SEARCH · Engineering Papers

Results for “CFD”

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.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,729 records · Page 96

All sources NASA NTRS DOE OSTI

NASA NTRS2019

Generation of a Reduced-Order Model of an Unmanned Combat Air Vehicle Using Indicial Response Functions

Within the NATO STO AVT-251 Task Group, a generic Unmanned Combat Air Vehicle (UCAV) planform was redesigned based on requirements derived from parts of the flight envelope of the defined mission. Because of the lambda-shape planform and associated flow phenomena including shocks and vortices, the aerodynamic design process relies heavily on high-fidelity predictions from computational fluid dynamics (CFD). These simulations enable accurate flight dynamics predictions, allowing for the identification of any potential performance issues early in the design process. Potentially, a complete flight dynamics model can be derived from CFD calculations provided that the underlying reduced order model (ROM) captures the underlying physics contained within the CFD results. This work investigates the creation of a nonlinear ROM using indicial response functions. The response functions are obtained using a grid motion approach that separates the effects of angle of attack and pitch rate. This approach is demonstrated using three different CFD codes from various organizations: ENSOLV at Royal Netherlands Aerospace Centre, USM3D at NASA Langley Research Center, and Kestrel at the United States Air Force Academy. ROM predictions were generated for a manoeuver resembling a low-speed pull-up. The predictions are found to be sensitive to the quality of the steady-state solutions from which step-response calculations were started, as well as the convergence of the step responses themselves. Nevertheless, the indicial response method is shown to provide aerodynamic predictions with acceptable accuracy including transient effects, and as such is a promising method for computationally efficient flight dynamics predictions.

Michel P.C. van Rooij↗

NASA NTRSDate not supplied

Improvements in Simulating a Mach 0.80 Transonic Truss-Braced Wing Configuration using the Spalart-Allmaras and k-ω SST Turbulence

Teams from the NASA Ames Research Centers (ARC) and Langley Research Center (LaRC) have been working on validating their computational fluid dynamics (CFD) results for the Boeing Mach 0.80 Transonic Truss-Braced Wing (TTBW) configuration. Experimental data used for the validation were gathered from a test conducted of a 4.5% scale Mach 0.80 TTBW model in the NASA Ames Research Center 11-by 11-Foot Transonic Wind Tunnel. The CFD simulations were initially run with both LAVA and USM3D Mixed Element solvers utilizing the Spalart-Allmaras (SA) turbulence model. A discrepancy was observed between CFD and experimental loads and moments ranging on average from 0.047 to 0.063 for lift coefficient, 16.5 to 27 in drag counts, and -0.02 to -0.032 for pitching moment, varying with the solver used. With introducing the refactored version of LAVA these values dropped to 0.034 to 0.047 for lift coefficient, 7.74 to 16.5 in drag counts, and lastly -0.012 to -0.02 for pitching moment. Based on findings from Boeing, who observed an improved comparison to experimental data when using the k-ω Shear Stress Transport (SST) turbulence model, the NASA teams conducted simulations with SST to investigate turbulence modeling sensitivities. OVERFLOW and USM3D V6 solvers were used for the comparison of SST and SA simulations. CFD results using the SST turbulence model demonstrate an improvement in matching with experimental CL values, reducing the discrepancies seen by 0.021-0.055 (62-93% reduction in discrepancy respective to the OVERFLOW and USM3D V6). The SST model has varying effects on CD, based on the solver / grid paradigm; for USM3D this prediction is only improved at higher angles of attack (above the cruise design point) resulting in a reduced discrepancy of 8.8–25 drag counts However, at the mid-to-low angles of attack, SST increases the CD discrepancy by 4-55 counts, increasing at lower angles of attack. For OVERFLOW this discrepancy only exists at the low angles of attack and maxes out at 10 counts of drag. Above α = 1.5° OVERFLOW SST reduces discrepancy by 9.3 drag counts on average.

AATT↗

NASA NTRSDate not supplied

Numerical Prediction of Heat Transfer Coefficients during Xenon Tank Fill in Microgravity

NASA’s Gateway will serve as an orbital outpost to enable sustained human lunar exploration and facilitate access to destinations beyond the Moon. To maintain sustainable deep space missions, on-orbit propellant transfer and refueling must be realized. One of Gateway’s critical components, the Power and Propulsion Element (PPE), will use solar electric propulsion to perform attitude control maneuvers and orbit transfers. Xenon will be used as the propellant for the PPE and stored in a composite overwrapped pressure vessel (COPV). During the propellant refueling process, xenon will compress causing the fluid and tank walls to warm. There is concern that the bond between the tank liner and composite may degrade at elevated temperatures, along with the possibility of the tank exceeding design pressure. While on-orbit testing is being proposed, numerical models are being used to predict the thermal response and reduce risk during a refueling operation in microgravity where natural convection is diminished. Accurate numerical models may be used to inform decisions for follow-on testing, or design and operation of refueling architectures. Challenges of modeling xenon tank fill in microgravity include non-ideal gas behavior, operating near the fluid’s critical point, and lack of experimental flight data. This study presents a computational fluid dynamics (CFD) model with conjugate heat transfer that is used to predict averaged heat transfer coefficients during tank filling. At the anticipated injection flow rates, the CFD results showed that forced convection dominates in microgravity. Initial results from a multi-node Thermal Desktop model that assumed a purely conducting fluid (Nu=1) over-predicted the CFD fluid temperature and pressure, while a similar model allowing for natural convection under-predicted. The heat transfer coefficients predicted by CFD were implemented into the multi-node model, and the COPV thermal response from the two computational approaches were compared with good agreement.

Heat Transfer↗

NASA NTRSDate not supplied

Conceptual Design of Propulsors for the SUSAN Electrofan Transport Aircraft

Conceptual designs of the propulsor modules for the SUSAN electro-fan aircraft are sought after. Shaft power requirement is reduced by using boundary layer ingesting propulsion technology. There are several obstacles to designing feasible BLI propulsion systems such as inlet distortion, flow blockage from ingesting low-momentum flow, strong coupling between airframe and propulsion systems. Thus, the high fidelity CFD tool is indispensable to assess the performance of the propulsion systems and evaluate the inlet profiles during the design process. Consequently, the CFD data is used to update the inputs for the NPSS model from the initial stage of the system design. Hence, the inlet sizing, fan design, and estimation of the power saving are carried out. Various installation concepts of the mail-slot nacelle, such as under-/over-wing and trailing edge configurations, are investigated for the wing-mounted turbo-electric distributed propulsor module, and their power-saving is evaluated. As for the tail-mounted turbofan engine, the fan diameter and inlet captured area is determined based on the CFD profiles. An appropriate area ratio of the bypass and core ducts is derived from meeting the target bypass ratio from the system design. The baseline fan stage is analyzed by full annulus URANS CFD to assess the efficiency penalty due to the ingested pressure and swirl distortions.

SUSAN Electro-Fan Aircraft↗

NASA NTRSDate not supplied

Conceptual Design of Propulsors for the SUSAN Electrofan Transport Aircrafts

Electric Aircraft↗

NASA NTRSDate not supplied

Recent Advancements in the PATO Material Response Code

Introduction: Predicting the complicated multiphysics phenomena during atmospheric entry requires high-fidelity modeling tools to refine estimates of mission risks during entry. To this end, new capabilities are being added to the Porous-material Analysis Toolbox based on OpenFOAM (PATO). PATO is an open-source software for Computational Material Response (CMR) of reactive porous materials submitted to high-temperature environments. The objective of this work is to highlight current efforts to add to and improve upon the modeling capabilities of PATO. These include efforts to loosely couple PATO with other discipline specialized codes including hypersonic Computational Fluid Dynamics (CFD), to assess the interaction effects between pyrolysis gas blowing and the boundary layer, and Computational Solid Mechanics (CSM), to address modeling of mechanical erosion. Other refinements include surface phenomena modeling capabilities to address the effects of silicone-based coatings applied to the TPS during flight preparation, and a unified multiphase solver for a mixed porous-material and plain-fluid domain. Coupling CMR with CFD (CMR/CFD): A loose coupling between PATO and the Data Parallel Line Relaxation (DPLR) CFD code has been achieved by making use of a blowing boundary condition at the heatshield surface available in DPLR. Starting with heat flux estimates with no pyrolysis gas blowing at the surface, blowing gases are computed by the CMR and passed to the CFD such that aerothermal properties of the environment can be recomputed for a new CMR computation. This leads to an iterative process which is supplemented with an estimate of the radiative heat flux using the Nonequilibrium air radiation (NEQAIR) program. The entire iterative process is illustrated in Figure 1. This coupling strategy has been utilized in computing the MSL material response. The goal is to compare the coupled CMR/CFD results with material response results obtained using traditional blowing corrections. Coupling CMS with CMR: A mechanical erosion model is currently being implemented in PATO to account for the additional mass removal induced by high shear conditions. The modeling process at each timestep consists of updating the mechanical properties as a function of temperature and computing the stress tensor and displacement fields of the material. Then, a failure criteria model determines the regions in which the stress exceeds the ultimate strength values resulting in mesh movement to account for mass removal. This model allows the material response simulation to compute the recession due to both oxidation and shear-induced erosion. The model is demonstrated by computing material response of sphere-cone arc jet samples. Surface Modeling Capabilities: NuSil, a silicone-based coating, was sprayed onto the MSL and Mars 2020 heatshields to mitigate shedding of phenolic dust. To better understand the effects of the NuSil coating on the material response, a novel model has been implemented in PATO. In this model, the equilibrium of the charred NuSil surface is modeled as pure silica, and a constant offset, inspired by the classical spallation model, is added to the the char blowing rate and wall enthalpy to reproduce HyMETS experimental results. The model has also been used to estimate the 3D material response of the MSL heatshield. Unified Solver: In addition to the iterative loose coupling approach mentioned above, a multiphase unified solver is being developed to couple the environment (plain-fluid phase) and the porous-material phase. The solver is based on the volume averaged conservation of mass, momentum, and energy for the macroscale with closure models which include microscale effects through effective physicochemical properties. The unified solver has been used to compute flow through a porous plug and solve the Beavers and Joseph problem. Since the strong coupling between phases is inherent to this solver, modeling assumptions present in other coupling methods of material response are mitigated. This strategy also makes it feasible to capture the competition between surface and volume ablation in the same computational domain, which is usually not possible with other coupling approaches.

Material Response↗

NASA NTRSDate not supplied

Recent Advancements in the PATO Material Response Code

Introduction: Predicting the complicated multiphysics phenomena during atmospheric entry requires high-fidelity modeling tools to refine estimates of mission risks during entry. To this end, new capabilities are being added to the Porous-material Analysis Toolbox based on OpenFOAM (PATO) [1,2,3]. PATO is an open-source software for Computational Material Response (CMR) of reactive porous materials submitted to high-temperature environments. The objective of this work is to highlight current efforts to add to and improve upon the modeling capabilities of PATO. These include efforts to loosely couple PATO with other discipline specialized codes including hypersonic Computational Fluid Dynamics (CFD), to assess the interaction effects between pyrolysis gas blowing and the boundary layer, and Computational Solid Mechanics (CSM), to address modeling of mechanical erosion. Other refinements include surface phenomena modeling capabilities to address the effects of silicone-based coatings applied to the TPS during flight preparation, and a unified multiphase solver for a mixed porous-material and plain-fluid domain. Coupling CMR with CFD (CMR/CFD): A loose coupling between PATO and the Data Parallel Line Relaxation (DPLR) [4] CFD code has been achieved by making use of a blowing boundary condition at the heatshield surface available in DPLR. Starting with heat flux estimates with no pyrolysis gas blowing at the surface, blowing gases are computed by the CMR and passed to the CFD such that aerothermal properties of the environment can be recomputed for a new CMR computation. This leads to an iterative process which is supplemented with an estimate of the radiative heat flux using the Nonequilibrium air radiation (NEQAIR) [5] program. The entire iterative process is illustrated in Figure 1. This coupling strategy has been utilized in computing the MSL material response. The goal is to compare the coupled CMR/CFD results with material response results obtained using traditional blowing corrections. Coupling CMS with CMR: A mechanical erosion model is currently being implemented in PATO to account for the additional mass removal induced by high shear conditions. The modeling process at each timestep consists of updating the mechanical properties as a function of temperature and computing the stress tensor and displacement fields of the material. Then, a failure criteria model determines the regions in which the stress exceeds the ultimate strength values resulting in mesh movement to account for mass removal. This model allows the material response simulation to compute the recession due to both oxidation and shear-induced erosion. The model is demonstrated by computing material response of sphere-cone arc jet samples. Surface Modeling Capabilities: NuSil, a silicone-based coating, was sprayed onto the MSL and Mars 2020 heatshields to mitigate shedding of phenolic dust. To better understand the effects of the NuSil coating on the material response, a novel model has been implemented in PATO. In this model, the equilibrium of the charred NuSil surface is modeled as pure silica, and a constant offset, inspired by the classical spallation model, is added to the the char blowing rate and wall enthalpy to reproduce HyMETS experimental results. The model has also been used to estimate the 3D material response of the MSL heatshield [6]. Unified Solver: In addition to the iterative loose coupling approach mentioned above, a multiphase unified solver is being developed to couple the environment (plain-fluid phase) and the porous-material phase. The solver is based on the volume averaged conservation of mass, momentum, and energy for the macroscale with closure models which include microscale effects through effective physicochemical properties. The unified solver has been used to compute flow through a porous plug and solve the Beavers and Joseph problem [7]. Since the strong coupling between phases is inherent to this solver, modeling assumptions present in other coupling methods of material response are mitigated. This strategy also makes it feasible to capture the competition between surface and volume ablation in the same computational domain, which is usually not possible with other coupling approaches.

Thermal Protection Systems↗

NASA NTRSDate not supplied

Time-Accurate Computational Fluid Dynamics Analysis of a Supersonic Turbine to Assess Structural Mode Excitation

Gas turbines can experience significant unsteady loading during operation which can cause damage such as blade cracking or bearing wear. While computational fluid dynamics (CFD) modeling of full three-dimensional gas turbine flow to verify meanline design performance is standard practice within the industry, the use of time-accurate CFD makes it possible to assess the unsteady loading which can be especially problematic at operating conditions where unsteady fluid loading aligns with structural modes. The Fluid Dynamics branch (ER42) at NASA Marshall Space Flight Center has developed a time-accurate CFD analysis methodology using the unstructured, density-based, Loci-CHEM solver which is capable of simulating the relative motion required for turbomachinery applications through sliding interface meshes. The highly-parallel unstructured solver allows for full three-dimensional, time-accurate simulation of turbines including upstream and downstream components such as manifolds, nozzles, and stators, any of which could excite a turbine structural mode. Recently, Loci-CHEM was used to analyze the FASTRAC supersonic turbine to assess the loading on the rotor. Overall performance and time-averaged loading on the rotor was computed, including the axial force over the entire rotor disk which is required for force balance calculations on the turbopump. The FASTRAC turbine has supersonic nozzles upstream of the rotor that result in significant unsteady loading on the rotor blades at multiples of the nozzle pass frequency. The unsteady blade pressure loading was decomposed into the frequency domain for direct use in structural forced response analysis. Utilizing this type of CFD analysis coupled with structural analysis has proven to be a valuable tool in identifying structural modes that are being excited by unsteady fluid loading.

M.V. Fischels↗

NASA NTRSDate not supplied

Time-Accurate Computational Fluid Dynamics Analysis of a Supersonic Turbine to Assess Structural Mode Excitation

M V Fischels↗

NASA NTRSDate not supplied

Modeling Autogenous Pressurization and Draining of a Cryogenic Storage Tank in Normal Gravity

A two-phase CFD model for autogenous pressurization and draining of a cryogenic storage tank is presented using both the Sharp Interface and Volume-Of-Fluid (VOF) approaches for capturing the front and the associated interfacial heat, mass and momentum transfer between the liquid and vapor regions. Both models are validated against data provided by the Cryogenic Propellant Storage and Transfer (CPST) Engineering Development Unit (EDU) experiment. The results of the autogenous pressurization are presented first, focusing on the phase change and turbulence effects on the tank pressure and temperature predictions. Both the Sharp Interface (SI-CFD) and VOF (VOF-CFD) multiphase models predict tank pressure during pressurization within 3% of the measured values. The sensitivity of key physical and numerical parameters of the problem are tested using the Sharp Interface model. Effects of the accommodation coefficient (AC), and the computational grid structure are studied. The second part of this paper is devoted to validating the SI-VOF model, with an enhanced capability of moving the liquid-vapor interface, against the draining segment of the EDU experiment. The VOF-CFD model was also used to simulate tank draining and its results were compared with the results of the Sharp Interface model with the moving interface. Both models predict tank pressure during draining within 3.5% of the measured values. Pressure decrease rate is underpredicted by both models during the first 100 seconds of draining but matches the experimental rate for the rest of the simulation.

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

Modeling Autogenous Pressurization and Draining of a Cryogenic Storage Tank in Normal Gravity

A two-phase CFD model for autogenous pressurization and draining of a cryogenic storage tank is presented using both the Sharp Interface and Volume-Of-Fluid (VOF) approaches for capturing the front and the associated interfacial heat, mass and momentum transfer between the liquid and vapor regions. Both models are validated against data provided by the Cryogenic Propellant Storage and Transfer (CPST) Engineering Development Unit (EDU) experiment1. The results of the autogenous pressurization are presented first, focusing on the phase change and turbulence effects on the tank pressure and temperature predictions. Both the Sharp Interface (SI-CFD) and VOF (VOF-CFD) multiphase models predict tank pressure during pressurization within 3% of the measured values. The sensitivity of key physical and numerical parameters of the problem are tested using the Sharp Interface model. Effects of the accommodation coefficient (AC), and the computational grid structure are studied. The second part of this paper is devoted to validating the SI-VOF model, with an enhanced capability of moving the liquid-vapor interface, against the draining segment of the EDU experiment. The VOF-CFD model was also used to simulate tank draining and its results were compared with the results of the Sharp Interface model with the moving interface. Both models predict tank pressure during draining within 3.5% of the measured values. Pressure decrease rate is underpredicted by both models during the first 100 seconds of draining but matches the experimental rate for the rest of the simulation.

Computational Fluid Dynamics↗

NASA NTRSDate not supplied

Automated Unstructured Grid Adaptation on a Strut Fuel Injector at Hypervelocity Flow Conditions

Computational fluid dynamics (CFD) analysis is presented with the use of an automated unstructured grid adaptation tool on a strut fuel injector at hypervelocity flow conditions. The analysis was carried out with the VULCAN-CFD solver using Reynolds-averaged simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). The current work utilizes an automated grid adaptation tool recently implemented into VULCAN-CFD, and explores this tool’s ability to solve high-speed mixing problems. Simulation results obtained using the unstructured adaptive grid approach are compared to those on a user generated structured grid. These results are evaluated by analyzing how efficiently comparable fidelity results are obtained from both adapted and structured simulations. In addition, two adaptation strategies were used to explore the impact on the final solution. In the current work, the unstructured grid adaptation tool automatically generates unstructured grids and performs adaptation of the grid based on a Hessian error estimate of a specified flow field parameter. Multiple adaptations were executed using each run strategy with the one-dimensional values of the mixing efficiency used to determine grid convergence and for comparison with the structured grid simulation results. It was found that the unstructured adaptive grid simulations were able to produce results that matched closely with those on structured grids using far fewer grid cells, and thus, requiring far less computational time to reach the solution. It was also discovered that the adaptation run strategy influenced the total number of grid cells and the efficiency with which a final grid-adapted solution was reached. Overall, the investigation demonstrated that the automated unstructured grid adaptation tool implemented in VULCAN-CFD is capable of accurately and efficiently solving complex highspeed mixing problems using only a fraction of the grid cells required to obtain comparable results using a user-generated structured grid.

grid adaptation↗

NASA NTRS2020

Implicit Large-Eddy Simulations of Hot and Cold Supersonic Jets in Loci-CHEM

Turbulence is a critical factor in most aero- and propulsion-related flows. For many years, the industry-standard approach to incorporating the effect of turbulence in computational fluid dynamics (CFD)calculations has been the Reynolds Averaged Navier Stokes (RANS) modeling approach. RANS implicitly time-averages turbulent motion, and models its effect on the mixing of species, momentum, and energy in a flow field. The approach is economical and often allows a CFD calculation to proceed to steady-state. However, it makes several simplifying assumptions, and the results will only be as good as the RANS turbulence model. Meanwhile, the last two decades have witnessed rapid and explosive growth in computational power, the development of CFD codes which scale well over thousands of processors, and the refinement of unstructured grid-generation tools which facilitate rapid surface and volume gridding of complex geometries. CFD engineering calculations of 108– 109 finite-volume cells have become routine. Thus, large eddy simulation (LES), which attempts to directly resolve the unsteady motion of the largest scales of turbulence, is increasingly of interest for many fluids engineering problems. Additionally, hybrid RANS-LES approaches such as Spalart’s Detached Eddy Simulation (DES), which attempt to marry the best strengths of both methods, are in active use and development. Because it seeks to accurately resolve the unsteady motion of the largest scales of turbulence, LES places certain requirements on the numerical method used to discretize and advance the Navier Stokes equations. In particular, LES requires high time accuracy and minimal intrinsic numerical dispersion and dissipation over a wide range of length scales.

C I Morris↗

NASA NTRSDate not supplied

Automated Unstructured Grid Adaptation on a Strut Fuel Injector at Hypervelocity Flow Conditions

Computational fluid dynamics (CFD) analysis is presented with the use of an automated unstructured grid adaptation tool on a strut fuel injector at hypervelocity flow conditions. The analysis was carried out with the VULCAN-CFD solver using Reynolds-averaged simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). The current work uses an automated grid adaptation tool recently implemented in VULCAN-CFD, and explores this tool’s ability to solve highspeed mixing problems. Simulation results obtained using the unstructured adaptive grid approach are compared to those on a user-generated structured grid. These results are evaluated by analyzing how efficiently comparable fidelity results are obtained from both adapted and structured simulations. In addition, two adaptation strategies were used to explore the impact on the final solution. In the current work, the unstructured grid adaptation tool automatically generates unstructured grids and performs adaptation of the grid based on a Hessian error estimate of a specified flowfield parameter. Multiple adaptations were executed using each run strategy with the one-dimensional values of the mixing efficiency used to determine grid convergence and for comparison with the structured grid simulation results. It was found that the unstructured adaptive grid simulations were able to produce results that matched closely with those on structured grids using far fewer grid cells, and thus, requiring far less computational time to reach the solution. It was also discovered that the adaptation run strategy influenced the total number of grid cells and the efficiency with which a final grid-adapted solution was reached. Furthermore, motivated by the grid convergence index (GCI) used for structured grid simulations, a grid convergence estimate (GCE) was developed and demonstrated for the grid adaptation. Overall, the investigation demonstrated that the automated unstructured grid adaptation tool implemented in VULCAN-CFD is capable of accurately and efficiently solving complex high-speed mixing problems with only a fraction of the grid cells required to obtain comparable results on a user-generated structured grid.

hypersonics↗

NASA NTRSDate not supplied

Automated Unstructured Grid Adaptation on a Strut Fuel Injector at Hypervelocity Flow Conditions

Computational fluid dynamics (CFD) analysis is presented with the use of an automated unstructured grid adaptation tool on a strut fuel injector at hypervelocity flow conditions. The analysis was carried out with the VULCAN-CFD solver using Reynolds-averaged simulations (RAS). The hypervelocity flow conditions match the high Mach number flow of the experiments conducted as part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). The current work uses an automated grid adaptation tool recently implemented in VULCAN-CFD, and explores this tool’s ability to solve highspeed mixing problems. Simulation results obtained using the unstructured adaptive grid approach are compared to those on a user-generated structured grid. These results are evaluated by analyzing how efficiently comparable fidelity results are obtained from both adapted and structured simulations. In addition, two adaptation strategies were used to explore the impact on the final solution. In the current work, the unstructured grid adaptation tool automatically generates unstructured grids and performs adaptation of the grid based on a Hessian error estimate of a specified flowfield parameter. Multiple adaptations were executed using each run strategy with the one-dimensional values of the mixing efficiency used to determine grid convergence and for comparison with the structured grid simulation results. It was found that the unstructured adaptive grid simulations were able to produce results that matched closely with those on structured grids using far fewer grid cells, and thus, requiring far less computational time to reach the solution. It was also discovered that the adaptation run strategy influenced the total number of grid cells and the efficiency with which a final grid-adapted solution was reached. Furthermore, motivated by the grid convergence index (GCI) used for structured grid simulations, a grid convergence estimate (GCE) was developed and demonstrated for the grid adaptation. Overall, the investigation demonstrated that the automated unstructured grid adaptation tool implemented in VULCAN-CFD is capable of accurately and efficiently solving complex high-speed mixing problems with only a fraction of the grid cells required to obtain comparable results on a user-generated structured grid.

hypersonics↗

NASA NTRSDate not supplied

Deep Neural Network Based Convergence Classification for Computational Fluid Dynamics

A supervised deep learning approach is coupled with heuristic convergence criteria to construct a classification model for detecting the completion (convergence) of computational fluid dynamics (CFD) simulations. Heuristic convergence criteria alone are not always sufficient and more complex decisions are often left to a human analyst. The proposed approach leverages heuristic convergence criteria as well as two deep neural network (DNN) models, one binary and one multi-class, to improve the efficiency and consistency of convergence classification across a wide range of flight regimes. The DNN models presented are each trained on a subset of ascent aerodynamic CFD simulations for NASA’s Space Launch System and were produced using NASA’s unstructured Navier-Stokes solver FUN3D. Individual solutions are analyzed intermittently and are classified as sufficiently converged, further iterations required, or switch from steady Reynolds Averaged Navier-Stokes (RANS) to unsteady RANS CFD based on the iterative histories of four aerodynamic coefficients. The implemented classification model is shown to produce solutions that closely correlate to solutions produced by a human analyst. This work lays groundwork for expanding the capabilities of DNNs for automating and improving more of the CFD process.

SLS↗

NASA NTRSDate not supplied

A Wall-Distance Method for Turbulence Modeling

The distance from a grid point to the closest wall surface, wall distance, is a funda- mental quantity in turbulence modeling. Efficiency of wall-distance calculations has become more critical as the size of computational grids has significantly increased in recent years. This paper reports on an initial implementation of a new search-based wall-distance method that is suitable for general unstructured computational fluid dynamics (CFD) grids and tailored for requirements specific for turbulence modeling. The method represents a two-step approach to calculate the wall distance. In the first step, the wall distance is approximated for each grid point as the minimum distance from this point to a vertex of a triangular face at the wall. The point-to-vertex distance calculation is relatively inexpensive but may lead to a significant error in the wall-distance ap- proximation, especially for grid points near the wall. In the second step, for grid points located within a predefined distance ( threshold ) from the wall, the wall distance is computed as the minimum distance to wall faces. As a result, the wall distance is exact for all grid points within the threshold. This two-step approach reduces the computational cost yet achieves high and controllable accuracy in the evaluation of the wall distance. Algorithmic enhancements are presented to improve efficiency of wall-distance computations. Comprehensive assessment of the new method is reported for large-scale unstructured CFD grids generated for the Fifth AIAA CFD High-Lift Prediction Workshop. The performance of the new wall-distance method compares favorably with performance of two established methods implemented in high-performance CFD codes.

Wall Distance↗

NASA NTRSDate not supplied

Computational Modeling of Two Mars Powered Descent Vehicle Concepts Tested in the Langley Unitary Plan Wind Tunnel

Future human Mars missions will require powered descent starting at supersonic conditions, something which has never been done before at Mars. Computational powered descent flowfield simulations have been completed at full-scale Mars conditions, but the available ground test data are not suitable for calibrating computational uncertainties for aerodynamic interference on proposed Mars descent vehicles. Testing was conducted in the NASA Langley Unitary Plan Wind Tunnel in order to investigate the aerodynamic interference of sub-scale versions of two Mars powered descent vehicle concepts at supersonic Mach numbers (2.4 and 3.5): a model based on a blunt hypersonic inflatable aerodynamic decelerator (HIAD) and the second representing a more slender rigid vehicle with body flaps (CobraMRV). This paper covers computational flowfield predictions completed at wind tunnel conditions and comparisons to the test data. On the blowing HIAD models, the time-averaged pressure inboard of the nozzles was generally well-predicted, especially if the nozzles are canted outward, when the nozzles are located further from the nose. At intermediate CobraMRV thrust coefficients, CFD pressures are more accurately predicted than they are for the HIAD models, largely due to the nozzle locations and pointing directions. Overall, the CFD pressure coefficients were predicted within 0.2 of the steady pressure measurements for all blowing models, with smaller discrepancies at higher HIAD thrust, and larger discrepancies at lower HIAD CobraMRV thrust. All HIAD models were predicted to have a gradually decreasing axial force coefficient as the total thrust increases, in agreement with available pressure sensitive paint data. On models with canted nozzles or with nozzles further from the nose, the force coefficient was slightly higher for a given thrust. On the CobraMRV model, the CFD also shows consistent results between solvers and follows trends revealed in the data; the aerodynamic force coefficient remains near the non-blowing value at a tunnel Mach number of 2.4 regardless of thrust, and increases above that level at a Mach number of 3.5, consistent with the discrete pressure data. CFD analysis at tunnel and flight conditions will continue as flight system designs concepts mature.

Supersonic Retropropulsion↗