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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Computational and Experimental Study of Film-Cooling Effectiveness With and Without Downstream Vortex Generators

Downstream vortex generators that involve a pair of rectangular plates arranged in an open V-shape placed just downstream of each film-cooling hole were shown to create flow and vortical structures that entrain lifted film-cooling flow back to the surface and increase its lateral spreading on the surface (GT2020–14317). In this study, computations and measurements were performed to examine the flow mechanisms this vortex generator induces to improve film-cooling effectiveness of a flat plate with the cooling flow emanating from one row of inclined holes. Parameters studied include blowing ratio (BR = 0.75 and 1.0) and temperature ratio (TR = 1.07 and 1.9). Here, the computational study is based on steady Reynolds-averaged Navier–Stokes (RANS) closed by the shear-stress transport (SST) turbulence model with and without conjugate analysis. The experimental study was conducted by using a conjugate heat transfer test rig with a plenum, where cooling flow is introduced. Measurements made include velocity and temperature profiles upstream and downstream of the film-cooling holes as well as the temperature at several locations on the hot and cold sides of the film-cooled flat plate. The computational study was validated by comparing computed results with those from measurements at BR = 0.75 and 1.0 and TR = 1.9. Computational and experimental results are presented to show the effects of BR and TR on the flow structures and how those structures improve the effectiveness of film cooling with and without the downstream vortex generators and with and without conjugate heat transfer.

42 ENGINEERING↗
DOE OSTI2022

Electric Vehicle and Infrastructure Systems Modeling in Washington D.C. and Baltimore

This report documents the Argonne-Exelon effort to develop and utilize an agent-based model (ATEAM) of charging demand and infrastructure expansion applicable to the Washington, DC–Baltimore, MD consolidated metropolitan area. This study extends the ATEAM model time horizon to 10 years (from 2020 to 2030), expands agent behavior modeling capabilities, incorporates more granular and extensive empirical data on charging behavior, and analyzes charging needs for a much larger population of PEVs, in keeping with regional goals for significant adoption of ZEVs. With given targets for annual BEV adoption, five scenarios were developed to examine public infrastructure needs and resulting charging load, considering different home charging availabilities, as well as different PEV consumer profiles and public charging infrastructure deployment strategies. Scenario results show that if new chargers (both L2 and DCFC) are spread more widely (as with ubiquitous deployment strategies), there will be less variation in the number of chargers added to each census tract in the study area. More importantly, widespread public charging infrastructure with ubiquitous deployment strategies reduces unmet charging demand and improves charging success, even with heavy reliance on public charging. About 80 percent of BEV drivers can charge on their first attempt in scenarios with ubiquitous deployment strategies. Moreover, widespread public charging infrastructure better meets the demand for more charging, and in return, increases BEV adoption. Low home charging availability produces higher charging loads in public locations, especially during the early morning (around 8:00 a.m.) and late afternoon (around 6:00 p.m.). The evening peak load indicates that drivers are taking advantage of public charging before heading home. Study results also indicate that even with 20 percent home charging availability in 2030, just 20 percent of drivers attempt to charge on a given day. With their relatively high electric range (200+ miles), the BEVs expected to be on the road in 2030 can handle daily commutes without re-charging for a couple of days.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Regional Tau Effects on Prospective Cognitive Change in Cognitively Normal Older Adults

Studies suggest that tau deposition starts in the anterolateral entorhinal cortex (EC) with normal aging, and that the presence of β-amyloid (Aβ) facilitates its spread to neocortex, which may reflect the beginning of Alzheimer's disease (AD). Functional connectivity between the anterolateral EC and the anterior-temporal (AT) memory network appears to drive higher tau deposition in AT than in the posterior-medial (PM) memory network. Here, we investigated whether this differential vulnerability to tau deposition may predict different cognitive consequences of EC, AT, and PM tau. Using 18 F-flortaucipir (FTP) and 11 C-Pittsburgh compound-B (PiB) positron emission tomography (PET) imaging, we measured tau and Aβ in 124 cognitively normal human older adults (74 females, 50 males) followed for an average of 2.8 years for prospective cognition. We found that higher FTP in all three regions was individually related to faster memory decline, and that the effects of AT and PM FTP, but not EC, were driven by Aβ+ individuals. Moreover, when we included all three FTP measures competitively in the same model, only AT FTP significantly predicted memory decline. Our data support a model whereby tau, facilitated by Aβ, transits from EC to cortical regions that are most closely associated with the anterolateral EC, which specifically affects memory in the initial stage of AD. Memory also appears to be affected by EC tau in the absence of Aβ, which may be less clinically consequential. These findings may provide clarification of differences between normal aging and AD, and elucidate the transition between the two stages. SIGNIFICANCE STATEMENT Tau and β-amyloid (Aβ) are hallmarks of Alzheimer's disease (AD) but are also found in cognitively normal people. It is unclear whether, and how, this early deposition of tau and Aβ may affect cognition in normal aging and the asymptomatic stage of AD. We show that tau deposition in the entorhinal cortex (EC), which is common in advanced age, predicts memory decline in older adults independent of Aβ, likely reflecting normal, age-related memory loss. In contrast, tau in anterior-temporal (AT) regions is most predictive of memory decline in Aβ+ individuals. These data support the idea that tau preferentially spreads to specific cortical regions, likely through functional connections, which plays a primary role in memory decline in the early stage of AD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Corona splashing triggered by a loose monolayer of particles

In nature, high-speed raindrops often impact and spread on particulate surfaces (e.g., soil, plant leaves with spores or pollen). We study the dynamics of droplet impact on a loosely packed monolayer of particles by combining experimental and mathematical approaches. We find that the presence of mobile particles lowers the critical impact velocity at which the droplet exhibits corona splashing, as the particle area fraction is systematically increased. We rationalize this experimental observation by considering the jamming of frictional particles at the spreading rim. Elucidating the splashing transition of the drop on a particulate bed can lead to a better understanding of soil loss and erosion from falling raindrops

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Nramp: Deprive and conquer?

Solute carriers 11 (Slc11) evolved from bacterial permease (MntH) to eukaryotic antibacterial defense (Nramp) while continuously mediating proton (H+)-dependent manganese (Mn2+) import. Also, Nramp horizontal gene transfer (HGT) toward bacteria led to mntH polyphyly. Prior demonstration that evolutionary rate-shifts distinguishing Slc11 from outgroup carriers dictate catalytic specificity suggested that resolving Slc11 family tree may provide a function-aware phylogenetic framework. Hence, MntH C (MC) subgroups resulted from HGTs of prototype Nramp (pNs) parologs while archetype Nramp (aNs) correlated with phagocytosis. PHI-Blast based taxonomic profiling confirmed MntH B phylogroup is confined to anaerobic bacteria vs. MntH A (MA)’s broad distribution; suggested niche-related spread of MC subgroups; established that MA-variant MH, which carries ‘eukaryotic signature’ marks, predominates in archaea. Slc11 phylogeny shows MH is sister to Nramp. Site-specific analysis of Slc11 charge network known to interact with the protonmotive force demonstrates sequential rate-shifts that recapitulate Slc11 evolution. 3D mapping of similarly coevolved sites across Slc11 hydrophobic core revealed successive targeting of discrete areas. The data imply that pN HGT could advantage recipient bacteria for H+-dependent Mn2+ acquisition and Alphafold 3D models suggest conformational divergence among MC subgroups. It is proposed that Slc11 originated as a bacterial stress resistance function allowing Mn2+-dependent persistence in conditions adverse for growth, and that archaeal MH could contribute to eukaryogenesis as a Mn2+ sequestering defense perhaps favoring intracellular growth-competent bacteria.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Characteristics of grassy ELMs and their impact on the divertor heat flux width

We report BOUT++ turbulence simulations are conducted for a 60 s steady-state long pulse high β p EAST grassy ELM discharge. BOUT++ linear simulations show that the unstable mode spectrum covers a range of toroidal mode numbers from low-n (n = 10–15) peeling–ballooning modes (P–B) to high-n (n = 40–80) drift-Alfvén instabilities. Nonlinear simulations show that the ELM crash is triggered by low-n peeling modes and fluctuation is generated at the peak pressure gradient position and radially spread outward into the scrape-off-layer, even though the drift-Alfvén instabilities dominate the linear growth phase. However, drift-Alfvén turbulence delays the onset of the grassy ELM and enhances the energy loss with the fluctuation extending to pedestal top region. Simulations further show that if the peeling drive is removed, the fluctuation amplitude drops by an order of magnitude and the ELM crashes disappear. The divertor heat flux width is ~2 times larger than the estimates based on the HD model and the Eich's ITPA multi-tokamak scaling (or empirical Eich scaling) due to the strong radial turbulence transport.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Nucleon-induced inelastic scattering with statistical strength functions and the ECIS direct reaction code

Modern theoretical descriptions of inelastic scattering make use of multi-step direct reaction approaches together with transition potentials obtained from sophisticated nuclear structure models. Here we demonstrate how the complexity of such calculations can be reduced to permit simpler ones, also using the ECIS code, but providing an almost equally precise alternative to a much more detailed calculation. We have studied the transition form factors within the random phase approximation (RPA), where these are obtained as linear combinations of particle–hole states. At moderate to high excitation energies, where interference effects tend to disappear, we have proposed an independent particle–hole formalism in which particle–hole states are spread in energy with an appropriate strength function obtained from the RPA. The effects of more complex modes, such as 2p–2h ones, are simulated with widths calculated in a semi-classical context. In this work, we verify the validity of our approximations for pre-equilibrium proton-induced reactions on 90 Zr target. Our calculations provide a good description of the reaction data and point toward a simplification of the description of nucleon-induced reactions based on averages of microscopic details of the projectile–target interaction.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2021

How New Mexico Leveraged a COVID-19 Case Forecasting Model to Preemptively Address the Healthcare Needs of the State: Quantitative Analysis

Prior to the COVID-19 pandemic, US hospitals relied on static projections of future trends for long-term planning and were only beginning to consider forecasting methods for short-term planning of staffing and other resources. With the overwhelming burden imposed by COVID-19 on the health care system, an emergent need exists to accurately forecast hospitalization needs within an actionable timeframe. Our goal was to leverage an existing COVID-19 case and death forecasting tool to generate the expected number of concurrent hospitalizations, occupied intensive care unit (ICU) beds, and in-use ventilators 1 day to 4 weeks in the future for New Mexico and each of its five health regions. We developed a probabilistic model that took as input the number of new COVID-19 cases for New Mexico from Los Alamos National Laboratory’s COVID-19 Forecasts Using Fast Evaluations and Estimation tool, and we used the model to estimate the number of new daily hospital admissions 4 weeks into the future based on current statewide hospitalization rates. The model estimated the number of new admissions that would require an ICU bed or use of a ventilator and then projected the individual lengths of hospital stays based on the resource need. By tracking the lengths of stay through time, we captured the projected simultaneous need for inpatient beds, ICU beds, and ventilators. We used a postprocessing method to adjust the forecasts based on the differences between prior forecasts and the subsequent observed data. Thus, we ensured that our forecasts could reflect a dynamically changing situation on the ground. Forecasts made between September 1 and December 9, 2020, showed variable accuracy across time, health care resource needs, and forecast horizon. Forecasts made in October, when new COVID-19 cases were steadily increasing, had an average accuracy error of 20.0%, while the error in forecasts made in September, a month with low COVID-19 activity, was 39.7%. Across health care use categories, state-level forecasts were more accurate than those at the regional level. Although the accuracy declined as the forecast was projected further into the future, the stated uncertainty of the prediction improved. Forecasts were within 5% of their stated uncertainty at the 50% and 90% prediction intervals at the 3- to 4-week forecast horizon for state-level inpatient and ICU needs. However, uncertainty intervals were too narrow for forecasts of state-level ventilator need and all regional health care resource needs. Real-time forecasting of the burden imposed by a spreading infectious disease is a crucial component of decision support during a public health emergency. Our proposed methodology demonstrated utility in providing near-term forecasts, particularly at the state level. This tool can aid other stakeholders as they face COVID-19 population impacts now and in the future.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Resource distribution under spatiotemporal uncertainty of disease spread: Stochastic versus robust approaches

We consider the problem of optimizing locations of distribution centers (DCs) and plans for distributing resources such as test kits and vaccines, under spatiotemporal uncertainties of disease spread and demand for the resources. We aim to balance the operational cost (including costs of deploying facilities, shipping, and storage) and quality of service (reflected by demand coverage), while ensuring equity and fairness of resource distribution across multiple populations. We compare a sample-based stochastic programming (SP) approach with a distributionally robust optimization (DRO) approach using a moment-based ambiguity set. Numerical studies are conducted on instances of distributing COVID-19 vaccines in the United States and test kits, to compare SP and DRO models with a deterministic formulation using estimated demand and with the current resource distribution plans implemented in the US. We demonstrate the results over distinct phases of the pandemic to estimate the cost and speed of resource distribution depending on scale and coverage, and show the “demand-driven” properties of the SP and DRO solutions. Furthermore, our results further indicate that if the worst-case unmet demand is prioritized, then the DRO approach is preferred despite of its higher overall cost. Nevertheless, the SP approach can provide an intermediate plan under budgetary restrictions without significant compromises in demand coverage.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

First study of the nuclear response to fast hadrons via angular correlations between pions and slow protons in electron-nucleus scattering

We report on the first measurement of angular correlations between high-energy pions and slow protons in electron-nucleus (eA) scattering, providing a new probe of how a nucleus responds to a fast-moving quark. The experiment employed the CLAS detector with a 5-GeV electron beam incident on deuterium, carbon, iron, and lead targets. For heavier nuclei, the pion-proton correlation function is more spread-out in azimuth than for lighter ones, and this effect is more pronounced in the πp channel than in earlier ππ studies. The proton-to-pion yield ratio likewise rises with nuclear mass, although the increase appears to saturate for the heaviest targets. These trends are qualitatively reproduced by state-of-the-art eA event generators, including BeAGLE, eHIJING, and GiBUU, indicating that current descriptions of target fragmentation rest on sound theoretical footing. At the same time, the precision of our data exposes model-dependent discrepancies, delineating a clear path for future improvements in the treatment of cold-nuclear matter effects in eA scattering.

Correlations↗
DOE OSTI2020

The impact of long-range dispersal on gene surfing

Range expansions lead to distinctive patterns of genetic variation in populations, even in the absence of selection. These patterns and their genetic consequences have been well studied for populations advancing through successive short-ranged migration events. However, most populations harbor some degree of long-range dispersal, experiencing rare yet consequential migration events over arbitrarily long distances. Although dispersal is known to strongly affect spatial genetic structure during range expansions, the resulting patterns and their impact on neutral diversity remain poorly understood. Here, we systematically study the consequences of long-range dispersal on patterns of neutral variation during range expansion in a class of dispersal models which spans the extremes of local (effectively short-ranged) and global (effectively well-mixed) migration. We find that sufficiently long-ranged dispersal leaves behind a mosaic of monoallelic patches, whose number and size are highly sensitive to the distribution of dispersal distances. We develop a coarse-grained model which connects statistical features of these spatial patterns to the evolution of neutral diversity during the range expansion. We show that growth mechanisms that appear qualitatively similar can engender vastly different outcomes for diversity: Depending on the tail of the dispersal distance distribution, diversity can be either preserved (i.e., many variants survive) or lost (i.e., one variant dominates) at long times. Our results highlight the impact of spatial and migratory structure on genetic variation during processes as varied as range expansions, species invasions, epidemics, and the spread of beneficial mutations in established populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Void coalescence and ductile failure in IN718 investigated via high-energy synchrotron X-ray tomography and diffraction

Ductile failure through the growth and coalescence of voids is of particular relevance for many engineering materials. Yet, the lack of experimental measurements of the mechanical state of the material at an appropriate length scale has limited further understanding of the regime of ductile failure. In this study, local grain-scale experimental measurements are used to determine their relevance for describing and predicting ductile failure. Additive manufactured materials, due to the presence of inherent porosity, as well as the ability to tailor internal porosity, provides a promising avenue to study ductile failure. Selective laser melting is used to manufacture a specially designed specimen with two large, internal voids, in addition to the natural porosity, which is characteristic to the production process. The initial porosity and its evolution upon tensile loading are characterized via micro tomography. Several locations of void coalescence are captured in the sample, revealing activity of multiple modes of failure. Finite element simulations, with a simplified J2 plasticity model, instantiated with the initial void structure, is deployed, in which geometric localizations of stress concentration corresponded with experimentally observed sites of coalescence but was inadequate in capturing the appropriate failure mechanism. Furthermore, from the experimental results of the far-field high-energy diffraction microscopy, the heterogeneous micromechanical state is identified and tracked around voids due to the local grain interactions. These experiments determine narrow bands of low stress triaxiality, at the onset of failure, which highlight the path of coalescence through intervoid shearing. Diffraction spot spreading analysis aided characterization of intragranular plasticity and strain heterogeneity, which can be coupled with high triaxiality to form the conditions inducive for coalescence through intervoid necking. In this study, the experimentally determined grain-scale description of the micromechanical state provides a physical basis that can accurately capture both the sites and the mechanism of void coalescence at the onset of ductile failure.

36 MATERIALS SCIENCE↗
DOE OSTI2023

X-ray induced electron and ion fragmentation dynamics in IBr

Characterization of the inner-shell decay processes in molecules containing heavy elements is key to understanding x-ray damage of molecules and materials and for medical applications with Auger-electron-emitting radionuclides. The 1s hole states of heavy atoms can be produced by absorption of tunable x rays and the resulting vacancy decays characterized by recording emitted photons, electrons, and ions. The 1s hole states in heavy elements have large x-ray fluorescence yields that transfer the hole to intermediate electron shells that then decay by sequential Auger-electron transitions that increase the ion’s charge state until the final state is reached. In molecules, the charge is spread across the atomic sites, resulting in dissociation to energetic atomic ions. We have used x-ray/ion coincidence spectroscopy to measure charge states and energies of Iq+ and Brq′+ atomic ions following 1s ionization at the I and Br K-edges of IBr. We present the charge states and kinetic energies of the two correlated fragment ions associated with core-excited states produced during the various steps of the cascades. Here, to understand the dynamics leading to the ion data, we develop a computational model that combines Monte-Carlo/Molecular-Dynamics (MC/MD) simulations with a classical over-the-barrier model to track inner-shell cascades and redistribution of electrons in valence orbitals and nuclear motion of fragments.

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

Discriminative versus generative approaches to simulation-based inference

Most of the fundamental, emergent, and phenomenological parameters of particle and nuclear physics are determined through parametric template fits. Simulations are used to populate histograms which are then matched to data. This approach is inherently lossy, since histograms are binned and low-dimensional. Deep learning has enabled unbinned and high-dimensional parameter estimation through neural likelihood(-ratio) estimation. We compare two approaches for neural simulation-based inference (NSBI): one based on discriminative learning (classification) and one based on generative modeling. These two approaches are directly evaluated on the same datasets, with a similar level of hyperparameter optimization in both cases. In addition to a Gaussian dataset, we study NSBI using a Higgs boson dataset from the FAIR Universe Challenge. We find that both the direct likelihood and likelihood ratio estimation are able to effectively extract parameters with reasonable uncertainties. For the numerical examples and within the set of hyperparameters studied, we found that the likelihood ratio method is more accurate and/or precise. Both methods have a significant spread from the network training and would require ensembling or other mitigation strategies in practice.

high energy physics↗
DOE OSTI2021

Predicting Antimicrobial Resistance Using Partial Genome Alignments

Antimicrobial resistance (AMR) is an important global health threat that impacts millions of people worldwide each year. Developing methods that can detect and predict AMR phenotypes can help to mitigate the spread of AMR by informing clinical decision making and appropriate mitigation strategies. Many bioinformatic methods have been developed for predicting AMR phenotypes from whole-genome sequences and AMR genes, but recent studies have indicated that predictions can be made from incomplete genome sequence data. In order to more systematically understand this, we built random forest-based machine learning classifiers for predicting susceptible and resistant phenotypes for Klebsiella pneumoniae (1,640 strains), Mycobacterium tuberculosis (2,497 strains), and Salmonella enterica (1,981 strains). We started by building models from alignments that were based on a reference chromosome for each species. We then subsampled each chromosomal alignment and built models for the resulting subalignments, finding that very small regions, representing approximately 0.1 to 0.2% of the chromosome, are predictive. In K. pneumoniae, M. tuberculosis, and S. enterica, the subalignments are able to predict multiple AMR phenotypes with at least 70% accuracy, even though most do not encode an AMR-related function. We used these models to identify regions of the chromosome with high and low predictive signals. Finally, subalignments that retain high accuracy across larger phylogenetic distances were examined in greater detail, revealing genes and intergenic regions with potential links to AMR, virulence, transport, and survival under stress conditions. IMPORTANCE Antimicrobial resistance causes thousands of deaths annually worldwide. Understanding the regions of the genome that are involved in antimicrobial resistance is important for developing mitigation strategies and preventing transmission. Machine learning models are capable of predicting antimicrobial resistance phenotypes from bacterial genome sequence data by identifying resistance genes, mutations, and other correlated features. They are also capable of implicating regions of the genome that have not been previously characterized as being involved in resistance. In this study, we generated global chromosomal alignments for Klebsiella pneumoniae, Mycobacterium tuberculosis, and Salmonella enterica and systematically searched them for small conserved regions of the genome that enable the prediction of antimicrobial resistance phenotypes. In addition to known antimicrobial resistance genes, this analysis identified genes involved in virulence and transport functions, as well as many genes with no previous implication in antimicrobial resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

A fluctuating hydrodynamics model for nanoscale surfactant-laden interfaces

A multispecies diffuse interface model is formulated in a fluctuating hydrodynamics framework for the purpose of simulating surfactant interfaces at the nanoscale. The model generalizes previous work to ternary mixtures, employing a Cahn-Hilliard free energy density combined with incompressible, isothermal fluctuating hydrodynamics where dissipative fluxes include both deterministic and stochastic terms. The intermolecular parameters in the free energy are chosen such that one species acts as a partially miscible surfactant. From Laplace pressure measurements, we show that in this model the surface tension decreases linearly with surfactant concentration, leading to Marangoni convection for interfaces with concentration gradients. In the capillary wave spectrum for interfaces with and without surfactant, we find that for the former, the spectrum deviates significantly from classical capillary wave theory, presumably due to Gibbs elasticity. In non-equilibrium simulations of the Rayleigh-Plateau instability, deterministic simulations showed that the surfactant delays pinching of a fluid cylinder into droplets. However, stochastic simulations indicate that thermal fluctuations disrupt the surfactant's stabilizing effect. Similarly, the spreading of a patch of surfactant, driven by Marangoni convection, was found to be partially suppressed by thermal fluctuations.

Capillary waves↗
DOE OSTI2023

Magnetically driven coupling in relativistic radiation-mediated shocks

ABSTRACT The radiation drag in photon-rich environments of cosmic explosions can seed kinetic instabilities by inducing velocity spreads between relativistically streaming plasma components. Such microturbulence is likely imprinted on the breakout signals of radiation-mediated shocks. However, large-scale, transverse magnetic fields in the deceleration region of the shock transition can suppress the dominant kinetic instabilities by preventing the development of velocity separations between electron–positron pairs and a heavy ion species. We use a 1D five-fluid radiative transfer code to generate self-consistent profiles of the radiation drag force and plasma composition in the deceleration region. For increasing magnetization, our models predict rapidly growing pair multiplicities and a substantial radiative drag developing self-similarly throughout the deceleration region. We extract the critical magnetization parameter σc, determining the limiting magnetic field strength at which a three-species plasma can develop kinetic instabilities before reaching the isotropized downstream. For a relativistic, single ion plasma drifting with γu = 10 in the upstream of a relativistic radiation-mediated shock, we find the threshold σc ≈ 10−7 for the onset of microturbulence. Suppression of plasma instabilities in the case of multi-ion composition would likely require much higher values of σc. Identifying high-energy signatures of microturbulence in shock breakout signals and combining them with the magnetization limits provided in this work will allow a deeper understanding of the magnetic environment of cosmic explosions like supernovae, gamma-ray bursts, and neutron star binary mergers.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Wettability Effects on Falling Film Flow and Heat Transfer Over Horizontal Tubes in Jet Flow Mode

The performance of a falling-film heat exchanger is strongly linked to the surface characteristics and the heat transfer processes that take place over the tubes. The primary aim of this numerical study is to characterize the influence of surface wettability on the film flow behavior and its associated surface heat transfer in the jet-flow mode. Volume of fluid (VOF) based simulations are carried out for horizontal tubes with different surface wettabilities. The wettability of the tube surfaces is represented using the Kistler's dynamic contact angle model. Surface wettability effects ranging from superhydrophilic to superhydrophobic are studied by varying the equilibrium contact angle from 2 deg to 175 deg. Two different liquid mass flow rates of 0.06 and 0.18 kg/m-s corresponding to the inline and staggered jet flow modes are studied. Results are presented in terms of the liquid film thickness, the contact areas between the different phases (solid–liquid and liquid–air), and the heat transfer coefficient or Nusselt number. The resistance imposed by the increasing contact angles inhibits the extent of the liquid spreading over the tube surface, and this, in turn, influences the liquid film thickness, and the wetted area of the tube surface. A significant decrement in the heat transfer rate from the tube surfaces was observed as the equilibrium contact angle increased from 2 deg to 175 deg. The local distributions of the Nusselt number over the tube surface are strongly influenced by the flow recirculation in the liquid bulk.

Engineering↗