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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Dynamics of virtual water networks: Role of national socio-economic indicators across the world

Intensified water usage due to rapid industrialization is often dictated by economic policies based on monetary growth rather than sustainable use of environmental resources. In addition, interdependence within economic sectors further interweaves water usage through product transactions, which further makes it difficult to quantify the dynamics of hydro-economic systems at regional, national and global scale. In this study, we investigated the dynamics of domestic virtual water networks (VWN) of 189 countries based on concept of information theory by quantifying network metrics that describes VWN flow capacity, robustness, efficiency and flexibility. These networks represent virtual water interconnected through economic sectors within a specified country built based on environmentally extended multi region input output (EE-MRIO) approach. We further estimated trends associated with network metrics, as well as coupling intensity between metrics with respect to socio-economic indicators, such as, population, Gross Domestic Product (GDP) and Gross National Income (GNI). It was observed that capacity and flexibility of VWNs are strongly and positively correlated indicating that a high capacity VWN can be more flexible. Our results also indicate that, in general a higher percentage of developing countries (i.e. both least developing and developing nations) have exhibited increasing trends in capacity, robustness, efficiency and flexibility of VWN compared to developed nations. It was revealed that the dynamics of VWNs are positively coupled with socio-economic growth for few countries, which indicates the sustainable behavior of VWN with socio-economic growth. Our results argue that the information theory-based metrics by embedding water footprints can holistically capture sustainability aspect of the VWN dynamics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

A Comparison between Invariant and Equivariant Classical and Quantum Graph Neural Networks

Machine learning algorithms are heavily relied on to understand the vast amounts of data from high-energy particle collisions at the CERN Large Hadron Collider (LHC). The data from such collision events can naturally be represented with graph structures. Therefore, deep geometric methods, such as graph neural networks (GNNs), have been leveraged for various data analysis tasks in high-energy physics. One typical task is jet tagging, where jets are viewed as point clouds with distinct features and edge connections between their constituent particles. The increasing size and complexity of the LHC particle datasets, as well as the computational models used for their analysis, have greatly motivated the development of alternative fast and efficient computational paradigms such as quantum computation. In addition, to enhance the validity and robustness of deep networks, we can leverage the fundamental symmetries present in the data through the use of invariant inputs and equivariant layers. In this paper, we provide a fair and comprehensive comparison of classical graph neural networks (GNNs) and equivariant graph neural networks (EGNNs) and their quantum counterparts: quantum graph neural networks (QGNNs) and equivariant quantum graph neural networks (EQGNN). The four architectures were benchmarked on a binary classification task to classify the parton-level particle initiating the jet. Based on their area under the curve (AUC) scores, the quantum networks were found to outperform the classical networks. However, seeing the computational advantage of quantum networks in practice may have to wait for the further development of quantum technology and its associated application programming interfaces (APIs).

Forestano, Roy T. (ORCID:0000000203552076)↗
DOE OSTI2020

Resilient Autonomous Wind Farms: Preprint

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm-level, taking account of individual wind turbine information to achieve wind farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have seen significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms which can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as supervisory control and data acquisition (SCADA) data, local meteorological stations, and nearby radars/sodars/lidars. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and enabling more efficient overall control of farm-wide optimizations through mechanisms such as wake-steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach which distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks which seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft Byzantine Fault Tolerance algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will be shown to achieve overall consensus, provided the number of faults/malicious nodes is less than 3$n$+1, where $n$ is the number of turbine cluster faults which may occur, and to be robust in the face of multiple arbitrary faults.

autonomous↗
DOE OSTI2025

A multifunctional sesquiterpene synthase integrates with cytochrome P450s to reinforce the terpenoid defense network in maize

Terpenoids, the largest and most structurally diverse class of plant natural products, play essential roles in maize defense and ecological interactions. In this study, we identified and functionally characterized a sesquiterpenoid-based defense pathway in maize centered on α-santalenoic acid, a pathogen-inducible sesquiterpenoid antibiotic. Using a combination of metabolite-based genome-wide association studies (mGWAS), linkage mapping, and heterologous expression assays, we identified ZmTPS9 as a multiproduct terpene synthase that primarily produces α-santalene and β-bisabolene. Sequence analysis and site-directed mutagenesis revealed that threonine at position 413 is critical for enzyme activity, with its deletion resulting in a complete loss of enzyme activity. The sesquiterpene hydrocarbons produced by ZmTPS9 are further oxidized by three cytochrome P450 monooxygenases, ZmCYP71Z16, ZmCYP71Z18, and ZmCYP71Z19, to yield antimicrobial metabolites including α-santalenoic acid, zealexin D1 (ZD1), and zealexin D2 (ZD2). Together, these findings demonstrate a convergent biosynthetic strategy in maize, where multiproduct terpene synthases and promiscuous P450s collaboratively generate a flexible and robust terpenoid defense network.

a-santalenoic acid↗
DOE OSTI2022

Disruption-Robust Community Detection Using Consensus Clustering in Complex Networks

Topological (graph-theoretic) analysis of critical infrastructure networks provides insight on several aspects of resilience. Graph clustering or community detection, which identifies densely connected components in a graph, has been employed for analysis. In this paper, we propose employing consensus clustering, which is a technique to determine consensus from a collection of different clusters on an input, such that the resulting clustering is robust to disruptions, where a disruption is represented as loss of one or more vertices or edges in the graph. Using two critical infrastructure networks as case studies, we empirically demonstrate the need to compute consensus clustering in order to address the drastic changes in the topology due to disruptions in the network.

Hussain, Md Taufique↗
DOE OSTI2024

Investigating the influence of particle distribution on force and torque statistics using hierarchical machine learning

An accurate representation of hydrodynamic force and torque experienced by every particle in a distribution can be obtained from particle resolved (PR) simulations. These unique quantities are influenced by the deterministic position of surrounding particles. However, systems simulated with this methodology are typically limited to particles due to the involved computational cost. This resource requirement is a major bottleneck in analyzing the effect of variations in particle distribution. Here, this article attempts to address this bottleneck by availing relatively inexpensive deep learning models. The surrogate models that we employ in this article use a physics‐based hierarchical framework and symmetry‐preserving neural networks to achieve robustness with limited training data. This article first performs additional generalizability tests on PR data of distinct distributions that are not involved in the training process. The models are then deployed on several different particle distributions. Impact of clustering and structure on the observed statistics are investigated.

42 ENGINEERING↗
DOE OSTI2020

Xylose Assimilation for the Efficient Production of Biofuels and Chemicals by Engineered Saccharomyces cerevisiae

Microbial conversion of plant biomass into fuels and chemicals offers a practical solution to global concerns over limited natural resources, environmental pollution, and climate change. Pursuant to these goals, researchers have put tremendous efforts and resources towards engineering the yeast Saccharomyces cerevisiae to efficiently convert xylose, the second most abundant sugar in lignocellulosic biomass, into various fuels and chemicals. Here, we summarize recent advances in metabolic engineering of yeast to address bottlenecks on xylose assimilation, and to enable simultaneous co-utilization of xylose and other carbon sources in lignocellulosic hydrolysates. We also highlight distinct characteristics of xylose metabolism that can be harnessed to produce advanced biofuels and chemicals. Although many challenges remain, recent research investments have facilitated efficient fermentation of xylose and simultaneous co-consumption of xylose and glucose. In particular, understanding of xylose-induced metabolic rewiring in engineered yeast has encouraged use of xylose as a carbon source for producing various non-ethanol bioproducts. Furthermore, to boost the lignocellulosic biomass-based bioeconomy, much attention is expected to promote xylose-utilizing efficiency via reprogramming cellular regulatory networks, to attain robust co-fermentation of xylose and other cellulosic carbon sources under industrial conditions, and to exploit the advantageous traits of yeast xylose metabolism for producing diverse fuels and chemicals.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

High-performance organic photodetectors enabled by a refined fibrillar multiphase morphology

Film morphology of donor:acceptor blend layers plays a critical role in photon-to-current efficiency and dark/noise current of organic photodetectors (OPDs). One effective approach to manipulate crystallization and mesoscale phase separation of such blend layers is sequential casting (SC). However, the guiding strategies to control the morphology and the impacts on OPD performance of the SC films remain elusive, as the film structural evolution during SC is different from conventional blend casting (BC). In this report a refined fibrillar multiphase morphology is demonstrated by SC in an NT812:IEICO-4F blend film, where the high-quality IEICO-4F crystallites infiltrate the robust NT812 fibril network from the surface to the bulk. Such a morphology leads to improved charge generation and collection, reduced trap states, and enhanced charge block capability of resultant device, enabling simultaneous achievement of high external quantum efficiency and low dark/noise current. A maximum special detectivity of 5 × 10 13 Jones is achieved at 860 nm under –0.1 V, which is among the highest detectivities for vis-to-NIR OPDs. The linear dynamic range and response speed are also improved. Such enhancements are parallelly observed from OPD devices based on other blend systems with similar fibrillar refinement, which provides guidelines for film structure manipulation towards high OPD performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Improving the prediction of daily reservoir releases over the CONUS using conditioned LSTM

Reservoirs play a vital role in regulating streamflow timing and variability for hydroelectricity, flood control, water supply, irrigation, and recreation. Despite their importance, many reservoirs lack comprehensive operational guidelines, making their management complex due to conflicting operational objectives. Hence traditional policy-based reservoir models often fail to capture real-world conditions accurately and they depend on perfect streamflow predictions, which are not always available. In contrast, data-driven models like Long Short-Term Memory (LSTM) networks offer a robust alternative. This study introduces an approach that integrates reservoir characteristics—such as main use, climate, and maximum capacity—into the LSTM model to enhance reservoir release predictions. Using data from nearly 200 reservoirs in the contiguous United States (CONUS), our conditioned LSTM model (LSTM_cond) was compared with both the vanila LSTM and a traditional policy-based approach. Furthermore, our results show that while both LSTM_cond and LSTM perfoms better than the policy-based approach, LSTM_cond consistently outperforms LSTM for hydroelectric, water supply, irrigation, and recreation reservoirs. The KGE median values for LSTM_cond for out-sample reservoirs are 0.764, 0.565, 0.821, and 0.779, respectively, for the aforementioned reservoir types, which are consistently higher that the corresponding KGE values of 0.737, 0.413, 0.775, and 0.713 of LSTM, demonstrating its advantages in improving generalizability.

CONUS↗
DOE OSTI2026

Microphase Separation of Randomly Linked Branched Polystyrene/Polylactic Acid for Formation of Cocontinuous Nanostructures

Cocontinuous polymeric nanomaterials have gained attention for their ability to preserve distinct properties of constituent microphases within a single material. Randomly linked copolymer networks have shown very wide stability windows for disordered cocontinuous phases (extending over ≈ 30 wt % in composition), but the reliance on a network architecture prevents subsequent solution- or melt-processing. Furthermore, the key factors contributing to cocontinuity have remained unclear. We recently found that randomly linked star copolymers (RSCs) can exhibit a cocontinuous window as wide as 25 wt % in the case of 4-arm stars, suggesting that while a network architecture is not essential for the formation of disordered cocontinuous phases, the presence of random elastic forces in such architectures may indeed facilitate their formation. In addition, the behavior was found to be highly sensitive to arm number, with 6-arm RSCs exhibiting almost no cocontinuous phase. These results raised a key mechanistic question regarding the contribution of random elastic forces, originating from strands that bridge between junctions, in stabilizing disordered cocontinuous phases. In the current study, we synthesized randomly linked branched copolymers (RBCs) of polystyrene (PS) and poly(D,L-lactic acid) (PLA), which represent an intermediate architecture between networks and stars. This approach allows for the introduction of elastic contributions from strands bridging between different junctions, while still maintaining the processability advantages of a non-network architecture. The cocontinuous regions of the PS/PLA RBCs, with varying polymer and linker functionalities (f p and f l , respectively), were characterized by small-angle X-ray scattering, gravimetry, and scanning electron microscopy. We found that the cocontinuous windows of RBCs typically expanded with increasing elastic contributions and exhibited reduced sensitivity to junction-functionality compared to RSCs. Notably, RBCs with f p = 1.50 and f l = 3, which had large molecular weights due to proximity to the gel point, achieved a cocontinuous window of ≈ 34 wt %, which is almost twice as wide as analogous 3-arm RSCs and comparable to randomly linked networks. Leveraging this robust cocontinuity and solution-processability, we fabricated a film of interconnected nanoporous PS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Enhanced Electrochemical Performance of Disordered Rocksalt Cathodes Enabled by a Graphite Conductive Additive

Cobalt-free cation-disordered rocksalt (DRX) cathodes are a promising class of materials for next-generation Li-ion batteries. Although they have high theoretical specific capacities (>300 mA h/g) and moderate operating voltages (~3.5 V vs Li/Li + ), DRX cathodes typically require a high carbon content (up to 30 wt %) to fully utilize the active material which has a detrimental impact on cell-level energy density. To assess pathways to reduce the electrode’s carbon content, the present study investigates how the carbon’s microstructure and loading (10–20 wt %) influence the performance of DRX cathodes with the nominal composition Li 1.2 Mn 0.5 Ti 0.3 O 1.9 F 0.1 . While electrodes prepared with conventional disordered carbon additives (C65 and ketjenblack) exhibit rapid capacity fade due to an unstable cathode/electrolyte interface, DRX cathodes containing 10 wt % graphite show superior cycling performance (e.g., reversible capacities ~260 mA h/g with 85% capacity retention after 50 cycles) and rate capability (~135 mA h/g at 1000 mA/g). Furthermore, a suite of characterization tools was employed to evaluate the performance differences among these composite electrodes. Overall, these results indicate that the superior performance of the graphite-based cathodes is largely attributed to the: (i) formation of a uniform graphitic coating on DRX particles which protects the surface from parasitic reactions at high states of charge and (ii) homogeneous dispersion of the active material and carbon throughout the composite cathode which provides a robust electronically conductive network that can withstand repeated charge–discharge cycles. Overall, this study provides key scientific insights on how the carbon microstructure and electrode processing influence the performance of DRX cathodes. Based on these results, exploration of alternative routes to apply graphitic coatings is recommended to further optimize the material performance.

25 ENERGY STORAGE↗
DOE OSTI2022

Three‐Dimensional Permeability Inversion Using Convolutional Neural Networks and Positron Emission Tomography

Abstract Quantification of heterogeneous multiscale permeability in geologic porous media is key for understanding and predicting flow and transport processes in the subsurface. Recent utilization of in situ imaging, specifically positron emission tomography (PET), enables the measurement of three‐dimensional (3‐D) time‐lapse radiotracer solute transport in geologic media. However, accurate and computationally efficient characterization of the permeability distribution that controls the solute transport process remains challenging. Leveraging the relationship between local permeability variation and solute advection behavior, an encoder‐decoder based convolutional neural network (CNN) is implemented as a permeability inversion scheme using a single PET scan of a radiotracer pulse injection experiment as input. The CNN can accurately capture the 3‐D spatial correlation between the permeability and the radiotracer solute arrival time difference maps in geologic cores. We first test the inversion accuracy using synthetic test datasets and then test the accuracy on a suite of experimental PET imaging datasets acquired on four different geologic cores. The network‐predicted permeability maps from the geologic cores are used to parameterize forward numerical models that are directly compared with the experimental PET imaging data. The results indicate that a single trained network can generate robust 3‐D permeability inversion maps in seconds. Numerical models parameterized with these permeability maps closely capture the experimentally observed solute arrival time behavior. This work provides an unprecedented approach for efficiently characterizing multiscale permeability heterogeneity in complex geologic samples.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Water-mediated ion transport in an anion exchange membrane

Water is a critical component in polyelectrolyte anion exchange membranes (AEMs). It plays a central role in ion transport in electrochemical systems. Gaining a better understanding of molecular transport and conductivity in AEMs has been challenged by the lack of a general methodology capable of capturing and connecting water dynamics, water structure, and ionic transport over time and length scales ranging from those associated with individual bond vibrations and molecular reorientations to those pertaining to macroscopic AEM performance. In this work, we use two-dimensional infrared spectroscopy and semiclassical simulations to examine how water molecules are arranged into successive solvation shells, and we explain how that structure influences the dynamics of bromide ion transport processes in polynorbornene-based materials. We find that the transition to the faster transport mechanism occurs when the reorientation of water molecules in the second solvation shell is fast, allowing a robust hydrogen bond network to form. Our findings provide molecular-level insights into AEMs with inherent transport of halide ions, and help pave the way towards a comprehensive understanding of hydroxide ion transport in AEMs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

HSNMon: High-Speed Monitoring Software for Omni-Path

HSNMon is a robust high speed network monitoring daemon that provides insight into network availability, hardware failure, and network performance for switches and network cards. HSNMon collects counters, forwarding tables, and error information at high resolution using publicly available Omni-Path Management APIs. The collected data can be forwarded into multiple logging infrastructures including syslog and message queuing systems. HSNMon is only the infrastructure for collecting the network data. It does not include any collected data or proprietary LANL information

Holman, Brett↗
DOE OSTI2024

Designing Ti-6Al-4V microstructure for strain delocalization using neural networks

Abstract The deformation behavior of Ti-6Al-4V titanium alloy is significantly influenced by slip localized within crystallographic slip bands. Experimental observations reveal that intense slip bands in Ti-6Al-4V form at strains well below the macroscopic yield strain and may serially propagate across grain boundaries, resulting in long-range localization that percolates through the microstructure. These connected, localized slip bands serve as potential sites for crack initiation. Although slip localization in Ti-6Al-4V is known to be influenced by various factors, an investigation of optimal microstructures that limit localization remains lacking. In this work, we develop a novel strategy that integrates an explicit slip band crystal plasticity technique, graph networks, and neural network models to identify Ti-6Al-4V microstructures that reduce the propensity for strain localization. Simulations are conducted on a dataset of 3D polycrystals, each represented as a graph to account for grain neighborhood and connectivity. The results are then used to train neural network surrogate models that accurately predict localization-based properties of a polycrystal, given its microstructure. These properties include the ratio of slip accumulated in the band to that in the matrix, fraction of total applied strain accommodated by slip bands, and spatial connectivity of slip bands throughout the microstructure. The initial dataset is enriched by synthetic data generated by the surrogate models, and a grid search optimization is subsequently performed to find optimal microstructures. Describing a 3D polycrystal with only a few features and a combination of graph and neural network models offer robustness compared to the alternative approaches without compromising accuracy. We show that while each material property is optimized through a unique microstructure solution, elongated grain shape emerges as a recurring feature among all optimal microstructures. This finding suggests that designing microstructures with elongated grains could potentially mitigate strain localization without compromising strength.

Ahmadikia, Behnam↗
DOE OSTI2020

Resilient Autonomous Wind Farms

With the advent of an increasing number of control strategies that seek to optimize wind turbine performance on a farm level, taking into account individual wind turbine information to achieve wind-farm-level objectives has become an increasingly important goal. Methods for controlling wind turbines on an individual and farm level have experienced significant development, and an abundance of new implementations for gathering and using data from turbines have created potential for novel control mechanisms that can further optimize the performance and delivery characteristics of a wind farm. A key element of making these wind farms more efficient is to develop reliable algorithms that use local sensor information that is already being collected, such as from local meteorological stations, nearby radars, sodars, and lidars, and supervisory control and data acquisition (SCADA) data. Making use of information from all wind turbines in a wind farm can enable such approaches as determining the atmospheric conditions across the farm, improving fault-finding, and ensuring more efficient overall control of farmwide optimizations through mechanisms such as wake steering. However, these approaches typically involve a centralized communications and control center. In order to ensure the resilient operation of the farm, it is necessary to develop an approach that distributes the calculation and communication amongst multiple nodes throughout the farm. In this fashion, a redundant, robust, and secure network can be created, which can tolerate faults in calculation, communication, and even external attacks that seek to disrupt the operation of the wind farm. This paper introduces the use of the Raft-Byzantine-Fault-Tolerant algorithm in the implementation of autonomous control of a wind farm. This implementation will allow for fault tolerance for malfunctioning nodes, sensors, transmitters, and connectors. This approach is equally extensible to account for malicious actors. It will...

fault tolerance↗
DOE OSTI2024

DMTN-260: Failure Modes and Error Handling for Prompt Processing

The Prompt Processing system will be responsible for processing roughly a thousand visits per night, and distributing the results in near real time, for at least ten years of Rubin Observatory operations. As such, it must be highly robust to algorithmic, network, and infrastructure failures, ranging from momentary glitches to extended downtimes. DMTN-219 introduced the initial design for the Prompt Processing framework; this document expands on the design to address expected failure modes and recovery strategies for each.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Closure Learning for Nonlinear Model Reduction Using Deep Residual Neural Network

Developing accurate, efficient, and robust closure models is essential in the construction of reduced order models (ROMs) for realistic nonlinear systems, which generally require drastic ROM mode truncations. We propose a deep residual neural network (ResNet) closure learning framework for ROMs of nonlinear systems. The novel ResNet-ROM framework consists of two steps: (i) In the first step, we use ROM projection to filter the given nonlinear system and construct a spatially filtered ROM. This filtered ROM is low-dimensional, but is not closed. (ii) In the second step, we use ResNet to close the filtered ROM, i.e., to model the interaction between the resolved and unresolved ROM modes. We emphasize that in the new ResNet-ROM framework, data is used only to complement classical physical modeling (i.e., only in the closure modeling component), not to completely replace it. We also note that the new ResNet-ROM is built on general ideas of spatial filtering and deep learning and is independent of (restrictive) phenomenological arguments, e.g., of eddy viscosity type. The numerical experiments for the 1D Burgers equation show that the ResNet-ROM is significantly more accurate than the standard projection ROM. The new ResNet-ROM is also more accurate and significantly more efficient than other modern ROM closure models.

Xie, Xuping↗