Numerical Study on Interactional Aerodynamics of a Quadcopter in Hover with Overset Mesh in OpenFOAM

Interactional aerodynamics of a quadcopter in hover is numerically investigated in this study. The main objective is to understand major flow structures associated with unsteady airloads on multirotor aircraft. The overset mesh approach is used to resolve flow structures in unsteady simulation using the flow solver OpenFOAM. The current computational study demonstrates that aerodynamic interaction between quadcopter components strongly affects the rotor wake, generating interesting vortical structures. Multiple rotors in close proximity generate $\Omega$-shaped vortical structures merged from rotor-tip vortices. The fuselage of the current quadcopter deflects the wake flow of the four rotors towards the center of the vehicle. Such interactional aerodynamics, i.e., rotor-rotor and rotor-fuselage interaction, varies the inflow condition of a rotor blade during the rotor revolution. Therefore, the quadcopter experiences unsteady airloads per rotor revolution. Our study indicates that a typical quadcopter would experience 8/rev thrust variations, which are a combined outcome from 4/rev thrust variations on the rotor and 2/rev fluctuations on the fuselage. The current understanding of interactional aerodynamics could help to design reliable and efficient multicopter aircraft

Physical interpretation of neural network-based nonlinear eddy viscosity models

Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high--fidelity data. However, the interpretability of the learned model is often not fully analyzed, which has been one of the main criticism of neural network-based turbulence modeling. Therefore, it is increasingly demanding to provide physical interpretation of the trained model, which is of significant interest for guiding the development of interpretable and unified turbulence models. The present work aims to interpret the predictive improvement of turbulence flows based on the behavior of the learned model, represented with tensor basis neural networks. The ensemble Kalman method is used for model learning from sparse observation data due to its ease of implementation and high training efficiency. Two cases, i.e., flow over the S809 airfoil and flow in a square duct, are used to demonstrate the physical interpretation of the ensemble-based turbulence modeling. For the flow over the S809 airfoil, our results show that the ensemble Kalman method learns an optimal linear eddy viscosity model, which improves the prediction of the aerodynamic lift by reducing the eddy viscosity in the upstream boundary layer and promoting the early onset of flow separation. For the square duct case, the method provides a nonlinear eddy viscosity model, which predicts well secondary flows by capturing the imbalance of the Reynolds normal stresses. The flexibility of the ensemble-based method is highlighted to capture characteristics of the flow separation and secondary flow by adjusting the nonlinearity of the turbulence model

Computations of Combustion-Powered Actuation for Dynamic Stall Suppression

A computational framework for the simulation of dynamic stall suppression with combustion-powered actuation (COMPACT) is validated against wind tunnel experimental results on a VR-12 airfoil. COMPACT slots are located at 10% chord from the leading edge of the airfoil and directed tangentially along the suction-side surface. Helicopter rotor-relevant flow conditions are used in the study. A computationally efficient two-dimensional approach, based on unsteady Reynolds-averaged Navier-Stokes (RANS), is compared in detail against the baseline and the modified airfoil with COMPACT, using aerodynamic forces, pressure profiles, and flow-field data. The two-dimensional RANS approach predicts baseline static and dynamic stall very well. Most of the differences between the computational and experimental results are within two standard deviations of the experimental data. The current framework demonstrates an ability to predict COMPACT efficacy across the experimental dataset. Enhanced aerodynamic lift on the downstroke of the pitching cycle due to COMPACT is well predicted, and the cycleaveraged lift enhancement computed is within 3% of the test data. Differences with experimental data are discussed with a focus on three-dimensional features not included in the simulations and the limited computational model for COMPACT

Combustion-Powered Actuation for Dynamic Stall Suppression - Simulations and Low-Mach Experiments

An investigation on dynamic-stall suppression capabilities of combustion-powered actuation (COMPACT) applied to a tabbed VR-12 airfoil is presented. In the first section, results from computational fluid dynamics (CFD) simulations carried out at Mach numbers from 0.3 to 0.5 are presented. Several geometric parameters are varied including the slot chordwise location and angle. Actuation pulse amplitude, frequency, and timing are also varied. The simulations suggest that cycle-averaged lift increases of approximately 4% and 8% with respect to the baseline airfoil are possible at Mach numbers of 0.4 and 0.3 for deep and near-deep dynamic-stall conditions. In the second section, static-stall results from low-speed wind-tunnel experiments are presented. Low-speed experiments and high-speed CFD suggest that slots oriented tangential to the airfoil surface produce stronger benefits than slots oriented normal to the chordline. Low-speed experiments confirm that chordwise slot locations suitable for Mach 0.3-0.4 stall suppression (based on CFD) will also be effective at lower Mach numbers

Physical interpretation of neural network-based nonlinear eddy viscosity models

Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high fidelity data. However, the interpretability of the learned model is often not fully analyzed, which has been one of the main criticisms of neural network-based turbulence modeling. Therefore, it is increasingly demanding to provide physical interpretation of the trained model, which is of significant interest for guiding the development of interpretable and unified turbulence models. The present work aims to interpret the predictive improvement of turbulence flows based on the behavior of the learned model, represented with tensor basis neural networks. The ensemble Kalman method is used for model learning from sparse observation data due to its ease of implementation and high training efficiency. Two cases, i.e., flow over the S809 airfoil and flow in a square duct, are used to demonstrate the physical interpretation of the ensemble-based turbulence modeling. For the flow over the S809 airfoil, our results show that the ensemble Kalman method learns an optimal linear eddy viscosity model, which improves the prediction of the aerodynamic lift by reducing the eddy viscosity in the upstream boundary layer and promoting the early onset of flow separation. For the square duct case, the method provides a nonlinear eddy viscosity model, which predicts well secondary flows by capturing the imbalance of the Reynolds normal stresses. The flexibility of the ensemble-based method is highlighted to capture characteristics of the flow separation and secondary flow by adjusting the nonlinearity of the turbulence model.(c) 2023 Elsevier Masson SAS. All rights reserved

Physical interpretation of neural network-based nonlinear eddy viscosity models

Neural network-based turbulence modeling has gained significant success in improving turbulence predictions by incorporating high fidelity data. However, the interpretability of the learned model is often not fully analyzed, which has been one of the main criticisms of neural network-based turbulence modeling. Therefore, it is increasingly demanding to provide physical interpretation of the trained model, which is of significant interest for guiding the development of interpretable and unified turbulence models. The present work aims to interpret the predictive improvement of turbulence flows based on the behavior of the learned model, represented with tensor basis neural networks. The ensemble Kalman method is used for model learning from sparse observation data due to its ease of implementation and high training efficiency. Two cases, i.e., flow over the S809 airfoil and flow in a square duct, are used to demonstrate the physical interpretation of the ensemble-based turbulence modeling. For the flow over the S809 airfoil, our results show that the ensemble Kalman method learns an optimal linear eddy viscosity model, which improves the prediction of the aerodynamic lift by reducing the eddy viscosity in the upstream boundary layer and promoting the early onset of flow separation. For the square duct case, the method provides a nonlinear eddy viscosity model, which predicts well secondary flows by capturing the imbalance of the Reynolds normal stresses. The flexibility of the ensemble-based method is highlighted to capture characteristics of the flow separation and secondary flow by adjusting the nonlinearity of the turbulence model.(c) 2023 Elsevier Masson SAS. All rights reserved

Micro-Viscometer for Measuring Shear-Varying Blood Viscosity over a Wide-Ranging Shear Rate

In this study, a micro-viscometer is developed for measuring shear-varying blood viscosity over a wide-ranging shear rate. The micro-viscometer consists of 10 microfluidic channel arrays, each of which has a different micro-channel width. The proposed design enables the retrieval of 10 different shear rates from a single flow rate, thereby enabling the measurement of shear-varying blood viscosity with a fixed flow rate condition. For this purpose, an optimal design that guarantees accurate viscosity measurement is selected from a parametric study. The functionality of the micro-viscometer is verified by both numerical and experimental studies. The proposed micro-viscometer shows 6.8% (numerical) and 5.3% (experimental) in relative error when compared to the result from a standard rotational viscometer. Moreover, a reliability test is performed by repeated measurement (N = 7), and the result shows 2.69 ± 2.19% for the mean relative error. Accurate viscosity measurements are performed on blood samples with variations in the hematocrit (35%, 45%, and 55%), which significantly influences blood viscosity. Since the blood viscosity correlated with various physical parameters of the blood, the micro-viscometer is anticipated to be a significant advancement for realization of blood on a chip

Numerical Investigation of Jet Angle Effect on Airfoil Stall Control

Numerical study on flow separation control is conducted for a stalled airfoil with steady-blowing jet. Stall conditions relevant to a rotorcraft are of interest here. Both static and dynamic stalls are simulated with solving compressible Reynolds-averaged Navier-Stokes equations. It is expected that a jet flow, if it is applied properly, provides additional momentum in the boundary layer which is susceptible to flow separation at high angles of attack. The jet angle can influence on the augmentation of the flow momentum in the boundary layer which helps to delay or suppress the stall. Two distinct jet angles are selected to investigate the impact of the jet angle on the control authority. A tangential jet with a shallow jet angle to the surface is able to provide the additional momentum to the flow, whereas a chord-normal jet with a large jet angle simply averts the external flow. The tangential jet reduces the shape factor of the boundary layer, lowering the susceptibility to the flow separation and delaying both the static and dynamic stalls