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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,171 records · Page 65
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Charge of the massive leptons

A simple picture of the massive leptons emerges by invoking a πλ 2 cross section for vacuum virtual photons to stimulate an isolated lepton to emit an additional virtual photon. This reaction mechanism leads to the storage of a lepton’s energy and momentum in the surrounding cloud of virtual photons. The stimulated-emission process allows for the exchange of virtual photons between a pair of leptons, and thus an estimate the fine-structure constant. A simple sharp low-energy cutoff for the exchanged photons, associated with near-field effects, results in a calculated charge of 1.65 x 10 –19 C. The discrepancy with experiment might be reduced by including additional computational complexity.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

Nanostructures for Electrical Energy Storage (NEES) (2020 Final Technical Report)

Nanostructures for Electrical Energy Storage (NEES, www.efrc.umd.edu) was an Energy Frontier Research Center supported by the DOE Office of Science, Basic Energy Sciences, from 8/1/2009 to 7/31/2020. Led by the University of Maryland, NEES enjoyed extensive collaborations with its funded partners, including two DOE Laboratories and six universities. The NEES vision has been to reveal a set of scientific insights and design principles that can underpin a next-generation electrical energy storage approach, building on advances in nanoscale science and technology to achieve simultaneous high power and high energy over extended charge/discharge cycling. The vision is motivated by the recognition that scaling into the nano regime opens the door to new physical phenomena and that the tools enlisted in nanoscale research provide major new opportunities for the synthesis not only of materials at molecular scale but for structures at nano scale and above. NEES has translated this vision into its research program based on two observations. First, while the behavior of ions and electrons in electrolytes and in electrode materials is crucial to electrical energy storage (or more appropriately electrochemical energy storage), it is the transport of ion and electron charge between different structural components of a storage device that ultimately determine its performance. With it well recognized that the choice of electrode materials typically constrain ion transport kinetics as well as maximum ion concentration, the search for better electrode materials has been a primary driver of battery research. At the same time the synthesis of electrodes is typically based on aggregation of particles with varying size, shape, and orientation in the electrode. Together with the presence of additional materials to impart electrical conductivity and cohesion to the composite electrode, change in electrode materials is necessarily accompanied by structural changes at the nano/micro scale that are difficult to categorize and manage. From the beginning, NEES’ vision has been to create and study simpler, highly controlled spatial arrangements of known materials as battery components (electrodes, current collectors, and electrolyte) and to understand how design and structure above the molecular scale determines the energy storage performance available from known materials. Second, advances in nanoscience dramatically expanded the portfolio of synthesis methods, structural motifs, and new phenomena available for research. Some of these gave rapid access to new building blocks at the deep nanoscale (e.g., carbon nanotubes grown by self-assembly, nanoscale arrays formed by electrochemical self-alignment, monolayer films controlled by self-limiting reaction). Such advances served as the enabler for the NEES vision to be pursued experimentally through study of 3D structures created and controlled at the nano, micro, and meso scales. Here, we use meso as in the BES MESO Report, implying not only intermediate or varying length scales, but very much the way behavior is influenced by other factors including aggregation of nanocomponents at different densities and spatial configurations, statistical variations in the aggregates, hierarchical architectures in which they can be assembled, or local 3D configurations that result from the architectures. Over its life cycle, NEES has pursued two overarching goals: (1) to understand the scientific fundamentals of electrochemical storage from the nanoscale to the mesoscale; and (2) to create and learn from innovative, controlled, heterogeneous nanostructures, where such nanostructures can enable the first goal and serve as models for future paradigms in energy storage. Specific goals have included: Synthesize heterogeneous nanostructures comprised of multiple materials arranged in controlled fashion and characterize their behavior; Demonstrate and elucidate design principles for achieving simultaneous high power and high energy; Develop materials processes which enable precision control of thin layers and 3D structures; Investigate the impact of artificial interphases on electrode stability during ion insertion/deinsertion; Create dense arrays of nanostructures to understand how the architecture of these assemblies, along with nanostructure design, influences energy storage behavior at the mesoscale; Identify and understand the consequences of nanoconfinement and local inhomogeneities in 3D mesoscale arrays; Develop and apply computational models to stimulate, guide and interpret experiments.

25 ENERGY STORAGE↗
DOE OSTI2022

Design of Transition-Metal/Zeolite Catalysts for Direct Conversion of Coal-derived CO 2 to Aromatics

This project introduces a lab-scale, stand-alone process to produce aromatics from CO 2 . Besides reducing the amount of coal-derived CO 2 emission, this process can provide new approaches to produce valuable chemicals such as benzene, toluene, and xylene (BTX). These chemicals are the starting material in various industries such as pharmaceuticals, polymers, paints, and coatings. The focus of the project is on developing a suitable reaction configuration, feed composition, and catalyst combination that leads to high aromatics selectivity and CO 2 conversions. To achieve these objectives, computational catalysis will accompany hands-on experiments to provide insights to potential transition metals or alloys that may serve as optimal active sites for different steps of the reaction mechanism, such as reduction of CO 2 and dehydration (in the case of alcohol intermediates) and oligomerization (in the case of paraffins or olefins) of intermediate hydrocarbons.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Design of Transition-Metal/Zeolite Catalysts for Direct Conversion of Coal-derived CO 2 to Aromatics

This project introduces a lab-scale, stand-alone process to produce aromatics from CO 2 . Besides reducing the amount of coal-derived CO 2 emission, this process can provide new approaches to produce valuable chemicals such as benzene, toluene, and xylene (BTX). These chemicals are the starting material in various industries such as pharmaceuticals, polymers, paints, and coatings. The focus of the project is on developing a suitable reaction configuration, feed composition, and catalyst combination that leads to high aromatics selectivity and CO 2 conversions. To achieve these objectives, computational catalysis will accompany hands-on experiments to provide insights to potential transition metals or alloys that may serve as optimal active sites for different steps of the reaction mechanism, such as reduction of CO 2 and dehydration (in the case of alcohol intermediates) and oligomerization (in the case of paraffins or olefins) of intermediate hydrocarbons.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

A reactive tracer method for predicting EGS reservoir geometry and thermal lifetime: development and field validation

The project summarized here (DOE Award No. DE-EE0006764) was intended to develop a methodology for predicting advective heat transfer in fracture-dominated crystalline rock. Our goal was to determine if a combination of inert and reactive tracers could adequately constrain the effective heat transfer surface area between an injection-production well pair. Our approach consisted of: 1. developing a novel computational framework; 2. Performing heat and tracer experiments at meso-scale; and 3. Comparing predictions of advective heat transfer to the “true” thermal breakthrough measured at the Altona site. Below is a summary of project activities/findings, a summary of project tasks, and a conclusion

15 GEOTHERMAL ENERGY↗
DOE OSTI2022

Spark-Assisted HCCI Residential CHP

A transformative small spark ignition (SI) internal combustion (IC) engine fueled with natural gas was developed for combined heat and power (CHP) applications using a combination of cycle simulations, computational fluid dynamics (CFD) modeling, and engine experiments. The resulting 1 kW CHP engine was demonstrated to achieve 36.1% brake thermal efficiency (BTE) while meeting aggressive exhaust emissions targets using a low-cost three-way catalyst. Starting from a “clean-sheet” design, modeling tools were used to select the optimal engine parameters and operating characteristics. The disruptive technologies developed for the CHP system have a target lifetime of at least 10 years. The engine cost at volume of 10,000 units was estimated to be $\$$1,050. A combination of experimental and simulation results were used to identify a path to the program target of 38.6% BTE. Base engine design parameters (i.e., displacement, speed, bore, stroke, valve timings, and compression ratio) were optimized to reduce friction, heat transfer, and exhaust losses. The base clean sheet prototype engine achieved 32% BTE. Optimization of the lube oil and coolant temperatures added 1.8% BTE. The addition of exhaust gas recirculation added 1.1% BTE. Novel thermal barrier coatings (TBCs) developed over the course of the project added 1.2% BTE. Further improvements in engine friction reduction (1.3% BTE) and improved combustion (1.2% BTE) are estimated to meet the program target 38.6% BTE, while also meeting the stringent criteria pollutant emissions targets.

42 ENGINEERING↗
DOE OSTI2022

Current and Future γ-Ray Searches for Dark Matter Annihilation Beyond the Unitarity Limit

For decades, searches for electroweak-scale dark matter (DM) have been performed without a definitive detection. This lack of success may hint that DM searches have focused on the wrong mass range. A proposed candidate beyond the canonical parameter space is ultraheavy DM (UHDM). In this work, we consider indirect UHDM annihilation searches for masses between 30 TeV and 30 PeV—extending well beyond the unitarity limit at ~100 TeV—and discuss the basic requirements for DM models in this regime. We explore the feasibility of detecting the annihilation signature, and the expected reach for UHDM with current and future very-high-energy (VHE; >100 GeV) γ-ray observatories. Specifically, we focus on three reference instruments: two Imaging Atmospheric Cherenkov Telescope arrays, modeled on VERITAS and CTA-North, and one extended air shower array, motivated by HAWC. With reasonable assumptions on the instrument response functions and background rate, we find a set of UHDM parameters (mass and cross section) for which a γ-ray signature can be detected by the aforementioned observatories. Here, we further compute the expected upper limits for each experiment. With realistic exposure times, the three instruments can probe DM across a wide mass range. At the lower end, it can still have a point-like cross section, while at higher masses the DM could have a geometric cross section, indicative of compositeness.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A Scalable Parallel Hypergraph Generator (HyGen)

Graphs are extensively used to model real-world complex systems. An edge in a graph can model pairwise relationships. However, multiway relationships (connections between three or more vertices) are common in many complex systems such as cellular process, image segmentation, and circuit design. A graph edge cannot model multiway relationships. A hypergraph, which can connect more than two vertices, is thus a better option to model multiway relationships. A large-scale hypergraph analysis has the potential to find useful insights from a complex system and assist in knowledge discovery. Currently a limited number of hypergraphs exists that are representative of real-world datasets. Moreover, real-world hypergraph datasets are small in size and inadequate to incorporate future needs. A graph generator that can produce large-scale synthetic hypergraphs can solve the above mentioned problems. In this paper, we present a scalable parallel hypergraph generator (HyGen) based on the Message Passing Interface (MPI) standard. To generate hypergraphs, HyGen takes the following parameter values as inputs: i) number of vertices, ii) number of hyperedges, iii) number of clusters, iv) vertex distribution, v) hyperedge distribution, vi) local cluster cardinality, and vii) global cluster cardinality. We have demonstrated that HyGen can generate hypergraphs of various sizes in a scalable fashion. HyGen takes approximately four minutes to generate a hypergraph with 4.8 million vertices, 1.6 million hyperedges, and 800 clusters using 1,024 processes on a leadership class computing platform. Our strong and weak scaling experiments on supercomputers demonstrate that HyGen can quickly create large-scale hypergraphs in a parallel manner, thus providing a useful capability for hypergraph analysis.

Hasan, S M Shamimul↗
DOE OSTI2019

End-Gas Autoignition Fraction and Flame Propagation Rate in Laser-Ignited Primary Reference Fuel Mixtures at Elevated Temperature and Pressure

Engine knock in spark-ignited (SI) engines is initiated by autoignition and detonation in the unburned gases (i.e., end-gas) upstream of the spark-ignited, propagating, turbulent premixed flame. Knock propensity of fuel/air mixtures is quantified by research octane number (RON), motor octane number (MON), or methane number (MN; for gaseous fuels), which are typically measured using single-cylinder, variable compression ratio research engines. In this study, we demonstrate the ability to evaluate knock propensity of SI fuels via observations of end-gas autoignition (EGAI) in unburned gases upstream of laser-ignited, premixed flames at elevated pressures and temperatures in a rapid compression machine. Stoichiometric primary reference fuel (PRF; n-heptane/isooctane) blends of varying reactivity (50 ≤ PRF ≤ 100) were ignited using an Nd:YAG laser at five discrete temperature and pressure conditions, all in excess of 485 K and 16.75 bar. Laser ignition produced outwardly-propagating, laminar premixed flames. High-speed pressure measurements, paired with high-speed Schlieren imaging, clearly indicated the presence of EGAI. The fraction of the total heat release attributed to EGAI (e.g. EGAI fraction) varied inversely with octane number. EGAI fraction and flame propagation rate were both influenced by the magnitude of convection in the unburned gases upstream of the propagating flame caused by variation in piston timing. Experiments were accompanied by three-dimensional computational modeling with detailed chemical kinetics performed using CONVERGETM. The model results reveal low-temperature heat release and hydrogen peroxide formation in the end-gas upstream of the propagating laminar flame. Low-temperature heat release increases the temperature and degree of chain branching in the end-gas and ultimately leads to EGAI.

End-Gas Autoignition, Engine Knock, Rapid Compress↗
DOE OSTI2023

An Update on the Colossus mK platform at Fermilab

As part of the efforts of the Superconducting Quantum Materials and Systems National Quantum Center at Fermilab, we will construct a large millikelvin refrigeration platform known as Colossus. The Colossus platform will be used for quantum computing applications, along with physics and sensing experiments. At the preceding CEC/ICMC meeting in 2021, we reported on the conceptual design of the platform. In the intervening time, the design of the system has been completed and passed through review, with construction now underway. This paper provides an update on the overall design of the system, and the status of and timeline for construction.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Improved tomographic reconstruction of large-scale real-world data by filter optimization

In advanced tomographic experiments, large detector sizes and large numbers of acquired datasets can make it difficult to process the data in a reasonable time. At the same time, the acquired projections are often limited in some way, for example having a low number of projections or a low signal-to-noise ratio. Direct analytical reconstruction methods are able to produce reconstructions in very little time, even for large-scale data, but the quality of these reconstructions can be insufficient for further analysis in cases with limited data. Iterative reconstruction methods typically produce more accurate reconstructions, but take significantly more time to compute, which limits their usefulness in practice. In this paper, we present the application of the SIRT-FBP method to large-scale real-world tomographic data. The SIRT-FBP method is able to accurately approximate the simultaneous iterative reconstruction technique (SIRT) method by the computationally efficient filtered backprojection (FBP) method, using precomputed experiment-specific filters. We specifically focus on the many implementation details that are important for application on large-scale real-world data, and give solutions to common problems that occur with experimental data. We show that SIRT-FBP filters can be computed in reasonable time, even for large problem sizes, and that precomputed filters can be reused for future experiments. Reconstruction results are given for three different experiments, and are compared with results of popular existing methods. The results show that the SIRT-FBP method is able to accurately approximate iterative reconstructions of experimental data. Furthermore, they show that, in practice, the SIRT-FBP method can produce more accurate reconstructions than standard direct analytical reconstructions with popular filters, without increasing the required computation time.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Automated and efficient local adaptive regression for principal component-based reduced-order modeling of turbulent reacting flows

Principal Component Analysis can be used to reduce the cost of Computational Fluid Dynamics simulations of turbulent reacting flows by reducing the dimensionality of the transported variables through projection of the thermochemical state onto a lower-dimensional manifold. However, because of the nonlinearity of the principal component source terms, nonlinear regression techniques must be utilized for the source terms in terms of the principal components. Unfortunately, widely available and utilized nonlinear regression techniques can have prohibitive computational requirements and/or accuracy that is highly dependent on user experience in ad hoc tuning of model architecture and hyperparameters. Here, in this work, a new nonlinear regression approach is proposed that is both computationally efficient and automated so does not require any user input. The approach is evaluated through a priori prediction of principal component source terms using data from a Direct Numerical Simulation of a turbulent nonpremixed n-heptane/air jet flame. In particular, the proposed framework consists of local regressions whose complexity is adapted according to the local nonlinearity of the data: local linear regression when accurate enough and local Artificial Neural Networks when nonlinear regression is required. The number of local clusters for local regression is determined automatically using the Davies-Bouldin index. In addition, Bayesian optimization is utilized for model training (i.e., to select the best architectures and hyperparameters of the nonlinear regressions in an unsupervised fashion), eliminating ad hoc hand-tuning and/or expensive grid searches. Overall, compared to a single, global neural network, the new local adaptive regression approach is shown to have comparable accuracy but 69% less training time due to the utilization of local linear regression and faster training of local neural networks.

42 ENGINEERING↗
DOE OSTI2026

Sensitivity of the CUPID experiment to $0\nu \beta \beta$ decay of 100 Mo

CUPID is a next-generation bolometric experiment to search for neutrinoless double-beta decay ($0\nu \beta \beta$ ) of 100 Mo using Li 2 MoO 4 scintillating crystals. It will operate 1596 crystals at ~10 mK in the CUORE cryostat at the Laboratori Nazionali del Gran Sasso in Italy. Each crystal will be facing two Ge-based bolometric light detectors for α rejection. We compute the discovery and the exclusion sensitivity of CUPID to $0\nu \beta \beta$ in a Frequentist and a Bayesian framework. This computation is done numerically based on pseudo-experiments. For the CUPID baseline scenario, with a background and an energy resolution of 1.0 x 10 –4 counts/keV/kg/yr and 5 keV FWHM at the Q-value, respectively, this results in a Bayesian exclusion sensitivity (90% c.i.) of $\hat{T}_{1/2} > 1.6 \times 10^{27} \ \textrm{yr}$, corresponding to the effective Majorana neutrino mass of $\hat{m}_{\beta \beta } < \ 9.6$ – 28 meV. The Frequentist discovery sensitivity (3σ ) is $\hat{T}_{1/2}= 1.0 \times 10^{27} \ \textrm{yr}$, corresponding to $\hat{m}_{\beta \beta }= \ 12$ – 36 meV.

Alfonso, K. [Virginia Polytechnic Institute and St↗
DOE OSTI2021

10 Years Later: Cloud Computing is Closing the Performance Gap

Can cloud computing infrastructures provide HPC-competitive performance for scientific applications broadly? Despite prolific related literature, this question remains open. Answers are crucial for designing future systems and democratizing high-performance computing. We present a multi-level approach to investigate the performance gap between HPC and cloud computing, isolating different variables that contribute to this gap. Our experiments are divided into (i) hardware and system microbenchmarks and (ii) user application proxies. The results show that today’s high-end cloud computing can deliver HPC-competitive performance not only for computationally intensive applications, but also for memory- and communication-intensive applications – at least at modest scales – thanks to the high-speed memory systems and interconnects and dedicated batch scheduling now available on some cloud platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Ocean Testing of the Azura Prototype Wave Energy Converter in Hawaii.

The Azura scaled prototype has successfully completed 19 months of grid-connected open ocean testing at the US Navy’s Wave Energy Test Site (WETS) in Kaneohe, Hawaii. The Azura was deployed from June 2015 to December 2016. Data collection and processing was done by The Hawaii Natural Energy Institute, providing independent data validation. Figure 1 shows the Azura deployed at WETS. The test concluded with some major accomplishments, including validation of computer simulations, demonstration of a robust design, and marine operations experience.

16 TIDAL AND WAVE POWER↗
DOE OSTI2019

Use cases of lossy compression for floating-point data in scientific data sets

Architectural and technological trends of systems used for scientific computing call for a significant reduction of scientific data sets that are composed mainly of floating-point data. Here, this article surveys and presents experimental results of currently identified use cases of generic lossy compression to address the different limitations of scientific computing systems. The article shows from a collection of experiments run on parallel systems of a leadership facility that lossy data compression not only can reduce the footprint of scientific data sets on storage but also can reduce I/O and checkpoint/restart times, accelerate computation, and even allow significantly larger problems to be run than without lossy compression. In conclusion, these results suggest that lossy compression will become an important technology in many aspects of high performance scientific computing. Because the constraints for each use case are different and often conflicting, this collection of results also indicates the need for more specialization of the compression pipelines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Synergy of coupled kink and tearing modes in fast ion transport

One aspect of the interaction between fast ions and magnetohydrodynamic (MHD) instabilities is the fast ion transport. Coupled kink and tearing MHD instabilities have also been reported to cause fast ion transport. Recently, the 'kick' model has been developed to compute the evolution of the fast ion distribution from neutral beam injection using instabilities as phase-space resonance sources. Overall, the goal of this paper is to utilize the kick model to understand the physics of fast ion transport caused by the coupled kink and tearing modes. Soft x-ray diagnostics are used to identify the mode parameters in the National Spherical Torus Experiment. The comparison of neutron rates measured and computed from time-dependent TRANSP simulation with the kick model shows that the coupling of kink and tearing mode is important in determination of the fast ion transport. The numerical scan of the mode parameters shows that the relative phase of the kink and tearing modes and the overlapping of kink and tearing mode resonances in the phase space can affect the fast ion transport, suggesting that the synergy of the coupled modes may be causing the fast ion transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗