SEARCH · Engineering Papers

Results for “complex network”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,765 records · Page 98

All sources NASA NTRS DOE OSTI

DOE OSTI2024

Carbodiimide‐Driven Toughening of Interpenetrated Polymer Networks

Abstract Recent work has demonstrated that temporary crosslinks in polymer networks generated by chemical “fuels” afford materials with large, transient changes in their mechanical properties. This can be accomplished in carboxylic‐acid‐functionalized polymer hydrogels using carbodiimides, which generate anhydride crosslinks with lifetimes on the order of minutes to hours. Here, the impact of the polymer network architecture on the mechanical properties of transiently crosslinked materials was explored. Single networks (SNs) were compared to interpenetrated networks (IPNs). Notably, semi‐IPN precursors that give IPNs on treatment with carbodiimide give much higher fracture energies (i.e., resistance to fracture) and superior resistance to compressive strain compared to other network architectures. A precursor semi‐IPN material featuring acrylic acid in only the free polymer chains yields, on treatment with carbodiimide, an IPN with a fracture energy of 2400 J/m 2 , a fourfold increase compared to an analogous semi‐IPN precursor that yields a SN. This resistance to fracture enables the formation of macroscopic complex cut patterns, even at high strain, underscoring the pivotal role of polymer architecture in mechanical performance.

Rajawasam, Chamoni W. H.↗

DOE OSTI2024

Carbodiimide‐Driven Toughening of Interpenetrated Polymer Networks

Rajawasam, Chamoni W. H.↗

DOE OSTI2021

The deep-DRT: A deep neural network approach to deconvolve the distribution of relaxation times from multidimensional electrochemical impedance spectroscopy data

Electrochemical impedance spectroscopy (EIS) is an experimental technique ubiquitously used to study electrochemical systems. However, conventional EIS data interpretation through physical and equivalent circuit models is challenging because physical models are problem-specific, and equivalent circuits are often just lumped-element analogs lacking physical meaning. The distribution of relaxation times (DRT) has emerged as a complementary approach to resolve these issues. One drawback of conventional DRT deconvolution is that the EIS data is understood to be (only) a function of frequency (i.e. 1D data) and deconvolved accordingly. This work proposes a novel deconvolution method based on deep neural networks (DNNs), allowing the analysis of multidimensional EIS spectra to bridge data dependency on both frequencies and experimental conditions. Two particularly appealing traits of the deep-DRT method developed in this article are that neither regularization nor specific spacing on the state variables defining the experiment are required. Finally, leveraging DNN to examine complex EIS spectra and their dependence on experimental conditions, this work opens a new research direction in the area of EIS analysis and DRT deconvolution.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2025

Stochastic 3D reconstruction of cracked polycrystalline NMC particles using 2D SEM data

Li-ion battery performance is strongly influenced by the 3D microstructure of its cathode particles. Cracks within these particles develop during calendaring and cycling, reducing connectivity but increasing reactive surface, making their impact on battery performance complex. Understanding these contradictory effects requires a quantitative link between particle morphology and battery performance. However, informative 3D imaging techniques are time-consuming, costly and rarely available, such that analyses often have to rely on 2D image data. This paper presents a novel stereological approach for generating virtual 3D cathode particles exhibiting crack networks that are statistically equivalent to those observed in 2D sections of experimentally measured particles. Consequently, 2D image data suffices for deriving a full 3D characterization of cracked cathodes particles. Such virtually generated 3D particles could serve as geometry input for spatially resolved electro-chemo-mechanical simulations to enhance our understanding of structure-property relationships of cathodes in Li-ion batteries.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Quantized electronic transitions in electrodeposited copper indium selenide nanocrystalline homojunctions

Pairing semiconductors with electrochemical processing offers an untapped opportunity to create novel nanostructures for practical devices. Here we report the results of one such pairing: the in-situ formation of highly-doped, interface-matched, sharp nanocrystalline homojunctions (NHJs) with single step electrodeposition of two copper-indium-selenide (CISe) compounds on flexible foil. It produces a homogenous film, comprising inherently ordered, 3-dimensional interconnected network of pn -CISe NHJs. These CISe NHJs exhibit surprising non-linear emissions, quantized transitions, large carrier mobility, low trap-state-density, long carrier lifetime and possible up-conversion. They facilitate efficient separation of minority carriers, reduce recombination and essentially function like quantum materials. This approach mitigates the material issues and complex fabrication of incumbent nanoscale heterojunctions; it also overcomes the flexibility and scale-up challenges of conventional planar pn junctions. The self-stabilized CISe NHJ film can be roll-to-roll processed in ambient atmosphere, thus providing a promising platform for a range of optoelectronic technologies. This concept exemplified by CISe compounds can be adapted to create nano-scale pn junctions with other inorganic semiconductors.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Insights on seasonal solifluction processes in warm permafrost Arctic landscape using a dense monitoring approach across adjacent hillslopes

Solifluction processes in the Arctic are highly complex, introducing uncertainties in estimating current and future soil carbon storage and fluxes, and assessment of hillslope and infrastructure stability. This study aims to enhance our understanding of triggers and drivers of soil movement of permafrost-affected hillslopes in the Arctic. To achieve this, we established an extensive soil deformation and temperature sensor network, covering 48 locations across multiple hillslopes within a 1 km² watershed on the Seward Peninsula, AK. We report depth-resolved measurements down to 1.8 m depth for May to September 2022, a period conducive to soil movement due to deepening thaw layers and frequent rain events. Over this period, surface movements of up to 334 mm were recorded. In general, these movements occur close to the thawing front, and are initiated as thawing reaches depths of 0.4 to 0.75 m. The largest movements were observed at the top of the south-east facing slope, where soil temperatures are cold (mean annual soil temperatures averaging -1.13°C) and slopes are steeper than 15°. Our analysis highlights three primary factors influencing movements: slope angle, soil thermal conditions, and thaw depth. The latter two significantly impact the generation of pore water pressures at the thaw–freeze interface. Specifically, soil thermal conditions govern the liquid water content, while thaw depth influences both the height of the water column and, consequently, the pressure at the thawing front. These factors affect soil properties, such as cohesion and internal friction angle, which are crucial determinants of slope stability. This underscores the significance of a precise understanding of subsurface thermal conditions, including spatial and temporal variability in soil temperature and thaw depth, when assessing and predicting slope instabilities. Based on our observations, we developed a Factor of Safety proxy that consistently falls below the triggering threshold for all probes exhibiting displacements exceeding 50 mm. This study offers novel insights into patterns and triggers of hillslope movements in the Arctic and provides a venue to evaluate their impact on soil redistribution.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Characterizing the gene–environment interaction underlying natural morphological variation in Neurospora crassa conidiophores using high-throughput phenomics and transcriptomics

Abstract Neurospora crassa propagates through dissemination of conidia, which develop through specialized structures called conidiophores. Recent work has identified striking variation in conidiophore morphology, using a wild population collection from Louisiana, United States of America to classify 3 distinct phenotypes: Wild-Type, Wrap, and Bulky. Little is known about the impact of these phenotypes on sporulation or germination later in the N. crassa life cycle, or about the genetic variation that underlies them. In this study, we show that conidiophore morphology likely affects colonization capacity of wild N. crassa isolates through both sporulation distance and germination on different carbon sources. We generated and crossed homokaryotic strains belonging to each phenotypic group to more robustly fit a model for and estimate heritability of the complex trait, conidiophore architecture. Our fitted model suggests at least 3 genes and 2 epistatic interactions contribute to conidiophore phenotype, which has an estimated heritability of 0.47. To uncover genes contributing to these phenotypes, we performed RNA-sequencing on mycelia and conidiophores of strains representing each of the 3 phenotypes. Our results show that the Bulky strain had a distinct transcriptional profile from that of Wild-Type and Wrap, exhibiting differential expression patterns in clock-controlled genes (ccgs), the conidiation-specific gene con-6, and genes implicated in metabolism and communication. Combined, these results present novel ecological impacts of and differential gene expression underlying natural conidiophore morphological variation, a complex trait that has not yet been thoroughly explored.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Characterizing the Evolution of Trapped scCO 2 Curvature in Bentheimer and Nugget Sandstone Pore Geometry

During a Geologic Carbon Storage process, supercritical CO 2 (scCO 2 ) is subjected to a series of dynamic and static conditions where the relationship between pore geometry and the trapped scCO 2 curvature remains to be established. To mimic the dynamic process, two sandstones, Bentheimer and Nugget, were subjected to two successive drainage and imbibition (D-I) cycles and X-ray computed tomography scanned at each residual state to capture the wettability evolution at static conditions in the same pore geometry. Both sandstones contain similar grain size distributions, pore size distributions, and pore interconnectivity but differ in that the Nugget formation contains approximately half the porosity of the Bentheimer sandstone, and the pore network contains dead-end pores. scCO 2 size distributions, strain calculations, and geometric contact angle measurements were used to characterize the curvature of scCO 2 in different pore types between cycles. An increase in geometric contact angle was the greatest when advancement along the pore network of the same ganglion occurred between cycles while strain increased the most with pore-filling trapping. Moreover, Nugget sandstone results in a greater aggregated residual saturation and shows a clear increase in scCO 2 sizes with an additional D-I cycle while scCO 2 in the Bentheimer core shows a more complex response with some ganglion increasing and some decreasing in size with an additional D-I cycle. From this work, we suspect the pore geometry is playing a role in scCO 2 size distributions and use this information to suggest using water pulses to enhance trapping capacity in lower porosity sandstones.

58 GEOSCIENCES↗

DOE OSTI2020

Hyper Parameter Tuning in Neural Optical Image Categorizer for the E-log (NOICE

The Fermilab Accelerator Division Electronic logbook (E-log) is a record of all the activities and events in the Division for the past 10 years and more. The Elog search function is a valuable resource and the institutional memory of the accelerator complex. About 300,000 files are stored in the E-log, of which the vast majority are images attached to entries and comments. The visual information contained in the images is not presently searchable. The goal of team NOICE (Neural Optical Image Categorizer for the E-log) was to design a neural network able to produce label categories for these images for use by searches. The group developed a dataset and trained a convolutional neural network (CNN) with optimized hyperparameter. Final performance metrics are presented.

Njekeu, Terence Franck↗

DOE OSTI2022

Multi-state Catalysts Modulated by Mechanical Force (Final Report)

The development of more efficient catalytic processes and new approaches to control catalytic activity and selectivity are central to the realization of more selective, atom economic, and energy efficient routes to value added chemicals and polymers. The reactivity and selectivity of a transition metal catalyst is intimately related to the ligand-sphere geometry and, in many cases, the ideal ligand geometry for one step of a catalytic cycle is poorly matched to the ideal ligand geometry for another, resulting in sub-optimal efficiency. Macroscopic mechanical forces are both large, potentially much larger than interatomic forces, and are directional and localized to an extent that differentiates them from other forms of energy input such as heat or light. As such, mechanical force represents a heretofore untapped approach to modulate catalyst geometry, with the potential to reversibly modulate catalyst geometry on the timescale of catalytic turnover or monomer enchainment. This project has addressed the fundamental challenges in material-to-molecule strain coupling associated with the development of a new class of mechanically responsive catalysts (mechanocatalysts) in which active organotransition metal catalysts are strategically embedded in a flexible polymer network such that application of external mechanical force (stretching or deformation) leads to modulation of catalyst geometry, and hence reactivity and selectivity. Our efforts during the tenure of this grant were directed toward the elucidation of force-reactivity relationships of elementary transformations that occur within the first coordination sphere of a transition metal complex employing stiff stilbene photoswitches tethered to a flexible bidentate phosphine ligand derived from MeOBiphep as molecular force probes which provide a range of compressive and extension forces to the coupled transition metal complex depending on the geometry of the stiff stilbene and length of the tethering chains. During the tenure of this grant, we have quantified the rate of C(sp 2 )-C(sp 2 ) reductive elimination from platinum(II) diaryl complexes containing bis(phosphine) force probe ligands as a function of mechanical force; compressive forces decreased the rate of reductive elimination whereas extension forces increased the rate relative to the strain-free MeOBiphep complex with a 3.4-fold change in rate over a ~290 pN range of restoring forces. In a similar manner, we have quantified the rate of oxidative addition of bromobenzene to low-ligated palladium(0) complexes containing force probe ligands as a function of mechanical force; compressive forces increase the rate of oxidative addition, whereas tensile forces decrease the rate with a ~6 fold change in rate across ~340 pN of force applied to the complexes. In both cases, experimental and computational analyses argue strongly against any significant force-induced perturbation of ground state geometry within the first coordination sphere of the reactant complexes. Rather, the force/rate behavior observed for these transformations across these ranges of forces is attributed to the coupling of force to the nuclear motion comprising the reaction coordinates for reductive elimination and oxidative addition. These results together inform the development of catalysts whose activity can be tuned by an external force that is adjusted within a catalytic cycle and suggest opportunities to experimentally map geometry changes associated with reactions in transition metal complexes and potential strategies for force-modulated catalysis.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2022

Linking climate change and human systems: a case study of Arctic pipelines

This case study estimates the potential economic risk from permafrost thaw on oil and gas pipelines in the Russian Arctic as part of a larger effort to better understand complex interactions between human and earth systems in the Arctic. Pan-Arctic simulations of permafrost thaw-depth from the Community Land Model version 4.5 and ground ice characteristics were used to generate ground subsidence projections over the period 2020 to 2040 with a quantification of uncertainty. Russian oil and gas transmission pipeline networks were then overlaid on the permafrost thaw projections in ArcGIS to identify pipelines vulnerable to damage from permafrost thaw. Recent pipeline construction costs were used to estimate the total replacement costs for at-risk pipelines under several thaw scenarios. The results indicate that permafrost thaw poses a major threat to pipeline infrastructure, especially gas pipelines, in the Russian Arctic. Over the twenty-year study period, total replacement costs for oil and gas pipelines were estimated at $\$$110 billion in 2020 USD. Reduced economic viability of pipelines under climate change will likely trigger major shifts in the Russian oil and gas industry, which would have impacts on markets, emissions, and geopolitics.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2020

(NOICE) Neural Optical Image Categorizer for the E-log

The Fermilab Accelerator Division Electronic logbook (E-log) is a record of all the activities and events in the Division for the past 10 years and more. The E-log search function is a valuable resource and the institutional memory of the accelerator complex. About 300,000 files are stored in the E-log, of which the vast majority are images attached to entries and comments. The visual information contained in the images is not presently searchable. The goal of team NOICE (Neural Optical Image Categorizer for the E-log) was to design a neural network able to produce label categories for these images for use by searches. The group developed a dataset and trained a convolutional neural network (CNN) with optimized hyperparameter

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

Data augmentation for disruption prediction via robust surrogate models

The goal of this work is to generate large statistically representative data sets to train machine learning models for disruption prediction provided by data from few existing discharges. Such a comprehensive training database is important to achieve satisfying and reliable prediction results in artificial neural network classifiers. Here, we aim for a robust augmentation of the training database for multivariate time series data using Student t process regression. We apply Student t process regression in a state space formulation via Bayesian filtering to tackle challenges imposed by outliers and noise in the training data set and to reduce the computational complexity. Thus, the method can also be used if the time resolution is high. We use an uncorrelated model for each dimension and impose correlations afterwards via colouring transformations. We demonstrate the efficacy of our approach on plasma diagnostics data of three different disruption classes from the DIII-D tokamak. To evaluate if the distribution of the generated data is similar to the training data, we additionally perform statistical analyses using methods from time series analysis, descriptive statistics and classic machine learning clustering algorithms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Sound propagation in realistic interactive 3D scenes with parameterized sources using deep neural operators

We address the challenge of acoustic simulations in three-dimensional (3D) virtual rooms with parametric source positions, which have applications in virtual/augmented reality, game audio, and spatial computing. The wave equation can fully describe wave phenomena such as diffraction and interference. However, conventional numerical discretization methods are computationally expensive when simulating hundreds of source and receiver positions, making simulations with parametric source positions impractical. To overcome this limitation, we propose using deep operator networks to approximate linear wave-equation operators. This enables the rapid prediction of sound propagation in realistic 3D acoustic scenes with parametric source positions, achieving millisecond-scale computations. By learning a compact surrogate model, we avoid the offline calculation and storage of impulse responses for all relevant source/listener pairs. Our experiments, including various complex scene geometries, show good agreement with reference solutions, with root mean squared errors ranging from 0.02 to 0.10 Pa. Notably, our method signifies a paradigm shift as—to our knowledge—no prior machine learning approach has achieved precise predictions of complete wave fields within realistic domains.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2020

Adaptive Anomaly Detection for Dynamic Clinical Event Sequences

Over the past decade, health information technology (IT) has enabled the amount of digital information stored in electronic health records (EHRs) to expand greatly. However, according to some studies, hazards in health IT can lead to changes in clinical decisions, care processes, and care outcomes, as well as other issues. Thus, the effects of health IT hazards on patient safety have been at the forefront of recent patient safety research. Nonetheless, hazard detection in health IT remains a challenge. In this paper, the authors assume that safety-related issues in health IT would exhibit anomalous characteristics in EHR data. Although all hazards will exhibit some anomalous characteristics, not all anomalies can be regarded as hazards. The authors hypothesize that errors in health IT could lead to interruptions in the sequence of clinical actions. To this end, the problem of detecting anomalous sequences in big EHR data is considered. This paper focuses on dynamic event sequences, which are a series of clinical actions in motion. The authors propose an adaptive anomaly detection approach that uses higher-order network representation to detect anomalous sequences. Furthermore, the authors propose a contiguous subsequence anomaly detection approach that identifies abnormal subsequences in the detected anomalous sequences. The proposed approaches are tested by using synthetic and real-world EHR data. The proposed methods outperform existing state of the art anomaly detection techniques. To reduce the computational complexity associated with the operational implementation of the proposed approaches, the Apache Spark environment was leveraged, and a much shorter run time together with improved performance were achieved, especially for data with more than 60,000 sequences.

Niu, Haoran↗

DOE OSTI2022

Latent Space Simulation for Carbon Capture Design Optimization

The CO2 capture efficiency in solvent-based carbon capture systems (CCSs) critically depends on the gas-solvent interfacial area (IA), making maximization of IA a foundational challenge in CCS design. While the IA associated with a particular CCS design can be estimated via a computational fluid dynamics (CFD) simulation, using CFD to derive the IAs associated with numerous CCS designs is prohibitively costly. However, previous works such as Deep Fluids (Kim et al., 2019) show that large simulation speed-ups and low error are achievable by replacing CFD simulators with neural-network (NN) surrogates that mimic the CFD simulation process. This raises the possibility for a fast, accurate replacement for a CFD simulator, and thus computationally feasible IA-based CCS-design optimization. As such, here, we explore whether an existing NN-surrogate approach (and variants we develop) can successfully be applied to our complex carbon-capture CFD simulations, with the ultimate goal of obtaining a fast and accurate simulator for our CCS-design application. Our experiments build on the Deep Fluids approach and find that resulting surrogates can produce large speed ups (4000x) while maintaining IA relative errors as low as 4% on unseen CCS configurations (interpolating between configurations seen during training). Thus, despite less faithfulness to the underlying physics of the problem, NN surrogates may be a promising tool for our CCS design optimization problem. Notably, though, the Deep Fluids approach has limitations for our application (such as model non-transferability to CCS-packing changes), which we discuss. We conclude with potential directions for future work that may, like innovations we introduced here (e.g., transformer-based dynamics prediction), improve performance on our complicated dataset of CCS CFD simulations.

AI Surrogate Simulation, Carbon Capture, design op↗

DOE OSTI2026

Generalizing synthetic data-trained acoustic predictive models to real-world measurements

Acoustic Resonance Spectroscopy (ARS) is highly sensitive to structural properties such as material, geometry, and environmental conditions; as a consequence, it can noninvasively measure internal properties that are unobservable by most other methods. Because of its sensing capabilities and low implementation cost and complexity, ARS has potential as a paradigm shift in noninvasive sensing, characterization, and monitoring applications. However, extracting specific properties from ARS measurements, comprising the vibration spectrum of a test object, is challenging due to the sensitivity of the spectra to other structural changes not being measured, e.g. manufacturing tolerances, component coupling, environmental variation, etc. Neural Networks are promising tools for identifying trends in ARS measurements, but their training typically requires large datasets, which are often impractical to obtain for real-world systems. Synthetic data can be simulated efficiently, but discrepancies between synthetic and real-world data frequently lead to poor generalization when testing on the real-world data. We propose a novel ARS model training framework that enables networks trained exclusively on synthetic ARS data to generalize effectively to real-world measurements. Our approach leverages the Correlation Alignment (CORAL) technique to enforce the extraction of features common to both synthetic and real-world domains. As a case study, we demonstrate noninvasive ARS-based pressure measurements in sealed systems. Finite element method (FEM) simulations were used to generate synthetic training data across diverse vessel configurations and pressure conditions, and model performance was then tested on real-world measurements. We demonstrate that robust machine learning models for ARS can be developed without large real-world datasets, significantly broadening the applicability of ARS for noninvasive sensing. Moreover, the approach is extensible to other sensing modalities where synthetic data are abundant but real-world data are limited.

36 MATERIALS SCIENCE↗

DOE OSTI2026

SuperLab 2.0 Showcase: Connecting Five Labs to Tackle Grid Complexity and Unlock Unique Grid Asset Potential

SuperLab 2.0 (5-Lab Demo) is a collaborative, national-scale experiment showcasing the coordination of geographically distributed energy assets in real time. The demonstration integrates 25 physical and digital assets, spanning wind, PV, batteries, electrolyzers, DC fast chargers, microgrid controllers, building automation systems, small modular reactor (SMR), control centers, and gas turbines, across five DOE national laboratories-NLR, INL, NETL, LBNL, and SNL. These assets are unified using Energy Sciences Network (ESnet), a low-latency, high-performance U.S. Department of Energy's (DOE) network, and controlled via a centralized energy controller hosted at NLR's ARIES facility. The demonstration validates the ability to stress-test hybrid energy systems under dynamic scenarios to de-risk advanced control strategies for greater resilience and flexibility. SuperLab 2.0 (5-Lab Demo) showcased a major advancement in federated national laboratory collaboration, enabling real-time, cross-laboratory experimentation to coordinate geographically dispersed distributed energy resources (DERs) using various communication protocols and networks. SuperLab 2.0 (5-Lab Demo) built on previous demonstrations conducted between NLR-PNNL and NLR-INL connecting diverse assets including distant protection devices, a SMR simulator, and a high temperature electrolyzer (HTE). Previous demos were based on a single connection between two labs with minimal coordination challenges. The 5-Lab demo with a centralized controller, distributed testbeds across different geographical locations, and use of protocols-based communication represents a scenario closer to real-world grid operations that coordinate resources across a region to meet system needs. This experiment studied how local DER controllers interact with a centralized energy controller during normal and abnormal events to maintain reliability. The SuperLab team across the five labs implemented a notional power system model equivalent of transmission and distribution lines, represented by the data networks interconnecting the labs. Each lab continuously exchanged local parameters (such as P and Q) from its Hardware-In-Loop (CHIL) and Power Hardware-In-Loop (PHIL) assets through centralized energy controller at NLR, enabling real-time interaction and coordination across sites. By leveraging ESnet as the communication backbone, the team successfully operated the distributed assets as a unified power system, with each bus represented by a different laboratory. This setup mirrors how assets interact in real-world power systems across dispersed locations with various protocols and latencies. At each lab site, assets were operated using their own local controllers which were coordinated through an overarching operation and control layer of centralized energy controller, equivalent to how an energy management system (EMS) orchestrates assets across a regional or national grid. SuperLab's federated connectivity utilized a Digital Real-Time Simulators (DRTS)-type gateway to connect Controller Hardware-In-Loop (CHIL) and PHIL assets between labs. To enable this federated connection through ESnet, a deterministic network was established where latency variations were consistent. This consistency allowed the development of digital filters for the power system assets across CHIL and PHIL interfaces to avoid unstable and unreliable grid conditions. This report provides an overview of the cross-laboratory configuration and offers insights into interconnecting geographically distributed research assets to test them as if they were co-located. This experiment represents a step toward linking nine DOE national laboratories, enabling nation-wide simulations that can address utility-driven challenges with grid resilience, flexibility, and modernization.

24 POWER TRANSMISSION AND DISTRIBUTION↗