arXiv subjects

Keisuke Asai

Publications and source records attributed to Keisuke Asai.

10 recordsLinked to original sources

arXiv2025

Computational and Experimental Comparison of CLF5605 and roamx-0201 Martian Helicopter Rotor Airfoils

This study compares aerodynamic performance of the CLF5605 rotor airfoil -- which flew on Ingenuity from 2021 to 2024 -- with that of a new optimized roamx-0201 airfoil designed for Martian conditions at NASA Ames. Specifically, performance is studied at a Reynolds number of 20,000 and a Mach number of 0.60, across a range of angles of attack, using three independent state-of-the-art methodologies: implicit large eddy simulations (ILES) using NASA's OVERFLOW solver, direct numerical simulations (DNS) using the high-order GPU-accelerated PyFR solver, and experimental testing in the Mars Wind Tunnel at Tohoku University. Discrepancies between results obtain using the various methodologies are analyzed and explained. Across all methodologies it can be seen that the roamx-0201 airfoil is able to achieve a given lift with less drag compared to the CLF5605 airfoil. Moreover, OVERFLOW and PyFR results show that the roamx-0201 airfoil has superior stall characteristics, and can achieve a maximum lift ~20% higher than that achieved by the CLF5605 airfoil. The work provides a strong body of evidence to support further studies into use of rotors based on the optimized roamx-0201 airfoil for future Mars helicopter missions.

physics.flu-dyn↗

arXiv2022

Proof-of-concept Study of Sparse Processing Particle Image Velocimetry for Real Time Flow Observation

In this paper, we overview, evaluate, and demonstrate the sparse processing particle image velocimetry (SPPIV) as a real-time flow field estimation method using the particle image velocimetry (PIV), whereas SPPIV was previously proposed with its feasibility study and its real-time demonstration is conducted for the first time in this study. In the wind tunnel test, the PIV measurement and real-time measurement using SPPIV were conducted for the flow velocity field around the NACA0015 airfoil model. The off-line analysis results of the test show that the flow velocity field can be estimated from a small number of processing points by applying SPPIV, and also illustrates the following characteristics of SPPIV. The estimation accuracy improves as the number of processing points increases, whereas the processing time per step increases in proportion to the number of processing points. Therefore, it is necessary to set an optimal number of processing points. In addition, the application of the Kalman filter significantly improves the estimation accuracy with a small number of processing points while suppressing the processing time. When the flow velocity fields with different angles of attack are used as the training data with that of test data, the estimation using SPPIV is found to be reasonable if the difference in angle of attack between the training and test data is equal to or less than 2 deg and the flow phenomena of the training data are similar to that of the test data. For this reason, training data should be prepared at least every 4 deg. Finally, the demonstration of SPPIV as a real-time flow observation was conducted for the first time. In this demonstration, the real-time measurement is found to be possible at a sampling rate of 2000 Hz at 20 or less processing points in the top 10 modes estimation as expected by the off-line analyses.

eess.SP↗

arXiv2022

Greedy Sensor Selection for Weighted Linear Least Squares Estimation under Correlated Noise

Optimization of sensor selection has been studied to monitor complex and large-scale systems with data-driven linear reduced-order modeling. An algorithm for greedy sensor selection is presented under the assumption of correlated noise in the sensor signals. A noise model is given using truncated modes in reduced-order modeling, and sensor positions that are optimal for generalized least squares estimation are selected. The determinant of the covariance matrix of the estimation error is minimized by efficient one-rank computations in both underdetermined and overdetermined problems. The present study also reveals that the objective function with correlated noise is neither submodular nor supermodular. Several numerical experiments are conducted using randomly generated data and real-world data. The results show the effectiveness of the selection algorithm in terms of accuracy in the estimation of the states of large-dimensional measurement data.

eess.SP↗

arXiv2022

Optimization of Sparse Sensor Placement for Estimation of Wind Direction and Surface Pressure Distribution Using Time-Averaged Pressure-Sensitive Paint Data on Automobile Model

This study proposes a method for predicting the wind direction against the simple automobile model (Ahmed model) and the surface pressure distributions on it by using data-driven optimized sparse pressure sensors. Positions of sparse pressure sensor pairs on the Ahmed model were selected for estimation of the yaw angle and reconstruction of pressure distributions based on the time-averaged surface pressure distributions database of various yaw angles, whereas the symmetric sensors in the left and right sides of the model were assumed. The surface pressure distributions were obtained by pressure-sensitive paint measurements. Three algorithms for sparse sensor selection based on the greedy algorithm were applied, and the sensor positions were optimized. The sensor positions and estimation accuracy of yaw angle and pressure distributions of three algorithms were compared and evaluated. The results show that a few optimized sensors can accurately predict the yaw angle and the pressure distributions.

physics.flu-dyn↗

arXiv2021

Determinant-based Fast Greedy Sensor Selection Algorithm

In this paper, the sparse sensor placement problem for least-squares estimation is considered, and the previous novel approach of the sparse sensor selection algorithm is extended. The maximization of the determinant of the matrix which appears in pseudo-inverse matrix operations is employed as an objective function of the problem in the present extended approach. The procedure for the maximization of the determinant of the corresponding matrix is proved to be mathematically the same as that of the previously proposed QR method when the number of sensors is less than that of state variables (undersampling). On the other hand, the authors have developed a new algorithm for when the number of sensors is greater than that of state variables (oversampling). Then, a unified formulation of the two algorithms is derived, and the lower bound of the objective function given by this algorithm is shown using the monotone submodularity of the objective function. The effectiveness of the proposed algorithm on the problem using real datasets is demonstrated by comparing with the results of other algorithms. The numerical results show that the proposed algorithm improves the estimation error by approximately 10% compared with the conventional methods in the oversampling case, where the estimation error is defined as the ratio of the difference between the reconstructed data and the full observation data to the full observation. For the NOAA-SST sensor problem, which has more than ten thousand sensor candidate points, the proposed algorithm selects the sensor positions in few seconds, which required several hours with the other algorithms in the oversampling case on a 3.40 GHz computer.

eess.SP↗

arXiv2021

Fast Greedy Optimization of Sensor Selection in Measurement with Correlated Noise

A greedy algorithm is proposed for sparse-sensor selection in reduced-order sensing that contains correlated noise in measurement. The sensor selection is carried out by maximizing the determinant of the Fisher information matrix in a Bayesian estimation operator.The Bayesian estimation with a covariance matrix of the measurement noise and a prior probability distribution of estimating parameters, which are given by the modal decomposition of high dimensional data, robustly works even in the presence of the correlated noise. After computational efficiency of the algorithm is improved by a low-rank approximation of the noise covariance matrix, the proposed algorithms are applied to various problems. The proposed method yields more accurate reconstruction than the previously presented method with the determinant-based greedy algorithm, with reasonable increase in computational time.

math.OC↗

arXiv2021

Quantitative Evaluation of a Linear Reduced-order Model based on Particle-image-velocimetry Data of Separated Flow Field around Airfoil

A quantitative evaluation method for a reduced-order model of the flow field around an NACA0015 airfoil based on particle image velocimetry (PIV) data is proposed in the present paper. The velocity field data obtained by the time-resolved PIV measurement were decomposed into significant modes by a proper orthogonal decomposition (POD) technique, and a linear reduced-order model was then constructed by the linear regression of the time advancement of the POD modes or the sparsity promoting dynamic mode decomposition (DMD). The present evaluation method can be used for the evaluation of the estimation error and the model predictability. The model was constructed using different numbers of POD or DMD modes for order reduction of the fluid data and different methods of estimating the linear coefficients, and the effects of these conditions on the model performance were quantitatively evaluated. The results illustrates that forward (standard) model works the best with two to ten significant DMD modes selected by sparsity promoting DMD.

physics.flu-dyn↗

arXiv2020

Effect of Oxygen Mole Fraction on Static Properties of Pressure-Sensitive Paint

The effects of oxygen mole fraction on the static properties of pressure-sensitive paint (PSP) were investigated. Sample coupon tests using a calibration chamber were conducted for polymer-based PSP (PHFIPM-PSP), polymer/ceramic PSP (PC-PSP), and anodized-aluminium PSP (AA-PSP). The oxygen mole fraction was set to be between 0.1-100% and the ambient pressure was set to be between 0.5-140 kPa. The localized Stern-Volmer coefficient $B_{\rm local}$ once increases and then decreases as the oxygen mole fraction increases. The value of $B_{\rm local}$ depends on both ambient pressure and oxygen mole fraction, but the effect of this parameter can be characterized as a function of the partial pressure of oxygen. The value of $B_{\rm local}$ of AA-PSP and PHFIPM-PSP, which are low-pressure type and relatively low-pressure type PSP, have a peak at the relatively low partial pressure of oxygen, and $B_{\rm local}$ of PC-PSP, which are atmospheric pressure type PSP, has a peak at the relatively high partial pressure of oxygen. The peak of the intensity change with respect to pressure fluctuation proportional to the ambient pressure $S_{\mathcal{PR}}$ appears at the lower partial pressure of oxygen than that of $B_{\rm local}$. This is because the intensity of PSP becomes quite low at the high partial pressure of oxygen even if $B_{\rm local}$ is higher. Hence, an optimal partial oxygen mole fraction exists depending on the type of PSP and ambient pressure range of the experiment, and its optimal value can be found based on the partial pressure of oxygen.

physics.chem-ph↗

arXiv2020

Data-driven Vector-measurement-sensor Selection based on Greedy Algorithm

A vector-measurement-sensor problem for the least squares estimation is considered, by extending a previous novel approach in this paper. An extension of the vector-measurement-sensor selection of the greedy algorithm is proposed and is applied to particle-image-velocimetry data to reconstruct the full state based on the information given by sparse vector-measurement sensors.

eess.SP↗

arXiv2017

Airfoil wake modification with Gurney flap at Low-Reynolds number

The complex wake modifications produced by a Gurney flap on symmetric NACA airfoils at low Reynolds number are investigated. Two-dimensional incompressible flows over NACA 0000 (flat plate), 0006, 0012 and 0018 airfoils at a Reynolds number of $Re = 1000$ are analyzed numerically to examine the flow modifications generated by the flaps for achieving lift enhancement. While high lift can be attained by the Gurney flap on airfoils at high angles of attack, highly unsteady nature of the aerodynamic forces are also observed. Analysis of the wake structures along with the lift spectra reveals four characteristic wake modes (steady, 2S, P and 2P), influencing the aerodynamic performance. The effects of the flap over wide range of angles of attack and flap heights are considered to identify the occurrence of these wake modes, and are encapsulated in a wake classification diagram. Companion three-dimensional simulations are also performed to examine the influence of three-dimensionality on the wake regimes. The spanwise instabilities that appear for higher angles of attack are found to suppress the emergence of the 2P mode. The use of the wake classification diagram as a guidance for Gurney flap selection at different operating conditions to achieve the required aerodynamic performance is discussed.

physics.flu-dyn↗