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A two-dimensional hydrodynamic numerical model has been used to study the motion of the termination shock in response to an 11 year variation in the solar wind ram pressure. For a total variation in the ram pressure by a factor of 2, a termination shock at 89 AU moves inward and outward about +/- 8 percent of its distance with a typical velocity of 12 km/s.
Microplastics (MPs) pose significant risks to marine ecosystems and human health, necessitating accurate predictions of their distributions in aquatic environments for effective risk mitigation. However, understanding MP transport dynamics is challenging because of the inadequate representation of MP characteristics such as size, shape, and density in numerical models. Further, the accuracy of the MP vertical profiles in existing models has not been thoroughly validated. Thus, we developed an MP transport model within the Finite Volume Community Ocean Model framework (FVCOM-MP) by integrating MP characteristics. We validated FVCOM-MP against experimental and analytical data, focusing on various MP transport modes and transitions. FVCOM-MP successfully replicates MP profiles in different transport modes, including the bedload, surface-load, suspended-load, and mixed-load modes. Additionally, we introduce phase diagrams for classifying MP transport modes based on particle characteristics, enhancing our understanding of MP dynamics in aquatic systems. The transport modes for a number of real-world MP particles, including fishing line, plastic bag/bottle fragments, synthetic fibers, tire wear particles, polyvinyl chloride and expanded polystyrene pellets, were analyzed with these phase diagrams.
FVCOM-ICM is a water quality model that simulates the influence of nutrients and carbon pollution on biogeochemical oceanographic processes such as phytoplankton primary productivity, nutrient consumption, mineralization, decay and settling of particulate organic matter and computes resulting dissolved oxygen (DO) concentrations and pH levels. An “offline” approach of coupling Finite Volume Coastal Ocean Model (FVCOM) hydrodynamic solutions to CE-QUAL-ICM biogeochemical model was used. FVCOM and CE-QUAL-ICM are public domain Fortran Codes that were developed independently by US Army Corps of Engineers and University of Massachusetts. PNNL developed FVCOM-ICM that has the ability to conduct water quality calculations using a previously computed hydrodynamic solution. The biogeochemical calculations using CE-QUAL-ICM kinetics are conducted over the same finite-volume mesh and in the FVCOM framework.
In this work, we employ thermoreflectance thermal imaging to directly measure the steady-state two-dimensional (2D) temperature field generated by nanostructured heat sources deposited on silicon substrate with different geometrical configurations and characteristic sizes down to 400nm. The analysis of the results using Fourier’s law not only breaks down as size scales down, but it also fails to capture the impact of the geometry of the heat source. The substrate effective Fourier thermal conductivities fitted to wire-shaped and circular-shaped structures with identical characteristic lengths are found to display up to 40% mismatch. Remarkably, a hydrodynamic heat transport model reproduces the observed temperature fields for all device sizes and shapes using just intrinsic Si parameters, i.e., a geometry and size-independent thermal conductivity and nonlocal length scale. The hydrodynamic model provides insight into the observed thermal response and of the contradictory Fourier predictions. We discuss the substantial Silicon hydrodynamic behavior at room temperature and contrast it to InGaAs, which shows less hydrodynamic effects due to dominant phonon-impurity scattering.
Continental-scale river forecasting platforms forecast streamflow at reaches that can be used as boundary conditions to drive a local-scale flood inundation model. Uncertainty accumulated during this process stems not only from any part of the forecasting chain but can also be caused by the daily variations in weather forcing that keeps evolving as the event advances. This work aims to examine the influence of the evolving forecast streamflow on predicting the maximum inundation for extreme floods. A diagnostic case study was made on the basis of a hindcast of Hurricane Matthew striking the eastern U.S. in 2016. The U.S. National Water Model was one-way coupled to a hydrodynamic inundation model through a developed automated workflow. Although the river forcing has significantly mismatched hydrographs versus observations, the simulated peak water surface elevations and maximum extents were validated to be comparable with the observations, which indicates that the inundation model may not be sensitive to the inherited uncertainty from the weather forcing. Moreover, the uncertainty of the forecast streamflow time series caused only one order of magnitude fewer variations in inundation prediction; this dampening effect may become clearer for extreme events with large areas inundated. In addition, the forecast total volume of stream discharge appears to be an important metric for assessing the performance of river forcing for inundation mapping, as a linear correlation between the total volume and the accuracy of the predicted peak water surface elevation and maximum extent was found, with the coefficients of determination all above 0.8. Extra best-practice experience of running similar operational tasks demonstrated the tradeoff between the cost and accuracy gain.
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A hydrodynamic electromagnetic model is developed in order to provide a qualitative and quantitative description of the relationship between Seasat synthetic aperture radar (SAR) signatures and the bottom topography of the ocean in the English Channel region of the North Sea. The model is based on environmental data for winds, currents, and depth changes, and the SAR parameters of frequency polarization, incidence angle, and resolution cell size. The data are used as inputs and SAR backscatter changes are predicted for individual topographic changes on the ocean floor. It is found that the model estimates of backscatter values are in good agreement with actual Seasat SAR-observed backscatter values. A comparison of the model and actual data shows agreement to be within 1.5 dB. The model is considered to be valid for only shallow water areas (less than 50 meters in depth). It is suggested that for bottom features to be visible on SAR imagery at greater depths, a moderate-to-high velocity current of at least 0.4 m/s and a moderate wind no more than 7.5 m/sec must be present.
Triboelectrically-charged objects may create threshold sparks, electrostatic discharge (ESD) events, to equilibrate charge between themselves and other relatively charged objects. ESD events exhibit many complex physical phenomena. They are a nexus of several fields of physics with disparate characteristic scales: plasma physics, chemical kinetics, hydrodynamics, circuit models, etc. These scales can span many orders of magnitude from the varied collisions thermalizing information within a plasma on the $\mathcal{O}(fs/ps)$ to the physical size of the plasma channel on the $\mathcal{O}(100µm)$, to the speed of a nonlinear hydrodynamic wave propagating at $\mathcal{O}(µm, ns)$. These threshold ESD events may occur in situations of programmatic importance, delivering energy and power profiles to a “victim load” generating deleterious consequences. To predict and mitigate these consequences we must answer questions about the spark’s energy budget: how much energy goes into producing the spark channel; how much gets radiated away; how much energy is advected away into the hydrodynamics; and how much energy is delivered to a victim load. An ESD simulation toolset has been created and evolved in order to answer these questions. An appropriate, physics guided implementation for simulation can be done by gaining insight into its constituent physics and leveraging that intuition to choose a suitable numerical operator. We examine in detail the chemical kinetics, circuit discharge, and hydrodynamics to deter mine dominant regimes, values, timescales, and interactions to uncover the underlying physical dynamics. We also examine and propose model reduction schemes for high-dimensional chemical kinetics. We use past and current work with experimentally validated and theoretically-verified hydrodynamics to calculate applicability limits of the non-ionizing strong shock limit. We quantify the energy budget from a hydrodynamic perspective and demonstrate that a significant fraction of the stored energy is “earmarked” for hydrodynamic advection as an energy terminus. Lastly, we combine the constituent physics of an ESD event (chemical kinetics, circuit model, and hydrodynamics) into a cohesive, actionable toolset and obtain promising results from an isothermal test case. We then propose a viable, modular evolution of the ESD toolset based upon the performed examination of the physics uncovering dominant physical scales and the stiffness of the compositional differential system.
Based on a 14-moment closure for nonresistive (general-) relativistic viscous plasmas, here we describe a new numerical scheme that is able to handle all first-order dissipative effects (heat conduction, bulk and shear viscosities), as well the anisotropies induced by the presence of magnetic fields. The latter is parametrized in terms of a thermal gyrofrequency or, equivalently, a thermal Larmor radius and allows to correctly capture the thermal Hall effect. By solving an extended Israel-Stewart-like system for the dissipative quantities that enforces algebraic constraints via stiff-relaxation, we are able to cast all first-order dissipative terms in flux-divergence form. This allows us to apply traditional high-resolution shock capturing methods to the equations, making the system suitable for the numerical study of highly turbulent flows. We present several numerical tests to assess the robustness of our numerical scheme in flat spacetime. The 14-moment closure can seamlessly interpolate between the highly collisional limit found in neutron star mergers, and the highly anisotropic limit of relativistic Braginskii magnetohydrodynamics appropriate for weakly collisional plasmas in black-hole accretion problems. We believe that this new formulation and numerical scheme will be useful for a broad class of relativistic magnetized flows.
Context. We investigate iPTF13bvn, a core-collapse (CC) supernova (SN) in the nearby spiral galaxy NGC 5806. This object was discovered by the intermediate Palomar Transient Factory (iPTF) very close to the estimated explosion date and was classified as a stripped-envelope CC SN, likely of Type Ib. Furthermore, a possible progenitor detection in pre-explosion Hubble Space Telescope (HST) images was reported, making this the only SN Ib with such an identification. Based on the luminosity and color of the progenitor candidate, as well as on early-time spectra and photometry of the SN, it was argued that the progenitor candidate is consistent with a single, massive Wolf-Rayet (WR) star. Aims. We aim to confirm the progenitor detection, to robustly classify the SN using additional spectroscopy, and to investigate if our follow-up photometric and spectroscopic data on iPTF13bvn are consistent with a single-star WR progenitor scenario. Methods. We present a large set of observational data, consisting of multi-band light curves (UBVRI, g′r′i′z′) and optical spectra. We perform standard spectral line analysis to track the evolution of the SN ejecta. We also construct a bolometric light curve and perform hydrodynamical calculations to model this light curve to constrain the synthesized radioactive nickel mass and the total ejecta mass of the SN. Late-time photometry is analyzed to constrain the amount of oxygen. Furthermore, image registration of pre- and post-explosion HST images is performed. Results. Our HST astrometry confirms the location of the progenitor candidate of iPTF13bvn, and follow-up spectra securely classify this as a SN Ib. We use our hydrodynamical model to fit the observed bolometric light curve, estimating the total ejecta mass to be 1.9 solar mass and the radioactive nickel mass to be 0.05 solar mass. The model fit requires the nickel synthesized in the explosion to be highly mixed out in the ejecta. We also find that the late-time nebular r′-band luminosity is not consistent with predictions based on the expected oxygen nucleosynthesis in very massive stars. Conclusions. We find that our bolometric light curve of iPTF13bvn is not consistent with the previously proposed single massive WR-star progenitor scenario. The total ejecta mass and, in particular, the late-time oxygen emission are both significantly lower than what would be expected from a single WR progenitor with a main-sequence mass of at least 30 solar mass.
In this work, we compare large scale structure observables for stellar mass selected samples at z = 0, as predicted by two galaxy models, the hydrodynamical simulation IllustrisTNG and the Santa-Cruz semi-analytic model (SC-SAM). Although both models have been independently calibrated to match observations, rather than each other, we find good agreement between the two models for two-point clustering and galaxy assembly bias signatures. The models also show a qualitatively similar response of occupancy and clustering to secondary halo parameters other than mass, such as formation history and concentration, although with some quantitative differences. Thus, our results demonstrate that the galaxy–halo relationships in SC-SAM and TNG are quite similar to first order. However, we also find areas in which the models differ. For example, we note a strong correlation between halo gas content and environment in TNG, which is lacking in the SC-SAM, as well as differences in the occupancy predictions for low-mass haloes. Moreover, we show that higher order statistics, such as cumulants of the density field, help us to accurately describe the galaxy distribution and discriminate between models that show degenerate behaviour for two-point statistics. Our results suggest that SAMs are a promising cost-effective and intuitive method for generating mock catalogues for next generation cosmological surveys.
The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.
Total coliform group bacteria were selected to expand the mathematical modeling capabilities of the hydrodynamic and salinity models to understand their relationship to commercial fishing ventures within bay waters and to gain a clear insight into the effect that rivers draining into the bay have on water quality conditions. Parametric observations revealed that temperature factors and river flow rate have a pronounced effect on the concentration profiles, while wind conditions showed only slight effects. An examination of coliform group loading concentrations at constant river flow rates and temperature shows these loading changes have an appreciable influence on total coliform distribution within Mobile Bay.
Collisions between galaxies can induce large morphological changes in the participants and, in the case of colliding disk galaxies, bridges and tails are often formed. Observations of such systems indicate a wide variation in color (see Larson and Tinsley, 1978) and that some of the particpants are experiencing enhanced rates of star formation, especially in their central regions (Bushouse 1986, 1987; Kennicutt et al., 1987, Bushouse, Lamb, and Werner, 1988). Here the authors describe progress made in understanding some of the dynamics of interacting galaxies using N-body stellar dynamical computer experiments, with the goal of extending these models to include a hydrodynamical treatment of the gas so that a better understanding of globally enhanced star formation will eventually be forthcoming. It was concluded that close interactions between galaxies can produce large perturbations in both density and velocity fields. The authors measured, via computational experiments that represent a galaxy's stars, average radial velocity flows as large as 100 km/sec and 400 percent density increases. These can occur in rings that move outwards through the disk of a galaxy, in roughly homologous inflows toward the nucleus, and in off center, non-axisymmetric regions. Here the authors illustrate where the gas is likely to flow during the early stages of interaction and in future work they plan to investigate the fate of the gas more realistically by using an N-body/Smoothed Particle Hydrodynamics code to model both the stellar and gaseous components of a disk galaxy during a collision. Specifically, they will determine the locations of enhanced gas density and the strength and location of shock fronts that form during the interaction.
This study presents a newly coupled model capable of treating the superthermal electron population in the global polar wind solution. The model combines the hydrodynamic Polar Wind Outflow Model (PWOM) with the kinetic SuperThermal Electron Transport (STET) code. The resulting PWOM-STET coupled model is described and then used to investigate the role of photoelectrons in the polar wind. We present polar wind results along single stationary field lines under dayside and nightside conditions, as well as the global solution reconstructed from nearly 1000 moving field lines. The model results show significant day-night asymmetries in the polar wind solution owing to the higher ionization and photoelectron fluxes on the dayside compared to the nightside. Field line motion is found to modify this dependence and create global structure by transporting field lines through different conditions of illumination and through the localized effects of Joule heating.
With the help of a theoretical model and finite-difference time-domain (FDTD) simulations based on the hydrodynamic–Maxwell model, we examine the effect of difference-frequency generation (DFG) in an array of L-shaped metal nanoparticles (MNPs) characterized by intrinsic plasmonic nonlinearity. The outcomes of the calculations reveal the spectral interplay between gain and loss in the vicinity of the fundamental frequency of the localized surface plasmon resonances. Subsequently, we identify different array thicknesses and pumping regimes facilitating parametric amplification and spontaneous parametric downconversion. Our results suggest that the parametric amplification regime becomes feasible on a scale of hundreds of nanometers and spontaneous parametric downconversion on the scale of tens of nanometers, opening up new exciting opportunities for developing building blocks of photonic metasurfaces.
This report is the final report of a SBIR Phase I project. It is identical to the final report submitted, after some proprietary information of administrative nature has been removed. The development of a numerical simulation tool for dielectric barrier discharge (DBD) plasma actuator is reported. The objectives of the project were to analyze and predict DBD operation at wide range of ambient gas pressures. It overcomes the limitations of traditional DBD codes which are limited to low-speed applications and have weak prediction capabilities. The software tool allows DBD actuator analysis and prediction for subsonic to hypersonic flow regime. The simulation tool is based on the VORPAL code developed by Tech-X Corporation. VORPAL's capability of modeling DBD plasma actuator at low pressures (0.1 to 10 torr) using kinetic plasma modeling approach, and at moderate to atmospheric pressures (1 to 10 atm) using hydrodynamic plasma modeling approach, were demonstrated. In addition, results of experiments with pulsed+bias DBD configuration that were performed for validation purposes are reported.
Abstract. Vegetation plays a key role in the global carbon cycle and thus is an important component within Earth system models (ESMs) that project future climate. Many ESMs are adopting methods to resolve plant size and ecosystem disturbance history, using vegetation demographic models. These models make it feasible to conduct more realistic simulation of processes that control vegetation dynamics. Meanwhile, increasing understanding of the processes governing plant water use, and ecosystem responses to drought in particular, has led to the adoption of dynamic plant water transport (i.e., hydrodynamic) schemes within ESMs. However, the extent to which variations in plant hydraulic traits affect both plant water stress and the risk of mortality in trait-diverse tropical forests is understudied. In this study, we report on a sensitivity analysis of an existing hydrodynamic scheme (HYDRO) model that is updated and incorporated into the Functionally Assembled Terrestrial Ecosystem Simulator (FATES) (FATES–HYDRO V1.0). The size- and canopy-structured representation within FATES is able to simulate how plant size and hydraulic traits affect vegetation dynamics and carbon–water fluxes. To better understand this new model system, and its functionality in tropical forest systems in particular, we conducted a global parameter sensitivity analysis at Barro Colorado Island, Panama. We assembled 942 observations of plant hydraulic traits on 306 tropical plant species for stomata, leaves, stems, and roots and determined the best-fit statistical distribution for each trait, which was used in model parameter sampling to assess the parametric sensitivity. We showed that, for simulated leaf water potential and loss of hydraulic conductivity across different plant organs, the four most important traits were associated with xylem conduit taper (buffers increasing hydraulic resistance with tree height), stomatal sensitivity to leaf water potential, maximum stem hydraulic conductivity, and the partitioning of total hydraulic resistance above vs. belowground. Our analysis of individual ensemble members revealed that trees at a high risk of hydraulic failure and potential tree mortality generally have a lower conduit taper, lower maximum xylem conductivity, lower stomatal sensitivity to leaf water potential, and lower resistance to xylem embolism for stem and transporting roots. We expect that our results will provide guidance on future modeling studies using plant hydrodynamic models to predict the forest responses to droughts and future field campaigns that aim to better parameterize plant hydrodynamic models.