Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “high performance”

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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Redox-stable symmetrical solid oxide fuel cells with exceptionally high performance enabled by electrode/electrolyte diffuse interface

Here, in this study, we report a high performance and redox-stable symmetrical solid oxide fuel cell (SOFC) based on (Ba 0.5 Sr 0.5 ) (Mo 0.1 Fe 0.9 )O 3-δ (BSMF) electrode and La 0.8 Sr 0.2 Ga 0.8 Mg 0.2 O 3-δ (LSGM) electrolyte. BSMF is able to operate both as anode and cathode. Excellent electrocatalytic activity has been achieved on BSMF towards hydrogen oxidation and oxygen reduction. Due to its closely matched lattice parameter to LSGM electrolyte, a unique diffuse interface is formed between BSMF and LSGM. Compared to a clean interface, e.g. BSMF/gadolinium doped ceria interface, this diffuse interface promotes the performance of BSMF electrode 1–1.8 times in 600–800 °C. Polarization resistance of the BSMF/LSGM specimen is as low as 0.047 and 0.007 Ωcm 2 in humidified H 2 and in air at 800 °C, respectively. On the BSMF/LSGM/BSMF symmetrical cell, a maximum power density of 2.28 W/cm 2 is achieved at 800 °C, the highest among with redox-stable ceramic electrodes to the best of our knowledge. Redox stability of this cell is confirmed. The role of anode and cathode is reversed back and forth in different operation modes. No apparent degradation is observed through 4 cycles within a 110 h operation period. These findings demonstrate that (Ba 0.5 Sr 0.5 ) (M o0.1 Fe 0.9 )O 3-δ coupled with LSGM electrolyte is an excellent choice to build a high performance, redox-stable SOFC.

25 ENERGY STORAGE↗
DOE OSTI2020

Teaching Software Sustainability for High Performance Computing at ATPESC

The Argonne Training Program in Extreme Scale Computing (ATPESC) was started by Argonne National Laboratory with the objective of expanding the ranks of better-prepared users of high-performance computing (HPC) machines. One of the unique aspects of the program was inclusion of a track on software engineering and community codes. The inclusion was motivated by the observation that the projects with good software processes were better able to meet their scientific goals. Over the years, with greater awareness of software sustainability issues in the community, the track has evolved into a software productivity and sustainability track. In this paper we present our experience in choosing and disseminating the content related to the topic of software engineering in high performance computing science from the beginning of the program until now. We discuss the motivations and the reception of the tracks. We also document the evolution of the track over the years based on student feedback and also the growth of awareness about software productivity in high performance computing.

Dubey, Anshu↗
DOE OSTI2021

Engineering donor–acceptor conjugated polymers for high-performance and fast-response organic electrochemical transistors

To date, high-performance organic electrochemical transistors (OECTs) have mostly been based on polythiophene systems. Donor–acceptor (D–A) conjugated polymers are expected to be promising materials for OECTs owing to their high mobility and comparatively low crystallinity (good for ion diffusion). However, the OECT performance of D–A polymers lags far behind that of the polythiophenes. Here we synergistically engineered the backbone and side chain of a series of diketopyrrolopyrrole (DPP)-based D–A polymers and found that redox potential, molecular weight, solution processability, and film microstructures all have a severe impact on their performance. After systematic engineering, P(bgDPP-MeOT2) exhibited the best figure-of-merit (μC*) of 225 F cm -1 V -1 s -1 , amongst the highest performance of the reported D–A polymers. Besides, the DPP polymers exhibited high hole mobility of over 1.6 cm 2 V -1 s -1 , leading to fast response OECTs with a record low turn-off response time of 30 μs. The polymer also exhibited good operation stability with a current retention of 98.8% over 700 electrochemical switching cycles. This work reveals the complexity and systematicness in the development of D–A polymer based high-performance OECTs.

36 MATERIALS SCIENCE↗
DOE OSTI2022

US Department of Energy, Office of Science High Performance Computing Facility Operational Assessment 2021: Oak Ridge Leadership Computing Facility

Oak Ridge National Laboratory’s (ORNL’s) Leadership Computing Facility (OLCF) continues to surpass its operational target goals of supporting users; delivering fast, reliable computational ecosystems; creating innovative solutions for high-performance computing (HPC) needs; contributing to the community to build the next generation HPC workforce, and managing risks, safety, and security associated with operating some of the most powerful computers in the world. The results can be seen in the cutting-edge science conducted by users and the praise from the research community. Calendar year (CY) 2021 saw continued excellence in research supported by the OLCF’s leadership-class computing resources, including Summit (the nation’s most powerful supercomputer), the global scratch file system Alpine, the Scalable Protected Infrastructure (SPI), the Exploratory Visualization Environment for Research in Science and Technology (EVEREST), and the archival mass-storage resource High-Performance Storage System (HPSS). While maintaining access and exceptional user support for Summit, the OLCF continued to make progress on the installation and deployment of Frontier, which will be the nation’s first exascale system when it comes online at the start of CY 2023. Users have already begun running and optimizing scientific codes on Crusher, the OLCF test and development system equipped with Frontier’s architecture. Throughout the year, the OLCF maintained a strong culture of operational excellence, including risk management, workplace safety, and cybersecurity. The OLCF’s rigorous risk management strategy anticipated and mitigated risks, and at this time there are no high-priority operational risks. Similarly, ORNL and the OLCF were committed to operating under the US Department of Energy’s (DOE’s) safety regulations that ensure a safe workplace. Technical staff tracked and monitored existing threats and vulnerabilities within the OLCF while continually developing tools and practices to enhance operations without increasing the facility’s risk. CY 2021 was filled with outstanding results and accomplishments, including a very high rating from users on overall satisfaction for the eighth consecutive year; a tremendous number of node hours delivered to 1,671 researchers on Summit; and the successful delivery of the allocation split of roughly 60%, 20%, and 20% of core-hours offered for the Innovative and Novel Computational Impact on Theory and Experiment (INCITE), Advanced Scientific Computing Research Leadership Computing Challenge (ALCC), and Director’s Discretionary (DD) programs, respectively (Section 2). COVID-19 research remained a focus in 2021, and the ALCC and DD programs allocated over 1 million Summit hours to the COVID-19 High Performance Computing Consortium. These accomplishments, coupled with the high utilization rates (i.e., overall and capability usage), represent the fulfillment of the promise of leadership class machines: efficient facilitation of leadership-class computational applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗
DOE OSTI2022

Quantifying the Impact of Advanced Web Platforms on High Performance Computing Usage

The deployment of Science Gateways for High Performance Computing (HPC) systems can alter long-accepted usage patterns on supercomputing systems in positive ways as an ever-increasing number of users migrate their workflows to HPC systems. Idaho National Laboratory (INL) has deployed two separate advanced web platforms, Open OnDemand and NICE DCV, for integration with HPC resources to improve web accessibility for HPC users. Researchers conducted a multi-year study on how HPC usage pat- terns changed in the presence of these platforms. This work reports the results of that study and quantifies the observed impacts, including adoption by visualization and Jupyter Notebook/Lab users, decreased job submission friction, rapid uptake of HPC by Windows users, and increased overall system utilization. The most significant impacts were observed from the deployment of Open OnDemand, and this work also identifies some best practices for Open OnDemand deployment for HPC datacenters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

High Performance Computing Systems Tools, Visualization, and Management

High Performance Computing (HPC) systems are complex setups of servers, storage devices, network switches, and cables that are specifically designed to accommodate hundreds of users running highly computationally intensive applications at a time. These applications require numerous softwares, licenses, and various levels of storage as well. All of these resources must be monitored and managed by HPC administrators, which presents a daunting task. In this project, I created numerous software tools as part of an HPC Visualization and Management system, which is now used by HPC administrators on a daily basis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Porous potassium tantalate-reduced graphene oxide nano cube architecture for high performance hybrid supercapacitors

Energy storage has always been a major concern in the present-day situation. Advanced energy storage devices are batteries, supercapacitor and solar cells. However, advancements have been noteworthy in the field of high-performance hybrid supercapacitors. On this note, we have fabricated a hybrid supercapacitor electrode material Potassium tantalate nano cube (KT NCs) and its reduced graphene oxide composite (KT-rGO NCs) and tested its electrochemical performance. The materials showed high performances with specific capacitance of 565 F/g for KT NCs and 850 F/g for KT-rGO NCs respectively. Energy densities of both KT NCs & KT-rGO NCs are 28.24 Wh/Kg and 29.50 Wh/kg with good retention capacities. Further detailed study of both KT NCs and KT-rGO NCs are carried out with characterization techniques like XRD, FTIR, BET, Raman and HRTEM for structural analysis and electrochemical measurements to analyse various parameters pertaining to its charge storage capacity.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Terephthalic Acid Copolyesters Containing Tetramethylcyclobutanediol for High‐Performance Plastics

Abstract There is a need for high‐performance applications for terephthalic acid (TPA) polyesters with high heat resistance, impact toughness, and optical clarity. Bisphenol A (BPA) based polycarbonates and polyarylates have such properties, but BPA is an endocrine disruptor. Therefore, new TPA polyesters that are less hazardous to health and the environment are becoming popular. Tetramethylcyclobutanediol (TMCD) is a difunctional monomer that can be polymerized with TPA and other diols to yield copolyesters with superior properties to conventional TPA polyesters. It has a cyclobutyl ring that makes it more rigid than cyclohexanedimethanol (CHDM) and EG. Thus, TMCD containing TPA copolyesters can have high heat resistance and impact strength. TPA can be made from abundantly available upcycled polyethylene terephthalate (PET). Therefore, this review discusses the synthesis of monomers and copolyesters, the impact of diol composition on material properties, molecular weight, effects of photodegradation, health safety, and substitution of cyclobutane diols for future polyesters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Automatic Code Generation for High-Performance Graph Algorithms

Graph problems are common across fields of scientific computing and social sciences. However, despite their importance, implementing graph algorithms effectively on modern computing systems is a challenging task that requires significant programming effort and generally results in customized implementations. Current computing and memory hierarchies are not architected for irregular computations resulting in challenges for graph algorithms to achieve high performance on those architectures. In this paper, we present GraphX, a novel compiler framework and DSL designed to simplify the development of efficient graph algorithms and achieve high performance on modern computing systems. GraphX consists of a DSL for efficient implementation of graph algorithms, various optimizations, such as support for sparse linear algebra and workspace transformations, optimized graph primitives, including semiring and masking, and a high-performance code generation engine. Using GraphX, users can implement graph algorithms using a semantically-rich language with graph-oriented operators. GraphX uses these semantics to automatically generate efficient code for target architectures, increasing performance and portability across architectures. The composable nature of GraphX makes it possible to extend the set of optimizations and architectures without modifying the source code. We demonstrate GraphX outperforms state-of-the-art graph libraries, such as LAGraph, up to $3.7 speedup in semiring operations, $2.19 speedup in an important sparse computational kernel, and $9.05 speedup in graph processing algorithms.

compiler, graph algorithms, semiring, masking, wor↗
DOE OSTI2020

Contrasting SnTe–NaSbTe 2 and SnTe–NaBiTe 2 Thermoelectric Alloys: High Performance Facilitated by Increased Cation Vacancies and Lattice Softening

Defect chemistry is critical to designing high performance thermoelectric materials. In SnTe, the naturally large density of cation vacancies results in excessive hole doping and frustrates the ability to control the thermoelectric properties. Yet, recent work also associates the vacancies with suppressed sound velocities and low lattice thermal conductivity, underscoring the need to understand the interplay between alloying, vacancies, and the transport properties of SnTe. Here, we report solid solutions of SnTe with NaSbTe 2 and NaBiTe 2 (NaSn m SbTe m +2 and NaSn m BiTe m +2 , respectively) and focus on the impact of the ternary alloys on the cation vacancies and thermoelectric properties. We find introduction of NaSbTe 2 , but not NaBiTe 2 , into SnTe nearly doubles the natural concentration of Sn vacancies. Furthermore, DFT calculations suggest that both NaSbTe 2 and NaBiTe 2 facilitate valence band convergence and simultaneously narrow the band gap. These effects improve the power factors but also make the alloys more prone to detrimental bipolar diffusion. Indeed, the performance of NaSn m BiTe m +2 is limited by strong bipolar transport and only exhibits modest maximum ZTs ≈ 0.85 at 900 K. In NaSn m SbTe m +2 however, the doubled vacancy concentration raises the charge carrier density and suppresses bipolar diffusion, resulting in superior power factors than those of the Bi-containing analogues. Lastly, NaSbTe 2 incorporation lowers the sound velocity of SnTe to give glasslike lattice thermal conductivities. Facilitated by the favorable impacts of band convergence, vacancy-augmented hole concentration, and lattice softening, NaSn m SbTe m +2 reaches high ZT ≈ 1.2 at 800–900 K and a competitive average ZT avg of 0.7 over 300–873 K. The difference in ZT between two chemically similar compounds underscores the importance of intrinsic defects in engineering high-performance thermoelectrics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Probing Particle Impingement in Boilers Using High-Performance Computing with Parallel CPUs and GPUs

The major goals of the project are to calculate and analyze particle impingement within boilers, quantify effects of particulates in boilers, and predict damage rates of boilers under different cycling modes. Collectively, these initiatives develop insight into existing coal plant challenges using advanced modeling tools, particularly those leveraging high-performance computing resources. High-performance CFD computing forms a central theme in this project that will employ a high degree of coordination and communication between these initiatives to realize a final, rigorously sound, and validated computational capability upon completion. These results will create a holistic, comprehensive, systems-level assessment of damage rates under different cycling modes. Together, these objectives will develop critical insight into damage mechanisms in existing coal plant challenges for accurately and efficiently assessing operating performance in fossil energy power plants.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2025

HP-MDR: High-performance and Portable Data Refactoring and Progressive Retrieval with Advanced GPUs

Scientific applications produce vast amounts of data, posing grand challenges in the underlying data management and analytic tasks. Progressive compression is a promising way to address this problem, as it allows for on-demand data retrieval with significantly reduced data movement cost. However, most existing progressive methods are designed for CPUs, leaving a gap for them to unleash the power of today’s heterogeneous computing systems with GPUs.In this work, we propose HP-MDR, a high-performance and portable data refactoring and progressive retrieval framework for GPUs. Our contributions are four-fold: (1) We carefully optimize the bitplane encoding and lossless encoding, two key stages in progressive methods, to achieve high performance on GPUs; (2) We propose pipeline optimization and incorporate it with data refactoring and progressive retrieval workflows to further enhance the performance for large data process; (3) We leverage our framework to enable high-performance data retrieval with guaranteed error control for common Quantities of Interest; (4) We evaluate HP-MDR and compare it with state of the arts using five real-world datasets. Experimental results demonstrate that HP-MDR delivers an average 13.68 × and 6.31 × throughput in data refactoring and progressive retrieval tasks, respectively. It also leads to 11.22 × throughput for recomposing required data representations under Quantity-of-Interest error control and 6.04 × performance for the corresponding end-to-end data retrieval, when compared with state-of-the-art solutions.

Li, Yanliang [University of Oregon]↗
DOE OSTI2024

Accurate machine-learning predictions of coercivity in high-performance permanent magnets

Increased demand for high-performance permanent magnets in the electric vehicle and wind-turbine industries has prompted the search for cost-effective alternatives. Discovering magnetic materials with the desired intrinsic and extrinsic permanent magnet properties presents a significant challenge to researchers because of issues with the global supply of rare-earth elements, material stability, and a low maximum magnetic energy product BH max . While first-principles density functional theory (DFT) predicts materials’ magnetic moments, magnetocrystalline anisotropy constants, and exchange interactions, it cannot compute extrinsic properties such as coercivity (H c ). Although it is possible to calculate H c theoretically with micromagnetic simulations, the predicted value is larger than the experiment by almost an order of magnitude due to the Brown paradox. To circumvent these issues, we employ machine-learning (ML) methods on an extensive database obtained from experiments, DFT calculations, and micromagnetic modeling. The use of a large experimental dataset enables realistic H c predictions for materials such as Ce-doped Nd 2 ⁢Fe 14 ⁢B, comparing favorably against micromagnetically simulated coercivities. Remarkably, our ML model accurately identifies uniaxial magneto-crystalline anisotropy as the primary contributor to H c . With DFT calculations, we predict the Nd-site-dependent magnetic anisotropy behavior in Nd 2 ⁢Fe 14 ⁢B, confirming that Nd 4⁢g sites mainly contribute to uniaxial magnetocrystalline anisotropy, and also calculate the Curie temperature (T c ). Finally, both calculated results are in good agreement with the experiments. The coupled experimental dataset and ML modeling with DFT input predict H c with far greater accuracy and speed than was previously possible using micromagnetic modeling. Further, we reverse engineer the grain-boundary and intergrain exchange coupling with micromagnetic simulations by employing the ML predictions.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Demonstration and Evaluation of Lightweight High Performance Quad-Pane Windows

Ten high-performance quad-pane windows (five quad-pane with suspended film and five quad-pane with thin glass) were installed at Building 41 of the Denver Federal Center in Colorado as part of a demonstration project to assess their thermal performance, life cycle costs, and deployment potential for replacing older single-pane windows. The U.S. Department of Energy's National Renewable Energy Laboratory performed several different evaluations to assess the viability of the quad-pane windows for GSA applications. Some of these assessments were performed with models, while others required onsite evaluations including time series measurements.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Near-infrared absorbing acceptor with suppressed triplet exciton generation enabling high performance tandem organic solar cells

Reducing the energy loss of sub-cells is critical for high performance tandem organic solar cells, while it is limited by the severe non-radiative voltage loss via the formation of non-emissive triplet excitons. Herein, we develop an ultra-narrow bandgap acceptor BTPSeV-4F through replacement of terminal thiophene by selenophene in the central fused ring of BTPSV-4F, for constructing efficient tandem organic solar cells. The selenophene substitution further decrease the optical bandgap of BTPSV-4F to 1.17 eV and suppress the formation of triplet exciton in the BTPSV-4F-based devices. The organic solar cells with BTPSeV-4F as acceptor demonstrate a higher power conversion efficiency of 14.2% with a record high short-circuit current density of 30.1 mA cm –2 and low energy loss of 0.55 eV benefitted from the low non-radiative energy loss due to the suppression of triplet exciton formation. We also develop a high-performance medium bandgap acceptor O1-Br for front cells. By integrating the PM6:O1-Br based front cells with the PTB7-Th:BTPSeV-4F based rear cells, the tandem organic solar cell demonstrates a power conversion efficiency of 19%. The results indicate that the suppression of triplet excitons formation in the near-infrared-absorbing acceptor by molecular design is an effective way to improve the photovoltaic performance of the tandem organic solar cells.

14 SOLAR ENERGY↗
DOE OSTI2021

Dual-Solvent Li-Ion Solvation Enables High-Performance Li-Metal Batteries

Novel electrolyte designs to further enhance the lithium (Li) metal battery cyclability are highly desirable. Here, fluorinated 1,6-dimethoxyhexane (FDMH) is designed and synthesized as the solvent molecule to promote electrolyte stability with its prolonged –CF 2 – backbone. Meanwhile, 1,2-dimethoxyethane is used as a co-solvent to enable higher ionic conductivity and much reduced interfacial resistance. Combining the dual-solvent system with 1 m lithium bis(fluorosulfonyl)imide (LiFSI), high Li-metal Coulombic efficiency (99.5%) and oxidative stability (6 V) are achieved. Using this electrolyte, 20 µm Li||NMC batteries are able to retain ≈80% capacity after 250 cycles and Cu||NMC anode-free pouch cells last 120 cycles with 75% capacity retention under ≈2.1 µL mAh -1 lean electrolyte conditions. Such high performances are attributed to the anion-derived solid-electrolyte interphase, originating from the coordination of Li-ions to the highly stable FDMH and multiple anions in their solvation environments. This work demonstrates a new electrolyte design strategy that enables high-performance Li-metal batteries with multisolvent Li-ion solvation with rationally optimized molecular structure and ratio.

25 ENERGY STORAGE↗
DOE OSTI2021

High performance ionic-liquid-gated air doped diamond field-effect transistors

Here we report successful fabrication of high performance ion-gated field-effect transistors (FETs) on hydrogenated diamond surface. Investigations on the hydrogen (H)-terminated diamond by Hall effect measurements shows Hall mobility as high as ~200 cm 2 V –1 s –1 . In addition we demonstrate a rapid fabrication scheme for achieving stable high performance devices useful for determining optimal growth and fabrication conditions. We achieved H-termination using hydrogen plasma treatment with a sheet resistivity as low as ~1.3 kΩ/sq. Conductivity through the FET channel is studied as a function of bias voltage on the liquid ion-gated electrode from –3.0 to 1.5 V. Stability of the H-terminated diamond surface was studied by varying the substrate temperature up to 350 °C. It was demonstrated that the sheet resistance and carrier densities remain stable over 3 weeks in ambient air atmosphere even at substrate temperatures up to 350 °C, whereas increasing temperature beyond this limit has effected hydrogenation. This study opens new avenues for carrying out fundamental research on diamond FET devices with ease of fabrication and high throughput.

77 NANOSCIENCE AND NANOTECHNOLOGY↗