Search Results (253)

Corner modification is an effective passive control strategy for mitigating aerodynamic loads on bluff-body structures. The aerodynamic characteristics of an asymmetric rounded square cylinder with rounded upstream corners and sharp downstream corners were investigated numerically at a Reynolds number of 10⁴. Unsteady simulations based on the Transition SST model were performed for corner radius ratios ranging from 0 to 0.20. The results indicate that leading-edge rounding produces a pronounced non-monotonic influence on the aerodynamic characteristics. As the rounding ratio increases, the mean drag coefficient and the root-mean-square lift coefficient are reduced by up to 49.3% and 95.9%, respectively. During this stage, the Strouhal number remains nearly unchanged. Further increases in the rounding ratio result in a recovery of both drag and lift fluctuations, accompanied by a rapid increase in the Strouhal number. Analysis of the wake structure reveals a non-monotonic evolution of the recirculation region, with the recirculation length first increasing and then decreasing sharply as the rounding ratio increases. The wake undergoes a transition in its dominant development mechanism as the recirculation region reaches its maximum extent and the base pressure recovers most effectively. The results suggest that the aerodynamic response is primarily governed by the competition between delayed separation and shear-layer interaction. Delayed separation appears to dominate at moderate rounding ratios, whereas enhanced shear-layer interaction becomes increasingly important at larger rounding ratios. These findings provide physical insight into the aerodynamic optimization of bluff-body structures through asymmetric leading-edge modifications. The proposed wake-transition mechanism is primarily interpreted from the 2D URANS simulations and is supported by representative 3D simulations. Full article

An adaptive immersed boundary method (IBM) for simulating non-isothermal incompressible flows with convective heat transfer involving a rotating circular cylinder is developed. Both Dirichlet- (isothermal) and Neumann (zero heat flux)-type temperature boundary conditions are implemented. In addition to the discrete momentum forcing and energy forcing adopted to effectively satisfy the prescribed velocity and temperature boundary conditions, a mass source/sink term is introduced into the continuity equation to meet the mass conservation at the immersed boundary. The Navier–Stokes equations are solved using the fractional step method implemented on a staggered Cartesian grid system. Time stepping is performed using a second-order Adams–Bashforth/backward-differentiation method, while spatial derivatives are approximated with a second-order centered scheme. Testing of the flow induced by a rotating disk demonstrates that the spatial accuracy of the presented algorithm is second-order. Furthermore, the proposed method is validated by forced convective flow past a rotating isothermal circular cylinder. Finally, mixed Rayleigh–Bénard convection in a square cavity with an embedded adiabatic rotating circular cylinder is simulated, showing that heat transport can be greatly enhanced by increasing the rotating rate and radius of the cylinder at larger Prandtl numbers in the laminar regime. Full article

Flow interference among bluff bodies can strongly amplify or suppress unsteady aerodynamic forces and induced vibrations thereof. However, the behaviour of tandem square-cylinder interference and its passive control in the laminar regime remains insufficiently quantified, particularly near the known critical tandem spacing ratio (L/D ≈ 4.5). This study systematically analysed and determined how a splitter plate placed downstream of the second cylinder modulates the aerodynamic forces, symmetry of vortex shedding, wake topology, and associated wake metrics in two-dimensional incompressible laminar flows. Unsteady finite-volume numerical simulations were conducted using ANSYS Fluent (2022 R1) at Re = 150. The tandem cylinder spacing (L) was varied over L/D = 2–6 (D, the cylinder side length), and the splitter gap (G) was varied over G/D = 1–6. The splitter plate acted as a strong wake stabiliser at small gaps (G/D = 1), where vortex shedding was largely suppressed and lift fluctuations were minimal (for example, C_l,rms ≈ 0.055), and the DC experienced negative drag (C_d ≈ −0.13), consistent with elongated and weakly rolled-up shear layers and extended recirculation. The splitter plate acted as a sharp control “switch” at a critical splitter gap G/D ≈ 2, where the wake transitioned to unsteady shedding, the Strouhal number (St) jumped to values that remained nearly constant for G/D = 2–6, and the wake metrics indicated earlier roll-up (reduced vortex formation length and recirculation length) and greater lateral spreading (increased wake width). The outcome was influenced by the tandem regimes: for 1.5 < L/D < 4, persistent shielding and negative downstream drag predominated. However, near the critical gap (L/D ≈ 4.5), the restoration of shear-layer impingement at G/D ≥ 2 resulted in a downstream drag surpassing the isolated-cylinder baseline (C_d,SC ≈ 1.49) by approximately 3–8%. In the co-shedding regime, when L/D = 5, there was a notable increase in drag, approximately 12.3% more than that on an isolated cylinder. Conversely, when L/D = 6, the system approached aerodynamic independence without any amplification (approaching the single-cylinder value). The interference metrics showed a maximum combined drag reduction of ~68.6% at L/D = 4.5 and G/D = 1, whereas the upstream cylinder drag was only weakly affected. The results of the present study establish splitter placement as an effective passive control method for suppressing or recovering interference-driven unsteadiness, thereby supporting designs in bluff-body aerodynamics, heat-transfer equipment, and vibration-mitigation systems. Full article

Valve-controlled asymmetric hydraulic cylinders inherently exhibit bidirectional dynamic asymmetry attributable to differential chamber areas and heterogeneous gravitational coupling. This study proposes an event-triggered asymmetric gain RBF-PID strategy, wherein real-time directional identification enables differentiated gain scheduling between extension and retraction strokes to compensate for direction-dependent dynamic discrepancies inherent to asymmetric actuators. A sparse RBF mechanism with heterogeneous event-triggering thresholds is further introduced to achieve synergistic coordination between adaptive compensation and computational lightweighting. Uniform ultimate boundedness of the closed-loop tracking errors is rigorously established via Lyapunov-based stability analysis. Simulation results demonstrate that steady-state errors of the three joints are constrained within ±1°; compared with standard PID, the root-mean-square error is reduced for all joints, with directional switching overshoot suppressed below 2%. Relative to conventional RBF-PID, the proposed strategy achieves an event-triggering rate below 5% while reducing FLOPs by approximately 86%, effectively reconciling the inherent conflict between tracking accuracy and computational burden. Full article

Rapid surrogate models are attractive for iterative computational fluid dynamics (CFD) design loops, though defining their operating envelope remains crucial. This study proposes RenaNet, a Reynolds-aware convolutional gated recurrent unit (ConvGRU) surrogate, for predicting two-dimensional laminar and transitional flows past cylinder and square obstacles. Using two initial flow snapshots and a Reynolds-number map, the model predicts spatiotemporal flow states up to 2000 time steps into the future, with Lattice Boltzmann Method (LBM) simulations serving as ground truth. Trained on Reynolds numbers of $1\le \mathrm{Re}\le 500$ (cylinder) and $1\le \mathrm{Re}\le 250$ (square), RenaNet achieves a minimum validation mean squared error (MSE) of $1.47\times {10}^{-5}$. A Reynolds-number ablation shows that removing the conditioning channel increases the validation MSE to $1.17\times {10}^{-3}$, while a ConvLSTM baseline gives $9.94\times {10}^{-4}$ with 24% more parameters. RenaNet also uses a direct long-horizon prediction interface for distant target frames. Auxiliary physics diagnostics confirm that predictions trained via MSE maintain acceptable continuity residuals across fitting, interpolation, and extrapolation cases. The average inference time for a 1000-step prediction horizon is approximately 1.25 s, delivering a 500-fold speedup over the reference LBM solver. Interpolation errors range from ${10}^{-4}$ to ${10}^{-2}$ depending on Reynolds number and geometry, while extrapolation beyond the training regime increases errors to the order of ${10}^{-2}$. These results establish RenaNet as a robust, parameter-efficient surrogate for laminar and transitional flows, with a clearly characterized operational boundary that informs future extensions into turbulent regimes. Full article

Alternative-fuel engines, such as diesel–compressed natural gas (CNG) dual fuel systems, exhibit increased cycle-to-cycle combustion variability, placing demanding requirements on the accuracy and robustness of prediction models for key combustion parameters. Echo state networks (ESNs), owing to their reservoir computing architecture, can capture nonlinear temporal dynamics, yet their performance is sensitive to reservoir hyperparameters, and the conventional linear readout trained by minimizing mean squared error is susceptible to outliers in noisy observations. This paper proposes a robust ESN framework based on the equilibrium optimizer (EO), termed EO-Robust-ESN, that automatically searches for key model hyperparameters and replaces the conventional squared loss with the $ϵ$-insensitive loss by adopting linear support vector regression (SVR) to train the readout weights, thereby enhancing prediction robustness under noisy conditions. Results on the Mackey–Glass chaotic time series benchmark and a peak in-cylinder pressure series from a diesel–CNG dual fuel engine demonstrate that EO significantly outperforms the genetic algorithm and manual tuning on the Mackey–Glass benchmark, reducing the mean RMSE by approximately 5.6% relative to GA, and achieves comparable accuracy with higher search stability on the $P_{max}$ series. The $ϵ$-insensitive SVR readout further reduces prediction errors, with the MSE on the noisy $P_{max}$ series reduced by approximately 42% compared with the ridge regression readout, suggesting that the proposed framework provides an effective data-driven tool for robust combustion prediction in alternative-fuel engines. Full article

Invasive species require long-term characterization of their shifting population dynamics to understand their environmental impacts and socio-economic effects. The dynamics of freshwater bivalves depend on their biology, which is influenced by the vulnerability of the ecosystems. This study evaluates the coexistence of Corbicula fluminea and C. largillierti in a stream in the Argentine Pampa, integrating density, size, and reproduction. In this stream, characterized by a temperate climate and hydrological fluctuations, live specimens of both species were sampled monthly using a 0.07 m² cylinder. The anteroposterior length of each specimen was measured, and size distributions were analyzed by decomposing normal modes using the least-squares method. Gonadal cycles were compared with data from the previous literature. Since 2004, C. fluminea has dominated the system with significantly higher densities. Both species exhibited contrasting reproductive cycles. Hydrological instability in the stream limited both species. C. fluminea dominated due to its greater resilience and longevity under stress. In contrast, C. largillierti prioritized initial rapid growth, making it more sensitive to fluctuations. These results highlight that environmental instability conditions invasive success and interspecific competition in freshwater systems. Full article

In this article, a fuzzy payload compensation algorithm is proposed. In the context of simulating a machine vision model reconstruction, the target object is regarded as a cylinder to obtain the corresponding geometric size data. The first fuzzy mass prediction system is then used to predict the mass of the target object. During operation, real-time processing and calculation of the robotic arm’s joint motor current data are performed. Based on the mathematical relationship between the identified basic parameter set from the dynamic parameters and the end-effector payload, the second fuzzy compensation system was used to calculate the root mean square error (RMSE) of the predicted versus collected current data of the 6-th joint motor, thereby predicting and compensating for the payload mass. The final prediction is generated upon completion of the operation. The overall experiment is conducted on the HSR-CR607 robot. The experimental results indicated that the proposed prediction algorithm consistently operates within the acceptable error range (15%) in most test cases. Full article

This study presents field measurements for a non-conventional mobile square cylinder fish enclosure, with permeable ends, evaluated under both towed and moored conditions at a semi-open ocean test site. Novelty lies within the enclosure design that enables both mobility and control of internal flow through the incorporation of an adjustable aft-end perimeter constriction to regulate internal flow and support fish welfare by enabling control of swimming conditions. Enclosure motion, internal flow speeds and hydrodynamic loads were measured for three constrictions (0%, 40% and 60%). The primary objective was to assess the effectiveness of aft-end constriction in regulating internal flow to levels compatible with sustainable swimming speeds for finfish culture. The enclosure remained stable at approximately 9 m depth across all vessel speeds and constriction settings in both towed and moored scenarios. During towing, increasing constriction to 40% reduced time-averaged internal flow by up to 24% without a significant increase in hydrodynamic load. A 60% constriction achieved a larger reduction (~51%) but resulted in a substantial load increase (~90% relative to 0% constriction), indicating a trade-off between flow control and towing resistance. Under moored conditions, aft-end constriction had minimal influence on both internal flow and hydrodynamic load. Mooring loads showed no clear relationship with wave height and only a weak correlation with ambient current speed. Overall, the results demonstrate that aft-end constriction is an effective mechanism for controlling internal flow during towing, but has limited impact when moored. The enclosure’s stability and controllability highlight its potential advantages over conventional gravity cages for mobile open-ocean finfish aquaculture applications. Full article

The continuous rotating valve plate piston pump (CRVPPP) can efficiently drive actuators such as hydraulic cylinders or hydraulic motors to generate excitation motion. This CRVPPP-driven excitation system can avoid the throttling losses associated with servo-valve-controlled excitation systems. However, this excitation system exhibits an asymmetric excitation phenomenon during actual operation. Through theoretical analysis and experimental research on the mechanical characteristics of the valve plate pair in the CRVPPP, it was found that the asymmetric excitation originates from the annular grooves of the fixed valve plate alternating between oil suction and discharge states. This alternation subjects the rotating valve plate to an overturning moment, which in turn causes a periodic variation in the end-face clearance of the valve plate. Targeting the asymmetric and nonlinear leakage characteristics of the CRVPPP, an adaptive neural network module was established based on the Amesim-Matlab/Simulink co-simulation framework. This module incorporates the mapping from the rotational speeds of the rotating valve plate and cylinder block to the equivalent leakage opening of the distribution grooves. By training with experimental data, the CRVPPP- driven excitation system model was formulated. Experimental results show that the established model achieves a correlation coefficient of 0.99786 on the training set, indicating its excellent fitting accuracy. Furthermore, the mean squared error on the test set is within 0.04 mm², demonstrating the model’s good generalization ability. It can reproduce the dynamic characteristics of the CRVPPP-driven excitation system with high precision, thereby laying a solid modeling foundation for the characteristic analysis, structural optimization, and high-precision control of such excitation systems. Full article

The transportation sector is the primary consumer of vehicle fuel worldwide and is thus a major contributor to climate change via carbon dioxide (CO₂) emissions. In addition to severe environmental impacts, such as global warming, droughts, floods, and rising sea levels, these emissions have a negative effect on public health by increasing the prevalence of respiratory disease. Achieving environmental sustainability through regulatory oversight requires a strong understanding of vehicular fuel consumption and CO₂ emissions. However, accurate modeling of these remains challenging due to the complex non-linear relationships between various vehicular characteristics and the lack of interpretability of many predictive models. Traditional linear models often fail to capture high-dimensional data complexities, while black-box methods provide few actionable insights for policymaking. To address these gaps, we developed a robust and data-driven two-stage machine-learning (ML) framework designed to enhance model performance and reliability. First, we implemented standard data preprocessing, enhanced feature engineering, and hyperparameter tuning for 14 cutting-edge ML algorithms and three advanced modeling techniques to explore their predictive performance. Second, we introduced three interpretable explainable AI (XAI) approaches. These were evaluated on a publicly available Kaggle static dataset of 550 vehicles, dominated by gasoline-powered vehicles, with only two diesels and two electric vehicles. The tuned CatBoost model demonstrated strong predictive performance, achieving an impressive R² of 0.9260, a root mean square error (RMSE) of 1.1759, and a mean absolute error (MAE) of 0.8147. In parallel, we deterministically estimated CO₂ emissions from fuel consumption, which provide direct estimates of tailpipe emissions. To ensure transparency and model interpretability, we employed Shapley additive explanations, local interpretable model-agnostic explanations, and permutation importance to identify the key factors contributing to the model predictions. Across the explainability analyses, cylinder count, front-wheel drive (drive_fwd), and the displacement–year interaction were the primary contributors to the predicted combined miles per gallon; in other words, they strongly affected fuel consumption. Collectively, these findings demonstrate the ability of the proposed model to capture complex feature relationships; thus, it offers a valuable tool for researchers and policymakers in sustainability planning and emission control. Future research should focus on real-time driving or dynamic measurements data and enhancing practical applications to further reduce emissions and promote environmental sustainability. Full article

Accurate prediction of hydrodynamic forces on confined oscillating structures is essential in applications related to nuclear engineering, energy systems, offshore devices, and mechanical components subjected to flow-induced vibrations. In this work, two computational fluid dynamics (CFD) methodologies implemented in ANSYS CFX are compared to determine the added-mass coefficients for a square cross-section cylinder confined within a square container: a deforming-mesh method (DMM) and an immersed-boundary method (IBM). Unlike previous studies restricted either to concentric square cylinders or to eccentric configurations treated with potential flow, the present study addresses eccentric confined configurations by solving the incompressible Navier–Stokes equations and focuses primarily on the prediction of added mass under strong confinement. Horizontal, vertical, and combined eccentric displacements are analyzed in detail. Mesh-independence, domain-size sensitivity, and temporal-convergence analyses are performed. Results show that both methods provide closely matching added-mass predictions over a wide range of eccentricities, with relative differences typically below 1% for moderate eccentricities, although discrepancies increase under extreme confinement. Relative to the concentric configuration, the added-mass coefficient increases by about 44% for the most eccentric vertical case and by about 87% for the most eccentric corner-approach case. Force decomposition and pressure-field analysis show that this increase is governed primarily by pressure-induced inertial effects, whereas viscous shear plays a secondary role under the conditions considered. From a practical standpoint, the immersed-boundary method reduced the computational time by approximately 92% in the most demanding case. Full article

Local scour is a typical hydro-sediment coupled process around near-bed obstacles. Its intensity and spatial distribution are jointly controlled by the surrounding-flow structure, sediment transport, and bed-feedback deformation. To address the relative lack of studies on local scour around non-circular double-obstacle systems, this study conducts a two-dimensional parametric numerical investigation of local scour around double triangular prisms based on an existing DBM-LBM hydro-morphodynamic framework that couples the D2Q16 discrete Boltzmann method with the D2Q9 lattice Boltzmann method. First, a single circular cylinder local-scour experiment is selected as the benchmark case, and a square-pier local-scour case is further introduced as a supplementary validation case to examine the applicability of the adopted framework in reproducing the magnitude of typical local scour and the main bed morphology. Then, three arrangement patterns (tandem, side-by-side, and staggered), two prism orientations (vertex-facing and face-facing), and nine spacing ratios, S/Bp = 2, 2.5, 3, 3.5, 4, 4.5, 5, 5.5, and 6, are considered for the double triangular prism cases. The local scour responses under different geometric configurations are systematically compared. The results show that, under the present two-dimensional numerical setting, the side-by-side arrangement produces the strongest local-scour amplification, with the peak occurring near S/Bp = 2.5. The tandem arrangement is mainly governed by sheltering suppression, and its group amplification factor is generally lower than 1. The scour intensity of the staggered arrangement lies between those of the side-by-side and tandem arrangements, and asymmetric scour is more likely to occur. Face-facing flow produces a larger scour depth in most cases, but its influence varies with the arrangement pattern and spacing ratio. Therefore, the double triangular-prism cases are interpreted as parametric numerical results within the adopted two-dimensional DBM–LBM framework. The reported effects of arrangement pattern, prism orientation, and spacing ratio should be understood as relative numerical trends rather than direct experimental predictions for this specific geometry. The results can provide a reference for subsequent physical-model experiments, three-dimensional numerical simulations, and scour-protection analysis for non-circular double-obstacle systems. Full article

The discovery of dynamical quantum phase transitions (DQPTs) has fundamentally challenged the traditional view that phase transitions only occur in thermal equilibrium. Experimental platforms and 1D numerical methods, like matrix product states (MPS), have made great progress. However, exploring true 2D DQPTs remains difficult due to finite-size limitations and the geometric biases of quasi-1D cylinder mappings. Here, we bypass these limitations by deploying a tree tensor network (TTN) approach. This allows us to directly compute the quench dynamics of the transverse-field Ising model (TFIM) on an open 2D square lattice. Because the TTN architecture naturally mirrors 2D lattice connectivity, we can extract the global Loschmidt echo. Our simulations reveal that while deep quenches yield standard DQPTs, quenching within the ferromagnetic phase produces an anomalous dynamical response. In this regime, the rate function exhibits sharp non-analytic peaks even as the macroscopic order parameter maintains its initial sign. This decoupled behavior strongly indicates that local spin excitations drive 2D DQPTs, rather than the macroscopic domain-wall motions seen in 1D chains. These results provide a quantitative numerical baseline for understanding non-equilibrium quantum matter in higher dimensions. Full article

In-cylinder pressure is a key parameter for evaluating combustion processes and engine performance in spark-ignition engines. However, acquiring high-resolution pressure data over a wide range of operating conditions, particularly under varying spark advance (SA), is costly and technically challenging, which limits its practical application. To address this issue, this study proposes two artificial neural network (ANN)-based methods for in-cylinder pressure reconstruction using data from a three-cylinder gasoline engine under different spark advance conditions. Both methods employ crank angle and spark advance as input features. The first method (ANN-P) directly predicts the in-cylinder pressure profile, achieving a coefficient of determination (R²) exceeding 0.99 on both training and validation datasets, with a root mean square error (RMSE) below 0.13 bar. The model accurately reproduces the pressure evolution throughout the compression, combustion, and expansion processes and enables reliable estimation of indicated mean effective pressure (IMEP). The second method (ANN-HRR) adopts an indirect strategy by first predicting the heat release rate (HRR) and subsequently reconstructing the pressure trace through thermodynamic integration based on a single-zone model. This approach avoids error amplification associated with numerical differentiation and demonstrates improved accuracy in predicting combustion phasing metrics, such as CA10 and CA50. The results indicate that both methods effectively capture the influence of spark timing on combustion characteristics and peak pressure. While ANN-P provides higher accuracy in pressure reconstruction, ANN-HRR offers superior performance in characterizing combustion features. Overall, this study presents a cost-effective and accurate framework for combustion diagnostics, performance calibration, and control optimization of gasoline engines. Full article