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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Determining flow directions in river channel networks using planform morphology and topology

Abstract. The abundance of global, remotely sensed surface water observations has accelerated efforts toward characterizing and modeling how water moves across the Earth's surface through complex channel networks. In particular, deltas and braided river channel networks may contain thousands of links that route water, sediment, and nutrients across landscapes. In order to model flows through channel networks and characterize network structure, the direction of flow for each link within the network must be known. In this work, we propose a rapid, automatic, and objective method to identify flow directions for all links of a channel network using only remotely sensed imagery and knowledge of the network's inlet and outlet locations. We designed a suite of direction-predicting algorithms (DPAs), each of which exploits a particular morphologic characteristic of the channel network to provide a prediction of a link's flow direction. DPAs were chained together to create “recipes”, or algorithms that set all the flow directions of a channel network. Separate recipes were built for deltas and braided rivers and applied to seven delta and two braided river channel networks. Across all nine channel networks, the recipe-predicted flow directions agreed with expert judgement for 97 % of all tested links, and most disagreements were attributed to unusual channel network topologies that can easily be accounted for by pre-seeding critical links with known flow directions. Our results highlight the (non)universality of process–form relationships across deltas and braided rivers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

DLSIA: Deep Learning for Scientific Image Analysis

DLSIA (Deep Learning for Scientific Image Analysis) is a Python-based machine learning library that empowers scientists and researchers across diverse scientific domains with a range of customizable convolutional neural network (CNN) architectures for a wide variety of tasks in image analysis to be used in downstream data processing. DLSIA features easy-to-use architectures, such as autoencoders, tunable U-Nets and parameter-lean mixed-scale dense networks (MSDNets). Additionally, this article introduces sparse mixed-scale networks (SMSNets), generated using random graphs, sparse connections and dilated convolutions connecting different length scales. For verification, several DLSIA-instantiated networks and training scripts are employed in multiple applications, including inpainting for X-ray scattering data using U-Nets and MSDNets, segmenting 3D fibers in X-ray tomographic reconstructions of concrete using an ensemble of SMSNets, and leveraging autoencoder latent spaces for data compression and clustering. As experimental data continue to grow in scale and complexity, DLSIA provides accessible CNN construction and abstracts CNN complexities, allowing scientists to tailor their machine learning approaches, accelerate discoveries, foster interdisciplinary collaboration and advance research in scientific image analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Data Quality Monitoring for the Hadron Calorimeters Using Transfer Learning for Anomaly Detection

The proliferation of sensors brings an immense volume of spatio-temporal (ST) data in many domains, including monitoring, diagnostics, and prognostics applications. Data curation is a time-consuming process for a large volume of data, making it challenging and expensive to deploy data analytics platforms in new environments. Transfer learning (TL) mechanisms promise to mitigate data sparsity and model complexity by utilizing pre-trained models for a new task. Despite the triumph of TL in fields like computer vision and natural language processing, efforts on complex ST models for anomaly detection (AD) applications are limited. In this study, we present the potential of TL within the context of high-dimensional ST AD with a hybrid autoencoder architecture, incorporating convolutional, graph, and recurrent neural networks. Motivated by the need for improved model accuracy and robustness, particularly in scenarios with limited training data on systems with thousands of sensors, this research investigates the transferability of models trained on different sections of the Hadron Calorimeter of the Compact Muon Solenoid experiment at CERN. The key contributions of the study include exploring TL’s potential and limitations within the context of encoder and decoder networks, revealing insights into model initialization and training configurations that enhance performance while substantially reducing trainable parameters and mitigating data contamination effects.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Towards Next-Generation Urban Decision Support Systems through AI-Powered Construction of Scientific Ontology Using Large Language Models—A Case in Optimizing Intermodal Freight Transportation

The incorporation of Artificial Intelligence (AI) models into various optimization systems is on the rise. However, addressing complex urban and environmental management challenges often demands deep expertise in domain science and informatics. This expertise is essential for deriving data and simulation-driven insights that support informed decision-making. In this context, we investigate the potential of leveraging the pre-trained Large Language Models (LLMs) to create knowledge representations for supporting operations research. By adopting ChatGPT-4 API as the reasoning core, we outline an applied workflow that encompasses natural language processing, Methontology-based prompt tuning, and Generative Pre-trained Transformer (GPT), to automate the construction of scenario-based ontologies using existing research articles and technical manuals of urban datasets and simulations. From these ontologies, knowledge graphs can be derived using widely adopted formats and protocols, guiding various tasks towards data-informed decision support. The performance of our methodology is evaluated through a comparative analysis that contrasts our AI-generated ontology with the widely recognized pizza ontology, commonly used in tutorials for popular ontology software. We conclude with a real-world case study on optimizing the complex system of multi-modal freight transportation. Our approach advances urban decision support systems by enhancing data and metadata modeling, improving data integration and simulation coupling, and guiding the development of decision support strategies and essential software components.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

LENS: Learning Enabled Network Synthesis

RTRC and UMD have developed novel machine learning based methods under the ARPA-E DIFFERENTIATE program for rapid acceleration of hypothesis generation in complex architecture design spaces involving both discrete choices of component inclusion and interconnection and continuous parametric decisions. The project named Learning Enabled Network Synthesis (LENS) further demonstrated the developed methods on challenging electrical power converter design problems by identifying the most suitable circuit topologies and simultaneously selecting the most appropriate components to achieve optimized design of power converter with improved performances. We demonstrated that LENS could enable exploration of very large design space of circuit topologies and components by addressing the limitations of conventional design process in non-linear, high switching speed, multi-dimensional power converter design and optimization. The key innovation developed in LENS is the seamless integration of statistical learning and logical reasoning techniques and building on the individual strengths of these techniques for rapid hypothesis discovery. The main component of LENS comprises of: 1) Graph Reasoning Engine (GRE) to enforce composition rules that rapidly reject all discrete architectures that are composed incorrectly and generates an adaptive database of feasible designs which can be used by ML modules, 2) Graph Generative Learning module which is a deep neural network based generative model for graph architectures which can enable design space exploration beyond the dataset generated by the GRE, 3) Graph Reduced Order Model (ROM) for graph domains for accelerating computation of output metrics, and 4) Active learning and Rule Discovery module for sample efficient learning and extracting logical rules from the learned ML models which will be integrated in the GRE to enhance the filtering effectiveness. LENS approach can be applied to any design domains where designs can be represented as multi-attribute graphs. The LENS team integrated the various technical innovations listed above into an optimization pipeline and exercised the optimization pipeline on the converter design problem. The LENS project demonstrated that the developed AI/ML technologies can be used to generate novel converter circuits >45x faster than experts on chosen use-cases. This can enable faster design space exploration and identification of new designs which are not considered by experts due to the increasing design space complexity. This has significant potential impact on the public and energy needs of the country. It is currently estimated that 30% of all electrical powers generated passes through power converters. The future estimate is that 80% of all power generated would be passing through converters. LENS fills a critical gap in this space since by accelerating the design process the designers would be able to generate more efficient converters which can lead to significant energy savings for the country.

42 ENGINEERING↗
DOE OSTI2022

Network analysis uncovers the communication structure of SARS-CoV-2 spike protein identifying sites for immunogen design

The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has triggered myriad efforts to understand the structure and dynamics of this complex pathogen. The spike glycoprotein of SARS-CoV-2 is a significant target for immunogens as it is the means by which the virus enters human cells, while simultaneously sporting mutations responsible for immune escape. These functional and escape processes are regulated by complex molecular-level interactions. Our study presents quantitative insights on domain and residue contributions to allosteric communication, immune evasion, and local- and global-level control of functions through the derivation of a weighted graph representation from all-atom MD simulations. Focusing on the ancestral form and the D614G-variant, we provide evidence of the utility of our approach by guiding the selection of a mutation that alters the spike’s stability. Taken together, the network approach serves as a valuable tool to evaluate communication “hot-spots” in proteins to guide design of stable immunogens.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Miniaturized Magnetoelastic Sensor System

This article describes the design, assembly, and implementation of a hand-held, magnetic-field-based sensor system that can be adapted for a variety of sensing applications. The miniaturized system is based on Chemical Identification by Magneto-Elastic Sensing (ChIMES) technology, which uses three concentric solenoid coils to wirelessly interrogate a sensor body comprised of a response material coupled to a magnetoelastic wire. The response material expands when it encounters a target, imposing mechanical stress on the wire and altering its magnetic permeability. The sensor bodies are passive, requiring no external power source, and they are small, measuring about 15 mm in length and 3.0 mm in diameter. Up to four sensor bodies can be configured as an evenly-spaced linear array. The sensor system operates by applying a low-frequency, current-stabilized, filtered triangle wave to a uniform-density excitation coil to switch the magnetic domains within the wire. Further, the responses from the sensors are picked up by a detection coil as stress-induced changes in the Faraday voltage, and the strong magnetic field induced by the excitation coil in the detection coil is nullified by a cancellation coil reverse-wound in series with the detection coil. The responses of the sensors in an array are separated in time by a linear gradient dc biasing coil. The sensors can be interrogated through metallic and nonmetallic barriers. The signals from the detection coil and the excitation coil are digitized by a pair of bipolar analog-to-digital converters (ADCs). A Raspberry Pi single-board computer (SBC) and associated software perform data acquisition and control all aspects of the sensor system hardware. The program allows the user to select the number of sensors in the array, the type of signal that is being collected, and the number of samples to take. The program also allows for signal processing of the sensor data, such as baseline correction. The program can differentiate sensor peaks from each other and calculate the magnitude of each sensor response with less than 1% error. The data are then displayed along with a graph of the signal.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2017

Distributed memory parallel Markov random fields using graph partitioning

Markov random fields (MRF) based algorithms have attracted a large amount of interest in image analysis due to their ability to exploit contextual information about data. Image data generated by experimental facilities, though, continues to grow larger and more complex, making it more difficult to analyze in a reasonable amount of time. Applying image processing algorithms to large datasets requires alternative approaches to circumvent performance problems. Aiming to provide scientists with a new tool to recover valuable information from such datasets, we developed a general purpose distributed memory parallel MRF-based image analysis framework (MPI-PMRF). MPI-PMRF overcomes performance and memory limitations by distributing data and computations across processors. The proposed approach was successfully tested with synthetic and experimental datasets. Additionally, the performance of the MPI-PMRF framework is analyzed through a detailed scalability study. We show that a performance increase is obtained while maintaining an accuracy of the segmentation results higher than 98%. The contributions of this paper are: (a) development of a distributed memory MRF framework; (b) measurement of the performance increase of the proposed approach; (c) verification of segmentation accuracy in both synthetic and experimental, real-world datasets

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Optimizing power system restoration with damaged communications

Utility procedures for power system blackstart and restoration typically assume that energization decisions can be reliably communicated across the grid. In reality, the communications and control network would likely also be affected in power outages, such as those caused by extreme weather events or cyber-attacks. This paper studies the effect of damage to the power system communications and control infrastructure on restoration operations following a blackout. We model the communications infrastructure as a graph, overlaying the power grid, and imposing the requirement that every energized element in the power grid be observable from a control center. We expand on a specialized branch-and-bound algorithm from the literature to optimize the restoration process and devise an initialization heuristic and a rounding heuristic to improve solution speed. We perform numerical experiments on synthetic systems for Illinois and Texas with outages based on a solar flare or hurricane. We compare the results of our specialized branch-and-bound algorithm to the results from (i) the initialization heuristic alone, (ii) a variation of this heuristic that we use as a baseline, and (iii) the restoration optimization for the power system without communications constraints. Here, we find that damage to the communications infrastructure significantly increases the time required to re-energize the grid. Moreover, by simultaneously optimizing communications repairs and grid energization decisions, we are able to re-energize the grid significantly faster than if communications repairs and energization decisions were made independently or with partial coordination, motivating improvements to current industry practice.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Two-Stage Quantum Reinforcement Learning Method for Multi-Objective Transmission Switching

Multi-objective transmission switching (MO-TS) problems involve the strategic reconfiguration of network topology to simultaneously optimize multiple objectives. As the system scale increases, finding feasible solutions becomes increasingly challenging due to the problem's nonlinearity and high computational complexity. To address these challenges, this paper proposes a two-stage quantum reinforcement learning method that leverages potential quantum advantages for MO-TS. In the first stage, candidate switching lines are identified using a graph-theoretical approach to reduce the problem's dimensionality. The second stage introduces a quantum-classical reinforcement learning framework, where a learnable measurement-based CNN-ResVQC architecture is developed to effectively reduce the input dimension for quantum processing, mitigate vanishing gradients, and enhance trainability while improving the quantum circuit's flexibility in modeling complex decision policies for MO-TS. Numerical studies on IEEE 14-bus, 57-bus, and 118-bus systems demonstrate that the proposed algorithm achieves superior training stability and faster convergence with approximately 1% of the network parameters required by classical algorithms, highlighting its effectiveness, efficiency, and scalability. Furthermore, the practicality is validated through its stable convergence under three common quantum noise channels.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Multiscale characterization and representation of variability in ceramic matrix composites

Low density, high strength, and high creep and oxidation resistance properties of ceramic matrix composites (CMCs) make them an ideal choice for use in extreme environments in space and military applications. This paper presents a detailed characterization study of structural and manufacturing flaws in Carbon fiber Silicon-Carbide-Nitride matrix (C/SiNC) CMCs at different length-scales. Energy-dispersive spectroscopy (EDS) is used for the chemical characterization of the material’s elemental constituents. High-resolution multiscale graphs obtained from scanning electron microscope (SEM) and confocal laser scanning microscope (LSM) are used to characterize the distribution and morphology of defects at different length scales. This is followed by the classification and quantification of the common manufacturing defects. An image processing algorithm based on the image segmentation process is developed to quantify the variability of various scale-dependent architectural parameters. Finally, a three-dimensional stochastic representative volume element (SRVE) generation algorithm is developed to provide precise representations of material textures at multiple length scales. The developed algorithm accurately accounts for material features and flaws based on a range of multiscale structural and defects characterization results.

36 MATERIALS SCIENCE↗
DOE OSTI2024

DNA parts and gene constructs for plant biodesign

Plant biodesign requires the knowledge of DNA parts (e.g., genes, promoters, terminators), along with their combinations (as gene constructs) linked to engineered traits. DNA parts with validated or predicted functions in plants have been deposited in various online databases. However, these existing databases focus on basic biological functions of individual DNA parts, leaving a gap between basic knowledge and bioengineering applications. To fill this knowledge gap, we have created a user-friendly, open-ended database as a knowledge graph linking DNA parts to gene constructs to traits. This database contains experimentally validated DNA parts and gene constructs documented in peer-reviewed publications. The DNA parts include 1) molecular components with biological functions, such as genes involved in various biological processes (e.g., metabolic and signal transduction pathways) and 2) molecular components with technical functions, such as gene expression, genome engineering and sequence splicing. The gene constructs deposited in this database include both single-gene and multi-gene constructs. This database allows users to submit DNA parts and gene construct compositions linked to engineered traits described in peer-reviewed publications, providing a public digital repository for sharing the biodesign information among the researchers in the fields of plant biotechnology and plant synthetic biology.

plant biodesign synthetic biology gene constructs ↗
DOE OSTI2023

Cyber-Physical Reconfiguration for Disaster Resilience of Power Distribution Systems

Cyber-physical distribution systems (CPDS) have emerged from the integration of information technology into distribution systems. While offering substantial benefits, this integration also introduces vulnerabilities. The interaction between cyber networks and distribution systems renders CPDS susceptible to disasters. To ensure critical load supply and system resilience, rapid post-disaster load restoration is required. The paper proposes a critical load restoration (CLR) framework in CPDS using a network reconfiguration approach that exploits the existing post-disaster resources to restore critical loads within the shortest possible time. Using graph theory, the cyber network and distribution system are integrated into a single digraph, minimizing the CLR complexity in CPDS. A cost metric is also defined to satisfy network-specific objectives and constraints. A heuristic is proposed to guide the load restoration process using the cost metric within the integrated digraph. Simulation results confirm the framework's superiority over existing literature, which either overlooks cyber components or prolongs restoration with additional resource deployment.

cyber-physical system↗
DOE OSTI2019

Chokepoints in Mechanical Coupling Associated with Allosteric Proteins: The Pyruvate Kinase Example

Although the critical role of allostery in controlling enzymatic processes is well appreciated, there is a current dearth in our understanding of its underlying mechanisms, including communication between binding sites. One potential key aspect of inter-site communication is the mechanical coupling between residues in a protein. Here, we introduce a graph-based computational approach to investigate the mechanical coupling between distant parts of a protein, highlighting effective pathways via which protein motion can transfer energy between sites. In this method, each residue is treated as a node on a weighted, undirected graph, where the edges are defined by locally correlated motions of those residues and weighted by the strength of the correlation. The method was validated against experimental data on allosteric regulation in the human liver pyruvate kinase as obtained from full-protein alanine-scanning mutagenesis (systematic mutation) studies. The method provides semi-quantitative information on the regulatory importance of specific structural elements. It is shown that these elements are key for the mechanical coupling between distant parts of the protein by providing effective pathway for energy transfer. It is also shown that, while there are a multitude of energy transfer pathways between distant parts of a protein, these pathways share a few common nodes that represent effective “chokepoints.”

Johnson, Lewis E.↗
DOE OSTI2019

Essential Signal Simulation Enabled by Netlist Transforms (ESSENT) v1.0

Hardware simulation is an indispensable tool for designing chips as well as researching future architectures. Unfortunately, the slow rate of simulation can often bottleneck the design process, as designers must wait to see the results of a simulation before changing the design and starting a new simulation. The Essential Signal Simulation Enabled by Netlist Transforms (ESSENT) is a tool designed to accelerate hardware simulations. ESSENT takes in a design, uses graph algorithms to identify opportunities for conditional execution, and generates a simulator that leverages these opportunities. The generated simulator produces the exact same simulation results that would be returned by an unoptimized simulator, but in substantially less time. For input, ESSENT takes in designs formatted in FIRRTL (an open format).

Beamer, Scott↗
DOE OSTI2023

Experimental Observations of the Topology of Convolutional Neural Network Activations

Topological data analysis (TDA) is a branch of computational mathematics, bridging algebraic topology and data science, that provides compact, noise-robust representations of complex structures. Deep neural networks (DNNs) learn millions of parameters associated with a series of transformations defined by the model architecture resulting in high-dimensional, difficult to interpret internal representations of input data. As DNNs become more ubiquitous across multiple sectors of our society, there is increasing recognition that mathematical methods are needed to aid analysts, researchers, and practitioners in understanding and interpreting how these models' internal representations relate to the final classification. In this paper we apply cutting edge techniques from TDA with the goal of gaining insight towards interpretability of convolutional neural networks used for image classification. We use two common TDA approaches to explore several methods for modeling hidden layer activations as high-dimensional point clouds, and provide experimental evidence that these point clouds capture valuable structural information about the model's process. First, we demonstrate that a distance metric based on persistent homology can be used to quantify meaningful differences between layers and discuss these distances in the broader context of existing representational similarity metrics for neural network interpretability. Second, we show that a mapper graph can provide semantic insight as to how these models organize hierarchical class knowledge at each layer. These observations demonstrate that TDA is a useful tool to help deep learning practitioners unlock the hidden structures of their models.

topological data analysis, deep learning↗
DOE OSTI2023

AMORE-Isoprene v1.0: a new reduced mechanism for gas-phase isoprene oxidation

Abstract. Gas-phase oxidation of isoprene by ozone (O3) and the hydroxyl (OH) and nitrate (NO3) radicals significantly impacts tropospheric oxidant levels and secondary organic aerosol formation. The most comprehensive and up-to-date chemical mechanism for isoprene oxidation consists of several hundred species and over 800 reactions. Therefore, the computational expense of including the entire mechanism in large-scale atmospheric chemical transport models is usually prohibitive, and most models employ reduced isoprene mechanisms ranging in size from ∼ 10 to ∼ 200 species. We have developed a new reduced isoprene oxidation mechanism using a directed-graph path-based automated model reduction approach, with minimal manual adjustment of the output mechanism. The approach takes as inputs a full isoprene oxidation mechanism, the environmental parameter space, and a list of priority species which are protected from elimination during the reduction process. Our reduced mechanism, AMORE-Isoprene (where AMORE stands for Automated Model Reduction), consists of 12 species which are unique to the isoprene mechanism as well as 22 reactions. We demonstrate its performance in a box model in comparison with experimental data from the literature and other current isoprene oxidation mechanisms. AMORE-Isoprene's performance with respect to predicting the time evolution of isoprene oxidation products, including isoprene epoxydiols (IEPOX) and formaldehyde, is favorable compared with other similarly sized mechanisms. When AMORE-Isoprene is included in the Community Regional Atmospheric Chemistry Multiphase Mechanism 1.0 (CRACMM1AMORE) in the Community Multiscale Air Quality Model (CMAQ, v5.3.3), O3 and formaldehyde agreement with Environmental Protection Agency (EPA) Air Quality System observations is improved. O3 bias is reduced by 3.4 ppb under daytime conditions for O3 concentrations over 50 ppb. Formaldehyde bias is reduced by 0.26 ppb on average for all formaldehyde measurements compared with the base CRACMM1. There was no significant change in computation time between CRACMM1AMORE and the base CRACMM. AMORE-Isoprene shows a 35 % improvement in agreement between simulated IEPOX concentrations and chamber data over the base CRACMM1 mechanism when compared in the Framework for 0-D Atmospheric Modeling (F0AM) box model framework. This work demonstrates a new highly reduced isoprene mechanism and shows the potential value of automated model reduction for complex reaction systems.

58 GEOSCIENCES↗
DOE OSTI2020

Accelerating Scientific Computing in the Post-Moore’s Era

Novel uses of graphical processing units for accelerated computation revolutionized the field of high-performance scientific computing by providing specialized workflows tailored to algorithmic requirements. As the era of Moore’s law draws to a close, many new non–von Neumann processors are emerging as potential computational accelerators, including those based on the principles of neuromorphic computing, tensor algebra, and quantum information. While development of these new processors is continuing to mature, the potential impact on accelerated computing is anticipated to be profound. We discuss how different processing models can advance computing in key scientific paradigms: machine learning and constraint satisfaction. Significantly, each of these new processor types utilizes a fundamentally different model of computation, and this raises questions about how to best use such processors in the design and implementation of applications. While many processors are being developed with a specific domain target, the ubiquity of spin-glass models and neural networks provides an avenue for multi-functional applications. Furthermore, this also hints at the infrastructure needed to integrate next-generation processing units into future high-performance computing systems.

97 MATHEMATICS AND COMPUTING↗