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This is a presentation that outlines the past successes of the INL led Charging Experience Consortium and lists current projects related to vehicle-grid integration. The primary purpose of this presentation is to engage with industry experts and practitioners about the use of the protocol OCPP to enable key improvements to the charging experience for both drivers and the grid.

We describe an integrated modelling approach to accelerate the search for novel, single-phase, multicomponent materials with high magnetocrystalline anisotropy (MCA). For a given system we predict the nature of atomic ordering, its dependence on the magnetic state, and then proceed to describe the consequent MCA, magnetisation, and magnetic critical temperature (Curie temperature). Crucially, within our modelling framework, the same ab initio description of a material’s electronic structure determines all aspects. We demonstrate this holistic method by studying the effects of alloying additions in FeNi, examining systems with the general stoichiometries Fe₄Ni₃X and Fe₃Ni₄X, for additives including X = Pt, Pd, Al, and Co. The atomic ordering behaviour predicted on adding these elements, fundamental for determining a material’s MCA, is rich and varied. Equiatomic FeNi has been reported to require ferromagnetic order to establish the tetragonal L1₀ order suited for significant MCA. Our results show that when alloying additions are included in this material, annealing in an applied magnetic field and/or below a material’s Curie temperature may also promote tetragonal order, along with an appreciable effect on the predicted hard magnetic properties.

Invar 36 alloy is a material of high interest in the composite tooling sector due to its low coefficient of thermal expansion. Current production of Invar 36 tooling using traditional manufacturing such as casting and forging is associated with long lead times due to a multitude of factors such as labor and component shortages, high material costs, foreign competition, and supply chain issues. An attractive alternate process is the use of an integrated 5-axis CNC hybrid Laser Hot Wire Deposition System (LHWD) for manufacturing invar molds. Here, the hybrid process provides a combination of the additive and subtractive technologies resulting in a synergistic platform for producing and repairing structures and molds. The main novelty and goal of this work is to study the properties of Invar deposited by a LHWD and to provide guidelines for the manufacture of parts using this process. In this study, the thermal expansion behavior of the manufactured specimens has been analyzed and related to its printing parameters and direction. Multiple specimens were extracted for mechanical, dilatometry and metallographic testing. A thermal IR recording of the printing process was also carried out to observe the thermal history of the produced parts to establish thermal influence on performance-property-processing relationship. The results of these tests show the advantage of LHWD technology for the manufacture of Invar alloy parts, as it presents similar thermal expansion behavior as those commercially available with minimal presence of precipitates and no macrostructural failures such as pores, cracks and lacks of fusion.

A brief overview of the Transient test reactor facility (TREAT), what working at TREAT entails and a brief overview of current and recently finished experiment campaigns.

Directed atomic fabrication using an aberration-corrected scanning transmission electron microscope (STEM) opens new pathways for atomic engineering of functional materials. In this approach, the electron beam is used to actively alter the atomic structure through electron beam induced irradiation processes. One of the impediments that has limited widespread use thus far has been the ability to understand the fundamental mechanisms of atomic transformation pathways at high spatiotemporal resolution. Here, we develop a workflow for obtaining and analyzing high-speed spiral scan STEM data, up to 100 fps, to track the atomic fabrication process during nanopore milling in monolayer MoS₂. An automated feedback-controlled electron beam positioning system combined with deep convolution neural network (DCNN) was used to decipher fast but low signal-to-noise datasets and classify time-resolved atom positions and nature of their evolving atomic defect configurations. Through this automated decoding, the initial atomic disordering and reordering processes leading to nanopore formation was able to be studied across various timescales. Using these experimental workflows a greater degree of speed and information can be extracted from small datasets without compromising spatial resolution. This approach can be adapted to other 2D materials systems to gain further insights into the defect formation necessary to inform future automated fabrication techniques utilizing the STEM electron beam.

Here, we developed a process for the fabrication of tunable single crystal diamond micro- and nanopillars, with tip widths ranging from 40 to 460 nm, densities ranging from 0.5 to 53.5 pillars/μm², and heights greater than 4.5 μm. A self-assembled Au nanodot ensemble etch mask was formed from an annealed Au thin film. The nanodot diameter and density can be tuned using the initial film thickness. The pillars were etched from the nanodot mask using an RIE O₂ plasma, which has infinite selectivity for the diamond when applied at low RF powers (50 W). Finally, the pillars can be sharpened to ~40 nm tip widths by annealing in air at 650 °C. These pillars can be used for applications such as field effect enhancement of diamond photocathode devices, enhancement of optical emission from N-V centers, and antireflective coatings.

Quantum algorithms are emerging tools in the design of functional materials due to their powerful solution space search capability. How to balance the high price of quantum computing resources and the growing computing needs has become an urgent problem to be solved. We propose a novel optimization strategy based on an active learning scheme that combines the Quantum-inspired Genetic Algorithm (QGA) with machine learning surrogate model regression. Using Random Forests as the surrogate model circumvents the time-consuming physical modeling or experiments, thereby improving the optimization efficiency. QGA, a genetic algorithm embedded with quantum mechanics, combines the advantages of quantum computing and genetic algorithms, enabling faster and more robust convergence to the optimum. Using the design of planar multilayer photonic structures for transparent radiative cooling as a testbed, we show superiority of our algorithm over the classical genetic algorithm (CGA). Additionally, we show the precision advantage of the Random Forest (RF) model as a flexible surrogate model, which relaxes the constraints on the type of surrogate model that can be used in other quantum computing optimization algorithms (e.g., quantum annealing needs Ising model as a surrogate).

Horizontal falling film evaporators are widely utilized in desalination industries to increase fresh water supply. However, universal correlations for seawater falling film evaporation under varied operational and geometrical conditions are simply unavailable in open literature. Thus, this study aims to develop such a universal correlation for both plain and enhanced tubes. The detailed heat transfer mechanisms are reviewed, and rational parameters are incorporated to develop the universal correlation. A dataset of 994 data points from 9 sources covering a wide range of conditions was compiled. These conditions include Reynolds numbers from 10 to 7235, heat fluxes from 7.7 to 208 kW/m^-2, saturation temperatures from 278 to 401 K, salinities from 0 to 60 gsalt kg^-1water, tube diameters from 15.8 to 50.8 mm, and liquid feeder height to diameter ratios from 1 to 2.25. Upon analysis, it was found that most of the recommended existing correlations exhibited poor predictive accuracy, as evidenced by larger MADs. The developed correlation in this study demonstrated the best predictive accuracy for the entire dataset, yielding a MAD of 16.8 % and an R² of 0.82. Furthermore, the performance of the new correlation was individually assessed across a broader spectrum of operational and design conditions, reflecting the individual conditions’ influences with an overall MAD of 20 %.

High-performance electronics are continuously demanding cooling of higher heat fluxes. Phase-change cooling, including pool boiling, is a useful approach to address this challenge; however, competition between liquid and vapor flows generally limit the heat fluxes that can be dissipated. A range of strategies to control these flows have been investigated previously, including capillary guides. Here a manifold structure formed from a metallic mesh is investigated to control the disposition of liquid and vapor phases above a pool fed boiling surface enhanced with porous structures. Copper mesh forms defined liquid flow paths, using capillary action to guide and distribute liquid evenly over the heated surface, along with open channels to facilitate vapor escape. The mesh provides a novel structure for liquid guidance that imposes low resistance to liquid flow while occluding a minimal area of heated surface underneath. The manifold performance is characterized in boiling fed by a pool of water above a laser-textured aluminum nitride heat dissipation surface with pin–fin structures having heights of 110 µm and spacing of 30 µm with a heated area of 5 mm x 5 mm. A maximum heat flux of 490 W/cm² is reached with the manifold in the pool fed configuration, representing an increase of more than 65% over the porous pin fin surface alone. The maximum stable superheat observed for the manifold of 36K is 14K higher than that for the porous surface without the manifold. The factors limiting performance of the manifold are analyzed. High superheat is attributed to partial flooding of the boiling surface as suggested by the reduction in superheat using external suction. Similar systems and structures for enhanced two-phase cooling are compared.

This article aims at discovering the unknown variables in the system through data analysis. The main idea is to use the time of data collection as a surrogate variable and try to identify the unknown variables by modeling gradual and sudden changes in the data. We use Gaussian process modeling and a sparse representation of the sudden changes to efficiently estimate the large number of parameters in the proposed statistical model. The method is tested on a realistic dataset generated using a one-dimensional implementation of a Magnetized Liner Inertial Fusion (MagLIF) simulation model, and encouraging results are obtained.