Simulation Training in U.K. General Aviation: An Undervalued Aid to Reducing Loss of Control Accidents

Analysis of data from 1,007 U.K. general aviation (GA) accidents demonstrates the predominant cause of accidents is loss of control, exacerbated by a lack of recent flying experience. These are long-standing problems that can be targeted effectively with simulation training. Discussion on training strategies in commercial aviation reinforces the logic of introducing simulation training for the GA pilot. Conclusions drawn affirm the notion that GA safety would benefit from implementation of regulated simulation training

A Risk Assessment Architecture for Enhanced Engine Operation

On very rare occasions, in-flight emergencies have occurred that required the pilot to utilize the aircraft's capabilities to the fullest extent possible, sometimes using actuators in ways for which they were not intended. For instance, when flight control has been lost due to damage to the hydraulic systems, pilots have had to use engine thrust to maneuver the plane to the ground and in for a landing. To assist the pilot in these situations, research is being performed to enhance the engine operation by making it more responsive or able to generate more thrust. Enabled by modification of the propulsion control, enhanced engine operation can increase the probability of a safe landing during an inflight emergency. However, enhanced engine operation introduces risk as the nominal control limits, such as those on shaft speed, temperature, and acceleration, are exceeded. Therefore, an on-line tool for quantifying this risk must be developed to ensure that the use of an enhanced control mode does not actually increase the overall danger to the aircraft. This paper describes an architecture for the implementation of this tool. It describes the type of data and algorithms required and the information flow, and how the risk based on engine component lifing and operability for enhanced operation is determined

Intelligent Systems for Unmanned Aircraft Safety Certification

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/97143/1/AIAA2012-958.pd

Investigating the Heat Generation Efficiency of Electrically-Conductive Asphalt Mastic Using Infrared Thermal Imaging

One of the emerging technologies for producing sustainable ice-and snow-free pavements is the use of electrically-conductive surface courses, e.g., electrically-conductive asphalt concrete (ECAC) that can melt ice and snow through resistive heating. Modifying the mastic in asphalt concrete with electrically-conductive materials is a promising approach for producing high-quality ECAC. The objective of this study is to evaluate electrical conductivity and heat generation efficiency of electrically-conductive asphalt mastic (ECAM) specimens at a below-freezing temperature—simulating the harsh weather conditions in North America during the wintertime. To this end, asphalt mastic was electrically modified with carbon fiber (CF) at varying volume contents. The ECAM specimens were then powered by 60V AC during a time window of 10 minutes so that their heat generation capacity could be characterized through infrared thermography (IRT). Based on the resistivity measurements and thermal data analysis, the most reasonable CF content enabling rapid heat-generating ECAM was identified; this has future implications with respect to achieving efficient highway, bridge, and airport pavement operations during wintertime

Burnout in the ICU : potential consequences for staff and patient well-being

Peer reviewedAuthor versio

Formally Bounding UAS Behavior to Concept of Operation with Operation-Specific Scenario Description Language

Previous work introduced an approach for formally describing the concept of operations for unmanned aircraft. For this purpose, an existing language for simulation scenario description was adapted. In the context of the specific operation category, an upcoming European regulation for the operation of unmanned aircraft, the description and acceptance of the concept of operations plays a major role for flight approval on a per mission basis. This paper extends the previous approach further with combining the formalized description of the concept of operations with our existing approach for runtime monitoring. Monitoring the behavior at runtime can be used to enforce certain limits on the behavior. Therefore, the concept of operations is an ideal input for the monitoring approach. As a basis for the information relevant for the concept of operations the official annex to the guidelines document for the specific operation risk assessment is used, as well as an internal concept of operations document for a DLR research unmanned aircraft system

Prediction and Analysis of Ground Stops with Machine Learning

A flight is considered to be delayed when it arrives 15 or more minutes later than scheduled. Delays attributed to the National Airspace System are one of the most common type of delays. Such delays may be caused by Traffic Management Initiatives (TMI) such as Ground Stops (GS), issued at affected airports. Ground Stops are implemented to control air traffic volume to specific airports where the projected traffic demand is expected to exceed the airports’ acceptance rate over a short period of time due to conditions such as inclement weather, volume constraints, closed runways, etc. Ground Stops can be considered to be the strictest Traffic Management Initiative (TMI), particularly because all flights destined to affected airports are grounded until conditions improve. Efforts have been made over the years to reduce the impact of Traffic Management Initiatives on airports and flight operations. However, these efforts have largely focused on otherTraffic Management Initiatives such as Ground Delay Programs (GDP), due to their frequency and duration compared to Ground Stops. Limited work has also been carried out on Ground Stops because of the limited amount of time that traffic management personnel often have between planning and implementing Ground Stops and external factors that influence decisions of traffic management personnel. Consequently, this research primarily focuses on the prediction of weather-related Ground Stops at Newark Liberty International (EWR) and LaGuardia (LGA) airports, with the secondary goal of gaining insights into factors that influence their occurrence. It is expected that this research will provide stakeholders with further insights into factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by benchmarking Machine Learning algorithms in order to identify the best suited algorithm(s) for the prediction models, and identifying and analyzing key factors that influence the occurrence of weather-related Ground Stops at both airports. This is achieved by 1) fusing data from the Traffic Flow Management System (TFMS) and Automated Surface Observing Systems (ASOS) datasets, and 2) leveraging supervised Machine Learning algorithms to predict the occurrence of weather-related Ground Stops. The performance of these algorithms is evaluated using balanced accuracy, and identifies the Boosting Ensemble algorithm as the best suited algorithm for predicting the occurrence of Ground Stops at EWR and LGA. Further analysis also revealed that model performance is significantly better when using balanced datasets compared to imbalanced datasets

Validation of Proposed Go-Around Criteria Under Various Environmental Conditions

This paper evaluates the effects of environmental conditions on touchdown performance under varying approach states and validates proposed go-around criteria developed using data from a previously conducted study under these various environmental conditions. An experiment was conducted using Boeing 737-800 and Airbus A330-200 Level D full-flight simulators in which 24 pilots flew multiple approaches under different approach conditions and environmental variables. Pilots were instructed to always land the aircraft, even from conditions considered to be an unstable approach. Various touchdown performance metrics were analyzed. In addition, pilots perceptions of risk under the various unstable approach conditions and resulting landings were assessed. The results of the study revealed that wind speed/direction and visibility had a stronger effect on touchdown performance than the approach parameters. Specifically, wind had a highly significant effect on longitudinal and lateral touchdown point, as well as a significant effect on sink-rate at touchdown. Wind and visibility, along with localizer deviation, also had a strong effect on pilots perception of risk and workload ratings. Furthermore, the study confirmed that touchdown performance was similar among the runs with a 300-foot and 500-foot starting gate, as was found in the previously conducted experiment. These results support the previous finding that lowering the go-around decision gate to 300-foot might be acceptable, but suggest that certain environmental conditions might warrant altered thresholds of the proposed go-around criteria at this gate. Finally, the findings of this experiment highlight the importance of environmental factors in the assessment of risk of unwanted outcomes on approach and landing