Computational Fluid Dynamics Simulations Research Articles

Thermally Conductive Polydimethylsiloxane-Based Composite with Vertically Aligned Hexagonal Boron Nitride

The considerable heat generated in electronic devices, resulting from their high-power consumption and dense component integration, underscores the importance of developing effective thermal interface materials. While composite materials are ideal for this application, the random distribution of filling materials leads to numerous interfaces, limiting improvements in thermal transfer capabilities. An effective method to improve the thermal conductivity of composites is the alignment of anisotropic fillers, such as hexagonal boron nitride (BN). In this study, the repeat blade coating method was employed to horizontally align BN within a polydimethylsiloxane (PDMS) matrix, followed by flipping and cutting to prepare BN/PDMS composites with vertically aligned BN (V-BP). The V-BP composite with 30 wt.% BN exhibited an enhanced out-of-plane thermal conductivity of up to 1.24 W/mK. Compared to the PDMS, the V-BP composite exhibited outstanding heat dissipation capacities. In addition, its low density and exceptional electrical insulation properties showcase its potential for being used in electronic devices. The impact of coating velocity on the performance of the composites was further studied through computational fluid dynamics simulation. The results showed that increasing the coating velocity enhanced the out-of-plane thermal conductivity of the V-BP composite by approximately 40% compared to those prepared at slower coating velocities. This study provides a promising approach for producing thermal interface materials on a large scale to effectively dissipate the accumulated heat in densely integrated electronic devices.

Computational Fluid Dynamics Analysis and Optimization of a Doublesuction Turbine Agitator

Background: As one of the essential pieces of chemical equipment, a reactor provides the necessary reaction space and conditions for the materials involved in the reaction during the stirring process. However, under typical operating conditions, issues such as uneven gas distribution, suboptimal gas-liquid mixing, and low product yield often arise in gas-liquid phase reactors. Purpose: To address the issues prevalent in current stirred reactors, a new design for a stirred reactor equipped with a double-suction turbine agitator was developed. Method: In this paper, a stirred reactor equipped with a double-suction turbine agitator was designed, and its three-dimensional modeling was conducted using SolidWorks. Computational Fluid Dynamics (CFD) simulations, based on the Euler-Euler two-phase approach with the RNG k −ε turbulence model, were performed to assess variables such as stirring speed, installation height, blade diameter and agitator inner diameter. The dispersion characteristics and flow field behaviors of the gas-liquid two-phase under varying conditions were comparatively analyzed. Optimizations were conducted across various parameters to enhance the gas mixing efficiency in the liquid phase. Result: The results show that a diameter of 370mm for the double-suction turbine agitator, an installation height of 640mm, a blade diameter of 500mm, and an inner hole diameter of 200mm yield optimal gas-liquid two-phase mixing performance. This configuration results in a broad and uniform gas distribution within the reactor, maintaining a desired high level of gas holdup at specific positions. Conclusion: The double suction turbine agitator is a type of radial agitator. During operation, it induces significant centrifugal forces in the liquid, exerts a robust shear effect, and enhances the mixing of the gas-liquid phases, thereby increasing the production efficiency of the product.

How does turbulence intensity affect the fatigue life of composite airfoils based on existing CFD and experimental data?

This research investigates the effect of airflow turbulence intensity on the fatigue life of composite airfoil structures in aerospace engineering. Using advanced Computational Fluid Dynamics (CFD) simulations, this study provides a comprehensive analysis of how varying levels of turbulence intensity—ranging from mild (5%) to severe (30%)—impact the mechanical degradation and fatigue performance of carbon-fiber-reinforced polymers (CFRPs) used in airfoil designs. The research quantifies the relationship between turbulence levels and changes in stress distribution, highlighting critical points of fatigue crack initiation and subsequent propagation. The study includes detailed comparisons of CFD results to experimental data to validate the predictive models and discusses how localized stress fluctuations under high-intensity turbulence accelerate the onset of fatigue failure. We present significant findings that demonstrate a non-linear relationship between turbulence intensity and the reduction in fatigue life, suggesting optimal design modifications for enhanced durability. Furthermore, this paper explores current challenges in merging CFD data with real-world fatigue testing and proposes advanced techniques such as adaptive mesh refinement and hybrid simulation models to improve predictive accuracy and computational efficiency in future research.

Single-Loop Triple-Diameter Pulsating Heat Pipes at Reduced Heat Input: A CFD Study on Inner Diameter Optimization

The geometric configuration, particularly the inner tube diameter, plays a significant role in the thermal performance of pulsating heat pipes (PHPs). Previous experimental research has demonstrated that single-loop triple-diameter PHPs (TD-PHPs) outperform single-loop single-diameter PHPs (SD-PHPs) and dual-diameter PHPs (DD-PHPs) in terms of thermal performance under moderate heating input powers ranging from 25 W to 75 W. However, a reduction in heat input from 75 W to 25 W leads to a diminished impact of TD-PHPs on the thermal performance. Therefore, to improve the overall performance of TD-PHPs, this study used two-dimensional transient computational fluid dynamics simulations to identify the optimal inner tube diameters for TD-PHPs at a low heat input by evaluating the thermal resistance of five TD-PHPs with various inner diameters. The findings reveal that the TD-PHP configuration exhibits minimum thermal resistance, with inner diameters of 4.5 mm for the upper arch (the condenser section), 4.0 mm for the wide branch, and 2.5 mm for the narrow branch, primarily due to its full circulation flow pattern. Furthermore, the overall heat transfer performance of the optimal TD-PHP was compared with that of an SD-PHP at low heat inputs (10 and 18 W), indicating that although the optimal TD-PHP shows lower thermal resistance, it does not significantly affect the start-up time.

A study on the thermal behavior of coal gangue mountains under airflow influence based on MD and CFD simulations

The long-term accumulation of coal gangue can lead to an increase in internal temperature. If the temperature becomes excessive, it can cause spontaneous combustion, posing serious environmental risks. The process is significantly influenced by external airflow. This study analyzes the thermodynamic properties and oxidation heat release rate of coal gangue samples through laboratory experiments. Subsequently, molecular dynamics (MD) simulations are used to investigate the mechanism of oxidation heat release during the heating process of coal gangue under varying oxygen supply conditions. Based on these findings, a coupled three-field model for the heating process of coal gangue mountains is developed. Using computational fluid dynamics (CFD) methods, transient simulations are conducted to analyze the internal thermal behavior of coal gangue mountains under the influence of external airflow, clarifying the risks of spontaneous combustion. The results indicate that after reaching T2 (295.16 °C), coal gangue enters an accelerated oxidation phase, with an increased heat release rate, heightening the risk of spontaneous combustion. The oxidation heat release rate of coal gangue accelerates with rising temperature and oxygen concentration, entering a rapid reaction phase after 300 °C, with a sharp increase in reaction rate. Compared to N2, coal gangue surfaces exhibit stronger adsorption of O2, and temperature changes affect N2 diffusion more sensitively (O2 diffusion coefficient increases by 0.312 Å2/ps, N2 diffusion coefficient increases by 0.334 Å2/ps). The isosteric heat of O2 adsorption in coal gangue decreases significantly with increasing adsorption amount, and higher temperatures hinder competitive adsorption of O2. The temperature in the deep and foot areas of the coal gangue mountains is lower, while the middle and upper areas near the slope surface exhibit higher temperatures, indicating a higher risk of spontaneous combustion in these regions. When environmental wind speed is too high or too low, both the internal temperature of the coal gangue mountains and the area of spontaneous combustion risk zones decrease. At a wind speed of 3 m/s, the spontaneous combustion risk zone reaches its maximum area of 73.47 m2, but when the wind speed increases to 7 m/s, the risk area decreases to 65.72 m2.

Modeling for formation of microdroplets prepared by pulsated orifice ejection method

Abstract This study investigated the preparation of metallic microdroplets by pulsed orifice ejection method. Computational fluid dynamics simulations, combined with volume-of-fluid model, were employed to analyze the jet break-up and droplet formation behaviors of various metallic materials within a certain Weber number (We) range. An energy-based physical model under inlet boundary conditions was established to precisely control the generation of droplets. The results indicate that the break-up state of metal jets is significantly influenced by material properties within the range that We = 12 ∼ 24, which makes the materials exhibiting higher surface tension and density relatively favorable to the production of mono-sized microdroplets. Moreover, the break-up behavior of the jet is closely related to the ratio of volume force to surface tension (Bond number). And the proposed model effectively predicts the properties of droplets. This research provides theoretical guidance and technical support for the precise preparation of metallic microdroplets.

Experimental Characterization of a Bladeless Air Compressor

Abstract The Tesla compressor is an innovative technology that offers a unique approach to fluid compression. Unlike traditional compressors that use rotating blades, bladeless compressors utilize closely spaced disks to create compression. The purpose of this article is to design a prototype Tesla air compressor with optimal design parameters and investigate the performance and loss characteristics based on numerical analysis and experimental demonstration. The prototype model has been numerically investigated at different rotational speeds, and the results have been compared with those obtained in experiments. Computational fluid dynamics (CFD) simulations indicate that the rotor-only efficiency is greater than 90% at very low mass flowrates, while the coupling of the rotor and volute leads to a total-to-static efficiency of approximately 58% (without losses) at 14 g/s. At a nominal mass flow of 4 g/s, the highest total-to-static pressure ratio would be around 1.27. Experimental results indicate leakage losses greatly reduce net mass flow, while pressure ratio values are in good agreement with CFD predictions. During this experiment, a maximum isentropic efficiency of 32.4% is measured. Indeed, the prototype included ventilation and leakage losses, which were not modeled in the CFD analysis. It is remarkable that the compressor does not show any unstable behavior down to zero mass flow (closed valve test), where the CFD and the experiment show consistent pressure ratios. An estimation of the losses from end-wall friction and leakage flow is carried out using numerical simulations at different exit radial clearances. Increasing radial clearance results in an increase in leakage and end-wall power loss, the latter being driven mainly by the axial clearance with the casing, which remained unchanged. To minimize leakage, a Teflon ring has been used as a first measure. Numerical calculations have indicated that the leakage rate is approximately 6 g/s at design speed. A brush seal-type solution can improve the sealing system to reduce leakage.

Optimization design of a two-step microreactor with spiral structure for high-performance catalytic reactions

Abstract In order to enhance the efficiency of diphenyldimethoxysilane preparation in microreactors, this study utilized the computational fluid dynamics simulation based on the finite element method to explore the impact of the internal structural parameters of the spiral two-step microreactor (STMR) on the reaction outcomes, with the aim of optimizing its structure for high-performance catalytic reactions. By designing a microreactor based on the Archimedean spiral shape and introducing two ribbed obstacles, the structure was optimized through adjusting the relevant ratios. The effects of different-sized structures and obstacles within the reaction zone and non-reaction zone on the product concentration and reaction results were discussed. The results demonstrate that lower obstacle heights and smaller aspect ratios (P = 2:7, R = 5:6) are beneficial for improving the reaction efficiency and product concentration. This study offers a theoretical foundation for microreactor design and is anticipated to further drive the development of microreactor technology.

The Effect of Bioalcohol Additives on Biofuel Diesel Engines

This study experimentally investigated a water-cooled four-cylinder turbocharged diesel engine (DE) under different loads and fuel blend ratios. The integration of Computational Fluid Dynamics (CFD) simulations enables a deeper analysis of the combustion process. Through an in-depth analysis of the combustion process, the focus was placed on investigating the specific impacts of ethanol and n-butanol additives on diesel engine performance. Research shows that a fuel mixture consisting of 70% diesel, 10% biodiesel, and 20% ethanol reduced NOx emissions by 5.56% compared to pure diesel at 75% load. Furthermore, this study explores the combustion performance of diesel/biodiesel blended with butanol/ethanol. The findings indicate that n-butanol improves thermal efficiency, particularly at 100% load, with the D70B10E20 and D70B10BU20 blends demonstrating thermal efficiencies of 9.94%and 8.72% higher than that of diesel alone, respectively. All mixed fuels exhibited reduced hydrocarbon and CO emissions under different loads, with a notable reduction in hydrocarbon emissions of 34.4% to 46.1% at 75% load.

The Aerodynamic Assessment and Shot Point Position Accuracy of UAV Seismic Source

An unmanned aerial vehicle (UAV) seismic source, suitable for complex terrains due to its eco-friendly, cost-effective, and efficient nature, operates by hovering and releasing impact objects to generate seismic waves. The aerodynamic behavior of these impact objects is vital for shotpoint position accuracy and drag magnitude. Excessive drag will result in the loss of seismic source energy, whereas the deviations of the shotpoint position will disrupt the geometric structure of the observation system, significantly impacting the quality of seismic data acquisition. Through theoretical calculations, the influencing factors of air resistance and cross-wind deflection are analyzed. Air resistance increases with the increase in mass, cross-sectional area, speed, and drag coefficient. Increasing the mass and throwing height can increase the upper limit of the output, but after the air resistance and gravity are balanced, the output kinetic energy of the drone’s seismic source will no longer increase when the throwing height is increased. Using computational fluid dynamics simulations and field tests, this study examines the aerodynamics of four prevalent impact object designs. The results show that shuttle and spherical structures have the least drag during descent and are less influenced by crosswinds, thus being optimal for this application. Field measurement data are also presented for reference.

Study on a novel dual-row vertical axis wind turbine with J-Shaped blades by using numerical methods and optimization

ABSTRACT This study investigates the benefits of using J-shaped blades in a dual-row Darrieus wind turbine (DDWT), combining two innovative ideas whose simultaneous impact on the self-starting capability of Darrieus turbines has not been explored. The performance of the turbine was simulated using Computational Fluid Dynamics (CFD) and optimized using both the Taguchi method and a combined Machine Learning and Genetic Algorithm (ML-GA) technique. Three main operating parameters, including the tip speed ratio (λ), radial ratio (δ), and angular distance (ϕ) of dual-row turbines, were investigated across four levels using an L25 orthogonal array design. According to the Taguchi analysis, the impact of the parameters on the power output was ranked in the order of λ > δ > ϕ. After comparison, the optimized turbine predicted by the ML-GA technique was chosen due to the superior accuracy of the ML-GA approach compared to the Taguchi method. The optimal configuration of a DDWT with J-shaped blades was predicted at λ = 2.15, δ = 1.39, and ϕ = 75.36, with a Cp of 0.49 based on CFD simulations. Compared to the single-row wind turbine, the proposed turbine showed a significant increase of the Cp at low tip speed ratios resulting in a better self-starting performance.

Multi-objective optimization of a novel hybrid battery thermal management system using response surface method

This study evaluates the thermal performance of a Z-type battery thermal management system (BTMS) designed for nine lithium-ion batteries discharged at a high rate of 5C, using Computational Fluid Dynamics (CFD) simulations. The investigation employs Response Surface Methodology (RSM) to optimize two critical thermal performance parameters: the maximum battery temperature (Tmax) and the maximum temperature difference between cells (ΔTmax). Various cooling strategies are explored to comprehensively assess the BTMS, including natural convection, forced convection, cooling fins, phase change material (PCM), and composite PCM. These methods are analyzed to determine their effectiveness in controlling the thermal behavior of the battery pack. The simulation results indicate that integrating different cooling techniques can significantly lower Tmax from 352.38 K to 309.14 K and reduce ΔTmax from 14.6 K to 3.31 K, depending on the method used. Under critical conditions, such as the failure of the active cooling system, the BTMS still maintained a Tmax of 310.64 K and a ΔTmax of 0.95 K, demonstrating its robustness and reliability. Further optimization identified the ideal configuration for the system, including an inlet air speed of 1.2 m/s, an inlet temperature of 297.15 K, and a PCM thickness of 3.8 mm, achieving optimal thermal performance with a Tmax of 303.97 K and ΔTmax of 3.17 K. This study offers valuable insights into the design and optimization of effective BTMS for enhanced battery safety and longevity.

Ultra-Short-Term Wind Power Forecasting in Complex Terrain: A Physics-Based Approach

This paper proposes a method based on Computational Fluid Dynamics (CFD) and the detection of Wind Energy Extraction Latency for a given wind turbine (WT) designed for ultra-short-term (UST) wind energy forecasting over complex terrain. The core of the suggested modeling approach is the Wind Spatial Extrapolation model (WiSpEx). Measured vertical wind profile data are used as the inlet for stationary CFD simulations to reconstruct the wind flow over a wind farm (WF). This wind field reconstruction helps operators obtain the wind speed and available wind energy at the hub height of the installed WTs, enabling the estimation of their energy production. WT power output is calculated by accounting for the average time it takes for the turbine to adjust its power output in response to changes in wind speed. The proposed method is evaluated with data from two WTs (E40-500, NM 750/48). The wind speed dataset used for this study contains ramp events and wind speeds that range in magnitude from 3 m/s to 18 m/s. The results show that the proposed method can achieve a Symmetric Mean Absolute Percentage Error (SMAPE) of 8.44% for E40-500 and 9.26% for NM 750/48, even with significant simplifications, while the SMAPE of the persistence model is above 15.03% for E40-500 and 16.12% for NM 750/48. Each forecast requires less than two minutes of computational time on a low-cost commercial platform. This performance is comparable to state-of-the-art methods and significantly faster than time-dependent simulations. Such simulations necessitate excessive computational resources, making them impractical for online forecasting.

Measurement of intelligent computing via Levenberg Marquardt algorithm (LMA) for accurate prediction of fluid forces in a transient non-Newtonian thermal flow

Predicting precise results for the quantities of interest in time-dependent Computational Fluid Dynamics (CFD) simulations requires a significant investment of computational resources and time. To get around these issues, CFD simulations have been joined with Artificial Neural Networks (ANN). An optimally configured artificial neural network (ANN) is given the training and validation data sets produced by computational fluid dynamics (CFD). The flow around a cylinder, which is a well-known benchmark problem for incompressible flows, has been taken into consideration by the hybrid-CFD system. The mathematical model is based on the non-stationary Navier-Stokes and energy equations with viscosity. The basic architecture of the ANN model consists of 10 hidden layers, three output levels, and five input layers. Fast second-order LMA, a top-tier approach, was used to train the network. Both the Mean Square Error (MSE) and the coefficient of determination (R) provide statistical evidence that the ANN projected values for the drag and lift coefficients and average Nusselt number obtained from the finite element analysis are accurate. This analysis suggests that ANNs have the potential to significantly cut down on the amount of time and energy needed to run time-dependent simulations.

Explainable and Reduced-Feature Machine learning models for shape and drag prediction of a freely moving drop in the sub-critical Weber number regime

Accurately predicting the shape and drag of a moving drop is crucial in many spray applications. However, due to the complex interaction between the drag force and drop shape deformation, accurate prediction of drop shape and drag from Computational Fluid Dynamics (CFD) simulation requires large computational resources and time. A novel data-driven approach using NARXNN (Non-linear Auto Regressive eXogenous input Neural Network) is proposed in this study, which recurrently predicts the drop shape and drag for a given Weber number (We) and Reynolds number (Re), in the sub-critical We regime where there is no drop breakup. The average error in radius and drag coefficient for the test cases, as low as 0.36% and 0.49%, respectively, is achieved. Most importantly, SHAP (SHapley Additive exPlanations) analysis is performed to identify important features for the predictions, hence enabling the explainability of the physics involved in the predictions of machine learning (ML) models for drop deformation and the interaction between the drop and gas flows. Global interpretation of SHAP values identifies We and Re as the most important features to predict the drop shape and drag. Based on the SHAP analysis, several reduced-feature models are developed. All the reduced-feature models can predict drop shape and drag coefficient within 4% average error in radius and drag coefficient for any We and Re combinations within the prescribed parameter space. The ML models developed in this study demonstrate a tremendous computational advantage over the traditional CFD simulations. The drop shape and drag coefficient evolution can be predicted within seconds for a single case, whereas the CFD simulation takes several days to a week on a single processor.

State-of-the-Art CFD Simulation: A Review of Techniques, Validation Methods, and Application Scenarios

This review examines recent advancements in Computational Fluid Dynamics (CFD) simulation, focusing on state-of-the-art techniques, validation methods, and their application across various fields. CFD techniques, including Direct Numerical Simulation (DNS), Large Eddy Simulation (LES), Reynolds-Averaged Navier-Stokes (RANS), and hybrid methods, are compared in terms of computational demands, accuracy, and suitability for different fluid flow scenarios. Effective validation methods such as wind tunnel testing, analytical solutions, and field measurements are highlighted to ensure simulation reliability and align models with experimental data. Additionally, this review explores application scenarios in aerospace, automotive, environmental, and biomedical engineering, discussing how each field adapts CFD techniques to optimize performance and ensure realistic simulation outcomes. The comparative analysis offers insights into the trade-offs between accuracy and computational cost. It underscores the need for careful technique selection and robust validation to enhance CFD's applicability in both research and industry. The review aims to guide practitioners in selecting the most appropriate CFD approaches for their specific engineering challenges.

Convolutional neural networks-based surrogate model for fast computational fluid dynamics simulations of indoor airflow distribution

Computational fluid dynamics (CFD) is a powerful but time-consuming simulation tool for building indoor environment analysis. Artificial intelligence (AI)-based surrogate models, especially artificial neural networks (ANN)-based models which are the dominated ones, have demonstrated a great potential in accelerating CFD simulations. However, the published AI-based models are not good at capturing local spatial features in indoor airflow distribution, leading to poor local simulation accuracy. To overcome this challenge, this study proposes a convolutional neural networks (CNN)-based surrogate model for fast CFD simulations of indoor airflow distribution. Compared with other published AI-based models, this model can capture local spatial features in indoor airflow distribution datasets simulated by CFD, leading to higher accuracy. To enable this model to process room geometry information, a geometry representation strategy is proposed to convert room geometry information into inputs suitable for CNN models. Simulation data of 2000 indoor airflow velocity fields with various boundary conditions are generated using COMSOL Multiphysics. Five cases with various model training strategies are designed based on these simulation data to verify the performance of the CNN-based model by comparing this model with ANN-based and GNN-based models. The results show that the CNN-based model outperforms other models in all cases. The CNN-based and GNN-based models have significantly smaller local simulation errors than the ANN-based model. The simulation accuracy of the CNN-based model is improved by an average of 45.55 % and 32.90 % compared with the ANN-based and GNN-based models, respectively. Moreover, the computational time of the CNN-based model is reduced to about 0.05 % of the computational time of CFD simulations.

The Role of Thermal Analysis in Engine Fin Design: Insights and Perspectives

Objective: The study aims to address the significance of fins in enhancing the heat transfer rate of engine cylinders and highlight their applications and advancements in various fields. Methods: Thermal analysis of fins using ANSYS Workbench is an invaluable tool for engineers and researchers in determining and optimizing the heat transfer characteristics of finned structures. ANSYS Workbench provides a comprehensive platform for evaluating the performance of fins in various applications using simulations of computational fluid dynamics (CFD) and finite element analysis (FEA). With ANSYS Workbench, it is possible to assess the thermal behaviour of fins under a variety of conditions, such as variable boundary conditions and thermal loads. Some significant recent patents on Engine fin are also discussed in this review article. Results: Fins are designed to maximize contact with the surrounding air or coolant, thereby facilitating engine heat transfer to the surrounding environment. Engineers can enhance heat dissipation capabilities by considering parameters such as fin geometry, density, material selection, and manufacturing techniques while minimizing associated drawbacks. Conclusion: The numerous applications of fins highlighted in this review article will encourage researchers to find novel strategies to improve heat transfer by employing fins for use in a variety of sectors.

Computational multiphysics modeling of radioactive aerosol deposition in diverse human respiratory tract geometries

The evaluation of aerosol exposure relies on generic mathematical models that assume uniform particle deposition profiles over the human respiratory tract and do not account for subject-specific characteristics. Here we introduce a hybrid-automated computational workflow that generates personalized particle deposition profiles in 3D reconstructed human airways from computed tomography scans using Computational Fluid and Particle Dynamics simulations. This is the first large-scale study to consider realistic airways variability, where 380 lower and 40 upper human respiratory tract 3D geometries are reconstructed and parameterized. The data is clustered into nine groups using random forest regression. Computational fluid and particle dynamics simulations are conducted on these representative geometries using a realistic heavy-breathing respiratory cycle and radioactive iodine-131 as a source term. Monte Carlo radiation transport simulations are performed to obtain detailed energy deposition maps. Our findings emphasize the importance of personalized studies, as minor respiratory tract variations notably influence deposition patterns rather than global parameters of the lower airways, observing more than 30% variance in the mass deposition fraction.

Dual-functional adsorptive membranes for PFAS removal: Mechanism, CFD simulation, and selective enrichment

The remediation of emerging water contaminants, particularly per- and polyfluoroalkyl substances (PFAS), presents challenges due to their refractory nature and the presence of competing substances. Dual-functional adsorptive membranes, with hydrophobic backbone and quaternary ammonium moieties, were thereby designed to selectively intercept organic competitors while enrich PFAS. A 96.8 % removal of perfluorooctanoic acid (PFOA) was achieved and this effective removal (>90 %) maintained across five reuse cycles with a total treatment capacity of 650 L m−2. Rather than the rejection mechanism of nanofiltration process, these adsorptive membranes utilize synergistic electrostatic attraction and hydrophobic interactions, leading to a greater enrichment factor of 18.5 (PFOA over humic acid) and a permeability of 34.6 L m-2h−1 bar−1 (1.9- and 4.5-fold higher than reported NF 270 membranes, respectively). Furthermore, computational fluid dynamics (CFD) modeling revealed that the sponge-like matrix effectively prevents channeling flow and enhance access to adsorption sites. Sensitivity analysis and the high Damkohler number indicated that the adsorption process is mass transfer-controlled, with the key parameters ranked in order of significance: residence time > fluid viscosity > intrinsic adsorption rate. With consistent removal performance with co-existing competitors, efficient regeneration, and reusability, the dual-functional adsorptive membranes offer promising practical efficacy for PFAS remediation.