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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 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Comparison of Sequence Component-Based Fault Detection and Relay Coordination Algorithms in Inverter-Based Networks

Protection of inverter-based microgrids using sequence component-based relaying schemes is a promising solution. These methods offer several advantages, including lower computational requirements, compatibility with commercial relay systems, and cost-effectiveness compared to communication-based approaches. This article investigate the performance of various sequence component based schemes with the objective of identifying the algorithms that provide the best fault detection and relay coordination, solely relying on local voltages and current at relay terminals. Positive, negative and zero sequence impedance, admittance and power detection algorithms were tested on modified IEEE 13 bus test network for various shunt faults (LG, LL, LLG, LLL). Hardware-in-the-loop validation was achieved using the Typhoon real-time simulator, interfacing with a SEL 751 relay. This research demonstrates that while several algorithms are capable of detecting faults with sufficient accuracy, only a few are effective in achieving proper coordination. Validation results indicate that the negative sequence power approach provides the best performance in both fault detection and coordination.

Patel, Deepika [ORNL] (ORCID:0000000341099994)↗
DOE OSTI2017

On Parallelizing Single Dynamic Simulation Using HPC Techniques and APIs of Commercial Software

Time-domain simulations are heavily used in today’s planning and operation practices to assess power system transient stability and post-transient voltage/frequency profiles following severe contingencies to comply with industry standards. Because of the increased modeling complexity, it is several times slower than real time for state-of-the-art commercial packages to complete a dynamic simulation for a large-scale model. With the growing stochastic behavior introduced by emerging technologies, power industry has seen a growing need for performing security assessment in real time. This paper presents a parallel implementation framework to speed up a single dynamic simulation by leveraging the existing stability model library in commercial tools through their application programming interfaces (APIs). Several high performance computing (HPC) techniques are explored such as parallelizing the calculation of generator current injection, identifying fast linear solvers for network solution, and parallelizing data outputs when interacting with APIs in the commercial package, TSAT. The proposed method has been tested on a WECC planning base case with detailed synchronous generator models and exhibits outstanding scalable performance with sufficient accuracy.

Diao, Ruisheng↗
DOE OSTI2017

A New Remote Communications Link to Reduce Residential PV Solar Costs

Monitoring of PV/DER site production is expensive to install and unreliable. Among third party systems providers, lost communications links are a growing concern. Nearly 20% of links are failing, provisioning is complex, recovery is expensive, production data is lost, and access is fragmented. FleetLink is a new concept in DER system communications, purpose built for lowering the cost of maintaining active contact with residential end user sites and ensuring that production data is reliably available to third party systems providers. Systems providers require accurate, secure system monitoring and reporting of production data and system faults while driving down overall costs to compete effectively. This plug and play, independently operating communications solution lowers the cost of fleet contact from typically .08 dollars-$.12/W down to .02 dollars -.03/W including installation and maintenance expenses. FleetLink establishes a breakthrough in simplicity that facilitates rapid expansion of residential solar by reducing initial capital outlay and lowering installation labor time and skill levels. The solution also facilitates higher DER installation growth rates by driving down maintenance costs and eliminating communications trouble calls. This is accomplished by the FleetLink’s unique network technology that enables dynamic network configuration for fast changes, and active, self-healing DER site contact for uptime assurance. Using an open source network framework with proprietary, application specific enhancements, FleetLink independently manages connectivity, security, recovery, grid control communications, and fleet expansion while presenting a compliant SunSpec interface to the third party operations centers. The net system cost savings of at least .05 dollars/W supports the SunShot cost goals and the flexibility and scalability of the solution accelerates the velocity and ubiquitous adoption of solar.

14 SOLAR ENERGY↗
DOE OSTI2022

Understanding Biomass and Polymer Feedstock Variability Through Analytical Pyrolysis and Two Dimensional Gas Chromatography and Mass Spectrometry

Moving toward a circular carbon economy requires enabling reuse of carbon-based macromolecules like those in biomass and polymers. Meeting this goal requires increased upcycling of waste products into higher value products. For biomass waste such as corn stover, pine needles, or bark, one approach is using pyrolysis to convert these feedstocks into bio-oil that can be used for liquid fuel or transformed into other hydrocarbon based products, like bio-polymers. Waste plastics can be upcycled or recycled into new products, or alternatively converted into bio-oil by pyrolysis. Macromolecules from biomass and polymers are challenging to convert into liquid fuel or chemical feedstocks, due to large and sometimes heterogeneous networks of polymeric bonds. We work with the Idaho National Laboratory’s Biomass Feedstock National User Facility and Department of Energy’s Feedstock Conversion Interface Consortium to develop a molecular understanding of biomass variability that occurs naturally and as a result of storage or preprocessing techniques applied to feedstocks, to understand chemical reactions occurring during pyrolysis and other biomass or polymer conversion techniques. Using two dimensional gas chromatography mass spectrometry coupled to analytical pyrolysis provides insight into the molecular changes that occur during the pyrolysis event, and these insights can extend to bench or pilot scale pyrolysis conversions. Understanding pyrolysis of biomass and polymers at a molecular level contributes to development of new tools to predict and manipulate the optimum conditions and identify the best storage, preprocessing, and conversion techniques to maximize output of specific high-value chemical species in the conversion to fuels, or to characterize feedstocks for blending before pyrolysis. Identification and understanding of the molecular level changes resulting from novel preprocessing techniques that can be used to control and manipulate the chemical output of bench or pilot scale pyrolysis is another focus of investigation, including the use of gamma irradiation, acid, or enzyme pre-treatments. We also focus on the impacts of storage conditions and techniques, which can affect moisture levels, degradation, and impacts from naturally occurring microbes in the environment.

09 BIOMASS FUELS↗
DOE OSTI2021

Programmable intrusion detection for distributed energy resources in cyber–physical networked microgrids

We present a programmable intrusion detection method is presented to identify the malicious attacks to distributed energy resources (DERs) in the cyber-physical networked microgrids. The proposed method injects small programmable signals into the system and uses the response to identify abnormal conditions. Because of the low or even zero inertia induced by integrations of DER power-electronic-interfaces, microgrids have very limited resilience capability; and thus, being sensitive to attacks. One microgrid's malfunction caused by attacks can easily propagate to its neighboring systems when several microgrids are connected, leading to catastrophic electricity supply failures. Through the presented method, malicious intrusions can be effectively detected, located, and defended for securing microgrids. Theoretical derivations are provided to define the programmable detection rules. The detection rule is easy and flexible to update, making it difficult for attack actors to gain the knowledge of the detection rules, in order to avoid being detected. Numerical results on a cyber-physical networked microgrids system show that the proposed method is effective and efficient in precisely locating intrusion attacks to the microgrids system.

42 ENGINEERING↗
DOE OSTI2018

Structural Evidence for Dimer-Interface-Driven Regulation of the Type II Cysteine Desulfurase, SufS

SufS is a type II cysteine desulfurase and acts as the initial step in the Suf Fe–S cluster assembly pathway. In Escherichia coli, this pathway is utilized under conditions of oxidative stress and is resistant to reactive oxygen species. Mechanistically, this means SufS must shift between protecting a covalent persulfide intermediate and making it available for transfer to the next protein partner in the pathway, SufE. Here, we report five X-ray crystal structures of SufS including a new structure of SufS containing an inward-facing persulfide intermediate on C364. Additional structures of SufS variants with substitutions at the dimer interface show changes in dimer geometry and suggest a conserved β-hairpin structure plays a role in mediating interactions with SufE. Here, these new structures, along with previous HDX-MS and biochemical data, identify an interaction network capable of communication between active-sites of the SufS dimer coordinating the shift between desulfurase and transpersulfurase activities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Traction-separation response of bilayer graphene interfaces: The role of an intercalated single atomic layer of water molecules and hydroxyl groups

Molecular dynamics simulations of the normal and shear traction-separation response of dry and wet bilayer graphene interfaces were performed. The influence of a single, atomic layer of water molecules sandwiched between graphene layers and hydroxyl functionalization on one or both layers were investigated. Hydroxyl groups provided a shielding effect during normal separation while favoring stick-slip friction under sliding conditions. The single, atomic layer of water molecules confined between hydrophilic and asymmetric hydrophobic/hydrophilic surfaces was found to ease sliding friction. The mechanisms arose from the balance between the attractive and repulsive graphene/water interactions through bridges of single water molecules between the two surfaces and the structure of the water monolayer under confinement for which an out-of-plane water/water hydrogen bond network could not be developed. The single, atomic layer of water molecules reduced the sliding friction the most when both surfaces were hydrophilic and lowered the sliding friction of the asymmetric hydrophobic/hydrophilic interfaces to that when both surfaces were hydrophobic. An optimal hydroxyl surface coverage of less than 10% may exist for efficient water lubrication, with diminishing effect with increasing number of hydroxyl groups. Finally, the findings offer a design strategy for assembling bilayer graphene interfaces with tailored friction and sliding contact properties.

36 MATERIALS SCIENCE↗
DOE OSTI2017

Multi-actor involvement for integrating ecosystem services in strategic environmental assessment of spatial plans

Integrating an ecosystem services (ES) approach into Strategic Environmental Assessment (SEA) of spatial plans potentially enhances the consideration of the value of nature in decision making and policy processes. However, there is increasing concern about the institutional context and a lack of a common understanding of SEA and ecosystem services for adopting them as an integrated framework. This paper addresses this concern by analysing the current understanding and network relations in a multi-actor arrangement as a first step towards a successful integration of ES in SEA and spatial planning. Our analysis focuses on a case study in Chile, where we administered a questionnaire survey to some of the main actors involved in the spatial planning process. The questionnaire focused on issues such as network relations among actors and on conceptual understanding, perceptions and challenges for integrating ES in SEA and spatial planning, knowledge on methodological approaches, and the connections and gaps in the science-policy interface. Our findings suggest that a common understanding of SEA and especially of ES in a context of multiple actors is still at an initial stage in Chile. Additionally, the lack of institutional guidelines and methodological support is considered the main challenge for integration. We conclude that preconditions exist in Chile for integrating ES in SEA for spatial planning, but they strongly depend on appropriate governance schemes that promote a close science-policy interaction, as well as collaborative work and learning. - Highlights: • Linking ecosystem services in SEA is an effective framework for sustainability. • Multi-actor understanding and networks in ecosystem services and SEA were analyzed. • Understanding of SEA and especially of ES is still in an initial stage in Chile. • A lack of institutional guidelines is one of the key challenges for this link.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Solid State Power Substations (SSPS): A Multi-Hierarchical Architecture from Substation to Grid Edge

With the growing deployment of distributed generation, or power electronic interfaced renewable energy and storage technologies, the nature and behavior of the grid is changing. Synchronous machine-driven asset contributions to the generation mix are shrinking, leading to concerns regarding grid stability. Furthermore, the scale of smaller distributed PE resources needed for managing the electrical network could dwarf the existing system leading to more complex optimization problems and communication interconnections. This paper introduces the concept of a hierarchal system of controllers that spans the grid edge or the customer end to distribution scale substations or solid-state power substation (SSPS). This concept focuses on minimizing the number of interfaces and optimization considerations in the grid by clustering resources into nodes and hubs. The work validates the concept in a controller hardware-in-the-loop (cHIL) platform.

Chinthavali, Madhu Sudhan↗
DOE OSTI2024

Improving the reliability of machine learned potentials for modeling inhomogeneous liquids

The atomic-scale response of inhomogeneous fluids at interfaces and surrounding solute particles plays a critical role in governing chemical, electrochemical, and biological processes. Classical molecular dynamics simulations have been applied extensively to simulate the response of fluids to inhomogeneities directly, but are limited by the accuracy of the underlying interatomic potentials. Here, we use neural network potentials (NNPs) trained to ab initio simulations to accurately predict the inhomogeneous responses of two distinct fluids: liquid water and molten NaCl. Although NNPs can be readily trained to model complex bulk systems across a range of state points, we show that to appropriately model a fluid's response at an interface, relevant inhomogeneous configurations must be included in the training data. In order to sufficiently sample appropriate configurations of such inhomogeneous fluids, we develop protocols based on molecular dynamics simulations in the presence of external potentials. We demonstrate that NNPs trained on inhomogeneous fluid configurations can more accurately predict several key properties of fluids—including the density response, surface tension and size-dependent cavitation free energies—for liquid water and molten NaCl, compared to both empirical interatomic potentials and NNPs that are not trained on such inhomogeneous configurations. This work therefore provides a first demonstration and framework to extract the response of inhomogeneous fluids from first principles for classical density-functional treatment of fluids free from empirical potentials.

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

Helical dislocation in twisted bilayer graphene

Here, we observe helical dislocation network in twisted bilayer graphene (tBLG), which is accompanied by large out-of-plane deformation. By atomistic calculations, we demonstrate two distinct out-of-plane deformation modes, breathing mode with small out-of-plane deformation and bending mode with one order larger corrugation magnitude compared to the breathing mode. The out-of-plane deformation is caused by inhomogeneous interlayer coupling resulting from the periodic stacking order of the tBLG moiré superlattice. Instead of commonly observed screw dislocation in tBLG, we demonstrate a slip-induced helical dislocation network in the bending mode tBLG. We show that bending mode deformation is more stable at low twist angles, as the energy savings due to interface energy exceeds the energy penalty due to the strain energy caused by the large out-of-plane deformation. Our work provides a detailed picture of a new helical dislocation structure in tBLG and establishes a direct connection between the dislocation and deformation. Therefore, understanding the dislocation mechanics of tBLG may open up the possibility to control the corrugation, and reveal an opportunity to tune the intriguing physical properties of twisted bilayer graphene.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High‐Resolution National‐Scale Water Modeling Is Enhanced by Multiscale Differentiable Physics‐Informed Machine Learning

Abstract The National Water Model (NWM) is a key tool for flood forecasting, planning, and water management. Key challenges facing the NWM include calibration and parameter regionalization when confronted with big data. We present two novel versions of high‐resolution (∼37 km 2 ) differentiable models (a type of hybrid model): one with implicit, unit‐hydrograph‐style routing and another with explicit Muskingum‐Cunge routing in the river network. The former predicts streamflow at basin outlets whereas the latter presents a discretized product that seamlessly covers rivers in the conterminous United States (CONUS). Both versions use neural networks to provide a multiscale parameterization and process‐based equations to provide a structural backbone, which were trained simultaneously (“end‐to‐end”) on 2,807 basins across the CONUS and evaluated on 4,997 basins. Both versions show great potential to elevate future NWM performance for extensively calibrated as well as ungauged sites: the median daily Nash‐Sutcliffe efficiency of all 4,997 basins is improved to around 0.68 from 0.48 of NWM3.0. As they resolve spatial heterogeneity, both versions greatly improved simulations in the western CONUS and also in the Prairie Pothole Region, a long‐standing modeling challenge. The Muskingum‐Cunge version further improved performance for basins >10,000 km 2 . Overall, our results show how neural‐network‐based parameterizations can improve NWM performance for providing operational flood predictions while maintaining interpretability and multivariate outputs. The modeling system supports the Basic Model Interface (BMI), which allows seamless integration with the next‐generation NWM. We also provide a CONUS‐scale hydrologic data set for further evaluation and use.

Song, Yalan [Civil and Environmental Engineering T↗
DOE OSTI2017

Open-source framework for power system transmission and distribution dynamics co-simulation

The promise of the smart grid entails more interactions between the transmission and distribution networks, and there is an immediate need for tools to provide the comprehensive modelling and simulation required to integrate operations at both transmission and distribution levels. Existing electromagnetic transient simulators can perform simulations with integration of transmission and distribution systems, but the computational burden is high for large-scale system analysis. For transient stability analysis, currently there are only separate tools for simulating transient dynamics of the transmission and distribution systems. In this paper, we introduce an open source co-simulation framework “Framework for Network Co-Simulation” (FNCS), together with the decoupled simulation approach that links existing transmission and distribution dynamic simulators through FNCS. FNCS is a middleware interface and framework that manages the interaction and synchronization of the transmission and distribution simulators. Preliminary testing results show the validity and capability of the proposed open-source co-simulation framework and the decoupled co-simulation methodology.

Huang, Renke↗
DOE OSTI2025

Entanglement swapping systems toward a quantum internet

We demonstrate conditional entanglement swapping, i.e. teleportation of entanglement, between time-bin qubits at the telecommunication wavelength of 1536.4 nm with high fidelity of 87%. Our system is deployable, utilizing modular, off-the-shelf, fiber-coupled, and electrically controlled components such as electro-optic modulators. It leverages the precise timing resolution of superconducting nanowire detectors, which are controlled and read out via a custom developed graphical user interface. The swapping process is described, interpreted, and guided using characteristic function-based analytical modeling that accounts for realistic imperfections. Our system supports quantum networking protocols, including source-independent quantum key distribution, with an estimated secret key rate of approximately 0.5 bits per sifted bit.

Davis, Samantha I. [Caltech] (ORCID:00000001999481↗
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↗
DOE OSTI2019

Biophysical Characterization of a Disabled Double Mutant of Soybean Lipoxygenase: The “Undoing” of Precise Substrate Positioning Relative to Metal Cofactor and an Identified Dynamical Network

Soybean lipoxygenase (SLO) has served as a prototype for understanding the molecular origin of enzymatic rate accelerations. The double mutant (DM) L546A/L754A is considered a dramatic outlier, due to the unprecedented size and near temperature-independence of its primary kinetic isotope effect, low catalytic efficiency, and elevated enthalpy of activation. To uncover the physical basis of these features, we herein apply three structural probes: hydrogen–deuterium exchange mass spectrometry, room-temperature X-ray crystallography and EPR spectroscopy on four SLO variants (wild-type (WT) enzyme, DM, and the two parental single mutants, L546A and L754A). DM is found to incorporate features of each parent, with the perturbation at position 546 predominantly influencing thermally activated motions that connect the active site to a protein–solvent interface, while mutation at position 754 disrupts the ligand field and solvation near the cofactor iron. However, the expanded active site in DM leads to more active site water molecules and their associated hydrogen bond network, and the individual features from L546A and L754A alone cannot explain the aggregate kinetic properties for DM. Lastly, using recently published QM/MM-derived ground-state SLO-substrate complexes for WT and DM, together with the thorough structural analyses presented herein, we propose that the impairment of DM is the combined result of a repositioning of the reactive carbon of linoleic acid substrate with regard to both the iron cofactor and a catalytically linked dynamic region of protein.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Distributed-Memory Sparse Deep Neural Network Inference Using Global Arrays

Partitioned Global Address Space (PGAS) models exhibit tremendous promise in developing efficient and productive distributed-memory parallel applications. They have been used extensively in scientific computations due to conveniently offering a ``shared-memory''-like model and convenient interfaces that separate communication with synchronization. Traditionally, PGAS communication models have been applied to dense/contiguously distributed data, but most modern applications depict varied levels of sparsity. Existing PGAS models require certain adaptations to support distributed sparse computations, since associated computations often require matrix arithmetic, in addition to data movement. The Global Arrays toolkit from Pacific Northwest National Laboratory (PNNL) is one of the earliest PGAS models to combine one-sided data communication and distributed matrix operations and is still used in the popular NWChem quantum chemistry suite. Recently, we have expanded the Global Arrays toolkit to support common sparse operations, like sparse matrix-dense matrix multiplies (SpMM), sparse matrix-sparse matrix multiplication (SpGEMM) and Sampled Dense-Dense Matrix Multiplication (SDDMM). As it turns out, these operations are the bedrock of sparse Deep Learning (DL); sparse deep neural networks and Graph Neural Networks (GNNs) have gained increasing attention recently in achieving speedups on training and inference with reduced memory footprints. Unlike scientific applications in High Performance Computing (HPC), modern (distributed-memory capable) DL toolkits often rely on non-standardized and closed-source vendor software optimizations, creating challenges in software-hardware co-design at scale. Our goal is to support a variety of distributed-memory sparse matrix operations and helper functions in the newly created Sparse Global Arrays (SGA), such that it is possible to build portable and productive Machine Learning scenarios for algorithm/software and hardware codesign purposes. Contemporary data-parallel schemes for training/inference are undergoing a major overhaul since model replication limits scalability and causes resource inefficiencies. As such, we have adopted tensor parallelism in decomposing the model and inputs, to mitigate memory issues. Current implementation is built on top of MPI and uses CPUs to maximize the portability across the platforms.

Distributed computing, machine learning↗
DOE OSTI2017

Packet Capture Solutions: PcapDB Benchmark for High-Bandwidth Capture, Storage, and Searching

PcapDB stands alone when looking at the overall field of competitors, from the cost-effective COTS hardware, to the efficient utilization of disk space that enables a longer packet history. A scalable, 100GbE-enabled system that indexes every packet and indexes flow data without complicated load-balancing requirements. The Transport Layer search and indexing approach led to patent-pending flow indexing technology, providing a specialized database system specifically optimized around providing fast flow searches. While there are a plethora of options in network packet capture, there are very few that are able to effectively manage capture rates of more than 10 Gb/s, distributed capture and querying, and a responsive user interface. By far, the primary competitor in the market place is Endace and DeepSee; in addition to meeting the technical requirements we set out in this document, they provide technical support and a fully 'appliance like' system. In terms of cost, however, our experience has been that the yearly maintenance charges alone outstrip the entire hardware cost of solutions like PcapDB. Investment in cyber security research and development is a large part of what has enabled us to build the base of knowlegable workers needed to defend government resources in the rapidly evolving cyber security landscape. We believe projects like Bro, WireCap, and Farm do more than just fill temporary gaps in our capabilities. They give allow us to build the firm foundation needed to tackle the next generation of cyber challenges. PcapDB was built with loftier ambitions than simply solving the packet capture of a single lab site, but instead to provide a robust, scaleable packet capture solution to the DOE complex and beyond.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗