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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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DOE OSTI2015

NCAR Contribution to A U.S. National Multi-Model Ensemble (NMME) ISI Prediction System

NCAR brought the latest version of the Community Earth System Model (version 1, CESM1) into the mix of models in the NMME effort. This new version uses our newest atmospheric model CAM5 and produces a coupled climate and ENSO that are generally as good or better than those of the Community Climate System Model version 4 (CCSM4). Compared to CCSM4, the new coupled model has a superior climate response with respect to low clouds in both the subtropical stratus regimes and the Arctic. However, CESM1 has been run to date using a prognostic aerosol model that more than doubles its computational cost. We are currently evaluating a version of the new model using prescribed aerosols and expect it will be ready for integrations in summer 2012. Because of this NCAR has not been able to complete the hindcast integrations using the NCAR loosely-coupled ensemble Kalman filter assimilation method nor has it contributed to the current (Stage I) NMME operational utilization. The expectation is that this model will be included in the NMME in late 2012 or early 2013. The initialization method will utilize the Ensemble Kalman Filter Assimilation methods developed at NCAR using the Data Assimilation Research Testbed (DART) in conjunction with Jeff Anderson’s team in CISL. This methodology has been used in our decadal prediction contributions to CMIP5. During the course of this project, NCAR has setup and performed all the needed hindcast and forecast simulations and provide the requested fields to our collaborators. In addition, NCAR researchers have participated fully in research themes (i) and (ii). Specifically, i) we have begun to evaluate and optimize our system in hindcast mode, focusing on the optimal number of ensemble members, methodologies to recalibrate individual dynamical models, and accessing our forecasts across multiple time scales, i.e., beyond two weeks, and ii) we have begun investigation of the role of different ocean initial conditions in seasonal forecasts. The completion of the calibration hindcasts for Seasonal to Interannual (SI) predictions and the maintenance of the data archive associated with the NCAR portion of this effort has been the responsibility of the Project Scientist I (Alicia Karspeck) that was partially supported on this project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

GoAmazon 2014/15. SRI-PTR-ToFMS Field Campaign Report

Our science team, including Dr. Alex Guenther (previously at Pacific Northwest National Laboratory (PNNL) and now at the University of California, Irvine) Dr. Saewung Kim and Dr. Roger Seco, and Dr. Jim Smith (previously at NCAR and now at UC Irvine), deployed a selected reagent ion – proton transfer reaction – time-of-flight mass spectrometer (SRI-PTR-TOFMS) to the T3 site during the GoAmazon study. One of the major uncertainties in climate model simulations is the effects of aerosols on radiative forcing, and a better understanding of the factors controlling aerosol distributions and life cycle is urgently needed. Aerosols contribute directly to the Earth’s radiation balance by scattering or absorbing light as a function of their physical properties and indirectly through particle-cloud interactions that lead to cloud formation and the modification of cloud properties. On a global scale, the dominant source of organic aerosol is biogenic volatile organic compounds (BVOC) emitted from terrestrial ecosystems. These organic aerosols are a major part of the total mass of all airborne particles and are currently not sufficiently represented in climate models. To incorporate quantitatively the effects of BVOCs and their oxidation products on biogenic organic aerosol (BOA) requires parameterization of their production in terrestrial ecosystems and their atmospheric transformations. This project was designed to reduce the gaps in our understanding of how these processes control BVOCs and BOAs, and their impact on climate. This was accomplished by wet and dry season measurements at the U.S. Department of Energy (DOE)’s Atmospheric Radiation Measurement (ARM) Climate Research Facility T3 site along with a comprehensive suite of complementary measurements. The specific goals were to 1) measure and mechanistically understand the factors affecting aerosol distributions over a tropical rain forest, especially the effects of anthropogenic pollution as a perturbation to the natural state, and 2) develop and implement an upscaling analysis from this new data set and knowledge of aerosol distributions to prognosticate possible climatic impacts of present-day urban pollution and possibly greater pollution in the future.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Nonlinear Ultrasonic Diagnosis and Prognosis of ASR Damage in Dry Cask Storage

Alkali-silica reaction (ASR) is a deleterious chemical process that may occur in cement-based materials such as mortars and concretes, where the hydroxyl ions in the highly alkaline pore solution attack the siloxane groups in the siliceous minerals in the aggregates. The reaction produces a cross-linked alkali-silica gel. The ASR gel swells in the presence of water. Expansion of the gel results in cracking when the swelling-induced stress exceeds the fracture toughness of the concrete. As the ASR continues, cracks may grow and eventually coalesce, which results in reduced service life and a decrease safety of concrete structures. Since concrete is widely used as a critical structural component in dry cask storage of used nuclear fuels, ASR damage poses a significant threat to the sustainability of long term dry cask storage systems. Therefore, techniques for effectively detecting, managing and mitigating ASR damage are needed. Currently, there are no nondestructive methods to accurately detect ASR damage in existing concrete structures. The only current way of accurately assessing ASR damage is to drill a core from an existing structure, and conduct microscopy on this drilled cylindrical core. Clearly, such a practice is not applicable to dry cask storage systems. To meet these needs, this research is aimed at developing (1) a suite of nonlinear ultrasonic quantitative nondestructive evaluation (QNDE) techniques to characterize ASR damage, and (2) a physics-based model for ASR damage evolution using the QNDE data. Outcomes of this research will provide a nondestructive diagnostic tool to evaluate the extent of the ASR damage, and a prognostic tool to estimate the future reliability and safety of the concrete structures in dry cask storage systems

42 ENGINEERING↗
DOE OSTI2016

Marine Aerosol Precursor Emissions for Earth System Models

Dimethyl sulfide (DMS) is generated by marine ecosystems and plays a major role in cloud formation over the ocean. Currently, Earth System Models use imposed flux of DMS from the ocean to the atmosphere that is independent of the climate state. We have added DMS as a prognostic variable to the Community Earth System Model (CESM) that depends on the distribution of phytoplankton species, and thus changes with climate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Predictive Battery Management System for Commercial Hybrid Vehicles: Cooperative Research and Development Final Report, CRADA Number CRD-13-520

NREL and Eaton Corporation will perform work together on DOE Advanced Research Projects Agency-Energy (ARPA-E) 'Advanced Management and Protection of Energy Storage Devices' program (DE-FOA0000675). NREL will experimentally characterize aging behavior of Eaton cells and packs. Eaton and NREL will implement NREL's prognostic life model in Eaton HEV supervisory controllers and demonstrate the algorithms in accelerated life hardware-in-the loop testing conducted at NREL.

25 ENERGY STORAGE↗
DOE OSTI2015

Prototypic Enhanced Risk Monitor Framework and Evaluation - Advanced Reactor Technology Milestone: M3AT-15PN2301054

This research report summaries the development and evaluation of a prototypic enhanced risk monitor (ERM) methodology (framework) that includes alternative risk metrics and uncertainty analysis. This updated ERM methodology accounts for uncertainty in the equipment condition assessment (ECA), the prognostic result, and the probabilistic risk assessment (PRA) model. It is anticipated that the ability to characterize uncertainty in the estimated risk and update the risk estimates in real time based on equipment condition assessment (ECA) will provide a mechanism for optimizing plant performance while staying within specified safety margins. These results (based on impacting active component O&M using real-time equipment condition information) are a step towards ERMs that, if integrated with AR supervisory plant control systems, can help control O&M costs and improve affordability of advanced reactors.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2018

Uncertainty Quantification in Vibro-Acoustics Diagnosis of Alkali-Silica Reaction Degradation in Medium-Sized Concrete Samples

The objectives of this ongoing research project focus on health monitoring and data analytics of concrete slabs containing reactive aggregates and thus subjected to degradation due to alkali-silica reaction (ASR). A controlled concrete slab with four pockets of reactive aggregates (pure silica, wells, placitas, and spratt) was cast at Vanderbilt University and cured in representative conditions to accelerate degradation due to ASR. A set of four concrete samples were also cast and cured at the University of Alabama for ASR testing. Of these four samples, two slabs contained reactive aggregates while the other two had the non-reactive aggregate counterparts mixed throughout the samples. Vibro-acoustic testing was used on these slabs to locate ASR damage within the reactive samples. Vibro-acoustic modulation (VAM) is a vibration-based NDE method that utilizes signatures of nonlinear dynamic interactions on contact surfaces of crack or delamination damage to detect and localize the damage. VAM analysis was conducted on both the Vanderbilt and Alabama samples using multiple variables for damage detection and localization. The results from the data analysis of the vibro-acoustic testing on concrete slabs cured at Vanderbilt University and the University of Alabama are discussed in detail in this report. Results for damage localization are dependent on multiple variables used in the vibro-acoustic modulation experiments. A major focus of this report is to quantify the uncertainty in the diagnosis due to multiple factors and uncertainty sources. The uncertainty quantification methodology presented in this report was applied to VAM-based diagnosis and prognosis. However, the methodology is general, and is capable of being applied to multiple technique that collect spatially distributed data. Future work needs to investigate the incorporation of uncertainty quantification in developing a robust Prognostics and Health Management framework. Digital image correlation is a three-dimensional, full-field, optical NDE technique to measure contour, deformation, vibration, and strain. The application of the digital image correlation technique to study ASR-related degradation on a large concrete specimen at the University of Tennessee is discussed and observations are presented in this report.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

High Fidelity Fast Running Multi-scale Multi-physics Battery Pack Software

The project team comprising of General Motors LLC (GM), ANSYS, ESim, and the National Renewable Energy Laboratory (NREL) proposed to develop multiscale multiphysics battery pack engineering software that significantly surpassed the most advanced state-of-the-art software in computational speed and accuracy. The successful software was to run faster than current state-of-the-art software by a factor of 100 while resolving complex multiphysics interplays across the scales from materials to large automotive battery pack systems. The proposed project was to shift the paradigm to using software for battery pack system design and evaluation, potentially revolutionizing the standard development process for the broader electric vehicle industry. The project objectives were to: (a) enhance computation speed and stability of the pack level multiscale model while still resolving nonlinearities of the battery’s dynamic responses across the scales from particles to a battery pack and (b) enhance multiphysics integrity at the pack level model enabling model-based diagnostics and prognostics for life and safety.

25 ENERGY STORAGE↗
DOE OSTI2019

Jiangsu Nanjing – Lawrence Berkeley Life Sciences Center for Translational Medicine (CRADA AWD00000392)

Project 1: Our previous work demonstrated that the CES biomarker can be an approximate measure for the levels of chromosomal instability (CIN) in cancer, and that the CES (for Centromere and kinetochore gene Expression Score) biomarker effectively predicts which cancer patients benefit from adjuvant chemotherapies in lung and other cancer types. Such a utility can potentially reduce overtreatment that is common in lung cancer treatment. We determined performance of the multiplex Quantigene RNA assay platform for measuring mRNA levels of CES genes using clinical tumor samples. These experiments determined sensitivity, specificity and detection limit of 12 CES genes and 5 control genes using clinical tumor samples with paired normal tissue samples. We determined how sample concentration and storage conditions affect measurement sensitivity and specificity, and whether sample storage over time affects result reproducibility. Importantly, we confirmed that early stage lung cancer CES values are significantly higher than those of paired normal tissues in all cases. This result greatly extended our bioinformatic data mining by confirming that we can measure CES values directly from patient tumor tissue, and that tumors have higher CES values than normal tissues. Unfortunately, our CRADA partner in China did not finish measuring CES expression levels for a larger group of ~100 tumor samples, due to the long period time required for human subject review in China. This delay prevented us from generating enough additional data to generate a significant statistical model. We finished analyzing all the data acquired during the CRADA, and are trying to work collaboratively with the Shanghai University Lung Cancer Hospital to complete the analysis of more tumor samples. Although no publications have resulted from this research, the findings are crucial for ongoing efforts to obtain funding to develop the technology to address problems in deciding on appropriate treatment regimens for individual patients, in clinical settings. Project 2: The overarching milestones of CRADA Project 2 were to investigate the role of FAM83 family members in cancer progression. Two publications resulted from this work which are highlighted below. The first major goal of the CRADA was to broadly assess the role of FAM83 family oncogenes in human cancer using a multi-omic bioinformatic approach (Snijders et al 2017 Mol. Oncol 11). The second major goal was to specifically assess the role of FAM83D in a cancer progression and metastasis (Yin et al 2019 Cell Oncol; in revision). Role of FAM83 family oncogenes in human cancers Recent studies from our laboratory as well as other groups have demonstrated that FAM83 family members have oncogenic properties and have significantly elevated expression levels in multiple human tumor types, including breast cancers (BC). We performed an integrative multi-omics bio-informatic analysis of the role of FAM83 family oncogenes in human cancers. Our findings were published in the journal Molecular Oncology (Snijders et al; Mol Oncol. 2017 Feb;11(2):167-179). Highlights from this manuscript are: • Transcriptional levels of FAM83 family members are frequently and dramatically elevated across tumor types in human • DNA copy number increase appears to be a potential mechanism for increased expression of FAM83 genes • FAM83 family gene signature is associated with poor survival across human tumor types • FAM83 gene alterations are significantly correlated with TP53, PIK3CA and Ecadherin mutations. In conclusion, this analysis of FAM83 family members opened up a new horizon for further understanding the significance of FAM83 genes in cancer and clinical applications in diagnosis, prognosis and therapy. Role of FAM83D in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy. Lung cancer is one of the most common malignant tumors in the world and non-small cell lung cancer (NSCLC) accounts for 80~85% of all diagnosed lung cancer cases, one of the deadliest cancers worldwide. FAM83D has been proposed as an oncoprotein in several types of human cancer, yet its role and molecular mechanism in human non-small cell lung cancer (NSCLC) metastasis and its impact on chemotherapy are poorly understood. We evaluated the expression of FAM83D in NSCLC cells and cancerous tissues and its prognostic significance, then investigated the effect of FAM83D on tumorigenic phenotypes such as cell proliferation, EMT and invasive capability, and finally assessed impact of FAM83D on NSCLC chemo-drug resistance. The results of this study are currently in revision at the journal Cellular Oncology. Highlights from this manuscript are: • FAM83D expression is elevated in NSCLC cells and tissues and associated with poor prognosis • FAM83D promotes NSCLC cell proliferation • FAM83D induces EMT in NSCLC cells • FAM83D promotes NSCLC cell migration and invasion • FAM83D promotes EMT and invasive capability of NSCLC cells by regulating AKT/mTOR pathway • FAM83D silencing increases cisplatin sensitivity of NSCLC cells In summary, this second study demonstrated for the first time that FAM83D promoted EMT and metastasis of NSCLC cells through AKT/mTOR signal pathway as well as increased the sensitivity of NSCLC to cisplatin. This study may provide potential targets for advanced NSCLC treatment and personalized therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Cyber Secure Sensor Network for Fossil Fuel Power Generation Assets Monitoring

The energy sector is undergoing digital transformation which is to say more and more power generation assets have become automated and connected to the internet. The connected sensors can tap into plants to monitor the health of assets and manage fleets remotely. These are just some of the benefits digitalization is bringing to the power industry. Within a power plant, control systems are no longer concerned with one system or one piece of equipment, but rather whole fleet of assets inter-connected with smart sensors which have the function of continuously monitoring and transmitting real-time operational data to operators. These connected systems will form a part of the industrial internet of things (IIoT). The big data scenarios provide benefits of performing system prognostics and optimization which is a key selling point of power plant digitalization.

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2022

Intelligent Process Visualization through Nuclear Operation Process Modeling, Reasoning, and Object Detection from Field Videos (Final Report)

This report is a deliverable for the “Final Report” task of DOE NEET Project 19-16790, "Context-Aware Safety Information Display for Nuclear Field Workers." This project's overall goal is to test the hypothesis that integrating computer vision and process reasoning methods will enable proactive visualization of the safe operation and maintenance processes of Nuclear Power Plants (NPP) for field workers. Augmented Reality (AR) glasses adopting such proactive safety information visualization techniques can significantly increase personnel safety and reduce the NPP’s operating costs. The current practice of monitoring NPPs requires workers to switch between digital models, data, and physical workspaces in identifying relevant but potentially occluded objects and in assessing the risks of operation and maintenance processes. On the other hand, frequently changed field conditions require field workers to report to supervisors for real-time guidance. Such guidance is essential to ensure that changing conditions will not invalidate or endanger the work order and other ongoing processes that may jeopardize NPP operations. Additionally, incorrect recognition of equipment objects can result in communication errors and safety problems. AR techniques can assist engineers in viewing the physical workspaces with objects labeled with detailed operation procedures and safety reminders during field operations. The project team developed an “Intelligent Context-Aware Safety Information Display” (ICAD) for supporting Nuclear Power Plant (NPP) field workers in achieving safe and efficient execution of a series of operational tasks in uncertain and changing workspaces of an NPP. Before designing the ICAD-AR prototype, the project team synthesized NPP operational knowledge models through literature review studies, surveys, interviews with domain experts, and knowledge modeling. The project team conducted an extensive study of the operational procedures of various NPPs, and digital technologies that can support the safe and efficient execution of those procedures in different NPP operational contexts. This literature review helped the project team conduct surveys and interviews with nuclear engineers and field workers to identify three categories of information. The NPP knowledge modeling efforts reveal that the three categories of information identified have different levels of importance in a typical procedure of carrying out a series of tasks to achieve a specific NPP operation goal (e.g., shutdown, mode changes). These three categories of information include 1) Workspace dynamics – the changing spatial arrangements of workspaces, tools, protection equipment, and supporting materials, 2) Workflow prognostics – the dynamic dependencies between different parts of an NPP that functionally support and influence each other in terms of safety and efficiency, and 3) Hazards – objects and spaces that contain hazardous materials or physical conditions that can pose risks to workers or mechanical systems. The project team has profiled the importance levels of these categories of information into a knowledge model. This knowledge model specifies what types of information are more critical for a given task in a given workspace so that computers can automatically identify critical objects and sensors in a scene for delivering context-ware safety information to field workers through AR devices. Significant research development of this project results in technical research outcomes and a prototyping system that illustrates the technical feasibility of establishing an ICAD-AR system supporting the proactive safety information display for nuclear field workers. This final report summarizes the project team’s technological achievements in the past three years. Overall, the project team completed the development and integration of five techniques into a prototype ICAD Augmented Reality (ICAD-AR) system and demonstrated the developed system’s real-time execution in a mechanical room. The project team completed the analysis of using this prototype in other types of workspaces based on 3D image data and digital design models collected from two additional workspaces (a water treatment plant and a flow loop training facility). The integrated techniques include 1) Natural Language Processing (NLP) algorithms supporting the generation and updates of nuclear fieldwork process models based on text analysis of work packages and operation manuals; 2) sensor log analysis for predicting control actions in given sensor reading contexts; 3) computer vision algorithms for automatic localization and navigation of workers; 4) object detection algorithms for identifying task-related objects and correlated sensors for safety checking; 5) AR technique as a platform for supporting the integration. The testing results of these five techniques have shown that 1) the sensor log analysis model can predict the next control action with an accuracy of 0.883; 2) the trained natural language processing model can extract more than 80% of the critical information from paper-based procedures (PBPs); 3) the navigation algorithm with the integration of Visual Inertial Odometry (VIO) and Non-Recursive Bayesian Filter methods make operator’s trajectory estimation resilient to drift error; 4) the computer vision algorithm can detect task-specific and safety-critical objects with an average accuracy of 95.3%. The project team used work procedures collected from a flow loop training facility and two datasets collected from two mechanical rooms simulating the workspaces of NPPs to demonstrate the technical capabilities of the developed ICAD-AR prototype. The demonstration validated the technical feasibility of establishing the ICAD-AR system for nuclear field workers and identified the challenges in 1) automatic text analysis of work packages; 2) use of limited samples of sensor logs for predicting the proper timings of control actions; 3) reliably tracking workers and their task progress in mechanical rooms with many similar objects.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Printed Strain Gauges for High Temperature Applications (>300°C)

The real-time understanding of strain and deformation of materials provides prognostic health monitoring of components of current operational reactors and valuable data that shortens the timeline for the development of new nuclear-relevant materials in test reactor experiments. This report discusses the current development and testing of additively manufactured strain gauges. This has potential to improve the sensor design and manufacturing techniques to meet the requirements of the current and advanced nuclear reactors (i.e., in terms of environment conditions, sample geometry, and materials compatibility). The developmental additively manufactured strain gauges are exposed to separate effects testing (i.e., mechanical strain, high temperature) to determine environmental factors that affect the strain gauge. In addition, sensor qualification methodologies are further developed for determining the reliability and robustness at the interface of the AM strain gauge materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An Operations and Maintenance Roadmap for U.S. Offshore Wind: Enabling a Cost-Effective and Sustainable U.S. Offshore Wind Energy Industry Through Innovative Operations and Maintenance

The United States is currently targeting 30GW of offshore wind to be installed by 2030, and 150GW by 2050. Even considering future turbine sizes, this represents thousands of new turbines installed in a diverse set of environments, each with their unique design, installation, and maintenance challenges. While much can be learned from European and Asian experience with offshore wind over the past two decades, it is important to understand the unique circumstances of the U.S. This document explores operations and maintenance of offshore wind energy, specific to the U.S. and attempts to lay out a roadmap for needed activities to ensure reliability of future installations. The roadmap was informed through dozens of interviews with a wide cross-section of the industry, including representatives from OEMs, owner/operators, service companies, certification agencies, service providers, and researchers. The roadmap first describes the problem by component - blades, drivetrain and nacelle, structures and foundations, and electrical systems - through a look at current practices and opportunities for improvement in the areas of Failure Mode Analysis and Mitigation; Monitoring, Sensing, and Inspection; and Maintenance Execution. Crosscutting areas of Digitalization, Robotics and Automation, Prognostics and Health Management and O&M Optimization, Experimentation and Demonstration, Standardization, and Design Optimization Considering Reliability and O&M are then discussed. Finally, the roadmap summarizes all of these topics with recommendations for short (1-3 years), medium (4-7 years), and long term (8-12 years) activities, with a description of needed public and private sector contributions.

17 WIND ENERGY↗
DOE OSTI2022

Analytics-at-scale of Sensor Data for Digital Monitoring in Nuclear Plants (4th Annual Report)

Nuclear plant sites collect and store large volumes of data collected from various equipment and systems. These datasets typically include plant process parameters, maintenance records, technical logs, online monitoring data, and equipment failure data. The collection of such data affords an opportunity to leverage data-driven machine learning and artificial intelligence technologies to provide diagnostic and prognostic capabilities within the nuclear power industry to reduce operating and maintenance costs. In this way, nuclear energy can become more economically competitive with other energy sources, and premature closures can be avoided. From a maintenance standpoint, savings can be achieved by leveraging machine learning and artificial intelligence technologies to develop data-driven algorithms to better diagnose and predict potential faults within the system. Improved model accuracy can lead to reductions in unnecessary maintenance and more efficient planning of future maintenance, thus lowering the costs associated with parts, labor, and unnecessary planned, forced, or extended outages. From an operations perspective, cost savings can be generated by shifting from route-based monitoring to wireless technologies for online monitoring, and by transitioning from onsite- to cloud-based computing and storage services. Wireless monitoring would reduce the operator manhours required for taking routine measurements, while cloud computing services would generate cost savings by reducing the amount of hardware needing to be purchased and maintained—all while scaling to both computational and storage demands. This report summarizes this project’s effort to shift from costly, labor-intensive preventative maintenance to cheaper predictive maintenance.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Ice-shelf freshwater triggers for the Filchner–Ronne Ice Shelf melt tipping point in a global ocean–sea-ice model

Some ocean modeling studies have identified a potential tipping point from a low to a high basal melt regime beneath the Filchner–Ronne Ice Shelf (FRIS), Antarctica, with significant implications for subsequent Antarctic ice sheet mass loss. To date, investigation of the climate drivers and impacts of this possible event have been limited because ice-shelf cavities and ice-shelf melting are only now starting to be included in global climate models. Using a global ocean–sea-ice configuration of the Energy Exascale Earth System Model (E3SM) that represents both ocean circulations and melting within ice-shelf cavities, we explore freshwater triggers (iceberg melt and ice-shelf basal melt) of a transition to a high-melt regime at FRIS in a low-resolution (30 km in the Southern Ocean) global ocean–sea-ice model. We find that a realistic spatial distribution of iceberg melt fluxes is necessary to prevent the FRIS melt regime change from unrealistically occurring under historical-reanalysis-based atmospheric forcing. Further, improvement of the default parameterization for mesoscale eddy mixing significantly reduces a large regional fresh bias and weak Antarctic Slope Front structure, both of which precondition the model to melt regime change. Using two different stable model con figurations, we explore the sensitivity of FRIS melt regime change to regional ice-sheet freshwater fluxes. Through a series of sensitivity experiments prescribing incrementally increasing melt rates from the smaller, neighboring ice shelves in the eastern Weddell Sea, we demonstrate the potential for an ice-shelf melt “domino effect” should the upstream ice shelves experience increased melt rates. The experiments also reveal that modest ice-shelf melt biases in a model, especially at coarse ocean resolution where narrow continental shelf dynamics are not well resolved, can lead to an unrealistic melt regime change at downstream ice shelves. Thus, we find that remote connections between melt fluxes at different ice shelves are sensitive to baseline model conditions. Our results highlight both the potential and the peril of simulating prognostic Antarctic ice-shelf melt rates in a low-resolution global model.

58 GEOSCIENCES↗
DOE OSTI2022

Low-Cost, Easy-To-Integrate and Reliable Grid Energy Storage System with 2 nd Life Lithium Batteries

Batteries retired from electric vehicles have the potential to extend their service as low-cost stationary energy storage systems. However, disperse battery state of health (SOH) and nonuniform battery characters often lead to compromised battery performance and reliability, which greatly hinder their adoption. A Heterogenous Unifying Battery (HUB) system is proposed to stage 2 nd life battery bricks for a period, and enable them to attain improved SOH uniformity, performance, and reliability before being sold for 2 nd life applications, while simultaneously providing grid services. It may offer a technically and economically advantageous solution for the broad utilization of 2 nd use batteries. The goal of this project was to develop the hardware and software that enables the key functions of the HUB system. The first achievement of the project was the development of a 1kW scale proof-of-concept (POC) system, which comprises (i) a modular plug-n-play DC-DC power converter matrix with isolated series output connections to achieve fully independent control of energy flow to each of the connected battery units at low voltage; (ii) enhanced model based control that drives each batteries’ SOH towards uniformity while collectively providing grid energy storage services; and (iii) comprehensive procedures to perform battery diagnostics and prognostics. The second achievement was the development of a 100kW scale HUB system and demonstrated its performance of re-establishing battery SOH uniformity through a period of battery cycling operation. The final HUB system incorporates six DC-DC power converter matrices paired with six battery bricks. Hot swapping of a single battery brick while maintaining consistent system power was demonstrated and system operation was validated to be capable of implementing the approved grid duty cycle and of balancing and conditioning the battery bricks. Through the course of the project, the team optimized the building-block design, form-factors, and adjusted life balancing control. An up-sized 250kW Scale was developed and deployed in October 2022 with pack-level battery form factors, see photo in Figure 1 The third achievement of the project was to perform a techno-economic analysis in order to better understand the cost and revenue potentials in this new “recondition-then-resell" value proposition. The final TEA quantified the economics of new Li-ion batteries as well as second-life batteries processed via reconditioning and traditional binning. Results showed the reconditioned second-life batteries in this project to be economically favorable and viable in grid energy storage markets. The TEA results were published in the Applied Energy journal. The fourth achievement of the project was to deliver a tech-to-market plan for the HUB system that includes funding, IP, and manufacturing strategies. The final T2M plan outlines a business strategy in which the HUB provides a B2B service to EV companies as an alternative to battery recycling that can prepare batteries for 2nd life applications. A company named Smartville Inc. was founded to carry on the commercialization, funding, and technical IP licensing activities of the OPEN project.

25 ENERGY STORAGE↗
DOE OSTI2020

Wireless Sensor Modalities at a Nuclear Plant Site to Collect Vibration Data

One of the major contributors to the total operating costs of domestic nuclear fleet of reactors today is the operation and maintenance (O&M) costs. These include labor-intense preventive maintenance programs involving manually-performed inspection, calibration, testing, and maintenance of plant assets at periodic frequency and time-based replacement of assets at periodic frequency, irrespective of their conditions. This has resulted in a labor-centric business model to achieve high capacity factors. To build an optimal maintenance program, it’s time to transition from this labor-centric business model to a technology-centric business model. Fortunately, there are technologies (advanced sensor, data analytics, and risk assessment methodologies) that will support this transition. The technology-centric business model will result in significant plant life extension and reduction of time-based maintenance activities. This will drive down O&M costs as labor is a rising cost and technology is a declining cost. This approach will lay the foundation for real-time condition assessment of plant assets, allowing condition-based maintenance to enhance plant safety, reliability, and economics of operation. The goal of this project is to address challenges in the area of digital monitoring, i.e., the application of advanced sensor technologies (particularly wireless sensor technologies) and science-based data analytic capabilities to advance online monitoring and predictive maintenance in nuclear plants to improve plant performance (efficiency gain and economic competitiveness). To achieve the project goal, in partnership with Exelon Generating Company (Exelon), researchers from Idaho National Laboratory (INL) and Oak Ridge National Laboratory (ORNL) are performing research and development (R&D) to demonstrate application of wireless sensors using the distributed antenna system and advanced data analytics to achieve predictive maintenance. In the report, wireless vibration sensors, vibration data and its indicator are described. The wireless vibration sensors presented in this report support three types of wireless communication, namely, Wi-Fi, cellular, and 900 MHz. These wireless vibration sensors are considered by partner plant site for installation on plant asset to enable online vibration monitoring to replace periodic measurements. These vibration data along with other plant process data will be utilized to develop diagnostic and prognostic models.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Identifying Northern Hemisphere Stratospheric and Surface Temperature Responses to the Mt. Pinatubo Eruption within E3SMv2-SPA

The Mt. Pinatubo eruption on 15 June 1991 is often associated with surface warming in the subsequent Northern Hemisphere winter. Employing E3SMv2 with prognostic aerosol modifications, we generated an ensemble of simulations initialized on 1 June 1991 to limit the intra-ensemble variability at the time of the eruption and a more traditional ensemble representing the full range of intra-ensemble variability. For each ensemble member we generated a paired counterfactual simulation with the Pinatub forcing removed allowing for isolation of the Pinatubo impact. In general, the limited variability ensemble has greater coherence in the Pinatubo impact across ensemble members which leads to more statistically robust signals compared to the full variability ensemble. Stratospheric warming patterns from Pinatubo were approximately zonally symmetric and confined between 30°S and 50°N. Isolating localized surface temperature impacts was more difficult, but the limited variability simulation did identify a preferential region of cooling between 20°S to 50°N.

54 ENVIRONMENTAL SCIENCES↗