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. aa 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). aa 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. aa 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. aa aa 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). aa 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. aa 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. aa 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. aa The understanding of system environments
will allow the users to be able to interpret the variable with
insight using methods from data analytics. aa 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.
