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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 1,747 records · Page 97
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

A transformed path integral approach for solution of the Fokker–Planck equation

A novel path integral (PI) based method for solution of the Fokker–Planck equation is presented. The proposed method, termed the transformed path integral (TPI) method, utilizes a new formulation for the underlying short-time propagator to perform the evolution of the probability density function (PDF) in a transformed computational domain where a more accurate representation of the PDF can be ensured. The new formulation, based on a dynamic transformation of the original state space with the statistics of the PDF as parameters, preserves the non-negativity of the PDF and incorporates short-time properties of the underlying stochastic process. New update equations for the state PDF in a transformed space and the parameters of the transformation (including mean and covariance) that better accommodate nonlinearities in drift and non-Gaussian behavior in distributions are proposed (based on properties of the SDE). Owing to the choice of transformation considered, the proposed method maps a fixed grid in transformed space to a dynamically adaptive grid in the original state space. The TPI method, in contrast to conventional methods such as Monte Carlo simulations and fixed grid approaches, is able to better represent the distributions (especially the tail information) and better address challenges in processes with large diffusion, large drift and large concentration of PDF. Additionally, in the proposed TPI method, error bounds on the probability in the computational domain can be obtained using the Chebyshev's inequality. The benefits of the TPI method over conventional methods are illustrated through simulations of linear and nonlinear drift processes in one-dimensional and multidimensional state spaces. The effects of spatial and temporal grid resolutions as well as that of the diffusion coefficient on the error in the PDF are also characterized.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

Modeling Analysis of Ball-Milling Process for Battery-Electrode Synthesis

The mechanical alloying process is a promising method for synthesizing electrode materials for batteries owing to its benefits such as the ability to produce nanostructured, high-performing electrode alloys, no adverse effects on the solid electrolyte for solid-state batteries, stable production of thick electrodes, simple processing steps, and low processing costs. It is gaining intensive attention in the battery industry as one of the best methods to replace the conventional wet-slurry-solvent method, and its application is rapidly increasing these days. However, the operation is currently conducted purely based on trial-and-error methods without fully utilizing the features of its functions. Here, this may be attributed to a lack of understanding of the effect of operating parameters on the alloying process and final products. Surprisingly, there is a scarcity of the literature conducting fundamental research to comprehend the underlying physics of the entire mechanical alloying process, resulting in a significant knowledge gap. To address this knowledge gap, extensive research was conducted. The existing literature on mechanical alloying was reviewed to comprehend the current state of understanding and to discuss the direction for future research. Mathematical expressions were developed to create physics-based models capable of capturing the entire mechanical alloying process, including milling kinetics and defect-enhanced phase evolution. These methods were then applied to investigate the impact of operating parameters such as milling frequency, initial mole ratio of the alloyed materials, density of grinding balls, and energy required for the powders to become amorphous (i.e., the amorphization energy threshold). This research aimed not only to comprehend the direct effects of these operating parameters but also to unveil the physics underlying the ball-milling process. The results of our study can serve as crucial information for the battery industry in designing or operating the ball-milling process.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Cold Commissioning Simulant Selection for the Hanford Tank Waste Treatment Plant LAW Facility - 18465

Commissioning the Low-Activity Waste (LAW) Facility of the Hanford Tank Waste Treatment and Immobilization Plant (WTP) will require large volumes (∼2,000,000 liters) of a non-radioactive, chemically analogous waste simulant. Cold commissioning will provide for operator training in an integrated facility, demonstration of mechanical process equipment capacity, environmental performance demonstration of emissions control systems, and process control systems shakedown and tuning. Selection of the cold commissioning simulant is following a structured four-phase process to arrive at the preferred composition and recipe. Phases I, II and III are complete. Phase I laid the framework for decision making with preparation of a Project Management Plan, an update to the Simulant Development, Approval, Validation, and Documentation procedure, and a kickoff meeting to gather stakeholder input into the simulant definition and selection criteria. Phase II consisted of a comprehensive literature search and compilation of LAW simulant recipes, followed by initial screening against the selection criteria identified in Phase I. This process identified 72 candidate simulant recipes and down-selected 9 for further study. Phase III studies involved a) statistical comparisons of the similarity of candidate simulant compositions to the tank waste feed vector, b) process modelling of the commissioning sequence with each simulant to estimate glass former and simulant material consumption, offgas and effluent volumes and compositions, and product glass volumes and compositions, and c) evaluation of simulants against the immobilized LAW glass composition algorithm to determine allo able waste loading to ensure compliance with product specifications and process constraints. The results of these studies identified two of the candidate recipes to be carried forward to full-scale testing. Phase IV consists of testing these two recipes at vendor facilities to determine scalability, fabricability, and stability of the simulants when produced at industrial scale. Scalability is the ability to take a recipe from bench scale (1 to 40 liters) batch size to full scale (>35,000 liters) batch size. Fabricability is the ability of a recipe to be prepared in large batches without the need for complex or expensive mechanical equipment, heating, or cooling systems. Stability is the ability of a simulant to withstand long term storage and variations in environmental conditions without unacceptable changes in key characteristics (the set of chemical, physical, and rheological parameters that the simulant is intended to exhibit during cold commissioning tests). Simulants will also be tested for compatibility with other startup material (i.e., frit), tuning simulants, and environmental performance demonstration test spiking solutions to ensure against unexpected situations. Using a structured process is essential when making high impact decisions such as waste simulant selection. Proper planning and a clear explanation of how the decision process will unfold is key to obtaining stakeholder buy-in as more information is gathered. The documentation resulting from this structured selection process will be compiled and summarized in a Cold Commissioning Simulant Definition deliverable due to the Department of Energy 24 months before the start of Cold Commissioning. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Utah FORGE: InSAR Data 2019

This dataset contains Interferometric Synthetic Aperture Radar (InSAR) data used for ground deformation monitoring during Phase 2C of the Utah FORGE project. The dataset includes measurements of the mean rate of range change and associated standard errors, provided in both CSV and NetCDF formats. Supporting materials include histograms, maps, and MATLAB scripts used for data processing, as well as unit vector information describing the satellite's sensor orientation. Compressed archives contain additional metadata and masked range change data for individual interferometric pairs. A README file is included to provide further details on the dataset's structure and contents.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

SEED: Semantic Energy Exploration and Discovery

The Bioenergy Knowledge Discovery Framework (KDF) hosts a vast repository of specialized data, yet traditional keyword-based search methods often struggle to provide direct answers, requiring significant domain expertise and manual effort to filter through raw documents. To overcome these barriers, this software introduces a semantic search engine that enables both specialists and non-specialists to query the KDF using natural language. By shifting from rigid keyword matching to intent-based retrieval, the tool automatically identifies and ranks the most relevant sources within the database. The system functions by processing natural language queries to extract the most pertinent information, delivering an AI-generated plain-language summary alongside exact supporting quotes from retrieved documents. This integrated approach provides users with immediate, evidence-based answers while eliminating the need for exhaustive manual review. By surfacing direct insights and contextual evidence, the software enhances the usability of existing KDF resources and democratizes access to complex bioenergy data. Ultimately, this semantic search solution accelerates the discovery process and supports faster, more informed decision-making across the bioenergy sector.

Pan, Meiyu (Melrose) [Oak Ridge National Laborator↗
DOE OSTI2024

Development and Demonstration of a Risk Assessment Approach for Approval of a Transportation Package of a Transportable Nuclear Power Plant for Domestic Highway Shipment

The U.S. Department of Defense (DoD) Strategic Capabilities Office (SCO) has tasked PNNL to address the regulatory challenges associated with confirming the safe transport of Transportable Nuclear Power Plants (TNPPs) containing irradiated nuclear fuel. A previous report—Proposed Risk-Informed Regulatory Framework for Approval of Microreactor Transportation Packages (PNNL-31867)—determined that the expected radioactive inventory in the irradiated fuel of a TNPP would likely require shipment in an NRC-approved Type B package (or spent nuclear fuel cask) but that a TNPP “package” is unlikely to meet the entire suite of NRC requirements set forth in Part 71 of Title 10 of the Code of Federal Regulations (CFR) for a Type B package. It was therefore concluded that shipment of this initial TNPP transportation package, as well as possibly others, under existing regulations would likely require NRC approval using the 10 CFR 71.12 (“Specific exemptions”) process that relies on risk-informed decision making supported by quantitative risk assessment (i.e., Probabilistic Risk Assessment).

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Assessing Alignment Between a Top-Down LCA Model and a Bottom-Up Supply Chain Model for Data Gap Identification and Prioritization

This work presents a method for evaluating the alignment between the results of bottom-up and top-down models and using the evaluation to enhance the bottom-up model and underlying data set. Disaggregation is used to enhance the embodied energy results of top-down models, which are then compared to bottom-up model results. Using the disaggregated top-down results makes it possible to identify specific data gaps and deficiencies within the bottom-up model and its data set. The top-down results will also provide information around which data gaps in the bottom-up model are contributing the most error to the results; in this way, data gaps can be identified as either critical or non-critical, and the effort needed to acquire and add data can be prioritized. Although the proposed method could be applied to any physical quantity or environmental impact that can be quantified with both bottom-up models (typically in physical units) and top-down models (typically in monetary units), embodied energy is the focus of this work. The disaggregated energy baseline is calculated from the throughput vector x, which gives the total economic activity required from each sector to provide a final demand. Using the direct requirements matrix, each element in x is disaggregated into a vector of inputs to the corresponding sector. Energy data from the interventions matrix B is then used to convert the monetary flows of the disaggregated x into physical energy flows. For each energy flow, the origin sector, the destination sector, and the energy carrier type will all be known. These energetic flows are analogous but not identical to direct energy inputs that would appear in a unit process used in life cycle analysis and other bottom-up methods, and thus can be used to evaluate the alignment between the top-down results and the results of a bottom-up model. To demonstrate and further develop the proposed method, embodied energy for chemicals assessed in the 2015 U.S. DOE bandwidth study on energy use within the chemical sector will be calculated using a top-down and a bottom-up model. The Materials Flows through Industry (MFI) modeling tool developed at the National Renewable Energy Laboratory is used as the bottom-up model, and the U.S. EEIO model, recently published by the U.S. Environmental Protection Agency, is used as the top-down model. U.S. EEIO and MFI results will be evaluated to determine how well the model results align. Where the two models are not in good alignment, the disaggregation method described above will be applied to the top-down model to generate a more detailed set of results that will aid in identifying data gaps in the MFI database. Using only MFI, data gaps can be identified in terms of which sectors have few or no unit processes in the database, but no information is available regarding the impact of the missing data on MFI results. The disaggregated top-down results will provide this information on which gaps in the MFI data are critical and have a significant impact on the results, and which are less important.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Risk-Informed Performance-Based Technology. Inclusive Guidance for Advanced Reactor Licensing Basis Development (NEI 18-04)

This guideline presents a modern, technology-inclusive, risk-informed, and performance-based (TI-RIPB) process for selection of Licensing Basis Events (LBEs); safety classification of structures, systems, and components (SSCs) and associated risk-informed special treatments; and determination of defense-in-depth (DID) adequacy for non-LWRs. This guidance document provides one acceptable means for addressing the aforementioned topics as part of demonstrating a specific design provides reasonable assurance of adequate radiological protection.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Modernization of Technical Requirements for Licensing of Advanced Non-Light Water Reactors: Risk-Informed and Performance-Based Evaluation of Defense-in-Depth Adequacy

This document supports the work contained in Nuclear Energy Institute (NEI) 18-04 “Risk-Informed Performance-Based Technology Inclusive Guidance for Advanced Reactor Licensing Basis Development” Revision 0.[19] NEI 18-04 presents a modern, technology-inclusive, risk-informed, and performance-based (TI-RIPB) process for selection of Licensing Basis Events (LBEs); safety classification of structures, systems, and components (SSCs) and associated risk-informed special treatments; and determination of defense-in-depth (DID) adequacy for non-LWRs.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2018

Stakeholder Engaged Structured Decision Making to Improve Performance and Reduce Costs While Protecting Human Health and the Environment - 18645

The objective of the Best Practice is to facilitate a paradigm shift when it comes to approaches to the decision making process for nuclear waste management, disposal, and remediation decisions. The stakeholder-engaged structured decision making (SDM) paradigm shift provides a transparent framework for developing optimal solutions to complex problems (Keeney, 1992, Gregory et al, 2012). This is a deliberative-analytical process. The deliberative side centers on understanding stakeholder concerns and values, and developing objectives while identifying options that may achieve those objectives. SDM is implemented with the aid of computer tools that are aimed specifically at capturing these deliberative aspects of a decision analysis. SDM captures this information in a structured format that allows access to the values, objectives, and options that have been identified to help. Through this approach we achieve transparency, traceability, and reproducibility. The goal of SDM at this stage is to provide a formal structure for capturing and processing deliberative information. The deliberative part sets the stage for the analytical part of SDM. A variety of Subject matter experts (SMEs) are engaged in how to evaluate the options through the objectives. Objectives often include minimizing human health risk and minimizing cost, but in the full scope of a sustainability-based approach to decision making, it can include objectives related to economic, environmental and social issues. Subject matter experts might provide other options for achieving the objectives, but their primary role is to evaluate/model the options to the endpoint defined by the objectives or in other words perform a consequence analysis. The structure of SDM makes it clear exactly what is needed from the SMEs, because the options are identified, and the endpoint (objectives) are defined - evaluations and/or models are needed to connect options to objectives. This consequence analysis completes the evaluation, and directly addresses which of the options is the best option. The same SDM approach to finding the best options can also be used for prioritization and resource allocation. Because all of the information is captured in a formal system with the help of computer tools (a software framework for implementing SDM), the decision models that are developed for an application can be fully evaluated numerically and for the insights gained from using a formal process for managing the multiple factors that need to be considered for complex decisions. This includes uncertainty and sensitivity analysis that can be used to guide the need for further data/information collection if the optimal decision is not adequately supported - that is, if there is insufficient confidence in the decision. Also, this feeds directly into adaptive management therefore if more data/information are collected then the efficacy of the current decision can be evaluated, potentially leading to a change in decisions if warranted. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Computer Vision-Enabled Smart Traffic Monitoring for Sustainable Transportation Management

Transportation accounts for a significant portion of total global energy consumption. Excessive energy consumption usually occurs in urban traffic environments with congestion and travel delays. With the advancement of remote sensing and computer vision technologies, real-time traffic conditions can be monitored. Therefore, sustainable transportation management strategies can be developed to optimize the overall energy and environment performance and reduce congestion and emissions. This work presents a smart traffic monitoring system based on remote camera sensors. Real-time and historical traffic conditions at the US Department of Energy’s Oak Ridge National Laboratory (ORNL) were monitored and analyzed to develop optimal transportation management strategies for sustainability. Computer vision algorithms were developed and applied to process the real-time camera data to obtain complete traffic information across the ORNL campus. Weeks of historical data were collected and processed to analyze the traffic and identify bottlenecks. The proposed traffic monitoring and management approach can be applied and extended to benefit other campuses or urban areas.

Shao, Yunli↗
DOE OSTI2017

The strain-dependent spatial evolution of garnet in a high- P ductile shear zone from the Western Gneiss Region (Norway): a synchrotron X-ray microtomography study

Reaction and deformation microfabrics provide key information to understand the thermodynamic and kinetic controls of tectono-metamorphic processes, however they are usually analysed in two dimensions, omitting important information regarding the third spatial dimension. We applied synchrotron-based X-ray microtomography to document the evolution of a pristine olivine gabbro into a deformed omphacite-garnet eclogite in four dimensions, where the 4th dimension is represented by the degree of strain. In the investigated samples, which cover a strain gradient into a shear zone from the Western Gneiss Region (Norway), we focused on the spatial transformation of garnet coronas into elongated clusters of garnets with increasing strain. Our microtomographic data allowed quantification of garnet volume, shape and spatial arrangement evolution with increasing strain. We combined microtomographic observations with light microscope- and backscatter electron images as well as electron microprobe- (EMPA) and electron backscatter diffraction (EBSD) analysis to correlate mineral composition and orientation data with the X-ray absorption signal of the same mineral grains. With increasing deformation, the garnet volume almost triples. In the low strain domain, garnets form a well interconnected large garnet aggregate that develops throughout the entire Page 1 of 52 sample. We also observed that garnet coronas in the gabbros never completely encapsulate olivine grains. In the most highly deformed eclogites, the oblate shapes of garnet clusters reflect a deformational origin of the microfabrics. We interpret the aligned garnet aggregates to direct synkinematic fluid flow and consequently influence the transport of dissolved chemical components. EBSD analyses reveal that garnet show a near-random crystal preferred orientation that testifies no evidence for crystal plasticity. There is, however evidence for minor fracturing, neo-nucleation and overgrowth. Microprobe chemical analysis revealed that garnet compositions progressively equilibrate to eclogite facies, becoming more almandine-rich. We interpret these observations as pointing to a mechanical disintegration of the garnet coronas during strain localisation, and their rearrangement into individual garnet clusters through a combination of garnet coalescence and overgrowth while the rock was deforming.

garnet↗
DOE OSTI2016

Walker Ranch 3D seismic images

Amplitude images (both vertical and depth slices) extracted from 3D seismic reflection survey over area of Walker Ranch area (adjacent to Raft River). Crossline spacing of 660 feet and inline of 165 feet using a Vibroseis source. Processing included depth migration. Micro-earthquake hypocenters on images. Stratigraphic information and nearby well tracks added to images. Images are embedded in a Microsoft Word document with additional information. Exact location and depth restricted for proprietary reasons. Data collection and processing funded by Agua Caliente. Original data remains property of Agua Caliente.

15 GEOTHERMAL ENERGY↗
DOE OSTI2017

Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

1. To develop a comprehensive model characterization framework to describe epidemiological models in an operational context. 2. To apply the framework to characterize “operational” models for specific infectious diseases and provide a web-based directory, the biosurveillance analytics resource directory (BARD) to the global infectious disease surveillance community. Epidemiological modeling for infectious disease is useful for disease management and routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. Los Alamos National Laboratory(LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context. We offer this framework and an associated database to stakeholders of the infectious disease modeling field as a tool for standardizing model description and facilitating the use of epidemiological models. Such a framework could help the understanding of diverse models by various stakeholders with different preconceptions, backgrounds, expertise ,and needs, and can foster greater use of epidemiological models as tools in infectious disease surveillance. We define, “operational” as the application of an epidemiological model to a real-world event for decision support and can be used by experts and non-experts alike. The term “model” covers three major types, risk mapping, disease dynamics and anomaly detection. To develop a framework for characterizing epidemiological models we collected information via a three-step process: a literature search of model characteristics, a review of current operational infectious disease epidemiological models, and subject matter expert (SME)panel consultation. We limited selection of operational models to five infectious diseases: influenza, malaria, dengue, cholera and foot-and-mouth disease (FMD). These diseases capture a variety of transmission modes, represent high or potentially high epidemic or endemic burden, and are well represented in the literature. We also developed working criteria for what attributes can be used to comprehensively describe an operational model including a model’s documentation, accessibility, and sustainability. To apply the model characterization framework, we built the BARD, which is publicly available (http://brd.bsvgateway.org).A document was also developed to describe the usability requirements for the BARD; potential users (and non-users) and use cases are formally described to explain the scope of use. 1. Framework for model characterization. The framework is divided into six major components (Figure 1):Model Purpose, Model Objective, Model Scope, Biosurveillance(BSV) goals, Conceptual Model and Model Utility; each of which has several sub-categories for characterizing each aspect of a model.2. Application to model characterization Models for five infectious diseases—cholera, malaria, influenza, FMD and dengue were characterized using the framework and are included in the BARD database. Our framework characterized disparate models in a streamlined fashion. Model information could be binned into the same categories, allowing easy manual comparison and understanding of the models.3. Development of the BARD. Our model characterization framework was implemented into an actionable tool which provides specific information about a model that has been systematically categorized. It allows manual category-to-category comparison of multiple models for a single disease and while the tool does not rank models it provides model information in a format that allows a user to make a ranking or an assessment of the utility of the model. With the model characterization framework we hope to encourage model developers to start describing the many features of their models using a common format. We illustrate the application of the framework through the development of the BARD which is a scientific and non-biased tool for selecting an appropriate epidemiological model for infectious disease surveillance. Epidemiological models are not necessarily being developed with decision makers in mind. This gap between model developers and decision makers needs to be narrowed before modeling becomes routinely implemented in decision making. The characterization framework and the tool developed (BARD) area first step towards addressing this gap.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

AI for Interpreting Nuclear Power Plant Documents for Power Uprates

To reduce the cost and time needed for regulatory compliance, nuclear power plants (NPPs) can utilize artificial intelligence (AI) to assist in interpreting complex and voluminous documents that typically span thousands of pages. Usually, the process of interpreting a plant’s technical specifications (TSs) and associated documents is labor intensive. This study aims to understand what processes state-of-the-art large language models (LLMs) can automate and to identify the pitfalls associated with using LLMs to reduce human labor costs and time. This research uses a recent AI technology called retrieval augmented generation (RAG), which retrieves pages of information from TSs and associated documents to assist with NPP power uprates (cleared to produce more power). LLMs are integral to RAG because they create human-like responses based on the retrieved information, aiding in the interpretation and application processes. A baseline case demonstrates how LLMs can operate successfully for a power uprate application. Then five use cases show five types of potential failures: (1) RAG retrieving the incorrect information, (2) RAG misinterpreting the retrieved information, (3) RAG relying on knowledge not contained in the retrieved information, (4) RAG hallucinating, and (5) RAG refusing to answer. The results of the five use cases suggest that automating the human interpretation of TSs and associated documents with AI should be approached with caution. A subject-matter expert reviewed the AI outputs from the five use cases and concluded that an LLM can produce technical information that is needed to produce power uprate applications in certain instances.

21 - SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLAN↗
DOE OSTI2025

Valued peaks: Sustainable water allocation for small hydropower plants in an era of explicit ecological needs

Optimizing hydropower operations to balance economic profitability and support functioning ecosystem services is integral to river management policy. In this article, we propose a dynamic, constrained optimization framework for small hydropower plants (SHPs) to evaluate trade-offs between economic profitability and socio-ecological requirements. Specifically, we examine the balance between short-term losses in hydropower generation and the potential for compensatory benefits in the form of revenue from recreational ecosystem services, irrespective of the direct beneficiary. Our framework integrates a fish habitat model, a hydropower optimization model, and a recreational ecosystem service estimate to evaluate different environmental flow scenarios. The optimization process gives three outflow release scenarios, informed by previous streamflow realisations (dam inflow), and designed environmental flow constraints. The framework is applied and tested for the river Kuusinkijoki in North-eastern Finland, which is a habitat for migratory brown trout and grayling populations. We show that the revenue loss due to the environmental flow constraints arises through a reduction in revenue per generated energy unit and through a reduction in turbine efficiency. Additionally, the simulation results reveal that all the designed environmental flow constraints cannot be met simultaneously. Under the environmental flow scenario with both minimum flow and flow ramping rate constraints, the annual hydropower revenue decreases by 16.5 %. An annual increase of 8 % in recreational fishing visits offsets the revenue loss. In conclusion, the developed framework provides knowledge of the costs and benefits of hydropower environmental flow constraints and guides the prioritizing process of environmental measures.

13 HYDRO ENERGY↗
DOE OSTI2024

Characterization of ultrafast-laser ablation of micro-structures in Li-ion battery anode and cathode materials: Morphology, rate, and efficiency

Characterization of the rate and quality of ultrafast-laser ablation of Li-ion battery (LIB) electrode materials is presented for a collection of common and next-generation electrodes. Laser ablated micro-structures on the surface of LIB electrodes have been shown to provide dramatic enhancement of high-rate capability and electrode wetting. However, industrial adoption is hampered by a lack of data enabling informed choice of laser parameters and predicting process throughput. Here this work bridges this gap by providing characterization of the ablation process at more laser parameters (laser fluence and number of pulses used) than are currently available in the literature. Further, we expand on previous graphite and lithium iron phosphate (LFP) ablation work by extending ablation characterization to new LIB materials, providing high data resolution and adopting new characterization metrics which are relevant for industrial application of this technology. Ablated pores are characterized by their ablated depth, volume, and how the depth and volume ablation rate changes as a function of pore depth. Finally, we provide a detailed characterization of the morphology of laser ablated micro-structures which informs how material and laser parameters affect the quality of laser-processed electrodes.

25 ENERGY STORAGE↗
DOE OSTI2018

Data-Driven Machine Learning for Wind Plant Flow Modeling

In this study, we introduce a data-driven machine learning framework for improving the accuracy of wind plant flow models by learning turbulence model corrections based on data from higher-fidelity simulations. First, a high-dimensional PDE-constrained optimization problem is solved using gradient-based optimization with adjoints to determine optimal eddy viscosity fields that improve the agreement of a medium-fidelity Reynolds-Averaged Navier Stokes (RANS) model with large eddy simulations (LES). A supervised learning problem is then constructed to find general, predictive representations of the optimal turbulence closure. A machine learning technique using Gaussian process regression is trained to predict the eddy viscosity field based on local RANS flow field information like velocities, pressures, and their gradients. The Gaussian process is trained on LES simulations of a single turbine and implemented in a wind plant simulation with 36 turbines. We show improvement over the baseline RANS model with the machine learning correction, and demonstrate the ability to provide accurate confidence levels for the corrections that enable future uncertainty quantification studies.

17 WIND ENERGY↗