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NASA NTRS2012

Risk Assessment Update: Russian Segment

BUMPER-II version 1.95j source code was provided to RSC-E- and Khrunichev at January 2012 MMOD TIM in Moscow. MEMCxP and ORDEM 3.0 environments implemented as external data files. NASA provided a sample ORDEM 3.0 g."key" & "daf" environment file set for demonstration and benchmarking BUMPER -II v1.95j installation at the Jan-12 TIM. ORDEM 3.0 has been completed and is currently in beta testing. NASA will provide a preliminary set of ORDEM 3.0 ".key" & ".daf" environment files for the years 2012 through 2028. Bumper output files produced using the new ORDEM 3.0 data files are intended for internal use only, not for requirements verification. Output files will contain these words ORDEM FILE DESCRIPTION = PRELIMINARY VERSION: not for production. The projectile density term in many BUMPER-II ballistic limit equations will need to be updated. Cube demo scripts and output files delivered at the Jan-12 TIM have been updated for the new ORDEM 3.0 data files. Risk assessment results based on ORDEM 3.0 and MEM will be presented for the Russian Segment (RS) of ISS.

Christiansen, Eric↗

NASA NTRS1989

Advanced NSTS propulsion system verification study

The merits of propulsion system development testing are discussed. The existing data base of technical reports and specialists is utilized in this investigation. The study encompassed a review of all available test reports of propulsion system development testing for the Saturn stages, the Titan stages, and the Space Shuttle main propulsion system. The knowledge on propulsion system development and system testing available from specialists and managers was also 'tapped' for inclusion.

Wood, Charles↗

NASA NTRS2011

Grazing Incidence Wavefront Sensing and Verification of X-Ray Optics Performance

Evaluation of interferometrically measured mirror metrology data and characterization of a telescope wavefront can be powerful tools in understanding of image characteristics of an x-ray optical system. In the development of soft x-ray telescope for the International X-Ray Observatory (IXO), we have developed new approaches to support the telescope development process. Interferometrically measuring the optical components over all relevant spatial frequencies can be used to evaluate and predict the performance of an x-ray telescope. Typically, the mirrors are measured using a mount that minimizes the mount and gravity induced errors. In the assembly and mounting process the shape of the mirror segments can dramatically change. We have developed wavefront sensing techniques suitable for the x-ray optical components to aid us in the characterization and evaluation of these changes. Hartmann sensing of a telescope and its components is a simple method that can be used to evaluate low order mirror surface errors and alignment errors. Phase retrieval techniques can also be used to assess and estimate the low order axial errors of the primary and secondary mirror segments. In this paper we describe the mathematical foundation of our Hartmann and phase retrieval sensing techniques. We show how these techniques can be used in the evaluation and performance prediction process of x-ray telescopes.

Saha, Timo T.↗

NASA NTRS1994

Precise orbit analysis and global verification results from ERS-1 altimetry

A technique which employs dual satellite crossover measurements from ERS-1 and Topology Ocean Experiment (TOPEX)/Poseidon together with laser tracking data and single satellite crossover measurements for ERS-1 precision orbit determination is described. The accuracy assessment of the resulting ERS-1 orbit is provided. Results of global verification of the ERS-1 Ocean Products (OPR02) and the Interim Geophysical Data Records (IGDR) data products in terms of altimeter bias, time lag bias and sea state bias are presented.

Shum, C. K.↗

NASA NTRS1986

Space radiation studies

Instrument design and data analysis expertise was provided in support of several space radiation monitoring programs. The Verification of Flight Instrumentation (VFI) program at NASA included both the Active Radiation Detector (ARD) and the Nuclear Radiation Monitor (NRM). Design, partial fabrication, calibration and partial data analysis capability to the ARD program was provided, as well as detector head design and fabrication, software development and partial data analysis capability to the NRM program. The ARD flew on Spacelab-1 in 1983, performed flawlessly and was returned to MSFC after flight with unchanged calibration factors. The NRM, flown on Spacelab-2 in 1985, also performed without fault, not only recording the ambient gamma ray background on the Spacelab, but also recording radiation events of astrophysical significance.

Gregory, J. C.↗

NASA NTRS2019

Assimilation of Aerosol Observations in the NASA GEOS Model

In the GEOS near real-time system, as well as in MERRA-2 which is the latest reanalysis produced at NASA's Global Modeling Assimilation Office (GMAO), the assimilation of aerosol observations is performed by means of a so-called analysis splitting method. The prognostic model is based on the GEOS model radiatively coupled to GOCART aerosol module and includes assimilation of bias-corrected Aerosol Optical Depth (AOD) at 550 nm from various space-based remote sensing platforms. In line with the transition of the GEOS meteorological data assimilation system to a hybrid Ensemble-Variational formulation, we are updating the aerosol component of our assimilation system to a variational ensemble type of scheme. In this talk we will examine the impact of replacing the current analysis splitting scheme with this new approach. Starting with the assimilation of satellite based single-channel retrievals; we will discuss the impact of this aerosol data assimilation technique on the 3D aerosol distributions by means of innovation statistics and verification against independent datasets such as the Aerosol Robotic Network (AERONET) and surface PM2.5. We will also present preliminary results related to the introduction of new aerosol data types in GEOS, including multi-spectral AOD retrievals.

AERONET↗

NASA NTRSDate not supplied

Actor-based Runtime Verification with MESA

This work presents a runtime verification approach implemented in the tool MESA (MEssage-based System Analysis) which allows for using concurrent monitors to check for properties specified in data parameterized temporal logic and state machines. The tool is implemented as an internal Scala DSL. We employ the actor programming model to implement MESA where monitors are captured by concurrent actors that communicate via messaging. The paper presents a case study in which MESA is used to effectively monitor a large number of flights from live US airspace data streams. We also perform an empirical study by conducting experiments using monitoring systems with different numbers of concurrent monitors and different layers of indexing on the data contained in events. The paper describes the experiments, evaluates the results, and discusses challenges faced during the study. The evaluation shows the value of combining concurrency with indexing to handle data rich events.

runtime verification↗

NASA NTRS2019

The NASA Orbital Debris Engineering Model 3.1: Development, Verification, and Validation

The NASA Orbital Debris Program Office has developed the Orbital Debris Engineering Model (ORDEM) primarily as a tool for spacecraft designers and other users to understand the long-term risk of collisions with orbital debris. The newest version, ORDEM 3.1, incorporates the latest and highest fidelity datasets available to build and validate representative orbital debris populations encompassing low Earth orbit (LEO) to geosynchronous orbit (GEO) altitudes for the years 2016-2050. ORDEM 3.1 models fluxes for object sizes > 10 μm within or transiting LEO and > 10 cm in GEO. The deterministic portion of the populations in ORDEM 3.1 is based on the U.S. Space Surveillance Network (SSN) catalog, which provides coverage down to approximately 10 cm in LEO and 1 m in GEO. Observational datasets from radar, in situ, and optical sources provide a foundation from which the model populations are statistically extrapolated to smaller sizes and orbit regions that are not well-covered by the SSN catalog, yet may pose the greatest threat to operational spacecraft. Objects in LEO ranging from approximately 5 mm to 10 cm are modeled using observational data from ground-based radar, namely the Haystack Ultrawideband Satellite Imaging Radar (HUSIR – formerly known as Haystack). The LEO population smaller than approximately 3 mm in size is characterized based on a reanalysis of in situ data from impacts to the windows and radiators of the U.S. Space Transportation System orbiter vehicle, i.e., the Space Shuttle. Data from impacts on the Hubble Space Telescope are also used to validate the sub-millimeter model populations in LEO. Debris in GEO with sizes ranging from 10 cm to 1 m is modeled using optical measurement data from the Michigan Orbital DEbris Survey Telescope (MODEST). Specific, major debris-producing events, including the Fengyun-1C, Iridium 33, and Cosmos 2251 debris clouds, and unique populations, such as sodium-potassium droplets, have been re-examined and are modeled and added to the ORDEM environment separately. The debris environment greater than 1 mm is forecast using NASA’s LEO-to- GEO ENvironment Debris model (LEGEND). Future explosions of intact objects and collisions involving objects greater than 10 cm are assessed statistically, and the NASA Standard Satellite Breakup Model is used to generate fragments from these events. Fragments smaller than 10 cm are further differentiated based on material density categories, i.e., high-, medium-, and low-density, to better characterize the potential debris risk posed to spacecraft. The future projection of the sub-millimeter environment is computed using a special small-particle degradation model where small particles are created from intact spacecraft and rocket bodies. This work discusses the development, features, and capabilities of the ORDEM 3.1 model; the ne new data analyses used to build the model populations; and sample verification and validation results.

Matney, M.↗

NASA NTRS2010

Experimental Space Shuttle Orbiter Studies to Acquire Data for Code and Flight Heating Model Validation

In an experimental study to obtain detailed heating data over the Space Shuttle Orbiter, CUBRC has completed an extensive matrix of experiments using three distinct models and two unique hypervelocity wind tunnel facilities. This detailed data will be employed to assess heating augmentation due to boundary layer transition on the Orbiter wing leading edge and wind side acreage with comparisons to computational methods and flight data obtained during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM during STS-119 reentry. These comparisons will facilitate critical updates to be made to the engineering tools employed to make assessments about natural and tripped boundary layer transition during Orbiter reentry. To achieve the goals of this study data was obtained over a range of Mach numbers from 10 to 18, with flight scaled Reynolds numbers and model attitudes representing key points on the Orbiter reentry trajectory. The first of these studies were performed as an integral part of Return to Flight activities following the accident that occurred during the reentry of the Space Shuttle Columbia (STS-107) in February of 2003. This accident was caused by debris, which originated from the foam covering the external tank bipod fitting ramps, striking and damaging critical wing leading edge heating tiles that reside in the Orbiter bow shock/wing interaction region. During investigation of the accident aeroheating team members discovered that only a limited amount of experimental wing leading edge data existed in this critical peak heating area and a need arose to acquire a detailed dataset of heating in this region. This new dataset was acquired in three phases consisting of a risk mitigation phase employing a 1.8% scale Orbiter model with special temperature sensitive paint covering the wing leading edge, a 0.9% scale Orbiter model with high resolution thin-film instrumentation in the span direction, and the primary 1.8% scale Orbiter model with detailed thin-film resolution in both the span and chord direction in the area of peak heating. Additional objectives of this first study included: obtaining natural or tripped turbulent wing leading edge heating levels, assessing the effectiveness of protuberances and cavities placed at specified locations on the orbiter over a range of Mach numbers and Reynolds numbers to evaluate and compare to existing engineering and computational tools, obtaining cavity floor heating to aid in the verification of cavity heating correlations, acquiring control surface deflection heating data on both the main body flap and elevons, and obtain high speed schlieren videos of the interaction of the orbiter nose bow shock with the wing leading edge. To support these objectives, the stainless steel 1.8% scale orbiter model in addition to the sensors on the wing leading edge was instrumented down the windward centerline, over the wing acreage on the port side, and painted with temperature sensitive paint on the starboard side wing acreage. In all, the stainless steel 1.8% scale Orbiter model was instrumented with over three-hundred highly sensitive thin-film heating sensors, two-hundred of which were located in the wing leading edge shock interaction region. Further experimental studies will also be performed following the successful acquisition of flight data during the Orbiter Entry Boundary Layer Flight Experiment and HYTHIRM on STS-119 at specific data points simulating flight conditions and geometries. Additional instrumentation and a protuberance matching the layout present during the STS-119 boundary layer transition flight experiment were added with testing performed at Mach number and Reynolds number conditions simulating conditions experienced in flight. In addition to the experimental studies, CUBRC also performed a large amount of CFD analysis to confirm and validate not only the tunnel freestream conditions, but also 3D flows over the orbiter acreage, wing leading edge, and controlurfaces to assess data quality, shock interaction locations, and control surface separation regions. This analysis is a standard part of any experimental program at CUBRC, and this information was of key importance for post-test data quality analysis and understanding particular phenomena seen in the data. All work during this effort was sponsored and paid for by the NASA Space Shuttle Program Office at the Johnson Space Center in Houston, Texas.

Wadhams, T. P.↗

NASA NTRS1980

Operational Applications of Satellite Snowcover Observations

The history of remote sensing of snow cover is reviewed and the following topics are covered: various techniques for interpreting LANDSAT and NOAA satellite data; the status of future systems for continuing snow hydrology applications; the use of snow cover observations in streamflow forecasts by Applications Systems Verification and Transfer participants and selected foreign investigators; and the benefits of using satellite snow cover data in runoff prediction.

Rango, A.↗

NASA NTRS1974

Simulation verification techniques study. Task report 4: Simulation module performance parameters and performance standards

Shuttle simulation software modules in the environment, crew station, vehicle configuration and vehicle dynamics categories are discussed. For each software module covered, a description of the module functions and operational modes, its interfaces with other modules, its stored data, inputs, performance parameters and critical performance parameters is given. Reference data sources which provide standards of performance are identified for each module. Performance verification methods are also discussed briefly.

Source record↗

NASA NTRS1985

Loads and low frequency dynamics data base: Version 1.1 November 8, 1985

Structural design data for the Shuttle are presented in the form of a data base. The data can be used by designers of Shuttle experiments to assure compliance with Shuttle safety and structural verification requirements. A glossary of Shuttle design terminology is given, and the principal safety requirements of Shuttle are summarized. The Shuttle design data are given in the form of load factors.

Garba, J. A.↗

NASA NTRS1975

Prediction and verification of creep behavior in metallic materials and components for the space shuttle thermal protection system. Volume 3, phase 3: Full size heat shield data correlation and design criteria

Analysis methods for predicting cyclic creep deflection in stiffened metal panel structures, were applied to full size panels. Results were compared with measured deflections from cyclic tests of thin gage L605, Rene' 41, and TDNiCr full size corrugation stiffened panels. A design criteria was then formulated for metallic thermal protection panels subjected to creep. A computer program was developed to calculate creep deflections.

Cramer, B. A.↗

NASA NTRS1981

Command system output bit verification

An automatic test was developed to test the ability of the deep space station (DSS) command subsystem and exciter to generate and radiate, from the exciter, the correct idle bit sequence for a given flight project or to store and radiate received command data elements and files without alteration. This test, called the command system output bit verification test, is an extension of the command system performance test (SPT) and can be selected as an SPT option. The test compares the bit stream radiated from the DSS exciter with reference sequences generated by the SPT software program. The command subsystem and exciter are verified when the bit stream and reference sequences are identical. It is a key element of the acceptance testing conducted on the command processor assembly (CPA) operational program (DMC-0584-OP-G) prior to its transfer from development to operations.

Odd, C. W.↗

NASA NTRS1989

Time and frequency-domain identification and verification of BO-105 dynamic models

Mathematical models for the dynamics of the DLR BO 105 helicopter are extracted from flight test data using two different approaches: frequency-domain and time-domain identification. Both approaches are reviewed. Results from an extensive data consistency analysis are given. Identifications for 6 degrees of freedom (DOF) rigid body models are presented and compared in detail. The extracted models compare favorably and their prediction capability is demonstrated in verification results. Approaches to extend the 6 DOF models are addressed and first results are presented. System identification is broadly defined as the deduction of system characteristics from measured data. It provides the only possibility to extract both non-parametric (e.g., frequency responses) and parametric (e.g., state space matrices) aircraft models from flight test data and therefore gives a reliable characterization of the dynamics of the actually existing aircraft. Main applications of system identification are seen in areas where higher accuracies of the mathematical models are required: Simulation validation, control system design (in particular model-following control system design for in-flight simulation), and handling qualities.

Kaletka, Juergen↗

NASA NTRS1989

Time and frequency-domain identification and verification of BO-105 dynamic models

Kaletka, Juergen↗

NASA NTRS2014

Simscape Modeling Verification in the Simulink Development Environment

The purpose of the Simulation Product Group of the Control and Data Systems division of the NASA Engineering branch at Kennedy Space Center is to provide a realtime model and simulation of the Ground Subsystems participating in vehicle launching activities. The simulation software is part of the Spaceport Command and Control System (SCCS) and is designed to support integrated launch operation software verification, and console operator training. Using Mathworks Simulink tools, modeling engineers currently build models from the custom-built blocks to accurately represent ground hardware. This is time consuming and costly due to required rigorous testing and peer reviews to be conducted for each custom-built block. Using Mathworks Simscape tools, modeling time can be reduced since there would be no custom-code developed. After careful research, the group came to the conclusion it is feasible to use Simscape's blocks in MatLab's Simulink. My project this fall was to verify the accuracy of the Crew Access Arm model developed using Simscape tools running in the Simulink development environment.

SIMSCAPE↗

NASA NTRS2010

Symbolic Analysis of Concurrent Programs with Polymorphism

The current trend of multi-core and multi-processor computing is causing a paradigm shift from inherently sequential to highly concurrent and parallel applications. Certain thread interleavings, data input values, or combinations of both often cause errors in the system. Systematic verification techniques such as explicit state model checking and symbolic execution are extensively used to detect errors in such systems [7, 9]. Explicit state model checking enumerates possible thread schedules and input data values of a program in order to check for errors [3, 9]. To partially mitigate the state space explosion from data input values, symbolic execution techniques substitute data input values with symbolic values [5, 7, 6]. Explicit state model checking and symbolic execution techniques used in conjunction with exhaustive search techniques such as depth-first search are unable to detect errors in medium to large-sized concurrent programs because the number of behaviors caused by data and thread non-determinism is extremely large. We present an overview of abstraction-guided symbolic execution for concurrent programs that detects errors manifested by a combination of thread schedules and data values [8]. The technique generates a set of key program locations relevant in testing the reachability of the target locations. The symbolic execution is then guided along these locations in an attempt to generate a feasible execution path to the error state. This allows the execution to focus in parts of the behavior space more likely to contain an error.

Rungta, Neha Shyam↗