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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

PV Hosting Capacity Estimation: Experiences with Scalable Framework; Preprint

Hosting capacity is an indication of the amount of photovoltaics (PV) can be hosted in a distribution system. This paper presents a framework for estimating distributed PV hosting capacity at scale. We first analyze the key challenges of performing relevant large scale simulation including computational and modeling challenges. Then, we develop two python-based software tools in order to conduct repeatable distribution analyses: Distribution Integration Solution Cost Options (DISCO) for configuring and analyzing simulations, and JADE for parallelizing jobs on HPC clusters. A case study of hosting capacity estimation for SMART-DS SFO 2000+ synthetic feeders is used to demonstrate the capability of the developed framework and tools. The framework and tools can help utilities assess the overall hosting capacity of their service territory, through which the overall upgrade cost can be better planned in order to integrate more PV in the future.

distributed energy resources↗
DOE OSTI2017

Isopycnal eddy mixing across the Kuroshio Extension: Stable versus unstable states in an eddying model

The Kuroshio Extension (KE) jet transitions between stable and unstable states on interannual time scales. Cross-jet eddy mixing in the two states is contrasted in the KE region (28°-240°N; 125°-165°E), using a global eddying 0.1° configuration of the Parallel Ocean Program with online numerical particles. The 4 year period chosen (June 1994 to May 1998) covers a full cycle of the stable state, unstable state and the transition period. Large values of cross-jet eddy diffusivities within the KE jet are concentrated in the upper 1000 m. In the upper ocean, elevated cross-jet mixing within the KE jet is mainly concentrated in the downstream part of the KE jet, where the jet is weak but eddy activity is strong. The simulated time-mean KE jet is more intense and extends further east in the stable state than in the unstable state. Consequently, strong cross-jet mixing within the KE jet is located west of 150°E during June 1996 to May 1997 (a typical unstable state), but east of 150°E during June 1995 to May 1996 (a typical stable state). However, average mixing within the KE jet is indistinguishable in the typical stable and unstable states. In the deep ocean, mixing is strongly influenced by topography, and thus their horizontal structures have less inter-annual variability than in the upper ocean. One caveat is that results here cover one representative cycle of the two states. To obtain the climate mean mixing structures for the stable or unstable state, one would need numerical output covering a period much longer than 4 years.

58 GEOSCIENCES↗
DOE OSTI2023

Giant Nonlinear Optical Response via Coherent Stacking of In-Plane Ferroelectric Layers

Thin ferroelectric materials hold great promise for compact nonvolatile memory and nonlinear optical and optoelectronic devices. Herein, an ultrathin in-plane ferroelectric material that exhibits a giant nonlinear optical effect, group-IV monochalcogenide SnSe, is reported. Nanometer-scale ferroelectric domains with ≈90°/270° twin boundaries or ≈180° domain walls are revealed in physical-vapor-deposited SnSe by lateral piezoresponse force microscopy. Atomic structure characterization reveals both parallel and antiparallel stacking of neighboring van der Waals ferroelectric layers, leading to ferroelectric or antiferroelectric ordering. Ferroelectric domains exhibit giant nonlinear optical activity due to coherent enhancement of second-harmonic fields and the as-resulted second-harmonic generation was observed to be 100 times more intense than monolayer WS 2 . This work demonstrates in-plane ferroelectric ordering and giant nonlinear optical activity in SnSe, which paves the way for applications in on-chip nonlinear optical components and nonvolatile memory devices.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Coupled Thermo-Hydrological-Mechanical-Chemical Behavior of Anisotropic Granite for Geologic Disposal of High-Level Radioactive Waste: A Core-Scale Laboratory Investigation

The coupled thermo-hydrological-mechanical-chemical (THMC) behavior of rock within an Excavation Damaged Zone (EDZ) is critical for the safety and long-term performance of a geological repository for high-level radioactive wastes. While many laboratory experiments have been conducted to investigate EDZ rocks, the flow and deformation characteristics resulting from anisotropic rock textures and microcrack distribution under triaxial loading and elevated temperatures remain poorly understood. Particularly, cracks at various scales serve as fast paths for fluid flow and solute transport and present as focal points of mechanical weakness, which complicate the coupled THMC processes in anisotropic EDZ rocks and challenge modeling predictions. Here, in this study, a series of core-scale experiments was conducted on three granite samples under repository-relevant conditions. These rock samples were obtained from the Grimsel Underground Research Laboratory (URL), featured by anisotropic minerals (represented by bedding layers) and microcrack distributions and coarse cm-scale grain sizes. During the experiments, samples were subjected to an elevated temperature at 90 °C and different triaxial loading conditions either by radial (normal to bedding layers) or axial (parallel to bedding layers) compaction. Water was injected into the samples, and the rock permeability evolutions and effluent water chemistry were monitored closely. For intact samples, thermal expansion of minerals at 90 °C resulted in a large, 75% irreversible permeability reduction and rock strengthening under radial compaction, while thermal impact was limited to a 15% permeability reduction under axial compaction. In contrast, for a sample containing open cracks, the growth of fractures during the experiment resulted in an abrupt permeability increase and fast failure at 90 °C. The effluent water chemistry indicates much more considerable mineral dissolution from large shear sliding than that in rocks dominated by mechanical compaction. These results helped better understand the coupled THMC processes in anisotropic rocks containing cracks, evaluate the behaviors of EDZ rocks, and predict the long-term evolution of EDZ for the performance of the repositories.

Coupled THMC processes↗
DOE OSTI2025

A scoping study of far-SOL main-wall protection limiters for steady-state operation of compact pilot plant tokamaks

We present a novel method for handling steady-state heat fluxes incident on the main wall of pilot plant-scale magnetic fusion devices, based on the utilization of protection limiters in the far scrape-off layer (SOL). This method helps avoid large plasma-wall gaps, without excessively compromising blanket performance. We present an optimization algorithm for determining the appropriate size and scale of these protection limiters given (1) probability distributions of SOL plasma parameters and (2) assumed risk tolerance. As part of this optimization, we have developed an analytic description of parallel heat fluxes across limiter shadows, and an objective cost function (the ‘Far-SOL Marginal Cost’) to quantify the impact that different main-wall thermal management design choices have on reactor capital cost. Applying the model to a midscale fusion pilot plant concept shows that making use of far-SOL protection limiters can reduce capital costs on the order of $500 M, relative to naively increasing the plasma-wall gap. Our analysis demonstrates that the far-SOL power decay length is the highest-leverage plasma assumption for thermal loading of the first wall, and the primary cost driver for main wall thermal management. The relative cost efficiency of protection limiters increases as assumptions on the far-SOL heat flux become more pessimistic. The concepts described in this paper motivate the further development of far-SOL protection limiters as part of larger efforts to design economical core-edge-wall compatible solutions for a fusion pilot plant.

Design under uncertainty↗
DOE OSTI2017

Design and performance of the spin asymmetries of the nucleon experiment

The Spin Asymmetries of the Nucleon Experiment (SANE) performed inclusive, double-polarized electron scattering measurements of the proton at the Continuous Electron Beam Facility at Jefferson Lab. A novel detector array observed scattered electrons of four-momentum transfer 2.5 < Q 2 < 6.5 GeV 2 and Bjorken scaling 0.3 < x < 0.8 from initial beam energies of 4.7 and 5.9 GeV. Employing a polarized proton target which could be rotated with respect to the incident electron beam, both parallel and near perpendicular spin asymmetries were measured, allowing model-independent access to transverse polarization observables A 1 , A 2 , g 1 , g 2 and moment d 2 of the proton. This article summarizes the operation and performance of the polarized target, polarized electron beam, and novel detector systems used during the course of the experiment, and describes analysis techniques utilized to access the physics observables of interest.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Memory access optimization for particle operations in computational fluid dynamics-discrete element method simulations

Computational Fluid Dynamics - Discrete Element Method is used to model gas-solid systems in several applications in energy, pharmaceutical and petrochemical industries. Computational performance bottlenecks often limit the problem sizes that can be simulated at industrial scale. The data structures used to store several millions of particles in such large-scale simulations have a large memory footprint that does not fit into the processor cache hierarchies on current high-performance-computing platforms, leading to reduced computational performance. This paper specifically addresses this aspect of memory access bottlenecks in industrial scale simulations. The use of space-filling curves to improve memory access patterns is described and their impact on computational performance is quantified in both shared and distributed memory parallelization paradigms. The Morton space filling curve applied to uniform grids and k-dimensional tree partitions are used to reorder the particle data-structure thus improving spatial and temporal locality in memory. The performance impact of these techniques when applied to two benchmark problems, namely the homogeneous-cooling-system and a fluidized-bed, are presented. We report these optimization techniques lead to approximately two-fold performance improvement in particle focused operations such as neighbor-list creation and data-exchange, with ~ 1.5 times overall improvement in a fluidization simulation with 1.27 million particles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Broadband unidirectional visible imaging using wafer-scale nano-fabrication of multi-layer diffractive optical processors

We present a broadband and polarization-insensitive unidirectional imager that operates at the visible part of the spectrum, where image formation occurs in one direction, while in the opposite direction, it is blocked. This approach is enabled by deep learning-driven diffractive optical design with wafer-scale nano-fabrication using high-purity fused silica to ensure optical transparency and thermal stability. Our design achieves unidirectional imaging across three visible wavelengths (covering red, green, and blue parts of the spectrum), and we experimentally validated this broadband unidirectional imager by creating high-fidelity images in the forward direction and generating weak, distorted output patterns in the backward direction, in alignment with our numerical simulations. This work demonstrates wafer-scale production of diffractive optical processors, featuring 16 levels of nanoscale phase features distributed across two axially aligned diffractive layers for visible unidirectional imaging. This approach facilitates mass-scale production of ~0.5 billion nanoscale phase features per wafer, supporting high-throughput manufacturing of hundreds to thousands of multi-layer diffractive processors suitable for large apertures and parallel processing of multiple tasks. Beyond broadband unidirectional imaging in the visible spectrum, this study establishes a pathway for artificial-intelligence-enabled diffractive optics with versatile applications, signaling a new era in optical device functionality with industrial-level, massively scalable fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2017

X-ray transparent microfluidic chips for high-throughput screening and optimization of in meso membrane protein crystallization

Elucidating and clarifying the function of membrane proteins ultimately requires atomic resolution structures as determined most commonly by X-ray crystallography. Many high impact membrane protein structures have resulted from advanced techniques such as in meso crystallization that present technical difficulties for the set-up and scale-out of high-throughput crystallization experiments. In prior work, we designed a novel, low-throughput X-ray transparent microfluidic device that automated the mixing of protein and lipid by diffusion for in meso crystallization trials. Here, in this work, we report X-ray transparent microfluidic devices for high-throughput crystallization screening and optimization that overcome the limitations of scale and demonstrate their application to the crystallization of several membrane proteins. Two complementary chips are presented: (1) a high-throughput screening chip to test 192 crystallization conditions in parallel using as little as 8 nl of membrane protein per well and (2) a crystallization optimization chip to rapidly optimize preliminary crystallization hits through fine-gradient re-screening. We screened three membrane proteins for new in meso crystallization conditions, identifying several preliminary hits that we tested for X-ray diffraction quality. Further, we identified and optimized the crystallization condition for a photosynthetic reaction center mutant and solved its structure to a resolution of 3.5 Å.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Structure of 30 Mg explored via in-beam γ-ray spectroscopy

In the island of inversion, ground states of neutron-rich sd-shell nuclei exhibit strong admixtures of intruder configurations from the fp shell. The nucleus 30 Mg, located at the boundary of the island of inversion, serves as a cornerstone to track the structural evolution as one approaches this region. Spin-parity assignments for excited states in 30 Mg, especially negative-parity levels, have yet to be established. In the present work, the nuclear structure of 30Mg was investigated by in-beam gamma-ray spectroscopy mainly focusing on firm spin-parity determinations. High-intensity rare-isotope beams of 31 Mg, 32 Mg, 34 Si, and 35 P bombarded a Be target to induce nucleon removal reactions populating states in 30 Mg. Gamma rays were detected by the state-of-the-art gamma-ray tracking array GRETINA. For the direct one-neutron removal reaction, final-state exclusive cross sections and parallel momentum distributions were deduced. Multi-nucleon removal reactions from different projectiles were exploited to gain complementary information. With the aid of the parallel momentum distributions, an updated level scheme with revised spin-parity assignments was constructed. Spectroscopic factors associated with each state were also deduced. Results were confronted with large-scale shell-model calculations using two different effective interactions, showing excellent agreement with the present level scheme. Furthermore, a marked difference in the spectroscopic factors indicates that the full delineation of the transition into the island of inversion remains a challenge for theoretical models.

20 ≤ A ≤ 38↗
DOE OSTI2022

A Universal Controller for Grid-Forming Inverters in Microgrid during Islanding for Low Transient Current

This paper presents a universal controller to reduce the transient current during islanding of microgrid. In the proposed universal controller, two sets of semi-parallel paths are formed, where one set produces voltage and phase-angle references for the grid-following (GFL) mode, and the other set produces the voltage and phase-angle references for the grid-forming (GFM) mode of operation for the inverters. Herein, the controller enables an inverter to universally operate in GFL and GFM mode, whereas, only one set of voltage and phase-angle reference is engaged for PWM signal generation. When the microgrid is disconnected from the grid, the universal controller switches the voltage and phase-angle references from GFL to GFM mode. The proposed controller ensures lower transient current by keeping the two sets of semi-parallel paths synchronized throughout the operation of the inverter and enabling seamless transition between the modes. The proposed controller is verified through hardware experiments under different scenarios in a laboratory scale microgrid for inverters transitioning from GFL to GFM mode after islanding.

Sadeque, Fahmid↗
DOE OSTI2023

Distributed-Memory Parallel JointNMF

Joint Nonnegative Matrix Factorization (JointNMF) is a hybrid method for mining information from datasets that contain both feature and connection information. We propose distributed-memory parallelizations of three algorithms for solving the JointNMF problem based on Alternating Nonnegative Least Squares, Projected Gradient Descent, and Projected Gauss-Newton. We extend well-known communication-avoiding algorithms using a single processor grid case to our coupled case on two processor grids. We demonstrate the scalability of the algorithms on up to 960 cores (40 nodes) with 60% parallel efficiency. The more sophisticated Alternating Nonnegative Least Squares (ANLS) and Gauss-Newton variants outperform the first-order gradient descent method in reducing the objective on large-scale problems. We perform a topic modelling task on a large corpus of academic papers that consists of over 37 million paper abstracts and nearly a billion citation relationships, demonstrating the utility and scalability of the methods.

Eswar, Srinivas↗
DOE OSTI2025

DIMPLES: Distributed Influence Maximization for Pandemic pLanning on Exascale Systems

We study exascale parallel algorithms for the selection of intervention or monitoring strategies in massive realistic socio-technical networks through scalable Influence Maximization (InfMax) algorithms. We employ novel techniques to enable efficient scaling on up to 8k nodes of OLCF Frontier, with 65k AMD GPUs and 458k AMD CPU cores. Current state-of-the-art InfMax tools are limited to networks with only a few million actors (vertices) and a few hundred million interactions (edges). By overcoming these limitations, we show that our approach is capable of processing a realistic social contact network of the United States with 285 million nodes and about 8 billion edges. This two orders-of-magnitude improvement over the previous state-of-the-art is obtained by leveraging algorithmic advancements for the InfMax problem and designing several problem-specific approaches to overlap communication with computation, improve GPU efficiency, and lower the application’s memory requirements. We evaluate strong scaling for computing 10k most influential seeds using up to 8k nodes of an exascale system, and weak scaling from 128 to 8k system nodes for seed sets ranging from 625 to 40k seeds. We achieve the fastest-known runtime of 25 minutes while performing 48 million diffusion simulations totaling 2.31 petabytes to identify 40k influential seeds using 8k nodes, and take 5.75 minutes to identify 10k seeds while using 4k nodes.

Minutoli, Marco [Pacific Northwest National Labora↗
DOE OSTI2017

Alstom’s Limestone Chemical Looping Gasification Process for High Hydrogen Syngas Generation

Alstom Power Inc., a wholly owned subsidiary of the General Electric Company (GE), is developing Limestone-based Chemical Looping (LCL™) Processes, utilizing limestone derived calcium sulfate as an oxygen carrier to transport oxygen from air to the fuel. The LCL™ process is a breakthrough technology with the potential to capture CO 2 and SO 2 from new and existing coal-fired power plants at reduced costs while maintaining high plant power generation efficiency. Chemical looping is a next generation advancement to oxy-combustion which would not require the significant investment in a cryogenic distillation type oxygen production plant. Instead of the energy-consuming oxygen plant, chemical looping uses a benign material to directly remove oxygen from the air and bring that oxygen into contact with the fuel. This "game changing" technology is the lowest cost-of-electricity technical approach that GE Power has identified to date for coal power with carbon capture. The technology has significantly lower capital costs compared to a conventional steam power plant with first generation carbon capture technologies. This new power plant concept is based on a hybrid combustion-gasification process utilizing high temperature chemical and thermal looping technology. In the air reactor, a solid oxygen carrier picks up oxygen from air through an oxidation reaction and leaves nitrogen behind. The hot oxygen-carrying solid carrier is then transported to a fuel reactor, where it releases oxygen. The released oxygen then converts the coal in the fuel reactor into combustion gases. The solid carrier is recycled back to the air reactor for regeneration completing the “chemical loop.” The regeneration step produces heat from which steam is produced for power generation while the process results in a concentrated carbon dioxide stream in which the carbon is captured for use or sequestration (CCUS). Limestone as a precursor for the oxygen carrier is abundant, benign, low cost, and is used in CFB and PC boilers for sulfur control. Challenges associated with this carrier include a complex reaction mechanism with possible side reactions leading to the release of SO 2 . The development of the Limestone Chemical Looping Gasification (LCL-G™) technology was co-funded by the Department of Energy under the DOE/NETL Cooperative Agreement No. DE- FE0023497, in parallel with the collaborative combustion (LCL-C™) program. In this report, the LCL-G™ experimental testing from the electrically-heated, once-through, 100 kW Pilot-Scale Test Facility (PSTF) is analysed and reviewed. The PSTF Oxidizer is a 50-foot tall, 100-mm diameter transport reactor, while the PSTF Reducer reactor is based upon a bubbling-bed design. The experimental data from four (4) test campaigns (denoted “Weeks”) are reviewed and discussed. The LCL-G™ (DE-FE0023497) project objectives for gasification are >90% carbon gasified in the Reducer and a H 2 :CO molar generation ratio of 2:1. These project performance metrics were not met by a single coal test, but they were individually approached in two separate tests.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2021

Distributed Training for High Resolution Images: A Domain and Spatial Decomposition Approach

In this work we developed two Pytorch libraries using the PyTorch RPC interface for distributed deep learning approaches on high resolution images. The spatial decomposition library allows for distributedtraining on very large images, which otherwise won’t be possible on a single GPU. The domain parallelism library allows for distributed training across multiple domain unlabeled data, by leveraging the domain separation architecture. Both of those libraries were tested on the Summit supercomputer at a moderate scale, and we are releasing the code for both of them.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

BM3DORNL

BM3DORNL is a high-performance, open-source library for removing streak and ring artifacts from computed-tomography (CT) data, developed for neutron imaging at Oak Ridge National Laboratory's Spallation Neutron Source (VENUS beamline) and applicable to X-ray CT as well. Ring artifacts — concentric rings in reconstructed slices caused by detector pixel-to-pixel response non-uniformities — appear as vertical streaks in the sinogram and degrade both image quality and quantitative analysis. BM3DORNL operates in the sinogram domain using an adaptation of the BM3D (block-matching and 3D collaborative filtering) algorithm (Dabov et al., 2007). It provides a dedicated streak-removal mode, a true multi-scale BM3D variant (after Mäkinen et al., 2021) that suppresses wide streaks single-scale methods miss, and an alternative Fourier–SVD method (~2.6× faster) combining FFT-based energy detection with rank-1 SVD. The computationally intensive core is implemented in Rust with parallel (Rayon) block matching, integral-image pre-screening, and optimized transforms, and is exposed through a simple Python API (with an optional GUI) so it integrates directly into existing tomography reconstruction pipelines. It processes both 2D sinograms and 3D sinogram stacks, is pip-installable for Linux and macOS, and is documented at https://bm3dornl.readthedocs.io.

Zhang, Chen [Oak Ridge National Laboratory (ORNL),↗
DOE OSTI2018

Simulation-Based Parameter Optimization Framework for Large-Scale Hybrid Smart Grid Communications Systems Design

The design of reliable, dynamic, fault-tolerant hybrid smart grid communication networks is a challenge to achieve for autonomous power grids. Hybrid networks use different communications technologies for different area networks. A simulation-based parameter optimization framework is proposed to tune parameters of hybrid communication technologies to achieve the optimal network performance. It consists of three main components: a parallel executor used to speedup a list of simulations; a sampler running simulations using the parallel executor at each generation; and a hybrid stochastic optimization algorithm for tuning configurable parameters of hybrid designs and applications. The proposed hybrid metaheuristic optimization algorithm combines an evolutionary algorithm with a gradient method to quickly achieve an approximately global optimum solution. Three optimization test functions are employed to train the adjustable parameters of the hybrid algorithm. Results show the proposed parameter optimization framework can help the designer choose the right hybrid architecture with an optimal parameter set for a large-scale broadband PLC-WiMAX hybrid smart grid communication network.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2017

Scaling deep learning workloads: NVIDIA DGX-1/Pascal and Intel Knights Landing

Deep Learning (DL) algorithms have become ubiquitous in data analytics. As a result, major computing vendors --- including NVIDIA, Intel, AMD, and IBM --- have architectural road-maps influenced by DL workloads. Furthermore, several vendors have recently advertised new computing products as accelerating large DL workloads. Unfortunately, it is difficult for data scientists to quantify the potential of these different products. This paper provides a performance and power analysis of important DL workloads on two major parallel architectures: NVIDIA DGX-1 (eight Pascal P100 GPUs interconnected with NVLink) and Intel Knights Landing (KNL) CPUs interconnected with Intel Omni-Path or Cray Aries. Our evaluation consists of a cross section of convolutional neural net workloads: CifarNet, AlexNet, GoogLeNet, and ResNet50 topologies using the Cifar10 and ImageNet datasets. The workloads are vendor-optimized for each architecture. Our analysis indicates that although GPUs provide the highest overall performance, the gap can close for some convolutional networks; and the KNL can be competitive in performance/watt. We find that NVLink facilitates scaling efficiency on GPUs. However, its importance is heavily dependent on neural network architecture. Furthermore, for weak-scaling --- sometimes encouraged by restricted GPU memory --- NVLink is less important.

Gawande, Nitin A.↗