Reply with your response in at least 80 words. Provide substantive feedback to classmates’ main posts. Responses should be thoughtful and include thought-provoking feedback. In other words, “I agree” or “Great post” are not considered valid responses. Whether you agree or disagree, explain why and support your response with valid references. There are total of six posts, two peers’ main posts for each questions.
Aviation Accident Analysis
- On February 22, 2022, at about 1020 Hawaii-Aleutian Standard Time, a Sikorsky S-61N helicopter, N615CK, was destroyed when involved in an accident at the Pacific Missile Range Facility (PMRF), Barking Sands, Kekaha, Hawaii, on the island of Kauai. The two pilots and two crew members were fatally injured. The helicopter was operated as a Title 14 Code of Federal Regulations Part 133 flight. The accident helicopter, owned and operated by Croman Corporation, was under contract to the United States Navy, being used to retrieve inert training torpedoes from the Pacific Ocean as part of the Navy’s ongoing, Pacific submarine training operations. According to the operator’s director of operations, the accident mission involved locating a training torpedo in the open waters, retrieving the torpedo using a recovery basket/cage system, and returning the torpedo to the PMRF sling load (NTSB, 2022). The root cause of the crash is still under investigation, but the risk associated with their mission was high. I can say firsthand from flying and working with this group of individuals this was not the first time they were conducting this mission. The crew faced multiple risk factors, including low levels over water, flying within constant changing wind directions, and performing external load operations. The Navy is continuously working with the NTSB to find the root cause. Still, the preliminary reports discuss possible mechanical failure forcing the aircraft to make rapid spinning movement before impact.
- On June 6, 2013 a helicopter pilot was supposed to fly a Robinson R44 II, N915BW from Redlands California following some maintenance to Southern California Helicopter Flight School in Long Beach California. The pilot had with him another less experienced pilot with intent of introducing the junior pilot to a helicopter he had never flown before. The flight that actually occurred departed from Redlands, to Brackett Field Airport in Pomona, California. There he picked up two passengers one of which the pilot was related to. From Brackett the group of four flew west towards the coast over to Santa Monica and proceeded back in an easterly direction. They flew over Universal Studios, Forest Lawn Cemetery, the “HOLLYWOOD” sign and finally over towards Griffith Observatory. Shortly after arriving to the Griffith Observatory area the Main Rotor Gearbox Chip warning light eliminated, and the pilot proceeded on trying to put the helicopter down somewhere safely. At the first chosen spot to land the pilot was unable due to people being in the immediate area. The pilot proceeded to an alternate location. After spotting another available spot to land the pilot put it down but felt the helicopter slip before having a chance to put collective all the way down. As the pilot felt the helicopter start to slip, he pulled up on the collective causing the helicopter spin to the right and finally roll over. No one was seriously hurt but the helicopter was basically totaled. The NTSB determined that the pilot failed to maintain directional control over the aircraft (NTSB, 2014.). In hindsight I have to have to wonder what the pilot with 5000 hours, 1500 hours in this particular aircraft, was doing with people in the aircraft after a maintenance session. I think the pilot was wrong in having those people with him at the time of the accident. Now if the pilot was found at fault, I believe the accident was caused by negligence on part of the helicopter pilot. If that flight would have happened and the pilot just went straight to Long Beach as he was supposed to none this would have happened. I am of the opinion that landing a helicopter in a real-world scenario is tougher than it may seem when it’s not on straight and level concrete pad. The pilot might have needed to practice more real world what happens if my engine goes out scenarios.
White Paper Analysis
Respond to your classmates post with an explanation of what you think the author’s intent was, and any bias that might introduce.
- The “Why IA is the right way to navigate around cancelled flights,” white paper examines the benefits of using intelligent automation (IA) to combat the operational difficulties of flight cancellations, in the post-pandemic airline schedule environment. Due to many schedule cancellations, airlines have updated their policies to allow passengers to change flights without fees and in some cases issues travel vouchers. However, with the increase in schedule changes came an increase in customer service congestion. The paper researched the use of IA to issue travel vouchers, updates to customers calendars so they do not miss booking deadlines and to keep track of all issued vouchers. The IA was also used to handle minor rebooking issues, thus, leaving the more complex rebooking issues for the customer service agents. The use of IA reduced airline response times and subsequently increased customer satisfaction. With IA, there was a 90% reduction in manual work and a 98% data reconciliation accuracy, improving compliance. Additionally, these IA systems made for a seamless voucher issuance procedures as they updated the airline, customer and any third-party travel companies at the same time. (Molchanova, 2020) Overall, the use of IA systems are beneficial. These IA systems reduce employee workload, improve response times, allow for more accurate voucher tracking and help customers keep track of deadlines. In the current environment, with many cancellations and schedules that can and in most cases, do change frequently. It is a huge benefit to have sophisticated IA systems, to handle the easy rebooking issues and saving the employee resources for the more complex rebooking issues. Though in most cases and which has already been proven in the paper, IA can lead to customer satisfaction through improved response times. I believe the IA maybe also upset some customers if it cannot handle the customers issues. It has happened to me before, not with airlines, where the IA becomes annoying because it is trying to solve my issues which it cannot and takes longer to speak with an agent.
- The white paper I chose to look at is “Transforming the Future of Airports with Artificial Intelligence, Machine Learning and Generative Design”. This white paper looks at how to better design airports. The author looks at two interesting design models. One is called a digital twin. “A digital twin is a digital 3D replica of a physical asset, system or process — such as an airport. Built from massive, cumulative, real-time data, a digital twin simulates its real-world counterpart in a live setting, creating an evolving digital profile of its historical and current behavior (Sims, 2019).” This means that the digital twin can take real world information to try to optimize performance on the digital twin, and eventually improve performance in the real world. For instance, a digital twin can see that there is always a line up at the female washroom near gate 20. It can then use its intelligence to come up with solutions in the digital twin. A solution like adding a second bathroom, or adjusting the flight gates so that multiple high passenger flights aren’t in that location at the same time. From there, it can learn what the best possible solution is and help airport planners to make the best decision. The second design model is a generative design approach. This approach takes “design goals, criteria and parameters on everything from materials and manufacturing methods to cost constraints (Sims, 2019)” and feeds that information into AI software to come up with multiple design solutions. This allows for the opportunity to come up with unbiased designs that the designers weren’t initially thinking of. This could be beneficial when designing new aircraft. When we think of the cabin of an aircraft we are already biased about which way the seats face, where the bathrooms typically are, how large the aisles are, etc. With the generative design approach, one could come up with multiple different cabin designs that were possibly not ever thought of!
Topic for Research Paper
- * Research Problem
There seem to be some outliers with individual state-reported General Aviation generated Gross Domestic Product (GDP) and the proportion of state reported General Aviation jobs that are created within this economic sector during the fiscal year of 2016 (FAA, 2020). The outliers do not match up per state and an individual state may show a significant GDP with very little job creation and vice versa. These minority unbalanced results between some state’s GDP and jobs demonstrate an unclear relationship that needs clarifying whether GDP and Job creation correlate or if no relationship exists between the two variables.
- Research Question
Does a State’s General Aviation GDP have a significant influence on General Aviation Job Creation? - Hypotheses
The hypothesis revolves around investigating if there is a direct correlation for the majority of states where their GDP is within the standard deviation of average job creation. I will look for the overall mean and individual standard deviations for each state’s GDP and jobs and see if there is any correlation. A state’s significant influence of GDP on jobs would have a standard deviation closer to the mean. There would be no significant influence if the standard deviations are further from the mean since there is no apparent relationship between the two variables. The hypotheses would be stated as:
Null (H0): A state’s General Aviation GDP has no significant influence on General Aviation job creation?
Alternative (H1): A state’s General Aviation GDP has a significant influence on General Aviation job creation? - Variables
Since most economic sectors are judged on their contribution to the state’s or country’s GDP, this will be considered the independent and influencing variable for this research. Job growth can be a direct result of strong economic growth and increased GDP lowers unemployment rates (Levine, 2013). With these considerations in mind my variables are comprised of the following:
State GDP (Independent Variable)
Job Creation (Dependent Variable)
- For my research topic I initially wanted to analyze air carrier delay data for specific airports across the United States to attempt and identify a region of the country that is more prone to on time arrival delays. However, after some feedback I am attempting to approach this from a different angle. The intent is to pool data for a single year from three major airports on the west coast and three on the east coast. I will take the averages of the delays for each region and then use the t-test hypothesis method to determine if a flight is more likely to be delayed on one side of the country versus the other. This analysis will not factor in causes for delays, but I am attempting to mitigate potential influence from weather by using airports on similar latitudes that should experience roughly the same weather patterns to help normalize results.
Research Problem: Air carrier travel delays are a routine part of travel but are often costly and arriving even a minute after a scheduled arrival time can have cascading effects on future aircraft logistics and operations. The average flight delay in the US is approximately 12 minutes, and for each minute of that delay companies and passengers are paying a price. These delays can be exacerbated by maintenance, weather, and airport congestion.
Research question: Is there a difference in the length of flight delays between the west coast and east coast of the United States?
Research Hypothesis: An airliner will experience longer delays when flying to the east coast.
Null Hypothesis: There is no difference in the arrival time delays between the west coast and east coast.
Independent Variable: Arrival airport location.
Dependent Variable: Flight delay length.
