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This is a presentation for LANL's Young Women's Summer Camp. After explaining what fluids are and the wide range of applications studying fluids has, properties, patterns, and instabilities are discussed followed by an explanation of turbulence and why it is important. This is placed in the context of what scientists and engineers try to do to unlock the physics of the study of fluids.
We report on the verification of elastic collisions in EMPIRE-PIC and EMPIRE-Fluid in support of the ATDM L2 V&V Milestone. The thermalization verification problem and the theory behind it is presented along with an analytic solution for the temperature of each species over time. The problem is run with both codes under multiple parameter regimes. The temperature over time is compared between the two codes and the theoretical results. A preliminary convergence analysis is performed on the results from EMPIRE-PIC and EMPIRE-Fluid showing the rate at which the codes converge to the analytic solution in time (EMPIRE-Fluid) and particles (EMPIRE-PIC).
This report summarizes the findings and outcomes of the LDRD-express project with title “Fluid models of charged species transport: numerical methods with mathematically guaranteed properties”. The primary motivation of this project was the computational/mathematical exploration of the ideas advanced aiming to improve the state-of-the-art on numerical methods for the one-fluid Euler-Poisson models and gain some understanding on the Euler-Maxwell model. Euler-Poisson and Euler-Maxwell, by themselves are not the most technically relevant PDE plasma-models. However, both of them are elementary building blocks of PDE-models used in actual technical applications and include most (if not all) of their mathematical difficulties. Outside the classical ideal MHD models, rigorous mathematical and numerical understanding of one-fluid models is still a quite undeveloped research area, and the treatment/understanding of boundary conditions is minimal (borderline non-existent) at this point in time. This report focuses primarily on bulk-behaviour of Euler-Poisson’s model, touching boundary conditions only tangentially.
Multiphysics and analytical calculations were conducted for a heat exchanger with passive, natural circulation flow. A glycol/water working fluid convects the heat to a dimpled heat exchanger shell, which subsequently transfers the heat to the soil, which acts as the ultimate heat sink. Because the system is fully-passive, it is not subject to the expenses, maintenance, and mechanical breakdowns associated with moving parts. Density, heat capacity, and thermal conductivity material properties were measured for various soil samples, and subsequently included as input for the soil heat conduction model. The soil model was coupled to a computational fluid dynamics (CFD) heat exchanger model that included the dynamic Smagorinsky large eddy simulation and k- omega turbulence models. The analysis showed that the fluid dynamics and heat transfer models worked properly, albeit at a slow pace. Nevertheless, the coupled CFD/heat conduction simulation ran long enough to determine a key parameter—the amount of heat conducted from the heat exchanger to the ground. This unique performance value, along with experimental data, was used as input for stand-alone, fast-running CFD models, as well as boundaries to obtain solutions to partial differential equations for soil heat conduction.
Introduction: In the current fleet of fossil-fired power plants, creep strength enhanced ferritic steels (CSEF) are used to sustain the harsh service conditions. Enhanced properties of Grade 91 steel result from tempered martensite with a fine distribution of MX and M23C6 carbides. Grade 91 steel is subjected to onsite welding repair to remedy their degradation due to extreme service condition. Knowledge of weld repairability of these steels, such as as-welded hardness distribution, is essential to establishing sound repair procedures. Experimental trial and error tests can consume a lot of time as many welding variables need to be studied. For numerical modelling, most of the multi-pass multi-layer models are based on finite element method, which are limited to solve the heat conduction equation and ignore convective heat transfer due to melt flow. Moreover, the mesh has to be pre-built based on a known or assumed weld cross-section geometry. These finite element based models thus have limited predictive capability as defects are not considered and nugget size are pre-assumed. This research aims at developing a thermal and microstructure evolution model incorporating molten pool dynamics in a multi-pass multi-layer material deposition to predict the as-welded hardness distribution. Technical Approach: All the thermal, physical, and metallurgical properties of Grade 91 as a function of temperature are collected from the literature and inputted into the thermo-fluid model based on Flow-3D, a computational fluid dynamics software. A multi-pass, multi-layer material deposition is simulated where the melting of filler wire into the molten pool is directly considered based on the volume of fluid (VOF) method. The flow behaviour of the molten pool is used to understand the formation of deposition geometry and defects. The temperature profiles during the multi-pass, multi-layer welding are calculated. The results computed using the new model are compared against the experimental data of fusion zone geometry and thermal cycles. Hardness prediction in the heat-affected zone (HAZ) are made using Johnson-Mehl-Avrami (JMA) equation for solid-state phase transformation kinetics. The JMA parameters are extracted from the experimental data available in the literature. For comparison, a standard finite element heat conduction model is also developed to predict the thermal cycles and hardness distribution in the multi-pass, multi-layer weld. Expected Result: Results obtained using the molten pool dynamic simulation versus the finite element heat conduction model are compared. Specifically, the effects of convective heat transfer on the accuracy of the calculated thermal history, bead shape and size, and HAZ hardness distribution are examined.
Overview This project focused on developing a high-fidelity numerical framework to simulate the multiphysics environment within Liquid Argon Time Projection Chamber (LArTPC) detectors. The primary objective was to characterize the complex interplay between ion transport, background fluid dynamics, and electric field distortions—a critical factor for the calibration and sensitivity of next-generation High Energy Physics experiments, such as DUNE. Technical Achievements The research successfully yielded a hybrid numerical space-charge solver utilizing a Cell-Centered Finite Volume Method (FVM) for ion transport coupled with a Finite Element Method (FEM) for electric potential. Key accomplishments include: • Verification & Validation: The 3-D solver was rigorously verified against 1-D analytical solutions, demonstrating high numerical accuracy in predicting space-charge-induced field deviations. • Field Distortion Analysis: 3D simulations revealed that space charge effects introduce significant non-uniformities in the electric field. Critically, the research identified that background LAr flow velocities, when comparable to ion drift velocities, markedly exacerbate these distortions. • Technology Transfer: The resulting source code and comprehensive user manuals were successfully transferred to collaborators at Fermilab, providing a portable computational tool for the broader scientific community. Challenges and Future Directions While the space-charge solver achieved all performance metrics, the integrated fluid dynamics modeling encountered convergence challenges stemming from the extreme 200-fold disparity in length scales between the detector's 37 mm inlet pipes and the 8-meter global domain. To address this, the project has identified a clear technical pivot toward Hierarchical Geometric Adaptive Mesh Refinement (HG-AMR). By implementing an h-type refinement strategy with hanging nodes, future iterations of this solver will be capable of resolving localized high-gradient inlet flows without the prohibitive computational costs of regular grids. This advancement, combined with data-driven uncertainty quantification based on MicroBooNE-style calibration, will enable the precise modeling of detector responses in large-scale cryogenic environments where direct measurement remains difficult. Impact The computational tools developed under this award provide a foundation for enhancing the energy resolution and spatial reconstruction of noble liquid detectors. By bridging the gap between theoretical fluid dynamics and experimental field calibration, this work supports the DOE’s mission to advance the frontiers of neutrino physics and dark matter detection.
The ability to sustain high energy extraction efficiency from Enhanced Geothermal Systems (EGS) is affected by the ability to measure, characterize, and predict the effect of different perturbations that arise from fluid injection rates, fluid temperature, and shut-in conditions on existing and stimulated fracture systems throughout a subsurface reservoir’s lifecycle. The difficulty arises from limited knowledge of the interaction of fluid and temperature driven fractures with frictional interfaces that govern local deformation and frictional behavior in the surrounding rock. Thermo-poro-mechanical coupled processes tend to dominate this interaction and strongly affect the permeability and local stress distributions that impact efficiency and prevent optimal stimulation conditions over multi-decadal time frames.
Until recently, modern theory has lacked a fundamentally based model to predict the operating pressures where classical sprays transition to dense-fluid mixing with diminished surface tension. In this paper, such a model is presented to quantify this transition for liquid-oxygen–hydrogen and n-decane–gaseous-oxygen injection processes. The analysis reveals that respective molecular interfaces break down not necessarily because of vanishing surface tension forces but instead because of the combination of broadened interfaces and a reduction in mean free molecular path. When this occurs, the interfacial structure itself enters the continuum regime, where transport processes rather than intermolecular forces dominate. Using this model, regime diagrams for the respective systems are constructed that show the range of operating pressures and temperatures where this transition occurs. The analysis also reveals the conditions where classical spray dynamics persists even at high supercritical pressures. As a result, it demonstrates that, depending on the composition and temperature of the injected fluids, the injection process can exhibit either classical spray atomization, dense-fluid diffusion-dominated mixing, or supercritical mixing phenomena at chamber pressures encountered in state-of-the-art liquid rocket engines.
Numerical solutions for fluid flow problems are challenging and have been focus of Computational Fluid Dynamics (CFD) research for past several decades. The advent of quantum computing promises exponential speedup in comparison to existing classical methods and alleviate computational constraints posed by CFD problems. Although solutions for most problems of interest in fluid dynamics using quantum computing are distant, recent advances in algorithms, software and hardware provide a path towards realizing this goal. Quantum linear solver algorithms (QLSA) such as Harrow–Hassidim–Lloyd (HHL) and Variational Quantum Linear Solver (VQLS) have been successfully implemented to solve for canonical problems such as Hele-Shaw flow. However, these algorithms still suffer to scale and address problems with ill-conditioned Jacobians. In the current paper, we alleviate these restrictions with a new quantum solver based on Singular Value Decomposition (SVD) and simulate flow past a 2D cylinder. The fidelity of the SVD based quantum solver in predicting the flow past 2D cylinder is computed along with an assessment of errors. Classical and quantum solutions for the flow are compared for different resolutions. Finally, we discuss variation in the solutions based on number of shots used.
Natural gas extracted from tight shale formations, such as the Marcellus Shale, represents a significant and developing front in energy exploration. By fracturing these formations using pressurized fracturing fluid, previously unobtainable hydrocarbon reserves may be tapped. While pursuing this resource, hydraulic fracturing operations leave chemically complex fluids in the shale formation for at least two weeks. This provides a substantial opportunity for the hydraulic fracturing fluid (HFF) to react with the shale formation at reservoir temperature and pressure. In this study, we investigated the effects of the carbonates on shale-HFF reactions with a focus on the Marcellus Shale. We performed autoclave experiments at high temperature and pressure reservoir conditions using a carbonate-rich and a decarbonated or carbonate-free version of the same shale sample. We observed that carbonate minerals buffer the pH of the solution, which in turn prevents clay dissolution. Carbonate and bicarbonate ions also scavenge reactive oxidizing species (ROS), which prevents oxidation of shale organic matter and volatile organic compounds (VOCs). Carbonate-free samples also show higher pyrite dissolution compared to the carbonate-rich sample due to chelation reactions. This study demonstrates how carbonate minerals (keeping all other variables constant) affect shale-HFF reactions that can potentially impact porosity, microfracture integrity, and the release of heavy metals and volatile organic contaminants in the produced water.
Adsorption of fluids in nanoporous materials is important for several applications including gas storage and catalysis. The pore network in natural, as well as engineered, materials can exhibit different degrees of connectivity between pores. While this might have important implications for the sorption of fluids, the effects of pore connectivity are seldom addressed in the studies of fluid sorption. We have carried out Monte Carlo simulations of the sorption of ethane and CO2 in silicalite, a nanoporous material characterized by sub-nanometer pores of different geometries (straight and zigzag channel like pores), with varied degrees of pore connectivity. The variation in pore connectivity is achieved by selectively blocking some pores by loading them with methane molecules that are treated as a part of the rigid nanoporous matrix in the simulations. Normalized to the pore space available for adsorption, the magnitude of sorption increases with a decrease in pore connectivity. The increased adsorption in the systems where pore connections are removed by blocking them is because of additional, albeit weaker, adsorption sites provided by the blocker molecules. By selectively blocking all straight or zigzag channels, we find differences in the absorption behavior of guest molecules in these channels.
Thermo-fluid science is a foundational discipline for numerous mechanical systems, particularly in energy production and building equipment, where thermal and mechanical energy transfer play critical roles. Advancements in heat transfer and fluid mechanics have significantly enhanced these systems, driving progress in associated market sectors. For instance, evaporator and condenser coils are essential for optimizing vapor compression cycles in building equipment. Heat pumps used for space and water heating constitute a major share of building systems, with microchannel heat exchanger technology at the forefront of these innovations. It is worth mentioning that advancements in heat transfer and fluid mechanics significantly alleviate the challenge of designing energy-efficient building equipment. The development of research techniques has contributed significantly to the advancement of science; for example, in the experimental field, non-intrusive measurement techniques such as Particle Image Velocimetry (PIV) are now capable of resolving flow behavior in a 3D format for different length and time scales.
A Bargmann spacetime is a constrained five-dimensional setting that, while introducing no new physical degrees of freedom beyond those of ordinary four-dimensional spacetime, permits Galilei physics to be expressed with a tensor formalism that respects the distinction between mass and energy while affording the conceptual and technical advantages of a spacetime metric. This framework offers a route to a strong-field ‘Galilei general relativity’ approximating the usual Poincaré general relativity introduced by Einstein. In preparation for modeling core-collapse supernovae, where such an approximation would be useful, this work generalizes the kinetic-energy–momentum–mass 5-flux 𝒯 and its associated spacetime tensor law from a simple fluid of constant particle mass to a baryon fluid whose multiple nuclear species can interconvert rest mass and internal energy. The spacetime tensor law on Bargmann–Galilei spacetime 𝐵𝒢 and its decompositions relative to comoving (‘Lagrangian’) and fiducial (‘Eulerian’) observers are derived in detail. The formalism is rendered more suitable for core-collapse supernova modeling by an extension from strict 𝐵𝒢 to a regime that might be denoted as 𝐵𝒢+: microscopically Poincaré yet macroscopically Galilei. This extension accommodates energy generation by nuclear composition changes and allows comoving energy density and pressure to contribute relative to mass density, while preserving the simplifications of Galilei bulk fluid flow and the streamlined geometry governed by the Bargmann–Galilei spacetime metric.
Reservoir simulation models are the major tools for studying fluid flow behavior in hydrocarbon reservoirs. These models are constructed based on geological models, which are developed by integrating data from geology, geophysics, and petro-physics. As the complexity of a reservoir simulation model increases, so does the computation time. Therefore, to perform any comprehensive study which involves thousands of simulation runs, a very long period of time is required. Several efforts have been made to develop proxy models that can be used as a substitute for complex reservoir simulation models. These proxy models aim at generating the outputs of the numerical fluid flow models in a very short period of time. This research is focused on developing a proxy fluid flow model using artificial intelligence and machine learning techniques. In this work, the proxy model is developed for a real case CO2 sequestration project in which the objective is to evaluate the dynamic reservoir parameters (pressure, saturation, and CO2 mole fraction) under various CO2 injection scenarios. The data-driven model that is developed is able to generate pressure, saturation, and CO2 mole fraction throughout the reservoir with significantly less computational effort and considerably shorter period of time compared to the numerical reservoir simulation model.
In order to study chromospheric magnetosonic wave propagation including, for the first time, the effects of ion–neutral interactions in the partially ionized solar chromosphere, we have developed a new multi-fluid computational model accounting for ionization and recombination reactions in gravitationally stratified magnetized collisional media. The two-fluid model used in our 2D numerical simulations treats neutrals as a separate fluid and considers charged species (electrons and ions) within the resistive MHD approach with Coulomb collisions and anisotropic heat flux determined by Braginskiis transport coefficients. The electromagnetic fields are evolved according to the full Maxwell equations and the solenoidality of the magnetic field is enforced with a hyperbolic divergence-cleaning scheme. The initial density and temperature profiles are similar to VAL III chromospheric model in which dynamical, thermal, and chemical equilibrium are considered to ensure comparison to existing MHD models and avoid artificial numerical heating. In this initial setup we include simple homogeneous flux tube magnetic field configuration and an external photospheric velocity driver to simulate the propagation of MHD waves in the partially ionized reactive chromosphere. In particular, we investigate the loss of chemical equilibrium and the plasma heating related to the steepening of fast magnetosonic wave fronts in the gravitationally stratified medium.
The presence of neutral species in a plasma has been shown to greatly affect the properties of magnetohydrodynamic waves. For instance, the interaction between ions and neutrals through momentum transfer collisions causes the damping of Alfvén waves and alters their oscillation frequency and phase speed. When the collision frequencies are larger than the frequency of the waves, single-fluid magnetohydrodynamic approximations can accurately describe the effects of partial ionization, since there is a strong coupling between the various species. However, at higher frequencies, the single-fluid models are not applicable and more complex approaches are required. Here, we use a five-fluid model with three ionized and two neutral components, which takes into consideration Hall’s current and Ohm’s diffusion in addition to the friction due to collisions between different species. We apply our model to plasmas composed of hydrogen and helium, and allow the ionization degree to be arbitrary. By analyzing the corresponding dispersion relation and numerical simulations, we study the properties of small-amplitude perturbations. We discuss the effect of momentum transfer collisions on the ion-cyclotron resonances and compare the importance of magnetic resistivity, and ion–neutral and ion–ion collisions on the wave damping at various frequency ranges. Applications to partially ionized plasmas of the solar atmosphere are performed.
Here, we present a "multipatch" infrastructure for the numerical simulation of fluid problems in which subregions require different grid scales, different grid geometries, different physical equations, or different reference frames. Its key element is a sophisticated client–router–server framework for efficiently linking processors supporting different regions ("patches") that must exchange boundary data. This infrastructure may be used with a wide variety of fluid dynamics codes; the only requirement is that their primary dependent variables be the same in all patches, e.g., fluid mass density, internal energy density, and velocity. Its structure can accommodate either Newtonian or relativistic dynamics. The overhead imposed by this system is both problem and computer cluster architecture dependent. Compared to a conventional simulation using the same number of cells and processors employed on a problem not requiring multipatch methods, the cell update per processor rate decreases by an amount that can range from negligible to a factor of a few; however, even in these problems, the infrastructure can permit substantial decreases in the total number of cell updates required.
Multiple sclerosis is a chronic autoimmune disease of the central nervous system. It is the main cause of non-traumatic neurological disability in young adults. Multiple sclerosis mostly affects people aged 20–50 years; however, it can occur in young children and much older adults. Factors identified in the distribution of MS include age, gender, genetics, environment, and ethnic background. Multiple sclerosis is usually associated with progressive degrees of disability. The disease involves demyelination of axons of the central nervous system and causes brain and spinal cord neuronal loss and atrophy. Diagnosing multiple sclerosis is based on a patient’s medical history including symptoms, physical examination, and various tests such as magnetic resonance imaging, cerebrospinal fluid and blood tests, and electrophysiology. The disease course of multiple sclerosis is not well correlated with the biomarkers presently used in clinical practice. Blood-derived biomarkers that can detect and distinguish the different phenotypes in multiple sclerosis may be advantageous in personalized treatment with disease-modifying drugs and to predict response to treatment. The studies reviewed have shown that the expression levels of a large number of miRNAs in peripheral blood, serum, exosomes isolated from serum, and cerebrospinal fluid are altered in multiple sclerosis and can distinguish the disease phenotypes from each other. Further studies are warranted to independently validate these findings so that individual or pairs of miRNAs in serum or cerebrospinal fluid can be used as potential diagnostic markers for adult and pediatric multiple sclerosis and for monitoring disease progression and response to therapy.