Project Scope: The project scope is to explore a theoretical framework
regarding data analytics and the methods to examine the influences of
identification, characterization and analysis of rotorcraft flight
maneuvers using simulated and flight data. j This approach considers
the optimization of flight performances as a framework to improve and
enhance the safety demand-capacities for system management using
concepts and techniques associated with data analytics (Dorneich et
al., 2016). In creating an integrative framework for mapping, the types
and characteristics for analysis must be adaptive to the system
environments and the components according to conditions introduced
the level of complexity through a systematic analysis (Dorneich et al.,
2016). To design an integrative framework, the analysis will have to
identify a formal approach to determine the inputs and parameters of
the system environment requiring a solution with reference of system
states (e.g., identification, characterization, and analysis) of rotorcraft
flight maneuvers using simulated and flight data. The data analytics
problem that I am analyzing is the ability to create an integrated flight
envelop to assess the risk associated with rotorcraft performances
uniquely to the environment and the maneuvering conditions using
events related to operational parameters for evaluation modeling
purposes.
In the selection of this project, the aviation safety culture regarding an
increase occurrence of risk concerns in general aviation can potential
assist in the decision-making process by introducing an integrative
framework for data analysis. This project introduces an opportunity
to create a formal approach that will define how to cross-examine
both external and internal operations for modeling purposes of aircraft
systems (e.g., rotorcraft flight maneuvers). This is important to the
implementation of a safety culture environment to assess with
verification and validation for cross-examination of significant
differences in operation responses of human performances. As such,
the general aviation community could benefit from this investigation
with potential impact to improve data collection strategies based on
an integrative framework concepts and conditions.
Analysis Plan: The study expectation from the data analysis includes
the ability to compare and implement a modeling framework with
features that will explore the implications about system environments.
As such, the expected approach will be used to improve the
development of emerging practices to explore the difference of
solutions for the recommendation needs in practices. j The
understanding of system environments will allow the users to be able
to interpret the variable with insight using methods from data
analytics. The practical framework also demonstrates the benchmark
of algorithms to improve results and coordination among the
algorithmically guided systems (Jiao et al., 2021). The predicted
action-value based for planning and risk assessment enabling the
performed decision to be model according to the baseline of the
algorithm and draw results from the outputs.
Reference:
Bottasso, C. L., & Montinari, P. (2015). Rotorcraft Flight Envelope
Protection by Model Predictive Control. Journal of the American
Helicopter Society, 60(2), 1–13.
Chen, R., Yuan, Y., & Thomson, D. (2021). A review of mathematical
modelling techniques for advanced rotorcraft configurations. Progress
in Aerospace Sciences, 120.
Collins, C., Andrienko, N., Schreck, T., Yang, J., Choo, J., Engelke, U.,
Jena, A., & Dwyer, T. (2018). Guidance in the human–machine
analytics process. Visual Informatics, 2(3), 166–180.
Dorneich, M. C., Rogers, W., Whitlow, S. D., & DeMers, R. (2016).
Human performance risks and benefits of adaptive systems on the
flight deck. International Journal of Aviation Psychology, 26(1/2), 15-
35
Filho, J. O. & Andrade, D. (2016). Management process of a frequency
response flight test for rotorcraft flying qualities evaluation. Journal of
Aerospace Technology and Management, 8(3), 289–306
Jiao, Y., Tang, X., Qin, Z., Li, S., Zhang, F., Zhu, H., & Ye, J. (2021). Real-
world ride-hailing vehicle repositioning using deep reinforcement
learning. Transportation Research: Part C, 130,
Li, X., Zhang, J., & Han, J. (2021). Trajectory planning of load
transportation with multi-quadrotors based on reinforcement learning
algorithm. Aerospace Science and Technology, 116.
Ud-Din, S. & Yoon, Y. (2018). Analysis of loss of control parameters for
aircraft maneuvering in general aviation. Journal of Advanced
Transportation, 2018.
