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Safety related problems are reported which occur as a consequence of information transfer deficiencies that arise when air/ground communications are (or should be) used as a resource in inflight emergency situations. The system factors, the human errors, and the associated causes of these problem are defined.
Atlas Centaur pad safety report for launch complex 36A and 36B
The work described in this report is done under contract with the National Aeronautics and Space Administration (NASA) to support the Advanced Air Transportation Technology (AATR) program. The goal of this program is to contribute to and accelerate progress in Advanced Air Transportation Technologies. Wyndemere Incorporated is supporting this goal by studying the complexity of the Air Traffic Specialist's role in maintaining the safety of the Air Transportation system. It is envisioned that the implementation of Free Flight may significantly increase the complexity and difficulty of maintaining this safety. Wyndemere Incorporated is researching potential methods to reduce this complexity. This is the final report for the contract.
The enhanced safety, superior energy, and power density of rechargeable metal‐air batteries make them ideal energy storage systems for application in energy grids and electric vehicles. However, the absence of a cost‐effective and stable bifunctional catalyst that can replace expensive platinum (Pt)‐based catalyst to promote oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) at the air cathode hinders their broader adaptation. Here, in this work, it is demonstrated that Tin (Sn) doped β‐gallium oxide (β‐Ga 2 O 3 ) in the bulk form can efficiently catalyze ORR and OER and, hence, be applied as the cathode in Zn‐air batteries. The Sn‐doped β‐Ga 2 O 3 sample with 15% Sn (Sn x =0.15 ‐Ga 2 O 3 ) displayed exceptional catalytic activity for a bulk, non‐noble metal‐based catalyst. When used as a cathode, the excellent electrocatalytic bifunctional activity of Sn x =0.15 ‐Ga 2 O 3 leads to a prototype Zn‐air battery with a high‐power density of 138 mW cm −2 and improved cycling stability compared to devices with benchmark Pt‐based cathode. The combined experimental and theoretical exploration revealed that the Lewis acid sites in β‐Ga 2 O 3 aid in regulating the electron density distribution on the Sn‐doped sites, optimize the adsorption energies of reaction intermediates, and facilitate the formation of critical reaction intermediate (O*), leading to enhanced electrocatalytic activity.
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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.
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Optical air flow measurements can support the testing of aircraft and can be instrumental to in-flight investigations of the atmosphere or atmospheric phenomena. Furthermore, optical air flow meters potentially contribute as avionics systems to flight safety and as air data systems. The qualification of these instruments for the flight environment is where we encounter the systems in flight testing. An overview is presented of different optical air flow measurement techniques applied in flight and what can be achieved with the techniques for flight test purposes is reviewed. All in-flight optical airflow velocity measurements use light scattering. Light is scattered on both air molecules and aerosols entrained in the air. Basic principles of making optical measurements in flight, some basic optical concepts, electronic concepts, optoelectronic interfaces, and some atmospheric processes associated with natural aerosols are reviewed. Safety aspects in applying the technique are shortly addressed. The different applications of the technique are listed and some typical examples are presented. Recently NASA acquired new data on mountain rotors, mountain induced turbulence, with the ACLAIM system. Rotor position was identified using the lidar system and the potentially hazardous air flow profile was monitored by the ACLAIM system.
Turbine airfoils are subjected to increasingly higher heat loads which escalate the cooling requirements in order to satisfy life goals for the component materials. If turbine efficiency is to be maintained, however, cooling requirements should be as low as possible. To keep the quantity of cooling air bounded, a more efficient internal cooling scheme must be developed. One approach is to employ airfoils with multipass cooling passages that contain devices to augment internal heat transfer while limiting pressure drop. Design experience with multipass cooling passage airfoils has shown that a surplus of cooling air must be provided as a margin of safety. This increased cooling air leads to a performance penalty. Reliable methods for predicting the internal thermal and aerodynamic performance of multipass cooling passage airfoils would reduce or eliminate the need for the safety margin of surplus cooling air. The objective of the program is to develop and verify improved analytical methods that will form the basis for design technology which will result in efficient turbine components with improved durability without sacrificing performance. The objective will be met by: (1) establishing a comprehensive experimental data base that can form the basis of an empirical design system; (2) developing computational fluid dynamic techniques; and (3) analyzing the information in the data base with both phenomenological modeling and mathematical modeling to derive a suitable design and analysis procedure.
We present a framework, called AIR, for verifying safety properties of assembly language programs via software model checking. AIR extends the applicability of predicate abstraction and counterexample guided abstraction refinement to the automated verification of low-level software. By working at the assembly level, AIR allows verification of programs for which source code is unavailable-such as legacy and COTS software-and programs that use features-such as pointers, structures, and object-orientation-that are problematic for source-level software verification tools. In addition, AIR makes no assumptions about the underlying compiler technology. We have implemented a prototype of AIR and present encouraging results on several non-trivial examples.
Airspace safety must be assured for Urban Air Mobility (UAM) to become reality. Emerging operations need to safely integrate with existing and future air traffic. UAM is anticipated to evolve in stages. Early stages, characterized by low-density operations, may be accommodated by traditional air traffic management approaches. However, to enable the scale of UAM operations necessary to reach ubiquitous integration into daily life, new paradigms that utilize collaborative and automated systems are being considered. Correspondingly, traditional approaches for determining a safe number of simultaneous operations in an airspace must be adapted to suit evolving traffic management paradigms. In this paper, we examine dynamic density as an approach to determine when an airspace is excessively populated, and safety may become compromised. We review previous work on dynamic density as it relates to air traffic controller workload and propose factors that may be more fitting for UAMs. Rather than considering controller workload, we propose factors that suggest increased likelihood of loss of separation and may lead to conflicts that require tactical avoidance maneuvers. In addition to monitoring dynamic density for tactical decision making, we endeavor to predict airspace dynamic density with adequate look-ahead to allow for strategic route selection.
Over half of Alaska homes are at risk of poor indoor air quality, which increases the risk of respiratory and other illnesses for people and animals. Heat recovery ventilators (HRVs) provide warm, fresh air to a building interior while minimizing energy use. They can be programmed to provide varying amounts of fresh air to ensure adequate ventilation for occupants and can be configured to filter air during wildfires or other events. In addition to improving the safety of indoor air and extending the life of a building, HRVs can improve surrounding outdoor air because they reduce the amount of fossil fuels needed to heat a building. Recent research by CCHRC has focused on lowering barriers to using HRVs in cold climates through laboratory evaluations and homeowner education. This poster will cover data on indoor air quality in Alaska, results of CCHRC research, and future research questions.
This SIG focuses on the engineering of automation in interactive critical systems. Automation has already been studied in a number of (sub-) disciplines and application fields: design, human factors, psychology, (software) engineering, aviation, health care, games. One distinguishing feature of the area we are focusing on is that in the field of interactive critical systems properties such as reliability, dependability, fault tolerance are as important as usability, user experience or overall acceptance issues. The SIG targets at two problem areas: first the engineering of the user interaction with (partly-) autonomous systems: how to design, build and assess autonomous behavior, especially in cases where there is a need to represent on the user interface both autonomous and interactive objects. An example of such integration is the representation of an unmanned aerial vehicle (UAV) (where no direct interaction is possible), together with aircrafts (that have to be instructed by an air traffic controller to avoid the UAV). Second the design and engineering of user interaction in general for autonomous objects/systems (for example a cruise control in a car or an autopilot in an aircraft). The goal of the SIG is to raise interest in the CHI community on the general aspects of automation and to identify a community of researchers and practitioners interested in those increasingly prominent issues of interfaces towards (semi)-autonomous systems. The expected audience should be interested in addressing the issues of integration of mainly unconnected research domains to formulate a new joint research agenda.
This SIG focuses on the engineering of automation in interactive critical systems. Automation has already been studied in a number of (sub-) disciplines and application fields: design, human factors, psychology, (software) engineering, aviation, health care, games. One distinguishing feature of the area we are focusing on is that in the field of interactive critical systems properties such as reliability, dependability, fault-tolerance are as important as usability, user experience or overall acceptance issues. The SIG targets at two problem areas: first the engineering of the user interaction with (partly-) autonomous systems: how to design, build and assess autonomous behavior, especially in cases where there is a need to represent on the user interface both autonomous and interactive objects. An example of such integration is the representation of an unmanned aerial vehicle (UAV) (where no direct interaction is possible), together with aircrafts (that have to be instructed by an air traffic controller to avoid the UAV). Second the design and engineering of user interaction in general for autonomous objects systems (for example a cruise control in a car or an autopilot in an aircraft). The goal of the SIG is to raise interest in the CHI community on the general aspects of automation and to identify a community of researchers and practitioners interested in those increasingly prominent issues of interfaces towards (semi)-autonomous systems. The expected audience should be interested in addressing the issues of integration of mainly unconnected research domains to formulate a new joint research agenda.
The recent emergence of distributed electric propulsion Vertical Takeoff and Landing (VTOL) aircraft has created a rapid introduction of new design concepts with unique stability and control characteristics. A challenge to realizing the full potential of these vehicles for urban transportation is to gain public acceptance which is largely driven by flight safety. This paper describes ongoing research to address the feasibility of integrating flying qualities safety metrics into conceptual design of VTOL Urban Air Mobility (UAM) vehicles. The discussion is composed of the approach and progress toward a toolbox that integrates with existing NASA rotorcraft design software and processes. Several key challenges are highlighted including modeling requirements for failures, identification of critical failures, and capturing critical failures, with robustness to model uncertainty. A discussion of requirements for safety metrics, specific to UAM vehicles, as well as the effects of the control system design is also included. Results to date have demonstrated the degradation in flying qualities metrics due to propulsion failures.
Rising traffic density, along with autonomy and diversity of vehicles in the air, will fundamentally change the safety environment of the future air transportation system. The change in risk is two-fold: increasing chances of mid-air collisions with non-cooperative objects and increasing chances of crashes over highly populated areas. The changing nature of the vehicles populating the airspace means that civilian aircraft design must now explicitly include considerations of survivability in the event of collision with other vehicles, as well as prevention of damage to people, animals and property on the ground, to a much greater extent than today. This paper offers a preliminary perspective on how MDAO could contribute toward these goals. One of the conclusions is that, in contrast to traditional vehicle design, to accommodate the complexity of the future airspace safely and efficiently, vehicle design requirements, modeling, and design optimization must be closely connected to the properties of the airspace, including those of other vehicles in the air. Thus, the total measure of a vehicle's survivability should include the traditional survivability in malfunction scenarios, combined with new considerations of survivability in collisions and survivability of the public on the ground.
The envisioned transformation of the National Airspace System to integrate an In-time Aviation Safety Management System(IASMS)to assure safety in Advanced Air Mobility(AAM)brings unprecedented challenges to the design of human interfaces and management of safety information. Safety in design and operational safety assurance are critical factors for how humans will interact with increasingly autonomous systems. The IASMS Concept of Operations builds from traditional commercial operator safety management and scales in complexity to AAM. The transformative changes in future aviation systems pose potential new critical safety risks with novel types of aircraft and other vehicles having different performance capabilities, flying in increasingly complex airspace, and using adaptive contingencies to manage normal and non-normal operations. These changes compel development of new and emerging capabilities that enable innovative ways for humans to interact with data and manage information. In-creasing complexity of AAM corresponds with use of predictive modeling, data analytics, machine learning, and artificial intelligence to effectively address known hazards and emergent risks. The roles of humans will dynamically evolve in increments with this technological and operational evolution. The interfaces for how humans will interact with increasingly complex and assured systems designed to operate autonomously and how information will need to be presented are important challenges to be resolved.
Air traffic controllers are responsible for the safety and efficiency of air traffic and therefore must maintain a consistently high standard of performance. However, performance can be negatively affected by factors such as workload and fatigue, potentially leading to performance decline and performance-related incidents. Real-time identification of negative influences would facilitate timely implementation of supportive strategies prior to performance decline. The current study aimed to explore the concept of ‘behavioral indicators’ to identify when a controller was reaching a performance limit. A second aim was to capture behavioral indicators associated with performance influencing factors. A total of 65 controllers spanning Tower, Approach and Enroute facilities across the United States of America were interviewed. Findings revealed that controllers were familiar with the concept of behavioral indicators, and that indicators were associated with specific performance-influencing factors. Implications for implementing behavioral indicators training in control environments are discussed.