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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

High Temperature Stability of Aqueous Foams for Potential Application in Enhanced Geothermal System (EGS)

Enhanced Geothermal Systems (EGS) are typically constructed by injecting high-pressure water into the deep hot dry rocks (HDR) under carefully controlled conditions to create new or re-open existing fractures, which usually utilizes an immense quantity of water. Alternatively, aqueous foam-based fracturing fluids may offer potential advantages over conventional water-based fracturing fluid, including reduced consumption of water and environmental impact. Although foam-based fracturing has shown promising results in oil and gas industries, its applicability to EGS is unknown. Foams are complex mixtures of liquids with gaseous phases that are thermodynamically unstable. Their stability will decrease over time due to liquid drainage, bubble coarsening, and coalescence. Therefore, it is essential to stabilize foam fluids at high temperatures for EGS related applications such as fracking of HDRs.This paper is focused on investigating high-temperature stability of N2-in-water or N2 foams containing different surfactants and stabilizing agents. Foam stability was characterized by the half-life, which is defined as the time a foam decreases to 50% of its original height due to drainage. Surfactants with various concentrations dissolved in deionized water were investigated, including alfa olefin sulfonate (AOS), sodium dodecyl sulfonate (SDS), TergitolTM (NP – 40), and cetyltrimethylammonium chloride (CTAC). Also, the effects of other stabilizing agents, including guar gum, bentonite clay, crosslinker, graphene oxide (GO) dispersion on foam stability were tested. Initial results showed that foam stability decreased dramatically as temperature increased. On the other hand, foams became relatively more stable as pressure increased. Certain stabilizing agents, such as guar gum and crosslinker, can enhance foam stability at a wide range of temperatures. Our results indicate that it may be possible to obtain stable foams at high-temperature, high-pressure conditions with appropriate stabilizing agents.

Thakore, Virensinh↗
DOE OSTI2021

Verdant Power: Thermoplastic Blade Validation at Full Scale

For the first time in history, thermoplastic composite blades, which have the potential to revolutionize the marine energy industry, are being validated on a large-scale tidal power turbine. Thanks to funding from the U.S. Department of Energy's Water Power Technologies Office and a collaboration with Verdant Power, NREL researchers constructed thermoplastic composite turbine blades and are validating their performance on one of Verdant Power's tidal turbines deployed in New York City's East River.

HYDRO ENERGY↗
DOE OSTI2022

Ion Coulomb Crystals in Storage Rings for Quantum Information Science

Quantum information science is a growing field that promises to take computing into a new age of higher performance and larger scale computing as well as being capable of solving problems classical computers are incapable of solving. The outstanding issue in practical quantum computing today is scaling up the system while maintaining interconnectivity of the qubits and low error rates in qubit operations to be able to implement error correction and fault-tolerant operations. Trapped ion qubits offer long coherence times that allow error correction. However, error correction algorithms require large numbers of qubits to work properly. We can potentially create many thousands (or more) of qubits with long coherence states in a storage ring. For example, a circular radio-frequency quadrupole, which acts as a large circular ion trap and could enable larger scale quantum computing. Such a Storage Ring Quantum Computer (SRQC) would be a scalable and fault tolerant quantum information system, composed of qubits with very long coherence lifetimes. With computing demands potentially outpacing the supply of high-performance systems, quantum computing could bring innovation and scientific advances to particle physics and other DOE supported programs. Increased support of R$\&$D in large scale ion trap quantum computers would allow the timely exploration of this exciting new scalable quantum computer. The R$\&$D program could start immediately at existing facilities and would include the design and construction of a prototype SRQC. We invite feedback from and collaboration with the particle physics and quantum information science communities.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Tantalum pentoxide: a new material platform for high-performance dielectric metasurface optics in the ultraviolet and visible region

Dielectric metasurfaces, composed of planar arrays of subwavelength dielectric structures that collectively mimic the operation of conventional bulk optical elements, have revolutionized the field of optics by their potential in constructing high-efficiency and multi-functional optoelectronic systems on chip. The performance of a dielectric metasurface is largely determined by its constituent material, which is highly desired to have a high refractive index, low optical loss and wide bandgap, and at the same time, be fabrication friendly. Here, we present a new material platform based on tantalum pentoxide (Ta 2 O 5 ) for implementing high-performance dielectric metasurface optics over the ultraviolet and visible spectral region. This wide-bandgap dielectric, exhibiting a high refractive index exceeding 2.1 and negligible extinction coefficient across a broad spectrum, can be easily deposited over large areas with good quality using straightforward physical vapor deposition, and patterned into high-aspect-ratio subwavelength nanostructures through commonly-available fluorine-gas-based reactive ion etching. We implement a series of high-efficiency ultraviolet and visible metasurfaces with representative light-field modulation functionalities including polarization-independent high-numerical-aperture lensing, spin-selective hologram projection, and vivid structural color generation, and the devices exhibit operational efficiencies up to 80%. Our work overcomes limitations faced by scalability of commonly-employed metasurface dielectrics and their operation into the visible and ultraviolet spectral range, and provides a novel route towards realization of high-performance, robust and foundry-manufacturable metasurface optics.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Stability Frontiers in the AM 6 X 6 Kagome Metals: The Ln Nb 6 Sn 6 ( Ln :Ce–Lu,Y) Family and Density-Wave Transition in LuNb 6 Sn 6

The kagome motif is a versatile platform for condensed matter physics, hosting rich interactions between magnetic, electronic, and structural degrees of freedom. In recent years, the discovery of a charge density wave (CDW) in the AV 3 Sb 5 superconductors and structurally-derived bond density waves (BDW) in FeGe and ScV 6 Sn 6 have stoked the search for new kagome platforms broadly exhibiting density wave (DW) transitions. Here, in this work, we evaluate the known AM 6 X 6 chemistries and construct a stability diagram that summarizes the structural relationships among the >125 member family. Subsequently, we introduce our discovery of the broader LnNb 6 Sn 6 (Ln:Ce–Nd,Sm,Gd–Tm,Lu,Y) family of kagome metals and an analogous DW transition in LuNb 6 Sn 6 . Our X-ray scattering measurements clearly indicate a (1/3, 1/3, 1/3) ordering wave vector (√$\bar{3}$ x √$\bar{3}$ x $3$ superlattice) and diffuse scattering on half-integer L-planes. Our analysis of the structural data supports the “rattling mode” DW model proposed for ScV 6 Sn 6 and paints a detailed picture of the steric interactions between the rare-earth filler element and the host Nb–Sn kagome scaffolding. We also provide a broad survey of the magnetic properties within the HfFe 6 Ge 6 -type LnNb 6 Sn 6 members, revealing a number of complex antiferromagnetic and metamagnetic transitions throughout the family. This work integrates our new LnNb 6 Sn 6 series of compounds into the broader AM 6 X 6 family, providing new material platforms and forging a new route forward at the frontier of kagome metal research.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

A de novo sacrificial-MOF strategy to construct enhanced-flux nanofiltration membranes for efficient dye removal

Nanofiltration membanes play a critical role in alleviating global water scarcity, affecting more than 4 billion people. Here, a new metal organic framework (MOF) sacrificicial strategy is designed to synthesize flux-enhancing nanofiltration membrane for efficient dye removal. With the aid of polyphenol chemistry, ZIF-67 MOF was first coated onto a porous support and in situ transformed into a selective layer by virtue of pyrrole polymerization. Trigerred by ammonium persulfate, polypyrrole selective layer functioalized nanofiltration membrane was fabricated when accompanying pore regulation by MOF self-sacrifice. The membrane demonstrated a flux enhancement of 745% than pristine polypyrrole (PPy) one with maintained rejection. Furthermore, the synergistic interactions among polypyrrole, polyphenol, and porous support endowed membrane with excellent stability during long term test. The designed MOF sacrificed route may open a new avenue for fabricating advanced energetic-efficient membranes with the reconstructed architecture towards diverse separation applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Knowledge-informed deep learning for hydrological model calibration: an application to Coal Creek Watershed in Colorado

Abstract. Deep learning (DL)-assisted inverse mapping has shown promise in hydrological model calibration by directly estimating parameters from observations. However, the increasing computational demand for running the state-of-the-art hydrological model limits sufficient ensemble runs for its calibration. In this work, we present a novel knowledge-informed deep learning method that can efficiently conduct the calibration using a few hundred realizations. The method involves two steps. First, we determine decisive model parameters from a complete parameter set based on the mutual information (MI) between model responses and each parameter computed by a limited number of realizations (∼50). Second, we perform more ensemble runs (e.g., several hundred) to generate the training sets for the inverse mapping, which selects informative model responses for estimating each parameter using MI-based parameter sensitivity. We applied this new DL-based method to calibrate a process-based integrated hydrological model, the Advanced Terrestrial Simulator (ATS), at Coal Creek Watershed, CO. The calibration is performed against observed stream discharge (Q) and remotely sensed evapotranspiration (ET) from the water year 2017 to 2019. Preliminary MI analysis on 50 realizations resulted in a down-selection of 7 out of 14 ATS model parameters. Then, we performed a complete MI analysis on 396 realizations and constructed the inverse mapping from informative responses to each of the selected parameters using a deep neural network. Compared with calibration using observations covering all time steps, the new inverse mapping improves parameter estimations, thus enhancing the performance of ATS forward model runs. The Nash–Sutcliffe efficiency (NSE) of streamflow predictions increases from 0.53 to 0.8 when calibrating against Q alone. Using ET observations, on the other hand, does not show much improvement on the performance of ATS modeling mainly due to both the uncertainty of the remotely sensed product and the insufficient coverage of the model ET ensemble in capturing the observation. By using observed Q only, we further performed a multiyear analysis and show that Q is best simulated (NSE > 0.8) by including in the calibration the dry-year flow dynamics that show more sensitivity to subsurface characteristics than the other wet years. Moreover, when continuing the forward runs till the end of 2021, the calibrated models show similar simulation performances during this evaluation period as the calibration period, demonstrating the ability of the estimated parameters in capturing climate sensitivity. Our success highlights the importance of leveraging data-driven knowledge in DL-assisted hydrological model calibration.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

The Present and Future of QCD: QCD Town Meeting White Paper – An Input to the 2023 NSAC Long Range Plan

It is currently understood that there are four fundamental forces in nature: gravitational, electromagnetic, weak and strong forces. The strong force governs the interactions between quarks and gluons, elementary particles whose interactions give rise to the vast majority of visible mass in the universe. The mathematical description of the strong force is provided by the non-Abelian gauge theory Quantum Chromodynamics (QCD). While QCD is an exquisite theory, constructing the nucleons and nuclei from quarks, and furthermore explaining the behavior of quarks and gluons at all energies, remain to be complex and challenging problems. Such challenges, along with the desire to understand all visible matter at the most fundamental level, position the study of QCD as a central thrust of research in nuclear science. Experimental insight into the strong force can be gained using large particle accelerator facilities, which are necessary to probe the very short distance scales over which quarks and gluons interact. The Long Range Plans (LRPs) exercise of 1989 and 1996 led directly to the construction of two world-class facilities: the Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Lab (JLab) that is focused on studying how the structure of hadrons emerges from QCD (cold QCD research), and the Relativistic Heavy Ion Collider (RHIC) at Brookhaven National Lab (BNL) that aims at the discovery and study of a new state of matter, the quark-gluon plasma (QGP), at extremely high temperatures (hot QCD research). These past investments have produced major advances. Nucleons and nuclei are being studied with increasing precision with a unified description of the partonic structure utilizing multi-dimensional imaging. Significant progress has been made, paving the way towards a complete picture of how quarks and gluons give rise to the mass, spin, and momentum of the nucleon. In hot QCD, the QGP is created in the collisions of nuclei at RHIC and the Large Hadron Collider (LHC) and is observed to behave like a fluid with very low specific shear viscosity; the current goals are to understand how the fluid behavior emerges from QCD and to characterize the temperature (and chemical potential) dependence of the properties of the QGP. As this White Paper is written, current experimental programs at CEBAF, RHIC and the LHC continue to provide exciting near term opportunities to capitalize on the investments in experimental equipment and accelerator operations. Most importantly, the QCD community looks forward to the construction of the Electron Ion Collider (EIC) as a major new facility to push forward QCD research in the next decades, with significant focus on exploring the properties of gluons, the mediators of the strong force.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Systematic quark/gluon identification with ratios of likelihoods

Discriminating between quark- and gluon-initiated jets has long been a central focus of jet substructure, leading to the introduction of numerous observables and calculations to high perturbative accuracy. At the same time, there have been many attempts to fully exploit the jet radiation pattern using tools from statistics and machine learning. We propose a new approach that combines a deep analytic understanding of jet substructure with the optimality promised by machine learning and statistics. After specifying an approximation to the full emission phase space, we show how to construct the optimal observable for a given classification task. This procedure is demonstrated for the case of quark and gluons jets, where we show how to systematically capture sub-eikonal corrections in the splitting functions, and prove that linear combinations of weighted multiplicity is the optimal observable. In addition to providing a new and powerful framework for systematically improving jet substructure observables, we demonstrate the performance of several quark versus gluon jet tagging observables in parton-level Monte Carlo simulations, and find that they perform at or near the level of a deep neural network classifier. Combined with the rapid recent progress in the development of higher order parton showers, we believe that our approach provides a basis for systematically exploiting subleading effects in jet substructure analyses at the Large Hadron Collider (LHC) and beyond.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Multilevel Hierarchical Decomposition of Finite Element White Noise with Application to Multilevel Markov Chain Monte Carlo

In this work we develop a new hierarchical multilevel approach to generate Gaussian random field realizations in an algorithmically scalable manner that is well suited to incorporating into multilevel Markov chain Monte Carlo (MCMC) algorithms. This approach builds off of other partial differential equation (PDE) approaches for generating Gaussian random field realizations; in particular, a single field realization may be formed by solving a reaction-diffusion PDE with a spatial white noise source function as the right-hand side. While these approaches have been explored to accelerate forward uncertainty quantification tasks, e.g., multilevel Monte Carlo, the previous constructions are not directly applicable to multilevel MCMC frameworks which build fine-scale random fields in a hierarchical fashion from coarse-scale random fields. Our new hierarchical multilevel method relies on a hierarchical decomposition of the white noise source function in $L^2$ which allows us to form Gaussian random field realizations across multiple levels of discretization in a way that fits into multilevel MCMC algorithmic frameworks. After presenting our main theoretical results and numerical scaling results to showcase the utility of this new hierarchical PDE method for generating Gaussian random field realizations, this method is tested on a four-level MCMC algorithm to explore its feasibility.

algebraic multigrid↗
DOE OSTI2020

Bis–Calix[4]pyrroles: Preparation, structure, complexation properties and beyond

Calix[4]pyrrole and its derivatives are key members of the supramolecular Parthenon along with other well-recognized receptor systems, such as crown ethers, cyclodextrins (CDs), cucurbiturils, calixarenes and pillararenes. Calix[4]pyrroles are relatively easy–to–make and widely recognized for their ability to bind anions and ion pairs. Many review papers relating to aspects of calix[4]pyrrole chemistry have been published within the past decades. These reviews have focused primarily on monomeric calix[4]pyrroles. The emergent area of Bis–calix[4]pyrroles, species wherein two calix[4]pyrrole subunits are linked by one or more “walls”, has not benefited from such treatment, even though the species in question often exhibit enhanced binding affinities and selectivities relative to their single calix[4[pyrrole congeners. This review is designed to summarize recent progress involving bis–calix[4]pyrroles. Advances in the design, synthesis, and coordination chemistry of bis–calix[4]pyrroles containing “one wall”, “two walls”, “three walls”, and “four walls”, as well as their possible application in ion recognition, sensing and logic gate construction, will be detailed. Lastly, the hope is that this review will provide a guide for the design and preparation of new multi-component calix[4]pyrrole receptors possessing improved recognition properties, thereby advancing host–guest chemistry in new and useful directions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Structural delineation and phase-dependent activation of the costimulatory CD27:CD70 complex

CD27 is a tumor necrosis factor (TNF) receptor, which stimulates lymphocytes and promotes their differentiation upon activation by TNF ligand CD70. Activation of the CD27 receptor provides a costimulatory signal to promote T cell, B cell, and NK cell activity to facilitate antitumor and anti-infection immunity. Aberrant increased and focused expression of CD70 on many tumor cells renders CD70 an attractive therapeutic target for direct tumor killing. However, despite their use as drug targets to treat cancers, the molecular basis and atomic details of CD27 and CD70 interaction remain elusive. Here we report the crystal structure of human CD27 in complex with human CD70. Analysis of our structure shows that CD70 adopts a classical TNF ligand homotrimeric assembly to engage CD27 receptors in a 3:3 stoichiometry. By combining structural and rational mutagenesis data with reported disease-correlated mutations, we identified the key amino acid residues of CD27 and CD70 that control this interaction. We also report increased potency for plate-bound CD70 constructs compared with solution-phase ligand in a functional activity to stimulate T-cells in vitro. These findings offer new mechanistic insight into this critical costimulatory interaction.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Machine learning-guided discovery of polymer membranes for CO 2 separation with genetic algorithm

Designing polymer membranes with high gas permeability and selectivity is a difficult multi-task constrained problem due to the trade-off between these two properties. In this work, we present a machine learning (ML) driven genetic algorithm to tackle the design problem of polymer membranes for CO 2 separation from N 2 and O 2 . Using literature data of permeability for three gases, we constructed multiple ML models with different fingerprinting featurization schemes to predict gas permeabilities. Then, we employed a genetic algorithm to design new polymers and evaluated their performance using our ML models. We were able to identify new polymer membranes that are promising for both CO 2 /N 2 and CO 2 /O 2 separations. Further, the top discovered polymers are predicted to have high glass transition temperatures. Similarly, the pyridine functionality was found in ≈20% of the predicted polymers. This framework can be used to design polymers for any application involving constrained optimization. Finally, we outlined the challenges and opportunities with using ML guided data-driven inverse design of polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Homomorphic data compression for real time photon correlation analysis

The construction of highly coherent X-ray sources, combined with next-generation detectors that are larger and faster, has enabled new research opportunities across the scientific landscape. Among the techniques that benefit most from these advancements is X-ray photon correlation spectroscopy (XPCS), where faster acquisition unlocks the ability to study faster dynamics within samples. However, faster acquisition on larger detectors also introduces unprecedented challenges for online data processing and offline data storage. Such challenges are particularly prominent for XPCS, where real time analyses require simultaneous calculation of all the previously acquired data in the time series. We present a homomorphic compression scheme to effectively reduce the computational time and memory space required for XPCS analysis. Leveraging similarities in the mathematical expression between a matrix-based compression algorithm and the correlation calculation, our approach allows direct operation on the compressed data without their decompression. The offline compression scheme extends storage capacity by a factor of 40 while preserving key features in the lossy compressed data. Meanwhile, the online compression scheme reduces the computational time to below 1 ms, enabling real time calculation of the correlation functions at kHz framerate. Our demonstration of a homomorphic compression of scientific data provides an effective solution to the big data challenge at coherent light sources. Beyond the example shown in this work, the framework can be extended to facilitate real-time operations directly on a compressed data stream for other techniques.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Bayesian Cloud Property Retrievals from ARM Active and Passive Measurements

The optimum use of the continuous measurements of thermodynamics, radiation, aerosols, clouds and precipitation from the DOE Atmospheric Radiation Measurement (ARM) program is key to achieve the DOE Atmospheric System Research (ASR)’s objectives. One of the key mission requirements is to retrieve cloud and precipitation properties, as well as vertical motion parameters, along the vertical cross- section defined by the profiling active sensors. Such retrievals are challenging to perform continuously in the entire spectrum of cloud and precipitation conditions due to the large natural microphysical and dynamical variability, the often-limited information content in the measurements, and the lack of proper characterization of measurement quality and uncertainty. Today, the acquisition of new remote and in-situ sensors by the ARM program creates opportunities to address the microphysical retrieval problem by exploiting new, more robust retrieval techniques and integrating various scattered advancements in both sensor techniques and retrieval algorithms. During this project, we constructed a robust Bayesian Markov chain Monte Carlo (MCMC) cloud property retrieval algorithm that includes a state of the art radar forward model. Our MCMC-based retrieval produces both the best estimate of height-resolved cloud and precipitation properties in the radar profile, as well as an estimate of the in-cloud vertical motion and turbulence. In addition, the MCMC algorithm automatically produces robust and flexible estimates of retrieval uncertainty. We tested the algorithm on several synthetic cloud profiles obtained from large eddy simulation (LES) models with bin-resolved microphysics.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Pilot Unit Testing at NCCC of Sorbent based CO2 Capture

TDA Research, Inc. is developing a new CO2 sorbent (alkalized alumina) technology for post-combustion CO2 capture. In this DOE sponsored project (DE-FE0012870), TDA constructed a pilot-scale skid, which was installed and tested at National Carbon Capture Center (NCCC). It was designed to process coal derived flue gas equivalent to 0.5 MWe of power generation. The pilot test data shows that TDA’s process can achieve 90% capture rate and 95% CO2 purity (the performance target) for both coal and natural gas (NG) flue gases. The techno-economic analysis (TEA) for a 550-MWe supercritical coal fired power plant with CO2 capture shows that the capture cost for TDA’s process is $34.9/tonne CO2 captured, which meets DOE’s goal of $40/tonne and is 17.1% less than the DOE baseline Case 12 (an amine solvent technology).

20 FOSSIL-FUELED POWER PLANTS↗
DOE OSTI2023

A Smart Silicon Carbide Power Module With Pulse Width Modulation Over Wi-Fi and Wireless Power Transfer-Enabled Gate Driver, Featuring Onboard State of Health Estimator and High-Voltage Scaling Capabilities: Preprint

A wide range of utility applications require controllable switches with features such as high-voltage (HV) blocking and high-current carrying capacity especially at high pulse width modulation (PWM) frequency. Low and medium voltage utility applications such as motor drives and flexible AC transmission systems (FACTS) as well as solid state transformers (SST) could also benefit from a low-cost HV switching module. Wide band gap (WBG) semiconductors such as SiC and GaN MOSFETs are considered to be the present and next generation device choices, although they have their own limitations. For relatively HV applications with demanding thermal management, SiC is still the only choice, and GaN dominates the low voltage regime. This manuscript proposes a new half bridge power MOSFET module that is suitable for conventional H-bridge of multilevel configurations used in HV applications. Constructed from bare SiC dies, this half bridge module takes advantage of (1) optimized MOSFET placement inside the module, (2) customized heat exchanger, manifold and cooling, (3) integrated gate driver module with PWM over wi-fi to eliminate the need for low voltage signals, (4) wireless power transfer (WPT) enabled gate driver and other ancillary circuits, (5) and the option to incorporate an onboard state of health (SOH) estimator module onboard. The entire architecture has been designed and built at National Renewable Energy Laboratory (NREL), Golden, CO.

baseplate design↗