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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Development and Application of Virtual Nuclear Power Plant Platform Concerning Environment and Society Digitally

Virtual Nuclear Power Plant, named Virtual4DS, which is developed by the advanced nuclear energy research team (FDS Team), is a platform for full range, whole cycle safety assessment and emergency decision of nuclear power plant in the digital environment and society. The function framework of Virtual4DS is shown in Fig. 1, and it is designed to ensure an overall optimization design of nuclear power plant, achieve comprehensive evaluation of safety and control of risk, and supply the decision support for accident emergency by the accident probability analysis, environmental consequence assessment and public risk evaluation built on the core of digital reactor and the foundation of the nuclear energy big data. Therefore, Virtual4DS is eligible to be applied in reactor safety, radiation safety and environment impact, emergency and public safety. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Photosynthetic biohybrid systems for solar fuels catalysis

Photosynthetic reaction center (RC) proteins are finely tuned molecular systems optimized for solar energy conversion. RCs effectively capture and convert sunlight with near unity quantum efficiency utilizing light-induced directional electron transfer through a series of molecular cofactors embedded within the protein core to generate a long-lived charge separated state with a useable electrochemical potential. Of current interest are new strategies that couple RC chemistry to the direct synthesis of energy-rich compounds. This Feature Article highlights recent work from our lab on RC and RC-inspired hybrid systems that capture the Sun's energy and convert it to chemical energy in the form of H2, a carbon-neutral energy source derived from water. Further, biohybrids made from the Photosystem I (PSI) RC are among the best photocatalytic H2-producing protein hybrids to date. Targeted self-assembly strategies that couple abiotic catalysts to PSI translate to catalyst incorporation at intrinsic PSI sites within thylakoid membranes to achieve complete solar water-splitting systems. RC-inspired biohybrids interface synthetic photosensitizers and molecular catalysts with small proteins to create photocatalytic systems and enable the spectroscopic discernment of the structural features and electron transfer processes that underpin solar-driven proton reduction. In total, these studies showcase the incredible scientific opportunities photosynthetic biohybrid research provides for harnessing the optimal qualities of both artificial and natural photosynthetic systems and developing materials that capture, convert, and store solar energy as a fuel.

14 SOLAR ENERGY↗
DOE OSTI2022

A second-order distributed memory parallel fast sweeping method for the Eikonal equation

The Eikonal equation is used to calculate wave propagation and distance fields, and due to its complexity requires numerical treatment for its solution. In this work, we present a second-order distributed memory parallel fast sweeping method. The second-order solution switches on a two-point stencil when two upwind points are available, and reverts to first-order otherwise. In all examples, the second-order method improves the solution over the first-order, allowing for significant savings in memory while achieving the same accuracy. Parallelization over distributed memory saw good weak scaling with optimal convergence. The computational time for second-order was approximately 2.5 times slower than first-order, where the largest amount of mesh points ran on 144 cores (512 GB) was ≈20 billion. The savings in memory from the second-order method combined with the distributed memory algorithm result in the ability to solve problems much larger than are possible with the serial first-order method.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2015

Excitonic condensation in spatially separated one-dimensional systems

We show theoretically that excitons can form from spatially separated one-dimensional ground state populations of electrons and holes, and that the resulting excitons can form a quasicondensate. We describe a mean-field Bardeen-Cooper-Schrieffer theory in the low carrier density regime and then focus on the core-shell nanowire giving estimates of the size of the excitonic gap for InAs/GaSb wires and as a function of all the experimentally relevant parameters. We find that optimal conditions for pairing include small overlap of the electron and hole bands, large effective mass of the carriers, and low dielectric constant of the surrounding media. Therefore, one-dimensional systems provide an attractive platform for the experimental detection of excitonic quasicondensation in zero magnetic field.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (> 85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. Furthermore, this suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2015

SU-E-T-437: Four-Dimensional Treatment Planning for Lung VMAT-SBRT

Purpose: To assess optimal treatment planning approach of Volumetric Modulated Arc Therapy for lung Stereotactic Body Radiation Therapy (VMAT-SBRT). Methods: Subjects were 10 patients with lung cancer who had undergone 4DCT. The internal target volume (ITV) volume ranged from 2.6 to 16.5cm{sup 3} and the tumor motion ranged from 0 to 2cm. From 4DCT, which was binned into 10 respiratory phases, 4 image data sets were created; maximum intensity projection (MIP), average intensity projection (AIP), AIP with the ITV replaced by 0HU (RITV-AIP) and RITV-AIP with the planning target volume (PTV) minus the internal target volume was set to −200 HU (HR-AIP). VMAT-SBRT plans were generated on each image set for a patient. 48Gy was prescribed to 95% of PTV. The plans were recalculated on all phase images of 4DCT and the dose distributions were accumulated using a deformable image registration software MIM Maestro™ as the 4D calculated dose to the gross tumor volume (GTV). The planned dose to the ITV and 4D calculated dose to the GTV were compared. Results: In AIP plan, 10 patients average of all dose parameters (D1%, D-mean, and D99%) discrepancy were 1Gy or smaller. MIP and RITV-AIP plans resulted in having common tendency and larger discrepancy than AIP plan. The 4D dose was lower than the planned dose, and 10 patients average of all dose parameters discrepancy were in range 1.3 to 2.6Gy. HR-AIP plan had the largest discrepancy in our trials. 4D calculated D1%, D-mean, and D99% were resulted in 3.0, 4.1, and 6.1Gy lower than the expected in plan, respectively. Conclusion: For all patients, the dose parameters expected in AIP plan approximated to 4D calculated. Using AIP image set seems optimal treatment planning approach of VMAT-SBRT for a mobile tumor. Funding Support: This work was supported by the Japan Society for the Promotion of Science Core-to-Core program (No. 23003)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Unified Communication Optimization Strategies for Sparse Triangular Solver on CPU and GPU Clusters

This paper presents a unified communication optimization framework for sparse triangular solve (SpTRSV) algorithms on CPU and GPU clusters. The framework builds upon a 3D communication-avoiding (CA) layout of Px × Py × Pz processes that divides a sparse matrix into Pz submatrices, each handled by a Px × Py 2D grid with block-cyclic distribution. We propose three communication optimization strategies: First, a new 3D SpTRSV algorithm is developed, which trades the inter-grid communication and synchronization with replicated computation. This design requires only one inter-grid synchronization, and the inter-grid communication is efficiently implemented with sparse allreduce operations. Second, broadcast and reduction communication trees are used to reduce message latency of the intra-grid 2D communication on CPU clusters. Finally, we leverage GPU-initiated one-sided communication to implement the communication trees on GPU clusters. With these nested inter- and intra-grid communication optimization strategies, the proposed 3D SpTRSV algorithm can attain up to 3.45x speedups compared to the baseline 3D SpTRSV algorithm using up to 2048 Cori Haswell CPU cores. In addition, the proposed GPU 3D SpTRSV algorithm can achieve up to 6.5x speedups compared to the proposed CPU 3D SpTRSV algorithm with Pz up to 64. Finally it is remarkable that the proposed GPU 3D SpTRSV can scale to 256 GPUs using the Perlmutter system while the existing 2D SpTRSV algorithm can only scale up to 4 GPUs.

Sao, Piyush↗
DOE OSTI2023

HFTS-1 (Final Report)

Despite the long history of hydraulic fracturing, the optimal number of fracturing stages during multi-stage fracture stimulation in horizontal wells can be a complex subject. In addition to the increased expense of multistage fracturing in horizontal wells, increasing the number of fracturing stages does not always correlate with a commensurate increase in production. The problem is the application of a uniform fracture stimulation design to all stages with no consideration for geological variations along the wellbore. The result is an inefficient use and costly waste of energy and water. Optimization of the fracturing process requires an understanding of the cause-and-effect relationship between fracturing parameters and local geological properties at a given location along the wellbore. Realizing that the generalized rock mechanics theories and hypotheses are not truly applicable to fractured and laminated shales, quantifiable impacts of a shale’s geomechanical and depositional features are a prerequisite for design and implementation of optimized hydraulic fractures. The overarching goal of this project is to understand and define the relationships of shale geology and fracture dynamics using detailed field data that includes coring of the fracture domain. Finally, resource recovery from shale formations is estimated to be less than fifteen percent. Research proposed in this project will establish the foundations for investigating enhanced recovery techniques for increased resource recovery in existing fracture treated wells. Natural gas as an EOR fluid is in the initial stages of being used broadly in the oil and gas industry and as such, many aspects of the process need to be researched and addressed prior to widespread acceptance. In addition, re-fracturing techniques, particularly using cemented liners also need to be further investigated. This project explores several of these EOR techniques in greater detail, not just at the theoretical level, but through actual field test sites. This report delves into the research program, identified tasks, several studies, observations, results, and conclusions under the Hydraulic Fracturing Test Site (HFTS) project. This test project is subdivided into three distinct phases and each phase is discussed in detail under separate sections. The original test site for Phase 1 as well as the Phase 2 site are in Midland Basin in Texas and operated by Laredo Petroleum. In the first two phases, the program targeted the Wolfcamp formation in Midland Basin. Phase 3 of the study targeted the EagleFord play in South Texas with Devon Energy as the operator. Significant conclusions from this study include: 1) lateral and vertical fracture growth mapping as well as proppant transport behavior, 2) Use of field gas and its applicability for huff n puff EOR injection in the Midland Basin, 3) Development and validation of proppant log, both at core scale as well as in-fill well implementation by analyzing drilling mud return samples, 4) Applicability of liner refracturing including best practices for enhanced unit level productivity. This report will detail all these studies as well as technical results and conclusions from said studies. These include general subsurface characterization work, completion designs as implemented, studies looking at fracture geometry, high resolution microseismic study, advanced crosswell strain mapping studies including drainage characterization, core characterization results, proppant analysis (core and observation wells), geochemistry, etc. to name a few.

02 PETROLEUM↗
DOE OSTI2025

Reliable and Efficient Machine Learning (Final Technical Report)

Modern scientific experiments generate massive amounts of data at a pace much faster than humans can manually analyze. While machine learning has revolutionized commercial data analysis (such as recommending movies or recognizing faces), applying these tools to complex scientific discovery is challenging because scientific answers must be precise, interpretable, and adhere to physical laws. The research under this project aims to develop new mathematical tools and computer algorithms specifically designed for scientific applications. Major progress has been made in automatically cleaning and deconstructing messy experimental data, analyzing the visual information of physical phenomena, determining the underlying physical variables, and providing rig orous mathematical analysis of interesting algorithms and concepts widely used in machine learning. This project addressed the critical gap between our ability to generate massive scientific data and our ability to extract interpretable information from it. We established mathematical foundations for Scientific Machine Learning (SciML) aimed at effective data analytics and automated discovery. Our work focused on three core objectives: (1) developing reliable feature extraction methods for dynamic high-dimensional data, (2) establishing mathematical foundations for discovering dynamics via neural networks, and (3) creating rigorous optimization techniques for these models. Key outcomes come from two fronts. On the practical side, they include the development of algorithms that significantly enhance the extraction of signals from field data, as well as the capability to handle situations that exhibit smooth variations or physical stretching due to temperature changes. They also include the creation of an automated framework for discovering fundamental state variables from raw experimental data, demonstrating the ability to identify intrinsic physical dimensions without prior knowledge of the governing laws. On the theoretical front, the research results in theoretical advances in Optimal Transport, a widely used notion in SciML, specifically regarding functions with fixed-size nodal sets, provide sharp bounds relevant to uncertainty quantification. Meanwhile, the outcomes also include the establishment of convergence theories for nonlocal gradient descent methods, enabling robust optimization with noisy data in high-dimensional settings commonly encountered in scientific modeling. The project also helps creating opportunities to train the next generation of researchers, equipping them with the necessary technical skills for today’s workplace and preparing them for future advances.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Precision Polishing of Spheres Via In-Situ Process Monitoring and Machine-Learning-Based Optimization

Precision polishing for high surface finish is a vital step in the making of many high-tech components for optical and semiconductor modules as well as for wide ranging experimental samples for HED science that is at the core of LLNL’s mission. In particular, the fabrication of extremely smooth and round spheres comprised of low atomic number materials (low-Z such as high-density carbon or HDC, beryllium, hydrocarbon polymers, etc.) for Inertial Confinement Fusion (ICF) is entirely reliant on specialized polishing processes to achieve the surface smoothness.

36 MATERIALS SCIENCE↗
DOE OSTI2023

CO 2 -Responsive Fracturing Fluids for Enhanced Geothermal Systems (Final Report)

Our group has recently developed StimuFrac, a non-toxic stimuli-responsive fracturing fluid consisting of a CO 2 -reactive polymer which has shown at the lab-scale to consistently fracture rock cores at significantly lower net pressures in a range of representative geothermal pressure/temperature conditions. However, until now the mechanism/s responsible for more effective fracturing, of critical importance to optimize fracturing performance as well as strategize injection methodologies for field deployment, was not understood. In this document, we report (1) on the two main mechanisms responsible for fracturing rock at lower net pressures with StimuFrac; (2) the phase behavior of StimuFrac/CO 2 under geothermal wellbore conditions; and (3) based on high-temperature true triaxial stimulations, detailed evidence that StimuFrac/CO 2 is the best performing stimulation fluid under EGS T/P conditions as compared to water, waterless CO 2 , and CO 2 /water fracturing fluids. This is because i) it requires significantly lower volumes of CO 2 due to its reduced leak off into the formation by the crosslinked polymer solution; ii) large fractures can be generated reproducibly at both low and high CO 2 injection flow rates, and iii) the reversible (previously reported) viscosity increase of StimuFrac could be beneficial to transport proppants when they become available for EGS. These results were particularly evident for hot nearly dry rock as well as partially and nearly fully water-saturated granitic rock. GTO requires StimuFrac to be evaluated in fully water-saturated rock to determine whether the above-described performance applies under these conditions. Since (1) GTO considers there is no enough evidence for Sa different StimuFrac formulation to work below 90C (where full water-saturation in an open system is possible) and (2) no polyaxial loading frame larger than a few centimeters that can do hydraulic fracturing tests while maintaining the rock sample fully saturated with water at 200 C exists; PNNL concludes that the only way to determine if StimuFrac represents an advanced fracturing fluid alternative for EGS, is to perform a stimulation in an actual EGS reservoir.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

A semi-supervised machine learning detector for physics events in tokamak discharges

Databases of physics events have been used in various fusion research applications, including the development of scaling laws and disruption avoidance algorithms, yet they can be time-consuming and tedious to construct. This paper presents a novel application of the label spreading semi-supervised learning algorithm to accelerate this process by detecting distinct events in a large dataset of discharges, given few manually labeled examples. A high detection accuracy (>85%) for H-L back transitions and initially rotating locked modes is demonstrated on a dataset of hundreds of discharges from DIII-D with manually identified events for which only 3 discharges are initially labeled by the user. Lower yet reasonable performance (~75%) is also demonstrated for the core radiative collapse, an event with a much lower prevalence in the dataset. Additionally, analysis of the performance sensitivity indicates that the same set of algorithmic parameters is optimal for each event. This suggests that the method can be applied to detect a variety of other events not included in this paper, given that the event is well described by a set of 0D signals robustly available on many discharges. Procedures for analysis of new events are demonstrated, showing automatic event detection with increasing fidelity as the user strategically adds manually labeled examples. Detections on Alcator C-Mod and EAST are also shown, demonstrating the potential for this to be used on a multi-tokamak dataset.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Grid pressure drop prediction in a fuel assembly

In fuel assembly, spacer grids have strong effects on pressure drop and other thermal-hydraulic behaviors. Considering the design and safety operation of a reactor, it is important to predict pressure drop accurately in the fuel assembly. For a mixed core, a reactor with mixed fuel designs in the reactor core, undesired cross flow were often caused by differences in pressure drop among neighboring bundles. The pressure drop is often estimated for designs of new reactor core fuel assemblies. In the past, due to complex flow conditions in fuel assembly, the pressure drop of a fuel assembly relied greatly on costly and time-consuming full length thermal hydraulic tests. Some empirical pressure drop correlations and CFD methodology were applied, however, only limited to a single gird span. Without considering pressure drop in the entire fuel assembly it is quite difficult to optimize the fuel assembly design. Most experimental fuel assemblies consist of mixing vane grids (MVG) and simple support grids (SSG). The MVG's were the main tested spacer grids in the reactor and SSG's were used only in experimental study to support the fuel assembly. In fuel reactor core, the fuel assembly heats the coolant, which could turn the single-phase water system into a two-phase flow system and eventually lead to critical heat flux (CHF). Because of the complexity of phase change, it is very difficult to predict pressure drop in rod bundles with two-phase flow. In this paper, a new method to predict the pressure drop in the fuel assembly is proposed and verified by experimental data.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2017

Nitrogen–doped graphitized carbon shell encapsulated NiFe nanoparticles: A highly durable oxygen evolution catalyst

Oxygen evolution reaction (OER) plays a crucial role in various energy conversion devices such as water electrolyzers and metal–air batteries. Precious metal catalysts such as Ir, Ru and their oxides are usually used for enhancing reaction kinetics but are limited by their scarce resource. The challenges associated with alternative non–precious metal catalysts such as transition metal oxides and (oxy)hydroxides etc. are their low electronic conductivity and durability. Herein, we report a highly active (360 mV overpotential at 10 mA cm–2GEO) and durable (no degradation after 20000 cycles) OER catalyst derived from bimetallic metal–organic frameworks (MOFs) precursors. This catalyst consists of NiFe nanoparticles encapsulated by nitrogen–doped graphitized carbon shells. The electron-donation/deviation from Fe and tuned electronic structure of metal cores by Ni are revealed to be primary contributors to the enhanced OER activity, whereas N concentration contributes negligibly. We further demonstrated that the structure and morphology of encapsulating carbon shells, which are the key factors influencing the durability, are facilely controlled by the chemical state of precursors.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2021

Phosphorolytic degradation of leaf starch via plastidic α-glucan phosphorylase leads to optimized plant growth and water use efficiency over the diel phases of Crassulacean acid metabolism

In plants with Crassulacean acid metabolism (CAM), it has been proposed that the requirement for nocturnal provision of phosphoenolpyruvate as a substrate for CO 2 uptake has resulted in a re-routing of chloroplastic starch degradation from the amylolytic route to the phosphorolytic route. To test this hypothesis, we generated and characterized four independent RNAi lines of the obligate CAM species Kalanchoë fedtschenkoi with a >10-fold reduction in transcript abundance of plastidic α-glucan phosphorylase (PHS1). The rPHS1 lines showed diminished nocturnal starch degradation, reduced dark CO 2 uptake, a reduction in diel water use efficiency (WUE), and an overall reduction in growth. A re-routing of starch degradation via the hydrolytic/amylolytic pathway was indicated by hyperaccumulation of maltose in all rPHS1 lines. Further examination indicated that whilst operation of the core circadian clock was not compromised, plasticity in modulating net dark CO 2 uptake in response to changing photoperiods was curtailed. The data show that phosphorolytic starch degradation is critical for efficient operation of the CAM cycle and for optimizing WUE. This finding has clear relevance for ongoing efforts to engineer CAM into non-CAM species as a means of boosting crop WUE for a warmer, drier future.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

Radial direct bandgap p-i-n GaNP microwire solar cells with enhanced short circuit current

We report the demonstration of dilute nitride heterostructure core/shell microwire solar cells utilizing the combination of top-down reactive-ion etching to create the cores (GaP) and molecular beam epitaxy to create the shells (GaNP). Systematic studies of cell performance over a series of microwire lengths, array periods, and microwire sidewall morphologies examined by transmission electron microscopy were conducted to shed light on performance-limiting factors and to optimize the cell efficiency. We show by microscopy and correlated external quantum efficiency characterization that the open circuit voltage is degraded primarily due to the presence of defects at the GaP/GaNP interface and in the GaNP shells, and is not limited by surface recombination. Compared to thin film solar cells in the same growth run, the microwire solar cells exhibit greater short circuit current but poorer open circuit voltage due to greater light absorption and number of defects in the microwire structure, respectively. The comprehensive understanding presented in this work suggests that performance benefits of dilute nitride microwire solar cells can be achieved by further tuning of the epitaxial quality of the underlying materials.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗