SEARCH · Search NASA
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.
Differentiable land surface model reimplementing an existing simulator, CANOAK, in JAX—a Google-developed Python package for high-performance machine learning research using automatic differentiation. The model's purpose is to perform hybrid land surface modeling that seamlessly couples process-based components with deep neural networks
INL’s General Line Ampacity State Solver (GLASS) is a high performance software platform that enables advanced grid system operations and planning efforts to be computed in real time. It enables the combination of advanced sensors and sensor networks, communication and data transfer channels, secure and reliable data management, and real-time processing through a flexible system architecture, with the ability to support and integrate with any utility enterprise system. GLASS is designed to help resolve the problems associated with thermally limited transmission and distribution networks, namely bare overhead power transmission lines. GLASS enables the methodology of Concurrent Cooling (Dynamic Line Rating) by reducing the need of using a cost-prohibited number of weather stations (reduce the number of weather stations per mile of overhead power line) to achieve the resolution necessary for individual electric transmission line span capacity calculations. GLASS achieves this by coupling real-time grid operations data and infrastructure architecture information with real-time field deployed weather station measurements and pre-computed Computational Fluid Dynamics (CFD)-based wind modeling interpolation parameters (look-up tables). GLASS uses mathematical models based on the IEEE 738 standard and builds off of the provided iterative solution using modern software paradigms and programming styles to provide a more robust and modular software system. GLASS also interfaces in real-time with utility companies using known transmission line spans and structures. GLASS provides real-time ampacity calculations based on recent weather station data as well as transmission line specifications. GLASS also allows the user to interact graphically with internal databases, which include information such as transmission line conductor types and thermal rating limits, weather station names and locations, specific transmission line span and structure locations, as well as recent ampacity calculations.
The discrete Fourier transform (DFT) is of fundamental interest in photonic quantum information, yet the ability to scale it to high dimensions depends heavily on the physical encoding, with practical recipes lacking in emerging platforms such as frequency bins. In this article, we show that d -point frequency-bin DFTs can be realized with a fixed three-component quantum frequency processor (QFP), simply by adding to the electro-optic modulation signals one radio-frequency harmonic per each incremental increase in d . We verify gate fidelity F W > 0.9997 and success probability P W > 0.965 up to d = 10 in numerical simulations, and experimentally implement the solution for d = 3, utilizing measurements with parallel DFTs to quantify entanglement and perform tomography of multiple two-photon frequency-bin states. Our results furnish new opportunities for high-dimensional frequency-bin protocols in quantum communications and networking.
Energy is the fundamental barrier to Exascale supercomputing and is dominated by the cost of moving data from one point to another, not computation. Similarly, performance is dominated by data movement, not computation. The solution to this problem requires three critical technologies: 3D integration, optical chip-to-chip communication, and a new communication model. The central goal of the Sandia led "Data Movement Dominates" project aimed to develop memory systems and new architectures based on these technologies that have the potential to lower the cost of local memory accesses by orders of magnitude and provide substantially more bandwidth. Only through these transformational advances can future systems reach the goals of Exascale computing with a manageable power budgets. The Sandia led team included co-PIs from Columbia University, Lawrence Berkeley Lab, and the University of Maryland. The Columbia effort of Data Movement Dominates focused on developing a physically accurate simulation environment and experimental verification for optically-connected memory (OCM) systems that can enable continued performance scaling through high-bandwidth capacity, energy-efficient bit-rate transparency, and time-of-flight latency. With OCM, memory device parallelism and total capacity can scale to match future high-performance computing requirements without sacrificing data-movement efficiency. When we consider systems with integrated photonics, links to memory can be seamlessly integrated with the interconnection network-in a sense, memory becomes a primary aspect of the interconnection network. At the core of the Columbia effort, toward expanding our understanding of OCM enabled computing we have created an integrated modeling and simulation environment that uniquely integrates the physical behavior of the optical layer. The PhoenxSim suite of design and software tools developed under this effort has enabled the co-design of and performance evaluation photonics-enabled OCM architectures on Exascale computing systems.
Ocean surface radiation measurement best practices have been developed as a first step to support the interoperability of radiation measurements across multiple ocean platforms and between land and ocean networks. This document describes the consensus by a working group of radiation measurement experts from land, ocean, and aircraft communities. The scope was limited to broadband shortwave (solar) and longwave (terrestrial infrared) surface irradiance measurements for quantification of the surface radiation budget. Best practices for spectral measurements for biological purposes like photosynthetically active radiation and ocean color are only mentioned briefly to motivate future interactions between the physical surface flux and biological radiation measurement communities. Topics discussed in these best practices include instrument selection, handling of sensors and installation, data quality monitoring, data processing, and calibration. It is recognized that platform and resource limitations may prohibit incorporating all best practices into all measurements and that spatial coverage is also an important motivator for expanding current networks. Thus, one of the key recommendations is to perform interoperability experiments that can help quantify the uncertainty of different practices and lay the groundwork for a multi-tiered global network with a mix of high-accuracy reference stations and lower-cost platforms and practices that can fill in spatial gaps.
Recently, graph neural networks (GNNs) have been successfully used for a variety of particle reconstruction problems in high energy physics, including particle tracking. The Exa.TrkX pipeline based on GNNs demonstrated promising performance in reconstructing particle tracks in dense environments. It includes five discrete steps: data encoding, graph building, edge filtering, GNN, and track labeling. All steps were written in Python and run on both GPUs and CPUs. In this work, we accelerate the Python implementation of the pipeline through customized and commercial GPU-enabled software libraries, and develop a C++ implementation for inferencing the pipeline. The implementation features an improved, CUDA-enabled fixed-radius nearest neighbor search for graph building and a weakly connected component graph algorithm for track labeling. GNNs and other trained deep learning models are converted to ONNX and inferenced via the ONNX Runtime C++ API. The complete C++ implementation of the pipeline allows integration with existing tracking software. We report the memory usage and average event latency tracking performance of our implementation applied to the TrackML benchmark dataset.
Linear state estimation (LSE) is a phasor measurement unit (PMU) data-based power system state estimation that incorporates a linear measurement model in rectangular coordinates. Due to the high computational efficiency and high observational time-resolution, LSE can act as a supplementary state estimation in a wide-area monitoring system (WAMS). The performance of LSE is relatively sensitive to noises in measurements. Therefore, the estimation accuracy relies heavily on the accuracy of the estimation matrix, which is directly influenced by the measurement weight matrix. This paper proposes two novel calibration method of the estimation matrix using neural networks. One is based on the minimum absolute network loss (ANL), and the other is based on the minimum average squared network loss (ASNL). Both methods are tested and compared with LSE algorithms on the IEEE 14-bus system
Abstract All‐polymer solar cells (all‐PSCs) exhibit excellent stability and readily tunable ink viscosity, and are therefore especially suitable for printing preparation of large‐scale devices. At present, the efficiency of state‐of‐the‐art all‐PSCs fabricated by the spin‐coating method has exceeded 11%, laying the foundation for the preparation and practical utilization of printed devices. A high power conversion efficiency (PCE) of 11.76% is achieved based on PTzBI‐Si:N2200 all‐PSCs processing with 2‐methyltetrahydrofuran (MTHF, an environmentally friendly solvent) and preparation of active layers by slot die printing, which is the top efficient for all‐PSCs. Conversely, the PCE of devices processed by high‐boiling point chlorobenzene is less than 2%. Through the study of film formation kinetics, volatile solvents can freeze the morphology in a short time, and a more rigid conformation with strong intermolecular interaction combined with the solubility limit of PTzBI‐Si and N2200 in MTHF results in the formation of a fibril network in the bulk heterojunction. The multilength scaled morphology ensures fast transfer of carriers and facilitates exciton separation, which boosts carrier mobility and current density, thus improving the device performance. These results are of great significance for large‐scale printing fabrication of high‐efficiency all‐PSCs in the future.
Single-cell gene expression measurements offer opportunities in deriving mechanistic understanding of complex diseases, including cancer. However, due to the complex regulatory machinery of the cell, gene regulatory network (GRN) model inference based on such data still manifests significant uncertainty. The goal of this paper is to develop optimal classification of single-cell trajectories accounting for potential model uncertainty. Partially-observed Boolean dynamical systems (POBDS) are used for modeling gene regulatory networks observed through noisy gene-expression data. We derive the exact optimal Bayesian classifier (OBC) for binary classification of single-cell trajectories. The application of the OBC becomes impractical for large GRNs, due to computational and memory requirements. To address this, we introduce a particle-based single-cell classification method that is highly scalable for large GRNs with much lower complexity than the optimal solution. The performance of the proposed particle-based method is demonstrated through numerical experiments using a POBDS model of the well-known T-cell large granular lymphocyte (T-LGL) leukemia network with noisy time-series gene-expression data.
The IBM Blue Gene®/Q platform presents scientists and engineers with a rich set of hardware features such as 16 cores per chip sharing a Level 2 cache, a wide SIMD (single-instruction, multiple-data) unit, a five-dimensional torus network, and hardware support for collective operations. Especially important is the feature related to cores that have four “hardware threads,” which makes it possible to hide latencies and obtain a high fraction of the peak issue rate from each core. All of these hardware resources present unique performance-tuning opportunities on Blue Gene/Q. We provide an overview of several important applications and solvers and study them on Blue Gene/Q using performance counters and Message Passing Interface profiles. We also discuss how Blue Gene/Q tools help us understand the interaction of the application with the hardware and software layers and provide guidance for optimization. Furthermore, on the basis of our analysis, we discuss code improvement strategies targeting Blue Gene/Q. Information about how these algorithms map to the Blue Gene® architecture is expected to have an impact on future system design as we move to the exascale era.
Semiempirical methods like density functional tight-binding (DFTB) allow extensive phase space sampling, making it possible to generate free energy surfaces of complex reactions in condensed-phase environments. Such a high efficiency often comes at the cost of reduced accuracy, which may be improved by developing a specific reaction parametrization (SRP) for the particular molecular system. Thiol–disulfide exchange is a nucleophilic substitution reaction that occurs in a large class of proteins. Its proper description requires a high-level ab initio method, while DFT-GAA and hybrid functionals were shown to be inadequate, and so is DFTB due to its DFT-GGA descent. We develop an SRP for thiol–disulfide exchange based on an artificial neural network (ANN) implementation in the DFTB+ software and compare its performance to that of a standard SRP approach applied to DFTB. Furthermore, as an application, we use both new DFTB-SRP as components of a QM/MM scheme to investigate thiol–disulfide exchange in two molecular complexes: a solvated model system and a blood protein. Demonstrating the strengths of the methodology, highly accurate free energy surfaces are generated at a low cost, as the augmentation of DFTB with an ANN only adds a small computational overhead.
Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and resulting in reduced performance and service life. A coupled electro-chemo-mechanical reproducing kernel particle method (RKPM) formulation has been developed to analyze this system. With microstructural images supplied by the National Renewable Energy Laboratory (NREL), pixel-based model construction by RKPM is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. Here, a neural network-enhanced reproducing kernel particle method (NN-RKPM) [1, 2] is introduced to effectively model damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RKPM is additionally used to inform how crack opening and closure in turn affect the coupled chemical equations and material microstructure. Reference: [1] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, pp 4422-4454, https://doi.org/10.1002/nme.7040, 2022. [2] Baek, J., Chen, J. S., "A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture", Computer Methods in Applied Mechanics and Engineering Vol. 410, 116590, 2024.
Sodium all-solid-state batteries (NaSSBs) with an alloy-type anode (e.g., Sn and Sb) offer superior capacity and energy density compared to hard carbon anode. However, the irreversible loss of Na + at the alloy anode during the initial cycle results in diminished capacity and stability, impairing full-cell performance. Here, this study presents an easy-to-implement cathode presodiation strategy by employing a Na-rich material to address these challenges. Leveraging the high theoretical capacity and suitable voltage window, Na 2 S is chosen as the Na donor, which is activated by creating a mixed electron-ion conducting network, delivering a high capacity of 511.7 mAh g −1 . By adding a small amount (i.e., 3 wt.%) of Na 2 S to the cathode composite, a NaCrO 2 || Sn full cell demonstrated capacity improvement from 90.8 to 118.2 mAh g −1 (based on cathode mass). The capacity-balanced full cell can thus cycle to more than 300 times with >90% capacity retention. This work provides a practical solution to enhance the full-cell performance and advance the transformation from half-cell to full-cell applications of NaSSBs.
The complex nature of manufacturing processes stipulates electrodes to possess high variability with increased heterogeneity during production. X-ray computed tomography imaging has proved to be critical in visualizing the complicated stochastic particle distribution of as-manufactured electrodes in lithium-ion batteries. However, accurate prediction of their electrochemical performance necessitates precise evaluation of kinetic and transport properties from real electrodes. Image segmentation that characterizes voxels to particle/pore phase is often meticulous and fraught with subjectivity owing to a myriad of unconstrained choices and filter algorithms. Here we utilize a Bayesian convolutional neural network to tackle segmentation subjectivity and quantify its pertinent uncertainties. Otsu inter-variance and Blind/Referenceless Imaging Spatial Quality Evaluator are used to assess the relative image quality of grayscale tomograms, thus evaluating the uncertainty in the derived microstructural attributes. We analyze how image uncertainty is correlated with the uncertainties and magnitude of kinetic and transport properties of an electrode, further identifying pathways of uncertainty propagation within microstructural attributes. The coupled effect of spatial heterogeneity and microstructural anisotropy on the uncertainty quantification of transport parameters is also understood. This work demonstrates a novel methodology to extract microstructural descriptors from real electrode images through quantification of associated uncertainties and discerning the relative strength of their propagation, thus facilitating feedback to manufacturing processes from accurate image based electrochemical simulations.
Not provided.
The large scale control over thousands of quantum emitters desired by quantum network technology is limited by the power consumption and cross-talk inherent in current microwave techniques. Here we propose a quantum repeater architecture based on densely-packed diamond color centers (CCs) in a programmable electrode array, with quantum gates driven by electric or strain fields. This ‘field programmable spin array’ (FPSA) enables high-speed spin control of individual CCs with low cross-talk and power dissipation. Integrated in a slow-light waveguide for efficient optical coupling, the FPSA serves as a quantum interface for optically-mediated entanglement. We evaluate the performance of the FPSA architecture in comparison to a routing-tree design and show an increased entanglement generation rate scaling into the thousand-qubit regime. Our results enable high fidelity control of dense quantum emitter arrays for scalable networking.
This paper explores the interactions between micro-pin concentrated receiver designs with overall solar thermal energy system performance, with different operating conditions, working fluid, and required materials of construction. A 320 MW thermal plant coupled to a 160 MW electric sCO2 Brayton cycle is considered as the baseline. The circulating fluid enters the receiver at 550°C, and leaves at 720°C. The thermal storage/power block are located 150 m from the receiver at the base of the receiver tower. A resistance network based thermal and hydraulic model is used to predict heat transfer and pressure drop performance of the micro-pin receiver. This output of this model is coupled to a system level model of the pressure loss and compressor power required in the remainder of the high temperature gas loop. Overall performance is investigated for supercritical carbon dioxide and helium as working fluids, at pressures from 7.5 to 25 MPa, and at delivery temperatures of 720°C. The results show that by modifying pin depth and flow lengths, there are design spaces for micro-pin devices that can provide high thermal performance without significantly reducing the overall solar thermal system output at lower operating pressures. Use of lower pressure fluids enables lower cost materials of construction in the piping and distribution system, reducing the cost of electricity.
It remains a grand challenge to replace platinum group metal (PGM) catalysts with earth-abundant materials for the oxygen reduction reaction (ORR) in acidic media, which is crucial for large-scale deployment of proton exchange membrane fuel cells (PEMFCs). Here, we report a high-performance atomic Fe catalyst derived from chemically Fe-doped zeolitic imidazolate frameworks (ZIFs) by directly bonding Fe ions to imidazolate ligands within 3D frameworks. Although the ZIF was identified as a promising precursor, the new synthetic chemistry enables the creation of well-dispersed atomic Fe sites embedded into porous carbon without the formation of aggregates. The size of catalyst particles is tunable through synthesizing Fe-doped ZIF nanocrystal precursors in a wide range from 20 to 1000 nm followed by one-step thermal activation. Similar to Pt nanoparticles, the unique size control without altering chemical properties afforded by this approach is able to increase the number of PGM-free active sites. The best ORR activity is measured with the catalyst at a size of 50 nm. Further size reduction to 20 nm leads to significant particle agglomeration, thus decreasing the activity. Using the homogeneous atomic Fe model catalysts, we elucidated the active site formation process through correlating measured ORR activity with the change of chemical bonds in precursors during thermal activation up to 1100 °C. The critical temperature to form active sites is 800 °C, which is associated with a new Fe species with a reduced oxidation number (from Fe3+ to Fe2+) likely bonded with pyridinic N (FeN4) embedded into the carbon planes. Further increasing the temperature leads to continuously enhanced activity, linked to the rise of graphitic N and Fe–N species. The new atomic Fe catalyst has achieved respectable ORR activity in challenging acidic media (0.5 M H2SO4), showing a half-wave potential of 0.85 V vs RHE and leaving only a 30 mV gap with Pt/C (60 μgPt/cm2). Enhanced stability is attained with the same catalyst, which loses only 20 mV after 10 000 potential cycles (0.6–1.0 V) in O2 saturated acid. The high-performance atomic Fe PGM-free catalyst holds great promise as a replacement for Pt in future PEMFCs.