The Rise of Model-Based Engineering Environments in the MBSE Disruption
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The concept of Aircraft Engine Life Extending Control is introduced. A brief description of the tradeoffs between performance and engine life are first explained. The overall goal of the life extending controller is to reduce the engine operating cost by extending the on-wing engine life while improving operational safety. The research results for NASA's Rocket Engine life extending control program are also briefly described. Major building blocks of the Engine Life Extending Control architecture are examined. These blocks include: life prediction models, engine operation models, stress and thermal analysis tools, control schemes, and intelligent control systems. The technology areas that would likely impact the successful implementation of an aircraft engine life extending control are also briefly described. Near, intermediate, and long term goals of NASA's activities are also presented.
The fatigue damage of a space propulsion system component is computed using probabilistic structural analysis methods. The analysis takes into account the variations in static and dynamic loads, the uncertainty in structural damping, and the scatter in material fatigue resistance. The key elements of the probabilistic approach include: (1) a numerical engine model for describing the global component interface loads consistent with engine balance, (2) models for computing the local mode boundary conditions on the component, (3) a static structural analysis model that captures the strains at a damage critical location as a function of engine performance variables, (4) a finite element model for assessment of the random amplitude stress response due to random base and pressure excitations with uncertain power and correlation length, and (5) advanced first order reliability methods for computing the probabilities associated with the fatigue damage.
An efficient diagnosis engine - a combination of mathematical models and algorithms - has been developed for identifying faulty components in a possibly complex engineering system. This model-based diagnosis engine embodies a twofold approach to reducing, relative to prior model-based diagnosis engines, the amount of computation needed to perform a thorough, accurate diagnosis. The first part of the approach involves a reconstruction of the general diagnostic engine to reduce the complexity of the mathematical-model calculations and of the software needed to perform them. The second part of the approach involves algorithms for computing a minimal diagnosis (the term "minimal diagnosis" is defined below). A somewhat lengthy background discussion is prerequisite to a meaningful summary of the innovative aspects of the present efficient model-based diagnosis engine. In model-based diagnosis, the function of each component and the relationships among all the components of the engineering system to be diagnosed are represented as a logical system denoted the system description (SD). Hence, the expected normal behavior of the engineering system is the set of logical consequences of the SD. Faulty components lead to inconsistencies between the observed behaviors of the system and the SD (see figure). Diagnosis - the task of finding faulty components - is reduced to finding those components, the abnormalities of which could explain all the inconsistencies. The solution of the diagnosis problem should be a minimal diagnosis, which is a minimal set of faulty components. A minimal diagnosis stands in contradistinction to the trivial solution, in which all components are deemed to be faulty, and which, therefore, always explains all inconsistencies.
As part of the Numerical Propulsion System Simulation (NPSS) effort of NASA Lewis in conjunction with the United States aeropropulsion industry, a new system simulation framework, the National Cycle Program (NCP), capable of combining existing empirical engine models with new detailed component-based computational models is being developed. The software architecture of the NCP program involves a generalized object- oriented framework and a base-set of engine component models along with supporting tool kits which will support engine simulation in a distributed environment. As the models are extended to contain two and three dimensions the computing load increases rapidly and it is intended that this load be distributed across multiple work stations executing concurrently in order to get acceptably fast results. The research carried out was directed toward performance analysis of the distributed object system. More specifically, the performance of the actor-based distributed object design I created earlier was desired. To this end, the research was directed toward the design and implementation of suitable performance-analysis techniques and software to demonstrate those techniques. There were three specific results which are reported in two separate reports submitted separately as NASA Technical Memoranda. The results are: (1) Design, implementation, and testing of a performance analysis program for a set of active objects (actor based objects) which allowed the individual actors to be assigned to arbitrary processes on an arbitrary set of machines. (2) The global-balance-equation approach has the fundamental limitation that the number of equations increases exponentially with the number of actors. Hence, unlike many approximate approaches to this problem, the nearest-neighbor approach allows checking of the solution and an estimate of the error. The technique was demonstrated in a prototype analysis program as part of this research. The results of the program were checked against the global-balance solution discussed above. Late during the grant, a much better approximation was developed and this is discussed in result below. As a consequence, a proposal was submitted to continue the research by developing the new approximation including development of a complete program from the prototype. (3) The source of approximation in the nearest-neighbor algorithm is the requirement for estimating some joint probabilities from some marginal distributions. A completely ad hoc estimate was used in the prototype.
Engineering models of lunar topography including dielectric, optical, terrain, and crater models
An engineering model built for droplet combustion drop tower tests is described. The model was built using a design with mechanical and electrical assemblies of the same level of complexity as they will have in flight. The model was tested for functional operation and integrated into a 5-sec drop tower. Test data obtained to date are presented together with model and test cell diagrams.
Detailed acoustic engineering models that explore noise propagation mechanisms associated with noise attenuation and transmission paths created when using hearing protectors such as earplugs and headsets in high noise environments. Biomedical finite element (FE) models are developed based on volume Computed Tomography scan data which provides explicit external ear, ear canal, middle ear ossicular bones and cochlea geometry. Results from these studies have enabled a greater understanding of hearing protector to flesh dynamics as well as prioritizing noise propagation mechanisms. Prioritization of noise mechanisms can form an essential framework for exploration of new design principles and methods in both earplug and earcup applications. These models are currently being used in development of a novel hearing protection evaluation system that can provide experimentally correlated psychoacoustic noise attenuation. Moreover, these FE models can be used to simulate the effects of blast related impulse noise on human auditory mechanisms and brain tissue.
In 2012, NASA's mission of landing the Mars Science Laboratory (MSL) on Mars was successful. MSL was protected by an ablative heatshield of tiled low density material known as PICA (phenolic impregnated carbon ablator). The heatshield was instrumented with MEDLI (MSL Entry Descent Landing Instrument) a suite of sensors & thermocouples at discrete locations in order to monitor the in-depth ablator temperature response and surface pressure. MEDLI was designed and developed by NASA Langley, in partnership with NASA Ames Research Center for the purposes of probing Mars and evaluating the performance of the spacecraft upon entry into the Martian atmosphere. The flight data reduces the uncertainty in engineer models for predicting the response of a spacecraft towards the extreme heating environment of an entry into the Martian atmosphere. MEDLI2 is a part of the Mars 2020 mission and is the next-generation sensor suite for entry, descent, and landing. This data will again help engineers validate their flight models. Additionally, the atmospheric data, can help us understand atmospheric density and winds. This is a critical study for reducing risks to both robotic and future human missions to Mars. Engineered models for Mars 2020 are dependent upon parameters related to the materials response to heating and radiation. In this work, the thermal properties and other measurements of various ablative materials are analyzed to achieve greater utility of the 2012 MEDLI flight data and more accurately determine the parameters being used in Mars 2020. The purpose of this study was to measure specific heat, thermal conductivity, char yield, reflectance as a function of wavelength, and other thermal parameters and optical properties of various ablative materials. CO2(g) at extreme temperatures emits radiation impacting MEDLI2 flight predictions. Emissivity & absorptivity as a function of temperature were calculated from FTIR and UV-Vis data as a means of investigating whether these materials might absorb CO2 radiation upon entry into the Mars atmosphere.
Aerothermal analysis of spacecraft planetary entry is heavily dependent on heritage engineering models. The film coefficient heat transfer model examined in this paper estimates the convective heating to the vehicle for a laminar, dissociated, chemically reacting boundary layer for an Earth atmosphere. This model requires information about the vehicle and flow field for a given trajectory point and estimates a proportional relationship between enthalpy potential and convective heat flux. In practice it is the aerothermal engineer who must decide which assumptions are appropriate for their application. This work looks at numerous CFD simulations for an arbitrary, axisymmetric flight vehicle to analyze the relative importance of both the mass and energy constraints imposed at the wall boundary, as well as the effect of various diffusion models. The sensitivity of the engineering model is assessed based on the impact of the CFD assumptions as an effort to make it available to design engineers across industry.
Introduction: The Global Reference Atmospheric Model (GRAM) is one of the most widely used engineering models of planetary atmospheres. The GRAM upgrades are being developed by NASA Marshall Space Flight Center and NASA Langley Research Center. This presentation will provide details regarding the upgrades to the existing GRAMs, the development of new GRAMs, and the ongoing objectives, tasks, and milestones related to the GRAM upgrades funded by the NASA Science Mission Directorate (SMD). GRAM: The GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of the atmospheric properties for numerous planetary destinations. They provide mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path. They are extensively used by the engineering community because of their ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. GRAMs are currently available for Earth, Mars, Venus, Neptune, Titan, and Uranus. Outer Planet GRAM Upgrade Status: Code Modernization. The outer planet GRAMs have been rearchitected from Fortran to a common object-oriented C++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ releases of the rearchitected legacy outer planet GRAMs (Neptune and Titan-GRAM) are straight conversions from the latest Fortran version. Model Upgrades. The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemeris has been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. In FY20, the GRAM project established a contract with Hampton University to develop empirical global models for Jupiter, Saturn, Uranus, Neptune, and Titan. Upgraded Outer Planet GRAM Releases. GRAM Suite Version 1.0 was released in May 2020 and contains the rearchitected Neptune-GRAM, including the common GRAM framework and planet–specific code. GRAM Suite Version 1.1 was released in September 2020 and added the rearchitected Titan-GRAM to the GRAM Suite. A User Guide and Programmer’s Manual are released with all GRAMs. New Outer Planet GRAM Releases. New GRAMs have been developed for Uranus and Jupiter. Uranus-GRAM is based on the NASA Ames Research Center (ARC) Uranus Atmospheric Model [1,2] and was released in GRAM Suite Version 1.2 in July 2021. Jupiter-GRAM is based on Galileo probe Atmospheric Structure Instrument (ASI) data from Seiff et al. [3] Saturn-GRAM is also under development. Both Jupiter and Saturn-GRAM will be released in future versions of the GRAM Suite. Conclusions: GRAMs are vital and frequently used toolsets. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. Titan-GRAM atmosphere model upgrades will be included in the next phase of GRAM tasks. References: [1] Allen Jr., G.A. et al. (2014) 11th International Planetary Probe Workshop, Abstract #8023. [2] Allen Jr., G.A. et al. (2014) Workshop on the Study of the Ice Giant Planets, Abstract #2001. [3] Seiff, A. et al. (1998) JGR, 103, 22,857 -22,889. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD.
Introduction:The Global Reference Atmospheric Model (GRAM) is one of the most widely used engineering models of the atmosphere. GRAM development and maintenance has been led by NASA Marshall Space Flight Center (MSFC). The NASA Science Mission Directorate (SMD) has provided funding support to upgrade the GRAMs since Fiscal Year 2018. NASA Langley Research Center has been working with MSFC on the upgrades.This presentation will provide details regarding the upgrades that have been made to the existing GRAMs, the development of new GRAMs, as well as the ongoing objectives, tasks, and milestones re-lated to the GRAM upgrades funded by NASA SMD. GRAM: The GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of the atmospheric properties for numerous planetary destinations.They provide mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path.Theyhave been widely used by the engineering community because of their ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. MSFC has been developing and updating GRAMs since 1974; GRAMs are currently available for Earth, Mars, Venus, Neptune, and Titan. GRAM Upgrade Status: Code Moderization.The planetary GRAMs are being rearchitected from Fortran to a common object-ori-entedC++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ releases of the existing planetary GRAMs (Mars, Venus, Neptune, and Titan-GRAM) in the GRAM Suite are straight conversions from the latest Fortran version. Model Upgrades.The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemerishas been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. The GRAM team has received updated Mars General Circulation Model (MGCM) datasets from NASA Ames Research Center. Mars Global Ionosphere-Thermosphere Model (M-GITM) data is being obtained toreplace the Mars Thermospheric General Circulation Model (MTGCM) data in legacy Mars-GRAM. M-GITM and updated MGCM data will be incorporated into a future GRAM Suite release.Twoprojects that will improve the atmospheric model data in the GRAMs have been funded by the GRAM team since Fiscal Year 2020. Sanjay Limaye and Patrick Fry at the University of Wisconsin are reanalyzing the Venus Express radio occultation observations and analyzing the Akatsukiradio occultation observations.This will lead tothe calculation of number density, temperature, and pressure profiles for the 40-90 km altitude range. Kunio Sayanagi, Justin Garland, and Ryan McCabeat Hampton University are developing empirical global models for Venus,Jupiter, Saturn, Uranus, Neptune, and Titan that incorporates the latest data available for each of these planetary destinations. Upgraded GRAM Releases. GRAM Suite Version 1.0 was released in May 2020 and contains the rearchitected Neptune-GRAM, including the common GRAM framework and planet–specific code. GRAM Suite Version 1.1 was released in September 2020 and addedthe rearchitected Titan-GRAM to the GRAM Suite. A User Guide and Programmer’s Manualarereleased with all GRAMs. The rearchitected Mars and Venus-GRAMs will be released in upcomingversions of the GRAM Suite. New GRAM Releases. New GRAMs have been de-veloped for Uranus and Jupiter. Uranus-GRAM is based on an individual profile generated by Gary Allen (ARC) from Voyager 2 occultation data and will be released in GRAM Suite Version 1.2. Jupiter-GRAM is based on individual profile produced from Al Seiff’s Ju-piter model[1].Jupiter-GRAM will be released in GRAM Suite Version 1.3. Saturn-GRAM is currently under development and will be released in a future version of the GRAM Suite. Conclusions: GRAMs are vital and frequently used toolsets. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. NASA SMD funding has been essential to addressing current limitations and accomplishing GRAM developmental goals. Continua-tion ofSMD fundingwill ensure the development, up-grades, and maintenance of the GRAMs. References: [1] Seiff, A., et al. (1998) JGR, 103, 22,857-22,889. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD
The Software Architectures Engineering (SAE) Project at the Software Engineering Institute (SEI) has developed engineering modeling techniques that both reduce the complexity of software for domain-specific computer systems and result in systems that are easier to build and maintain. These techniques allow maximum freedom for system developers to apply their domain expertise to software. We have applied these techniques to several types of applications, including training simulators operating in real time, engineering simulators operating in non-real time, and real-time embedded computer systems. Our modeling techniques result in software that mirrors both the complexity of the application and the domain knowledge requirements. We submit that the proper measure of software complexity reflects neither the number of software component units nor the code count, but the locus of and amount of domain knowledge. As a result of using these techniques, domain knowledge is isolated by fields of engineering expertise and removed from the concern of the software engineer. In this paper, we will describe kinds of domain expertise, describe engineering by domains, and provide relevant examples of software developed for simulator applications using the techniques.
The program is divided into six tasks: preliminary design and performance; materials development; advanced concepts; economic assessment; demonstration/verification models; and a 15 meter engineering model. All support the main objective of the program which is the technology development necessary to evaluate, design, manufacture, package, transport and deploy the Hoop/Column reflector by means of a ground-based test program. Progress for each task is reported.
The development of lithium magnetoplasmadynamic (MPD) thrusters at JPL is discussed. The following topics are presented in vugraph form: mercury vapor mass flow control; porous tungsten vaporizer and housing; the lithium vaporizer experiment; a dry box for handling solid lithium; MPD thruster electrode modeling; engine lifetime definitions; cathode failure modeling; cathode erosion modeling; cathode thermal modeling; near cathode plasma model regions; cathode work function modeling; anode work function modeling; and radiation-cooled anodes.
An integrated methodology for propulsion and airframe control has been developed and evaluated for a Short Take-Off Vertical Landing (STOVL) aircraft using a fixed base flight simulator at NASA Lewis Research Center. For this evaluation the flight simulator is configured for transition flight using a STOVL aircraft model, a full nonlinear turbofan engine model, simulated cockpit and displays, and pilot effectors. The paper provides a brief description of the simulation models, the flight simulation environment, the displays and symbology, the integrated control design, and the piloted tasks used for control design evaluation. In the simulation, the pilots successfully completed typical transition phase tasks such as combined constant deceleration with flight path tracking, and constant acceleration wave-off maneuvers. The pilot comments of the integrated system performance and the display symbology are discussed and analyzed to identify potential areas of improvement.
Station Power and Energy Evaluation Determiner (SPEED) is a Java application program for analyzing the supply and demand aspects of the electrical power system of the International Space Station (ISS). SPEED can be executed on any computer that supports version 1.4 or a subsequent version of the Java Runtime Environment. SPEED includes an analysis module, denoted the Simplified Battery Solar Array Model, which is a simplified engineering model of the ISS primary power system. This simplified model makes it possible to perform analyses quickly. SPEED also includes a user-friendly graphical-interface module, an input file system, a parameter-configuration module, an analysis-configuration-management subsystem, and an output subsystem. SPEED responds to input information on trajectory, shadowing, attitude, and pointing in either a state-of-charge mode or a power-availability mode. In the state-of-charge mode, SPEED calculates battery state-of-charge profiles, given a time-varying power-load profile. In the power-availability mode, SPEED determines the time-varying total available solar array and/or battery power output, given a minimum allowable battery state of charge.
The NASA Glenn Research Center (GRC) is developing flywheel system requirements and designs for terrestrial and spacecraft applications. Several generations of flywheels have been designed and tested at GRC using in-house expertise in motors, magnetic bearings, controls, materials and power electronics. The maturation of a flywheel system from the concept phase to the preliminary design phase is accompanied by maturation of the Integrated Systems Performance model, where estimating relationships are replaced by physics based analytical techniques. The modeling can incorporate results from engineering model testing and emerging detail from the design process.