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The LOTO Training Flow Simulator Design poster discusses the intended purpose of the simulator along with the steps taken in the design process. It highlights the two main systems designed in the primary flow loop and the bubbler system, along with a brief description of the flow calculations made to ensure the functionality of the system.
Flow simulation on wind tunnel test models for aerodynamic characteristics of aircraft design, discussing boundary layer and external flow coupling
Gas hydrate-bearing sands on the Alaska North Slope (ANS) host minor volumes of crude oil whose impact on formation permeability has never been quantified relative to a gas hydrate reservoir system. Here, we combine in situ pressure-core microcomputed-tomography (μ-CT), thermogravimetric analysis (TGA), gas-chromatography–mass-spectrometry (GC–MS), scanning-electron microscopy coupled with energy-dispersive spectroscopy (SEM-EDS), and Stokes-flow simulation to (i) quantify oil saturation, (ii) infer its source, and (iii) evaluate its influence on permeability after gas-hydrate dissociation. μ-CT and SEM-EDS imaging identify the host sediment as silty, with some grain-coating clay present. Eight representative 3003-voxel subvolumes extracted from a preserved pressure core (Core 15P-3c, from the B1 sand (unit B), HYDRATE 02 Geo Data Well (GDW), 886.96–887.29 m measured depth, MD) exhibit porosities of 0.34–0.37 and an average oil saturation of 0.09 ± 0.03, in comparison to an independent TGA-based estimate of 0.16. GC-MS fingerprints obtained from an adjacent depressurized core (Core 17P-1, 891.37–891.44 m MD), together with oil pore habit revealed by μ-CT, suggest that the oil is partly native but also includes contributions from mineral oil-based drilling fluid contamination. Further analysis of oil–porewater interactions and flushing tests confirms that native oil saturation in the analyzed samples ranges from 0.04 to 0.08 with an average of approximately 0.06, and the oil is highly immobile. Flow simulations demonstrate that the native oil saturation of ≈0.06 reduces permeability by approximately 50% due to both pore blockage and increased flow-path tortuosity. These findings reveal the potential presence of native oil within the ANS gas hydrate reservoir and suggest that future simulation models may need to account for its impact on permeability to improve long-term performance predictions of gas and water production.
In order to meet the requirements of U.S. Department of Energy (DOE) in DOE O 435.1, Radioactive Waste Management, a composite analysis (CA) must be completed for the Central Plateau at the Hanford Site. The CA requires estimates of fate and transport of radionuclides in the groundwater from multiple sources within the modeling domain. This Environmental Calculation File (ECF) details the application of the Plateau-to-River (P2R) Model (CP-57037, Rev. 3, Model Package Report for the P2R Model: Version 9.1) to predict the flow of groundwater on the Central Plateau for the 10,000-year simulation to support a special analysis associated with the Hanford Site CA. The simulated flow field will support the simulation of fate and transport of contaminants for comparison to the results obtained as part of the CA to evaluate impact of the updated version of the P2R Model.
Abstract An iterative dynamic chemical stiffness removal method (IDCSR) based on quasi-steady-state approximation (QSSA) is proposed. The IDCSR method is built on a previously developed non-iterative method which has proved to work well for small timestep sizes. A novel iterative procedure is designed in IDCSR to enable explicit time integration of stiff chemistry at relatively large timestep sizes relevant to practical reacting flow simulations. The effectiveness of the iterative procedure is first demonstrated with a toy problem and homogeneous auto-ignition with fixed integration step sizes, showing that larger timestep sizes can be allowed for explicit time integration using IDCSR compared with the previous non-iterative method. IDCSR is then compared with existing explicit chemistry solvers for simulations of homogeneous auto-ignition and shows similar or lower computational cost but significantly higher accuracy across a wide range of timestep sizes. IDCSR is further combined with an automatic adaptive time-stepping scheme for simulations of 0-D homogeneous auto-ignition and a 2-D laminar lifted n -dodecane jet flame. For the 0-D auto-ignition simulations, IDCSR is shown to reduce both the error (by 43%–90%) and computational cost (by 6–15 times) compared with existing explicit solvers, while achieving speed-up factors of up to 400 compared with VODE for a wide range of timestep sizes and reaction mechanisms. For the 2-D jet flame simulations, speed-up factors of 15 and 31 for chemistry integration, and 5 and 9 for overall simulation, are achieved by IDCSR compared with CVODE with and without analytic Jacobian, respectively.
We present a generalizable uncertainty quantification (UQ) and visualization framework for lattice Boltzmann method simulations of high Reynolds number vascular flows, demonstrated on a patient-specific stenosed aorta. The framework combines EasyVVUQ for parameter sampling with large-eddy simulation turbulence modeling in HemeLB, and executes ensembles on the Frontier exascale supercomputer. Spatially resolved metrics, including entropy and isosurface-crossing probability, are used to map uncertainty in pressure and wall shear stress fields directly onto vascular geometries. Two sources of model variability are examined: inlet peak velocity and the Smagorinsky constant. Inlet velocity variation produces high uncertainty downstream of the stenosis where turbulence develops, while upstream regions remain stable. Smagorinsky constant variation has little effect on the large-scale pressure field but increases WSS uncertainty in localized high-shear regions. In both cases, the stenotic throat manifests low entropy, indicative of robust identification of elevated WSS. By linking quantitative UQ measures to three-dimensional anatomy, the framework improves interpretability over conventional 1D UQ plots and supports clinically relevant decision-making, with broad applicability to vascular flow problems requiring both accuracy and spatial insight.
We developed and verified a high-performance open-source discrete element method (DEM) solver with simultaneously-supported feedstock-specific interaction models, including bonded-sphere, liquid bridge, cohesion, and non-linear contact models. Our solver uses parallel data structures on hybrid central and graphics processing unit (CPU/GPU) architectures, with favorable strong scaling performance observed for large problem sizes comprised of (100 M particles), and 4X single-node GPU speedup. The particles for corn stover feedstock were conceptualized and calibrated based on experimental measurements and results. Sensitivity analyses demonstrate that the mass flow rate from a wedge hopper is governed primarily by moisture content, friction coefficient, and cohesion energy density. The model is used to reproduce experimentally observed hopper jamming results, highlighting that the experimental no-flow trends can only be achieved by using non-spherical particles, liquid bridge and cohesion models, highlighting the importance of using concurrent feedstock specialized models for the effective representation of biomass material handling problems.
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Wind tunnel method for simulating flow fields around blunt vehicles entering planetary atmospheres without involving high temperatures
Simulating downstream flow blockage doors in bypass air duct of turbofan engine with axial flow fan rotor
Wind farms, particularly offshore clusters, are becoming larger than ever before. Besides influencing the surface wind flow and the inflow for downstream wind farms, large wind farms can trigger atmospheric gravity waves in the inversion layer and the free atmosphere aloft. Wind-farm-induced gravity waves can cause adverse pressure gradients upstream of the wind farm, which contribute to the global blockage effect, and can induce favorable pressure gradients above and downstream of the wind farm that enhance wake recovery. Numerical modeling is a powerful means of studying these wind-farm-induced atmospheric gravity waves, but it comes with the challenge of handling spurious reflections of these waves from domain boundaries. Typically, approaches which employ radiation boundary conditions and forcing zones are used to avoid these reflections. However, the simulation setup of these approaches relies heavily on ad hoc processes. For instance, the widely used Rayleigh damping method requires ad hoc tuning to produce a setup that may only produce satisfactory results for a particular case. To provide more systematic guidance on setting up realistic simulations of atmospheric gravity waves, we conduct a large-eddy simulation (LES) study of flow over a 2D hill and through a wind farm canopy that explores the optimum domain size and damping layer setup depending on the fundamental parameters which determine the flow characteristics. In this work, we only consider linearly stratified conditions (i.e., no inversion layer), thereby focusing on internal gravity waves in the free atmosphere and their reflections from the domain boundaries. This type of flow is governed by a single Froude number, which dictates most of the internal wave properties, such as wavelength, amplitude, and direction. This, in turn, will dictate the optimum domain size and Rayleigh damping layer setup. We find the effective horizontal and vertical wavelengths (the representative wavelengths of the entire wave spectrum) to be the appropriate length scales to size the domain and damping layer thickness, and the optimal Rayleigh damping coefficient scales with the Brunt–Väisälä frequency. Considering Froude numbers seen in wind farm applications, we propose recommendations to limit the reflections to less than 10 % of the total upward-propagating wave energy. Typically, damping is done at the top boundary, but given the non-periodic lateral boundary conditions of practical wind farm simulation domains, we find that damping the inflow–outflow boundaries is of equal importance to damping the top boundary. The Brunt–Väisälä frequency-normalized damping coefficient should be between 1 and 10. The damping layer thickness should be at least one effective vertical wavelength; damping layers exceeding 1.5 times the vertical wavelength are found to be unnecessary. The domain length and height should accommodate at least one effective horizontal and vertical wavelength, respectively. Moreover, Rayleigh damping does not damp the waves completely, and the non-damped energy might accumulate over the simulation time.
Tabulated chemistry models, including the flamelet/progress variable approach, have been successfully used for a variety of turbulent flame simulations. The progress variable describes the progress of reactions in a system and parameterizes a lookup table of thermochemical variables. This approach reduces the cost of simulations, transporting only one scalar (progress variable) instead of the many species mass fractions required for detailed chemistry. Originally developed for low Mach number flame simulations, recent works have focused on extensions of this approach to compressible flames, supersonic combustion, and detonations, with applications such as scramjet combustors and rotating detonation engines. Unlike low Mach simulations, compressible flow simulations require solving the energy transport equation, which is coupled to the equation of state. This leads to additional modeling challenges regarding the thermodynamics and its impact on the chemistry. The validity of modeling assumptions, for example the relationship between energy and temperature, also varies with the combustion regime. The present work provides a detailed assessment of the existing strategies for chemistry tabulation for compressible/supersonic combustion, including detonations. A priori analysis indicates that approximations which are reasonable for weakly compressible flames may break down for shock-induced combustion. Furthermore, the analysis identifies specific assumptions and approximations that do not hold for detonations, emphasizing that care must be taken when applying tabulated chemistry models outside their intended combustion regimes.
Aerodynamic hinge moments for scale model boost stage nozzle of Saturn V launch vehicle during cold flow jet simulation
Fluid transport through fractured geological formations is strongly influenced by the redistribution of solutes at fracture intersections. In this study, we perform detailed numerical simulations of flow and scalar transport within the intersection of two smooth, planar fractures. The analysis focuses on the mixing ratio, the proportion of solute flux exiting along the outlet branch aligned with the primary inlet flow direction, relative to the total solute flux at the outlets. We systematically investigate how the mixing ratio varies with four key parameters: Peclet number, Reynolds number, flow rate ratio between outlet branches, and fracture intersection angle. Results show that the mixing ratio decreases with increasing Peclet number and outlet flow rate ratio, consistent with reduced diffusive spreading and enhanced streamline routing. While low Reynolds numbers have minimal impact, inertial effects at higher Reynolds numbers significantly increase the mixing ratio. Additionally, acute and obtuse intersection angles alter flow partitioning and modify the solute distribution at the outlets. These findings provide a quantitative basis for incorporating physically realistic mixing behavior—intermediate between complete mixing and streamline-following assumptions—into network-scale transport models. The results have direct relevance to subsurface energy systems, including geothermal energy production, carbon sequestration, and contaminant remediation.
Liquid hydrogen nuclear reactor simulator tests - determination of flow iniation with turbine accelerated tank pressure 50 psia
In wildfires, atmospheric turbulence plays a major role in the transfer of turbulent kinetic energy. Understanding how turbulence feeds back into a dynamical system is important, down to the varying small scales of fuel structures (i.e. pine needles, grass). Large eddy simulations (LES) are a common way of numerically representing turbulence. The Smagorinsky model (1963) serves as one of the most studied sub-grid scale representations in LES. In this investigation, the Smagorinsky model was implemented in HIGRAD/FIRETEC, LANL’s coupled fire-atmosphere model. This study was motivated by the need to quantitatively investigate the vorticity budget equation in HIGRAD/FIRETEC. The Smagorinsky turbulent kinetic energy (TKE) was compared to FIRETEC’s 1.5-order TKE eddy-viscosity subgrid-scale model, known as the Linn turbulence model. This was done in simulations of flow over flat terrain with a homogeneous, cuboidal canopy in the center of the domain. Examinations of the modeled vertical TKE profile and turbulent statistics at the leading edge, and throughout the canopy, show that the Smagorinsky model provides comparable results to that of the original closure model posed in FIRETEC.
Non-invasive simulations of coronary hemodynamics have improved clinical risk stratification and treatment outcomes for coronary artery disease, compared to relying on anatomical imaging alone. However, simulations typically use empirical approaches to distribute total coronary flow amongst the arteries in the coronary tree, which ignores patient variability, the presence of disease, and other clinical factors. Further, uncertainty in the clinical data often remains unaccounted for in the modeling pipeline. We present an end-to-end uncertainty-aware pipeline to (1) personalize coronary flow simulations by incorporating vessel-specific coronary flows as well as cardiac function; and (2) predict clinical and biomechanical quantities of interest with improved precision, while accounting for uncertainty in the clinical data. We assimilate patient-specific measurements of myocardial blood flow from clinical CT myocardial perfusion imaging to estimate branch-specific coronary artery flows. Simulated noise in the clinical data is used to estimate the joint posterior distributions of the model parameters using adaptive Markov Chain Monte Carlo sampling. Additionally, the posterior predictive distribution for the relevant quantities of interest is determined using a new approach combining multi-fidelity Monte Carlo estimation with non-linear, data-driven dimensionality reduction. This leads to improved correlations between high- and low-fidelity model outputs. Our framework accurately recapitulates clinically measured cardiac function as well as branch-specific coronary flows under measurement noise uncertainty. We observe substantial reductions in confidence intervals for estimated quantities of interest compared to single-fidelity Monte Carlo estimation and state-of-the-art multi-fidelity Monte Carlo methods. This holds especially true for quantities of interest that showed limited correlation between the low- and high-fidelity model predictions. In addition, the proposed multi-fidelity Monte Carlo estimators are significantly cheaper to compute than traditional estimators, under a specified confidence level or variance. The proposed pipeline for personalized and uncertainty-aware predictions of coronary hemodynamics is based on routine clinical measurements and recently developed techniques for CT myocardial perfusion imaging. The proposed pipeline offers significant improvements in precision and reduction in computational cost.
This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.