Penetration model explanation for turbulent forced-convection heat transfer observed in near-critical fluids
Penetration model explanation for turbulent forced convection heat transfer in near-critical fluids
Graham, R. W.↗
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Extended dynamic mode decomposition for model reduction in fluid dynamics simulations
High computational cost and storage/memory requirements of fluid dynamics simulations constrain their usefulness as a predictive tool. Reduced-order models (ROMs) provide a viable solution to this challenge by extracting the key underlying dynamics of a complex system directly from data. We investigate the efficacy and robustness of an extended dynamic mode decomposition (xDMD) algorithm in constructing ROMs of three-dimensional cardiovascular computations. Focusing on the ROMs' accuracy in representation and interpolation, we relate these metrics to the truncation rank of singular value decomposition, which underpins xDMD and other approaches to ROM construction. Our key innovation is to relate the truncation rank to the singular values of the original flow problem. This result establishes a priori guidelines for the xDMD deployment and its likely success as a means of data compression and reconstruction of the system's dynamics from dominant spatiotemporal structures present in the data.
Mechanics↗
Validation and parameterization of a novel physics-constrained neural dynamics model applied to turbulent fluid flow
We report, in fluid physics, data-driven models to enhance or accelerate time to solution are becoming increasingly popular for many application domains, such as alternatives to turbulence closures, system surrogates, or for new physics discovery. In the context of reduced order models of high-dimensional time-dependent fluid systems, machine learning methods grant the benefit of automated learning from data, but the burden of a model lies on its reduced-order representation of both the fluid state and physical dynamics. In this work, we build a physics-constrained, data-driven reduced order model for Navier–Stokes equations to approximate spatiotemporal fluid dynamics in the canonical case of isotropic turbulence in a triply periodic box. The model design choices mimic numerical and physical constraints by, for example, implicitly enforcing the incompressibility constraint and utilizing continuous neural ordinary differential equations for tracking the evolution of the governing differential equation. We demonstrate this technique on a three-dimensional, moderate Reynolds number turbulent fluid flow. In assessing the statistical quality and characteristics of the machine-learned model through rigorous diagnostic tests, we find that our model is capable of reconstructing the dynamics of the flow over large integral timescales, favoring accuracy at the larger length scales. More significantly, comprehensive diagnostics suggest that physically interpretable model parameters, corresponding to the representations of the fluid state and dynamics, have attributable and quantifiable impact on the quality of the model predictions and computational complexity.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
A mathematical model for investigating the stability of thermally stratified flow of unbounded viscous incompressible fluid
Mathematical models for investigating stability of thermally stratified shear flow in atmosphere
Gage, K. S.↗
Computational Modeling of Cephalad Fluid Shift for Application to Microgravity-Induced Visual Impairment
An improved understanding of spaceflight-induced ocular pathology, including the loss of visual acuity, globe flattening, optic disk edema and distension of the optic nerve and optic nerve sheath, is of keen interest to space medicine. Cephalad fluid shift causes a profoundly altered distribution of fluid within the compartments of the head and body, and may indirectly generate phenomena that are biomechanically relevant to visual function, such as choroidal engorgement, compromised drainage of blood and cerebrospinal fluid (CSF), and altered translaminar pressure gradient posterior to the eye. The experimental body of evidence with respect to the consequences of fluid shift has not yet been able to provide a definitive picture of the sequence of events. On earth, elevated intracranial pressure (ICP) is associated with idiopathic intracranial hypertension (IIH), which can produce ocular pathologies that look similar to those seen in some astronauts returning from long-duration flight. However, the clinically observable features of the Visual Impairment and Intracranial Pressure (VIIP) syndrome in space and IIH on earth are not entirely consistent. Moreover, there are at present no experimental measurements of ICP in microgravity. By its very nature, physiological measurements in spaceflight are sparse, and the space environment does not lend itself to well-controlled experiments. In the absence of such data, numerical modeling can play a role in the investigation of biomechanical causal pathways that are suspected of involvement in VIIP. In this work, we describe the conceptual framework for modeling the altered compartmental fluid distribution that represents an equilibrium fluid distribution resulting from the loss of hydrostatic pressure gradient.
Nelson, Emily S.↗
Characteristics of Fluid‐Solid Interaction Constitutive Models Within Poroelastodynamics at Higher Strain‐Rates and Large Deformations Implemented in 1D
The large deformation, mixed formulation, finite element (FE) modeling approach presented in Irwin et al. 2024 is extended herein to include improved constitutive models for representing dynamic solid-fluid interactions at higher strain rates (𝒪(10 2 −10 3 )s −1 ) and larger overpressure magnitudes (𝒪(10 2 )kPa) within a biphasic soft porous material using Theory of Porous Media (TPM) at finite strain. Specifically, these constitutive modeling improvements are the following: (i) a more physically robust constitutive model for pore fluid seepage velocity via inclusion of pore fluid viscous stress, and (ii) a modified deformation-dependent-permeability model and updated hyperelastic constitutive model better suited for handling larger volumetric compressions and extensions. The novelty of the present work is mainly the contribution (i): inclusion of pore fluid viscous stress at higher strain-rate and large deformations, which requires 𝐶 1 continuity in the weak formulation, accomplished by employing Hermite cubic interpolation functions within a mixed nonlinear poromechanical finite element formulation. In (ii), the model is updated to weakly enforce solid phase incompressibility, such that this assumption is not violated numerically, which provides improved numerical stability for achieving larger overpressure magnitudes on 𝒪(10 2 ) kPa, which were not achievable with the previous Kozeny–Carman model in Irwin et al. 2024. Also in (ii), the volumetric part of the solid skeleton free energy function is modified to ensure proper bounds on the solid skeleton Jacobian of deformation 𝐽 s related to incompressibility of the solid phase. Uniaxial strain, unidirectional flow examples at higher strain rates (𝒪(10 2 −10 3 )s −1 ) and larger deformations (up to 0.2 (or 20%) nominal axial strain) demonstrate the improved physical representation—and numerical stability—of these constitutive model improvements.
42 ENGINEERING↗
Supercritical (and Subcritical) Fluid Behavior and Modeling: Drops, Streams, Shear and Mixing Layers, Jets and Sprays
A critical review of recent investigations in the real of supercritical (and subcritical) fluid behavior is presented with the goal of obtaining a perspective on the peculiarities of high pressure observations.
supercritical fluids↗
Mathematical modeling of fluid-electrolyte alterations during weightlessness
Fluid electrolyte metabolism and renal endocrine control as it pertains to adaptation to weightlessness were studied. The mathematical models that have been particularly useful are discussed. However, the focus of the report is on the physiological meaning of the computer studies. A discussion of the major ground based analogs of weightlessness are included; for example, head down tilt, water immersion, and bed rest, and a comparison of findings. Several important zero g phenomena are described, including acute fluid volume regulation, blood volume regulation, circulatory changes, longer term fluid electrolyte adaptations, hormonal regulation, and body composition changes. Hypotheses are offered to explain the major findings in each area and these are integrated into a larger hypothesis of space flight adaptation. A conceptual foundation for fluid electrolyte metabolism, blood volume regulation, and cardiovascular regulation is reported.
Leonard, J. I.↗
Fundamentals of Plasma Sails Propulsion Concept
The Mini-Magnetospheric Plasma Propulsion (M2P2), originally proposed by Winglee et al. [2000], is based on the two-fluid plasma model and requires a 15-km frontal standoff distance (or 20-km cross-sectional diameter) in order for the magnetic bubble to absorb sufficient momentum from the SW to accelerate a spacecraft to the unprecedented speeds of 50-80 km/s after an acceleration period of about three months. Winglee et al. [2000] derived the above size requirement based on an extrapolation of their simulated results in which a system much smaller than a M2P2 was used (p. 21,074 of their study). We submit, however, that a fluid model has no validity for such a small scale size-even in the region near the plasma source! It is assumed in the MHD fluid model, normally applied to the magnetosphere, that the characteristic scale-size is much greater than the Larmor radius and ion skin depth of the SW. In the case of the M2P2, however, the size of the magnetic bubble is actually less than or, at best, comparable to, the scale of these characteristic parameters and, therefore, a kinetic approach, which addresses the smallscale physical mechanisms involved, must be used. A fully three-dimensional version of the hybrid code is used in our M2P2 (Plasma Sails) studies was originally developed by Delamere et al. [1999]. The M2P2 plasma sail is an excellent application for this hybrid code. The primary advantage of this code is the seamless interface between fluid and kinetic descriptions of the ion populations. A kinetic description is not necessary for the dense inner regions of the magnetic bubble and tremendous computational savings can be realized by treating this dense, magnetized ion population with the fluid description. It is essential, however, that the outer bubble regions be treated kinetically as well as the SW protons. Comparison of full size M2P2 simulation based on 3D MHD and kinetic models show that kinetic treatment introduces much more asymmetry to the considering problem and the possibility of kinetic instabilities development.
Khazanov, G. V.↗
Coupling a Computational Fluid Dynamics (CFD) Model to a Spacecraft Thermal System Model for the DraMS Instrument Thermal Analysis
The Dragonfly Mass Spectrometer (DraMS) is an instrument on the Dragonfly mission, which will spend 7 years in deep space cruise before landing and operating on the surface of Titan. Vacuum thermal analyses are required for deep space cruise, and convection analyses are required for the Titan surface operations. Model exchanges across multiple thermal teams are needed for all phases of the mission. For DraMS, Thermal Desktop (TD) has been the main thermal analytical tool of choice due to its capability in modeling complex thermal systems with relatively low computational power and for its availability across thermal teams. However, TD does not have computational fluid dynamics (CFD) capability and struggles to accurately capture complex convective behavior. DraMS has fans operating in tandem and gas flow behaviors are not easily predicted due to its complex flow paths. CFD software, such as Fluent, can model and predict such complex flow behaviors, but CFD models are computationally expensive, and its workflow processes are not tailored towards simulating large and complex systems. Therefore, a coupled modeling approach was chosen for DraMS: A TD model was used for simulating all the conductive, radiative, and source terms, while a Fluent CFD model was added on, as needed, to the TD model to provide the convective boundary conditions using the System Coupling software. The coupling software allows the TD and Fluent models to communicate data and arrive at a co-solved and co-converged solution. Furthermore, Thermal Iso-value Exchange (TIE) method was developed to facilitate and improve the TD-Fluent data exchange process. This paper will discuss the analytical studies that were done to verify the accuracy and usability of the coupled approach and the challenges associated, which lead to the development of the TIE approach. DraMS thermal design and co-solved analysis results will also be discussed.
heat transfer↗
Self-Pressurization and Spray Cooling Simulations of the Multipurpose Hydrogen Test Bed (MHTB) Ground-Based Experiment
This paper presents a CFD (computational fluid dynamics) model for simulating the self-pressurization of a large scale liquid hydrogen storage tank. In this model, the kinetics-based Schrage equation is used to account for the evaporative and condensing interfacial mass flows. Laminar and turbulent approaches to modeling natural convection in the tank and heat and mass transfer at the interface are compared. The flow, temperature, and interfacial mass fluxes predicted by these two approaches during tank self-pressurization are compared against each other. The ullage pressure and vapor temperature evolutions are also compared against experimental data obtained from the MHTB (Multipuprpose Hydrogen Test Bed) self-pressurization experiment. A CFD model for cooling cryogenic storage tanks by spraying cold liquid in the ullage is also presented. The Euler- Lagrange approach is utilized for tracking the spray droplets and for modeling interaction between the droplets and the continuous phase (ullage). The spray model is coupled with the VOF (volume of fluid) model by performing particle tracking in the ullage, removing particles from the ullage when they reach the interface, and then adding their contributions to the liquid. Droplet ullage heat and mass transfer are modeled. The flow, temperature, and interfacial mass flux predicted by the model are presented. The ullage pressure is compared with experimental data obtained from the MHTB spray bar mixing experiment. The results of the models with only droplet/ullage heat transfer and with heat and mass transfer between the droplets and ullage are compared.
cryogenic fluid storage↗
Data-Driven Many-Body Models for Molecular Fluids: CO 2 /H 2 O Mixtures as a Case Study
In this study, we extend the scope of the many-body TTM-nrg and MB-nrg potential energy functions (PEFs), originally introduced for halide ion–water and alkali-metal ion–water interactions, to the modeling of carbon dioxide (CO2) and water (H2O) mixtures as prototypical examples of molecular fluids. Both TTM-nrg and MB-nrg PEFs are derived entirely from electronic structure data obtained at the coupled cluster level of theory and are, by construction, compatible with MB-pol, a many-body PEF that has been shown to accurately reproduce the properties of water. Although both TTM-nrg and MB-nrg PEFs adopt the same functional forms for describing permanent electrostatics, polarization, and dispersion, they differ in the representation of short-range contributions, with the TTM-nrg PEFs relying on conventional Born–Mayer expressions and the MB-nrg PEFs employing multidimensional permutationally invariant polynomials. By providing a physically correct description of many-body effects at both short and long ranges, the MB-nrg PEFs are shown to quantitatively represent the global potential energy surfaces of the CO 2 –CO 2 and CO 2 –H 2 O dimers and the energetics of small clusters, as well as to correctly reproduce various properties in both gas and liquid phases. Furthermore, building upon previous studies of aqueous systems, our analysis provides further evidence for the accuracy and efficiency of the MB-nrg framework in representing molecular interactions in fluid mixtures at different temperature and pressure conditions.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Highly Accurate Experimental Heave Decay Tests with a Floating Sphere: A Public Benchmark Dataset for Model Validation of Fluid–Structure Interaction
Highly accurate and precise heave decay tests on a sphere with a diameter of 300 mm were completed in a meticulously designed test setup in the wave basin in the Ocean and Coastal Engineering Laboratory at Aalborg University, Denmark. The tests were dedicated to providing a rigorous benchmark dataset for numerical model validation. The sphere was ballasted to half submergence, thereby floating with the waterline at the equator when at rest in calm water. Heave decay tests were conducted, wherein the sphere was held stationary and dropped from three drop heights: a small drop height, which can be considered a linear case, a moderately nonlinear case, and a highly nonlinear case with a drop height from a position where the whole sphere was initially above the water. The precision of the heave decay time series was calculated from random and systematic standard uncertainties. At a 95% confidence level, uncertainties were found to be very low—on average only about 0.3% of the respective drop heights. Physical parameters of the test setup and associated uncertainties were quantified. A test case was formulated that closely represents the physical tests, enabling the reader to do his/her own numerical tests. The paper includes a comparison of the physical test results to the results from several independent numerical models based on linear potential flow, fully nonlinear potential flow, and the Reynolds-averaged Navier–Stokes (RANS) equations. A high correlation between physical and numerical test results is shown. The physical test results are very suitable for numerical model validation and are public as a benchmark dataset.
42 ENGINEERING↗
Coupled 2D MHD and runaway electron fluid simulations of SPARC disruptions
Runaway electrons (REs) generated during disruption events in tokamaks can carry mega-Ampère level currents, potentially causing damage to plasma-facing components. Understanding RE evolution during disruption events is important for evaluating strategies to mitigate RE damage. Using two-dimensional toroidally symmetric magnetohydrodynamic (MHD) simulations in M3D-C1, which incorporates a fluid RE model evolved self-consistently with the bulk MHD fluid, we examine the seeding and avalanching of REs during disruptions in the SPARC tokamak – a compact, high-field, high-current device designed to achieve a fusion gain Q > 2 in deuterium–tritium plasmas. The M3D-C1 simulations of unmitigated disruptions demonstrate RE plateau formation and peaking of the final current density, which agree well with the results of lower-fidelity reduced RE fluid models. This work provides the first systematic comparison and benchmarking of different primary sources, including activated tritium beta decay and Compton scattering, in SPARC disruption simulations with self-consistent MHD and RE coupling.
Compton scattering↗
Reactive Transport Modeling of Shale–Fluid Interactions after Imbibition of Fracturing Fluids
Injection of hydraulic fracturing fluid (HFF) into shale formations for unconventional oil/gas production results in chemical reactions in the shale matrix. Our recent experimental study determined the depths to which different types of reactions between the shale matrix and the HFF extended. In the present study, we built continuum-scale reactive transport models to understand the coupling of chemical reactions and the transport of aqueous species in these shale–HFF systems. Calibration of the model with our previous experimental results reveals that it takes hours to months for the shale matrix to completely neutralize the imbibed acids, depending primarily upon the carbonate content of the shale. Both the HFF pH and pore pH affect the location of barite precipitation, resulting in unique barite precipitation profiles extending millimeters into calcite-rich Eagle Ford shale but only tens of micrometers into low-carbonate Marcellus shale. In addition, dissolved oxygen and extracted bitumen are key to reproducing the experimental observation of Fe(III) (oxyhydr)oxide formation in the shale matrix as a result of pyrite dissolution in the shales. A comparison between the modeling results of porosity in the present study to experimentally measured permeabilities in our previous study suggests that chemical reactions occurring at a greater depth than the observable reaction zone might have impacted permeability. Our model serves as a benchmark for efficiently modeling water–rock interactions in similar systems where bulk rock samples react with a solution in batch reactors. Important reactive transport processes were ascertained via modeling, which allows for quantitative prediction of shale–HFF interactions in shale matrices given the shale and HFF compositions.
58 GEOSCIENCES↗
Performance Model for Reversible Fluid Balloons
The Altitude Control Experiment (ALICE) is a balloon system that consists of a helium lifting balloon and a buoyancy-controlling freon R114 balloon. Condensation of freon gas in the cooler upper atmosphere and vaporization of liquid freon in the warmer lower atmosphere can be effectively used to adjust the net buoyant force so that the balloon system may float between desirable ranges of altitudes.This report summarizes efforts to produce an analytical model to predict the performance of this type of altitude control balloon in the earth's atmosphere. Various interactive parameters that are influential to the dynamics of the system were identified and treated. The analytical model developed herein correlated well with three ALICE day flight trajectories and one ALICE night flight trajectory in the earth's atmosphere. A similar approach can be applied to the design of a Venusian balloon system that could repeatedly travel to the lower, hot altitudes or surface of Venus for imaging or science experiments and then float higher in order to cool the electronics.
balloon↗
Dynamic response of a freely rotating butterfly valve in the advanced test reactor – dynamic fluid-body interaction modeling
To regulate primary coolant flow in the Advanced Test Reactor (ATR), a butterfly valve was installed between the primary coolant pumps and the reactor core. If the mechanical connection between the valve's disk and its shaft ever fails, the disk will rotate freely. Rapid disk rotation may induce pressure surges that could damage upstream pipes. In the present work, the rotational trajectory and pressure evolution during a disk free-rotation scenario were analyzed via the dynamic fluid-body interaction (DFBI) approach in STAR-CCM+, with the movement of a solid being driven by the forces and moment/torque imposed by its surrounding fluid. Assuming a large initial opening angle, the disk accelerates slowly, but swiftly passes the closed position. As a result of the sudden valve closure, a pressure surge occurs in the upstream pipes, exceeding their maximum allowable pressure. Furthermore, the disk does not stabilize at the closed position but continually oscillates due to the unsteady nature of the coolant flow. Because of the significant and continuous water hammer effect, a fix to the butterfly valve is being implemented to prevent rapid valve closure due to failure at the valve's disk/shaft mechanical connection.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Machine learning surrogate models for Landau fluid closure
The first result of applying the machine/deep learning technique to the fluid closure problem is presented in this letter. As a start, three different types of neural networks (multilayer perceptron (MLP), convolutional neural network (CNN) and two-layer discrete Fourier transform (DFT) network) were constructed and trained to learn the well-known Hammett-Perkins Landau fluid closure in configuration space. We found that in order to train a well-preformed network, a minimum size of training data set is needed; MLP also requires a minimum number of neurons in the hidden layers equals to the degrees of freedom in Fourier space despite training data is fed in configuration space. Out of three models DFT performs the best for the clean data most likely due to the existence of nice Fourier expression for Hammett-Perkins closure but it is least robust with respect to input noise. Overall, with appropriate tuning and optimization, all three neural networks are able to accurately predict Hammett-Perkins closure and reproduce the inherit nonlocal feature, suggesting a promising path to calculate more sophisticated closures with the machine/deep learning technique.
70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗