Search Results (1,730)

To improve the indoor thermal comfort of embedded atriums and corridors in office buildings during summer, this study aims to optimize air conditioning airflow organization in atriums using computational fluid dynamics (CFD) simulations. Field measurements were carried out to collect air parameters, which were subsequently used to validate the established CFD model. Taking a six-story office building in Xi’an as the research subject and stratified air conditioning as the baseline case, this study investigated the effects of air inlet layout, air inlet type, and air volume distribution on the indoor thermal environment. The results revealed significant vertical temperature stratification within the atrium, with average temperatures ranging from 23.5 °C to 46.1 °C. Based on comparative analysis of multiple optimization scenarios, the following conclusions are drawn: adopting swirl diffusers in the corridors with an air inlet quantity ratio of 1:1:1:1:2 from the first to fifth floors, combined with uniform air supply volume across the first to fourth floors, can maintain the average Predicted Mean Vote (PMV) of each floor within the range of −0.1 to 0.3. Conversely, excessive air supply volume on upper floors and insufficient air supply volume on lower floors significantly degrade the corridor thermal comfort. Full article

Computational fluid dynamics (CFD)-based simulation of coronary blood flow provides valuable hemodynamic markers, such as pressure gradients, for diagnosing coronary artery disease (CAD). However, CFD is computationally expensive, time-consuming, and difficult to integrate into large-scale clinical workflows. These limitations restrict the availability of labeled hemodynamic data for training AI models and hinder the broad adoption of non-invasive, physiology-based CAD assessment. To address these challenges, we develop an end-to-end pipeline that automates coronary geometry extraction from coronary computed tomography angiography (CCTA), streamlines simulation data generation, and enables efficient learning of coronary blood pressure distributions. The pipeline reduces the manual burden associated with traditional CFD workflows while producing consistent training data. Furthermore, we introduce a diffusion-based regression model. Specifically, the inverted conditional diffusion (ICD) model is designed to predict coronary blood pressure directly from CCTA-derived features, thereby bypassing the need for computationally intensive CFD during inference. The proposed model is trained and validated on two CCTA datasets using the Adam optimizer with a weight decay of $1\times {10}^{-3}$, a learning rate of $1\times {10}^{-5}$, a batch size of 100, and Huber loss. It is then evaluated on a test set of ten simulated coronary hemodynamic cases. Experimental results demonstrate state-of-the-art performance. Compared with Long Short-Term Memory (LSTM), the proposed model improves the $R^2$ score by 19.78%, reduces the root mean squared error (RMSE) by 19.44%, and lowers the normalized root mean squared error (NRMSE) by 18%. Compared with a multilayer perceptron (MLP), it improves the $R^2$ score by 8.38%, reduces RMSE by 4.3%, and reduces NRMSE by 5.4%. This work represents a first step toward a scalable and accessible framework for rapid, non-invasive, CFD-based blood pressure prediction, with the potential to support CAD diagnosis. Full article

This study presents a surrogate-assisted evolutionary optimization framework for parametric design under limited data conditions, integrating computational fluid dynamics (CFD), machine learning, and evolutionary algorithms to optimize spiked blunt body geometries in supersonic flow. A dataset of CFD simulations covering a range of Mach numbers and geometric ratios, including spike length () and diameter (), was used to train regression-based surrogate models.Among the evaluated models, the Gradient Boosting Regressor (GBR) achieved the highest predictive accuracy (, RMSE = 0.00775), effectively capturing the nonlinear relationship between flow conditions, geometry, and drag coefficient (). The trained surrogate model was coupled with three evolutionary algorithms—Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and Genetic Algorithm (GA)—to identify optimal geometric configurations across different Mach regimes. To validate the proposed framework, the optimal solutions obtained from the surrogate-based optimization were re-evaluated using CFD simulations. A strong agreement between predicted and simulated drag coefficients was observed, confirming the reliability of the surrogate model for guiding optimization within the explored design space. The results reveal consistent geometric trends, with the optimal spike length ratio decreasing as Mach number increases, while the diameter ratio converges to a narrow range around . Additionally, SHapley Additive exPlanations (SHAP) analysis identified as the most influential parameter affecting drag, followed by Mach number and , supporting the physical interpretation of the flow behavior. Overall, the proposed framework demonstrates that the integration of CFD, machine learning, and evolutionary algorithms provides an efficient and reliable approach for geometric optimization in supersonic applications, enabling accurate design exploration with a limited number of high-fidelity simulations. Full article

The global transition toward carbon-neutral energy solutions has established Solid Oxide Fuel Cells (SOFCs) as a key technology for next-generation power generation. This work presents a comprehensive numerical study and multi-factor statistical analysis of an anode-supported SOFC fueled by synthetic diesel. A three-dimensional computational fluid dynamics model, validated against experimental data, was integrated with a Taguchi L₂₇ orthogonal array to systematically evaluate the influence of six key parameters: temperature, fuel mass flow rate, operating pressure, current load, flow channel configuration, and methane molar fraction. Statistical analysis through the signal-to-noise ratio and analysis of variance identified the operating current as the most significant factor affecting cell voltage, followed by the fuel mass flow rate and temperature. The experiments showed that the highest levels of all factors (except for the current, which had the lowest level) maximize electrochemical performance while maintaining a steam-to-carbon ratio (S/C) within a range of 0.83 to 0.92, calculated based on total carbon content, ensuring sufficient humidification for internal reforming across all tested fuel compositions. Furthermore, a multiple linear regression model was developed as a computationally efficient surrogate, demonstrating exceptional predictive accuracy with an R² of 0.9954 and a mean relative error of 1.76% across independent validation cases. These results provide a robust methodology for rapid design and sensitivity analysis of internal-reforming SOFCs, offering a precise tool for optimizing fuel utilization in high-temperature electrochemical systems. Full article

Decarbonizing air travel poses a major technological challenge, driven by the substantial power requirements of the drivetrain and the demanding weight and volume constraints of airborne systems. One promising avenue involves leveraging the high specific energy of hydrogen by designing compact, high-power fuel cell stacks to supply power for electric drivetrains. However, a key drawback of such propulsion architectures is the substantial heat generated within the fuel cells, which necessitates bulky and heavy thermal management systems to ensure safe and continuous operation. This study investigates a proposed air-based thermal management system, which operates by introducing pulsating heat pipes into the bipolar plates of a High-Temperature Polymer Electrolyte Membrane Fuel Cell (HT-PEM FC) stack. If proven to be feasible, heat pipe assisted air cooling may provide the benefit of reducing overall system complexity by decreasing the number of components in the thermal management system. To evaluate the thermal performance of the proposed system, a one-dimensional thermal model was initially developed in a previous study to describe the temperature distribution along the length of a heat pipe. Building upon this foundation, the present work extends the model by incorporating a two-dimensional Computational Fluid Dynamic (CFD) analysis to account for geometry-specific effects within the hexagonal design. Results indicate that the heat transfer from the hexagonal heat pipe geometry to the coolant air flow was marginally overestimated in previous analytical calculations. Revised heat transfer rates led to a shift in the predicted temperature distributions, resulting in the need for either increased external airflow, extended condenser sections, or reduced inlet temperatures to maintain target operating conditions. Although these adjustments may result in a slight increase in system mass and parasitic power consumption, the overall impact is limited, and the heat pipe-assisted air cooling approach remains theoretically feasible. Based on the results, design modifications are proposed and their impact on thermal performance is evaluated to address the challenges of heat rejection and temperature uniformity. A modification based on variation and optimization of PHP meander lengths was evaluated using the updated model and it significantly improved temperature homogeneity across the evaporator. Full article

Predicting the maneuverability of ships equipped with twin azimuth thrusters remains challenging due to their complex hydrodynamic interactions. This study develops an integrated framework that combines Computational Fluid Dynamics (CFD) with an enhanced Manoeuvring Mathematical Group (MMG) Model. Using the platform supply vessel Hai Yang Shi You 661 as a case study, all requisite hydrodynamic derivatives and propeller coefficients were efficiently obtained through CFD-based captive model tests, including oblique towing and Planar Motion Mechanism tests, conducted in STAR-CCM+ 2206. A core contribution of this work is the systematic evaluation of how hydrodynamic model fidelity affects prediction accuracy. Numerical turning circle simulations were executed with three models of increasing complexity: one with only linear derivatives, a second incorporating nonlinear higher-order terms, and a third, full model that additionally includes nonlinear velocity coupling terms. The results, rigorously validated against full-scale trial data, demonstrate that while the basic CFD-MMG approach is feasible, the inclusion of nonlinear coupling terms is critical for achieving accurate predictions in large-amplitude maneuvers. This enhancement reduced the maximum error in tactical diameter prediction from over 25% to approximately 11.8%. Consequently, this study provides a validated and cost-effective framework for maneuvering the prediction of azimuth-thruster vessels and offers clear, quantitative guidance on the necessary level of model complexity for practical engineering applications. Full article

Subsoiling can break the plough pan and improve the root growth environment. The effect of the traditional subsoiler is poor, as it relies only on the chisel tine, but pneumatic subsoiling can improve the soil structure more efficiently through the negative pressure generated by the jet hole. This research used computational fluid dynamics and the discrete element method to optimize the biomimetic structure of the jet hole, model the pneumatic subsoiling process at a depth of 330 mm, and observe the movement of soil particles as airflow passes through. The effect of the jet hole at different positions and sizes on the plough pan soil was analyzed, and fluid domains and measurement areas were set up to observe the upward movement, diffusion, stabilization, and settling of soil particles under the action of airflow. The results of the soil bin experiment validated the accuracy of the simulation model through draft force and vertical force, and the average error between the simulation and experimental data was 2.8%. The study revealed that the increase in the rate of soil porosity reached a maximum of 3.65% when the jet hole was positioned above the chisel tine with a radius of 4 mm. The biomimetic jet hole pneumatic subsoiler designed in this study, along with the established CFD-DEM coupled simulation model capable of predicting pneumatic subsoiling performance, can provide references for the design and application of a pneumatic subsoiler. Furthermore, it also provides a theoretical basis for understanding the mechanism of airflow on soil during pneumatic subsoiling operations. Full article

This paper presents a data-driven surrogate model for predicting cabin airflow and its integration into system-level electric vehicle simulations for energy management analysis. The model employs a graph-based neural network with a mirror-symmetric predictor–corrector architecture and is trained on a dataset generated using computational fluid dynamics (CFD) covering a defined range of inlet velocities and temperatures. The surrogate appropriately reconstructs temperature fields and captures the dominant airflow structures at significantly lower computational cost than CFD. Quantitative evaluation shows high accuracy in passenger-relevant regions, while localized discrepancies remain confined mainly to shear-layer zones. The model enables near-real-time inference and is coupled with a system-level modeling framework for control-oriented simulations that are impractical with CFD. The study is tailored to a specific geometry and operating range, showing that targeted training strategies and physics-based extensions improve robustness, particularly under limited data conditions. Full article

Electrohydrodynamic effects can significantly alter transport processes in reacting flows, even when the plasma is weakly ionized. However, predictive modeling of such plasma–flame interactions remains challenging due to the multiscale coupling among charge transport, fluid motion, and chemical kinetics. This study presents a charge-transport closure model to investigate electrohydrodynamic influences on laminar non-premixed flames. A two-dimensional computational framework in cylindrical coordinates is used to simulate plasma-assisted methane–air diffusion flames under weak electric-field conditions representative of practical combustion environments. To represent plasma–flow coupling in a computationally feasible yet physically consistent manner, a charge-transport formulation based on the drift–diffusion approximation is employed. The model solves transport equations for representative positive and negative charge carriers coupled with Poisson’s equation for the electric potential to obtain a self-consistent electric field. This formulation assumes a weakly ionized regime for low-temperature plasma-assisted combustion, in which neutral species dominate the mass and momentum transport, while ionization chemistry is simplified and charge transport primarily influences the flow through electrohydrodynamic body forces and Joule heating. Assuming a weak electric field, the steady flamelet model is applied, in which plasma effects primarily influence scalar transport and local thermal balance rather than inducing significant bulk ionization dynamics. The governing equations are discretized using a high-order compact finite-difference scheme that provides improved resolution of steep gradients in temperature, species concentration, and space-charge density near thin reaction zones. The canonical laminar flame model configuration was validated using the established laminar methane–air diffusion flame benchmark, and steady-state spatial profiles of key transport properties were evaluated. Two-dimensional analysis identified the discharge coupling location as an important factor. The application of discharge in the fuel-air mixing region leads to a clear restructuring of the flame. When the discharge is activated, electrohydrodynamic forcing and ion-driven momentum transfer produce a highly localized, columnar flame with sharp gradients and a confined reaction zone. Compared with the baseline case, the plasma-assisted flame localizes the OH-rich reaction zone, confines the high-temperature region into a narrow column, and enhances downstream H₂O formation. Full article

The transition toward renewable energy sources has positioned wind energy as a critical technology for achieving global carbon neutrality targets. While large-scale wind farms dominate current installations, micro-scale horizontal-axis wind turbines present significant potential for distributed energy generation in remote and rural areas. This study presents a comprehensive methodology for designing micro-scale wind turbine blades through comparative analysis of three computational approaches: classical blade element momentum theory (BEMT), QBlade 2.0.9.6 software, and Computational Fluid Dynamics (CFD) simulations, with the design methodology selected based on a trade-off between accuracy and computational cost. A numerical campaign for airfoil assessment was conducted to identify optimal blade geometries, with performance evaluated based on power coefficient distribution, peak power output, and cut-in wind speed. The investigation reveals that steady CFD simulations predict peak power coefficients 23.34% higher than those predicted by BEMT and 22.46% higher than those predicted by QBlade due to three-dimensional effects, including rotational stall delay. Considering unsteady effects, the CFD simulations show a decrease of 4.08% with respect to steady simulations. The addition of endplates to the optimized blade design demonstrates significant performance improvements. This multi-fidelity approach provides a robust framework for micro-scale wind turbine design, balancing computational efficiency with accuracy requirements, and examines the impact of adding endplates. Full article

The foaming of hydrogels presents a promising strategy for tailoring mechanical and radiological properties to replicate biological soft tissues for biomedical phantom applications. A computational fluid dynamics (CFD) framework is developed to predict void fraction distribution in alginate hydrogel precursor solutions aerated by air injection through a bottom nozzle. The objective is to use the framework for the design of the foaming system to match the desired gas-fraction distribution and radiological property. Seven parametric cases are investigated, varying inlet air velocity, alginate concentration, and surface tension. Results show that higher inlet velocities promote stronger jet penetration and greater gas accumulation, while increasing alginate concentration confines the bubble plume, with quasi-steady gas fractions displaying a non-monotonic trend with concentration. Elevated surface tension yields broader plume coverage and improved gas distribution uniformity at the expense of peak void fraction. The predicted void fractions map to Hounsfield Unit (HU) values of −34 to −103, corresponding to adipose and fatty breast tissue attenuation (−50 to −150 HU). The peak gas fraction at 5.0 wt% alginate yields −307 HU, approaching published experimental CT measurements for the same formulation (−460 to −233 HU). Full article

Airfoil flow-field prediction is important for aerodynamic design, but wind-tunnel testing and computational fluid dynamics (CFD) remain costly and time-consuming. Deep learning enables fast inference, yet many existing models still rely on fixed grid representations, which may lead to insufficient learning in high-gradient regions and larger local errors. This study proposes Spatial Multilayer Perceptron (Spatial MLP) together with an Error-Gradient-Guided Data Sampling (EGDS) strategy for airfoil flow-field prediction. Spatial MLP adopts a coordinate-based point-wise prediction framework. A spatial decoder is introduced as an auxiliary branch to enhance global flow consistency during pretraining, while channel-wise multi-head attention is incorporated to improve cross-variable feature coupling. EGDS prioritizes physically informative points according to relative prediction error and gradient magnitude, while retaining random samples to preserve data diversity. Experiments on an independent test set show that Spatial MLP reduces the mean relative error (averaged over the velocity components u, v, and pressure p) by 15.2% relative to the MLP baseline. With EGDS, the overall mean relative error is further reduced by 34.5% relative to the MLP baseline. These results demonstrate that combining global consistency constraints with targeted sampling effectively improves both global prediction accuracy and local reconstruction quality in high-gradient flow regions. Full article

Organic Rankine Cycles (ORCs) are widely recognized as an effective solution for Waste heat recovery (WHR). However, the design and optimization of these systems must address the tradeoff between computational efficiency and the need to capture complex component behavior. This requires moving beyond purely energetic 0D modeling approaches to account for constructional, spatial, and operational constraints. This work presents a novel modeling framework with a specific focus on the expansion device. Radial inflow turbine stages are selected for their capability to achieve high pressure ratios while maintaining compactness and high efficiency. Heat exchangers follow a generic one-dimensional counterflow configuration, with a shell-and-tube geometry adopted for sizing purposes. The turbine stages are modeled by resolving several internal sections in order to capture local thermofluid dynamic conditions. The framework predicts turbine efficiency and incorporates a newly developed formulation for shock-induced losses, improving performance prediction under trans-sonic flow conditions. After validation against experimental data, the model is applied to a WHR system integrated with an internal combustion engine fueled by biofuels. The results highlight the existence of optimal operating conditions arising from competing physical mechanisms. The analysis also shows the transition from single-stage to two-stage turbine configurations at high pressure ratios and emphasizes the role of real gas effects in determining stage performance and optimal expansion distribution. The results of simulations carried out for three different working fluids (ethanol, toluene, and R1234ze(E)) highlight that the available mechanical power ranges from 10 to 22 kW for single-stage turbine configurations and from 24 to 36 kW for two-stage configurations, with total system volumes varying between approximately 600 and 9000 L. Among the working fluids considered here, ethanol provides the best overall performance for the present case study. Overall, the proposed approach provides a reliable and computationally efficient tool for the preliminary design and optimization of ORC-based WHR systems. Full article

This study presents an optimized numerical and empirical modeling framework for ionic wind-driven electrostatic precipitators designed for atmospheric particulate matter (PM) removal. While traditional particle tracing models in long ducts often suffer from transient evaluation errors (the “flight time paradox”), this work introduces a Fate-based Steady-state Evaluation (FSE) method. By coupling Electrostatics, Laminar Flow, and Particle Tracing in a high-fidelity 2D axisymmetric model, we achieved a baseline validation with a Mean Absolute Error (MAE) of 5.3% compared to experimental data (20 kV, 0.5 m/s). Furthermore, a non-linear regression engine based on a physical-exponential decay function was developed to provide real-time performance predictions. The resulting hybrid model demonstrates a high scientific reliability (R² = 0.98), establishing it as a robust tool for the design and optimization of air purification systems targeting fine atmospheric aerosols (0.1–3.0 μm). In addition, the proposed Fate-based Steady-state Evaluation (FSE) method eliminates transient bias commonly observed in long-duct Lagrangian particle simulations. This methodological improvement enables statistically consistent efficiency estimation for electrohydrodynamic filtration systems and can be applied to a broad class of Computational Fluid Dynamics (CFD)-based particulate capture studies. The developed framework enables rapid design optimization of compact electrohydrodynamic filtration systems and provides a practical alternative to computationally expensive full-scale Computational Fluid Dynamics (CFD) simulations. Full article

Cavitation damage poses a serious threat to the reliability of morning-glory spillways. This study aims to develop a reliability framework for predicting cavitation damage probability under uncertain operational conditions for the Haraz Dam spillway. Cavitation analysis in such structures exhibits inherent nonlinearity and uncertainty, complicating accurate damage prediction. This study incorporates model uncertainties to assess cavitation responses at multiple points on the Haraz Dam morning-glory spillway. Three-dimensional flow simulations were performed using Computational Fluid Dynamics (CFD) and validated against an experimental model from the Iran Water Research Institute, showing satisfactory agreement. Statistical parameters and probability density functions (PDFs) for key uncertainties were determined using the Shapiro–Wilk test. A total of 35 simulation runs, designed via the Central Composite Design (CCD) method, were conducted using Latin Hypercube Sampling (LHS). These simulations incorporated inter-uncertainty correlations and predicted cavitation damage responses at ten critical spillway locations through Response Surface Methodology (RSM). Both linear and second-order response functions were formulated based on interactions among model uncertainties. The results indicated a strong correlation (R² > 0.95) between numerical model outputs and RSM predictions, with the maximum RSM errors remaining within acceptable thresholds. Among the uncertainty factors, the inflow velocity demonstrated the highest contribution (>50%) to cavitation damage responses. These outcomes advance the understanding of cavitation mechanisms and provide a reliable methodology for evaluating damage risks in morning-glory spillways under uncertain operational conditions. Full article