NASA Langley Research Center, Simulation Development and Analysis Branch - Flight Simulation Facilities
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The role of the mean state on MJO simulation in CESM2 ensemble simulation
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Daehyun Kang↗
An Observing System Simulation Experiment (OSSE) to Assess the Impact of Doppler Wind Lidar (DWL) Measurements on the Numerical Simulation of a Tropical Cyclone
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Lei Zhang↗
NASA LSII Lunar Simulant Project
Introduction: America has entered a new era of exploration. NASA’s Artemis program will lead humanity forward to the Moon and prepare us for the next giant leap, the exploration of Mars [1]. To champion technologies needed to live on and explore the Moon, NASA’s Space Technology Mission Directorate (STMD) established the Lunar Surface Innovation Initiative (LSII)[2].LSII's technology development portfolio includes: Utilizing the Moon’s resources; Establishing sustainable surface power; Building machinery and electronics that work in extreme environments, like super-chilly permanently shadowed craters; Mitigating lunar dust; Carrying out surface excavation, manufacturing and construction duties; and Extreme access which includes navigating and exploring the surface/subsurface. To support the development and testing of these technologies, LSII created the lunar simulant project, to create and/or acquire low-, medium-and high-fidelity lunar simulants to match the needs of STMD projects at all levels of technology readiness levels (TRL), as well as other NASA programs. There is not one bulk lunar simulant that will satisfy the needs of all projects. NASA's Approach to Simulants: Just as LSII's activities are being implemented through a combination of unique NASA work and public-private partnerships, NASA will work with commercial simulant providers to acquire simulants that meet NASA's needs. If warranted, NASA will develop simulants using government agencies, as was done with the NASA/USGS Lunar Highlands Type (NU-LHT)series of lunar simulants [3].NASA is also collaborating with the Johns Hopkins University Applied Physics Laboratory(JHUAPL) Lunar Surface Innovation Consortium (LSIC) in the development and characterization of lunar simulants [4]. Within NASA, a small team (< 10 people) is coordinating simulant activities across the agency, with team members located at several NASA centers. The overall objective of the project is to procure lunar simulants in sufficient amounts for earth-based testing of subsystems and systems in a variety of environments (i.e., laboratory, high-bay, thermal-vacuum chambers), required for Artemis missions to the Moon, as well as other missions carrying NASA lunar payloads, such as the Commercial Lunar Payload Services (CLPS)program [5]. Lunar Highlands Simulant: NASA's Artemis Program is targeting the lunar south pole region for initial human missions and the Artemis Base Camp. Hence, the LSII lunar simulant project is currently focusing on the mineralogy and properties of lunar highlands regolith [6 and 7]. Plagioclase-rich rocks (e.g., anorthosite, norite) are the dominant constituent in highlands simulants, with Shawmere, Stillwater, and White Mountain anorthosites being used in commercially available simulants. However, because these feedstocks are terrestrial in nature, they include hydrated minerals, carbon-bearing minerals, and other chemical signatures that are not present on the Moon, and these minor mineral assemblages need to be taken into account when trying to understand test procedures and results. Glass Component: While much attention has been placed on the rock/mineral component of lunar simulants, glass is just as important when creating simulants. The glass component in lunar regoliths is often greater than 50% by volume [8]. This component includes impact melt glass, dark matrix breccias, and agglutinates. However, this component is difficult, time-consuming and expensive to make. Most lunar simulants, past and current, have relied on basaltic cinder as a feedstock for glass. Getting better glass components at a lower cost, particularly agglutinates and glass with an anorthositic composition is a near-term objective that the NASA simulant project is trying to address . Characterization: It is extremely important for lunar simulants to be characterized by several analytical methods. Gruener et al. [9] and JHUAPL LSIC [10] conducted initial assessments of some of the commercially available simulants in 2019 and early 2020, before the global pandemic. Further analyses are needed to better quantify important parameters such as, modal mineralogy and glass content, particle shape, and particle size distribution. These quantified results can then be used in determining figures of merit (FOM) that show how well simulants compare to lunar regolith [11 and 12].
J E Gruener↗
Integrated Simulation Design Challenges to Support TPS Repair Operations
During the Orbiter Repair Maneuver (ORM) operations planned for Return to Flight (RTF), the Shuttle Remote Manipulator System (SRMS) must grapple the International Space Station (ISS), undock the Orbiter, maneuver it through a long duration trajectory, and orient it to an EVA crewman poised at the end of the Space Station Remote Manipulator System (SSRMS) to facilitate the repair of the Thermal Protection System (TPS). Once repair has been completed and confirmed, then the SRMS proceeds back through the trajectory to dock the Orbiter to the Orbiter Docking System. In order to support analysis of the complex dynamic interactions of the integrated system formed by the Orbiter, ISS, SRMS, and SSMS during the ORM, simulation tools used for previous ‘nominal’ mission support required substantial enhancements. These upgrades were necessary to provide analysts with the capabilities needed to study integrated system performance. Prevalent throughout this ORM operation is a dynamically varying topology. In other words, the ORM starts with the SRMS grappled to the mated Shuttle/ISS stack (closed loop topology), moves to an open loop chain topology consisting of the Shuttle, SRMS, and ISS, and then, at the repair configuration, extends the chain topology to one consisting of the Shuttle, SMS, ISS, and SSRMS/EVA crewman. The resulting long dynamic chain of vehicles and manipulators may exhibit significant motion between the Shuttle worksite and the EVA crewman due to the system flexibility throughout the topology (particularly within the SRMS/SSRMS joints and links). Since the attachment points of both manipulators span the flexible structure of the ISS, simulation analysis may also need to take that into consideration. Moreover, due to the lengthy time duration associated with the maneuver and repair, orbital effects become a factor and require the ISS vehicle control system to maintain active attitude control. Several facets of the ORM operation make the associated analytical efforts different from previous mission support, including: (1) the magnitude of the SRMS handled payload (Le., Orbiter class), (2) the orbital effects induced on the integrated system consisting of the large Shuttle and ISS masses connected by a light flexible SRMS, (3) long duration environmental consequences due to the lengthy operational times associated with the maneuver and repair of the TPS, (4) active attitude control (as opposed to free drift) interacting with the SRMS and SSRMS manipulators (also due to the length of the maneuver and repair), (5) relative dynamics between the EVA crewman and thc worksite influenced by the extended flexible topology. In order to meet these analysis challenges, an ORM simulation architecture was developed leveraging upon numerous pre-existing simulation elements to analyze the various subsystems individually. For example, core manipulator subsystem simulations for both the SRMS and SSRMS were originally combined to provide the dual-arm dynamics topology simulation (in the absence of orbital dynamics and vehicle control). This capability was later merged with the simulation used to analyze SRMS loading with a heavy payload in the orbital environment with an active payload control system (in this case, the ISS Attitude Control System (ACS)), configured for the ORM. The resulting worksite dynamics simulation, based off of the modified ORM simulation, provided the extended topological chain of vehicles and manipulators, while taking into account the orbital effects of both the Shuttle and ISS (as well as its ACS). Verification and validation (V&V) of these integrated simulations became a challenge in itself. A systematic approach needed to be developed such that integration simulation results could be tested against previous constituent simulations upon which these simulations were built. General V&V categories included: (1) core orbital state propagation, (2), stand-alone SRMS, (3) stand-alone SSRMS, (4) stand-alone ISS ACS, (5) integrated Shuttle, SRMS, ISS (with active ACS) in the orbital environment, and (5) dual-arm SRMS/SSRMS dynamics topology. Integrated simulation V&V run suites were created and correlated to verification runs from subsystem simulations, in order to establish the validity of the results. This paper discusses the simulation design challenges encountered while developing simulation capabilities to mirror the ORM operations. The paper also describes the incremental build approach that was utilized, starting with the subsystem simulation elements and integration into increasing more complex simulations until the resulting ORM worksite dynamics simulation had been assembled. Furthermore, the paper presents an overall integrated simulation V&V methodology based upon a subsystem level testing, integrated comparisons, and phased checkout.
Thermal Protection System↗
2007 Lunar Regolith Simulant Workshop Overview
The National Aeronautics and Space Administration (NASA) vision has as a cornerstone, the establishment of an Outpost on the Moon. This Lunar Outpost will eventually provide the necessary planning, technology development, and training for a manned mission to Mars in the future. As part of the overall activity, NASA is conducting Earth-based research and advancing technologies to a Technology Readiness Level (TRL) 6 maturity under the Exploration Technology Development Program that will be incorporated into the Constellation Project as well as other projects. All aspects of the Lunar environment, including the Lunar regolith and its properties, are important in understanding the long-term impacts to hardware, scientific instruments, and humans prior to returning to the Moon and living on the Moon. With the goal of reducing risk to humans and hardware and increasing mission success on the Lunar surface, it is vital that terrestrial investigations including both development and verification testing have access to Lunar-like environments. The Marshall Space Flight Center (MSFC) is supporting this endeavor by developing, characterizing, and producing Lunar simulants in addition to analyzing existing simulants for appropriate applications. A Lunar Regolith Simulant Workshop was conducted by MSFC in Huntsville, Alabama, in October 2007. The purpose of the Workshop was to bring together simulant developers, simulant users, and program and project managers from ETDP and Constellation with the goals of understanding users' simulant needs and their applications. A status of current simulant developments such as the JSC-1A (Mare Type Simulant) and the NASA/U.S. Geological Survey Lunar Highlands-Type Pilot Simulant (NU-LHT-1 M) was provided. The method for evaluating simulants, performed via Figures of Merit (FoMs) algorithms, was presented and a demonstration was provided. The four FoM properties currently being assessed are: size, shape, density, and composition. Some of the Workshop findings include: simulant developers must understand simulant users' needs and applications; higher fidelity simulants are needed and needed in larger quantities now; simulants must be characterized to allow "apples-to-apples" comparison of test results; simulant users should confer with simulant experts to assist them in the selection of simulants; safety precautions should be taken in the handling and use of simulants; shipping, storing, and preparation of simulants have important implications; and most importantly, close communications among the simulant community must be maintained and will be continued via telecoms, meetings, and an annual Lunar Regolith Simulant Workshop.
McLemore, Carole A.↗
2007 Lunar Regolith Simulant Workshop Overview
The National Aeronautics and Space Administration (NASA) vision has as a cornerstone, the establishment of an Outpost on the Moon. This Lunar Outpost will eventually provide the necessary planning, technology development, and training for a manned mission to Mars in the future. As part of the overall activity, NASA is conducting Earth-based research and advancing technologies to a Technology Readiness Level (TRL) 6 maturity under the Exploration Technology Development Program that will be incorporated into the Constellation Project as well as other projects. All aspects of the Lunar environment, including the Lunar regolith and its properties, are important in understanding the long-term impacts to hardware, scientific instruments, and humans prior to returning to the Moon and living on the Moon. With the goal of reducing risk to humans and hardware and increasing mission success on the Lunar surface, it is vital that terrestrial investigations including both development and verification testing have access to Lunar-like environments. The Marshall Space Flight Center (MSFC) is supporting this endeavor by developing, characterizing, and producing Lunar simulants in addition to analyzing existing simulants for appropriate applications. A Lunar Regolith Simulant Workshop was conducted by MSFC in Huntsville, Alabama, in October 2007. The purpose of the Workshop was to bring together simulant developers, simulant users, and program and project managers from ETDP and Constellation with the goals of understanding users' simulant needs and their applications. A status of current simulant developments such as the JSC-1A (Mare Type Simulant) and the NASA/U.S. Geological Survey Lunar Highlands-Type Pilot Simulant (NU-LHT-1M) was provided. The method for evaluating simulants, performed via Figures of Merit (FoMs) algorithms, was presented and a demonstration was provided. The four FoM properties currently being assessed are: size, shape, density, and composition. Some of the Workshop findings include: simulant developers must understand simulant users' needs and applications; higher fidelity simulants are needed and needed in larger quantities now; simulants must be characterized to allow "apples-to-apples" comparison of test results; simulant users should confer with simulant experts to assist them in the selection of simulants; safety precautions should be taken in the handling and use of simulants; shipping, storing, and preparation of simulants have important implications; and most importantly, close communications among the simulant community must be maintained and will be continued via telecoms, meetings, and an annual Lunar Regolith Simulant Workshop.
McLemore, Carole A.↗
Simulation Technology at NASA
NASA Ames Research Center is home to several high-fidelity research flight and air-traffic control simulation facilities which, together with an experienced workforce, produce high-quality research data and findings that have proven to be applicable in the real world. These assets include the Vertical Motion Simulator (VMS), Crew Vehicle Systems Research Facility (CVSRF), Future Flight Central (FFC) air traffic control tower simulator, and several air-traffic control (ATC) simulators. The VMS combines a high-fidelity simulation capability with an adaptable simulation environment, enabling customization for numerous human-in-the-loop research applications. The distinctive feature of the VMS is its unparalleled large amplitude, high-fidelity motion capability. In over 30 years of continuous operation, the VMS has contributed significantly to the body of knowledge in a range of disciplines directly benefiting several aerospace programs and flight safety, including the design and development of flight control systems for the Joint Strike Fighter, Space Shuttle Orbiter, and rotorcraft. It continues to be used for researching new vehicle configurations, vehicle control and safety, transfer-of-training, etc., by NASA, other government agencies, and Industry.The CVSRF includes two motion-based flight simulators: a Boeing 747-400 full flight simulator and the reconfigurable Advanced Concepts Flight Simulator (ACFS). These simulators are primarily used to research air-traffic management concepts and procedures, advanced navigation and avionics concepts, and cockpit human factors. FFC is a full-sized control tower simulator with a 360-degree external field-of-view display system and reconfigurable system architecture. FFC and the ATC simulators are used for testing air-traffic management automation and decision support tools and demonstrate their feasibility in a realistic environment prior to technology transfer for implementation in the National Airspace System (NAS).To support integrated simulations and flight-tests for NASA's Unmanned Aircraft Systems (UAS) in the National Airspace System (NAS) Project, NASA developed a distributed test environment incorporating Live, Virtual, Constructive, (LVC) concepts. Development of the software enabling the LVC is conducted primarily at the Distributed Simulation Research Lab (DSRL) at NASA Ames. The LVC components provide the core infrastructure supporting simulation of UAS operations by integrating live and virtual aircraft in a realistic air traffic environment. This provides the ability to conduct tests more efficiently by promoting the use of existing distributed assets. The LVC infrastructure was used in several human-in-the-loop simulations to evaluate acceptance of Detect and Avoid (DAA) advisories used by UAS pilots to maintain well clear of other virtual traffic and to negotiate maneuvers with air traffic control. It is currently being used to support testing of self-separation algorithms between unmanned and manned aircraft in live flight. Further simulations with more comprehensive air traffic scenarios mixing live and virtual aircraft is planned.In the current fiscal environment, maintaining and upgrading these high-fidelity simulation assets and retaining the skilled workforce necessary to meet future research needs is the primary non-technical challenge.
CVSRF↗
Aviation Simulators for the Desktop: Panel and Demonstrations
Panel Members are: Christine M. Mitchell (Georgia Tech), Michael T. Palmer (NASA Langley), Greg Pisani (NASA Ames), and Amy R. Pritchett (MIT). The Panel members are affiliated with aviation human factors groups from NASA Ames, NASA Langley, MITCHELL Department of Aerospace and Aeronautical Engineering, and Georgia Technics Center for Human-Machine Systems Research. Panelists will describe the simulator(s) used in their respective institutions including a description of the FMS aircraft models, software, hardware, and displays. Panelists will summarize previous, on-going, and planned empirical studies conducted with the simulators. Greg Pisanich will describe two NASA Ames simulation systems: the Stone Soup Simulator (SSS), and the Airspace Operations Human Factors Simulation Laboratory. The the Stone Soup Simulator is a desktop-based, research flight simulator that includes mode control, flight management, and datalink functionality. It has been developed as a non-proprietary simulator that can be easily distributed to academic and industry researchers who are collaborating on NASA research projects. It will be used and extended by research groups represented by at least two panelists (Mitchell and Palmer). The Airspace Operations Simulator supports the study of air traffic control in conjunction with the flight deck. This simulator will be used provide an environment in which many AATT and free flight concepts can be demonstrated and evaluated. Mike Palmer will describe two NASA Langley efforts: The Langley Simulator and MD-11 extensions to the NASA Amesbury simulator. The first simulator is publicly available and combines a B-737 model with a high fidelity flight management system. The second simulator enhances the S3 simulator with MD-11 electronic flight displays together with modifications to the flight and FMS models to emulate MD-11 dynamics and operations. Chris Mitchell will describe GT-EFIRT (Georgia Tech-Electronic Flight Instrument Research Tool) and B-757 enhancements to the NASA Ames S3. GT-EFIRT is a medium fidelity simulator used to conduct preliminary studies of the CATS (crew activity tracking system). Like the Langley efforts with S3, the Georgia Tech enhancements will allow it to emulate the dynamics and operations of a widely used glass cockpit. Amy Pritchett will describe the MIT simulator(s) that have been used in a range of research investigating cockpit displays, warning devices, and flight deck-ATC interaction.
Pisanich, Greg↗
Flight Mechanics Modeling and Simulation of the Earth Entry System
Introduction: The Mars Sample Return (MSR) Campaign being planned by NASA and ESA has the ambitious goal to return Mars samples back to Earth. This international collaboration had developed a concept of operations that included a ESA-designed Earth Return Orbiter (ERO) and NASA-designed Capture, Containment, and Return System (CCRS). The Earth Entry System (EES), consisting of a protective aeroshell that houses the samples as well as sample containment vessels, would conduct entry, descent, and landing (EDL) on a direct Earth trajectory. The EES would enter on a spin-stabilized ballistic trajectory with the goal to passively achieve aerodynamic stability throughout all regions of flight. The EDL sequence would end with the EES impacting the soft playa soil of the Utah Test and Training Range (UTTR). As of the submission of this abstract, the MSR campaign is undergoing a re-architecture leading to a pause in EES development. However, the novel approaches developed in flight mechanics modeling and simulation can significantly benefit the greater IPPW community in the development of Earth return vehicles. This paper will present the latest state of EES flight mechanics modeling and simulation. The paper will highlight the simulation architecture developed and key lessons learned from understanding of EDL trajectory sensitivities. Modeling and Simulation: Figure 1 provides a high-level concept of operations for the approach, entry, descent, and landing (AEDL) phase of the CCRS-portion of MSR. The objective of EES flight mechanics is to model and simulate the EES trajectory from ERO separation to ground impact at UTTR. A variety of flight mechanics simulation models were utilized to model both exo-atmopsheric and atmospheric portions of flight. 42, a 6-DOF simulation developed at Goddard Space Flight Center, is utilized for propagating the attitude of EES during exo-atmospheric flight. 42 allows for a variety of spin eject mechanism scenarios to be simulated for analysis. 10 minutes prior to entry, the 42 states are handed off to the EDL sims. The prime EDL sim utilized by EES is the Program to Optimize Simulated Trajectories II (POST2), a 6-DOF sim developed at Langley Research Center, and the independent verification and validation EDL sim utilized is DSENDS, a 6-DOF sim developed at Jet Propulsion Laboratory. Figure 2 provides a visualization of the flight mechanics simulation model flow through various points in the AEDL phase. Due to the existence of a variety of sim models, the EES flight mechanics team developed processes for data hand-off. These processes included the development of a centralized coordinate frame document, utilization of a single, centralized simulation input document for all sims to reference, and hand-off files containing both the technical data to be ingested by other flight mechanics sims as well as annotations of modeling assumptions utilized to generate the data. Figure~\ref{fig:post2simarchitecture} provides an overview of the POST2 sim architecture wherein POST2 ingests numerous subsystem models and input files. The dispersed state file generated by MONTE provides the position/velocity state of the trajectory while the 42 Handoff file provides the attitude. The aerodynamics database, delivered by the EES aeroscience team, is utilized to simulate the aerodynamic forces and moments experienced during EDL. A custom atmosphere model, developed by EES atmosphere team, is utilized to simulate the anticipated atmosphere environment around the region of Earth through which the EES trajectory flys. These inputs and subsystem models can be varied depending on the AEDL flight mechanics scenario being simulated. Monte Carlo simulations are utilized to generate statistical AEDL performance metrics in the form of scorecards and violin plots. Furthermore, outputs from the POST2 simulation are utilized for follow-on analyses including aerothermal and landing performance. \section{Flight Mechanics Lessons Learned} Though the EES flight mechanics team uncovered a variety of lessons learned through the analysis conducted to support CCRS through preliminary design review, this paper will highlight the most important lessons. A key AEDL performance goal is to ensure the landing footprint of EES remains on the UTTR south range. A common modeling strategy used in EDL analysis is One-Variable-At-a-Time (OVAT). OVAT analysis provides insight into the key drivers that affect AEDL performance metrics. Figure 3 shows the landing ellipses for single dispersion sources as compared to the baseline aggregate of all dispersions. The figure shows that atmosphere winds alone dominate the size of the footprint ellipse (note: EES does not use a parachute unlike previous Earth-return missions and is in wind-driven free fall for ~5min). The significance of the wind led the EES flight mechanics team to pursue the development of a Custom Atmosphere Model [4], in lieu of EarthGRAM [1], built on actual radiosonde wind measurements around the UTTR-region. This decision was driven by the realism in the generated footprint ellipses and lessons-learned from Stardust [5]. These findings will be invaluable for future Earth-return missions in providing an early understanding of the key drivers affecting footprint size and modeling considerations for which to account. Another lesson learned is tied to the AEDL performance goal of achieving passive stability throughout all regions of flight. It is well understood that blunt-body aeroshells are less stable as they transition from supersonic to subsonic. Eliminating a backshell does help improvestability; however, other phenomena such as roll-induced instability during terminal descent can still arise. The EES flight mechanics team developed stability metrics as tools to better understand the causes of and better predict the onset of dynamic instability. These tools were built upon analytical models developed by Jaffe [3] and Murphy [2]. The tools were shown to both be very accurate in correlation with actual unstable cases and useful in developing stability margin policies based on the vehicle design and simulation considerations (e.g. sphere-cone angle change, mass change, wind turbulence). These tools allowed for the current EES design to demonstrate the ability to achieve passive stability and can be an invaluable tool for consideration in the design of parachute-less Earth-return vehicles.
Rohan Deshmukh↗
Spatially Local Surrogate Modeling of Subgrid-Scale Effects in Idealized Atmospheric Flows: A Deep Learned Approach Using High-Resolution Simulation Data
Abstract We introduce a machine learned surrogate model from high-resolution simulation data to capture the subgrid-scale effects in dry, stratified atmospheric flows. We use deep neural networks (NNs) to model the spatially local state differences between a coarse-resolution simulation and a high-resolution simulation. The setup enables the capture of both dissipative and antidissipative effects in the state differences. The NN model is able to accurately capture the state differences in offline tests outside the training regime. In online tests intended for production use, the NN-coupled coarse simulation has higher accuracy over a significant period of time compared to the coarse-resolution simulation without any correction. We provide evidence of the capability of the NN model to accurately capture high-gradient regions in the flow field. With the accumulation of the errors, the NN-coupled simulation becomes computationally unstable after approximately 90 coarse simulation time steps. Insights gained from these surrogate models further pave the way for formulating stable, complex, physics-based spatially local NN models which are driven by traditional subgrid-scale turbulence closure models. Significance Statement Flows in the atmosphere are highly chaotic and turbulent, comprising flow structures of broad scales. For effective computational modeling of atmospheric flows, the effects of the small- and large-scale structures need to be captured by the simulations. Capturing the small-scale structures requires fine-resolution simulations. Even with the current state-of-the-art supercomputers, it can be prohibitively expensive to simulate these flows when computed for the entire earth over climate time scales. Thus, it is necessary to focus on the larger-scale structures using a coarse-resolution simulation while capturing the effects of the smaller-scale structures using some parameterization (approximation) scheme and incorporating it into the coarse-resolution simulation. We use machine learning to model the effects of the small-scale structures (subgrid-scale effects) in atmospheric flows. Data from a fine-resolution simulation is used to compute the missing subgrid-scale effects in coarse-resolution simulations. We then use machine learning models to approximate these differences between the coarse- and fine-resolution simulations. We see improved accuracy for the coarse-resolution simulations when corrected using these machine learned models.
54 ENVIRONMENTAL SCIENCES↗
The Shuttle Mission Simulator computer generated imagery
Equipment available in the primary training facility for the Space Transportation System (STS) flight crews includes the Fixed Base Simulator, the Motion Base Simulator, the Spacelab Simulator, and the Guidance and Navigation Simulator. The Shuttle Mission Simulator (SMS) consists of the Fixed Base Simulator and the Motion Base Simulator. The SMS utilizes four visual Computer Generated Image (CGI) systems. The Motion Base Simulator has a forward crew station with six-degrees of freedom motion simulation. Operation of the Spacelab Simulator is planned for the spring of 1983. The Guidance and Navigation Simulator went into operation in 1982. Aspects of orbital visual simulation are discussed, taking into account the earth scene, payload simulation, the generation and display of 1079 stars, the simulation of sun glare, and Reaction Control System jet firing plumes. Attention is also given to landing site visual simulation, and night launch and landing simulation.
Henderson, T. H.↗
Development of Monte Carlo Capability for Orion Parachute Simulations
Parachute test programs employ Monte Carlo simulation techniques to plan testing and make critical decisions related to parachute loads, rate-of-descent, or other parameters. This paper describes the development and use of a MATLAB-based Monte Carlo tool for three parachute drop test simulations currently used by NASA. The Decelerator System Simulation (DSS) is a legacy 6 Degree-of-Freedom (DOF) simulation used to predict parachute loads and descent trajectories. The Decelerator System Simulation Application (DSSA) is a 6-DOF simulation that is well suited for modeling aircraft extraction and descent of pallet-like test vehicles. The Drop Test Vehicle Simulation (DTVSim) is a 2-DOF trajectory simulation that is convenient for quick turn-around analysis tasks. These three tools have significantly different software architectures and do not share common input files or output data structures. Separate Monte Carlo tools were initially developed for each simulation. A recently-developed simulation output structure enables the use of the more sophisticated DSSA Monte Carlo tool with any of the core-simulations. The task of configuring the inputs for the nominal simulation is left to the existing tools. Once the nominal simulation is configured, the Monte Carlo tool perturbs the input set according to dispersion rules created by the analyst. These rules define the statistical distribution and parameters to be applied to each simulation input. Individual dispersed parameters are combined to create a dispersed set of simulation inputs. The Monte Carlo tool repeatedly executes the core-simulation with the dispersed inputs and stores the results for analysis. The analyst may define conditions on one or more output parameters at which to collect data slices. The tool provides a versatile interface for reviewing output of large Monte Carlo data sets while preserving the capability for detailed examination of individual dispersed trajectories. The Monte Carlo tool described in this paper has proven useful in planning several Crew Exploration Vehicle parachute tests.
Moore, James W.↗
Global Positioning System Simulator Field Operational Procedures
Global Positioning System (GPS) simulation is an important activity in the development or qualification of GPS signal receivers for space flight. Because a GPS simulator is a critical resource it is highly desirable to develop a set of field operational procedures to supplement the basic procedures provided by most simulator vendors. Validated field procedures allow better utilization of the GPS simulator in the development of new test scenarios and simulation operations. These procedures expedite simulation scenario development while resulting in scenarios that are more representative of the true design, as well as enabling construction of more complex simulations than previously possible, for example, spacecraft maneuvers. One difficulty in the development of a simulation scenario is specifying various modes of test vehicle motion and associated maneuvers requiring that a user specify some (but not all) of a few closely related simulation parameters. Currently this can only be done by trial and error. A stand-alone procedure that implements the simulator maneuver motion equations and solves for the motion profile transient times, jerk and acceleration would be of considerable value. Another procedure would permit the specification of some configuration parameters that would determine the simulated GPS signal composition. The resulting signal navigation message, for example, would force the receiver under test to use only the intended C-code component of the simulated GPS signal. A representative class of GPS simulation-related field operational procedures is described in this paper. These procedures were developed and used in support of GPS integration and testing for many successful spacecraft missions such as SAC-A, EO-1, AMSAT, VCL, SeaStar, sounding rockets, and by using the industry standard Spirent Global Simulation Systems Incorporated (GSSI) STR series simulators.
Kizhner, Semion↗
Automated Knowledge Discovery From Simulators
A computational method, SimLearn, has been devised to facilitate efficient knowledge discovery from simulators. Simulators are complex computer programs used in science and engineering to model diverse phenomena such as fluid flow, gravitational interactions, coupled mechanical systems, and nuclear, chemical, and biological processes. SimLearn uses active-learning techniques to efficiently address the "landscape characterization problem." In particular, SimLearn tries to determine which regions in "input space" lead to a given output from the simulator, where "input space" refers to an abstraction of all the variables going into the simulator, e.g., initial conditions, parameters, and interaction equations. Landscape characterization can be viewed as an attempt to invert the forward mapping of the simulator and recover the inputs that produce a particular output. Given that a single simulation run can take days or weeks to complete even on a large computing cluster, SimLearn attempts to reduce costs by reducing the number of simulations needed to effect discoveries. Unlike conventional data-mining methods that are applied to static predefined datasets, SimLearn involves an iterative process in which a most informative dataset is constructed dynamically by using the simulator as an oracle. On each iteration, the algorithm models the knowledge it has gained through previous simulation trials and then chooses which simulation trials to run next. Running these trials through the simulator produces new data in the form of input-output pairs. The overall process is embodied in an algorithm that combines support vector machines (SVMs) with active learning. SVMs use learning from examples (the examples are the input-output pairs generated by running the simulator) and a principle called maximum margin to derive predictors that generalize well to new inputs. In SimLearn, the SVM plays the role of modeling the knowledge that has been gained through previous simulation trials. Active learning is used to determine which new input points would be most informative if their output were known. The selected input points are run through the simulator to generate new information that can be used to refine the SVM. The process is then repeated. SimLearn carefully balances exploration (semi-randomly searching around the input space) versus exploitation (using the current state of knowledge to conduct a tightly focused search). During each iteration, SimLearn uses not one, but an ensemble of SVMs. Each SVM in the ensemble is characterized by different hyper-parameters that control various aspects of the learned predictor - for example, whether the predictor is constrained to be very smooth (nearby points in input space lead to similar output predictions) or whether the predictor is allowed to be "bumpy." The various SVMs will have different preferences about which input points they would like to run through the simulator next. SimLearn includes a formal mechanism for balancing the ensemble SVM preferences so that a single choice can be made for the next set of trials.
Burl, Michael↗
A Functional Comparison of Lunar Regoliths and Their Simulants
Lunar regolith simulants are essential to the development of technology for human exploration of the Moon. Any equipment that will interact with the surface environment must be tested with simulant to mitigate risk. To reduce the greatest amount of risk, the simulant must replicate the lunar surface as well as possible. To quantify the similarities and differences between simulants, the Figures of Merit were developed. The Figures of Merit software compares the simulants and regolith by particle size, particle shape, density, and bulk chemistry and mineralogy; these four properties dictate the majority of the remaining characteristics of a geologic material. There are limitations to both the current Figures of Merit approach and simulants in general. The effect of particle textures is lacking in the Figures of Merit software, and research into this topic has only recently begun with applications to simulants. In addition, not all of the properties for lunar regolith are defined sufficiently for simulant reproduction or comparison; for example, the size distribution of particles greater than 1 centimeter and the makeup of particles less than 10 micrometers is not well known. For simulants, contamination by terrestrial weathering products or undesired trace phases in feedstock material is a major issue. Vapor deposited rims have not yet been created for simulants. Fortunately, previous limitations such as the lack of agglutinates in simulants have been addressed and commercial companies are now making agglutinate material for simulants. Despite some limitations, the Figures of Merit sufficiently quantify the comparison between simulants and regolith for useful application in lunar surface technology. Over time, the compilation and analysis of simulant user data will add an advantageous predictive capability to the Figures of Merit, accurately relating Figures of Merit characteristics to simulant user parameters.
Rickman, D.↗
Developing an Automated Microscopic Traffic Simulation Scenario Generation Tool
Traffic simulation is an effective tool for urban planners, traffic engineers, and researchers to study traffic. In particular, microscopic traffic simulation, which simulates individual vehicles’ movements within a transportation network, has demonstrated its importance in analyzing and managing transportation systems. However, integrating data from various sources, generating traffic scenarios, and importing information into traffic simulators to conduct microscopic simulations have always been a challenge. This paper presents a solution to overcome this challenge: RealTwin, a comprehensive tool for automated scenario generation for microscopic traffic simulation. Following a streamlined scenario generation and calibration workflow, RealTwin effectively bridges gaps between traffic data from various sources and traffic simulators, making microscopic traffic simulation more accessible for researchers and engineers across various levels of expertise. Using RealTwin to generate a real-world traffic scenario in Simulation of Urban Mobility (SUMO), VISSIM, and AIMSUN, RealTwin’s ability is demonstrated in the construction of realistic and consistent traffic scenarios in different simulators. Furthermore, this paper introduces and illustrates RealTwin’s capability for technology (e.g., autonomous vehicle) scenario generation. This feature can contribute to more comprehensive microscopic simulations, facilitating the analysis of potential effects of various technological innovations on mobility, energy efficiency, and safety. Finally, RealTwin is used to calibrate a simulation in SUMO. In conclusion, the calibration module enhances RealTwin’s ability to generate consistent simulations across different platforms and more realistic simulations that reflect real-world traffic operations.
autonomous vehicle↗
On Fast Post-Processing of Global Positioning System Simulator Truth Data and Receiver Measurements and Solutions Data
Post-processing of data, related to a GPS receiver test in a GPS simulator and test facility, is an important step towards qualifying a receiver for space flight. Although the GPS simulator provides all the parameters needed to analyze a simulation, as well as excellent analysis tools on the simulator workstation, post-processing is not a GPS simulator or receiver function alone, and it must be planned as a separate pre-flight test program requirement. A GPS simulator is a critical resource, and it is desirable to move off the pertinent test data from the simulator as soon as a test is completed. The receiver and simulator databases are used to extract the test data files for postprocessing. These files are then usually moved from the simulator and receiver systems to a personal computer (PC) platform, where post-processing is done typically using PC-based commercial software languages and tools. Because of commercial software systems generality their functions are notoriously slow and more than often are the bottleneck even for short duration simulator-based tests. There is a need to do post-processing faster and within an hour after test completion, including all required operations on the simulator and receiver to prepare and move off the post-processing files. This is especially significant in order to use the previous test feedback for the next simulation setup or to run near back-to-back simulation scenarios. Solving the post-processing timing problem is critical for a pre-flight test program success. Towards this goal an approach was developed that allows to speed-up post-processing by an order of a magnitude. It is based on improving the post-processing bottleneck function algorithm using a priory information that is specific to a GPS simulation application and using only the necessary volume of truth data. The presented postprocessing scheme was used in support of a few successful space flight missions carrying GPS receivers.
Kizhner, Semion↗