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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 1,621 records · Page 90
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Separable physics-informed DeepONet: Breaking the curse of dimensionality in physics-informed machine learning

The deep operator network (DeepONet) has shown remarkable potential in solving partial differential equations (PDEs) by mapping between infinite-dimensional function spaces using labeled datasets. However, in scenarios lacking labeled data, the physics-informed DeepONet (PI-DeepONet) approach, which utilizes the residual loss of the governing PDE to optimize the network parameters, faces significant computational challenges, particularly due to the curse of dimensionality. This limitation has hindered its application to high-dimensional problems, making even standard 3D spatial with 1D temporal problems computationally prohibitive. Additionally, the computational requirement increases exponentially with the discretization density of the domain. Here, to address these challenges and enhance scalability for high-dimensional PDEs, we introduce the Separable physics-informed DeepONet (Sep-PI-DeepONet). This framework employs a factorization technique, utilizing sub-networks for individual one-dimensional coordinates, thereby reducing the number of forward passes and the size of the Jacobian matrix required for gradient computations. By incorporating forward-mode automatic differentiation (AD), we further optimize computational efficiency, achieving linear scaling of computational cost with discretization density and dimensionality, making our approach highly suitable for high-dimensional PDEs. We demonstrate the effectiveness of Sep-PI-DeepONet through three benchmark PDE models: the viscous Burgers’ equation, Biot’s consolidation theory, and a parameterized heat equation. Our framework maintains accuracy comparable to the conventional PI-DeepONet while reducing training time by two orders of magnitude. Notably, for the heat equation solved as a 4D problem, the conventional PI-DeepONet was computationally infeasible (estimated 289.35 h), while the Sep-PI-DeepONet completed training in just 2.5 h. These results underscore the potential of Sep-PI-DeepONet in efficiently solving complex, high-dimensional PDEs, marking a significant advancement in physics-informed machine learning.

Neural operator↗
DOE OSTI2020

SHARP-Net: Platform for Self-Healing and Attack Resilient PMU Networks

Synchrophasor technology plays a pivotal role in developing the next generation of wide-area monitoring, protection, and control in the smart grid environment. As technology and communications infrastructures evolve, however, so do the attack surfaces in the synchrophasor network that can be exploited by advanced persistent threat (APT) actors to affect power system stability and reliability. In this paper, we propose a novel platform for developing a self-healing and attack-resilient PMU network (SHARP-Net) by instituting a state-of-the-art intrusion detection system (IDS) with an intrusion mitigation system (IMS) and an alert management system (AMS). In particular, the proposed platform detects anomalies during cyberattacks on phasor data concentrators (PDCs) based on the rules defined in the IDS, then the generated alerts are published to the IMS through the AMS. The proposed IMS proceeds to take automated corrective responses to mitigate cyberattacks by reconfiguring the synchrophasor network to isolate the compromised PDCs, and it orchestrates new PDCs to prevent the future propagation of attacks. Further, the IMS restores the system's observability by reconnecting the new PDCs to make the grid attack-resilient. In this work, the SHARP-Net platform is developed by using Python-based libraries, minimega's software-defined network, and virtual machine orchestration. We implement and validate the proposed SHARP-Net architecture by testing a PMU network in the smart grid environment. SHARP-Net showed promising performance in detecting cyberattacks and mitigating them through the network reconfiguration.

computer architecture↗
DOE OSTI2016

Understanding Water Transport in Polymer Electrolyte Fuel Cells Using Coupled Continuum and Pore-Network Models

Water management remains a critical issue for polymer electrolyte fuel cell performance and durability, especially at lower temperatures and with ultrathin electrodes. To understand and explain experimental observations better, water transport in gas diffusion layers (GDLs) with macroscopically heterogeneous morphologies was simulated using a novel coupling of continuum and pore-network models. X-ray computed tomography was used to extract GDL material parameters for use in the pore-network model. The simulations were conducted to explain experimental observations associated with stacking of anode GDLs, where stacking of the anode GDLs increased the limiting current density. Through imaging, it is shown that the stacked anode GDL exhibited an interfacial region of high porosity. The coupled model shows that this morphology allowed more efficient water movement through the anode and higher temperatures at the cathode compared to the single GDL case. As a result, the cathode exhibited less flooding and hence better low temperature performance with the stacked anode GDL.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2022

Examination of Haines Jump in Microfluidic Experiments via Evolution Graphs and Interface Tracking

This work examines a type of rapid pore-filling event in multiphase flow through permeable media that is better known as Haines Jump. While existing microfluidic experiments on Haines Jump mostly seek to maintain quasi-steady states through very low bulk flow rates over long periods of time, this work explores the combined use of a highly structured microscale transport network, high-speed fluorescent microscopy, displacement front segmentation algorithms, and a tracking algorithm to build evolution graphs that track displacement fronts as they evolve through high-speed video recording. The resulting evolution graph allows the segmentation of a high-speed recording in both space and time, potentially facilitating topology-cognitive computation on the transport network. Occurrences of Haines Jump are identified in the microfluidic displacement experiments and their significance in bulk flow rates is qualitatively analyzed. The bulk flow rate has little effect on the significance of Haines Jump during merging and splitting, but large bulk flow rates may obscure small bursts at the narrowest part of the throat.

permeable media↗
DOE OSTI2025

Three-dimensional modeling of hyphal fusion, branching, and nutrient transport in filamentous fungi

Fungi exhibit behaviors distinct from other microbes. Filamentous fungi grow by extending complex networks of branched filaments collectively referred to as the mycelium. These networks can expand over large distances and traverse low-nutrient areas by translocating nutrients through the filament network. This spatial characteristic makes filamentous fungi crucial for soil ecosystems, supporting stable microbial communities and promoting plant growth. However, simulating these behaviors is complex. The elongated nature of fungal compartments results in different mechanical interactions compared to the commonly modeled spherical bacteria. These detailed hyphal mechanics require specialized consideration and are often excluded from conventional fungal simulation packages. Additionally, the extensive fungal networks in nature demand computationally intensive simulations, necessitating high-performance algorithms. Therefore, realistic fungi simulations require specialized software. Here, we introduce a fungal modeling expansion to the high-performance biological modelling and interface exchange (bmx) software suite. bmx leverages adaptive mesh refinement in AMReX for chemical diffusion and incorporates a full mechanical model for bacterial cells, accelerated by GPUs. By extending bmx to model filamentous particles, we demonstrate the formation of complex filament networks through interactions like hyphal branching and fusion (anastomosis). We show that the networks produced match real-world fungal structures through various metrics. This work supports computational studies of fungal growth dynamics and can be adapted to investigate the growth of other filamentous structures in biology or materials science. The expanded-BMX package is open-sourced and is available online.

Cell mechanics↗
DOE OSTI2012

End-to-End Performance Management for Large Distributed Storage

Storage systems for large distributed clusters of computer servers are themselves large and distributed. Their complexity and scale make it hard to ensure that applications using them get good, predictable performance. At the same time, shared access to the system from multiple applications, users, and internal system activities leads to a need for predictable performance. This research investigates mechanisms for improving storage system performance in large distributed storage systems through mechanisms that integrate the performance aspects of the path that I/O operations take through the system, from the application interface on the compute server, through the network, to the storage servers. The research focuses on five parts of the I/O path in a distributed storage system: I/O scheduling at the storage server, storage server cache management, client-to-server network flow control, client-to-server connection management, and client cache management.

42 ENGINEERING↗
NASA NTRS1999

Integration of a Decentralized Linear-Quadratic-Gaussian Control into GSFC's Universal 3-D Autonomous Formation Flying Algorithm

A decentralized control is investigated for applicability to the autonomous formation flying control algorithm developed by GSFC for the New Millenium Program Earth Observer-1 (EO-1) mission. This decentralized framework has the following characteristics: The approach is non-hierarchical, and coordination by a central supervisor is not required; Detected failures degrade the system performance gracefully; Each node in the decentralized network processes only its own measurement data, in parallel with the other nodes; Although the total computational burden over the entire network is greater than it would be for a single, centralized controller, fewer computations are required locally at each node; Requirements for data transmission between nodes are limited to only the dimension of the control vector, at the cost of maintaining a local additional data vector. The data vector compresses all past measurement history from all the nodes into a single vector of the dimension of the state; and The approach is optimal with respect to standard cost functions. The current approach is valid for linear time-invariant systems only. Similar to the GSFC formation flying algorithm, the extension to linear LQG time-varying systems requires that each node propagate its filter covariance forward (navigation) and controller Riccati matrix backward (guidance) at each time step. Extension of the GSFC algorithm to non-linear systems can also be accomplished via linearization about a reference trajectory in the standard fashion, or linearization about the current state estimate as with the extended Kalman filter. To investigate the feasibility of the decentralized integration with the GSFC algorithm, an existing centralized LQG design for a single spacecraft orbit control problem is adapted to the decentralized framework while using the GSFC algorithm's state transition matrices and framework. The existing GSFC design uses both reference trajectories of each spacecraft in formation and by appropriate choice of coordinates and simplified measurement modeling is formulated as a linear time-invariant system. Results for improvements to the GSFC algorithm and a multiple satellite formation will be addressed. The goal of this investigation is to progressively relax the assumptions that result in linear time-invariance, ultimately to the point of linearization of the non-linear dynamics about the current state estimate as in the extended Kalman filter. An assessment will then be made about the feasibility of the decentralized approach to the realistic formation flying application of the EO-1/Landsat 7 formation flying experiment.

Folta, David C.↗
DOE OSTI2014

Big Data over a 100G network at Fermilab

As the need for Big Data in science becomes ever more relevant, networks around the world are upgrading their infrastructure to support high-speed interconnections. To support its mission, the high-energy physics community as a pioneer in Big Data has always been relying on the Fermi National Accelerator Laboratory to be at the forefront of storage and data movement. This need was reiterated in recent years with the data-taking rate of the major LHC experiments reaching tens of petabytes per year. At Fermilab, this resulted regularly in peaks of data movement on the Wide area network (WAN) in and out of the laboratory of about 30 Gbit/s and on the Local are network (LAN) between storage and computational farms of 160 Gbit/s. To address these ever increasing needs, as of this year Fermilab is connected to the Energy Sciences Network (ESnet) through a 100 Gb/s link. To understand the optimal system-and application-level configuration to interface computational systems with the new highspeed interconnect, Fermilab has deployed a Network Research & Development facility connected to the ESnet 100G Testbed. For the past two years, the High Throughput Data Program (HTDP) has been using the Testbed to identify gaps in data movement middleware [5] when transferring data at these high-speeds. The program has published evaluations of technologies typically used in High Energy Physics, such as GridFTP [4], XrootD [9], and Squid [8]. Furthermore, this work presents the new R&D facility and the continuation of the evaluation program.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Stream network analysis and geomorphic flood plain mapping from orbital and suborbital remote sensing imagery application to flood hazard studies in central Texas

The author has identified the following significant results. Development of a quantitative hydrogeomorphic approach to flood hazard evaluation was hindered by (1) problems of resolution and definition of the morphometric parameters which have hydrologic significance, and (2) mechanical difficulties in creating the necessary volume of data for meaningful analysis. Measures of network resolution such as drainage density and basin Shreve magnitude indicated that large scale topographic maps offered greater resolution than small scale suborbital imagery and orbital imagery. The disparity in network resolution capabilities between orbital and suborbital imagery formats depends on factors such as rock type, vegetation, and land use. The problem of morphometric data analysis was approached by developing a computer-assisted method for network analysis. The system allows rapid identification of network properties which can then be related to measures of flood response.

Baker, V. R.↗
DOE OSTI2022

A Roadmap for Edge Computing Enabled Automated Multidimensional Transmission Electron Microscopy

The advent of modern, high-speed electron detectors has made the collection of multidimensional hyperspectral transmission electron microscopy datasets, such as 4D-STEM, a routine. However, many microscopists find such experiments daunting since analysis, collection, long-term storage, and networking of such datasets remain challenging. Some common issues are their large and unwieldy size that often are several gigabytes, non-standardized data analysis routines, and a lack of clarity about the computing and network resources needed to utilize the electron microscope. The existing computing and networking bottlenecks introduce significant penalties in each step of these experiments, and thus, real-time analysis-driven automated experimentation for multidimensional TEM is challenging. One solution is to integrate microscopy with edge computing, where moderately powerful computational hardware performs the preliminary analysis before handing off the heavier computation to high-performance computing (HPC) systems. In this work, we trace the roots of computation in modern electron microscopy, demonstrate deep learning experiments running on an edge system, and discuss the networking requirements for tying together microscopes, edge computers, and HPC systems.

47 OTHER INSTRUMENTATION↗
NASA NTRS1993

A comparison of queueing, cluster and distributed computing systems

Using workstation clusters for distributed computing has become popular with the proliferation of inexpensive, powerful workstations. Workstation clusters offer both a cost effective alternative to batch processing and an easy entry into parallel computing. However, a number of workstations on a network does not constitute a cluster. Cluster management software is necessary to harness the collective computing power. A variety of cluster management and queuing systems are compared: Distributed Queueing Systems (DQS), Condor, Load Leveler, Load Balancer, Load Sharing Facility (LSF - formerly Utopia), Distributed Job Manager (DJM), Computing in Distributed Networked Environments (CODINE), and NQS/Exec. The systems differ in their design philosophy and implementation. Based on published reports on the different systems and conversations with the system's developers and vendors, a comparison of the systems are made on the integral issues of clustered computing.

Kaplan, Joseph A.↗
DOE OSTI2017

A convolutional neural network approach to calibrating the rotation axis for X-ray computed tomography

This paper presents an algorithm to calibrate the center-of-rotation for X-ray tomography by using a machine learning approach, the Convolutional Neural Network (CNN). The algorithm shows excellent accuracy from the evaluation of synthetic data with various noise ratios. It is further validated with experimental data of four different shale samples measured at the Advanced Photon Source and at the Swiss Light Source. The results are as good as those determined by visual inspection and show better robustness than conventional methods. CNN has also great potential for reducing or removing other artifacts caused by instrument instability, detector non-linearity, etc . An open-source toolbox, which integrates the CNN methods described in this paper, is freely available through GitHub at tomography/xlearn and can be easily integrated into existing computational pipelines available at various synchrotron facilities. Source code, documentation and information on how to contribute are also provided.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

Micro- and nano-X-ray computed-tomography: A step forward in the characterization of the pore network of a leached cement paste

Pore structure of leached cement pastes (w/c = 0.5) was studied for the first time from micro-scale down to the nano-scale by combining micro- and nano-X-ray computed tomography (micro- and nano-CT). This allowed assessing the 3D heterogeneity of the pore network along the cement profile (from the core to the altered layer) of almost the entire range of cement pore size, i.e. from capillary to gel pores. We successfully quantified an increase of porosity in the altered layer at both resolutions. Porosity is increasing from 1.8 to 6.1% and from 18 to 58% at the micro-(voxel = 1.81 μm) and nano-scale (voxel = 63.5 nm) respectively. The combination of both CT allowed to circumvent weaknesses inherent of both investigation scales. In addition the connectivity and the channel size of the pore network were also evaluated to obtain a complete 3D pore network characterization at both scales.

36 MATERIALS SCIENCE↗
NASA NTRS1990

Neural computation of arithmetic functions

An area of application of neural networks is considered. A neuron is modeled as a linear threshold gate, and the network architecture considered is the layered feedforward network. It is shown how common arithmetic functions such as multiplication and sorting can be efficiently computed in a shallow neural network. Some known results are improved by showing that the product of two n-bit numbers and sorting of n n-bit numbers can be computed by a polynomial-size neural network using only four and five unit delays, respectively. Moreover, the weights of each threshold element in the neural networks require O(log n)-bit (instead of n-bit) accuracy. These results can be extended to more complicated functions such as multiple products, division, rational functions, and approximation of analytic functions.

Siu, Kai-Yeung↗
DOE OSTI2024

Feasibility of Hybrid Electro- and Acousto-Dynamical Systems for Quantum Optical Networks

We present results on an exploratory research program that aims to study the feasibility of novel, sustained, dense, and efficient quantum information generation, storage, retrieval, relay and distribution devices and systems towards future advanced quantum networking architectures. The pathfinder program is anchored on original ideas and device conceptual designs that turn the phononic-sourced challenges into an opportunity towards optimized transduction chains over a broad range of length and energy/temperature scales. The proposal involves conceptual design and feasibility towards integration, commissioning and benchmarking of challenging transduction devices in a scaled quantum network setup.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗