Tohoku University · Engineering
Professor Yuichi Kuya's research lab specializes in experimental and computational fluid dynamics, with a focus on flow control and aerodynamic optimization in complex configurations such as racing car wings and turbulent boundary layers. The lab investigates vortex generator applications for separation control in ground effect flows, combining wind tunnel experiments, surface and off-surface flow measurements, and high-fidelity simulations. Innovative approaches such as multifidelity surrogate modeling and quantum annealing-based lattice-gas automata are also explored to enhance computational efficiency and physical fidelity in flow prediction. The lab emphasizes interdisciplinary methods, integrating design of experiments, advanced diagnostics, and emerging quantum computing techniques for fluid dynamics.
Figures are computed from collected data and may differ slightly.
This study presents a multifidelity surrogate modeling approach, combining experimental and computational aerodynamic data sets. A multifidelity cokriging regression surrogate model is used. This study highlights how lowfidelity data from computations contribute to improving surrogate models built with limited high-fidelity data from experiments. Various types of sampling design for low fidelity data are also examined to study the impact of characteristics of the sampling design on the final sur
Flow separation control using vortex generators on an inverted wing in ground effect is experimentally investigated, and its performance is characterized in terms of forces and pressure distributions over a range of incidence and ride height. Counter-rotating and co-rotating rectangular-vane type vortex generators are tested on the suction surface of the wing. The effect of device height and spacing is investigated. The counter-rotating sub-boundary layer vortex generators and counter-rotating l
This paper experimentally investigates the use of vortex generators for separation control on an inverted wing in ground effect using off-surface flow measurements and surface flow visualization. A typical racing car wing geometry is tested in a rolling road wind tunnel over a wide range of incidences and ride heights. Rectangular vane type of sub-boundary layer and large-scale vortex generators are attached to the suction surface, comprising counter-rotating and corotating configurations. The e
Vortex generators can be applied to control separation in flows with adverse pressure gradients, such as wings. In this paper, a study using three-dimensional steady computations for an inverted wing with vortex generators in ground effect is described. The main aim is to provide understanding of the flow physics of the vortex generators, and how they affect the overall aerodynamic performance of the wing to complement previous experimental studies of the same configuration. Rectangular vane typ
This study proposes a quantum annealing-based algorithm for flow computation based on lattice-gas automata (LGA). Since the state of the lattice gas is determined by Boolean variables, 0 (absence) or 1 (presence), in LGA, it is well suited for implementation in quantum annealing and simulated annealing computers. The quantum annealing-based algorithm proposed in this study is constructed so that conservation of mass and momentum is satisfied at the particle collision process. Verification tests
This paper presents an experimental study of film cooling on a flat surface using swirling jets through a row of discrete holes of 45° along streamwise direction. Experiments were conducted in a low speed wind tunnel and swirling jet was made by a twisted tape set in the injectant path. A thermal probe with thermo-couples was used to measure both film cooling effectiveness and dimensionless temperature. Three pairs of swirling jets were used, i.e., "clockwise-counterclockwise", "counterclockwise
In this study multi-fidelity surrogate modelling for combining data sets of wind tunnel experiments and computations is examined, dealing with different types of errors. Co- kriging regression is constructed with the low-fidelity sample data of the computations and the high-fidelity data of the wind tunnel experiments, and is compared with co-kriging and polynomial response surface approaches. Face-centred central composite design is used to obtain the high-fidelity sample data for the co-krigin
Open papers in the app to read, cite, and organize with AI.
