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Penetration rates and meteoritic encounter frequency obtained from explorer xiii and explorer xvi satellites to reappraise meteoroid hazards to spacecraft
The active development community surrounding electric vertical takeoff and landing (eVTOL) aircraft has demonstrated potential to bring new technological capabilities to market, encouraging visions of widespread and diverse Urban Air Mobility (UAM) applications. New capabilities always come with safety considerations, some familiar and others less so. OEMs who intend to obtain FAA type certification for their eVTOL designs must plan for and execute sufficient safety engineering processes to demonstrate that credible hazards associated with these eVTOL designs are adequately mitigated. This work provides an orientation for eVTOL stakeholders, especially new entrants and those in hybrid roles, to the safety and regulatory context for assuring eVTOL aircraft for UAM applications. We further present selections of functional hazard assessment (FHA) performed on a reference eVTOL concept, with process and decision narration. The exercise allows exploration of several safety considerations specific to eVTOL systems and demonstrates the FHA process together with negotiation of some of its options and variations.
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.
An experimental investigation was conducted in a supersonic wind tunnel to determine the effect a sudden high velocity headwind had on the physical deformation and structural breakup characteristics of birds. Several sizes of recently killed birds were dropped into the test section at free-stream Mach numbers ranging from 0.2 to 0.8 and photographed with high-speed motion-picture cameras. These conditions simulated flow conditions encountered when birds are ingested into the inlets of high speed aircraft, thereby constituting a safety hazard to the aircraft and its occupants. The investigation shows that, over the range of headwind conditions tested, the birds remained structurally intact and did not suffer any appreciable deformation or structural breakup.
The potential hazard to electrical and electronic devices should there be a release of free carbon fibers due to an aircraft crash and fire was assessed. Exposure and equipment sensitivity data were compiled for a risk analysis. Results are presented in the following areas: DC-9/DC-10 electrical/electronic component characterization; DC-9 and DC-10 fiber transfer functions; potential for transport aircraft equipment exposure to carbon fibers; and equipment vulnerability assessment. Results reflect only a negligible increase in risk for the DC-9 and DC-10 fleets either now or projected to 1993.
Spatial and temporal patterns in bird abundance within the five-mile airspace at the Shuttle Landing Facility (SLF) on John F. Kennedy Space Center (KSC), Florida, USA were investigated for purposes of quantifying Bird Aircraft Strike Hazards (BASH). The airspace is surrounded by the Merritt Island National Wildlife Refuge (MINWR) which provides habitat for approximately 331 resident and migratory bird species. Potential bird strike hazards were greatest around sunrise and sunset for most avian taxonomic groups, including wading birds, most raptors, pelicans, gulls/terns, shorebirds, and passerines. Turkey Vultures and Black Vultures were identified as a primary threat to aircraft operations and were represented in 33% of the samples. Diurnal vulture activity varied seasonally with the development of air thermals in the airspace surrounding the SLF. Variation in the presence and abundance of migratory species was shown for American Robins, swallows, and several species of shorebirds. Analyses of bird activities provides for planning of avionics operations during periods of low-dsk and allows for risk minimization measures during periods of high-risk.
The Forward-Looking Interferometer (FLI) is an airborne sensor concept for detection and estimation of potential atmospheric hazards to aircraft. The FLI concept is based on high-resolution Infrared Fourier Transform Spectrometry technologies that have been developed for satellite remote sensing. The FLI is being evaluated for its potential to address multiple hazards, during all phases of flight, including clear air turbulence, volcanic ash, wake vortices, low slant range visibility, dry wind shear, and icing. In addition, the FLI is being evaluated for its potential to detect hazardous runway conditions during landing, such as wet or icy asphalt or concrete. The validation of model-based instrument and hazard simulation results is accomplished by comparing predicted performance against empirical data. In the mountain lee wave data collected in the previous FLI project, the data showed a damped, periodic mountain wave structure. The wave data itself will be of use in forecast and nowcast turbulence products such as the Graphical Turbulence Guidance and Graphical Turbulence Guidance Nowcast products. Determining how turbulence hazard estimates can be derived from FLI measurements will require further investigation.
This paper presents a new wide field of view laser beacon system for measurement, in three dimensions, of aircraft or other remote objects. The system is developed for aircraft collision hazard warning independent of ground-based hardware, as well as for flight research, helicopter-assisted construction and rescue, and robotic manipulation applications. Accurate information describing the relative range, elevation, and azimuth of the aircraft are generated by the sweep of a low-power fan-shaped rotating laser beacon past an array of optical detectors. The system achieves a wide angle of acceptance of laser beacon light through use of compound parabolic concentrators, which collimate the light for spectral filtering to minimize solar interference. An on-board microprocessor system converts the pulse sequence to aircraft position in real time. System reliability and performance are enhanced through narrow pass filtering of the pulse signals, digital logic design to mask spurious signals, and adaptive modulation of trigger threshold levels.
Low-altitude windshear (LAWS) has been identified as a major hazard to aircraft, particularly during takeoff and landing. The Federal Aviation Administration (FAA) has been involved with developing technology to detect LAWS. A key element in this technology is high resolution pulse Doppler weather radar equipped with signal and data processing to provide timely information about possible hazardous conditions.
The relative safety of passengers in LH2 - fueled aircraft, as well as the safety of people in areas surrounding a crash scene, has been evaluated in an analytical study. Four representative circumstances were postulated involving a transport aircraft in which varying degrees of severity of damage were sustained. Potential hazard to the passengers and to the surroundings posed by the spilled fuel was evaluated for each circumstance. Corresponding aircraft fueled with liquid methane, Jet A, and JP-4 were also studied in order to make comparisons of the relative safety. The four scenarios which were used to provide a basis for the evaluation included: (1) a small fuel leak internal to the aircraft, (2) a survivable crash in which a significant quantity of fuel is spilled in a radial pattern as a result of impact with a stationary object while taxiing at fairly low speed, (3) a survivable crash in which a significant quantity of fuel is spilled in an axial pattern as a result of impact during landing, and (4) a non-survivable crash in which a massive fuel spill occurs instantaneously.
Results of a NOAA/NASA research field program (1982-1983) which focused on dynamic and thermodynamic characterization of arc cloud lines are discussed. The program combines research aircraft flights with rapid-scan (3-minute interval) GOES imagery and covers the subcloud layer above and below the density surge line. Strong differences in the dynamic characteristics of air are noted to exist within and above the density surge line (DSL); air motions within the DSL are much more turbulent, with stronger upward and downward motions, than in the subcloud layer above the DSL. The arc cloud lines and the associated DSL regions are concluded to pose an extreme potential hazard to aircraft and Space Shuttle operations.
The effectiveness of a forward-located spoiler, a spline, and span load alteration due to a flap configuration change as trailing-vortex-hazard alleviation methods was investigated. For the transport aircraft model in the normal approach configuration, the results indicate that either a forward-located spoiler or a spline is effective in reducing the trailing-vortex hazard. The results also indicate that large changes in span loading, due to retraction of the outboard flap, may be an effective method of reducing the trailing-vortex hazard.
Results of studies of wind shear hazard to aircraft operation are summarized. Existing wind shear profiles currently used in computer and flight simulator studies are reviewed. The governing equations of motion for an aircraft are derived incorporating the variable wind effects. Quantitative discussions of the effects of wind shear on aircraft performance are presented. These are followed by a review of mathematical solutions to both the linear and nonlinear forms of the governing equations. Solutions with and without control laws are presented. The application of detailed analysis to develop warning and detection systems based on Doppler radar measuring wind speed along the flight path is given. A number of flight path deterioration parameters are defined and evaluated. Comparison of computer-predicted flight paths with those measured in a manned flight simulator is made. Some proposed airborne and ground-based wind shear hazard warning and detection systems are reviewed. The advantages and disadvantages of both types of systems are discussed.
The vortex wakes of large transport aircraft can pose a hazard to smaller following aircraft in the vicinity of airports during landing and take-off operations if certain separation guidelines are not observed. In order to reduce the hazard potential, and thereby the separation distances, efforts are being made to find more rapid wake-dissipation mechanisms. In this paper numerical simulations are made of the three-dimensional time-dependent instabilities that might be initiated in a vortex pair by sinusoidal displacements of the filaments. The objective of the study was to find those displacements and phase angles that would produce the most rapid destruction of the vortices. It is concluded that, of the wave patterns tried on the one pair of wake filaments, the only instability mode that leads to destructive interactions of the vortices is the Scorer-Crow process.
The natural mechanisms that lead to the development of deep convective storms through the integration of radio scan satellite data with research aircraft observations is discussed. The aircraft measurements are designed to provide detailed air motion and thermodynamic data near and in the arc cloud line region at the same time GOES rapid scan data is taken. Inspection of the data indicates: (1) Arc cloud lines are important in both the production of convergence and vorticity, and in the interaction with intense thunderstorms which may act to trigger tornado activity. (2) The lateral extent of the vertical motion field compared to the cloud scale indicates that the main driving force for the initial cloud development along the arc-line is controlled by the thunderstorm outflow(s) interacting with the convectively unstable air of the environment. (3) Arc cloud lines and their associated DSL region can pose extreme hazards to aircraft operations. (4) An arc cloud line's major threat to space shuttle operations lie in its ability to generate new thunderstorm activity along the shuttle glide path.
Research on forecasting, detection, and incidents of clear air turbulence
A model of turbulent flow in the atmospheric boundary layer was used to simulate the low-level wind and turbulence profiles associated with both local thunderstorm gust fronts and synoptic-scale warm fronts. Dimensional analyses of both type fronts provided the physical scaling necessary to permit normalized simulations to represent fronts for any temperature jump. The sensitivity of the thunderstorm gust front to five different dimensionless parameters as well as a change from axisymmetric to planar geometry was examined. The sensitivity of the warm front to variations in the Rossby number was examined. Results of the simulations are discussed in terms of the conditions which lead to wind shears which are likely to be most hazardous for aircraft operations.
Smaller scale severe storms with the expectation that the larger storms associated weather-hazard-to-aircraft problems are discussed. Improvements in the detection capability for hail, turbulence, wind shear, and lightning are reported. Improvements in communications with limited surveillance capabilities are summarized. Education and training of pilots in severe storms are examined. Weather forecasts improvements by increasing accuracy in the short term are described. Aircraft operations, aircraft design, weather services, data acquisition and utilization are reviewed.