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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Characteristics of Non-Fullerene Acceptor-Based Organic Photovoltaic Active Layers Using X-ray Scattering and Solid-State NMR

With the recent development of non-fullerene acceptors, power conversion efficiencies of bulk-heterojunction organic solar cells have exceeded 18%. The morphology of the BHJ active layer, including packing, ordering, orientation, and phase behavior of the donor(s) and acceptor(s), plays critical roles in determining the device performance. We characterized the morphology of active layers consisting of mixtures of a PTB7/PTB7-Th (donor) and ITIC (acceptor) using grazing incidence wide-angle X-ray scattering, resonant soft X-ray scattering, and solid-state nuclear magnetic resonance (ssNMR) to correlate the morphology with device performance. PTB7-Th/ITIC shows a better device performance than that of PTB7/ITIC due to a smaller π-πstacking distance and smaller domain size of the phase separated morphology. One of PTB7/ITIC samples, processed from mixed solvents (chlorobenzene/benzene = 1:1 by volume), shows a possible partial miscibility and smaller domain size as revealed by ssNMR T 1ρ relaxation times, which is detrimental to device performance. ssNMR results showed that ITIC could crystallize into different forms depending on processing conditions, which may have implications on the manufacturing of devices using it as an ingredient, as well as its long-term stability in service.

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

Dynamic Behavior of Pt Multimetallic Alloys for Active and Stable Propane Dehydrogenation Catalysts

Improving the use of platinum in propane dehydrogenation catalysts is a crucial aspect to increasing the efficiency and sustainability of propylene production. A known and practiced strategy involves incorporating more abundant metals in supported platinum catalysts, increasing its activity and stability while decreasing the overall loading. Here, using colloidal techniques to control the size and composition of the active phase, we show that Pt/Cu alloy nanoparticles supported on alumina (Pt/Cu/Al 2 O 3 ) displayed elevated rates for propane dehydrogenation at low temperature compared to a monometallic Pt/Al 2 O 3 catalyst. We demonstrate that the enhanced catalytic activity is correlated with a higher surface Cu content and formation of a Pt-rich core and Cu-rich shell that isolates Pt sites and increases their intrinsic activity. However, rates declined on stream because of dynamic metal diffusion processes that led to a more uniform alloy structure. This transformation was only partially inhibited by adding excess hydrogen to the feed stream. Instead, cobalt was introduced to provide trimetallic Pt/Cu/Co catalysts with stabilized surface structure and stable activity and higher rates than the original Pt/Cu system. In conclusion, the structure–activity relationship insights in this work offer improved knowledge of propane dehydrogenation catalyst development featuring reduced Pt loadings and notable thermal stability for propylene production.

Alloys↗
DOE OSTI2022

Automatic derivation of many-body theories based on general Fermi vacua

This paper describes Wick&D, an implementation of the algebra of second-quantized operators normal ordered with respect to general correlated references and the corresponding Wick theorem [D. Mukherjee, Chem. Phys. Lett. 274, 561 (1997) and W. Kutzelnigg and D. Mukherjee, J. Chem. Phys. 107, 432 (1997)]. Wick&D employs a compact representation of operators and a backtracking algorithm to efficiently evaluate Wick contractions. Since Wick&D can handle both fully and partially contracted terms, it can be applied to both projective and Fock-space many-body formalisms. To demonstrate the usefulness of Wick&D, here we use it to evaluate the single-reference coupled cluster equations up to octuple excitations and report an automated derivation and implementation of the second-order driven similarity renormalization group multireference perturbation theory.

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

Multi-fidelity Bayesian neural networks: Algorithms and applications

Here we propose a new class of Bayesian neural networks (BNNs) that can be trained using noisy data of variable fidelity, and we apply them to learn function approximations as well as to solve inverse problems based on partial differential equations (PDEs). These multi-fidelity BNNs consist of three neural networks: The first is a fully connected neural network, which is trained following the maximum a posteriori probability (MAP) method to fit the low-fidelity data; the second is a Bayesian neural network employed to capture the cross-correlation with uncertainty quantification between the low- and high-fidelity data; and the last one is the physics-informed neural network, which encodes the physical laws described by PDEs. For the training of the last two neural networks, we first employ the mean-field variational inference (VI) to maximize the evidence lower bound (ELBO) to obtain informative prior distributions for the hyperparameters in the BNNs, and subsequently we use the Hamiltonian Monte Carlo (HMC) method to estimate accurately the posterior distributions for the corresponding hyperparameters. We demonstrate the accuracy of the present method using synthetic data as well as real measurements. Specifically, we first approximate a one- and four-dimensional function, and then infer the reaction rates in one- and two-dimensional diffusion-reaction systems. Moreover, we infer the sea surface temperature (SST) in the Massachusetts and Cape Cod Bays using satellite images and in-situ measurements. Taken together, our results demonstrate that the present method can capture both linear and nonlinear correlation between the low- and high-fidelity data adaptively, identify unknown parameters in PDEs, and quantify uncertainties in predictions, given a few scattered noisy high-fidelity data. Finally, we demonstrate that we can effectively and efficiently reduce the uncertainties and hence enhance the prediction accuracy with an active learning approach, using as examples a specific one-dimensional function approximation and an inverse PDE problem.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Magneto-structural studies of an unusual [Mn III Mn II Gd III (OR) 4 ] 4– partial cubane from 2,2'-bis- p- t Bu-calix[4]arene

Reaction of 2,2'-bis- p - t Bu-calix[4]arene (H 8 L) with MnCl 2 ·4H 2 O, GdCl 3 ·6H 2 O and 2,6-pyridinedimethanol (H 2 pdm) affords [Mn III Mn II Gd III (H 3 L)(pdmH)(pdm)(MeOH) 2 (dmf)]·3MeCN·dmf ( 3 ·3MeCN·dmf) upon vapour diffusion of MeCN into the basic dmf/MeOH mother liquor. 3 crystallises in the tetragonal space group P 4 1 2 1 2 with the asymmetric unit comprising the entire cluster. The highly unusual core contains a triangular arrangement of Mn III Mn II Gd III ions housed within a [Mn III Mn II Gd III (OR) 4 ] 4– partial cubane. Magnetic susceptibility and magnetisation data reveal best fit parameters J Mn(II)–Mn(III) = +0.415 cm –1 , J Mn(III)–Gd(III) = +0.221 cm –1 , J Mn(II)–Gd(III) = –0.258 cm –1 and D Mn(III) = –4.139 cm –1 . Theoretically derived magnetic exchange interactions, anisotropy parameters, and magneto-structural correlations for 3 are in excellent agreement with the experimental data.

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

Sizing Accuracy of Low-Cost Optical Particle Sensors Under Controlled Laboratory Conditions

Low-cost particulate matter sensors have seen increased use for monitoring at personal and local levels due to their affordability, ease of operation, and high time resolution. However, the quality of data reported by these sensors can be questionable, and a thorough evaluation of their performance is necessary. This study evaluated the particle sizing accuracy of several commonly used optical sensors, including the Alphasense optical particle counter (OPC), TSI DustTrak DRX aerosol monitor, Plantower PMS5003 sensor, and Sensirion SPS30 sensor, using laboratory-generated monodisperse particles. The OPC and DRX agreed partially with reference instruments and showed promise in detecting coarse-size particles. However, the PMS5003 and SPS30 did not correctly size fine and coarse particles. Furthermore, their reported mass distributions do not directly correspond to their number distribution. Despite these limitations, field measurements involving a dust storm period showed that the SPS30 correlated reasonably well with reference instruments for both PM2.5 and PM10, though the regression slopes differed significantly. These findings underscore the need for caution when interpreting data from low-cost optical sensors, particularly for coarse particles. Recommendations for improving the performance of these sensors are also provided.

Gautam, Prakash↗
DOE OSTI2023

Multi-omics of NET formation and correlations with CNDP1, PSPB, and L-cystine levels in severe and mild COVID-19 infections

We report that we performed a multi-omics analysis of an immunologically naïve SARS-CoV-2 clinical cohort to characterize overall changes in plasma among control (uninfected), mild, and severe infections. A comparison of healthy controls and patient samples showed activation of neutrophil degranulation pathways. Consistent with this observation, we characterized neutrophil extracellular trap (NET) complexes that were partially initiated in a subset of the mild infections (showing partially formed NETs) and fully-formed NETs in a subset of severe infections (containing multiple NET proteins in individual patient samples). As a potential mechanism to suppress NET formation, multiple redox enzymes were elevated in the mild and severe population. Analysis of metabolites from the same cohort showed a 24 and 60-fold elevation in plasma L-cystine, the oxidized form of cysteine and substrate of the powerful antioxidant glutathione in mild and severe patients, respectively. Unique to patients with mild infections, the carnosine dipeptidase modifying enzyme (CNDP1) was up-regulated. The strong protein and metabolite oxidation signatures suggest multiple compensatory pathways working to suppress both free radical and NET formation in SARS-CoV-2 infections.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Comparison of Spectroscopic Techniques for Determining the Peroxide Value of 19 Classes of Naturally Aged, Plant-Based Edible Oils

The peroxide value of edible oils is a measure of the degree of oxidation, which directly relates to the freshness of the oil sample. Several studies previously reported in the literature have paired various spectroscopic techniques with multivariate analyses to rapidly determine peroxide values using field portable and process instrumentation; those efforts presented “best-case scenarios” with oils from narrowly defined training and test sets. The purpose of this paper is to evaluate the use of near- and mid-infrared absorption and Raman scattering spectroscopies on oil samples from different oil classes, including seasonal and vendor variations, to determine which measurement technique or combination thereof is best for predicting peroxide values. Following peroxide value assays of each oil class using an established titration-based method, global and global-subset calibration models were constructed from spectroscopic data collected on the 19 oil classes used in this study. Spectra from each optical technique were used to create partial least squares regression calibration models to predict the peroxide value of unknown oil samples. A global peroxide value model based on near-infrared (8 mm optical path length) oil measurements produced the lowest RMSEP (4.9), followed by 24 mm optical path length near infrared (5.1), Raman (6.9) and 50 µm optical path length mid-infrared (7.3). However, it was determined that the Raman RMSEP resulted from chance correlations. Global peroxide value models based on low-level fusion of the NIR (8 and 24 mm optical path length) data and all infrared data produced the same RMSEP of 5.1. Global subset models, based on any of the spectroscopies and olive oil training sets from any class (pure, extra light, extra virgin), all failed to extrapolate to the non-olive oils. However, the near-infrared global subset model built on extra virgin olive oil could extrapolate to test samples from other olive oil classes. This work demonstrates the difficulty of developing a truly global method for determining peroxide value of oils.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Machine learning elastic constants of multi-component alloys

The present manuscript explores application of machine learning methods for determining elastic constants and other derived mechanical properties of multi-component alloys. Here, a number of machine learning models, including linear regression, neural network and random forest based models, are trained and tested on a dataset of binary alloys generated using density functional theory (DFT) calculations and spanning over a large number of elemental species in the periodic table. Starting with a wide range of simple and easily accessible compositionally-averaged elemental features, a correlation-based feature selection strategy was used to systematically down-select a set of most relevant features towards the prediction of the elasticity tensor components. The true predictive performance and the associated uncertainties of the models were established by testing on unseen data and bootstrapping, respectively. A single and pair-wise feature partial dependence analysis was performed to visualize the average property trends in the multi-dimensional feature space in order to further understand the achieved predictive performance. The utility of the trained model is further demonstrated by obtaining sufficiently accurate yet highly efficient approximations for bulk modulus, Young’s modulus, shear modulus and Poisson’s ratio for alloys beyond the binary space (i.e., two-component alloys) on which the model was originally trained. More importantly, we test and validate the predictive performance of the developed model directly against the experimentally measured elastic constants of technologically relevant multi-component alloys (such as, Ni- and Ti-based alloys). Finally, utility of such a data-enabled route is demonstrated by predicting the possible range of various elastic properties for vast composition space available within the five component Ni-Cr-Fe-Mo-W alloy system in a high-throughput manner.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Community structure and function during periods of high performance and system upset in a full-scale mixed microalgal wastewater resource recovery facility

Microalgae have the potential to exceed current nutrient recovery limits from wastewater, enabling water resource recovery facilities (WRRFs) to achieve increasingly stringent effluent permits. The use of photobioreactors (PBRs) and the separation of hydraulic retention and solids residence time (HRT/SRT) further enables increased biomass in a reduced physical footprint while allowing operational parameters (e.g., SRT) to select for desired functional communities. However, as algal technology transitions to full-scale, there is a need to understand the effect of operational and environmental parameters on complex microbial dynamics among mixotrophic microalgae, bacterial groups, and pests (i.e., grazers and pathogens) and to implement robust process controls for stable long-term performance. Here, we examine a full-scale, intensive WRRF utilizing mixed microalgae for tertiary treatment in the US (EcoRecover, Clearas Water Recovery Inc.) during a nine-month monitoring campaign. We investigated the temporal variations in microbial community structure (18S and 16S rRNA genes), which revealed that stable system performance of the EcoRecover system was marked by a low-diversity microalgal community (D INVSIMPSON = 2.01) dominated by Scenedesmus sp. (MRA = 55 %-80 %) that achieved strict nutrient removal (effluent TP < 0.04 mg·L -1 ) and steady biomass concentration (TSS monthly avg . = 400–700 mg·L −1 ). Operational variables including pH, alkalinity, and influent ammonium (NH 4 + ), correlated positively (p < 0.05, method = Spearman) with algal community during stable performance. Further, the use of these parameters as operational controls along with N/P loading and SRT allowed for system recovery following upset events. Importantly, the presence or absence of bacterial nitrification did not directly impact algal system performance and overall nutrient recovery, but partial nitrification (potentially resulting from NO 2 − accumulation) inhibited algal growth and should be considered during long-term operation. The microalgal communities were also adversely affected by zooplankton grazers (ciliates, rotifers) and fungal parasites (Aphelidium), particularly during periods of upset when algal cultures were experiencing culture turnover or stress conditions (e.g., nitrogen limitation, elevated temperature). Altogether, the active management of system operation in order to maintain healthy algal cultures and high biomass productivity can result in significant periods (>4 months) of stable system performance that achieve robust nutrient recovery, even in winter months in northern latitudes (WI, USA).

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Separating the intrinsic alignment signal and the lensing signal using self-calibration in photo- z surveys with KiDS450 and KV450 Data

ABSTRACT To reach the full potential for the next generation of weak lensing surveys, it is necessary to mitigate the contamination of intrinsic alignments (IAs) of galaxies in the observed cosmic shear signal. The self-calibration (SC) of IAs provides an independent method to measure the IA signal from the survey data and the photometric redshift information. It operates differently from the marginalization method based on the IA modelling. In this work, we present the first application of SC to the KiDS450 data and the KV450 data, to split directly the intrinsic shape–galaxy density (Ig) correlation signal and the gravitational shear–galaxy density (Gg) correlation signal, using the information from photometric redshift (photo-z). We achieved a clear separation of the two signals and performed several validation tests. Our measured signals are found to be in general agreement with the KiDS450 cosmic shear best-fitting cosmology, for both lensing and IA measurements. For KV450, we use partial (high-z) data, and our lensing measurements are also in good agreement with KV450 cosmic shear best fit, while our IA signal suggests a larger IA amplitude for the high-z sample. We discussed the impact of photo-z quality on IA detection and several other potential systematic biases. Finally, we discuss the potential application of the information extracted for both the lensing signal and the IA signal in future surveys.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

Light-Induced Charge Order Mode in a Metastable Cuprate Ladder

We report the observation of an emergent charge order mode in the optically excited cuprate ladder Sr 14⁢ Cu 24⁢ O 41 . Near-infrared light in the ladder plane drives a symmetry-protected electronic metastable state together with a partial melting of the equilibrium charge order. Our time-resolved resonant inelastic x-ray scattering measurements at the upper Hubbard band reveal a collective excitation dispersing from the charge order wave vector up to 0.8 eV with a slope on the order of the quasiparticle velocity. These findings reveal a regime where correlated carriers acquire itinerant character at finite momentum, and charge order becomes dynamically fluctuating, offering a platform to explore light-induced pairing instabilities.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2026

Probing Surface Hydration and Isotope Exchange Kinetics in Proton-Conducting Ceramics Using DRIFTS and EIS

This internship project investigates how water vapor partial pressure (pH2O) affects surface hydration and H/D isotope-exchange kinetics in BaZr0.4Ce0.4Y0.1Yb0.1O3?d (4411) proton-conducting ceramic electrolytes. Diffuse reflectance infrared Fourier-transform spectroscopy (DRIFTS) is used to monitor surface hydroxyl and deuteroxyl species under controlled humidified atmospheres during gas-switching experiments. These surface kinetic trends are to be correlated with bulk, grain-boundary, and total electrolyte resistance measured by electrochemical impedance spectroscopy (EIS) under matching conditions. The future goals of this study are to clarify the relationship between surface hydration dynamics and proton conduction behavior, improving interpretation of EIS data and guiding the optimization of ceramic electrolytes for energy applications.

08 - HYDROGEN↗
DOE OSTI2025

Enhanced Phase Stability of Sm 2 (Fe, Al) 17 C x

Aluminum doping can improve the phase stability of metastable compound Sm 2 Fe 17 C x with a high carbon content (x > 1.5). We investigated the preferential site substitution of Al, chemical bonding, and structural stability in Sm 2 (Fe,Al) 17 C 3 using first-principle calculations. Our results reveal a strong correlation between the preferential substitution of Fe by Al and the atomic site chemical environment, which affects the overall phase stability. Specifically, Al preferentially occupies the 9d site in Sm 2 (Fe,Al) 17 C 3 . At the same time, Al prefers the site 6c in its parent phase Sm 2 (Fe,Al) 17 . Partial replacement of Fe with Al leads to a more negative formation energy, indicating enhanced thermodynamic stability. Crystal Orbital Hamilton Population (COHP) and Crystal Orbital Bond Index (COBI) analysis suggest that insertion of carbon weakens the bonding strength of Sm-Fe (18f) and Sm-Fe (18h), resulting in metastability of Sm 2 Fe 17 C x . Doping Al strengthens Al-Fe, Al-Sm, Sm-Fe (18f, 18h) and Fe–C bonding in Sm 2 (Fe,Al) 17 C 3 , as revealed by calculated COHP and COBI. These effects contribute to improved phase stability in the Al-doped 2:17 interstitial compound.

chemical bonding↗
DOE OSTI2022

Z-Contrast Enhancement in Au–Pt Nanocatalysts by Correlative X-ray Absorption Spectroscopy and Electron Microscopy: Implications for Composition Determination

The properties of bimetallic nanoparticles (BNPs) vary widely as a function of their composition and size distributions. X-ray absorption fine structure analysis is commonly used to characterize their structure, but its application to elements that are close to each other in the periodic table is hampered by poor Z-contrast. For this study, we trained an artificial neural network to recognize the partial coordination numbers in AuPt NPs synthesized via peptide templating using their X-ray absorption near-edge structure spectra. This approach, combined with scanning transmission electron microscopy analysis, revealed unique details of this prototype catalytic system that has different forms of heterogeneities at both the intra- and inter-particle levels. Our method based on the enhancement of Z-contrast of metal species will have implications for compositional studies of BNPs.

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

Accelerating defect predictions in semiconductors using graph neural networks

First-principles computations reliably predict the energetics of point defects in semiconductors but are constrained by the expense of using large supercells and advanced levels of theory. Machine learning models trained on computational data, especially ones that sufficiently encode defect coordination environments, can be used to accelerate defect predictions. Here, we develop a framework for the prediction and screening of native defects and functional impurities in a chemical space of group IV, III–V, and II–VI zinc blende semiconductors, powered by crystal Graph-based Neural Networks (GNNs) trained on high-throughput density functional theory (DFT) data. Using an innovative approach of sampling partially optimized defect configurations from DFT calculations, we generate one of the largest computational defect datasets to date, containing many types of vacancies, self-interstitials, anti-site substitutions, impurity interstitials and substitutions, as well as some defect complexes. We applied three types of established GNN techniques, namely crystal graph convolutional neural network, materials graph network, and Atomistic Line Graph Neural Network (ALIGNN), to rigorously train models for predicting defect formation energy (DFE) in multiple charge states and chemical potential conditions. We find that ALIGNN yields the best DFE predictions with root mean square errors around 0.3 eV, which represents a prediction accuracy of 98% given the range of values within the dataset, improving significantly on the state-of-the-art. We further show that GNN-based defective structure optimization can take us close to DFT-optimized geometries at a fraction of the cost of full DFT. The current models are based on the semi-local generalized gradient approximation-Perdew–Burke–Ernzerhof (PBE) functional but are highly promising because of the correlation of computed energetics and defect levels with higher levels of theory and experimental data, the accuracy and necessity of discovering novel metastable and low energy defect structures at the PBE level of theory before advanced methods could be applied, and the ability to train multi-fidelity models in the future with new data from non-local functionals. The DFT-GNN models enable prediction and screening across thousands of hypothetical defects based on both unoptimized and partially optimized defective structures, helping identify electronically active defects in technologically important semiconductors.

Rahman, Md Habibur (ORCID:000000027705984X)↗
DOE OSTI2020

Scalable Substitutional Re‐Doping and its Impact on the Optical and Electronic Properties of Tungsten Diselenide

Abstract Reliable, controlled doping of 2D transition metal dichalcogenides will enable the realization of next‐generation electronic, logic‐memory, and magnetic devices based on these materials. However, to date, accurate control over dopant concentration and scalability of the process remains a challenge. Here, a systematic study of scalable in situ doping of fully coalesced 2D WSe 2 films with Re atoms via metal–organic chemical vapor deposition is reported. Dopant concentrations are uniformly distributed over the substrate surface, with precisely controlled concentrations down to <0.001% Re achieved by tuning the precursor partial pressure. Moreover, the impact of doping on morphological, chemical, optical, and electronic properties of WSe 2 is elucidated with detailed experimental and theoretical examinations, confirming that the substitutional doping of Re at the W site leads to n‐type behavior of WSe 2 . Transport characteristics of fabricated back‐gated field‐effect‐transistors are directly correlated to the dopant concentration, with degrading device performances for doping concentrations exceeding 1% of Re. The study demonstrates a viable approach to introducing true dopant‐level impurities with high precision, which can be scaled up to batch production for applications beyond digital electronics.

Kozhakhmetov, Azimkhan↗
DOE OSTI2022

Disentangling the Influences of Storm-Relative Flow and Horizontal Streamwise Vorticity on Low-Level Mesocyclones in Supercells

Sufficient low-level storm-relative flow is a necessary ingredient for sustained supercell thunderstorms and is connected to supercell updraft width. Assuming a supercell exists, the role of low-level storm-relative flow in regulating supercells’ low-level mesocyclone intensity is less clear. One possibility considered in this article is that storm-relative flow controls mesocyclone and tornado width via its modulation of overall updraft extent. This hypothesis relies on a previously postulated positive correspondence between updraft width, mesocyclone width, and tornado width. An alternative hypothesis is that mesocyclone characteristics are primarily regulated by horizontal streamwise vorticity irrespective of storm-relative flow. A matrix of supercell simulations was analyzed to address the aforementioned hypotheses, wherein horizontal streamwise vorticity and storm-relative flow were independently varied. Among these simulations, mesocyclone width and intensity were strongly correlated with horizontal streamwise vorticity, and comparatively weakly correlated with storm-relative flow, supporting the second hypothesis. Accompanying theory and trajectory analysis offers the physical explanation that, when storm-relative flow is large and updrafts are wide, vertically tilted streamwise vorticity is projected over a wider area but with a lesser average magnitude than when these parameters are small. These factors partially offset one another, degrading the correspondence of storm-relative flow with updraft circulation and rotational velocity, which are the mesocyclone attributes most closely tied to tornadoes. In conclusion, these results refute the previously purported connections between updraft width, mesocyclone width, and tornado width, and emphasize horizontal streamwise vorticity as the primary control on low-level mesocyclones in sustained supercells.

54 ENVIRONMENTAL SCIENCES↗