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Modified computer program mathematical theory and mathematical model description for predicting rocket engine ablative liner thickness, char depth, and temperature distribution
In this paper a new computational method for accurate simulation of the nonlinear dynamic behavior of single- and multi-spool core engines, turbofan engines, and power generation gas turbine engines is presented. In order to perform the simulation, a modularly structured computer code has been developed which includes individual mathematical modules representing various engine components. The generic structure of the code enables the dynamic simulation of arbitrary engine configurations ranging from single-spool thrust generation to multi-spool thrust/power generation engines under adverse dynamic operating conditions. For precise simulation of turbine and compressor components, row-by-row calculation procedures were implemented that account for the specific turbine and compressor cascade and blade geometry and characteristics. The dynamic behavior of the subject engine is calculated by solving a number of systems of partial differential equations, which describe the unsteady behavior of the individual components. In order to ensure the capability, accuracy, robustness, and reliability of the code, comprehensive critical performance assessment and validation tests were performed. As representatives, three different transient cases with single- and multi-spool thrust and power generation engines were simulated. The transient cases range from operating with a prescribed fuel schedule, to extreme load changes, to generator and turbine shut down.
Computer programs, routines, and subroutines for aiding engineers, scientists, and mathematicians in direct problem solving are presented. Also included is a group of items that affords the same users greater flexibility in the use of software.
The underlying engineering and mathematical models as well as the computational methods used by the SOUP5 analysis programs, which are part of the R2BCSAT-83 Broadcast Satellite Computational System, are described. Included are the algorithms used to calculate the technical parameters and references to the relevant technical literature. The system provides the following capabilities: requirements file maintenance, data base maintenance, elliptical satellite beam fitting to service areas, plan synthesis from specified requirements, plan analysis, and report generation/query. Each of these functions are briefly described.
The underlying engineering and mathematical models as well as the computational methods used by the Spectrum Orbit Utilization Program 5 (SOUP5) analysis programs are described. Included are the algorithms used to calculate the technical parameters, and references to the technical literature. The organization, capabilities, processing sequences, and processing and data options of the SOUP5 system are described. The details of the geometric calculations are given. Also discussed are the various antenna gain algorithms; rain attenuation and depolarization calculations; calculations of transmitter power and received power flux density; channelization options, interference categories, and protection ratio calculation; generation of aggregrate interference and margins; equivalent gain calculations; and how to enter a protection ratio template.
The Pointing History Engine (PHE) is a computer program that provides mathematical transformations needed to reconstruct, from downlinked telemetry data, the attitude of the Spitzer Space Telescope (formerly known as the Space Infrared Telescope Facility) as a function of time. The PHE also serves as an example for development of similar pointing reconstruction software for future space telescopes. The transformations implemented in the PHE take account of the unique geometry of the Spitzer telescope-pointing chain, including all data on relative alignments of components, and all information available from attitude-determination instruments. The PHE makes it possible to coordinate attitude data with observational data acquired at the same time, so that any observed astronomical object can be located for future reference and re-observation. The PHE is implemented as a subroutine used in conjunction with telemetry-formatting services of the Mission Image Processing Laboratory of NASA s Jet Propulsion Laboratory to generate the Boresight Pointing History File (BPHF). The BPHF is an archival database designed to serve as Spitzer s primary astronomical reference documenting where the telescope was pointed at any time during its mission.
A computer program for modeling transient flow behavior of the GOX control valve (GCV) has been developed which makes it possible to model a variety of operating conditions and changes of physical hardware. The SSME GCV controls the flow of GOX that pressurizes the Pogo accumulator. The model simulates the transient flow behavior of the original, tight stacked, and redesigned valve configurations during start and shutdown. The model reads a user-prepared input file that defines the valve configurations and thus provides maximum flexibility.
Educational NASA Computational and Scientific Studies (enCOMPASS) is an educational project of NASA Goddard Space Flight Center aimed at bridging the gap between computational objectives and needs of NASA's scientific research, missions, and projects, and academia's latest advances in applied mathematics and computer science. enCOMPASS achieves this goal via bidirectional collaboration and communication between NASA and academia. Using developed NASA Computational Case Studies in university computer science/engineering and applied mathematics classes is a way of addressing NASA's goals of contributing to the Science, Technology, Education, and Math (STEM) National Objective. The enCOMPASS Web site at http://encompass.gsfc.nasa.gov provides additional information. There are currently nine enCOMPASS case studies developed in areas of earth sciences, planetary sciences, and astrophysics. Some of these case studies have been published in AIP and IEEE's Computing in Science and Engineering magazines. A few university professors have used enCOMPASS case studies in their computational classes and contributed their findings to NASA scientists. In these case studies, after introducing the science area, the specific problem, and related NASA missions, students are first asked to solve a known problem using NASA data and past approaches used and often published in a scientific/research paper. Then, after learning about the NASA application and related computational tools and approaches for solving the proposed problem, students are given a harder problem as a challenge for them to research and develop solutions for. This project provides a model for NASA scientists and engineers on one side, and university students, faculty, and researchers in computer science and applied mathematics on the other side, to learn from each other's areas of work, computational needs and solutions, and the latest advances in research and development. This innovation takes NASA science and engineering applications to computer science and applied mathematics university classes, and makes NASA objectives part of the university curricula. There is great potential for growth and return on investment of this program to the point where every major university in the U.S. would use at least one of these case studies in one of their computational courses, and where every NASA scientist and engineer facing a computational challenge (without having resources or expertise to solve it) would use enCOMPASS to formulate the problem as a case study, provide it to a university, and get back their solutions and ideas.
The ability of computers to solve hitherto intractable problems and simulate complex processes using mathematical models makes them an indispensable part of modern science and engineering. Computer simulations of large-scale realistic applications usually require solving a set of non-linear partial differential equations (PDES) over a finite region. For example, one thrust area in the DOE Grand Challenge projects is to design future accelerators such as the SpaHation Neutron Source (SNS). Our colleagues at SLAC need to model complex RFQ cavities with large aspect ratios. Unstructured grids are currently used to resolve the small features in a large computational domain; dynamic mesh adaptation will be added in the future for additional efficiency. The PDEs for electromagnetics are discretized by the FEM method, which leads to a generalized eigenvalue problem Kx = AMx, where K and M are the stiffness and mass matrices, and are very sparse. In a typical cavity model, the number of degrees of freedom is about one million. For such large eigenproblems, direct solution techniques quickly reach the memory limits. Instead, the most widely-used methods are Krylov subspace methods, such as Lanczos or Jacobi-Davidson. In all the Krylov-based algorithms, sparse matrix-vector multiplication (SPMV) must be performed repeatedly. Therefore, the efficiency of SPMV usually determines the eigensolver speed. SPMV is also one of the most heavily used kernels in large-scale numerical simulations.
This report summarizes research conducted at the Institute for Computer Applications in Science and Engineering in the areas of (1) applied and numerical mathematics, including numerical analysis and algorithm development; (2) theoretical and computational research in fluid mechanics in selected areas of interest, including acoustics and combustion; (3) experimental research in transition and turbulence and aerodynamics involving Langley facilities and scientists; and (4) computer science.
A methodology for automatic mathematical modeling and generating simulation models is described. The models will be verified by running in a test environment using standard profiles with the results compared against known results. The major objective is to create a user friendly environment for engineers to design, maintain, and verify their model and also automatically convert the mathematical model into conventional code for conventional computation. A demonstration program was designed for modeling the Space Shuttle Main Engine Simulation. It is written in LISP and MACSYMA and runs on a Symbolic 3670 Lisp Machine. The program provides a very friendly and well organized environment for engineers to build a knowledge base for base equations and general information. It contains an initial set of component process elements for the Space Shuttle Main Engine Simulation and a questionnaire that allows the engineer to answer a set of questions to specify a particular model. The system is then able to automatically generate the model and FORTRAN code. The future goal which is under construction is to download the FORTRAN code to VAX/VMS system for conventional computation. The SSME mathematical model will be verified in a test environment and the solution compared with the real data profile. The use of artificial intelligence techniques has shown that the process of the simulation modeling can be simplified.
Computer program contains mathematical model which provides relationship between engine gas dynamics and combustion processes. Mathematically simulated explosions initiate gas disturbances. Design methods for damping disturbances can be studied to prevent future engine shutdown or destruction.
Computational support of the engineering design process routinely requires mathematical models of behavior to inform designers of the system response to external stimuli. However, designers also need to know the effect of the changes in design variable values on the system behavior. For large engineering systems, the conventional way of evaluating these effects by repetitive simulation of behavior for perturbed variables is impractical because of excessive cost and inadequate accuracy. An alternative is described based on recently developed system sensitivity analysis that is combined with extrapolation to form a model of design. This design model is complementary to the model of behavior and capable of direct simulation of the effects of design variable changes.
Intelligent systems are nature-inspired, mathematically sound, computationally intensive problem solving tools and methodologies that have become extremely important for advancing the current trends in information technology. Artificially intelligent systems currently utilize computers to emulate various faculties of human intelligence and biological metaphors. They use a combination of symbolic and sub-symbolic systems capable of evolving human cognitive skills and intelligence, not just systems capable of doing things humans do not do well. Intelligent systems are ideally suited for tasks such as search and optimization, pattern recognition and matching, planning, uncertainty management, control, and adaptation. In this paper, the intelligent system technologies and their application potential are highlighted via several examples.
Fluid dynamic computer codes for the mathematical simulation of problems in gas turbine engine combustion systems are required as design and diagnostic tools. To eventually achieve a performance standard with these codes of more than qualitative accuracy it is desirable to use benchmark experiments for validation studies. Typical of the fluid dynamic computer codes being developed for combustor simulations is the TEACH (Teaching Elliptic Axisymmetric Characteristics Heuristically) solution procedure. It is difficult to find suitable experiments which satisfy the present definition of benchmark quality. For the majority of the available experiments there is a lack of information concerning the boundary conditions. A standard TEACH-type numerical technique is applied to a number of test-case experiments. It is found that numerical simulations of gas turbine combustor-relevant flows can be sensitive to the plane at which the calculations start and the spatial distributions of inlet quantities for swirling flows.
Development of advanced algorithms for simulating engine flow paths requires the integration of fundamental experiments with the validation of enhanced mathematical models. In this paper, we provide an overview of statistical methods to strategically and efficiently conduct experiments and computational model refinement. Moreover, the integration of experimental and computational research efforts is emphasized. With a statistical engineering perspective, scientific and engineering expertise is combined with statistical sciences to gain deeper insights into experimental phenomenon and code development performance; supporting the overall research objectives. The particular statistical methods discussed are design of experiments, response surface methodology, and uncertainty analysis and planning. Their application is illustrated with a coaxial free jet experiment and a turbulence model refinement investigation. Our goal is to provide an overview, focusing on concepts rather than practice, to demonstrate the benefits of using statistical methods in research and development, thereby encouraging their broader and more systematic application.
Three-dimensional Inelastic Analysis Methods are described. These methods were incorporated into a series of new computer codes embodying a progression of mathematical models (mechanics of materials, specialty finite element, boundary element) for streamlined analysis of hot engine structures such as: (1) combustor liners, (2) turbine blades, and (3) turbine vanes. These models address the effects of high temperatures and thermal/mechanical loadings on the local (stress/strain) and global (displacements, frequencies, amplitudes, buckling) structural behavior of the three respective components. The methods and the three computer codes, referred to as MOMM (Mechanics Of Materials Model), MHOST (MARC-Hot Section Technology), and BEST3D (Boundary Element Stress Technology), have been developed and are briefly described.
The unprecedented progress in celestial mechanics (orbital mechanics, astrodynamics, space dynamics) is reviewed from 1957 to date. The engineering, astronomical and mathematical aspects are synthesized. The measuring and computational techniques developed parallel with the theoretical advances are outlined. Major unsolved problem areas are listed with proposed approaches for their solutions. Extrapolations and predictions of the progress for the future conclude the paper.