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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Failure Mode Identification of Insulin Drug Products – Impact of Relevant Stress Conditions on the Quality of the Drug

Fast-acting insulin drug products (DPs) are carried and administered by diabetic patients to maintain their blood glucose level throughout the day, exposing the DPs to stress conditions. Apidra, Novolog, and Humalog insulin DPs were tested under various stress conditions. Dynamic light scattering (DLS), and size exclusion chromatography (SEC) were used to monitor the stability and aggregation. Thermal stress alone did not influence the stability. However, 24 hr exposure to vigorous mechanical stress shifted the DLS size peaks of Novolog and Humalog from 5 ± 1 nm to > 50.9 ± 25.6 nm, and the SEC native protein peak areas decreased 52% for Novolog and 18.4% for Humalog. Combined stress accelerated protein aggregation more drastically. Novolog and Humalog size shifted (>75 nm) after 3 hr and the peak area decreased > 97.9% after 6 hr exposure, indicating that high temperature accelerated the aggregation triggered by agitation. Soluble aggregates were captured by DLS early on compared to SEC. Apidra was comparably stable indicating DP formulation plays a critical role in stability. Here this study provides a greater understanding of potential failure modes patients and care givers may encounter while handling insulin DPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.

big scientific dataset↗
DOE OSTI2024

Chapter 7.1: Microalgae

Microalgae is a unique biomass resource that does not need to compete for land and water with other biomass feedstocks because it can be cultivated on low-quality unencumbered land using noncompetitive water types including saline and wastewater. It is included as a complementary resource alongside other biomass feedstocks reported in this study, albeit at higher biomass production costs reflective of more capital-intensive farming operations than typical for terrestrial biomass. Higher biomass costs can be offset by the potential to produce value-added coproducts unique to compositional constituents of microalgae. Relative to BT16, this chapter reflects the latest analysis from the 2022 Algae Harmonization Update, which uses the latest parameterized and high-performing saline algal strain, second-generation carbon capture of point-source waste CO2 and high-pressure pipeline transport resolved to specific point-source types, saline water sourcing up to 40,000 mg/L total dissolved solids for source and makeup water salinity, blowdown water treatment and recycle, and brine disposal handling. National-scale algal biomass availability potential was calculated at 152 million tons/yr ash-free dry weight (AFDW) (191 million tons/yr dry weight) at an average biomass productivity of 26.2 g/m2 -day AFDW (about 50 tons/acre/yr dry weight).1 The algal biomass was cultivated on 3.9 million acres of multi-criteria screened and potentially available land for CONUS and fixed 268 million tons of waste CO2 based on biomass uptake. The algae biomass can be produced at an average MBSP of $674/ton AFDW ($536/ton dry weight) in 2020 dollars,2 corresponding to a total energy potential of 3.3 quads/yr at an average MBSP of $31.2/MMBtu (higher-heating-value [HHV] basis).

algae↗
DOE OSTI2022

Integration of software tools for integrative modeling of biomolecular systems

Integrative modeling computes a model based on varied types of input information, be it from experiments or prior models. Often, a type of input information will be best handled by a specific modeling software package. In such a case, we desire to integrate our integrative modeling software package, Integrative Modeling Platform (IMP), with software specialized to the computational demands of the modeling problem at hand. After several attempts, however, we have concluded that even in collaboration with the software's developers, integration is either impractical or impossible. The reasons for the intractability of integration include software incompatibilities, differing modeling logic, the costs of collaboration, and academic incentives. In the integrative modeling software ecosystem, several large modeling packages exist with often redundant tools. So we reason, therefore, that the other development groups have similarly concluded that the benefit of integration does not justify the cost. As a result, modelers are often restricted to the set of tools within a single software package. The inability to integrate tools from distinct software negatively impacts the quality of the models and the efficiency of the modeling. As the complexity of modeling problems grows, we seek to galvanize developers and modelers to consider the long-term benefit that software interoperability yields. In this article, we formulate a demonstrative set of software standards for implementing a model search using tools from independent software packages and discuss our efforts to integrate IMP and the crystallography suite Phenix within the Bayesian modeling framework.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

2023 Billion-Ton Report: An Assessment of U.S. Renewable Carbon Resources - Microalgae

Microalgae is a unique biomass resource that does not need to compete for land and water with other biomass feedstocks because it can be cultivated on low-quality unencumbered land using noncompetitive water types including saline and wastewater. It is included as a complementary resource alongside other biomass feedstocks reported in this study, albeit at higher biomass production costs reflective of more capital-intensive farming operations than typical for terrestrial biomass. Higher biomass costs can be offset by the potential to produce value-added coproducts unique to compositional constituents of microalgae. Relative to the 2016 Billion-Ton Report, this chapter reflects the latest analysis from the 2022 Algae Harmonization Update, which uses the latest parameterized and high-performing saline algal strain, second-generation carbon capture of point-source waste CO 2 , and high-pressure pipeline transport resolved to specific point-source types, saline water sourcing up to 40,000 mg/L total dissolved solids for source and makeup water salinity, blowdown water treatment and recycle, and brine disposal handling. National-scale algal biomass availability potential was calculated at 152 million tons/yr ash-free dry weight (AFDW) (191 million tons/yr dry weight) at an average biomass

09 BIOMASS FUELS↗
DOE OSTI2022

A review of computing-based automated fault detection and diagnosis of heating, ventilation and air conditioning systems

We report faults in Heating, Ventilation, and Air Conditioning (HVAC) systems of buildings result in significant energy waste in building operation. With fast-growing sensing data availability and advancement in computing, computational modeling has demonstrated strong capability to detect and diagnose HVAC system faults, hence, ensuring efficient building operation. This paper comprehensively reviews the state-of-the-art computing-based fault detection and diagnosis (FDD) for HVAC systems. Overall, the reviewed computing-based FDD methods are classified as two major approaches: knowledge-based and data-driven approaches. We then identify multiple important topics, including data availability, training data size, data quality, approach generality, capability, interpretability, and required modeling efforts, along with corresponding metrics to summarize the most updated FDD development. Generally, the knowledge-based approaches are further divided as physics-based modeling, Diagnostic Bayesian Network, and performance indicator-based methods while data-driven approaches include supervised learning, unsupervised learning, and regression and statistics-based methods. State-of-the-art FDD development, remaining challenges, and future research directions are further discussed to push forward FDD in practice. Availability of fault data, capability of existing methods to deal with complex fault situations (such as simultaneous faults), modeling interpretability for data-driven methods, and required engineering efforts for physics-based methods are identified as remaining challenges in FDD development. Improving modeling fidelity and reducing modeling efforts are essential for applying physics-based methods in real buildings. Meanwhile, addressing fault data availability, increasing algorithm adaptability, and handling multiple faults are essential to further enhance the applicability of data-driven FDD approaches.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Designing Ta C Virtual Substrates for Vertical Al x Ga 1 − x N Power Electronics Devices

Power electronics are critical for a sustainable energy future, playing a key role in electrification and integration of renewable energy sources into the grid. Advances in ultrawide band gap materials are needed to handle higher powers in smaller form factors while reducing electrical and thermal losses. High Al content Al x Ga 1 − x N is theoretically capable of meeting these demands, but its impact in power electronics has been severely restricted by a lack of substrates that can satisfy conductivity, lattice matching, and/or thermal expansion requirements. We demonstrate that electrically conductive Ta C can be used as a virtual substrate for Al x Ga 1 − x N heteroepitaxy. Scaleably sputtered Ta C grown on Al 2 O 3 , followed by high-temperature face-to-face annealing, produces a thin film Ta C template with an effective hexagonal lattice constant matched to Al 0.70 Ga 0.30 N . Annealing of the Ta C promotes recrystallization, significantly improving crystallinity and reducing crystalline defects from as-deposited columnar grains to a step-and-terrace surface morphology, enabling the subsequent growth of high-quality Al 0.70 Ga 0.30 N by molecular beam epitaxy. X-ray diffraction and scanning transmission electron microscopy confirm that the Al x Ga 1 − x N layer is heteroepitaxially aligned, strain-free, and lattice-matched, transitioning abruptly from Ta C to Al x Ga 1 − x N without intermediate phases. These results demonstrate Ta C virtual substrates as electrically conductive, lattice-matched, and thermally compatible templates for vertical Al x Ga 1 − x N devices that can meet the growing power needs of a sustainable energy future. Published by the American Physical Society 2024

36 MATERIALS SCIENCE↗
DOE OSTI2021

Low-Cost Aero Technology Demonstrations

The main focus of this work was to demonstrate the use of polymeric additive manufacturing (AM) to create tooling for both preforming and consolidation. Polymeric tooling was utilized where both modest and higher pressures are used for part consolidation. The key focus for the AM tooling development was for fabrication of complex structures such as ducting, C-channel stiffened skins, and airfoils where conventional male tooling would typically be trapped in the cured part. The AM tooling was evaluated for use as a tool master used to fabricate and re-shape deformable/re-formable mandrels based on SpinTech’s shape memory composite technology known as Smart Tooling. The AM tooling was also evaluated for use as a mold for composite infusion and consolidation. Key performance parameters were tracked for project schedule completion with each step comprising of “art to part” cycle time, cost, and model fidelity for dimensions, performance, and cost. The primary focus of this demonstration was to determine if a 50% cost reduction was achievable, for each AM tooling-set, as compared to conventional processes. UDRI leveraged project partner SpinTech, who manufactures tools and parts in these categories and thus provided a baseline regarding current best practices and provided valuable feedback during the entirety of this demonstration. This demonstration primarily focused on the use of AM tooling for fabrication of three composite component structures which are typically utilized in aircraft and comprise salient geometric features of broad interest. These components are often tooling intensive and have features requiring extraction of male tools which are usually trapped by the geometry. The three structures selected by the team included: 1) A one-piece airfoil shell comprised of compound contours where male tooling would be trapped unless the part were manufactured in two halves as is typically the case. 2) A one-piece duct used for air handling, comprised of compound contours where male tooling would be trapped unless the part were manufactured in two halves, or a washout mandrel were to be used. 3) A co-cured C-channel stiffened skin where typically C-channels would be individually manufactured and then bonded to a cured skin. The demonstration was comprised of three main tasks: • Task 1: AM Tool Feasibility Study – ensure the AM tooling meets the performance requirements as specified by SpinTech to match baseline performance. • Task 2: Complex Tool Demonstration – Fabricate tooling, preforms, and parts representative of an airfoil and duct. • Task 3: Large Aerostructure Fabrication Demonstration – Fabricate tooling, preforms, and part representative of a C-channel stiffened skin. With the conclusion of this project, a decision tree was developed to determine the key considerations necessary to determine if use of AM tooling for the three selected structures was able to attain the same quality as historically achieved on metallic tooling, while providing a significant cost reduction.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Designing reinforcement learning algorithms for building HVAC control: From experimental observation to simulation comparisons

Advanced supervisory-level control with reinforcement learning (RL) is regarded as a promising solution for HVAC systems to minimize energy consumption while maintaining thermal comfort and indoor air quality. However, most RL applications were conducted in the simulation environment rather than real-world HVAC systems. This paper developed a value-based RL controller termed Deep Q-Network (DQN) for a typical central HVAC system and evaluated its performance in a building test facility. By comparing DQN with a rule-based controller, the study not only demonstrated the cases where DQN could properly maintain indoor comfort but also discussed possible reasons why DQN failed in some other situations. Recognizing the limitations of value-based RL algorithms from the experimental tests, a simulation study was conducted to compare DQN with an alternative RL approach, an actor–critic algorithm termed Deep Deterministic Policy Gradient (DDPG). In scenarios with a relatively large action space, DDPG outperformed DQN by requiring fewer computational resources and achieving better thermal comfort, lower energy consumption, and more stable control actions. The findings suggest that the ability of DDPG to handle continuous control variables more effectively allows for faster convergence in training and more precise control in practice, which enhances the overall efficiency and reliability of the HVAC system.

Guo, Fangzhou↗
DOE OSTI2024

Improving precision and accuracy of genetic mapping with genotyping‐by‐sequencing data in outcrossing species

Abstract Genotyping‐by‐sequencing (GBS) is a widely used strategy for obtaining large numbers of genetic markers in model and non‐model organisms. In crop plants, GBS‐derived marker datasets are frequently used to perform quantitative trait locus (QTL) mapping. In some plant species, however, high heterozygosity and complex genome structure mean that researchers must use care in handling GBS data to conduct QTL mapping most effectively. Such outbred crops include most of the perennial grass and tree species used for bioenergy. To identify strategies for increasing accuracy and precision of QTL mapping using GBS data in outbred crops, we conducted an empirical study of SNP‐calling and genetic map‐building pipeline parameters in a Miscanthus sinensis population, and a complementary simulation study to estimate the relationship between genome‐wide error rate, read depth, and marker number. The bioenergy grass Miscanthus is an obligate outcrossing species with a recent (diploidized) whole‐genome duplication. For the study of empirical M. sinensis data, we compared two SNP‐calling methods (one non‐reference‐based and one reference‐based), a series of depth filters (12×, 20×, 30×, and 40×) and two map‐construction methods (i.e., marker ordering: linkage‐only and order‐corrected based on a reference genome). We found that correcting the order of markers on a linkage map by using a high‐quality reference genome improved QTL precision (shorter confidence intervals). For typical GBS datasets of between 1000 and 5000 markers to build a genetic map for biparental populations, a depth filter set at 30× to 40× applied to outbred populations provided a genome‐wide genotype‐calling error rate of less than 1%, improved accuracy of QTL point estimates and minimized type I errors for identifying QTL. Based on these results, we recommend using a reference genome to correct the marker order of genetic maps and a robust genotype depth filter to improve QTL mapping for outbred crops.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

River Dissolved Oxygen Prediction Using Machine Learning Models and Wireless Sensor Measurements

Simultaneous flooding&heat and droughts&heat events can potentially destabilize hydro-meteorological conditions to deteriorate the water quality of Neches River. Machine learning (ML) models utilizing wireless sensor measurements have been applied to predict water quality and optimize various water management strategies. This study aims to develop ML models to predict dissolved oxygen (DO) prediction under various hydro-meteorological conditions and enhance water management decision-making. Wireless sensor measurements of DO, water temperature, sample depth, conductivity, turbidity, and pH, along with discharge from the United States Geological Survey stations, are collected for model inputs at the Pine Island Bayou C749 station (PIB-C749) and Neches River Saltwater Barrier (SWB). Multilayer perceptron neural networks, recurrent neural networks, long short-term memory (LSTM), and bidirectional LSTM (BiLSTM) with and without attention mechanism (AT) are tested to determine the best model, which is applied the rolling forecast method to predict 14-day DO. Traditional and recurrent transfer learning (TL and RTL) methods are adopted to overcome insufficient data at the SWB. The input feature importance analysis using the integrated gradients (IG) algorithm is applied to determine dominant inputs. The results show LSTM-based models are capable handling long sequential data. AT-BiLSTM and RTL-LSTM demonstrate the best performance at the PIB-C749 (RMSE=0.054) and the SWB (RMSE=0.028), respectively. TL and RTL methods significantly improve model performance at the SWB. DO, temperature, and pH show higher importance, consistent with hydrodynamics and water chemistry. Both best models are applied to predict 14-day DO and demonstrate reasonable performance for decision-making. Hydro-meteorological conditions of 2017 flood and 2012 drought events are simulated and reveal that possible hypoxia occurs after flooding due to increasing temperature and turbidity, and DO concentration decreases significantly under heat and drought conditions. In conclusion, LSTM-based models utilizing wireless sensor data can be a timely and effective approach to make appropriate decisions on water resource management.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Properties and Autoignition Reactivity of Diesel Boiling Range Ethers Produced from Guerbet Alcohols

We examine the properties of diesel boiling range ethers made from coupling of alcohols produced by oligomerization of ethanol (Guerbet alcohols) for their utility as low-carbon liquid fuel blendstocks. Basic properties of boiling point, flash point, freezing point, density and viscosity are well suited for blending into diesel fuels. For the mixture of ethers the lightest component, di-n-butyl ether, can be present at up to 20 vol% while still having adequately high flashpoint for safe handling. Soot formation tendency (as yield sooting index) is well below that of conventional diesel. The ethers have similar compatibility with elastomers as conventional diesel, based on Hansen solubility parameter analysis. Oxidation stability was assessed for 30 vol% blends of individual ethers in a conventional diesel fuel using a long-term storage test. Over 6 weeks we observed no formation of peroxides or degradation. n-alkyl ethers with carbon number of 8 or higher have cetane number over 100, which is outside the defined range of cetane number, while branched ethers are over 70. The ethers also blend antagonistically into conventional diesel for cetane number, meaning that the blend cetane value is lower than predicted based on a linear by volume, mass, or mole model. We show that aromatics and naphthenes likely act as radical scavengers to slow or shut down autoignition of the highly reactive ethers at low to medium blend levels. Overall, diesel boiling range ethers show significant promise as high quality low-net carbon diesel blendstocks.

09 BIOMASS FUELS↗
DOE OSTI2020

Impacts of uncertain feedstock quality on the economic feasibility of fast pyrolysis biorefineries with blended feedstocks and decentralized preprocessing sites in the Southeastern United States

Abstract This study performs techno‐economic analysis and Monte Carlo simulations (MCS) to explore the effects that variations in biomass feedstock quality have on the economic feasibility of fast pyrolysis biorefineries using decentralized preprocessing sites (i.e., depots that produce pellets). Two biomass resources in the Southeastern United States, that is, pine residues and switchgrass, were examined as feedstocks. A scenario analysis was conducted for an array of different combinations, including different pellet ash control levels, feedstock blending ratios, different biorefinery capacities, and different biorefinery on‐stream capacities, followed by a comparison with the traditional centralized system. MCS results show that, with depot preprocessing, variations in the feedstock moisture and feedstock ash content can be significantly reduced compared with a traditional centralized system. For a biorefinery operating at 100% of its designed capacity, the minimum fuel selling price (MFSP) of the decentralized system is $3.97–$4.39 per gallon gasoline equivalent (GGE) based on the mean value across all scenarios, whereas the mean MFSP for the traditional centralized system was $3.79–$4.12/GGE. To understand the potential benefits of highly flowable pellets in decreasing biorefinery downtime due to feedstock handling and plugging problems, this study also compares the MFSP of the decentralized system at 90% of its designed capacity with a traditional system at 80%. The analysis illustrates that using low ash pellets mixed with switchgrass and pine residues generates a more competitive MFSP. Specifically, for a biorefinery designed for 2,000 oven dry metric ton per day, running a blended pellet made from 75% switchgrass and 25% pine residues with 2% ash level, and operating at 90% of designed capacity could make an MFSP between $4.49 and $4.71/GGE. In contrast, a traditional centralized biorefinery operating at 80% of designed capacity marks an MFSP between $4.72 and $5.28.

09 BIOMASS FUELS↗
DOE OSTI2022

Implement and Test 3D Mortar Contact in BISON

We leverage the extension of the generation of mortar segment meshes to three dimensions in MOOSE’s framework to extend thermomechanical modeling capabilities to problems with three dimensions. A modular approach to gap heat transfer physics using the mortar finite element method was created and documented, mechanical contact was extended to three dimensions—including frictional behavior, performance and ease of use were improved, and steps towards scalability of solid mechanics problems involving contact were taken. Many of these new developments are demonstrated in the simulation of 3D light-water reactor (LWR) problems, where the thermomechanical interface problem is solved using the mortar finite element method. Usage of the mortar framework has improved convergence in 2D problems and has enabled employing friction in 3D problems, of which we show results of a short, local stack of 3D pellets. Consequently, the benefits of mortar in terms of solution convergence and quality are extended to three dimensions. Section 2 discusses fundamental developments that enabled the simulation of practical mortar problems in three dimensions and other general improvements, including the reduction of the derivative container size, the modification of dual basis computations when edge dropping (lack of secondary element projection) takes place, the improvement of conditioning when employing the VCP in-edge dropping conditions, and code usability and quality improvements. These latter code enhancements include the migration of tests using “old” mortar contact constraints to using dual mortar with a semi-smooth Newton solution strategy and the reuse of lower dimensional domains for straightforwardly setting up a mortar thermomechanical LWR problem, i.e. the MOOSE action is employed for mechanical contact and the thermal LWR action is employed to capture the gas conductance, contact, and radiation components of gap heat transfer physics. Independently of the mortar LWR thermal action, we developed a modular approach to gap heat transfer that resides in MOOSE and can be leveraged, e.g., in metallic fuel problems. This approach, whose code design based on MOOSE’s user objects to model specific physics was proposed by the maintenance activity, is detailed in Section 3. Based on the dual mortar finite element method, the frictional contact constraints were extended to three dimensions. A block sheared in two directions in and out of contact with a rigid plane is employed in Section 4 to show the way the approach handles changes in frictional states (e.g. stick to slip) within a competitive number of Newton iterations. Equations and numerical results on the use of the VCP with Cartesian Lagrange multipliers, whose combination enables their direct condensation, are described in Section 5.3. Two-dimensional and three-dimensional BISON LWR simulations are discussed in Section 6. Particularly, a stack of five eccentric pellets with a surface defect is simulated and the effect of pellet-cladding friction is assessed. Finally, conclusions are outlined in Section 7.

42 ENGINEERING↗
DOE OSTI2020

Drop Analysis of the Advanced Test Reactor Fresh Fuel Shipping Container with Heavier Low-Enriched Uranium Fuel Contents

The Advanced Test Reactor Fresh Fuel Shipping Container (ATR FFSC) is a rectangular stainless steel container used for shipping radioactive material. The container is described in the ATR FFSC Safety Analysis Report (SAR). Per the ATR FFSC SAR, the ATR FFSC is designated a Type AF-96 packaging per the definition of 10 CFR §71.4, and was originally designed to transport high enriched uranium (HEU) reactor fuel elements for the Advanced Test Reactor (ATR), the Advanced Test Reactor Critical (ATRC) facility, the Massachusetts Institute of Technology Reactor (MITR), and the University of Missouri Research Reactor (MURR). The Department of Energy, National Nuclear Security Administration’s (NNSA), Office of Material Management and Minimization (M3) is working with the Idaho National Laboratory (INL) to develop and qualify new low enriched uranium (LEU) fuels and technologies for use in the ATR, ATRC, MITR, and MURR reactors. The LEU fuel elements will weigh significantly more than the current HEU designs and, combined with their associated Fuel Handling Enclosures for packaging, some configurations will exceed the 50 lbf used in the ATR FFSC qualifying drop tests. There are LEU versions of MITR, MURR, and ATR fuel elements. However, for this evaluation, drop analysis of the ATR FFSC with only the heavier ATR Low Enrichment (LOWE) fuel element is considered in this evaluation because the LOWE fuel element is the heaviest of the considered LEU fuel elements. The ATR HEU fuel element and the ATR LOWE fuel element are identical in every design aspect except for the fuel meat inside the 19 fuel plates. The LEU fuel meats are made using a U-10Mo high-density foil rather than uranium dispersed in aluminum in the HEU fuel elements. The high density of the uranium in the LEU fuel meat increases the LOWE fuel element weight to just under 44 lbf (versus the 22.1 lbf weight of the tested ATR HEU fuel element). ATR fuel elements are placed in a thin-gauge aluminum weldment called a "Fuel Handling Enclosure" during packaging. The Fuel Handling Enclosure is used to cover and protect the element during loading and unloading operations. The ATR Fuel Handling Enclosure weighs about 15 lbf per the drawings in the ATR FFSC SAR and the weight is accounted for in this evaluation. Transporting the heavier LEU fuel elements require evaluation of two issues. The first is the effect of the increased mass of the LEU fuel elements on the survivability of the ATR FFSC package following the requisite drop qualifications. The second is the effect of the increased mass of the fuel plates on the fuel element during the same drops. The ATR FFSC containing an ATR HEU fuel element in an ATR Fuel Handling Enclosure was physically dropped multiple times to qualify the container as a Type AF-96 package. The ATR FFSC SAR describes the drop tests performed with an actual ATR HEU fuel element weighing 22.1 lbf contained in a 14.3 lbf Fuel Handling Enclosure for a total payload of 36.4 lbf. Those drop tests showed that the ATR FFSC maintained containment of the ATR HEU fuel element and the fuel element was not significantly damaged. (Containment herein is not defined as a leak tight but is retention of the radioactive contents.) The purpose of the evaluation is to analytically show that, for a similar set of tests, the ATR FFSC maintains containment of the heavier ATR LOWE fuel element and to assess the damage to the fuel element during the drops. The approach was to create finite element analysis (FEA) models that produce the same results as the physical drops. Those models were then used as the benchmarks for the follow-on analyses using the heavier contents. FEA models of the drops of ATR FFSC using up to a 115 lbf fuel element were run and evaluated. Likewise, drops of a LOWE fuel element weighing 44 lbf in the ATR FFSC were run and evaluated. It is important to note that this report was done at the quality level necessary to be included in a nuclear facility safety basis. However, it is not the intent of this report to conclude the suitability of the ATR FFSC for transporting the heavier payloads. This report only describes the results of the FEA as related to the required drop scenarios. Incorporation of the FEA into the safety basis will be evaluated by the ATR FFSC design authority. The physical drop tests of the HEU fuel element and FEA drop analysis for the LOWE fuel element showed noteworthy damage to the fuel plates. An aluminum protective block was conceived to mitigate the damage. The concept requires the blocks to be placed in the fuel element between the end boxes and fuel plates. Additional FEA drop analyses were performed using the protective block. The addition of the blocks is primarily intended to mitigate the damage to the LOWE fuel element fuel plates. However, FEA drop analyses of the ATR HEU fuel element with the blocks were also performed and included for information.

42 ENGINEERING↗
DOE OSTI2025

Unveiling Feedstock Variability: Insights into Corn Stover Conversion - Part I: Physicochemical Properties and Self-Degradation

Transforming agricultural waste into biofuels and bioproducts is crucial to advancing a low-carbon bioeconomy. However, the inherent variability in the composition and quality introduces uncertainties in the conversion efficiency and poses challenges in process development. Through integrating a high-throughput conversion system, material characterization techniques, and advanced data analysis tools, this study investigates the variability of corn stover and its subsequent impacts on carbohydrate conversion. The findings reveal that indoor storage substantially reduces the moisture and ash content and soil contamination, while other properties remain largely unchanged. Self-degradation due to microbial activity during storage decreases the carbohydrate content of corn stover but enhances glucose and xylose yields. A negative correlation is observed between sugar yields and lignin content across samples with varying ash and moisture content. The inhibitory effect of lignin diminishes in self-degraded samples likely due to the disrupted cell wall structure. Although self-degradation slightly increases cellulose crystallinity, no strong correlation was observed between the crystallinity and sugar yield. Hot water pretreatment under mild conditions effectively mitigates inherent variability, consistently improving the sugar yield from corn stover by up to 50%. By elucidating the feedstock variability and its impact on convertibility, these findings offer valuable insights into appropriate feedstock handling and management, highlighting potential strategies to address variability challenges.

09 BIOMASS FUELS↗
DOE OSTI2025

Advancements on Multi-Fidelity Random Fourier Neural Networks: Application to Hurricane Modeling for Wind Energy

Multi-fidelity approaches are emerging as effective strategies in computational science to handle otherwise intractable tasks like Uncertainty Quantification (UQ), training of Machine Learning (ML) models, and optimization, for expensive high-fidelity applications in which the amount of available simulations or data is limited. The main idea is simple: large datasets generated for low-fidelity approximations of the problem at hand are fused with a much sparser dataset for the target (high-fidelity) system. In this paper, we build on our recent success in designing random Fourier Neural Networks (rFNNs) [1] to target problems arising in wind energy applications and in particular problems of interest for hurricane modeling. In this context, data for the high-fidelity models are limited and lower fidelity alternatives are needed. In this work, we introduce a novel multi-fidelity training approach for our rFNNs and demonstrate its use on a simple verification problem and on a hurricane modeling problem in which high-fidelity data are generated via Large-Eddy Simulations (LES), while low-fidelity data are given by a mesoscale model. Initial results demonstrate how the multi-fidelity training approach can improve the quality of the resulting surrogate.

Fourier Neural Networks↗
DOE OSTI2023

Neutron Absorber Plate Characterization Plan for Criticality Experiments Design

After being used in nuclear installations, depleted fuel can still be highly reactive and must be handled securely to prevent any radiological or criticality concerns. In particular, spent fuel from use in nuclear power reactors must be stored and transported in specifically designed containers using neutron absorber materials to prevent criticality. Various neutron absorber material types exist and are manufactured by various entities, as thoroughly described in the Handbook of Neutron Absorber Materials for Spent Nuclear Fuel Storage and Transportation Applications written by EPRI. Presently, one of the most modern and most widely used types of neutron absorber material contains particles of boron carbide, or B 4 C, embedded in aluminum matrix: Boralcan, manufactured by Rio Tinto. It is very important for the community to know as much as possible about such neutron absorber materials. Therefore, in the recent years, a US Department of Energy National Nuclear Security Administration–Nuclear Criticality Safety Program funded project initiated design of an experiment that places Boralcan neutron-absorbing plates in an established critical assembly using low-enriched uranium fuel at the Sandia Pulsed Reactor Facility/Critical Experiments (SPRF/CX) apparatus at Sandia National Laboratories. The goal of the experiment is to produce high-quality benchmark data to submit to the International Criticality Safety Benchmark Evaluation Project (ICSBEP), for use in validating calculational tools and nuclear data by criticality safety analysts. The project, named IER-554, is currently in its final design stage, following a successful preliminary design. In the work documented in the design study, ten critical configurations using Boralcan neutron absorber plates were designed, and the experiment was proven to be feasible, with a predicted low k eff uncertainty around 100 pcm. An overview of the modeled cutout of the critical assembly with a Boralcan plate is shown in Figure 1, representing one of the configurations planned for the critical experiments. Before the plates are inserted in the critical assembly, it is necessary to know more about their composition and uniformity. This summary focuses on the plate characterization plans. Each plate will undergo (1) neutron transmission measurements at different locations to determine the 10 B areal density and (2) an in-depth x-ray computed tomography (XCT) examination to obtain the exact Sizes and distribution of the B4C powder particles inside the plates. In parallel, plate modeling studies are performed with a goal to determine the validity of the currently used approximation of modeling the neutron absorber plates as a homogeneous mixture of Aluminum 1100 alloy and B4C— instead of explicitly modeling the B4C particles. By using the experimental 10 B areal density measurements, and the exact size and location of the B4C particles obtained by XCT, a plate model can theoretically be built that reproduces the plate with extremely high fidelity. The results of this modeling study could increase the confidence of the criticality safety community in its modeling methods when using this type of neutron absorber material, and the industry could use these validations to change the boron loading credit limits from the U.S. Nuclear Regulatory Commission standard review plan for dry cask storage of spent nuclear fuel. The modeling calculations are performed with SCALE 6.3.0 using the KENO V.a sequence for criticality calculations with the ENDF/B-VIII.0 continuous-energy cross section library.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗