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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 307 records · Page 17
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Design characteristics of the Idaho National Laboratory high-temperature gas-cooled test reactor - 18703

A point design for a graphite-moderated, high-temperature, gas-cooled test reactor (HTG-TR) has been developed by Idaho National Laboratory as part of a U.S. Department of Energy initiative to explore and potentially expand existing U.S. test reactor capabilities. This paper provides a summary of the design and its main attributes. The 200-MW HTG-TR is a thermal-neutron spectrum reactor composed of hexagonal prismatic fuel and graphite reflector blocks. The HTG-TR is designed to operate at 7 MPa with a coolant inlet/outlet temperature of 325/650 deg. C and utilizes tri-structural isotropic particle fuel with an enrichment of 15.5-wt% {sup 235}U. The primary mission of the HTG-TR is material irradiation, and therefore the core has been specifically designed and optimized to provide the highest possible thermal and fast-neutron fluxes. The highest thermal-neutron flux (3.90 E+14 n/cm{sup 2}/s) occurs in the outer reflector, and the maximum fast-flux levels (1.17 E+14 n/cm{sup 2}s) are produced in the central reflector column, where most of the graphite has been removed. The core features a large number of irradiation positions with large test volumes and long test lengths, providing an ideal environment for thermal-neutron irradiation of large test articles. The total available test volume is more than 1,100 L. Up to four test loop facilities can be accommodated with pressure tube boundaries to isolate test articles and test fluids (e.g., liquid metal, liquid salt, and light water) from the helium primary coolant system. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2016

CTF Parallel Performance Improvements

CTF is a modernized version of COBRA-TF (Coolant Boiling in Rod Arrays-Two Fluid), which is a thermal hydraulic (T/H) sub-channel code based on a two-phase, two-fluid model that formulates the conservation equations of mass, energy, and momentum for three fields of vapor, continuous liquid, and entrained liquid droplets. Under the Consortium for Advanced Simulation of Light Water Reactors (CASL) program sponsored by the U.S. Department of Energy, CTF is being further developed and improved for modeling and simulation of light water reactors. The code improvements for predicting T/H responses of pressurized water reactor (PWR) cores are focused on its applications to specific challenge problems deemed significant to the industry, such as departure from nucleate boiling (DNB) and crud-induced power shift. The code is also being used for T/H feedback in normal operating condition depletions in PWR and boiling water reactor designs in the reactor core simulator, Virtual Environment for Reactor Applications Core Simulator (VERA-CS), being developed by CASL. CTF modifications under the CASL program include software optimization, new closure models, and parallelization for modeling full reactor core T/H responses. The purpose of this work was to improve the parallel performance of CTF. CTF was originally parallelized via a domain-decomposition strategy using the Message Passing Interface (MPI), in which the basis of a solution domain was a physical assembly in the model. In other words, a user who wanted to perform a parallel solution with CTF had to provide the code one processor per assembly in the model (e.g., 56 cores for a 56 assembly quarter-core model, or 193 cores for a 193 assembly full-core model). This design was rigid, demanding a very specific number of processors for a model; furthermore, when CTF was run in VERA-CS-which typically uses thousands of processors- many processors sat idle during the CTF solve because CTF was unable to use any more processors than there are physical assemblies. This was not necessarily a problem if the CTF execution speed was very fast, but the average time spent in a CTF solve for prototypical PWR operating conditions was significant (i.e., over 20 % of the simulation wall time was being spent in CTF for a standard core depletion simulation). Larger, more computationally intensive simulations performed in support of the CASL DNB challenge problem demonstrated a need for improvements in this original parallelization approach. The specific goal of this work was to allow CTF to use more processors than there are physical assemblies in the model, which would not only speed up CTF and improve the overall efficiency of VERA-CS but also give users more-flexible options for executing their simulations. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. Here, in this study, we report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core–shell upconverting nanoparticles (UCNPs). UCNPs, which have applications in fields such as biosensing, super-resolution microscopy and three-dimensional printing, can emit visible and ultraviolet light from near-infrared excitations. We report a large-scale dataset of UCNP emission spectra based on accurate but expensive kinetic Monte Carlo simulations (N > 6,000) and use these data to train a heterogeneous graph neural network using a physically motivated representation of UCNP nanostructure. Applying gradient-based optimization on the trained graph neural network, we identify structures with 6.5× higher predicted emission under 800-nm illumination than any UCNP in our training set. Our work reveals design principles for UCNP heterostructures and presents a roadmap for DL-based inverse design of nanomaterials.

Sivonxay, Eric [Lawrence Berkeley National Laborat↗
DOE OSTI2017

Understanding the I/O Performance Gap Between Cori KNL and Haswell

The Cori system at NERSC has two compute partitions with different CPU architectures: a 2,004 node Haswell partition and a 9,688 node KNL partition, which ranked as the 5th most powerful and fastest supercomputer on the November 2016 Top 500 list. The compute partitions share a common storage configuration, and understanding the IO performance gap between them is important, impacting not only to NERSC/LBNL users and other national labs, but also to the relevant hardware vendors and software developers. In this paper, we have analyzed performance of single core and single node IO comprehensively on the Haswell and KNL partitions, and have discovered the major bottlenecks, which include CPU frequencies and memory copy performance. We have also extended our performance tests to multi-node IO and revealed the IO cost difference caused by network latency, buffer size, and communication cost. Overall, we have developed a strong understanding of the IO gap between Haswell and KNL nodes and the lessons learned from this exploration will guide us in designing optimal IO solutions in many-core era.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

LMFR design and optimization methodology

This paper presents and applies a methodology for designing and optimizing Liquid Metal-cooled Fast Reactors (LMFRs). The methodology, called the Design and Optimization Methodology (DOM), achieves its goals by the effective exploration of a search space. The paper presents two case studies to demonstrate DOM. Both studies were based on the WEC long-life core LFR design. In the first study, DOM was used to explore the search space in the vicinity of the WEC (Westinghouse Electric Company LLC) long-life core LFR design trying to find a more optimized design. The objective of this optimization attempt was to reduce the fuel cycle cost. The WEC long-life core LFR design uses uranium mononitride (UN) fuel with natural nitrogen enrichment. In the second study, DOM was used to design a competitive UN fueled Lead-cooled Fast Reactor (LFR) which uses {sup 15}N enriched UN fuel. Based on the results from the second study, an estimation of the {sup 15}N enrichment cost required to obtain an economic core was performed. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Increased activity of core photorespiratory enzymes and CO 2 transfer conductances are associated with higher and more optimal photosynthetic rates under elevated temperatures in the extremophile Rhazya stricta

Increase photorespiration and optimising intrinsic water use efficiency are unique challenges to photosynthetic carbon fixation at elevated temperatures. To determine how plants can adapt to facilitate high rates of photorespiration at elevated temperatures while also maintaining water-use efficiency, we performed in-depth gas exchange and biochemical assays of the C 3 extremophile, Rhazya stricta. These results demonstrate that R. stricta supports higher rates of photorespiration under elevated temperatures and that these higher rates of photorespiration correlate with increased activity of key photorespiratory enzymes; phosphoglycolate phosphatase and catalase. The increased photorespiratory enzyme activities may increase the overall capacity of photorespiration by reducing enzymatic bottlenecks and allowing minimal inhibitor accumulation under high photorespiratory rates. Additionally, we found the CO 2 transfer conductances (stomatal and mesophyll) are re-allocated to increase the water-use efficiency in R. stricta but not necessarily the photosynthetic response to temperature. These results suggest important adaptive strategies in R. stricta that maintain photosynthetic rates under elevated temperatures with optimal water loss. The strategies found in R. stricta may inform breeding and engineering efforts in other C 3 species to improve photosynthetic efficiency at high temperatures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2012

Itinerant electrons, local moments, and magnetic correlations in the pnictide superconductors CeFeAsO₁₋ x F x and Sr(Fe₁₋ x Co x )₂As₂

A direct and element-specific measurement of the local Fe spin moment has been provided by analyzing the Fe 3s core level photoemission spectra in the parent and optimally doped CeFeAsO₁₋ x F x (x = 0, 0.11) and Sr(Fe₁₋ x Co x )2As2 (x = 0, 0.10) pnictides. The rapid time scales of the photoemission process allowed the detection of large local spin moments fluctuating on a 10⁻¹⁵ s time scale in the paramagnetic, antiferromagnetic, and superconducting phases, indicative of the occurrence of ubiquitous strong Hund's magnetic correlations. The magnitude of the spin moment is found to vary significantly among different families, 1.3μ B in CeFeAsO and 2.1μB in SrFe₂As₂. Surprisingly, the spin moment is found to decrease considerably in the optimally doped samples, 0.9μ B in CeFeAsO₀.₈₉F₀.₁₁ and 1.3μ B in Sr(Fe₀.₉Co₀.₁)₂As₂. The strong variation of the spin moment against doping and material type indicates that the spin moments and the motion of itinerant electrons are influenced reciprocally in a self-consistent fashion, reflecting the strong competition between the antiferromagnetic superexchange interaction among the spin moments and the kinetic energy gain of the itinerant electrons in the presence of a strong Hund's coupling. By describing the evolution of the magnetic correlations concomitant with the appearance of superconductivity, these results constitute a fundamental step toward attaining a correct description of the microscopic mechanisms shaping the electronic properties in the pnictides, including magnetism and high-temperature superconductivity.

Vilmercati, Paolo↗
DOE OSTI2012

Towards Scalable Optimal Sequence Homology Detection

Abstract—The field of bioinformatics and computational biol- ogy is experiencing a data revolution — experimental techniques to procure data have increased in throughput, improved in accuracy and reduced in costs. This has spurred an array of high profile sequencing and data generation projects. While the data repositories represent untapped reservoirs of rich information critical for scientific breakthroughs, the analytical software tools that are needed to analyze large volumes of such sequence data have significantly lagged behind in their capacity to scale. In this paper, we address homology detection, which is a funda- mental problem in large-scale sequence analysis with numerous applications. We present a scalable framework to conduct large- scale optimal homology detection on massively parallel super- computing platforms. Our approach employs distributed memory work stealing to effectively parallelize optimal pairwise alignment computation tasks. Results on 120,000 cores of the Hopper Cray XE6 supercomputer demonstrate strong scaling and up to 2.42 × 107 optimal pairwise sequence alignments computed per second (PSAPS), the highest reported in the literature.

Daily, Jeffrey A.↗
DOE OSTI2013

Recent developments in DYNSUB: New models, code optimization and parallelization

DYNSUB is a high-fidelity coupled code system consisting of the reactor simulator DYN3D and the sub-channel code SUBCHANFLOW. It describes nuclear reactor core behavior with pin-by-pin resolution for both steady-state and transient scenarios. In the course of the coupled code system's active development, super-homogenization (SPH) and generalized equivalence theory (GET) discontinuity factors may be computed with and employed in DYNSUB to compensate pin level homogenization errors. Because of the largely increased numerical problem size for pin-by-pin simulations, DYNSUB has bene fitted from HPC techniques to improve its numerical performance. DYNSUB's coupling scheme has been structurally revised. Computational bottlenecks have been identified and parallelized for shared memory systems using OpenMP. Comparing the elapsed time for simulating a PWR core with one-eighth symmetry under hot zero power conditions applying the original and the optimized DYNSUB using 8 cores, overall speed up factors greater than 10 have been observed. The corresponding reduction in execution time enables a routine application of DYNSUB to study pin level safety parameters for engineering sized cases in a scientific environment. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NEAMS Milestone Report: M2MS–20OR030102 FW–CADIS PWR Ex-Core Analysis with Shift Through VERA

This report presents the work completed for the NEAMS milestone M2MS–20OR030102 titled "FW–CADIS PWR Ex-Core Analysis with Shift through VERA." The work completed for this milestone includes the implementation, integration, and optimization of memory and performance improvement methods in Shift for fully coupled ex-core calculations through Virtual Environment for Reactor Applications (VERA). Fully coupled in this context means the transfer of moderator boron concentration, pin-wise fission source, depleted compositions, temperatures, and moderator densities from MPACT (with COBRA-TF (CTF)) to Shift. The ability to run ex-core calculations with VERA has been enabled and used for several years by Consortium for Advanced Simulation of Light Water Reactors (CASL) partners. However, this implementation was limited and potentially computationally burdensome. This work has enabled the ability to run higher-fidelity ex-core calculations on moderate computing clusters by focusing on multithreading, domain decomposition, and Forward-Weighted CADIS (FW-CADIS) variance reduction. Tests performed on small cores, a small modular reactor (SMR), and CASL progression problems show very promising memory reduction and computational performance. Recommendations for settings when running fully coupled high-fidelity ex-core calculations with VERA are documented. Without these optimization methods, many processors on a compute node would be left unused for the entire ex-core calculation. Therefore, these methods enable the user to better use the resources available and reduce computation time.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Simultaneous ELM suppression and divertor detachment via synergistic boron powder and neon injection in EAST

A novel approach for simultaneous power exhaust and edge-localized mode (ELM) control is presented in the Experimental Advanced Superconducting Tokamak discharges, which utilize an ITER-like tungsten divertor. Real-time injection of boron (B) powder and neon (Ne) gas overcomes their limitations encountered when used separately. Pure Ne seeding leads to a narrow operational window constrained by core impurity accumulation and H-mode to L-mode back transitions, while pure solid B injection (SBI) is insufficient for effective divertor cooling. In comparison, their combined use achieves a stable, stationary, ELM-suppressed H-mode with adequate power exhaust. This synergistic scenario features partial energy detachment at the outer divertor while maintaining good plasma confinement (H 98 ∼ 1) with minimal degradation. Two key features of this scenario are: (1) the SBI triggers a persistent Edge Harmonic Mode (EHM), which provides a crucial continuous particle transport channel, preventing Ne and tungsten/molybdenum accumulation without flushing out by ELM, and (2) the B + Ne mixture allows for active optimization of the radiated power profile. Core radiation can be reduced by substituting a portion of the Ne with B, leveraging their complementary non-coronal equilibrium radiation efficiencies. This combined B + Ne injection scheme presents a promising pathway toward integrated core-edge scenarios, offering the potential to minimize total impurity throughput while leveraging an actuator (powder injection) already being considered for ITER.

Boron↗
DOE OSTI2025

Physics of Edge-Core Coupling by Inward Turbulence Propagation

The dynamics of edge-core coupling is critically important to the optimization of magnetically confined fusion plasmas. Since early proposals, there has been persistent speculation that inward propagation of turbulence from the boundary is a possible means to energize the edge-core coupling region. However, the detailed mechanism of this process has remained a mystery until recent experiments observed that regular, intense gradient relaxation events generated blob-void pairs very close to the last closed flux surface. Blobs ($\tilde{n}$ >0) propagate outward and detach from the bulk plasma, while voids ($\tilde{n}$ <0) propagate inward, and so stir the core plasma. Here, in this work, we demonstrate that this heretofore ignored process of void emission can drive a broad turbulent layer of width ∼100 𝜌 𝑠 , for typical parameters. The mechanism is the Cherenkov emission of drift waves from inward-propagating voids. The model shows promise to resolve several questions surrounding the shortfall problem and the strong turbulence in the edge-core coupling region.

drift waves↗
DOE OSTI2013

Validation of transport models using additive flux minimization technique

A new additive flux minimization technique is proposed for carrying out the verification and validation (V and V) of anomalous transport models. In this approach, the plasma profiles are computed in time dependent predictive simulations in which an additional effective diffusivity is varied. The goal is to obtain an optimal match between the computed and experimental profile. This new technique has several advantages over traditional V and V methods for transport models in tokamaks and takes advantage of uncertainty quantification methods developed by the applied math community. As a demonstration of its efficiency, the technique is applied to the hypothesis that the paleoclassical density transport dominates in the plasma edge region in DIII-D tokamak discharges. A simplified version of the paleoclassical model that utilizes the Spitzer resistivity for the parallel neoclassical resistivity and neglects the trapped particle effects is tested in this paper. It is shown that a contribution to density transport, in addition to the paleoclassical density transport, is needed in order to describe the experimental profiles. It is found that more additional diffusivity is needed at the top of the H-mode pedestal, and almost no additional diffusivity is needed at the pedestal bottom. The implementation of this V and V technique uses the FACETS::Core transport solver and the DAKOTA toolkit for design optimization and uncertainty quantification. The FACETS::Core solver is used for advancing the plasma density profiles. The DAKOTA toolkit is used for the optimization of plasma profiles and the computation of the additional diffusivity that is required for the predicted density profile to match the experimental profile.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

BEAM CORE: A Flexible Ecosystem for Freight, Demographics, and Vehicle Analysis

The Behavior, Energy, Autonomy, and Mobility Comprehensive Regional Evaluator (BEAM CORE) is an open-source, modular ecosystem of highly refined, agent-based modeling tools developed by Lawrence Berkeley National Laboratory and the National Laboratory of the Rockies. Organizations such as metropolitan planning organizations, agencies, and companies can use BEAM CORE to analyze freight movement and optimize logistics solutions, assess the impacts of emerging freight technologies or e-commerce trends, model dynamic population growth and evolution, and understand the drivers and impacts of electric vehicle adoption across households in a given region. Users can choose from a flexible suite of modeling modules according to their needs and priorities. The modules integrate with travel demand models to support enhanced analysis of diverse scenarios involving freight movement, vehicle technologies, and other key factors relevant to regional planning.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2016

BEAM OPTIMIZATION STUDY FOR AN X-RAY FEL OSCILLATOR AT THE LCLS-II

The 4 GeV LCLS-II superconducting linac with high repetition beam rate enables the possibility to drive an X-Ray FEL oscillator at harmonic frequencies *. Compared to the regular LCLS-II machine setup, the oscillator mode requires a much longer bunch length with a relatively lower current. Also a flat longitudinal phase space distribution is critical to maintain the FEL gain since the X-ray cavity has extremely narrow bandwidth. In this paper, we study the longitudinal phase space optimization including shaping the initial beam from the injector and optimizing the bunch compressor and dechirper parameters. We obtain a bunch with a flat energy chirp over 400 fs in the core part with current above 100 A. The optimization was based on LiTrack and Elegant simulations using LCLS-II beam parameters.

Qin, Weilun↗
DOE OSTI2012

Application of Fully Ceramic Microencapsulated Fuel for Transuranic Waste Recycling in PWRs

Presented is an investigation of the utilization of Tristructural-Isotropic (TRISO) particle-based fuel designs for the recycling of transuranic (TRU) wastes in typical Westinghouse four-loop pressurized water reactors (PWRs). Though numerous studies have evaluated the recycling of TRU in light water reactors (LWRs), this work differentiates itself by employing TRU-loaded TRISO particles embedded within a SiC matrix and formed into pellets that can be loaded into standard 17 x 17 fuel element cladding. This approach provides the capability of TRU recycling and, by virtue of the TRISO particle design, will allow for greater burnup (i.e., removal of the need for UO2 mixing) and improved fuel reliability. In this study, a variety of assembly layouts and core loading patterns were analyzed to demonstrate the feasibility of TRU-loaded TRISO fuel. The assembly and core design herein reported are a work in progress, so they still require some fine-tuning to further flatten power peaks; however, the progress achieved thus far strongly supports the conclusion that with further rod/assembly/core loading and placement optimization, TRU-loaded TRISO fuel and core designs that are capable of balancing TRU production and destruction can be designed within the standard constraints for thermal and reactivity performance in PWRs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2012

Design study of Thorium-232 and Protactinium-231 based fuel for long life BWR

A preliminary design study for the utilization of thorium added with {sup 231}Pa based fuel on BWR type reactor has been performed. In the previous research utilization of fuel based Thorium-232 and Uranium-233 show 10 years operation time with maximum excess-reactivity about 4.075% dk/k. To increase reactor operation time and reduce excess-reactivity below 1% dk/k, Protactinium (Pa-231) is used as Burnable Poison. Protactinium-231 has very interesting neutronic properties, which enable the core to reduce initial excess-reactivity and simultaneously increase production of {sup 233}U to {sup 231}Pa in burn-up process. Optimizations of the content of {sup 231}Pa in the core enables the BWR core to sustain long period of operation time with reasonable burn-up reactivity swing. Based on the optimization of fuel element composition (Th and Pa) in various moderation ratio we can get reactor core with longer operation time, 20 {approx} 30 years operation without fuel shuffling or refuelling, with average power densities maximum of about 35 watt/cc, and maximum excess-reactivity 0.56% dk/k.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗