In this study, a nondominated-solution-based multi-objective greedy method is proposed and applied to a sensor selection problem based on the multiple indices of the optimal design of experiments. The proposed method simultaneously considers multiple set functions and applies the idea of Pareto ranking for the selection of sets. Specifically, a new index is iteratively added to the nondominated solutions of sets, and the multi-objective functions are evaluated for new sets. The nondominated solutions are selected from the examined solutions, and the next sets are then considered. With this procedure, the multi-objective optimization of multiple set functions can be conducted with reasonable computational costs. This paper defines a new class of greedy algorithms which includes the proposed nondominated-solution-based multi-objective greedy algorithm and the group greedy algorithm, and the characteristics of those algorithms are theoretically discussed. Then, the proposed method is applied to the sensor selection problem and its performance is evaluated. The results of the test case show that the proposed method not only gives the Pareto-optimal front of the multi-objective optimization problem but also produces sets of sensors in terms of D-, A-, and E-optimality, that are superior to the sets selected by pure greedy methods that consider only a single objective function.

Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-invariant, and discrete-time dynamical system are examined under the assumption of independent and identically distributed measurement noise. This study offers two novel selection algorithms, namely an approximate convex relaxation method with the Newton method and a gradient greedy method, and confirms the performance of the selection methods, including a convex relaxation method with semidefinite programming (SDP) and a pure greedy optimization method proposed in the previous studies. The matrix determinant of the observability Gramian was employed for the evaluations of the sensor subsets, while its gradient and Hessian were derived for the proposed methods. In the demonstration using numerical and real-world examples, the proposed approximate greedy method showed superiority in the run time when the sensor numbers were roughly the same as the dimensions of the latent system. The relaxation method with SDP is confirmed to be the most reasonable approach for a system with randomly generated matrices of higher dimensions. However, the degradation of the optimization results was also confirmed in the case of real-world datasets, while the pure greedy selection obtained the most stable optimization results.

Objective functions for sensor selection are investigated in linear time-invariant systems with a large number of sensor candidates. This study compared the performance of sensor sets obtained using three types of D-optimality-based indices as objective functions for sensor selection based on the greedy method. The compared indices are computed based on the snapshot-to-snapshot Fisher information matrix, the observability Gramian and the Kalman filter-based matrix. Both random systems and systems with eigenmodes are considered, indices for selecting the best-performing sensor set for each are identified, as well as computational complexity and corresponding wall clock times. The sensor optimized for each index works best for that index, as expected. We also clarified the trend of the sensor sets selected by the greedy method based on each objective function in terms of the other objective function.

This paper presents the experimental results of separation and reattachment transient flow processes over a NACA0015 airfoil wing when using a plasma actuator for flow control. In addition, it addresses the flow behavior in the transient processes when the flow control device is activated or deactivated, providing insights for future feedback-based active flow control. This approach offers the benefit of enhanced aerodynamic capabilities. The experiments were conducted at a Reynolds number of 66,000 and an angle of attack of 13 degrees for leading-edge separation without control. The plasma actuator was installed on the leading edge of the wing, with a voltage of 8 kV, base frequency of 30 kHz, and burst frequencies ranging from 100 Hz to 600 Hz. Particle image velocimetry was employed for the flow field velocity measurements, and surface pressure data were obtained using eight piezoelectric pressure sensors. The first proper orthogonal decomposition mode of the transient flow velocity field is the focus of this paper and the flow behavior is quantitatively discussed. The results reveal details about the flow separation and reattachment transient processes such as their flow structures and their evolution over time. It is concluded that the time asymmetry between the separation and reattachment transient processes could be leveraged for further improvements to the efficiency of actuators.

Complexity of online computation is a drawback of model predictive control (MPC) when applied to the Navier–Stokes equations. To reduce the computational complexity, we propose a method to approximate the MPC with an explicit control law by using regression analysis. In this paper, we extracted two state-feedback control laws and two output-feedback control laws for flow around a cylinder as a benchmark. The state-feedback control laws that feed back different quantities to each other were extracted by ridge regression, and the two output-feedback control laws, whose measurement output is the surface pressure, were extracted by ridge regression and Gaussian process regression. In numerical simulations, the state-feedback control laws were able to suppress vortex shedding almost completely. While the output-feedback control laws could not suppress vortex shedding completely, they moderately improved the drag of the cylinder. Moreover, we confirmed that these control laws have some degree of robustness to the change in the Reynolds number. The computation times of the control input in all the extracted control laws were considerably shorter than that of the MPC.

This study investigated the effectiveness of a dielectric barrier discharge (DBD) plasma actuator operating in burst-in-burst (BIB) mode for flow separation control on a NACA 0015 airfoil. Time-resolved particle image velocimetry measurements were conducted at a Reynolds number of 66,000 and 13° angle of attack. Various BIB signal configurations were tested, with actuation periods of 70 ms and 150 ms, non-actuation periods ranging from 5 ms to 50 ms, and burst frequencies of 300 Hz and 600 Hz. Proper orthogonal decomposition was applied to analyze the flow field dynamics. The results showed that BIB actuation maintained flow attachment with reduced power consumption compared with continuous burst actuation. However, the effectiveness was highly sensitive to the BIB parameters, with some configurations failing to achieve consistent reattachment and becoming unstable. This study reveals complex interactions between actuation vortices and separation processes, highlighting both the potential and challenges of intermittent plasma actuation for efficient flow control.