In this research, a proper orthogonal decomposition (POD)-based re-parameterization approach is utilized to realize efficient multi-objective aerodynamic shape optimization (MOASO) by reducing the number of design variables, which results in reducing the number of computational fluid dynamics evaluations. The approach developed is examined in a three-dimensional wing-shape optimization problem of a supersonic transport configuration. As for the multi-objective design optimization problem, aerodynamic/sonic boom evaluations are performed to minimize the drag coefficient and sonic boom strength. By introducing a variable fidelity concept in the POD approach, the optimization problem can be solved with much smaller computational cost. Important design insights can be extracted and discussed from the most dominant (first) POD mode.

Combustion oscillation in a rocket combustor was analyzed using a Variational Auto-Encoder (VAE). Dimension reductions were conducted on various physical quantities (p′, ˙q′, etc.) in the combustor. Orthogonal modes were obtained using proper orthogonal decomposition for the output of VAE. Pumping and damping regions in the combustor were identified using the distribution of the Rayleigh Index for each mode. Three characteristic modes were identified that supply energy to the oscillation. A phase plane was spanned by the latent variables obtained by the reductions. Activation of each of the characteristic six modes was mapped on the plane. The three characteristic modes were found to be activated at different phases. From the trajectory of the oscillation on the plane, the plane was confirmed to satisfy the requirements to be a state space. In the high-pressure amplitude phase, the pumping regions near the combustor wall were clarified.

Fuel injection is one of the most crucial components for scramjet engines, a promising hypersonic airbreathing technology for economical and flexible space transportation systems. While surrogate modeling based on machine learning has been employed to replace computational simulations for performance evaluation in design optimization of such components, it can inherently predict performance parameters only as scalar quantities. This study investigates the capability of deep learning to predict the fuel injection flowfields, aiming to assist with data-driven approaches for data mining and optimization. Two-dimensional flowfields with sonic fuel injection into a Mach 3.8 crossflow have been trained using the multilayer perceptron. The resultant model has been found to be able to predict the flowfields instantaneously with reasonable accuracy. Local sensitivity analysis has been performed to examine the influence of the design variables on flow properties to gain insights into the effects of their variations on local flow phenomena.

Performance changes due to aerodynamic interference between the main rotor and propeller are investigated using Computational Fluid Dynamics (CFD) for a winged high-speed compound helicopter proposed by JAXA. The study focused on hovering conditions and confirmed that the propeller exposed to the downwash and swirl of the main rotor wake tended to increase the generated thrust and propeller efficiency. On the other hand, periodic thrust fluctuations occur in the propeller and, the shift of the thrust center changes the moment arm length of side propellers, which compensates for the aircraft’s anti-torque, changing the magnitude of thrust required to maintain the aircraft’s attitude. These facts provide important insights for the future design of compound helicopters with side propellers.