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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Fuel Property Effects on Knock Propensity and Thermal Efficiency in a Direct-Injection Spark-Ignition Engine

Engine knock remains one of the major barriers to further improvement in thermal efficiency of Direct-Injection Spark-Ignition (DISI) engines. While Research Octane Number and Motor Octane Number are often used as standard rating methods for knock resistance of fuels, the impacts of other fuel properties on knock propensity in modern engines such as heat of vaporization (HoV) and laminar flame speed (LFS) require better understanding in order to co-optimize fuels and engine designs to achieve higher thermal efficiency and lower CO2 emission. In the present study, computational fluid dynamics (CFD) is used to model a boosted DISI engine with a focus on knock prediction and fuel property effects. A level-set G-equation model is employed to capture turbulent premixed combustion, and is coupled with a transported Livengood-Wu (L-W) integral approach to predict autoignition in the unburnt region. A criterion associated with the L-W integral is developed to accurately predict knock onset and knock-limited spark-advance. This model is then applied to a sensitivity analysis of HoV and LFS on knock tendency and thermal efficiency. The pressure-temperature trajectory framework is applied and extended to study the fuel effects on auto-ignition process in the engine. An existing efficiency-based merit function, which is derived from experiments for boosted SI engines, is evaluated and improved based on the current CFD results.

DISI↗
DOE OSTI2022

Thermal Management System for an Electric Machine with Additively Manufactured Hollow Conductors with Integrated Heat Pipes: Preprint

This paper discusses steps taken to size a thermal management system for an aircraft propulsion electric machine containing additively manufactured coils integrated with heat pipes aimed at boosting its specific power. Experimental setups are used to size and characterize heat pipes for the application and 3D thermal FEA is used to determine optimum heat transfer coefficient of convective boundaries. Geometric details of fin-based surface area enhancement required to reach target combined overall heat transfer coefficient (U) and surface area (A) performance (UA) in W/K, is worked out for relevant boundaries and the resulting UA is verified in 3D thermal FEA. Thermal management system's UA (by extension specific power) sensitivity to coolant temperature is explored and temperature distribution plots of optimized machine components are presented and discussed.

additive manufacturing↗
DOE OSTI2023

The connection between nonzero density and spontaneous symmetry breaking for interacting scalars

We consider U(1)-symmetric scalar quantum field theories at zero temperature. At nonzero charge densities, the ground state of these systems is usually assumed to be a superfluid phase, in which the global symmetry is spontaneously broken along with Lorentz boosts and time translations. We show that, in d > 2 spacetime dimensions, this expectation is always realized at one loop for arbitrary non-derivative interactions, confirming that the physically distinct phenomena of nonzero charge density and spontaneous symmetry breaking occur simultaneously in these systems. We quantify this result by deriving universal scaling relations for the symmetry breaking scale as a function of the charge density, at low and high density. Moreover, we show that the critical value of μ above which a nonzero density develops coincides with the pole mass in the unbroken, Poincaré invariant vacuum of the theory. The same conclusions hold non-perturbatively for an O(N) theory with quartic interactions in d = 3 and 4, at leading order in the 1/N expansion. We derive these results by computing analytically the zero-temperature, finite-μ one-loop effective potential, paying special attention to subtle points related to the iε terms. We check our results against the one-loop low-energy effective action for the superfluid phonons in λΦ 4 theory in d = 4 previously derived by Joyce and ourselves, which we further generalize to arbitrary potential interactions and arbitrary dimensions. As a byproduct, we find analytically the one-loop scaling dimension of the lightest charge-n operator for the λΦ 6 conformal superfluid in d = 3, at leading order in 1/n, reproducing a numerical result of Badel et al. For a λΦ 4 superfluid in d = 4, we also reproduce the Lee-Huang-Yang relation and compute relativistic corrections to it. Finally, we discuss possible extensions of our results beyond perturbation theory.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Comparison of temperature adaptive calibration methods for laser induced fluorescence based fuel-in-oil instrument

Fuel dilution of engine oil (or fuel-in-oil, FiO) is an important issue as multiple and late-cycle fuel injection, integral to many combustion efficiency and emissions improvements (e.g., downsized boosted gasoline engines and catalyst thermal management) increases FiO rate. In addition to causing general wear and corrosion in engine due to decreased oil viscosity and pH buffering, FiO is also believed to cause destructive low speed pre-ignition (or super knock) in boosted SI engines. To understand the effects of engine operating conditions on the FiO rate, an optical diagnostic capable of measuring transient FiO on minute timescales was recently developed and demonstrated on a modified GM Ecotech engine system (Neupane et al., Applied Spectroscopy 2021). The measurement is based on adding a dye to the fuel and monitoring for its presence in oil via laser-induced fluorescence (LIF). The measured LIF signal is related to FiO concentration via pre-determined calibration factors using a multivariate classical least square (CLS) method.Since fluorescence quantum yield is a function of temperature, measured LIF intensity not only depends on FiO concentration but also oil temperature. To expand the applicability of the FiO diagnostic to transient oil-temperature conditions (e.g., cold start in practical engines), this study develops a method to account for oil-temperature variations. The effect of oil temperature (20°C - 95°C) on the LIF spectra of eight FiO samples ranging from ~0.8-15% was investigated. LIF intensities of the FiO samples decreased linearly with increasing temperature; the reductions being more significant at dye peaks. We develop a new calibration model (T-adaptive CLS) incorporating the temperature (T) effects on LIF intensity that enables simultaneous calculation of FiO and oil temperature. The improved FiO diagnostic with T-adaptive calibration is more robust, and applicable to varying oil-temperature conditions. For example, when strategies such as multiple/late fuel injections are applied to overcome cold-start instability due to use of low vaporization bio-based fuels such as ethanol, FiO rate is expected to be very high; the improved T-adaptive FiO diagnostic is hence relevant for engine- and fuel-system calibration and optimization. The diagnostic could also provide validation data for flow-field and spray interaction CFD models, further broadening the diagnostic’s utility for advancing engine technology and efficiency.

Neupane, Sneha↗
DOE OSTI2025

Charge density fluctuations with enhanced superconductivity at the proposed quantum critical point of Sr0.77⁢Ba0.23⁢Ni2⁢As2

A quantum critical point (QCP) represents a continuous phase transition at absolute zero. In unconventional superconductors, enhanced superconducting transition temperature and magnetic fluctuation strength are often observed together, indicating magnetism-mediated superconductivity. This raises the question of whether quantum fluctuations in other degrees of freedom, such as charge, could similarly boost superconductivity. However, because charge is frequently intertwined with magnetism, isolating and understanding its specific role in Cooper pair formation pose a significant challenge. Here, we report persistent charge density fluctuations (CDFs) down to 15 K in the nonmagnetic superconductor Sr0.77⁢Ba0.23⁢Ni2⁢As2, which lie near a proposed nematic QCP associated with a sixfold enhancement of superconductivity. Our results show that the quasielastic CDFs do not condense into resolution-limited Bragg peaks but, rather, display nonsaturated strength. The phonons associated with CDFs completely soften at 25 K, with their critical behavior described by the same mathematical framework as the antiferromagnetic Fermi liquid model, yielding a fitted Curie-Weiss temperature of 𝜃≈0K. Additionally, we find that the nematic fluctuations are weakly coupled to the lattice, as evidenced by the absence of softening in nematic-coupled in-plane transverse acoustic phonons. Our discovery positions Sr𝑥⁢Ba1−𝑥⁢Ni2⁢As2 as a promising candidate for charge-fluctuation-driven superconductivity.

Aczel, Adam [ORNL] (ORCID:0000000319641943)↗
DOE OSTI2023

Phase-Field Modeling of Materials Interfaces and Nanostructures

Nanostructured materials offer unique properties for a wide range of energy applications. Among various known processing methods, liquid metal dealloying (LMD)—the selective dissolution of a base alloy element into a metallic melt—has emerged as a powerful technique to produce a new class of nano-/meso-scale open porous and bicontinuous composite structures with ultra-high interfacial area. LMD has recently been complemented by the advent of vapor phase dealloying (VPD), a novel technique that exploits the selective evaporation of one element from a parent alloy containing elements with very different vapor pressures, thereby enabling to fabricate open nanoporous structures of various elements from less-noble metals to inorganic elements regardless of their chemical activity without requiring high LMD temperatures and chemical etching. Together LMD and VPD have greatly expanded the scope of dealloying techniques, limited by traditional electrochemical means to noble metals, and boosted the design of new functional and structural materials that combine a wide variety of elements. Topologically-connected open porous structures with ultra-high surface area allow mass transport within the structure while preserving structural integrity, enabling them to serve as catalysts, fuel cells, supercapacitors, or high-capacity battery materials. Bicontinuous composite structures in turn can display high strength and high ductility or superior radiation-damage resistance due to the ultra-high interface area between interpenetrating solid phases. This research program makes use of state-of-the-art computational methods to understand at a basic level the self-organizing dealloying process with main focus on dealloying kinetics and interfacial pattern formation at the dealloying front controlling initial structure size, topology, and phase compositions. Phase-field simulation studies of LMD focus on solid solutions, line compounds, and intermetallic systems that can form ternary composites by nucleation and growth of a new phase. Studies of VPD employ phase-field modeling and a hybrid method combining a kinetic Monte Carlo (KMC) model of evaporation and surface diffusion with molecular dynamics for vapor-phase transport inside nanopores. Simulations explore mechanisms of interface- and diffusion-controlled dealloying kinetics, both observed in VPD but not fundamentally understood. In addition, our newly developed multi-physics phase-field approach of large-volume-change phase transformations is being used to model novel 3D anode geometries based on dealloyed nanoporous structures including novel sandwiched graphene/Si/silica for highrate Li ion battery. Those studies are aimed at elucidating geometric design principles that improve mechanical stability. We expect this research to enhance the capability to tailor nano-/mesoscale structures for a wide range of energy-related materials applications and to yield further advances in computational methodologies that benefit a broad materials research community.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2022

Methods for microwave characterization of electro-optic crystals for quantum transduction

Microwave-optic quantum transducers are essential devices to develop distributed quantum networks and implement related quantum communication protocols. Three dimensional high-coherence time microwave cavities embedded with electro-optic nonlinear dielectric materials provide promising platforms to boost the efficiency of the microwave-optic frequency conversion. However, so far, the properties for such dielectric crystals operating at milli-Kelvin cryogenic temperatures have not been well understood. Here, we propose a scheme to precisely measure and benchmark the dielectric constant and analyze the dissipation mechanisms of electro-optic materials, such as Lithium Niobate, at the quantum threshold. We will use Fermilab’s three dimensional superconducting cavities with long coherence time. The proposed method of microwave characterization lays the foundations for engineering quantum transduction devices and quantum sensors with high conversion efficiency and fidelity.

Zorzetti, Silvia↗
DOE OSTI2022

Spinel Cu–Mn–Cr Oxide Nanoparticle-Pigmented Solar Selective Coatings Maintaining >94% Efficiency at 750 °C

High-temperature concentrating solar power (CSP) system is capable of harvesting and storing solar energy as heat towards cost-effective dispatchable solar electricity. Solar selective coating is a critical component to boost its efficiency by maximizing solar absorptance and minimizing thermal emittance losses. However, maintaining a high solar-thermal conversion efficiency >90% for long-term operation at ≥750 ºC remains a significant challenge. Herein, we report spray-coated spinel Cu-Mn-Cr oxide nanoparticle-pigmented solar selective coatings on Inconel tube sections maintaining ≥94% efficiency at 750 ºC and ≥92.5% at 800 ºC under 1000x solar concentration after 60 simulated day-night thermal cycles in air, each cycle comprising 12h at 750 ºC/800 ºC and 12h cooling to 25 ºC. The solar spectral selectivity is intrinsic to the band-to-band and d-d transitions of non-stoichiometric spinel Cu-Mn-Cr oxide nanoparticles. Furthermore, this feature offers a large fabrication tolerance in nanoparticle volume fraction and coating thickness, facilitating low-cost and scalable spray-coated high-efficiency solar selective absorbers for high-temperature CSP systems.

14 SOLAR ENERGY↗
DOE OSTI2025

Optimizing superconducting Nb film cavities by mitigating medium-field Q -slope through annealing

Niobium films are of interest in applications in various superconducting devices, such as superconducting radiofrequency cavities for particle accelerators and superconducting qubits for quantum computing. In this study, we address the persistent medium-field Q-slope issue in Nb film cavities, which, despite their high-quality factor at low RF fields, exhibit a significant Q-slope at medium RF fields compared to bulk Nb cavities. Traditional heat treatments, effective in reducing surface resistance and mitigating the Q-slope in bulk Nb cavities, are challenging for Nb-coated copper cavities. To overcome this challenge, we employed DC bias high-power impulse magnetron sputtering to deposit Nb film onto a 1.3 GHz single-cell elliptical bulk Nb cavity, followed by annealing treatments aimed at modifying the properties of the Nb film. In-situ annealing at 340 °C increased the quench field from 10.0 to 12.5 MV m −1 . Vacuum furnace annealing at 600 °C and 800 °C for 3 h resulted in a quench field increase of 13.5 and 15.3 MV m −1 , respectively. Further annealing at 800 °C for 6 h boosted the quench field to 17.5 MV m −1 . Additionally, the annealing treatments significantly reduced the field dependence of the surface resistance. However, increasing the annealing temperature to 900 °C induced a Q-switch phenomenon in the cavity. The analysis of RF performance and material characterization before and after annealing has provided critical insights into how the microstructure and impurity levels in Nb films influence the evolution of the Q-slope in Nb film cavities. Our findings highlight the significant roles of hydrides, high local misorientation, and lattice and surface defects in driving field-dependent losses. By strategically optimizing film properties and controlling impurity levels, we demonstrate a promising pathway to mitigate the medium-field Q-slope, paving the way for more efficient superconducting RF technologies.

Nb film↗
DOE OSTI2025

Machine learning for fundamental spectroscopic and thermodynamic data of actinides and lanthanides

Accurately modeling optical spectra with absolute radiometric intensities is vital for nuclear forensics applications that depend on characterizing optical emissions from energetic nuclear phenomena. This requires precise knowledge of the individual atomic transition probabilities, known as Einstein A-coefficients, for each emission line. Obtaining these values theoretically or experimentally is often impractical due to the complex electronic structures and the number of transitions involved in atoms relevant to nuclear applications. In this study, we explore the use of machine learning to predict the Einstein A coefficients for atomic transitions. Seven models were evaluated that ranged from deep learning to decision tree algorithms, and found that gradient boosting performed best, specifically the Extreme Gradient Boosting (XGB) architecture, achieving a precision of 86% across transitions of 36 elements. Furthermore, the model was cross-validated using published transition probabilities reported in the literature and applied to estimate Pu plasma temperatures from a previous experiment conducted at Savannah River National Laboratory.

Atomic spectroscopy↗
DOE OSTI2021

Machine Learning Augmented Predictive and Generative Model for Rupture Life in Ferritic and Austenitic Steels

The Larson-Miller parameter (LMP) offers an efficient and fast scheme to estimate the creep rupture life of alloy materials for high temperature applications. However, owing to poor generalizability and dependence on the constant C, which is typically not known a-priori, estimations using the Larson-Miller parameter often result in suboptimal performance for a wide range of materials. At best it is useful in comparing alloys of similar composition. In this work, three machine learning (ML) schemes were developed for rupture life prediction for 9-12% Cr ferritic-martensitic steels and austenitic stainless steels, i.e., a hierarchical model to parameterize LMP using the LMP constant C to compute rupture life, a hierarchical model to parameterize both C and LMP to compute rupture life, and a direct prediction of rupture life. Specifically, we show that the third scheme, using a gradient boosting algorithm, can be used to train ML models for very accurate prediction of rupture life in a variety of alloys (Pear-son Correlation Coefficient > 0.9 for 9-12% Cr and > 0.8 for austenitic stainless steels). In addition, the Shapley value was used to quantify feature importance, making the model interpretable by identifying the effect of various features on the model performance. Furthermore, a variational autoencoder-based generative model was built by conditioning on the experimental dataset to sample hypothetical synthetic candidate alloys from the learnt joint distribution not existing in both 9-12 % Cr ferritic-martensitic steel and austenitic stainless steel datasets. Finally, based on the predictive and generative model, a reinforcement learning strategy has been proposed to guide experimentalists into designing better heat resistant alloys.

Mamun, Md Osman G.↗
DOE OSTI2024

Microwave-Assisted dry reforming of toluene as a model tar compound using low-cost iron catalyst for syngas clean-up

Gasification of waste feedstock such as biomass, waste plastics suffers from high tar yields during hydrogen-rich syngas production. The presence of tars result in lower quality syngas, lower syngas yields, reactor blockage, reactor down time, and costly maintenance. Therefore, removal of tar from syngas during gasification is essential. Catalytic reforming of tars via in-situ syngas cleanup is an effective way of mitigating tars. This work explores the possibility of microwave-assisted catalytic dry reforming of tars for syngas production. Further, due to its chemical complexity, toluene, which is one of the main tar constituents, could be used as a model tar compound. Toluene dry reforming was studied using the Fe/Al 2 O 3 catalyst under CO 2 under the temperature range of 400–700 °C. The toluene reforming reaction was conducted using microwave and conventional thermal reactors. Under microwave irradiation, CO 2 and toluene conversions are boosted to 80% at 500 °C. Hydrogen and carbon monoxide yields were approximately five times and ten times higher in the microwave reactor at 500 °C, respectively, compared to the productions obtained in the conventional fixed-bed reactor at 700 °C. Filamentous carbon was also produced as a valuable side product to improve the economy of this process and such value-added carbon was only observed in the microwave reactor. Three reaction pathways were observed during microwave reaction: the toluene decomposition produces an initial hydrogen and carbon deposit on the catalyst; the formation of methane and benzene suggests toluene hydrodemethylation as a secondary reaction; and toluene hydrogenolysis forms light alkanes such as methane, and through reforming reaction under CO 2 to syngas.

10 SYNTHETIC FUELS↗
DOE OSTI2022

Low Temperature Combustion Exploration with Negative Valve Overlap

Progressively stringent emission regulations and increasing regulatory demands on fuel economy have led to advanced combustion development. Low temperature combustion (LTC), specifically homogenous charge compression ignition (HCCI), is a promising technology for reducing exhaust emissions and improving efficiency. However, its operating range is limited to low load without boosting and EGR, due to low volumetric efficiency and high pressure rise rates. In addition, effectively controlling the combustion phasing is another challenge in realizing the associated combustion gains. In this work, advanced valve control mechanisms known as continuously variable valve duration (CVVD) and continuously variable valve timing (CVVT) were used for both intake and exhaust valvetrains to enable negative valve overlap (NVO) for trapping hot exhaust residuals and to promote multipoint simultaneous ignition. Heat release phasing was controlled by varying the fueling scheme and by adjusting the amount of NVO. Parametric studies on valve timing and duration, fueling strategy, lambda, spark assist, etc., were carried out first. Afterwards the LTC strategy was proposed and then LTC operation was explored at different engine speeds. Various approaches for extending load limits were summarized and discussed. Finally, combustion performance was compared to that of spark ignition combustion, demonstrating the combustion gains of LTC.

02 PETROLEUM↗
DOE OSTI2024

Isolated Single Metal Atoms Supported on Silica for One Step Non-Oxidative Methane Upgrading to Hydrogen and Value-Added Hydrocarbons

Natural gas in the United States offers substantial economic opportunities due to its abundance but its transportation is challenging because its primary component, methane (CH 4 ), does not liquefy at ambient temperature and typical pressures. As a result, a significant portion of natural gas is either used for heat, flared in remote locations, or remains unutilized, presenting a lost economic opportunity and an environmental harm. Converting natural gas to larger hydrocarbons in an economically competitive manner would enable transportation of products and further boost the economy and reduce environmental footprint. Our research goal is to enable efficient non-oxidative methane conversion (NMC) via catalyst innovation to convert CH 4 in one-step to olefins and aromatics and hydrogen (H 2 ) co-product. The catalysts are made of supported single metal atoms and operated at medium-high temperatures. The single metal atoms achieve methane activation by heterogeneous surface dehydrogenation to generate a hydrocarbon pool and importantly limit coke formation. The integration of novel single atom catalysts in a short contact time microreactor enable unprecedented NMC performance that could lead to economically-feasible, distributed natural gas upgrading by advanced manufacturing and process intensification. Our innovated catalyst and reactor technology promise to tap into previously uneconomic natural gas resources, such as stranded, vented, and flared methane.

03 NATURAL GAS↗
DOE OSTI2026

Plasmon-Assisted Electrochemical Epoxidation using Water as an Oxidant

Olefin epoxidation, an important industrial reaction, often uses hazardous oxidants, causing challenges in waste disposal. Here, we demonstrate the use of water as an oxidant by a plasmon-assisted electrochemical strategy. The electrocatalyst is comprised of a hybrid of a water oxidation catalyst, manganese oxide, and plasmonic gold nanoparticles. Visible-light irradiation of the electrocatalyst enhanced the epoxidation of 4-styrenesulfonate 5-fold as compared to dark conditions at the same temperature. From electrochemical analyses conducted under plasmon excitation conditions, complemented by real-time time-dependent density functional tight binding simulations, it is found that the plasmonic boost of the electrochemical styrene epoxidation is due to energetic holes generated by the excitation of localized surface plasmon resonances of gold nanoparticles. These photogenerated holes activate adsorbed water for oxidation and enhance the binding of 4-styrenesulfonate at interfacial sites. Here, this work demonstrates a proof of concept and establishes the mechanistic basis for plasmon-assisted activation of water as an O atom source for electrochemical epoxidations.

Gold↗
DOE OSTI2023

Recursive Blind Forecasting of Photovoltaic Generation and Consumer Load for Microgrids

Existing forecasting frameworks that predict time-series photovoltaic (PV) generation and consumer load for micro-grids' operation and control assume near-continuous availability of real-time predictors from the field. The incoming data are used to periodically re-train the models and update forecast snapshots over a moving horizon window. However, such frameworks are not resilient to disruptions in data availability caused by losses in communications between the field sensors and data loggers. This paper bridges the shortcoming by leveraging a previously proposed forecasting framework that is resilient to abrupt changes in data quality caused by communication losses. Assuming no availability of real-time field system data, which is typical in extreme weather events such as hurricanes, the framework uses lightweight recursive time-series models to independently forecast solar irradiance, ambient temperature, PV power, and consumer load for three horizon windows: 24 hours, 12 hours, and 1 hour. Four types of ensemble-based regression trees-simple gradient boosted trees (GBR), GBR with an adaptive component (A-GBR), random forests (RF), and extra trees (ExTR)-are leveraged and their performances are compared against a simple historical weekly mean. Numerical results show that A-GBR performs better on average by 32% for 24-hour horizon and 39% for 12-hour horizon, whereas ExTR outdoes the other models on average by 10% for 1-hour horizon.

Sundararajan, Aditya↗
DOE OSTI2024

High-Throughput Screening and Accurate Prediction of Ionic Liquid Viscosities Using Interpretable Machine Learning

Ionic liquids (ILs) are a novel group of green solvents with great promise for various industrial applications, including carbon capture and lignocellulosic biomass deconstruction. However, the use of ILs at the industrial scale remains challenging due to their high viscosities at ambient temperatures. To develop ILs with lower viscosities, a systematic study of their quantitative structure–property relationship (QSPR) is desirable. Here, we developed four machine learning (ML) models to predict viscosity at various temperature and pressure ranges, trained over a wide range of ILs consisting of various cationic and anionic families. ML methods including two-factor polynomial regression (two-factor PR), support vector regression (SVR), feed-forward neural networks (FFNN), and categorical boosting (CATBoost) were developed based on features that have proven useful in previous ML studies: COSMO-RS (conductor-like screening model for real solvents)-derived surface screening charge densities (sigma profiles). FFNN and CATBoost were the most accurate in predicting IL viscosities with lower average absolute relative deviation and higher R2 values on the test set. Tanimoto similarity scores were calculated to characterize the chemical space and structural similarity of the investigated ions. Furthermore, SHapley Additive exPlanation (SHAP) analysis was employed to interpret the ML results. Temperature, the polar area of ILs, and the nonpolar regions of ions are key features that influence the viscosity predictions. Importantly, the IL viscosity prediction here is the most accurate reported to date.

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

Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms

Fires, including wildfires, harm air quality and essential public services like transportation, communication, and utilities. These fires can also influence atmospheric conditions, including temperature and aerosols, potentially affecting severe convective storms. Here, we investigate the remote impacts of fires in the western United States (WUS) on the occurrence of large hail (size: $\geqslant$ 2.54 cm) in the central US (CUS) over the 20-year period of 2001–20 using the machine learning (ML), Random Forest (RF), and Extreme Gradient Boosting (XGB) methods. The developed RF and XGB models demonstrate high accuracy (> 90%) and F1 scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide, particularly in four states (Wyoming, South Dakota, Nebraska, and Kansas). The key contributing variables identified from both ML models include the meteorological variables in the fire region (temperature and moisture), the westerly wind over the plume transport path, and the fire features (i.e., the maximum fire power and burned area). Importantly, the results confirm a linkage between WUS fires and severe weather in the CUS, corroborating the findings of our previous modeling study conducted on case simulations with a detailed physics model.

54 ENVIRONMENTAL SCIENCES↗