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At least 199 records · Page 11

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learningautonomous systems; flight simulat↗

Tradeoffs When Considering Deep Reinforcement Learning for Contingency Management in Advanced Air Mobility

Air transportation is undergoing a rapid evolution globally with the introduction of Advanced Air Mobility (AAM) and with it comes novel challenges and opportunities for transforming aviation. As AAM operations introduce increasing heterogeneity in vehicle capabilities and density, increased levels of automation are likely necessary to achieve operational safety and efficiency goals. This paper focuses on one example where increased automation has been suggested. Autonomous operations will need contingency management systems that can monitor evolving risk across a span of interrelated (or interdependent) hazards and, if necessary, execute appropriate control interventions via supervised or automated decision making. Accommodating this complex environment may require automated functions (autonomy) that apply artificial intelligence (AI) techniques that can adapt and respond to a quickly changing environment. This paper explores the use of Deep Reinforcement Learning (DRL) which has shown promising performance in complex and high-dimensional environments where the objective can be constructed as a sequential decision-making problem. An extension of a prior formulation of the contingency management problem as a Markov Decision Process (MDP) is presented and uses a DRL framework to train agents that mitigate hazards present in the simulation environment. A comparison of these learning-based agents and classical techniques is presented in terms of their performance, verification difficulties, and development process.

machine learning↗

Overview of the NASA LISA Laser System Development

NASA Goddard Space Flight Center (GSFC) is developing the Laser System (LS) for the Laser Interferometer Space Antenna (LISA) mission, led by the European Space Agency (ESA) with a launch date of 2035. The LS under development at NASA GSFC consists of the Laser Head (LH), the Frequency Reference System (FRS), and the Power Monitor (PMON) Detector Assemblies. Since late 2017, we have been developing various models to advance the technology readiness level (TRL) for the LH from prototype (TRL4) to a system model demonstration in a relevant environment (TRL6). For the LH and FRS, the models further breakdown into the optical and electronics modules where the LH is made up of the Laser Optical Module (LOM) and the Laser Electronics Module (LEM) and the FRS consists of the FRS Optical Reference Cavity (FRS-O) and the FRS Electronics (FRS-E). The LS development follows the established NASA process in demonstrating the performance requirements [1] through the TRL4 effort and then advancing the form factor and package design to meet relevant environment requirements and qualifying the TRL6 design through rigorous testing and performance verification for space applications. The LOM for the LH is a main oscillator power amplifier (MOPA) with wavelength of 1064 nm and nominal output power of 2 Watt throughout the mission. The low-power, low-noise main oscillator (MO) is a custom micro non-planar ring oscillator (µNPRO) [2] that is phase modulated then amplified by the forward pumped ytterbium-dope fiber power amplifier (PA) stage to meet the output power requirement. The FRS is baselined on the GRACE Follow-On (GFO) [3] approach with updated FRS-E design to work with the LH-LEM. The PMON is located on the Optical Bench Assembly (OBA) [4] that samples a small portion of the LH output signal and provides the feedback signal to the LH-LEM for relative intensity noise (RIN) control. In this paper, we will report on the latest status of the LH testing and system level tests, explain the other subsystems involved in the test campaigns, and discuss the path to bring each LS subsystem to TRL6. We will also present the NASA GSFC roadmap in advancing the LISA LS to TRL6+ and plans for future system level testing as well as the preparation for space flight development to meet the LISA launch date of 2035.

amplifier↗

On Orbit Performance Validation and Verification of the SMAP Instrument Antenna

NASA’s Soil Moisture Active Passive (SMAP) Mission is currently flying in a 685 km orbit. Featuring a Synthetic Aperture Radar (SAR) and a radiometer sharing the same antenna, SMAP was developed in collaboration between Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). While the radar requirements on the instrument antenna were more benign from an RF point of view, the radiometer requirement were more difficult to meet because of the stability required by the radiometer to operate to its full potential. The instrument antenna performance was predicted by a very detailed RF model and verified by measuring a 1/10th scale model with great accuracy before launch. Once in orbit, we had the opportunity to measure the antenna performance for both the radiometer and the radar and compare it with the predicted performance given by our RF model. This paper discusses the work done both at JPL and GSFC in order to verify and validate the on orbit performance of the SMAP instrument antenna.

offset reflector↗

SMAP Instrument Antenna, on Orbit Performance Validation and Verification

NASA’s Soil Moisture Active Passive (SMAP) Mission is currently flying in a 685 km orbit. Featuring a Synthetic Aperture Radar (SAR) and a radiometer sharing the same antenna, SMAP was developed in collaboration between Jet Propulsion Laboratory (JPL) and Goddard Space Flight Center (GSFC). While the radar requirements on the instrument antenna were more benign from an RF point of view, the radiometer requirement were more difficult to meet because of the stability required by the radiometer to operate to its full potential. The instrument antenna performance was predicted by a very detailed RF model and verified by measuring a 1/10th scale model with great accuracy before launch. Once in orbit, we had the opportunity to measure the antenna performance for both the radiometer and the radar and compare it with the predicted performance given by our RF model. This paper discusses the work done both at JPL and GSFC in order to verify and validate the on orbit performance of the SMAP instrument antenna.

reflector antenna↗

Analytical prediction with multidimensional computer programs and experimental verification of the performance, at a variety of operating conditions, of two traveling wave tubes with depressed collectors

Experimental and analytical results are compared for two high performance, octave bandwidth TWT's that use depressed collectors (MDC's) to improve the efficiency. The computations were carried out with advanced, multidimensional computer programs that are described here in detail. These programs model the electron beam as a series of either disks or rings of charge and follow their multidimensional trajectories from the RF input of the ideal TWT, through the slow wave structure, through the magnetic refocusing system, to their points of impact in the depressed collector. Traveling wave tube performance, collector efficiency, and collector current distribution were computed and the results compared with measurements for a number of TWT-MDC systems. Power conservation and correct accounting of TWT and collector losses were observed. For the TWT's operating at saturation, very good agreement was obtained between the computed and measured collector efficiencies. For a TWT operating 3 and 6 dB below saturation, excellent agreement between computed and measured collector efficiencies was obtained in some cases but only fair agreement in others. However, deviations can largely be explained by small differences in the computed and actual spent beam energy distributions. The analytical tools used here appear to be sufficiently refined to design efficient collectors for this class of TWT. However, for maximum efficiency, some experimental optimization (e.g., collector voltages and aperture sizes) will most likely be required.

Dayton, J. A., Jr.↗

The Verification-based Analysis of Reliable Multicast Protocol

Reliable Multicast Protocol (RMP) is a communication protocol that provides an atomic, totally ordered, reliable multicast service on top of unreliable IP Multicasting. In this paper, we develop formal models for R.W using existing automatic verification systems, and perform verification-based analysis on the formal RMP specifications. We also use the formal models of RW specifications to generate a test suite for conformance testing of the RMP implementation. Throughout the process of RMP development, we follow an iterative, interactive approach that emphasizes concurrent and parallel progress between the implementation and verification processes. Through this approach, we incorporate formal techniques into our development process, promote a common understanding for the protocol, increase the reliability of our software, and maintain high fidelity between the specifications of RMP and its implementation.

Wu, Yunqing↗

Process Sensitivity, Performance, and Direct Verification Testing of Adhesive Locking Features

Phase I: The use of adhesive locking features or liquid locking compounds (LLCs) (e.g., Loctite) as a means of providing a secondary locking feature has been used on NASA programs since the Apollo program. In many cases Loctite was used as a last resort when (a) self-locking fasteners were no longer functioning per their respective drawing specification, (b) access was limited for removal & replacement, or (c) replacement could not be accomplished without severe impact to schedule. Long-term use of Loctite became inevitable in cases where removal and replacement of worn hardware was not cost effective and Loctite was assumed to be fully cured and working. The NASA Engineering & Safety Center (NESC) and United Space Alliance (USA) recognized the need for more extensive testing of Loctite grades to better understand their capabilities and limitations as a secondary locking feature. These tests, identified as Phase I, were designed to identify processing sensitivities, to determine proper cure time, the correct primer to use on aerospace nutplate, insert and bolt materials such as A286 and MP35N, and the minimum amount of Loctite that is required to achieve optimum breakaway torque values. The .1900-32 was the fastener size tested, due to wide usage in the aerospace industry. Three different grades of Loctite were tested. Results indicate that, with proper controls, adhesive locking features can be successfully used in the repair of locking features and should be considered for design. Phase II: Threaded fastening systems used in aerospace programs typically have a requirement for a redundant locking feature. The primary locking method is the fastener preload and the traditional redundant locking feature is a self-locking mechanical device that may include deformed threads, non-metallic inserts, split beam features, or other methods that impede movement between threaded members. The self-locking resistance of traditional locking features can be directly verified during assembly by measuring the dynamic prevailing torque. Adhesive locking features or LLCs are another method of providing redundant locking, but a direct verification method has not been used in aerospace applications to verify proper installation when using LLCs because of concern for damage to the adhesive bond. The reliability of LLCs has also been questioned due to failures observed during testing with coupons for process verification, although the coupon failures have often been attributed to a lack of proper procedures. It is highly desirable to have a direct method of verifying the LLC cure or bond integrity. The purpose of the Phase II test program was to determine if the torque applied during direct verification of an adhesive locking feature degrades that locking feature. This report documents the test program used to investigate the viability of such a direct verification method. Results of the Phase II testing were positive, and additional investigation of direct verification of adhesive locking features is merited.

Golden, Johnny L.↗

STS 41-D Solar Array Flight Experiment

The Solar Array Flight Experiment (SAFE) developed under the direction of the Marshall Space Flight Center, scheduled for launch on STS 41-D, will demonstrate a lightweight solar array technology which offers a factor of 3 improvement in weight and a factor of 10 improvement in specific volume over solar array systems currently in use in the space program. The experiment, which will include multiple deployment and retraction demonstration, verification of electrical and thermal performance, and verification of structural dynamic math models is 15 feet by 105 feet in size and, if completely covered with solar cells, would produce approximately 12.5 kW of electrical power. The unit has now been developed, tested, and is at Kennedy Space Center (KSC) being prepared for launch.

Turner, G. F.↗

Space station definitions, design, and development. Task 5: Multiple arm telerobot coordination and control: Manipulator design methodology

A proposed methodology applicable to the design of manipulator systems is described. The current design process is especially weak in the preliminary design phase, since there is no accepted measure to be used in trading off different options available for the various subsystems. The design process described uses Cartesian End-Effector Impedance as a measure of performance for the system. Having this measure of performance, it is shown how it may be used to determine the trade-offs necessary to the preliminary design phase. The design process involves three main parts: (1) determination of desired system performance in terms of End-Effector Impedance; (2) trade-off design options to achieve this desired performance; and (3) verification of system performance through laboratory testing. The design process is developed using numerous examples and experiments to demonstrate the feasability of this approach to manipulator design.

Stoughton, R. M.↗

Pre-Installation Acceptance (PIA) Functional Performance of the Design Verification Test (DVT) Exploration Extra-vehicular Mobility Unit (xEMU)

In an effort that began with technology investment by NASA in a few key components during the Constellation Program and then evolved to demonstrate a packaged Portable Life Support System (PLSS) as part of the Advanced Exploration Systems (AES) Program, the next evolution of the PLSS is now a key component of the Exploration Extra-vehicular Mobility Unit (xEMU) and is assembled as a Design Verification Test (DVT) unit. The xEMU has been detailed with respect to completing a demonstration on the International Space Station (ISS) with support of units for initial lunar capability. The xEMU completed the Preliminary Design Review (PDR) with subsequent Safety Review Panel (SRP) Phase I reviews in 2019-2020. The objectives for DVT are to validate requirements, train the team, learn how to fabricate the hardware with appropriate process controls, assemble the hardware, test the hardware, determine the failure mechanisms/limits of the hardware design and buy down the most risk possible for the qualification and flight phases of the development. With completion of the assembly and initial functional testing of the DVT PLSS in laboratory ambient conditions and vacuum conditions the xEMU has progressed significantly into the DVT objectives. A key part of the test sequences for DVT and all future phases is the Pre-Installation Acceptance (PIA) functional testing which validates the performance of integrated systems including: primary thermal control, auxiliary thermal control, suit ventilation, primary oxygen, secondary oxygen, power distribution, as well as caution and warning all with respect to the applied requirements. This discussion will include an overview of the assembly, summary of the PIA functional testing, lessons learned, and corrective actions implemented moving forward into the remainder of the DVT phase for xEMU.

Colin Campbell↗

Lay out, test verification and in orbit performance of HELIOS a temperature control system

HELIOS temperature control system is described. The main design features and the impact of interactions between experiment, spacecraft system, and temperature control system requirements on the design are discussed. The major limitations of the thermal design regarding a closer sun approach are given and related to test experience and performance data obtained in orbit. Finally the validity of the test results achieved with prototype and flight spacecraft is evaluated by comparison between test data, orbit temperature predictions and flight data.

Brungs, W.↗