Fluid-structure Interaction Model Research Articles

Configuration Optimization of a Skippable Anti-Ship Missile Considering Initial Kinematic Parameters

Skimming the sea at ultralow altitudes can be achieved by repeatedly touching and bouncing off water surface (commonly referred to as skipping), thus giving birth to skippable anti-ship missiles. However, the high impact load during skipping poses a safety hazard to the structure. Whether a missile can rebound primarily depends on the initial kinematic parameters, which also influence the impact load along with the configuration parameters. Therefore, it is important to optimize both of them. Due to the differences in types and complicated coupling of these parameters, the optimization problem becomes highly complex. In this paper, the grouped sequential quadratic programming (GSQP) algorithm is proposed to reduce the nonlinearity and nonconvexity of the problem. By bringing together the global search capability of cooperative co-evolutionary algorithm and the local search strength of GSQP, a combined optimization framework using decomposition strategies is constructed. With the support of fluid–structure interaction models, the optimization is performed to reduce impact load for the missile. The results demonstrate that simultaneously optimizing the configuration and initial kinematic parameters yields a reduction of 12.2% in the impact load on the premise of successful skipping. This research provides a valuable reference for the design of future skippable anti-ship missiles.

Time-Dependent Fluid-Structure Interaction Simulations of a Simplified Human Soft Palate.

Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep-related disorder. It is characterized by recurrent partial or total collapse of pharyngeal upper airway accompanied by induced vibrations of the soft tissues (e.g., soft palate). The knowledge of the tissue behavior subject to a particular airflow is relevant for realistic clinic applications. However, in-vivo measurements are usually impractical. The goal of the present study is to develop a 3D fluid-structure interaction model for the human uvulopalatal system relevant to OSA based on simplified geometries under physiological conditions. Numerical simulations are performed to assess the influence of the different breathing conditions on the vibrational dynamics of the flexible structure. Meanwhile, the fluid patterns are investigated for the coupled fluid-structure system as well. Increasing the respiratory flow rate is shown to induce larger structural deformation. Vortex shedding induced resonance is not observed due to the large discrepancy between the flow oscillatory frequency and the natural frequency of the structure. The large deformation for symmetric breathing case under intensive respiration is mainly because of the positive feedback from the pressure differences on the top and the bottom surfaces of the structure.

Biomechanical Evaluation of Aortic Valve Stenosis by Means of a Virtual Stress Test: A Fluid-Structure Interaction Study.

The impact of aortic valve stenosis (AS) extends beyond the vicinity of the narrowed leaflets into the left ventricle (LV) and into the systemic vasculature because of highly unpredictable valve behavior and complex blood flow in the ascending aorta that can be attributed to the strong interaction between the narrowed cusps and the ejected blood. These effects can become exacerbated during exercise and may have implications for disease progression, accurate diagnosis, and timing of intervention. In this 3-D patient-specific study, we employ strongly coupled fluid-structure interaction (FSI) modeling to perform a comprehensive biomechanical evaluation of systolic ejection dynamics in a stenosed aortic valve (AV) during increasing LV contraction. Our model predictions reveal that the heterogeneous ∆P vs. Q relationship that was observed in our previous clinical study can be attributed to a non-linear increase (by ~ 1.5-fold) in aortic valve area as LV heart rate increases from 70 to 115bpm. Furthermore, our results show that even for a moderately stenotic valve, increased LV contraction during exercise can lead to high-velocity flow turbulence (Re = 11,700) in the aorta similar to that encountered with a severely stenotic valve (Re ~ 10,000), with concomitant greater viscous loss (~3-fold increase) and elevated wall stress in the ascending aorta. Our FSI predictions also reveal that individual valve cusps undergo distinct and highly non-linear increases (>100%) in stress during exercise, potentially contributing to progressive calcification. Such quantitative biomechanical evaluations from realistic FSI workflows provide insights into disease progression and can be integrated with current stress testing for AS patients to comprehensively predict hemodynamics and valve function under both baseline and exercise conditions.

Enhancing Adaptive Physics Refinement Simulations Through the Addition of Realistic Red Blood Cell Counts.

Simulations of cancer cell transport require accurately modeling mm-scale and longer trajectories through a circulatory system containing trillions of deformable red blood cells, whose intercellular interactions require submicron fidelity. Using a hybrid CPU-GPU approach, we extend the advanced physics refinement (APR) method to couple a finely-resolved region of explicitly-modeled red blood cells to a coarsely-resolved bulk fluid domain. We further develop algorithms that: capture the dynamics at the interface of differing viscosities, maintain hematocrit within the cell-filled volume, and move the finely-resolved region and encapsulated cells while tracking an individual cancer cell. Comparison to a fully-resolved fluid-structure interaction model is presented for verification. Finally, we use the advanced APR method to simulate cancer cell transport over a mm-scale distance while maintaining a local region of RBCs, using a fraction of the computational power required to run a fully-resolved model.

Design of a polymeric cerebral aneurysm based on numerical modelling for the development of an aneurysm mechanical characterisation device

Intracranial aneurysm is a life-threatening pathology related to the arterial wall alteration. As yet there is no method capable of predicting rupture risk based on quantitative in vivo mechanical data. This work is part of a large-scale project aimed at providing clinicians with a non-invasive patient-specific decision support tool, based on the in vivo mechanical characterisation of the aneurysm wall. First, an original wall deformation device was developed on polymeric phantom arteries. These artery models were obtained by 3D printing and an injection moulding process, each one showing pros and cons of designs of a biofidelic phantom in terms of thickness and local stiffness. A numerical modelling of this experimental study was built as a support for designing phantoms as design process choices and determining the geometrical and mechanical parameters of arteries. A numerical Fluid–Structure Interaction model based on the finite element method was developed. Several wall thicknesses, mechanical properties and deformation device locations were considered. Regarding the 3D printed phantom artery, the numerical model demonstrated that a thin wall thickness should be emphasised instead of a low Young’s modulus to reach a significant and experimentally observable strain. Regarding the injection moulded phantom, the results pointed to a locally reduced aneurysm thickness with a Young’s modulus of 0.7 MPa for the strain analysis. The numerical study provided helpful information regarding the scientific challenges of the experimental study. This work is the keystone of further animal studies and associated patient-specific models.

Gas Evolution Principle during Gas Drainage from a Drill Hole along the Coal Seam and Reasonable Borehole Layout Spacing.

The efficiency of coal seam gas drainage can be further improved by the accurate mastery of the gas evolution principle during gas drainage from the drill hole along the coal seam and reasonable optimization of borehole layout spacing. Based on the actual geological conditions of the no. 2 coal seam in a coal mine in Guizhou, China, and relevant control equations, a fluid-structure interaction model of gas drainage from a drill hole along the coal seam was established in this paper. Besides, numerical simulation research on the gas evolution principle during gas drainage along the coal seam and optimization of the borehole layout spacing was carried out with the COMSOL simulation software; and these were verified in combination with the project site. The results showed that in the early stage of gas drainage the gas pressure in the area near the drill holes decreased significantly. As the gas drainage went on, the degree of influence decreased gradually. During the gas drainage from adjacent drill holes, the gas pressure in the coal body between the holes decreased rapidly, and the migration was obvious. With the increase of the spacing between the drill holes, the drainage superposition effect between these holes gradually decreased until the influence area around a single hole is independently distributed in a circle-like shape, indicating that the optimization and the reasonable spacing of the drill holes are directly related to the effect of the gas drainage. With the increase of drilling spacing, the superposition effect of extraction between the holes gradually decreased until the influence area around a single hole is independently distributed in the shape of a circle, indicating that the optimization of drilling spacing is directly related to the effect of gas drainage. The results of numerical simulation were verified by analyzing the three-dimensional map of the gas pressure during the period of gas drainage at the project site and by comparing and examining the rational borehole layout spacing of the drill hole along the coal seam. The results of this study can be used to determine the spacing of gas extraction boreholes and improve the efficiency of gas extraction in the no. 2 coal seam of a coal mine in Guizhou, China, as well as to provide a reference for the gas pressure evolution, velocity field distribution, the prediction of effective drainage area, and the selection of rational borehole layout spacing during gas drainage.

Patient-specific air puff-induced loading using machine learning.

Introduction: The air puff test is a contactless tonometry test used to measure the intraocular pressure and the cornea's biomechanical properties. Limitations that most challenge the accuracy of the estimation of the corneal material and the intraocular pressure are the strong intercorrelation between the intraocular pressure and the corneal parameters, either the material properties that can change from one person to another because of age or the geometry parameters like central corneal thickness. This influence produces inaccuracies in the corneal deformation parameters while extracting the IOP parametric equation, which can be reduced through the consideration of the patient-specific air puff pressure distribution taking into account the changes in corneal parameters. This air puff pressure loading distribution can be determined precisely from the fluid-structure interaction (FSI) coupling between the air puff and the eye model. However, the computational fluid dynamics simulation of the air puff in the coupling algorithm is a time-consuming model that is impractical to use in clinical practice and large parametric studies. Methods: By using a supervised machine learning algorithm, we predict the time-dependent air puff pressure distribution for different corneal parameters via a parametric study of the corneal deformations and the gradient boosting algorithm. Results: The results confirmed that the algorithm gives the time-dependent air puff pressure distribution with an MAE of 0.0258, an RMSE of 0.0673, and an execution time of 93s, which is then applied to the finite element model of the eye generating the corresponding corneal deformations taking into account the FSI influence. Using corneal deformations, the response parameters can be extracted and used to produce more accurate algorithms of the intraocular pressure and corneal material stress-strain index (SSI). Discussion: Estimating the distribution of air pressure on the cornea is essential to increase the accuracy of intraocular pressure (IOP) measurements, which serve as valuable indicator of corneal disease. We find that the air puff pressure loading is largely influenced by complex changes in corneal parameters unique to each patient case. With our innovative algorithm, we can preserve the same accuracy developed by the CFD-based FSI model, while reducing the computational time from approximately 101000s (28h) to 720s (12min), which is about 99.2% reduction in time. This huge improvement in computational cost will lead to significant improvement in the parametric equations for IOP and the Stress-Strain Index (SSI).

Segmental biomechanics of the normal and glaucomatous human aqueous outflow pathway

The conventional aqueous outflow pathway, encompassing the trabecular meshwork (TM), juxtacanalicular connective tissue (JCT), and inner wall endothelium of Schlemm's canal (SC), governs intraocular pressure (IOP) regulation. This study targets the biomechanics of low-flow (LF) and high-flow (HF) regions within the aqueous humor outflow pathway in normal and glaucomatous human donor eyes, using a combined experimental and computational approach. LF and HF TM/JCT/SC complex tissues from normal and glaucomatous eyes underwent uniaxial tensile testing. Dynamic motion of the TM/JCT/SC complex was recorded using customized green-light optical coherence tomography during SC pressurization in cannulated anterior segment wedges. A hyperviscoelastic model quantified TM/JCT/SC complex properties. A fluid-structure interaction model simulated tissue-aqueous humor interaction. FluoSpheres were introduced into the pathway via negative pressure in the SC, with their motion tracked using two-photon excitation microscopy. Tensile test results revealed that the elastic moduli of the LF and HF regions in glaucomatous eyes are 3.5- and 1.5-fold stiffer than the normal eyes, respectively. The FE results also showed significantly larger shear moduli in the TM, JCT, and SC of the glaucomatous eyes compared to the normal subjects. The LF regions in normal eyes demonstrated larger elastic moduli compared to the HF regions in glaucomatous eyes. The resultant strain in the outflow tissues and velocity of the aqueous humor in the FSI models were in good agreement with the digital volume correlation and 3D particle image velocimetry data, respectively. This study uncovers stiffer biomechanical responses in glaucomatous eyes, with LF regions stiffer than HF regions in both normal and glaucomatous eyes. Statement of significanceThis study delves into the biomechanics of the conventional aqueous outflow pathway, a crucial regulator of intraocular pressure and ocular health. By analyzing mechanical differences in low-flow and high-flow regions of normal and glaucomatous eyes, this research unveils the stiffer response in glaucomatous eyes. The distinction between regions' properties offers insights into aqueous humor outflow regulation, while the integration of experimental and computational methods enhances credibility. These findings have potential implications for disease management and present a vital step toward innovative ophthalmic interventions. This study advances our understanding of glaucoma's biomechanical basis and its broader impact on ocular health.

Fluid–Structure Interaction Aortic Valve Surgery Simulation: A Review

The complicated interaction between a fluid flow and a deformable structure is referred to as fluid–structure interaction (FSI). FSI plays a crucial role in the functioning of the aortic valve. Blood exerts stresses on the leaflets as it passes through the opening or shutting valve, causing them to distort and vibrate. The pressure, velocity, and turbulence of the fluid flow have an impact on these deformations and vibrations. Designing artificial valves, diagnosing and predicting valve failure, and improving surgical and interventional treatments all require the understanding and modeling of FSI in aortic valve dynamics. The most popular techniques for simulating and analyzing FSI in aortic valves are computational fluid dynamics (CFD) and finite element analysis (FEA). By studying the relationship between fluid flow and valve deformations, researchers and doctors can gain knowledge about the functioning of valves and possible pathological diseases. Overall, FSI is a complicated phenomenon that has a great impact on how well the aortic valve works. Aortic valve diseases and disorders can be better identified, treated, and managed by comprehending and mimicking this relationship. This article provides a literature review that compiles valve reconstruction methods from 1952 to the present, as well as FSI modeling techniques that can help advance valve reconstruction. The Scopus, PubMed, and ScienceDirect databases were used in the literature search and were structured into several categories. By utilizing FSI modeling, surgeons, researchers, and engineers can predict the behavior of the aortic valve before, during, and after surgery. This predictive capability can contribute to improved surgical planning, as it provides valuable insights into hemodynamic parameters such as blood flow patterns, pressure distributions, and stress analysis. Additionally, FSI modeling can aid in the evaluation of different treatment options and surgical techniques, allowing for the assessment of potential complications and the optimization of surgical outcomes. It can also provide valuable information on the long-term durability and functionality of prosthetic valves. In summary, fluid–structure interaction modeling is an effective tool for predicting the outcomes of aortic valve surgery. It can provide valuable insights into hemodynamic parameters and aid in surgical planning, treatment evaluation, and the optimization of surgical outcomes.

Finite volume analysis of dam breaking subjected to earthquake accelerations

ABSTRACT Because of the complexity of the dam failure mechanism due to earthquakes that occur under the simultaneous influence of hydraulic and seismic forces, a single model has not been obtained so far and this study was conducted to achieve this model. In this paper, dam failure models including both sudden and gradual failure have been investigated using volume of fluid techniques (VOF) to simulate water fluxes, general moving object (GMO) to simulate moving bodies, and the fluid–structure interaction model for finite volume analysis. In order to be sure of the accuracy of the results, before examining the failure mechanisms, the verifications of utilized methods in the case of dam failure were proven using experimental and numerical studies from literature. The Koyna Dam earthquake of magnitude 6.5 (11 December 1967) is investigated as a test case. Comparison of the output discharges due to dam failure in the two failure models reveals that the peak discharge of sudden failure is recorded three times faster than the gradual failure mode. Another achievement that should be mentioned is that although large oscillating periods carry a higher risk of cracking of the dam’s body, smaller periods propagate the resulting cracks more rapidly.

Research on internal flow field analysis and power loss modeling of the expansion seal ring

Expansion seal rings are widely used in automobile transmission systems, and their sealing performance plays a vital role in the pressure stability and transmission efficiency of the transmission system. However, investigations into revealing the internal flow mechanism of the expansion seal ring and predicting the power loss of the expansion seal ring are still insufficient. In this study, a two-way fluid–structure interaction model consisting of the shaft, oil sleeve, and expansion seal ring was established. The flow and structural physical fields under a series of operating conditions were simulated, and the effects of oil temperature, rotational speed, and seal pressure differential on leakage rate, deformation, power loss, and contact pressure were analyzed. Further, a power loss prediction model was developed by multiple linear regression method. The results demonstrate that in the internal flow field, the oil flow into and out of the groove is characterized by backward step flow and jet flow, respectively. In the structural field, the maximum deformation and the maximum contact pressure of the main sealing surface appear at the notch of the expansion seal ring, and the seal pressure differential has the most significant influence on it. The relative error between the predicted value of the developed model and experimental values is within 9%, which meets the industrial requirements. This work facilitates scholars to understand the internal flow mechanism and deformation process of expanding seal rings and provides a method to predict sealing ring power loss.

Whirling dynamics of a drill-string with fluid–structure interaction

Fluid–structure interaction models for drill-string vibrations are often of reduced order. However, both the structure and the surrounding fluid are non-linear, which can lead to complex coupled dynamic. In this paper, a coupled fluid–structure model is developed, where the flow is reduced to multiple cross-sections and solved with the lattice-Boltzmann method, while the finite element method is employed to discretize a region of the drill-string. In sequence, the whirling dynamics and the fluid forces are analysed for different configurations. The process is repeated disregarding the fluid-interaction with the aim of evaluating the fluid forces. The fluid forces estimated through the solution of the Navier–Stokes equation shows that the fluid acts in both dissipation and excitation of the vibrations. The dissipation is seen when high frequency dynamics is expected, whereas the lower frequencies are excited.

Differentiable hybrid neural modeling for fluid-structure interaction

Solving complex fluid-structure interaction (FSI) problems, which are described by nonlinear partial differential equations, is crucial in various scientific and engineering applications. Traditional computational fluid dynamics based solvers are inadequate to handle the increasing demand for large-scale and long-period simulations. The ever-increasing availability of data and rapid advancement in deep learning (DL) have opened new avenues to tackle these challenges through data-enabled modeling. The seamless integration of DL and classic numerical techniques through the differentiable programming framework can significantly improve data-driven modeling performance. In this study, we propose a differentiable hybrid neural modeling framework for efficient simulation of FSI problems, where the numerically discretized FSI physics based on the immersed boundary method is seamlessly integrated with sequential neural networks using differentiable programming. All modules are programmed in JAX, where automatic differentiation enables gradient back-propagation over the entire model rollout trajectory, allowing the hybrid neural FSI model to be trained as a whole in an end-to-end, sequence-to-sequence manner. Through several FSI benchmark cases, we demonstrate the merit and capability of the proposed method in modeling FSI dynamics for both rigid and flexible bodies. The proposed model has also demonstrated its superiority over baseline purely data-driven neural models, weakly-coupled hybrid neural models, and purely numerical FSI solvers in terms of accuracy, robustness, and generalizability.

Dynamic stratified porosity computation from canopy interaction simulation between airflow and leaves.

The main goal of wind-driven spraying is to use assisted airflow to disrupt the structure of branches and leaves and broaden the air delivery channel, so as to achieve uniform droplet deposition in the middle and lower parts of the canopy. Due to the complex branch and leaf structure inside the canopy, there is currently no effective method to express the dynamic changes of canopy porosity and the law of airflow attenuation under assisted airflow. In this study, based on the two-way fluid-structure interaction numerical simulation method, the relating between the assisted airflow and the structural parameters of the cotton canopy is analyzed, and a new method for predicting and simulating the dynamic porosity of the canopy is proposed. Firstly, a two-way fluid-structure interaction model based on Lattice Boltzmann (LB) solver and Finite Element (FE) solver is developed to simulate the deformation motion of cotton leaves and the spatial distribution of airflow field, and the correctness of the numerical simulation is verified based on indoor measurement data. Secondly, the post-processing method of Computational Fluid Dynamics (CFD) is used to obtain images of leaves at different canopy positions under assisted airflow, and the porosity changes are calculated and analyzed by image processing. The research results show that under different initial wind speeds (5 m·s-1, 10 m·s-1, 15 m·s-1), the maximum normalized mean absolute error (NMAE) between the simulated values and the measured values is 13.99%, 20.72% and 16.08%, respectively. The coefficient of determination (R2) for linear fitting between simulated values and measured values is 0.9221. These validation results indicate the effectiveness of the numerical simulation method. The validated CFD model is applied to predict leaf deformation and porosity changes within the canopy under various wind loads and times. The application results have well revealed the interaction between crop leaves and airflow, and will be beneficial to make a better understanding of the effect of assisted airflow on droplet deposition.

Locomotion performance of an axisymmetric ‘flapping fin’

Inspired by the jet-propulsion mechanism of aquatic creatures such as sea salps, a novel locomotion system based on an axisymmetric body design is proposed. This system consists of an empty tube with two ends open. When the diameters of the front and back openings are changed periodically, the forward-backward symmetry is broken so that the system starts swimming. Viewed within a cross section, this system resembles a two-dimensional flapping fin with its leading edge located at the front opening and the trailing edge at the back opening. The feasibility of this system has been proven via numerical simulations using a fluid-structure interaction model based on the immersed-boundary framework. According to the results, at relatively low Reynolds number (O(102)), this simple locomotion method can easily achieve a mean swimming speed of 2 to 3 body lengths per deformation period. Further simulations illustrate the following characteristics: (1) within the chamber, the hydrodynamic interactions among different parts of the body leads to a performance-enhancing mechanism similar to the ground effect; (2) reducing the diameter of the body can strengthen this effect so that both the swimming speed and the energy efficiency are improved; (3) for better performance the amplitude of diameter oscillation at the trailing edge should be larger or at least equal to the one at the leading edge.

Effect of TAVR commissural alignment on coronary flow: A fluid-structure interaction analysis

Background and ObjectivesCoronary obstruction is a complication that may affect patients receiving Transcatheter Aortic Valve Replacement (TAVR), with catastrophic consequences and long-term negative effects. To enable healthy coronary perfusion, it is fundamental to appropriately position the device with respect to the coronary ostia. Nonetheless, most TAVR delivery systems do not control commissural alignment to do so. Moreover, no in silico study has directly assessed the effect of commissural alignment on coronary perfusion. This work aims to evaluate the effect of TAVR commissural alignment on coronary perfusion and device performance. MethodsA two-way computational fluid-structure interaction model is used to predict coronary perfusion at different commissural alignments. Moreover, in each scenario, hemodynamic biomarkers are evaluated to assess device performance. ResultsCommissural misalignment is shown to reduce the total coronary perfusion by −3.2% and the flow rate to a single coronary branch by −6.8%. It is also observed to impair valvular function by reducing the systolic geometric orifice area by −2.5% and increasing the systolic transvalvular pressure gradients by +5.3% and the diastolic leaflet stresses by +16.0%. ConclusionsThe present TAVR patient model indicates that coronary perfusion, hemodynamic and structural performance are minimized when the prosthesis commissures are fully misaligned with the native ones. These results support the importance of enabling axial control in new TAVR delivery catheter systems and defining recommended values of commissural alignment in upcoming clinical treatment guidelines.

Simulating the mechanical stimulation of cells on a porous hydrogel scaffold using an FSI model to predict cell differentiation.

3D-structured hydrogel scaffolds are frequently used in tissue engineering applications as they can provide a supportive and biocompatible environment for the growth and regeneration of new tissue. Hydrogel scaffolds seeded with human mesenchymal stem cells (MSCs) can be mechanically stimulated in bioreactors to promote the formation of cartilage or bone tissue. Although in vitro and in vivo experiments are necessary to understand the biological response of cells and tissues to mechanical stimulation, in silico methods are cost-effective and powerful approaches that can support these experimental investigations. In this study, we simulated the fluid-structure interaction (FSI) to predict cell differentiation on the entire surface of a 3D-structured hydrogel scaffold seeded with cells due to dynamic compressive load stimulation. The computational FSI model made it possible to simultaneously investigate the influence of both mechanical deformation and flow of the culture medium on the cells on the scaffold surface during stimulation. The transient one-way FSI model thus opens up significantly more possibilities for predicting cell differentiation in mechanically stimulated scaffolds than previous static microscale computational approaches used in mechanobiology. In a first parameter study, the impact of the amplitude of a sinusoidal compression ranging from 1% to 10% on the phenotype of cells seeded on a porous hydrogel scaffold was analyzed. The simulation results show that the number of cells differentiating into bone tissue gradually decreases with increasing compression amplitude, while differentiation into cartilage cells initially multiplied with increasing compression amplitude in the range of 2% up to 7% and then decreased. Fibrous cell differentiation was predicted from a compression of 5% and increased moderately up to a compression of 10%. At high compression amplitudes of 9% and 10%, negligible areas on the scaffold surface experienced high stimuli where no cell differentiation could occur. In summary, this study shows that simulation of the FSI system is a versatile approach in computational mechanobiology that can be used to study the effects of, for example, different scaffold designs and stimulation parameters on cell differentiation in mechanically stimulated 3D-structured scaffolds.

A hybrid Finite Volume-Smoothed Particle Hydrodynamics approach for shock capturing applications

A hybrid Finite Volume Method (FVM)-Smoothed Particle Hydrodynamics (SPH) approach for shock capturing in compressible fluids is presented. The Python framework Pyro2 is employed to simulate a coarse FVM mesh, while the Python framework PySPH is utilized to model the fluid in regions with high gradients through SPH particles. New FVM-SPH coupling approaches are explored, including online SPH particle injection and deletion around time persistent SPH regions around shocks. The SPH simulation is coupled to the FVM simulation through boundary particles surrounding the active SPH regions, with FVM values interpolated to SPH boundary particles at each timestep. Values in active SPH regions are similarly interpolated to the overlying FVM mesh at each timestep. The performance of the hybrid FVM-SPH scheme in comparison to the individual FVM and SPH methods on two-dimensional test problems is assessed. Our results indicate that the hybrid approach offers higher computational efficiency than SPH while preserving its accuracy, although it remains slower than the FVM. This hybrid SPH algorithm may enable future compressible fluid simulations with more extensive capabilities than grid-based methods alone, offering potential applications in modeling fluid–structure interaction and solid deformation and fracturing in blast simulations. Further research will focus on applying the hybrid method to three-dimensional blast problems, where the relative computational cost reduction is expected to be more significant.

A computational study of fluid transport characteristics in the brain parenchyma of dementia subtypes

The cerebral environment is a complex system consisting of parenchymal tissue and multiple fluids. Dementia is a common class of neurodegenerative diseases, caused by structural damages and functional deficits in the cerebral environment. In order to better understand the pathology of dementia from a cerebral fluid transport angle and provide clearer evidence that could help differentiate between dementia subtypes, such as Alzheimer’s disease and vascular dementia, we conducted fluid–structure interaction modelling of the brain using a multiple-network poroelasticity model, which considers both neuropathological and cerebrovascular factors. The parenchyma was further subdivided and labelled into parcellations to obtain more localised and detailed data. The numerical results were converted to computed functional images by an in-house workflow. Different cerebral blood flow (CBF) and cerebrospinal fluid (CSF) clearance abnormalities were identified in the modelling results, when comparing Alzheimer’s disease and vascular dementia. This paper presents our preliminary results as a proof of concept for a novel clinical diagnostic tool, and paves the way for a larger clinical study.

Effect of Groove Structure on Lubrication Performance of Water-Lubricated Stern Tube Bearings

This study investigated the lubrication characteristics (i.e., the groove ratio and width) of water-lubricated stern tube bearings, based on the flexibility matrix method and lubrication theory. Considering the elastic deformation of the lining, a fluid structure interaction (FSI) model of the surface micro-groove texture of a water-lubricated stern tube bearing was established, and the correctness and rationality of the model were verified by experiments. Micro-grooved, surface-lubricated, water-lubricated stern tube bearings with three different cross-sectional shapes (rectangular, circular, and triangular) were designed. The influences of the groove area ratio and width on the bearing load-carrying capacity and friction coefficient were analyzed. At a groove area ratio of 0.31, the load-carrying capacity of the rectangular grooved stern tube bearing reached the maximum value and the friction coefficient reached the minimum value. It is recommended to design and use water-lubricated stern tube bearings, especially Thordon water-lubricated stern tube bearings, with rectangular micro-grooves, with a groove area ratio of 0.30–0.32, so that the best lubrication performance can be obtained. With the increase in the micro-groove width, the lubrication of water-lubricated stern tube bearings with partial rectangular micro-grooves is significantly better than that of others. Under the same conditions, the bearing load-carrying capacity and friction performance of local groove stern bearings is significantly better than that of global groove stern bearings.