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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The DESI One-Percent survey: exploring the Halo Occupation Distribution of Emission Line Galaxies with AbacusSummit simulations

The One-Percent survey of the Dark Energy Spectroscopic Instrument collected ~ 270k emission line galaxies (ELGs) at 0.8 < z < 1.6. The high completeness of the sample allowed the clustering to be measured down to scales never probed before, 0.04 Mpc/h in rp for the projected 2-point correlation function (2PCF) and 0.17 Mpc/h in galaxy pair separation s for the 2PCF monopole and quadrupole. The most striking feature of the measurements is a strong signal at the smallest scales, below 0.2 Mpc/h in rp and 1 Mpc/h in s. We analyse these data in the halo occupation distribution framework. We consider different distributions for central galaxies, a standard power law for satellites with no condition on the presence of a central galaxy and explore several extensions of these models. For all considered models, the mean halo mass of the sample is found to be log10 $\langle$Mh$\rangle$ ~ 11.9. We obtain a satellite mean occupation function which agrees with physically motivated ELG models only if we introduce central-satellite conformity, meaning that the satellite occupation is conditioned by the presence of central galaxies of the same type. To achieve in addition a good modelling of the clustering between 0.1 and 1 Mpc/h in rp, we allow for ELG positioning outside of the halo virial radius and find 0.5% of ELGs residing in the outskirts of halos. Furthermore, the satellite velocity dispersion inside halos is found to be ~ 30% larger than that of the halo dark matter particles. These are the main findings of our work. Here we investigate assembly bias as a function of halo concentration, local density or local density anisotropies and observe no significant change in our results. We split the data sample in two redshift bins and report no significant evolution with redshift. Lastly, changing the cosmology in the modelling impacts only slightly our results.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Assembly of MiniFuel Targets for Irradiation of TRISO Fuel Compacts in the High Flux Isotope Reactor

To support the development of Kairos Power’s fluoride-salt-cooled high-temperature reactor, irradiation testing of tristructural isotropic (TRISO) fuel compacts was performed at the Oak Ridge National Laboratory (ORNL) High Flux Isotope Reactor (HFIR) to collect experimental data on TRISO fuel during high particle-power operation and validate fuel performance models. Fuel compacts containing enriched uranium oxycarbide (UCO), natural UCO (NUCO), or uranium dioxide (UO 2 ) TRISO particles were fabricated at ORNL and inserted into MiniFuel targets for HFIR irradiation. Five MiniFuel targets were successfully assembled, welded, tested, and delivered to HFIR, along with their quality assurance documentation. The targets were inserted into HFIR’s inner vertical experiment facility within the permanent beryllium reflector. Each target contains six fuel compacts and will be irradiated in HFIR for four cycles, with target temperatures of 500, 700, and 900°C. This report summarizes the experiment design, test matrix, and fabrication. This work was performed under the Nuclear Science User Facility program.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Multifidelity uncertainty quantification with models based on dissimilar parameters

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the highest-fidelity model, provided that the low-fidelity models are well correlated. However, maintaining a high level of correlation can be challenging, especially when models depend on different input uncertain parameters, which drastically reduces the correlation. Existing MF UQ approaches do not adequately address this issue. In this work, we propose a new sampling strategy that exploits a shared space to improve the correlation among models with dissimilar parameterization. We achieve this by transforming the original coordinates onto an auxiliary manifold using the adaptive basis (AB) method (Tipireddy and Ghanem, 2014). The AB method has two main benefits: (1) it provides an effective tool to identify the low-dimensional manifold on which each model can be represented, and (2) it enables easy transformation of polynomial chaos representations from high- to low-dimensional spaces. This latter feature is used to identify a shared manifold among models without requiring additional evaluations. Here we present two algorithmic flavors of the new estimator to cover different analysis scenarios, including those with legacy and non-legacy high-fidelity (HF) data. We provide numerical results for analytical examples, a direct field acoustic test, and a finite element model of a nuclear fuel assembly. For all examples, we compare the proposed strategy against both single-fidelity and MF estimators based on the original model parameterization.

42 ENGINEERING↗
DOE OSTI2021

GAMA/DEVILS: constraining the cosmic star formation history from improved measurements of the 0.3–2.2 μm extragalactic background light

We present a revised measurement of the optical extragalactic background light (EBL), based on the contribution of resolved galaxies to the integrated galaxy light (IGL). The cosmic optical background radiation (COB), encodes the light generated by star formation, and provides a wealth of information about the cosmic star formation history (CSFH). We combine wide and deep galaxy number counts from the Galaxy And Mass Assembly survey (GAMA) and Deep Extragalactic VIsible Legacy Survey (DEVILS), along with the Hubble Space Telescope (HST) archive and other deep survey data sets, in nine multiwavelength filters to measure the COB in the range from 0.35 μm to 2.2 μm. We derive the luminosity density in each band independently and show good agreement with recent and complementary estimates of the optical-EBL from very high-energy (VHE) experiments. Our error analysis suggests that the IGL and γ-ray measurements are now fully consistent to within $\sim 10{{\ \rm per\ cent}}$, suggesting little need for any additional source of diffuse light beyond the known galaxy population. We use our revised IGL measurements to constrain the CSFH, and place amplitude constraints on a number of recent estimates. As a consistency check, we can now demonstrate convincingly, that the CSFH, stellar mass growth, and the optical-EBL provide a fully consistent picture of galaxy evolution. Here, we conclude that the peak of star formation rate lies in the range 0.066–0.076 M ⊙ yr -1 Mpc -3 at a lookback time of 9.1 to 10.9 Gyr.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

HERO WEC - Bills of Materials: WEC, RO System, and Submersible Pump

This submission includes detailed Bills of Materials for the NREL-designed and -built Hydraulic and Electric Reverse Osmosis Wave Energy Converter (HERO WEC), as well as the reverse osmosis assembly and submersible pump that are used in the HERO WEC. The WEC file is specific to the components and sub-components that are included on the in-water buoy portion of the WEC. The RO file is specific to the components and sub-components that are included on the reverse osmosis module that is used for both the hydraulic and electric configuration. The submersible pumps file is specific to the components and sub-components that are included on the submersible pump module that is used feed the reverse osmosis module when the HERO WEC is in the electric configuration. In addition to this submission, an additional submission available for the WEC model itself, including the power electronics enclosure, and reverse osmosis assembly that is needed to supply water in the electric submission. A link is provided below. More details on this project including data, CAD drawings, etc. can be found in the HERO-WEC main page link below. This data set has been developed by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Water Power Technologies Office.

16 TIDAL AND WAVE POWER↗
DOE OSTI2023

Phylogenomics and diversification of Sordariomycetes

The Sordariomycetes is a specious, morphologically diverse, and widely distributed class of the phylum Ascomycota that forms a well-supported clade diverged from Leotiomycetes. Aside from their ecological significance as plant and human pathogens, saprobes, endophytes, and fungicolous taxa, species of Sordariomycetes produces a wide range of chemically novel and diverse metabolites used in important fields. Recent phylogenetic analyses derived from a small number of genes have considerably increased our understanding of the family, order, and subclass relationships within Sordariomycetes, but several important groups have not been resolved well. In addition, there are various paraphyletic or polyphyletic groups. Moreover, the criteria used to establish higher ranks remain highly variable across different studies. Therefore, the taxonomy of Sordariomycetes is in constant flux, remains poorly understood, and is subject to much controversy. Here, for the first time, we have assembled a phylogenetic dataset containing 638 genomes representing the 156 genera, 50 families, and 17 orders and 5 subclasses of Sordariomycetes. This data set is based on 1124 genes and results in a well-resolved phylogenomic tree. We further constructed an evolutionary timeline of Sordariomycetes diversification based on the genomic data sets. Our divergence time estimate results are inconsistent with previous studies, suggesting estimates of node ages are less precise and varied. Based on these results, we discuss the higher ranks of Sordariomycetes and empirically propose an unprecedented taxonomic framework for the class.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Probing the Self-Assembly dynamics of cellulose nanocrystals by X-ray photon correlation spectroscopy

Charge-stabilized colloidal cellulose nanocrystals (CNCs) can self-assemble into higher-ordered chiral nematic structures by varying the volume fraction. The assembly process exhibits distinct dynamics during the isotropic to liquid crystal phase transition, which can be elucidated using X-ray photon correlation spectroscopy (XPCS). Anionic CNCs were dispersed in propylene glycol (PG) and water spanning a range of volume fractions, encompassing several phase transitions. Coupled with traditional characterization techniques, XPCS was conducted to monitor the dynamic evolution of the different phases. Additionally, simulated XPCS results were obtained using colloidal rods and compared with the experimental data, offering additional insights into the dynamic behavior of the system. The results indicate that the particle dynamics of CNCs undergo a stepped decay in three stages during the self-assembly process in PG, coinciding with the observed phases. The phase transitions are associated with a total drop of Brownian diffusion rates by four orders of magnitude, a decrease of more than a thousand times slower than expected in an ideal system of repulsive Brownian rods. Given the similarity in the phase behaviors in CNCs dispersed in PG and in water, we hypothesize that these dynamic behaviors can be extrapolated to other polar solvent environments. Importantly, these findings represent the direct measurement of CNC dynamics using XPCS, underscoring the feasibility of directly assessing the dynamic behavior of other rod-like colloidal suspensions.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Methodology for inferring reactor core power distribution from an optical fiber based gamma thermometer array

Here, a 4-step data analytic methodology has been devised for the purpose of inferring the distribution of power in a reactor core, based on the response of an array of optical fiber based gamma thermometers (OFBGTs). This data analytic methodology is crucial for the development of a system of OFBGTs for the purpose of calibrating local power range monitors in boiling water reactors. Such a system would be an improvement to the present calibration system in boiling water reactors, in terms of safety, efficiency, and permanence. The first step of this methodology is to establish an energy balance method. In this method, one uses MCNP to determine response functions, which allow one to convert from gamma thermometer response to power. The gamma thermometers and the reactor core are segmented, such that each gamma thermometer segment provides an estimate of power for each reactor core segment. The estimates of power for the reactor core segments (hereafter referred to as fuel assembly segments) are calculated based on the measured response of the gamma thermometers. The second step of the methodology is to employ a weighting scheme to combine the estimates of the power of the various fuel assembly segments, based on the response of the various OFGBT segments and the incremental dose rates of the various fuel assembly segments to the OFBGT segments. The third step is to iteratively calculate the estimates of the power of the fuel assembly segments, until a convergence criterion is met, which indicates that the calculation has converged. The fourth and final step of the methodology is to estimate the uncertainty of the power for each fuel assembly segment. The mathematical basis for this step is not the focus of this paper. We have used a 3-D homogeneous reactor model to demonstrate the data analytic methodology; and have found that the data analytic methodology operates as intended.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Integral Experiment Final Design for Thermal/Epithermal eXperiments (TEX) using Highly Enriched Uranium with Polyethylene at Low Temperature (IER-479 CED-2 Report)

The goal of IER-479 is to design uranium critical experiments that can be used to validate low temperature cross sections and criticality safety analyses over multiple neutron energy regimes. Currently, there are no benchmarks in the International Criticality Safety Benchmark Evaluation Project (ICSBEP) handbook at temperatures lower than room temperature (International Criticality Safety Benchmark Evaluation Project Handbook, 2019). However, there are many needs for validation of criticality safety analysis at lower temperatures, including meeting transportation requirements and operations conducted outside or in unheated facilities. Additionally, NCSP has funded North Carolina State (NCSU) to generate new thermal scattering laws, including at lower temperatures, and the lack of integral benchmarks impedes data testing of these new cross sections. To address these needs, this report will present a critical experiment design covering various fission energy regimes with a goal temperature of -40°C (-40°F), which is based on the lower bound of expected non-cryogenic operational temperatures. The goal of the U.S. Nuclear Criticality Safety Program’s (NCSP) Thermal/Epithermal eXperiments (TEX) is to design and conduct new critical experiments to address high priority nuclear data needs from the nuclear criticality safety and nuclear data communities. The TEX program includes two series of baseline experimental configurations, one based on plutonium fuel (plutonium-aluminum Zero Power Physics Reactor (ZPPR) plates) and the other based on uranium fuel (highly enriched uranium (HEU) plates), that are moderated with varying thickness of polyethylene to create assemblies which span the thermal, intermediate, and fast fission energy regimes. The configurations are designed to be easily modified (for example, to add diluent materials of interest) to allow for efficient generation of additional benchmark configurations and allow for added nuclear data testing utility when comparing modified configurations to baseline configurations. The goal of IER-479 is to use the TEX-HEU concept (stack of HEU plates and polyethylene moderators) to design a critical experiment that can be used to validate low temperature cross sections and criticality safety analyses.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

INTEGRATE - Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements

The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design of wind turbine rotors using invertible neural networks. This AI-based design technology can capture complex non-linear aerodynamic effects while being 100 times faster than design approaches based on computational fluid dynamics. This project enables innovation in wind turbine design by accelerating time to market through higher-accuracy early design iterations to reduce the levelized cost of energy. INVERTIBLE NEURAL NETWORKS Researchers are leveraging a specialized invertible neural network (INN) architecture along with the novel dimension-reduction methods and airfoil/blade shape representations developed by collaborators at the National Institute of Standards and Technology (NIST) learns complex relationships between airfoil or blade shapes and their associated aerodynamic and structural properties. This INN architecture will accelerate designs by providing a cost-effective alternative to current industrial aerodynamic design processes, including: - Blade element momentum (BEM) theory models: limited effectiveness for design of offshore rotors with large, flexible blades where nonlinear aerodynamic effects dominate - Direct design using computational fluid dynamics (CFD): cost-prohibitive - Inverse-design models based on deep neural networks (DNNs): attractive alternative to CFD for 2D design problems, but quickly overwhelmed by the increased number of design variables in 3D problems AUTOMATED COMPUTATIONAL FLUID DYNAMICS FOR TRAINING DATA GENERATION - MERCURY FRAMEWORK The INN is trained on data obtained using the University of Marylands (UMD) Mercury Framework, which has with robust automated mesh generation capabilities and advanced turbulence and transition models validated for wind energy applications. Mercury is a multi-mesh paradigm, heterogeneous CPU-GPU framework. The framework incorporates three flow solvers at UMD, 1) OverTURNS, a structured solver on CPUs, 2) HAMSTR, a line based unstructured solver on CPUs, and 3) GARFIELD, a structured solver on GPUs. The framework is based on Python, that is often used to wrap C or Fortran codes for interoperability with other solvers. Communication between multiple solvers is accomplished with a Topology Independent Overset Grid Assembler (TIOGA). NOVEL AIRFOIL SHAPE REPRESENTATIONS USING GRASSMAN SPACES We developed a novel representation of shapes which decouples affine-style deformations from a rich set of data-driven deformations over a submanifold of the Grassmannian. The Grassmannian representation as an analytic generative model, informed by a database of physically relevant airfoils, offers (i) a rich set of novel 2D airfoil deformations not previously captured in the data , (ii) improved low-dimensional parameter domain for inferential statistics informing design/manufacturing, and (iii) consistent 3D blade representation and perturbation over a sequence of nominal shapes. TECHNOLOGY TRANSFER DEMONSTRATION - COUPLING WITH NREL WISDEM Researchers have integrated the inverse-design tool for 2D airfoils (INN-Airfoil) into WISDEM (Wind Plant Integrated Systems Design and Engineering Model), a multidisciplinary design and optimization framework for assessing the cost of energy, as part of tech-transfer demonstration. The integration of INN-Airfoil into WISDEM allows for the design of airfoils along with the blades that meet the dynamic design constraints on cost of energy, annual energy production, and the capital costs. Through preliminary studies, researchers have shown that the coupled INN-Airfoil + WISDEM approach reduces the cost of energy by around 1% compared to the conventional design approach. This page will serve as a place to easily access all the publications from this work and the repositories for the software developed and released through this pr...

aerodynamics↗
DOE OSTI2024

DOE BSSD Performance Management Metrics Report Q1

Microbes play key roles in our biosphere, from driving global nutrient cycling to impacting plant, animal and human health and disease. Complex data from microbial genomes, proteins, and metabolites provide a window into these tiny engines that drive life on our planet. Yet these data are dispersed among researchers’ laboratories and various repositories, making it difficult to access. This calls for new ways of managing data, improving data interoperability, advancing community standards, and creating an infrastructure where data are shared efficiently. We have built the National Microbiome Data Collaborative (NMDC) to advance how scientists create, use, and reuse data to redefine the way we understand and harness the power of microbes. The vision of the National Microbiome Data Collaborative (NMDC) is to drive a microbiome data sharing network connecting data, people, and ideas to advance microbiome innovation and discovery. The NMDC was launched in 2019 and brought together DOE National Laboratories to collaborate across resources, capabilities, and expertise. The NMDC team was strategically assembled to include software developers, microbial researchers, metadata experts, and multi-omics specialists. The diversity of the NMDC team reflects the inherently interdisciplinary nature of microbiome science, and we leverage the strengths of the DOE National Laboratory system. Towards BER’s goal of advancing an iterative systems biology approach to the understanding of microbial genomes, the NMDC serves as a foundation for infrastructure, data standards, and community building. Together with the flagship DOE User Facilities, the Joint Genome Institute (JGI) and the Environmental Molecular Sciences Laboratory (EMSL), we are developing core capabilities in metadata standards for environmental descriptors and sample handling and processing; standardized bioinformatic workflows; an interface for data search and access; and robust community engagement activities. The NMDC production platform supports long-term data infrastructure and community building for BER’s bioenergy and environmental research goals. Our approach leverages lessons learned and an ambitious framework for collaborative, interdisciplinary data infrastructure to support microbiome research. The NMDC supports data, information, and knowledge access through three defined software tools – the Submission Portal, NMDC EDGE, and the Data Portal – driven by community needs. Herein, we describe the value proposition for the microbiome research community, our overarching strategy, and challenges and opportunities for developing the NMDC as both an infrastructure and community engagement program.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Quantification of neural networks uncertainties with applications to SAFARI-1 axial neutron flux profiles

Deep Neural Networks (DNNs) have been widely used as a data-driven modelling tool in nuclear engineering. However, as a Machine Learning model, Artificial Neural Network (ANN) predictions are subjected to uncertainties originating from the noise in training data, incomplete coverage of the domain, and imperfect neural network architectures. In this work, we target at quantifying the prediction/approximation uncertainties of ANNs using Monte Carlo Dropout (MCD), as well as Bayesian Neural Networks (BNNs) which are solved by variational inference. With a demonstration problem in which neural networks are used to predict the assembly axial neutron flux profiles, the results have shown that the three different neural network models (regular DNNs, DNNs solved with MCD and BNNs) can produce results that agree very well among each other and with the measurement data, on cycles that are not used in the training process. Besides the excellent generalization capability, the uncertainty bands produced by MCD and BNN agree very well, and in general, they can fully envelop the noisy measurement data points. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

An underground, wireless, open-source, low-cost system for monitoring oxygen, temperature, and soil moisture

Abstract. The use of wireless sensor networks to measure soil parameters eliminates the need to remove sensors for field operations, such as tillage, thus allowing long-term measurements without multiple disturbances to soil structure. Wireless sensors also reduce above-ground cables and the risk of undesired equipment damage and potential data loss. However, implementing wireless sensor networks in field studies usually requires advanced and costly engineering knowledge. This study presents a new underground, wireless, open-source, low-cost system for monitoring soil oxygen, temperature, and soil moisture. The process of system design, assembly, programming, deployment, and power management is presented. The system can be left underground for several years without the need to change the battery. Emphasis was given on modularity so that it can be easily duplicated or changed if needed and deployed without previous engineering knowledge. Data from this type of system have a wide range of applications, including precision agriculture and high-resolution modelling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

AN ESTIMATE OF SPENT NUCLEAR FUEL MECHANICAL LOADS IN THE GENERAL 30 CM PACKAGE DROP SCENARIO

The US Department of Energy Spent Fuel and Waste Science and Technology (SFWST) program is performing research to determine the mechanical loading conditions applied to spent nuclear fuel (SNF) during normal conditions of transport to inform mechanical tests of SNF and close an important knowledge gap related to the practical disposition of SNF in the US. Researchers at Pacific Northwest National Laboratory (PNNL) have completed an extensive finite element study to characterize and estimate the potential mechanical loads on SNF during a hypothetical 30 cm drop of an SNF transportation package. This modeling study is validated with test data collected by the SFWST program during a physical test campaign that included one-third scale package drop tests and full-scale single fuel assembly drop tests. The test campaign was led by Sandia National Laboratories (SNL) and included international collaboration with Equipos Nucleares S.A, S.M.E (ENSA) and Bundesanstalt für Materialforschung und -prüfung (BAM). The key contribution of the modeling study is to go beyond the limitations of the limited number of physical tests to estimate the impact response to variations in impact angle, initial gap conditions, fuel assembly design, burnup and other parameters that affect the mechanical loads. The methodology of this study included a classic parametric study to calculate the impact response of highly detailed fuel assemblies over many combinations of parameters. Models of a 17x17 pressurized water reactor fuel assembly and a generic 10x10 boiling water reactor fuel assembly were both used in this study to cover the major fuel assembly types in the US inventory. Over 2,000 impact responses were calculated. The results of the parametric study were evaluated using traditional methods and basic statistics. The results were also used to construct a damage model using multiple nonlinear regression techniques to predict the mechanical loads over the full range of all input parameters. The damage model was found to work very well for all impact angle cases where the cask came to rest on its side. It was concluded that end drop cases where the cask remained vertical (instead of tipping over onto its side) were not sufficiently characterized by the current set of parametric study cases to include in the damage model, but it was not a priority to fully investigate that range because the highest mechanical loads were observed in the broader range of side impact cases. This modeling work provides sufficient insight into the mechanical loads on SNF during a hypothetical 30 cm package drop that, when considered along with the physical test data collected by the SNL-led team, the SFSWT program can consider the knowledge gap closed.

Klymyshyn, Nicholas A.↗
DOE OSTI2022

Full Length Assembly Testing in PELICAN (Final Report)

In support of the development of the U.S. Department of Energy (DOE) Versatile Test Reactor (VTR), a thermal hydraulics test facility was constructed to generate experimental measurement of the pressure drop across a single full-scale assembly containing prototypic axial reflectors, fuel, and plena components. Constructed and operated at Argonne National Laboratory, the Pressure drop Experimental Loop for Investigations of Core Assemblies in Nuclear reactors (PELICAN) facility was designed to achieve hydraulic conditions identical to those anticipated for a full-scale fuel assembly located in the VTR core in the region with the highest flow rate. Using water as surrogate for liquid sodium, the flow loop was operated at elevated temperatures and pressures to match the thermophysical properties of liquid sodium and ensure matching Reynolds and Euler numbers. The measurement objectives for data generated from this test facility was driven primarily by the validation needs for code calculations and simulations of the reference VTR core. These objectives focused on the need to validate pressure drop results across the various segments of the fuel assembly as they relate directly to the pumping power and safety of the reactor. Presented in this report are experimental results and analytical comparisons based on testing of a full-length assembly in PELICAN. Housed within a hexagonal test section extending 3.4 m in length, the tested assembly features a prototypic lower reflector, grid plates, wire-wrapped rod bundle, upper reflector, and exit region. The rod bundle extends over 1.5 m in length and contains 217 individual wire-wrapped rods with dimensions that best reflect the reference VTR design. The as-tested bundle assembly was fabricated using 316 stainless steel 0.25-inch (6.35-mm) diameter rods wrapped with 0.04-inch (1.016-mm) diameter wire at a helical pitch of 10.51 inch (26.6 cm). Details of the method for in-house wire-wrapping, assembly, and installation are provided later in this report. Experimental measurements of pressure drop at 19 positions along the test assembly were recorded for a range of flow conditions, with special attention paid to key locations within the assembly, including component inlet and outlet, transition, and wire-wrapped rod bundle regions. Testing conditions were based on 110°C water with flow rates ranging from 50 to 450 GPM (3 to 27 kg/s) at the inlet of the test assembly generating Reynolds numbers and velocities up to ~8.0×10 4 and ~7.8 m/s, respectively, within the rod bundle region. Non-dimensional values for the friction factor were then calculated based on these experimental measurements and compared against those predicted by various analytical correlations available from open literature. Predictions by the upgraded Cheng and Todreas, Rehme, and Novendstern correlations fell within 4% to those values measured experimentally.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2020

Holistic Measurement Driven Resilience: Combining Operational Fault and Failure Measurements and Fault Injection for Quantifying Fault Detection, Propagation and Impact. Final report

For HPC systems to date, application resilience to faults and failures has been accomplished by the brute- force method of checkpoint/restart, which allows an application to make forward progress in the face of system and application faults, errors, and failures independent of root cause or end result. It has remained the primary resilience mechanism because we lack a way to identify faults and anticipate consequences early enough to take meaningful mitigating action. However, checkpoint/restart implementations put a tremendous burden on system resources and on the applications themselves and is becoming less feasible at scale. Because we have not yet operated at scales at which checkpoint/restart fails to provide forward progress, despite increasing costs, vendors have had little motivation to provide the instrumentation necessary for early identification of faults and failures. However, as we move from petascale to exascale, component mean time to failure (MTTF) will render the existing techniques ineffectual and/or too expensive. Furthermore, fault recovery mechanisms such as failover and/or error correction introduce performance inconsistency. Instrumentation allowing early indication of problems and tools to enable use of such information by systems, operating systems, and applications offer an alternative, more scalable and less costly solution. In the HMDR project, we built on our experience and expertise developed and accumulated over years of research on design, monitoring, measurement, and assessment of resilient computing systems. Analysis of field data on the current and past generations of extreme-scale systems revealed several challenges that, if not addressed in increasingly larger and more complex systems, may hinder the effectiveness of future exascale computing systems. Specifically, i) file systems and interconnects in current-generation large-scale systems already operate at the margins of resiliency, including consistent performance, and may not scale to larger deployments; ii) automated, software-based failover mechanisms are frequently inadequate and can introduce wider failures, such that failures during recovery may lead to system/application failures, including system-wide outages; and iii) silent data corruption represents a critical fault mode and will require efficient detection mechanisms if next-generation applications are to take full advantage of exascale hardware. To address the above challenges, we assembled a team of world-renowned experts in resilient extreme- scale computing from the University of Illinois (Electrical and Computer Engineering, Computer Science, and NCSA), SNL, LANL, NERSC, and Cray. Our team includes representatives from centers that house many of the largest HPC resources in the world, both today and over the coming years. The team has a unique track record of research in i) system and application failure characterization based on the analysis of field data, ii) data-driven design of fault/error detection mechanisms, and iii) experimental characterization of system/application resiliency. The team includes system owners/operators who provide continuous data collection and access and ensure installation of appropriate analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Comparative genomic and transcriptomic analyses of trans-kingdom pathogen Fusarium solani species complex reveal degrees of compartmentalization

Background: The Fusarium solani species complex (FSSC) comprises fungal pathogens responsible for mortality in a diverse range of animals and plants, but their genome diversity and transcriptome responses in animal pathogenicity remain to be elucidated. We sequenced, assembled and annotated six chromosome-level FSSC clade 3 genomes of aquatic animal and plant host origins. We established a pathosystem and investigated the expression data of F. falciforme and F. keratoplasticum in Chinese softshell turtle (Pelodiscus sinensis) host. Results: Comparative analyses between the FSSC genomes revealed a spectrum of conservation patterns in chromosomes categorised into three compartments: core, fast-core (FC), and lineage-specific (LS). LS chromosomes contribute to variations in genomes size, with up to 42.2% of variations between F. vanettenii strains. Each chromosome compartment varied in structural architectures, with FC and LS chromosomes contain higher proportions of repetitive elements with genes enriched in functions related to pathogenicity and niche expansion. We identified differences in both selection in the coding sequences and DNA methylation levels between genome features and chromosome compartments which suggest a multi-speed evolution that can be traced back to the last common ancestor of Fusarium. We further demonstrated that F. falciforme and F. keratoplasticum are opportunistic pathogens by inoculating P. sinensis eggs and identified differentially expressed genes also associated with plant pathogenicity. These included the most upregulated genes encoding the CFEM (Common in Fungal Extracellular Membrane) domain. Conclusions: The high-quality genome assemblies provided new insights into the evolution of FSSC chromosomes, which also serve as a resource for studies of fungal genome evolution and pathogenesis. This study also establishes an animal model for fungal pathogens of trans-kingdom hosts.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Structural basis of RNA conformational switching in the transcriptional regulator 7SK RNP

7SK non-coding RNA (7SK) negatively regulates RNA polymerase II (RNA Pol II) elongation by inhibiting positive transcription elongation factor b (P-TEFb), and its ribonucleoprotein complex (RNP) is hijacked by HIV-1 for viral transcription and replication. Methylphosphate capping enzyme (MePCE) and La-related protein 7 (Larp7) constitutively associate with 7SK to form a core RNP, while P-TEFb and other proteins dynamically assemble to form different complexes. Here, we present the cryo-EM structures of 7SK core RNP formed with two 7SK conformations, circular and linear, and uncover a common RNA-dependent MePCE-Larp7 complex. Further, together with NMR, biochemical, and cellular data, these structures reveal the mechanism of MePCE catalytic inactivation in the core RNP, unexpected interactions between Larp7 and RNA that facilitate a role as an RNP chaperone, and that MePCE-7SK-Larp7 core RNP serves as a scaffold for switching between different 7SK conformations essential for RNP assembly and regulation of P-TEFb sequestration and release.

59 BASIC BIOLOGICAL SCIENCES↗