Chao Yuan

Surface riblets have emerged as a highly efficient passive drag-reduction strategy for enhancing the endurance of autonomous underwater vehicles (AUVs). Nevertheless, current research less frequently deploys riblets in the complex aft-body region of underwater vehicles. To address this gap, we propose a drag-reduction scheme utilizing streamwise rectangular riblets distributed along the tail contraction section of the Defense Advanced Research Projects Agency submarine (DARPA SUBOFF) bare-hull model. Employing Reynolds-averaged Navier–Stokes simulations coupled with the shear-stress transport k – ω turbulence model, we systematically evaluate the impact of riblet height ( h ), width ( w ), axial length ( l ), and circumferential count ( n ) on hydrodynamic drag characteristics across varying velocities. The results demonstrate that, within the investigated operating conditions, the best-performing configuration among the tested cases ( h = 0.3 mm, w = 0.6 mm, l = 0.5 L R , n = 50 ) achieves the largest total drag reduction of approximately 2.6%, primarily driven by an 11%–16% decrease in pressure drag. Comprehensive flow field analysis reveals that the riblets significantly attenuate near-wall turbulent kinetic energy, effectively accelerating pressure recovery. Ultimately, this study not only elucidates the underlying physical mechanisms by which riblets modulate complex tail flow fields but also offers a reference for the drag-reduction design of AUV surfaces.

The hydrodynamic interaction between closely spaced autonomous underwater vehicles (AUVs) in formation produces strongly nonlinear drag responses. In the present configuration space, two dominant pressure-related mechanisms are identified: stern pressure recovery at the leader and bow low-pressure immersion at the follower. To map these mechanisms over a wide formation parameter space, a physics-guided surrogate framework is constructed from 666 Reynolds-averaged Navier–Stokes simulations of dual-AUV configurations. Total resistance is decomposed into pressure and viscous contributions and modeled separately. Guided by the effective-boundedness principle of Learning and Inference assisted by feature-space engineering, the feature space incorporates bounded geometric descriptors, a regularized turbulent-wake power-law decay, a Gaussian lateral wake profile, and pressure-interaction proxies. The resulting surrogate achieves pressure-drag R2≈0.974–0.986 and enables formation optimization over the prescribed feasible design space. At V=5.14 m/s (approximately 10 kn), the system-optimal arrangement yields a 5.9% total-drag reduction relative to two isolated AUVs, with computational fluid dynamics verification errors below 1%. The individual optima are associated with geometrically incompatible mechanism-favorable regions: the leader optimum is located on the centerline (b/D=0), whereas the follower optimum occurs at b/D=1.5, where D denotes the maximum hull diameter. This lateral separation of 1.5D places the two individual minima in different regions of the static formation space, so they are not attained simultaneously by a single feasible configuration. A nondominated leader–follower Pareto front quantifies the resulting tradeoff and locates the system optimum as a compromise between the two competing drag objectives.

Optimal sensor placement is a crucial issue in scientific and engineering research. This study proposes an autoencoder-based deep learning framework for automated optimal sensor layout and flow field reconstruction. A dataset is established based on transient Computational Fluid Dynamics (CFD) simulation results of a three-dimensional finite-span airfoil under various Reynolds numbers and angles of attack, enabling high-precision reconstruction of airfoil surface pressure distribution using sparse pressure coefficient data. The multilayer perceptron of both the encoder and decoder adopts an optimal five-layer structure with 200 nodes per layer, and the ReLU activation function delivers superior performance with a training loss reduction of over 45%. When using 50 sensors, the proposed architecture determined detailed placement and obtained a reconstruction error of 0.0604, which outperforms traditional manual sensor placement. The reconstruction accuracy of aerodynamic loads improves with increasing sensor count, but exhibits diminishing returns beyond the optimal threshold 50, necessitating a balanced selection that optimizes performance-to-cost ratio. The proposed method adaptively captures critical flow regions with high gradients, cutting sensor quantity by 60–80% versus grid-based placement. This method can flexibly use either CFD or experimental data in practical applications, offering an efficient solution for aerodynamic field reconstruction.

Disc-shaped Autonomous Underwater Helicopters (AUHs) offer superior maneuverability but suffer from high hydrodynamic drag, which limits their operational endurance. To address this challenge, this study proposes a robust optimization framework for a novel sea-turtle-inspired AUH. A parametric hull, governed by two dimensionless shape factors based on modified Myring equations, was established to facilitate systematic exploration. To reduce the high computational cost of direct CFD evaluations, a high-precision Gaussian Process Regression (GPR) surrogate model was constructed from a small dataset of 24 samples. The core methodological innovation is T-NSGA-II, an algorithm featuring hyperparameters that are systematically optimized by the Optuna framework. In comparative evaluations, the T-NSGA-II-generated Pareto front demonstrated clear superiority over the standard NSGA-II, identifying designs with significantly lower drag for an equivalent vertical force. A key scientific contribution of this research is the identification of a distinct performance gap on the Pareto front. This phenomenon is interpreted not as an algorithmic artifact but as a ‘natural gap’, reflecting a deep physical trade-off, with potential underlying causes including a critical transition in flow physics or a topological shift in the optimal hull geometries. This work not only delivers a suite of optimized, practical AUH designs but also presents a powerful, intelligent optimization methodology that is capable of revealing fundamental physical trade-offs in complex engineering problems.