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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 1,459 records · Page 81
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Enabling scientific machine learning in MOOSE using Libtorch

A neural-network-based machine learning interface has been developed for the Multiphysics Object-Oriented Simulation Environment (MOOSE). The interface relies on Libtorch, the C++ front-end of PyTorch, and enables an online interaction between modern machine learning algorithms and all the existing simulation, modeling, and analysis processes available in MOOSE. New capabilities in MOOSE include the native generation and training of artificial neural networks together with options to load pretrained neural networks in TorchScript format. Furthermore, the MOOSE stochastic tools module (MOOSE-STM) has been enhanced with neural network-based surrogate and reduced-order model generation options for efficient stochastic analyses. Lastly, a reinforcement learning capability has been added to MOOSE-STM for the interactive control and optimization of complex multiphysics problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Probing Accuracy-Speedup Tradeoff in Machine Learning Surrogates for Molecular Dynamics Simulations

The performance promise of machine learning surrogates of molecular dynamics simulations of soft materials is significant but generally comes at the cost of acquiring large training datasets to learn the complex relationships between input soft material attributes and output properties. Under the constraint of limited high-performance computing resources, optimizing the size of the training datasets becomes paramount. Using an artificial neural network based surrogate for molecular dynamics simulations of confined electrolytes, we explore the tradeoff between surrogate accuracy and computational gains. Accuracy is assessed by computing the root-mean-square errors between the surrogate predictions and the ground truth results obtained via molecular dynamics simulations. The computational performance is judged by evaluating the speedup which incorporates the training dataset creation time. Improvement in accuracy occurs with a loss of speedup, which scales as the inverse of the training dataset size. Furthermore, the link between surrogate generalizability and the accuracy-speedup tradeoff is assessed by examining the errors incurred in surrogate predictions on unseen, interpolated input variables and developing a net speedup metric to capture the associated gains.

Anions↗
DOE OSTI2022

Cation–Ligand Interactions Dictate Salt Partitioning and Diffusivity in Ligand-Functionalized Polymer Membranes

Membranes are an attractive alternative to current thermal separations due to their scalability and energy efficiency in desalinating water. Unfortunately, many of the conventional membrane materials available today are unable to differentiate between ionic solutes, especially alkali cations, compromising their use in ion–ion separations. Inspired by the ion-specific interactions exhibited by biological ion channels, recent research efforts have focused on synthesizing and characterizing new polymeric materials that incorporate ligands into polymer networks to bias solubility and/or diffusivity of one cationic species over another. Despite these efforts, little is known about the influence of incorporating ligands into polymer membranes on solubility and diffusivity of the complexing species. In this study, we first build a qualitative model of salt partitioning, diffusivity, and permeability in generic cation-complexing ligand-functionalized polymer membranes. Next, to validate our model and hypotheses, we perform atomistic molecular dynamics simulations of a 12-crown-4-functionalized membrane in the presence of alkali halide salts at low concentration. Generally, cation complexation enhances cation solubility but decreases diffusivity. Interestingly, the reduction in diffusivity is predicted to be larger than the enhancement in solubility for materials which operate by the mechanisms proposed in our physical picture, ultimately resulting in a reduction in the permeability of the selectively complexing ion.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Atomically Dispersed Iron–Nitrogen Sites on Hierarchically Mesoporous Carbon Nanotube and Graphene Nanoribbon Networks for CO 2 Reduction

Atomically dispersed metal and nitrogen co-doped carbon (M-N/C) catalysts hold great promise for electrochemical CO 2 conversion. However, there is a lack of cost-effective synthesis approaches to meet the goal of economic mass production of single-atom M-N/C with desirable carbon support architecture for efficient CO 2 reduction. Herein, we report facile transformation of commercial carbon nanotube (CNT) into isolated Fe-N 4 sites anchored on carbon nanotube and graphene nanoribbon (GNR) networks (Fe-N/CNT@GNR). The oxidization-induced partial unzipping of CNT results in the generation of GNR nanolayers attached to the remaining fibrous CNT frameworks, which reticulates a hierarchically mesoporous complex and thus enables a high electrochemical active surface area and smooth mass transport. The Fe residues originating from CNT growth seeds serve as Fe sources to form isolated Fe-N-4 moieties located at the CNT and GNR basal plane and edges with high intrinsic capability of activating CO 2 and suppressing hydrogen evolution. The Fe-N/CNT@GNR delivers a stable CO Faradaic efficiency of 96% with a partial current density of 22.6 mA cm -2 at a low overpotential of 650 mV, making it one of the most active M-N/C catalysts reported. This work presents an effective strategy to fabricate advanced atomistic catalysts and highlights the key roles of support architecture in single-atom electrocatalysis.

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

Cross-facility science with the Superfacility Project at LBNL

As data sets from DOE user science facilities grow in both size and complexity there is an urgent need for new capabilities to transfer, analyze and manage the data underlying scientific discoveries. LBNL's Superfacility project brings together experimental and observational research instruments with computational and network facilities at the National Energy Research Scientific Computing Center (NERSC) and the Energy Sciences Network (ESnet) with the goal of enabling user science. Here, we report on recent innovations in the Superfacility project, including advanced data management, API-based automation, real-Time interactive user interfaces, and supported infrastructure for 'edge' services.

Enders, B↗
DOE OSTI2021

Training Spiking Neural Networks with Synaptic Plasticity under Integer Representation

Neuromorphic computing is emerging as a promising Beyond Moore computing paradigm that employs event-triggered computation and non-von Neumann hardware. Spike Timing Dependent Plasticity (STDP) is a well-known bio-inspired learning rule that relies on activities of locally connected neurons to adjust the weights of their respective synapses. In this work, we analyze a basic STDP rule and its sensitivity on the different hyperparameters for training spiking neural networks (SNNs) with supervision, customized for a neuromorphic hardware implementation with integer weights. We compare the classification performance on four UCI datasets (iris, wine, breast cancer and digits) that depict varying levels of complexity. We perform a search for optimal set of hyperparameters using both grid search and Bayesian optimization. Through the use of Bayesian optimization, we show the general trends in hyperparameter sensitivity in SNN classification problem. With the best sets of hyperparameters, we achieve accuracies comparable to some of the best performing SNNs on these four datasets. With a highly optimized supervised STDP rule we show that these accuracies can be achieved with just 20 epochs of training.

Kulkarni, Shruti↗
DOE OSTI2024

Semi-supervised Learning of Dynamical Systems with Neural Ordinary Differential Equations: A Teacher-Student Model Approach

Modeling dynamical systems is crucial for a wide range of tasks, but it remains challenging due to complex nonlinear dynamics, limited observations, or lack of prior knowledge. Recently, data-driven approaches such as Neural Ordinary Differential Equations (NODE) have shown promising results by leveraging the expressive power of neural networks to model unknown dynamics. However, these approaches often suffer from limited labeled training data, leading to poor generalization and suboptimal predictions. On the other hand, semi-supervised algorithms can utilize abundant unlabeled data and have demonstrated good performance in classification and regression tasks. We propose TS-NODE, the first semi-supervised approach to modeling dynamical systems with NODE. TS-NODE explores cheaply generated synthetic pseudo rollouts to broaden exploration in the state space and to tackle the challenges brought by lack of ground-truth system data under a teacher-student model. TS-NODE employs an unified optimization framework that corrects the teacher model based on the student's feedback while mitigating the potential false system dynamics present in pseudo rollouts. TS-NODE demonstrates significant performance improvements over a baseline Neural ODE model on multiple dynamical system modeling tasks.

Wang, Yu↗
DOE OSTI2024

Utilizing HYSPLIT for Emergency Response Modeling at SRS

At SRS, emergency responders use a variety of tools to detect, track, and mitigate hazardous material releases into the atmosphere. Two models currently used at SRS are Puff-Plume and the Lagrangian Particle Dispersion Model (LPDM), a Gaussian and Lagrangian model, respectively. A decision has been made to replace LPDM with the more widely-supported Hybrid Single-Particle Lagrangian Integrated Trajectory (HYSPLIT) model for evaluating inhalation and ingestion doses following a release. HYSPLIT is designed to compute complex dispersion and deposition simulation To achieve the implementation of HYSPLIT, we have developed a preliminary UI framework that will allow HYSPLIT to be run on ATG computers without the need for active network connections, thus avoiding the loss of capabilities in the event of a network outage during an emergency.

Earley, Ian↗
DOE OSTI2023

The Feasibility of Incorporating a 3D Velocity Model Into Earthquake Location Around Salt Lake City, UT Using a Physics Informed Neural Network

Earthquake location algorithms typically require travel time calculation. Doing this calculation in 3D, despite advances in algorithm efficiency and computational power, can still be prohibitively expensive in terms of resources and storage. Implementation of high-resolution 3D models in routine earthquake location would be a significant step forward in most of the world. Machine learning algorithms have potential to act as substitutes for travel time calculation algorithms or stored travel time tables. We investigate EikoNet - a physics informed neural network machine learning model that estimates travel times very quickly and comes with negligible memory-overhead. Specifically, we apply EikoNet to the Wasatch Fault Community Velocity Model (WFCVM), a highly detailed and complex 3D velocity model of the Salt Lake City, UT region. While routine locations in the area and studies of the 2020 Magna, UT earthquake sequence used a 1D velocity model, a 3D model may help better our understanding the structure of the major fault in the region. Our primary goal was to test the speed, memory requirements, and accuracy of EikoNet compared to a reference eikonal solver. We find that while the EikoNet is exceedingly fast and requires little memory overhead, achieving acceptable accuracy in estimated travel times is difficult and requires extensive computational resources.

58 GEOSCIENCES↗
DOE OSTI2020

CBRN Situational Awareness Tools for the Modern Age

CBRN Situational awareness (SA) should be a key operational requirement in the CNI area of discussion, not only for the “boots-on-the-ground” warfighter, but at a strategic level. For example, maneuver plans will need to be developed for contamination avoidance and battlefield survivability. Large datasets from instruments dispersed across the battlefield will thus need to be collected and processed into a holistic assessment of the radiological hazard. From there, hazard maps then must be distributed at the tactical level, all while continuing to effectively engage a peer adversary. This vision represents a network of sensors, software tools, data processing algorithms, communications suites, and common operational picture (COP) platforms working in concert to inform warfighters and command echelons alike of evolving nuclear and radiological hazards. This complex system-of-systems and the ultimate goal of effective engagement in a nuclear or radiological-hazard battlefield can be achieved through pursuit of improvement in collaborative SA tools.

Becker, Eric M.↗
DOE OSTI2020

Deep Learning Prediction of Interspecies Interactions from Self-organized Spatiotemporal Patterns of Co-evolving Organisms

Microorganisms colonizing natural habits such as soils co-evolve to form specific spatial patterns through interspecies interactions. These self-organized patterns are a key ecological phenotype, which provides critical information on their interaction mechanisms. However, conventional network inference techniques that analyze species population data in bulk have yet to be extended to account for such spatial heterogeneity. Here we proposed supervised deep learning as a new network inference tool for predicting interspecies interactions from spatiotemporal patterns of microbial evolution. Due to lack of biological imaging data that can be used for training deep learning networks, we used in silico data generated from high-fidelity agent-based models to determine model structure and parameters. Even though networks were trained under simple configurations where interaction coefficients are assumed to be spatially invariant, we demonstrated that the resulting model can be utilized to successfully predict spatial variation of interactions in more complex domains (i.e., configured with a context-dependent mixture of interaction coefficients) as well as in simple domains without further training. In the further test against real biological data obtained through imaging experiments of a binary consortium (Pseudomonas fluorescens and a mutant of Escherichia coli), our model also predicted the dramatic shifts in interactions of the two organisms across different environmental contexts. Through various successful demonstrations in this work, the combined use of the agent-based model and machine learning algorithm provides a means to use new type of data - microscopic images - for extracting microbial interactions, therefore presenting itself as a useful tool for the analysis of more complex microbial community interactions.

Lee, Joon-Yong↗
DOE OSTI2021

Cybersecurity Risk Assessment Framework for Externally Exposed Energy Delivery Systems

Securing the energy delivery system (EDS) from complex, nonlinear, and evolving cyber threats requires a complex set of changing and interwoven classes of technologies, policies, relationships, and personnel. One key area in this technological milieu is assessment methodologies to compare information, gathered by a variety of means, about networked devices with publicly known possible threat information about said devices. This information is used to generate risk-based characterizations that allow for the adjudication and proper corresponding management action chains to be assigned.

Gourisetti, Sri Nikhil G.↗
DOE OSTI2021

Neural Lyapunov Control for Power System Transient Stability: A Deep Learning-Based Approach

We report that power system control and transient stability analysis play essential roles in secure system operation. Control of power systems typically involves highly nonlinear and complex dynamics. Most of the existing works address such problems with additional assumptions in system dynamics, leading to a requirement for a complete and general solution. This paper, therefore, proposes a novel control framework for various power system control and stability problems leveraging a learning-based approach. The proposed framework includes a two-module structure that iteratively and jointly learns the candidate Lyapunov function and control law via deep neural networks in a learning module. Meanwhile, it guides the learning procedure towards valid results satisfying Lyapunov conditions in a falsification module. The introduced termination criteria ensure provable system stability. This control framework is verified through several studies handling different types of power system control problems. The results show that the proposed framework is generalizable and can simplify the control design for complex power systems with the stability guarantee and enlarged region of attraction.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Entanglement features of random neural network quantum states

Restricted Boltzmann machines (RBMs) are a class of neural networks that have been successfully employed as a variational ansatz for quantum many-body wave functions. Here, we develop an analytic method to study quantum many-body spin states encoded by random RBMs with independent and identically distributed complex Gaussian weights. By mapping the computation of ensemble-averaged quantities to statistical mechanics models, we are able to investigate the parameter space of the RBM ensemble in the thermodynamic limit. We discover qualitatively distinct wave functions by varying RBM parameters, which correspond to distinct phases in the equivalent statistical mechanics model. Notably, there is a regime in which the typical RBM states have near-maximal entanglement entropy in the thermodynamic limit, similar to that of Haar-random states. However, these states generically exhibit nonergodic behavior in the Ising basis, and do not form quantum state designs, making them distinguishable from Haar-random states.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Connecting particle interactions to agglomerate morphology and rheology of boehmite nanocrystal suspensions

Rheology imposes significant challenges on processing of complex suspensions such as nuclear waste slurries at the Hanford and Savannah River sites. Understanding rheology connecting to microstructures and underlying particle interactions in complex slurries is therefore important for both fundamental knowledge and practical applications. Here, we use suspensions of aluminum oxyhydroxide minerals in the form of boehmite as an analog of the radioactive waste slurry to gain physical insights on the correlation between particle interactions, microstructures, and slurry rheology. Specifically, we use a combination of Couette rheometry and small-angle scattering techniques (independently and simultaneously) to understand how the slurry microstructure changes under flow and how these structural changes manifest themselves in the bulk rheology of the suspensions. Our experiments show that the boehmite slurries are thixotropic, with the rheology and structure of the suspensions changing with increasing exposure to flow. In the slurries, particle aggregates begin as loose, system-spanning clusters, but exposure to moderate shear rates causes the aggregates to irreversibly consolidate into denser clusters of finite size. The microstructural changes directly influence the rheological properties of the slurries such as viscosity and viscoelasticity. More importantly, our study shows that solution pH affects the amount of structural rearrangement and the kinetics of the rearrangement process, with an increase in pH leading to faster and more dramatic changes in the bulk rheology. Such dynamic microstructural changes and resultant rheology are understood via correlations between particle interactions and strength of particle network, coupled with surface chemistry and anisotropic nature of particle interactions. Nearly identical structural changes are also observed in Poiseuille flow geometries, implying that the observed changes are relevant in the pipe flow conditions present during waste processing.

Weston, Javen S.↗
DOE OSTI2021

An artificial spiking quantum neuron

Abstract Artificial spiking neural networks have found applications in areas where the temporal nature of activation offers an advantage, such as time series prediction and signal processing. To improve their efficiency, spiking architectures often run on custom-designed neuromorphic hardware, but, despite their attractive properties, these implementations have been limited to digital systems. We describe an artificial quantum spiking neuron that relies on the dynamical evolution of two easy to implement Hamiltonians and subsequent local measurements. The architecture allows exploiting complex amplitudes and back-action from measurements to influence the input. This approach to learning protocols is advantageous in the case where the input and output of the system are both quantum states. We demonstrate this through the classification of Bell pairs which can be seen as a certification protocol. Stacking the introduced elementary building blocks into larger networks combines the spatiotemporal features of a spiking neural network with the non-local quantum correlations across the graph.

Kristensen, Lasse Bjørn (ORCID:0000000239398170)↗
DOE OSTI2022

Islands Within Islands: Bacterial Phylogenetic Structure and Consortia in Hawaiian Lava Caves and Fumaroles

Lava caves, tubes, and fumaroles in Hawai‘i present a range of volcanic, oligotrophic environments from different lava flows and host unexpectedly high levels of bacterial diversity. These features provide an opportunity to study the ecological drivers that structure bacterial community diversity and assemblies in volcanic ecosystems and compare the older, more stable environments of lava tubes, to the more variable and extreme conditions of younger, geothermally active caves and fumaroles. Using 16S rRNA amplicon-based sequencing methods, we investigated the phylogenetic distinctness and diversity and identified microbial interactions and consortia through co-occurrence networks in 70 samples from lava tubes, geothermal lava caves, and fumaroles on the island of Hawai‘i. Our data illustrate that lava caves and geothermal sites harbor unique microbial communities, with very little overlap between caves or sites. We also found that older lava tubes (500–800 yrs old) hosted greater phylogenetic diversity (Faith's PD) than sites that were either geothermally active or younger (<400 yrs old). Geothermally active sites had a greater number of interactions and complexity than lava tubes. Average phylogenetic distinctness, a measure of the phylogenetic relatedness of a community, was higher than would be expected if communities were structured at random. This suggests that bacterial communities of Hawaiian volcanic environments are phylogenetically over-dispersed and that competitive exclusion is the main driver in structuring these communities. This was supported by network analyses that found that taxa (Class level) co-occurred with more distantly related organisms than close relatives, particularly in geothermal sites. Network “hubs” (taxa of potentially higher ecological importance) were not the most abundant taxa in either geothermal sites or lava tubes and were identified as unknown families or genera of the phyla, Chloroflexi and Acidobacteria. These results highlight the need for further study on the ecological role of microbes in caves through targeted culturing methods, metagenomics, and long-read sequence technologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exploring the determinants of organic matter bioavailability through substrate-explicit thermodynamic modeling

Microbial decomposition of organic matter (OM) in river corridors is a major driver of nutrient and energy cycles in natural ecosystems. Recent advances in omics technologies enabled high-throughput generation of molecular data that could be used to inform biogeochemical models. With ultrahigh-resolution OM data becoming more readily available, in particular, the substrate-explicit thermodynamic modeling (SXTM) has emerged as a promising approach due to its ability to predict OM degradation and respiration rates from chemical formulae of compounds. This model implicitly assumes that all detected organic compounds are bioavailable, and that aerobic respiration is driven solely by thermodynamics. Despite promising demonstrations in previous studies, these assumptions may not be universally valid because OM degradation is a complex process governed by multiple factors. To identify key drivers of OM respiration, we performed a comprehensive analysis of diverse river systems using Fourier-transform ion cyclotron resonance mass spectrometry OM data and associated respiration measurements collected by the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) consortium. In support of our argument, we found that the incorporation of all compounds detected in the samples into the SXTM resulted in a poor correlation between the predicted and measured respiration rates. The data-model consistency was significantly improved by the selective use of a small subset (i.e., only about 5%) of organic compounds identified using an optimization method. Through a subsequent comparative analysis of the subset of compounds (which we presume as bioavailable) against the full set of compounds, we identified three major traits that potentially determine OM bioavailability, including: (1) thermodynamic favorability of aerobic respiration, (2) the number of C atoms contained in compounds, and (2) carbon/nitrogen (C/N) ratio. We found that all three factors serve as “filters” in that the compounds with undesirable properties in any of these traits are strictly excluded from the bioavailable fraction. This work highlights the importance of accounting for the complex interplay among multiple key traits to increase the predictive power of biogeochemical and ecosystem models.

59 BASIC BIOLOGICAL SCIENCES↗