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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Core-shell structured tow-pregs enabled additive manufacturing of continuously reinforced thermoset composites

One of the major challenges in thermally curable polymer composite additive manufacturing is the slow curing time of thermally curable resins, coupled with a significant drop in thermoset viscosity upon heating, making the formation of complex shapes difficult. To tackle these issues, we have introduced a novel rapid composite preformation strategy called Tow-Preg Cladding (TPC). This technique involves the use of a thin and rapidly curable dual-cure resin, which combines a thermally curable resin (such as epoxy) with a fast photo-curable resin. The dual-cure resin is applied as a coating over a thermally curable resin tow-preg, enabling the formation of a rigid cladding that supports and shapes the tow-pregs into the desired geometries and patterns. The key advantage of this approach lies in the sequential formation of an interpenetrating polymer network (IPN) at the interlayer between laminates, resulting from the curing of the photocurable resin and the thermally curable resin sequentially. This IPN formation enhances interlaminar bonding in the composite, contributing to its overall strength and performance. The composites produced using this technique exhibited impressive mechanical properties, including longitudinal tensile strength of 1058.2 MPa, longitudinal tensile modulus of 74.2 GPa, flexural strength of 1080.4 MPa, and flexural modulus of 41.7 GPa. Overall, our Tow-Preg Cladding approach shows great promise in overcoming the challenges associated with thermally curable polymer composite additive manufacturing. It offers a viable solution to creating strong, shape-specific thermoset composites efficiently with enhanced mechanical properties.

42 ENGINEERING↗
DOE OSTI2012

Implementing Molecular Dynamics on Hybrid High Performance Computers - Particle-Particle Particle-Mesh

The use of accelerators such as graphics processing units (GPUs) has become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power requirements. Hybrid high-performance computers, machines with nodes containing more than one type of floating-point processor (e.g. CPU and GPU), are now becoming more prevalent due to these advantages. In this paper, we present a continuation of previous work implementing algorithms for using accelerators into the LAMMPS molecular dynamics software for distributed memory parallel hybrid machines. In our previous work, we focused on acceleration for short-range models with an approach intended to harness the processing power of both the accelerator and (multi-core) CPUs. To augment the existing implementations, we present an efficient implementation of long-range electrostatic force calculation for molecular dynamics. Specifically, we present an implementation of the particle-particle particle-mesh method based on the work by Harvey and De Fabritiis. We present benchmark results on the Keeneland InfiniBand GPU cluster. We provide a performance comparison of the same kernels compiled with both CUDA and OpenCL. We discuss limitations to parallel efficiency and future directions for improving performance on hybrid or heterogeneous computers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Implementing Molecular Dynamics on Hybrid High Performance Computers - Three-Body Potentials

The use of coprocessors or accelerators such as graphics processing units (GPUs) has become popular in scientific computing applications due to their low cost, impressive floating-point capabilities, high memory bandwidth, and low electrical power re- quirements. Hybrid high-performance computers, defined as machines with nodes containing more than one type of floating-point processor (e.g. CPU and GPU), are now becoming more prevalent due to these advantages. Although there has been extensive research into methods to efficiently use accelerators to improve the performance of molecular dynamics (MD) employing pairwise potential energy models, little is reported in the literature for models that include many-body effects. 3-body terms are required for many popular potentials such as MEAM, Tersoff, REBO, AIREBO, Stillinger-Weber, Bond-Order Potentials, and others. Because the per-atom simulation times are much higher for models incorporating 3-body terms, there is a clear need for efficient algo- rithms usable on hybrid high performance computers. Here, we report a shared-memory force-decomposition for 3-body potentials that avoids memory conflicts to allow for a deterministic code with substantial performance improvements on hybrid machines. We describe modifications necessary for use in distributed memory MD codes and show results for the simulation of water with Stillinger-Weber on the hybrid Titan supercomputer. We compare performance of the 3-body model to the SPC/E water model when using accelerators. Finally, we demonstrate that our approach can attain a speedup of 5.1 with acceleration on Titan for production simulations to study water droplet freezing on a surface.

Brown, W Michael↗
DOE OSTI2021

Accelerated impurity solver for DMFT and its diagrammatic extensions

Here, we present ComCTQMC, a GPU accelerated quantum impurity solver. It uses the continuous-time quantum Monte Carlo (CTQMC) algorithm wherein the partition function is expanded in terms of the hybridisation function (CT-HYB). ComCTQMC supports both partition and worm-space measurements, and it uses improved estimators and the reduced density matrix to improve observable measurements whenever possible. ComCTQMC efficiently measures all one and two-particle Green's functions, all static observables which commute with the local Hamiltonian, and the occupation of each impurity orbital. ComCTQMC can solve complex-valued impurities with crystal fields that are hybridized to both fermionic and bosonic baths. Most importantly, ComCTQMC utilizes graphical processing units (GPUs), if available, to dramatically accelerate the CTQMC algorithm when the Hilbert space is sufficiently large. We demonstrate acceleration by a factor of over 600 (100) in a simulation of δ-Pu at 600 K with (without) crystal fields. In easier problems, the GPU offers less impressive acceleration or even decelerates the CTQMC. Here we describe the theory, algorithms, and structure used by ComCTQMC in order to achieve this set of features and level of acceleration.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2016

Lessons from the Crystal Structure of the S. aureus>/em> Surface Protein Clumping Factor A in Complex With Tefibazumab, an Inhibiting Monoclonal Antibody

The Staphylococcus aureus fibrinogen binding MSCRAMM (Microbial Surface Components Recognizing Adhesive Matrix Molecules), ClfA (clumping factor A) is an important virulence factor in staphylococcal infections and a component of several vaccines currently under clinical evaluation. The mouse monoclonal antibody aurexis (also called 12-9), and the humanized version tefibazumab are therapeutic monoclonal antibodies targeting ClfA that in combination with conventional antibiotics were effective in animal models but showed less impressive efficacy in a limited Phase II clinical trial. We here report the crystal structure and a biochemical characterization of the ClfA/tefibazumab (Fab) complex. The epitope for tefibazumab is located to the “top” of the N3 subdomain of ClfA and partially overlaps with a previously unidentified second binding site for fibrinogen. A high-affinity binding of ClfA to fibrinogen involves both an interaction at the N3 site and the previously identified docking of the C-terminal segment of the fibrinogen γ-chain in the N2N3 trench. Although tefibazumab binds ClfA with high affinity we observe a modest IC50 value for the inhibition of fibrinogen binding to the MSCRAMM. This observation, paired with a common natural occurring variant of ClfA that is not effectively recognized by the mAb, may partly explain the modest effect tefibazumab showed in the initial clinic trail. This information will provide guidance for the design of the next generation of therapeutic anti-staphylococcal mAbs targeting ClfA.

General & Internal Medicine↗
DOE OSTI2015

Impacts of software and its engineering on the carbon footprint of ICT

The energy consumption of information and communication technology (ICT) is still increasing. Even though several solutions regarding the hardware side of Green IT exist, the software contribution to Green IT is not well investigated. The carbon footprint is one way to rate the environmental impacts of ICT. In order to get an impression of the induced CO{sub 2} emissions of software, we will present a calculation method for the carbon footprint of a software product over its life cycle. We also offer an approach on how to integrate some aspects of carbon footprint calculation into software development processes and discuss impacts and tools regarding this calculation method. We thus show the relevance of energy measurements and the attention to impacts on the carbon footprint by software within Green Software Engineering.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Decentralized control of units in smart grids for the support of renewable energy supply

Due to the significant environmental impact of power production from fossil fuels and nuclear fission, future energy systems will increasingly rely on distributed and renewable energy sources (RES). The electrical feed-in from photovoltaic (PV) systems and wind energy converters (WEC) varies greatly both over short and long time periods (from minutes to seasons), and (not only) by this effect the supply of electrical power from RES and the demand for electrical power are not per se matching. In addition, with a growing share of generation capacity especially in distribution grids, the top-down paradigm of electricity distribution is gradually replaced by a bottom-up power supply. This altogether leads to new problems regarding the safe and reliable operation of power grids. In order to address these challenges, the notion of Smart Grids has been introduced. The inherent flexibilities, i.e. the set of feasible power schedules, of distributed power units have to be controlled in order to support demand–supply matching as well as stable grid operation. Controllable power units are e.g. combined heat and power plants, power storage systems such as batteries, and flexible power consumers such as heat pumps. By controlling the flexibilities of these units we are particularly able to optimize the local utilization of RES feed-in in a given power grid by integrating both supply and demand management measures with special respect to the electrical infrastructure. In this context, decentralized systems, autonomous agents and the concept of self-organizing systems will become key elements of the ICT based control of power units. In this contribution, we first show how a decentralized load management system for battery charging/discharging of electrical vehicles (EVs) can increase the locally used share of supply from PV systems in a low voltage grid. For a reliable demand side management of large sets of appliances, dynamic clustering of these appliances into uniformly controlled appliance sets is necessary. We introduce a method for self-organized clustering for this purpose and show how control of such clusters can affect load peaks in distribution grids. Subsequently, we give a short overview on how we are going to expand the idea of self-organized clusters of units into creating a virtual control center for dynamic virtual power plants (DVPP) offering products at a power market. For an efficient organization of DVPPs, the flexibilities of units have to be represented in a compact and easy to use manner. We give an introduction how the problem of representing a set of possibly 10{sup 100} feasible schedules can be solved by a machine-learning approach. In summary, this article provides an overall impression how we use agent based control techniques and methods of self-organization to support the further integration of distributed and renewable energy sources into power grids and energy markets. - Highlights: • Distributed load management for electrical vehicles supports local supply from PV. • Appliances can self-organize into so called virtual appliances for load control. • Dynamic VPPs can be controlled by extensively decentralized control centers. • Flexibilities of units can efficiently be represented by support-vector descriptions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

Flexible sulfur wires (Flex-SWs)—A new versatile platform for lithium-sulfur batteries

In this paper, a simple electrospinning methodology is described for generating novel fiber configurations of sulfur with the potential of yielding high performance sulfur electrodes for use in lithium-sulfur batteries. The unique fiber morphology derived by electrospinning has the capability of generating flexible sulfur yarns for the first time rendering them a highly attractive platform for small-scale mobile device applications such as textile-batteries. The electrospinning methodology reported herein also allows for the formation of a polymer-sulfur interface which acts as a physical barrier to liquid lithium electrolyte facilitating the reduction of polysulfide dissolution, a primary barrier to the progress of Li-S systems. Such flexible sulfur wire materials when converted into pellet sulfur electrodes, exhibit very stable capacities over 135 cycles with high areal capacities (~2.5 mAh cm -2 ). Finally, coating the electrodes with an inorganic lithium ion conductor coating results in further improvement of cycling behavior with electrodes of ~650 mAh g -1 capacity and an impressive low fade rate of ~0.003% fade cycle -1 demonstrating the promise of this unique platform.

25 ENERGY STORAGE↗
DOE OSTI2020

Self-supported MoO 2 /MoS 2 nano-sheets embedded in a carbon cloth as a binder-free substrate for high-energy lithium–sulfur batteries

As one of the most prospective candidates for next-generation rechargeable batteries, lithium-sulfur (Li-S) batteries currently still encounter great challenges associated with the low conductivity, severe shuttle effects, and sluggish redox kinetics. Herein, a self-supported sulfur host is fabricated by an in-situ growth of MoO 2 /MoS 2 nano-sheets on a carbon cloth (CC). First, carbon cloth serves as a good template for the morphology-controlled synthesis of nanostructured materials, which not only alleviates the agglomeration of MoO2/MoS2, but also enhances the flexibility and mechanical strengthen of the hybrid architecture as a free-standing host. Second, the polysulfide-trapping ability can be greatly enhanced by both physical and chemical adsorption from the MoO 2 /MoS 2 -anchored carbon cloth. Moreover, the partially sulfurized MoO 2 /MoS 2 nano-sheets integrate the benefits of conductive MoO 2 and sulfiphilic MoS 2 , thus facilitating fast charge transfer and redox kinetics of polysulfide conversion. As a result, these attributes enable the host to hold a high sulfur loading (up to 7.6 mg cm -2 ), which exceeds most of the reported carbon cloth-related cathode work in the literature. Furthermore, the Li-S cells can achieve a high peak capacity of 1350 mA h g -1 , excellent rate capability (C/20 - 2C rate), impressive areal capacity (up to 6.3 mA h cm -2 ), and a high capacity retention of 85% after 100 cycles.

25 ENERGY STORAGE↗
DOE OSTI2021

Controlling MoO 2 and MoO 3 phases in MoO x /CNTs nanocomposites and their application to anode materials for lithium-ion batteries and capacitors

Molybdenum oxides (MoO 2 and MoO 3 ) are attractive anode materials for Li- and Na- ion batteries. Although there have been extensive studies on them individually, systematic and comparative studies are still lacking. Here, we demonstrate a facile and straightforward synthesis method to control the phase and oxidation state in the MoO x /CNTs nanocomposites via hydrothermal reaction followed by heat-treatment. By changing the gas atmosphere during the annealing process, well-dispersed MoO 2 /CNTs and MoO 3 /CNTs nanocomposites are formed without altering their overall morphology. This strategy enables us to investigate the true structure-property correlation of MoO x /CNTs nanocomposites by comparing the structure and electrochemical properties of MoO 2 /CNTs and MoO 3 /CNTs. When tested as anode materials for lithium-ion batteries, both HT-MoO 2&3 /CNTs electrodes show much-improved cycling stability and rate performance compared to the rod-shaped bulk MoO 3 electrode. In situ Mo K-edge x-ray absorption spectroscopy (XAS) has been further employed to compare and elucidate Li + storage mechanisms of both electrodes. When employed to the negative electrode of a high-power lithium-ion capacitor (LIC), the LIC full-cell composed of HT-MoO 3 /CNTs negative and activated carbon positive electrodes demonstrates impressive energy and power densities (~ 90 Wh kg –1 with 2000 W kg –1 ) and excellent cycling stability (96.8 % capacity retention after 300 cycles), revealing the versatility of the MoO x /CNTs electrodes in energy applications.

25 ENERGY STORAGE↗
DOE OSTI2023

C@SnS 2 core-shell 0D/2D nanocomposite with excellent electrochemical performance as lithium-ion battery anode

C@SnS 2 core-shell 0D/2D nanocomposite was successfully prepared by a one-step hydrothermal method. The SnS 2 nanosheets were heterogeneously nucleated and grown on the surface of carbon spheres. As an anode for lithium-ion batteries, the electrochemical performance of the C@SnS 2 composite outperforms that of SnS 2 nanoflowers. After 100 cycles, the reversible discharge specific capacity reaches an impressive value of 802 mAh g -1 at a current density of 100 mA g -1 . Even after 600 cycles, the discharge specific capacity remains a value of 442 mAh g -1 , under a high current density of 1 A g -1 . Further, this remarkable lithium-ion storage performance can be attributed to the unique core-shell nanostructure and the synergy between SnS 2 nanosheets and carbon spheres. This study advances our understanding of the vital role of carbon in fabricating nano-heterojunction or composite electrodes and provides a feasible route to significantly improve the electrochemical properties of SnS 2 and other metal sulfides.

25 ENERGY STORAGE↗
DOE OSTI2020

Single crystal cathodes enabling high-performance all-solid-state lithium-ion batteries

All-solid-state lithium-ion batteries (ASSLIBs) are receiving significant attention owing to their improved safety and energy density over liquid counterparts. However, single-crystal cathodes have never been investigated in ASSLIBs. In this work, single-crystal Li(Ni 0.5 Mn 0.3 Co 0.2 )O 2 (SC-NMC532) is used as the cathode material for ASSLIBs, which exhibits 6~14 times higher Li + diffusion coefficient than polycrystalline Li(Ni 0.5 Mn 0.3 Co 0.2 )O 2 (PC-NMC532). As a result, SC-NMC532 exhibits an initial specific capacity of 156.4 mAh.g -1 while PC-NMC532 shows an initial capacity of only 127.5 mAh.g -1 . After 150 cycles, SC-NMC532 retains the capacity of 94.5 mAh.g -1 . More impressively, under a high current density of 1.3 mA.cm -2 , SC-NMC532 exhibits a capacity of 82 mAh.g -1 , much higher than that of PC-NMC532 (2.1 mAh.g -1 ). This work demonstrates that single-crystal NMC cathodes could enable both high power density and high energy density of ASSLIBs.

25 ENERGY STORAGE↗
DOE OSTI2020

Freestanding vanadium nitride nanowire membrane as an efficient, carbon-free gas diffusion cathode for Li–CO 2 batteries

A freestanding vanadium nitride nanowire (VN-NW) membrane is employed as a carbon-free gas diffusion cathode (GDC) for high-performance Li–CO 2 batteries. Li–CO 2 cells built with the VN-NW GDC exhibit excellent electrochemical performance, achieving 100 discharge/charge cycles with an ultralow round-trip overpotential of <1 V. The VN-NW GDC also delivers vastly improved capacity and rate capability compared to a conventional multiwalled carbon nanotube GDC. The morphology of the discharge products (Li 2 CO 3 + C) is greatly improved with the VN-NW GDC compared to the carbon-based cathode, allowing for facile decomposition on charge. Furthermore, the absence of carbon in the VN-NW GDC allows for easy characterization of the discharge products with a variety of techniques, including transmission electron microscopy, X-ray diffraction, and X-ray photoelectron spectroscopy. The impressive performance of the hierarchically structured VN-NW GDC in Li–CO 2 batteries justifies further investigation into noble metal-free and carbon-free materials for gas diffusion cathodes.

25 ENERGY STORAGE↗
DOE OSTI2022

Watershed Workflow: A toolset for parameterizing data-intensive, integrated hydrologic models

Integrated, distributed hydrologic models leverage advances in computational power and data accessibility to improve predictive understanding of the water cycle. While impressive advances in this area of environmental modeling have been accomplished, such models are still rarely used, partially because of difficulty integrating model and data. This research describes the release of Watershed Workflow version 1.2, a new library aiming to automate and enable complex workflows defining inputs to high resolution, integrated, distributed hydrologic models. Watershed Workflow provides tools enabling the discovery, acquisition, mapping, and coordination of watershed geometry, land cover, soil properties, and meteorological data. It enables the construction of unstructured meshes that incorporate this data, and provides tools for automating a “first” simulation on any watershed in the United States. We present the design of the workflow tool, and describe best practices for its usage, culminating in a final example from watershed specification to simulation at the Coweeta Hydrologic Laboratory.

Integrated hydrologic modeling↗
DOE OSTI2022

Ultralow platinum loading for redox-flow battery by electrospinning the electrocatalyst and the ionomer in core-shell fibers

Hydrogen Bromine Redox Flow Batteries (HBRFB) are promising candidates for large scale energy storage, having an excellent balance of system, inexpensive and abundant electrolytes, high power density and near zero kinetic limitations. However, they suffer from corrosion of the hydrogen electrode due to bromine species crossover, which requires a high loading of precious group metal (PGM) electrocatalyst. Herein, a standard catalyst has been used in an electrospun (ES) fiber mat electrode, allowing for a significant (six-fold) reduction in platinum loading from 0.3 mg Pt /cm 2 down to 0.05 mg Pt /cm 2 . At this very low loading, the electrospun electrode attained an impressive specific power of 11.5W/mg Pt , and exhibited excellent durability, with constant power output for 140 charge/discharge cycles. Further, the excellent performance of the electrospun hydrogen electrode is attribute to its unique core-shell nanofiber structure, which improves the stability of the catalyst by preventing bromide species from reaching the catalyst surface.

25 ENERGY STORAGE↗
DOE OSTI2023

xLi2MnO3 (1-x)LiMeO2 and Li4Ti5O12 Cell Chemistry for Behind-the-Meter Storage Applications

Li- and Mn-rich layered oxide material (xLi2MnO3 (1-x)LiMeO2, Me = Ni, Mn, and etc., LMR-NM) is paired with Li4Ti5O12 (LTO) in a full cell and evaluated for the Behind-the-Meter Storage (BTMS) applications. The LMR-NM/LTO full cell shows very high capacities and excellent long-term cycle life. It delivers 192 mAh g-1 after 500 cycles at C/2 and 45 degrees C with a capacity retention of 75% and coulombic efficiency higher than 99.95%. It also has impressive rate capabilities. A capacity of 220 mAh g-1 is achieved at 2C which is 88 % of the initial capacity at C/10. The high cycling temperature clearly enhances electrochemical kinetics and activates more Li2MnO3 component, which gives high capacities, low cell impedance, and better rate capabilities. Moreover, it helps to form a relatively thick cathode-electrolyte interphase (CEI) film to suppress transition metal dissolution from the cathode surface. The upper cut-off voltage (UCV) of 3.0 V keeps the structural integrity of the cathode during cycling. A higher UCV of 3.2 V accelerates structural instabilities of the cathode as well as growth of the solid-electrolyte interphase (SEI) via transition metal dissolution and deposition on the anode surface. It results in higher cell impedance, worse capacity retention and faster capacity fade.

behind-the-meter storage↗
DOE OSTI2026

Unraveling the Synergistic effects of La and Al co-doping on Ni/Co-free P2-type Na 0.67 Mn 0.67 Fe 0.33 O 2 layered oxide cathodes for sodium-ion batteries

Recent developments in high-performance cathodes for sodium-ion batteries (SIBs) tend to mitigate use of critical Ni and Co active species to satisfy sustainability. P2-type layered oxide cathodes based on Mn and Fe promise high capacity, sustainability, and low cost on account of the abundance and multiple redox characteristics of Mn and Fe. However, their commercialization is still challenged due to poor cycling stability caused by phase changes emanating from inherent Jahn-Teller distortions. Herein, we propose a novel strategy of trivalent La and Al co-doping to alleviate the drawbacks, innovating a new cathode of Na 0.67 Mn 0.62 Al 0.05 Fe 0.31 La 0.02 O 2 (AlLa). The co-doped cathode delivers an impressive capacity of 128.7 mAh/g at 1C and 106.5 mAh/g at high rate of 5C with a good capacity retention of 85% after 100 cycles. These are superior to the unmodified material, whose specific capacity was 118.6 mAh/g at 1C and 61.1 mAh/g at 5C with a retention of only 65% after 100 cycles. The synergistic contribution from La and Al associated with their improved oxygen bonding, pillaring, and local electronic structural modification stemming from rigid Lasingle bondO and Alsingle bondO bonding resulted in the observed structural and electrochemical improvement. Overall, this research offers novel strategies for designing high-performance cathode materials for sodium-ion batteries.

25 ENERGY STORAGE↗
DOE OSTI2020

Neural-based time series forecasting of loss of coolant accidents in nuclear power plants

During the last few years, deep learning in neural networks has demonstrated impressive successes in the areas of computer vision, speech and image recognition, text generation, and many others. However, sensitive engineering areas such as nuclear engineering benefited less from these efficient techniques. In this work, deep learning expert systems are utilized to model and predict time series progression of a design-basis nuclear accident, featuring a loss of coolant accident. Two major findings are accomplished in this work. First, the ability to train expert systems with high accuracy, which could help nuclear power plant operators to figure out plant responses during the accident. Second, building fast, efficient, and accurate deep models to simulate nuclear phenomena, which could be valuable to nuclear computational science. In this work, large amount of time series data is obtained from simulation tools by simulating different conditions of the base-case/nominal accident scenario. Four critical outputs/responses are monitored during the accident (e.g. temperature, pressure, break flow rate, water level). Two approaches are adopted in this work. The first approach is to use feedforward deep neural networks (DNN) to fit all time steps and outputs in a single model. The second approach is to use long short-term memory (LSTM) to fit all time steps together for each reactor response separately. Both DNN and LSTM demonstrate very good performance in predicting the test and base-case scenarios, with accuracy as low as 92% and as high as 99%, where these test scenarios are unknown to the expert systems and are not included in the model training. In addition, both approaches demonstrate a significant reduction in computational costs, as the deep expert system is able to accurately predict the accident 100,000 times faster than the original simulation tool. Given sufficient data, the methodology adopted in this study demonstrates that DNN/LSTM expert systems can be used as a decision support system to model advanced time series phenomena within nuclear power plants with high accuracy and negligible computational costs.

42 ENGINEERING↗