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. f 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). f 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. f 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. f f 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). f 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. f 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. f 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. f
The understanding of system environments will allow the users to
be able to interpret the variable with insight using methods from
data analytics. f The practical framework also demonstrates the
benchmark of algorithms to improve results and coordination
among the algorithmically guided systems (Jiao et al., 2021). f 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
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