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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 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

GHG Emissions Inventory Report for LvivTeploEnergo (LTE)

PNNL prepared the greenhouse gas (GHG) emissions inventory for LTE to help gain international funding for modernization requirements necessary to decarbonize LTE’s DH network. The inventory itself provides a comprehensive account of total greenhouse gas emissions from (a) LTE’s transportation (commuting and product transport), (b) purchased electricity (for water heating, heat production, and other uses), and (c) heat production for the DH network. The greenhouse gases analyzed in the inventory are Carbon Dioxide (CO 2 ), Carbon Monoxide (CO), and Nitrogen Oxide (NO x ). All data gathered for the inventory came directly from previous years of LTE data, with total emissions of each activity calculated by using emission factors from several internationally reputable sources (e.g., United National Framework Convention on Climate Change (UNFCCC), the ECLIPSE v6 dataset from the International Institute for Applied Systems Analysis (IIASA), the European Modeling and Evaluation Programme (EMEP), and the World Resources Institute (WRI)). This report further explains the methodology and justification for which scopes of emissions were included in the GHG emissions inventory spreadsheet and a description of references from which the emission factors were taken.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Real-Sim Interface: Enabling Multi-resolution Simulation and X-in-the-Loop Development for Connected and Automated Vehicles

We report connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications. This interface is built upon Oak Ridge National Laboratory’s (ORNL’s) Real-Sim approach, can support various simulation tools and real-time X-in-the-loop (XIL) simulations, and is based on network communication protocols. The network communication delays are analyzed for simultaneous connection of up to 20 simulators. Then, two example applications are studied to demonstrate the potential usage of the Real-Sim interface: (1) a centralized merging scenario where the dynamics of two ego vehicles are emulated with detailed vehicle and powertrain dynamics models and (2) a signal-controller-in-the-loop (SCIL) evaluation where one intersection of the simulated traffic scenario is controlled by a physical signal controller. This Real-Sim interface is a tool that allows researchers to bring real, tangible hardware and software into simulated environments to comprehensively evaluate and support a wide variety of CAV applications.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

An Autonomous Robot for Shell and Tube Heat Exchanger Inspection

Shell and tube heat exchangers (STHEs) are critical to energy conversion efficiency of power plants. Eddy current examination is a way to evaluate working conditions of these tubes. However, the current testing apparatus requires human to manually insert an eddy current testing (ECT) probe into and extract it out of individual tubes, and meanwhile monitor measurement results for diagnosis. It is a time-consuming and labor-intensive procedure even for an experienced technician. To tackle this challenge, in this work, we developed a robot enabled ECT system for autonomous inspection of STHEs. The robotic platform employs Mecanum wheeled chassis for high mobility, machine vision to locate tube bundle and tube inlets, a rotational Cartesian mechanism to operate at planes with all possible inclinations, and a task-specific mechanism for ECT probe delivery. Machine vision locates tube bundle and tube inlets by an April tag detection algorithm and a Circle Hough Transform (CHT) algorithm, respectively. Assisted by a guidance cone, the ECT probe is continuously fed into the tubes with a fill factor of 0.819. During this process, the eddy current data are automatically collected and real-time analyzed by convolutional neural networks (CNN), showing accuracy of nearly 100% for identifying defective and non-defective tubes and 85% for four types of defective tubes and non-defective tubes.

autonomy, deep learning, eddy current testing, hea↗
DOE OSTI2022

GNN-based end-to-end reconstruction in the CMS Phase 2 High-Granularity Calorimeter

We present the current stage of research progress towards a one-pass, completely Machine Learning (ML) based imaging calorimeter reconstruction. The model used is based on Graph Neural Networks (GNNs) and directly analyzes the hits in each HGCAL endcap. The ML algorithm is trained to predict clusters of hits originating from the same incident particle by labeling the hits with the same cluster index. We impose simple criteria to assess whether the hits associated as a cluster by the prediction are matched to those hits resulting from any particular individual incident particles. The algorithm is studied by simulating two tau leptons in each of the two HGCAL endcaps, where each tau may decay according to its measured standard model branching probabilities. The simulation includes the material interaction of the tau decay products which may create additional particles incident upon the calorimeter. Using this varied multiparticle environment we can investigate the application of this reconstruction technique and begin to characterize energy containment and performance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2025

Accurate and reliable thermochemistry by data analysis of complex thermochemical networks using Active Thermochemical Tables: the case of glycine thermochemistry

Active Thermochemical Tables (ATcT) were successfully used to resolve the existing inconsistencies related to the thermochemistry of glycine, based on statistically analyzing and solving a thermochemical network that includes >3350 chemical species interconnected by nearly 35 000 thermochemically-relevant determinations from experiment and high-level theory. Here, the current ATcT results for the 298.15 K enthalpies of formation are −394.70 ± 0.55 kJ mol −1 for gas phase glycine, −528.37 ± 0.20 kJ mol −1 for solid α-glycine, −528.05 ± 0.22 kJ mol −1 for β-glycine, −528.64 ± 0.23 kJ mol −1 for γ-glycine, −514.22 ± 0.20 kJ mol −1 for aqueous undissociated glycine, and −470.09 ± 0.20 kJ mol −1 for fully dissociated aqueous glycine at infinite dilution. In addition, a new set of thermophysical properties of gas phase glycine was obtained from a fully corrected nonrigid rotor anharmonic oscillator (NRRAO) partition function, which includes all conformers. Corresponding sets of thermophysical properties of α-, β-, and γ-glycine are also presented.

Active Thermochemical Tables↗
DOE OSTI2021

Risk-Averse Bi-Level Stochastic Network Interdiction Model for Cyber-Security Risk Management

Security of cyber networks is crucial; recent severe cyber-attacks have had a devastating effect on many large organizations. The attack graph, which maps the potential attack paths of a cyber network, is a popular tool for analyzing cyber system vulnerability. In this study, we propose a bi-level stochastic network interdiction model on an attack graph to enable a risk-averse, resource constrained cyber network defender to optimally deploy security countermeasures that protect against attackers with an uncertain budget. This risk- averse conditional-value-at-risk (CVaR) model minimizes a weighted sum of the expected maximum loss over all scenarios and the expected maximum loss from the most damaging attack scenarios. We develop a customized constraint and column generation algorithm to solve our model as well as several acceleration techniques to improve the computational efficiency. Numerical experiments demonstrate that the acceleration techniques enable the solution of relatively large problems within a reasonable amount of time: applying all the acceleration techniques also reduces the average computation time of the basic algorithm by 71% for 100-node graphs. Using metrics called mean-risk value of stochastic solution and value of risk-aversion, computational results suggest that our stochastic risk-averse model significantly outperforms deterministic and risk-neutral models when 1) the distribution of attacker budget is heavy-right-tailed and 2) the defender is highly risk-averse.

Bhuiyan, Tanveer H.↗
DOE OSTI2024

Small-Signal Stability Impacts of Load and Network Dynamics on Grid-Forming Inverters

This paper presents several stability analyses for grid-forming inverters and synchronous generators considering the dynamics of transmission lines and different load models. Load models are usually of secondary importance compared to generation source models, but as the results show, they play a crucial role in stability studies with the introduction of inverter-based resources. Given inverter control time scales, the implications of considering or neglecting electromagnetic transients of the network are very relevant in the stability assessments. In this paper, we perform eigenvalue analyses for inverter-based resources and synchronous machines connected to a load and explore the effects of multiples models under different network representations. We explore maximum loadability of inverter-based resources and synchronous machines, while analyzing the effects of load and network dynamic models on small-signal stability. The results show that the network representation plays a fundamental role in the stability of the system of different load models. The resulting stability regions are significantly different depending on the source and load model considered.

grid forming inverters↗
DOE OSTI2024

Malcolm Deployment Guide for Solar Power Generation Plants

This guide provides detailed instructions for deploying Malcolm in Solar Power Generation systems. It covers the deployment process, from understanding the network architecture of these systems to configuring network switches and Switched Port Analyzer (SPAN) ports or mirror ports or TAPs. The guide also includes best practices for deploying Hedgehog sensors, another critical component in these systems. Following this guide, users can enhance network visibility, improve their system’s security, and effectively troubleshoot common issues.

14 SOLAR ENERGY↗
DOE OSTI2021

Analyzing acoustic emission data to identify cracking modes in cement paste using an artificial neural network

This research is focused on the identification of cracking mechanisms for cement paste using acoustic emission data, recorded from compression and notched four-point bending tests. A procedure is developed for analyzing the data by employing an agglomerative hierarchical clustering method, an artificial neural network, and a ray-tracing source location algorithm. An agglomerative hierarchical clustering method is utilized to cluster the AE data from a compression test using frequency-dependent features. A neural network is trained using the compression test data and applied to the AE data emitted during the four-point bending test. The clustered data from the four-point bending test is localized using a ray-tracing algorithm. Based on the occurrence and locations of the clustered events and signal feature analyses, potential cracking mechanisms are identified and assigned.

36 MATERIALS SCIENCE↗
DOE OSTI2026

SENTRA: A Modular Computational Graph Framework for Critical Mineral and Materials Supply Chains: Part I: Network Construction Latent-Quantity Estimation, and Temporal Graph Forecasting

Global supply chains for critical minerals and materials are complex, evolving networks of countries, products, production stages, and trade relationships. Existing analytical approaches are limited by fragmented data and static network representations that do not capture the dynamic production dependencies linking raw materials, intermediate products, and final goods across multiple countries. Trade and production statistics provide only a partial view of domestic production, inventories, and material flows, making it difficult to identify indirect sourcing pathways, hidden dependencies, and embedded foreign exposures. This paper introduces the Supply Chain Exposure Network Tracking and Risk Assessment (SENTRA) framework, a modular graph-based computational framework for constructing, analyzing, and forecasting dynamic supply chain networks. As the first paper in a three-part methodological series, it establishes the computational foundation of SENTRA by constructing a temporal attributed multi-relational graph whose nodes represent product–country pairs and whose edges encode observed trade and within-country value-chain relationships. Statistical estimation and constrained optimization recover latent production, final demand, and product input dependency coefficients while enforcing economic accounting constraints. Graph-derived exposure measures quantify direct, transshipment, value-chain, and multi-hop supply chain dependencies independently of the forecasting model. A temporal graph forecasting architecture based on a relational graph neural network then forecasts the evolution of the graph under mass-balance constraints with distribution-free conformal uncertainty quantification. Validation on the global aluminum supply chain shows that the learned graph representations recover economically meaningful supply chain structure, accurately forecast out-of-sample trade relationships, and produce well-calibrated prediction intervals. Subsequent papers apply this computational foundation to exposure assessment, disruption analysis, and scenario-based policy analysis, and extend the framework to multimaterial supply chain modeling and decision support.

36 MATERIALS SCIENCE↗
DOE OSTI2022

FORCE Development Status Update: Vertical Integration and Benchmarking of System Dynamics

Recent efforts to establish effective models for grid energy analysis, especially given the increase in variable renewable energy (VRE) sources and the economic challenges faced by traditional nuclear energy, have generated new technological considerations. One effort to improve the economic viability of nuclear power involves investigating integrated energy systems (IES) which include secondary energy systems that introduce flexibility and secondary market possibilities to existing and perceived future nuclear energy generation technologies. To analyze the technical and economic potential of IES, the Framework for Optimization of Resources and Economics (FORCE) tool suite was developed through a collaboration among national laboratories. Within the FORCE tool suite, the Holistic Energy Resource Optimization Network (HERON) provides algorithms for analyzing the long-term viability of potential IES technologies, while HYBRID provides algorithms and models to achieve high-resolution analysis of coupling physics over a short time period. Continued maturing of the FORCE tool suite requires further interconnections between the various tools in the suite in order to ensure consistent analysis. Analyses performed by applying HYBRID to transient process modeling should be easily harvestable as inputs to HERON analyses. The first item in this status update is a demonstration of an automated data pipeline for loading data from HYBRID into HERON analyses. Application of HYBRID results to HERON, as part of using the FORCE tool suite, relies on robust modeling assumptions for the various models included in HYBRID. The second result of this status update is the benchmarking and validation of cost and operational data, with a particular focus on natural gas energy generators. These generators are benchmarked with a focus on contrasting them with proposed thermal energy storage (TES) technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2022

Hybrid Quantum-Classical Neural Networks

Deep learning is one of the most successful and far-reaching strategies used in machine learning today. However, the scale and utility of neural networks is still greatly limited by the current hardware used to train them. These concerns have become increasingly pressing as conventional computers are soon expected to approach the physical limitations that will slow their performance improvements in the near future. For these reasons, scientists have begun to explore alternative computing platforms, like quantum computers, for training neural networks. In recent years, variational quantum circuits have emerged as one of the most successful approaches to quantum deep learning on noisy intermediate scale quantum devices. We propose a hybrid quantum-classical neural network architecture where each neuron is a variational quantum circuit. We empirically analyze the performance of this hybrid neural network on a series of binary classification data sets using a simulated IBM universal quantum computer and a state-of-the-art IBM universal quantum computer. On the simulated hardware, we observe that the hybrid neural network achieves around 10% higher classification accuracy and 20% better minimization of the cost function than an individual variational quantum circuit. On the quantum hardware, we observe that each model only performs well when the qubit and gate count is sufficiently small.

Arthur, Davis↗
DOE OSTI2020

Using new edges for anomaly detection in computer networks

Creation of new edges in a network may be used as an indication of a potential attack on the network. Historical data of a frequency with which nodes in a network create and receive new edges may be analyzed. Baseline models of behavior among the edges in the network may be established based on the analysis of the historical data. A new edge that deviates from a respective baseline model by more than a predetermined threshold during a time window may be detected. The new edge may be flagged as potentially anomalous when the deviation from the respective baseline model is detected. Probabilities for both new and existing edges may be obtained for all edges in a path or other subgraph. The probabilities may then be combined to obtain a score for the path or other subgraph. A threshold may be obtained by calculating an empirical distribution of the scores under historical conditions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Transcriptomic Network Analysis of Cyanobacterial-Methylotroph Interactions in Coculture and Axenic Conditions

A previous study demonstrated the potential for Cyanobacterial-Methylotroph cocultures to facilitate biogas processing as well as to be used in other biotechnological applications. To advance this technology, we investigated potential interactions between Cyanobacterium stanieri HL-69 (HL69) and Methylotuvimicrobium alkaliphilum 20Z (20Z) by inferring and analyzing gene co-expression networks under co-culture and axenic conditions. Five different co-expression networks were examined. These networks were inferred using gene expression profiles for 20Z axenic condition, HL-69 axenic, HL-69, 20Z coculture, HL-69 coculture, and cross-species HL-69-20Z coculture. Through the analysis of node (gene) betweenness and node normalized degree values in all five network cases, we compared adjustments in gene expression between growth conditions (axenic vs co-culture) as well as identify biological functions relevant to interspecies interactions. This analysis was done to distinguish between gene interactions within an organism and gene interactions between two organisms. Moreover, for all five cases we investigated two different network cutoff levels of 3,000 and 10,000. By shedding light on inter- and intra- species interactions, we hope to gain a better understanding of how these two organisms interact. This research will allow the investigation of further biotechnological applications of coculture systems and optimization of such applications for biotechnological purposes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Network model for periodically strained graphene

The long-wavelength physics of monolayer graphene in the presence of periodic strain fields has a natural chiral scattering network description. When the strain field varies slowly compared to the graphene lattice and the effective magnetic length of the induced valley pseudomagnetic field, the low-energy physics can be understood in terms of valley-polarized percolating domain-wall modes. Inspired by a recent experiment, we consider a strain field with threefold rotation and mirror symmetries but without twofold rotation symmetry, resulting in a system with the connectivity of the oriented kagome network. Scattering processes in this network are captured by a symmetry-constrained phenomenological S matrix. Here, we analyze the phase diagram of the kagome network and show that the bulk physics of the strained graphene can be qualitatively captured by the network when we account for a percolation transition at charge neutrality. We also discuss the limitations of this approach to properly account for boundary physics.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Porosity evolution under increasing tension in wire-arc additively manufactured aluminum using in-situ micro-computed tomography and convolutional neural network

Internal defects such as porosities are often formed in additively manufactured metal components. The pores nucleate, grow, and coalesce to form cracks under loads, leading to eventual catastrophic failure. Here, in this paper, the full-field porosity evolution, including pore growth and coalescence in a wire-arc additively manufactured (WAAM) aluminum alloy cylinder under tension is observed with in-situ X-ray micro-computed tomography (μCT). The pore size distribution, density, and tensile stress are calculated from the volumetric images analyzed by a convolutional neural network (CNN) algorithm, which provides rapid analysis of 12,950 slice images from μCT volumetric images at the reference state and 13 tensile strains. The results show the quantitative evolution of the growth and coalescence of macropores under tension. A strong correlation is found between the local pore volume fraction and the true tensile stress when the tensile strain is larger than 5%.

36 MATERIALS SCIENCE↗
DOE OSTI2021

User Role Identification in Software Vulnerability Discussions over Social Networks

Understanding and early awareness of software vulnerabilities is vital for preventing and mitigating potential impacts from cybersecurity events. One step toward early characterization of software vulnerabilities may involve analyzing discussion and spread of information in online social networks. Prior work has used information from such discussions over multiple online forums to develop dynamic networks among users followed by analysis of structure, spread, and information evolution. In this work, we advance the state-of-the-art by focusing on data-driven learning of types, roles, and transition of roles exhibited by users over time. In social networks, users take on particular roles based on their actions and structure of the network. Identifying “meaningful” roles can help separate potential users of interest from the larger community, and identify patterns in a network. We will identify and compare roles found in online forums (e.g., Twitter) using techniques such as feature-based Non-negative Matrix Factorization coupled with topological and influence-based measures of centrality. Since users’ activities change over time, we also analyze role evolution in dynamic networks.

Jones, Rebecca D.↗
DOE OSTI2022

Stardust Network Measurement Components (esnet-stardust-components) v1

This software contains a set of components that allow us to measure and analyze the use and performance of computer networks using existing off the shelf Open Source components organized into a scalable and extensible system. The components were developed for and used within ESnet's Stardust Measurement Environment. The components are used to collect, process and display network measurement data in an architecture that employs a combination of Elasticsearch, Logstash and Grafana. The advantage of this approach is we can tailor system behavior to our analytical needs and expand its capability with new types of network measurement without waiting for a commercial vendor to support our fairly niche use cases. Additionally we have existing collaborations within our community that came about from prior research efforts that will benefit from the release of this software so that others can also contribute features.

Balas, Edward↗