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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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This paper will describe the methodology used to allocate Reliability and Maintainability (R&M) goals to Ground Systems Development and Operations (GSDO) subsystems currently being designed or upgraded.
An analysis was performed to identify the engineering rationale for the existing particulate limits in MSFC-SPEC-164, Cleanliness of Components for Use in Oxygen, Fuel, and Pneumatic Systems, determine the applicability of this rationale to fibers, identify potential risks that may result from fiber contamination in liquid oxygen/fuel bipropellant systems, and bound each of these risks. The objective of this analysis was to determine whether fiber contamination exceeding the established quantitative limits for particulate can be tolerated in these systems and, if so, to derive and recommend quantitative allocations for fibers beyond the limits established for other particulate. Knowledge gaps were identified that limit a complete understanding of the risk of promoted ignition from an accumulation of fibers in a gaseous oxygen system.
Two human-in-the-loop simulation experiments were conducted in coordinated fashion to investigate the allocation of separation assurance functions between ground and air and between humans and automation. The experiments modeled a mixed-operations concept in which aircraft receiving ground-based separation services shared the airspace with aircraft providing their own separation service (i.e., self-separation). Ground-based separation was provided by air traffic controllers without automation tools, with tools, or by ground-based automation with controllers in a managing role. Airborne self-separation was provided by airline pilots using self-separation automation enabled by airborne surveillance technology. The two experiments, one pilot-focused and the other controller-focused, addressed selected key issues of mixed operations, assuming the starting point of current-day operations and modeling an emergence of NextGen technologies and procedures. In the controller-focused experiment, the impact of mixed operations on controller performance was assessed at four stages of NextGen implementation. In the pilot-focused experiment, the limits to which pilots with automation tools could take full responsibility for separation from ground-controlled aircraft were tested. Results indicate that the presence of self-separating aircraft had little impact on the controllers' ability to provide separation services for ground-controlled aircraft. Overall performance was best in the most automated environment in which all aircraft were data communications equipped, ground-based separation was highly automated, and self-separating aircraft had access to trajectory intent information for all aircraft. In this environment, safe, efficient, and highly acceptable operations could be achieved for twice today's peak airspace throughput. In less automated environments, reduced trajectory intent exchange and manual air traffic control limited the safely achievable airspace throughput and negatively impacted the maneuver efficiency of self-separating aircraft through high-density airspace. In a test of scripted conflicts with ground-managed aircraft, flight crews of self-separating aircraft prevented separation loss in all conflicts with detection time greater than one minute. In debrief, pilots indicated a preference for at least five minute's alerting notice and trajectory intent information on all aircraft. When intent information on ground-managed aircraft was available, self-separating aircraft benefited from fewer conflict alerts and fewer required deviations from trajectory-based operations.
Two human-in-the-loop simulation experiments were conducted to investigate allocation of separation assurance functions between ground and air and between humans and automation. The experiments modeled a mixed-operations concept in which aircraft receiving ground-based separation services shared the airspace with aircraft providing their own separation service (i.e., self-separation). The two experiments, one pilot-focused and the other controller-focused, addressed selected key issues of mixed operations and modeling an emergence of NextGen technologies and procedures. This paper focuses on the results of the subjective assessments of pilots collected during the pilot-focused human-in-the-loop simulation, specifically workload and situation awareness. Generally the results revealed that across all conditions, pilots' perceived workload was low to medium, with the highest reported levels of workload occurring when the pilots experienced a loss of separation during the scenario. Furthermore, the results from the workload data and situation awareness data were complimentary such that when pilots reported lower levels of workload they also experienced higher levels of situation awareness.
Integration of the Control Allocation technique to recover from Pilot Induced Oscillations (CAPIO) System into the control system of a Short Takeoff and Landing Mobility Concept Vehicle simulation presents a challenge because the CAPIO formulation requires that constrained optimization problems be solved at the controller operating frequency. We present a solution that utilizes a modified version of the well-known L-BFGS-B solver. Despite the iterative nature of the solver, the method is seen to converge in real time with sufficient reliability to support three weeks of piloted runs at the NASA Ames Vertical Motion Simulator (VMS) facility. The results of the optimization are seen to be excellent in the vast majority of real-time frames. Deficiencies in the quality of the results in some frames are shown to be improvable with simple termination criteria adjustments, though more real-time optimization iterations would be required.
The performance of the conflict detection function in a separation assurance system is dependent on the content and quality of the data available to perform that function. Specifically, data quality and data content available to the conflict detection function have a direct impact on the accuracy of the prediction of an aircraft's future state or trajectory, which, in turn, impacts the ability to successfully anticipate potential losses of separation (detect future conflicts). Consequently, other separation assurance functions that rely on the conflict detection function - namely, conflict resolution - are prone to negative performance impacts. The many possible allocations and implementations of the conflict detection function between centralized and distributed systems drive the need to understand the key relationships that impact conflict detection performance, with respect to differences in data available. This paper presents the preliminary results of an analysis technique developed to investigate the impacts of data quality and data content on conflict detection performance. Flight track data recorded from a day of the National Airspace System is time-shifted to create conflicts not present in the un-shifted data. A methodology is used to smooth and filter the recorded data to eliminate sensor fusion noise, data drop-outs and other anomalies in the data. The metrics used to characterize conflict detection performance are presented and a set of preliminary results is discussed.
To date, the most extensive space-based inventory management operation has been the International Space Station (ISS). Approximately 20,000 items are tracked with the Inventory Management System (IMS) software application that requires both flight and ground crews to update the database daily. This audit process is manually intensive and laborious, requiring the crew to open cargo transfer bags (CTBs), then Ziplock bags therein, to retrieve individual items. This inventory process contributes greatly to the time allocated for general crew tasks.
In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well-formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA's proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.
The following work details a study into real-time failure adaptive control allocation method for powered descent vehicle systems. The motivation for this work is to enable future human and robotic missions utilizing a powered descent system to tolerate engine failures in flight without the loss of crew or assets. This study is conducted using a six degree-of-freedom trajectory simulation of a PDV (Powered Descent Vehicle) experiencing either a loss of thrust or an engine stuck full on failure scenario. Sequential least squares in the frequency domain is used on-board to process inertial measurement unit (IMU) data and generate an estimate of the PDV plant model, which is then fed to the guidance and control system. Data used by the sequential least squares method is generated from an in-flight maneuver. The work herein focuses on determining a maneuver that is least impactful to the PDV trajectory and enables a suitable plant model estimate. A 1.5-second-long maneuver with an amplitude of 5 percent throttle is determined to provide suitable data for the sequential least squares method to estimate a plant model. A PDV implementing this method can adapt to a single engine failure and continue to reach its touchdown conditions.
Precision landing of large payloads on Mars presents a challenge to the Entry, Descent, and Landing (EDL) community. Previous studies indicated that by incorporating the capability for a Hypersonic Inflatable Aerodynamic Decelerator (HIAD) to morph during reentry would result in a more accurate landing footprint by allowing modulation of the lift- to-drag (L/D) vector directly instead of through bank angle control. However, morphing the HIAD shape for trajectory control may expose the HIAD to potential structural loads or aero heating concerns. In this study, the application of an optimal control allocation (OCA) technique was investigated that would to enable the morphing HIAD to maximize trajectory control capabilities while simultaneously keeping the structural loads and aero heating below some thresholds. This concept was demonstrated in a 3 degree-of-freedom (DOF) EDL simulation and provides basis for future research.
This paper describes the development and application of a Generic Control Allocation Toolbox developed at NASA Langley Research Center (LGCAT) intended to aid engineers during the preliminary design phase of an aerospace vehicle. The static controllability space in the forms of a Theoretical Attainable Moment Set, Φ, or Theoretical Attainable Force Set, φ is difficult to visualize for modern vehicles with multiple types of redundant control effectors. The objective of LGCAT is to provide system engineers and designers early in the vehicle design phase with quick insights on how control effector parameters such as quantity, sizing, location, orientation, redundancy, etc., affect the overall controllability and other performance metrics. Having such information in hand allows system engineers to make more informed decisions on overall mission objectives such as performance vs. reliability vs. cost, etc. early in a vehicle design phase and reduce the number of iterations necessary in the design and analysis cycles. LGCAT can accept a variety of control effector types including aerodynamic surfaces, rotors, thrust vector control (TVC) engines, and reaction control systems (RCS). LGCAT is MATLAB based, user friendly, and is capable of performing the analysis in the Graphical User Interface (GUI) or script mode. Current add-on features include interfacing with engineering level codes such as Vehicle Sketch Pad (VSP) and generating the corresponding Φ and φ for an arbitrary vehicle design. These capabilities potentially make LGCAT an integral part of the preliminary design phase for any vehicle.
In a Model Based Systems Engineering (MBSE) infusion effort, there is a usually a concerted effort to define the information architecture, ontologies, and patterns that drive the construction and architecture of MBSE models, but less attention is given to the logical follow-on of that effort: how to practically leverage the resulting semantic richness of a well formed populated model to enable systems engineers to work more effectively, as MBSE promises. While ontologies and patterns are absolutely necessary, an MBSE effort must also design and provide practical demonstration of value (through human-understandable representations of model data that address stakeholder concerns) or it will not succeed. This paper will discuss opportunities that exist for visualization in making the richness of a well-formed model accessible to stakeholders, specifically stakeholders who rely on the model for their day-to-day work. This paper will discuss the value added by MBSE-driven visualizations in the context of a small case study of interactive visualizations created and used on NASA’s proposed Europa Mission. The case study visualizations were created for the purpose of understanding and exploring targeted aspects of requirements flow, allocation, and comparing the structure of that flow-down to a conceptual project decomposition. The work presented in this paper is an example of a product that leverages the richness and formalisms of our knowledge representation while also responding to the quality attributes SEs care about.
As NASA looks to the moon as a proving ground for the technologies required to conduct the first human exploration campaign of Mars, this study assesses the potential propellant demands that a sustained human lunar presence would require. In particular, this study evaluates how the allocation and distribution of vehicle functions to a different number of vehicles can impact the propellant required to conduct a human lunar exploration mission. Four different transportation architectures for human lunar exploration and three different refueling assumptions were defined. The impact on the propellant required for a mission was assessed for all architectures and refueling assumptions for ranges of crew payload mass, vehicle inert mass fraction, and a sensitivity to the mission maneuver velocity magnitudes. The results of this analysis show that aggregating vehicle functions to a single-stage lander vehicle can have a positive impact on the architecture's level of vehicle reusability and that an increase in the number of refueling opportunities reduces the mass of the vehicles and of the propellant required per mission.
The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.
The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.
Increased space and ground sensing is enabling dramatic new measurements of a wide range of Earth Science and Applied Earth Science phenomena, including: volcanism, flooding, wildfires, and weather. Large scale observations constellations of hundreds of assets already exist (e.g. Planet) with several constellations with 10,000’s of assets planned. New challenges exist to rapidly assimilate available data and to optimize measurements (e.g. direct assets) to best observe these complex and dynamic spatiotemporal phenomena. We describe automated centralized and distributed Artificial Intelligence methods for allocating observations within a constellation.
Increased space and ground sensing is enabling dramatic new measurements of a wide range of Earth Science and Applied Earth Science phenomena, including: volcanism, flooding, wildfires, and weather. Large scale observations constellations of hundreds of assets already exist (e.g. Planet) with several constellations with 10,000’s of assets planned. New challenges exist to rapidly assimilate available data and to optimize measurements (e.g. direct assets) to best observe these complex and dynamic spatiotemporal phenomena. We describe automated centralized and distributed Artificial Intelligence methods for allocating observations within a constellation.
Near-Angle Scatter (NAS) of the host star’s light may limit the ability of a potential Habitable Worlds Observatory (HWO) to detect and characterize an Earth-like planet around a Sun-like star via coronagraphy. NAS from each optical surface before the coronagraph’s focal plane mask produces an E-field across the dark hole that is coherent. These E-fields sum and could be as large or larger than the coronagraph mask leakage E-field. This paper assumes an error budget allocation for scatter of 20 ppt. NAS E-fields contribute to the dark hole noise floor via both shot noise and heterodyne amplification of the wavefront instability. While previous papers have developed specifications for scatter from surface scatter, this paper develops specifications for scatter from surface contamination and micrometeoroid impacts. The development process utilizes an expression that predicts scatter throughput into the dark hole based on surface BRDF. Analysis does not include scatter from coating columnar structure, edges, contamination, micrometeoroid impacts, or polarization.