Search NASA⌕ Search

SEARCH · Search NASA

Results for “core optimization”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Savannah River National Laboratory (FY2021 LDRD Annual Report)

Research efforts to build upon the unique knowledge base of the Savannah River National Laboratory (SRNL) are funded through the Laboratory Directed Research and Development (LDRD) Program. The program yields foundational scientific research and development (R&D) essential to SRNL’s core business areas, while aligning optimally and continuously with SRNL’s Strategic Plan to provide long-term benefits to the Department of Energy (DOE), the National Nuclear Security Administration (NNSA), and other customers and stakeholders.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Particle In Cell Scalable Application Resource (PICSAR) v2

PICSAR, for Particle-In-Cell Scalable Application Resource, is a high-performance Particle-in-Cell library developed in Fortran 90 and Python. This library was first developed to be coupled with WARP and provide efficient subroutines optimized for Many-Integrated Core (MIC) architectures such as the forthcoming system Cori Phase II that will be equipped with Intel Xeon Phi KNL processors. It is a compact self-contained proxy that adequately portrays the computational loads and dataflow of the more complex WARP code but can also be used by other PIC codes. PICSAR also contains a Fortran stand-alone Particle-In-Cell "mini-app" that includes the key functionalities of a PIC code. This stand-alone code is used for testing implementation performances and to do some benchmarks.

VINCENTI, HENRI↗
DOE OSTI2020

NREL's Capabilities in Organism Development and Fermentation Sciences Research and Development (R&D)

NREL is developing robust microbial strains and scalable fermentation technologies for converting biomass sugars to fuels and biochemicals, and one-carbon (C1) gaseous substrates (e.g., CO2, CO, CH4) to high-value products. Core capabilities include: (1) Fermentation optimization of aerobic, microaerophilic, and anaerobic cultivation processes of gas and traditional liquid fermentation R&D; (2) Gas mass transfer R&D and CFD modeling for improved reactor design and gas mixing (3) Metabolic engineering in CRISPR editing tools to improve titer, rates, and yields; (4) Probing and optimizing carbon, redox, and energy flux using transcriptomics and fluxomic techniques; and (5) Consolidated bioprocessing (CBP) using cellulose and hemicellulose directly to minimize pretreatment.

BASIC BIOLOGICAL SCIENCES,BIOMASS FUELS,SYNTHETIC ↗
DOE OSTI2023

Method and apparatus for constructing informative outcomes to guide multi-policy decision making

In Multi-Policy Decision-Making (MPDM), many computationally-expensive forward simulations are performed in order to predict the performance of a set of candidate policies. In risk-aware formulations of MPDM, only the worst outcomes affect the decision making process, and efficiently finding these influential outcomes becomes the core challenge. Recently, stochastic gradient optimization algorithms, using a heuristic function, were shown to be significantly superior to random sampling. In this disclosure, it was shown that accurate gradients can be computed-even through a complex forward simulation—using approaches similar to those in dep networks. The proposed approach finds influential outcomes more reliably, and is faster than earlier methods, allowing one to evaluate more policies while simultaneously eliminating the need to design an easily-differentiable heuristic function.

Olson, Edwin↗
DOE OSTI2017

Core Reactivity Control for a Soluble Boron Free Small Modular Reactor

Soluble boron has been used in pressurized water reactors (PWRs) to control the core excess reactivity in a uniform and stable way without distorting power distributions. It also enables current commercial PWRs to have high core power density. Despite its outstanding advantages, the soluble boron raises several significant concerns such as soluble boron-induced corrosion of structures, a large amount of liquid waste generation, and a possibility of positive moderator temperature coefficient (MTC) at high boron concentration. These concerns become more serious as the concentration of boron becomes higher. Therefore boron in the primary coolant has been a hindrance to the power uprate and cycle extension operation of PWRs. Currently a government-supported research project is underway to develop technologies for the soluble-boron-free (SBF) operation of a PWR-type small modular reactor (SMR). This paper presents preliminary results of the SBF SMR core reactivity control methods including optimization of burnable absorbers (BA) and control rod design, and development of secondary shutdown system (SSS)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

3D full core BEAVRS using OpenMOC with transport equivalence

Using an optimized implementation of the three-dimension method of characteristics for neutron transport, as well as a novel equivalence method for transport calculations de- signed to recover self-shielding errors from neglecting the angular dependence of reso- nant group absorption, a 3D full core light water reactor hybrid stochastic-deterministic eigenvalue calculation was achieved. This paper presents the optimizations developed and compares the transport solutions obtained. It realizes a runtime near 6,000 CPU hours for the statepoint, an improvement of an order of magnitude on previous works, with less than 1% error on pin fission/U-238 capture ratios and a few dozen pcms on the eigenvalue.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Three-Dimensional Full-Core BEAVRS Using OpenMOC with Transport Equivalence

Using an optimized implementation of the three-dimensional (3D) method of characteristics for neutron transport, along with a novel equivalence method for transport calculations that was designed to correct self-shielding errors from neglecting the angular dependence of resonant group absorption, a 3D full-core light water reactor hybrid stochastic-deterministic eigenvalue calculation was achieved. This paper presents the optimizations developed and compares the transport solutions obtained. For the statepoint, run times near 10 000 CPU hours are achieved—improving on previous works by an order of magnitude—with near 1% error on pin fission to 238 U capture ratios and a few dozen pcms on the eigenvalue.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Physics basis for design of 3D coils in tokamaks

Recent progress in 3D tokamak modeling is now leveraged to create a conceptual design of new external 3D field coils for the DIII-D tokamak. In this work generalized perturbed equilibrium code is used to determine optimally efficient spectrum for driving total, core, and edge neoclassical toroidal viscosity torque. These fundamental modes of 3D control are shown to have consistent outboard structures across a wide variety of plasma scenarios and machines. Given these target spectra, the currents and 3D geometry of multiple coils can be optimized to increase efficient drive for the physics of interest without undesired secondary effects. Here, this nonlinear optimization is demonstrated using the flexible optimized coils using space-curves code. The optimized coils are individually distorted in space, creating toroidal 'arrays' containing a variety of shapes that often wrap around a significant poloidal extent of the machine. Importantly, efficient coupling can be maintained even when enforcing large distances between coils and the plasma during the geometric optimization of coil designs. Finally, the physics-driven optimization presented here thus provides a practical path to utilizing coils built on the exterior of the vacuum chamber in future reactors to obtain the powerful 3D field benefits demonstrated on current machines with close, internal coils.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Local Electric Field Effects on Water Dissociation in Bipolar Membranes Studied Using Core–Shell Catalysts

The local electric field strength is thought to affect the rate of water dissociation (WD) in bipolar membranes (BPMs) at the catalyst–nanoparticle surfaces. Here, we study core–shell nanoparticles, where the core is metallic, semiconducting, or insulating, to understand this effect. The nanoparticle cores were coated with a WD catalyst layer (TiO2 or HfO2) via atomic layer deposition (ALD), and the morphology was imaged with transmission electron microscopy. Irrespective of the core material, these core–shell catalysts displayed comparable WD overpotentials at optimal mass loading, despite the hypothesized differences in the electric field strength across the catalyst particle suggested by continuum electrostatic simulations. Substantial atomic interdiffusion between the core and shell was ruled out by X-ray absorption spectroscopy, X-ray photoelectron spectroscopy, and diffuse reflectance optical measurements. However, the optimal mass loading of catalyst was roughly 1 order of magnitude higher for the conductive and high dielectric core materials than for the low dielectric insulating cores. These findings are consistent with the hypothesis that electric field screening within the core material focuses the electric field drop between particles such that larger film thicknesses can be tolerated. Collectively, these data support the idea that it is the local electric field at the molecular level that controls proton-transfer rates and that the metal core/dielectric-shell constructs introduced here modulate that field. Further materials and synthetic design may enable optimization of the electric field strength across the proton-transfer trajectory at the material surface.

Sarma, Prasad V↗
DOE OSTI2016

DESIGN CHARACTERISTICS OF THE IDAHO NATIONAL LABORATORY HIGH-[TEMPERATURE GAS-COOLED TEST REACTOR

A point design for a graphite-moderated, high-temperature, gas-cooled test reactor (HTG TR) has been developed by Idaho National Laboratory (INL) as part of a United States (U.S.) Department of Energy (DOE) initiative to explore and potentially expand the 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. Twelve fuel columns (96 fuel blocks total and 6.34 m active core height) are arranged in two hexagonal rings to form a relatively compact, high-power density, annular core sandwiched between inner, outer, top, and bottom graphite reflectors. The HTG-TR is designed to operate at 7 MPa with a coolant inlet/outlet temperature of 325°C/650°C, and utilizes TRISO particle fuel from the DOE AGR Program with 425 ?m uranium oxycarbide (UCO) kernels and an enrichment of 15.5 wt% 235U. 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 (3.90E+14 n/cm2s) occurs in the outer reflector, and the maximum fast flux levels (1.17E+14 n/cm2s) are produced in the central reflector column where most of the graphite has been removed. Due to high core temperatures under accident conditions, all the irradiation test facilities have been located in the inner and outer reflectors where fast flux levels decline. The core features a large number of irradiation positions with large test volumes and long test lengths, ideal for thermal neutron irradiation of large test articles. The total available test volume is more than 1100 liters. 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, light water) from the helium primary coolant system.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Design Optimization of Medium-Frequency Transformer for DAB Converters With DC Bias Capacity

Medium-frequency transformer (MFT) dc bias is critical for the safe operation of dual-active-bridge (DAB) converters. To analyze the dc bias, expressions of dc bias are derived for SiC MOSFET DAB converters. It shows that the dc bias is a current source in steady state; thus, a dc bias current capacity is defined and used as a design constraint in the optimization of MFT. The dc bias current capacity is related to core materials, geometric parameters, and flux density, which is analyzed with analytical expression. Optimization equations with geometric variables are derived, which can be solved theoretically or numerically for both interleaved winding (IW) and separated winding (SW) MFT without and/or with leakage inductance design. Based on the optimization equations, different MFT designs for a 150-kW SiC MOSFET DAB converter are compared on the aspects of core materials, winding structures, and without/with leakage inductance design. The combination of IW structure MFT with auxiliary phase shift inductors is chosen, and the assembled prototype is tested with the 150-kW DAB converter. Here, the designed dc bias capacity, temperature rise, and efficiency are verified.

42 ENGINEERING↗
DOE OSTI2019

Scalable Preconditioning of Block-Structured Linear Algebra Systems using ADMM

In this paper, we study the solution of block-structured linear algebra systems arising in optimization by using iterative solution techniques. These systems are the core computational bottleneck of many problems of interest such as parameter estimation, optimal control, network optimization, and stochastic programming. Our approach uses a Krylov solver (GMRES) that is preconditioned with an alternating method of multipliers (ADMM). We show that this ADMM-GMRES approach overcomes well-known scalability issues of Schur complement decomposition in problems that exhibit a high degree of coupling. The effectiveness of the approach is demonstrated using linear systems that arise in stochastic optimal power flow problems and that contain up to 2 million total variables and 4,000 coupling variables. We find that ADMM-GMRES is nearly an order of magnitude faster than Schur complement decomposition. Moreover, we demonstrate that the approach is robust to the selection of the augmented Lagrangian penalty parameter, which is a key advantage over the direct use of ADMM.

97 MATHEMATICS AND COMPUTING↗
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 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↗