Technologically-Enabled Design and Operations
09/10/2019
hazard or hazardous condition and numbers of persons involved
09/10/2019

Operational Improvements

Operational Improvements

There is a multitude of improvements in operations of the aviation industry which are developing or has already been introduced that can reduce GHG emissions significantly in the near future. For instance, one of the leading operational improvements is the reduction of inefficiencies in Air Traffic Management (ATM) – the introduction of the Next Generation Air Transportation System (NextGen) and Boeing’s tailored arrivals system can help to provide a suitable solution to the current issues at hand (Agarwal, 2012).

NextGen. The Next Generation Air Transportation System is the modernization of an active networking technology that updates itself with real-time shared information and adapts itself to the individual needs of all United States aircraft (FAA, 2018). It has a digital air transportation network which promotes versatility in its systems by allowing aircrafts to instantly conform to ever-changing circumstances such as flight trajectory patterns, aircraft position by GPS, traffic congestion, weather and security matters (FAA, 2018). Boehm-Davis (2008), reports that there are plans to connect all aircraft and airports in U.S. airspace to the NextGen network and will constantly share information in real time to improve safety and productivity especially since there is an expected increase in air transportation. In addition, Agarwal (2012) reports that the improvement in operational measures, which covers almost all of world’s aircraft, is possible to attain a bigger impact in the short term as compared to the introduction of newer engine and aircraft technologies.

Tailored arrivals. Currently, the ATM system is tremendously safe but not entirely efficient. The ATM system still uses a step system designed in the 1950s, which has a high fuel wastage, longer flight times and a higher potential to contribute to aircraft noise (Upstarter, 2015). Hence, this prompted action from one of aviation industry’s biggest aircraft manufacturer – Boeing. Boeing together with several other airlines, airports and partners worldwide has developed a system which helps to save an estimated 400 to 800 pounds of fuel per aircraft touchdown (Upstarter, 2015). In addition, this ingenious system can also reduce air traffic controllers’ workload and allow for better scheduling and passenger connections (Agarwal, 2012). For instance, between December 2007 and June 2009, more than 1700 tailored arrivals of B777 and B747 have been completed at San Francisco airport (Agarwal, 2012). It is estimated that Boeing’s tailored arrivals system has helped airlines save an average of 950 kg of fuel and approximately $950 every landing (Agarwal, 2012). Through a one-year period, four participating airlines using the tailored arrivals system has managed to save more than 524,000 kg of fuel, reducing an estimated 1.6 million kg in GHG emissions (Agarwal, 2012).

Discussion and Recommendations

This capstone paper has delved into the aviation industry to explore how humanity has impacted the environment and how the Earth would reach a point of no return by the year 2035 if nothing is done to address the issues of energy and sustainability (Aengenheyster et al., 2018). The main purpose of this paper is to urge stakeholders – aircraft manufacturers, airlines, airports, policy makers and industry leaders to the adoption of newer sustainable alternative technologies such as, the usage of alternative fuels, biomimetics engineering and machine learning in order to create a sustainable aviation industry. The findings of this paper indicate that current methods used by the industry are barely adequate to keep up with the exponential growth of the aviation industry. However, GHG emissions and environmental issues can be easily mitigated with technological innovations in aircraft and engine designs, by use of modern composites and alternative, sustainable biofuels and operational improvements.

Agarwal (2018), believes that some of the changes in operations can be readily put into effect such as Boeing’s tailored arrivals system and the ongoing development of NextGen by the FAA. Even though innovations in creating sustainable biofuels, biomimicry of aircraft design and machine learning may take some time to implement, the author believes that it can be made achievable in the short term through the coordinated efforts of policy makers and aviation industry stakeholders (Agarwal, 2018).

In addition, this paper has further investigated about how the utilization of machine learning can help improve aviation security through facial recognition. Machine learning can furthermore, be used to create predictions in electricity demand and generation to create a more environmentally friendly industry.

Last, there needs to be a combined effort from the aviation industry and government alike to consider the adoption of new modern technology such as alternative fuels, biomimetics and design technology for the future construction of aircraft so as to reduce GHG emissions and climate change. Immediate adoption of aforementioned methods may conceivably be achievable; however, the author feels that there needs to be a further refinement of such technologies to be fully safe and tested before it can be used for aviation operations which involves the safety of millions of lives worldwide.

Conclusion

Global warming and climate change are occurring at a faster pace than expected. Hence, this ongoing phenomenon requires humankind’s immediate attention. The aviation industry being one of the largest influencers in the economy has the obligation to lessen its environmental impact so as to safeguard the sustainability of its operations. Current traditional methods and policies used by the aviation industry is beginning to lose its effectiveness and therefore there is a need for the adoption of newer design approaches, computer technologies, operational improvements and policies to ensure the sustainability of the environment.

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