scholarly journals Estimation of unsteady aerodynamic forces using pointwise velocity data

2016 ◽  
Vol 804 ◽  
Author(s):  
F. Gómez ◽  
A. S. Sharma ◽  
H. M. Blackburn
Keyword(s):  
Aerodynamic Force ◽  
Mean Flow ◽  
Time Resolved ◽  
Time Stepping ◽  
Unsteady Aerodynamic ◽  
Novel Method ◽  
Unsteady Aerodynamic Force ◽  
Lift And Drag ◽  
Matrix Free ◽  
Good Agreement

A novel method to estimate unsteady aerodynamic force coefficients from pointwise velocity measurements is presented. As opposed to other existing methodologies, time-resolved full velocity fields are not required. The methodology is based on a resolvent-based reduced-order model which requires the mean flow to obtain physical flow structures and pointwise measurement to calibrate their amplitudes. A computationally affordable time-stepping methodology to obtain resolvent modes in non-trivial flow domains is introduced and compared with previous existing matrix-free and matrix-forming strategies. The technique is applied to the unsteady flow around an inclined square cylinder at low Reynolds number. The potential of the methodology is demonstrated through good agreement between the fluctuating pressure distribution on the cylinder and the temporal evolution of the unsteady lift and drag coefficients predicted by the model and those computed by direct numerical simulation.

Unsteady aerodynamic force generation by a model fruit fly wing in flapping motion

2002 ◽  
Vol 205 (1) ◽  
pp. 55-70 ◽  
Author(s):  
Mao Sun ◽  
Jian Tang
Keyword(s):  
Aerodynamic Force ◽  
Lift Coefficient ◽  
Unsteady Aerodynamics ◽  
Fruit Fly ◽  
Force Generation ◽  
Generation Process ◽  
Flapping Motion ◽  
Unsteady Aerodynamic ◽  
The Mean ◽  
Unsteady Aerodynamic Force

SUMMARY A computational fluid-dynamic analysis was conducted to study the unsteady aerodynamics of a model fruit fly wing. The wing performs an idealized flapping motion that emulates the wing motion of a fruit fly in normal hovering flight. The Navier–Stokes equations are solved numerically. The solution provides the flow and pressure fields, from which the aerodynamic forces and vorticity wake structure are obtained. Insights into the unsteady aerodynamic force generation process are gained from the force and flow-structure information. Considerable lift can be produced when the majority of the wing rotation is conducted near the end of a stroke or wing rotation precedes stroke reversal (rotation advanced), and the mean lift coefficient can be more than twice the quasi-steady value. Three mechanisms are responsible for the large lift: the rapid acceleration of the wing at the beginning of a stroke, the absence of stall during the stroke and the fast pitching-up rotation of the wing near the end of the stroke. When half the wing rotation is conducted near the end of a stroke and half at the beginning of the next stroke (symmetrical rotation), the lift at the beginning and near the end of a stroke becomes smaller because the effects of the first and third mechanisms above are reduced. The mean lift coefficient is smaller than that of the rotation-advanced case, but is still 80 % larger than the quasi-steady value. When the majority of the rotation is delayed until the beginning of the next stroke (rotation delayed), the lift at the beginning and near the end of a stroke becomes very small or even negative because the effect of the first mechanism above is cancelled and the third mechanism does not apply in this case. The mean lift coefficient is much smaller than in the other two cases.

Study on Unsteady Aerodynamic Force of Turbine Blade With Different Mass Flow

2008 ◽  
Author(s):  
Jibing Lan ◽  
Yonghui Xie ◽  
Di Zhang
Keyword(s):  
Turbine Blade ◽  
Mass Flow ◽  
Aerodynamic Force ◽  
Incidence Angle ◽  
Wake Flow ◽  
Unsteady Aerodynamic ◽  
Design Case ◽  
Excitation Force ◽  
Unsteady Aerodynamic Force ◽  
Flow Case

The traditional turbomachinery design systems are always based on the assumption of steady or quasi-steady flows. However, unsteady flows such as wake flow, separated flow and shedding vortices are the main factors inducing the excitation force on turbine blade which leads to high cycle fatigue failure of blade. In this paper, the three-dimensional, time dependent, Reynolds-Averaged Navier-Stokes (RANS) equations were resolved using a commercial program CFX based on finite volume method. The unsteady flow fields of three mass flow cases (design case, 110% design mass flow and 85% design mass flow) in a one-and-a-half stage axial turbine (stator/rotor/stator) were investigated in detail and then the unsteady aerodynamic force on the rotational blade was obtained. Frequencies of unsteady disturbances and excitation force factors were obtained by spectrum analysis. It can be seen clearly that the excitation factors at 110% mass flow case are larger than that at the design case. On the other side, the unsteady aerodynamic force on the rotational blade at 85% mass flow case is quite different from the design case. There are two peaks during a stator passing period and the dominate frequency of the tangential blade force is 6000Hz due to large amount of negative incidence angle. The 6000Hz component tangential aerodynamic force amplitude is 6.533N, which is 5.93 times of that at design case and 2.92 times of that at 110% mass flow case. Because of the large amplitude, the unsteady aerodynamic force at small mass flow case is necessary to be taken into account in the forced vibration analysis of blade.

scholarly journals Study on the Intelligent Modeling of the Blade Aerodynamic Force in Compressors Based on Machine Learning

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 476
Author(s):  
Mingming Zhang ◽  
Shurong Hao ◽  
Anping Hou
Keyword(s):  
Machine Learning ◽  
Aerodynamic Force ◽  
Three Dimensional ◽  
Learning Model ◽  
Aerodynamic Load ◽  
Blade Vibration ◽  
Unsteady Aerodynamic ◽  
Machine Learning Model ◽  
Unsteady Aerodynamic Force ◽  
Intelligent Model

In order to obtain the aerodynamic loads of the vibrating blades efficiently, the eXterme Gradient Boosting (XGBoost) algorithm in machine learning was adopted to establish a three-dimensional unsteady aerodynamic force reduction model. First, the database for the unsteady aerodynamic response during the blade vibration was acquired through the numerical simulation of flow field. Then the obtained data set was trained by the XGBoost algorithm to set up the intelligent model of unsteady aerodynamic force for the three-dimensional blade. Afterwards, the aerodynamic load could be gained at any spatial location during blade vibration. To evaluate and verify the reliability of the intelligent model for the blade aerodynamic load, the prediction results of the machine learning model were compared with the results of Computation Fluid Dynamics (CFD). The determination coefficient R2 and the Root Mean Square Error (RMSE) were introduced as the model evaluation indicators. The results show that the prediction results based on the machine learning model are in good agreement with the CFD results, and the calculation efficiency is significantly improved. The results also indicate that the aerodynamic intelligent model based on the machine learning method is worthy of further study in evaluating the blade vibration stability.

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