Flight tests and wind tunnel experiments face difficulties in investigating the impact of aircraft carrier air-wake on the landing process. Meanwhile, numerical methods generally exhibit low overall computational efficiency in solving such problems. To address the computational challenges posed by the disparate spatiotemporal scales of the ship air-wake and aircraft motion, a domain precursor inflow method is developed to efficiently generate unsteady inflow boundary conditions from precomputed full-domain air-wake simulations. This study investigates the aerodynamic variability of carrier-based aircraft during landing through the turbulent air-wake generated by an aircraft carrier, employing a hybrid RANS-LES methodology on dynamic unstructured overset grids. The numerical framework integrates a delayed detached-eddy simulation (DDES) model with a parallel dynamic overset grid approach, enabling high-fidelity simulations of coupled aircraft carrier interactions. Validation confirms the accuracy of the precursor inflow method in reproducing air-wake characteristics and aerodynamic loads compared to full-domain simulations. Parametric analyses of 15 distinct landing trajectories reveal significant aerodynamic variability, particularly within 250 m of the carrier, where interactions with island-generated vortices induce fluctuations in lift (up to 25%), drag (18%), and pitching moments (30%). Ground effects near the deck further amplify load variations, while lateral deviations in landing paths generate asymmetric forces and moments. The proposed methodology demonstrates computational efficiency for multi-scenario analysis, providing critical insights into aerodynamic uncertainties during carrier operations.

At cryogenic temperatures, gases exhibit significant deviations from ideal behaviour, and the commonly employed gas model may inadequately represent the thermodynamic properties of cryogenic gases, subsequently impacting numerical simulations using various thermodynamic and transport models at cryogenic temperatures. The findings of this study reveal that the relative errors in aerodynamic characteristics obtained through different isentropic relations are noteworthy, with the maximum relative error in the drag coefficient reaching 16%. The impact of the equation of state, viscosity model, and thermal conductivity model is relatively minor, with relative errors in the pressure drag coefficient and viscous drag coefficient remaining well below 1%. Nevertheless, the relative error in the skin friction coefficient cannot be ignored due to transonic shock wave/boundary layer interactions. Consequently, when conducting numerical simulations of cryogenic flow, it is imperative to select appropriate gas models to attain precise results.

Accurate unsteady aerodynamic surrogate models are important for the aerodynamic analysis and model-based design of flapping-wing air vehicles (FWAVs). However, the construction of such models is often limited by the cost of generating high-quality unsteady aerodynamic samples. Traditional design of experiment (DoE) sampling methods have fundamental limitations in sampling cases involving strongly nonlinear time-varying kinematic equations for flapping airfoil. While DoE generates uniformly distributed samples in the kinematic parameter space, these samples undergo significant distribution distortion in the actual input feature space of the deep neural network (DNN) after being mapped through the parameterized harmonic kinematic model. Ultimately, the local prediction accuracy and generalization ability of the DNN model are affected by nonlinear kinematic mapping. A space-filling sampling method for sample generation in aerodynamic modeling of the flapping airfoil over complex motion state spaces is proposed to overcome the limitations. The proposed sampling method integrates two stages: an initial sample distribution optimization using a Genetic Algorithm, followed by a sample augmentation stage via active learning. This method is applied to the training-sample selection problem for a two-dimensional NACA0012 flapping airfoil within complex motion state spaces. The results indicate that space-filling sampling is particularly useful for reducing local prediction failures caused by nonlinear kinematic mapping, rather than merely increasing the average accuracy over already well-covered regions.