Engineering topics

Tomasz G Drozda

Publications and source records attributed to Tomasz G Drozda.

12 records

All sources NASA NTRS DOE OSTI

NASA NTRS2022

VULCAN-CFD Theory Manual: Ver. 7.2.0

VULCAN-CFD Theory Manual↗

NASA NTRS2022

VULCAN-CFD User Manual: Ver. 7.2.0

VULCAN-CFD offers a comprehensive set of capabilities to enable the simulation of continuum flowfields from subsonic to hypersonic conditions. The governing equations that are employed include allowances for both chemical and thermal nonequilibrium processes, coupled with a wide variety of turbulence models for both Reynolds-averaged and large eddy simulations. The software package can simulate two-dimensional, axisymmetric, or three-dimensional problems on structured multiblock meshes or fully unstructured meshes. A parabolic (i.e., space-marching) treatment can also be used for any subset of a structured mesh that can accommodate this solution strategy. The flow solver provides a significant level of geometric flexibility for structured grid simulations by allowing for arbitrary face-to-face C(0) continuous and non-C(0) continuous block interface connectivities. The unstructured grid paradigm allows for mixed element unstructured meshes that contain any combination of tetrahedral, prismatic, pyramidal, and hexahedral cell elements. The flow solver is also fully parallelized using MPI (Message Passing Interface) libraries in a data-parallel fashion, allowing for efficient simulations on modern High Performance Computing (HPC) systems. This document provides information related to the installation and execution of the VULCAN-CFD software package. A detailed description of the physical and numerical models available in the software are provided in the VULCAN-CFD Theory Manual.

VULCAN-CFD User Manual↗

NASA NTRS2019

Surrogate Modeling and Optimization of a Combustor with an Interdigitated Flushwall Injector

Design and Analysis of Computer Experiments (DACE) methods are applied and used to perform surrogate modeling and optimization of a simplified combustor flowpath with an interdigitated flushwall injector. The objectives of the optimization are the thrust potential and combustion efficiency, which are evaluated across a range of flight Mach numbers, duct heights, spanwise spacings, and injection angles. The focus of this work is to highlight the application of a sequential learning approach, in order to learn about the responses of the objective functions over the design space and to identify local regions of interest for further analysis. This approach is contrasted to a previous effort where only a single sampling set was used to fit surrogate models and perform optimization. The optimal solutions resulting from the previous and present approaches are different, due to surrogate model-guided local refinement of the design space allowed by the sequential learning method. The values of the global error estimates between the previous and present approaches are comparable, but the sequential method proved more computationally cost-effective. Further refinement in the optimal regions might be needed to improve predictive capability of surrogate models and to obtain the optimal solutions sets.

Rajiv R Shenoy↗

NASA NTRS2018

Analysis of the Two-Level NO PLIF Model for Low-Temperature High-Speed Flow Applications

The current work compares experimentally obtained nitric oxide (NO) laser-induced fluorescence (LIF) spectra with the equivalent spectra obtained analytically. The experimental spectra are computed from captured images of fluorescence in a gas cell and from a laser sheet passing through the fuel-air mixing flowfield produced by a high-speed fuel injector. The fuel injector is a slender strut that is currently being studied as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. This injector is placed downstream of a Mach 6 facility nozzle, which simulates the high Mach number airflow at the entrance of a scramjet combustor, and injects helium, which is used as an inert substitute for hydrogen fuel. Experimental planar (P) LIF is obtained by using a UV laser to excite fluorescence from the NO molecules that are present in either a gas cell or the facility air used for the EIMP experiments. The experimental data are obtained for several segments of the NO fluorescence spectrum. The selected segments encompass LIF lines with rotational quantum numbers appropriate for low-to-moderate temperature flows similar to those corresponding to the nominal experimental flow conditions. The experimental LIF spectra are then evaluated from the data and compared with those obtained from the theoretical models. The theoretical spectra are obtained from LIFBASE and LINUS software, and from a simplified version of the two-level fluorescence model. The equivalent analytic PLIF images are also obtained by applying only the simplified model to the results of the Reynolds-averaged simulations (RAS) of the mixing flowfield. Good agreement between the experimental and theoretical results provides increased confidence in both the simplified LIF modeling and CFD simulations for further investigations of high-speed injector performance using this approach.

Tomasz G Drozda↗

NASA NTRSDate not supplied

Analytical and Experimental Demonstration of an Alternate Mixing Performance Metric for High-Speed Fuel Mixing Studies

To experimentally assess the fuel/air mixing performance of high-speed fuel injectors, one-dimensional metrics that quantify the degree of mixing completeness downstream of the fuel injection location are required. The most accurate assessment of mixing performance is achieved with the mixing efficiency parameter. In order to experimentally determine the mixing efficiency parameter, the spatial distributions of both mass flux and fuel mass fraction must be measured. In-stream gas sampling techniques are commonly used to measure the fuel mass fraction distribution; however, the mass flux distribution is not easily determined because it requires the measurement of three independent aerothermodynamic variables in addition to the gas composition. Therefore, to experimentally determine the mixing efficiency parameter, the spatial distributions of four independent properties must be measured, with each property generally requiring its own unique probe. Because of this difficulty, it is commonly assumed that alternate metrics, which rely solely on the fuel distribution, are good indicators of mixing performance. However, since these alternate metrics do not provide a mass flux-weighted measure of mixing completeness, they can lead to incorrect conclusions being drawn about the mixing performance of the studied fuel injector configuration. Recognizing this shortcoming, this work proposes two new alternate mixing performance metrics that are easier to obtain than the mixing efficiency parameter. The analytical development of the new metrics, as well as their application to relevant CFD and experimental data of high-speed fuel injector configurations, is presented in this work. For two different experiments, the new metrics are shown to provide an excellent representation of the true mass flux-weighted mixing performance, unlike the traditionally-used alternatives. The results presented herein suggest that the new metrics can serve as accurate surrogates for mixing efficiency in future high-speed fuel/air mixing studies.

Cody R Ground↗

NASA NTRSDate not supplied

Comparisons Between NO PLIF Imaging and CFD Simulations of Mixing Flowfields for High-Speed Fuel Injectors

The current work compares experimentally and computationally obtained nitric oxide (NO) planar laser-induced fluorescence (PLIF) images of the mixing flowfields for three types of high-speed fuel injectors: a strut, a ramp, and a rectangular flushwall. These injection devices, which exhibited promising mixing performance at lower flight Mach numbers, are currently being studied as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center. The EIMP aims to investigate scramjet fuel injection and mixing physics, and improve the understanding of underlying physical processes relevant to flight Mach numbers greater than eight. In the experiments conducted in the NASA Langley Arc-Heated Scramjet Test Facility (AHSTF), the injectors are placed downstream of a Mach 6 facility nozzle, which simulates the high Mach number air flow at the entrance of a scramjet combustor. Helium is used as an inert substitute for hydrogen fuel. Both schlieren and PLIF techniques are applied to obtain mixing flowfield flow visualizations. The experimental PLIF is obtained by using a UV laser sheet to interrogate a plane of the flow by exciting fluorescence from the NO molecules that are present in the AHSTF air. Consequently, the absence of signal in the resulting PLIF images is an indication of pure helium (fuel). The computational PLIF is obtained by applying a fluorescence model for NO to the results of the Reynolds-averaged simulations (RAS) of the mixing flowfield carried out using the VULCAN-CFD solver. This approach is required because the PLIF signal is a nonlinear function of not only NO concentration, but also pressure, temperature, and the flow velocity. This complexity allows additional flow features to be identified and compared with those obtained from the computational fluid dynamics (CFD) simulations, however, such comparisons are only semiquantitative. Three-dimensional image reconstruction, similar to that used in magnetic resonance imaging, is also used to obtain images in the streamwise and spanwise planes from select cross-stream PLIF plane data. Synthetic schlieren is also computed from the RAS data. Good agreement between the experimental and computational results provides increased confidence in the CFD simulations for investigations of injector performance.

PLIF↗

NASA NTRSDate not supplied

Cold-Flow Experiments for CFD Validation of Mixing Flowfields for High-Speed Fuel Injectors for Scramjet Applications

Explore the source record for details and available documents.

mixing↗

NASA NTRSDate not supplied

“Cold-Flow” Experiments Supporting CFD of Mixing Flowfields for High-Speed Fuel Injectors for Scramjet Applications

Recent flight demonstrations of supersonic combustion ramjet (scramjet) vehicles prove their increasing promise for military (rapid response and strike capability on global scale), aerospace (safer and more affordable access to space), and civil aviation (hypersonic point-to-point transport) applications. Currently, these technologies are still in their early development stages with commercial interest and investment at only a fraction of that of government organizations such as NASA and DoD. To advance hypersonic air-breathing propulsion technologies to the technology readiness levels necessary for access to space or widespread commercialization, further government investments and university engagement are needed over the next decade and likely beyond. This is because designing scramjet propulsion devices capable of robust high-speed air-breathing operation, characterized by rapid fuel and air mixing, short combustion times, and ensuring stable flame, has proven difficult. In this presentation, after a brief engineering-level introduction to scramjets technology, we will discuss one of the key challenges in high-speed propulsion design, namely the fuel injector design. Attempts at improving fuel injection to enhance fuel-air mixing while simultaneously reducing engine thrust losses have received a great deal of attention over the years. Although some amount of loss is thermodynamically unavoidable and occurs due to the desired effect of molecular mixing of fuel and air, any losses beyond this minimum required amount reduces the thrust potential of the engine. The Enhanced Injection and Mixing Project (EIMP) at NASA Langley Research Center aims at addressing this design challenge by analyzing the performance of a number of baseline and novel fuel injectors for high-speed applications. The project leverages computational and experimental capabilities with the goal to investigate scramjet fuel injection and mixing physics, improve our understanding of the underlying physical process, and develop enhancement strategies relevant to hypersonic flight Mach numbers. The talk will discuss the current computational and experimental research approaches using one of the baseline injectors considered by the EIMP as an example.

Scramjet↗

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 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

Comparisons of Mixing Efficiency for the Strut Fuel Injector Obtained from Large-Eddy and Reynolds-Averaged Simulations, and Experiments

Mixing efficiency is obtained for a strut fuel injector at hypervelocity flow conditions by using large-eddy simulations (LES), Reynolds-averaged simulations (RAS), and experiments. The injector and flow conditions have been previously investigated by using RAS and experiments as a part of the Enhanced Injection and Mixing Project (EIMP) at the NASA Langley Research Center (LaRC). Because the fidelity of LES is a strong function of the grid, the mixing efficiency is obtained on two grids, the coarser of which is a factor of two coarser in each of the three dimensions with respect to the fine grid. The RAS uses the two-equation linear eddy viscosity and diffusivity modeling of Menter. In RAS, the species diffusivity model exhibits a strong dependence on the turbulent Schmidt number, which is often adjusted until some metric of engineering interest, such as the mixing efficiency, matches the experimental data. In the absence of experimental data, scale-resolving simulations, such as LES, have been proposed as surrogates for experiments that could provide the data needed to “calibrate” the turbulent Schmidt number in the RAS models. This approach is followed because LES requires significantly more computational resources (CPU, data storage, and time) than RAS, making it prohibitive for use in many engineering applications and specifically for parameter exploration or optimization. Here we examine the mixing efficiency obtained from several RAS with different values of the turbulent Schmidt number, and compare the results with those obtained from the LES and experiments. In addition, the least squares fitting approach was used to demonstrate how to obtain an estimate for the turbulent Schmidt number from LES analytically. These estimates were then used together with prior knowledge about RAS model sensitivity to select a turbulence model that was expected to best match the LES data.

LES↗

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↗