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NASA NTRS2015

A Researcher's Perspective on Function Allocation and Its Application to Air Traffic Management

Functional Allocation is an important research area for NASA going forward, and aims at exploring a range of potential options for future ATM (Air Traffic Management) operation concepts. The goal of this work is to gain some insight into the relative strengths and limitations of a range of different concepts, and to identify the important variables as well as any critical or threshold values for those variables for each of those concepts. This information will be captured in a form that can be easily referenced, and can then be used by decision makers to chose which concept or concepts best suit the needs of the US NAS (National Airspace System). The work presented here is one researcher's perspective on this process. A brief overview of Functional Allocation will be followed by more detailed discussion about two of the topics being currently investigated, as well as why and how this work differs from the way we have done ATM research in the past. More specifically, the goal of the analysis work is to understand the reasons for any differences in our metrics (such as delay) when adjusting input variables rather than trying to analyze the raw numbers themselves. In other words, finding which variables different concepts are sensitive to is important for this work, while finding a best or recommended operational concept is not. This will be followed by some examples of this process using results from our current studies. Finally, there will be a discussion on where we are in the process as well as our next steps.

Air-Ground Studies↗
NASA NTRS2019

Alternative Surveillance Fast Time Simulation with Sensor Uncertainties and Mitigation

This presentation describes a simulation plan for the fast time simulation 3 for the modeling and simulation team in the UAS (Unmanned Aircraft Systems) in the NAS (National Airspace System)project. Objectives: Investigate the effect of limited surveillance volume and realistic radar noise on DAA (Detect and Avoid) performance; Evaluate the ability of the sensor uncertainty mitigation (SUM) feature of the DAIDALUS [Dectect and Avoid...] algorithm to improve DAA alerting and guidance [Includes content on SWaP-C (size, weight, power, and cost) of unmanned aircraft; and Near-mid-air-collision (NMAC).]

UAS↗
NASA NTRS2019

Evaluation of Ultem 1000, 1010, and 9085 for Radome Applications at 24.5 GHz

The UAS in the NAS project Flight Test 6 (FT6) campaign scheduled for FY19Q3 will evaluate the proficiency of a Honeywell DAPA-Lite Radar installed on a Tiger Shark unmanned vehicle to detect the presence of air traffic operating in its vicinity. A 3D printed radome will be manufactured for the front of the Tiger Shark to enclose the radar during FT6 operations. The DAPA-Lite radar operates in the 24.5 GHz frequency band. Material properties of 3D printer filaments are widely available for the mechanical and thermal properties, but limited knowledge exists on the electrical properties for radome applications and no data was found to correspond at the 24.5 Ghz frequency band. To minimize project risk associated with the radome performance, transmissivity and reflectivity measurements were conducted on two candidate 3D printed dielectric material filaments (Ultem 1010 Natural and Ultem 9085 Black) and two thicknesses of a solid laminate (Ultem 1000) material. The 3D printed Ultem coupons were tested shortly after being printed and again 8 months later to examine ageing effects of the open cell structure. This paper presents the transmissivity and reflectivity measurement results collected on the Ultem coupons and concludes the 3D printed 1010 Natural coupon is a suitable candidate filament for radome applications at 24.5 GHz. The design of the structure’s open cell matrix has a significant impact on the material’s surface reflectivity.

Szatkowski, George N.↗
NASA NTRS2014

Airspace Safety Threshold Study: Status Update to SC-228 DAA Safety Sub-Group

Realization of the expected proliferation of Unmanned Aircraft System (UAS) operations in the National Airspace System (NAS) depends on the development and validation of performance standards for UAS Detect and Avoid (DAA) Systems. The RTCA Special Committee 228 is charged with leading the development of draft Minimum Operational Performance Standards (MOPS) for UAS DAA Systems. NASA, as a participating member of RTCA SC-228, is committed to supporting the development and validation of draft requirements as well as the safety substantiation and end-to-end assessment of DAA system performance. A recent study conducted using NASA's ACES (Airspace Concept Evaluation System) simulation capability begins to address questions surrounding the development of draft MOPS for DAA systems. ACES analyses were conducted to determine: 1) the rate at which IFR aircraft encounter other IFR and VFR aircraft, and 2) the rate at which UAS aircraft encounter VFR aircraft. Five different separation thresholds were used (two for encounter and one each for well-clear, near mid-air collision, and closest point of approach). The results will be used by SC228 to inform decisions about the safety aspect of UAS DAA systems and future requirements development and validation efforts.

Risk ratio↗
NASA NTRS2015

Terminal Sequencing and Spacing (TSS)

The Federal Aviation Administration's (FAA) Next Generation Air Transportation System (or NextGen) is being designed to support the predicted increases in traffic volume and to increase the capacity, efficiency and safety of the National Airspace System (NAS). The Federal Aviation Administration (FAA) identifies Performance-Based Navigation (PBN) as a key enabling capability of NextGen and is actively publishing PBN procedures at major airports throughout the United States. Standard Terminal Arrival Routes (STARs), procedures, and approaches are designed to facilitate fuel-efficient continuous descent operations. However, their use is limited during periods of high traffic demand due to the complexity of merging multiple streams of aircraft to the same airport. As a result, most arrivals in the Terminal Radar Approach Control (TRACON) area continue to be controlled using radar vectoring and step-down descents, resulting in high workload for controllers and diverting aircraft from efficient PBN trajectories. To address this issue, NASA developed the Terminal Sequencing and Spacing (TSS) system, an advanced arrival management technology that combines time-based scheduling and controller-based precision spacing tools. TSS is a ground-based controller automation tool that facilitates sequencing and merging arrivals on Performance-Based Navigation (PBN) routes, especially during highly congested demand periods. The two main components of TSS are: 1) a scheduler that de-conflicts merging arrivals in the terminal area by computing appropriate arrival times to the runway threshold and upstream terminal merge points, and 2) a set of Controller-Managed Spacing (CMS) decision support tools to efficiently assist schedule conformance. Sixteen high-fidelity human-in-the-loop simulations involving more than five hundred hours of evaluation time, were conducted to mature TSS from proof-of-concept design to a fully functional prototype. Results indicate high controller use and acceptability of the CMS tools as well as improved PBN route conformance (Figure 2). The TSS technology was transferred to the FAA in 2014, and it is targeted for deployment to several busy airports in the U.S. starting in 2018. Potential enhancements to TSS using DataComm will also be presented.

controller-managed spacing↗
NASA NTRS2015

Evaluating Performance of UAS Detect-And-Avoid System Using a Fast-Time Simulation Tool

Most unmanned aircraft systems will be required to be equipped with a Detect-and-Avoid (DAA) system. The surveillance performance of the DAA system to detect and track intruder aircraft will depend on the encounter geometries that unmanned aircraft are expected to have with other aircraft in the airspace. The performance of DAA alerting and avoidance system is also dependent on the timeliness of alerting for UAS pilots to give a sufficient time to determine and command a resolution maneuver to avoid well clear separation violations. This presentation introduces general background of UAS DAA systems and concept of well clear separation standard to satisfy see and avoid regulations. The presentation shows the several UAS mission profiles and analysis of the encounter geometries that were simulated using historical VFR traffic data and some proposed UAS missions. This presentation introduces several potential metrics for evaluating the performance of a DAA system and shows the results that measured through fast-time simulation with traffic scenarios that include NAS-wide VFR manned aircraft and IFR UAS flights. At the end, some research areas will be briefly discussed.

UAS↗
NASA NTRS2017

Current Safety Nets Within the U.S. National Airspace System

There are over 70,000 flights managed per day in the National Airspace System, with approximately 7,000 aircraft in the air over the United States at any given time. Operators of each of these flights would prefer to fly a user-defined “4D” trajectory (4DT), which includes arrival and departure times; preferred gates and runways at the airport; efficient, wind-optimal routes for departure, cruise and arrival phase of flight; and fuel efficient altitude profiles. To demonstrate the magnitude of this achievement a single flight from Los Angeles to Baltimore, accesses over 35 shared or “constrained” resources that are managed by roughly 30 air traffic controllers (at towers, approach control and en route sectors); along with traffic managers at 12 facilities, using over 22 different, independent automation system (including TBFM, ERAM, STARS, ASDE-X, FSM, TSD, GPWS, TCAS, etc.). In addition, dispatchers, ramp controllers and others utilize even more systems to manage each flight’s access to operator-managed resources. Flying an ideal 4DT requires successful coordination of all flight constraints among all flights, facilities, operators, pilots and controllers. Additionally, when conditions in the NAS change, the trajectories of one or more aircraft may need to be revised to avoid loss of flight efficiency, predictability, separation or system throughput. The Aviation Safety Network has released the 2016 airliner accident statistics showing a very low total of 19 fatal airliner accidents, resulting in 325 fatalities1. Despite several high profile accidents, the year 2016 turned out to be a very safe year for commercial aviation, Aviation Safety Network data show. Over the year 2016 the Aviation Safety Network recorded a total of 19 fatal airliner accidents [1], resulting in 325 fatalities. This makes 2016 the second safest year ever, both by number of fatal accidents as well as in terms of fatalities. In 2015 ASN recorded 16 accidents while in 2013 a total of 265 lives were lost. How can we keep it that way and not upset the apple cart by premature insertion of innovative technologies, functions, and procedures? In aviation, safety nets function as the last system defense against incidents and accidents. Current ground-based and airborne safety nets are well established and development to make them more efficient and reliable continues. Additionally, future air traffic control safety nets may emerge from new operational concepts.

Smith, Brian E.↗
NASA NTRS2019

Prescheduling with EOBT’s

As the surface system is deployed across the NAS and fully integrated with existing FAA decision support, the roles of both ATC and Operators are expected to change. This will discuss first hand examples of this evolution as experienced at CLT.

Capps, Al↗
NASA NTRS2019

High-Performance Computing Optimization for Aladyn – Adaptive Neural Network Molecular Dynamics Mini-Application

This report provides a description and performance evaluation of the optimization techniques for high performance computing (HPC) implementation of the open source Computational Materials mini-application Aladyn (https://github.com/nasa/aladyn). Aladyn is a basic molecular dynamics code written in FORTRAN 2003, which is designed to demonstrate the use of adaptive neural networks (ANNs) in atomistic simulations. The role of ANNs is to efficiently reproduce the very complex energy landscape resulting from the atomic interactions in materials with the accuracy of the more expensive quantum mechanics-based calculations. The ANN is trained on a large set of atomic structures calculated using the density functional theory (DFT) method. While achieving orders of magnitude faster computational performance than DFT, the ANN-based approach was still very computationally demanding compared to the conventional approach of using empirically fitted energy functions. After its initial development, Aladyn was evaluated and optimized by experts at the NASA Advanced Supercomputing (NAS) division to exploit modern supercomputer architectures. The code has been optimized for execution on multicore central processing units (CPUs), including Intel® Skylake microarchitecture, and on graphic accelerators, such as Nvidia® V100 graphic processing units (GPUs), using Open Multi-Processing (OpenMP) and Open Accelerators (OpenACC) programming interfaces. The optimization achieved a speedup of 4.7 times the baseline version on CPU performance and an additional 2.4 times on CPU+GPU performance. Atomistic computer simulations are a fundamental tool in materials research to model material properties form physics-based first principles. Atomic interaction, governed by Quantum Mechanics (QM) require sophisticated and highly computationally demanding mathematical models to calculate [1]. Classical methods use approximate functional forms, empirically fitted through a set of variable parameters to emulate atomic energies as direct functions of atomic coordinates [2]. While empirical potentials are computationally much simpler, allowing simulations of large-scale systems of up to a trillion (1012) atoms [3], they are substantially less accurate compared to quantum calculations and applicable only to very specific atomic configurations or predefined crystallographic phases. A recently suggested approach is to use heuristic machine learning methods [4], such as those based on Adaptive Neural Networks (ANNs) to predict atomic energies, after being trained on a sufficiently large database of QM-calculated structures [5,6]. This approach reduces significantly the computational complexity, allowing for simulations of orders of magnitude larger systems compared to QM-based methods without compromising accuracy. Still, compared to classical methods using empirical energy functions, ANN methods remain two- to three orders of magnitude more computationally demanding. Hence, the computational cost of simulations, together with the need for extensive training of ANNs, still makes the practical implementation of ANN-based methods quite challenging. The purpose of the Aladyn mini-application software [7], available as open source at https://github.com/nasa/aladyn, is to be a testbed for exploring possible optimization strategies to develop highly scalable parallel algorithms for ANN-based atomistic simulations. Aladyn is aimed at utilizing the architecture of the high-end modern highperformance computing (HPC) hardware based on multicore central processing units (CPUs) equipped with graphic processing unit (GPU) accelerators. Specifically, the goal is to optimize the performance on a single HPC compute node, before implementing scaling to multi-node parallelization using message passing interface (MPI). At the same time, the open source code of Aladyn can serve as a training model for students and professors in academia.

Yamakov, Vesselin I.↗
NASA NTRS2019

GEONEX Data Products: Geostationary Satellite Derived Land Surface and Atmospheric Public Data Products

The latest generation of geostationary satellites carry sensors such as the Advanced Baseline Imager (GOES-16/17) and the Advanced Himawari Imager (Himawari-8/9) that closely mimic the spatial and spectral characteristics of MODIS and VIIRS, useful for monitoring land surface conditions. The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort among scientists from NASA and NOAA exploring the feasibility of producing operational land surface products similar to those from MODIS/VIIRS. The team built a processing pipeline called GEONEX that is capable of converting raw geostationary data into routine products of Fires, surface reflectances, vegetation indices, LAI/FPAR, ET and GPP/NPP using algorithms adapted from both NASA/EOS and NOAA/GOES-R programs. The GEONEX pipeline will begin to produce provisional data products to be consumed by external collaborators and the academic community. In order to better inform and introduce the GEONEX products to the science community, the provisional products shall be distributed from the NAS data portal, located at data.nas.nasa.gov, and simple webpage at www.nasa.gov/geonex has been deployed, which describes any algorithms used in deriving the products, user manuals and data file information. We will also update the status of the data processing, on the website and provide links to the latest datasets, and use a geonex mailing list, using lists.nasa.gov.

Wang, Weile↗
NASA NTRS2019

Collaborative Seamless Manager for Airspace Resources and Traffic

In today's NAS, airlines conduct pre-departure flight planning with limited information about traffic congestion. Then, as events evolve (pre-departure, on the surface, or inflight) and are impacted by congestion, limited options for changing flight plans are offered to the airlines, and, in some cases, flight plans are changed by air traffic managers on behalf of the airlines. For the airlines, this produces flight plan uncertainty and possible disruption to their business plans. The Collaborative Seamless Manager of Airspace Resources and Traffic (CSMART) is a tool being developed for the 2045 Next Gen System that will enhance the airlines' ability to flight plan, both pre-departure and during flight. CSMART achieves this, using the UTM architecture approach, by connecting via the internet airlines with other airlines and airlines with FAA agents. Flight plans, including digital, discrete planned trajectories with tolerances, are passed through the connection. Planned trajectories are used to create probabilistic predictions of when and where congestion will occur. Pre-departure, airline agents will use the predictions to collaboratively and seamlessly create flight plans that avoid congestion or go through it, depending on business objectives. In cases, where demand exceeds capacity and priorities need to be set, airline agents will have the ability to negotiate with each other to set them. Moreover, if flights are impacted by congestion during flight, CSMART will allow airlines more real-time decision making options for changing flight plans. This seminar presents the general CSMART concept and tool and research needed to develop it.

Windhorst, Robert D.↗
NASA NTRS2019

Flight Test 6 VIP Day: A Little Bit of How We Got Here

This presentation provides an overview of the Detect and Avoid subproject of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project. It discusses efforts and contributions for Phase 1 and on-going work for Phase 2.

Shively, Jay↗
NASA NTRS2017

Managing Cyber Risk: from the Ground to the Air and Beyond Out There

Experts from the aerospace sector with the focus of presenting current state-of-the-art in aviation cyber security and discussing major challenges, requirements, and developments. A variety of topics will be addressed by the panel, including: Aviation cyber security regulations, standards, and best practices Aviation information sharing and analysis challenges Aircraft systems and avionics cyber security challenges Airline and airport stakeholder cyber perspectives CNSATM cyber security challenges UAS cyber security and privacy requirements Military aviation cyber security issues in the NAS

Davis, Jerry L.↗
NASA NTRS2020

Urban Air Mobility System Testbed Using CAVE Virtual Reality Environment

Urban Air Mobility (UAM) refers to a system of air passenger and small cargo transportation within an urban area. The UAM framework also includes other urban Unmanned Aerial Systems (UAS) services that will be supported by a mix of onboard, ground, piloted, and autonomous operations. Over the past few years UAM research has gained wide interest from companies and federal agencies as an on-demand innovative transportation option that can help reduce traffic congestion and pollution as well as increase mobility in metropolitan areas. The concepts of UAM/UAS operation in the National Airspace System (NAS) remains an active area of research to ensure safe and efficient operations. With new developments in smart vehicle design and infrastructure for air traffic management, there is a need for methods to integrate and test various components of the UAM framework. In this work, we report on the development of a virtual reality (VR) testbed using the Cave Automatic Virtual Environment (CAVE) technology for human-automation teaming and airspace operation research of UAM. Using a four-wall projection system with motion capture, the CAVE provides an immersive virtual environment with real-time full body tracking capability. We created a virtual environment consisting of San Francisco city and a vertical take-off-and-landing passenger aircraft that can fly between a downtown location and the San Francisco International Airport. The aircraft can be operated autonomously or manually by a single pilot who maneuvers the aircraft using a flight control joystick. The interior of the aircraft includes a virtual cockpit display with vehicle heading, location, and speed information. The system can record simulation events and flight data for post-processing. The system parameters are customizable for different flight scenarios; hence, the CAVE VR testbed provides a flexible method for development and evaluation of UAM framework.

Marayong, Panadda↗
NASA NTRS2020

ACAS-Xu Run 5 HITL (June 2019) SC-228 WG 1.3 Results Outbrief

This presentation provides an overview of a recently completed human-in-the-loop simulation, conducted as part of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project. This study examined how to present resolution advisories (RAs) issued by Airborne Collision Avoidance System (ACAS) Xu, which can be in the vertical dimension, the horizontal dimension, or both (i.e., “blended”). The study varied the location of the ACAS Xu traffic information - it was either presented within an 'integrated' display or a 'standalone' display, where the traffic information was separated from the vehicle control interfaces and navigational information. Results revealed generally positive feedback on the visual and aural presentation of RAs, with special considerations and performance implications noted throughout.

Monk, Kevin J.↗
NASA NTRS2020

Detect-and-Avoid: Flight Test 6 Scripted Encounters Data Analysis

The Unmanned Aircraft System (UAS) in the National Airspace System (NAS) project conducted Flight Test 6 (FT6) in 2019. The ultimate goal of this flight test was to produce data to inform RTCA SC-228's Phase II Minimum Operational Performance Standards (MOPS) for Detect and Avoid (DAA) and Low Size, Weight, and Power Sensors. This report documents the analysis of scripted encounters' data. Scripted encounters own were analyzed and categorized based on the outcome of alert, maneuver guidance, and effectiveness of pilots' maneuver in resolving conflicts. Results indicate that UAS pilots' decisions as well as intruder maneuvers are leading factors that contribute to ineffective DAA maneuvers. Results also show that adding buffers to the DAA's suggested minimum turn angle improves effectiveness of the DAA maneuvers.

Wang, Wei-Ching↗