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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 343 records · Page 19

All sources NASA NTRS DOE OSTI

DOE OSTI2020

Evaluation of simulated soil carbon dynamics in Arctic-Boreal ecosystems

Given the magnitude of soil carbon stocks in northern ecosystems, and the vulnerability of these stocks to climate warming, land surface models must accurately represent soil carbon dynamics in these regions. We evaluate soil carbon stocks and turnover rates, and the relationship between soil carbon loss with soil temperature and moisture, from an ensemble of eleven global land surface models. We focus on the region of NASA's Arctic-Boreal vulnerability experiment (ABoVE) in North America to inform data collection and model development efforts. Models exhibit an order of magnitude difference in estimates of current total soil carbon stocks, generally under- or overestimating the size of current soil carbon stocks by greater than 50 PgC. We find that a model's soil carbon stock at steady-state in 1901 is the prime driver of its soil carbon stock a hundred years later—overwhelming the effect of environmental forcing factors like climate. The greatest divergence between modeled and observed soil carbon stocks is in regions dominated by peat and permafrost soils, suggesting that models are failing to capture the frozen soil carbon dynamics of permafrost regions. Using a set of functional benchmarks to test the simulated relationship of soil respiration to both soil temperature and moisture, we find that although models capture the observed shape of the soil moisture response of respiration, almost half of the models examined show temperature sensitivities, or Q10 values, that are half of observed. Significantly, models that perform better against observational constraints of respiration or carbon stock size do not necessarily perform well in terms of their functional response to key climatic factors like changing temperature. This suggests that models may be arriving at the right result, but for the wrong reason. The results of this work can help to bridge the gap between data and models by both pointing to the need to constrain initial carbon pool sizes, as well as highlighting the importance of incorporating functional benchmarks into ongoing, mechanistic modeling activities such as those included in ABoVE.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Mitigating drought mortality by incorporating topography into variable forest thinning strategies

Abstract Drought-induced productivity reductions and tree mortality have been increasing in recent decades in forests around the globe. Developing adaptation strategies hinges on an adequate understanding of the mechanisms governing the drought vulnerability of forest stands. Prescribed reduction in stand density has been used as a management tool to reduce water stress and wildfire risk, but the processes that modulate fine-scale variations in plant water supply and water demand are largely missing in ecosystem models. We used an ecohydrological model that couples plant hydraulics with groundwater hydrology to examine how within-stand variations in tree spatial arrangements and topography might mitigate forest vulnerability to drought at individual-tree and stand scales. Our results demonstrated thinning generally ameliorated plant hydraulic stress and improved carbon and water fluxes of the remaining trees, although the effectiveness varied by climate and topography. Variable thinning that adjusted thinning intensity based on topography-mediated water availability achieved higher stand productivity and lower mortality risk, compared to evenly-spaced thinning at comparable intensities. The results from numerical experiments provided mechanistic evidence that topography mediates the effectiveness of thinning and highlighted the need for an explicit consideration of within-stand heterogeneity in trees and abiotic environments when designing forest thinning to mitigate drought impacts.

Environmental Sciences & Ecology↗

DOE OSTI2022

Characterizing manufacturing sector disruptions with targeted mitigation strategies

It has become clear in recent decades that manufacturing supply chains are increasingly vulnerable to disruptions of varying geographical scales and intensities. These disruptions—whether intentional, accidental, or resulting from natural disasters—cause failures and capacity reductions to manufacturing infrastructure, with lasting effects that can cascade throughout the manufacturing network. An overall lack of understanding of solutions to mitigate disturbances has rendered the challenge of reducing manufacturing supply chain vulnerability even more difficult. Additionally, the variability of disruptions and their impacts complicates policy maker and stakeholder efforts to plan for specific disruptive scenarios. It is necessary to comprehend different kinds of disturbances and group them based on stakeholder-provided metrics to support planning processes and modeling efforts that promote adaptable, resilient manufacturing supply chains. This paper reviews existing methods for risk management in manufacturing supply chains and the economic and environmental impacts of disruptions. In addition, we develop a framework using agglomerative hierarchical clustering to classify disruptions using U.S. manufacturing network data between 2000 and 2021 and characteristic metrics defined in the literature. Our review identifies five groups of disruptions and discusses both general mitigation methods and strategies targeting each identified group. Further, we highlight gaps in the literature related to estimating and including environmental costs in disaster preparedness and mitigation planning. We also discuss the lack of easily available metrics to quantify environmental impacts of disruptions and how such metrics could be included into our methodology.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Impacts of benchmarking choices on inferred model skill of the Arctic–Boreal terrestrial carbon cycle

Abstract Land surface models require continuous validation against observations to improve and reduce simulation uncertainty. However, inferred model performance can be heavily influenced by subjective choices made in the selection and application of observational data products. A key area often misrepresented by models is the Arctic–Boreal region, which is a potential tipping point region in Earth’s climate system due to large permafrost carbon stocks that are vulnerable to release with climate warming. We use the International Land Model Benchmarking (ILAMB) framework to evaluate how the model skill of TRENDY-v9 models varies based on the choice of observational-based benchmark and how benchmarks are applied in model evaluation. This analysis uses global datasets integrated into ILAMB and new, regionally-specific observational products from the Arctic–Boreal Vulnerability Experiment. Our results cover the overall time period of 1979–2019 and show that model scores can vary substantially depending on the data product applied, with higher model scores indicating better model performance against observations. The lowest model scores occur when benchmarked against regional, compared to global, datasets. We also evaluate observed and modeled functional relationships between ecosystem respiration and air temperature and between gross primary production and precipitation. Here, we find that the magnitude and shape of the responses are strongly impacted by the choice of observational dataset and the approach used to construct the functional relationship benchmark. These results suggest that model evaluation studies could conclude a false sense of model skill if only using a single benchmark data product or if not applying regional data products when performing a regional model analysis. Collectively, our findings highlight the influence of benchmarking choices on model evaluation and point to the need for benchmarking guidelines when assessing model skill.

Poe, Jeralyn (ORCID:0000000318495278)↗

DOE OSTI2025

Comparing multi-source urban flood indicators: satellite, simulation, and citizen-reported data

Urban flooding arises from complex mechanisms, making it challenging to capture accurately with a single detection method. This study evaluates three complementary approaches to detect flooding across three Chicago neighborhoods: (i) Sentinel-1 synthetic aperture radar (SAR), offering weather-independent, high-resolution (10 m) imagery of surface inundation; (ii) the storm water management model (SWMM), simulating combined sewer overflow and drainage performance; and (iii) citizen-generated 311 service requests, capturing observed flooding impacts. By analyzing six storms ranging from severe to mild, we examine how each source uniquely contributes to identifying urban flood events. SAR imagery effectively identifies standing water but can miss brief flooding due to satellite revisit constraints. SWMM provides detailed insights into system-wide drainage behavior yet may underestimate localized street-level flooding. Meanwhile, 311 calls reflect real-world flooding impacts but are vulnerable to underreporting. Statistical overlap analysis highlights chronic flood hotspots repeatedly identified across multiple detection methods, indicating persistent infrastructure and topographic vulnerabilities. Temporal analysis further reveals that while SWMM flooding aligns closely with rainfall peaks, 311 calls typically precede or persist beyond these peaks. Our findings emphasize the value of using satellite observations, hydrological modeling, and resident-reported data in a complementary manner to better interpret patterns in flood timing, severity, and spatial distribution—providing insights that can inform targeted infrastructure improvements and contribute to urban flood resilience planning.

311↗

DOE OSTI2024

The Ecosystem as Super-Organ/ism, Revisited: Scaling Hydraulics to Forests under Climate Change

Synopsis Classic debates in community ecology focused on the complexities of considering an ecosystem as a super-organ or organism. New consideration of such perspectives could clarify mechanisms underlying the dynamics of forest carbon dioxide (CO2) uptake and water vapor loss, important for predicting and managing the future of Earth’s ecosystems and climate system. Here, we provide a rubric for considering ecosystem traits as aggregated, systemic, or emergent, i.e., representing the ecosystem as an aggregate of its individuals or as a metaphorical or literal super-organ or organism. We review recent approaches to scaling-up plant water relations (hydraulics) concepts developed for organs and organisms to enable and interpret measurements at ecosystem-level. We focus on three community-scale versions of water relations traits that have potential to provide mechanistic insight into climate change responses of forest CO2 and H2O gas exchange and productivity: leaf water potential (Ψcanopy), pressure volume curves (eco-PV), and hydraulic conductance (Keco). These analyses can reveal additional ecosystem-scale parameters analogous to those typically quantified for leaves or plants (e.g., wilting point and hydraulic vulnerability) that may act as thresholds in forest responses to drought, including growth cessation, mortality, and flammability. We unite these concepts in a novel framework to predict Ψcanopy and its approaching of critical thresholds during drought, using measurements of Keco and eco-PV curves. We thus delineate how the extension of water relations concepts from organ- and organism-scales can reveal the hydraulic constraints on the interaction of vegetation and climate and provide new mechanistic understanding and prediction of forest water use and productivity.

Zoology↗

DOE OSTI2024

Cyber-Secure and Safe Operation of Solar Photovoltaic Power Distribution Systems

Solar photovoltaic (PV)-rich power distribution systems are networked Cyber-Physical Systems (CPS). These are control systems where multiple computing nodes and diverse intelligent agents interact with the physical world in real-time. However, the presence of networked components renders them vulnerable to potential cyber-attacks, cyber-intrusions, and other malicious events. This is because these systems depend on the measurements reported from their heterogeneous sensors. This makes them vulnerable to potential cyber-attacks where malicious agents can compromise the sensors or the communication networks carrying the sensor measurements. This paper proposes a novel methodology for enhancing the cyber-security and cyber-resilient post-attack safe operation of solar PV-rich power distribution systems against potential cyber-attacks through the Dynamic Watermarking (DW), using online system identification. The resiliency of the proposed technique is tested and validated with several attack scenarios on both a lab-scale 3kW grid-connected PV inverter and a Hardware-in-the-Loop (HiL) system. The proposed approach can be applied to other types of power distribution systems to enhance their cyber-secure and cyber-resilient safe operation. This paper thereby contributes to the field of cyber-security of Cyber-Physical Energy Systems (CPES).

Kim, Jaewon↗

DOE OSTI2020

Distributed Detection of Malicious Attacks on Consensus Algorithms with Applications in Power Networks

Consensus-based distributed algorithms are well suited for coordination among agents in a cyber-physical system. These distributed schemes, however, suffer from their vulnerability to cyber attacks that are aimed at manipulating data and control ow. In this article, we present a novel distributed method for detecting the presence of such intrusions for a distributed multi-agent system following ratio consensus. We employ a Max-Min protocol to develop low cost, easy to implement detection strategies where each participating node detects the intrusion independently, eliminating the need for a trusted certifying agent in the network. The effectiveness of the detection method is demonstrated by numerical simulations on a 1000 node network to demonstrate the efficacy and simplicity of implementation.

27 ARPA - Advanced Research Projects Agency-Energy↗

DOE OSTI2021

Cyber Protection of Grid-Connected Devices Through Embedded Online Security

Cybersecurity research regarding the electric power grid has primarily been focused on protecting the communication layer of grid-connected devices against cyber-attack threats. Although many developed methods have greatly reduced the effects of a cyber-attack on the vulnerabilities of grid-connected devices, discovering new vulnerabilities is inevitable and a constant threat. As a result, the overall reliability and security of network communications with regard to grid-connected devices is a concern. Here, this paper proposes a method that further secures a system by focusing on the control and hardware layer of grid-connected devices. The device’s controller firmware will be validated and authenticated using integrated device emulation resources prior to being activated to control the grid-connected device. This verification process is performed while the controller is online and actively controlling power flows related to the device. Therefore, an attack to the system through a malicious firmware patch would be detected by the online security and rejected while safely maintaining continuous and stable control of the device. This method integrates the concepts of firmware hot-patching, digital twins, and active monitoring into an overall cybersecurity protection system.

cybersecurity↗

DOE OSTI2022

Ransomware Security Threat Modeling for Photovoltaic Systems

Ransomware attacks are one of the most dangerous cyber-attacks which can disrupt the operation of photovoltaic (PV) systems and incur an enormous economic loss. This paper introduces a ransomware security threat modeling method that identifies potential vulnerabilities, threats, and impacts of ransomware attacks targeting a PV system. Here, the security threat modeling consists of three steps: 1) system identification, 2) threat modeling that finds existing vulnerabilities, 3) attack modeling that designs attack profiles to succeed ransomware attacks, and 4) penetration testing that performs authorized cyber-attacks and analyzes impacts of the ransomware attack profiles using a real-time hardware-in-the-loop (HIL) PV system security testbed.

attack modeling↗

DOE OSTI2023

Optimization and Comparison of Modern Offshore Wind Turbine Generators Using GeneratorSE 2.0

As the offshore wind industry keeps growing at a rapid pace, developers are bracing themselves for a huge demand in critical rare earth metals which will threaten an already vulnerable supply chain. The wind energy industry is addressing this problem by investing in modern generator technologies that employ magnets with reduced rare earth content and high-field magnets enabled by rare-earth-free superconductors. In this paper we introduce the National Renewable Energy Laboratory's newly advanced GeneratorSE 2.0, which is a design and optimization tool that was developed to investigate the feasibility of such modern generators. Two direct-drive generator topologies with different magnet materials and mounting arrangements are investigated: an outer-rotor, V-shaped interior permanent magnet generator, and an inner-rotor normally conducting armature, paired with a low-temperature superconducting field with race-track coils. These technologies were evaluated for a range of power ratings between 15 and 25 MW, which represent the next generation of offshore wind turbines for both fixed-bottom and floating applications. The analyses indicate a new trend favoring the low-temperature superconducting technology for the direct-drive system.

direct-drive generators↗

DOE OSTI2024

Cybersecurity Assessment in DER-rich Distribution Operations: Criticality Levels and Impact Analysis

The integration of distributed energy resources (DERs) in distribution networks has become a pivotal strategy for achieving decarbonization, enhancing grid resilience, and optimizing grid efficiency. Remote monitoring and control op- erations of such resources rely on a network of sensors and communication infrastructure, exposing the system to potential cyber threats. Therefore, as the deployment of DERs increases, ensuring secure monitoring and control becomes an imperative challenge. This paper utilizes real-time feeder models, which are instrumental in developing cybersecurity testbeds tailored for hardware-in-loop (HIL) systems. These models enable users to simulate cyber attacks in a real-world environment and analyze the power distribution operations during vulnerabilities. Furthermore, we discuss several practical sets of grid parameters to identify critical levels of DERs and evaluate various scenarios that simulate cyber threats on sensitive DERs. The modified IEEE 123-bus model is used as the test case for demonstrating the proposed scenarios. The findings from this study provide valuable insights into the vulnerabilities and potential consequences of cyber attacks on DERs, allowing for better mitigation strategies and improved cyber resilience in future distribution networks.

Maharjan, Manisha↗

DOE OSTI2022

Adversarial Attacks on Deep Neural Network-based Power System Event Classification Models

Online event classification is essential to strengthening the reliability of the power transmission system. Recently, deep learning based methods have achieved great success in numerous domains such as computer vision and natural language processing. Researchers began to adopt deep learning based methods to solve the power system event identification problem and achieved effective results. However, these previous works do not consider that deep learning models are vulnerable to adversarial attacks, potentially influencing real-world applications' reliability. In this paper, we adopt several adversarial attack mechanisms by adding tailored noise signal to the input Phasor Measurement Units (PMU) time series and make the deep learning model misclassify the power system event. This numerical study discloses that current state-of-the-art deep learning based power system event classifiers are extremely vulnerable to adversarial attacks, which may jeopardize the reliability of the power transmission system.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2021

Cybersecurity for Electric Vehicle Fast-Charging Infrastructure

The integration of electric vehicles (EVs) into electric grid operations can potentially leave the grid vulnerable to cyberattacks from both legacy and new equipment and protocols, including extreme fast-charging infrastructure. This paper introduces a co-simulation platform to perform cyber vulnerability analysis of EV charging infrastructure and its dependencies on communications and control systems. Grid impact scenarios through linkages to power system simulation tools such as OpenDSS and vehicle infrastructure-specific attack paths are discussed. An adaptive platform that assists with predicting and solving evolving cybersecurity challenges is demonstrated with a cyber-energy emulation that accelerates the analysis of cyberattacks and system behavior.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Safeguarding the Grid: Diverse Resources for Resilience [In My View]

Reports on the vulnerability of the power grid. From wildfires, hurricanes, and floods to cyberattacks, the grid is vulnerable to natural and human-made disasters, which can cause widespread outages that last from hours to months and leave lasting economic impacts. Imagine a coastal region in the United States that has just been hit by a hurricane and lost all power. Besides the electricity infrastructure going out, communications networks, rail transportation, and gasoline pumps may also stop working after the emergency event, which often delays repair efforts to the grid. Unresponsive communications make it difficult to dispatch workers to repair the grid, and missing transportation links delay shipments of replacement equipment for the grid’s restoration.

cybersecurity↗

DOE OSTI2025

Cybersecurity Challenges in Low-Inertia Power-Electronics-Dominated Grids

Here, the low inertia characteristics of the power electronics dominated grid (PEDG) introduces challenges while restoring voltage and frequency to their nominal values. These stability challenges create new cybersecurity vulnerabilities that are not thoroughly discussed in the literature. Cyber events such as false data injection (FDI), denial of service (DoS), man-in-the-middle attacks, stealthy attacks, and advanced persistent threats target PEDG to disrupt grid stability or gain financial benefits. The low inertia of PEDG (< 2s) compared to traditional grids (~10s) exacerbates these vulnerabilities. In response to stealthy attacks on state variables that supervisory layers cannot detect until significant harm occurs, the low inertia characteristics of PEDG offer substantial stealthy attack surfaces. To counteract such threats, PEDG must be equipped with ultra-fast real-time anomaly detection system and trajectory prediction mechanism to achieve effective cyberattack resiliency.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2023

Outage Forecast-Based Preventative Scheduling Model for Distribution System Resilience Enhancement

Distribution system resilience enhancement is an important topic to ensure customers have access to power supply during extreme events. In fact, certain weather-related extreme events can be predicted ahead of time. Therefore, it is important to investigate how to predict grid outages using extreme weather forecasts, and how outage predictions can be incorporated into distribution system resilience enhancement. In this paper, a preventative scheduling model for distribution systems is proposed. The model targets at allocating resources, especially mobile responsive resources such as mobile backup generators and mobile energy storage systems, to prepare for an extreme event in the day-ahead context. To achieve efficient resource allocation and scheduling, a machine learning-based outage prediction module is developed to predict vulnerable or risky segments of the distribution system based on historical operating records and extreme weather event forecast. By integrating the outage prediction results into the scheduling model, optimal resource allocation can be derived to help distribution systems prepare for an upcoming event and improve resilience performance. A real distribution feeder in North Carolina, U.S. is used in the case study to validate the proposed approach.

distributed energy resources↗

DOE OSTI2021

A Methodology for Assessing Risk to Inform Technology Integration

When new technology, such as artificial intelligence (AI), is introduced into an existing workflow it may impact risk by mitigating some vulnerabilities and threats in the workflow while introducing others. We present a versatile methodology for assessing the vulnerabilities and threats that impact overall risk in a workflow to inform technology integration. Our method involves both qualitative and quantitative assessment of risk and includes a formula for generating a risk score to guide technology integration. We describe our methodology and demonstrate its application to a specific workflow (the Derivative Classification review process). This work was funded by the Department of Energy (DOE) Automated Classification Tools for the Identification of Classified Information (ACTICI) program.

risk, technology integration↗