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At least 235 records · Page 13

Estimation of secondary organic aerosol viscosity from explicit modeling of gas-phase oxidation of isoprene and α -pinene

Abstract. Secondary organic aerosols (SOA) are major components of atmospheric fine particulate matter, affecting climate and air quality. Mounting evidence exists that SOA can adopt glassy and viscous semisolid states, impacting formation and partitioning of SOA. In this study, we apply the GECKO-A (Generator of Explicit Chemistry and Kinetics of Organics in the Atmosphere) model to conduct explicit chemical modeling of isoprene photooxidation and α-pinene ozonolysis and their subsequent SOA formation. The detailed gas-phase chemical schemes from GECKO-A are implemented into a box model and coupled to our recently developed glass transition temperature parameterizations, allowing us to predict SOA viscosity. The effects of chemical composition, relative humidity, mass loadings and mass accommodation on particle viscosity are investigated in comparison with measurements of SOA viscosity. The simulated viscosity of isoprene SOA agrees well with viscosity measurements as a function of relative humidity, while the model underestimates viscosity of α-pinene SOA by a few orders of magnitude. This difference may be due to missing processes in the model, including autoxidation and particle-phase reactions, leading to the formation of high-molar-mass compounds that would increase particle viscosity. Additional simulations imply that kinetic limitations of bulk diffusion and reduction in mass accommodation coefficient may play a role in enhancing particle viscosity by suppressing condensation of semi-volatile compounds. The developed model is a useful tool for analysis and investigation of the interplay among gas-phase reactions, particle chemical composition and SOA phase state.

54 ENVIRONMENTAL SCIENCES↗

Mesoscale Effects of Composition and Calendering in Lithium-Ion Battery Composite Electrodes

Macrohomogeneous battery models are widely used to predict battery performance, necessarily relying on effective electrode properties, such as specific surface area, tortuosity, and electrical conductivity. While these properties are typically estimated using ideal effective medium theories, in practice they exhibit highly non-ideal behaviors arising from their complex mesostructures. In this paper, we computationally reconstruct electrodes from X-ray computed tomography of 16 nickel–manganese–cobalt-oxide electrodes, manufactured using various material recipes and calendering pressures. Due to imaging limitations, a synthetic conductive binder domain (CBD) consisting of binder and conductive carbon is added to the reconstructions using a binder bridge algorithm. Reconstructed particle surface areas are significantly smaller than standard approximations predicted, as the majority of the particle surface area is covered by CBD, affecting electrochemical reaction availability. Finite element effective property simulations are performed on 320 large electrode subdomains to analyze trends and heterogeneity across the electrodes. Significant anisotropy of up to 27% in tortuosity and 47% in effective conductivity is observed. Electrical conductivity increases up to 7.5× with particle lithiation. We compare the results to traditional Bruggeman approximations and offer improved alternatives for use in cell-scale modeling, with Bruggeman exponents ranging from 1.62 to 1.72 rather than the theoretical value of 1.5. We also conclude that the CBD phase alone, rather than the entire solid phase, should be used to estimate effective electronic conductivity. This study provides insight into mesoscale transport phenomena and results in improved effective property approximations founded on realistic, image-based morphologies.

25 ENERGY STORAGE↗

Driving force and pathway in polyelectrolyte complex coacervation

There is notable discrepancy between experiments and coarse-grained model studies regarding the thermodynamic driving force in polyelectrolyte complex coacervation: experiments find the free energy change to be dominated by entropy, while simulations using coarse-grained models with implicit solvent usually report a large, even dominant energetic contribution in systems with weak to intermediate electrostatic strength. Here, using coarse-grained, implicit-solvent molecular dynamics simulation combined with thermodynamic analysis, we study the potential of mean force (PMF) in the two key stages on the coacervation pathway for symmetric polyelectrolyte mixtures: polycation–polyanion complexation and polyion pair–pair condensation. We show that the temperature dependence in the dielectric constant of water gives rise to a substantial entropic contribution in the electrostatic interaction. By accounting for this electrostatic entropy, which is due to solvent reorganization, we find that under common conditions (monovalent ions, room temperature) for aqueous systems, both stages are strongly entropy-driven with negligible or even unfavorable energetic contributions, consistent with experimental results. Furthermore, for weak to intermediate electrostatic strengths, this electrostatic entropy, rather than the counterion-release entropy, is the primary entropy contribution. From the calculated PMF, we find that the supernatant phase consists predominantly of polyion pairs with vanishingly small concentration of bare polyelectrolytes, and we provide an estimate of the spinodal of the supernatant phase. Finally, we show that prior to contact, two neutral polyion pairs weakly attract each other by mutually induced polarization, providing the initial driving force for the fusion of the pairs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Bonding and Acidity of the Formal Hydride in the Prototypical Au 9 (PPh 3 ) 8 H 2+ Nanocluster

Abstract The role of hydrogen atoms as surface ligands on metal nanoclusters is of profound importance but remains difficult to directly study. While hydrogen atoms often appear to be incorporated formally as hydrides, evidence suggests that they donate electrons to the cluster's delocalized superatomic orbitals and may consequently behave as acidic protons that play key roles in synthetic or catalytic mechanisms. Here we directly test this assertion for the prototypical Au 9 (PPh 3 ) 8 H 2+ nanocluster, formed by addition of a hydride to the well‐characterized Au 9 (PPh 3 ) 8 3+ . Using gas‐phase infrared spectroscopy, we were able to unambiguously isolate Au 9 (PPh 3 ) 8 H 2+ and Au 9 (PPh 3 ) 8 D 2+ , revealing an Au−H stretching mode at 1528 cm −1 that shifts to 1038 cm −1 upon deuteration. This shift is greater than the maximum expected for a typical harmonic potential, suggesting a potential governing cluster‐H bonding that has some square‐well character consistent with the hydrogen nucleus behaving as a metal atom in the cluster core. Complexing this cluster with very weak bases reveals a redshift of 37 cm −1 in the Au−H vibration, consistent with those typically seen for moderately acidic groups in gas phase molecules and providing an estimate of the acidity of Au 9 (PPh 3 ) 8 H 2+ , at least with regard to its surface reactivity.

Hernández, Hanna Morales↗

Bonding and Acidity of the Formal Hydride in the Prototypical Au 9 (PPh 3 ) 8 H 2+ Nanocluster

The role of hydrogen atoms as surface ligands on metal nanoclusters is of profound importance but remains difficult to directly study. While hydrogen atoms often appear to be incorporated formally as hydrides, evidence suggests that they donate electrons to the cluster's delocalized superatomic orbitals and may consequently behave as acidic protons that play key roles in synthetic or catalytic mechanisms. Here we directly test this assertion for the prototypical Au 9 (PPh 3 ) 8 H 2+ nanocluster, formed by addition of a hydride to the well-characterized Au 9 (PPh 3 ) 8 3+ . Using gas-phase infrared spectroscopy, we were able to unambiguously isolate Au 9 (PPh 3 ) 8 H 2+ and Au 9 (PPh 3 ) 8 D 2+ , revealing an Au–H stretching mode at 1528 cm –1 that shifts to 1038 cm –1 upon deuteration. This shift is greater than the maximum expected for a typical harmonic potential, suggesting a potential governing cluster-H bonding that has some square-well character consistent with the hydrogen nucleus behaving as a metal atom in the cluster core. Complexing this cluster with very weak bases reveals a redshift of 37 cm –1 in the Au–H vibration, consistent with those typically seen for moderately acidic groups in gas phase molecules and providing an estimate of the acidity of Au 9 (PPh 3 ) 8 H 2+ , at least with regard to its surface reactivity.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

REGAL International Program: Analysis of experimental data for depletion code validation

The Rod-Extremity and Gadolinia AnaLysis (REGAL) Program is a joint international effort to expand the nuclide inventory experimental data for irradiated nuclear fuel, with a specific focus on addressing two challenging needs associated with the characterization of modern, high duty, nuclear fuel. The first challenge is filling the gaps in experimental nuclide inventory data for gadolinia (UO 2 –Gd 2 O 3 ) fuel rods. The huge absorption cross sections of Gd-155 and Gd-157 in the Gd dopant in these rods lead to atypical spatial self-shielding patterns and have an impact on the neutronic environment within the fuel assembly compared to regular UO 2 fuel rods. Additionally, the second challenge is investigating the impact of burnup gradients at rod extremities on fuel composition and neutron leakage, to provide relevant experimental data for assessing computational capabilities to model such impact. A benchmark has been defined as a first step in the development of best-estimate models in the preliminary phase of the experimental data evaluation. Comparison of experimental results obtained in Phase I of the program for two measured pressurized water reactor (PWR) samples, one UO 2 and one UO 2 –Gd 2 O 3 sample, with calculated results obtained with different computational tools based on the defined benchmark are presented and discussed.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

Predictive Design of Hybrid Improper Ferroelectric Double Perovskite Oxides

The computational design of suitable multiferroic double perovskite oxides requires finding materials that exhibit sizable polarization, magnetization, and coupling between them. Oxides with the chemical formula of AA'BB'O 6 with building blocks of ABO 3 single perovskite oxides in centrosymmetric Pnma symmetry are strong candidates that have been reported to satisfy such criteria. The system lowers to noncentrosymmetric, polar P2 1 symmetry if A/A' layered and B/B' rocksalt cation orderings are imposed. A detailed compositional search over a variety of chemical spaces followed by evaluating their polarization may lead to the identification of more of these compounds with ferroelectric ordering. The standard density functional theory practices to estimate polarization within the Berry phase formalism require the systems to be perfectly insulating. The number of compounds that can be evaluated using this method is therefore limited. In this work, we introduce a predictive learning strategy based on importance sampling to build a series of machine learning models using results from first-principles simulations to predict polarization and the corresponding switching barrier. The geometry-driven features related to charge states and cationic radii play key roles in predicting the switching barrier with complementary contributions from the key structural mode-based order parameters. These modes become important to draw reasonable predictions of polarization components from machine learning models. In conclusion, our predictive models identify candidates with high polarizations and low switching barriers from a pool of double perovskite oxides, suitable for future investigation for their potential applications in spintronic devices.

36 MATERIALS SCIENCE↗

Palladium at high pressure and high temperature: A combined experimental and theoretical study

Palladium is one of the most important technological materials, yet its phase diagram remains poorly understood. At ambient conditions, its solid phase is face-centered cubic (fcc). However, another solid phase of Pd, body-centered cubic (bcc), was very recently predicted in two independent theoretical studies to occur at high pressures and temperatures. In this work, we report an experimental study on the room-temperature equation of state (EOS) of Pd to a pressure of 80 GPa, as well as a theoretical study on the phase diagram of Pd including both fcc-Pd and bcc-Pd. Our theoretical approach consists in ab initio quantum molecular dynamics (QMD) simulations based on the Z methodology which combines both direct Z method for the simulation of melting curves and inverse Z method for the calculation of solid–solid phase transition boundaries. We obtain the melting curves of both fcc-Pd and bcc-Pd and an equation for the fcc–bcc solid–solid phase transition boundary as well as the thermal EOS of Pd which is in agreement with experimental data and QMD simulations. We uncover the presence of another solid phase of Pd on its phase diagram, namely, random hexagonal close-packed (rhcp), and estimate the location of the rhcp-bcc solid–solid phase transition boundary and the rhcp–fcc–bcc triple point. We also discuss the topological similarity of the phase diagrams of palladium and silver, the neighbor of Pd in the periodic table. We argue that Pd is a reliable standard for shock-compression studies and present the analytic model of its principal Hugoniot in a wide pressure range.

36 MATERIALS SCIENCE↗

Simulations of saturated MHD activity in the HBT-EP tokamak

The NIMROD code is used to perform simulations of MHD activity in the HBT-EP tokamak, including the effect of a resistive wall. Linear simulations are conducted to scan current and pressure profiles to establish self-consistent baseline equilibria that result in MHD instabilities with low error between experimental diagnostics and synthetic equivalents. A family of such equilibria is found with relatively peaked current and pressure profiles. Nonlinear simulations are performed using the optimal equilibrium, which is linearly unstable to a Resistive Wall Mode—stable in the ideal wall limit and with an Alfvénic growth rate in the no-wall limit. Using resistive wall boundary conditions, nonlinear Hall MHD simulations yield a rotating, saturated n=1 mode similar to the type observed experimentally in HBT-EP discharges. An n=1 mode around the q=2 surface mediates a localized axisymmetric perturbation that flattens the plasma current from a linearly unstable state to a 3D, rotating, stable state. During the saturated period, periodic changes in inter-diagnostic phase lag allow for qualitative estimations of effective, macroscopic transport coefficients used in MHD closure models and could be extended to model post-disruption MHD structure rotation after thermal quenches seen in HBT-EP.

Arnold, David A. (ORCID:0009000059535761)↗

Structure Factors for Hot Neutron Matter from Ab Initio Lattice Simulations with High-Fidelity Chiral Interactions

We present the first ab initio lattice calculations of spin and density correlations in hot neutron matter using high-fidelity interactions at next-to-next-to-next-to-leading order in chiral effective field theory. These correlations have a large impact on neutrino heating and shock revival in core-collapse supernovae and are encapsulated in functions called structure factors. Unfortunately, calculations of structure factors using high-fidelity chiral interactions were well out of reach using existing computational methods. In this Letter, we solve the problem using a computational approach called the rank-one operator (RO) method. The RO method is a general technique with broad applications to simulations of fermionic many-body systems. It solves the problem of exponential scaling of computational effort when using perturbation theory for higher-body operators and higher-order corrections. Using the RO method, we compute the vector and axial static structure factors for hot neutron matter as a function of temperature and density. Here, the ab initio lattice results are in good agreement with virial expansion calculations at low densities but are more reliable at higher densities. Random phase approximation codes used to estimate neutrino opacity in core-collapse supernovae simulations can now be calibrated with ab initio lattice calculations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

Fate of Listeria monocytogenes Serotypes on Frozen Mixed Vegetables During Consumer‐Simulated Thawing and Storage

ABSTRACT Recent outbreaks and recalls associated with frozen vegetables in the United States and Europe have been linked to Listeria monocytogenes . This study aims to understand the extent to which frozen vegetables support the growth of L. monocytogenes once thawed and held at different temperatures. Six L. monocytogenes strains, two of each from serotypes 1/2a, 1/2b, and 4b, were individually inoculated onto frozen vegetables and stored at −18°C for 7 days. After 7 days, the vegetables were thawed and stored at 5°C or 10°C for up to 14 days or at 25°C for up to 7 days. L. monocytogenes was enumerated from the thawed vegetables throughout the storage period. Population data were fitted to the primary Baranyi model to estimate growth rates and lag phase durations; the secondary Ratkowsky square root model was used to model the relationship of the growth rates with storage temperature. Five of the L. monocytogenes strains survived and grew on the thawed vegetables (population increases of > 1 log CFU/g) stored at 5°C, and all six of the strains proliferated at 10°C and 25°C (population increases of > 3 log CFU/g after 14 days and > 4 log CFU/g after 7 days, respectively). A secondary model was successfully generated based on the growth rates of the six L. monocytogenes strains on the thawed vegetables ( r 2 = 0.8888, RMSE = 0.2057). Results from this study fill a data gap associated with L. monocytogenes survival on thawed vegetables and can be used to determine safe handling and storage practices for these products to protect public health.

Salazar, Joelle K. [Division of Food Processing Sc↗

Etching of Si 3 N 4 induced by electron beam plasma from hollow cathode plasma in a downstream reactive environment

An etching system based on the interaction of electrons extracted from a direct current (DC) hollow-cathode (HC) Ar plasma and injected towards a Si3N4 covered silicon substrate located in the downstream reactive environment created by an additional remote CF 4 /O 2 plasma source was developed and evaluated. By controlling the properties of the injected beam electrons, this approach allows to deliver energy to a surface functionalized by exposure to reactive species, and initiate surface etching. The energy of the primary beam electrons is controlled by the acceleration voltage relative to the HC discharge. Ar atoms flow from the high-pressure HC discharge into the low pressure downstream reactive environment in the process chamber. For an acceleration voltage greater than the ionization potential of Ar and/or process gas species, the energetic primary beam electrons produce a secondary plasma in the process chamber, and can also cause additional dissociation. We have characterized the properties of the secondary plasma and also surface etching of Si 3 N 4 as a function of process parameters, including acceleration voltage (0-80 V), discharge current of the HC discharge (1-2 A), pressure (3.5-20 mTorr), source to substrate distance (1.5 to 5 cm), and feed gas composition (20% and 80% O2 in CF 4 /O 2 ). The electron energy probability function (EEPF) measured with a Langmuir probe about 2.5 cm below the extraction ring suggests several major groups of electrons for this situation, including high energy primary beam electrons with an energy that varies as the acceleration voltage is changed and low energy electrons produced by beam electron-induced ionization of the Ar gas in the process chamber. When a remote CF 4 /O 2 plasma is additionally coupled to the process chamber, Si 3 N 4 surfaces can be functionalized, and by varying the energy of the beam electrons, Si 3 N 4 etching can be induced by electron-neutral synergy effect with plasma-surface interaction. For conditions without beam electron injection, the remote plasma etching rate (ER) of Si 3 N 4 depends strongly on the O 2 concentration in the CF 4 /O 2 processing gas mixture and can be suppressed for O 2 -rich process conditions by the formation of a SiONF passivation layer on the Si 3 N 4 surface. The combination of the HC electron beam source with the remote plasma source (HCEB-RP) makes it possible to induce Si 3 N 4 etching for O 2 -rich remote plasma conditions where remote plasma by itself produces negligible Si 3 N 4 etching. The electron enhanced etching of Si 3 N 4 depends strongly on O 2 /CF 4 mixing ratio reflecting changing arrival rates of O and F species at the surface. Optical emission spectroscopy was used to estimate the ratio of gas phase F and O densities and found to be controlled by the gas mixing ratio and independent of HC EB operating conditions. At this time the detailed sequence of events operative in the etching mechanism is unclear. While the increase of the electron energy is ultimately responsible for initiating surface etching, at present we cannot rule out a role of ions from the simultaneously produced secondary plasma in plasma surface interaction mechanisms.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

BioSTEAMDevelopmentGroup/thermosteam

BioSTEAM is a fast and flexible package for the design, simulation, and techno-economic analysis of biorefineries under uncertainty. BioSTEAM’s framework is built to streamline and automate early-stage technology evaluations and to enable rigorous sensitivity and uncertainty analyses. Complete biorefinery configurations are available at the Bioindustrial-Park GitHub repository, BioSTEAM’s premier repository for biorefinery models and results. The long-term growth and maintenance of BioSTEAM is supported through both community-led development and the research institutions invested in BioSTEAM. Through the open-source and community-lead platform, BioSTEAM aims to foster communication and transparency within the biorefinery research community for an integrated effort to expedite the evaluation of candidate biofuels and bioproducts. Additionally, an agile life cycle assessment (LCA) platform has been designed to interface with BioSTEAM, BioSTEAM-LCA. This open-source, installable package allows users to perform streamlined LCAs of biorefineries. The focus of BioSTEAM-LCA is to streamline and automate early-stage environmental impact analyses of processes and technologies, and to enable rigorous sensitivity and uncertainty analyses linking process design, performance, economics, and environmental impacts. ThermoSTEAM is a standalone thermodynamic engine capable of estimating mixture properties, solving thermodynamic phase equilibria, and modeling stoichiometric reactions. ThermoSTEAM builds upon chemicals, the chemical properties component of the Chemical Engineering Design Library, with a robust and flexible framework that facilitates the creation of property packages. The Biorefinery Simulation and Techno-Economic Analysis Modules (BioSTEAM) is dependent on ThermoSTEAM for the simulation of unit operations.

Cortes-Peña, Yoel↗

Palladium at high pressure and high temperature: A combined experimental and theoretical study

Palladium is one of the most important technological materials, yet its phase diagram remains poorly understood. At ambient conditions, its solid phase is face-centered cubic (fcc). However, another solid phase of Pd, body-centered cubic (bcc), was very recently predicted in two independent theoretical studies to occur at high pressures and temperatures. In this work, we report an experimental study on the room-temperature equation of state (EOS) of Pd to a pressure of 80 GPa, as well as a theoretical study on the phase diagram of Pd including both fcc-Pd and bcc-Pd. Our theoretical approach consists in ab initio quantum molecular dynamics (QMD) simulations based on the Z methodology which combines both direct Z method for the simulation of melting curves and inverse Z method for the calculation of solid–solid phase transition boundaries. We obtain the melting curves of both fcc-Pd and bcc-Pd and an equation for the fcc–bcc solid– solid phase transition boundary as well as the thermal EOS of Pd which is in agreement with experimental data and QMD simulations. We uncover the presence of another solid phase of Pd on its phase diagram, namely, random hexagonal close-packed (rhcp), and estimate the location of the rhcp-bcc solid–solid phase transition boundary and the rhcp–fcc–bcc triple point. We also discuss the topological similarity of the phase diagrams of palladium and silver, the neighbor of Pd in the periodic table. We argue that Pd is a reliable standard for shock-compression studies and present the analytic model of its principal Hugoniot in a wide pressure range.

36 MATERIALS SCIENCE↗

NRAP Task 5: Preliminary Evaluation of the Cost of Responding to a Hypothetical Leakage Scenario Using the NRAP/SMART TALES Model and other NRAP Tools

Poster presentation illustrating the use of tools developed as part of Task 5 of Phase 3 of NRAP to estimate the technical performance and costs of implementing remedial responses to address a leak of fluid out of the storage formation at a CO2 saline storage project. Presented at the 2024 FECM - NETL Carbon Management Research Project Review Meeting, 5-9 August, 2024, Pittsburgh, PA.

Morgan, David↗

Quantum Filtering and Analysis of Multiplicities in Eigenvalue Spectra

Fine-grained spectral properties of quantum Hamiltonians, including both eigenvalues and their multiplicities, provide useful information for characterizing many-body quantum systems as well as for understanding phenomena such as topological order. Extracting such information with small additive error is #BQP-complete in the worst case. In this work, we introduce QFAMES (quantum filtering and analysis of multiplicities in eigenvalue spectra), a quantum algorithm that efficiently identifies clusters of closely spaced dominant eigenvalues and determines their multiplicities under physically motivated assumptions, which allows us to bypass worst-case complexity barriers. QFAMES also enables the estimation of observable expectation values within targeted energy clusters, providing a powerful tool for studying quantum phase transitions and other physical properties. We validate the effectiveness of QFAMES through numerical demonstrations, including its applications to characterizing quantum phases in the transverse-field Ising model and estimating the ground-state degeneracy of a topologically ordered phase in the two-dimensional toric code model. We also generalize QFAMES to the setting of mixed initial states. Our approach offers rigorous theoretical guarantees and significant advantages over existing subspace-based quantum spectral analysis methods, particularly in terms of the sample complexity and the ability to resolve degeneracies.

97 MATHEMATICS AND COMPUTING↗

Combined Force-Frequency Sampling for Simulation of Systems Having Rugged Free Energy Landscapes.

An adaptive, machine learning-based sampling method is presented for simulation of systems having rugged, multidimensional free energy landscapes. The method's main strength resides in its ability to learn both from the frequency of visits to distinct states and the generalized force estimates that arise in a system as it evolves in phase space. This is accomplished by introducing a self-integrating artificial neural network, which generates an estimate of the free energy directly from its derivatives. The usefulness of the proposed combined approach is examined in the context of two concrete examples, namely, an alanine dipeptide molecule in water and a polymer diffusing through a narrow pore. This new method is found to be robust, faster, and more accurate than approaches that rely only on frequency-based or generalized force-based estimations. After combining the proposed approach with overfill protection and support for sparse data storage and training, the method is shown to be more effective than comparable, previously available techniques and capable of scaling efficiently to larger numbers of collective variables.

Sevgen, Emre↗

Velocity structure functions in multiphase turbulence: interpreting kinematics of Hα filaments in cool-core clusters

ABSTRACT The central regions of cool-core galaxy clusters harbour multiphase gas, with gas temperatures ranging from $10$ to $10^7\, \mathrm{K}$. Feedback from active galactic nuclei jets prevents the gas from undergoing a catastrophic cooling flow. However, the exact mechanism of this feedback energy input is unknown, mainly due to the lack of velocity measurements of the hot-phase gas. However, recent observations have measured the velocity structure functions (VSFs) of the cooler molecular (${\sim} 10\, \mathrm{K}$) and Hα filaments (${\sim} 10^4\, \mathrm{K}$) and used them to indirectly estimate the motions of the hot phase. In the first part of this study, we conduct high-resolution (3843–15363 resolution elements) simulations of homogeneous isotropic subsonic turbulence, without radiative cooling. We analyse the second-order velocity structure functions (VSF2) in these simulations and study the effects of varying spatial resolution, the introduction of magnetic fields, and the effect of projection along the line of sight (LOS) on it. In the second part of the study, we analyse high-resolution (7683 resolution elements) idealized simulations of multiphase turbulence in the intracluster medium from the companion study Mohapatra et al. We compare the VSF2 for both the hot ($T\sim 10^7\, \mathrm{K}$) and cold ($T\sim 10^4\, \mathrm{K}$) phases and find that their amplitude depends on the density contrast between the phases. They have similar scaling with separation, but introducing magnetic fields steepens the VSF2 of only the cold phase. We also find that projection along the LOS steepens the VSF2 for the hot phase and mostly flattens it for the cold phase.

Mohapatra, Rajsekhar (ORCID:0000000216007552)↗