Inverse problem of determining a time-dependent friction coefficient in the hydraulic hammer problem

Maritime safety is the top priority in ship operations, especially during mooring and unmooring activities at ports, which carry a high risk of accidents due to the use of mechanical equipment, mooring line tension, and crew activities on deck. Mooring winch plays an important role as the main equipment in supporting these operations, therefore proper maintenance is required to ensure optimal performance. This study analyzes the maintenance of mooring winch on board MT. Defiance and its role in supporting the safety and smoothness of mooring and unmooring operations. This research uses a qualitative method with data collection techniques including observation, interviews, and document studies. The results show that the implementation of maintenance has not been fully optimal, as indicated by the lack of routine inspection on components such as brake, drum, clutch, and hydraulic system, as well as the incomplete implementation of the Planned Maintenance System (PMS). The challenges faced include technical factors (component wear, decreased brake performance, and hydraulic system problems) and internal factors (lack of crew discipline, limited understanding of maintenance procedures, and suboptimal supervision). To address these issues, comprehensive measures have been taken: conducting routine inspections before and after mooring operations; applying maintenance checklists consistently; improving crew awareness through safety meetings, training, and evaluations; and strengthening supervision and coordination in maintenance activities. With proper and systematic maintenance, the mooring winch is expected to function optimally so that mooring and unmooring operations can run safely, smoothly, and efficiently.

The spreading of a thin viscous fluid film on a horizontal surface is an interesting problem in fluid mechanics with many practical applications ranging from coating processes to biological systems and environmental flows. It can even be observed in everyday situations, such as syrup spreading on a pancake. We present a project-based learning approach to this problem, in which engineering or physics undergraduates apply classroom knowledge to understand and solve it, using dimensional analysis, experiments, and theoretical modeling. First, a dimensional analysis is conducted to guide the design of the experiment suitable for an undergraduate laboratory or even at home. The problem is then simplified to obtain a mathematical model that accounts for the experimental results. Through this process, students are able to obtain a solution compatible with those published in fluid mechanics journals with minimal supervision from the instructor. This project not only develops important skills but also motivates students by showing that they have the ability to solve complex problems.

Bubble dynamics constitutes a fundamental scientific problem in fluid mechanics. Although the oscillation can be predicted through theories for bubble dynamics in previous studies, the viscous effects on the bubble migration remains difficult to predict accurately. In this study, we establish a theoretical model for bubble migration across the entire cycle. The theoretical model derives a drag coefficient expression under dynamic Reynolds numbers, and incorporates corrections to account for non-spherical bubble dynamics. A key advance is the capability to account for viscous drag without relying on constant empirical drag coefficients. Validation against experimental results demonstrates that the theoretical model effectively predicts the bubble migration. Furthermore, we discuss the correlation between drag coefficient and Reynolds number, and elucidate the effects of viscous domain range and bubble deformation on the drag coefficient of the present model.

The stability analysis of a saturated soil slope subjected to seepage flow generated by rapid water level drawdown is investigated in this study by means of the limit analysis kinematic approach. Adopting the framework of effective stresses for formulating the strength failure condition of the saturated porous medium, it is shown that the effect of seepage flow can be accounted for in the stability analysis by means of driving body forces computed from the gradient of pore pressure distribution. The hydraulic boundary value problem governing the water filtration velocity is addressed by resorting to a specific analytical variational approach. Comparisons with traditional pore pressure coefficient methods and finite element analyses demonstrate the effectiveness of the proposed methodology. Numerical analyses are conducted to explore the influence of hydraulic-related parameters, highlighting in particular the critical effects of water drawdown height and soil permeability anisotropy.

Pressure fluctuations in a hydraulic jump can prove critical in achieving the safe, economical design of stilling basins. This paper describes the application of data-driven methods and experimental measurements to estimate the dimensionless coefficient of pressure fluctuations ($${C}_{p}^{{\prime}}$$) for forced and free hydraulic jumps in a horizontal channel. White-box data-driven methods, including Multivariate Adaptive Regression Splines (MARS), Gene Expression Programming (GEP), Group Method of Data Handling (GMDH), M5 Model Tree (M5MT), Classification and Regression Tree (CART) method, Stronger Variable Creator Machines (SVCM), Evolutionary Polynomial Regression (EPR), and Multi Expression Programming (MEP), were used to estimate $${C}_{p}^{{\prime}}$$. The methods were evaluated using statistical indices and graphical plots. The results indicated that white-box models perform well in predicting $${C}_{p}^{{\prime}}$$. Two black-box data-driven models were also examined based on an artificial neural network (ANN) and Support Vector Regression (SVR), and both were hybridized with the Particle Swarm Optimization (PSO) algorithm. SHapley Additive exPlanations (SHAP) method and sensitivity analysis were used to assess effective parameters for $${C}_{p}^{{\prime}}$$. Although the accuracy of black-box and white-box models in estimating $${C}_{p}^{{\prime}}$$ is close. Of all the methods considered, the ANN-PSO and SVR-PSO were the best predictive models, achieving the lowest values of Objective Function (OBJ) of 0.0035 and 0.0022 for forced and free jumps, respectively. Our findings highlight the potential of data-driven methods for solving complex nonlinear problems in environmental fluid mechanics.

The analysis of cavitation pressure for high-speed underwater moving bodies is critical for hydrodynamic performance and stability. However, traditional methods relying on physical experiments or high-fidelity CFD simulations are computationally expensive and time-consuming, often resulting in small datasets that challenge data-driven approaches. This study aimed to evaluate efficient feature extraction techniques to overcome the limitations of small-sample scenarios. This paper systematically evaluated three feature extraction methods—Principal Component Analysis (PCA), Fast Independent Component Analysis (Fast ICA), and a customized 1-dimensional Convolutional Auto-encoder (Conv1D AE)—for processing cavitation pressure data. The evaluation was conducted through two experiments: the first assessed the ability of these methods to reconstruct the pressure evolution across the body surface in an unsupervised manner, while the second investigated their performance in predicting the peak pressure using a small set of labeled samples.The findings demonstrated a clear trade-off: Fast ICA exhibited the best performance in reconstructing the overall pressure evolution, followed closely by PCA, while the Conv1D AE showed limitations in capturing sharp pressure gradients. Conversely, for the critical task of peak pressure prediction from limited labeled data, the Conv1D AE model achieved a significant 10% increase in accuracy compared to the baseline model without feature extraction, with PCA providing a 3% improvement. Fast ICA, however, was less effective for this specific prediction task.These results underscore the effectiveness of tailored feature extraction in automating cavitation analysis. By reducing reliance on manual intervention and accelerating the extraction of key features like peak pressure, these methods offer a practical pathway to enhance the design and analysis cycle of underwater moving bodies. The study establishes a foundation for applying machine learning to small-sample fluid mechanics problems, with future work focused on optimizing network architectures for improved precision.

The flow around circular cylinders is a classic problem in fluid mechanics with significant implications for offshore engineering. While extensive numerical and experimental research has focused on the subcritical and critical Reynolds regimes, the supercritical and postcritical regimes remain challenging and relatively unexplored, primarily due to the complex nature of turbulence and the high computational requirements. In this study, we perform three-dimensional detached eddy simulations using the finite volume method in OpenFOAM v1906, employing Menter’s k-ω SST turbulence model, to systematically investigate the flow past an infinitely long smooth cylinder from the subcritical through the postcritical regimes. The numerical setup ensures accurate near-wall resolution and reliable representation of unsteady flow features. We present a detailed analysis of vortex shedding patterns, wake evolution, and statistical properties of lift and drag coefficients for selected Reynolds numbers representative of each regime. The simulation results are benchmarked against experimental data from the literature, demonstrating good agreement for Strouhal number and mean drag. Special emphasis is placed on the evolution of wake topology and force coefficients as the flow transitions from laminar to fully turbulent conditions. The findings contribute to the limited numerical literature on flow around circular cylinders across subcritical, critical, supercritical, and postcritical Reynolds number regimes, providing insights that are fundamentally relevant to the broader scope of understanding vortex shedding phenomena.

In some problems of fluid mechanics, it is possible to be confronted with data that are not regular, that is why we are interested here in the search for the so-called very weak solutions for the stationary Stokes problem with Navier-type boundary conditions in a three-dimensional exterior domain. The problem describes the flow of a viscous and incompressible fluid past an obstacle where we assume that the fluid may slip on the boundary of the obstacle. Because the flow domain is unbounded, we set the problem in weighted Sobolev spaces in order to control the behavior at infinity of the solutions. Our purpose is to prove the existence and the uniqueness of a very weak solution in a Hilbertian framework.

Understanding the dynamics of particles suspended in a flowing liquid is a fundamental fluid mechanics problem. Over the last several decades, significant advances in our theoretical and experimental understanding of these particle-laden flows have been used to manipulate particles in a variety of applications. In particular, recent developments in micro- and nanoscale fabrication and nanotechnology have increased the range of applications, as well as requirements, for manipulating suspended particles with radii less than a few micrometers. We focus here on the surprising and largely unexplained dynamics of neutrally buoyant particles suspended in two common microscale flows, namely Poiseuille and electroosmotic flows, where the particles are subject to both surface forces (e.g., due to pressure gradients) and body forces (e.g., due to electric fields). This perspective review summarizes current developments and identifies opportunities for future advances. Particles suspended in flows can demonstrate both individual and collective behaviors that lead to unusual and unexpected physicochemical hydrodynamics. These dynamics are a long-standing subject of interest, and there has been significant research on the fundamentals of particle-fluid interactions and suspension dynamics because of their relevance to nano- and microscale robotics, drug delivery, biosensing, nanomaterials, optical systems, and biotechnology. The review focuses on the dynamics of nanoscale colloidal particles within confined microscale flows, discussing past discoveries and current state-of-art research, and concluding with suggestions for future research directions.

The primary purpose of the investigation is to highlight the significance of artificial intelligence and machine learning techniques in engineering and fluid mechanics problems. With the advancement in Artificial Intelligence (AI) and Machine Learning (ML) techniques, the computational efficiency and accuracy of numerical results have enhanced. The present study aims to examine MHD boundary layer flow of Casson Nanofluids (CNFs) over a cylinder with thermal radiation effects. Tiwari and Das' model is used to develop a governing Casson nanofluid problem that contains nonlinear PDEs. ANN and numerical models are utilized to examine the effect of numerous physical parameters on heat and fluid transfer properties. The Keller-Box technique, a second-order accurate implicit finite difference scheme, is used to solve the modified fundamental differential equations computationally. However, the predicted solution is examined with MLP-ANN. The efficiency of the proposed model is examined using MSE, the correlation index, and the optimal curve fitness function. An optimal performance of MLP-ANN is examined with the computation of MSE [2.56×10^(-8), 4.8×10^(-8), 4.42×10^(-8), 3.23×10^(-8), and 4.81×10^(-8)] is attained against the epoch [1000, 608, 555, 822, and 787] for scenarios 1-5 with case 1. Graphs and tables illustrate how the Nusselt number ( Gr^(-1/5) ) and skin friction (Gr^(1/5) C_f ) affect several physical variables related to the Casson nanofluid flow. One of the most important conclusions is that (Gr^(1/5) C_f) decreases and (Gr^(-1/5) increases when the Casson parameter increases.

The characteristics of air resistance during free fall are a classic problem in fluid mechanics, with significant value for aerospace, airdrop rescue, and meteorological detection. Previous studies on single geometric bodies are relatively complete, but few works compare spheres, cubes, and cones under the same conditions. Wind tunnel experiments are laborious and limited in demonstrating resistance laws across Reynolds number ranges, while the influence of shape parameters such as corner curvature and cone angle lacks quantitative explanation. Based on literature data and theoretical models, this paper proposes a framework for analyzing resistance differences of the three types of bodies without experiments. The resistance coefficient ranges are determined as sphere 0.47-0.50, cube 1.05-1.20, and cone 0.75-0.85. Results show the geometric influences resistance by changing boundary layer separation and wake distribution. At high Reynolds numbers, shape parameters adjust resistance efficiency by 40%-60%, far exceeding secondary factors such as surface roughness and turbulence intensity (<5%). The prediction method achieves 5% error compared with experiments. This study establishes an air resistance theory for low-speed, lightweight, non-spherical objects, providing engineering value for drag reduction, attitude control, and terminal velocity prediction while reducing research and development costs.

ABSTRACT The outflow of a gas from a pressurized vessel is one of the classical fundamental problems in fluid mechanics. In a recent work, Fischer developed a fast and accurate model for the prediction of transient gas outflow from vessels. The system is considered adiabatic, isentropic, and subcritical, and the gas is considered ideal and compressible. However, the model was limited to thin‐wall vessels, where frictional pressure losses are negligible. In the case of a thick‐wall vessel or a vessel with a pipe outlet, the model must be extended. This leads to the Frictional Inertial Loss Model (FILM). The FILM is validated by experimental investigations using the phase Doppler anemometry. The mean deviation between the experimentally determined outflow durations and the FILM is 2.60%. With respect to the maximal outlet velocity, the mean deviation is even smaller at −0.14%. This shows that it is possible to determine the outflow duration, even for more complex problems with frictional pressure losses, quickly and accurately.

ABSTRACT The Jeffery–Hamel flow, a classic benchmark in fluid dynamics, describes the motion of an incompressible viscous fluid within convergent or divergent channels. Although extensively studied for Newtonian fluids, the dynamics of such flows in channels with stretching or shrinking walls, especially for couple‐stress fluids, remain largely unexplored. In this study, we pioneer the use of artificial neural networks (ANNs) to solve a fifth‐order nonlinear differential equation arising from the two‐dimensional Jeffery–Hamel flow of couple‐stress fluids within stretching/shrinking channels, addressing a complex, nonlinear fluid dynamics problem. By capturing microstructural effects and the unique rheology of couple‐stress fluids, our approach enables high‐accuracy solutions for complex flow behaviour influenced by wall deformation. We focus exclusively on fluid flow behaviour, analysing the influence of key parameters such as Reynolds number, magnetic parameter, channel angle, stretching parameter, and couple stress parameter on velocity distribution and flow structure. Our results reveal new flow topologies and response patterns that are unattainable with traditional analytical or numerical methods. The proposed ANN‐based methodology bridges significant gaps in the literature and provides a powerful tool for modelling biological, industrial, and microfluidic flows in adaptive geometries. This work advances the understanding of the dynamics of Jeffery–Hamel flow in couple‐stress fluids within magnetically influenced stretching/shrinking channels, demonstrating unprecedented microstructural interactions absent in prior Newtonian or non‐Newtonian studies, and unveiling the effectiveness of intelligent methods for solving problems in computational fluid mechanics.

Review of fluid mechanics problems on Ocean Thermal Energy Conversion (OTEC) pipeline systems

The integration of digital technology in automotive engineering education has become a pressing necessity to prepare competent workforce for the Industry 4.0 era. This Community Partnership Empowerment Program aims to enhance the competence of Automotive Engineering Subject Teacher Group (MGMP) members and vocational high school (SMK) students in West Sumatra through training on hydraulic and pneumatic systems integrated with digital technology. The service methodology included intensive workshops, hands-on training, mentorship in developing learning media, and industry site visits, all delivered through a blended learning approach utilizing an e-learning platform for teachers and physical simulators for students. Evaluation results demonstrate a significant increase in teachers' digital technology competence, rising from an average score of 42 (basic level) to 87 (proficient level). Furthermore, students' critical thinking skills in solving hydraulic and pneumatic system problems improved by an average of 30 points, increasing from an initial score of 45 to 75. Additionally, the program successfully developed various video tutorials and interactive learning media, and established sustainable partnerships between the university, schools, and industry, following the triple helix model.

ABSTRACT Planning is the first and most crucial step in problem management and decision-making. Waterway rehabilitation is a widely adopted solution to address the various challenges faced by irrigation canals, such as improving hydraulic efficiency and increasing capacity to meet rising water demands. Uninformed decisions regarding rehabilitation can lead to wasteful expenditure of time, resources, and effort. Therefore, the need for rehabilitation should be guided by a well-defined strategy that identifies hydraulic problems and evaluates the potential impact of rehabilitation measures in addressing these issues. This study aims to develop a strategy to assist decision-makers in assessing the necessity of canal rehabilitation. It proposes a combination of field investigations and numerical modeling to inform decision-making processes. The Suez Waterway in Egypt, an earthen canal, was chosen as a case study. This canal faces significant challenges due to increased sedimentation, which has reduced its flow velocity and discharge capacity. To meet the growing water demands driven by population growth, a need to increase its flow capacity from 3.74 million to 4.53 million cubic meters per day. This study investigates the feasibility of enhancing the hydraulic performance of the canal by modifying its cross-sectional geometry through reshaping and sediment removal, to restore or increase its flow capacity by improving flow velocity and reducing the friction coefficient. In conclusion, this study presents a strategic, data-driven approach for earthen and lined canal rehabilitation decision-making, emphasizing the importance of informed, evidence-based solutions to address hydraulic challenges in irrigation canals. Additionally, it highlights the need to leverage non-traditional water resources to meet future water management demands.

We derive a mathematical model for the overflow fusion glass manufacturing process. In the limit of zero wedge angle, the model leads to a canonical fluid mechanics problem in which, under the effects of gravity and surface tension, a free-surface viscous flow transitions from lubrication flow to extensional flow. We explore the leading-order behaviour of this problem in the limit of small capillary number, and find that there are four distinct regions where different physical effects control the flow. We obtain leading-order governing equations, and determine the solution in each region using asymptotic matching. The downstream behaviour reveals appropriate far-field conditions to impose on the full problem, resulting in a simple governing equation for the film thickness that holds at leading order across the entire domain.

Nowadays, there is more and more attention from researchers in solving various problems in the applied field of fuzzy equations, one of which is the fluid mechanics problem of reservoir piping. In this article, we investigate a solution to a dual fully fuzzy equations system involving triangular and trapezoidal fuzzy numbers using a numerical approach, especially meta-heuristic methods. We consider the equations system in crisp equations form as an optimization problem, and then its problems are solved. Here we compare the performance of four meta-heuristic methods, i.e., genetic algorithm (GA), chaos optimization algorithm (COA), particle swarm optimization (PSO), and grey wolf optimizer (GWO), to solve some cases in our problems. The results obtained show that PSO gives performance that is better than of other methods. Furthermore, the type of initial population number in GA affects how close the numerical solutions are to the exact solution.

Experimental dataset on wave–structure interaction in a sloshing tank with a flexible floating body