Simulation of the Apollo command module aerodynamics at high altitudes.
Aerodynamic measurements on Apollo CM model at hypersonic flow simulating earth orbital reentry trajectory
Boylan, D. E.↗
SEARCH · Engineering Papers
Results for “flow simulation”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Identifying and tracking bubbles and drops in simulations: A toolbox for obtaining sizes, lineages, and breakup and coalescence statistics
Knowledge of bubble and drop size distributions in two-phase flows is important for characterizing a wide range of phenomena, including combustor ignition, sonar communication, and cloud formation. The physical mechanisms driving the background flow also drive the time evolution of these distributions. Accurate and robust identification and tracking algorithms for the dispersed phase are necessary to reliably measure this evolution and thereby quantify the underlying mechanisms in interface-resolving flow simulations. The identification of individual bubbles and drops traditionally relies on an algorithm used to identify connected regions. This traditional algorithm can be sensitive to the presence of spurious structures. A cost-effective refinement is proposed to maximize volume accuracy while minimizing the identification of spurious bubbles and drops. An accurate identification scheme is crucial for distinguishing bubble and drop pairs with large size ratios. The identified bubbles and drops need to be tracked in time to obtain breakup and coalescence statistics that characterize the evolution of the size distribution, including breakup and coalescence frequencies, and the probability distributions of parent and child bubble and drop sizes. An algorithm based on mass conservation is proposed to construct bubble and drop lineages using simulation snapshots that are not necessarily from consecutive time steps. These lineages are then used to detect breakup and coalescence events, and obtain the desired statistics. Accurate identification of large-size-ratio bubble and drop pairs enables accurate detection of breakup and coalescence events over a large size range. Accurate detection of successive breakup and coalescence events requires that the snapshot interval be an order of magnitude smaller than the characteristic breakup and coalescence times to capture these successive events while minimizing the identification of repeated confounding events. Together, these algorithms serve as a toolbox for detailed analysis of two-phase simulations, and enable insights into the mechanisms behind bubble and drop formation and evolution in flows of practical importance.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Feature Selection Clustering and Prototype Placement for Turbulence Data Sets.
Abstract not provided.
Barone, Matthew F.↗
A Large-Eddy Simulation Of Turbulent Flow Over A Backward Facing Step
Large-eddy simulation (LES) was used to study the turbulent flow over a backward facing step. The simulations examine the details of the turbulent flow’s separated and reattaching regions created by the step geometry. An explicit high-order of accuracy/high resolution finite-difference computational fluid dynamics solver was used for both LES and Reynolds-averaged Navier-Stokes (RANS) simulations. The experimental data from the backward facing step experiment of Driver and Seegmiller was used for comparison. Data presented are derived from a RANS simulation using the Spalart-Allmaras turbulence model and LES simulations using the implicit LES assumption on three different grids of varying resolution. The RANS solution agrees with past work. It does an adequate job of predicting the flowfield and reattachment point, but misses details such as the strength of the recirculating region and magnitudes of the Reynolds stresses. The synthetic eddy method was used to provide a turbulent inflow to the quasi-2D LES domains. For the LES, the coarse grid was of insufficient resolution and under-predicted the skin friction coefficient and over-predicted the normal stresses. The finer grid LES results accurately predict the skin friction coefficient and provide more accurate Reynolds stresses. Triple product velocity correlations are also computed from the LES and the finer grid results did a good job predicting the magnitudes of the curves and the shapes were reasonably represented. It is recommended that future work include investigating the turbulent inflow domain length, width of the quasi-2D domain, and additional grid refinement
turbulence↗
A Large-Eddy Simulation Of Turbulent Flow Over A Backward Facing Step
Large-eddy simulation (LES) was used to study the turbulent flow over a backward facing step. The simulations examine the details of the turbulent flow's separated and reattaching regions created by the step geometry. An explicit high-order of accuracy/high resolution finite-difference computational fluid dynamics solver was used for both LES and Reynolds-averaged Navier-Stokes (RANS) simulations. The experimental data from the backward facing step experiment of Driver and Seegmiller was used for comparison. Data presented are derived from a RANS simulation using the Spalart-Allmaras turbulence model and LES simulations using the implict LES assumption on three different grids of varying resolution. The RANS solution agrees with past work. It does an adequate job of predicting the flowfield and reattachment point, but misses details such as the strength of the recirculating region and magnitudes of the Reynolds stresses. The synthetic eddy method was used to provide a turbulent inflow to the quasi-2D LES domains. For the LES, the coarse grid was of insufficient resolution and under-predicted the skin friction coefficient and over-predicted the normal stresses. The finer grid LES results accurately predict the skin friction coefficient and provide more accurate Reynolds stresses. Triple product velocity correlations are also computed from the LES and the finer grid results did a good job predicting the magnitudes of the curves and the shapes were reasonably represented. It is recommended that future work include investigating the turbulent inflow domain length, width of the quasi-2D domain, and additional grid refinement.
turbulence↗
Calibration of drag models for mesoscale simulation of gas–liquid flow through packed beds
Not Available
Finn, Justin R.↗
Sandia's Current Energy Conversion module for Flexible-Mesh Delft3D flow solver v. 1.0
The DOE has funded Sandia National Labs (SNL) to develop an open-source modeling tool to guide the design and layout of marine hydrokinetic (MHK) arrays to maximize power production while minimizing environmental effects. This modeling framework simulates flows through and around a MHK arrays while quantifying environmental responses. As an augmented version of the Dutch company, Deltares’s, environmental hydrodynamics code, Delft3D, SNL-Delft3D-CEC-FM includes a new module that simulates energy conversion (momentum withdrawal) by MHK current energy conversion devices with commensurate changes in the turbulent kinetic energy and its dissipation rate. SNL-Delft3D-CEC-FM modified the Delft3D flexible mesh flow solver, DFlowFM. The Following is a description of Deltares’s open-source code Delft3D from which Delft3D-CEC is built upon. “Delft3D is a world leading 3D modeling suite to investigate hydrodynamics, sediment transport and morphology and water quality for fluvial, estuarine and coastal environments. As per 1 January 2011, the Delft3D flow (FLOW), morphology (MOR) and waves (WAVE) modules are available in open source. The software is used and has proven his capabilities on many places around the world, like the Netherlands, USA, Hong Kong, Singapore, Australia, Venice, etc. The software is continuously improved and developed with innovating advanced modelling techniques as consequence of the research work of our institute and to stay world leading. The FLOW module is the heart of Delft3D and is a multi-dimensional (2D or 3D) hydrodynamic (and transport) simulation program which calculates non-steady flow and transport phenomena resulting from tidal and meteorological forcing on a curvilinear, boundary fitted grid or spherical coordinates. In 3D simulations, the vertical grid is defined following the so-called sigma coordinate approach or Z-layer approach. The MOR module computes sediment transport (both suspended and bed total load) and morphological changes for an arbitrary number of cohesive and non-cohesive fractions. Both currents and waves act as driving forces and a wide variety of transport formulae have been incorporated. For the suspended load this module connects to the 2D or 3D advection-diffusion solver of the FLOW module; density effects may be taken into account. An essential feature of the MOR module is the dynamic feedback with the FLOW and WAVE modules, which allow the flows and waves to adjust themselves to the local bathymetry and allows for simulations on any time scale from days (storm impact) to centuries (system dynamics). It can keep track of the bed composition to build up a stratigraphic record. The MOR module may be extended to include extensive features to simulate dredging and dumping scenarios. For over 30 years Deltares has been in the forefront of these types of combined morphological simulation techniques.”
Chartand, Chris↗
Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network
The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Numerical simulation of fluid flow around a scramaccelerator projectile
Numerical simulations of the fluid motion and temperature distribution around a 'scramaccelerator' projectile are obtained for Mach numbers in the 5-10 range. A finite element method is used to solve the equations of motion for inviscid and viscous two-dimensional or axisymmetric compressible flow. The time-dependent equations are solved explicitly, using bilinear isoparametric quadrilateral elements, mass lumping, and a shock-capturing Petrov-Galerkin formulation. Computed results indicate that maintaining on-design performance for controlling and stabilizing oblique detonation waves is critically dependent on projectile shape and Mach number.
Pepper, Darrell W.↗
Characterizing the Influence of Fracture Density on Network Scale Transport
The topology of natural fracture networks is inherently linked to the structure of the fluid velocity field and transport therein. Here we study the impact of network density on flow and transport behaviors. We stochastically generate fracture networks of varying density and simulate flow and transport with a discrete fracture network model, which fully resolves network topology at the fracture scale. We study conservative solute trajectories with Lagrangian particle tracking and find that as fracture density decreases, solute channelization to large local fractures increases, thereby reducing plume spreading. Furthermore, in sparse networks mean particle travel distance increases and local network features, such as velocity zones where flow is counter to the primary pressure gradient, become increasingly important for transport. As the network density increases, network statistics homogenize and such local features have a reduced impact. We quantify local topological influence on transport behavior with an effective tortuosity parameter, which measures the ratio of total advective distance to linear distance at the fracture scale; large tortuosity values are correlated to slow-velocity regions. Additionally, these large tortuosity, slow-velocity regions delay downstream transport and enhance tailing on particle breakthrough curves. Finally, we predict transport with an upscaled, Bernoulli spatial Markov random walk model and parameterize local topological influences with a novel tortuosity parameter. Furthermore, Bernoulli model predictions improve when sampling from a tortuosity distribution, as opposed to a fixed value as has previously been done, suggesting that local network topological features must be carefully considered in upscaled modeling efforts of fracture network systems.
58 GEOSCIENCES↗
An inviscid three-dimensional analysis of the Space Shuttle main engine hot-gas manifold
A numerical study using an inviscid three-dimensional Lagrangian fluid dynamics code has been conducted as a part of an overall effort to understand the flow behavior in the SSME fuel side hot-gas manifold. The model simulates flow from the high-pressure fuel turbine exit through the transfer ducts, including the effects of swirl, inlet flow symmetry, and presence of straightening vanes and struts; a separate, more-detailed effort is in progress that includes viscosity and turbulence effects. The simplified model presented is divided into two parts, the first includes the 180-degree turnaround duct downstream of the turbine exit and the spherical fuel bowl section, while the second models the three transfer ducts. The two parts of the model are coupled together with the interface conditions being updated through iteration. Results indicate that a transverse pressure differential of 165 psi would be imposed on the turbine exit and that unstable flow separation occurs around the vanes, struts, and within the transfer ducts. The three transfer ducts show a mass flux split of approximately 41, 21, and 38 percent. Results to date are encouraging that certain flow characteristics can be usefuly represented using a relatively coarse grid inviscid code.
Liang, P. Y.↗
Toward particle-resolved accuracy in Euler–Lagrange simulations of multiphase flow using machine learning and pairwise interaction extended point-particle (PIEP) approximation
This study presents two different machine learning approaches for the modeling of hydrodynamic force on particles in a particle-laden multiphase flow. Results from particle-resolved direct numerical simulations (PR-DNS) of flow over a random array of stationary particles for eight combinations of particle Reynolds number (Re) and volume fraction (φ) are used in the development of the models. The first approach follows a two-step process. In the first flow prediction step, the perturbation flow due to a particle is obtained as an axisymmetric superposable wake using linear regression. In the second force prediction step, the force on a particle is evaluated in terms of the perturbation flow induced by all its neighbors using the generalized Faxén form of the force expression. In the second approach, the force data on all the particles from the PR-DNS simulations are used to develop an artificial neural network (ANN) model for direct prediction of force on a particle. Due to the unavoidable limitation on the number of fully resolved particles in the PR-DNS simulations, direct force prediction with the ANN model tends to over-fit the data and performs poorly in the prediction of test data. In contrast, due to the millions of grid points used in the PR-DNS simulations, accurate flow prediction is possible, which then allows accurate prediction of particle force. This hybridization of multiphase physics and machine learning is particularly important, since it blends the strength of each, and the resulting pairwise interaction extended point-particle model cannot be developed by either physics or machine learning alone.
42 ENGINEERING↗
Simulation of Fluid Flow and Collection Efficiency at Low Stokes Number
Numerical simulations of fluid flow and collection efficiency for a Science Engineering Associates (SEA) Robust probe are presented. The cases cover a range of freestream velocity from 10-135 m/s, and particle sizes from 1-200 µm. This combination produces results for Stokes numbers in the range of 0.07-1092. A blended modified Stokes (defined in the paper) is introduced which covers a range of 0.07-146. Of particular interest is the collection efficiency of the sensing element (a forward-facing half-pipe), Etot, in the low Stokes number regime. It was observed that the collection efficiency of the sensing area approached one for large Stokes numbers (>100) as expected. As blended modified Stokes number approached one, the Etot dropped to approximately 0.5. By blended modified Stokes numbers of 0.3, the Etot has dropped to essentially zero. Plotting collection versus blended modified Stokes number, the different freestream velocities collapsed quite well on to a single curve. The paper presents a discussion on the flowfield and local collection efficiency, beta, in an effort to explain the predictions. Of particular interest is what is essentially a shadow zone in plain sight. As the Stokes number is reduced, the local beta along the centerline of the half-pipe goes to zero. This occurs at Stokes numbers where the overall collection, Etot, is still appreciable. An empirical relation was developed to allow for corrected collection values at low Stokes numbers. The correlation remains within 2% for blended modified Stokes numbers above 1, and within 6% for all cases.
Instrumentation↗
Simulation of Fluid Flow and Collection Efficiency at Low Stokes Number
Numerical simulations of fluid flow and collection efficiency for a Science Engineering Associates (SEA) Robust probe are presented. The cases cover a range of freestream velocity from 10-135 m/s, and particle sizes from 1-200 µm. This combination produces results for Stokes numbers in the range of 0.07-1092. A blended modified Stokes (defined in the paper) is introduced which covers a range of 0.07-146. Of particular interest is the collection efficiency of the sensing element (a forward-facing half-pipe), Etot, in the low Stokes number regime. It was observed that the collection efficiency of the sensing area approached one for large Stokes numbers (>100) as expected. As blended modified Stokes number approached one, the Etot dropped to approximately 0.5. By blended modified Stokes numbers of 0.3, the Etot has dropped to essentially zero. Plotting collection versus blended modified Stokes number, the different freestream velocities collapsed quite well on to a single curve. The paper presents a discussion on the flowfield and local collection efficiency, beta, in an effort to explain the predictions. Of particular interest is what is essentially a shadow zone in plain sight. As the Stokes number is reduced, the local beta along the centerline of the half-pipe goes to zero. This occurs at Stokes numbers where the overall collection, Etot, is still appreciable. An empirical relation was developed to allow for corrected collection values at low Stokes numbers. The correlation remains within 2% for blended modified Stokes numbers above 1, and within 6% for all cases.
Instrumentation↗
The simulation of hypersonic flow using a zonal approach
The development of a Navier-Stokes code for the simulation of hypersonic flow will be described. The code uses a zonal philosophy which helps alleviate the grid generation problem for complex configurations and allows an efficient solution procedure. The code has been used to simulate equilibrium flow past a simple generic hypersonic configuration. Validation of the code on a blunt cone has also been conducted.
Flores, Jolen↗
Quantifying and reducing model-form uncertainties in Reynolds-averaged Navier–Stokes simulations: A data-driven, physics-informed Bayesian approach
Despite their well-known limitations, Reynolds-Averaged Navier–Stokes (RANS) models are still the workhorse tools for turbulent flow simulations in today's engineering analysis, design and optimization. While the predictive capability of RANS models depends on many factors, for many practical flows the turbulence models are by far the largest source of uncertainty. As RANS models are used in the design and safety evaluation of many mission-critical systems such as airplanes and nuclear power plants, quantifying their model-form uncertainties has significant implications in enabling risk-informed decision-making. In this work we develop a data-driven, physics-informed Bayesian framework for quantifying model-form uncertainties in RANS simulations. Uncertainties are introduced directly to the Reynolds stresses and are represented with compact parameterization accounting for empirical prior knowledge and physical constraints (e.g., realizability, smoothness, and symmetry). An iterative ensemble Kalman method is used to assimilate the prior knowledge and observation data in a Bayesian framework, and to propagate them to posterior distributions of velocities and other Quantities of Interest (QoIs). We use two representative cases, the flow over periodic hills and the flow in a square duct, to evaluate the performance of the proposed framework. Both cases are challenging for standard RANS turbulence models. Simulation results suggest that, even with very sparse observations, the obtained posterior mean velocities and other QoIs have significantly better agreement with the benchmark data compared to the baseline results. At most locations the posterior distribution adequately captures the true model error within the developed model form uncertainty bounds. The framework is a major improvement over existing black-box, physics-neutral methods for model-form uncertainty quantification, where prior knowledge and details of the models are not exploited. This approach has potential implications in many fields in which the governing equations are well understood but the model uncertainty comes from unresolved physical processes. - Highlights: • Proposed a physics–informed framework to quantify uncertainty in RANS simulations. • Framework incorporates physical prior knowledge and observation data. • Based on a rigorous Bayesian framework yet fully utilizes physical model. • Applicable for many complex physical systems beyond turbulent flows.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Computational study of the bubbling-to-slugging transition in a laboratory-scale fluidized bed
Here, we report results from a computational study of the transition from bubbling to slugging in a laboratory-scale fluidized-bed reactor with Geldart Group B glass particles. For simulating the three-dimensional fluidized-bed hydrodynamics, we employ MFiX, a widely studied multi-phase flow simulation tool, that uses a two-fluid Eulerian-Eulerian approximation of the particle and gas dynamics over a range of gas flows. We also utilize a previously published algorithm to generate bubble statistics that can be correlated with pressure fluctuations to reveal previously unreported details about the stages through which the hydrodynamics progress during the bubbling-to-slugging transition. We expect this new information will lead to improved approaches for on-line reactor diagnostics, as well as new approaches for validating the results of computational fluidized-bed simulations with experimental measurements.
42 ENGINEERING↗
DEMONSTRATION OF A DATA-DRIVEN PHYSICS-BASED APPROACH FOR COMPUTATIONALLY EFFICIENT CFD PREDICTION OF TWO-PHASE BUBBLY FLOW
To realize efficient computational fluid dynamics (CFD) prediction of two-phase flow, a multi-scale physics-guided data-driven approach, Feature Similarity Measurement (FSM) technique was developed for error estimation in two-phase flow simulation using coarse-mesh CFD, to achieve a comparable accuracy as fine-mesh simulations with fast-running feature. By defining physics-guided parameters and variable gradients as physical features, FSM has the capability to capture the underlying local patterns in the coarse-mesh CFD simulation. Massive low-fidelity data and respective high-fidelity data are used to explore the underlying information relevant to the main simulation errors and the effects of phenomenological scaling. By learning from previous simulation data, a surrogate model using deep feedforward neural network (DFNN) can be developed and trained to estimate the simulation error of coarse-mesh CFD. In a demonstration case of two-phase bubbly flow, the DFNN model well captured and corrected the unphysical “peaks” in the velocity and void fraction profiles near the wall in the coarse-mesh configuration, even for extrapolative predictions. The research documented supports the feasibility of the physics-guided deep learning methods for coarse mesh CFD simulations which has a potential for the efficient industrial design.
42 ENGINEERING↗