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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 1,099 records · Page 61
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Neutronics Analysis of TREAT Multi-SERTTA Calibration Test Vehicle (Multi-SERTTA CAL)

Transient testing is a necessary step in the performance evaluation of Accident Tolerant Fuels (ATF) developed as part of the Fuel Cycle Research and Development Advanced Fuels Campaign initiated and supported by the United States Department of Energy (DOE-NE). These advanced fuels are expected to be used in future reactors or replace the current fuel design used in existing power reactors for enhanced performance and safety. Upon completion of a process proscribed by the National Environmental Policy Act, the Transient Reactor Test (TREAT) facility, located at Idaho National Laboratory (INL), was chosen by DOE-NE to provide the desired specialized testing. TREAT has successfully operated from start-up in 1959 to 1994 when it was placed on operational stand-by. Careful preparations are being made for operational readiness and TREAT is projected to resume testing no later than 2018. Past TREAT testing primarily focused on sodium-cooled reactor fuels (SRF). Test vehicle designs varied to accommodate testing needs ranging from separate effects tests to integral tests approaching simulation of actual reactor environment conditions including forced convection cooling. The current focus on light water reactor (LWR) ATF requires design of new experiment vehicles to accommodate the water/steam environment and associated complexities regarding desired energy deposition into the specimen and proper containment. The first set of experiments in TREAT restart is expected to be conducted with the Multi-SERTTA (Static Environment Rodlet Transient Test Apparatuses). Difficult to quantify and calculate integral and time dependent effects inherent in transient testing limit predictive modeling capability; therefore, calibration testing in TREAT prior to the actual experiment is necessary to empirically determine the effectiveness of core-to-specimen power coupling in order to achieve the desired energy deposition into the test specimen during a nuclear power transient. This has been done in the past, in part, by evaluating the difference in energy deposition into dosimeter wires subjected to various TREAT time dependent power conditions. These calibration tests were typically conducted in a test vehicle neutronically similar to the actual test vehicle, but more simplistic in design in order to quickly retrieve and analyze dosimeter wires and minimize containment structure costs due to several trials. Prior calibration testing for SRF experiments could be conducted without coolant as sodium has no notable neutronic effect on the core-to-specimen power coupling factor (PCF). Such is not the case with LWR fuel where water serves as both moderator and coolant. This presents a design challenge as the calibration test vehicle must retain heated, high-pressure water to preserve neutronic equivalence representative of near prototypic reactor conditions while allowing for quick retrieval of dosimeters. The solution to this challenge comes in the form of a thin walled dry well positioned through the center of the basic Multi-SERTTA design. Test vehicle reactivity worth and PCF are two important factors with respect to ascertainment of neutronic equivalence. Similarity in test vehicle reactivity worth is desired to avoid the need to change operational core rod positions in TREAT which could significantly alter neutron flux distribution and consequently power distribution throughout the core and into the test specimen. The geometry and composition of various materials within the experiment containment structure can also significantly alter the quality of neutron signal delivery from the core to the test specimen. Introduction of an Inconel tube, albeit very thin, is expected to impact PCF. Simple modifications to the CAL design may compensate for the anticipated change. Evaluation of these factors and proposed modifications through neutronics analysis serves to inform experiment design and is a valuable tool towards achieving the optimal CAL design.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

National Spherical Torus Experiment Upgrade Collaborative Research on Configuration Optimization of Advanced Operating Scenarios and Control Including Macroscopy Stability EOG for Period 3/1/14 through 2/28/18

General Atomics has collaborated with PPPL since 1999 in support of NSTX and NSXT-U by leveraging DIII-D program expertise. John Ferron was the principal investigator (PI) for the first nine years (to 2008) and Rob La Haye since then. The research effort evolved over a number of three-year grants and originally included both the core (control, stability, resonant error fields) and the boundary (ELMs, divertor, error fields). In 2014 the current and last grant extension was stretched to four years and split into a core grant (advanced scenarios and control, macroscopic plasma stability and extending 3D magnetics diagnostics) and a boundary grant. Todd Evans is the PI for the new boundary grant which while also for four years expires later than the core grant; a separate end of grant (EOG) report for that is due in July 2018.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2019

Risk-informed Physical Security Using Dynamic Force-on-Force Modelling Tools (ABSTRACT)

Physical security requirement of nuclear power plants (NPPs) in the US is regulated in a mainly prescriptive manner. This “one size fits all” approach may consequently overlook plant-specific characteristics which are beneficial for the inherent safety and security of the plant. Although a performance-based security measure has been promulgated in 10 CFR 73.55(r), current evaluation methods generally emphasize static and conservative assumptions on physical security uncertainties. This conservatism contributes to the overall operational and maintenance costs, which account to 70% of total operating expenditures for an NPP. This paper describes an ongoing work at the Idaho National Laboratory within the Light Water Reactor Sustainability pathway, which aims to optimize physical protection system with regards to system effectiveness and dollar costs. To achieve this goal, a risk-informed approach to physical security is proposed. This approach leverages the Event Modeling Risk Assessment using Linked Diagrams (EMRALD) tool to model facility infiltration and Force-on-Force (FoF) engagement in a dynamic manner. The dynamic FoF analysis was conducted by coupling human performance uncertainties modeled in EMRALD to existing commercial 3-dimensional FoF software, i.e. SCRIBE3D, AVERT and Simajin. Meanwhile, risk analysis was accomplished by coupling the attack outcomes to existing plant Probabilistic Risk Assessment (PRA) model with FLEX strategy. This approach reduces the conservatisms in security modeling and further acknowledges the possibility for plant operators to mitigate the attack outcomes. A sabotage attack on a hypothetical NPP facility was devised to demonstrate the proposed methodology. Dynamic FoF rules were implemented in EMRALD coupled to SCRIBE3D. This dynamic evaluation revealed an additional 21% system effectiveness in protecting the target set relative to the static modelling approach. Furthermore, the conditional core damage probability could be reduced by up to 46% by crediting operator actions in mitigating the sabotage outcomes. We evaluated the relative importance of each component of the physical security posture included in the model and assessed tradeoffs between components to identify potential opportunities for cost reduction while maintaining a specific level of system effectiveness. Results of the cost analysis will be presented in the conference. This study is expected to help NPP operators in allocating resources optimally to satisfy the security requirements.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Carbon fiber synthesis from pitch: Insights from ReaxFF based molecular dynamics simulations

Despite the potential to lowering fabrication costs of pitch-based carbon fiber (CF) compared to polyacrylonitrile-based CF, large scale utilization is currently limited by the complexity in manufacturing. This is mainly due to the lack of consistency between the pitch structure/chemistry and CF properties under different processing conditions. Here, we employed full atomistic simulations based on reactive force fields to investigate the conversion of pitch precursors to CFs. Additionally, it was found that at the carbonization stage, the consumption rates of precursors, gas formations, and solid yields (carbonized CFs) are all dependent on the composition and arrangement of molecular moieties in the precursor. Specifically, precursors with sp 3 carbons in the cata-condensed core area have limited decomposition pathways (i.e., limited reaction sites), which leads to the formation of carbonized CFs with similar compositions. Furthermore, carbonized CFs possess distinct structural characteristics, and finally lead to the formation of CFs with different moduli. By analyzing CF crystallinity and elemental composition, general rationales are proposed for the selection of optimal precursors and processing conditions, and thus shed lights on parameter screening during industrial CF synthesis. For instance, inclusion of sp 3 carbons in cata- condensed rings can be preferred over the presence of sp 3 carbons on side chains.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2018

Structural basis for MTR4–ZCCHC8 interactions that stimulate the MTR4 helicase in the nuclear exosome-targeting complex

The nuclear exosome-targeting (NEXT) complex functions as an RNA exosome cofactor and is involved in surveillance and turnover of aberrant transcripts and noncoding RNAs. NEXT is a ternary complex composed of the RNA-binding protein RBM7, the scaffold zinc-knuckle protein ZCCHC8, and the helicase MTR4. While RNA interactions with RBM7 are known, it remains unclear how NEXT subunits collaborate to recognize and prepare substrates for degradation. Here, we show that MTR4 helicase activity is enhanced when associated with RBM7 and ZCCHC8. While uridine-rich substrates interact with RBM7 and are preferred, optimal activity is observed when substrates include a polyadenylated 3' end. We identify a bipartite interaction of ZCCHC8 with MTR4 and uncover a role for the conserved C-terminal domain of ZCCHC8 in stimulating MTR4 helicase and ATPase activities. A crystal structure reveals that the ZCCHC8 C-terminal domain binds the helicase core in a manner that is distinct from that observed for Saccharomyces cerevisiae exosome cofactors Trf4p and Air2p. Furthermore, our results are consistent with a model whereby effective targeting of substrates by NEXT entails recognition of elements within the substrate and activation of MTR4 helicase activity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

A.I. for nuclear physics

Nuclear science is concerned with the understanding of the nature of matter, its basic constituents and their interaction to form the elements and the properties we observe. This includes the forms of matter we see around us and also exotic forms such as those that existed in the first moments after the Big Bang and that exist today inside neutron stars. The techniques, tools, and expertise needed for nuclear physics (NP) research are therefore diverse in nature. State-of-the art accelerators are being developed to illuminate the dynamical basis of the core of the atom in terms of the fundamental constituents called quarks and gluons and to increase the number of isotopes with known properties. This scientific infrastructure is reaching scales and complexities that require computational methods for tasks such as anomaly detection in operational data. New methodologies are needed to detect anomalies and to optimize operating parameters, predict failures as well as to discover new optimization algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Divertor Plasma Detachment Control Neural Network

DivControlNN is a state-of-the-art software tool that leverages advanced machine learning techniques to predict and control divertor plasma behavior in fusion reactors. Plasma, a highly energetic and electrically charged gas, requires meticulous management to protect reactor components and maintain optimal energy production. Conventional simulation methods, although extremely detailed, typically demand extensive computational time-making them unsuitable for real-time control scenarios. DivControlNN addresses this challenge by learning from tens of thousands of high-fidelity simulations, thereby creating a rapid surrogate model that can deliver near-instantaneous predictions. At the core of its functionality is a sophisticated technique known as latent space mapping, which condenses complex, high-dimensional plasma data into a compact, lower-dimensional representation. This streamlined representation enables the system to quickly forecast essential plasma properties and determine the precise conditions required for effective detachment. Detachment is a crucial process in which the plasma is cooled before reaching the divertor plates, thereby reducing heat loads and mitigating material erosion. In recent experiments conducted on the KSTAR tokamak in South Korea, DivControlNN successfully guided the detachment process without any fine-tuning-even when applied to a new tungsten divertor configuration. By achieving a computational speed-up of over one hundred million times compared to traditional simulation methods while maintaining low prediction errors, DivControlNN stands to significantly enhance real-time control and diagnostic capabilities in future fusion reactors. This breakthrough paves the way for safer, more reliable reactor operation and represents a major advancement toward realizing fusion energy as a practical, sustainable, and clean power source.

Xu, Xueqiao [Lawrence Livermore National Laborator↗
DOE OSTI2017

MOOSE Implementation of MAMBA

The development of MAMBA is targeted at capturing both core wide CRUD induced power shifts (CIPS) as well as pin-­level CRUD induced localized corrosion (CILC). Both CIPS and CILC require some sort of information from thermal-­hydraulic, neutronics, and fuel performance codes, although the degree of coupling is different for the two effects. Since CIPS necessarily requires a core-­wide power distribution solve, it requires tight coupling with a neutronics code. Conversely, CIPS tends to be an individual pin phenomenon, requiring tight coupling a fuel performance code. As efforts are now focused on coupling MAMBA within the VERA suite, a natural separation has surfaced in which a FORTRAN rewrite of MAMBA is optimal for VERA integration to capture CIPS behavior, while a CILC focused calculation would benefit from a tight coupling with BISON, motivating a MOOSE version of MAMBA.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Deploy threading in Nalu solver stack

The goal of the ExaWind project is to enable predictive simulations of wind farms composed of many MW-scale turbines situated in complex terrain. Predictive simulations will require computational fluid dynamics (CFD) simulations for which the mesh resolves the geometry of the turbines, and captures the rotation and large deflections of blades. Whereas such simulations for a single turbine are arguably petascale class, multi-turbine wind farm simulations will require exascale-class resources. The primary code in the ExaWind project is Nalu, which is an unstructured-grid solver for the acousticallyincompressible Navier-Stokes equations, and mass continuity is maintained through pressure projection. The model consists of the mass-continuity Poisson-type equation for pressure and a momentum equation for the velocity. For such modeling approaches, simulation times are dominated by linear-system setup and solution for the continuity and momentum systems. For the ExaWind challenge problem, the moving meshes greatly affect overall solver costs as re-initialization of matrices and re-computation of preconditioners is required at every time step In this Milestone, we examine the effect of threading on the solver stack performance against flat-MPI results obtained from previous milestones using Haswell performance data full-turbine simulations. Whereas the momentum equations are solved only with the Trilinos solvers, we investigate two algebraic-multigrid preconditioners for the continuity equations: Trilinos/Muelu and HYPRE/BoomerAMG. These two packages embody smoothed-aggregation and classical Ruge-Stiiben AMG methods, respectively. In our FY18 Q2 report, we described our efforts to improve setup and solve of the continuity equations under flat-MPI parallelism. While significant improvement was demonstrated in the solve phase, setup times remained larger than expected. Starting with the optimized settings described in the Q2 report, we explore here simulation performance where OpenMP threading is employed in the solver stack. For Trilinos, threading is acheived through the Kokkos abstraction where, whereas HYPRE/BoomerAMG employs straight OpenMP. We examined results for our mid-resolution baseline turbine simulation configuration (229M DOF). Simulations on 2048 Haswell cores explored the effect of decreasing the number of MPI ranks while increasing the number of threads. Both HYPRE and Trilinos exhibited similar overal solution times, and both showed dramatic increases in simulation time in the shift from MPI ranks to OpenMP threads. This increase is attributed to the large amount of work per MPI rank starting at the single-thread configuration. Decreasing MPI ranks, while increasing threads, may be increasing simulation time due to thread synchronization and start-up overhead contributing to the latency and serial time in the model. These result showed that an MPI+OpenMP parallel decomposition will be more effective as the amount per MPI rank computation per MPI rank decreases and the communication latency increases. This idea was demonstrated in a strong scaling study of our low-resolution baseline model (29M DOF) with the Trilinos-HYPRE configuration. While MPI-only results showed scaling improvement out to about 1536 cores, engaging threading carried scaling improvements out to 4128 cores — roughly 7000 DOF per core. This is an important result as improved strong scaling is needed for simulations to be executed over sufficiently long simulated durations (i.e., for many timesteps). In addition to threading work described above, the team examined solver-performance improvements by exploring communication-overhead in the HYPRE-GMRES implementation through a communicationoptimal- GMRE algorithm (CO-GMRES), and offloading compute-intensive solver actions to GPUs. To those ends, a HYPRE mini-app was allow us to easily test different solver approaches and HYPRE parameter settings without running the entire Nalu code. With GPU acceleration on the Summitdev supercomputer, a 20x speedup was achieved for the overall preconditioner and solver execution time for the mini-app. A study on Haswell processors showed that CO-GMRES provides benefits as one increases MPI ranks.

17 WIND ENERGY↗
DOE OSTI2022

Eddy Current Flow Meter Measurements and Modeling Validation

The goal of the eddy current flow meter (ECFM) is to monitor velocities and temperatures of conductive liquid metals. The ECFM can also detect voids in two-phase liquid metal flows. Measurement of these flows can greatly improve the accuracy of the empirical formulas used for fluid dynamics near the reactor core and aid in mapping any non-uniformities in fluid flow such as estimates of temperature and phase. While the eventual goal is to develop the ECFM for the liquid sodium surrounding the Versatile Test Reactor (VTR) core, this report focuses on modeling and experimental validation of ECFM for a moving solid test rod and for liquid mercury in the Target Test Facility (TTF) flow loop to better understand and further develop ECFM measurement capabilities by validating model predictions with benchmark experiments. Eventually the models could be used to design and optimize future ECFMs for specific applications such as monitoring flow within the VTR pool.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Manufacturing Full-Scale High Gradient Copper Accelerators: Electron Beam Welding and Allied Processes

This DOE ARDAP-funded study examines the technical and business feasibility of manufacturing high-gradient normal conducting RF (NCRF) copper accelerating structures using electron beam welding (EBW) instead of conventional high-temperature brazing. The core motivation is material performance: brazing softens copper significantly, while hard copper alloys have demonstrated ~75% higher operational gradients in SLAC tests, making cold-joining techniques highly attractive. EBW, applied to split-cell (half or quadrant) structure designs, preserves copper hardness away from the weld joint and simplifies machining — but industrial process optimization remains immature and a substantial learning curve is expected. The business case was modeled for two scenarios: a greenfield EBW linac company (> $10M upfront costs, viable above ~30 units/year) and an EBW division added to an existing accelerator firm to reduce risk and upfront costs, at the expense of being less optimal structure for the higher volume production. The study concludes that without a significant increase in demand, private investment alone cannot sustain this capability, and recommends federal support through R&D grants, procurement incentives, and CAPEX cost-sharing to incubate domestic EBW-NCRF manufacturing — with the existing-company model.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Complete Performance Comparison Between the Optimized Image Construction Algorithm (U-MBIR) and the Existing Reconstruction Algorithm for Detecting Defects and Damage in Concrete

Reinforced concrete (RC) is a composite material subjected to mechanical, thermal, and chemical loads throughout its service life. Because of these external stressors and the susceptibility of RC structural members to shrinkage and microcracking, the material degrades throughout its life cycle. This deterioration can lead to a decrease in member capacity and, ultimately, poses a threat to the structural integrity. Thus, it is crucial that the damage caused by aging and degradation be monitored and assessed at regular intervals throughout the material’s service life. Since coring of the material is typically not feasible for in-service structural systems, non-destructive evaluation (NDE) methodologies are used to assess remaining structural capacity. NDE methods enable surface and subsurface examination without damaging or degrading the medium. Moreover, RC is a critical component of nuclear power plants; thus, its safety and reliability must be thoroughly examined throughout the life cycle of the structural system. Ultrasonic measurements have been an industry standard for both surface and subsurface inspections. To this end, Oak Ridge National Laboratory (ORNL) has researched and developed advanced image reconstruction algorithms to capture internal damage. The results and discussion presented herein summarize the current state of the ultrasonic model–based iterative reconstruction (U-MBIR) algorithm developed at ORNL. More specifically, this report presents a comparison between reconstruction images produced via a widely employed ultrasonic NDE technique—the synthetic aperture focusing technique (SAFT)—and the ORNL-developed U-MBIR algorithm. These NDE methodologies are demonstrated using ultrasonic data collected from four concrete specimens. Overall, the U-MBIR algorithm eliminates artifacts and noise that are typically present within the SAFT reconstructions, and it shows defects and anomalies more clearly than the SAFT images. In conclusion, this algorithm is suitable for identifying concrete defects, although more improvements and optimization could be done to better define internal defects.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Engineering of large-pore lipid-coated mesoporous silica nanoparticles for dual cargo delivery to cancer cells

Lipid-coated mesoporous silica nanoparticles (LC-MSNs) have recently emerged as a next-generation cargo delivery nanosystem combining the unique attributes of both the organic and inorganic components. The high surface area biodegradable inorganic mesoporous silica core can accommodate multiple classes of bio-relevant cargos in large amounts, while the supported lipid bilayer coating retains the cargo and increases the stability of the nanocarrier in bio-relevant media which should promote greater bio-accumulation of LC-MSNs in cancer sites. In this paper, we report on the optimization of various sol–gel synthesis (pH, stirring speed) and post-synthesis (hydrothermal treatment) procedures to enlarge the MSN pore size and tune the surface chemistry so as to enable loading and delivery of large biomolecules. Finally, the proof of concept of the dual cargo-loaded nanocarrier has been demonstrated in immortalized cervical cancer HeLa cells using MSNs of various fine-tuned pore sizes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Crystal structure of aminopentamide hydrogen sulfate, (C 19 H 25 N 2 O)(HSO 4 )

The crystal structure of aminopentamide hydrogen sulfate has been solved and refined using synchrotron X-ray powder diffraction data, and optimized using density functional techniques. Aminopentamide hydrogen sulfate crystallizes in space group P2 1 /c (#14) with a = 17.62255(14), b = 6.35534(4), c = 17.82499(10) Å, β = 96.4005(6)°, V = 1983.906(14) Å 3 , and Z = 4. The structure consists of layers parallel to the bc-plane with hydrogen sulfate anions at the core and aminopentamide cations on the outside. There is a strong charge-assisted O49–H53∙∙∙O52 hydrogen bond between the hydrogen sulfate anions. This hydrogen bond links the anions in a chain parallel to the b-axis. The cation forms a discrete N–H∙∙∙O hydrogen bond to the anion. The amide group also forms two weaker discrete hydrogen bonds to the anion. The three N–H∙∙∙O hydrogen bonds link the cations and anions into columns parallel to the b-axis. This commercial material from USP contained an unidentified impurity, the powder pattern of which could be indexed on a monoclinic unit cell. The powder pattern has been submitted to ICDD for inclusion in the Powder Diffraction File™ (PDF®).

36 MATERIALS SCIENCE↗
DOE OSTI2018

Assessing Substitution Effects on Surface Chemistry by in Situ Ambient Pressure X-ray Photoelectron Spectroscopy on Perovskite Thin Films, BaCe x Zr 0.9– x Y 0.1 O 2.95 ( x = 0; 0.2; 0.9)

Performance of proton-solid oxide fuel cells (H+-SOFC) is governed by ion transport through solid/gas interfaces. Major breakthroughs are then intrinsically linked to a detailed understanding of how parameters tailoring bulk proton conductivity affect surface chemistry in situ, at an early stage. In this work, we studied proton and oxygen transport at the interface between H+-SOFC electrolyte BaCexZr0.9–xY0.1O2.95 (x = 0; 0.2; 0.9) thin films and the gas (100 mTorr of H2O and O2) by using synchrotron-based ambient pressure X-ray photoelectron spectroscopy at operating temperature (>400 °C). We developed highly textured BaCexZr0.9–xY0.1O2.95 epitaxial thin films, which exhibit high level of in-plane proton conductivity, that is, up to 0.08 S cm–1 at 500 °C for x = 0.9. Upon applying 100 mTorr water partial pressure above 300 °C, major changes are observed only in the O 1s and Y 3d core level spectra, with a clear Zr/Ce ratio dependency. OH– formation is favored by Ce content while initiated near Y. Hydration is also associated with surface secondary phase growth comprising oxygen-under-coordinated yttrium and/or yttrium hydroxide. With BaCe0.2Zr0.7Y0.1O2.95, high levels of ionic conductivities and chemical stability are obtained as a result of the optimized surface reaction kinetics, with low activation energy barrier for proton transport while restraining formation of OH–/SO42– adsorb species.

bio-inspired, energy storage (including batteries ↗
DOE OSTI2024

Integrated edge-to-exascale workflow for real-time steering in neutron scattering experiments

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Spatial core-edge coupling of the particle-in-cell gyrokinetic codes GEM and XGC

Two existing particle-in-cell gyrokinetic codes, GEM for the core region and XGC for the edge region, have been successfully coupled with a spatial coupling scheme at the interface in a toroidal geometry. Additionally, a mapping technique is developed for transferring data between GEM's structured and XGC's unstructured meshes. Two examples of coupled simulations are presented to demonstrate the coupling scheme. The optimization of GEM for graphics processing unit is also presented.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

Scalable Parallel Methods for Analyzing Metagenomics Data at Extreme Scale

The field of bioinformatics and computational biology is currently experiencing a data revolution. The exciting prospect of making fundamental biological discoveries is fueling the rapid development and deployment of numerous cost-effective, high-throughput next-generation sequencing technologies. The result is that the DNA and protein sequence repositories are being bombarded with new sequence information. Databases are continuing to report a Moore’s law-like growth trajectory in their database sizes, roughly doubling every 18 months. In what seems to be a paradigm-shift, individual projects are now capable of generating billions of raw sequence data that need to be analyzed in the presence of already annotated sequence information. While it is clear that data-driven methods, such as sequencing homology detection, are becoming the mainstay in the field of computational life sciences, the algorithmic advancements essential for implementing complex data analytics at scale have mostly lagged behind. Sequence homology detection is central to a number of bioinformatics applications including genome sequencing and protein family characterization. Given millions of sequences, the goal is to identify all pairs of sequences that are highly similar (or “homologous”) on the basis of alignment criteria. While there are optimal alignment algorithms to compute pairwise homology, their deployment for large-scale is currently not feasible; instead, heuristic methods are used at the expense of quality. In this dissertation, we present the design and evaluation of a parallel implementation for conducting optimal homology detection on distributed memory supercomputers. Our approach uses a combination of techniques from asynchronous load balancing (viz. work stealing, dynamic task counters), data replication, and exact-matching filters to achieve homology detection at scale. Results for a collection of 2.56M sequences show parallel efficiencies of ~75-100% on up to 8K cores, representing a time-to-solution of 33 seconds. We extend this work with a detailed analysis of single-node sequence alignment performance using the latest CPU vector instruction set extensions. Preliminary results reveal that current sequence alignment algorithms are unable to fully utilize widening vector registers.

59 BASIC BIOLOGICAL SCIENCES↗