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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 325 records · Page 18

Novel Zwitterionic Polyurethane-in-Salt Electrolytes with High Ion Conductivity, Elasticity, and Adhesion for High-Performance Solid-State Lithium Metal Batteries

This study presents a novel polymer-in-salt (PIS) zwitterionic polyurethane-based solid polymer electrolyte (zPU-SPE) that offers high ionic conductivity, strong interaction with electrodes, and excellent mechanical and electrochemical stabilities, making it promising for high-performance all solid-state lithium batteries (ASSLBs). The zPU-SPE exhibits remarkable lithium-ion (Li+) conductivity (3.7 × 10⁻⁴ S cm−1 at 25 °C), enabled by exceptionally high salt loading of up to 90 wt.% (12.6 molar ratio of Li salt to polymer unit) without phase separation. It addresses the limitations of conventional SPEs by combining high ionic conductivity with a Li+ transference number of 0.44, achieved through the incorporation of zwitterionic groups that enhance ion dissociation and transport. The high surface energy (338.4 J m−2) and elasticity ensure excellent adhesion to Li anodes, reducing interfacial resistance and ensuring uniform Li+ flux. When tested in Li||zPU||LiFePO₄ and Li||zPU||S/C cells, the zPU-SPE demonstrated remarkable cycling stability, retaining 76% capacity after 2000 cycles with the LiFePO4 cathode, and achieving 84% capacity retention after 300 cycles with the S/C cathode. Molecular simulations and a range of experimental characterizations confirm the superior structural organization of the zPU matrix, contributing to its outstanding electrochemical performance. The findings strongly suggest that zPU-SPE is a promising candidate for next-generation ASSLBs.

Wang, Kun↗

Identifying High-Performance Metal–Organic Frameworks for Low-Temperature Oxygen Recovery from Helium by Computational Screening

Metal–organic frameworks (MOFs) are an important class of porous crystalline materials for applications ranging from gas adsorption and separation to catalysis. There are thousands of potential MOFs available for separation applications. Here, we developed a computational approach to screen MOFs for the separation of oxygen–helium mixtures at low temperatures (100–200 K), conditions that were motivated by issues associated with propulsion in space-based settings. We used detailed molecular simulations for a small number of MOFs to develop screening methods that were then used to estimate the optimum temperatures for separations using pressure swing adsorption for 2932 MOFs from the CoRE MOF database and the swing capacity and oxygen–helium selectivity at these temperatures. We used the stability of the best-performing structures in the presence of moisture as a means to provide a short list of high-performance materials. In addition to identifying specific materials for oxygen–helium separations, this approach could prove useful for selecting adsorbents for other gas separations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Replicated Computational Results (RCR) Report for "Adaptive Precision Block-Jacobi for High Performance Preconditioning in the Ginkgo Linear Algebra Software''

In, a practical implementation of a novel adaptive precision block-Jacobi preconditioner is introduced. In particular, the authors present a heavily-tuned GPU implementation of the adaptive precision block-Jacobi preconditioner within the Ginkgo numerical linear algebra library. The performance of the methodology and implementation is demonstrated using the proposed preconditioning scheme within Ginkgo’s high-performance Conjugate Gradient (CG) implementation on an NVIDIA Volta GPU. In this report, we replicate a subset of the computational results presented in. The focus is generating results from Fig. 9 to evaluate the performance of using Ginkgo’s CG solver integrated with either the full or the adaptive precision block-Jacobi preconditioner applied to a variety of test cases

97 MATHEMATICS AND COMPUTING↗

Job Scheduler-Driven Power Gateway for High Performance Computing

Power gateways in the form of a microgrid can incorporate multiple distributed energy resources (DER) in either grid forming or grid following mode and support high performance computing (HPC) power profiles including the large load-follow requirements observed in multi-user HPC systems. The microgrid’s flexibility to operate in either grid forming or grid following mode and to actively switch between these modes enables baseline power from multiple non-baseline DER while maintaining high power quality metrics for the HPC system. But this enormous flexibility in demand response and time of use shifting is generally programmed independently of any integration with an HPC job scheduler which can better inform the load shaping by the microgrid. While there are many existing approaches where the HPC job scheduler takes in information from the grid to make queue scheduling decisions, this work takes the opposite view and explores a scheduler where the jobs in the queue can directly impact the settings of the grid. Several HPC scheduler strategies are tested where the jobs in the queue directly impact the settings of a microgrid designed for HPC operation which is driving a datacenter with three classes of HPC architectures. The scheduler operation is shown using a microgrid with 64 kW of solar capacity and 320 kWh of battery over a period of 21 days operating with significant low-follow swings, a throttled grid, cloudy conditions, switching between grid following and grid forming modes, and a wide range of battery states-of-charge all while maintaining high quality power metrics. The scheduler provides a mechanism for the job queue to directly impact a power gateway like a microgrid and to improve HPC power outcomes such as maximizing renewable energy usage

microgrid↗

Design and Manufacturing of High Performance, Reduced Charge Heat Exchangers

A framework of stress analysis and fatigue analysis was developed. Utilizing the framework, stress analysis and fatigue analysis of high-performance heat exchangers with non-round shaped tubes were studied. The tubes, headers and welding layers were studied separately before analyzing the assembled heat exchangers. Results reveal different stress strength on the different shapes of tubes and headers. Unevenly welding distribution is also discussed in the report. Moreover, the fatigue analysis results of the heat exchanger are helping to estimate the lifetime of the heat exchanger from fatigue point of view.

42 ENGINEERING↗

High-performance computing leadership to enable advances in artificial intelligence and a thriving compute ecosystem

The past three decades have witnessed the widespread adoption of high-performance computing (HPC) as an essential tool in the advancement of science. From accelerating critical research in the wake of a global pandemic to climate modelling and national security, HPC has become integral to the most cutting-edge scientific research around the world and across application domains. The global competition to debut the next fastest supercomputer keeps pushing the field forward. Meanwhile, increasing capabilities are bringing hallmarks of science fiction, such as artificial intelligence (AI), into daily life. However, the tremendous power of new computing systems comes with an increased concern about equitable access to these resources and their impact on supporting a thriving workforce.

Tourassi, Georgia↗

A high-performance intermediate temperature reversible solid oxide cell with a new barrier layer free oxygen electrode

The best solution to address the critical durability issue of solid oxide electrolytic cells (SOECs) for high-efficiency and high-rate H 2 production is to lower the operating temperature without sacrificing the performance. Developing high performance oxygen electrodes (OEs) is a key to capitalizing this solution. Here, in this paper, we report on a highly active OE for intermediate temperature ZrO 2 -based SOECs without a CeO 2 barrier layer. The new barrier-layer-free (BLF) OE is a composite of two materials, (Bi 0.75 Y 0.25 ) 0.93 Ce 0.07 O 1.5±δ (BYC) that exhibits high oxide-ion conductivity and La 0.8 Sr 0.2 MnO 3 (LSM) that possesses a high electronic conductivity to enable fast oxygen reduction/evolution reactions (ORR/OER). Featuring a microscale porous BYC scaffold decorated with high surface area LSM nanoparticles (NPs), the new BLF-OE exhibited a low area specific resistance (ASR) of 0.10 Ω cm 2 at 650 °C in air. With 50%H 2 -50%H 2 O as a feed to hydrogen electrode (HE) and air to OE, the single cell performance achieved 588 mA cm -2 at 0.80 V in the fuel cell mode and 688 mA cm -2 at 1.30 V in the electrolytic mode at 650 °C. Our in-house testing showed that this level of performance was ~3.5x higher than the cell with the benchmark La 0.6 Sr 0.4 Co 0.2 Fe 0.8 O 3-δ -Ce 0.9 Gd 0.1 O 2-δ OE. The long-term durability testing under alternating fuel cell and electrolytic modes showed a low degradation rate of 0.10 mA cm -2 h -1 over 550 hours. These encouraging results showed the great promise of the newly developed BYC-LSM to be an excellent OE candidate for intermediate temperature SOECs.

25 ENERGY STORAGE↗

Exploring Flood Predictability in Taiwan through Coupled Atmospheric–Hydrological and High-Performance Hydrodynamic Models

Effective flood simulation capabilities can tremendously support early warning and disaster prevention. To examine the applicability of a fully physics-based and high-performance flood simulation and forecasting modeling framework for a flood-prone region in Taiwan, we conduct a numerical experiment that couples the Weather Research and Forecasting (WRF) Model, WRF-Hydrological modeling system (WRF-Hydro), and the Two-Dimensional Runoff Inundation Toolkit for Operational Needs (TRITON) to perform integrated rainfall, streamflow, and flood simulations. Furthermore, we first use the coupled WRF and WRF-Hydro (WWH) to predict rainfall and streamflow and then drive TRITON with the predicted streamflow hydrographs to simulate flood depth and inundation area. With the refined spatial resolution and parameterization, this framework can better predict rainfall with reasonable spatial patterns. Although WWH could overestimate the amount of rainfall in some areas, the uncertain rainfall–streamflow predictions produce reasonable flood maps able to pinpoint regions at risk of flooding. In terms of model efficiency, the graphics processing unit–based computation can yield a speed-up factor as high as ∼13 compared to the central processing unit–based computation, promoting the efficacy of the coupled modeling framework in practical real-time flood forecasting.

Coupled models↗

Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

36 MATERIALS SCIENCE↗

Developing the Next Generation of High-Performance Solid-State Batteries

The project team conducted cutting-edge battery research on electrode materials, electrolyte formulations, and cell designs for next-generation rechargeable lithium metal batteries designed for high energy density and safety. The research has focused on two separate but related technologies: 1) a high-temperature lithium metal battery designed for extreme environments, and 2) a high-energy lithium metal battery designed for more general applications (including aerospace, defense, transportation, electronics, etc.). We took these projects from the conceptual stage (an idea on paper) all the way to high performing lab prototypes and early commercial-format pouch and cylindrical cell prototypes. Both chemistries were improved substantially in terms of materials compatibility, lithium metal cyclability, high-voltage stability, high-temperature stability, and scalability.

25 ENERGY STORAGE↗

High-Performance/-Precision/-Z(HPPZ) Scintillator Grids via Advanced Electrochemistry. Phase I – Project # 20210572MFR- Mid year review [Slides]

Scintillation grids with improved imaging resolution in time and space, are required for future mission needs at various facilities (pRad user station, LANSCE, DARHT, Scorpius etc). Current technologies use inorganic crystal scintillators, which are positioned within a high-Z scintillator septa. Dense and high-Z materials are optimal for gamma-ray detection making them promising candidates for development of high -performance/-precision/-Z (HPPZ) scintillator grids. The current resolution of standard chemical etching and fabrication processes of high-Z materials is very low, leading to undesired undercutting and ultimately reducing crystal performance efficiency. Goals are to: Perform initial feasibility studies with the aim of producing complex parts for applications with difficult-to-process high-Z material; Use of pulse and pulse-reverse electrochemical methods on additive approaches to produce high-precision Au and Re scintillator grids at a small scale; and, Follow up in Phase II with the delivery of a large-scale scintillator grid of the best material candidate with unprecedented properties for LANL needs determined in Phase I.

36 MATERIALS SCIENCE↗

Mesophase pitch-based high performance carbon fiber production using coal extracts from mild direct coal liquefaction

Mild direct coal liquefaction (autogenous pressure, no catalyst, no H2 gas) of Springfield coal in fluid catalytic cracking decant oil is shown to effectively produce coal extract precursors to spinnable mesophase pitch. Here this work demonstrates that the coal extract can be thermally treated to obtain mesophase pitch in a facile one-step process, bypassing the production of an intermediate isotropic pitch. Furthermore, the presence of 25 wt.% coal in the initial slurry can increase the yield to mesophase pitch nearly twofold and yield to carbon fiber by approximately 70%. The coal extract-derived mesophase pitch was melt-spun and heat treated to produce carbon fiber with graphitic texture, high modulus (>400 GPa) and tensile strength up to 943 MPa. Overall, this work demonstrates that coal can be effectively utilized to markedly amplify the mesophase pitch and carbon fiber yield from fluid catalytic cracking decant oil by relatively simple processing, while conserving utility as a precursor to high performance carbon fiber and potentially other high value graphitic products.

36 MATERIALS SCIENCE↗

High-Fidelity and High-Performance Computational Simulations for Rapid Design Optimization of Sulfur Thermal Energy Storage (CRADA Report)

NREL and Element 16 collaborated on sulfur thermal energy storage modeling using NREL’s high performance computing (HPC) resources to assist its application in industrial processes. Industrial process heat (IPH) accounts for ~70% of US manufacturing energy use and is primarily produced by fossil fuel combustion. Approximately, 1500 TWht (~60% Terawatt hour thermal) of IPH demand is in the temperature range of 100-300°C. Industrial applications in this temperature range include drying, hydrothermal processing, thermal enhanced oil recovery, food and beverage, bioethanol production, etc. Cost-effective thermal energy storage (TES) that increases the utilization of waste and renewable heat (solar, geothermal, etc.) could provide significant energy savings and reliable heat sources, decrease emissions, and increase US manufacturing competitiveness through reductions in fuel consumption. This HPC4EI project facilitated Element 16’s development of low-cost and high-impact molten sulfur TES for dispatchable IPH. The development of a high-fidelity model validated by experimental data and HPC simulations enabled the successful resolution of the complex interplay between fluid dynamics and heat transfer processes during transient operation of sulfur TES, overcoming the numerical challenges posed by the non-linear temperature-dependent physical properties of sulfur. The project helped accelerate Element 16’s molten sulfur TES product design and support its broad applications.

25 ENERGY STORAGE↗

A2Cloud‐RF : A random forest based statistical framework to guide resource selection for high‐performance scientific computing on the cloud

Summary This article proposes a random‐forest based A2Cloud framework to match scientific applications with Cloud providers and their instances for high performance. The framework leverages four engines for this task: PERF engine, Cloud trace engine, A2Cloud‐ext engine, and the random forest classifier (RFC) engine. The PERF engine profiles the application to obtain performance characteristics, including the number of single‐precision (SP) floating‐point operations (FLOPs), double‐precision (DP) FLOPs, x87 operations, memory accesses, and disk accesses. The Cloud trace engine obtains the corresponding performance characteristics of the selected Cloud instances including: SP floating point operations per second (FLOPS), DP FLOPS, x87 operations per second, memory bandwidth, and disk bandwidth. The A2Cloud‐ext engine uses the application and Cloud instance characteristics to generate objective scores that represent the application‐to‐Cloud match. The RFC engine uses these objective scores to generate two types of random forests to assist users with rapid analysis: application‐specific random forests (ARF) and application‐class based random forests. The ARF consider only the input application's characteristics to generate a random forest and provide numerical ratings to the selected Cloud instances. To generate the application‐class based random forests, the RFC engine downloads the application profiles and scores of previously tested applications that perform similar to the input application. Using these data, the RFC engine creates a random forest for instance recommendation. We exhaustively test this framework using eight real‐world applications across 12 instances from different Cloud providers. Our tests show significant statistical agreement between the instance ratings given by the framework and the ratings obtained via actual Cloud executions.

Samuel, David↗

High-performance ionomerless cathode anion-exchange membrane fuel cells with ultra-low-loading Ag–Pd alloy electrocatalysts

Rapid translation of catalysts from fundamental studies to high-performance devices could facilitate the process of developing and commercializing anion-exchange membrane fuel cells (AEMFCs). Traditionally, translation from material screening in three-electrode rotating disk electrode (RDE) cells to AEMFCs is complicated by differences in microenvironments, e.g. solid ionomer/membrane vs liquid electrolyte. Herein, we introduce a platform for translation to devices that utilizes ionomerless ultra-low-loading Ag-Pd alloy electrocatalyst cathodes synthesized by co-physical vapor deposition (PVD). Our ionomerless cathodes allow for systematic H 2 -O 2 AEMFC experiments while demonstrating comparable activity trends to those in three-electrode cells. Furthermore, here we show that our Ag 10 Pd 90 -based AEMFC reaches a peak power density of ~1 W cm$_{geo}^{-2}$ and ~10 W mg$_{PGM Cathode}^{-1}$ satisfying the U.S. Department of Energy’s platinum-group-metal (PGM) loading and cost targets. Our approach shows promise in facilitating the rapid translation between three-electrode studies and AEMFCs, offering a simple and effective design for decreasing PGM loadings.

25 ENERGY STORAGE↗

Durable and High-Performance SOECs Based on Proton Conductors for Hydrogen Production

Proton-conducting solid oxide electrolysis cells (P-SOECs) are a promising technology for cost-effective and efficient production of green hydrogen. Breakthroughs in materials development, optimization of cell structure, and achievement of high performance and durability are essential to significantly increase the commercial competitiveness of these technologies. The main objective of this project is to gain scientific knowledge for the rational design, fabrication, and demonstration of a robust, highly efficient, and low-cost SOEC technology based on a proton-conducting electrolyte membrane for hydrogen production. We focused on better understanding the degradation mechanisms of proton-conducting electrolytes, air electrodes, and catalyst materials under electrolysis mode to develop an effective strategy for rationalizing new materials that are vital for enhancing cell performance and durability. The scope includes enhancing the performance and durability of the electrolyte and electrode materials under realistic operating conditions, developing highly active and robust catalysts to minimize electrode losses while improving tolerance to contaminant poisoning, revealing the mechanism of enhanced activity and stability of the catalyst, and understanding the underlying degradation mechanisms. In addition, various characterization techniques were employed to gain a fundamental understanding of the materials’ behavior and their impact on cell performance, providing vital information to guide materials discovery and cell design. After defect chemistry engineering, the optimized donor and acceptor co-doped electrolytes BaMo/W 0.03 Ce 0.71 Yb 0.26 O 3-δ (BM/W03) showed substantially improved chemical stability against high concentrations of CO 2 and H 2 O compared to the state-of-the-art electrolyte (BaZr 0.1 Ce 0.7 Y 0.1 Yb 0.1 O 3-δ , BZCYYb1711) while maintaining comparable ionic conductivity and ionic transference number. To bypass the inherent trade-off between conductivity and chemical stability, we fabricated a bi-layer electrolyte composed of BZCYYb1711 coated with a highly-stable thin layer of BaHf 0.83 Yb 0.17 O 3-δ (BHYb). This bi-layer electrolyte displayed excellent chemical stability against high concentration CO 2 ; there was no detectable formation of BaCO 3 after exposure to 97% CO 2 (with 3% H 2 O) at 500 °C for 1000 hours and the rate of degradation in resistance was about 0.4% per 1,000 hours (kh). In contrast, the same BZCYYb1711 electrolyte without a BHYb coating degraded significantly under the same testing conditions; the degradation rate was increased to 5.1%/kh. In addition, a triple conducting air electrode Ba 0.9 Pr 0.1 Hf 0.1 Y0.1Co 0.8 O 3-δ (BPHYC) was developed by heavily doping transition metal ions into a proton-conducting material. This air electrode material, composed of 3 distinct phases, exhibits superior electrocatalytic activity due to the synergistic effect from the three component phases. Moreover, an active and durable catalyst, La 2 Ni 0.5 Fe 0.5 O 4+δ (LNF), was developed, showing excellent catalytic activity and contaminant tolerance, with a degradation rate of only 0.49%/kh when exposed to high concentrations of steam and Cr. Finally, single cells were constructed from the best electrolytes, electrodes, and catalyst coatings developed in this project. These cells demonstrated superior high current density at a given cell voltage, high roundtrip efficiency, and remarkable durability (up to 1000 hours of operation).

08 HYDROGEN↗

The Scientific Justification for a U.S. Domestic High-Performance Reactor-Based Research Facility

The Basic Energy Science Advisory Committee (BESAC) was charged with forming a subcommittee to assess the scientific justification for a U.S. domestic high-performance reactor-based research facility in order to continue providing the U.S scientific community with leading neutron capabilities in support of DOE's missions in science, energy, environment, and national security. The assessment included consideration of current international plans and existing domestic facility infrastructure. The subcommittee held a series of meetings from August 19, 2019 to April 24, 2020 that included DOE senior officials, leaders of national and international neutron facilities (SNS, HFIR, NIST, ILL, FRM-II), chairs of the NAS and POPA HEU-LEU committees, and outside experts on important areas of science, technology, and industry where high flux nuclear reactor facilities make important contributions. Also included were tours of neutron facilities (SNS, HFIR, NIST, BR2 reactor, and the planned Jules Horowitz Reactor). This July 2020 (revised 10-28-2020) report describes scientific use cases, brief summaries of existing and planned neutron facilities in the US and Europe, a comprehensive review of HFIR, a comprehensive discussion of the current state of progress on HEU-LEU conversion, user information from NIST and ORNL, and three recommendations to DOE for moving forward.

36 MATERIALS SCIENCE↗