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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Fuel Property Effects on Stochastic Preignition Events During Engine Load Transitions

Stochastic preignition (SPI) is an abnormal combustion phenomenon that can cause catastrophic engine damage. There have been several proposed mechanisms of SPI, where a uniform source is still not certain, however, SPI tendencies have been shown to be influenced by engine operating conditions, oil composition, engine age, and fuel chemical and physical properties. Laboratory research and testing for SPI propensity is challenging given the stochastic nature of events, as well as the potential for significant degradation of the engine platform and measuring equipment over time. Thus, SPI specific experiments are generally conducted under either sustained or cyclic patterning of steady-state operating conditions to avoid the influence of transient engine boundary conditions on test parameters of interest (e.g. oil additive package, fuel properties, engine speed/load, etc.). In this work a cyclically varying SPI test sequence involves a 5 min engine warmup period at a low engine load of around 4 bar gross indicated mean effective pressure (IMEPg), followed by a transition to high load (~20 bar IMEPg) at a constant 2000 rev/min engine speed for a total of 25 min. This individual test sequence load schedule is then sequentially repeated 10 times to generate significant statistical data for analysis. This work examines the influence of fuel chemical and physical properties on SPI tendency during the unsteady portion of the 10-cycle sequence (the first 5 min of the high load operation in each sequence of the loading cycle) which has been discarded from previous analyses due to the uncertainty in engine operating and thermal boundary conditions. Results from this analysis suggest an increasing trend in the ratio of SPI events during the unsteady test period relative to the steady test period with increasing fuel Reid Vapor Pressure (RVP), implying differences in uncontrolled ignition source terms, possibly from, fuel wall interactions and retention during the load transition phase of the test.

Splitter, Derek [ORNL] (ORCID:0000000174044047)↗
DOE OSTI2014

The Hunt for Green Every April: Factors Affecting Fitness in Switchgrass

This grant funded work was undertaken to develop fundamental biological knowledge of the factors affecting the complex plant trait “fitness” in switchgrass (Panicum virgatum L.), a plant being developed as a biomass crop. Using a diverse range of latitudinally-adapted switchgrass plants, genomic, molecular and physiological studies were performed to track a number of different aspects of plant genetics and physiology over the course of the growing season. Work was performed on both genetically unrelated and genetically related plants. Plants were established in the field from seedlings raised in a greenhouse, or from clones present in other field nurseries. Field grown plants were used as the source of all tissues. The three objectives of this proposal were:(1) Transcript Profiling, Metabolomics, and C and N Partitioning and Recycling in Crowns and Rhizomes of Switchgrass over two growing seasons; (2) Gene Profiling During Regreening and Dormancy of Bulked Segregants; (3) Extent of Linkage Disequilibrium in Populations for Adaptation and Fitness Traits Being Developed for Central and Northern USA, that Show Significant Heterosis. Objective 1 results: Plants were labeled using 13 CO2 (a stable isotope) using an acrylic chamber constructed specifically for this purpose. Plants became labeled with 13 C and label decayed in aerial tissues over the course of the growing season. Varying amounts of 13 C were recovered in the rhizomes. These data are being analyzed. Plants were also labeled with 15 N-urea. Plants absorbed significant amounts of label that was remobilized to the growing shoots. N-dynamics would suggest that a portion of the 15 N absorbed into the crowns and rhizomes is sequestered below ground. Variable amounts of 15 N were translocated from the shoots to the roots over the course of the growing season. Polar metabolites extracted from a diverse array of rhizomes were analyzed using GCMS. Data indicated that there was a significant shift in metabolite pools over the course of the growing season, and differences in the levels of specific metabolites could be linked to the progression of dormancy. Several metabolites that accumulate in dormant rhizomes were identified. Some of these metabolites could be potentially linked to winter-survival of switchgrass. Extensive high-throughput sequencing was conducted on crown and rhizome samples collected from field grown plants. Initial work was performed on a Roche 454 system. All later work was performed on an Illumina sequencing-by-synthesis system. Some of these datasets have been published as peer-reviewed papers, other data are currently being analyzed and being readied for publication. Objective 2 results: Genetically related but phenotypically divergent plants from an octaploid switchgrass population were grown in a replicated field nursery. Rhizomes were harvested at four different times over the course of the growing season from plants with high winter survival and those with lower winter survival. RNA-Seq was performed on harvested materials. Initial analysis suggests that plants with lowered winter survival experience a greater level of cellular stress in dormant tissues. This aspect of plant function is being probed in greater depth. Objective 3 results: A total of 592 individual clones with three clonal replications in a randomized complete block design from each of five populations used in Objective 1 studies were rated for heading date in 2012 and 2014, green-up day of year in 2013, anthesis date in 2012, and yield in 2012, they were also subjected to NIR spectroscopy to derive cell wall composition estimates based on prior NIR calibrations. Plants were genotyped via a genotyping by sequencing (GBS) approach from reduced representation libraries constructed with adaptors that identified each individual. Libraries generated with the restriction enzyme PstI and called SNPs using Samtools after alignment to version 1.1 of the switchgrass genome sequence. A total of approximately 40,000 SNPs were found. These were then further filtered to eliminate markers with a minor allele frequency of < 0.05. The results of population analysis using STRUCTURE with expected population sizes or cluster numbers (K), clearly shows the hybrid composition of the KxS population and discriminated easily between upland (Summer) and lowland (Kanlow) populations. Under an assumption of 5 distinct populations there were detectable differences in allele frequencies between subpopulations within the three Kanlow populations particularly with respect to Kanlow EM and Kanlow base. We detected 110 SNPs with an allele frequency difference of ≥ 0.2 between Kanlow EM and Kanlow base populations, while 120 SNPs showed an allele frequency difference of ≥ 0.15 between Kanlow N1 and Kanlow base populations. These data are being readied for publication.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Planet Occurrence Rate Correlated to Stellar Dynamical History: Evidence from Kepler and Gaia

The dynamical history of stars influences the formation and evolution of planets significantly. To explore the influence of dynamical history on the planet formation and evolution using observations, we assume stars that experienced significantly different dynamical histories tend to have different relative velocities. Utilizing the accurate Gaia–Kepler Stellar Properties Catalog, we select single main-sequence stars and divide these stars into three groups according to their relative velocities, i.e., high-V, medium-V, and low-V stars. After considering the known biases from Kepler data and adopting prior and posterior correction to minimize the influence of stellar properties on planet occurrence rate, we find that high-V stars have a lower occurrence rate of super-Earths and sub-Neptunes (1–4 R {sub ⊕}, P < 100 days) and a higher occurrence rate of sub-Earth (0.5–1 R {sub ⊕}, P < 30 days) than low-V stars. Additionally, high-V stars have a lower occurrence rate of hot Jupiter-sized planets (4–20 R {sub ⊕}, P < 10 days) and a slightly higher occurrence rate of warm or cold Jupiter-sized planets (4–20 R {sub ⊕}, 10 < P < 400 days). After investigating multiplicity and eccentricity, we find that high-V planet hosts prefer a higher fraction of multiplanet systems and lower average eccentricity, which are consistent with the eccentricity–multiplicity dichotomy of Kepler planetary systems. All of these statistical results favor the scenario that high-V stars with large relative velocity may experience fewer gravitational events, while low-V stars may be influenced by stellar clustering significantly.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Dynamic Succession of Groundwater Sulfate-Reducing Communities during Prolonged Reduction of Uranium in a Contaminated Aquifer

To further understand the diversity and dynamics of SRB in response to substrate amendment, we sequenced genes coding for the dissimilatory sulfite reductase (dsrA) in groundwater samples collected after an emulsified vegetable oil (EVO) amendment, which sustained U(VI)-reducing conditions for one year in a fast-flowing aquifer. EVO amendment significantly altered the composition of groundwater SRB communities. Sequences having no closely related-described species dominated (80%) the indigenous SRB communities in nonamended wells. After EVO amendment, Desulfococcus , Desulfobacterium , and Desulfovibrio , known for long-chain-fatty-acid, short-chain-fatty-acid and H 2 oxidation and U(VI) reduction, became dominant accounting for 7 ± 2%, 21 ± 8%, and 55 ± 8% of the SRB communities, respectively. Succession of these SRB at different bioactivity stages based on redox substrates/products (acetate, SO 4 –2 , U(VI), NO 3 – , Fe(II), and Mn(II)) was observed. Desulfovibrio and Desulfococcus dominated SRB communities at 4–31 days, whereas Desulfobacterium became dominant at 80–140 days. By the end of the experiment (day 269), the abundance of these SRB decreased but the overall diversity of groundwater SRB was still higher than non-EVO controls. Up to 62% of the SRB community changes could be explained by groundwater geochemical variables, including those redox substrates/products. A significant ( P < 0.001) correlation was observed between groundwater U(VI) concentrations and Desulfovibrio abundance. Lastly, our results showed that the members of SRB and their dynamics were correlated significantly with slow EVO biodegradation, electron donor production and maintenance of U(VI)-reducing conditions in the aquifer.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Techno-economic analysis of the use of atomic layer deposited transition metal oxides in silicon heterojunction solar cells

The industry for producing silicon solar cells and modules has grown remarkably over the past decades, with more than a 100-fold reduction in price over the past 45?years. The main solar cell fabrication technology has shifted over that time and is currently dominated by the passivated emitter and rear cell (PERC). Other technologies are expected to increase in market share, including tunnel-oxide passivated contact (TOPCon) and heterojunction technology (HJT). In this paper, we examine the cost potential for using atomic layer deposition (ALD) to form transition metal oxide (TMO) layers (MoO x , TiO x and aluminium-doped zinc oxide [AZO]) to use as lower cost alternatives of the p-doped, n-doped and indium tin oxide (ITO) layers, respectively, the layers normally used in HJT solar cells. Using a bottom-up cost and uncertainty model with equipment cost data and process experience in the lab, we find that the production cost of these variations will likely be lower per wafer than standard HJT, with the main cost drivers being the cost of the ALD precursors at high-volume production. We then considered what efficiency is required for these sequences to be cost effective in $/W and discuss whether these targets are technically feasible. This work motivates further work in developing these ALD TMO processes to increase their efficiency towards their theoretical limits to take advantage of the processing cost advantage.

14 SOLAR ENERGY↗
DOE OSTI2021

Visible-Light-Enhanced Cobalt-Catalyzed Hydrogenation: Switchable Catalysis Enabled by Divergence between Thermal and Photochemical Pathways

The catalytic hydrogenation activity of the readily-prepared, coordinatively saturated cobalt(I) precatalyst, (R,R)-( iPr DuPhos)Co(CO) 2 H ((R,R)- iPr DuPhos = (+)-1,2-bis[(2R,5R)-2,5-diisopropylphospholano]benzene) is described. While efficient turnover was observed with a range of alkenes upon heating to 100 ºC, the catalytic performance of the cobalt catalyst was markedly enhanced upon irradiation with blue light at 35 °C. Here, this improved reactivity enabled hydrogenation of terminal, di- and trisubstituted alkenes, alkynes, and carbonyl compounds. A combination of deuterium labeling studies, hydrogenation of alkenes containing radical clocks and experiments probing relative rates support a hydrogen atom transfer pathway under thermal conditions that is enabled by a relatively weak cobalt–hydrogen bond of 56 kcal/mol. In contrast, data for the photocatalytic reactions support light-induced dissociation of a carbonyl ligand followed by a coordination-insertion sequence where the product is released by combination of a cobalt alkyl intermediate with the starting hy-dride, (R,R)-( iPr DuPhos)Co(CO) 2 H. These results demonstrate the versatility with catalysis with Earth-abundant metals as pathways involving open- versus closed-shell intermediates can be switched by the energy source.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

A compendium of multi-omics data illuminating host responses to lethal human virus infections

Human infections caused by viral pathogens trigger a complex gamut of host responses that limit disease, resolve infection, generate immunity, and contribute to severe disease or death. Here, we present experimental methods and multi-omics data capture approaches representing the global host response to infection generated from 45 individual experiments involving human viruses from the Orthomyxoviridae, Filoviridae, Flaviviridae, and Coronaviridae families. Analogous experimental designs were implemented across human or mouse host model systems, longitudinal samples were collected over defined time courses, and global multi-omics data (transcriptomics, proteomics, metabolomics, and lipidomics) were acquired by microarray, RNA sequencing, or mass spectrometry analyses. For comparison, we have included transcriptomics datasets from cells treated with type I and type II human interferon. Raw multi-omics data and metadata were deposited in public repositories, and we provide a central location linking the raw data with experimental metadata and ready-to-use, quality-controlled, statistically processed multi-omics datasets not previously available in any public repository. This compendium of infection-induced host response data for reuse will be useful for those endeavouring to understand viral disease pathophysiology and network biology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

Exploring stellar evolution models of sdB stars using MESA

Stellar evolution calculations have had great success reproducing the observed atmospheric properties of different classes of stars. Recent detections of g-mode pulsations in evolved He burning stars allow a rare comparison of their internal structure with stellar models. Asteroseismology of subdwarf B (sdB) stars suggests convective cores of 0.22–0.28 M{sub ⊙}, ≳45% of the total stellar mass. Previous studies found significantly smaller convective core masses (≲0.19 M{sub ⊙}) at a comparable evolutionary stage. We evolved stellar models with Modules for Experiments in Stellar Astrophysics (MESA) to explore how well the interior structures inferred from asteroseismology can be reproduced by standard algorithms. Our qualitative evolutionary paths, position in the logg−T{sub eff} diagram, and model timescales are consistent with previous results. The sdB masses from our full evolutionary sequences fall within the range of the empirical sdB mass distribution, but are nearly always lower than the median. Using standard MLT with atomic diffusion we find convective core masses of ∼0.17–0.18 M{sub ⊙}, averaged over the entire sdB lifetime. We can increase the convective core sizes to be as large as those inferred from asteroseismology, but only for extreme values of the overshoot parameter (overshoot gives numerically unstable and physically unrealistic behavior at the boundary). High resolution three-dimensional simulations of turbulent convection in stars suggest that the Schwarzschild criterion for convective mixing systematically underestimates the actual extent of mixing because a boundary layer forms. Accounting for this would decrease the errors in both sdB total and convective core masses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. Here, the explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973) tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Extending evolutionary forecasts across bacterial species

Improving evolutionary forecasting requires progressing from studying repeated evolution of a single genotype under identical conditions to formulating broad principles. These principles should enable predictions of how similar species will adapt to similar selective pressures. Evolve-and-resequence experiments with multiple species allow testing forecasts on different biological levels and elucidating the causes for failed predictions. Here, we show that forecasts for adaptation to static culture conditions can be extended to multiple species by testing previous predictions for Pseudomonas syringae and Pseudomonas savastanoi. In addition to sequence divergence, these species differ in their repertoire of biofilm regulatory genes and structural components. Consistent with predictions, both species repeatedly produced biofilm mutants with a wrinkly spreader phenotype. Predominantly, mutations occurred in the wsp operon, with less frequent promoter mutations near uncharacterized diguanylate cyclases. However, mutational patterns differed on the gene level, which was explained by a lack of conservation in relative fitness of mutants between more divergent species. The same mutation was the most frequent for both species suggesting that conserved mutation hotspots can increase parallel evolution. This study shows that evolutionary forecasts can be extended across species, but that differences in the genotype–phenotype–fitness map and mutational biases limit predictability on a detailed molecular level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Detecting Masquerade Attacks in Controller Area Networks Using Graph Machine Learning

Modern vehicles rely on a myriad of electronic control units (ECUs) interconnected via controller area networks (CANs) for critical operations. Despite their ubiquitous use and reliability, CANs are susceptible to sophisticated cyberattacks, particularly masquerade attacks, which inject false data that mimic legitimate messages at the expected frequency. These attacks pose severe risks such as unintended acceleration, brake deactivation, and rogue steering. Traditional intrusion detection systems (IDS) often struggle to detect these subtle intrusions due to their seamless integration into normal traffic. This paper introduces a novel framework for detecting masquerade attacks in the CAN bus using graph machine learning (ML). We hypothesize that the integration of shallow graph embeddings with time series features derived from CAN frames enhances the detection of masquerade attacks. We show that by representing CAN bus frames as message sequence graphs (MSGs) and enriching each node with contextual statistical attributes from time series, we can enhance detection capabilities across various attack patterns compared to using graph-based features only. Our method ensures a comprehensive and dynamic analysis of CAN frame interactions, improving robustness and efficiency. Extensive experiments on the ROAD dataset validate the effectiveness of our approach, demonstrating statistically significant improvements in the detection rates of masquerade attacks compared to a baseline that uses graph-based features only as confirmed by Mann-Whitney U and Kolmogorov-Smirnov tests (p < 0.05) .

Marfo, William [Univ. of Texas, El Paso, TX (Unite↗
DOE OSTI2017

Molecular basis of surface anchored protein A deficiency in the Staphylococcus aureus strain Wood 46

Protein A in Staphylococcus aureus is encoded by the spa (staphylococcal protein A) gene and binds to immunoglobulin (Ig). The S. aureus strain Wood 46 has been variously reported as protein A-deficient and/or spa negative and used as a control in animal models of staphylococcal infections. The results of this study indicate that Wood 46 has normal spa expression but transcribes very low levels of the srtA gene which encodes the sortase A (SrtA) enzyme. This is consistent with unique mutations in the srtA promoter. In this study, a low level of sortase A explains deficient anchoring of proteins with an LPXTG motif, such as protein A, fibrinogen-binding protein and fibronectin-binding proteins A and B on to the peptidoglycan cell wall. The activity of secreted protein A is an important consideration for use of Wood 46 in functional experiments and animal models.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Respiration data, microbial community assembly data, and FTICR-MS data associated with: “Disturbance Triggers Non-Linear Microbe-Environment Feedbacks. Sengupta et al., 2021, Biogeosciences”

This data package is associated with the manuscript “Disturbance Triggers Non-Linear Microbe-Environment Feedbacks, in revision in Biogeosciences (Sengupta et al. and 2021;https://bg.copernicus.org/preprints/bg-2021-51/). The study used hyporheic zone sediments as a model system to provide an integrated view of how disturbance modulates linkages among microbial ecology, biogeochemistry, and organic matter thermodynamics. Laboratory experiments exposed hyporheic sediment to varying wetting/drying dynamics. Data types include dissolved oxygen rates used to derive respiration rates, Fourier-transform ion cyclotron resonance mass spectrometry (FTICR-MS) data used to derive thermodynamic properties of organic matter, and microbial community assembly metrics derived from amplicon-sequence data of putatively active (cDNA) and whole community (gDNA). The outcomes of the study are condensed into a broadly applicable conceptual model linking external forcing, internal dynamics, and history. This data package is comprised of a file-level metadata (FLMD) csv, metadata csv, and seven folders that contain csv files, R scripts, xml files, and associated documentation: (1) Rates, (2) bNTI, and (3) FTICR, (4) Statistics_Analyses, (5) Raw OTU Beta dispersion Analysis, (6) bMNTD Randomizations, and (7) Data Dictionaries. The FLMD file has a description of each file included in the data package.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2013

Preliminary Evaluation of Removing Used Nuclear Fuel From Nine Shutdown Sites

The Blue Ribbon Commission on America’s Nuclear Future identified removal of stranded used nuclear fuel at shutdown sites as a priority so that these sites may be completely decommissioned and put to other beneficial uses. In this report, a preliminary evaluation of removing used nuclear fuel from nine shutdown sites was conducted. The shutdown sites included Maine Yankee, Yankee Rowe, Connecticut Yankee, Humboldt Bay, Big Rock Point, Rancho Seco, Trojan, La Crosse, and Zion. At these sites a total of 7649 used nuclear fuel assemblies and a total of 2813.2 metric tons heavy metal (MTHM) of used nuclear fuel are contained in 248 storage canisters. In addition, 11 canisters containing greater-than-Class C (GTCC) low-level radioactive waste are stored at these sites. The evaluation was divided in four components: • characterization of the used nuclear fuel and GTCC low-level radioactive waste inventory at the shutdown sites • an evaluation of the onsite transportation conditions at the shutdown sites • an evaluation of the near-site transportation infrastructure and experience relevant to the shipping of transportation casks containing used nuclear fuel from the shutdown sites • an evaluation of the actions necessary to prepare for and remove used nuclear fuel and GTCC low-level radioactive waste from the shutdown sites. Using these evaluations the authors developed time sequences of activities and time durations for removing the used nuclear fuel and GTCC low-level radioactive waste from a single shutdown site, from three shutdown sites located close to each other, and from all nine shutdown sites.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Hybrid deep learning architecture for general disruption prediction across tokamaks

In this paper, we present a new deep learning disruption prediction algorithm based on important findings from explorative data analysis which effectively allows knowledge transfer from existing devices to new ones, thereby predicting disruptions using very limited disruptive data from the new devices. The explorative data analysis conducted via unsupervised clustering techniques confirms that time-sequence data are much better separators of disruptive and non-disruptive behavior than the instantaneous plasma state data with further advantageous implications for a sequence-based predictor. Based on such important findings, we have designed a new algorithm for multi-machine disruption prediction that achieves high predictive accuracy on the C-Mod (AUC=0.801), DIII-D (AUC=0.947) and EAST (AUC=0.973). tokamaks with limited hyperparameter tuning. Through numerical experiments, we show that boosted accuracy (AUC=0.959) is achieved on EAST predictions by including in the training only 20 disruptive discharges, thousands of non-disruptive discharges from EAST, and combining this with more than a thousand discharges from DIII-D and C-Mod. The improvement of predictive ability obtained by combining disruptive data from other devices is found to be true for all permutations of the three devices. Furthermore, by comparing the predictive performance of each individual numerical experiment, we find that non-disruptive data are machine-specific while disruptive data from multiple devices contain device-independent knowledge that can be used to inform predictions for disruptions occurring on a new device.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
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↗
DOE OSTI2014

V1309 Sco—understanding a merger

One of the two outcomes of a common envelope (CE) event is a merger of the two stars. To date, the best known case of a binary merger is the V1309 Sco outburst, where the orbital period was known and observed to decay up to the outburst. Using the hydrodynamical code StarSmasher, we study in detail which characteristics of the progenitor binary affect the outburst and produce the best match with observations. We have developed a set of tools in order to quantify any CE event. We consider binaries consisting of a 1.52 M {sub ☉} giant and a 0.16 M {sub ☉} companion with P {sub orb} ∼ 1.4 days, varying the nature of the companion and its synchronization. We show that all considered progenitor binaries evolve toward the merger primarily because of Darwin instability. The merger is accompanied by mass ejection that proceeds in several separate mass outbursts and takes away a few percent of the donor mass. This very small mass, nonetheless, is important as it is not only sufficient to explain the observed light curve, but it also carries away up to one-third of both the initial total angular momentum and initial orbital energy. We find that all synchronized systems experience L {sub 2} mass loss that operates during just a few days prior to the merger and produces ring-shaped ejecta. The formed star is always a strongly heated radiative star that differentially rotates. We conclude that the case of a synchronized binary with a main-sequence companion gives the best match with observations of V1309 Sco.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Nitrogen addition alters soil fungal communities, but root fungal communities are resistant to change

Plants are colonized by numerous microorganisms serving important symbiotic functions that are vital to plant growth and success. Understanding and harnessing these interactions will be useful in both managed and natural ecosystems faced with global change, but it is still unclear how variation in environmental conditions and soils influence the trajectory of these interactions. In this study, we examine how nitrogen addition alters plant-fungal interactions within two species of Populus - Populus deltoides and P. trichocarpa. In this experiment, we manipulated plant host, starting soil (native vs. away for each tree species), and nitrogen addition in a fully factorial replicated design. After ~10 weeks of growth, we destructively harvested the plants and characterized plant growth factors and the soil and root endosphere fungal communities using targeted amplicon sequencing of the ITS2 gene region. Overall, we found nitrogen addition altered plant growth factors, e.g., plant height, chlorophyll density, and plant N content. Interestingly, nitrogen addition resulted in a lower fungal alpha diversity in soils but not plant roots. Further, there was an interactive effect of tree species, soil origin, and nitrogen addition on soil fungal community composition. Starting soils collected from Oregon and West Virginia were dominated by the ectomycorrhizal fungi Inocybe (55.8% relative abundance), but interestingly when P. deltoides was grown in its native West Virginia soil, the roots selected for a high abundance of the arbuscular mycorrhizal fungi, Rhizophagus. These results highlight the importance of soil origin and plant species on establishing plant-fungal interactions.

59 BASIC BIOLOGICAL SCIENCES↗