamics. We derive analytical results for the effective kinetics, which are validated by comparison to direct numerical simulations for a broad range of Damköhler numbers and reaction order. Our findings provide new insights into the interpretation of imperfectly mixed lab experiments, the effective kinetics of field systems characterized by intermittent reactant release and the integration of sub-scale concentration gradients in reactive transport models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Unseeded, spontaneous nucleation of spherulitic magnesium calcite

Most of the sedimentary carbonates deposited in the marine environments are composed of calcium carbonate minerals with varying amounts of incorporated Mg 2+ . However, understanding how interactions of impurities with carbonate and their incorporation affect sediments behavior remains a challenge. Here, a new insight is obtained by monitoring solution composition, morphology, and electrokinetic potential of carbonate particles formed in a spontaneous unseeded batch precipitation experiment using electrochemical and scanning electron microscopy methods. The solid composition and growth rate are extracted from changes in the bulk composition and fitted to chemical affinity rate law, revealing that the precipitation pathway consists of second-order dissolution and first-order precipitation. The molecular dynamics simulations show that the lattice strain induced by randomly substituting Ca 2+ by Mg 2+ stabilizes spherical nanoparticles and reduces their surface area and volume. Combining kinetics and thermodynamics insight, we conclude that variation in the carbonate bulk and interfacial energies, along with the solution supersaturation, lead to the dissolution-precipitation transformation pathway from Mg-rich to Mg-poor carbonate phase that preserves spherulitic morphology. Furthermore, our findings are relevant for long-standing questions of how impurities influence diagenesis of carbonate sediments and spherulitic carbonate particles' origin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

PyPVRPM: Photovoltaic Reliability and Performance Model in Python

The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Geometric GNNs for charged particle tracking at GlueX

Nuclear physics experiments are aimed at uncovering the fundamental building blocks of matter. The experiments involve high-energy collisions that produce complex events with many particle trajectories. Tracking charged particles resulting from collisions in the presence of a strong magnetic field is critical to enable the reconstruction of particle trajectories and precise determination of interactions. It is traditionally achieved through combinatorial approaches that scale worse than linearly as the number of hits grows. Since particle hit data naturally form a point cloud and can be structured as graphs, graph neural networks (GNNs) emerge as an intuitive and effective choice for this task. In this study, we evaluate the GNN model for track finding on the data from the GlueX experiment at Jefferson Lab. We use simulation data to train the model and test on both simulation and real GlueX measurements. We demonstrate that GNN-based track finding outperforms the currently used traditional method at GlueX in terms of segment-based efficiency at a fixed purity while providing faster inferences. We show that the GNN model can achieve significant speedup by processing multiple events in batches, which exploits the parallel computation capability of graphical processing units (GPUs). Finally, we compare the GNN implementation on GPU and field-programmable gate array and describe the trade-off.

batched GNN pipeline↗

DOE OSTI2021

RhizoVision Explorer: open-source software for root image analysis and measurement standardization

Abstract Roots are central to the function of natural and agricultural ecosystems by driving plant acquisition of soil resources and influencing the carbon cycle. Root characteristics like length, diameter and volume are critical to measure to understand plant and soil functions. RhizoVision Explorer is an open-source software designed to enable researchers interested in roots by providing an easy-to-use interface, fast image processing and reliable measurements. The default broken roots mode is intended for roots sampled from pots and soil cores, washed and typically scanned on a flatbed scanner, and provides measurements like length, diameter and volume. The optional whole root mode for complete root systems or root crowns provides additional measurements such as angles, root depth and convex hull. Both modes support providing measurements grouped by defined diameter ranges, the inclusion of multiple regions of interest and batch analysis. RhizoVision Explorer was successfully validated against ground truth data using a new copper wire image set. In comparison, the current reference software, the commercial WinRhizo™, drastically underestimated volume when wires of different diameters were in the same image. Additionally, measurements were compared with WinRhizo™ and IJ_Rhizo using a simulated root image set, showing general agreement in software measurements, except for root volume. Finally, scanned root image sets acquired in different labs for the crop, herbaceous and tree species were used to compare results from RhizoVision Explorer with WinRhizo™. The two software showed general agreement, except that WinRhizo™ substantially underestimated root volume relative to RhizoVision Explorer. In the current context of rapidly growing interest in root science, RhizoVision Explorer intends to become a reference software, improve the overall accuracy and replicability of root trait measurements and provide a foundation for collaborative improvement and reliable access to all.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

Simulated moving bed-inspired method for continuous adsorptive denitrogenation of model fuel

Efficient, continuous routes for removing nitrogen-containing compounds from hydrothermal liquefaction-derived synthetic aviation fuel are needed to enable direct blending with conventional jet fuels. Here, we report a simulated moving bed-inspired process for adsorptive denitrogenation of a model fuel. Unlike conventional simulated moving bed systems, which are designed for sharp separations between similar solutes, this approach was run deliberately outside the classical separation region so that both pyridine and indole were removed together from the hydrocarbon stream. Alcohol solvents were used to regenerate the silica adsorbent, maintaining performance over extended operation and avoiding the downtime and energy demand associated with calcination. Under these conditions, the system demonstrates removal of more than 98% of nitrogen while cutting solvent use by 28% compared to batch operation. Classical modeling tools predicted column concentration profiles even in this nontraditional regime, suggesting a straightforward path to scaling. Together, these results motivate solvent-efficient, continuous denitrogenation strategies that could be integrated with biorefinery processes.

Adsorption↗

DOE OSTI2025

Quantitative Encapsulation and Homogeneity Assessment of Sol–Gel Based Nuclear Explosive Debris Simulants

Nuclear explosive debris simulants are an important material in training and validating aspects of post-detonation nuclear forensic processes. Realistic simulants should replicate several aspects of nuclear explosive debris such as the size, shape, color, density, and chemical and radiological properties. Silica particles produced via sol-gel synthesis have recently been found to successfully reproduce many of these parameters including the controllable incorporation of radionuclide content. However, to be useful as a benchmarking material for validation and verification of laboratory methodologies, radionuclide content from batch-to-batch must be reproducible. Here, in this work, we explore the variance in radionuclide distribution incorporated into sol-gel benchmarking materials with respect to sample subdivision. Results will help inform the sample sizes required to minimize variance between samples.

36 - MATERIALS SCIENCE↗

DOE OSTI2021

A physics-informed and hierarchically regularized data-driven model for predicting fluid flow through porous media

This paper presents a new deep learning data-driven model for predicting structure dependent pore-fluid velocity fields in rock. The model is based on a Convolutional Auto-Encoder (CAE) artificial neural network capable of learning from image data generated by direct numerical simulations of fluid flow through pore-structures, such as by Lattice Boltzmann or molecular dynamics methods. The main novelty of the model in comparison to previous CAE-based data-driven approaches consists of three parts. The first is a methodology for decomposing the full-domain of the porous media into sub-regions, or “sub-domains”, in order to reduce the overall size of the CAE, batch process the sub-domains in parallel, and enable the CAE to learn local and generalizable nonlinear mappings of pore-fluid velocities. The second consists of embedding the finite difference solutions of the incompressible Navier-Stokes and continuity equations into convolutional layers prior to the CAE in order to provide the CAE with knowledge of fluid dynamics physics (PhyFlow). The third main novelty is that the training of the CAE is regularized with a hierarchical loss function that encourages the learning of fluid flow patterns (in a way similar to ranked modes in principal component analysis), ranking from most to least important. This is shown to increase the stability in learning, reduce over-fitting, and promote interpretability of the CAE neural network layers (HierCAE). The comprehensive new data-driven model, which we call the PhyFlow-HierCAE model, is shown to exhibit improved accuracy and generalizability of flow field predictions over conventional CAE models, attributable to the embedded physical knowledge and the hierarchical regularization, as well as realize orders of magnitude speed-ups in computation times as a surrogate for the direct numerical simulations. Examples of training and forward predictions on unseen pore-structures are provided and evaluated for data from Lattice Boltzmann and molecular dynamics simulations of pore-fluid flow. The model is shown to be a fast and accurate emulator (or “surrogate”) for predicting effective permeability of unseen pore-structures based on learning from relatively small direct numerical simulation datasets.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Interplay of physically different properties leading to challenges in separating lanthanide cations – an ab initio molecular dynamics and experimental study

Lanthanide elements have well-documented similarities in their chemical behavior, which make the valuable trivalent lanthanide cations (Ln 3+ ) particularly difficult to separate from each other in water. Here, we apply ab initio molecular dynamics simulations to compare the free energies (Δ G ads ) associated with the adsorption of lanthanide cations to silica surfaces at a pH condition where SiO – groups are present. The predicted Δ G ads for lutetium (Lu 3+ ) and europium (Eu 3+ ) are similar within statistical uncertainties; this is in qualitative agreement with our batch adsorption measurements on silica. This finding is remarkable because the two cations exhibit hydration free energies (Δ G hyd ) that differ by >2 eV, different hydration numbers, and different hydrolysis behavior far from silica surfaces. We observe that the similarity in Lu 3+ and Eu 3+ Δ G ads is the result of a delicate cancellation between the difference in Eu 3+ and Lu 3+ hydration (Δ G hyd ), and their difference in binding energies to silica. We propose that disrupting this cancellation at the two end points, either for adsorbed or completely desorbed lanthanides ( e.g. , via nanoconfinment or mixed solvents), will lead to effective Ln 3+ separation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Holistic Numerical Calibration of the iMESA Electrostatic Analyzer

A drifted Maxwellian velocity distribution is the most common model used to interpret the data from low-energy charged-particle instruments onboard spacecraft that are used to investigate the ambient plasma environment in the low Earth orbit (LEO). An original method is presented for determining the flow parameters (density, temperature, and flow energy) of such a distribution from the output of the integrated miniaturized electrostatic analyzer, which has been successfully flown on several LEO missions. Rather than attempting to deconvolve from the on-orbit data the analyzer’s response to an ideal, monoenergetic input, numerical simulation is used to predict and parameterize the response of the device to an input distribution that includes an isotropic, non-zero temperature, yielding a straightforward method for extracting the flow parameters from the spacecraft data. The method is computationally simple enough to be incorporated into a robust algorithm suitable for rapid batch processing or real-time analysis of data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗