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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 1,207 records · Page 67
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Learning-Accelerated ADMM for Distributed DC Optimal Power Flow

We suggest a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous observations of ADMM trajectories for a given system under varying load, the method trains a recurrent neural network (RNN) to predict the converged values of dual and consensus variables. Given a new realization of system load, a small number of initial ADMM iterations is taken as input to infer the converged values and directly inject them into the iteration. We empirically demonstrate that the online injection of these values into the ADMM iteration accelerates convergence by a significant factor for partitioned 14-, 118-and 2848-bus test systems under differing load scenarios. The proposed method has several advantages: it maintains the security of private decision variables inherent in consensus ADMM; inference is fast and so may be used in online settings; RNN-generated predictions can dramatically improve time to convergence but, by construction, can never result in infeasible ADMM subproblems; it can be easily integrated into existing software implementations. While we focus on the ADMM formulation of distributed DC-OPF in this paper, the ideas presented are naturally extended to other distributed optimization problems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Artificial Neural Network-based State Estimation for Low Observable, Unbalanced Microgrids for Microgrid Building Blocks

The microgrid building blocks (MBB) were proposed as microgrid components with combined sub-components with power conversion, communication, and microgrid control capability, or a subset of such sub-components. This work addresses the microgrid controller, present in an MBB, which requires accurate state estimation to perform its tasks, including for monitoring, power flow (dispatch), fault detection, etc. In this paper, an artificial neural network (ANN)-based framework for state estimation is proposed for an MBB, especially for unbalanced and low observable microgrids. To overcome the challenge of low observability in unbalanced systems, a concept of extended adjacent matrix is introduced to reduce the required number of measurements for state estimation. Addressing the challenges, a feed forward neural network (FNN) is utilized to enhance estimation accuracy and reliability with the reduced number of measurements. The proposed state estimation is validated through extensive simulations on a microgrid, which was achieved from the modified IEEE 34-bus distribution test feeder with multiple distributed energy resources (DERs) and demonstrated superior performance in estimation accuracy and low observability.

Choi, Jongchan↗
DOE OSTI2025

Molecular Dynamics Simulation and Theoretical Analysis of Structural Relaxation, Bond Exchange Dynamics, and Glass Transition in Vitrimers

Vitrimers are a class of polymer networks featuring dynamic covalent cross-links that can undergo associative bond exchange. There has been recent interest in these materials due to their promise as recyclable thermosets or self-healing polymers because of the ability of vitrimer networks to rearrange at the molecular level and undergo macroscopic flow. However, the practical use of these materials often occurs in the supercooled regime or glassy state, where the implications of dynamic bonds are complicated by the interplay between slow activated segmental dynamics, cross-link (i.e., bond-exchange) kinetics, and ultimately material properties. Here, in this paper, we combine coarse-grained molecular dynamics simulation and microscopic statistical mechanical theory to understand how cross-linking kinetics affect material dynamics and how this couples to segmental relaxation of the polymeric network strands across a spectrum of length and time scales, especially in the supercooled regime. We characterize the Kuhn segmental alpha relaxation time and bond exchange time for vitrimer systems across various cross-link densities, temperatures, and bond exchange rates. Simulation and theory both exhibit a bending-up behavior for bond exchange time upon cooling, suggesting a coupling between bond exchange dynamics and segmental relaxation that intensifies with faster bond exchange kinetics. We also found bond exchange dynamics have an impact on Kuhn segment alpha relaxation time, which is most significant at higher cross-link densities. Both these effects are most prominent when the bond exchange time is similar to the Kuhn segment alpha relaxation time, and the resulting coupling of these two relaxation processes is tied to both the probability of a free end to find a bonded pair and the time scale of the constraints imposed by the dynamic cross-links. This relationship is reflected by a cross-link dependence of a theoretical parameter which represents the quantitative degree of coupling between bond exchange and segmental dynamics. Overall, the combination of simulation and theory clarifies the intricate interaction between bond kinetics and segmental relaxation and demonstrates the ability to provide molecular-level insights into vitrimer dynamics over a wide temperature range.

dynamic relaxation↗
DOE OSTI2024

Holistic Small-Signal Stability Analysis for Large-Scale Inverter-Intensive Power Systems with Coupled and Full-Order Dynamics from Control Systems and Power Networks

The increasing penetration of inverter-based resources (IBRs) into the existing power systems introduces tremendous benefits for enhanced sustainability but also poses inevitable challenges in terms of insufficient inertia, potential instability, and complex network dynamics, among others. However, the additional coupling introduced by the interactions among gridfollowing (GFL) and grid-forming (GFM) IBRs and the other components (i.e., synchronous generators [SGs], loads, and network, etc.) has not been clearly explored. A holistic, scalable, and quantitative stability analysis framework with the control systems and power networks is still missing. Here, in this paper, to fill in the technical gaps, a holistic small-signal model of the entire system with both rotating generation units and IBRs is established. An extended power flow model with operation dynamics from both generator control schemes and power networks is proposed to provide the varying steady-state operating points for small-signal modeling. The proposed method is compared with MATLAB solvers, and the results show that the proposed approach has a minimum calculation time, which can be less than 12 seconds for a large-scale power system with up to 2,000 buses. Furthermore, a quantitative method is developed to identify the impacts of IBRs on system performance with emphases on the potential stability issues with GFL IBRs, additional benefits of employing GFM IBRs, the feasibility of replacing SGs with GFM IBRs, and the impact of penetration level of different kinds of generation units. Finally, a field island power system is used to verify the proposed approach, and hardware-in-the-loop (HIL) tests are provided to further demonstrate the effectiveness of the proposed analysis.

14 SOLAR ENERGY↗
DOE OSTI2024

Unsupervised Learning for Equitable DER Control: Preprint

In the context of managing distributed energy resources (DERs) within distribution networks (DNs), this work focuses on the task of developing local controllers. We propose an unsupervised learning framework to train functions that can closely approximate optimal power flow (OPF) solutions. The primary aim is to establish specific conditions under which these learned functions can collectively guide the network towards desired configurations asymptotically, leveraging an incremental control approach. The flexibility of the proposed methodology allows to integrate fairness-driven components into the cost function associated with the OPF problem. This addition seeks to mitigate power curtailment disparities among DERs, thereby promoting equitable power injections across the network. To demonstrate the effectiveness of the proposed approach, power flow simulations are conducted using the IEEE 37-bus feeder. The findings not only showcase the guaranteed system stability but also underscore its improved overall performance.

asymptotic stability↗
DOE OSTI2024

Enhancing Network Anomaly Detection Using Graph Neural Networks

In the world of Internet of Things (IoT) networks, where devices are constantly communicating, keeping them secure from cyber threats is critical. This paper introduces a novel approach to detecting unusual and potentially harmful activities in these networks using graph neural networks (GNNs). We combine two specific types of GNNs-GraphSAGE and graph attention networks (GAT)-to create a model that understands and represents the behaviors and interactions in a network. GraphSAGE creates an embedding of network activities by examining local data interactions, while GAT directs the model's focus to the most critical interactions. By integrating these two methods in a single model that considers different types of interactions (both host and flow nodes), we aim to create a system that accurately represents the current state of a network and can also spot anomalies effectively while reducing false positives and negatives. Our innovative approach has demonstrated promising results, achieving an accuracy of 98% on the UNSW-NB15 dataset, significantly outperforming standalone GraphSAGE and GAT models. This underscores its potential as a robust framework for securing IoT networks against cyber threats and anomalies.

Marfo, William↗
DOE OSTI2018

An Optimization-Based Approach for Simultaneous Chemical Process and Heat Exchanger Network Synthesis

We propose a mixed-integer nonlinear programming (MINLP) model for simultaneous chemical process and heat exchanger network synthesis. The model allows process stream inlet/outlet temperatures and flow rates to vary and can be extended to handle unclassified streams, thereby facilitating integration with a process synthesis model. The proposed model is based on a generalized transshipment approach in which the heat cascade is built upon a “dynamic” temperature grid. Both hot and cold streams can cascade heat so that exchanger inlet and outlet temperature, heat duty, and area can be calculated at each temperature interval. Here, we develop mixed-integer constraints to model the number of heat exchangers in the network. Finally, we present several solution strategies tailored to improve the computation performance of the proposed models.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Volume Flow Rate Estimation for Small Explosions at Mt. Etna, Italy, From Acoustic Waveform Inversion

Rapid assessment of the volume and the rate at which gas and pyroclasts are injected into the atmosphere during volcanic explosions is key to effective eruption hazard mitigation. Here, we use data from a dense infrasound network deployed in 2017 on Mt. Etna, Italy, to estimate eruptive volume flow rates (VFRs) during small gas-and-ash explosions. We use a finite-difference time-domain approximation to compute the acoustic Green's functions and perform a full waveform inversion for a multipole source, combining monopole and horizontal dipole terms. The inversion produces realistic estimates of VFR, on the order of 4 × 10 4 m 3 /s and well-defined patterns of source directivity. This is the first application of acoustic waveform inversion at Mt. Etna. Our results demonstrate that acoustic waveform inversion is a mature and robust tool for assessment of source parameters and holds potential as a tool to provide rapid estimates of VFR in near real time.

58 GEOSCIENCES↗
NASA NTRS2009

Evaluation of IEEE 802.11g and 802.16 for Lunar Surface Exploration Missions Using MACHETE Simulations

In this paper, we investigated the suitability of terrestrial wireless networking technologies for lunar surface exploration missions. Specifically, the scenario we considered consisted of two teams of collaborating astronauts, one base station and one rover, where the base station and the rover have the capability of acting as relays. We focused on the evaluation of IEEE 802.11g and IEEE 802.16 protocols, simulating homogeneous 802.11g network, homogeneous 802.16 network, and heterogeneous network using both 802.11g and 802.16. A mix of traffic flows were simulated, including telemetry, caution and warning, voice, command and file transfer. Each traffic type had its own distribution profile, data volume, and priority. We analyzed the loss and delay trade-offs of these wireless protocols with various link-layer options. We observed that 802.16 network managed the channel better than an 802.11g network due to controlled infrastructure and centralized scheduling. However, due to the centralized scheduling, 802.16 also had a longer delay. The heterogeneous (hybrid) of 802.11/802.16 achieved a better balance of performance in terms of data loss and delay compared to using 802.11 or 802.16 alone.

Segui, John↗
DOE OSTI2022

Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series

Anomaly detection is a widely studied task for a broad variety of data types; among them, multiple time series appear frequently in applications, including for example, power grids and traffic networks. Detecting anomalies for multiple time series, however, is a challenging subject, owing to the intricate interdependencies among the constituent series. We hypothesize that anomalies occur in low density regions of a distribution and explore the use of normalizing flows for unsupervised anomaly detection, because of their superior quality in density estimation. Moreover, we propose a novel flow model by imposing a Bayesian network among constituent series. A Bayesian network is a directed acyclic graph (DAG) that models causal relationships; it factorizes the joint probability of the series into the product of easy-to-evaluate conditional probabilities. We call such a graph-augmented normalizing flow approach GANF and propose joint estimation of the DAG with flow parameters. We conduct extensive experiments on real-world datasets and demonstrate the effectiveness of GANF for density estimation, anomaly detection, and identification of time series distribution drift.

Dai, Enyan↗
DOE OSTI2016

Simulation of Hydraulic and Natural Fracture Interaction Using a Coupled DFN-DEM Model

The presence of natural fractures will usually result in a complex fracture network due to the interactions between hydraulic and natural fracture. The reactivation of natural fractures can generally provide additional flow paths from formation to wellbore which play a crucial role in improving the hydrocarbon recovery in these ultra-low permeability reservoir. Thus, accurate description of the geometry of discrete fractures and bedding is highly desired for accurate flow and production predictions. Compared to conventional continuum models that implicitly represent the discrete feature, Discrete Fracture Network (DFN) models could realistically model the connectivity of discontinuities at both reservoir scale and well scale. In this work, a new hybrid numerical model that couples Discrete Fracture Network (DFN) and Dual-Lattice Discrete Element Method (DL-DEM) is proposed to investigate the interaction between hydraulic fracture and natural fractures. Based on the proposed model, the effects of natural fracture orientation, density and injection properties on hydraulic-natural fractures interaction are investigated.

Discrete Fracture Network↗
DOE OSTI2017

The Application of Radon for Mapping Open Fracture Networks in a Thin Vadose Zone

The use of naturally occurring tracers to elucidate subsurface flow and transport of water and gasses through fractured vadose zones represents a powerful and inexpensive methodology for understanding geologic systems. Our study was conducted at Sheply’s Hill, a highly fractured granite highland at the old Fort Devens military base. This study evaluates the applicability of using cheap, readily available passive radon detectors to identify conductive structures (e.g., fractures and fracture networks) in fracture-dominated vadose zones. Radon data from this study provides strong evidence that, even within an intensively fractured system, a relatively smaller subset of the fractures is responsible for the majority of the vertical migration of gas and water that recharges contaminated groundwater sites. Our study found that zones of high recharge potential associated with open fracture networks could be identified based on the 222 Rn flux within the highly fractured Shepley’s Hill. Based on the results of this study, 222 Rn is an environmental tracer capable of efficiently detecting major bedrock fracture sets in the vadose zone responsible for water infiltration into contaminated aquifers. Results from the Shepley’s Hill study show a clear spatial correlation of elevated 222 Rn concentrations emanating from fracture zones previously identified by independent geologic studies. At Shepley’s Hill, the 222 Rn concentrations measured from detector locations directly above the major bedrock fractures, located within the Disc Golf Fracture Zone (DGFZ) and the Nona-Shep Fracture Zone (NSFZ), were almost exclusively the highest values measured in the study. Conversely, probes located above areas of less-fractured bedrock, although highly fractured, showed relatively low 222 Rn concentrations. Furthermore, these two observations provide strong supporting evidence that not all fractures are equal when it comes to their ability to transmit gas or water into and out of the subsurface.

58 GEOSCIENCES↗
DOE OSTI2018

Gene flow simulations demonstrate resistance of long-lived species to genetic erosion from habitat fragmentation

Habitat fragmentation restricts the movement of individuals across a landscape. In terrestrial and aquatic systems, barriers to movement can modify population and community dynamics at local or regional scales. This research contrasted life history traits related to lifespan with habitat fragmentation to determine impacts on species population genetic structure in the Neuse River Basin, USA. For this, we simulated gene flow among evenly-spaced populations in a river network and tracked individual and population genetics for 200 years. The modeled scenarios represent a full cross between five life history strategies and four riverscapes representing varying degrees of fragmentation. The five life history strategies include species (based on freshwater mussels) with average lifespans ranging from 10 to 50 years and age at maturity from 2 to 6 years. The movement landscapes included a (1) panmictic, (2) stepping-stone landscape allowing movement to only neighboring populations during each dispersal event, (3) partially-fragmented landscape divided by dams currently in the network, and (4) fully-fragmented landscape. Results suggest species with shorter lifespans have higher population genetic structure in fragmented landscapes than species with longer lifespans. Additionally, species with shorter lifespans in highly fragmented landscapes may be harboring genetic degradation or decline as allele fixation and loss. Although anthropogenic fragmentation of many river systems is only 100–200 years old, the simulation indicates that species can respond genetically in that period of time. Moreover, the time frame of the simulation suggests that genetic impacts of habitat fragmentation in some species present in the Neuse River Basin may not yet be manifesting and restoration activities could be successful.

13 HYDRO ENERGY↗
DOE OSTI2019

Scaling the impacts of pore-scale characteristics on unstable supercritical CO 2 -water drainage using a complete capillary number

Geological carbon storage in deep aquifers involves displacement of resident brine by supercritical CO 2 (scCO 2 ), which is an unstable drainage process caused by the invasion of less viscous scCO 2 . The unstable drainage is greatly complicated by aquifer heterogeneity and anisotropy and regarded as one of the key factors accounting for the uncertainty in storage capacity estimates. The impacts of pore-scale characteristics on the unstable drainage remain poorly understood. In this study, scCO 2 drainage experiments were conducted at 40 °C and 9 MPa using a homogeneous elliptical micromodel with low or high anisotropy, a homogeneous/isotropic hexagonal micromodel, and a heterogeneous sandstone-analog micromodel. Each initially water-saturated micromodel was invaded by scCO 2 at different rates with logC a (the capillary number) ranging from -7.6 to -4.4, and scCO 2 /water images were obtained. The measured CO 2 saturations in these centimeter-scale micromodels vary considerably from 0.08 to 0.93 depending on the pore-scale characteristics and capillary number. It was also observed that scCO 2 drainage follows the classic flow-regime transition from capillary fingering through crossover to viscous fingering for either of the low-anisotropy elliptical and heterogeneous micromodels, but with disparate crossover zones. The crossover zones of scCO 2 saturation were then unified with the minimum scCO 2 saturation occurring at logC$_a^*$ = -4.0 using the complete capillary number (C$_a^*$)that considers pore characteristics. For the hexagonal and the high-anisotropy elliptical micromodels, a monotonic increase in scCO 2 saturation with increasing C$_a^*$ (without crossover) was observed. It appears that the complete capillary number is more appropriate than the classic capillary number when characterizing flow regimes and CO 2 saturation in different pore networks.

58 GEOSCIENCES↗
DOE OSTI2018

Simultaneous Process and Heat Exchanger Network Synthesis Using a Discrete Temperature Grid

We present mixed-integer linear programming models for utility targeting and heat exchanger network synthesis that account for variable stream temperatures and flow rates as well as unclassified streams and thus can be combined with process synthesis optimization models. By projecting temperature information onto a fixed discrete temperature grid, the model remains linear even under variable temperatures and flow rates, leading to a more tractable optimization model. The proposed model is well suited to address problems in the area of process intensification where the close interaction between different phenomena naturally leads to process synthesis problems with unclassified process streams. Several extensions are presented, including nonisothermal mixing as well as phase changes. Moreover, we show how a nonuniform grid can be adopted to reduce the complexity of the model for medium and large-size problems.

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

PathTracer Comprehensively Identifies Hypoxia-Induced Dormancy Adaptations in Mycobacterium tuberculosis

Mining large-scale data to discover biologically relevant information remains a challenge despite the rapid development of bioinformatics tools. Here, we have developed a new tool, PathTracer, to identify biologically relevant information flows by mining genome-wide protein–protein interaction networks following integration of gene expression data. PathTracer successfully mines interactions between genes and traces the most perturbed paths of perceived activities under the conditions of the study. Here, we further demonstrated the utility of this tool by identifying adaptation mechanisms of hypoxia-induced dormancy in Mycobacterium tuberculosis (Mtb).

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

High-temperature high-pressure microfluidic system for rapid screening of supercritical CO 2 foaming agents

CO 2 foam helps to increase the viscosity of CO 2 flood fluid and thus improve the process efficiency of the anthropogenic greenhouse gas’s subsurface utilization and sequestration. Successful CO 2 foam formation mandates the development of high-performance chemicals at close to reservoir conditions, which in turn requires extensive laboratory tests and evaluations. This work demonstrates the utilization of a microfluidic reservoir analogue for rapid evaluation and screening of commercial surfactants (i.e., Cocamidopropyl Hydroxysultaine, Lauramidopropyl Betaine, Tallow Amine Ethoxylate, N,N,N' Trimethyl-N'-Tallow-1,3-diaminopropane, and Sodium Alpha Olefin Sulfonate) based on their performance to produce supercritical CO 2 foam at high salinity, temperature, and pressure conditions. The microfluidic analogue was designed to represent the pore sizes of the geologic reservoir rock and to operate at 100 °C and 13.8 MPa. Values of the pressure drop across the microfluidic analogue during flow of the CO 2 foam through its pore network was used to evaluate the strength of the generated foam and utilized only milliliters of liquid. The transparent microfluidic pore network allows in-situ quantitative visualization of CO 2 foam to calculate its half-life under static conditions while observing if there is any damage to the pore network due to precipitation and blockage. The microfluidic mobility reduction results agree with those of foam loop rheometer measurements, however, the microfluidic approach provided more accurate foam stability data to differentiate the foaming agent as compared with conventional balk testing. The results obtained here supports the utility of microfluidic systems for rapid screening of chemicals for carbon sequestration or enhanced oil recovery operations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗