State of art in instrumentation for manned flight control in space
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This document describes a Concept of Operations (ConOps) for an Integrated Vehicle Health Assurance System (IVHAS). This ConOps is associated with the Maintain Vehicle Safety (MVS) between Major Inspections Technical Challenge in the Vehicle Systems Safety Technologies (VSST) Project within NASA s Aviation Safety Program. In particular, this document seeks to describe an integrated system concept for vehicle health assurance that integrates ground-based inspection and repair information with in-flight measurement data for airframe, propulsion, and avionics subsystems. The MVS Technical Challenge intends to maintain vehicle safety between major inspections by developing and demonstrating new integrated health management and failure prevention technologies to assure the integrity of vehicle systems between major inspection intervals and maintain vehicle state awareness during flight. The approach provided by this ConOps is intended to help optimize technology selection and development, as well as allow the initial integration and demonstration of these subsystem technologies over the 5 year span of the VSST program, and serve as a guideline for developing IVHAS technologies under the Aviation Safety Program within the next 5 to 15 years. A long-term vision of IVHAS is provided to describe a basic roadmap for more intelligent and autonomous vehicle systems.
Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth-Moon system. Using Multi-Reward Proximal Policy Optimization, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.
Multi-Reward Proximal Policy Optimization, a multi-objective deep reinforcement learning algorithm, is used to examine the design space of low-thrust trajectories for a SmallSat transferring between two libration point orbits in the Earth- Moon system. Using Multi-Reward Proximal Policy Optimiza- tion, multiple policies are simultaneously and efficiently trained on three distinct trajectory design scenarios. Each policy is trained to create a unique control scheme based on the trajectory design scenario and assigned reward function: a unique combination of weights scaling competing objectives that guide the spacecraft to the target mission orbit, incentivize faster flight times, and penalize propellant mass usage. Then, the policies are evaluated on the same set of perturbed initial conditions in each scenario to generate the propellant mass usages, flight times, and state discontinuities from a reference trajectory for each control scheme. This solution space of low-thrust trajectories for a SmallSat is used to examine the multi-objective trade space for the trajectory design scenario. By autonomously constructing the solution space, insights into the required propellant mass, flight time, and transfer geometry are rapidly achieved.
The use of decoupled longitudinal controls during the approach and landing of a typical twin-engine jet transport in the presence of wind shear was studied. The simulation included use of a localizer and flight director to capture and maintain a 3 deg glide slope. The pilot then completed the landing by using visual cues provided below an altitude of 200 m by closed-circuit television and a terrain model. The decoupled controls used constant prefilter and feedback gains to provide steady state decoupling of flight path angle, pitch angle, and forward velocity. The use of the decoupled control system improved pilot performance during the approach and at touchdown in the presence of wind shears. The pilots preferred the decoupled controls and rated the task 1 to 3 increments better on a pilot rating scale, depending on wind conditions, than was the case when conventional controls were used.
In the constant drive to further the safety and efficiency of air travel, the complexity of avionics-related systems, and the procedures for interacting with these systems, appear to be on an ever-increasing trend. While this growing complexity often yields productive results with respect to system capabilities and flight efficiency, it can place a larger burden on pilots to manage increasing amounts of information and to understand intricate system designs. Evidence supporting this observation is becoming widespread, yet has been largely anecdotal or the result of subjective analysis. One way to gain more insight into this issue is through experimentation using more objective measures or indicators. This study utilizes and analyzes eye-tracking data obtained during a high-fidelity flight simulation study wherein many of the complexities of current flight decks, as well as those planned for the next generation air transportation system (NextGen), were emulated. The following paper presents the findings of this study with a focus on electronic flight bag (EFB) usage, system state awareness (SSA) and events involving suspected inattentional blindness (IB).
The current simulation for the Capsule Parachute Assembly System (CPAS) lacks fidelity in representing added mass for the 116 ft Do ringsail Main parachute. The availability of 3-D models of inflating Main canopies allowed for better estimation the enclosed air volume as a function of time. This was combined with trajectory state information to estimate the components making up measured axial loads. A proof-of-concept for an alternate simulation algorithm was developed based on enclosed volume as the primary independent variable rather than drag area growth. Databases of volume growth and parachute drag area vs. volume were developed for several flight tests. Other state information was read directly from test data, rather than numerically propagated. The resulting simulated peak loads were close in timing and magnitude to the measured loads data. However, results are very sensitive to data curve fitting and may not be suitable for Monte Carlo simulations. It was assumed that apparent mass was either negligible or a small fraction of enclosed mass, with little difference in results.
The NASA Dryden Flight Research Facility embarked upon a project with the United States Army Aviation Engineering Flight Activity (USAAEFA) to develop and test a stall-speed warning system. NASA designed and built an automated stall-speed warning system which presents both airspeed and stall speed to the pilot. The airspeed and stall speed are computed in real time by monitoring the basic aerodynamic parameters {dynamic pressure, horizontal and vertical accelerations, and pressure altitude) and other parameters (elevator and flap positions, engine torques, and fuel flow). In addition, an aural warning at predetermined stall margins is presented to the pilot through a voice synthesizer. Once the system was designed and installed in the aircraft, a flight-test program of less than 20 hr was anticipated to determine the stall-speed software coefficients. These coefficients would then be inserted in the system's software and then test flown over a period of about 10 hr for the purposes of evaluation.
The NASA Dryden Flight Research Facility embarked upon a project with the United States Army Aviation Engineering Flight Activity (USAAEFA) to develop and test a stall-speed warning system. NASA designed and built an automated stall-speed warning system which presents both airspeed and stall speed to the pilot. The airspeed and stall speed are computed in real time by monitoring the basic aerodynamic parameters (dynamic pressure, horizontal and vertical accelerations, and pressure altitude) and other parameters (elevator and flap positions, engine torques, and fuel flow). In addition, an aural warning at predetermined stall margins is presented to the pilot through a voice synthesizer. Once the system was designed and installed in the aircraft, a flight-test program of less than 20 hr was anticipated to determine the stall-speed software coefficients. These coefficients would then be inserted in the system's software and then test flown over a period of about 10 hr for the purposes of evaluation.
We have conducted experiments to determine the equilibration rates of some major precipitants used in protein crystallography aboard the International Space Station (ISS). The solutions were placed in the Protein Crystallization Apparatus for Microgravity (PCAM) which mimic Cryschem sitting drop trays. The trays were placed in cylinders. These cylinders were placed inside a Single locker Thermal Enclosure System (STES), and were activated for different durations during the flight. Bumpers pressed against elastomers seal drops in a deactivated state during pre-flight and prior to transfer to the ISS. Activation occurs while in flight on the ISS by releasing the bumpers allowing the drops to be exposed to the reservoir. PCAM was flown to the ISS on STS 100, Flight 6A, on April 19, 2001. Six series of equilibration experiments were tested for each precipitant with a small amount of Green Fluorescent Protein (GFP). Cylinder 10 was never activated, 7 was activated for 40 days, 8 was activated for 20 days, 9 was activated for 10 days, 11 was activated for 4 days and 12 was activated for 2 days. Upon the return to Earth by STS 104 on July 24,2001 the samples were transferred to Marshall Space Flight Center. The samples were then brought to the lab and the volumes of each sample were measured.
Predicting raptor movements through a wind power plant under given atmospheric and topographical conditions is a crucial first step in the overall goal of quantifying the risk of turbine-related collisions and mortalities. Extracting behavioral traits of golden eagles (Aquila chrysaetos) from telemetry data requires the fusion of noisy and sparse movement data (location, heading, velocity) with a stochastic mathematical representation of the eagles' decision-making processes. In this study, we framed this problem in a Bayesian state-space framework where both observations and decision-making are assumed to be stochastic processes connected through hidden states (mode of flight, intent), and the unknown model parameters are assumed to be random variables that are calibrated using the available telemetry data. This framework allowed for rigorous consideration of underlying uncertainties while allowing for both data and prior biological knowledge to contribute to a probabilistic and predictive agent-based movement model. We implemented and applied the Bayesian framework to understand movement behavior of 23 GPS-tagged golden eagles travelling in the western US for years 2019 and 2020. Our preliminary findings show that the Bayesian state-space framework provides a robust inverse modeling apparatus to decode eagle behavioral characteristics from telemetry data. This study was primarily aimed at verifying and validating the framework with selected golden eagle tracks (both long- and short-ranged), with future research aimed at extending the framework to include multi-mode flight, consideration of atmospheric data and uplift mechanisms, eagle-to-eagle interaction, and eagle-to-turbine interaction.
Attitude and heading determination using GPS interferometry is a well-understood concept. However, efforts have been concentrated mainly in the development of robust algorithms and applications for low-dynamic, rigid platforms (e.g., shipboard). This paper presents results of what is believed to be the first real-time flight test of a GPS attitude and heading determination system. Signals from four antennas are processed by a 24-channel GPS receiver. Data from the receiver are sent to a microcomputer for storage and further computations. Attitude and heading data are sent to a second computer for display on a software-generated artificial horizon. Demonstration of this technique proves its candidacy for augmentation of aircraft state estimation for flight control and navigation, as well as for numerous other applications.
An iterative method, which is characterized as a maximum-likelihood minimum-variance technique, was used to extract the aerodynamic parameters of a Navion airplane from flight data. The purposes were to compare the results with parameters obtained from wind-tunnel tests and with results obtained by analog matching the same data, and to develop techniques for application of the parameter extraction program. Results from the study showed that the parameter-extraction program can produce aerodynamic parameters which will permit close estimation of the aircraft time histories used in the extraction process. The program determined an estimate of the standard deviations of the states and parameters. These estimates were used to indicate how well the calculated states fit the flight data and the confidence in the values of the estimated parameters. The study also showed that the values of the parameters were affected by the data and mathematical model used during the extraction process. Because of the lack of confidence in the parameters extracted by use of some of the sets of data, several parameters were estimated by other methods. By using a combination of methods, a set of parameters which gave a fit to the data was obtained.
Attitude and heading determination using GPS interferometry is a well-understood concept. However, efforts have been concentrated mainly in the development of robust algorithms and applications for low dynamic, rigid platforms (e.g., shipboard). This paper presents results of what is believed by the authors to be the first realtime flight test of a GPS attitude and heading determination system. The system is installed in Ohio University's Douglas DC-3 research aircraft. Signals from four antennas are processed by an Ashtech 3DF 24-channel GPS receiver. Data from the receiver are sent to a microcomputer for storage and further computations. Attitude and heading data are sent to a second computer for display on a software generated artificial horizon. Demonstration of this technique proves its candidacy for augmentation of aircraft state estimation for flight control and navigation as well as for numerous other applications.
The isothermal Dendritic Growth Experiment (IDGE), to be performed on three of the United States Microgravity Payload (USMP) flights, starting with USMP-2, is designed to provide microgravity data on dendritic growth for a critical test of theory. Ground based test data using succinonitrile (SCN), from both a flight growth chamber and a laboratory growth chamber, are compared to theoretical calculations of dendritic tip velocities and radii. The comparison shows that the data from the flight chamber are consistent with the historical data and that dendritic growth in a microgravity environment should exhibit significant differences from the dendritic growth of SCN at g sub 0.
It has been noted by industry that in addition to dramatic variations of temperature over a given blade surface, blade-to-blade variations also exist despite identical design. These variations result from manufacturing variations, uneven wear and deposition over the life of the part as well as limitations in the uniformity of coolant distribution in the baseline cooling design. It is proposed to combine recent advances in optical sensing, actuation, and film cooling concepts to develop a workable active, closed-loop modulated turbine cooling system to improve by 10 to 20 the turbine thermal state over the flight mission, to improve engine life and to dramatically reduce turbine cooling air usage and aircraft fuel burn. A reduction in oxides of nitrogen (NOx) can also be achieved by using the excess coolant to improve mixing in the combustor especially for rotorcraft engines. Recent patents filed by industry and universities relate to modulating endwall cooling using valves. These schemes are complex, add weight and are limited to the endwalls. The novelty of the proposed approach is twofold 1) Fluidic diverters that have no moving parts are used to modulate cooling and can operate under a wide range of conditions and environments. 2) Real-time optical sensing to map the thermal state of the turbine has never been attempted in realistic engine conditions.
One of the most severe forms of coupling between aeroelasticity and flight dynamics is an instability called freedom flutter. The existing tools often assume relatively weak coupling, and are therefore unable to accurately model body freedom flutter. Because the existing tools were developed from traditional flutter analysis models, inconsistencies in the final models are not compatible with control system design tools. To resolve these issues, a number of small, but significant changes have been made to the existing approaches. A frequency domain transformation is used with the unsteady aerodynamics to ensure a more physically consistent stability axis rational function approximation of the unsteady aerodynamic model. The aerodynamic model is augmented with additional terms to account for limitations of the baseline unsteady aerodynamic model and to account for the gravity forces. An assumed modes method is used for the structural model to ensure a consistent definition of the aircraft states across the flight envelope. The X-56A stiff wing flight-test data were used to validate the current modeling approach. The flight-test data does not show body-freedom flutter, but does show coupling between the flight dynamics and the aeroelastic dynamics and the effects of the fuel weight.
This paper describes real-time attitude determination results for the Solar, Anomalous, and Magnetospheric Particle Explorer (SAMPEX), a gyroless spacecraft, using a Kalman filter/Euler equation approach denoted the real-time sequential filter (RTSF). The RTSF is an extended Kalman filter whose state vector includes the attitude quaternion and corrections to the rates, which are modeled as Markov processes with small time constants. The rate corrections impart a significant robustness to the RTSF against errors in modeling the environmental and control torques, as well as errors in the initial attitude and rates, while maintaining a small state vector. SAMPLEX flight data from various mission phases are used to demonstrate the robustness of the RTSF against a priori attitude and rate errors of up to 90 deg and 0.5 deg/sec, respectively, as well as a sensitivity of 0.0003 deg/sec in estimating rate corrections in torque computations. In contrast, it is shown that the RTSF attitude estimates without the rate corrections can degrade rapidly. RTSF advantages over single-frame attitude determination algorithms are also demonstrated through (1) substantial improvements in attitude solutions during sun-magnetic field coalignment and (2) magnetic-field-only attitude and rate estimation during the spacecraft's sun-acquisition mode. A robust magnetometer-only attitude-and-rate determination method is also developed to provide for the contingency when both sun data as well as a priori knowledge of the spacecraft state are unavailable. This method includes a deterministic algorithm used to initialize the RTSF with coarse estimates of the spacecraft attitude and rates. The combined algorithm has been found effective, yielding accuracies of 1.5 deg in attitude and 0.01 deg/sec in the rates and convergence times as little as 400 sec.