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DOE OSTI2021

Towards Predictive Plasma Science and Engineering through Revolutionary Multi-Scale Algorithms and Models (Final Report)

This report describes the high-level accomplishments from the Plasma Science and Engineering Grand Challenge LDRD at Sandia National Laboratories. The Laboratory has a need to demonstrate predictive capabilities to model plasma phenomena in order to rapidly accelerate engineering development in several mission areas. The purpose of this Grand Challenge LDRD was to advance the fundamental models, methods, and algorithms along with supporting electrode science foundation to enable a revolutionary shift towards predictive plasma engineering design principles. This project integrated the SNL knowledge base in computer science, plasma physics, materials science, applied mathematics, and relevant application engineering to establish new cross-laboratory collaborations on these topics. As an initial exemplar, this project focused efforts on improving multi-scale modeling capabilities that are utilized to predict the electrical power delivery on large-scale pulsed power accelerators. Specifically, this LDRD was structured into three primary research thrusts that, when integrated, enable complex simulations of these devices: (1) the exploration of multi-scale models describing the desorption of contaminants from pulsed power electrodes, (2) the development of improved algorithms and code technologies to treat the multi-physics phenomena required to predict device performance, and (3) the creation of a rigorous verification and validation infrastructure to evaluate the codes and models across a range of challenge problems. These components were integrated into initial demonstrations of the largest simulations of multi-level vacuum power flow completed to-date, executed on the leading HPC computing machines available in the NNSA complex today. These preliminary studies indicate relevant pulsed power engineering design simulations can now be completed in (of order) several days, a significant improvement over pre-LDRD levels of performance.

42 ENGINEERING↗

DOE OSTI2023

ac susceptibility studies of intra- and intergrain properties of high-$J_c$ Bi-2212 wires

Bi 2 Sr 2 CaCu 2 O x (Bi-2212) is the only high-$T_c$ superconductor (HTS) available as a multifilamentary round wire with multiple architectures and it is a very promising conductor for the realization of high-field applications. Despite their relatively simple wire fabrication by the powder-in-tube technique, Bi-2212 wires require a tightly controlled overpressure heat treatment (HT) with a multi-parameter time-temperature schedule to achieve high critical current density, $J_c$. Further, the variation of these HT parameters, changes in the wire design, wire diameter and powder quality can lead to variations in both the microstructure and the superconducting performance. Particularly noticeable are variations in $J_c$ performance and degree of filament bridging. In this work, we focus on the use of different magnetic characterization techniques to estimate the bridging level and assess the balance of INTER-grain and intra-grain superconducting properties including the irreversibility field ($H_{\text{irr}}$) and the pinning energy ($U_0$) in differently processed wires. Regardless of the actual bridging level, we find that the supercurrent flows at the filament bundle level, not just at the individual filament level. Moreover, using AC susceptibility we identify two distinct supercurrent contributions, one related to the intra-grain and one to the INTER-grain properties, whose irreversibility fields are different but without large sample-to-sample variation. Moreover, an additional component of intra-grain pinning mechanism becomes effective at low temperatures with positive effects also on the INTER-grain performance. The work clearly shows that detailed magnetic characterizations can become valuable tools to investigate the performance of differently processed Bi-2212 wires, correlating their microstructure and overall transport $J_c$, to obtain a deeper understanding of the causes of performance variation and paths to achieve further improvement.

2212↗

DOE OSTI2022

Multiplexed Readout for an Experiment with a Large Number of Channels Using Single-Electron Sensitivity Skipper-CCDs

This paper presents the implementation of a multiplexed analog readout electronics system that can achieve single-electron counting using Skipper-CCDs with non-destructive readout. The proposed system allows the best performance of the sensors to be maintained, with sub-electron noise-level operation, while maintaining low-bandwidth data transfer, a minimum number of analog-to-digital converters (ADC) and low disk storage requirement with zero added multiplexing time, even for the simultaneous operation of thousands of channels. These features are possible with a combination of analog charge pile-up, sample and hold circuits and analog multiplexing. The implementation also aims to use the minimum number of components in circuits to keep compatibility with high-channel-density experiments using Skipper-CCDs for low-threshold particle detection applications. Performance details and experimental results using a sensor with 16 output stages are presented along with a review of the circuit design considerations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

Parasitic Effects in Impedance Spectrum of PEM Water Electrolysis Cells: Case Study of High-Frequency Inductive Effects

Electrochemical impedance spectroscopy (EIS) is a powerful tool to characterize and distinguish electrochemical, electrical, and diffusive processes in an electrolysis cell. The EIS response with small impedance (mΩ) depends on components and materials of the cell, but often also on the electrical setup, cables, and connectors, leading to incorrect conclusions about the performance of the electrolysis cells. These parasitic effects are assessed via a short-circuit measurement and confirmed to be external from the membrane electrode assembly (MEA). In the setup, the inductive characteristic is described by a modified inductive reactance and must be included in the equivalent circuit model (ECM) which is fitting the cell EIS spectra in operation (in situ). If points of the spectra showing inductive characteristic (below x-axis in Nyquist plot) are excluded from the ECM evaluation, an artificially increased ohmic resistance at high frequencies is obtained. In addition, considerations on other components of the ECM can be misleading and significantly incorrect. The only way to avoid that inductive behavior caused by the external setup is incorrectly assigned to the ECM describing properties of the MEA is to include the inductive effects in the ECM analysis of in situ EIS measurements.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2022

Developing novel electrodes with ultralow catalyst loading for high-efficiency hydrogen production in proton exchange membrane electrolyzer cells

Hydrogen plays more crucial roles for decarbonizing the planets and meeting the climate challenges because of its high energy density and zero-emission. It can be produced with proton exchange membrane electrolyzer cells (PEMECs) driven by sustainable and renewable energy resources. Although PEMECs have a number of advantages, including high purity production, quick response, and the ability to operate at high pressure facilitating the gas delivering, their performance and cost greatly hinder their commercial-scale applications. To achieve high-efficiency and cost-reduced hydrogen production in PEMECs, we proposed thin engineered liquid/gas diffusion layers (LGDLs) and associated electrodes, i.e., catalyst-coated LGDLs (CCLGDLs), over conventional porous transport layers (PTLs) and catalyst-coated membranes (CCMs). The research approaches in this project are based on material synthesis, in-situ and ex-situ characterizations, component design and treatment, numerical modeling, and cost analysis. The thin and tunable LGDLs (TT-LGDLs) and CCLGDLs were successfully developed with great performance improvement as demonstrated in lab-scale, bench-scale, and system-scale electrolyzer tests. The electrode thickness was reduced from 370 µm to less than 100 µm with simplified fabrication processes. With the catalytically enhanced Ir-based catalyst coating, the as-developed CCLGDLs with a catalyst loading of 0.34 mg Ir /cm 2 achieved a cell performance of 1.77 V at 2 A cm -2 , exhibiting the catalyst mass activity enhanced by >20 times with significant catalyst saving over conventional catalyst cell design. In-situ PEMEC characterizations, including the current distribution mapping and high-speed and multiscale visualizations, were conducted for a deeper understanding of mass transport and electrochemical reactions within an electrolyzer with LGDLs and CCLGDLs. A 2D cell model was developed and validated for the enhanced performance on TT-LGDL through reducing ohmic losses due to nonuniform hydration and water transport. Further, the cost analysis results have shown a path to move beyond equivalency and surpass costs associated with the project baseline. In this project, the design and fabrication of TT-LGDLs and CCLGDLs will contribute to the performance enhancement, manufacturing simplification, and cost reduction for PEMECs and other energy conversion devices, thus shortening their pathways towards commercialization. This project also provides a good foundation for furthering the in-situ reaction interface research.

08 HYDROGEN↗

DOE OSTI2025

Design of Novel Hot Gas Component for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

This CRADA project was the result of a project award under FOA-DOE-0001980. The overarching FOA project team consisted of researchers from Carpenter Technology Corporation (CTC), Solar Turbines Incorporated (Solar), Pennsylvania State University (PSU), University of California-Santa Barbara (UCSB), and Oak Ridge National Laboratory (ORNL). Evaluations were conducted on two high-γ’ superalloys that were designed by CTC and the UCSB. One alloy named GammaPrint-700 (GP-700) is a cobalt-base superalloy. The other alloy named GammaPrint-1100 (GP-1100) is a nickel-base (Ni-base) superalloy. PSU provided expertise and experimental testing of the thermal performance of AM micro-cooling architectures. ORNL provided expertise with the AM superalloy materials characterization and AM processing science. Solar provided turbine component design expertise. The focus of this CRADA report is to document the efforts between ORNL and CTC towards the development of superalloys designed for AM. The project goal was to use an AM processable high-temperature superalloy and design for Additive Manufacturing (DfAM) techniques to design an efficient turbine component (i.e. a turbine tip shoe) with enhanced cooling features that can only be fabricated through additive manufacturing (AM). The efficiencies of existing combined heat and power (CHP) engines are capped by both component design and materials limitations. However, AM of a tip shoe component from a γ’strengthened superalloy offers the design flexibility to increase the efficiency and power of an industrial gas turbine. This project brought about advancements in the DfAM tip shoe design space and in the area of high temperature superalloys processable through laser powder bed fusion (LPBF) AM. State of art computation design tools were utilized to optimize unique cooling features into a tip shoe component design. A two-prong materials development approach was taken to support development of the AM tip shoe geometry. The first approach centered on investigating the processability and the appropriate process science for the industry standard high-γ’ nickel-base (Ni-base) superalloy Mar-M247. This superalloy is typically cast and considered non-weldable by traditional welding standards. In the course of this work, the alloy was not deemed feasible for process scale-up due to significant cracking issues during printing. The second approach focused on the development and evaluation of a novel cobalt-base superalloy, GammaPrint™-700 (GP-700 and a Ni-base superalloy, GammaPrint™-1100 (GP-1100) designed to mitigate the significant AM processing issues with Mar-M247. The processability of these two alloys were investigated through electron beam melting (EBM) binder-jet AM (BJAM), and LPBF as a risk mitigation for manufacturability. To be considered a candidate material for down-selection to proceed to full-scale AM tip shoe engine testing trials, the high temperature creep rupture strength was required to achieve at a minimum, a Larsen Miller Parameter (LMP) increase of 10.9% over the baseline material LPBF AM Hastelloy X.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2025

Design of Novel Hot Gas Component for Gas Turbine Engines Enabled by Materials and Additive Manufacturing Process Development

This CRADA project was the result of a project award under FOA-DOE-0001980. The overarching FOA project team consisted of researchers from Carpenter Technology Corporation (CTC), Solar Turbines Incorporated (Solar), Pennsylvania State University (PSU), University of California-Santa Barbara (UCSB), and Oak Ridge National Laboratory (ORNL). Evaluations were conducted on two high-γ’ superalloys that were designed by CTC and the UCSB. One alloy named GammaPrint-700 (GP-700) is a cobalt-base superalloy. The other alloy named GammaPrint-1100 (GP-1100) is a nickel-base (Ni-base) superalloy. PSU provided expertise and experimental testing of the thermal performance of AM micro-cooling architectures. ORNL provided expertise with the AM superalloy materials characterization and AM processing science. Solar provided turbine component design expertise. The focus of this CRADA report is to document the efforts between ORNL and CTC towards the development of superalloys designed for AM. The project goal was to use an AM processable high-temperature superalloy and design for Additive Manufacturing (DfAM) techniques to design an efficient turbine component (i.e. a turbine tip shoe) with enhanced cooling features that can only be fabricated through additive manufacturing (AM). The efficiencies of existing combined heat and power (CHP) engines are capped by both component design and materials limitations. However, AM of a tip shoe component from a γ’strengthened superalloy offers the design flexibility to increase the efficiency and power of an industrial gas turbine. This project brought about advancements in the DfAM tip shoe design space and in the area of high temperature superalloys processable through laser powder bed fusion (LPBF) AM. State of art computation design tools were utilized to optimize unique cooling features into a tip shoe component design. A two-prong materials development approach was taken to support development of the AM tip shoe geometry. The first approach centered on investigating the processability and the appropriate process science for the industry standard high-γ’ nickel-base (Ni-base) superalloy Mar-M247. This superalloy is typically cast and considered non-weldable by traditional welding standards. In the course of this work, the alloy was not deemed feasible for process scale-up due to significant cracking issues during printing. The second approach focused on the development and evaluation of a novel cobalt-base superalloy, GammaPrint™-700 (GP-700 and a Ni-base superalloy, GammaPrint™-1100 (GP-1100) designed to mitigate the significant AM processing issues with Mar-M247. The processability of these two alloys were investigated through electron beam melting (EBM) binder-jet AM (BJAM), and LPBF as a risk mitigation for manufacturability. To be considered a candidate material for down-selection to proceed to fullscale AM tip shoe engine testing trials, the high temperature creep rupture strength was required to achieve at a minimum, a Larsen Miller Parameter (LMP) increase of 10.9% over the baseline material LPBF AM Hastelloy X.

36 MATERIALS SCIENCE↗

DOE OSTI2021

NREL’s Wind Turbine Drivetrain Condition Monitoring and Wind Plant Operation and Maintenance Research During the 2010s: A US Land-Based Perspective

The wind industry has seen tremendous growth during the past two decades, with the global cumulative installation capacity reaching more than 650 gigawatts by the end of 2019. Despite performance and reliability improvements of utility-scale wind turbines over the years, the industry still experiences premature component failures, leading to increased operation and maintenance (O&M) costs. Among various turbine components, gearboxes—and, more broadly, drivetrains—have shown to be costly to maintain throughout the design life of a wind turbine. The problem of premature component failure is industry wide. As early as 2007, the US Department of Energy (DOE) started to address this challenge through the National Renewable Energy Laboratory’s (NREL’s) reliability initiative that first focused on gearboxes and more recently expanded to entire drivetrains. The wind turbine drivetrain condition monitoring and wind plant O&M research that is the subject of this paper is part of the NREL initiative and includes a few research and development (R&D) activities conducted during the 2010s. These activities included technology evaluation during the first few years; novel monitoring technique investigation (specifically, compact filter analysis) during the middle years; and data and physics domain modeling for fault detection and prediction in recent years. A high-level summary of these activities is provided in this paper along with some key observations from each activity. Most of the work discussed has been published and can be referred to for more information. They reflect the expected evolution of wind turbine condition monitoring and O&M in the US market—primarily, a land-based perspective. In addition, we have identified several R&D opportunities that can be picked up by the research community to help industry advance in related areas, making wind power more cost competitive in the future.

17 WIND ENERGY↗

DOE OSTI2022

Non-intrusive reduced order modeling of natural convection in porous media using convolutional autoencoders: Comparison with linear subspace techniques

Natural convection in porous media is a highly nonlinear multiphysical problem relevant to many engineering applications (e.g., the process of CO 2 sequestration). Here, we extend and present a non-intrusive reduced order model of natural convection in porous media employing deep convolutional autoencoders for the compression and reconstruction and either radial basis function (RBF) interpolation or artificial neural networks (ANNs) for mapping parameters of partial differential equations (PDEs) on the corresponding nonlinear manifolds. To benchmark our approach, we also describe linear compression and reconstruction processes relying on proper orthogonal decomposition (POD) and ANNs. Further, we present comprehensive comparisons among different models through three benchmark problems. The reduced order models, linear and nonlinear approaches, are much faster than the finite element model, obtaining a maximum speed-up of 7 × 10 6 because our framework is not bound by the Courant–Friedrichs–Lewy condition; hence, it could deliver quantities of interest at any given time contrary to the finite element model. Our model’s accuracy still lies within a relative error of 7% in the worst-case scenario. We illustrate that, in specific settings, the nonlinear approach outperforms its linear counterpart and vice versa. We hypothesize that a visual comparison between principal component analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) could indicate which method will perform better prior to employing any specific compression strategy.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Current research in low temperature proton exchange membrane-based electrolysis and a necessary shift in focus

While hydrogen has limited use in energy markets today, significant growth is possible in grid storage and as an energy carrier in other sectors. Along with lower electricity prices, further hydrogen cost reduction can enable electrochemical water splitting, particularly as the use of intermittent, renewable power sources increases and the quantity and duration of storage requirements limit electrical storage options. With the improved viability of large-scale hydrogen production, research in low temperature electrolysis has dramatically increased. The overwhelming majority of research efforts within proton exchange membrane-based systems has focused on catalyst development, although a smaller set have developed transport layers as well. As proton exchange membrane-based electrolysis shifts into higher technology readiness levels, however, there is a growing need for work optimizing materials integration, developing component-specific and device-level accelerated stress tests, and addressing manufacturing considerations. These areas directly impact electrolyzer performance, durability, and cost, and are critical to advancing hydrogen’s value and electrochemical water splitting.

08 HYDROGEN↗

DOE OSTI2020

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

In the last 50 years there has been a tremendous progress in solving numerically the Navier-Stokes equations using finite differences, finite elements, spectral, and even meshless methods. Yet, in many real cases, we still cannot incorporate seamlessly (multi-fidelity) data into existing algorithms, and for industrial-complexity applications the mesh generation is time consuming and still an art. Moreover, solving ill-posed problems (e.g., lacking boundary conditions) or inverse problems is often prohibitively expensive and requires different formulations and new computer codes. Here, we employ physics-informed neural networks (PINNs), encoding the governing equations directly into the deep neural network via automatic differentiation, to overcome some of the aforementioned limitations for simulating incompressible laminar and turbulent flows. We develop the Navier-Stokes flow nets (NSFnets) by considering two different mathematical formulations of the Navier-Stokes equations: the velocity-pressure (VP) formulation and the vorticity-velocity (VV) formulation. Since this is a new approach, we first select some standard benchmark problems to assess the accuracy, convergence rate, computational cost and flexibility of NSFnets; analytical solutions and direct numerical simulation (DNS) databases provide proper initial and boundary conditions for the NSFnet simulations. The spatial and temporal coordinates are the inputs of the NSFnets, while the instantaneous velocity and pressure fields are the outputs for the VP-NSFnet, and the instantaneous velocity and vorticity fields are the outputs for the VV-NSFnet. This is unsupervised learning and, hence, no labeled data are required beyond boundary and initial conditions and the fluid properties. The residuals of the VP or VV governing equations, together with the initial and boundary conditions, are embedded into the loss function of the NSFnets. No data is provided for the pressure to the VP-NSFnet, which is a hidden state and is obtained via the incompressibility constraint without extra computational cost. Unlike the traditional numerical methods, NSFnets inherit the properties of neural networks (NNs), hence the total error is composed of the approximation, the optimization, and the generalization errors. Here, we empirically attempt to quantify these errors by varying the sampling (“residual”) points, the iterative solvers, and the size of the NN architecture. For the laminar flow solutions, we show that both the VP and the VV formulations are comparable in accuracy but their best performance corresponds to different NN architectures. The initial convergence rate is fast but the error eventually saturates to a plateau due to the dominance of the optimization error. For the turbulent channel flow, we show that NSFnets can sustain turbulence at , but due to expensive training we only consider part of the channel domain and enforce velocity boundary conditions on the subdomain boundaries provided by the DNS data base. We also perform a systematic study on the weights used in the loss function for balancing the data and physics components, and investigate a new way of computing the weights dynamically to accelerate training and enhance accuracy. In the last part, we demonstrate how NSFnets should be used in practice, namely for ill-posed problems with incomplete or noisy boundary conditions as well as for inverse problems. We obtain reasonably accurate solutions for such cases as well without the need to change the NSFnets and at the same computational cost as in the forward well-posed problems. As a result, we also present a simple example of transfer learning that will aid in accelerating the training of NSFnets for different parameter settings.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

High pressure effects on EBC systems in high temperature environments

Environmental barrier coatings (EBCs) are designed to protect SiCfiber/SiCmatrix ceramic matrix composites (CMCs) in turbine engines by mitigating wear in high-temperature water vapor environments. The failure of EBCs is frequently attributed to the accelerated oxidation of the silicon bond coating layer when exposed to high-temperature steam, leading to the formation of a thickened thermally grown oxide (TGO). TGO growth increases interfacial stress, weakens adhesion, and results in coating spallation. Understanding the impact of high-temperature oxidation of EBC systems is essential for developing accurate lifespan models for turbine components, although pressurized oxidation testing is extremely sparse in the open literature. In this work, oxidation tests were performed on rare earth silicate EBCs coated onto SiC substrates under increased pressure conditions. The coated specimens were tested at 1100°C, 1200°C, and 1300°C at both 1 atm and 10 atm total pressure in steam environments. Subsequent characterization focused on the microstructural evolution of the EBC/Si/SiC system. The experimental findings indicated that TGO behavior is dependent on high-pressure conditions, with elevated pressure leading to an increase in oxide scale thickness and modifications in its morphology.

Ardrey, Kristyn [ORNL] (ORCID:0000000184407796)↗

DOE OSTI2025

Constructing synthetic organosulfur additive for high voltage lithium-ion batteries

Despite its high anodic stability, common organosulfur solvents such as ethyl methyl sulfone and sulfolane typically exhibit poor solid-electrolyte interphase (SEI) formation capability. Here, to address this, the fluorinated organic sulfate 4-(trifluoromethyl)-1,3,2-dioxathiolane 2,2-dioxide (TFDTD) was developed as an effective additive for tailoring organosulfur-based electrolytes in lithium-ion batteries. This development was guided by the functionality selection principle and careful evaluation of feasibility in organic synthesis. TFDTD can be readily synthesized through the reaction between trifluoropropylene glycol and sulfuryl chloride. The ring structure of the organic sulfate enables the formation of a stable SEI on the anode, while the fluorination of the sulfate not only enhances its chemical stability and oxidation potential, but also its effectiveness to protect the anode by increasing its reduction potential, rendering it preferentially reduced on the anode surface before the decomposition of other electrolyte components. Introducing TFDTD facilitates the generation of a robust solidelectrolyte interphase on the graphite anode, significantly enhancing cell performance. Moreover, coupling the use of TFDTD with vinylene carbonate provides further protection on the cathode surface, enabling exceptionally stable, high-voltage, long-term cycling of Gr||NMC full cells.

Functionality selection principle↗

DOE OSTI2025

The fixed probe storage ring magnetometer for the Muon g-2 experiment at Fermi National Accelerator Laboratory

The goal of the FNAL E989 experiment is to measure the muon magnetic anomaly to unprecedented accuracy and precision at the Fermi National Accelerator Laboratory. To meet this goal, the time and space averaged magnetic environment in the muon storage volume must be known to better than 70 ppb. A new pulsed proton nuclear magnetic resonance (NMR) magnetometer was designed and built at the University of Washington, Seattle to track the temporal stability of the 1.45 T magnetic field in the muon storage ring at this precision. It consists of an array of 378 petroleum jelly based NMR probes that are embedded in the walls of muon storage ring vacuum chambers and custom electronics built with readily available modular radio frequency (RF) components. We give NMR probe construction details and describe the functions of the custom electronic subsystems. The excellent performance metrics of the magnetometer are discussed, where after 8 years of operation the median single shot resolution of the array of probes remains at 650 ppb.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2023

A Three-Dimensional, Analytical Wind Turbine Wake Model: Flow Acceleration, Empirical Correlations, and Continuity

A new, three-dimensional, analytical, steady-state wake model is presented that includes local flow acceleration near the rotor, improving the wake description compared to existing models. Wake structures such as the momentum deficit and regions of accelerated flow are concisely described with compound and normal Gaussian functions. Large-eddy simulations (LES) are used as training data to develop the model using two in-line turbines under various inflow conditions parameterized by hub-height wind speed and turbulence intensity. Mass conservation is considered by fixing two components of the wake velocity model and optimizing the third to best satisfy continuity; after which, the model performs comparably if not better than existing work with regards to both relative error and mass consistency. The final model demonstrates a high degree of flexibility, making use of empirical correlations to scale across different inflow conditions. The inclusion of these effects is capable of revealing unused opportunities for enhanced power generation by aligning wake trajectories with these regions of accelerated flow.

modeling↗

DOE OSTI2020

New Concepts in Electrolytes

Over the past decades, Li-ion battery (LIB) has turned into one of the most important advances in the history of technology due to its extensive and in-depth impact on our life. Its omnipresence in all electric vehicles, consumer electronics and electric grids relies on the precisely tuned electrochemical dynamics and interactions among the electrolytes and the diversified anode and cathode chemistries therein. Furthermore, with consumers' demand for battery performance ever increasing, more and more stringent requirements are being imposed upon the established equilibria among these LIB components, and it became clear that the state-of-the-art electrolyte systems could no longer sustain the desired technological trajectory. Driven by such gap, researchers started to explore more unconventional electrolyte systems. From superconcentrated solvent-in-salt electrolytes to solid-state electrolytes, the current research realm of novel electrolyte systems has grown to unprecedented levels. In this review, we will avoid discussions on current state-of-the-art electrolytes but instead focus exclusively on unconventional electrolyte systems that represent new concepts.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

O’Hare Airport Short-Term Ground Transportation Modal Demand Forecast Using Gaussian Processes

Here, the principal objective of this study is to analyze the spatial and temporal variation of ground transportation airport demand and provide demand forecast to inform planning capability and explore alternatives for investments to accommodate airport growth. Because of its good adaptability and strong generalization ability for dealing with high-dimensional input, small-sample, and nonlinear spatial data, Gaussian process (GP) regression is used to provide forecast estimates using data from transportation network company (TNC) trips and urban rail passengers at Chicago's O'Hare International Airport. TNC airport trips differ significantly, with three times more distance, more than twice the travel time, and half of the share requests compared with nonairport trips. This highlights the need for separate demand models. Hourly analysis of the rail service indicates that this is likely heavily used by airport workers, whereas TNC services focus on travelers because of variations in the peak demand hours. Heteroscedastic GP regression is implemented because of differences in trip variance between night and day hours. Estimates are given for weekdays and weekend trips, and the 95% confidence intervals are calculated. The introduction of flight schedule information into the models shows marginal improvements in their performance. However, fitting a GP regression becomes computationally expensive with increased sample size and the introduction of spatial components. Transportation planners and policymakers can use the results and methods implemented in this study to optimize transportation assets and provide long-range simulations of the current and future conditions in the area.

42 ENGINEERING↗

DOE OSTI2022

Nonlinear MHD modeling of n = 1 RMP -induced pedestal transport and mode coupling effects on ELM suppression in KSTAR

We report fully suppressing edge-localized modes (ELMs), e.g., with resonant magnetic perturbations (RMPs), is essential to reach and sustain high-performance steady-state H-mode plasmas because large ELMs can significantly reduce the lifetime of divertor components in future tokamak reactors. RMP-driven ELM suppression in KSTAR has been modeled by coupling the neoclassical transport code PENTRC to the nonlinear 3D MHD code JOREK. We have found that the radial transport from the combined effects of the kink-peeling, tearing response, and neoclassical toroidal viscosity can explain the pedestal degradation observed in experiments. In addition, it has been found that the RMP response can increase the inter-ELM heat flux on the lower outer divertor by redistributing the heat transport between the divertor plates. In addition to the degraded pedestal, ELM suppression is also attributable to the RMP-induced mode interactions. While the linear stability of peeling-ballooning mode (PBMs) improves owing to the degraded pedestal, the PBM and RMP interaction increases the spectral transfer between edge harmonics, preventing catastrophic growth and the crash of unstable modes. Here, it turns out that the magnetic islands near the pedestal top can play a vital role in mediating the mode interactions.

mode coupling↗