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All sourcesNASA NTRSDOE OSTI
NASA NTRS1993

Making tomorrow's mistakes today: Evolutionary prototyping for risk reduction and shorter development time

In the early days of JPL's solar system exploration, each spacecraft mission required its own dedicated data system with all software applications written in the mainframe's native assembly language. Although these early telemetry processing systems were a triumph of engineering in their day, since that time the computer industry has advanced to the point where it is now advantageous to replace these systems with more modern technology. The Space Flight Operations Center (SFOC) Prototype group was established in 1985 as a workstation and software laboratory. The charter of the lab was to determine if it was possible to construct a multimission telemetry processing system using commercial, off-the-shelf computers that communicated via networks. The staff of the lab mirrored that of a typical skunk works operation -- a small, multi-disciplinary team with a great deal of autonomy that could get complex tasks done quickly. In an effort to determine which approaches would be useful, the prototype group experimented with all types of operating systems, inter-process communication mechanisms, network protocols, packet size parameters. Out of that pioneering work came the confidence that a multi-mission telemetry processing system could be built using high-level languages running in a heterogeneous, networked workstation environment. Experience revealed that the operating systems on all nodes should be similar (i.e., all VMS or all PC-DOS or all UNIX), and that a unique Data Transport Subsystem tool needed to be built to address the incompatibilities of network standards, byte ordering, and socket buffering. The advantages of building a telemetry processing system based on emerging industry standards were numerous: by employing these standards, we would no longer be locked into a single vendor. When new technology came to market which offered ten times the performance at one eighth the cost, it would be possible to attach the new machine to the network, re-compile the application code, and run. In addition, we would no longer be plagued with lack of manufacturer support when we encountered obscure bugs. And maybe, hopefully, the eternal elusive goal of software portability across different vendors' platforms would finally be available. Some highlights of our prototyping efforts are described.

Friedman, Gary↗
NASA NTRS1998

Parallel 3D Multi-Stage Simulation of a Turbofan Engine

A 3D multistage simulation of each component of a modern GE Turbofan engine has been made. An axisymmetric view of this engine is presented in the document. This includes a fan, booster rig, high pressure compressor rig, high pressure turbine rig and a low pressure turbine rig. In the near future, all components will be run in a single calculation for a solution of 49 blade rows. The simulation exploits the use of parallel computations by using two levels of parallelism. Each blade row is run in parallel and each blade row grid is decomposed into several domains and run in parallel. 20 processors are used for the 4 blade row analysis. The average passage approach developed by John Adamczyk at NASA Lewis Research Center has been further developed and parallelized. This is APNASA Version A. It is a Navier-Stokes solver using a 4-stage explicit Runge-Kutta time marching scheme with variable time steps and residual smoothing for convergence acceleration. It has an implicit K-E turbulence model which uses an ADI solver to factor the matrix. Between 50 and 100 explicit time steps are solved before a blade row body force is calculated and exchanged with the other blade rows. This outer iteration has been coined a "flip." Efforts have been made to make the solver linearly scaleable with the number of blade rows. Enough flips are run (between 50 and 200) so the solution in the entire machine is not changing. The K-E equations are generally solved every other explicit time step. One of the key requirements in the development of the parallel code was to make the parallel solution exactly (bit for bit) match the serial solution. This has helped isolate many small parallel bugs and guarantee the parallelization was done correctly. The domain decomposition is done only in the axial direction since the number of points axially is much larger than the other two directions. This code uses MPI for message passing. The parallel speed up of the solver portion (no 1/0 or body force calculation) for a grid which has 227 points axially.

Turner, Mark G.↗
NASA NTRS2009

Jet Noise Modeling for Suppressed and Unsuppressed Aircraft in Simulated Flight

This document describes the development of further extensions and improvements to the jet noise model developed by Modern Technologies Corporation (MTC) for the National Aeronautics and Space Administration (NASA). The noise component extraction and correlation approach, first used successfully by MTC in developing a noise prediction model for two-dimensional mixer ejector (2DME) nozzles under the High Speed Research (HSR) Program, has been applied to dual-stream nozzles, then extended and improved in earlier tasks under this contract. Under Task 6, the coannular jet noise model was formulated and calibrated with limited scale model data, mainly at high bypass ratio, including a limited-range prediction of the effects of mixing-enhancement nozzle-exit chevrons on jet noise. Under Task 9 this model was extended to a wider range of conditions, particularly those appropriate for a Supersonic Business Jet, with an improvement in simulated flight effects modeling and generalization of the suppressor model. In the present task further comparisons are made over a still wider range of conditions from more test facilities. The model is also further generalized to cover single-stream nozzles of otherwise similar configuration. So the evolution of this prediction/analysis/correlation approach has been in a sense backward, from the complex to the simple; but from this approach a very robust capability is emerging. Also from these studies, some observations emerge relative to theoretical considerations. The purpose of this task is to develop an analytical, semi-empirical jet noise prediction method applicable to takeoff, sideline and approach noise of subsonic and supersonic cruise aircraft over a wide size range. The product of this task is an even more consistent and robust model for the Footprint/Radius (FOOTPR) code than even the Task 9 model. The model is validated for a wider range of cases and statistically quantified for the various reference facilities. The possible role of facility effects will thus be documented. Although the comparisons that can be accomplished within the limited resources of this task are not comprehensive, they provide a broad enough sampling to enable NASA to make an informed decision on how much further effort should be expended on such comparisons. The improved finalized model is incorporated into the FOOTPR code. MTC has also supported the adaptation of this code for incorporation in NASA s Aircraft Noise Prediction Program (ANOPP).

Stone, James R.↗
NASA NTRS2000

NCC: A Physics-Based Design and Analysis Tool for Combustion Systems

The National Combustion Code (NCC) is an integrated system of computer codes for physics-based design and analysis of combustion systems. It uses unstructured meshes and runs on parallel computing platforms. The NCC is composed of a set of distinct yet closely related modules. They are: (1) a gaseous flow module solving 3-D Navier-Stokes equations; (2) a turbulence module containing the non-linear k-epsilon models; (3) a chemistry module using either the conventional reduced kinetics approach of solving species equations or the Intrinsic Low Dimensional Manifold (ILDM) kinetics approach of table looking up in conjunction with solving the equations of the progressive variables; (4) a turbulence-chemistry interaction module including the option of solving the joint probability density function (PDF) for species and enthalpy; and (5) a spray module for solving the liquid phase equations. In early 1995, an industry-government team was formed to develop the NCC. In July 1998, the baseline beta version was completed and presented in two NCC sessions at the 34th AIAA/ASME/SAE/ASEE Joint Propulsion Conference & Exhibit, July 1998. An overview of this baseline beta version was presented at the NASA HPCCP/CAS Workshop 98, August 1998. Since then, the effort has been focused on the streamlining, validation, and enhancement of the th baseline beta version. The progress is presented in two NCC sessions at the AIAA 38 Aerospace Sciences Meeting & Exhibit, January 2000. At this NASA HPCCP/CAS Workshop 2000, an overview of the NCC papers presented at the AIAA 38 th Aerospace Sciences Meeting & Exhibit is presented, with emphasis on the reduction of analysis time of simulating the (gaseous) reacting flows in full combustors. In addition, results of NCC simulation of a modern turbofan combustor will also be reported.

Liu, Nan-Suey↗
NASA NTRSDate not supplied

A window on the composition of the early solar nebula: 2014MU69, Pluto, and Phoebe

The initial chemical composition of any solar nebula will depend upon the degree to which 1) organic and ice components form on dust grains, 2) organic and molecular species form in the gas phase, 3) organics and ices are exchanged between the gas and solid state, and 4) the precursor and newly formed (more complex) materials survive and are modified in the developing planetary system. Infrared and radio observations of star-forming regions reveal that complex chemistry occurs on icy grains, sometimes before stars even form. Additional processing, through the protosolar disk and within the solar nebula further modifies most, but probably not all, of the initial materials. In fact, the modern Solar System still carries a fraction of its interstellar inheritance [Alexander et al., 2017]. Here we focus on three examples of small bodies in our Solar System, each containing chemical and dynamical clues to its origin and evolution: the small, cold-classical Kuiper Belt object (KBO) 2014MU69 , Pluto and Saturn’s moon, Phoebe. The New Horizons flyby of 2014 MU69 has given the first view of an unaltered body composed of material originally in the solar nebula at ~45 AU. The spectrum reveals methanol ice (not commonly found), a possible detection of water ice (Stern et al. 2019). Pluto’s internal and surface inventory of volatiles and complex organics, together with active geological processes including cryo-volcanism, indicate a surprising level of activity on a body in the outermost region of the Solar System, and the fluid that emerges from subsurface reservoirs may contain material inherited from the solar nebula (Cruikshank et al. 2019). Meanwhile, Saturn's captured moon, Phoebe, carries high D/H in H2O [Clark et al. 2018)], and complex organics (Cruikshank et al. 2008), both consistent with its formation in, and inheritance from, the outer solar nebula. Together, these objects provide windows on the origin and evolution of our Solar System and constraints to be considered in future chemical and physical models of PPDs. The Spitzer Space telescope discovered the ring of dust around Saturn that emanated from the bombardment of Phoebe in the recent past, dislodging primitive Solar System material from deep within that satellite. In addition, Spitzer has provided detailed studies of the chemistry of star forming regions where methanol and other ices are readily observed. As a result of these and other observations, protoplanetary disk models are beginning to combine both dynamical and chemical complexities into their codes, resulting in new insights for exoplanetary systems.

Yvonne J. Pendleton↗
NASA NTRS2022

GBaTSv2: a revised synthesis of the likely basal thermal state of the Greenland Ice Sheet

The basal thermal state (frozen or thawed) of the Greenland Ice Sheet is under-constrained due to few direct measurements, yet knowledge of this state is becoming increasingly important to interpret modern changes in ice flow. The first synthesis of this state relied on inferences from widespread airborne and satellite observations and numerical models, for which most of the underlying datasets have since been updated. Further, new and independent constraints on the basal thermal state have been developed from analysis of basal and englacial reflections observed by airborne radar sounding. Here we synthesize constraints on the Greenland Ice Sheet's basal thermal state from boreholes, thermomechanical ice-flow models that participated in the Ice Sheet Model Intercomparison Project for CMIP6 (ISMIP6; Coupled Model Intercomparison Project Phase 6), IceBridge BedMachine Greenland v4 bed topography, Making Earth Science Data Records for Use in Research Environments (MEaSUREs) Multi-Year Greenland Ice Sheet Velocity Mosaic v1 and multiple inferences of a thawed bed from airborne radar sounding. Most constraints can only identify where the bed is likely thawed rather than where it is frozen. This revised synthesis of the Greenland likely Basal Thermal State version 2 (GBaTSv2) indicates that 33 % of the ice sheet's bed is likely thawed, 40 % is likely frozen and the remainder (28 %) is too uncertain to specify. The spatial pattern of GBaTSv2 is broadly similar to the previous synthesis, including a scalloped frozen core and thawed outlet-glacier systems. Although the likely basal thermal state of nearly half (46 %) of the ice sheet changed designation, the assigned state changed from likely frozen to likely thawed (or vice versa) for less than 6 % of the ice sheet. This revised synthesis suggests that more of northern Greenland is likely thawed at its bed and conversely that more of southern Greenland is likely frozen, both of which influence interpretation of the ice sheet's present subglacial hydrology and models of its future evolution. The GBaTSv2 dataset, including both code that performed the analysis and the resulting datasets, is freely available at https://doi.org/10.5281/zenodo.6759384 (MacGregor, 2022).

Joseph A. MacGregor↗
NASA NTRS2004

Probability Distribution Estimated From the Minimum, Maximum, and Most Likely Values: Applied to Turbine Inlet Temperature Uncertainty

Modern engineering design practices are tending more toward the treatment of design parameters as random variables as opposed to fixed, or deterministic, values. The probabilistic design approach attempts to account for the uncertainty in design parameters by representing them as a distribution of values rather than as a single value. The motivations for this effort include preventing excessive overdesign as well as assessing and assuring reliability, both of which are important for aerospace applications. However, the determination of the probability distribution is a fundamental problem in reliability analysis. A random variable is often defined by the parameters of the theoretical distribution function that gives the best fit to experimental data. In many cases the distribution must be assumed from very limited information or data. Often the types of information that are available or reasonably estimated are the minimum, maximum, and most likely values of the design parameter. For these situations the beta distribution model is very convenient because the parameters that define the distribution can be easily determined from these three pieces of information. Widely used in the field of operations research, the beta model is very flexible and is also useful for estimating the mean and standard deviation of a random variable given only the aforementioned three values. However, an assumption is required to determine the four parameters of the beta distribution from only these three pieces of information (some of the more common distributions, like the normal, lognormal, gamma, and Weibull distributions, have two or three parameters). The conventional method assumes that the standard deviation is a certain fraction of the range. The beta parameters are then determined by solving a set of equations simultaneously. A new method developed in-house at the NASA Glenn Research Center assumes a value for one of the beta shape parameters based on an analogy with the normal distribution (ref.1). This new approach allows for a very simple and direct algebraic solution without restricting the standard deviation. The beta parameters obtained by the new method are comparable to the conventional method (and identical when the distribution is symmetrical). However, the proposed method generally produces a less peaked distribution with a slightly larger standard deviation (up to 7 percent) than the conventional method in cases where the distribution is asymmetric or skewed. The beta distribution model has now been implemented into the Fast Probability Integration (FPI) module used in the NESSUS computer code for probabilistic analyses of structures (ref. 2).

Holland, Frederic A., Jr.↗
NASA NTRS1998

A Geometry Based Infra-Structure for Computational Analysis and Design

The computational steps traditionally taken for most engineering analysis suites (computational fluid dynamics (CFD), structural analysis, heat transfer and etc.) are: (1) Surface Generation -- usually by employing a Computer Assisted Design (CAD) system; (2) Grid Generation -- preparing the volume for the simulation; (3) Flow Solver -- producing the results at the specified operational point; (4) Post-processing Visualization -- interactively attempting to understand the results. For structural analysis, integrated systems can be obtained from a number of commercial vendors. These vendors couple directly to a number of CAD systems and are executed from within the CAD Graphical User Interface (GUI). It should be noted that the structural analysis problem is more tractable than CFD; there are fewer mesh topologies used and the grids are not as fine (this problem space does not have the length scaling issues of fluids). For CFD, these steps have worked well in the past for simple steady-state simulations at the expense of much user interaction. The data was transmitted between phases via files. In most cases, the output from a CAD system could go to Initial Graphics Exchange Specification (IGES) or Standard Exchange Program (STEP) files. The output from Grid Generators and Solvers do not really have standards though there are a couple of file formats that can be used for a subset of the gridding (i.e. PLOT3D data formats). The user would have to patch up the data or translate from one format to another to move to the next step. Sometimes this could take days. Specifically the problems with this procedure are:(1) File based -- Information flows from one step to the next via data files with formats specified for that procedure. File standards, when they exist, are wholly inadequate. For example, geometry from CAD systems (transmitted via IGES files) is defined as disjoint surfaces and curves (as well as masses of other information of no interest for the Grid Generator). This is particularly onerous for modern CAD systems based on solid modeling. The part was a proper solid and in the translation to IGES has lost this important characteristic. STEP is another standard for CAD data that exists and supports the concept of a solid. The problem with STEP is that a solid modeling geometry kernel is required to query and manipulate the data within this type of file. (2) 'Good' Geometry. A bottleneck in getting results from a solver is the construction of proper geometry to be fed to the grid generator. With 'good' geometry a grid can be constructed in tens of minutes (even with a complex configuration) using unstructured techniques. Adroit multi-block methods are not far behind. This means that a million node steady-state solution can be computed on the order of hours (using current high performance computers) starting from this 'good' geometry. Unfortunately, the geometry usually transmitted from the CAD system is not 'good' in the grid generator sense. The grid generator needs smooth closed solid geometry. It can take a week (or more) of interaction with the CAD output (sometimes by hand) before the process can begin. One way Communication. (3) One-way Communication -- All information travels on from one phase to the next. This makes procedures like node adaptation difficult when attempting to add or move nodes that sit on bounding surfaces (when the actual surface data has been lost after the grid generation phase). Until this process can be automated, more complex problems such as multi-disciplinary analysis or using the above procedure for design becomes prohibitive. There is also no way to easily deal with this system in a modular manner. One can only replace the grid generator, for example, if the software reads and writes the same files. Instead of the serial approach to analysis as described above, CAPRI takes a geometry centric approach. This makes the actual geometry (not a discretized version) accessible to all phases of the analysis. The connection to the geometry is made through an Application Programming Interface (API) and NOT a file system. This API isolates the top-level applications (grid generators, solvers and visualization components) from the geometry engine. Also this allows the replacement of one geometry kernel with another, without effecting these top-level applications. For example, if UniGraphics is used as the CAD package then Parasolid (UG's own geometry engine) can be used for all geometric queries so that no solid geometry information is lost in a translation. This is much better than STEP because when the data is queried, the same software is executed as used in the CAD system. Therefore, one analyzes the exact part that is in the CAD system. CAPRI uses the same idea as the commercial structural analysis codes but does not specify control. Software components of the CAD system are used, but the analysis suite, not the CAD operator, specifies the control of the software session. This also means that the license issues (may be) minimized and individuals need not have to know how to operate a CAD system in order to run the suite.

Haimes, Robert↗
NASA NTRS1998

The NASA Low-Pressure Turbine Flow Physics Program

An overview of the NASA Lewis Low-Pressure Turbine (LPT) Flow Physics Program will be presented. The program was established in response to the aero-engine industry's need for improved LPT efficiency and designs. Modern jet engines have four to seven LPT stages, significantly contributing to engine weight. In addition, there is a significant efficiency degradation between takeoff and cruise conditions, of up to 2 points. Reducing the weight and part count of the LPT and minimizing the efficiency degradation will translate into fuel savings. Accurate prediction methods of LPT flows and losses are needed to accomplish those improvements. The flow in LPT passages is at low Reynolds number, and is dominated by interplay of three basic mechanisms: transition, separation and wake interaction. The affecting parameters traditionally considered are Reynolds number, freestream turbulence intensity, wake frequency parameter, and the pressure distribution (loading). Three-dimensional effects and additional parameters, particularly turbulence characteristics like length scales, spectra and other statistics, as well as wake turbulence intensity and properties also play a role. The flow of most interest is on the suction surface, where large losses are generated as the flow tends to separate at the low Reynolds numbers. Ignoring wakes, a common flow scenario, there is laminar separation, followed by transition on the separation bubble and turbulent reattachment. If transition starts earlier the separation will be eliminated and the boundary layer will be attached leading to the well known bypass transition issues. In contrast, transition over a separation bubble is closer to free shear layer transition and was not investigated as well, particularly in the turbine environment. Unsteadiness created by wakes complicates the picture. Wakes induce earlier transition, and the calmed regions trailing the induced turbulent spots can delay or eliminate separation via shear stress modification. Three-dimensional flow physics and geometry will have strong effects. Altogether a very complex and challenging problem emerges. The objective of the program is to provide improved models and physical understanding of the complex flow, which are essential for accurate prediction of flow and losses in the LPT. Experimental, computational and analytical work as complementing and augmenting approaches are used. The program involves industry, universities and research institutes, and other government laboratories. It is characterized by strong interaction among participants, quick dissemination of results, and responsiveness to industry's needs. The presentation will describe the work elements. Highlighting some activities in progress are experiments on simulated blade suction surface in low-speed wind tunnels, on curved wall, and on a flat-plate, both with pressure gradient. In the area of computation, assessment of existing models is performed using RANS (Reynolds Averaged Navier Stokes) simulations. Laminar flow DNS was completed. Analytical studies of instability and receptivity in attached and separated flows were started. In the near future the program is moving to include wake effects and development of improved modeling. Experimental work in preparation stages are: (1) Addition of wakes to the curved tunnel experiment; (2) Low-speed rotating rig experiment on GE90 engine LPT; and (3) Transonic cascade. In the area of computation, it is expected to move from model assessment towards development of improved models. In addition, a new project of Large Eddy Simulation (LES) of LPT is to begin and will provide numerical data bases. It is planned to implement the emerging improved models in a multistage turbomachinery code and to validate against the GE90 engine LPT.

Ashpis, David E.↗