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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Normal or abnormal? Machine learning for the leakage detection in carbon sequestration projects using pressure field data

The international commitments for atmospheric carbon reduction will require a rapid increase in carbon capture and storage (CCS) projects. The key to any successful CCS project lies in the long term storage and prevention of leakage of stored carbon dioxide (CO 2 ). In addition to being a greenhouse gas, CO 2 leaks reaching the surface can accumulate in low-lying areas resulting in a serious health risk. Among several alternatives, some of the more promising CCS storage formations are depleted oil and gas reservoirs, where the reservoirs had good geological seals prior to hydrocarbon extraction. With more CCS wells coming online, it is imperative to implement permanent, automated monitoring tools. We apply machine learning models to automate the leakage detection process in carbon storage reservoirs using rates of (CO 2 ) injection and pressure data measured by simple harmonic pulse testing (HPT). To validate the feasibility of this machine learning based workflow, we use data from HPT experiments carried out in the Cranfield oil field, Mississippi, USA. The data consist of a series of pulse tests conducted with baseline parameters and with an artificially introduced leak. Here, in this study, we pose the leakage detection task as an anomaly detection problem where deviation from the predicted behavior indicates leaks in the reservoir. Results show that different machine learning architectures such as multi-layer feed forward network, Long Short-Term Memory, and convolutional neural network are able to identify leakages and can provide early warning. These warnings can then be used to take remedial measures.

58 GEOSCIENCES↗
DOE OSTI2025

Insulation Activation and Contamination in Low-Power Experiments

Among the diverse advanced nuclear reactor concepts being developed are designs that expect to operate at much higher temperatures than conventional light-water reactors, and therefore require alternative materials for components capable of withstanding these extreme temperatures. Insulation is one such component where alternative high-temperature industrial composites are being considered. Low-power experiments can provide an opportunity for advancing our understanding of material behavior under irradiation; however, these experiments are not without occupational hazards. The focus of this paper is the potential for activation of insulating composites when exposed to neutron fluence during reactor experiments, and to discuss the subsequent contamination of the testing area. This paper will also discuss practical measures for preventing the creation and inhalation of activated particulate matter (i.e., dust), such as engineering and administrative controls and personal protective equipment. This paper evaluates the suitability of several insulation materials for use in reactor experiments based on their activation from neutron irradiation. The materials considered are Pyrogel XTE by Aspen Aerogel, Cerablanket and Kaowool by Morgan Advanced Materials, Thermo-12 Gold by Johns Manville, and Maxsil CF6-2000 by McAllister Mills. Although these materials are not yet widely used in the nuclear industry, they are common in other industries due to their thermal insulating properties. These materials produce dust while handling that can pose health and safety hazards, which could be further exacerbated by activation. The analysis presented in this paper utilizes a deterministic method developed to predict the activation source term of the provided insulation materials. The method calculates the atomic density of each element within the composites and simulates their exposure to a specified neutron flux density representative of low power experimental conditions. During the irradiation phase and throughout the extensive cooling period, neutron absorptions and nuclear decays are tracked for relevant isotopes. The residual activity within each composite is then assessed to identify the material with the lowest potential radiological hazard to workers. Dust will be created during installation and removal of insulating materials and may also be generated during the experiment. Contamination of surrounding surfaces due to dispersion is highly likely unless preventative measures are taken. The results of this analysis are used to estimate an internal dose from inhalation of activated dust, which is significantly lower than the allowable whole-body dose. However, the risk of internal exposure may increase if the dust becomes a transport vector for nuclear material. The goals of this paper are to enhance the understanding of the potential activation and contamination hazards from the use of these insulating materials in low-power nuclear experiments and to foster safer work environments through informed decision-making and strategic planning. This work ultimately contributes to a broader understanding of how new non-nuclear materials for advanced reactors impact the radiological safety of co-located workers, helping to ensure the responsible management of radioactive materials during installation and decommissioning activities.

12 - MGMT OF RADIOACTIVE AND NON-RADIOACTIVE WASTE↗
DOE OSTI2021

Predicting Cement-Sheath Integrity with Consideration of Initial State of Stress and Thermoporoelastic Effects

In conventional wellbore-integrity analysis, the cement sheath’s initial state of stress and transient thermoporoelastic effects are often neglected. However, the initial state of stress is prerequisite information for accurately predicting the safe operating conditions that prevent a cemented well from being damaged. In addition, transient thermoporoelastic effects can have a profound effect on when damage will occur. Here in this paper, we propose a model that includes these effects to predict the safe operating pressures and temperatures that will prevent cement-sheath failure. For the initial state of stress, we proposed an empirical model using measurements. Subsequent stress changes are evaluated by a fully coupled transient thermoporoelastic model to analyze the mechanical behavior of the cement sheath. We predict the safe operating envelope (SOE) for shear, tensile, and debonding cement-sheath failures caused by pressure and temperature perturbations after the cement sets. Our model predicts that pore pressure is a key factor for cement failure, especially for rapid temperature changes. If the formation is low permeability, the transient pore pressures are amplified, causing the risk of damage to increase. Compared with conventional thermoelastic models, the thermoporoelastic model predicts a smaller SOE when heating the internal casing fluid and a larger envelope when cooling the internal casing fluid. Finally, the heating rate was considered with respect to field applications. The heating rate was also considered, and slower heating/cooling rates can prevent damage to the cement sheath. Finally, the thermoporoelastic model was applied to explain several laboratory and field experiments and achieved good matches.

02 PETROLEUM↗
DOE OSTI2020

Inferred inflow forecast horizons guiding reservoir release decisions across the United States

Abstract. Medium- to long-range forecasts often guide reservoir release decisions to support water management objectives, including mitigating flood and drought risks. While there is a burgeoning field of science targeted at improving forecast products and associated decision support models, data describing how and when forecasts are applied in practice remain undeveloped. This lack of knowledge may prevent hydrological modelers from developing accurate reservoir release schemes for large-scale, distributed hydrology models that are increasingly used to assess the vulnerabilities of large regions to hydrological stress. We address this issue by estimating seasonally varying, regulated inflow forecast horizons used in the operations of more than 300 dams throughout the conterminous United States (CONUS). For each dam, we take actual forward observed inflows (perfect foresight) as a proxy for forecasted flows available to the operator and then identify for each week of the year the forward horizon that best explains the release decisions taken. Resulting “horizon curves” specify for each dam the inferred inflow forecast horizon as a function of the week of the water year. These curves are analyzed for strength of evidence for contribution of medium- to long-range forecasts in decision making. We use random forest classification to estimate that approximately 80 % of large dams and reservoirs in the US (1553±50 out of 1927 dams with at least 10 Mm3 storage capacity) adopt medium- to long-range inflow forecasts to inform release decisions during at least part of the water year. Long-range forecast horizons (more than 6 weeks ahead) are detected in the operations of reservoirs located in high-elevation regions of the western US, where snowpack information likely guides the release. A simulation exercise conducted on four key western US reservoirs indicates that forecast-informed models of reservoir operations may outperform models that neglect the horizon curve – including during flood and drought conditions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Cyber risk assessment and investment optimization using game theory and ML-based anomaly detection and mitigation for wide-area control in smart grids

The electric power grid is increasingly becoming susceptible to cyber attacks that exploit vulnerabilities in the smart grid control, information, and physical layers. Successful cyber attacks can have catastrophic impacts on the social and economic well-being of any nation all over the globe. It has, thus, become imperative to secure the smart grid against such adversarial actions to ensure stable, secure, and reliable operation of the grid. The existing research and industry practices prove to be inadequate in terms of providing pragmatic and effective defense methodologies and measures for long-term cybersecurity planning and real-time cybersecurity for grid operation. For example, existing works lack models that incorporate uncertain behavior of cyber-attackers and pragmatic defense measures for cyber risk assessment and cybersecurity investment optimization which often provide unreliable and strictly qualitative solutions to these problems. At the same time, with the growing number of cyber incidents in the grid, there still exists a need to develop attack-resilient algorithms for wide-area monitoring, protection, and control (WAMPAC) applications like the wide-area voltage control systems (WAVCS) for Flexible AC Transmissions Systems (FACTS) that lack in scalable and feasible solutions from the cybersecurity perspective. This dissertation proposes novel models and methodologies for: (1) Cybersecurity planning, and (2) Cybersecurity for system operation. The cybersecurity planning is achieved through cyber risk assessment and cybersecurity resource investment optimization for long-term cybersecurity of the grid using game theory and attack-defense trees. Cybersecurity for system operation consists of development of cyber anomaly detection and mitigation algorithms for flexible AC transmission system (FACTS) controller-based wide-area voltage control systems (WAVCS) using machine learning (ML), and software defined networking-based moving target defense network routing for achieving real-time cyber-physical security for grid operations. This is followed by hardware-in-the-loop (HIL) implementation and evaluation of these attack prevention, detection, and mitigation algorithms and methodologies showcasing their feasibility in a close to real-world environment. For cybersecurity planning, a novel approach involving a combination of game theory and attack defense trees (ADT) for optimal cybersecurity resource allocation in the smart grid is proposed. This methodology involves modeling of the cyber-physical smart grid substations as ADTs, defining attacker costs, defense costs, and attack probabilities for attack access points. Using game theoretical formulation, optimal defense strategies for the defender of the system to invest cybersecurity resources in the grid are obtained. Additionally, a game-theoretic framework is developed for quantitative cyber-physical risk assessment of the grid under a dynamically changing cyber threat space and uncertain behavior of cyber attackers which is further used to optimize investments in the smart grid's cybersecurity resources. The attacker, defender, and the smart grid system are modeled while incorporating attacker-stochasticity and federal guidelines for smart grid cybersecurity. This allows quantification of threat, vulnerabilities, and attack impact of the grid for quantitative risk assessment. The defender's budget to invest in the security resources in the grid is optimized based on the strategies leading to minimum system risk. The evaluation of the proposed solutions highlight the feasibility for practical implementation of these methodologies and algorithms in the smart grid, while taking the federal requirements and guidelines for smart grid security into consideration. For achieving cybersecurity for system operation, attack prevention, detection, and mitigation algorithms and methodologies are developed specifically for FACTS-based WAVCS. Anomaly detection and mitigation in the WAVCS are achieved using algorithms based on machine learning which involves offline training and testing of ML models with CPS datasets incorporating physics-based features that allow accurate distinction between system faults and cyber attacks. For attack prevention, a methodology based on software defined network (SDN)-based moving target defense (MTD) network routing is proposed that enables prevention of Denial of Service (DoS) type attacks on the smart grid communication system. Subsequently, these methodologies and algorithms are implemented and evaluated on an HIL testbed that allows for real-time attack prevention, detection, and mitigation of emulated cyber attacks on the WAVCS in a close to real-world environment. The results show highly accurate and efficient performance of the implemented algorithms and methodologies with the smart grid system operating within the NERC's system operation limits even in the presence of DoS and data integrity cyber attacks. This work opens up future research opportunities in other directions such as (1) Expanding cybersecurity planning methodologies to real-time cyber contingency analysis with different game formulations; and (2) Applying the cybersecurity for system operation algorithms to broader categories of wide-area control applications.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Glovebox Ergonomics Basics for Ergonomists Course #50606 [Slides]

Upon completion of this course participants will: Have an understanding of common topics and terms associated with glovebox basics. Be able to identify common types of musculoskeletal disorders. Be able to identify high risk activities associated with glovebox activities. Be able to demonstrate knowledge of glovebox ergonomic controls and best practices. Be able to demonstrate knowledge of glovebox ergonomic design/usage guidance and best practices. Be able to demonstrate knowledge of preventable exercises for glovebox ergonomic injuries.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Critical steps in preventing future pandemics. Early lessons from the Covid-19 crisis for addressing natural and deliberate biological threats.

This report summarizes a virtual workshop on early lessons from the COVID-19 pandemic as they pertain to proactively addressing future biological threats. Co-hosted by Sandia National Laboratory (Sandia) and the Council on Strategic Risks (CSR) in August 2020, the discussion involved experts who at that time were leading innovative efforts in various U.S. government agencies, industry, and academia sharing observations from their ongoing pandemic response efforts. Based on the input by these expert participants, it is clear that even though the pandemic response is ongoing, the following recommendations will be important to consider for more successfully addressing biological threats in the future: Continue building on the cross-sector collaboration and agility shown in the COVID-19 response; Expand capabilities for detecting biological threats early; Prioritize ways to create and disseminate medical countermeasures even faster; Create the U.S. bio industrial base needed for rapid response to biological threats, and keep it healthy; and, Major government reorganization may not be needed if there is effective work to form coalitions, improve coordination, and expand steady-state and surge capacities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Multi-modal characterisation of early-stage, subclinical cardiac deterioration in patients with type 2 diabetes

Abstract Background Type 2 diabetes mellitus (T2DM) is a major risk factor for heart failure with preserved ejection fraction and cardiac arrhythmias. Precursors of these complications, such as diabetic cardiomyopathy, remain incompletely understood and underdiagnosed. Detection of early signs of cardiac deterioration in T2DM patients is critical for prevention. Our goal is to quantify T2DM-driven abnormalities in ECG and cardiac imaging biomarkers leading to cardiovascular disease. Methods We quantified ECG and cardiac magnetic resonance imaging biomarkers in two matched cohorts of 1781 UK Biobank participants, with and without T2DM, and no diagnosed cardiovascular disease at the time of assessment. We performed a pair-matched cross-sectional study to compare cardiac biomarkers in both cohorts, and examined the association between T2DM and these biomarkers. We built multivariate multiple linear regression models sequentially adjusted for socio-demographic, lifestyle, and clinical covariates. Results Participants with T2DM had a higher resting heart rate (66 vs. 61 beats per minute,p < 0.001), longer QTc interval (424 vs. 420ms,p < 0.001), reduced T wave amplitude (0.33 vs. 0.37mV,p < 0.001), lower stroke volume (72 vs. 78ml,p < 0.001) and thicker left ventricular wall (6.1 vs. 5.9mm,p < 0.001) despite a decreased Sokolow-Lyon index (19.1 vs. 20.2mm,p < 0.001). T2DM was independently associated with higher heart rate (beta = 3.11, 95% CI = [2.11,4.10],p < 0.001), lower stroke volume (beta = −4.11, 95% CI = [−6.03, −2.19],p < 0.001) and higher left ventricular wall thickness (beta = 0.133, 95% CI = [0.081,0.186],p < 0.001). Trends were consistent in subgroups of different sex, age and body mass index. Fewer significant differences were observed in participants of non-white ethnic background. QRS duration and Sokolow-Lyon index showed a positive association with the development of cardiovascular disease in cohorts with and without T2DM, respectively. A higher left ventricular mass and wall thickness were associated with cardiovascular outcomes in both groups. Conclusion T2DM prior to cardiovascular disease was linked with a higher heart rate, QTc prolongation, T wave amplitude reduction, as well as lower stroke volume and increased left ventricular wall thickness. Increased QRS duration and left ventricular wall thickness and mass were most strongly associated with future cardiovascular disease. Although subclinical, these changes may indicate the presence of autonomic dysfunction and diabetic cardiomyopathy. Graphical Abstract

Cardiovascular System & Cardiology↗
DOE OSTI2023

2022 Climate Change Vulnerability Assessment and Resilience Plan Summary

Los Alamos National Laboratory (LANL, or the Laboratory) produced a Vulnerability Assessment and Resilience Plan (VARP) following Department of Energy (DOE) Guidance to assess and manage climate change related risks to the Laboratory’s assets and operations. This is a condensed summary of the Laboratory’s 2022 VARP – the full version of the document is not publicly available at this time. The VARP was led by the Pollution Prevention (P2) Program in the Environmental Protection and Compliance Division (EPC-DO) and covers the entirety of the 36-square-mile LANL site in Los Alamos, New Mexico. A Steering Committee, composed of representatives from the three main Laboratory Directorates and the Los Alamos Field Office, identified real property Critical Assets based on their Mission Dependency Index (MDI) scores. LANL used MDI scores of 70 and above, which are considered Mission-Critical, to generate a list of 141 Critical Asset facilities.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Intern Deliverable Poster 2025

An Incident Response Plan (IRP) is a document that is created and maintained by an organization that provides guidance in the event of a cyber incident. The primary objectives of an IRP are to aid in the detection, response, and recovery from incidents, as well as to enhance preparation and preventative measures. An IRP should outline specific procedures at each stage of an incident, with the goal of minimizing asset damage, data leakage, and operational impact. By investing in a well-defined IRP, organizations can better manage and mitigate risks associated with cyber threats, ensuring business continuity and resilience. This poster summarizes incident response guidelines for wind energy, which faces unique cybersecurity and physical challenges.

17 - WIND ENERGY↗
DOE OSTI2022

Nature-based solutions in mountain catchments reduce impact of anthropogenic climate change on drought streamflow

Quantifying how well Nature-based Solutions can offset anthropogenic climate change impacts is important for adaptation planning, but has rarely been done. Here we show that a widely-applied Nature-based Solution in South Africa – invasive alien tree clearing – reduces the impact of anthropogenic climate change on drought streamflow. Using a multi-model joint-attribution of climate and landscape-vegetation states during the 2015–2017 Cape Town “Day Zero” drought, we find that anthropogenic climate change reduced streamflow by 12–29% relative to a counterfactual world with anthropogenic emissions removed. This impact on streamflow was larger than corresponding reductions in rainfall (7–15%) and reference evapotranspiration (1.7–2%). Clearing invasive alien trees could have ameliorated streamflow reductions by 3–16% points for moderate invasions levels. Preventing further invasive alien tree spread avoided potential additional reductions of 10–27% points. Total clearing could not have offset the anthropogenic climate change impact completely. Invasive alien tree clearing is an important form of catchment restoration for managing changing hydroclimatic risk, but will need to be combined with other adaptation options as climate change accelerates.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Prevalence of Americans reporting a family history of cancer indicative of increased cancer risk: Estimates from the 2015 National Health Interview Survey

The collection and evaluation of family health history in a clinical setting presents an opportunity to discuss cancer risk, tailor cancer screening recommendations, and identify people with an increased risk of carrying a pathogenic variant who may benefit from referral to genetic counseling and testing. National recommendations for breast and colorectal cancer screening indicate that men and women who have a first-degree relative affected with these types of cancers may benefit from talking to a healthcare provider about starting screening at an earlier age and other options for cancer prevention. The prevalence of reporting a first-degree relative who had cancer was assessed among adult respondents of the 2015 National Health Interview Survey who had never had cancer themselves (n = 27,999). We found 35.6% of adults reported having at least one first-degree relative with cancer at any site. Significant differences in reporting a family history of cancer were observed by sex, age, race/ethnicity, educational attainment, and census region. Nearly 5% of women under age 50 and 2.5% of adults under age 50 had at least one first-degree relative with breast cancer or colorectal cancer, respectively. We estimated that 5.8% of women had a family history of breast or ovarian cancer that may indicate increased genetic risk. A third of U.S. adults who have never had cancer report a family history of cancer in a first-degree relative. Furthermore, this finding underscores the importance of using family history to inform discussions about cancer risk and screening options between healthcare providers and their patients.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Study of the Heater-Coil Electrical Insulation for the HL-LHC Low Beta Quadrupoles

In the framework of the HL-LHC project, the present LHC low-β superconducting quadrupoles will be substituted with higher performance Nb 3 Sn magnets (MQXF) with 11.4 T coil peak field. MQXF coils are impregnated with epoxy resin to reduce risk of stress concentration on the brittle conductor. The magnet quench protection is provided by CLIQ and quench heaters to ensure a redundant system. Quench heaters are impregnated with the coils in order to have suitable thermal contact with them, and to prevent the hot spot temperature from exceeding 350K during normal operation in case of a quench. Quench heaters are insulated from the coil by S-2 Glass and polyimide. Here, the test of the first MQXF prototype (4 m long) MQXFAP1 was stopped by a coil-to-ground short circuit triggered by a heater-to-coil short. This issue triggered a root analysis of the causes of this short. Here we prove that the use of a non-conforming cloth in coil impregnation, further weakened by non-conforming high voltage test, has triggered the shorts. Moreover, we present an analysis of the heater-to-coil insulation strength, showing the role of blistering phenomena and how they are triggered by a combination of magnet powering and heater firing.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Optical Biosensor Platforms Display Varying Sensitivity for the Direct Detection of Influenza RNA

Detection methods that do not require nucleic acid amplification are advantageous for viral diagnostics due to their rapid results. These platforms could provide information for both accurate diagnoses and pandemic surveillance. Influenza virus is prone to pandemic-inducing genetic mutations, so there is a need to apply these detection platforms to influenza diagnostics. Here, we analyzed the Fast Evaluation of Viral Emerging Risks (FEVER) pipeline on ultrasensitive detection platforms, including a waveguide-based optical biosensor and a flow cytometry bead-based assay. The pipeline was also evaluated in silico for sequence coverage in comparison to the U.S. Centers for Disease Control and Prevention’s (CDC) influenza A and B diagnostic assays. The influenza FEVER probe design had a higher tolerance for mismatched bases than the CDC’s probes, and the FEVER probes altogether had a higher detection rate for influenza isolate sequences from GenBank. When formatted for use as molecular beacons, the FEVER probes detected influenza RNA as low as 50 nM on the waveguide-based optical biosensor and 1 nM on the flow cytometer. In addition to molecular beacons, which have an inherently high background signal we also developed an exonuclease selection method that could detect 500 pM of RNA. The combination of high-coverage probes developed using the FEVER pipeline coupled with ultrasensitive optical biosensors is a promising approach for future influenza diagnostic and biosurveillance applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Financial Analysis of the Smallmouth Bass Flows implemented at the Glen Canyon Dam during Water Year 2024

The Glen Canyon Dam (GCD) is a Colorado River Storage Project (CRSP) power resource that is a component of the Salt Lake City Area Integrated Projects (SLCA/IP). The 2016 record of decision (ROD) for the GCD long-term experimental and management plan (LTEMP) final Environmental Impact Statement (EIS) specifies criteria for GCD monthly water releases, daily and hourly operating limits, and experimental releases. This report presents a financial analysis of the Smallmouth bass (Micropterus dolomieu) (SMB) flows implemented at GCD during Water Year (WY) 2024. These bypass flows were introduced by the U.S. Bureau of Reclamation (USBR) as an emergency response to the growing threat posed by invasive SMB in the Colorado River ecosystem downstream of the dam. SMB are a non-native predatory species that pose a significant threat to native fish populations, including the endangered humpback chub (Gila cypha). The thermal regime below GCD, typically cold due to hypolimnetic releases from Lake Powell, has historically served as a thermal barrier limiting SMB establishment. However, persistently low reservoir levels in recent years have reduced stratification in Lake Powell, allowing warmer water to be released downstream. This has enabled SMB to spawn successfully below the dam, prompting urgent ecological concerns. To mitigate the risk of SMB proliferation, the USBR implemented a series of bypass flows in WY 2024. Drawn from a lower elevation than the penstocks, the bypass structures released cooler water downstream. These short-duration bypass flows aimed to keep temperatures cool enough to prevent SMB from spawning, thereby reducing the ecological threat posed by this invasive species. Although motivated by ecological objectives, these bypass flows came with financial tradeoffs. Releasing water through the bypass structures instead of the turbines at GCD reduced hydropower generation, resulting in a significantly lower financial position for Western Area Power Administration (WAPA), which is responsible for marketing the electricity produced by the GCD Powerplant. This report analyzes the financial impact of the SMB flows implemented from July to November 2024. These experimental releases led to an estimated financial cost of approximately $18.9 million, primarily driven by the substantial volume of water diverted through the bypass structures. This study applies an integrated set of tools to estimate WAPA financial impacts by simulating GCD under two types of cases; namely, (1) a “With Experiment” case that mimics the water operations that actually occurred, including the SMB bypass flows, and (2) a “Without Experiment” case that simulates operations under the assumption that the SMB flows did not occur. Both cases comply with LTEMP hourly and daily operating criteria, and the monthly water release volumes are assumed to be identical under both cases. The Colorado River Storage Project Python-based model (CRiSPPy) model was the main modeling tool used to simulate the dispatch of the GCD hydropower plant and associated water releases from Lake Powell. In the modeling process, the research team used extensive data sets and historical information on SLCA/IP power plant characteristics, hydrologic conditions, and WAPA’s power purchases and sales prices.

13 HYDRO ENERGY↗
DOE OSTI2025

Review on Preprocessing Strategies, Deactivation, Thermal Safety, and Future Perspectives in Lithium-Ion Battery Recycling

The rapid growth in the use of lithium-ion batteries (LIBs) in electric vehicles, consumer electronics, and renewable energy storage has made effective end-of-life management essential. Recycling LIBs is critical not only for resource recovery and environmental protection but also for ensuring safety and economic viability. This review focuses on the preprocessing technologies that precede typical recovery processes, including disassembly, sorting, discharging, electrolyte removal, dismantling, thermal treatment, separation, and flotation. These steps play a foundational role in determining the efficiency, safety, and environmental impact of LIB recycling. LIBs pose substantial fire and explosion risks due to residual charge, flammable electrolytes, and reactive materials. The conditions and successive progression of the exothermic reactions which lead to thermal runaway has been discussed. It also explores secure deactivation techniques such as external circuit discharge, saline immersion, and thermomechanical methods, alongside fire prevention strategies including the use of flame retardants, elimination of oxidants, and reduction of heat generation and accumulation. Challenges and future directions are outlined, highlighting the need for standardized designs, automation, and safer, more sustainable recycling infrastructure. Furthermore, this review is distinguished by its focused analysis of preprocessing and deactivation steps, with particular attention to the thermal safety engineering aspects of LIB recycling.

Battery deactivation↗
DOE OSTI2022

A Cybersecurity Threat Profile for a Connected Lighting System

In this paper we analyze a threat profile performed on a fault-detection use case for streetlights. A threat profile establishes security requirements, justifies security measures, yields actionable controls, and effectively communicates risk to stakeholders. This effort provides critical information for making threat-based decisions to increase security at a reasonable cost, and can effectively be used by development teams, software architects, and managers to make cybersecurity a part of their ongoing culture of awareness, training, and prevention. This leads to more secure systems and better-understood security. On-premise, cloud, and hybrid architectures with different authentication mechanisms were modeled and later categorized using the Microsoft STRIDE framework. An analysis of the recommended controls for each threat was performed to determine which controls could and should be put in place by manufacturers or third-party suppliers, and which controls need to be left up the end-user to implement.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A Cybersecurity Threat Profile for a Connected Lighting System

In anticipation of improved energy performance and cost savings, cities and building owners are increasingly considering “smart lighting initiatives” that aim to convert their collection of simple luminaires (i.e., lighting fixtures) into an intelligent connected lighting system (CLS) capable of remotely monitoring energy consumption and fault conditions, and possibly implementing adaptive lighting schemes. The U.S. Department of Energy (DOE) has set an national goal of tripling the energy efficiency and demand flexibility of the buildings sector by 2030, relative to 2020 levels 1. It is forecast that connected lighting systems can contribute to that goal by delivering 125 TWh of annual energy savings by 2035 2, equivalent to the annual output of 50 typical (500 MW) power plants. However, these energy savings and the DOE goal are put at significant risk if connected technologies are not adopted due to real or perceived cybersecurity concerns. Connected IoT devices such as these have historically been rife with vulnerabilities which sometimes put security considerations secondary to functionality and operability. What are the cybersecurity threats that will impact these systems, as formerly banal luminaires transition into intelligent connected devices that collect information about themselves, their surrounding environment, and possibly us? In this paper we analyze a threat profile performed on a fault-detection use case for streetlights. A threat profile establishes security requirements, justifies security measures, yields actionable controls, and effectively communicates risk to stakeholders. This effort provides critical information for making threat-based decisions to increase security at a reasonable cost, and can effectively be used by development teams, software architects, and managers to make cybersecurity a part of their ongoing culture of awareness, training, and prevention. This leads to more secure systems and better-understood security. On-premise, cloud, and hybrid architectures with different authentication mechanisms were modeled and later categorized using the Microsoft STRIDE framework. An analysis of the recommended controls for each threat was performed to determine which controls could and should be put in place by manufacturers or third-party suppliers, and which controls need to be left up the end-user to implement. Fifty-seven threats were identified. Among our key findings: (1) 65% (37/57) of the threats did not involve the luminaires, but rather the other components needed to communicate with and manage them; (2) 63% (36/57) of the threats could have been mitigated through manufacturer-implemented defensive techniques or “controls”; and (3) 23% (13/57) of the threats were dependent on the network configuration. Recommendations based on the results of this work are made to key stakeholder groups. Notably, lighting technology developers are advised to address all threats that can be reasonably controlled with baked-in technology solutions (e.g., encryption or authentication controls), and employ some form of secure supply chain management and tracking where other parts (e.g., sensors, microprocessors) of a luminaire must also be built and manufactured with the proper security controls in place. Developers should also review threats involving assets not developed in-house to understand how connectivity with other devices will affect their product during system operation and determine if a compensating control for a defense-in-depth strategy will be needed. Finally, those interested in deploying CLS should compare the differences between cloud and on-premise models to determine which is more suitable for their needs and the abilities of their security team.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗