The present research aims at providing a numerical fluid flow prediction of the less explored T-shaped lid-driven cavity problem. It is solved two ways, first using a finite-difference-method-based vorticity-stream function formulation that is applied to the two-dimensional incompressible Navier-Stokes equations and then by using the SIMPLE algorithm implemented in the finite-volume-method-based OpenFOAM system. Firstly, a numerical in-house code is developed for a classical lid-driven square cavity problem, and successively verified by comparing it with results from the literature. Once the code’s credibility is established, the fluid flow characteristics are thoroughly investigated considering three different wall motions: single-sided, double-sided with co-directional wall motion, and double-sided with counter-directional motion. The investigation also focuses on how the fluid flow characteristics are influenced by both the Reynolds number of the flow and the aspect ratio of the domain. Through numerical simulations, the centerline velocity profiles are plotted, and also the structure and formation of vortices in a T-shaped cavity are compared between both finite difference and finite volume schemes. As the Reynolds number of the domain rises, many recirculation zones are formed, the fluid is shown to flow faster in the wall-driven enclosures. As the aspect ratio increases the strength of the vortices diminishes at the walls of the computational domain.
Operating characteristics (e.g., working area, kinematic and dynamic responses) of hydraulic excavators equipped with standard buckets is inherently defined by the dimensions of links, the corresponding characteristics of driving cylinders, and working conditions. For dredging construction, it is often necessary to integrate additional equipment into the original hydraulic excavator. Excavators mounted on pontoons and equipped with drum cutters are commonly employed for this purpose. Investigating the working performance of these devices is essential for integrating replacement equipment into excavators to meet specific design criteria, technical requirements, and operational spaces. The installation of an extended boom and the modification of the working attachment to meet dredging requirements have resulted in fundamental changes in the kinematic and dynamic characteristics of the working mechanism. Specifically, the addition of a long dynamic link and the presence of resistive forces that vary continuously due to the cutter teeth alternately entering and exiting the cutting zone represent key differences compared to the original machine. This study introduces an innovative configuration for a standard excavator by incorporating a long-reach arm and substituting the bucket with a drum cutter. This modification aims to enhance the excavator’s effectiveness in dredging tasks. In addition to extending the reachable working area to more than twice that of the original configuration, the translational velocity of the drum cutter can exceed that of the actuating cylinder by over 30 times. From a dynamic response perspective, the cutting layer thickness is shown to have a pronounced influence on the variation of resistive force components, driving forces, and joint reaction forces, with the coefficient of variation (CV) reaching values of up to 38.02 %. When the cutting depth is increased by a factor of 1.5, the CV of the force components decreases by the same ratio; however, the magnitudes of these force components may increase by more than four times. Through a comprehensive kinematic and dynamic analysis, the modified working area and operating characteristics of the backhoe dredger are evaluated to ensure safe, efficient, and structurally optimized operations. The proposed design and the findings of the present research offer valuable insights and practical solutions for the manufacturing and improvement of dredging equipment.
This research addresses the uncertainty quantification of time-dependent partial differential equations (PDEs) with random parameters. The stochastic Galerkin method, a sampling-free intrusive approach, is employed instead of sampling- or quadrature-based methods to overcome the slow convergence and high computational cost associated with high-resolution models. An acoustic wave propagation problem with a log-normal random field approximation for wave speed is illustrated. The stochastic partial differential equation with the inputs and outputs expanded using polynomial chaos expansion (PCE) is transformed into a set of coupled deterministic PDEs and discretized to yield a system of linear equations. To handle the increased memory requirements with increasing mesh size, time step and number of random parameters, domain decomposition-based (DD-based) solvers are utilized. A conjugate gradient iterative solver with a two-level Neumann-Neumann preconditioner is applied to the symmetric positive definite system matrix showing their efficient scalability. This combination of the stochastic Galerkin method and DD-based solvers enables large-scale real-time applications involving acoustic waves.
This study presents a surrogate-assisted multi-objective framework for a GE 1.5XLE horizontal-axis wind turbine (HAWT) blade based on a one-way fluid-structure interaction (FSI) analysis. The objective is to identify optimal trade-offs between structural weight reduction and dynamic performance by simultaneously minimizing blade mass and maximizing the fundamental natural frequency. Aerodynamic loads are first computed using computational fluid dynamics (CFD) and subsequently transferred to a structural finite element analysis (FEA) model through a one-way FSI coupling. To reduce the computational cost associated with repeated high-fidelity simulations, a Kriging metamodel is constructed using a latin hypercube sampling (LHS) design of experiments. The resulting surrogate model demonstrated high predictive accuracy, with a maximum prediction error below 0.4 %. A multi-objective genetic algorithm (MOGA) is then employed to generate the Pareto optimal set of solutions and to identify the best compromise designs under stress and deformation constraints. The optimal design achieves an approximately 10% reduction in total mass and a 9.1% increase in fundamental frequency compared with the baseline blade, while satisfying all structural requirements. Independent high-fidelity numerical verification confirms the reliability of the proposed framework, with deviations of less than 0.2 %. The proposed FSI-Kriging-MOGA methodology provides an efficient and reliable computational tool for the aeroelastic optimization of wind turbine blades and can be extended to other complex fluid-structure interaction systems.
