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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 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Model of Operation-and-Maintenance Costs for Photovoltaic Systems

This article presents a method for calculating costs associated with operation and maintenance (O&M) of photovoltaic (PV) systems. It compiles details regarding the cost and frequency of multiple O&M services to estimate annual O&M costs ($\$$/year) for each year of an analysis period, the net present value ($\$$) of life cycle costs accumulated over the analysis period, and the reserve account amount ($\$$). Here we show that this method is an improvement over the previous averaged or levelized per-unit ($\$$/kW/year) valuations for estimating PV O&M costs, because it allows a detailed selection of services to perform based on system size, market served (e.g., residential, commercial, or utility), type and configuration of system components (e.g., micro-, string or central inverter) and site and environmental conditions (e.g., snow, pollen, bird populations). This model also distinguishes costs that vary from year to year and increase at different rates over time because of heuristic failure distributions (e.g. Weibull or Lognormal distribution) based on actuarial data for many of the services. This cost model was created by the PV O&M Working Group of researchers and industry, sponsored by DOE Solar Energy Technologies Office, and has been published in an on-line version hosted by SunSpec Alliance at apsuite.sunspec.org. A spreadsheet version is included with this paper as supplementary material.

14 SOLAR ENERGY↗
DOE OSTI2020

Workforce and Economic Development Considerations from the Operations and Maintenance of Wind Power Plants

As wind capacity in the United States grows, economic opportunities also emerge—supported by the development of the wind workforce, specifically in the operation and maintenance (O&M) phase. Moreover, almost all wind turbines in the United States—about 99%—are located in rural areas (American Wind Energy Association 2018). Communities in these rural areas can anticipate some degree of economic impact from the wind industry. However, the various stakeholders within these communities, such as local businesses and government officials, are not always fully informed of the workforce impacts caused by wind power plants during operational years (Stefek et al. 2019). Although several research studies have focused on workforce and economic impacts of land-based wind facilities (e.g., Tegen et al. 2014; Brown et al. 2012; Halvatzis and Keyser 2013), these studies focused on the quantity of workers and not on O&M workforce interactions with local communities, such as worker commuting and spending patterns. This report is intended to provide stakeholders and community decision makers with qualitative and quantitative information to enable informed decisions that support workforce planning around new wind energy deployment.

17 WIND ENERGY↗
DOE OSTI2021

Wind Turbine Drivetrain Reliability and Wind Plant Operations and Maintenance Research and Development Opportunities

The Drivetrain Reliability Collaborative holds annual meetings convened by the National Renewable Energy Laboratory, Argonne National Laboratory, and the U.S. Department of Energy to bring researchers and stakeholders throughout the wind turbine drivetrain supply, operations, and sustainment chain together to explore the state of the art in mechanical system reliability and operations and maintenance challenges that if solved could have significant benefits. Identification of these research and development opportunities are a prime focus of the meetings and are summarized in this report.

17 WIND ENERGY↗
DOE OSTI2025

PMDT: AI-Enabled Predictive Maintenance Digital Twins for Advanced Nuclear Reactors

Our team made substantial technical progress on various fronts during the course of the program. Multiple milestones were geared towards demonstrating the feasibility of machine learning based predictive maintenance digital twins towards reducing O&M costs, whereas some other milestones actually focused on identifying technical gaps and developing technologies such as humble AI to provide necessary robustness to the ML-based models. We were able to demonstrate in many cases that Machine learning-based methods can be successfully adapted for Nuclear plant environments especially for remote monitoring applications. Detailed analyses were carried out with plant and full scope simulation data along with capabilities of enhanced analytics to assess and set realistic expectations on cost reductions in O&M. These assessments are paving the way for investments towards reactor design improvements as well project planning for SMR projects as they develop and mature in the next few years. Technology developed under this program got direct visibility to GE Hitachi and their utility customers and resulted in positive intents to deploy some of the elements from design phase. The project additionally resulted in several reports, publications, software and data generation that will be useful in deployment and O&M services for BWRX300 fleets.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2026

Pumped Storage Hydropower Operation & Maintenance Cost Estimation

The National Laboratory of the Rockies (NLR) develops and hosts a pumped storage hydropower (PSH) cost model that is the most detailed bottom-up PSH cost model available to the public. It is available both as a spreadsheet and an interactive web tool, enabling users with a variety of PSH interests to transparently characterize costs of alternative PSH sites and designs. The NLR PSH cost model was designed originally to consider only upfront capital costs only. This slide deck describes methodology to expand the cost model to include operations and maintenance (OM) costs. OM costs are characterized as five distinct components with unique sources and methods for cost estimation. By combining methods for each of these components into a cumulative OM cost estimate, these methods allow a more complete estimation of total OM costs that agrees with existing literature values. The methods are scalable and transparent, allowing them to be readily to applied to any prospective PSH facility for a representative preliminary OM cost estimate in advance of detailed site-specific engineering and other studies.

13 HYDRO ENERGY↗
DOE OSTI2022

Modeling Predictive Maintenance for NuScale’s Condensate and Feedwater System Using EMRALD

Event Modeling Risk Assessment using Linked Diagrams (EMRALD) is used to model of NuScale’s feedwater and condenser system to capture component degradation and repairing process. To model the reliability of the components, a three-stage failure rate is used contrary to a constant rate leading to component failure. Results from EMRALD will be used to compare different maintenance strategies to reduce system downtime. The outcome of the project will assist in maximizing remaining useful life of the overall system and increasing the availability and revenue of the plant.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Foundations of Off-Grid Solar in Haiti: 6. Installation, Operations, and Maintenance of Off-Grid Solar Systems

This training lends insight into key information and concepts for off-grid solar in Haiti, building foundational knowledge and an ability to catalyze interest in off-grid solar for rural electrification in Haiti. This self-paced course is offered in both English and French and covers a variety of topics related to energy access in Haiti including off-grid solar products, market potential in Haiti, supply and demand side considerations, system design, installation and maintenance, off-grid solar business models, financial modeling, gender and energy access, productive use of energy, and climate adaptation. The training is intended for university students, government partners, NGOs, funders, development partners, professionals, and more. No prior knowledge of off-grid solar or Haiti is required to benefit from this training. See NREL/PR-7A40-89260 for the French translation of this document.

ENERGY PLANNING, POLICY, AND ECONOMY,SOLAR ENERGY↗
DOE OSTI2024

Integration of Condition-Based, Diagnostic, Prognostic, And Anomaly Detection Data into Reliability Models to Support a Predictive Maintenance Context

Reliability data employed in plant reliability models are an approximated integral representation of the past industrywide operational experience, and they neglect the present asset health status (available, for example, from online monitoring data and diagnostic assessments) and forecasted health projection (when available from prognostic models). Ideally, in a predictive maintenance context, system reliability models should support decision making by propagating actual health information from the asset to the system level in order to provide a quantitative snapshot of system health and identify the most critical assets. Asset health should be informed solely by that specific asset’s current and historical performance data and should not be an approximated integral representation of the past industrywide operational experience (as currently performed by system reliability models through Bayesian updating processes). This paper proposes a reliability modeling approach that relies on asset diagnostic and prognostic assessments, along with monitoring data to measure asset health. We show how state-of-the art condition-based, diagnostic, prognostic, and anomaly detection models can be linked to system reliability models not in probability terms, but in terms of margin where margin is defined as the “distance” between the present status and an undesired event (e.g., failure or unacceptable performance). Then, we show how the propagation of margin data from the asset to the system level is performed through classical reliability models such as fault trees or reliability block diagrams. The described method is in fact able to propagate heterogenous health data from the asset to the system level in order to analytically assess system health.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Investigation of the Performance and Explainability Tradeoffs for Machine-Learning Models for Predictive Maintenance of Circulating Water Systems in Nuclear Power Plants

Predictive maintenance (PdM) has shown great potential for achieving substantial cost savings and enhancing the economic competitiveness of nuclear power plants (NPPs) in today's energy market. Among the different modeling approaches that exist, machine learning (ML) tools in particular have a demonstrated ability to handle high dimensional and multivariate data and to extract hidden relationships within data in industrial environments. While ML methods show great potential, their lack of explainability---especially for black-box models---is a major hurdle to their adoption. Moreover, considering the supposed trade-off between explainability and performance challenges, careful consideration must be made as to which of these quality aspects takes precedence in light of multiple modeling options, resource availability, and domain characteristics. The present work evaluates the performance of six ML models, each with a different degree of explainability, in classifying the conditions of circulating water pumps (CWPs) by utilizing sensor data from nuclear power plants. To determine the drivers behind the trade-offs presented by this array of models, this work also tests different combinations of CWP units as the training and testing data, degrees of data imbalance, and objective functions for hyperparameter tuning. It was found that black-box models tend to afford superior performance in cases where there are far more instances of one type of labeled data than of any other type. It is recommended that a guided procedure be followed for designing and delivering an ML system that is sufficiently explainable to all involved stakeholders.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Operations and Maintenance for Electric Vehicle Charging Infrastructure in Indian Country

This fact sheet provides an overview of operations and maintenance (O&M) costs, responsibilities, and contracts to help ensure EV charging infrastructure remains functional and reliable in Indian Country. O&M planning is important for Tribes to consider at the beginning of EV charging infrastructure projects to help ensure charger uptime and longevity.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2026

Development of Digital Twin-Informed Predictive Maintenance for Critical Components in Advanced Reactors

Small modular reactors (SMRs) and microreactors, along with other advanced reactor (AR) technologies, are key to the future of nuclear energy. For these systems to achieve low operating costs, high reliability, and flexibility across applications, their operation and maintenance must be optimized. Digital twin (DT) technology is one of the technologies that enables real-time (or faster than real-time) monitoring and prognosis of critical components which are vital for operational efficiency, low costs, and enhanced safety of ARs, accelerating their deployment. DT technology provides dynamic virtual representation of physical assets by integrating real-time data, physics-based models, and advanced analytics, which is critical to optimizing the performance of the entire energy system throughout the life cycle. DTs empower engineers and operators to virtually explore different scenarios, configurations, and control strategies, allowing for the identification of optimal solutions that maximize reactor efficiency, safety, and economics.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

An in-depth field validation of “DUSST”: A novel low-maintenance soiling measurement device

This study presents indoor and field validation results for two versions of the “DUSST” optical soiling sensor, intended to be a low-cost and low-maintenance device for measuring photovoltaic soiling losses. Indoor testing covers irradiance calibration and temperature dependencies, which are necessary to achieve high accuracy, low uncertainty field measurements. Field testing includes an array of different environments including Saudi Arabia, California, Utah, and Colorado. DUSST versions include a configuration with a 530-nm light emitting diode (LED) (discussed in previous work) and a unit with seven white LEDs and a polycarbonate collimating optic. The new design increases light intensity fivefold and demonstrates a single linear calibration coefficient is effective to measure soiling losses as high as 75%. Field data from Utah and California demonstrate that daily soiling loss measurements and soiling rate calculations closely match both reference cell and full-size module measurements of soiling losses and soiling rates. Corrective methods employed on the Utah DUSST sensor suggest that it is possible to achieve measurement errors as low as ±0.1% at two standard deviations. Field data from both Colorado and Saudi Arabia demonstrate that LED lens soiling can occur and that further design optimizations are needed. The lesson learned from all the field deployment locations suggests directions for future design improvements.

14 SOLAR ENERGY↗
DOE OSTI2021

A role for Rab30 in retrograde trafficking and maintenance of endosome-TGN organization

Highlights: • Rab30 localised primarily to TGN and recycling endosomes. • Rab30 plays a major role in the spatial organization of the TGN and recycling endosomes. • Deficiency of Rab30 results in a perturbation in endosome-TGN retrograde transport. • Interactive partners of Rab30 include Arf1 and Arf4. • Findings highlight the interconnected network between the TGN and endosomal system. Rab30 is a poorly characterized small GTPase. Here we show that Rab30 is localised primarily to the TGN and recycling endosomes in a range of cell types, including primary neurons; minor levels of Rab30 were also detected throughout the Golgi stack and early endosomes. Silencing of Rab30 resulted in the dispersal of both early and recycling endosomes and TGN compartments in HeLa cells. By analyzing cargo trafficking in Rab30-silenced and Rab30-overexpressing HeLa cells, we demonstrate that Rab30 plays a role in retrograde trafficking of TGN38 from endosomes to the Golgi, but has no apparent role in the endocytic recycling of the transferrin receptor to the plasma membrane. Five interactive partners with Rab30 were identified by pull-down and MS analysis using GFP-tagged Rab30 mutant, Rab30(Q68L). Two of the interactive partners identified were Arf1 and Arf4, known regulators of endosome to TGN retrograde transport. Knockdown of Arf1 and Arf4 results in GFP-Rab30 decorated tubules arising from the recycling endosomes, suggesting association of Rab30 with tubular carriers. Overall our data demonstrates a role for Rab30 in regulating retrograde transport to the TGN and maintenance of endosomal-TGN organization.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Economic Analysis of Condition-Monitoring-Based Predictive Maintenance in Power Plants under Market Elasticity

Condition-monitoring-based predictive maintenance can increase the power plant availability by preventing forced outages. However, the actual on-stream time depends on market elasticity and dynamics, which are affected by cost and penetration of other power generation technologies. This paper develops a systematic approach for the economic analysis of investment in condition monitoring technologies with due consideration of market elasticity. Focus is on corrosion monitoring in coal-fired power plants (CFFPs) since corrosion in high-temperature coal-fired boilers is a leading cause of equipment failure. Investment in sensor networks for measuring corrosion and operating conditions like metal temperature and concentrations of O 2 and SO 2 is investigated. The unscented Kalman filter is used to estimate corrosion in the waterwall section of the boiler under multiple sensor networks. Electricity produced by CFPPs in the future in the U.S. due to changes in availability under market elasticity is studied. Sensitivity of the incremental net present value to factors like the number, type, and cost of sensors is analyzed.

Electrochemistry↗
DOE OSTI2022

The dynamic nature of risk in DOE facilities in the surveillance and maintenance program–with observations for risk communications

Here, we explore three case studies of facilities at two US Department of Energy (DOE) former nuclear weapons research and production sites—the Oak Ridge National Laboratory and the Hanford site—whose risk profiles have changed during their long-term management under the DOE's surveillance and maintenance (S&M) program. These case studies provide examples of the challenges faced in communicating to external stakeholders, such as federal and state regulators, local communities surrounding the site, as well as the general public, the circumstances surrounding unexpected events or the emergence/discovery of new risk-important information at historically high-risk sites. We identify common topics of importance from these case studies and suggest a taxonomy for risk communicators to use in informing the dialogues with individuals and organizations that may not be technically oriented or fully informed on the subject matter. The taxonomy is based on technical insights from the quintessential definition of risk known as the Kaplan–Garrick “risk triplet” as well as insights from regulatory guidance documents on risk communication with external stakeholders originally developed for the commercial nuclear power industry.

45 MILITARY TECHNOLOGY, WEAPONRY, AND NATIONAL DEF↗
DOE OSTI2022

HOMP (Hybrid Operation and Maintenance Platform) [SWR-22-81]

The HOMP software module is contained within NREL's Hybrid Optimization and Performance Platform (HOPP) at this URL: https://github.com/NREL/HOPP/tree/feature/HOMP. HOMP is a python-based software module which can be used concurrently with the Hybrid Optimization and Performance Platform (HOPP) to model, simulate, and optimize degradation, reliability, and operations and maintenance of hybrid power plant components. The program currently models lithium ion battery, PEM electrolyzer, wind turbine, and solar PV array components, which enables users to understand the design and operation tradeoffs of hybrid power plants. For instance, HOMP enables users to answer questions like: how large does a battery bank need to be to achieve optimal charge/discharge rates for performance and reliability? How does plant design change for different objectives (e.g., profit versus resilience)? Under what conditions is it better to produce electricity versus hydrogen? How do we optimally control hybrid power plants to reduce downtime and increase performance?

Clark, Caitlyn↗
DOE OSTI2025

Frontal Maintenance in Submesoscale Flows

Classic deformation theory includes parameters—divergence, total strain, and vorticity—that are invariant to changes in the coordinate system. However, these parameters are sometimes ambiguous with respect to characterizing how fronts are formed and maintained because the presence of a front imposes a reference coordinate system. To help remedy this ambiguity, we propose a framework in frontal coordinates based on along- and cross-front velocity gradients to better characterize frontal maintenance, which can also be used to define divergence and normal strain in frontal coordinates. The framework with these four parameters (along-, cross-front velocity gradients, divergence, and normal strain in frontal coordinate) defines eight characteristic flow types at a front, providing a complete characterization of the flow that strengthens or weakens a front. This framework highlights the importance of the “strain efficiency” concept, which unambiguously defines the contribution of total strain to frontogenesis. Two examples, one based on a realistic simulation of submesoscales in the northern Gulf of Mexico and the other based on an idealized model with similar flow characteristics, are provided to demonstrate how this framework can be used to enhance our understanding of frontal dynamics in submesoscale flows.

frontogenesis↗