Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Group Processes”

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 1,333 records · Page 74
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Polymer-Stabilized Liquid Metal Nanoparticles as a Scalable Current Collector Engineering Approach Enabling Lithium Metal Anodes

Dendrites and dead lithium formation over prolonged cycling have long been challenges that hinder the safe implementation of metallic Li anodes. In this study, we employ polymer-stabilized liquid metal nanoparticles (LM-P NPs) of eutectic gallium indium (EGaIn) to create uniform Li nucleation sites enabling homogeneous lithium electrodeposition. Block copolymers of poly(ethylene oxide) and poly(acrylic acid) (PEO-b-PAA) were grafted onto the EGaIn surface, forming stabilized, well-dispersed NPs. Using a scalable spray coating approach, LM-P NPs were fabricated on copper current collectors, providing lithiophilic PEO sites and interactive carboxyl groups to guide Li deposition. The Li-EGaIn alloying process greatly reduced the Li + diffusion barrier, enabling fast Li transport through the coating layer, resulting in decreased nucleation overpotential. Therefore, about five times lower Li nucleation overpotential was obtained on the LM-P modified Cu with an optimal composition of the polymers than the bare Cu substrates. DFT computations was used to reveal the binding properties between the LM-P layer and Li. Due to the regulated Li plating/stripping process, as-obtained 30 μm Li anodes paired with LiNi 0.8 Co 0.1 Mn 0.1 O 2 (NCM8 11 ) with a negative/positive electrode capacity (N/P) ratio ~ 10 exhibited stable cycling performance at 0.5C for over 250 cycles, with an average Coulombic efficiency of 99.55%. Ultrathin Li (1 μm) anodes with an N/P ratio ~ 0.6 were also demonstrated in Li|LiFePO 4 cells, which examined the stabilization of Li by LM-P NPs and monitored practical loadings of Li anodes that are close to anode-free systems.

25 ENERGY STORAGE↗
DOE OSTI2024

Data Analytics for Catalysis Predictions: Are We Ready Yet?

Catalysis informatics has received tremendous attention in recent years as a tool to design catalysts and discover unique descriptors that capture the relationships between chemical properties and catalytic performance. One of the stop-gaps in understanding catalytic effects, which is often ignored and limits the deployment of data science tools, relates to the lack of uniform data. The catalytic cleavage of C–X (X= H, C, N, and O) bonds is relevant to many fundamental catalytic processes. In this Perspective, we performed data analytics on four groups of C–X cleavage reactions that are common in production, upcycling, or reactive separation: the C–C cleavage in cyclopropyl alcohol, the C–H cleavage in hydroacylation reactions, the C–O cleavage in β-O-4 linkages, and the C–N cleavage in amides, using experimental data collected from the literature to understand their underlying correlations. Experimental variables of high impact are identified for each reaction by dimensionality reduction methods. We highlight the urgent need for experimental data sets that include full details on the reaction conditions, such as reagent concentration, reaction temperature, or time in machine-readable forms. We discuss the potential improvement of the data of these reactions and promising approaches such as autonomous experiments to fill the gaps in unbiased experimental data. Finally, we also address the early stage consideration of separation aspects in the experimental design of efficient catalytic systems for these fundamental examples of chemical reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Aqueous-Phase Acetic Acid Ketonization over Monoclinic Zirconia

The effect of aqueous phase on the acetic acid ketonization over monoclinic zirconia has been investigated using first-principles based density functional theory (DFT) calculations. To capture the aqueous phase chemistry over the solid zirconia catalyst surface, the aqueous phase is represented by 111 explicit water molecules with a liquid water density of 0.93 g/cm3 and the monoclinic zirconia is modeled by the most stable surface structure . The dynamic nature of aqueous phase/ interface was studied using ab initio molecular dynamics simulation, indicating that nearly half of the surface Zr sites are occupied by either adsorbed water molecules or hydroxyl groups at 550 K. DFT calculations show that the adsorption process of acetic acid from the liquid water phase to the surface is nearly thermodynamically neutral with a Gibbs free energy of -2.3 kJ/mol although the adsorption strength of acetic acid on the surface in aqueous phase is much stronger than in vapor phase. Therefore it is expected that the adsorption of acetic acid will dramatically affects aqueous phase ketonization reactivity over the monoclinic zirconia catalyst. Using the same ketonization mechanism via the β-keto acid intermediate, we have compared acetic acid ketonization to acetone in both vapor and aqueous phases. Our DFT calculation results show although the rate-determining step of the β-keto acid formation via the C-C coupling is not pronouncedly affected, the presence of liquid water molecules will dramatically affect dehydrogenation and hydrogenation steps via proton transfer mechanism. This work was financially supported by the United States Department of Energy (DOE)’s Bioenergy Technologies Office (BETO) and performed at the Pacific Northwest National Laboratory (PNNL). PNNL is a multi-program national laboratory operated for DOE by Battelle Memorial Institute. Computing time and advanced catalyst characterization use was granted by a user proposal at the William R. Wiley Environmental Molecular Sciences Laboratory (EMSL). EMSL is a national scientific user facility sponsored by the Department of Energy’s Office of Biological and Environmental Research and located at PNNL.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Olefin oligomerization by main group Ga 3+ and Zn 2+ single site catalysts on SiO 2

In heterogeneous catalysis, olefin oligomerization is typically performed on immobilized transition metal ions, such as Ni 2+ and Cr 3+ . Here we report that silica-supported, single site catalysts containing immobilized, main group Zn 2+ and Ga 3+ ion sites catalyze ethylene and propylene oligomerization to an equilibrium distribution of linear olefins with rates similar to that of Ni 2+ . The molecular weight distribution of products formed on Zn 2+ is similar to Ni 2+ , while Ga 3+ forms higher molecular weight olefins. In situ spectroscopic and computational studies suggest that oligomerization unexpectedly occurs by the Cossee-Arlman mechanism via metal hydride and metal alkyl intermediates formed during olefin insertion and β-hydride elimination elementary steps. Initiation of the catalytic cycle is proposed to occur by heterolytic C-H dissociation of ethylene, which occurs at about 250 °C where oligomerization is catalytically relevant. This work illuminates new chemistry for main group metal catalysts with potential for development of new oligomerization processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

Adsorption of a metalorganic complex at a metal surface: A density functional theory study vs. model description

A modification of the electronic and magnetic structure of a metalorganic complex by chemisorption at a metallic surface is addressed. The density functional theory (DFT) is applied to investigate a simplified form of a copper-dioxolene complex in a free state and connected to Au(111) surface with alkanethiol linkers. A systematic study of the dependence of the system electronic structure on the linker length is performed. It is found that the electronic structure of the complex is well preserved during the adsorption process. The magnetic moment of the Cu-dioxolene functional group is shown to be strictly correlated with the amount of the charge residing at the complex. On the basis of the DFT results, a model Hamiltonian of the adsorbed metalorganic system is proposed. The model is an extension of the Sandorfy's model of the alkanes and includes explicitly Coulomb interaction between electrons both within the alkane's backbone and the end group. We show that the latter feature is necessary to understand the evolution of the system's properties with the length of the linkers. The advantage of this approach is that it not only reproduces the main results of our DFT analysis but also provides a simple common basis to analyse a wide class of metal complexes bound to metal surfaces with alkanethiol linkers.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2017

Ultralow thermal conductivity in all-inorganic halide perovskites

Significance Discovery of materials with low thermal conductivity in simple, fully dense, and single-crystalline solids has proven extremely challenging. This paper reports the discovery of ultralow thermal conductivity (∼0.4 W⋅m −1 ⋅K −1 ) in single-crystalline, all-inorganic halide perovskite nanowires, which is comparable to their amorphous limit value. We attribute ultralow thermal conductivity to a cluster rattling mechanism, based on strong phonon–phonon scattering via the coexistence of collective motions involving various atom groups. These results call attention to the vital thermal transport processes and thermal management strategies for applications with all-inorganic halide perovskites. Further, CsSnI 3 shows a rare combination of ultralow thermal conductivity and high electrical conductivity, so it can be a promising material for unique applications as an electrically conductive thermal insulator.

36 MATERIALS SCIENCE↗
DOE OSTI2013

HIGH-RESOLUTION SIMULATIONS OF THE REIONIZATION OF AN ISOLATED MILKY WAY-M31 GALAXY PAIR

We present the results of a set of numerical simulations aimed at studying reionization at the galactic scale. We use a high-resolution realization of the formation of the Milky Way (MW)-M31 system to simulate the reionization of the Local Group. The reionization calculation was performed with the post-processing radiative transfer code ATON and the underlying cosmological simulation was performed as part of the CLUES project (http://www.clues-project.org). We vary the source models to bracket the range of source properties used in the literature. We investigate the structure and propagation of the galactic ionization fronts by a visual examination of our reionization maps. Within the progenitors, we find that reionization is patchy and proceeds locally inside-out. The process becomes patchier with decreasing source photon output. It is generally dominated by one major H II region and one to four additional isolated smaller bubbles, which eventually overlap. Higher emissivity results in faster and earlier local reionization. In all models, the reionization of the MW and M31 are similar in duration, i.e., between 203 Myr and 22 Myr depending on the source model, placing their z{sub reion} between 8.4 and 13.7. In all models except the most extreme, the MW and M31 progenitors reionize internally, ignoring each other despite being relatively close to each other, even during the epoch of reionization. Only in the case of strong supernova feedback suppressing star formation in halos less massive than 10{sup 9} M{sub ☉}, and using our highest emissivity, do we find that the MW is reionized by M31.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2019

Lipid Mini-On: mining and ontology tool for enrichment analysis of lipidomic data

Abstract Summary Here we introduce Lipid Mini-On, an open-source tool that performs lipid enrichment analyses and visualizations of lipidomics data. Lipid Mini-On uses a text-mining process to bin individual lipid names into multiple lipid ontology groups based on the classification (e.g. LipidMaps) and other characteristics, such as chain length. Lipid Mini-On provides users with the capability to conduct enrichment analysis of the lipid ontology terms using a Shiny app with options of five statistical approaches. Lipid classes can be added to customize the user’s database and remain updated as new lipid classes are discovered. Visualization of results is available for all classification options (e.g. lipid subclass and individual fatty acid chains). Results are also visualized through an editable network of relationships between the individual lipids and their associated lipid ontology terms. The utility of the tool is demonstrated using biological (e.g. human lung endothelial cells) and environmental (e.g. peat soil) samples. Availability and implementation Rodin (R package: https://github.com/PNNL-Comp-Mass-Spec/Rodin), Lipid Mini-On Shiny app (https://github.com/PNNL-Comp-Mass-Spec/LipidMiniOn) and Lipid Mini-On online tool (https://omicstools.pnnl.gov/shiny/lipid-mini-on/). Supplementary information Supplementary data are available at Bioinformatics online.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

HIPASS study of southern ultradiffuse galaxies and low surface brightness galaxies

ABSTRACT We present results from an H i counterpart search using the HI Parkes All Sky Survey (HIPASS) for a sample of low surface brightness galaxies (LSBGs) and ultradiffuse galaxies (UDGs) identified from the Dark Energy Survey (DES). We aimed to establish the redshifts of the DES LSBGs to determine the UDG fraction and understand their properties. Out of 409 galaxies investigated, none were unambiguously detected in H i. Our study was significantly hampered by the high spectral rms of HIPASS and thus in this paper we do not make any strong conclusive claims but discuss the main trends and possible scenarios our results reflect. The overwhelming number of non-detections suggest that (a) Either all the LSBGs in the groups, blue or red, have undergone environment aided pre-processing and are H i deficient or the majority of them are distant galaxies, beyond the HIPASS detection threshold. (b) The sample investigated is most likely dominated by galaxies with H i masses typical of dwarf galaxies. Had there been Milky Way (MW) size (Re) galaxies in our sample, with proportionate H i content, they would have been detected, even with the limitations imposed by the HIPASS spectral quality. This leads us to infer that if some of the LSBGs have MW-size optical diameters, their H i content is possibly in the dwarf range. More sensitive observations using the SKA precursors in future may resolve these questions.

Zhou, Yun-Fan↗
DOE OSTI2021

Validity of Machine Learning in the Quantitative Analysis of Complex Scanning Near-Field Optical Microscopy Signals Using Simulated Data

Scattering-type scanning near-field optical microscope (s-SNOM) is a modern technique for subdiffractional optical imaging and spectroscopy. Over the past two decades, tremendous efforts have been devoted to modeling complex tip-sample interactions in s-SNOM, aimed at understanding the electrodynamics of materials at the nanoscale. However, due to complexities in analytical methods and the limited computation power for fully numerical simulations, compromises must be made to facilitate the modeling of tip-sample interaction, such as using quasistatic approximation or unrealistic tip geometries. Here, we apply a variety of widely utilized machine-learning methods, including k nearest neighbor and feedforward neural network etc. to study the phase-resolved spectroscopic near-field response. With only a small set of training data, which is simulated using the finite-dipole model, we demonstrate that the relation between the experimental near-field signal and sample optical constant can be one to one mapped without the need for tip modeling: for a given material with a moderate dielectric function, its complex near-field spectrum can be accurately determined within the mid-IR spectral range, and vice versa. Our preliminary study sets the stage for future exploration using real experimental data. Our method is beneficial for processing the increasing amount of data accumulated across many research groups and especially useful for user facilities such as synchrotron-based national laboratories where a large amount of data is generated on a daily basis.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine learning analysis of perovskite oxides grown by molecular beam epitaxy

Reflection high-energy electron diffraction (RHEED) is a ubiquitous in situ molecular beam epitaxial (MBE) characterization tool. Although RHEED can be a powerful means for crystal surface structure determination, it is often used as a static qualitative surface characterization method at discrete intervals during a growth. A full analysis of RHEED data collected during the entirety of MBE growths is made possible using principle component analysis (PCA) and $\textit{k}$-means clustering to examine significant boundaries that occur in the temporal clusters grouped from RHEED data and identify statistically significant patterns. This process is applied to data from homoepitaxial SrTiO 3 growths, heteroepitaxial SrTiO 3 grown on scandate substrates, BaSnO 3 films grown on SrTiO 3 substrates, and LaNiO 3 films grown on SrTiO 3 substrates. We report this analysis may provide additional insights into the surface evolution and transitions in growth modes at precise times and depths during growth, and that video archival of an entire RHEED image sequence may be able to provide more insight and control overgrowth processes and film quality.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Parallel variable selection for effective performance prediction

Large data analysis problems often involve a large number of variables, and the corresponding analysis algorithms may examine all variable combinations to find the optimal solution. For example, to model the time required to complete a scientific workflow, we need to consider the impact of dozens of parameters. To reduce the model building time and reduce the likelihood of overfitting, we look to variable selection methods to identify the critical variables for the performance model. In this work, we create a combination of variable selection and performance prediction methods that is as effective as the exhaustive search approach when the exhaustive search could be completed in a reasonable amount of time. To handle the cases where the exhaustive search is too time consuming, we develop the parallelized variable selection algorithm. Additionally, we develop a parallel grouping mechanism that further reduces the variable selection time by 70%.As a case study, we exercise the variable selection technique with the performance measurement data from the Palomar Transient Factory (PTF) workflow. The application scientists have determined that about 50 variables and parameters are important to the performance of the workflows. Our tests show that the Sequential Backward Selection algorithm is able to approximate the optimal subset relatively quickly. By reducing the number of variables used to build the model from 50 to 4, we are able to maintain the prediction quality while reducing the model building time by a factor of 6. Using the parallelization and grouping techniques we developed in this work, the variable selection process was reduced from over 18 hours to 15 minutes while ending up with the same variable subset.

Wang, J↗
DOE OSTI2024

Divergence Reduction in Monte Carlo Neutron Transport with On-GPU Asynchronous Scheduling

While Monte Carlo Neutron Transport (MCNT) is near-embarrasingly parallel, the effectively unpredictable lifetime of neutrons can lead to divergence when MCNT is evaluated on GPUs. Divergence is the phenomenon of adjacent threads in a warp executing different control flow paths; on GPUS, it reduces performance because each work group may only execute one path at a time. The process of Thread Data Remapping (TDR) resolves these discrepancies by moving data across hardware such that data in the same warp will be processed through similar paths. A common issue among prior implementations of TDR is the synchronous nature of its remapping and processing cycles, which exhaustively sort data produced by prior processing passes and exhaustively evaluate the sorted data. In another work, we defined a method of remapping data through an asynchronous scheduler which allows for work to be stored in shared memory and deferred arbitrarily until that work is a viable option for low-divergence evaluation. This article surveys a wider set of cases, with the goal of characterizing performance trends across a more comprehensive set of parameters. These parameters include cross sections of scattering/capturing/fission, use of implicit capture, source neutron counts, simulation time spans, and tuned memory allocations. Across these cases, we have recorded minimum and average execution times, as well as a heuristically tuned near-optimal memory allocation size for both synchronous and asynchronous scheduling. Across the collected data, it is shown that the asynchronous method is faster and more memory efficient in the majority of cases, and that it requires less tuning to achieve competitive performance.

Computer Science↗
DOE OSTI2022

A review on the application of machine learning for combustion in power generation applications

Abstract Although the world is shifting toward using more renewable energy resources, combustion systems will still play an important role in the immediate future of global energy. To follow a sustainable path to the future and reduce global warming impacts, it is important to improve the efficiency and performance of combustion processes and minimize their emissions. Machine learning techniques are a cost-effective solution for improving the sustainability of combustion systems through modeling, prediction, forecasting, optimization, fault detection, and control of processes. The objective of this study is to provide a review and discussion regarding the current state of research on the applications of machine learning techniques in different combustion processes related to power generation. Depending on the type of combustion process, the applications of machine learning techniques are categorized into three main groups: (1) coal and natural gas power plants, (2) biomass combustion, and (3) carbon capture systems. This study discusses the potential benefits and challenges of machine learning in the combustion area and provides some research directions for future studies. Overall, the conducted review demonstrates that machine learning techniques can play a substantial role to shift combustion systems towards lower emission processes with improved operational flexibility and reduced operating cost.

Engineering↗
DOE OSTI2022

Scalable and Energy-Efficient Methods for Interactive Exploration of Scientific Data

The main scientific contributions of this project are the following novel concepts for multidimensional arrays: shape-based similarity join (SIGMOD 2016), incremental view maintenance (SIGMOD 2017), user-defined stencil functions (HPDC 2017), and distributed caching for in-situ processing (SSDBM 2018). Building on our collaboration with the astrophysics group at LBNL, we applied these techniques to the data generated in the Palomar Transient Factory (PTF) astronomical survey. They played a pivotal role in the first-ever observation of a neutron star merger, which produces gravitational waves and turns out to be the origin of heavy elements, including gold. This has lead to a Science magazine article that has received extensive media coverage on ACM TechNews, Slashdot, FiveThirtyEight, and Quanta Magazine, among others. Additionally, two other articles detailing related aspects of the same discovery have been published in the Astrophysical Journal Letters journal. These publications have more than 3,000 citations according to Google Scholar (as of February 2022). This cross-disciplinary collaboration provided very good opportunities to apply database techniques to real-life scientific problems. The fact that they facilitated major discoveries in astrophysics proves the importance of our research. In addition to the work on multidimensional array databases, this project has also developed stochastic gradient descent (SGD) optimization algorithms for training large scale machine learning models, methods for querying in-situ data, and a database query optimizer based on sketch synopses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Stakeholder Training for IEEE 1547-2018 (Abbreviated Final Technical Report)

This project identifies and makes publicly available educational materials to assist stakeholders in adopting the revised IEEE Std 1547-2018, Standard for Interconnection and Interoperability of Distributed Energy Resources with Associated Electric Power Systems Interfaces. The new standard is significantly different than the previous 2003 version, contains new concepts and new technical requirements, and requires educational material on the application of these changes. NREL had a principal role in accelerating the revision process though its chairmanship of the IEEE Std 1547 working group to revise the standard and through technical support provided to the working group. Under this project, NREL and partners compiled and developed publicly available educational materials for IEEE Std 1547-2018, including webinars, white papers, and other resources on related topics.

14 SOLAR ENERGY↗