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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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NASA NTRS1993

Fidelity assessment of a UH-60A simulation on the NASA Ames vertical motion simulator

Helicopter handling qualities research requires that a ground-based simulation be a high-fidelity representation of the actual helicopter, especially over the frequency range of the investigation. This experiment was performed to assess the current capability to simulate the UH-60A Black Hawk helicopter on the Vertical Motion Simulator (VMS) at NASA Ames, to develop a methodology for assessing the fidelity of a simulation, and to find the causes for lack of fidelity. The approach used was to compare the simulation to the flight vehicle for a series of tasks performed in flight and in the simulator. The results show that subjective handling qualities ratings from flight to simulator overlap, and the mathematical model matches the UH-60A helicopter very well over the range of frequencies critical to handling qualities evaluation. Pilot comments, however, indicate a need for improvement in the perceptual fidelity of the simulation in the areas of motion and visual cuing. The methodology used to make the fidelity assessment proved useful in showing differences in pilot work load and strategy, but additional work is needed to refine objective methods for determining causes of lack of fidelity.

Atencio, Adolph, Jr.↗

NASA NTRS1992

Simultaneous mapping of the unsteady flow fields by Particle Displacement Velocimetry (PDV)

Current experimental and computational techniques must be improved in order to advance the prediction capability of the longitudinal vortical flows shed by underwater vehicles. The generation, development, and breakdown mechanisms of the shed vortices at high Reynolds numbers are not fully understood. The ability to measure hull separated vortices associated with vehicle maneuvering does not exist at present. The existing point-by-point measurement techniques can only capture approximately the large 'mean' eddies but fail to meet the dynamics of small vortices during the initial stage of generation. A new technique, which offers a previously unavailable capability to measure the unsteady cross-flow distribution in the plane of the laser light sheet, is called Particle Displacement Velocimetry (PDV). PDV consists of illuminating a thin section of the flowfield with a pulsed laser. The water is seeded with microscopic, neutrally buoyant particles containing imbedded fluorescing dye which responds with intense spontaneous fluorescence with the illuminated section. The seeded particles in the vortical flow structure shed by the underwater vehicle are illuminated by the pulse laser and the corresponding particle traces are recorded in a single photographic frame. Two distinct approaches were utilized for determining the velocity distribution from the particle traces. The first method is based on matching the traces of the same particle and measuring the distance between them. The direction of the flow can be identified by keeping one of the pulses longer than the other. The second method is based on selecting a small window within the image and finding the mean shift of all the particles within that region. The computation of the auto-correlation of the intensity distribution within the selected sample window is used to determine the mean displacement of particles. The direction of the flow is identified by varying the intensity of the laser light between pulses. Considerable computational resources are required to compute the auto-correction of the intensity distribution. Parallel processing will be employed to speed up the data reduction. A few examples of measured unsteady vortical flow structures shed by the underwater vehicles will be presented.

Huang, Thomas T.↗

NASA NTRS2005

Downstream Effects on Orbiter Leeside Flow Separation for Hypersonic Flows

Discrepancies between experiment and computation for shuttle leeside flow separation, which came to light in the Columbia accident investigation, are resolved. Tests were run in the Langley Research Center 20-Inch Hypersonic CF4 Tunnel with a baseline orbiter model and two extended trailing edge models. The extended trailing edges altered the wing leeside separation lines, moving the lines toward the fuselage, proving that wing trailing edge modeling does affect the orbiter leeside flow. Computations were then made with a wake grid. These calculations more closely matched baseline experiments. Thus, the present findings demonstrate that it is imperative to include the wake flow domain in CFD calculations in order to accurately predict leeside flow separation for hypersonic vehicles at high angles of attack.

Buck, Gregory M.↗

NASA NTRS2019

Thermal & Fluids Analysis and the NASA Launch Services Program

This presentation provides the What, Where, Why, and How of the thermal and fluids analysis disciplines within the Launch Services Program (LSP). The LSP manages all commercial launch services to match NASA science mission payloads to the appropriate launch system. The Flight Analysis Division within the LSP provides multi disciplinary analysis support of these flight systems to characterize payload and launch vehicle environments from payload mate to upper stage through on orbit separation. The Thermal Analysis and Fluids Analysis teams are two distinct groups within the Flight Analysis Division of the LSP who cover a wide array of standard and non standard analytical tasks. These teams offer a significant benefit to both the payload design and integration team and the launch vehicle supplier. These benefits, along with the tools and methods are described in this talk to inform engineers and analysts from both sides of the launch system interface how the LSP Thermal and Fluids Analysis teams can benefit the success of their mission.

Fortenberry, Cindy↗

NASA NTRSDate not supplied

Instrumentation for Measuring Entry Radiative Heating - Past, Present, and Future

Radiative heating can be a significant contributor to the total heat load during atmospheric entry, especially for large vehicles with high entry velocties. For example, the stagnation region heat load derived from thermocouples embedded in the thermal protection system (TPS)of the Mars Science Laboratory (MSL) heatshield did not match the unmargined pre-flight predictions. This was at least partially attributed to the fact that radiative heating was not accounted for in the unmargined pre-flight predictions [2].Since MSL, significant work has been done to update radiative heating models and test how the models perform compared to ground-based test facilities such as shock tubes. The models and ground-based tests have shown that radiative heating can be significant for entry into the atmospheres of Venus, Earth, Mars, and Titan, among other planetary bodies. However, there are still discrepancies between the models and the test data, which is where the desire for flight data comes in to play. Missions that have had or are currently planned to have instrumentation to measure radiative heating during atmospheric entry are listed in Table 1.

R A Miller↗

NASA NTRS1990

Teleautonomous guidance for mobile robots

Teleautonomous guidance (TG), a technique for the remote guidance of fast mobile robots, has been developed and implemented. With TG, the mobile robot follows the general direction prescribed by an operator. However, if the robot encounters an obstacle, it autonomously avoids collision with that obstacle while trying to match the prescribed direction as closely as possible. This type of shared control is completely transparent and transfers control between teleoperation and autonomous obstacle avoidance gradually. TG allows the operator to steer vehicles and robots at high speeds and in cluttered environments, even without visual contact. TG is based on the virtual force field (VFF) method, which was developed earlier for autonomous obstacle avoidance. The VFF method is especially suited to the accommodation of inaccurate sensor data (such as that produced by ultrasonic sensors) and sensor fusion, and allows the mobile robot to travel quickly without stopping for obstacles.

Borenstein, J.↗

NASA NTRSDate not supplied

Diagnosing Noise Features Of Internally Mixed, External Plug Exhaust Systems

In internally mixed exhaust systems, a lobed or forced mixer is employed to mix the core and bypass streams before they exit the common nozzle. For moderate bypass-ratio engines this marginally improves thrust, and provides a significant acoustic benefit. Indeed, it has been thought that a fully mixed jet creates the lowest noise you can get from a two-stream engine. Typically, the forced mixer system produces a bit more noise than a fully mixed equivalent jet, noise that has been called “excess noise”. In recent testing, this excess noise seemed to be amplified when an external plug was employed in the exhaust system to reduce boattail drag. Identifying the mechanism of this excess noise and mitigating it is key to creating commercial supersonic vehicles that are acoustically acceptable around airports. This paper adds to that investigation, analyzing results from recent tests where far-field acoustic data were acquired on two sets of flows with matched fully mixed flow conditions. One set of flows had a heated core stream, making it a dual-stream jet. The other set had both streams heated to the same temperature, making a single-stream jet that matched the fully mixed temperature and velocity of the dual-stream jet. These flows were tested in two lobed-mixer exhaust systems with internal and external plugs. Analysis of flow and noise data from the nozzles with combinations of single- and dual-stream flows, and with and without flight streams, gives insight into how and why the noise of the dual-stream and single-stream jet differ. One additive source, present in nozzles both with and without external plugs, comes from inside the nozzle, likely the internal shear layer coming off the lobed mixer. This noise source is not impacted by a change in flight stream and has a distinctive spectral directivity that is amplified by the presence of the external plug. Finding the exact source mechanism of this new source will be key to making the internally mixed exhaust systems reach their lowest possible sound level.

Noise prediction, supersonic, jet noise↗

NASA NTRSDate not supplied

Diagnosing Noise Features Of Internally Mixed, External Plug Exhaust Systems

Noise prediction↗

NASA NTRS1992

Numerical analysis of hypersonic turbulent film cooling flows

As a building block, numerical capabilities for predicting heat flux and turbulent flowfields of hypersonic vehicles require extensive model validations. Computational procedures for calculating turbulent flows and heat fluxes for supersonic film cooling with parallel slot injections are described in this study. Two injectant mass flow rates with matched and unmatched pressure conditions using the database of Holden et al. (1990) are considered. To avoid uncertainties associated with the boundary conditions in testing turbulence models, detailed three-dimensional flowfields of the injection nozzle were calculated. Two computational fluid dynamics codes, GASP and FDNS, with the algebraic Baldwin-Lomax and k-epsilon models with compressibility corrections were used. It was found that the B-L model which resolves near-wall viscous sublayer is very sensitive to the inlet boundary conditions at the nozzle exit face. The k-epsilon models with improved wall functions are less sensitive to the inlet boundary conditions. The testings show that compressibility corrections are necessary for the k-epsilon model to realistically predict the heat fluxes of the hypersonic film cooling problems.

Chen, Y. S.↗

NASA NTRSDate not supplied

Error Localization Examples: Looking for a Needle in a Haystack

Finite element models (FEM) are routinely developed and used during fabrication of high dollar-value hardware. NASA, as part of the pre-flight certification of launch vehicles, routinely conducts vibration and static tests to calibrate models used for flight-risk assessments. During model calibration, certain areas of the model are modified, using engineering judgment and sensitivity analysis, to match the test results. Unfortunately, tools to identify problem areas in the FEM using test data directly are scarce and infrequently applied. Over the years, error localization algorithms have been proposed with very limited success. Recently, the Analytical Dynamics Model Improvement (ADMI) algorithm, which computes closed-form mass and stiffness corrections to match the test data exactly, have been shown to be an effective Error Localization Algorithm (ELA). The paper discusses three examples where ELA is used with simulated test data to locate problem areas. To gain confidence in the approach, the exact answer is shown along with ELA results. Results show that ELA is able to identify general problem areas consistent with known problem areas. In all examples, the ELA identified area is larger than the exact problem area. Nonetheless, with proper optimization tools, calibration results using the ELA identified areas provide excellent results.

model calibration↗

NASA NTRSDate not supplied

Comparison between Hayabusa 2 Spectral Measurements and Simulations

This paper compares recent Hayabusa 2 spectral measurements with state-of-the-art shock-layer radiation simulations resulting from the LAURA/HARA code suite. These simulations include coupled ablation, which accounts for the injection of ablation products into the flowfield, and coupled radiation, which accounts for radiative energy loss in the flowfield. To enable the coupled ablation simulations, a best-estimate model is developed for Hayabusa’s carbon-phenolic ablator, based on the limited available published information. The comparison between the simulations and measurements focuses on two atomic nitrogen lines and two atomic oxygen lines, as well as the CN Violet band system. For the atomic lines, the measurements and simulations agree within 25% over most of the trajectory. This excellent agreement is unprecedented for observed radiation measurements of atomic lines, where previous Stardust and Hayabusa 1 comparisons were significantly worse. The improved agreement for these Hayabusa 2 comparisons is both the result of improved measurement quality and enhanced flowfield/radiation modeling. To provide a link between these observed radiation measurements and the radiative heating to a vehicle surface, the recently developed flowfield property binning approach is used to identify flowfield properties that provide the dominant emission contribution to the measured spectrum. These identified flowfield properties are then shown to match those for the surface radiative heating to the currently developed Mars Sample Return (MSR) Earth Entry System (EES) at a specific trajectory point and surface location. This matching of flowfield properties indicates that this EES radiative heating and Hayabusa 2 observed radiation simulations are equivalent radiation problems. Therefore, the excellent agreement between Hayabusa 2 measurements and simulations may be leveraged to inform the radiation heating margin for the MSR EES.

Christopher O Johnston↗

DOE OSTI2021

Combined Mesonet and Tracker

Title: Combined Mesonet and Trackers (UNL Mobile Mesonets) Authors University of Nebraska PI: Adam Houston, UNL Professor (ahouston2@unl.edu) Mailing Address: 126 Bessey Hall P.O. Box 880340 Lincoln, NE 68588-0340 CoMeT Overview The University of Nebraska-Lincoln operates three Combined Mesonet and Tracker (CoMeTs). CoMeTs are Ford Explorers (model years 2015, 2017, and 2019) with forward-mounted suites of meteorological sensors and dual moonroofs, combining the capability of a mobile mesonet to collect near-surface observations with the capability of an unmanned aircraft systems (UAS) tracker vehicle, which enables an observer in the second row of seats to see the aircraft and maintain compliance with Federal Aviation Administration policies on UAS operation. The CoMeTs collect observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155A, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, and vehicle heading using a KVH Industries C-100 fluxgate compass (Barbieri et al. 2019). The HMP155A and 109SS-L thermistor are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). This list of sensors is also included in the CoMeT data file metadata. Manufacturer specifications for these instruments are given in Table 1 of Hanft and Houston (2018). The reported measured quantities are summarized below.CoMeT-3 was funded through an equipment allocation included in the NSF TORUS award (AGS-1824649). Instrument Description The specific sensors included on each CoMeT are summarized in the table at the end of this section. In general each CoMeT collects observations of slow temperature and humidity at ~2 m above ground level (AGL) using a Vaisala HMP155, fast temperature at ~2 m AGL using a Campbell Scientific 109SS-L thermistor, pressure at ~2.5 m AGL using a Vaisala PTB210 barometer with a Gill pressure port, wind speed and direction at ~3.25 m AGL using an R.M. Young 05103 propeller anemometer, position using a Garmin 19x HVS receiver, and vehicle heading using a KVH Industries C-100 fluxgate compass. The HMP155 and 109SS are shielded and aspirated within a U-tube (Waugh and Frederickson 2010; Houston et al. 2016). Fast temperature and corrected RH measurements (using sensors housed within the U-tube) have a time constant of 10-12 s based on data collected across a temperature and RH shock during the CLOUD-MAP 2017 calibration/validation tests on June 26, 2017. Vehicle speed was < 10 kts for this test. CoMeT-1 CoMeT-2 CoMeT-3 Slow Temperature Slow RH Vaisala HMP155A-L20-PT Part #: 22280-7 Vaisala HMP155E Part #: E1AA11A0B1A1A0A Vaisala HMP155E Part #: E1AA11A0B1A1A0A Fast temperature Campbell Scientific 109SS-L20-PT Part #: 21448-3 Campbell Scientific 109SS-L12-PW Part #: 21448-109 Campbell Scientific 109SS-L12-PW Part #: 21448-150 Pressure Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Vaisala PTB-210 Part #: A1A1B Gill Pressure Port Part #: 61002 Wind RM Young 05103-L20-PT Part #: 18435-310 RM Young 05103-L20-PW Part #: 18435-244 RM Young 05103-L20-PW Part #: 18435-244 GPS Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Garmin GPS 19x HVS (NMEA 0183) Part #: 010-01010-00 Compass KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 KVH C-100 Part #: 01-0177-15 Logger Campbell Scientific CR6-NA-XT-SW Part #: 28385-9 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Campbell Scientific CR6-WIFI-XT-SW Part #: 28385-6 Data Collection and Real-Time Processing The reported measured quantities are summarized in the table below. Quantity Units Source Epoch time Seconds GPS Latitude and longitude Degrees GPS Altitude m GPS Pressure hPa PTB210 Temperature (fast) deg C 109SS-L Temperature (slow) deg C HMP155 RH (slow) % HMP155 Vehicle speed m/s GPS Vehicle heading deg C-100 and GPS In addition to the measured variables, several derived variables are calculated. Corrected/fast relative humidity (%) Relative humidity is adjusted to the fast temperature following Richardson et al. (1998) and Houston et al. (2016). Water vapor mixing ratio (g/kg) Dew point temperature (°C) Potential temperature (Kelvin) Virtual potential temperature (Kelvin) Equivalent potential temperature (Kelvin) Regular intercomparisons between all three CoMeTs were performed during TORUS 2019. Comparisons were also conducted between CoMeT-1 and CoMeT-2 during LAPSE-RATE (2018) on 14 July. In these intercomparisons, the vehicles were parked adjacent to each other aligned perpendicular to (and facing into) the wind. To minimize engine heating effects, intercomparisons were only conducted when the wind speed was >10 kts. Data Format Original data files for each deployment are saved as text files and then converted to NetCDF. NetCDF versions have units that are CF compliant and may not match the original units in the txt files. The naming convention for the NetCDF files is as follows: UNL.CoMeT3.{deployment date YYYYMMDD}.{start time of observation collection in UTC HHMM}.L2.{post-processing codes}.cdf example: UNL.CoMeT3.20190627.1931.L2.g1.f1.cdf Post-processing codes are included to track modifications to the raw data. These codes are closely connected to error flags associated with each record. Each letter corresponds to a particular instrument: g: GPS p: Barometer tf: Fast temperature ts: Slow temperature rh: Relative humidity f: Compass w: Wind monitor a: All instruments Each number corresponds to a particular post-processing action described more below. Measured and derived variables are included in the following table. Variable Heading Standard Name Units time Time seconds since 00:00:00, 01-01-1970 Alt Altitude meters lat Latitude degrees north lon Longitude degrees east fast_temp Air Temperature kelvin slow_temp Air Temperature kelvin pressure Air Pressure pascals logger_RH Relative Humidity percent calc_corr_RH Relative Humidity percent wind_speed Wind Speed meters per second wind_dir Wind From Direction degrees vehicle_dir Vehicle Direction degrees dewpoint Dew Point Temperature kelvin mixing_ratio Humidity Mixing Ratio g/g theta Air Potential Temperature kelvin theta_v Virtual Potential Temperature kelvin theta_e Equivalent Potential Temperature Kelvin error_flag The error_flag variable is a string that matches the post-processing codes listed above. All instruments will have an associated code, but will have a “0” if the datum is unchanged from the initial processed value. Error Codes The following table summarizes the error codes for data collected before 2020: Error Code Relevant CoMeT Description g1 1,2,3 Exact correction. GPS position and time reprocessed from raw data g2 1 As far as we can tell this is an exact correction to an error in the GPS time. During the correct time periods the time suddenly went backwards ~250s and stayed at this offset for 750s when it corrected itself. The offset was applied to the “time warp” period. p1 2 Approximate correction. Hole in the pressure tube connecting the pressure port to the barometer. Resulted in erroneously low air pressure measurements when the vehicle was in motion. Derived variables recalculated (dew point temperature [e depends on qv and p], water vapor mixing ratio, potential temperature, virtual potential temperature, equivalent potential temperature) a1 3 Exact correction. Missing data reprocessed from raw data a2 1 Bug fix to bias correction for ts1, ts2, and rh1: water vapor mixing ratio was off by a factor of 10 and virtual potential temperature was wrong because of this. f1 3 No correction, missing data. Fluxgate compass inoperable. Wind speed and direction calculated using GPS-derived vehicle heading instead. rh1 1 Approximate correction. Constant bias of +1.7% removed from relative humidity. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts1 1 Approximate correction. Constant bias of +0.6 K removed from slow temperature. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) ts2 1 Approximate correction. Constant bias of +1.0 K removed. Derived variables recalculated (corrected/fast relative humidity, dew point temperature, water vapor mixing ratio, virtual potential temperature, equivalent potential temperature) References Bolton, D., 1980: The Computation of Equivalent Potential Temperature. Mon. Wea. Rev., 108, 1046–1053, https://doi.org/10.1175/1520-0493(1980)108<1046:TCOEPT>2.0.CO;2. Hanft, W., and A. L. Houston, 2018: An Observational and Modeling Study of Mesoscale Air Masses with High Theta-E. Mon. Wea. Rev., 146, 2503–2524, https://doi.org/10.1175/MWR-D-17-0389.1.Wexler Houston, A. L., R. J. Laurence III, T. W. Nichols, S. Waugh, B. Argrow, and C. L. Ziegler, 2016: Intercomparison of unmanned aircraft-borne and mobile mesonet atmospheric sensors. Journal of Atmospheric and Oceanic Technology. 33, 1569-1582, doi: 10.1175/JTECH-D-15-0178.1. Lowe, P. R., 1977: An Approximating Polynomial for the Computation of Saturation Vapor Pressure. J. Applied Meteorology, 16, 100–103. Richardson, S. J., S. E. Frederickson, F. V. Brock, and J. A. Brotzge, 1998: Combination temperature and relative humidity probes: Avoiding large air temperature errors and associated relative humidity errors. Preprints, 10th Symp. On Meteorological Observations and Instrumentation, Phoenix, AZ, Amer. Meteor. Soc., 278–283. Waugh, S., and S. E. Frederickson, 2010: An improved aspirated temperature system for mobile meteorological observations, especially in severe weather. 25th Conf. on Severe Local Storms, Denver, CO, Amer. Meteor. Soc., P5.2. [Available online at https://ams.confex.com/ams/25SLS/techprogram/paper_176205.htm.]

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2010

Ares I-X Ground Diagnostic Prototype

Automating prelaunch diagnostics for launch vehicles offers three potential benefits. First, it potentially improves safety by detecting faults that might otherwise have been missed so that they can be corrected before launch. Second, it potentially reduces launch delays by more quickly diagnosing the cause of anomalies that occur during prelaunch processing. Reducing launch delays will be critical to the success of NASA's planned future missions that require in-orbit rendezvous. Third, it potentially reduces costs by reducing both launch delays and the number of people needed to monitor the prelaunch process. NASA is currently developing the Ares I launch vehicle to bring the Orion capsule and its crew of four astronauts to low-earth orbit on their way to the moon. Ares I-X will be the first unmanned test flight of Ares I. It is scheduled to launch on October 27, 2009. The Ares I-X Ground Diagnostic Prototype is a prototype ground diagnostic system that will provide anomaly detection, fault detection, fault isolation, and diagnostics for the Ares I-X first-stage thrust vector control (TVC) and for the associated ground hydraulics while it is in the Vehicle Assembly Building (VAB) at John F. Kennedy Space Center (KSC) and on the launch pad. It will serve as a prototype for a future operational ground diagnostic system for Ares I. The prototype combines three existing diagnostic tools. The first tool, TEAMS (Testability Engineering and Maintenance System), is a model-based tool that is commercially produced by Qualtech Systems, Inc. It uses a qualitative model of failure propagation to perform fault isolation and diagnostics. We adapted an existing TEAMS model of the TVC to use for diagnostics and developed a TEAMS model of the ground hydraulics. The second tool, Spacecraft Health Inference Engine (SHINE), is a rule-based expert system developed at the NASA Jet Propulsion Laboratory. We developed SHINE rules for fault detection and mode identification. The prototype uses the outputs of SHINE as inputs to TEAMS. The third tool, the Inductive Monitoring System (IMS), is an anomaly detection tool developed at NASA Ames Research Center and is currently used to monitor the International Space Station Control Moment Gyroscopes. IMS automatically "learns" a model of historical nominal data in the form of a set of clusters and signals an alarm when new data fails to match this model. IMS offers the potential to detect faults that have not been modeled. The three tools have been integrated and deployed to Hangar AE at KSC where they interface with live data from the Ares I-X vehicle and from the ground hydraulics. The outputs of the tools are displayed on a console in Hangar AE, one of the locations from which the Ares I-X launch will be monitored. The full paper will describe how the prototype performed before the launch. It will include an analysis of the prototype's accuracy, including false-positive rates, false-negative rates, and receiver operating characteristics (ROC) curves. It will also include a description of the prototype's computational requirements, including CPU usage, main memory usage, and disk usage. If the prototype detects any faults during the prelaunch period then the paper will include a description of those faults. Similarly, if the prototype has any false alarms then the paper will describe them and will attempt to explain their causes.

Schwabacher, Mark↗

NASA NTRS1976

OAST Space Theme Workshop. Volume 3: Working Group Summary. 5: Propulsion (P-1). A. Summary Statement. B. Technology Needs (Form 1). C. Priority Assessments (Form 2)

All themes require some form of advanced propulsion capabilities to achieve their stated objectives. Requirements cover a broad spectrum ranging from a new generation of heavy lift launch vehicles to low thrust, long lift system for on-orbit operations. The commonality extant between propulsive technologies was established and group technologies were grouped into vehicle classes by functional capability. The five classes of launch vehicles identified by the space transportation theme were augmented with a sixth class, encompassing planetary and on-orbit operations. Propulsion technologies in each class were then ranked, and assigned priority numbers. Prioritized technologies were matched to theme requirements.

Source record↗

NASA NTRS1993

Compact Optical Correlator

Optical cross-correlator indicates position of target (if any) in scene matching target encoded in matched filter. Size reduced by use of prisms to fold optical path. Device designed, assembled from commercial components, and tested to verify feasibility of miniaturization for eventual incorporation into navigational and/or control systems of autonomous vehicles. For example, aircraft equipped with optical correlator recognizes prescribed target and measures its position, so that it lands on target under automatic control.

Scholl, Marija S.↗

NASA NTRS2005

Camera Image Transformation and Registration for Safe Spacecraft Landing and Hazard Avoidance

Inherent geographical hazards of Martian terrain may impede a safe landing for science exploration spacecraft. Surface visualization software for hazard detection and avoidance may accordingly be applied in vehicles such as the Mars Exploration Rover (MER) to induce an autonomous and intelligent descent upon entering the planetary atmosphere. The focus of this project is to develop an image transformation algorithm for coordinate system matching between consecutive frames of terrain imagery taken throughout descent. The methodology involves integrating computer vision and graphics techniques, including affine transformation and projective geometry of an object, with the intrinsic parameters governing spacecraft dynamic motion and camera calibration.

Jones, Brandon M.↗

NASA NTRSDate not supplied

A Low-Cost and Low-Risk Testbed for Control Design of Launch Vehicles and Landing Systems

This research seeks to bridge the gap between the simulation/theory and the flight-testing phases of control algorithmdesign for launch vehicles (LVs) and landing systems (LSs). Thispaper reviews contemporary testbeds for LV and LS control sys-tem design with an emphasis on reducing costs and risks. Also, anew low-cost and low-risk testbed utilizing a quadcopter, flexibleinverted pendulum, and a hanging pendulum is proposed. It is shown that the planar dynamic response of this proposed testbedcan match the planar dynamic response of an LV. As mission objectives for frequent cislunar and interplanetary travel become more complex, the algorithms that control LVs and LSs need to become more advanced to ensure mission safety and success. These control systems are slow to mature because it is difficult to perform experiments in an environment relevant to the mission in a safe and cost-effective manner. A solution to this problem frequently leveraged in the aviation andsatellite industries involves mimicking the dynamic response ofthe vehicle with a test platform that is low-cost and low-risk.

William J Elke III↗

NASA NTRSDate not supplied

CFD Predictions of Fluctuating Pressure Environments on NASA’s SLS Ascent Unsteady Aerodynamics Wind Tunnel Test

This paper details work that is being conducted on predicting fluctuating pressure environments on NASA launch vehicles using computational fluid dynamics (CFD). The accurate characterization of these aeroacoustics environments is necessary in the analysis of vehicle structural health and aerodynamic performance and in the vehicle design process. High-fidelity simulations of the unsteady flow over NASA’s SLS Ascent Unsteady Aerodynamics wind tunnel Test (AUAT) using the hybrid Reynolds-averaged Navier-Stokes (RANS)-large eddy simulations (LES) methodologies in Mississippi States’ Loci/CHEM solver are presented. The two available methods, Nelson-Nichols and dynamic hybrid RANS-LES (DHRL), are compared head-to-head on identical grids at two Mach numbers. At the high-subsonic Mach number, locally supersonic flow expanding over a shoulder induces a separation-reattachment system that is predicted poorly by the Nelson-Nichols method due to an under-prediction in unsteady content. The DHRL solution predicts the spatial extent and frequency domain response of this system well but under-predicts the peak noise levels due to a delay in the RANS-to-LES transition. At the low-supersonic Mach number, compression corner dynamics dominate the unsteady flow. While the Nelson Nichols solution captures this phenomenon well, another solution on a refined grid indicates that some model stress depletion is occurring. The DHRL solution again performs well at this Mach number and matches the wind tunnel data fairly closely, but perhaps most impressively, is fairly agnostic to increasing grid resolution. The findings in this paper are expected to be generally applicable and will be used to guide the prediction of aeroacoustics environments of other NASA launch vehicles.

CFD↗