Peripheral artery disease (PAD) is a leading cause of limb loss and morbidity worldwide, with chronic limb-threatening ischemia (CLTI) representing its most severe presentation. Although image-guided endovascular interventions are routinely performed, clinicians currently lack tools that provide real-time, patient-specific predictions of hemodynamic outcomes to guide revascularization decisions. Existing computational fluid dynamics (CFD) approaches can recover pre-operative hemodynamics but are typically too slow or insufficiently integrated into clinical workflows to support interactive, intraoperative planning. We extend HarVI (HARVEY Virtual Intervention), a previously established digital twin framework, to the peripheral circulation and evaluate its use for real-time prediction of postoperative blood flow in patients with superficial femoral artery (SFA) lesions. HarVI integrates one-dimensional CFD with machine learning to enable rapid assessment of patient-specific revascularization strategies. Key components include: (1) automated boundary condition tuning using patient-averaged and optimization-based approaches; (2) simulation of a wide range of endovascular interventions via a machine-learned surrogate model; and (3) validation of predicted postoperative hemodynamics against clinical duplex ultrasound measurements. Performance was evaluated retrospectively in a cohort of seven patients with SFA disease. HarVI accurately predicted postoperative peak systolic velocities and reproduced full 1D CFD results across a synthetic revascularization landscape. Surrogate model predictions closely matched high-fidelity simulations while enabling rapid exploration of intervention scenarios, supporting near-real-time evaluation of treatment options. These results establish HarVI as a promising digital twin platform for real-time, patient-specific intervention planning in PAD. By enabling rapid, data-driven prediction of postoperative hemodynamics, HarVI opens the door to interactive intraoperative decision support with the potential to improve revascularization outcomes in patients with CLTI.

Coupling computational fluid dynamics and kinetic models using a compartmental model applied to a full-scale agricultural digester.

Characterization of leak flow from pipes under the effect of cavitation in water distribution systems.

The presented case study benchmarks a novel design approach for evaluating the survival function of multidimensional dynamic systems subjected to stochastic, nonstationary environmental loading, with particular focus on naval architecture. The proposed design methodology combines a novel log-integral concept of the Integrated Cumulative Distribution Function (ICDF) for accurate modeling of failure probabilities with the Smoothed Particle Hydrodynamics (SPH) Computational Fluid Dynamics (CFD) method to simulate slamming forces on the vessel hull mid-section. The proposed design approach offers a robust tool for reliability and safety assessment of vessels and offshore structures, particularly in complex, nonlinear, adverse marine environments. A traditional four-parameter Weibull parametric fit is used to cross-validate the predicted design values. The combination of ICDF and SPH simulations may provide naval architects with a robust framework for enhancing the reliability analysis of marine structures under dynamic, rapidly changing loading conditions. The major novelty of this study lies in combining an SPH-based CFD approach with a successfully benchmarked novel probabilistic integral ICDF extrapolation scheme, which is particularly suitable for design when the underlying dataset is representative but limited in size. System performance or limit-state function depends on multiple random variables (e.g. load, resistance, and environmental factors). This method enables efficient estimation of extreme impact loads, thereby providing practical support for reliability-based design and operational safety assessment of high-speed craft undergoing underwater/above-water entry and exit processes under nonstationary sea conditions. Engineering relevance: assessing the reliability of high-reliability structures where failure probabilities P Failure are low (e.g. ≤ 10 − 6 ). Fundamental design concepts, such as the Most Probable Maximum (MPM) for non-Gaussian processes with clustering effects, are expressed in terms of a memory-modified mean up-crossing rate in a practical engineering context. The presented ICDF design scheme is shown to provide enhanced accuracy to the design values and P Failure estimates, when the underlying data sample is of limited size.

The objective of this work is to develop a one-way partitioned coupling computational method to account for aerodynamic effects, dynamic structural deformation, and fatigue damage of a 5[Formula: see text]MW wind turbine blade. We accurately reproduce a wind flow field using a large eddy simulation (LES)-based computational fluid dynamics (CFD) approach with a rotating high-fidelity wind turbine model. A high-fidelity finite element structural model of the blade is also constructed, where laminated composite solid elements are used for mesh discretization. A dynamic finite element method is employed for blade deformation analysis. The aerodynamic loading history calculated by the LES analysis is applied onto the blade surface as a loading boundary condition of the dynamic structural analysis via a one-way partitioned fluid–structure interaction (FSI) method. A fatigue damage distribution of the whole blade structure is finally estimated using an engineering fatigue life model with the help of stress history information outputted from the structural analysis. Based on the developed method, a high-performance computational system that combines a parallel finite element LES code named FrontFlow/Blue (FFB), a parallel data coupling tool named REVOCAP_Coupler, a parallel structural analysis code named ADVENTURE_Solid, and a fatigue evaluation tool named ADVENTURE_Fatigue is established on a latest high-performance computing environment (i.e., Supercomputer Fugaku). The effectiveness and accuracy of the computational system are first validated in terms of aerodynamic results and dynamic behaviors of the blade model. Finally, some parametric studies are performed to investigate the effects of gravitational and centrifugal forces, shear of atmospheric boundary layer, and tip speed ratios on structural behaviors and fatigue damages of the 5[Formula: see text]MW turbine blade.

Wave bioreactors are commonly used in biopharmaceutical upstream processes as an intermediate stage between shake flasks and stirred tanks within the seed train. They offer a controlled environment for cell cultivation while minimizing shear stress. Accurate characterization of these systems is essential for optimizing cell culture performance, particularly as state of the art cell lines require higher volumetric mass transfer coefficients k L a. This study aims to determine the volumetric mass transfer coefficient through experiments and computational fluid dynamics (CFD) simulations. An improved experimental method for the measurements of the volumetric mass transfer is presented, with results correlated to key process parameters: rocking angle, rocking rate, and filling volume. In addition, CFD simulations were caried out using M-Star CFD by means of a Lattice-Boltzmann Method-based solver. The mass transfer was calculated using Higbie's penetration theory, incorporating the Kolmogorov scale to define contact time. The analysis also integrates concepts from Friedl and the surface renewal model, introducing the surface normal velocity as an additional parameter in the mass transfer coefficient k L calculation. Analyzes were carried out for 10 and 50L wave bioreactors, with one degree of freedom movement. Optimized process parameters were identified and validated in biological cultivations, resulting in increased dissolved oxygen levels in the medium. These findings contribute to improved characterization and control of wave bioreactors, enabling more accurate prediction of process parameter effects.

Gas turbines (GTs) operating in offshore environments are highly vulnerable to performance degradation from airborne contaminants such as salt aerosols, mist, hydrocarbons, and particulate matter. This study develops and validates a computational fluid dynamics (CFD) model to optimize a multistage inlet-air filtration system for offshore GT applications, complementing prior experimental investigations. A three-dimensional CAD model of a wind tunnel housing six ASHRAE filter classes (F7, H12, E11, E10, G5, F9) was created in ANSYS Design Modeler, and simulations were performed under steady-state and transient conditions using Navier–Stokes, turbulence, and particle transport models. Contaminant mass loadings from 20–100% were evaluated at inlet velocities of 5 m/s and 10 m/s to characterize airflow distribution, static and total pressures, and filtration efficiency. Results revealed peak inlet velocities up to nine times the free-stream value, with mass flow concentration opposite the vertical inflow reaching 8.4 kg/s. Static and total pressures decreased progressively downstream, with the highest pressure drops occurring at 80% contaminant loading, indicating increased flow resistance. Transient analyses showed filtration efficiency degradation over time due to fouling. Model predictions for total pressure drop and volumetric flow rate deviated by ≤10% from experimental data, confirming robustness and accuracy. This work offers validated CFD insights into the complex aero–particle dynamics in offshore GT inlet filtration, providing a predictive framework for optimizing filter design, selection, and maintenance to enhance long-term turbine reliability and efficiency.

Maintaining adequate root-zone temperature in solar greenhouses during extreme cold is crucial for crop production. This study investigated the optimization of an auxiliary biomass heating system in a solar greenhouse. The heating performance was evaluated using an integrated methodology that combined orthogonal experimental design, Computational Fluid Dynamics (CFD) simulation, and Machine Learning (ML) surrogate modeling. First, a reliable CFD model, validated against experimental data (Index of Agreement, IA = 0.954), was used to generate high-fidelity temperature field data for nine layout schemes. Parameter sensitivity analysis revealed that the burning cave Diameter is the dominant factor (R = 6.01), followed by burial Depth (R = 2.00), with inter-pool Spacing having the least impact (R = 0.89). Subsequently, six ML algorithms were compared for use as a predictive surrogate model, with Lasso Regression demonstrating superior performance (R2 = 0.934). Comprehensive optimization focused on maximizing the Suitable Area Ratio (Rs) in the critical 0.2 m depth root zone. The analysis conclusively identified the 2.5 m diameter group as optimal, achieving a maximum Rs of 90% and the lowest temperature standard deviation. The final recommended optimal design (2.5 m diameter, 0.7 m depth, 10 m spacing) significantly improves heating uniformity and efficiency. This integrated CFD-ML approach provides a scientific basis and a rapid assessment tool for the design and structural optimization of similar underground thermal systems in cold-climate agriculture.

This study employed a combined approach of computational fluid dynamics (CFD), numerical simulations, and wind tunnel tests to investigate droplet drift characteristics and develop prediction models in order to address the issues of low pesticide utilization rates and high drift risk, associated with droplet drift during agricultural unmanned aerial vehicle (UAV) spraying, as well as the unreliable results of field experiments. Firstly, a numerical model of the rotor wind field was established using the multiple reference frame (MRF) method, while the realizable k-ε turbulence model was employed to analyze the flow field. The model’s reliability was verified through wind field tests. Next, the Euler–Lagrange method was used to couple the wind field with droplet movement. The drift characteristics of two flat-fan nozzles (FP90-02 and F80-02) were then compared and analyzed. The results showed that the relative error between the simulated and wind tunnel test values was within 20%. Centrifugal nozzle experiments were carried out using single-factor and orthogonal designs to analyze the effects of flight height, rotor wind speed, flight speed, and droplet size on drift. The priority order of influence was found to be “rotor wind speed > flight height > flight speed”, while droplet size (DV50 = 100–300 µm) was found to have no significant effect. Based on the simulation data, a multiple linear regression drift prediction model was constructed with a goodness of fit R2 value of 0.9704. Under the verification condition, the relative error between the predicted and simulated values was approximately 10%. These results can provide a theoretical basis and practical guidance for assessing drift risk and optimizing operational parameters for agricultural UAVs.

An in-house computational fluid dynamics (CFD) code based on the lattice Boltzmann method has been used to investigate the wake interactions between multiple wind turbines. This study assesses the accuracy of the numerical method by conducting a comparison computation on the Blind Test 2 (BT2) experiment, which aims to evaluate turbine performance and wake characteristics, such as mean velocity and turbulence intensity, for two turbines aligned in tandem and operating at different rotational speeds. In the present simulation, a hybrid approach is proposed for modeling the wind turbines, in which the blades are modeled using the actuator line method, and the nacelle and other structures are treated as solid bodies with the wall boundary condition implemented through an interpolated bounce-back scheme. The numerical results are validated against BT2 data and three earlier CFD studies. A mesh convergence test at four different resolutions shows excellent performance in predicting velocity deficits; moreover, the method effectively reproduces turbulence intensity profiles. Compared to previous CFD approaches, this hybrid method achieves superior accuracy, especially in regions where the nacelle and tower have a significant impact. Visualization of the three-dimensional flow field reveals that wake structures become highly asymmetrical due to the influence of the tower, and blade-tip vortices from the downstream turbine are breaking up in the high tip speed ratio region. Finally, the turbulence characteristics are analyzed based on three velocity fluctuation components, showing that the turbulence behind the nacelle and tower is isotropic. In contrast, the turbulence behind the rotor tips is highly anisotropic.

Computational fluid dynamics (CFD) simulations are essential for studying cardiovascular diseases, particularly atherosclerosis. The left coronary artery (LCA) and its bifurcations, which supply blood to a large portion of the myocardium, are recognized as high-risk sites for plaque formation. This systematic review addresses the question: “What are the main CFD approaches, modeling strategies, and hemodynamic parameters used to study LCA bifurcation over the last decade, and how have these advanced the understanding of atherosclerosis progression?” A comprehensive search was conducted in PubMed, Web of Science, and Scopus databases. Following preferred reporting items for systematic reviews and meta-analyses guidelines, the search focused on original studies published within the last decade employing CFD to analyze LCA bifurcation hemodynamics. The review includes 108 CFD studies, mostly three-dimensional (3D) simulations (104 studies). Forty-seven used idealized 3D LCA models and 48 used patient-specific models focusing on bifurcation. Recent studies increasingly focus on hemodynamic parameters linked to atherosclerosis and the impact of stenosis and post-stenting restenosis on local blood flow. Simulations incorporating physiological features, such as fluid–structure interaction, cardiac motion, and blood rheology, are also included. Additionally, artificial intelligence is being integrated to efficiently predict hemodynamic parameters, reducing simulation time and cost. There is a clear trend toward using high-resolution imaging and advanced modeling for patient-specific CFD simulations. Despite methodological differences, disturbed flow patterns, low wall shear stress, and high oscillatory shear index are studied in the LCA bifurcation, supporting their role in atherogenesis. Standardizing protocols and integrating clinical data are essential for translating CFD findings into practice.

Accurate wind prediction over complex coastal terrain is essential for mitigating the stochasticity and uncertainty of wind energy in power systems. This study focuses on day-ahead hourly wind speed forecasting for the coastal region around meteorological station no. 45032 in Hong Kong and develops a multiscale prediction methodology that combines a mesoscale Weather Research and Forecasting (WRF) model with a microscale computational fluid dynamics (CFD) model. Based on high-resolution WRF forecasts, the coupling framework dynamically assigns CFD open boundary conditions across multiple grid nodes and directions and incorporates momentum source terms and a relaxation zone in the CFD model to address inconsistencies arising from dynamical downscaling and turbulence parameterization. Results show that high-resolution WRF reduces the day-ahead hourly mean absolute percentage error (MAPE) from 37.2% to 22.8%, with the coupled WRF–CFD model further lowering the MAPE to 14.8%. Furthermore, the coupled WRF–CFD approach accurately captures terrain-induced wind variations, including the speed-up effect over ridges and the sheltering effect in valleys. The proposed forecasting framework achieves both high predictive accuracy and good timeliness, providing a practical and generalizable solution for refined wind resource assessment and wind farm planning in complex terrain.

Jet flow is a crucial fluid dynamic phenomenon that has been extensively studied. It is essential for various industrial applications, including surface cleaning, flow control, and cooling electronic components. Offset jet is an innovation in jet flow configuration that offers advantages in flow pattern control by expanding the impingement area and regulating surface pressure distribution. This study employed a Computational Fluid Dynamics (CFD) approach to investigate the influence of variations in the offset jet ratio on the aerodynamic characteristics of the flow, specifically the impingement zone area, pressure coefficient distribution, and skin friction coefficient. The standard k-ε turbulence model, utilizing a structured mesh and a Reynolds number of 10,000, was employed in this research. The number of mesh elements used was a fine mesh of 200,000 with an error percentage of 0.09436%. The results of the study show that an offset ratio of 3 produces the highest cf value of 0.0047 and a stable Cp distribution of 0.218, while also providing the best impingement zone area. These findings indicate that OR 3 is the most optimal configuration in terms of aerodynamics for precision system applications, with a focus on flow pattern control and wide impingement zone coverage.

The kinematic and dynamic characteristics of the grinding media during the wet grinding process are investigated using a coupled Discrete Element Method (DEM)–Computational Fluid Dynamics (CFD) approach. Firstly, a coupled DEM-CFD model of the vertical spiral agitator mill is established and validated with experimental torque measurements. Subsequently, a velocity analysis model is established using the vector decomposition method. The cylinder is then divided into multiple regions along its radial and axial directions. The effects of spiral agitator rotational speed, diameter, pitch, and media filling level are investigated with respect to the circumferential velocity, axial velocity, collision frequency, effective energy between media, and energy loss of the grinding media. The average effective energy between media is an innovative metric for evaluating the grinding effect. The results indicate that the peripheral region of the spiral agitator demonstrates superior kinematic and dynamic performance. The rotational speed of the spiral agitator exerts a highly significant influence on the kinematic and dynamic characteristics of the media. With a maximum rise of 0.2 m/s in circumferential velocity and a 16.7 J gain in total energy. The media filling level demonstrates a negligible influence on media kinematics, while it profoundly affects dynamic properties, evidenced by a substantial increase of 83.09 J in the total media–media energy. As the diameter increases, the peak media circumferential velocity shifts outward, and the total media–media energy rises by 5.4 J. The spiral agitator pitch has a minimal impact on both the kinematic and dynamic characteristics of the media.

This paper presents a numerical study of thermal pollution from a potential discharge of heated water from a nuclear power plant cooling system into the coastal zone of Lake Balkhash. The aim of the study was to determine the dynamics of the formation and spatial propagation of the thermal footprint, as well as to assess its impact on the hydrodynamic characteristics of the water area. A two-dimensional computational fluid dynamic (CFD) model based on the Navier-Stokes and energy equations, supplemented by a k-ω SST turbulence model, was used for the simulation. The simulation was conducted by using real shoreline geometry. The model was validated using a test jet injection problem, which demonstrated good agreement with experimental data. Modeling results showed that within the first hour after the discharge begins, a stable zone of thermal pollution, approximately 1.5 km² in area, forms, where the water temperature exceeds background values by 0.5–1.0 K. The high-temperature plume gradually transforms into a diffuse heat spot, which persists in the coastal zone due to weak water exchange and recirculation. Analysis of the velocity field revealed zones of local circulation that facilitate long-term heat retention. The scientific novelty of this study lies in the application of a CFD approach to assessing the thermohydrodynamics of the discharge under conditions of limited water exchange in a large lake with the actual morphology of the shoreline of Lake Balkhash. The practical significance lies in the potential use of the developed method for predicting environmental risks and optimizing the cooling systems of nuclear and thermal power plants located near the lake.

Objective: This study aims to investigate the local pressure losses for conical reducers used in sprinkler irrigation systems using experimental, analytical, and Computational Fluid Dynamics (CFD) methods. Material and Methods: Eight different reducers with nominal outer diameters of 90-75, 110-90, and 110-75 mm were considered. In the experiments, the pressure losses in the reducers were measured at different water flow rates. The CFD analysis was carried out using the Realizable k-ε, SST k-, and RSM turbulence models. The pressure loss coefficients were determined by measurements, analytically, and CFD analysis and were compared with each other. Results: Taking the experimental data into account, the local loss coefficients for the R19075, R29075, R411090, and R511090, reducers were determined to be values between 0.5 and 1.0. The R611075, and R711075, R811075 reducers local loss coefficients between 0.8 and 1.5 were determined. The local loss coefficients determined using the SST k- turbulence model considered in the CFD analysis were in better agreement with the experimental results. Conclusion: It can be said that the pressure losses in the newly designed reducers could be determined by the CFD analysis at the design stage, and it would be useful to use these values in the system design.

Drill pipe pullback gravel packing is a novel sand control method for marine natural gas hydrate reservoirs, enabling rapid and uniform filling by synchronizing fluid injection with pipe retraction. However, the complex liquid–solid two-phase flow mechanisms and parameter sensitivities in this dynamic process remain unclear. To address this gap, a coupled Computational Fluid Dynamics and Discrete Element Method (CFD-DEM) approach is adopted in accordance with the trial production requirements in the South China Sea. This investigation systematically analyzes the relative contributions of injection rate (0.8–2.2 m3/min) and sand-carrying ratio (30–60%) to the packing effectiveness. Additionally, the effects of carrier fluid viscosity and drill pipe pullback speed are explored. Results show that injection rate and sand-carrying ratio positively affect performance, with sand-carrying ratio as the decisive factor, exhibiting an impact approximately 73 times greater than that of the injection rate. Optimal parameters in this study are injection rate of 2.2 m3/min and sand-carrying ratio of 60%, which yield the highest gravel volume fraction and stable bed height. Furthermore, it is also found that while increasing carrier fluid viscosity improves bed height, excessive viscosity hinders particle settling and compaction. Similarly, a trade-off exists for the pullback speed to balance packing density and pipe burial risks. These findings provide a theoretical basis for optimizing sand control operations in hydrate trial productions.

Unthrottled Continuously Variable Valve Lift (CVVL) engines adopting the Early Intake Valve Closure (EIVC) strategy exhibit weak in-cylinder turbulence under low loads, leading to prolonged combustion duration. This study optimizes the combustion chamber of an E10-fueled CVVL engine (equipped with advanced intake port) to resolve this low-load issue while preserving high-load efficiency. It incorporates three core innovations: (1) For the first time, the performance of cylinder wall-side and spark plug-side valve masking structures is systematically compared under identical CVVL operating conditions; (2) It innovatively investigates the interaction between an advanced high-tumble intake port design and the CVVL operating mechanism; (3) It explores masking technology application in E10-fueled CVVL systems. The research employs an experimentally validated CFD approach. At 2000 r/min, simulations were performed for two masking structures (Cases 1 and 2) and a baseline (no masking) under partial-load (brake mean effective pressure, BMEP = 0.4 MPa) and high-load (BMEP = 1.1 MPa) conditions. Key results show: under partial load, the low-lift characteristic of CVVL conflicts with the advanced intake port’s flow-guiding principle, inhibiting tumble formation. Among the configurations, spark plug-side masking outperforms cylinder wall-side masking and the baseline significantly: it increases turbulent kinetic energy (TKE) at ignition timing by 89% (vs 6% for Case 1), shortens 10%–90% combustion duration by 26.4%, raises in-cylinder peak pressure by 13.3%, and has lower volumetric efficiency loss (9.4% vs 13.4% for Case 1) and reduced pumping losses. Under high load, the advanced intake port dominates in-cylinder flow, making masking’s effect on engine performance negligible. This study confirms that the synergistic effect of spark plug-side masking and the advanced intake port effectively alleviates low-load combustion deterioration in E10-fueled unthrottled CVVL engines, providing a feasible solution to balance combustion stability and efficiency.

Branched horizontal wells are widely applied in oil and gas development. However, their complex structures make cuttings transport and deposition problems more pronounced. In this study, a three-dimensional branched wellbore model was established based on an intelligent drilling and completion simulation system. A computational fluid dynamics (CFD) approach, incorporating the Eulerian–Eulerian two-fluid model and the kinetic theory of granular flow, was employed to investigate the effects of wellbore diameter, eccentricity, curvature, flow rate, and rheological parameters on cuttings transport behavior. Results from the steady-state simulations indicate that increasing the wellbore diameter and eccentricity intensifies cuttings deposition at the connection section, with the lower-region concentration rising significantly as the eccentricity increases from 0% to 60%. A larger curvature enhances local flow disturbance but reduces the overall cuttings transport efficiency. Increasing the flow rate improves hole cleaning but may promote cuttings accumulation near the bottom of the main wellbore. As the flow behavior index increases from 0.4 to 0.8, the average cuttings concentration rises from 0.0996 to 0.1008, and the pressure drop increases from 1,010,894 Pa to 1,042,880 Pa, indicating improved transport capacity but higher energy consumption. Experimental results are consistent with the numerical simulation trends, confirming the model’s reliability. This study provides both theoretical and experimental support for optimizing complex wellbore structures and drilling fluid parameters.

Additive manufacturing (AM) with clay and ceramic-based materials is gaining momentum as a sustainable alternative in construction, yet its advancement depends on bridging experimental practice with predictive modeling. This review synthesizes advances in mathematical formulations and numerical tools applied to clay, geopolymers, alumina, and related extrusion-based pastes. Classical rheological models, including the Bingham and Herschel–Bulkley formulations, remain central for characterizing yield stress, structuration, and flow stability. Meanwhile, finite element (FEM) and computational fluid dynamics (CFD) approaches are increasingly supporting predictions of deformation, shrinkage, drying, and sintering. Despite these advances, their application to natural clay systems remains limited due to heterogeneity, moisture sensitivity, and the lack of standardized constitutive parameters. Recent studies emphasize that validation is essential: rheometry, layer stability tests, in situ monitoring, and prototyping provide necessary calibration for reliable simulation. In parallel, parametric and generative design workflows, particularly through Rhino and Grasshopper ecosystems, illustrate how digital methods can link geometric logic, fabrication constraints, and performance criteria. Overall, the literature demonstrates a transition from isolated modeling efforts toward integrated, iterative frameworks where rheology, numerical simulation, and experimental validation converge to improve predictability, reduce trial-and-error, and advance scalable and sustainable clay- and ceramic-based AM.