Oceanic Responses to Wind Forcing as Observed by Satellite Sensors and Diagnosed by Ocean General Circulation Models
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This work presents a novel landing site relative topographic measurement model for aslant range sensor being utilized for precision planetary landing operations. The measurement model accounts for the local terrain the slant range sensor captures and leverages knowledge of the estimated landing site provided by the navigation filter. Notably, in contrast to previous works, the new model does not rely on surface normal approximation, reducing the model’s sensitivity to noisy digital elevation maps which represent the local topography. In addition to the measurement model, this work introduces a novel fault detection method, denoted the probabilistic inspection of topographic filter altitude likelihood (PITFAL) algorithm, that implements a statistical outlier rejection algorithm. PITFAL is designed for multi-beam slant range sensors, such as the Navigation Doppler LIDAR (NDL), and identifies statistically inconsistent range estimates through a consensus check on the set of apparent altitudes computed for each individual beam. These models are numerically validated by the Safe and Precise Landing Capability Evolution (SPLICE) project’s high-fidelity terrestrial and lunar lander simulations.
This work presents a novel landing site relative topographic measurement model for aslant range sensor being utilized for precision planetary landing operations. The measurement model accounts for the local terrain the slant range sensor captures and leverages knowledge of the estimated landing site provided by the navigation filter. Notably, in contrast to previous works, the new model does not rely on surface normal approximation, reducing the model’s sensitivity to noisy digital elevation maps which represent the local topography. In addition to the measurement model, this work introduces a novel fault detection method, denoted the probabilistic inspection of topographic filter altitude likelihood (PITFAL) algorithm, that implements a statistical outlier rejection algorithm. PITFAL is designed for multi-beam slant range sensors, such as the Navigation Doppler LIDAR (NDL), and identifies statistically inconsistent range estimates through a consensus check on the set of apparent altitudes computed for each individual beam. These models are numerically validated by the Safe and Precise Landing Capability Evolution (SPLICE) project’s high-fidelity terrestrial and lunar lander simulations.
A 'nonintrusive', multielement heat-transfer sensor was designed to study laminar-separation bubble characteristics on a NASA LRN (1)-1010 low-Reynolds number airfoil. The sensor consists of 30 individual nickel films, vacuum-deposited on a thin substrate (0.05 mm) that was bonded to the airfoil model with the sensor array placed streamwise on the airfoil upper surface. Experiments were conducted on a 15-cm chord model in the 50,000-300,000 chord Reynolds number range. Time history as well as spectral analysis of signals from surface film gauges were simultaneously obtained to determine the location of laminar separation and the subsequent behavior of the separated shear layer. In addition to the successful determination of laminar separation, a new phenomenon involving a large phase shift in dynamic shear stresses across the separation and reattachment points was observed.
Ground observations are critical in the validation of soil water content (SWC) estimates from both satellites and land surface models. Portable SWC sensors provide useful information to determine the amount of SWC in the topsoil layer for various applications; however, these probes are not accurate without site‐specific correction. In the present study, we examined and compared six different types of portable electromagnetic (EM) SWC sensors, including multiple sensors made by the same manufacturers, for a total of 16 EM‐based SWC probes equipped with portable data loggers. All SWC probes met the target accuracy after onsite correction—the RMSD was <0.025 m(exp 3) m(exp -3). Using the two‐sample t-tests, we observed that SWC data obtained from similar electrode lengths and from different manufacturers showed similar distributions over time with the same mean. Furthermore, using the maximize R method to combine SWC data from two different types of sensors increased the accuracy of the results. When datasets from two different types of sensors were combined, the Pearson's correlation coefficient (R value) and RMSD values were improved. The average R value improved from .930 to .945, and the RMSD decreased from 0.036 m(exp 3) m(exp -3) to 0.018 m(exp 3) m(exp −3). These results indicate that, along with site‐specific correction, synergetic use of multiple manufacturers’ EM‐based SWC probes can improve the R value and reduce systematic bias.
An exact mathematical model is presented of the Space Shuttle Main Engine computer voting logic in the presence of High Pressure Fuel Turbine (HPFT) overtemperature events and fuel turbine temperature sensor failures. The model provides estimates of the probability of erroneous engine shutdown and the probability of not detecting a HPFT overtemperature event. Because it is believed that the likelihood of sensor failures and overtemperature events in the HPFT greatly overshadows those in the High Pressure Oxygen Turbine (HPOT), this modeling effort focused on the HPFT. However, because the redline protection logic is the same for both turbines, estimation of the model parameters using relevant HPOT data would provide estimates of the risk of erroneous engine shutdown and an undetected overtemperature event in the HPOT. Because of the complexity of the model, it was necessary to program the solution which thus makes it feasible to accommodate a changing data base. This is considered to be of great interest because of the subjective nature in determining the relevancy of certain failures and the fact that the data base is constantly changing as a result of the frequent engine tests.
This paper presents an approach to evaluating sensor placements on the basis of how well they are able to discriminate between a given fault and normal operating modes and/or other fault modes. In this approach, a model of the system in both normal operations and fault modes is used to evaluate possible sensor placements upon the basis of three criteria. Discriminability measures how much of a divergence in expected sensor readings the two system modes can be expected to produce. Accuracy measures confidence in the particular model predictions. Timeliness measures how long after the fault occurrence the expected divergence will take place. These three metrics then can be used to form a recommendation for a sensor placement. This paper describes how these measures can be computed and illustrated these methods with a brief example.
A procedure for optimizing the performance of large flexible spacecraft that require active vibration suppression to achieve required performance is presented. The procedure is to conduct on-orbit testing and system identification followed by a control system design. It is applied via simulation to a spacecraft configuration currently being considered for flight test by NASA - the Controls, Astrophysics, and Structures Experiment in Space (CASES). The system simulator is based on a NASTRAN finite element structural model. A finite number of modes is used to represent the structural dynamics. The system simulator also includes models of the electronics, actuators, sensors, the digital controller, and the internal and external disturbances. Nonlinearities caused by quantization are included in the study to examine tolerance of the procedure to modelling errors. Disturbance and sensor noise is modelled as a Gaussian process. For system identification, the system is excited using sinusoidal inputs at the resonant frequencies of the structure using each actuator. Mode shapes, frequencies, and damping ratios are identified from the unforced response sensor data after each excitation. Then, the excitation data is used to identify the actuator influence coefficients. The results of the individual parameter identification analyses are assembled into an aggregate system model. The control design is accomplished based only on the identified model using multi-input/output linear quadratic Gaussian theory. Its performance is evaluated based on time-to-damp as compared with the uncontrolled structure.
Advanced teleoperators are discussed, with emphasis on the remote manipulation system designed to perform such actions as grasping, orienting, moving, placing, and inserting objects. Geometrical performance dimensions are considered, indicating that a manipulator is limited to three-orders-of-magnitude ratio of workspace extension to positioning accuracy. The control system is examined, showing that a manipulative task requires coordination of several joints, with the relationship between the task definers and the joint variables given by complex trigonometrical transformations. Control modes developed in the last 30 years are noted, including program controlled industrial 'robots' which can endlessly repeat a fixed sequence of motions without operator intervention, and the sensor-referenced/computer-controlled mode manipulators. Advanced proximity sensor systems are taken into account, with emphasis on the applications of the force-torque and slip models. The proximity sensor system for the shuttle-size manipulator is described.
Remote sensing of Earth from the International Space Station began with handheld camera astronaut photography in 2000. Subsequent and more sophisticated autonomous sensors have actualized the ISS as a useful platform for climate science data collection. This offers a useful model for future sensor suites on planned commercial LEO stations.
The status of a global-scale model of background aerosol backscatter cross-sections at CO2 wavelengths is described. The model needs, strategy, concept, parameters, and capabilities are addressed, and the data base is discussed, concluding data selection, CO2 backscatter measurements, aerosol optical measurements, aerosol microphysical measurements, water vapor measurements, and data analysis. Strong evidence is reported for a 'universal' background tropospheric aerosol population. Typical background backscatter values at CO2 wavelengths appear to be about 3 x 10 to the -11th to 8 x 10 to the -11th/m/sr. Background signatures are evident in most aerosol data sets which have global-scale coverage in space or time.
SMM flight system control errors occurring at spacecraft dawn and dusk are analyzed. The errors are associated with the fine pointing sun sensor (FPSS), which is a primary component of the SMM attitude control system. It is shown that the source of the FPSS dawn/dusk distortion is the incomplete masking of sunlight reflected off the earth by the optical baffle covering the FPSS sensor heads onboard the SMM during periods of orbit dawn and dusk. For the most part, the modeled behavior of the FPSS under dawn and dusk lighting conditions matches the observed behavior in the SMM flight data.
Attitude control problems for large flexible telecommunications spacecraft were investigated. A typical S/C configuration is described and modeled by modal data derived from a finite element analysis. The effect of structural flexibility on radio-frequency sensor is analyzed. Model reduction using modal gain considerations is applied. Two control concepts are investigated: separate central body and antenna pointing control using direct feedback laws, centralized control using modal observer and optimal control. Performances of each concept and the algorithm implementation are assessed.
Air quality managers, researchers, and concerned community scientists around the world have a variety of sources for air quality information, ranging from traditional regulatory monitoring networks and atmospheric chemistry models to remote sensing data products and low-cost sensor networks. However, the ability to incorporate data from these disparate sources and synthesize a comprehensive overview of the local air quality situation remains a considerable barrier for many end-users. This presentation will outline a tool, currently in development, which will address this need using a flexible data fusion approach. The tool will make use of air quality forecast model outputs (primarily from the NASA GEOS-CF composition forecast modeling system), satellite remote sensing data (from instruments including MODIS, VIIRS, TROPOMI, plus TEMPO for the US when available), and in-situ data from official regulatory and/or low-cost networks where these are available. The ability to incorporate data from low-cost sensor networks will be a key feature of the tool; it will make use of other available data sources to calibrate the low-cost sensor data on a regional scale, then use these calibrated low-cost sensor data for localized updating to resolve finer-scale air quality patterns. Development of this tool is taking place with the help of national and international partners and end-user groups, coordinated through the US EPA and the United Nations Environment Programme (UNEP). The tool is being developed on the Google Earth Engine cloud computing platform to facilitate integration of diverse data sources and free access by a broad community of end-users. Stewardship of the tool will be passed to US EPA and UNEP to support future activities with end-users in the US and around the world, and the tool itself will remain freely accessible. We hope that this tool will lower the barrier to entry for various user groups worldwide, including community scientists, who struggle to integrate disparate data sources to gain insight into their local air quality situations. This presentation will cover the early stages of the development of the tool, including the underlying methods and some pilot case studies in integrating low-cost sensor data.
The paper considers the modeling and analysis of the JPL Precision Pointing and Tracking System, a gyro stabilized platform pointing system using optical target tracking. Sensor and actuator models are described which realistically represent actual hardware, and a practical controller and state estimator is developed through modern control techniques. The effect of the actuator nonlinearities on the system performance evaluation is discussed along with the modeling of the noise sources in the gyro. Finally, the limitations of implementing the control algorithms on the spacecraft computer, and the resulting tradeoffs are presented.
The scope of this study covered steady-state, continuous-time vibration control under disturbances applied to the Space Shuttle and continuous-time models of actuators, sensors, and disturbances. Focus was on a clear illustration of the methodology, therefore sensor/actuator dynamics were initially ignored, and a finite element model of the NASA Spacecraft Control Laboratory Experiment (SCOLE) was conducted, including products of inertia and offset of reflector CM from the mast tip.
Carbon fiber composites are used in a wide range of structural applications due to their excellent specific strength and stiffness. However, the anisotropic mechanical and electrical properties associated with the fibers within each composite layer present challenges, and opportunities, for Nondestructive Testing (NDT) methods used to characterize and assess the structure condition. This includes composite condition after manufacture (such as fiber orientation and density, porosity, delamination, and bond strength) and during usage (such as damage from impact, fiber breakage, thermal exposure or applied stress). Ultrasonic and thermographic methods can address some of these challenges, but eddy current methods provide an alternative method for composite structures that contain a conducting material, such as carbon fibers or a metallic liner. This presentation reviews recent advances in the development of eddy current sensors and arrays for carbon fiber composite NDT and Structural Health Monitoring (SHM) applications. The focus is on eddy current sensor constructs with linear drive windings, such as MWM -Arrays, that induce currents primarily within the linear fibers of the composite. By combining this type of sensor construct with micromechanical models that relate composite constituent properties to measurable sensor responses, insight is obtained into the volumetric distribution of electrical properties within the composite and the associated manufacturing, damage, or strain conditions. With knowledge of the fiber layup, this MWM-Array technology is able to detect damage and strain/stress as a function of depth and fiber orientation. This work has been funded by NASA, NA V AIR and the Army for applications ranging from composite overwrapped pressure vessels (COPVs) to aircraft structures and rotorcraft blades. This presentation will specifically present background on the MWM-Array technology, results from the micromechanical modeling effort, and results from ongoing efforts for high resolution imaging and volumetric strain sensing.
An integrated approach to rotorcraft system identification is described. This approach consists of sequential application of (1) data filtering to estimate states of the system and sensor errors, (2) model structure estimation to isolate significant model effects, and (3) parameter identification to quantify the coefficient of the model. An input design algorithm is described which can be used to design control inputs which maximize parameter estimation accuracy. Details of each aspect of the rotorcraft identification approach are given. Examples of both simulated and actual flight data processing are given to illustrate each phase of processing. The procedure is shown to provide means of calibrating sensor errors in flight data, quantifying high order state variable models from the flight data, and consequently computing related stability and control design models.