Enabling Safety from Data: Machine Learning/Artificial Intelligence for In-Time Aviation Safety
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Nikunj C Oza↗
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Ames Contrail Simulation Model: Modeling Aviation Induced Contrails and the Computation of Contrail Radiative Forcing Using Air Traffic Data
NASA is developing traffic flow management strategies to reduce the impact of aviation on climate and improve efficiency of aircraft routes in the presence of limited airport and airspace capacity constraints, which are partly due to convective weather and natural calamities. Development of Strategic Traffic Flow Management (STFM) requires models of aircraft emissions and contrails, and models of their effect on climate. There is extensive literature on the modeling of contrails and the Radiative Forcing (RF) associated with contrails. This report captures the latest NASA developments in this research area and describes the integrated modeling, analysis, and software development to support STFM. Aircraft contrails are long, thin and often linear clouds triggered by aircraft engine exhausts in the high-altitude ice-saturated atmosphere. Contrails, similar to that of natural cirrus clouds, can impact global climate by reflecting shortwave radiation and trapping longwave radiation. Recent studies from the Intergovernmental Panel on Climate Change (IPCC) have shown that aircraft contrails are estimated to have greater impact on global warming than aircraft CO 2 emissions. The Ames Contrail Simulation Model (ACSM) presented in this report simulates the full life cycle of aircraft contrails, including their formation, dynamic evolution, and dissipation, and calculates the associated RF with actual meteorological and air traffic data. ACSM combines models from previous studies that focus on contrail formation and persistence based on the Schmidt-Appleman theoretical criteria, and it incorporates elements of cloud dynamics, microphysics, and climate modeling found in other surveyed contrail models, while also making adjustment for improved computational efficiency. In addition, ACSM is integrated with NASA's state-of-the-art flight simulation software for rapid assessment of aircraft contrail impacts. Applications include assessments of long-term global climate impact resulting from aviation-induced contrails and the design of optimal contrail-mitigation aircraft operation strategies.
aviation contrails↗
Heart-Lung Interactions in Aerospace Medicine
Few of the heart-lung interactions that are discussed have been studied in any detail in the aerospace environment, but is seems that many such interactions must occur in the setting of altered accelerative loadings and pressure breathing. That few investigations are in progress suggests that clinical and academic laboratory investigators and aerospace organizations are further apart than during the pioneering work on pressure breathing and acceleration tolerance in the 1940s. The purpose is to reintroduce some of the perennial problems of aviation physiology as well as some newer aerospace concerns that may be of interest. Many possible heart-lung interactions are pondered, by necessity often drawing on data from within the aviation field, collected before the modern understanding of these interactions developed, or on recent laboratory data that may not be strictly applicable. In the field of zero-gravity effects, speculation inevitably outruns the sparse available data.
Guy, Harold J. B.↗
Forecast of the general aviation air traffic control environment for the 1980's
The critical information required for the design of a reliable, low cost, advanced avionics system which would enhance the safety and utility of general aviation is stipulated. Sufficient data is accumulated upon which industry can base the design of a reasonably priced system having the capability required by general aviation in and beyond the 1980's. The key features of the Air Traffic Control (ATC) system are: a discrete address beacon system, a separation assurance system, area navigation, a microwave landing system, upgraded ATC automation, airport surface traffic control, a wake vortex avoidance system, flight service stations, and aeronautical satellites. The critical parameters that are necessary for component design are identified. The four primary functions of ATC (control, surveillance, navigation, and communication) and their impact on the onboard avionics system design are assessed.
Hoffman, W. C.↗
Sherlock Data Warehouse
Technical Interchange Meeting 2 of 2, with two linked events. Overall Purpose of the Technical Interchange Meeting was to facilitate collaboration on NAS data analytics between FAA and NASA teams, leveraging the great tool developed by NASA, Sherlock Data Warehouse. Conducted the two TIM events, next step is for the leadership to meet and discuss potential future collaborations. Purpose for Technical Interchange Meeting #2 (TIM 2o2); NASA presented data science related use-cases and applications where they have leveraged the NASA Sherlock Data Warehouse to address aviation transportation problems, with priority on aviation safety.
Two TIMs↗
A Machine Learning Approach to Improve Air Traffic Management Initiatives
Collaborating closely with commercial air carriers and related organizations, the Federal Aviation Administration(FAA) regulates air traffic and ensures the safety and efficiency of air operations. Air traffic controllers make strategic decisions, such as delaying, rerouting, or canceling flights, partly based on guidance provided by the FAA’s Air TrafficControl System Command Center (ATCSCC). The guidance includes, among other things, control measures known asTraffic Management Initiatives (TMIs) designed to enhance safety and improve operational efficiency. TMIs play a crucial role in managing the demand and capacity within the U.S. National Airspace System (NAS). Two major TMIs that are routinely used (primarily to mitigate the adverse effects of bad weather) are Ground Delay Programs (GDPs) andGround Stops (GSs). In a GDP, flights destined for airports facing thunderstorm activity experience delays at their origin airports. This proactive approach minimizes the risk of routing aircraft through hazardous weather conditions and also replaces (fuel burning) airborne delays with ground delays. In a GS, a temporary restriction is imposed on the departure or arrival of aircraft at a specific airport or within a designated airspace. Although other TMIs (e.g., miles-in-trail) are also implemented as part of (air) traffic flow management in the NAS, the focus of this work is on GDPs and GSs. Since TMIs, by design, lead to flight delays or cancellations, it is crucial to put in place the right set of parameters(e.g., scope and duration of the GDP). For example, when the end time of a GDP extends beyond what is necessary, it imposes unnecessary delays on departing flights. This situation could occur as a result of inaccurate prediction of the(required) duration of the GDP based on the weather forecast. On the other hand, if a GDP ends prematurely before the underlying capacity constraints are resolved at the destination airport, it may result in airborne holding. The delicate balance lies in matching the termination of the GDP precisely with the resolution of capacity constraints, avoiding both the imposition of unnecessary ground delays and the need for airborne holding due to premature program termination.Failing to specify the right parameters for TMIs also leads to flight delays, creating a significant obstacle in managing the increasing traffic volumes causing increased work load for the controllers. To address this issue, we propose the integration of Machine Learning (ML) models in the traffic flow management(TFM) pipeline. In current operations, decisions are made by human experts based on extensive training, historical patterns, available traffic and weather data. Since we have an abundance of data from past events that tell us the likely impact of various TMIs, by ingesting historical data, properly trained ML models can offer valuable insights and aid human decision-making. With the FAA increasingly exploring advanced analytics, ML emerges as a focal point for enhancing TFM within the National Airspace System (NAS). As a first step, this study aims to provide traffic controllers with decision-making support for the issuance and adjustment of TMIs. Data analytics and machine learning have been previously employed to address some of the challenges associated with TMIs. Numerous studies have concentrated on various facets of TMI issuance, exploring factors influencing TMI parameters, including arrival rate, airport capacity, and delay prediction. For example, using weather forecasts, several statistical methods were used to produce probabilistic capacity profiles which in conjunction with deterministic models provided insights into the GDP planning process [1–4]. The downside of using deterministic models is that they rely on fixed inputs and predetermined rules, which lack the ability to account for the inherent uncertainty and variability present in real-world scenarios. In a separate series of studies, researchers aimed to predict the occurrences of GDPs and GSs. The majority of these studies utilized various supervised learning methods, including Decision Trees, Naive Bayes, Support VectorMachines, and Random Forests to analyze the influence of weather conditions and arrival demand on TMI incidents[5–8]. However, these studies primarily focused on predicting the incidence of TMIs without explicitly addressing the scope of TMIs, including their duration and their geographical coverage. Furthermore, the emphasis of these studies was largely on GDPs, given their higher frequency and longer duration when compared to GSs. A limited number of studies focused on predicting the parameters of TMIs, specifically addressing their duration and extent. In one such study focusing on optimizing the TMI parameters at San Francisco International Airport (SFO),the authors utilized a probabilistic forecast of fog [9]. They simulated various capacity scenarios based on the (fog)burn-off forecasts, selecting GDP parameters that minimized airborne and overall ground delays. However, this approach exclusively emphasizes stratus (fog) burn-off as the primary determinant of GDP and GS, neglecting other influential factors like severe weather events, runway closures, lower capacity than traffic demand, and other important variables. Given the complexity of predicting the TMI and determining its scope, we seek a more holistic approach. We aim to consider all significant factors that could impact TMIs and their parameters. What sets this research apart is the fusion of all data sources relevant to the issuance and adjustment of TMIs and it represents the first comprehensive attempt to optimize TMIs in this manner. Since this comprehensive solution involves various aspects, we break down the problem into smaller components and input all parameters into a unified model called the “TMI Adjuster”. Figure 1 shows the overall framework and the list of datasets used in each model. The objective of the TMI Adjuster module is to deliver reliable, consistent and expedited recommendations for the progression, adjustment, and termination of TMIs. The ML solution entails developing a pipeline capable of predicting the necessity of a TMI (e.g., GS or GDP) along with its various parameters. For example, in the case of a GS, this includes the scope of the GS either in terms of distance from the destination airport or based on pre-defined airspace sectors. Here, scope refers to those regions and departing airports that are subject to the GS. In this paper, we concentrate on the issuance of GSs in the three major airports in the New York area — LaGuardia(LGA), John F. Kennedy International (JFK), and Newark Liberty International (EWR). We fuse traffic, weather and other relevant aviation data from years 2017 to 2019 to train and validate the ML models. In particular, we use the following datasets: •Terminal Aerodrome Forecast (TAF): meteorological forecasts specific to each airport, issued four times a day, covering predefined time periods. •TMI data: includes all GSs and GDPs along with their respective parameters. •Aviation System Performance Metrics (ASPM): includes traffic related data such as aircraft delays, arrival, and departure rates. •Notices to Airmen (NOTAMs): utilized to extract runway closure data and manage interdependencies between terminals in close proximity. •Flight cancellation data •Airspace Flow Programs (AFP): includes information on flight airborne holdings caused by TMIs. The data preprocessing entails transforming ASPM, TMI, AFP, NOTAMs, and weather data into an hourly format and consolidating all datasets by merging them based on date and time as the primary key. The TMI Adjuster framework comprises two parallel models: one dedicated to GS and a second model focused on GDP. As previously mentioned, our specific focus is on the GS model as a multi-classification problem. In this framework, each data point of the GS model input summarizes ten hours of data. Specifically, the data loader for the GS model generates the input and output of the model as follows: at a given time step, the input includes the actual traffic, weather, and TMI data from the two-hour window before the time step, alongside the weather forecast and scheduled traffic for the next 8 hours starting from the time step. Based on this information, the output of the GS model for each time interval consists of three dimensions. The first dimension represents a binary decision on whether there should be a GS in place for the next hour or not. The second dimension is related to the scope of the GS in the United States, and the third dimension is related to the scope of the GS in Canada (i.e., to determine if the GS impacts airports in Canada).One of the challenges with TMI modeling is the sparsity of TMI events, particularly regarding its scope. To address this challenge in the scope of the GS model output, we implement grouping. The GS scope for the US region is defined based on a list of centers that should be included when the GS is in place. With 20 centers in the US, we utilized historical data to group them into 4 categories. In particular, we summarized our historical data in a graph format where nodes represent centers, and link weights are defined based on the co-occurrence of centers in the scope parameter ofTMIs. By identified strongly connected components in this graph, we were able to partition the centers into four groups. We consider two model structures for the GS Model. Firstly, a hierarchical classification model [10], where the human decision-making for a GS is of hierarchical nature. The decision-maker first decides whether there is a need fora GS, and if the answer is yes, determines the scope. A hierarchical classification model organizes the problem into a class hierarchy, typically a tree or a Directed Acyclic Graph (DAG) structure, and considers the dependency of the decision in the previous step to the next component [10]. Here, we employ the local classifier per level approach, which involves training one multi-class classifier for each level of the class hierarchy. The second structure is the independent structure. In this setting, as the name suggests, we do not consider the dependency of the decisions in the different dimensions of the output of the model. Instead, for each dimension, we train a multi-class classifier independently. Table 1 summarizes GS model statistics for training, validation and testing. The table documents the effect of limiting data to the time steps when there was actually a TMI in place or when a TMI had just terminated. This resulted in a more balanced distribution of the GS class(GS positive class)versus “No GS”(GS negative class), which might help the training process. While JFK and LGA follow very similar distributions, with 40% and 42% GS positive class respectively, EWR has proportionally fewer GS incidents at 28%. Our subsequent phase involves evaluating the performance of both hierarchical structure and independent structure using different state-of-the-art multi-class classifier models such as Random Forest, Decision Trees, K-nearest Neighbors, and Logistic Regression and forecast the duration and scope of the GSs.
Farzan Masrour Shalmani↗
Verifying Data Integrity of Electronically Scanned Pressure Systems at the NASA Glenn Research Center
The proper operation of the Electronically Scanned Pressure (ESP) System critical to accomplish the following goals: acquisition of highly accurate pressure data for the development of aerospace and commercial aviation systems and continuous confirmation of data quality to avoid costly, unplanned, repeat wind tunnel or turbine testing. Standard automated setup and checkout routines are necessary to accomplish these goals. Data verification and integrity checks occur at three distinct stages, pretest pressure tubing and system checkouts, daily system validation and in-test confirmation of critical system parameters. This paper will give an overview of the existing hardware, software and methods used to validate data integrity.
Panek, Joseph W.↗
Seventh Annual Workshop on Meteorological and Environmental Inputs to Aviation Systems 26-28 October 1983, Tullahoma, Tennessee
A summary of reports and recommendations resulting from the Workshop on Meteorological and Environmental Inputs to Aviation Systems is presented. The Workshops have been held to characterize the interaction between aircraft and airport operators with the atmosphere, identify necessary meteorological enhancements, and establish criteria for meteorological data needed by the aviation community. The topics covered included winds and wind shear, turbulence, fog, visibility and ceiling, lightning and atmospheric electricity, icing, frost and snow, rain, ozone and acid rain. The recommendations issued included a denser meteorological network with more automated stations, the development of radar systems for icing forecasts and wind shear alerts, the accumulation of a greater data base on convective cells and lightning strikes, and further study of the impact of rain on aircraft performance.
Camp, D. W.↗
The FAA/NASA flight loads monitoring program - The prototype system and its benefits for the aviation community
The FAA established the flight load monitoring program to collect a data base of typical flight operational loads experienced by commercial transports. This system will provide a comprehensive monitoring of aircraft loading conditions with over 20 flight parameters being recorded simultaneously. NASA is designing and testing a prototype data collection and analysis system which will be implemented into an FAA operational program. This paper presents the program's objectives and the proposed development testing on a commercial Boeing 737-400. The prototype system, its data processing schemes, and reports are described. The searching criteria or flight attributes generated for each flight are listed. The data processing system will provide the aviation community with a powerful tool for the study of transport flight loading conditions and the system's flexibility will accommodate individual studies and specialized concerns.
Whitehead, Julia H.↗
Statistical summary: Study to determine the IFR operational profile of the general aviation single pilot
The statistical summary of data resulting from a mail questionnaire survey designed to develop information on the general aviation single pilot operating under instrument flight rules is presented.
Weislogel, G. S.↗
Weather data dissemination to aircraft
Documentation exists that shows weather to be responsible for approximately 40 percent of all general aviation accidents with fatalities. Weather data products available on the ground are becoming more sophisticated and greater in number. Although many of these data are critical to aircraft safety, they currently must be transmitted verbally to the aircraft. This process is labor intensive and provides a low rate of information transfer. Consequently, the pilot is often forced to make life-critical decisions based on incomplete and outdated information. Automated transmission of weather data from the ground to the aircraft can provide the aircrew with accurate data in near-real time. The current National Airspace System Plan calls for such an uplink capability to be provided by the Mode S Beacon System data link. Although this system has a very advanced data link capability, it will not be capable of providing adequate weather data to all airspace users in its planned configuration. This paper delineates some of the important weather data uplink system requirements, and describes a system which is capable of meeting these requirements. The proposed system utilizes a run-length coding technique for image data compression and a hybrid phase and amplitude modulation technique for the transmission of both voice and weather data on existing aeronautical Very High Frequency (VHF) voice communication channels.
Mcfarland, Richard H.↗
IT Data Mining Tool Uses in Aerospace
Data mining has a broad spectrum of uses throughout the realms of aerospace and information technology. Each of these areas has useful methods for processing, distributing, and storing its corresponding data. This paper focuses on ways to leverage the data mining tools and resources used in NASA's information technology area to meet the similar data mining needs of aviation and aerospace domains. This paper details the searching, alerting, reporting, and application functionalities of the Splunk system, used by NASA's Security Operations Center (SOC), and their potential shared solutions to address aircraft and spacecraft flight and ground systems data mining requirements. This paper also touches on capacity and security requirements when addressing sizeable amounts of data across a large data infrastructure.
Monroe, Gilena A.↗
Single-Frequency Multitransmitter Telemetry
Telemetry technique for data collection on general aviation aircraft involved in aeronautical research consists of many sensor/transmitter units at different locations on aircraft. Individual signaling and logic at each sensor/transmitter send signals to single receiver in airplane. Transmitters operate independently on same frequency and are not synchronized to receiver, eliminating erroneous data resulting from two-transmitter overlap.
Carreno, V. A.↗
Rotary balance data for a typical single-engine low-wing general aviation design for an angle-of-attack range of 30 deg to 90 deg
Aerodynamic characteristics obtained in a spinning flow environment utilizing a rotary balance located spin tunnel are presented in plotted form for a 1/5 scale single-engine low-wing general aviation airplane model. The configurations tested include the basic airplane, various airfoil shapes, tail designs, fuselage strakes and modifications as well as airplane components. Data are presented for pitch and roll angle ranges of 30 to 90 degrees and 10 to -10 degrees, respectively, and clockwise and counter-clockwise rotations covering an Omega b/2V range from 0 to .9. The data are presented without analysis.
Bihrle, W., Jr.↗
NASA's Aviation Safety and Modeling Project
The Aviation Safety Monitoring and Modeling (ASMM) Project of NASA's Aviation Safety program is cultivating sources of data and developing automated computer hardware and software to facilitate efficient, comprehensive, and accurate analyses of the data collected from large, heterogeneous databases throughout the national aviation system. The ASMM addresses the need to provide means for increasing safety by enabling the identification and correcting of predisposing conditions that could lead to accidents or to incidents that pose aviation risks. A major component of the ASMM Project is the Aviation Performance Measuring System (APMS), which is developing the next generation of software tools for analyzing and interpreting flight data.
Chidester, Thomas R.↗
Study of advanced rotary combustion engines for commuter aircraft
Performance, weight, size, and maintenance data for advanced rotary aircraft engines suitable for comparative commuter aircraft system evaluation studies of alternate engine candidates are provided. These are turbocharged, turbocompounded, direct injected, stratified charge rotary engines. Hypothetical engines were defined (an RC4-74 at 895 kW and an RC6-87 at 1490 kW) based on the technologies and design approaches used in the highly advanced engine of a study of advanced general aviation rotary engines. The data covers the size range of shaft power from 597 kW (800 hp) to 1865 kW (2500 hp) and is in the form of drawings, tables, curves and written text. These include data on internal geometry and configuration, installation information, turbocharging and turbocompounding arrangements, design features and technologies, engine cooling, fuels, scaling for weight size BSFC and heat rejection for varying horsepower, engine operating and performance data, and TBO and maintenance requirements. The basic combustion system was developed and demonstrated; however the projected power densities and performance efficiencies require increases in engine internal pressures, thermal loading, and rotative speed.
Berkowitz, M.↗
Optimization of Airport Runway Configuration with Forecast-Augmented Offline Reinforcement Learning
Runway configuration Management (RCM) governs the optimal utilization of runways based on variables such as traffic and meteorological conditions, making it a daunting task in air traffic management due to its dependency on volatile operational and environmental factors. This paper improves upon our previous work [1] on using offline model-free reinforcement learning for creating a Runway Configuration Assistance (RCA) decision-support tool. A novel integration of forecast data from LAMP (Localized Aviation Model Output Statistics Program) and TAF (Terminal Area Forecast) is introduced, enhancing the tool’s accuracy and also its adaptability to quick wind changes. The performance is evaluated using two major US airports, Charlotte Douglas International Airport (CLT) and Denver International Airport (DEN). To counter scalability issues presented by the addition of discrete forecast variables, we transitioned to a continuous state space model, ensuring scalability and inclusion of longer forecast data. The results of our experiments reflect significant improvements in the RCA tool’s prediction accuracy.
Sumanth Nethi↗
Aviation Safety Reporting System (ASRS)
This overview of the NASA Aviation Safety Reporting System (ASRS) will describe the genesis of the program, report processing procedures, and safety products that are produced from the ASRS data.
aviation safety reporting↗