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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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Scalable High-Performance Algorithm for the Simulation of Exciton Dynamics. Application to the Light-Harvesting Complex II in the Presence of Resonant Vibrational Modes
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Scalable Synthesis of Ultrathin Mn 3 N 2 Exhibiting Room-Temperature Antiferromagnetism
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Scalability of Partial Differential Equations Preconditioner Resilient to Soft and Hard Faults.
Abstract not provided.
Scalable synthesis and post-modification of a mesoporous metal-organic framework called NU-1000
The synthesis of NU-1000, a highly robust mesoporous (containing pores >2 nm) metal-organic framework (MOF), can be conducted efficiently on a multigram scale from inexpensive starting materials. Tetrabromopyrene and (4-(ethoxycarbonyl)phenyl)boronic acid can easily be coupled to prepare the requisite organic strut with four metal-binding sites in the form of four carboxylic acids, while zirconyl chloride octahydrate is used as a precursor for the well-defined metal oxide clusters. NU-1000 has been reported as an excellent candidate for the separation of gases, and it is a versatile scaffold for heterogeneous catalysis. In particular, it is ideal for the catalytic deactivation of nerve agents, and it shows great promise as a new generic platform for a wide range of applications. Multiple post-synthetic modification protocols have been developed using NU-1000 as the parent material, making it a potentially useful scaffold for several catalytic applications. The procedure for the preparation of NU-1000 can be scaled up reliably, and it is suitable for the production of 50 g of the tetracarboxylic acid containing organic linker and 200 mg–2.5 g of NU-1000. The entire synthesis is performed without purification by column chromatography and can be completed within 10 d.
Scalable Inference for Sparse Deep Neural Networks using Kokkos Kernels.
Abstract not provided.
Scalable Generation of Graphs for Benchmarking HPC Community-Detection Algorithms.
Abstract not provided.
Demonstrating GPU code portability and scalability for radiative heat transfer computations
High performance computing frameworks utilizing CPUs, Nvidia GPUs, and/or Intel Xeon Phis necessitate portable and scalable solutions for application developers. Nvidia GPUs in particular present numerous portability challenges with a different programming model, additional memory hierarchies, and partitioned execution units among streaming multiprocessors. This work presents modifications to the Uintah asynchronous many-task runtime and the Kokkos portability library to enable one single codebase for complex multiphysics applications to run across different architectures. Scalability and performance results are shown on multiple architectures for a globally coupled radiation heat transfer simulation, ranging from a single node to 16,384 Titan compute nodes.
Scalable hierarchical PDE sampler for generating spatially correlated random fields using nonmatching meshes: Scalable hierarchical PDE sampler using nonmatching meshes
This work describes a domain embedding technique between two nonmatching meshes used for generating realizations of spatially correlated random fields with applications to large-scale sampling-based uncertainty quantification. The goal is to apply the multilevel Monte Carlo (MLMC) method for the quantification of output uncertainties of PDEs with random input coefficients on general and unstructured computational domains. We propose a highly scalable, hierarchical sampling method to generate realizations of a Gaussian random field on a given unstructured mesh by solving a reaction–diffusion PDE with a stochastic right-hand side. The stochastic PDE is discretized using the mixed finite element method on an embedded domain with a structured mesh, and then, the solution is projected onto the unstructured mesh. This work describes implementation details on how to efficiently transfer data from the structured and unstructured meshes at coarse levels, assuming that this can be done efficiently on the finest level. We investigate the efficiency and parallel scalability of the technique for the scalable generation of Gaussian random fields in three dimensions. An application of the MLMC method is presented for quantifying uncertainties of subsurface flow problems. Here, we demonstrate the scalability of the sampling method with nonmatching mesh embedding, coupled with a parallel forward model problem solver, for large-scale 3D MLMC simulations with up to 1.9·109 unknowns.
Scalable Programming Workflows for Validation of Quantum Computers
Hybrid quantum-classical workflows have become standard methods for executing variational algorithms and other quantum simulation techniques, which are key applications for noisy intermediate scale quantum (NISQ) computers. Validating these simulations is an important task which helps gauge the progress of quantum computer development, and classical simulation can serve as a tool to this end. Both exact and more scalable approximate methods with quantifiable error bounds can be used in validation tasks where the applicable metrics include the distance from a calculable ground truth, the quality of an error model fit to data, etc. Here we present a library extension that includes methods for validation of quantum simulations based on scalable hybrid workflows executable on high performance computers. We provide examples that use approximate methods based on tensor networks and stabilizer simulators to bound the error of quantum simulations on NISQ hardware.
Towards robust and scalable dispatch modeling of long-duration energy storage
Not Available
IPL-Annealed Mixed-Cation Perovskites with Robust Coating Window toward Scalable Manufacturing of Commercial Perovskite Solar Cells
Perovskite solar cells (PSCs) are a promising alternative solar technology, but the technical challenges of (1) stability/durability, (2) efficiency at scale, and (3) manufacturability must be overcome to achieve widespread PSC commercialization. The challenge of balancing solution ink formulation and scalable manufacturing is often overlooked in the literature, where focus is on adopting inks from processes that will not scale. In this study, we apply a classical roll-to-roll manufacturing perspective, utilizing both compositional engineering and intense pulsed light (IPL) annealing, to develop a mixed-cation perovskite ink with a robust coating window that simultaneously solve issues of stability and manufacturability for PSCs. Our method resulted in blade-coated, flexible, mixed-cation PSCs on ITO-PET substrates with a champion power conversion efficiency (PCE) efficiency of 16.7% using IPL annealing of the absorber layer and, to our knowledge, is one of the fastest processing methods for the perovskite layer. This overall reduction in processing time with a stable ink represents an advance toward the scaled production of perovskite solar cells on flexible substrates.
A scalable geometric multigrid solver for nonsymmetric elliptic systems with application to variable-density flows
A geometric multigrid algorithm is introduced for solving nonsymmetric linear systems resulting from the discretization of the variable density Navier–Stokes equations on nonuniform structured rectilinear grids and high-Reynolds number flows. The restriction operation is defined such that the resulting system on the coarser grids is symmetric, thereby allowing for the use of efficient smoother algorithms. To achieve an optimal rate of convergence, the sequence of interpolation and restriction operations are determined through a dynamic procedure. A parallel partitioning strategy is introduced to minimize communication while maintaining the load balance between all processors. To test the proposed algorithm, we consider two cases: 1) homogeneous isotropic turbulence discretized on uniform grids and 2) turbulent duct flow discretized on stretched grids. Testing the algorithm on systems with up to a billion unknowns shows that the cost varies linearly with the number of unknowns. This O (N) behavior confirms the robustness of the proposed multigrid method regarding ill-conditioning of large systems characteristic of multiscale high-Reynolds number turbulent flows. The robustness of our method to density variations is established by considering cases where density varies sharply in space by a factor of up to 104, showing its applicability to two-phase flow problems. Strong and weak scalability studies are carried out, employing up to 30,000 processors, to examine the parallel performance of our implementation. Excellent scalability of our solver is shown for a granularity as low as 104 to 105 unknowns per processor. At its tested peak throughput, it solves approximately 4 billion unknowns per second employing over 16,000 processors with a parallel efficiency higher than 50%.
Scalable, low-cost synthesis of high volumetric capacity LiMn 0.5 Fe 0.5 PO 4 cathode for lithium-ion batteries
A facile, scalable, wastewater-free synthesis of high energy density LiMn0.5Fe0.5PO4 (LMFP) cathodes with high electrode press density is achieved by employing the spinel LiMnFeO4 (LMFO) as a precursor.
GUIDE: A Scalable Information Directory Service to Collect, Federate, and Analyze Logs for Operational Insights into a Leadership HPC Facility
In this paper, we describe the GUIDE framework to collect, federate, and analyze log data from the Oak Ridge Leadership Computing Facility's (OLCF), and how we use it derive insights into center operations. We collectsystem logs and extract monitoring data at every level of the various OLCF subsystems, and have developed a suite of pre-processing tools to make the raw data consumable. The cleansed logs are then ingested and federated into a central, scalable data warehouse, Splunk, that offers storage, indexing, querying, and visualization capabilities. We have further developed and deployed a set of analytics tools to analyze these multiple disparate log streams in concert, and derive operational insights. We describe our experience from developing and deploying the GUIDE infrastructure, and deriving valuable insights on the various subsystems, based on two years of operations in the production OLCF environment.
A Highly Effective Polysulfide-Trapping Approach for the Development of High Energy Density, Scalable Lithium-Sulfur Batteries
Lithium-sulfur (Li-S) batteries are identified as one of the most promising next-generation battery technologies owing to their high theoretical specific energy, sustainability, and affordability. However, the commercialization of Li-S batteries has been hindered by severe technical challenges, including the lithium polysulfide (PS) dissolution/shuttling effect, a major cause of fast capacity degradation over cycling. We demonstrated that, for the first time, nanolayer polymer coated high surface area porous carbons (NPCs) were coated directly on sulfur electrodes (NPC-S), which led to a high specific capacity of ∼1,600 mAh g −1 approaching the theoretical specific capacity limit in the NPC-S based Li-S batteries. The NPC-S based Li-S batteries maintained their large initial specific capacity gain compared with the Baseline-S based Li-S batteries (control) over extended cycles. A follow-on study indicated that the NPC-S approach is a necessary and critical step to boost the near-theoretical specific capacity while being stabilized over long cycles with a synergistic strategy. Our experimental and computational results suggest that NPC coated on sulfur electrodes provides not only an effective and strong PS-trapping power but also an increased redox reaction kinetics for sulfur ↔ PS’s conversions during battery charge and discharge, rendering the realization of near-theoretical discharge specific capacity in the NPC-S based Li-S batteries. The findings presented in this study may inspire a new, simple, low-cost, and commercially scalable approach, without adding any appreciable dead weight or volume to the batteries, in the effort to tackle the technical challenges facing SOA Li-S batteries.
A scalable Euler–Lagrange approach for multiphase flow simulation on spectral elements
Multiphase flow can be difficult to simulate with high accuracy due to the wide range of scales associated with various multiphase phenomena. These scales may range from the size of individual particles to the entire domain of interest. Traditionally, large scale systems can only be simulated using averaging approaches that filter out the locations of individual particles. In this work, the Euler–Lagrange method is used to simulate large-scale dense particle systems in which each individual particle is tracked. In order to accomplish this, the highly scalable spectral element code nek5000 has been extended to handle the multiple levels of multiphase coupling in these systems. These levels include what has been called one-, two-, and four-way coupling. Here, each level has been separated to detail the computational impact of each stage. A binned ghost particle algorithm has also been developed to efficiently handle the challenges of two- and four-way coupling in a parallel processing context. The algorithms and their implementations are then shown to scale to 65,536 Message Passing Interface (MPI) ranks in both the strong and weak limits. After this, validation is performed through simulation of a small-scale fluidized bed. Lastly, a large-scale fluidized bed is simulated with 65,536 MPI ranks and is able to capture the unique physics of the onset of fluidization.