On High-Order Space-Time Methods for Computational Fluid Dynamics (CFD)
High-order space-time methods for Computational Fluid Dynamics are studied. Both explicit and implicit schemes will be discussed
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High-order space-time methods for Computational Fluid Dynamics are studied. Both explicit and implicit schemes will be discussed
Additional results and a revised and improved computer program listing from the shuttle rocket booster computational fluid dynamics formulations are presented. Numerical calculations for the flame zone of solid propellants are carried out using the Galerkin finite elements, with perturbations expanded to the zeroth, first, and second orders. The results indicate that amplification of oscillatory motions does indeed prevail in high frequency regions. For the second order system, the trend is similar to the first order system for low frequencies, but instabilities may appear at frequencies lower than those of the first order system. The most significant effect of the second order system is that the admittance is extremely oscillatory between moderately high frequency ranges.
Computational Fluid Dynamics (CFD) using the commercial CFD package CFDesign has been performed at NASA Jet Propulsion Laboratory (JPL) California Institute of Technology (Caltech) in support of the Phaeton Early Career Hire Program's Optical Payload for Lasercomm Science (OPALS) mission. The OPALS project is one which involves an International Space Station payload that will be using forced convection cooling in a hermetically sealed enclosure at 1 atm of air to cool "off-the-shelf" vendor electronics. The CFD analysis was used to characterize the thermal and fluid flow environment within a complicated labyrinth of electronics boards, fans, instrumentation, harnessing, ductwork and heat exchanger fins. The paradigm of iteratively using CAD/CAE tools and CFD was followed in order to determine the optimum flow geometry and heat sink configuration to yield operational convective film coefficients and temperature survivability limits for the electronics payload. Results from this current CFD analysis and correlation of the CFD model against thermal test data will be presented. Lessons learned and coupled thermal / flow modeling strategies will be shared in this paper.
Distributed-memory parallel computers dominate today's parallel computing arena. These machines, such as Intel Paragon, IBM SP2, and Cray Origin2OO, have successfully delivered high performance computing power for solving some of the so-called "grand-challenge" problems. Despite initial success, parallel machines have not been widely accepted in production engineering environments due to the complexity of parallel programming. On a parallel computing system, a task has to be partitioned and distributed appropriately among processors to reduce communication cost and to attain load balance. More importantly, even with careful partitioning and mapping, the performance of an algorithm may still be unsatisfactory, since conventional sequential algorithms may be serial in nature and may not be implemented efficiently on parallel machines. In many cases, new algorithms have to be introduced to increase parallel performance. In order to achieve optimal performance, in addition to partitioning and mapping, a careful performance study should be conducted for a given application to find a good algorithm-machine combination. This process, however, is usually painful and elusive. The goal of this project is to design and develop efficient parallel algorithms for highly accurate Computational Fluid Dynamics (CFD) simulations and other engineering applications. The work plan is 1) developing highly accurate parallel numerical algorithms, 2) conduct preliminary testing to verify the effectiveness and potential of these algorithms, 3) incorporate newly developed algorithms into actual simulation packages. The work plan has well achieved. Two highly accurate, efficient Poisson solvers have been developed and tested based on two different approaches: (1) Adopting a mathematical geometry which has a better capacity to describe the fluid, (2) Using compact scheme to gain high order accuracy in numerical discretization. The previously developed Parallel Diagonal Dominant (PDD) algorithm and Reduced Parallel Diagonal Dominant (RPDD) algorithm have been carefully studied on different parallel platforms for different applications, and a NASA simulation code developed by Man M. Rai and his colleagues has been parallelized and implemented based on data dependency analysis. These achievements are addressed in detail in the paper.
The purpose of the workshop was to discuss experimental and computational fluid dynamic activities in rocket propulsion and launch vehicles. The workshop was an open meeting for government, industry, and academia. A broad number of topics were discussed including computational fluid dynamic methodology, liquid and solid rocket propulsion, turbomachinery, combustion, heat transfer, and grid generation.
This paper provides a personal view of computational fluid dynamics. The main theme is divided into two categories - one dealing with algorithms and engineering applications and the other with scientific investigations. The former category may be termed computational aerodynamics, with the objective of providing reliable aerodynamic or engineering predictions. The latter category is essentially basic research, where the algorithmic tools are used to unravel and elucidate fluid-dynamic phenomena hard to obtain in a laboratory. A critique of the numerical solution techniques for both compressible and incompressible flows is included. The discussion on scientific investigations deals in particular with transition and turbulence.
The role of experiment in the development of Computational Fluid Dynamics (CFD) for external aerodynamic flow-field prediction is discussed. Requirements for code verification from two sources pacing the development of CFD are described: (1) the need for developing adequate flow modeling, and (2) the need for establishing confidence in the use of CFD for predicting complex flows. The type of data needed to meet these needs differs in detail and scope and leads to definite wind tunnel requirements. Recent examples of testing to assess and develop turbulence models, and to verify code development are used to establish future requirements of wind tunnel testing for CFD verification.
The role of experiment in the development of Computational Fluid Dynamics (CFD) for aerodynamic flow field prediction is discussed. Requirements for code verification from two sources that pace the development of CFD are described for: (1) development of adequate flow modeling, and (2) establishment of confidence in the use of CFD to predict complex flows. The types of data needed and their accuracy differs in detail and scope and leads to definite wind tunnel requirements. Examples of testing to assess and develop turbulence models, and to verify code development, are used to establish future wind tunnel testing requirements. Versatility, appropriate scale and speed range, accessibility for nonintrusive instrumentation, computerized data systems, and dedicated use for verification were among the more important requirements identified.
The role of experiment in the development of computational fluid dynamics (CFD) for aerodynamic flow field prediction is discussed. Requirements for code verification from two sources that pace the development of CFD are described for: (1) development of adequate flow modeling, and (2) establishment of confidence in the use of CFD to predict complex flows. The types of data needed and their accuracy differs in detail and scope and leads to definite wind tunnel requirements. Examples of testing to assess and develop turbulence models, and to verify code development, are used to establish future wind tunnel testing requirements. Versatility, appropriate scale and speed range, accessibility for nonintrusive instrumentation, computerized data systems, and dedicated use for verification were among the more important requirements identified.
Information on computational fluid dynamics (CFD) research and applications carried out at the NASA Langley Research Center is given in viewgraph form. The Langley CFD strategy, the five-year plan in CFD and flow physics, 3-block grid topology, the effect of a patching algorithm, F-18 surface flow, entropy and vorticity effects that improve accuracy of unsteady transonic small disturbance theory, and the effects of reduced frequency on first harmonic components of unsteady pressures due to airfoil pitching are among the topics covered.
A computational fluid dynamics (CFD) analysis has been performed on the aft slot region of the Titan 4 Solid Rocket Motor Upgrade (SRMU). This analysis was performed in conjunction with MSFC structural modeling of the propellant grain to determine if the flow field induced stresses would adversely alter the propellant geometry to the extent of causing motor failure. The results of the coupled CFD/stress analysis have shown that there is a continual increase of flow field resistance at the aft slot due to the aft segment propellant grain being progressively moved radially toward the centerline of the motor port. This 'bootstrapping' effect between grain radial movement and internal flow resistance is conducive to causing a rapid motor failure.
A large step in the validation of Computational Fluid Dynamics (CFD) for Supersonic Retropropulsion (SRP) is shown through the comparison of three Navier-Stokes solvers (DPLR, FUN3D, and OVERFLOW) and wind tunnel test results. The test was designed specifically for CFD validation and was conducted in the Langley supersonic 4 x4 Unitary Plan Wind Tunnel and includes variations in the number of nozzles, Mach and Reynolds numbers, thrust coefficient, and angles of orientation. Code-to-code and code-to-test comparisons are encouraging and possible error sources are discussed.
The commercial computational fluid dynamic (CFD) code ANSYS Fluent and multiple research CFD codes (ez4d with uses the conservation element and solution element (CESE) method and codes that use the flux reconstruction (FR) method) were tested using three different benchmark problems from the International Workshop for High-Order CFD Methods. The benchmark problems included the transonic Ringleb flow, vortex transport by uniform flow, and laminar boundary layer on a flat plate. Simulation results from all three benchmark problems showed that the Fluent solutions had less error than the ez4d solutions for a given degree of freedom. As expected, both the Fluent and ez4d solutions had larger errors for a given degree of freedom than the simulations that used the FR method because both Fluent and ez4d utilized a second-order scheme whereas the FR codes utilized a fourth-order scheme.
A finite difference code was implemented for the compressible Navier-Stokes equations on the Connection Machine, a massively parallel computer. The code is based on the ARC2D/ARC3D program and uses the implicit factored algorithm of Beam and Warming. The codes uses odd-even elimination to solve linear systems. Timings and computation rates are given for the code, and a comparison is made with a Cray XMP.
Validation of cryogenic propellant tank filling was performed using the Computational Fluid Dynamics (CFD) solver Loci/STREAM-VoF. The validation effort helped identify modeling methodologies that enable NASA to best support its partners in both launch pad and on-orbit filling operations. Data from liquid hydrogen ground tests filled via jet injection were used for the validation effort which include a sensitivity to initial tank wall temperature. Initially hot walls are expected to yield rapid evaporation and possibly boiling. A Volume of Fluid (VoF) methodology was used to capture the gas-liquid interface. Rapid breakup of the liquid jet was observed in simulation results as liquid evaporated and expanded. Inflowing liquid transitioned to a contiguous jet as tank temperatures decreased, gas pressure increased, and saturation conditions at the incoming liquid temperature were approached. The final phase of filling was distinguished by rapid gas pressure rise due to a higher rate of gas volume compression than condensation at the liquid surface. Key physics of propellant tank filling were captured in the computational predictions, and opportunities for added simulation robustness and efficiency in future modeling efforts were identified.
This slide presentation reviews the use of computational fluid dynamics in performing analysis of the space shuttle with particular reference to the return to flight analysis and other shuttle problems. Slides show a comparison of pressure coefficient with the shuttle ascent configuration between the wind tunnel test and the computed values. the evolution of the grid system for the space shuttle launch vehicle (SSLv) from the early 80's to one in 2004, the grid configuration of the bipod ramp redesign from the original design to the current configuration, charts with the computations showing solid rocket booster surface pressures from wind tunnel data, calculated over two grid systems (i.e., the original 14 grid system, and the enhanced 113 grid system), and the computed flight orbiter wing loads are compared with strain gage data on STS-50 during flight. The loss of STS-107 initiated an unprecedented review of all external environments. The current SSLV grid system of 600+ grids, 1.8 Million surface points and 95+ million volume points is shown. The inflight entry analyses is shown, and the use of Overset CFD as a key part to many external tank redesign and debris assessments is discussed. The work that still remains to be accomplished for future shuttle flights is discussed.
High-performance computing (HPC) architectures are trending toward manycore paradigms such as graphics processing units (GPUs). Approximately half of the top 100 publicly disclosed supercomputers in the world utilize GPU accelerators for performance. This is in contrast to a decade ago, where there were only a few such machines in the top 100. It is not currently possible to compile and run legacy central processing unit (CPU) software efficiently on GPUs without significant refactoring. Though a number of frameworks offering performance portability exist, none offer a standardized specification that is supported by all major hardware vendors. Additionally, experiences show that obtaining a high percentage of peak performance often requires architecture-specific code. This work details a pragmatic multi-architecture computational fluid dynamics library focused on aerospace problems across the speed range from low subsonic to hypersonic flows involving thermochemical nonequilibrium. A thin abstraction layer above NVIDIA CUDA C++ is utilized, which enables primarily single-source software currently capable of running efficiently on multicore CPUs, NVIDIA GPUs, AMD GPUs, and Intel GPUs. Results on various problems of interest across the speed range are presented and performance is compared between various architectures.
High-performance computing (HPC) architectures are trending toward manycore paradigms such as graphics processing units (GPUs). Approximately half of the top 100 publicly disclosed supercomputers in the world utilize GPU accelerators for performance. This is in contrast to a decade ago, where there were only a few such machines in the top 100. It is not currently possible to compile and run legacy central processing unit (CPU) software efficiently on GPUs without significant refactoring. Though a number of frameworks offering performance portability exist, none offer a standardized specification that is supported by all major hardware vendors. Additionally, experiences show that obtaining a high percentage of peak performance often requires architecture-specific code. This work details a pragmatic multi-architecture computational fluid dynamics library focused on aerospace problems across the speed range from low subsonic to hypersonic flows involving thermochemical nonequilibrium. A thin abstraction layer above NVIDIA CUDA C++ is utilized, which enables primarily single-source software currently capable of running efficiently on multicore CPUs, NVIDIA GPUs, AMD GPUs, and Intel GPUs. Results on various problems of interest across the speed range are presented and performance is compared between various architectures.