Evaluation of Finite-State Dynamic Inflow for Rotors

2021 ◽  
pp. 1-15
Author(s):  
Jimmy C. Ho ◽  
Hyeonsoo Yeo
Keyword(s):  
State Dynamic ◽  
Finite State

Modal analysis of finite-state dynamic inflow for rotary wing systems

2016 ◽  
Vol 23 (7) ◽  
pp. 1086-1094 ◽  
Author(s):  
Zhongyang Fei ◽  
David A Peters
Keyword(s):  
Modal Analysis ◽  
Axial Flow ◽  
Helicopter Rotor ◽  
Mode Shapes ◽  
Harmonic Numbers ◽  
State Dynamic ◽  
Finite State ◽  
Skew Angles ◽  
Rotary Wing ◽  
Velocity Mode

In this paper, the finite-state helicopter rotor inflow modes have been studied based on eigenanalysis. The inflow velocity mode shapes with node lines have been displayed with various skew angles. The eigenvalues are highly coupled especially for higher skew angles, and the mode shapes change significantly for different angles. The changing of eigenvalues with different harmonic numbers is also exhibited in the tables for axial flow of both the Peters–He and Morillo dynamic inflow models. An easy way to estimate the eigenvalues of the Peters–He inflow model is also established.

An Approximate Finite State Dynamic Wake Model for Predictions of Inflow below the Rotor

2021 ◽  
Author(s):  
Feyyaz Guner ◽  
J. V. R. Prasad ◽  
David A. Peters
Keyword(s):  
Velocity Potential ◽  
Dynamic Pressure ◽  
Frequency Control ◽  
Flight Simulation ◽  
Low Frequencies ◽  
Simulation Fidelity ◽  
State Dynamic ◽  
Finite State ◽  
Time Marching ◽  
Wake Model

The velocity potential based finite state dynamic inflow model can predict inflow anywhere in the flow field once velocity potential states and costates are known. However, solution to costate equations requires backward time marching, making it incompatible for integration into real-time flight simulation. This paper explores two types of quasi-steady approximations to the costate equations, both of which eliminate the need for backward time marching. The fidelities of the resulting inflow models are assessed through comparisons of off-disk inflow predictions for an isolated rotor. Further, the implication of the inflow model approximations on the flight simulation fidelity is assessed using the coupled body/rotor/inflow dynamics model of a generic helicopter model. It is shown that, in both cases, the quasi-steady approximations to the inflow model retain simulation model fidelity at low frequencies, a typical frequency range of pilot control inputs. Notable fidelity loss is seen at high-frequency control inputs, specifically for cases where horizontal tail is operating at a higher dynamic pressure within the rotor wake.

Space-time accurate finite-state dynamic inflow modeling for aeromechanics of rotorcraft

2019 ◽  
Vol 95 ◽  
pp. 105454 ◽  
Author(s):  
Felice Cardito ◽  
Riccardo Gori ◽  
Jacopo Serafini ◽  
Giovanni Bernardini ◽  
Massimo Gennaretti
Keyword(s):  
Space Time ◽  
State Dynamic ◽  
Finite State

Rotorcraft simulation modelling and validation for control law design

2007 ◽  
Vol 111 (1116) ◽  
pp. 77-88 ◽  
Author(s):  
B. J. Manimala ◽  
D. J. Walker ◽  
G. D. Padfield ◽  
M. Voskuijl ◽  
A. W. Gubbels
Keyword(s):  
Flight Control ◽  
Degrees Of Freedom ◽  
Flight Test ◽  
Simulation Modelling ◽  
Order System ◽  
Element Formulation ◽  
Base Line ◽  
Line Model ◽  
State Dynamic ◽  
Finite State

AbstractThis paper describes the development and validation of a high fidelity simulation model of the Bell 412 helicopter for handling qualities and flight control investigations. The base-line model features a rigid, articulated blade-element formulation of the main rotor, with flap and lag degrees of freedom. The Bell 412 HP engine/governor dynamics are represented by a second-order system. Other key features of the base-line model include a finite-state dynamic inflow model and lag damper dynamics. The base-line model gives excellent agreement with flight-test data over the speed range 15-120kt for on-axis responses. Prediction of off-axis responses is less accurate. Several model enhancement options were introduced to obtain an improved off-axis response. It is shown that the pitch/roll off-axis responses in transient manoeuvres can be improved significantly by including wake geometry distortion effects in the Peters-He finite-state dynamic inflow model.

MARKOV MATHEMATICAL MODEL OF DYNAMIC ADAPTIVE TESTING OF AN ACTIVE AGENT

2018 ◽  
pp. 29-35
Author(s):  
N. V. Brovka ◽  
P. P. Dyachuk ◽  
M. V. Noskov ◽  
I. P. Peregudova
Keyword(s):  
Mathematical Model ◽  
Finite State Machine ◽  
Active Agent ◽  
Adaptive Testing ◽  
State Machine ◽  
Learning Activities ◽  
Independent Learning ◽  
Complex Object ◽  
Total Reward ◽  
Finite State

The problem and the goal.The urgency of the problem of mathematical description of dynamic adaptive testing is due to the need to diagnose the cognitive abilities of students for independent learning activities. The goal of the article is to develop a Markov mathematical model of the interaction of an active agent (AA) with the Liquidator state machine, canceling incorrect actions, which will allow mathematically describe dynamic adaptive testing with an estimated feedback.The research methodologyconsists of an analysis of the results of research by domestic and foreign scientists on dynamic adaptive testing in education, namely: an activity approach that implements AA developmental problem-solving training; organizational and technological approach to managing the actions of AA in terms of evaluative feedback; Markow’s theory of cement and reinforcement learning.Results.On the basis of the theory of Markov processes, a Markov mathematical model of the interaction of an active agent with a finite state machine, canceling incorrect actions, was developed. This allows you to develop a model for diagnosing the procedural characteristics of students ‘learning activities, including: building axiograms of total reward for students’ actions; probability distribution of states of the solution of the problem of identifying elements of the structure of a complex object calculate the number of AA actions required to achieve the target state depending on the number of elements that need to be identified; construct a scatter plot of active agents by target states in space (R, k), where R is the total reward AA, k is the number of actions performed.Conclusion.Markov’s mathematical model of the interaction of an active agent with a finite state machine, canceling wrong actions allows you to design dynamic adaptive tests and diagnostics of changes in the procedural characteristics of educational activities. The results and conclusions allow to formulate the principles of dynamic adaptive testing based on the estimated feedback.

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