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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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DOE OSTI2023

HERMES: A cyber-energy SAAS platform to provide real-time 2d and 3d interactive visualizations [SWR-23-15]

HERMES is a novel visualization SAAS platform for achieving real-time visualization of large-scale environments involving cyber-energy devices. The platform receives data from emulated environments which include physical hardware and network devices that represent a complex IT / OT system. The platform is capable of streaming, filtering, storing, and visualizing all data within the environment and scaling vertically and horizontally. This capability enables high fidelity visual analysis of events to be performed in real time as well as the collection and storage of historical data for forensic analysis.

Van Natta, Joshua↗

DOE OSTI2025

Applying Transfer Learning for Street-Scale Nuisance Flood Forecasting in Coastal-Urban Cities

An important challenge with Machine Learning (ML) is its transferability; that is, whether a ML model trained on one set of data can be applied to a second set of data without requiring a full re-training of the model. Transfer Learning (TL) addresses this challenge by transferring knowledge learned in the source domain (the data it was trained on) to the target domain (a second set of data that is statistically different but related, which the model was not trained on). This study investigates the use of TL for street-scale nuisance flood forecasting by exploring whether a ML model trained on data collected for one set of streets can effectively forecast flooding for another set of streets in the same city using TL. The envisioned use case is a city deploying a new flood depth monitoring sensor on a street and using TL to apply a ML model, trained on sensor data from an existing flood depth sensor network, to this new street. Eventually, the new flood depth sensor will have a sufficient dataset for training its own ML model, but TL can be used to fill the gap in time while this new dataset is being generated. This method is explored using a Long Short-Term Memory (LSTM) model trained on data for the flood-prone streets of Norfolk City, Virginia. The data used for training includes environmental time series (rainfall, tide), topographic features (Digital Elevation Model (DEM), Topographic Wetness Index (TWI), Depth To Water (DTW)), and street-scale flood depth time series obtained from a high-fidelity physics-based model, acting as a synthetic street-scale stream depth sensor dataset since actual stream depth sensor data is generally unavailable for most cities. A set of 180 flood-prone streets was used to train a base model, while another set of 180 flood-prone streets was used to re-train that model using different TL strategies. The results show that full-weight re-training proved most effective and minimal re-training of only the output layer was insufficient. The advantage of TL was most pronounced when target data was limited, meaning data collected at the new water depth sensor location included generally less than 18 flood events. As target data increased beyond 18 flood events, the benefit of TL diminished relative to training a ML model directly on the local flood events. These findings can assist cities as they implement street-scale flood sensing systems to create accurate forecasts for new sensing locations that do not yet have sufficient data records to train a local ML model.

Roy, Binata [Univ. of Virginia, Charlottesville, V↗

DOE OSTI2025

Spin-informed universal graph neural networks for simulating magnetic ordering

The screening and discovery of magnetic materials are hindered by the computational cost of first-principles density-functional theory (DFT) calculations required to find the ground state magnetic ordering. Although universal machine-learning interatomic potentials (uMLIPs), also known as atomistic foundation models, offer high-fidelity models of many atomistic systems with significant speedup, they currently lack the inputs required for predicting magnetic ordering. In this work, we present a data-efficient, spin-informed graph neural network framework that incorporates spin degrees of freedom as inputs and preserves physical symmetries, extending the functionality of uMLIPs to simulate magnetic orderings. This framework speeds up DFT calculations through better initial guesses for magnetic moments, determines the ground-state ordering of bulk materials and even generalizes to magnetic ordering in surfaces. Furthermore, we implement a closed-loop anomaly detection approach that effectively addresses the classic "chicken-and-egg" problem of creating a high-quality dataset while developing a uMLIP, unearthing anomalies in large benchmark datasets and boosting model accuracy.

Xu, Wenbin↗

DOE OSTI2024

Pipeline Hydrogen Decarbonization and Repurposing Analyzers (P-HyDRAs)

The Pipeline Hydrogen Decarbonization and Repurposing Analyzers (P-HyDRAs) are a set of prototype computational tools for simulating and optimizing midstream natural gas pipeline system operations subject to location and time-dependent hydrogen blending. The models can accurately resolve dynamic gas flows through large-scale pipeline networks using non-ideal gas equations of state. The codes can be used as decision support for planning and design decisions involving intra-day energy flow schedules as well as spatiotemporal economic values of natural gas, hydrogen, and net energy delivered to consumers while ensuring that pipeline hydraulic limitations, gas compressor station constraints, operational factors, and pre-existing shipping contracts are satisfied. The inputs to the codes are a model of the pipeline system as well as time-series data that specify boundary conditions on the network. For optimization, the code module requires price and quantity offers for natural gas and hydrogen and price and quantity bids for energy, which are used as time-dependent constraints in an optimal control problem. The outputs are time-series data that provide a predictive simulation of gas flows, mass fractions, and pressures, or with additional degrees of freedom give an approximately optimal solution for gas injections/withdrawals, compressor settings, and sensitivities to the objective function that provide locational values of energy.

Zlotnik, Anatoly↗

DOE OSTI2023

Design, execution, and interpretation of plant RNA-seq analyses

Genomics has transformed our understanding of the genetic architecture of traits and the genetic variation present in plants. Here, we present a review of how RNA-seq can be performed to tackle research challenges addressed by plant sciences. We discuss the importance of experimental design in RNA-seq, including considerations for sampling and replication, to avoid pitfalls and wasted resources. Approaches for processing RNA-seq data include quality control and counting features, and we describe common approaches and variations. Though differential gene expression analysis is the most common analysis of RNA-seq data, we review multiple methods for assessing gene expression, including detecting allele-specific gene expression and building co-expression networks. With the production of more RNA-seq data, strategies for integrating these data into genetic mapping pipelines is of increased interest. Finally, special considerations for RNA-seq analysis and interpretation in plants are needed, due to the high genome complexity common across plants. By incorporating informed decisions throughout an RNA-seq experiment, we can increase the knowledge gained.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2023

Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets

Abstract Cell type-specific gene expression patterns are outputs of transcriptional gene regulatory networks (GRNs) that connect transcription factors and signaling proteins to target genes. Single-cell technologies such as single cell RNA-sequencing (scRNA-seq) and single cell Assay for Transposase-Accessible Chromatin using sequencing (scATAC-seq), can examine cell-type specific gene regulation at unprecedented detail. However, current approaches to infer cell type-specific GRNs are limited in their ability to integrate scRNA-seq and scATAC-seq measurements and to model network dynamics on a cell lineage. To address this challenge, we have developed single-cell Multi-Task Network Inference (scMTNI), a multi-task learning framework to infer the GRN for each cell type on a lineage from scRNA-seq and scATAC-seq data. Using simulated and real datasets, we show that scMTNI is a broadly applicable framework for linear and branching lineages that accurately infers GRN dynamics and identifies key regulators of fate transitions for diverse processes such as cellular reprogramming and differentiation.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2025

Recent Experience with the CMS Data Management System

The CMS[1] experiment manages a large-scale data infrastructure, currently handling over 200 PB of disk and 500 PB of tape storage and transferring more than 1 PB of data per day on average between various WLCG[2] sites. Utilizing Rucio[3] for high-level data management, FTS[4] for data transfers, and a variety of storage and network technologies at the sites, CMS confronts inevitable challenges due to the system’s growing scale and evolving nature. Key challenges include managing transfer and storage failures, optimizing data distribution across different storages based on production and analysis needs, implementing necessary technology upgrades and migrations, and efficiently handling user requests. The data management team has established comprehensive monitoring to supervise this system and has successfully addressed many of these challenges. The team’s efforts aim to ensure data availability and protection, minimize failures and manual interventions, maximize transfer throughput and resource utilization, and provide reliable user support. This paper details the operational experience of CMS with its data management system in recent years, focusing on the encountered challenges, the effective strategies employed to overcome them and the ongoing challenges as we prepare for future demands.

Öztürk, Hasan [CERN]↗

DOE OSTI2024

EFIT-Prime: Probabilistic and physics-constrained reduced-order neural network model for equilibrium reconstruction in DIII-D

We introduce EFIT-Prime, a novel machine learning surrogate model for EFIT (Equilibrium FIT) that integrates probabilistic and physics-informed methodologies to overcome typical limitations associated with deterministic and ad hoc neural network architectures. EFIT-Prime utilizes a neural architecture search-based deep ensemble for robust uncertainty quantification, providing scalable and efficient neural architectures that comprehensively quantify both data and model uncertainties. Physically informed by the Grad–Shafranov equation, EFIT-Prime applies a constraint on the current density J tor and a smoothness constraint on the first derivative of the poloidal flux, ensuring physically plausible solutions. Furthermore, the spatial location of the diagnostics is explicitly incorporated in the inputs to account for their spatial correlation. Extensive evaluations demonstrate EFIT-Prime's accuracy and robustness across diverse scenarios, most notably showing good generalization on negative-triangularity discharges that were excluded from training. Timing studies indicate an ensemble inference time of 15 ms for predicting a new equilibrium, offering the possibility of plasma control in real-time, if the model is optimized for speed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

The use of seismic spatial gradients in a single layer neural network for seismic source discrimination: proof of concept

This report describes a proof-of-concept method of seismic source discrimination using seismic gradiometry and a common machine learning technique. The tests described here are purely numerical, using synthetic seismic data and well understood mathematical techniques. The primary innovation described here is the application of a richer seismic data set derived from seismic gradiometry. Seismic gradiometry is a method to estimate the time variable spatial gradient of the wavefield to compute various wavefield attributes such as slowness, dynamic strain, and rotational motions. With the addition of these wavefield attributes, we are afforded up to twenty "compo- nents" of time series data measured at a single point on, or in, the Earth. This is in direct contrast to conventional three-component seismic data collected at several locations using a seismic network. Using the gradiometrically-derived wavefield components directly in a single-layer neural network, I show that it is possible to discriminate between three common seismic source types (earthquakes, explosions, and opening fractures) for various noise conditions and gradiometry configurations.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗

DOE OSTI2021

Learning an Algebriac Multrigrid Interpolation Operator Using a Modified GraphNet Architecture

This work, building on previous efforts, develops a suite of new graph neural network machine learning architectures that generate data-driven prolongators for use in Algebraic Multigrid (AMG). Algebraic Multigrid is a powerful and common technique for solving large, sparse linear systems. Its effectiveness is problem dependent and heavily depends on the choice of the prolongation operator, which interpolates the coarse mesh results onto a finer mesh. Previous work has used recent developments in graph neural networks to learn a prolongation operator from a given coefficient matrix. In this paper, we expand on previous work by exploring architectural enhancements of graph neural networks. A new method for generating a training set is developed which more closely aligns to the test set. Asymptotic error reduction factors are compared on a test suite of 3-dimensional Poisson problems with varying degrees of element stretching. Results show modest improvements in asymptotic error factor over both commonly chosen baselines and learning methods from previous work.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Wireless Sensing and Communication Capability from In-Core to a Monitoring Center

Significant cost savings can be made if electrical cables can be replaced by wireless technology in current Nuclear Power Plants (NPP) and in advance reactor designs. Wireless technology can also provide in-core opportunities by significantly reducing the number of penetrations in the pressure vessel, cost and complexity of sensor installation and by increasing the efficiency of current and advanced reactors. Unlike other deployment scenarios for an industrial environment, operators need to have centralized control over all the networks. Centralized control will reduce implementation costs, provide single point control and enable monitoring of network devices, improve security, and enhance connectivity. Micro-sensors that can simultaneously monitor temperature and pressure within a fuel rod inside nuclear reactors will enable preventative actions during abnormal operating conditions. This ability could avert accidents and enable the expedient development of accident tolerant fuels. A novel micro-sensor suite (~ mm) to simultaneously measure multiple parameters such as temperature, strain, pressure, and neutron/gamma flux inside a fuel rod is being developed for use in reactors. The necessary communication architecture is also being developed to transmit measurement signals from the core to the plant's data cloud or control room. A three three-tier strategy has been developed to support wireless transmission of in-core measurements to the control room or to a secure cloud platform for control, analytics, and decision-making purposes. 1. In-core: data signal from in-core to outside of the pressure vessel within the containment building 2. Containment building: data signal from inside to the outside of the containment building and into the balance of the plant network 3. Balance of the plant network: information transmitted to the data cloud and control room This plan presents a wireless sensing and communication system for use within a reactor core and elsewhere. The communication technology is advantageous to compensate for network equipment failures and adverse data transmission conditions. Wireless technology will significantly increase the resiliency of the plants network system. The wireless system naturally provides multiple transmission path capability and data redundancy.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2022

Cross Inference of Throughput Profiles Using Micro Kernel Network Method

Dedicated network connections are being increasingly deployed in cloud, centralized and edge computing and data infrastructures, whose throughput profiles are critical indicators of the underlying data transfer performance. Due to the cost and disruptions to physical infrastructures, network emulators, such as Mininet, are often used to generate measurements needed to estimate throughput profiles, typically expressed as a function of the connection round trip time. The profiles estimated using measurements from such emulated networks are usually inaccurate for high bandwidth and high latency connections, since they do not accurately reflect the critical network transport dynamics mainly due to computing and memory constraints of the host. We present a machine learning (ML) method to estimate the throughput profiles using emulation measurements to closely match the testbed and production network profiles. In particular, we propose a micro Kernel Network (mKN) that provides baseline throughput measurements on the host running Mininet emulations, which are used to learn a regression map that converts them to the corresponding testbed measurement estimates. Once initially learned, this map is applied to measurements from subsequent network emulations on the same host. We present experimental measurements to illustrate this approach, and derive generalization equations for the proposed mKN-ML method. Using a four-site scenario emulation, we show the effectiveness of this method in providing accurate concave throughput profiles from inaccurate convex or non-smooth ones indicated by Mininet emulation.

Rao, Nageswara↗

DOE OSTI2024

Distributed and Secure Spectrum Sharing for 5G and 6G Networks

Secure spectrum sharing or spectrum co-existence of multiple 5G networks and future 6G networks is a powerful enabler technology. The National Spectrum Strategy (NSS) published by the White House in November, 2023, and the subsequent NSS implementation plan led by the National Telecommunication and Information Administration (NTIA) is the driver of a national effort to enable co-existence of government incumbents and commercial networks in selected spectrum bands. Cellular networks such as 5G & 6G and non-cellular Wi-Fi 6E & 7 are the prominent wireless technologies considered for co-existence with incumbent wireless links. Security of the spectrum sharing solutions is a must to make this transformation of spectrum use possible, specially for mission critical communications. However, current spectrum sharing solutions rely on centralized data bases with inherent vulnerabilities. This paper focuses on secure spectrum sharing among multiple 5G networks using unlicensed and shared frequency bands. It presents an innovative AI/ML based distributed spectrum sharing approach that can be autonomously used by multiple networks. Each sharing network uses its own observation of the Radio Frequency (RF) environment, which consists of RF measurements reported from the 5G User Equipment (UE), to adjust the transmission power levels for secure co-existence. Data is presented to illustrate the superior performance of this solution compared to other spectrum sharing solutions where each network can utilize usage data of the other networks. Finally it discusses how this efficient spectrum sharing solution can evolve in the future for the 6G networks.

5G↗

DOE OSTI2022

Synthetic Data Generation Using Machine Learning

Robust machine learning techniques for image analysis require a substantial amount of data to yield confident results. In the nuclear domain, data scarcity is a substantial challenge because there are so few facilities worldwide. This research focuses on being able alleviate the data scarcity problem by generating synthetic data to bridge the gap between large and small datasets. This work achieves that goal using a Generative Adversarial Network (GAN) architectural approach, by training a model on real-world data and expands that small dataset through synthetic data amendments. Model performance is impacted by the size of the real-world dataset and the number of training epochs utilized. This means that 1) It is important to develop your GAN to be optimized with the specific data type, and 2) approaches taken when training the GAN should be specialized to encompass important aspects of the dataset that it is generating. By taking a step to improve dataset sizes in this way, the gap between models trained by parties with significant amount of data and those without access to large data, closes, allowing for robust analyses of satellite imagery for nuclear domain applications.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Attention Network Forecasts Time-to-Failure in Laboratory Shear Experiments

Rocks under stress deform by creep mechanisms that include formation and slip on small-scale internal cracks. Intragranular cracks and slip along grain contacts release energy as elastic waves termed acoustic emissions (AE). AEs are thought to contain predictive information that can be used for fault failure forecasting. Here, we present a method using unsupervised classification and an attention network to forecast labquakes using AE waveform features. Our data were generated in a laboratory setting using a biaxial shearing device with granular fault gouge intended to mimic the conditions of tectonic faults. Here, we analyzed the temporal evolution of AEs generated throughout several hundred laboratory earthquake cycles. We used a Conscience Self-Organizing Map (CSOM) to perform topologically ordered vector quantization based on waveform properties. The resulting map was used to interactively cluster AEs. We examined the clusters over time to identify those with predictive ability. Finally, we used a variety of LSTM and attention-based networks to test the predictive power of the AE clusters. By tracking cumulative waveform features over the seismic cycle, the network is able to forecast the time-to-failure (TTF) of lab earthquakes. Our results show that analyzing the data to isolate predictive signals and using a more sophisticated network architecture are key to robustly forecasting labquakes. In the future, this method could be applied on tectonic faults to monitor earthquakes and augment early warning systems.

58 GEOSCIENCES↗

DOE OSTI2026

Prong Segmentation using Point Set Transformers in Multiple View Neutrino Detectors

NOvA is a long-baseline neutrino experiment studying neutrino oscillations by detecting neutrinos from the NuMI beam at Fermilab. Its physics analysis relies on accurate prong segmentation, which involves matching each hit to its source particle and identifying the particle type. This task has commonly been addressed using a combination of traditional clustering algorithms and convolutional neural networks (CNNs). However, NOvA’s detector design presents data as two sparse and decoupled 2D images (XZ and YZ views) rather than a native 3D representation, posing a significant challenge for traditional CNN-based models. In this talk, we propose a novel neural network based on the Point Set Transformer. By treating detector hits as sparse point clouds and implementing a cross-view attention mechanism, our model enables efficient information mixing between both views. Evaluated on NOvA simulated data, our model achieves superior accuracy while requiring significantly fewer computational resources compared to other models. Furthermore, the model demonstrates great performance when applied to Liquid Argon Time Projection Chamber (LArTPC) data, which shows its potential as a universal prong segmentation algorithm for multiple view neutrino detectors.

Liu, Jiaxi [UC, Irvine]↗

DOE OSTI2023

Scripts and data associated with a manuscript linking soil and sediment elemental composition with dissolved organic matter chemistry across CONUS

This data package provides scripts and geochemical data for a manuscript titled “Linkages between mineral element composition of soils and sediments with hyporheic zone dissolved organic matter chemistry across the contiguous United States” (preprint: doi: 10.22541/essoar.169447343.31694990/v1). This data is associated with the Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS, https://whondrs.pnnl.gov) and is an extension of the Summer 2019 Sampling campaign which crowdsourced samples from rivers and sediment across the continental United States. Data from this study can be found at https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1603775 and https://data.ess-dive.lbl.gov/datasets/doi:10.15485/1729719. The main objective of this manuscript was to couple sediment water extractable dissolved organic matter chemistry, defined by ultra-high resolution mass spectrometry, with localized sediment elemental composition and watershed scale soil elemental characteristics. This data package contains one main folder with four subfolders. The main data folder contains (1) readme; (2) data dictionary (dd); (3) file-level metadata (flmd); (4) an R markdown to reproduce manuscript figures and analyses; (5) a pdf of instructions to reproduce NGS interpolations with ArcGIS software; and (6) a python script to reproduce NGS extrapolations with python. The four subfolders contain files required to reproduce NGS extrapolations include (1) ‘CONUS_boundaries’ containing boundary layers (.shp) for the Continental United States; (2) ‘ngs_project’ containing files (.shp) with point level NGS soil elemental data (Grossman et al., 2004); (3) ‘raster_outputs’ containing the interpolated raster output files for various soil elements; and (4) ‘NGS_Chemistry_Final’ contain final extracted soil elemental data.

54 ENVIRONMENTAL SCIENCES↗