Zoekresultaten

The (congested) city in the sky

Energy, exergy, economic, environmental, and sustainability assessments of the CFM56-3 series turbofan engine used in the aviation sector

In this study, aviation, energy, exergy, environmental, exergoeconomic, and exergoenvironmental analyses are performed on a CFM56-3 series high by-pass turbofan engine fueled with Jet-A1 fuel. Specific fuel consumption and specific thrust of the engine are found to be 0.01098 kg/kN.s and 0.3178 kN/kg/s, respectively. Engine's energy efficiency is calculated as 35.37%, while waste energy ratio is obtained as 64.63%. Exergy efficiency, waste exergy rate, and fuel exergy waste ratio are forecasted as 33.32%, 33175.03 kW, and 66.68%, respectively. Environmental effect factor and ecological effect factor are computed as 2.001 and 3.001, while ecological objective function and its index are taken into account of −16597.22 kW and −1.001, respectively. Exergetic sustainability index and sustainable efficiency factor are determined as 0.5 and 1.5 for the CFM56-3 engine, respectively. Environmental damage cost rate is determined as 519.753 $/h, while the environmental damage cost index is accounted as 0.0314 $/kWh. Specific exergy cost of the engine production is found as 40.898 $/GJ from exergoeconomic analysis, while specific product exergy cost is expressed as 49.607 $/GJ from exergoenvironmental analysis. From exergoenvironmental economic analysis, specific exergy cost of fuel is computed as 10.103 $/GJ when specific exergy cost of production is determined as 40.898 $/GJ.

Aviation safety concerns for the future

The AVAC-COM communication model and taxonomy: results from application to aviation safety events

Communication problems are acknowledged as hazardous eventualities affecting operations negatively. However, a few systematic attempts have been made to understand the pattern of communication issues and their contribution to safety events. In this paper, we present the AVAC-COM communication model and taxonomy based on the cybernetics approach and a literature review. The model elements and taxonomy variables regard the actors, signals, coders, interference, direction and timing, predictability, decoders, and channels. To test the applicability and potential value of the AVAC-COM framework, we analysed 103 safety investigation reports from aviation published between 1997 and 2016 by the respective authorities of Canada, the United States, Australia, the United Kingdom and the Netherlands. The overall results of the 256 cases of communication flaws detected in the reports suggested that these regarded more frequently Human-Media and Human-Human interactions, verbal and local communications as well as unfamiliarity of the receivers with the messages transmitted. Further statistical tests revealed associations of the region, time period, event severity and operations type with various variables of the AVAC-COM taxonomy. Although the findings are only indicative, they showed the potential of the AVAC-COM model and taxonomy to be used to identify strong and weak communication elements and relationships in documented data such as investigation and hazard reports.

Aviation Data Analytics in MRO Operations: Prospects and Pitfalls

As every new generation of civil aircraft creates more on-wing data and fleets gradually become more connected with the ground, an increased number of opportunities can be identified for more effective Maintenance, Repair and Overhaul (MRO) operations. Data are becoming a valuable asset for aircraft operators. Sensors measure and record thousands of parameters in increased sampling rates. However, data do not serve any purpose per se. It is the analysis that unleashes their value. Data analytics methods can be simple, making use of visualizations, or more complex, with the use of sophisticated statistics and Artificial Intelligence algorithms. Every problem needs to be approached with the most suitable and less complex method. In MRO operations, two major categories of on-wing data analytics problems can be identified. The first one requires the identification of patterns, which enable the classification and optimization of different maintenance and overhaul processes. The second category of problems requires the identification of rare events, such as the unexpected failure of parts. This cluster of problems relies on the detection of meaningful outliers in large data sets. Different Machine Learning methods can be suggested here, such as Isolation Forest and Logistic Regression. In general, the use of data analytics for maintenance or failure prediction is a scientific field with a great potentiality. Due to its complex nature, the opportunities for aviation Data Analytics in MRO operations are numerous. As MRO services focus increasingly in long term contracts, maintenance organizations with the right forecasting methods will have an advantage. Data accessibility and data quality are two key-factors. At the same time, numerous technical developments related to data transfer and data processing can be promising for the future.

Transition towards sustainable aviation

The development of sustainable aviation turns out to be a 30 year transition process. How to manage this transition process is a crucial for the change and success of the aviation sector in future. The foreseen solutions are mostly driven by technological innovation and improvements of procedures and regulations. The question is if these tools are sufficient to manage the innovation of an entire sector with 100 years legacy or are changes in business models, societal values and human behaviour part of the instrument mix aviation can use? New or adapted innovation models and tools are needed to use the full mix of instruments. The article explores the use of a modified Cyclic Innovation Model which is developed by researchers of TU Delft. The development of Schiphol Airport in Amsterdam and the outlook for its next 100 years is used as a case to understand the complexity of sustainable airport development.