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At least 73 records · Page 4

Avionics and controls in review

The article surveys the changes which will occur in avionics and controls due to microprocessor technology. Attention is given to five broadly applicable technologies: (1) flight path management technology, (2) automatic control systems, (3) crew station technology, (4) integration and interfacing technology, and (5) fundamental technologies. Areas discussed include inertial navigation, guidance, weather avoidance, propulsion control systems, display technology, flight-system management, functional integration of avionics, and airborne information processing.

Smyth, R. K.↗

STS-63 Space Shuttle report

The STS-63 Space Shuttle Program Mission Report summarizes the Payload activities and provides detailed data on the Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME) systems performance during this sixty-seventh flight of the Space Shuttle Program, the forty-second since the return to flight, and twentieth flight of the Orbiter vehicle Discovery (OV-103). In addition to the OV-103 Orbiter vehicle, the flight vehicle consisted of an ET that was designated ET-68; three SSME's that were designated 2035, 2109, and 2029 in positions 1, 2, and 3, respectively; and two SRB's that were designated BI-070. The RSRM's that were an integral part of the SRB's were designated 360Q042A for the left SRB and 360L042B for the right SRB. The STS-63 mission was planned as an 8-day duration mission with two contingency days available for weather avoidance or Orbiter contingency operations. The primary objectives of the STS-63 mission were to perform the Mir rendezvous operations, accomplish the Spacehab-3 experiments, and deploy and retrieve the Shuttle Pointed Autonomous Research Tool for Astronomy-204 (SPARTAN-204) payload. The secondary objectives were to perform the Cryogenic Systems Experiment (CSE)/Shuttle Glo-2 Experiment (GLO-2) Payload (CGP)/Orbital Debris Radar Calibration Spheres (ODERACS-2) (CGP/ODERACS-2) payload objectives, the Solid Surface Combustion Experiment (SSCE), and the Air Force Maui Optical Site Calibration Tests (AMOS). The objectives of the Mir rendezvous/flyby were to verify flight techniques, communication and navigation-aid sensor interfaces, and engineering analyses associated with Shuttle/Mir proximity operations in preparation for the STS-71 docking mission.

Fricke, Robert W., Jr.↗

STS-66 Space Shuttle mission report

The primary objective of this flight was to accomplish complementary science objectives by operating the Atmospheric Laboratory for Applications and Science-3 (ATLAS-3) and the Cryogenic Infrared Spectrometers and Telescopes for the Atmosphere-Shuttle Pallet Satellite (CRISTA-SPAS). The secondary objectives of this flight were to perform the operations of the Shuttle Solar Backscatter Ultraviolet/A (SSBUV/A) payload, the Experiment of the Sun Complementing the Atlas Payload and Education-II (ESCAPE-II) payload, the Physiological and Anatomical Rodent Experiment/National Institutes of Health Rodents (PARE/NIH-R) payload, the Protein Crystal Growth-Thermal Enclosure System (PCG-TES) payload, the Protein Crystal Growth-Single Locker Thermal Enclosure System (PCG-STES), the Space Tissue/National Institutes of Health Cells STL/N -A payload, the Space Acceleration Measurement Systems (SAMS) Experiment, and Heat Pipe Performance Experiment (HPPE) payload. The 11-day plus 2 contingency day STS-66 mission was flown as planned, with no contingency days used for weather avoidance or Orbiter contingency operations. Appendix A lists the sources of data from which this report was prepared, and Appendix B defines all acronyms and abbreviations used in the report.

Fricke, Robert W., Jr.↗

STS-71, Space Shuttle Mission Report

The STS-71 Space Shuttle Program Mission Report summarizes the Payload activities and provides detailed data on the Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle main engine (SSME) systems performance. STS-71 is the 100th United States manned space flight, the sixty-ninth Space Shuttle flight, the forty-fourth flight since the return-to-flight, the fourteenth flight of the OV-104 Orbiter vehicle Atlantis, and the first joint United States (U.S.)-Russian docking mission since 1975. In addition to the OV-104 Orbiter vehicle, the flight vehicle consisted of an ET that was designated ET-70; three SSMEs that were designated 2028, 2034, and 2032 in positions 1, 2, and 3, respectively; and two SRBs that were designated Bl-072. The RSRMs that were an integral part of the SRBs were designated 360L045A for the left SRB and 360W045B for the right SRB. The STS-71 mission was planned as a 1 0-day plus 1-day-extension mission plus 2 additional days for contingency operations and weather avoidance. The primary objectives of this flight were to rendezvous and dock with the Mir Space Station and perform on-orbit joint U.S.-Russian life sciences investigations, logistical resupply of the Mir Space Station, return of the United States astronaut flying on the Mir, the replacement of the Mir-18 crew with the two-cosmonaut Mir-19 crew, and the return of the Mir-18 crew to Earth. The secondary objectives were to perform the requirements of the IMAX Camera and the Shuttle Amateur Radio experiment-2 (SAREX-2).

Frike, Robert W., Jr.↗

STS-77 Space Shuttle Mission Report

The STS-77 Space Shuttle Program Mission Report summarizes the Payload activities as well as the: Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle Main Engine (SSME) systems performance during the seventy-seventh flight of the Space Shuttle Program, the fifty-second flight since the return-to-flight, and the eleventh flight of the Orbiter Endeavour (OV-105). STS-77 was also the last flight of OV-105 prior to the vehicle being placed in the Orbiter Maintenance Down Period (OMDP). In addition to the Orbiter, the flight vehicle consisted of an ET that was designated ET-78; three SSME's that were designated as serial numbers 2037, 2040, and 2038 in positions 1, 2, and 3, respectively; and two SRB's that were designated BI-080. The RSRM's, designated RSRM-47, were installed in each SRB and the individual RSRM's were designated as 360TO47A for the left SRB, and 360TO47B for the right SRB. The STS-77 Space Shuttle Program Mission Report fulfills the Space Shuttle Program requirement as documented in NSTS 07700, Volume VII, Appendix E. The requirement stated in that document is that each organizational element supporting the Program will report the results of their hardware (and software) evaluation and mission performance plus identify all related in-flight anomalies. The primary objectives of this flight were to successfully perform the operations necessary to fulfill the requirements of Spacehab-4, the SPARTAN 207/inflatable Antenna Experiment (IAE), and the Technology Experiments Advancing Missions in Space (TEAMS) payload. Secondary objectives of this flight were to perform the experiments of the Aquatic Research Facility (ARF), Brilliant Eyes Ten-Kelvin Sorption Cryocooler Experiment (BETSCE), Biological Research in Canisters (BRIC), Get-Away-Special (GAS), and GAS Bridge Assembly (GBA). The STS-77 mission was planned as a 9-day flight plus 1 day, plus 2 contingency days, which were available for weather avoidance or Orbiter contingency operations. The sequence of events for the STS-77 mission is shown in Table 1, and the Space Shuttle Vehicle Management Office Problem Tracking List is shown in Table 11. The Government Fumished Equipment/Flight Crew Equipment (GFE/FCE) Problem Tracking List is shown in Table II. Appendix A lists the sources of data, both formal and informal, that were used to prepare this report. Appendix B provides the definition of acronyms and abbreviations used throughout the report. All times during the flight are given in Greenwich mean time (G.m.t.) and mission elapsed time (MET). The six-person crew for STS-77 consisted of John H. Casper, Col., U. S. Air Force, Commander; Curtis L. Brown, Jr., Lt. Col., U. S. Air Force, Pilot; Andrew S. W. Thomas, Civilian, Ph.D., Mission Specialist 1; Daniel W. Bursch, CDR., U. S. Navy, Mission Specialist 2; Mario Runco, Jr., Civilian, Mission Specialist 3; and Marc Gameau, Civilian, PhD, Mission Specialist 4.

Fricke, Robert W., Jr.↗

STS-78 Space Shuttle Mission Report

The STS-78 Space Shuttle Program Mission Report summarizes the Payload activities as well as the Orbiter, External Tank (ET), Solid Rocket Booster (SRB), Reusable Solid Rocket Motor (RSRM), and the Space Shuttle main engine (SSME) systems performance during the seventy-eighth flight of the Space Shuttle Program, the fifty-third flight since the return-to-flight, and the twentieth flight of the Orbiter Columbia (OV-102). In addition to the Orbiter, the flight vehicle consisted of an ET that was designated ET-79; three SSME's that were designated as serial numbers 2041, 2039, and 2036 in positions 1, 2, and 3, respectively; and two SRB's that were designated BI-081. The RSRM's, designated RSRM-55, were installed in each SRB and the individual RSRM's were designated as 360L055A for the left SRB, and 360L055B for the right SRB. The STS-78 Space Shuttle Program Mission Report fulfills the Space Shuttle Program requirement as documented in NSTS 07700, Volume 7, Appendix E. The requirement stated in that document is that each organizational element supporting the Program will report the results of their hardware (and software) evaluation and mission performance plus identify all related in-flight anomalies. The primary objective of this flight was to successfully perform the planned operations of the Life and Microgravity Spacelab experiments. The secondary objectives of this flight were to complete the operations of the Orbital Acceleration Research Experiment (OARE), Biological Research in Canister Unit-Block II (BRIC), and the Shuttle Amateur Radio Experiment II-Configuration C (SAREX-II). The STS-78 mission was planned as a 16-day, plus one day flight plus two contingency days, which were available for weather avoidance or Orbiter contingency operations. The sequence of events for the STS-78 mission is shown in Table 1, and the Space Shuttle Vehicle Management Office Problem Tracking List is shown in Table 2. The Government Furnished Equipment/Flight Crew Equipment (GFE/FCE) Problem Tracking List is shown in Table 3. The Marshall Space Flight Center (MSFC) Problem Tracking List is shown in Table 4. Appendix A lists the sources of data, both formal and informal, that were used to prepare this report. Appendix B provides the definition of acronyms and abbreviations used throughout the report. All times during the flight are given in Greenwich mean time (G.m.t.) and mission elapsed time (MET).

Fricke, Robert W., Jr.↗

Notional Airspace Operations Demonstration Plan

The airspace operations demonstration (AOD) is intended to show that the Access 5 Step 1 functional requirements can be met. The demonstration will occur in two phases. The initial on-range phase will be carried out in restricted airspace to demonstrate the cooperative collision avoidance (CCA) functional requirements and to provide risk-reduction for the AOD by allowing the test team to rehearse some elements of the demonstration mission. The CCA system to be used in these flights is based on Automatic Dependent Surveillance-Broadcast (ADS-B) which is a commercially-available system by which airplanes constantly broadcast their current position and altitude to other aircraft and ground resources over a dedicated radio datalink. The final phase will occur in the national airspace (NAS) and will be the formal demonstration of the remainder of the proposed functional requirements. The general objectives of the AOD are as follows: (1) Demonstrate that the UAS can aviate in the NAS (2) Demonstrate that the UAS can navigate in the NAS (3) Demonstrate that the UAS can communicate with the NAS (4) Demonstrate that the UAS can perform selected collision avoidance functions in the NAS (5) Demonstrate that the UAS can evaluate and avoid weather conflicts in the NAS (6) Demonstrate that the UAS can provide adequate command and control in the NAS In addition to the stated objectives, there are a number of goals for the flight demonstration. The demo can be accomplished successfully without achieving these goals, but these goals are to be used as a guideline for preparing for the mission. The goals are: (1) Mission duration of at least 24 hours (2) Loiter over heavy traffic to evaluate the data block issue identified during the Access 5 Airspace Operations Simulations (3) Document the contingency management process and lessons learned (4) Document the coordination process for Ground Control Stations (GCS) handoff (5) Document lessons learned regarding the process of flying in the NAS Preliminary planning for a notional mission to achieve the objectives and goals has been prepared. The planning is intended to serve as a guide for detailed planning of the AOD.

Trongale, Nicholas A.↗

A Near-Term Concept for Trajectory Based Operations with Air/Ground Data Link Communication

An operating concept and required system components for trajectory-based operations with air/ground data link for today's en route and transition airspace is proposed. Controllers are fully responsible for separation as they are today, and no new aircraft equipage is required. Trajectory automation computes integrated solutions to problems like metering, weather avoidance, traffic conflicts and the desire to find and fly more time/fuel efficient flight trajectories. A common ground-based system supports all levels of aircraft equipage and performance including those equipped and not equipped for data link. User interface functions for the radar controller's display make trajectory-based clearance advisories easy to visualize, modify if necessary, and implement. Laboratory simulations (without human operators) were conducted to test integrated operation of selected system components with uncertainty modeling. Results are based on 102 hours of Fort Worth Center traffic recordings involving over 37,000 individual flights. The presence of uncertainty had a marginal effect (5%) on minimum-delay conflict resolution performance, and windfavorable routes had no effect on detection and resolution metrics. Flight plan amendments and clearances were substantially reduced compared to today s operations. Top-of-descent prediction errors are the largest cause of failure indicating that better descent predictions are needed to reliably achieve fuel-efficient descent profiles in medium to heavy traffic. Improved conflict detections for climbing flights could enable substantially more continuous climbs to cruise altitude. Unlike today s Conflict Alert, tactical automation must alert when an altitude amendment is entered, but before the aircraft starts the maneuver. In every other failure case tactical automation prevented losses of separation. A real-time prototype trajectory trajectory-automation system is running now and could be made ready for operational testing at an en route Center in 1-2 years.

McNally, David↗

Functional Allocation for Ground-Based Automated Separation Assurance in NextGen

As part of an ongoing research effort into functional allocation in a NextGen environment, a controller-in-the-loop study on ground-based automated separation assurance was conducted at NASA Ames' Airspace Operations Laboratory in February 2010. Participants included six FAA front line managers, who are currently certified professional controllers and four recently retired controllers. Traffic scenarios were 15 and 30 minutes long where controllers interacted with advanced technologies for ground-based separation assurance, weather avoidance, and arrival metering. The automation managed the separation by resolving conflicts automatically and involved controllers only by exception, e.g., when the automated resolution would have been outside preset limits. Results from data analyses show that workload was low despite high levels of traffic, Operational Errors did occur but were closely tied to local complexity, and safety acceptability ratings varied with traffic levels. Positive feedback was elicited for the overall concept with discussion on the proper allocation of functions and trust in automation.

Prevot, Thomas↗

Allocation of Functions in a Far-Term Air Traffic Control Environment

A human-in-the-loop exploration of a ground-based automated separation assurance concept was conducted that involved the allocation of certain functions between humans and automation. This exploration included operations that were sustained for prolonged periods of time with high levels of traffic in the presence of convective weather and scheduling constraints. An investigation into the acceptability of the defined roles and performance of tasks was conducted where it was found that the participants rated the concept and allocation of functions with a high level of acceptability. However, issues were encountered with the automation related to the detection of and response to tactical conflicts. Lower ratings were given on account of these concerns, and it was found that a key contributor to the underlying problems was transitioning aircraft and the uncertainty of their trajectories. Stemming from those results, participants responded that they would rather have direct control over aircraft transitions as well as more control over the tactical conflict resolution automation. In contrast, participants responded that they would rather have the automation place aircraft back on trajectory, and perform weather avoidance and scheduling tasks.

Homola, Jeffrey↗

Defining Dynamic Route Structure

This poster describes a method for defining route structure from flight tracks. Dynamically generated route structures could be useful in guiding dynamic airspace configuration and helping controllers retain situational awareness under dynamically changing traffic conditions. Individual merge and diverge intersections between pairs of flights are identified, clustered, and grouped into nodes of a route structure network. Links are placed between nodes to represent major traffic flows. A parametric analysis determined the algorithm input parameters producing route structures of current day flight plans that are closest to todays airway structure. These parameters are then used to define and analyze the dynamic route structure over the course of a day for current day flight paths. Route structures are also compared between current day flight paths and more user preferred paths such as great circle and weather avoidance routing.

Zelinski, Shannon↗

Pilot Convective Weather Decision Making in En Route Airspace

The present research investigates characteristics exhibited in pilot convective weather decision making in en route airspace. In a part-task study, pilots performed weather avoidance under various encounter scenarios. Results showed that the margins of safety that pilots maintain from storms are as fluid as deviation decisions themselves.

Wu, Shu-Chieh↗

Upgrades to the Probabilistic NAS Platform Air Traffic Simulation Software

This document is the final report for the project entitled "Upgrades to the Probabilistic NAS Platform Air Traffic Simulation Software." This report consists of 17 sections which document the results of the several subtasks of this effort. The Probabilistic NAS Platform (PNP) is an air operations simulation platform developed and maintained by the Saab Sensis Corporation. The improvements made to the PNP simulation include the following: an airborne distributed separation assurance capability, a required time of arrival assignment and conformance capability, and a tactical and strategic weather avoidance capability.

Hunter, George↗

Provably Correct Floating-Point Implementation of a Point-In-Polygon Algorithm

The problem of determining whether or not a point lies inside a given polygon occurs in many applications. In air traffic management concepts, a correct solution to the point-in-polygon problem is critical to geofencing systems for Unmanned Aerial Vehicles and in weather avoidance applications. Many mathematical methods can be used to solve the point-in-polygon problem. Unfortunately, a straightforward floating- point implementation of these methods can lead to incorrect results due to round-off errors. In particular, these errors may cause the control flow of the program to diverge with respect to the ideal real-number algorithm. This divergence potentially results in an incorrect point-in- polygon determination even when the point is far from the edges of the polygon. This paper presents a provably correct implementation of a point-in-polygon method that is based on the computation of the winding number. This implementation is mechanically generated from a source- to-source transformation of the ideal real-number specification of the algorithm. The correctness of this implementation is formally verified within the Frama-C analyzer, where the proof obligations are discharged using the Prototype Verification System (PVS).

Moscato, Mariano M.↗

Shadow Evaluation of the ATD-2 Phase 3Trajectory Option Set Reroute Capability in the North Texas Metroplex

This paper presents results of NASA’s Airspace Technology Demonstration 2 Phase 3 Trajectory Option Set reroute capability designed to resolve a demand capacity imbalance along the terminal airspace boundary. We focus on Candidate flights generated during the Stormy 2020 Shadow Evaluation which was the result of using the system to passively collect predictions for each flight at the OUT event. Benefit metrics associated with the predictions are defined for the individual rerouted flight and also the system-wide savings. Candidate flights are grouped into three distinct use cases and the benefit mechanism is explained for each use case along with illustrative data. Analysis of the different use cases shed light on the underlying causes of the reroute opportunities. Lessons learned from the Stormy 2020 Shadow Evaluation including the importance of reroutes during recovery from Severe Weather Avoidance Programs and also reroutes in the absence of terminal airspace constraints will be incorporated into the Phase 3 Field Evaluation in 2021 to maximize reroute opportunities.

Airspace Technology Demonstration 2↗

Optimizing Air Traffic - Integrating Artificial Intelligence and Machine Learning in Flight Path Planning and 3D Airspace Visualization for Air Traffic Control

Air Traffic Control (ATC) systems are vital components of the National Airspace System (NAS). ATC, Airport Traffic Control Towers (ATCT), and Terminal Radar Approach Control (TRACON) are responsible for directing all flights departing from and arriving at airports, managing our nation’s airspace, preventing potential accidents, and ensuring that every flight is accounted for. However, these systems often face challenges in effectively monitoring the skies. Issues such as poor communication between operators, difficulty in performing operations, and the constant need for vigilance frequently burden ATC operators. Additionally, the projected increase in air traffic in the coming years will only exacerbate the stress associated with this role. To address these issues, we propose a system that assists ATC operators in situations such as handovers, emergencies, and routing aircraft to avoid weather hazards. Our solution includes an Artificial Intelligence (AI) and Machine Learning (ML)-based Flight Pathways Planning System (FPPS) designed to find the fastest and most optimal routes for aircraft, taking into account weather conditions, restricted terrain, and Extended-Range Twin-Engine Operational Performance Standards (ETOPS) ratings. The proposed Predictive Weather Planning Model, included in FPPS, adjusts routes based on real-time and forecasted weather conditions. Additionally, our NVIDIA Omniverse 3D Visualization System offers a highly interactive environment for better visualization and a clear view of the airspace. By incorporating these systems, the roles of ATC, ATCT, and TRACON operators will become more manageable and less stressful, equipping them to efficiently handle the growing density of airspace.

Regina Ayoubi↗

Enabling Civilian Low-Altitude Airspace and Unmanned Aerial System (UAS) Operations

UAS operations will be safer if a UTM system is available to support the functions associated with Airspace management and geo-fencing (reduce risk of accidents, impact to other operations, and community concerns); Weather and severe wind integration (avoid severe weather areas based on prediction); Predict and manage congestion (mission safety);Terrain and man-made objects database and avoidance; Maintain safe separation (mission safety and assurance of other assets); Allow only authenticated operations (avoid unauthorized airspace use).

Unmanned↗

An Integrated Decision-Making Model for Categorizing Weather Products and Decision Aids

The National Airspace System s capacity will experience considerable growth in the next few decades. Weather adversely affects safe air travel. The FAA and NASA are working to develop new technologies that display weather information to support situation awareness and optimize pilot decision-making in avoiding hazardous weather. Understanding situation awareness and naturalistic decision-making is an important step in achieving this goal. Information representation and situation time stress greatly influence attentional resource allocation and working memory capacity, potentially obstructing accurate situation awareness assessments. Three naturalistic decision-making theories were integrated to provide an understanding of the levels of decision making incorporated in three operational situations and two conditions. The task characteristics associated with each phase of flight govern the level of situation awareness attained and the decision making processes utilized. Weather product s attributes and situation task characteristics combine to classify weather products according to the decision-making processes best supported. In addition, a graphical interface is described that affords intuitive selection of the appropriate weather product relative to the pilot s current flight situation.

Elgin, Peter D.↗