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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2005

Quadrant CFD Analysis of a Mixer-Ejector Nozzle for HSCT Applications

This study investigates the sidewall effect on flow within the mixing duct downstream of a lobed mixer-ejector nozzle. Simulations which model only one half-chute width of the ejector array are compared with those which model one complete quadrant of the nozzle geometry and with available experimental data. These solutions demonstrate the applicability of the half-chute technique to model the flowfield far away from the sidewall and the necessity of a full-quadrant simulation to predict the formation of a low-energy flow region near the sidewall. The quadrant solutions are further examined to determine the cause of this low-energy region, which reduces the amount of mixing and lowers the thrust of the nozzle. Grid resolution and different grid topologies are also examined. Finally, an assessment of the half-chute and quadrant approaches is made to determine the ability of these simulations to provide qualitative and/or quantitative predictions for this type of complex flowfield.

Yoder, Dennis A.↗
DOE OSTI2025

Harnessing distributed GPU computing for generalizable graph convolutional networks in power grid reliability assessments

Although machine learning (ML) has emerged as a powerful tool for rapidly assessing grid contingencies, prior studies have largely considered a static grid topology in their analyses. This limits their application, since they need to be re-trained for every new topology. Here, this paper explores the development of generalizable graph convolutional network (GCN) models by pre-training them across a range of grid topologies and contingency types. We found that a GCN model with auto-regressive moving average (ARMA) layers with a line graph representation of the grid offered the best predictive performance in predicting voltage magnitudes (VM) and voltage angles (VA). We introduced the concept of phantom nodes to consider disparate grid topologies with a varying number of nodes and lines. For pre-training the GCN ARMA model across a variety of topologies, distributed graphics processing unit (GPU) computing afforded us significant training scalability. The predictive performance of this model on grid topologies that were part of the training data is substantially better than the direct current (DC) approximation. Although direct application of the pre-trained model to topologies that are not part of the grid is not particularly satisfactory, fine-tuning with small amounts of data from a specific topology of interest significantly improves predictive performance. In general, this paper highlights the feasibility of training large-scale GNN models to assess the reliability of power grids by considering a wide variety of grid topologies and contingency types. With the advent of foundational models in ML and the exponential increase in GPU computing clusters, generalizable ML models will significantly enhance how utilities manage power systems and make decisions in real-time or near-real-time.

24 - POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Grid Topology Discovery Algorithm Evaluation of Suitability for Utility Deployment (CRADA 606 Final Report)

This work presents the results of a field-informed demonstration aimed at evaluating the practical suitability of a topology discovery algorithm for utility environments. We demonstrated an algorithm that uses a graph-theory-informed state estimation approach for model selection. In collaboration with Survalent and Peninsula Light Co., the algorithm was applied to real feeder models and field measurements from supervisory control and data acquisition (SCADA) and advanced metering infrastructure (AMI) systems to identify the operational topology of a power distribution system. The demonstration assessed the algorithm’s performance under realistic data conditions, including sparse and noisy measurements, and examined its ability to identify the most likely network configurations. The results confirmed that the approach can effectively narrow down feasible topologies, providing operators with improved situational awareness of network status. Key lessons learned emphasize the need for systematic data validation and strategic sensor placement to enhance observability. These insights inform future deployment strategies and guide refinements for broader adoption in utility operations.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Optimal environmental and economic performance trade-offs for fifth generation district heating and cooling network topologies with waste heat recovery

Network topology greatly influences both the economic and environmental performance of fifth generation district heating and cooling (5GDHC) systems. In this study the optimal trade-offs between the environmental and economic performance of 5GDHC network topologies for a five-building district with waste heat recovery were explored. A life cycle assessment method was used to calculate the total life cycle CO 2 emissions (LCCO2) associated with the installation and operation of various network topologies. Twelve months of empirical data from a data center cooling system were analyzed to assess its suitability for integration into a 5GDHC system. The most suitable method for utilizing this waste heat was selected based on the ambient loop warm pipe setpoint, waste heat temperature, and district energy system configuration. A multi-objective optimization algorithm was used to select the 5GDHC network topology that provided the optimal trade-off between LCCO2 and life cycle cost (LCC). A trade-off parameter was employed to weigh the importance of each objective in the selection process. The results showed waste heat from the data center was suitable for integration into the 5GDHC system due to its availability and consistent temperature profile. When return temperatures of 25 °C or higher were available from the liquid-cooled system, direct pre-heating of the ambient loop warm pipe was found to be the most effective waste heat integration method. The selection of the network topology that provided the optimal trade-off between LCCO2 and LCC (optimal trade-off topology) was highly dependent on factors such as fuel prices, CO 2 prices, electricity CO 2 emissions factors, availability of waste heat, embodied CO 2 emissions associated with network installation and network infrastructure costs. Optimal trade-off topologies produced substantial LCCO2 reductions relative to corresponding LCC increases. LCCO2 reduction to LCC increase ratios from 5.78 to 117.79 were identified with CO 2 offset costs ranging from 4.77 to 60.08 ($/tCO 2 e).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2024

Scalable Risk Assessment of Rare Events in Power Systems With Uncertain Wind Generation and Loads

Risk assessment of rare events has become increasingly important in power system planning and operation with the increasing integration of renewable energy and the presence of system uncertainties. However, quantifying the risk posed by rare events via the traditional method, i.e., Monte Carlo sampling (MCS), incurs substantial computational expense stemming from the vast ensemble of power flow simulations. To accelerate the assessment, this paper proposes a Deep Neural Network (DNN)-kernelized vector-valued Gaussian Process (VVGP) approach with excellent computational efficiency while maintaining high accuracy. Consequently, serving as a surrogate model for the power flow solver, the DNN-kernelized VVGP enables significantly faster but accurate risk assessment compared to the power flow solver. The developed surrogate model evaluates low-order N - k events that contain more than 90% instances by adeptly capturing the topological features while the high-order N - k events are assessed via a power flow solver, thereby striking a balance between computational efficiency and uncertainty quantification accuracy. Moreover, the model incorporates a Support Vector Machine (SVM) classifier to resample concerning low-probability tail events to counteract the biases potentially introduced during the DNN-kernelized VVGP evaluations. Simulations conducted on the modified IEEE 24-bus, 118-bus, and European 1354-bus systems demonstrate that the proposed method maintains the accuracy benchmark set by MCS while significantly reducing computational demands in large-scale power systems as compared to other state-of-the-art methods.

17 WIND ENERGY↗
DOE OSTI2022

A Laplace-Domain Circuit Model for Fault and Stability Analysis Considering Unbalanced Topology

For systems subject to unbalanced faults, analytical model building for stability assessment is a challenging task. This letter presents a straightforward modeling approach. A generalized dynamic circuit representation is achieved by use of the Laplacian transform variable s . Here, we translate the voltage and current relationship at the fault location into the relationship of three subsystems. The final circuit model is an interconnected sequence network with impedances in the Laplace domain. This circuit can be directly converted from a steady-state sequence network. This modeling procedure is illustrated by an example case of an induction motor served by a grid through a series compensated line. Electromagnetic transient simulation results demonstrate that sub-synchronous oscillations can be mitigated when a single-line to ground fault is applied at the motor terminal. Stability analysis results based on the dynamic circuit corroborate the simulation results. What's more, the derived circuit effortlessly reveals why unbalance can enhance stability.

42 ENGINEERING↗
DOE OSTI2020

Risk Assessment of Rare Events in Probabilistic Power Flow via Hybrid Multi-Surrogate Method

The risks associated with rare events threatening the security of power system operation are of paramount importance to power system planners and operators. To analyze the risks caused by high-impact, low-frequency rare events, an immensely large number of samples are typically required for the Monte-Carlo (MC) method on the high-fidelity power system model to achieve a sufficient accuracy, thereby rendering this approach computationally prohibitive. To handle this problem efficiently, it is desirable to construct a surrogate model for the power system response. However, the straightforward MC sampling of the low-fidelity surrogate can lead to biased results in the low-probability tail regions that are vital to risk assessment. Moreover, a single surrogate is unable to handle the topology uncertainties caused by random branch outages. To overcome these issues, we propose a hybrid multi-surrogate (HMS) method based on the polynomial chaos expansion (PCE) with low-probability tail events reevaluated by the high-fidelity model through a probabilistic analysis. This method improves the computational efficiency of the MC method for rare-event risk assessment by leveraging multi-fidelity models while retaining the desired accuracy. Simulations conducted in three test systems verify the excellent performances of the HMS method.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

A Transferable Force Field for Predicting Adsorption and Diffusion of Water in Cationic Zeolites with Coupled Cluster Accuracy

We present a transferable force field for water in proton-exchanged, alkali (Li, Na, K, Rb, and Cs) metal-exchanged, and alkaline-earth (Mg, Ca, Sr, and Ba) metal-exchanged zeolites. The fitting methodology is based on adsorbate–adsorbent interaction energies obtained from periodic density functional theory calculations and corrected using the coupled-cluster method applied to small model clusters. To ensure an accurate prediction of both adsorption and diffusion properties of water, sets of configurations that sample both adsorption sites and intracrystalline hopping transition states were used in the fitting. The quality of the force field is assessed for a wide range of zeolites with different topologies and chemical compositions, demonstrating good agreement between theoretical predictions and experimental measurements of water adsorption and diffusion.

Adsorption↗
DOE OSTI2023

Experimental Investigation of Thermodynamic Stabilization in Boron Imidazolate Frameworks (BIFs) Synthesized by Mechanochemistry

This study experimentally explores the energetics for the formation of boron–imidazolate frameworks (BIFs), which are synthesized by mechanochemistry. The topologically similar frameworks employ the same tetratopic linker based on tetrakis(imidazolyl)boric acid but differ in the monovalent cation metal nodes. This permits assessment of the stabilizing effect of metal nodes in frameworks with sodalite (SOD) and diamondoid (dia) topologies. The enthalpy of formation from endmembers (metal oxide and linker), which define thermodynamic stability of the structures, has been determined by use of acid solution calorimetry. The results show that heavier metal atoms in the node promote greater energetic stabilization of denser structures. Overall, in BIFs the relation between cation descriptors (ionic radius and electronegativity) and thermodynamic stability depends on framework topology. Thermodynamic stability increases with the metallic character of the cation employed as the metal node, independent of the framework topology. In conclusion, the results suggest unifying aspects for thermodynamic stabilization across MOF systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1984

Photovoltaic central-station power conditioner subsystems

The potential of applying new advanced circuit topologies and semiconductors with improved switching characteristics to the design and development of central-station power conditioning subsystems is assessed. State-of-the-art large power conditioners suitable for photovoltaic central-station application are reviewed. Advanced topology power conditioning subsystems, when compared to available production hardware, promise improved performance, significant cost reduction, and lower cost-to-efficiency ratios.

Krauthamer, S.↗
DOE OSTI2022

Improving Solar and Solar+Storage Screening Techniques to Reduce Utility Interconnection Time and Costs (Final Technical Report)

Residential PV installations have increased rapidly over the last decade, and the increased application volume has caused permitting delays and lower overall adoption rates. In this project, we developed and evaluated whether data-driven secondary modeling and screening techniques can help utilities assess customer applications more accurately than traditional screening shortcuts. Secondary topologies are predicted using decision trees and commonly available information, such as service transformer, customer, and street locations. Conductors were predicted using a logistic regression method based on real world object (RWO) types, service transformer ratings, conductor length, and distance to transformer. After developing the combined primary and secondary distribution network model, hosting capacity results were used to train a random forest model to predict the pass/fail likelihood of a customer application. Powerflow based models with predicted secondaries and data-driven methods both increased the screening success rate, relative to common utility heuristics, by as much as 55 percentage points. Data-driven screening techniques were described by one utility as a "right-sized" approach for residential customers given the low-risk of small errors and the high-cost of accurate modeling.

14 SOLAR ENERGY↗
NASA NTRS2001

Considerations and Architectures for Inter-Satellite Communications in Distributed Spacecraft Systems

This paper will identify the important characteristics and requirements necessary for inter-satellite communications in distributed spacecraft systems and present analysis results focusing on architectural and protocol comparisons. Emerging spacecraft systems plan to deploy multiple satellites in various "distributed" configurations ranging from close proximity formation flying to widely separated constellations. Distributed spacecraft configurations provide advantages for science exploration and operations since many activities useful for missions may be better served by distributing them between spacecraft. For example, many scientific observations can be enhanced through spatially separated platforms, such as for deep space interferometry. operating multiple distributed spacecraft as a mission requires coordination that may be best provided through inter-satellite communications. For example, several future distributed spacecraft systems envision autonomous operations requiring relative navigational calculations and coordinated attitude and position corrections. To conduct these operations, data must be exchanged between spacecraft. Direct cross-links between satellites provides an efficient and practical method for transferring data and commands. Unlike existing "bent-pipe" relay networks supporting space missions, no standard or widely-used method exists for cross-link communications. Consequently, to support these future missions, the characteristics necessary for inter-satellite communications need to be examined. At first glance, all of the missions look extremely different. Some missions call for tens to hundreds of nano-satellites in constant communications in close proximity to each other. Other missions call for a handful of satellites communicating very slowly over thousands to hundreds of thousands of kilometers. The paper will first classify distributed spacecraft missions to help guide the evaluation and definition of cross-link architectures and approaches. Based on this general classification, the paper will examine general physical layer parameters, such as frequency bands and data rates, necessary to support the missions. The paper will also identify classes of communication architectures that may be employed, ranging from fully distributed to centralized topologies. Numerous factors, such as number of spacecraft, must be evaluated when attempting to pick a communications architecture. Also important is the stability of the formation from a communications standpoint. For example, do all of the spacecraft require equal bandwidth and are spacecraft allowed to enter and leave a formation? The type of science mission being attempted may also heavily influence the communications architecture. In addition, the paper will assess various parameters and characteristics typically associated with the data link layer. The paper will analyze the performance of various multiple access techniques given the operational scenario, requirements, and communication topologies envisioned for missions. This assessment will also include a survey of existing standards and their applicability for distributed spacecraft systems. An important consideration includes the interoperability of the lower layers (physical and data link) examined in this paper with the higher layer protocols(network) envisioned for future space internetworking. Finally, the paper will define a suggested path, including preliminary recommendations, for defining and developing a standard for intersatellite communications based on the classes of distributed spacecraft missions and analysis results.

Edwards, Bernard↗
DOE OSTI2020

Investigating the Interaction Between Circulating Tumor Cells and Local Hydrodynamics via Experiment and Simulations

Introduction: The biological and mechanical properties of circulating tumor cells (CTCs) in combination with the hemodynamics affect the preference of metastatic sites in the vasculature. Despite the extensive literature on the effects of biological properties on cell adhesion, the effects of hydrodynamic forces on primary attachment remains an active area of research. Here, using simulations in conjunction with experimentation, we provide new insight into the interplay of CTCs dynamics and local hydrodynamics. Methods: A flow experiment of CTC attachment was performed within a bioprinted, double branching endothelialized vessel. Simulations of fluid flow and CTC transport in the reconstructed and idealized bifurcated vessel were respectively performed by HARVEY, our in-house massively parallel computational fluid dynamics solver. HARVEY is based on the lattice Boltzmann and finite element methods to model the fluid and cells dynamics. The immersed boundary method is employed for resolving the fluid–structure interaction. Results: CTC attachment was quantified experimentally at all regions of the complex vessel. The results demonstrate a clear preference for CTCs to attach at the branch points. To elucidate the effect of the vessel topology on the location of attachment, a fluid-only simulation was performed assessing the differences in the hydrodynamics along the vessel. CTC transport in idealized bifurcated vessels was subsequently studied to examine the effects of cell deformability on the local hydrodynamics patterns and, thus, the preference of attachment sites. Conclusions: The current work provides evidence on the correlation of the hydrodynamics forces arising from the vessel topology and CTC properties on the attachment regions.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Evaluating State Space Discovery by Persistent Cohomology in the Spatial Representation System

Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Grid, head direction, and conjunctive cell populations each span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures for different dataset dimensions, variations in spatial tuning, and forms of noise. We quantify its ability to decode simulated animal trajectories contained within these topological structures. We also identify regimes under which mixtures of populations form product topologies that can be detected. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Dendritic cells focus CTL responses toward highly conserved and topologically important HIV-1 epitopes

During early HIV-1 infection, immunodominant T cell responses to highly variable epitopes lead to the establishment of immune escape virus variants. Here we assessed a type 1-polarized monocyte-derived dendritic cell (MDC1)-based approach to selectively elicit cytotoxic T lymphocyte (CTL) responses against highly conserved and topologically important HIV-1 epitopes in HIV-1-infected individuals from the Thailand RV254/SEARCH 010 cohort who initiated antiretroviral therapy (ART) during early infection (Fiebig stages I-IV). Autologous MDC1 were used as antigen presenting cells to induce in vitro CTL responses against HIV-1 Gag, Pol, Env, and Nef as determined by flow cytometry and ELISpot assay. Ultra-conserved or topologically important antigens were respectively identified using the Epigraph tool and a structure-based network analysis approach and compared to overlapping peptides spanning the Gag proteome. MDC1 presenting either the overlapping Gag, Epigraph, or Network 14-21mer peptide pools consistently activated and expanded HIV-1-specific T cells to epitopes identified at the 9-13mer peptide level. Interestingly, some CTL responses occurred outside known or expected HLA associations, providing evidence of new HLA-associated CTL epitopes. Comparative analyses demonstrated more sequence conservation among Epigraph antigens but a higher magnitude of CTL responses to Network and Gag peptide groups. Importantly, CTL responses against topologically constrained Gag epitopes contained in both the Network and Gag peptide pools were selectively enhanced in the Network pool-initiated cultures. Our study supports the use of MDC1 as a therapeutic strategy to induce and focus CTL responses toward putative fitness-constrained regions of HIV-1 to prevent immune escape and control HIV-1 infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Harnessing Particle Size‐Control and DNA‐Oligo Functionalization in ZIF‐76 for Biological Applications

Abstract Advanced therapeutics require novel nanocarriers to ensure their functionality is preserved during transit. Zeolitic imidazolate frameworks (ZIFs) have emerged as promising materials in this field owing to their combined biocompatibility, high porosity, and tunable chemistry. While a diverse family of ZIFs has been reported, few have been explored beyond the prototypical ZIF‐8. Herein, the size‐controlled synthesis of three distinct ZIF‐76 analogs is demonstrated, overcoming the unique synthetic challenges intrinsic to the lta topology and complex crystallization kinetics associated with the mixed linker approach. This assesses the materials’ platform effectiveness for intracellular delivery first by exploring the structural and colloidal stability in biologically relevant media. To circumvent particle aggregation, fluorescently labeled DNA oligonucleotides are post‐synthetically attached to the ZIF surface. This modification significantly improves the colloidal stability in media and facilitates particle internalization tracking. Finally, the particle‐cell interactions are assessed, revealing rapid cell membrane association with macrophages, but not lung epithelial cells, and ZIF accumulation within macrophages which increased over time. Importantly, this study outlines a generalized approach toward expanding the available library of ZIFs for biological applications, enabling the potential for targeted therapeutic delivery for intracellular infections treatment.

Deneff, Jacob I.↗
DOE OSTI2024

Altered cortical thickness-based structural covariance networks in type 2 diabetes mellitus

Cognitive impairment is a common complication of type 2 diabetes mellitus (T2DM), and early cognitive dysfunction may be associated with abnormal changes in the cerebral cortex. This retrospective study aimed to investigate the cortical thickness-based structural topological network changes in T2DM patients without mild cognitive impairment (MCI). Fifty-six T2DM patients and 59 healthy controls underwent neuropsychological assessments and sagittal 3-dimensional T1-weighted structural magnetic resonance imaging. Then, we combined cortical thickness-based assessments with graph theoretical analysis to explore the abnormalities in structural covariance networks in T2DM patients. Correlation analyses were performed to investigate the relationship between the altered topological parameters and cognitive/clinical variables. T2DM patients exhibited significantly lower clustering coefficient (C) and local efficiency (Elocal) values and showed nodal property disorders in the occipital cortical, inferior temporal, and inferior frontal regions, the precuneus, and the precentral and insular gyri. Moreover, the structural topological network changes in multiple nodes were correlated with the findings of neuropsychological tests in T2DM patients. Thus, while T2DM patients without MCI showed a relatively normal global network, the local topological organization of the structural network was disordered. Moreover, the impaired ventral visual pathway may be involved in the neural mechanism of visual cognitive impairment in T2DM patients. This study enriched the characteristics of gray matter structure changes in early cognitive dysfunction in T2DM patients.

Huang, Yang↗