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All sources NASA NTRS DOE OSTI

DOE OSTI2022

A High-Voltage High-Reliability Scalable Architecture for Electric Vehicle Power Electronics (Final Report)

This project developed and demonstrated new composite converter technologies that lead to high power density (> 20 kW/L) at power levels of 10s of kW, 100s of kW, or possibly higher, with fundamental advances in converter efficiency and Q that lead to substantial increases in mean time to failure (MTTF). These advantages were realized through development of new composite converter topologies that perform buck, boost, or other conversion functions and that are scalable to higher voltage and power levels through sharing of voltage and current stresses among multiple dissimilar partial-power converter modules. The project led to experimental demonstration of a125 kW multifunction electric vehicle power conversion system having in-creased dc bus voltage (950 V nominal, 1200 V peak) that interfaces a 200 V to 400 V battery pack, and that includes integrated level 2 wired charging and wireless charging functions. The project incorporated SiC MOSFET modules having switching frequencies in excess of 100 kHz, planar magnetics, a hierarchical control architecture that enables scaling to higher voltages and powers with additional converter modules, and a high-power density in excess of 20 kW/L. The research demonstrated how a more complex converter approach can increase mean-time-to-failure, even though the number of elements is increased. This is achieved through significant reduction of temperature rise through fundamentally superior converter circuit topologies. The research also demonstrated new high power planar magnetics that increase power density. The technology is appropriate to a variety of applications including EV power trains, EV charging, PV inverters, battery storage, and similar areas. These systems potentially can be manufactured in the U.S.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Kernel Enriched Meshfree Multiphysics Degradation Modeling of Energy Storage Materials

Energy storage materials exhibit strong electro-chemo-mechanical coupling and highly anisotropic material properties, contributing to the formation and propagation of micro-cracking during charge/discharge cycling and ultimately diminishing performance and service life. With microstructural images supplied by the National Laboratory of the Rockies (NLR), pixel-based meshfree model construction by the reproducing kernel particle method (RKPM) is used to represent the complex material microstructures that dictate the coupled physics of these systems. Traditional electro-chemo-mechanical models rely on mesh-based finite element methods, which can lead to difficulties in meshing such complex geometries and capturing crack propagation due to mesh dependency. The first kernel enrichment discussed will be the interface modified reproducing kernel (IM-RK) [1, 2], constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. The IM-RK is especially useful for areas in which a known discontinuity-type is expected a priori. The second kernel enrichment to be discussed is a neural network-enhanced reproducing kernel (NN-RK) [3, 4], which is introduced to effectively model non-obvious damage and crack propagation in the material microstructures; the location, orientation, and solution transition near a localization are automatically captured by superimposed block-level NN optimizations. This NN enrichment approach allows for effective modeling of localizations via a fixed background discretization, relieving tedious efforts for adaptive refinement in traditional mesh-based methods. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. NN-RK is additionally used to inform how crack opening and closure in turn affect the electro-chemo-mechanical responses in the material microstructure. References: [1] Wang, Y., Baek, J., Tang, Y. et al. "Support vector machine guided reproducing kernel particle method for image-based modeling of microstructures," Comput Mech 73, 907-942 (2024). https://doi.org/10.1007/s00466-023-02394-9. [2] Susuki, K., Allen, J. & Chen, J. S.. "Image-based modeling of coupled electro-chemo-mechanical behavior of Li-ion battery cathode using an interface-modified reproducing kernel particle method," Engineering with Computers (2024). https://doi.org/10.1007/s00366-024-02016-9. [3] Baek, J., Chen, J. S., Susuki, K., "Neural Network enhanced Reproducing Kernel Particle Method for Modeling Localizations," International Journal for Numerical Methods in Engineering, Vol. 123, 4422-4454 (2022). https://doi.org/10.1002/nme.7040.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

A Multidisciplinary Approach to Integrated Energy Systems: Advanced Nuclear Plants with Thermal Storage for Dynamic and Flexible Operation in Diverse Markets

Energy supply, distribution, and demand are continuously evolving in accordance with the integration of new power generation sources. In the United States, there is a progressive shift toward incorporating fluctuating energy sources such as wind and solar into the energy mix, alongside the distributed generation. Simultaneously, we are seeing a revolution in consumer technologies such as electric vehicles, energy devices for both residential and commercial use, and industrial processes—all contributing to a fundamental change in energy consumption dynamics. These scenarios have led to huge demand for enhanced flexibility from nuclear power plants (NPPs), requiring them to operate with unprecedented adaptability. Furthermore, advanced NPPs (A-NPPs) could potentially apply to scenarios in which power generation flexibility is prioritized over its current and conventional baseload generation capacity for traditional demand patterns. The present work covers several configurations for coupling nuclear energy production with thermal energy storage (TES). Previous work evaluated systems entailing a simple nuclear-TES configuration that used steam as a heat source for the TES of three different advanced reactors (ARs), but its heat diversion ratio (HDR) for charging was limited. The present work proposes a new configuration that features full decoupling of the nuclear reactor and the power cycle, using reactor coolant (i.e., gas) as a heat source for the TES and offering a theoretically unlimited HDR. This configuration is, however, more complex, as both high- and low-temperature TES (HT-TES and LT-TES) systems are required in order to cover the extended temperature range. Although this configuration imposes certain limitations on the discharge system configuration, it also presents the opportunity to design a more efficient system. For this case in particular, a steam reheat cycle is advantageous. Conventional TES systems take advantage of nominal efficiency when discharging, whereas the reheat cycle in the fully decoupled TES system affords a significant efficiency increase over conventional cases. Material selection, component/equipment sizing, and costing analyses were performed, followed by economic dispatch and size optimization. Dynamic models were developed for the decoupled system in order to generate insights into aspects of off design operation (e.g., drops in cycle efficiency), and these models enable exploration of potential mitigation strategies for addressing such drops. The models also highlight the need to design fail-safes that ensure continuous operational stability and minimize the impact of power ramping on the reactor parameters.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗

DOE OSTI2022

Multiscale Electrochemomechanics Interaction and Degradation Analytics of Sn Electrodes for Sodium-Ion Batteries

Sodium-ion batteries have emerged as a strong contender among the beyond lithium-ion chemistries due to elemental abundance and the low cost of sodium. Tin (Sn) is a promising alloying electrode with high capacity, redox reversibility, and earth abundance. Tin electrodes, however, undergo a series of intermediate reactions exhibiting multiple voltage plateaus upon sodiation/desodiation. Phase transformations related to incomplete sodiation in tin during cycling, in the presence of a frail solid electrolyte interphase layer, can quickly weaken the structural stability. The structural dynamics and reactivity of the electrode/electrolyte interface, being further dependent on the size and morphology of the active material particle in the presence of different electrolytes, dictate the electrode degradation and survivability during cycling. Here, we paint a comprehensive picture of the underpinnings of the electrochemical and mechanics coupling and electrode/electrolyte interfacial interactions in alloying Sn electrodes. We elicit the fundamental role of electrode/electrolyte complexations in the Sn electrode structure-property-performance relationship based on multimodal analytics, including electrochemical, microscopy, and tomography analyses.

25 ENERGY STORAGE↗

DOE OSTI2026

The Use of Microelectrodes in Molten Salt Electrochemistry

Molten salts have attracted considerable interest as essential media for advanced high-temperature technologies, including molten salt reactors, thermal energy storage, high-temperature electrolysis, and pyrochemical processing. Their ability to remain stable in liquid form at elevated temperatures, combined with favorable thermophysical properties and wide electrochemical windows, makes them highly suitable for applications involving heat transfer, energy storage, and hightemperature electrochemical processing. However, despite these advantages, molten salts present challenges due to their chemically reactive nature at high-temperatures, especially in the presence of oxidizing impurities. Salt chemistry can fluctuate through interactions with impurities over time, or fuel burnup in the case of molten salt reactors, often leading to the dissolution of metal species. This dynamic environment not only results in complex redox behavior but also promotes corrosion, which is rarely uniform and frequently manifests as localized degradation driven by structural materials’ compositional differences, electrochemical imbalances, and microstructural susceptibilities.

Kim, Changkyu [University of Wisconsin-Madison, WI↗

DOE OSTI2021

Designed and biologically active protein lattices

Versatile methods to organize proteins in space are required to enable complex biomaterials, engineered biomolecular scaffolds, cell-free biology, and hybrid nanoscale systems. Here, we demonstrate how the tailored encapsulation of proteins in DNA-based voxels can be combined with programmable assembly that directs these voxels into biologically functional protein arrays with prescribed and ordered two-dimensional (2D) and three-dimensional (3D) organizations. We apply the presented concept to ferritin, an iron storage protein, and its iron-free analog, apoferritin, in order to form single-layers, double-layers, as well as several types of 3D protein lattices. Our study demonstrates that internal voxel design and inter-voxel encoding can be effectively employed to create protein lattices with designed organization, as confirmed by in situ X-ray scattering and cryo-electron microscopy 3D imaging. The assembled protein arrays maintain structural stability and biological activity in environments relevant for protein functionality. The framework design of the arrays then allows small molecules to access the ferritins and their iron cores and convert them into apoferritin arrays through the release of iron ions. The presented study introduces a platform approach for creating bio-active protein-containing ordered nanomaterials with desired 2D and 3D organizations.

77 NANOSCIENCE AND NANOTECHNOLOGY↗

DOE OSTI2024

A Geodatabase Designed to Inform and Support Safe CO2 Transport-Route Planning

The National Energy Technology Laboratory developed the Carbon Capture and Storage (CCS) Pipeline Route Planning Database to inform safe and sustainable CO2 transport planning in support of decarbonization efforts. This comprehensive Esri Geodatabase contains over 90 GBs of data and 60+ spatial layers representing key considerations including natural hazards, infrastructure, energy, and social justice. Leveraging ArcGIS Pro, nationwide raster, and vector datasets containing millions of features were processed to be easily digestible for users and complex modeling software.

Romeo, Lucy↗

DOE OSTI2023

Low-Cost X-Ray CT System for Imaging of Roots

The goal of this project was to develop and demonstrate an innovative, low cost, field deployable, stationary 3D x-ray computed tomography (CT) system that will image total root phenotypes with a micron size resolution at a throughput of hundreds of plants per cycle. This system is based on UHV’s unique low cost linear x-ray tube technology and sophisticated reconstruction & image segmentation algorithms developed at University of Massachusetts, Lowel and University of Nottingham; and was tested for several types of soils at University of Wisconsin and Texas A&M University. Currently, no technologies exist that have been designed to image roots in complex media such as agricultural field conditions. Due to its small size, high resolution & fast imaging of fine roots, low power consumption, large penetration depth (i.e. ability to see through several feet of soil) and ease of field deployability, this CT system will increase the speed and efficacy of discovery, field translation, and deployment of improved crops and systems that improve soil carbon accumulation and storage, decrease N2O emissions, and improve water efficiency leading towards advancements that could mitigate 10% of the total US Greenhouse gases. This degree of imaging in the field has never been available and would be invaluable to scientists in understanding how environmental conditions and phenotypic variations contribute to carbon deposition through root development.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2023

An intelligent Data Delivery Service for and beyond the ATLAS experiment

The intelligent Data Delivery Service (iDDS) has been developed to cope with the huge increase of computing and storage resource usage in the coming LHC data taking. It has been designed to intelligently orchestrate workflows and data management systems, decoupling data pre-processing, delivery, and primary processing in large scale workflows. It is an experiment-agnostic service that has been deployed to serve data carousel (orchestrating efficient processing of tape-resident data), machine learning hyperparameter optimization, active learning, and other complex multi-stage workflows defined via DAG (Directed Acyclic Graph), CWL (Common Workflow Language) and other descriptions, including a growing number of analysis workflows. We will at first introduce some deployed use cases in a summary. Then we will focus on new improvements and use cases under developments in ATLAS, Rubin Observatory and sPHENIX, together with future efforts.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

Geologic carbon sequestration (GCS) is a promising technology for mitigating net carbon emissions and growing climate concern by storing CO 2 in reservoirs. Oil and gas brownfields are an attractive option for CO 2 storage, but these sites have many historical wellbores from petroleum production and can be a potential leakage pathway for CO 2 or formation brine. Therefore, risk management of GCS operations requires an assessment of potential well leakage. Due to the high uncertainty of the system, stochastic approaches are ideal for quantifying the range of risk behaviors, but they must be computationally efficient in the face of complex physics. Here, we develop a new physics-centric deep learning wellbore model to predict the leakage of CO 2 and brine through leaky wellbores. Multi-physics numerical simulations were used to generate data sets, and physics-informed features were introduced. Neural networks were optimized with an automated searching algorithm. Feature analysis quantifies the impact of each feature on model prediction and confirms the role of physics-inspired parameters. The model shows high predictive performance across a wide range of geologic and injection conditions and well attributes. In conclusion, a case study illustrates how the model is applied to assess well leakage in GCS operations.

58 GEOSCIENCES↗

DOE OSTI2023

Solution phase high repetition rate laser pump x-ray probe picosecond hard x-ray spectroscopy at the Stanford Synchrotron Radiation Lightsource

We present a dedicated end-station for solution phase high repetition rate (MHz) picosecond hard x-ray spectroscopy at beamline 15-2 of the Stanford Synchrotron Radiation Lightsource. A high-power ultrafast ytterbium-doped fiber laser is used to photoexcite the samples at a repetition rate of 640 kHz, while the data acquisition operates at the 1.28 MHz repetition rate of the storage ring recording data in an alternating on-off mode. The time-resolved x-ray measurements are enabled via gating the x-ray detectors with the 20 mA/70 ps camshaft bunch of SPEAR3, a mode available during the routine operations of the Stanford Synchrotron Radiation Lightsource. As a benchmark study, aiming to demonstrate the advantageous capabilities of this end-station, we have conducted picosecond Fe K-edge x-ray absorption spectroscopy on aqueous [FeII(phen)3]2+, a prototypical spin crossover complex that undergoes light-induced excited spin state trapping forming an electronic excited state with a 0.6–0.7 ns lifetime. In addition, we report transient Fe Kβ main line and valence-to-core x-ray emission spectra, showing a unique detection sensitivity and an excellent agreement with model spectra and density functional theory calculations, respectively. Notably, the achieved signal-to-noise ratio, the overall performance, and the routine availability of the developed end-station have enabled a systematic time-resolved science program using the monochromatic beam at the Stanford Synchrotron Radiation Lightsource.

47 OTHER INSTRUMENTATION↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

Distributed energy resources (DERs) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Impact of Mineral Reactive Surface Area on Forecasting Geological Carbon Sequestration in a CO2-EOR Field

Mineral reactive surface area (RSA) is one of the key factors that control mineral reactions, as it describes how much mineral is accessible and can participate in reactions. This work aims to evaluate the impact of mineral RSA on numerical simulations for CO2 storage at depleted oil fields. The Farnsworth Unit (FWU) in northern Texas was chosen as a case study. A simplified model was used to screen representative cases from 87 RSA combinations to reduce the computational cost. Three selected cases with low, mid, and high RSA values were used for the FWU model. Results suggest that the impact of RSA values on CO2 mineral trapping is more complex than it is on individual reactions. While the low RSA case predicted negligible porosity change and an insignificant amount of CO2 mineral trapping for the FWU model, the mid and high RSA cases forecasted up to 1.19% and 5.04% of porosity reduction due to mineral reactions, and 2.46% and 9.44% of total CO2 trapped in minerals by the end of the 600-year simulation, respectively. The presence of hydrocarbons affects geochemical reactions and can lead to net CO2 mineral trapping, whereas mineral dissolution is forecasted when hydrocarbons are removed from the system.

58 GEOSCIENCES↗

DOE OSTI2020

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning: Preprint

Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2021

Restoring Distribution System Under Renewable Uncertainty Using Reinforcement Learning

61 RADIATION PROTECTION AND DOSIMETRY↗

DOE OSTI2024

Image-Based Failure Assessment of Li-Ion Batteries

In energy storage materials, strong electrochemical-mechanical coupling and highly anisotropic material properties contribute to the formation and propagation of micro-cracking during charge/discharge cycling, resulting in reduced performance and service life. In this work, a digital twin is created to investigate the performance of a heterogeneous Li-ion battery cathode and simulate degradation accumulation. Pixel-based model construction is used to represent the complex material geometries from microstructural images supplied by the National Renewable Energy Laboratory (NREL). Because of the expected large deformation and crack opening, the reproducing kernel particle method (RKPM), a meshfree method with discretization at the image pixels, is used to approximate the field variables: electrostatic potential, concentration, and displacement. An interface modified reproducing kernel (IM-RK) is constructed by scaling a smooth kernel function with an interface-distance function to achieve strategic discontinuity types (i.e. weak discontinuities for strain discontinuities and strong discontinuities for cracks) and alleviate Gibbs oscillations near these transition zones. Applications to the heterogeneous microstructures of Li-ion battery cathodes will be presented to demonstrate the effectiveness of the proposed methods. IM-RK is additionally used to inform how crack evolution in turn affects the coupled electro-chemo-mechanical behavior of the Li-ion battery cathode.

image-based modeling↗

DOE OSTI2020

Review—Thermal Safety Management in Li-Ion Batteries: Current Issues and Perspectives

Approaches for thermal management of lithium-ion (Li-ion) batteries do not always keep pace with advances in energy storage and power delivering capabilities. Root-cause analysis and empirical evidence indicate that thermal runaway (TR) in cells and cell-to-cell thermal propagation are due to adverse changes in physical and chemical characteristics internal to the cell. However, industry widely uses battery management systems (BMS) originally designed for aqueous-based batteries to manage Li-ion batteries. Even the "best" BMS that monitor both voltage and outside-surface temperature of each cell are not capable of preventing TR or TR propagation, because voltage and surface-mounted temperature sensors do not track fast-emerging adverse events inside a cell. Most BMS typically include a few thermistors mounted on select cells to monitor their surface temperature. Technology to track intra-cell changes that are TR precursors is becoming available. Simultaneously, the complex pathways resulting in cell-to-cell TR propagation are being successfully modelled and mapped. Innovative solutions to prevent TR and thermal propagation are being advanced. These include modern BMS for rapid monitoring the internal health of each individual cell and physical as well as chemical methods to reduce the deleterious effects of rapid cell-to-cell heat and material transport in case of TR.

25 ENERGY STORAGE↗

DOE OSTI2023

Heterogenization of Homogeneous Ruthenium(II) Catalysts for Carbon-Neutral Dehydrogenation of Polyalcohols

Liquid organic hydrogen carrier (LOHC) systems are an excellent alternative to pressurized gas and liquid hydrogen storage technologies due to their high volumetric storage capacities and straightforward adaptation to existing infrastructure. Homogeneous catalysts are promising for the selective and reversible release of hydrogen from LOHC. However, separation from product mixtures and recycling inhibit their use, particularly when comprised of costly low-abundance elements, motivating the development of heterogeneous versions that are more easily recovered and reused. Here, we describe two methods for the heterogenization of molecular Ru catalysts that efficiently dehydrogenate the polyalcohol LOHCs ethylene glycol (EG) and 1,2-propanediol (1,2-PDO). The heterogeneous versions of these catalysts maintain catalytic activity for hydrogen production comparable to the homogeneous complexes, with up to 81% conversion and 99% selectivity. Further, DFT modeling indicates mechanistic similarities for the dehydrogenations of EG and 1,2-PDO, with the rate-limiting steps associated with protonation of the Ru–H bond to form H 2 and the alkoxide species coordinated to Ru(II), followed by β-hydride elimination to regenerate the Ru–H bond. Overall, the data suggest these heterogenized molecular catalysts have potential for practical use in polyalcohol-based LOHC systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗