Unsteady large-scale wake structure behind levitated freestream-aligned circular cylinder

The relationships between characteristic large-scale wake structures appearing behind a freestream-aligned circular cylinder are investigated and discussed from the velocity field obtained by wind tunnel tests. The tests were conducted under a supportless condition using a magnetic suspension and balance system and stereo PIV measurements at a Reynolds number of $3.46\times10^4$. The velocity fields were analysed with a modal decomposition combining azimuthal Fourier decomposition and proper orthogonal decomposition. The wake behind the freestream-aligned circular cylinder with three different fineness ratios of 1.0, 1.5 and 2.0 was investigated, and the wake structures in a nonreattaching flow formed by the cylinder at the fineness ratio of 1.0 are mainly discussed in the present study. Four characteristic large-scale wake structures of recirculation bubble pumping, azimuthal shear mode, large-scale vortex shedding and streaks are identified and mainly focused on in the present study. The state of the vortex shedding is classified into three; anticlockwise/clockwise circular and flapping patterns. Each state has a relationship with the azimuthal shear mode and it tends to appear when the state is circular. Furthermore, from the analysis of the relationship between modes, the recirculation bubble pumping is found to be related to the vortex shedding position in the radial direction and the strength of the streaks. Particularly, analysis of causality shows that the recirculation bubble pumping is affected by them in the low-frequency range

Dynamic mode decomposition using a Kalman filter for parameter estimation

A novel dynamic mode decomposition (DMD) method based on a Kalman filter is proposed. This paper explains the fast algorithm of the proposed Kalman filter DMD (KFDMD) in combination with truncated proper orthogonal decomposition for many-degree-of-freedom problems. Numerical experiments reveal that KFDMD can estimate eigenmodes more precisely compared with standard DMD or total least-squares DMD (tlsDMD) methods for the severe noise condition if the nature of the observation noise is known, though tlsDMD works better than KFDMD in the low and medium noise level. Moreover, KFDMD can track the eigenmodes precisely even when the system matrix varies with time similar to online DMD, and this extension is naturally conducted owing to the characteristics of the Kalman filter. In summary, the KFDMD is a promising tool with strong antinoise characteristics for analyzing sequential datasets

A Comparative Study on Evaluation Methods of Fluid Forces on Cartesian Grids

We investigate the accuracy and the computational efficiency of the numerical schemes for evaluating fluid forces in Cartesian grid systems. A comparison is made between two different types of schemes, namely, polygon-based methods and mesh-based methods, which differ in the discretization of the surface of the object. The present assessment is intended to investigate the effects of the Reynolds number, the object motion, and the complexity of the object surface. The results show that the mesh-based methods work as well as the polygon-based methods, even if the object surface is discretized in a staircase manner. In addition, the results also show that the accuracy of the mesh-based methods is strongly dependent on the evaluation of shear stresses, and thus they must be evaluated by using a reliable method, such as the ghost-cell or ghost-fluid method

Compressibility effects on Flat-Plates with Serrated Leading-Edges at a Low Reynolds Number

This study evaluates the influence of a serrated leading edge on flat-plate aerodynamics at low-Reynolds-number and subsonic high-Mach-number conditions. Forces are measured for a Mach number ranging from 0.2 to 0.64 at aReynolds number of (12,000 ± 1000). Pressure distributions are obtained under the same conditions using pressure sensitive paint (PSP) measurement. Three models are tested: a flat plate without serrations used as the baseline case and two flat plates with serrated leading edges of different wavelength-to-amplitude ratios. Results show that the aerodynamic performance of flat plates with serrations is slightly changed from the baseline case. The plate with short-wavelength serrations underperforms in terms of the lift-to-drag ratio under all the conditions compared to the baseline case while the plate with large-wavelength serrations slightly outperforms it at around the stall angle. The Mach number has little effect on the attached flow while the lift increases with the Mach number under deep stall conditions. Serrations maintain the lift even under high angle of attack conditions when Mach number varies. The twodimensional pressure distributions and the analyses of local chordwise pressure coefficient distributions at different spanwise locations and of periodicity of spanwise pressure coefficients allow categorisation of the complex flow structures into three types. These configurations feature different types of low pressure regions downstream of troughs. The periodicity of the pattern depends not only on the angle of attack but also on the Mach number

Fast Data-driven Greedy Sensor Selection for Ridge Regression

We propose a data-driven sensor-selection algorithm for accurate estimation of the target variables from the selected measurements. The target variables are assumed to be estimated by a ridge-regression estimator which is trained based on the data. The proposed algorithm greedily selects sensors for minimization of the cost function of the estimator. Sensor selection which prevents the overfitting of the resulting estimator can be realized by setting a positive regularization parameter. The greedy solution is computed in quite a short time by using some recurrent relations that we derive. Furthermore, we show that sensor selection can be accelerated by dimensionality reduction of the target variables without large deterioration of the estimation performance. The effectiveness of the proposed algorithm is verified for two real-world datasets. The first dataset is a dataset of sea surface temperature for sensor selection for reconstructing large data, and the second is a dataset of surface pressure distribution and yaw angle of a ground vehicle for sensor selection for estimation. The experiments reveal that the proposed algorithm outperforms some data-drive selection algorithms including the orthogonal matching pursuit

Coupled Simulation of Shock Waves in Gas-Particle Mixtures Introducing Motion Equations

In this work, direct numerical analyses for flow around particles passing a shock wave was carried out to predict effects of small particles in rocket plumes. A flow solver based on three-dimensional compressible Navier-Stokes equations is developed for the purpose of high accurate prediction of the acoustic field around rocket plumes. This flow solver is capable of analysing a flow around moving multiple particles and motion equations was introduced. The flow field and the drag coefficient after the shock wave passage were validated by comparing with the drag models at shock Mach number 1.2-2.8. The result was in good agreement with the drag models. In the flow around multiple particles, the interference between particles was confirmed

Randomized Group-Greedy Method for Large-Scale Sensor Selection Problems

The randomized group-greedy method and its customized method for large-scale sensor selection problems are proposed. The randomized greedy sensor selection algorithm is applied straightforwardly to the group-greedy method, and a customized method is also considered. In the customized method, a part of the compressed sensor candidates is selected using the common greedy method or other low-cost methods. This strategy compensates for the deterioration of the solution due to compressed sensor candidates. The proposed methods are implemented based on the D- and E-optimal design of experiments, and numerical experiments are conducted using randomly generated sensor candidate matrices with potential sensor locations of 10,000--1,000,000. The proposed method can provide better optimization results than those obtained by the original group-greedy method when a similar computational cost is spent as for the original group-greedy method. This is because the group size for the group-greedy method can be increased as a result of the compressed sensor candidates by the randomized algorithm. Similar results were also obtained in the real dataset. The proposed method is effective for the E-optimality criterion, in which the objective function that the optimization by the common greedy method is difficult due to the absence of submodularity of the objective function. The idea of the present method can improve the performance of all optimizations using a greedy algorithm