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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Storm-DEPART (Damage Estimate Prediction and Recovery Tool)

Storm-DEPART (Damage Estimate Prediction and Restoration Tool): Each year hurricanes and tropical storms in the United States damage critical infrastructure assets, disrupt the services they provide, and cause millions to billions of dollars in economic impacts due to extended recovery times. The Storm-DEPART tool and analytical output enable more impactful data-driven decision-making capabilities and strengthen national-level disaster preparedness, response, and recovery. Storm-DEPART, built through multi-month collaboration between Entergy and INL, combines Entergy’s critical infrastructure inventory data with weather forecasts to predict damages to Electric utility’s assets due to natural disasters and the estimated recovery support needed, including time, materials, and resource allocation. In the event of an approaching hurricane, this innovative solution can assess potential damage to power generation capacity, transmission grids, distribution networks, and communications assets from wind bands, storm surge, and flooding. With more effective predictions, Entergy can more efficiently allocate resources to mitigate impacts and optimize recovery for customers. Storm-DEPART also allows Electric utilities the ability to apply a planning scenario and model expected damage to better inform infrastructure restoration needs leading to enhance system resiliency. The technology is fully transferrable to other electric utilities with the same damage estimating challenges. The INL team is working on the evolution of Storm-DEPART to include ice event damage prediction framework.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Multi-Scenario Extreme Weather Simulator

The Multi-scenario Extreme Weather Simulator (MEWS) takes existing historical weather data and inserts trends and extreme events such as heat waves, hurricanes, extreme precipitation, or winter storms. The code includes functionality for projecting increasing frequency and duration of such events over a large range of statistically sampled realizations of the future. These weather files can then be used in building energy simulations through the Energy Plus software created by DOE. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525 SAND2021-8658 O

Villa, Daniel↗
DOE OSTI2024

Direct Air Capture and Utilization System (DACUS): FEED Study of CarbonCapture Inc. DAC and CarbonCure Utilization Technologies Using United States Steel’s Gary Works Plant Waste Heat

The effects of climate change are drastically perceived through the alarming rates of severe events such as flooding, hurricanes, wildfires, global temperature rise, sea-level rise, and ocean acidification etc., which has prompted the nations across the globe to control & reduce greenhouse gas (GHG) emissions, by massive adoption of electric vehicles, switching to renewal energy sources and promoting sustainable green products. However, recent climate studies have shown that such efforts will not be enough to keep the global temperature rising under 1.5° Celsius by the year 2050, which lead to the development of multiple Direct Air Capture (DAC) technologies. A DAC system extracts CO2 directly from the atmosphere, which is either stored underground for long term or utilized in other processes. The DACUS proposed at U.S. Steel Corporation’s Gary Works Plant in Gary, Indiana, will include an innovative DAC technology from CarbonCapture Inc. (CCI), which will be collocated and powered by waste heat from the steel plant to capture CO2 from ambient air, and CarbonCure CO2 utilization technology- which is a commercial technology that is already installed and in use by early adopters in more than 150 concrete mixing plants across North America and Asia. The University of Illinois at Urbana Champaign spearheaded this Front-End Engineering Design (FEED) study project, which was funded by US Department of Energy (DOE), aimed at designing a state-of-the art DACU system that can remove minimum of 5,000 tonne/year net CO2 from air (based on cradle-to-gate LCA) and converting the captured CO2 into low carbon intensity products. The off takers of the low Carbon product produced by the DACUS have also been identified near the steel plant in the Indiana, Illinois & Wisconsin Region. This creates a full CO2 value chain and provides a means to assess the impact of this holistic approach on job creation, regional economic impact, and environmental justice. DACUS is a promising new technology for reducing greenhouse gas in the atmosphere. Operation costs are minimized by locating CCI’s innovative DAC collector at United States Steel's Gary Works site where waste heat is utilized. Carbon emissions are minimized in CO2 transportation. The captured CO2 will be permanently sequestered in concrete used as construction material. This DOE funded FEED study demonstrates a full CO2 value chain for a DACU system, supported by an industrial facility. If this project were constructed, it would have a very positive and lasting impact on both the environment and the community.

42 ENGINEERING↗
DOE OSTI2021

Evaluation of Extreme Weather Impacts on Utility-scale Photovoltaic Plant Performance in the United States

The global energy system is undergoing significant changes, including a shift in energy generating technologies to more renewable energy sources. However, the dependence of renewable energy sources on local environmental conditions could also increase disruptions in service through exposures to compound, extreme weather events. By fusing three diverse datasets (operations and maintenance tickets, weather data, and production data), this analysis presents a novel methodology to identify and evaluate performance impacts arising from extreme weather events across diverse geographical regions. Text analysis of maintenance tickets identified snow, hurricanes, and storms as the leading extreme weather events affecting photovoltaic plants in the United States. Statistical techniques and machine learning were then implemented to identify the magnitude and variability of these extreme weather impacts on site performance. Impacts varied between event and non-event days, with snow events causing the greatest reductions in performance (54.5%), followed by hurricanes (12.6%) and storms (1.1%). Machine learning analysis identified key features in determining if a day is categorized as low performing, such as low irradiance, geographic location, weather features, and site size. The analysis improves our understanding of compound, extreme weather event impacts on photovoltaic systems, which can inform planning activities, especially as the industry continues to expand into new geographic and climatic regions around the world.

14 SOLAR ENERGY↗
DOE OSTI2023

Public values failure associated with Hurricane Ian power outages

Power outages from extreme weather events can diminish community resilience, making it difficult for the areas impacted to bounce back after such events. For socially vulnerable populations, the frequency and duration of power outages can be even more severe. Governments have an obligation to protect public values, or those values that are most fundamental to society, which includes equitable resilience. Using Jørgensen and Bozeman's inventory of public values, this manuscript explores how power outages from extreme weather events create public values failures. More specifically, the manuscript evaluates intraorganizational aspects of public administration during power outages in Florida during Hurricane Ian in 2022. Framing power outages as a public values failure may motivate greater time and effort toward improving equitable access to more resilient power systems.

Entress, Rebecca M.↗
DOE OSTI2022

PR100: Puerto Rico Grid Resilience and Transition to 100% Renewable Energy: Preventing and Responding to Extreme Events

This presentation discusses the aftermath of Hurricane Maria's landfall in Puerto Rico on September 20, 2017. Maria incapacitated Puerto Rico's power systems, leaving the entire island without electricity and access to fresh water. Some parts are still recovering years later. A resilient electric grid is vital to Puerto Rico's security, economy, and way of life.

demand impacts↗
DOE OSTI2020

Series of semi-Markov processes to model infrastructure resilience under multihazards

Civil infrastructure systems are subjected to multiple hazards, including natural and anthropogenic, that disrupt their function or the level of service offered. Estimating the function recovery of these systems (or how soon normalcy of operations will be restored) when subjected to repeated hazard events by considering the inter-event dependencies is an important problem in multihazard infrastructure resilience. However, this problem has been less addressed in the field. This paper proposes a series of semi-Markov processes model to capture the inter-event dependencies in infrastructure recovery when subjected to successive hazard events. Recovery after each new hazard event is represented by a unique semi-Markov process that models the reduced recovery rates and the increased recovery times caused by the system’s incomplete recovery from the preceding event. Two novel formulations of the inter-event dependency modeling, namely Maximal Effects Dependency (considers the worst impact of two successive hazard events) and Cumulative Effects Dependency (considers the aggregated impacts of two successive hazard events), are proposed and discussed. The model is demonstrated by considering the following applications: Three-state system subjected to deterministic and random occurrences of identical hazard events; and Multihazard resilience of a building in Charleston, SC, considering earthquake and hurricane hazards. Results indicate that considering inter-event dependencies in recovery modeling can lead to lesser-predicted resilience, thereby affecting resilience-based decision-making.

42 ENGINEERING↗
DOE OSTI2023

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

Abstract In low‐lying estuarine regions, compound flooding (CF) is caused by the co‐occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial‐coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial‐coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid‐Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial‐coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Improved microgrid resiliency through distributionally robust optimization under a policy-mode framework

Critical energy infrastructure are constantly under stress due to the ever increasing disruptions caused by wildfires, hurricanes, other weather related extreme events and cyber-attacks. Hence it becomes important to make critical infrastructure resilient to threats from such cyber-physical events. However, such events are hard to predict and numerous in nature and type and it becomes infeasible to make a system resilient to every possible such cyber-physical event. Such an approach can make the system operation overly conservative and impractical to operate. Furthermore, distributions of such events are hard to predict and historical data available on such events can be very sparse, making the problem even harder to solve. To deal with these issues, in this paper we present a policy-mode framework that enumerates and predicts the probability of various cyber-physical events and then a distributionally robust optimization (DRO) formulation that is robust to the sparsity of the available historical data. The proposed algorithm is illustrated on an islanded microgrid example: a modified IEEE 123-node feeder with distributed energy resources (DERs) and energy storage. Simulations are carried to validate the resiliency metrics under the sampled disruption events.

Nazir, Mohammad Nawaf↗
DOE OSTI2021

Optimization of Carbon-Nanotube-Reinforced Composite Gridlines Towards Commercialization

Solar cells in PV modules crack during field operation from environmental stressors, including extreme weather events, such as hailstorms and hurricanes. These cracks can lead to gradual or immediate acute power degradation. To directly address cell-crack-induced degradation, we have formulated a carbon nanotube additive for commercial screen printed silver pastes. We have shown in previous work that these metal matrix composites have little to no effect on the cell’s efficiency while enhancing the metallization’s fracture toughness and electrical gap-bridging capability. In this work, we focus on optimizing the composite metallization to achieve the best possible performance. We discover that reducing carbon nanotube agglomerations in the paste not only improves the printability for narrow gridlines, but also increases the modulus of toughness of the metallization by over 500%.

carbon nanotubes↗
DOE OSTI2020

Mid-Atlantic Bight Wave Hindcast To Support DOE Lidar Buoy Deployments: Model Validation

This study presents a shelf scale wave hindcast for the Mid-Atlantic Bight to provide accurate wave data to complement the measured data gathered by two DOE lidar buoy deployments off the coasts of Virginia and New Jersey. PNNL developed a ~2 km resolution model based on Wavewatch III and forced with analyzed winds and ocean currents; and executed a four-year hindcast for the period from January 2014 to December 2017. The model results are compared well against 16 wave-measuring buoys and data derived from six satellite-borne radar altimeters. In addition, the model results for two storm events that generated large waves, Hurricane Hermine and the January 2016 Blizzard, are discussed in detail.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Satellite Imagery of PV Site Storm Damage

"This repository contains multiple data sets focused on visible damage to photovoltaic (PV) installations following extreme weather events such as hailstorms and hurricanes. Data sets are split into two categories: the first category, the ‘manually labeled’ data, was compiled by researchers manually, and contains manually identified PV sites exposed to storms. The second data set, the ‘aggregated’ data, is a compilation of the manually labeled PV sites and deep learning-identified PV sites. The hail damage data set focuses on post-storm PV damage following a September 24, 2023 hailstorm in Austin, TX, which caused over $600 million in damages in the Austin metro area. The hurricane damage data set focuses on post-storm PV damage following Hurricanes Irma and Maria in Puerto Rico and the US Virgin Islands. Hurricanes Irma and Maria were back-to-back category 5 hurricanes, which pummeled the Caribbean and southeastern United States in September 2017, causing an estimated $115.2 billion in damages."

14 SOLAR ENERGY↗
DOE OSTI2025

Overcoming Resilience Challenges on the Outer Banks of North Carolina

In 2021, the Town of Nags Head, located in the Outer Banks region of North Carolina, was among the first cohort of coastal and island communities to participate in the U.S. Department of Energy's Energy Transitions Initiative Partnership Project (ETIPP), which offers technical assistance and cash awards to competitively selected coastal, remote, and island communities seeking to transform their energy systems and increase their energy resilience. As a barrier island separated from mainland North Carolina, Nags Head experiences impacts from extreme weather events, including power outages, especially during hurricane season. To address challenges, the Nags Head ETIPP team sought to improve the town's disaster resilience by ensuring that critical facilities can remain powered during and after extreme events. With support from experts at Sandia National Laboratories, the National Renewable Energy Laboratory, and the Coastal Studies Institute, the community team identified the following goals for its participation in ETIPP: 1. Identify and prioritize critical facilities that, when powered during disruptions, support the town's resilience. 2. Analyze the potential for on-site solar photovoltaic (PV) and storage systems at 2-4 prioritized critical facilities owned and/or operated by the town. 3. Analyze the potential for interconnecting solar PV and storage systems at a cluster of public and privately owned facilities into a resilient microgrid. 4. Explore finance options and utility interconnection requirements and compensation mechanisms. 5. Summarize outcomes and next steps for town leadership.

25 ENERGY STORAGE↗
DOE OSTI2021

A Meta-Level Framework for Evaluating Resilience in Net-Zero Carbon Power Systems with Extreme Weather Events in the United States

Important changes are underway in the U.S. power industry in the way that electricity is sourced, transported, and utilized. Disruption from extreme weather events and cybersecurity events is bringing new scrutiny to power-system resilience. Recognizing the complex social and technical aspects that are involved, this article provides a meta-level framework for coherently evaluating and making decisions about power-system resilience. It does so by examining net-zero carbon strategies with quantitative, qualitative, and integrative dimensions across discrete location-specific systems and timescales. The generalizable framework is designed with a flexibility and logic that allows for refinement to accompany stakeholder review processes and highly localized decision-making. To highlight the framework’s applicability across multiple timescales, processes, and types of knowledge, power system outages are reviewed for extreme weather events, including 2021 and 2011 winter storms that impacted Texas, the 2017 Hurricane Maria that affected Puerto Rico, and a heatwave/wildfire event in California in August 2020. By design, the meta-level framework enables utility decision-makers, regulators, insurers, and communities to analyze and track levels of resilience safeguards for a given system. Future directions to advance an integrated science of resilience in net-zero power systems and the use of this framework are also discussed.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Monte Carlo-based Transmission and Subtransmission Recovery Simulation of Hurricanes

High-impact low-probability (HILP) events can wreak havoc on electric power systems without appropriate preparedness. In this paper, the recent development of a tool, named Recovery Simulator and Analysis (RSA), is described and demonstrated. While there are many issues to consider when recovering electric power systems, the focus of RSA is on the coordination of transmission and subtransmission recovery with generation dispatch to minimize unserved energy. RSA focuses on recovery simulation to evaluate resilience as part of a planning process. RSA is demonstrated on an approximately 1400-bus Puerto Rican power system for 100 simulated instances of Hurricane Maria. Analysis of the recovery determines how many lines are critical to the recovery and which loads may experience delayed recovery. The results demonstrate the potential uses of RSA for identifying recovery decisions with low unserved energy and for identifying assets critical to recovery which can then be hardened prior to a HILP event.

Maloney, Patrick R.↗
DOE OSTI2022

A treatment-effect model to quantify human dimensions of disaster impacts: the case of Hurricane Maria in Puerto Rico

Here, we propose a supervised learning approach using publicly available panel data to statistically quantify the specific manifestations of human impacts of an extreme event, such as changes number of suicides, substance abuse, excess mortality, and unemployment. This allows us to conceptually focus our framework on human impacts and how by attributing them to disaster events along widely accepted psychological, economic, and social dimensions. Our modified treatment-effect model allows counterfactual baseline conditions to be posited for each manifestation from which an aggregated quantitative multi-faceted measure of human impacts can be determined. The developed statistical methodology could be beneficial to policymakers who must allocate scarce resources to those communities in greater need. We illustrate the applicability of our approach using annual and monthly panel data from 2012 to 2018 encompassing the 2017 Hurricane Maria event across various municipalities in Puerto Rico. Our statistical modeling methodology stands apart since (i) it explicitly and more realistically captures the effect of different human-oriented manifestations of an actual event and (ii) it is flexible enough to accommodate individual preferences of various stakeholders in how they assign importance to multiple manifestations of human impacts.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

A Comparative Study of Physics‐Informed and Data‐Driven Neural Networks for Compound Flood Simulation at River‐Ocean Interfaces: A Case Study of Hurricane Irene

Simulating compound flooding (CF) at the river-ocean interface within large-scale Earth System Models (ESMs) presents significant challenges due to complex interactions between river discharge, storm surge, and tides. This study assesses the comparative advantages of physics-informed and data-driven machine learning (ML) approaches for enhancing local ESM performance. We systematically compare data-driven neural network models (i.e., CNNs, U-Net, Long Short-Term Memory (LSTM), Gated Recurrent Unit), and physics-informed neural network (PINN) models, including vanilla PINN and a finite-difference-based PINN (FD-PINN). Specifically, FD-PINN is introduced to enhance computational efficiency, accelerating vanilla PINNs by ∼6.5 times while improving accuracy. To enhance data-driven model training, a new data-generation approach is developed to sample historical fluvial and coastal flood events, which ensures a robust data set for extreme event prediction. The models are evaluated using a realistic one-dimensional river domain extracted from an ESM's river mesh and the Hurricane Irene event as an independent test case. Results show that FD-PINN achieves accurate predictions with significantly reduced computational costs relative to vanilla PINNs. Among data-driven models, the best overall performance is achieved by a CNN-LSTM hybrid, which balances accuracy and efficiency. While a fully connected CNN (CNN-FC) provides the best accuracy, it incurs high computational cost. Architectures lacking strong temporal modeling tend to underperform on unseen events. These findings highlight the importance of sequence-aware designs for robust generalization. This study reveals the trade-offs between physics-informed and data-driven models and proposes an adaptive hybrid framework for integrating ML into ESMs to enhance local flood simulations.

Earth Systems Modeling↗
DOE OSTI2020

Impacts of Hurricane Disturbance on Water Quality across the Aquatic Continuum of a Blackwater River to Estuary Complex

Hurricanes cause landscape-scale disturbances that affect biogeochemical cycling and water quality in coastal ecosystems. During Hurricane Irma’s passage through northern Florida, water movements driven by wind velocities up to 105 km h-1 caused a salinity peak in an estuary/blackwater river complex. Water quality was monitored across the 15 km site to detect the magnitude and duration of disturbance. Saline water intruded 15 km inland into a freshwater portion of the river that peaked at a salinity of 2 psu. Due to the volume of precipitation from the hurricane, significant runoff of freshwater and dissolved organic matter (DOM) caused a decrease in salinity, dissolved oxygen (DO), and Chlorophyll-a concentrations while increasing turbidity and fluorescent dissolved organic matter (fDOM). The disturbance caused rapid changes observed by in-situ water quality monitors over a 3-week period, but some effects persisted for longer periods as shown by 3-month weekly water sampling. This disturbance caused shifts in DOM loading, altered salinity dynamics, and reshaped landscapes due to wind and wave surge both in upland marsh and downstream estuary. Hurricane disturbance temporarily and abruptly alters the aquatic continuum, and observations of system response can help us understand the mechanisms associated with ecosystem resilience and recovery.

54 ENVIRONMENTAL SCIENCES↗