Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “information storage”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Machine learning for automated experimentation in scanning transmission electron microscopy

Abstract Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-centric experiment workflow design and optimization. Here, we discuss the associated challenges with the transition to active ML, including sequential data analysis and out-of-distribution drift effects, the requirements for edge operation, local and cloud data storage, and theory in the loop operations. Specifically, we discuss the relative contributions of human scientists and ML agents in the ideation, orchestration, and execution of experimental workflows, as well as the need to develop universal hyper languages that can apply across multiple platforms. These considerations will collectively inform the operationalization of ML in next-generation experimentation.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Impact of Hydrogen on Methane and Pollutant Emissions over Three-Way Catalysts with Natural Gas–Hydrogen Blends

Blending natural gas (NG) with hydrogen (H₂) can improve combustion and engine performance while potentially facilitating the catalytic conversion of methane and other pollutants, resulting in cleaner tailpipe emissions. This study evaluates the impact of H2 on the conversion of methane, CO, and NOx emissions on a commercial three-way catalyst (TWC) in a flow reactor using synthetic gas mixtures that simulate stoichiometric engine exhausts with NG or NG+H₂ combustion. The work examines whether, and how, the additional amount of H₂ in the exhaust stream affects the conversion efficiency of methane and other pollutants. Experiments were conducted with both degreened and aged catalysts under controlled conditions, systematically varying temperature, the air-to-fuel equivalence ratio (λ), and λ modulation. Test conditions covered λ values from 0.996 to 1.000 to represent nominally stoichiometric engine operation with different λ modulation amplitudes, as well as a range of temperatures to inform control strategies for effective CH₄, CO, and NOₓ reduction. Overall, the results show that hydrogen addition significantly improves the conversion efficiency of CH₄ and NOₓ, particularly at temperatures below 500 °C. More significantly, this study highlights that exhaust gas composition, operating temperature, λ management, and the oxygen storage capacity of the TWC all play major roles in affecting the tailpipe emissions from NG and NG+H₂ combustion.

Prikhodko, Vitaly [ORNL] (ORCID:0000000244685836)↗
DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗
DOE OSTI2023

LA100 Equity Strategies. Chapter 12: Distribution Grid Upgrades for Equitable Resilience and Solar, Storage, and Electric Vehicle Access

The LA100 Equity Strategies project integrates community guidance with robust research, modeling, and analysis to identify strategy options that can increase equitable outcomes in Los Angeles' clean energy transition. As Los Angeles transitions toward clean energy, existing distribution grid infrastructure will need to be updated and expanded to support reliable service during routine operations, enable interconnection with distributed energy resources and electrified loads, and provide access to energy-related services during disasters. This chapter focuses on equity in distribution grid upgrades, reliability, and resilience in Los Angeles. Specifically, NREL performed grid upgrade and resilience analyses using a detailed model of the distribution grid and income-differentiated household load profiles, electric vehicle (EV) adoption patterns, distributed solar adoption, and grid reliability to explore two key questions to inform how the City of Los Angeles can ensure a resilient and reliable distribution grid for all communities during the clean energy transition: Where can distribution system upgrades can be prioritized to enable equitable access to, and adoption of, clean energy technologies and how can Los Angeles provide equitable, resilient access to electricity-related services (e.g., health care, food) during disaster events like earthquakes and flooding? The electric distribution system is the "last mile" of the grid, linking the multistate bulk power system with customers; new loads, including EVs; and distributed energy resources, such as customer and community solar and storage. This analysis focuses on the 4.8-kilovolt (kV) system, including service transformers that represent the utility-side of the grid connection for most residential customers. Chapter 17 looks at the customer-side of the grid connection with a focus on electric panel upgrade needs. The transition toward clean energy can put additional stress on the distribution system from distributed energy resources and electrification - especially EVs and increased use of electricity for heating, cooling, cooking, and hot water. This stress, measured here as the number of equipment overloads and voltage violations, correlates strongly to grid reliability and therefore is used as a proxy for understanding additional upgrades needed and to help ensure equitable access to electrification and distributed energy resources. NREL also conducted community resilience analysis to examine customer-level access to both electricity and a larger range of services, such as hospitals and grocery stores during a disaster. This analysis explicitly considers equity to understand differences in current resilience and resilience strategies to effectively improve critical services access for all Angelenos. Research was guided by input from the community engagement process, and associated equity strategies are presented in alignment with that guidance.

14 SOLAR ENERGY↗
DOE OSTI2026

Qualitative Risk Assessment of Legacy Wells within the Estimated Prairie State Generating Company Area of Review

This report details the digitization of a legacy wellbore database, including data processing assumptions, parameter estimation, and risk assessment methodology. The database, comprising 6,454 documents, was provided by ISGS. It includes valuable data from the Prairie State Generating Company (PSGC) and One Earth Energy (OEE) sites of the CarbonSAFE Phase III – Illinois Storage Corridor project. The report focuses on wells within a 15-mile radius from the Lively Grove #1 (LG#1) well at PSGC site, evaluating subsurface conditions and potential risks. A total of 4,386 wellbores within 15 miles of the LG#1 well were filtered based on depth and formation codes. LG#1 is the stratigraphic well at the PSGC site drilled in 2021. Ninety-four (94) wells penetrating the Maquoketa Shale Group (the primary confining unit) within the estimated area-of-review (AoR) for the PSGC site were evaluated using a qualitative risk assessment (QRA) methodology. The QRA developed by Arbad et al. 2022 focuses on legacy wells within the AoR and categorizes them based on well construction details. The QRA identifies wells that need immediate attention by categorizing them based on penetration depth and protection. Wells within the AoR were categorized into nine groups based on penetrations and protections. These categories range from Type 1 wells, with no documentation, to Type 9 wells, which do not penetrate the primary confining unit or storage reservoir (unit). Well accessibility within the AoR varies based on well status, including Dry & Abandoned (DA), Plugged & Abandoned (PA), Injection (INJ), Oil/Gas Producing (PROD), and Observation (Obs) wells. Accessibility levels were determined by well construction, with DA wells being the least accessible and Observation wells the most accessible, impacting gas leakage detection possibilities. Remedial action priority of wells decreases from Type 1 to Type 9 wells. Type 1 to Type 6 wells with status DA and PA require immediate attention, while Type 7 and Type 8 wells are low priority. A risk matrix used to prioritize corrective actions for legacy wells is proposed to categorize wells within an AoR based on penetrations, protections, and accessibility. The methodology involves data acquisition, well categorization into nine types, and determining CO 2 leakage pathways using well schematics and geospatial mapping. This approach is particularly useful for managing the integrity of legacy wells throughout the lifecycle of a Carbon Capture and Storage (CCS) project. A qualitative risk assessment of 94 wells within the AoR of the PSGC site identified 54 wells with high priority for corrective action due to penetration of the primary containment seal. The assessment utilizes color-coded maps to categorize well types and prioritize corrective actions, providing a comprehensive analysis. Schematics of wells penetrating the primary confining unit were drawn, and leakage pathways were identified. Details of all wells penetrating the confining zone are provided in the appendix, including information on well types, plugging, and casing status.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2022

Computational Fluid Dynamic Modeling of Dry Cask Simulator with Crosswind

The purpose of this study is to create a STAR-CCM+ model of a Belowground Vertical Dry Cask Simulator (BVDCS) at Sandia National Laboratories (SNL) and validate the model with SNL’s experimental results. The BVDCS consists of a single boiling water reactor assembly fitted with electric heaters encompassed by a containment vessel and shell to represent a belowground spent nuclear fuel (SNF) dry storage system. Blowers are located near the inlet and outlet of the BVDCS to simulate crosswind conditions. In addition to the experimental results, the STAR-CCM+ model developed for this study is compared with a previous computational fluid dynamics (CFD) model in a different software program, which is used as a software-to-software benchmark. The experimental results provide a dataset to compare the STAR-CCM+ model results for a variety of different conditions. The main objective is to validate and improve STAR-CCM+ CFD models for spent nuclear fuel storage systems with explicitly modeled external environments and “wind driven” crossflows. These CFD models aide in the study of external particle deposition in spent nuclear fuel storage systems, which is important to predicting the significance of chloride induced stress corrosion cracking (CISCC). In addition to experimental comparison, a sensitivity analysis study is performed using the STAR-CCM+ model. The sensitivity analysis provides a quantitative assessment of the sensitivity of various parameters. This helps provide information on various parameters that are of particular importance to constructing a model representative of real life systems. The STAR-CCM+ model compared well to the experimental results showing similar responses to changes in cross wind flow, and a number of parameters are identified for model improvement.

Jensen, Ben J.↗
DOE OSTI2020

Next-generation geospatial-temporal information technologies for disaster management

Traditional geographic information systems (GIS) have been disrupted by the emergence of Big Data in the form of geo-coded raster, vector, and time-series Internet-of-Things data. This article discusses the application of new scalable technologies that go far beyond relational databases and file-based storage on spinning disk or tape to incorporate both storage and processing data in the same platform. The roles of the Apache Hadoop Distributed File Systems and NoSQL key-value stores such as the Apache Hbase are discussed, along with indexing schemes that optimally support geospatial-temporal use. Here, we highlight how this new approach can rapidly search multiple GIS data layers to obtain insights in the context of early warning, impact evaluation, response, and recovery to earthquake and wildfire disasters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Front End Engineering Design of Linde-BASF Advanced Post-Combustion CO 2 Capture Technology at a Southern Company Natural Gas-Fired Power Plant (Final Scientific/Technical Report)

This document details the execution of Cooperative Agreement DE-FE0031847, “Front End Engineering Design of Linde-BASF Advanced Post-Combustion Carbon Dioxide (CO 2 ) Capture Technology at a Southern Company Natural Gas-Fired Power Plant” during the period of 10/1/2019 to 6/30/2022. The project was funded by the U.S. Department of Energy’s Office of Fossil Energy and Carbon Management (FECM) and managed by the National Energy Technology Laboratory (NETL). Southern Company Services, Inc. (SCS) was the prime recipient and led the project team. Other members of the project team included Linde, Inc. (Linde), Linde Engineering – Dresden (LED), and BASF. The overall goal of the project was to complete a front-end engineering design (FEED) study for installing the Linde-BASF post-combustion capture (PCC) technology at an existing domestic natural gas-fired combined cycle (NGCC) power plant within Southern Company’s portfolio of assets. The CO 2 capture plant was to be of commercial scale (at least 375 MWe) and include process units for pre-conditioning of the flue gas system, the CO 2 capture plant island, storage vessels, the CO 2 compression train, and any necessary components for integration into the NGCC plant. Mississippi Power’s Plant Daniel Unit #4 was chosen as the host site for the FEED with the target of capturing 90% of CO 2 emissions from the existing combustion turbines. The information produced by the FEED was used to develop a cost estimate of +/- 15% accuracy. Capital costs, excluding financing, are estimated at approximately $\$752$ million dollars (2021). The execution of a project based on this FEED study has an estimated duration of almost five years.

03 NATURAL GAS↗
DOE OSTI2025

Electron-Ion Collider (EIC) Hadron Storage Ring (HSR) Injection Transport Beamline (Y-Line Ext.) Lattice Design and Magnet Parameters

The preliminary design of the Electron-Ion Collider (EIC) Hadron Storage Ring (HSR) Normal Conducting Injection Beamline ( Y-Line Ext. ) lattice is discussed. The preliminary design includes the periodic cells, matching sections, state of the art Twiss parameters at the HSR injection, the magnet parameters and first order instrumentation placement. Furthermore, the beamline parameters, element coordinates, and other information are discussed. The names of the HSR Injection Beamline elements used in this technical report ( i.e. HD1, HQ1 ) are preliminary and not the official site wide names.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

Illuminating the Material World: Autonomous Microscopy to Understand Order, Disorder, and Everything In Between

Artificial intelligence (AI) holds immense promise for revolutionizing microscopy, yet its widespread adoption has been hindered by challenges ranging from user inexperience to limited model transferability and difficulties in operationalizing machine learning. This presentation showcases our approach to developing practical autonomy for materials discovery, aiming to accelerate the integration of AI into everyday microscopy workflows. As shown in Fig. 1, I will focus on three key areas: understanding order-disorder transitions, quantifying point defects, and achieving truly device-scale microscopy. First, I will demonstrate the power of multi-modal knowledge graphs for integrating diverse microscopy data. By combining imaging, spectroscopy, and diffraction data, these graphs provide a holistic view of material behavior, capturing the intricate relationships between different modalities [1,2]. I will present a case study on how these models illuminate the structural and chemical changes associated with irradiation in oxide thin films, revealing critical insights for designing materials for extreme environments like spaceflight and nuclear energy. Specifically, I will show how multi-modal analysis clarifies the evolution of order-disorder transitions under irradiation, a key factor influencing material performance in these applications. Next, I will address the challenge of quantifying point defects in 2D materials. We demonstrate the application of computer vision and transfer learning to accurately identify and classify various defect types, such as vacancies and substitutional atoms, and to quantify their concentrations. This information is crucial for understanding and tailoring the properties of 2D materials for applications in electronics, optoelectronics, and catalysis. For example, I will show how our models can characterize the topological distribution of point defects in MXene transition metal carbides, providing valuable insights for optimizing their performance in energy storage and separation science. Finally, I will discuss our progress toward autonomous device-scale microscopy [3,4]. We are fundamentally redesigning electron microscopes around the principles of machine reasoning, enabling automation beyond basic tasks like sample navigation and data acquisition to include sophisticated experimental design. This approach paves the way for truly reproducible and massively scaled analysis campaigns. I will emphasize the importance of autonomous microscopy platforms for high-throughput materials discovery and characterization, facilitating the rapid screening of materials for a broad range of applications and accelerating the development of next-generation technologies.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Scaling Sustainable Biochar Research & Commercialization for Agriculture & Conservation: A Summary from a Stakeholder Convening

Biochar is a carbon-rich substance with the potential to increase soil carbon sequestration, reduce greenhouse gas (GHG) emissions and improve soil health. When used as part of soil health management systems, biochar can provide multiple synergistic benefits for the agricultural sector and society, while also supporting the production of renewable, climate-neutral biofuels. Establishing a pyrolysis biochar bioenergy industry (PBBI) is necessary to harness the environmental benefits of biochar. To galvanize the necessary coordination to support a PBBI, the Foundation for Food & Agriculture Research (FFAR), the National Center for Appropriate Technology (NCAT), and American Farmland Trust (AFT) co-hosted a virtual convening event on biochar research and commercialization in March 2022. Participants represented a diverse set of stakeholders, including biochar producers, agricultural producers, and members of nonprofit, industry and government agencies. This whitepaper offers a summary of the convening, as well as insights from subsequent stakeholder engagement. Sustainably sourced, fit-for-purpose biochar can be a powerful tool in the soil health management systems and climate-smart toolbox, with decades of research supporting the soil, environment, production, and climate benefits. However, because biochar represents a range of feedstocks, processes and products, there is a critical need to: • Coordinate efforts to characterize and test the application of biochar types across locations with diverse soil and management conditions • Address research and decision-support gaps • Support the growing industry These objectives can be addressed or advanced through a coordinated strategy. To support establishment of this strategy, the convening participants identified key gaps in implementation and research. The experiences of foresters, ranchers, and farmers already using biochar, some of whom participated in the convening, helped inform priorities. Key gaps include: • Practical, applied on-farm research • Supporting management for agricultural productivity • Maximum benefits for conservation • Decision support • Life Cycle Assessment (LCA) • Quantification of climate mitigation potential Existing research gaps include: • Potential synergies among biochar, soil organic matter, and enhanced rock weathering • Development of new storage reservoirs for biochar in industrial products Convening participants further provided next steps for rapidly developing a PBBI to produce sustainable, fit-for-purpose biochars that increase soil health and mitigate climate change. Participants also stressed the need for actionable, coordinated, large-scale research relevant to commercial production in the next five years. To have a chance to reduce greenhouse gas emissions, we need an array of tools that would aid in mitigation; a coordinated strategy to enable biochar to be an effective tool in the climate-smart toolbox must be created; We cannot wait 50 years.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Charged Wellbore Casing Controlled Source Electromagnetics (CWC-CSEM) for Reservoir Imaging and Monitoring (Final report)

This project addresses the needs of the U.S. Department of Energy (DOE) to develop advanced monitoring technologies and protocols to track the fate of subsurface carbon dioxide (CO2) plumes for carbon storage. Specifically, the project seeks to develop and test a unique and novel system of technologies consisting of electromagnetic data acquisition, coupled multiphysics imaging, and reservoir model enhancement to understand the migration and long-term distribution of CO2 in the subsurface. The overarching objective is to develop an integrated approach for long term monitoring of carbon storage. The two main components of the project include the methodology development and the test of the method at a field site. The methodology component consists of 1) developing the field procedure and protocol for collecting time-lapse controlled-source electromagnetic (CSEM) data with source electric current injected into the subsurface through wellbore casings; 2) building of background 3D electrical conductivity utilizing multiple sources of data such as supplemental surface transient EM (TEM) surveys, well-logs, and seismic structural information, for enhancing CSEM signal from reservoir depths; and 3) coupled multiphysics simulations and inversion of CSEM data constrained by production data and by structural information from seismic imaging of the reservoir and overlying formations. The testing component used the field site of Bell Creek Oil Field, which served both as a field laboratory for the method development as well as a test site to evaluate the CSEM signal strengths and the methodology developed in this research project. We have accomplished all the proposed tasks and developed the methodology as planned. These include the procedure for time-lapse CSEM data acquisition, data processing techniques, integration with 3D conductivity model building, fast reservoir simulation for history matching using machine learning, and interpreting CSEM data with coupling to the reservoir modeling. Collectively, the outcome of these tasks form a coherent workflow that can be applied to monitor dedicated carbon storage in saline reservoirs. The testing component evaluated the applicability and limitations of the method, and concluded that the method would be ideal for monitoring dedicated carbon storage sites utilizing saline reservoirs.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Mapping thermal energy storage technologies with advanced nuclear reactors

Advanced nuclear power plants (NPPs) will potentially need to operate in environments where power generation flexibility is more highly valued than the stability or baseload generation capability for conventional demand curves. Thermal energy storage (TES) systems would enable NPPs to respond nimbly to market variability and could also position advanced NPPs to participate differently in restructured markets, thus further enhancing their economic competitiveness. TES systems could also benefit the electric grid by eliminating the need for peaking plants, as well as by improving the economic performance of baseload NPPs. While TES technologies afford a unique opportunity to address many of these challenges, the applicability of these systems is also complicated by the fact that various advanced NPPs are designed differently, each with its own temperature range, size, operating fluids, and operating conditions. Hence, TES systems face significant barriers to investment, as more information on their compatibility and performance metrics is needed to quantify the advantages provided by each, as well as the challenges these technologies might face if coupled with a particular type of advanced NPP. This study explores the possibility of integrating a wide variety of TES technologies with various categories of advanced NPPs, based on their operating characteristics. To help decision makers, users and developers decide which TES technology is best suited to a particular category of advanced NPPs, this research present a Phenomena Identification and Ranking Table (PIRT) analysis of 10 TES systems that could potentially be coupled with advanced NPPs, which themselves are divided into nine categories based on their operating conditions. Then, each advanced NPP category is evaluated for compatibility with the 10 TES systems by assembling and discussing a database of information concerning 10 engineering questions, defined herein in as figures of merit (FOMs), such as: technology readiness level (TRL), temperature compatibility, energy density, size, cycle frequency, ramp time, realignment frequency, geographic needs, environmental impact, and interventions. By assembling a database of information concerning the TES technologies' compatibility with various advanced NPP systems, this study can help developers acquaint themselves with a particular TES technology before choosing to build a new integrated installation.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2021

Net-Zero Carbon Microgrids

The microgrid concept has been effective in creating aggregations of distributed energy resources—generation, storage and loads—for resiliency, in the form of energy security. The success of microgrids in bringing energy security to a wide range of customers—from individual residences to commercial and industrial installations to military bases—has been exemplified during power disruptions and extended outages due to extreme weather events, cybersecurity attacks, and equipment failures. Now microgrids have an opportunity to meet the challenges of climate change and contribute to a carbon-free power delivery system. The transition to net-zero starts within microgrids themselves. In fact, today’s microgrids are largely dominated by generators using fossil fuels, natural gas and diesel, with high greenhouse gas emissions. In short, the transition to net-zero means replacing fossil fueled generators with renewable generation in microgrids. This transition is extended by including new dispatchable generation technologies that are 100% carbon-free and that offer additional advantage of a more-dependable and sustainable source of energy and power. Basically, the decarbonization of microgrids requires three elements: 1) maximizing generation from renewable energy resources, 2) management of storage and flexible loads to balance the variability and intermittency of renewable energy resources, and 3) introducing new clean power sources, including hydrogen-based generation and small modular reactors. This report affirms a need for specific focus by governmental agencies at national, regional, and local levels to establish technology, policy, and investment in this area. The intention of the Net-Zero Microgrid (NZM) Program is to inform these constituencies with cross-cutting research and tools for the reduction of GHG in microgrids – to net-zero in the near term eventually to zero in the longer term. The NZM Program is committed to achieving decarbonization for resiliency and for providing clean energy at the local or distribution level, from remote communities to underserved communities, and large industrial and military facilities. The NZM Planning and Design Platform is a core tool to be developed as an early deliverable of the NZM Program because only a fully integrated microgrid-design approach will ensure maximum carbon reduction in energy production.

13 HYDRO ENERGY↗
DOE OSTI2025

Battery‐Grade Lithium Materials: Virgin Production and Recycling, a Techno‐Economic Comparison

Lithium has been identified as an essential mineral to the economic and national security of the United States. It is vital for rechargeable batteries that surround us daily from the personal electronics to large-scale energy storage. With a comprehensive techno-economic analysis, the cost of battery-grade lithium compounds production, i.e., lithium carbonate (LC) is evaluated and lithium hydroxide monohydrate (LHM), from both virgin (spodumene ore and brine) and recycled feedstocks (spent lithium-ion batteries). The goal of this study is to inform about the economics of lithium compounds production with comprehensive insights into differences in manufacturing routes and pave a pathway to explore more innovative, domestic manufacturing processes in future for their cost competitiveness and environmental impact. The study includes details on mining and extraction operations as well as unit level operation in the refining process. Moreover, process level information has been collected for pyrometallurgical and hydrometallurgical battery recycling routes. This analysis shows that brine and direct lithium extraction are the cheapest pathways to produce LC or LHM (between $\$3.39$ and 6.20 kg −1 ). The cost of production in the ore and recycling routes can range between $\$4.17$ and $\$53.41$ kg −1 and is highly depending on capital equipment investment, plant location, and the price of spodumene concentrate SC6.0.

battery recycling↗
DOE OSTI2021

The Effect of Compressibility and Outer Boundaries on Incipient Viscous Fingering

The knowledge of the effects of instability and heterogeneity on displacements, primarily enhanced oil recovery, and carbon dioxide storage are well known, although they remain difficult to predict. Here, the usual recourse to modeling these effects is through numerical simulation. Simulation remains the gold standard for prediction; however, its results lack generality, being case-specific. There are also several analytic models for displacements that are usually more informative than simulation results. However, these methods apply to steady-state, incompressible flow.

02 PETROLEUM↗
DOE OSTI2020

Deep Learning for Land Cover Classification Using Only a Few Bands

There is an emerging interest in using hyperspectral data for land cover classification. The motivation behind using hyperspectral data is the notion that increasing the number of narrowband spectral channels would provide richer spectral information and thus help improve the land cover classification performance. Although hyperspectral data with hundreds of channels provide detailed spectral signatures, the curse of dimensionality might lead to degradation in the land cover classification performance. Moreover, in some practical applications, hyperspectral data may not be available due to cost, data storage, or bandwidth issues, and RGB and near infrared (NIR) could be the only image bands available for land cover classification. Light detection and ranging (LiDAR) data is another type of data to assist land cover classification especially if the land covers of interest have different heights. In this paper, we examined the performance of two Convolutional Neural Network (CNN)-based deep learning algorithms for land cover classification using only four bands (RGB+NIR) and five bands (RGB+NIR+LiDAR), where these limited number of image bands were augmented using Extended Multi-attribute Profiles (EMAP). The deep learning algorithms were applied to a well-known dataset used in the 2013 IEEE Geoscience and Remote Sensing Society (GRSS) Data Fusion Contest. With EMAP augmentation, the two deep learning algorithms were observed to achieve better land cover classification performance using only four bands as compared to that using all 144 hyperspectral bands.

58 GEOSCIENCES↗
DOE OSTI2024

Best Practices for Resilience in Smart Grid-Interactive Efficient Buildings

The Federal Energy Management Program (FEMP) supports federal agencies' energy decisions with information and guidance on design, funding, and operations to ensure federal buildings are efficient and resilient. The modernization of building infrastructure and the evolution of buildings to support decarbonization involves complex implementation of multiple components across several systems. This includes energy-efficient equipment, on-site energy generation and storage systems, and control systems. These systems have operational modes that can operate more efficiently if they are able to behave responsively to the conditions of the electrical grid. These grid-interactive efficient buildings (GEB) allow facilities to manage power demand according to operational constraints and market signals issued by grid operators. With proper design and planning, these same capabilities have the potential to enable building and facility resilience - coordinating with microgrids, maintaining power on critical circuits to sustain essential operations, and monitoring building health and safety status during an outage. By managing the load of buildings, GEBs can also reduce the cost of backup generation and make better use of renewable power sources on site. This document outlines some of the processes and considerations to guide the design and operation of GEBs in ways that promote facility resilience.

building decarbonization↗