Computational Fluid Dynamics Data Research Articles

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.

Toward aerodynamic surrogate modeling based on β-variational autoencoders

Surrogate models that combine dimensionality reduction and regression techniques are essential to reduce the need for costly high-fidelity computational fluid dynamics data. New approaches using β-variational autoencoder (β-VAE) architectures have shown promise in obtaining high-quality low-dimensional representations of high-dimensional flow data while enabling physical interpretation of their latent spaces. We propose a surrogate model based on latent space regression to predict pressure distributions on a transonic wing given the flight conditions: Mach number and angle of attack. The β-VAE model, enhanced with principal component analysis (PCA), maps high-dimensional data to a low-dimensional latent space, showing a direct correlation with flight conditions. Regularization through β requires careful tuning to improve overall performance, while PCA preprocessing helps to construct an effective latent space, improving autoencoder training and performance. Gaussian process regression is used to predict latent space variables from flight conditions, showing robust behavior independent of β, and the decoder reconstructs the high-dimensional pressure field data. This pipeline provides insight into unexplored flight conditions. Furthermore, a fine-tuning process of the decoder further refines the model, reducing the dependence on β and enhancing accuracy. Structured latent space, robust regression performance, and significant improvements in fine-tuning collectively create a highly accurate and efficient surrogate model. Our methodology demonstrates the effectiveness of β-VAEs for aerodynamic surrogate modeling, offering a rapid, cost-effective, and reliable alternative for aerodynamic data prediction.

Machine Learning-Based Predictions of Flow and Heat Transfer Characteristics in a Lid-Driven Cavity with a Rotating Cylinder

Machine learning-based predictions of heat transfer characteristics in lid-driven cavities are transforming the field of computational fluid dynamics (CFD). Lid-driven cavities are a fundamental problem in fluid mechanics, characterized by the motion of a fluid inside a square cavity driven by the motion of one of its walls. The goal of this study was to develop multiple machine-learning regression models and highlight the discrepancies between the predicted and actual average Nusselt numbers. Additionally, the study utilized physics-informed neural networks (PINNs) to model the flow and thermal behavior at both low and high Reynolds numbers. The results were compared among actual data from computational fluid dynamics (CFD) simulations, PINN models trained with CFD data, and purely PINN models created without any prior data input. The findings of this study showed that the random forest model exhibited an exceptional stability in its predictions, consistently maintaining low errors even as the Reynolds number increased compared with other machine-learning regression models. Further, the results of this study in terms of flow and thermal behavior within the cavity were found to depend significantly on the PINN method. The data-driven PINN exhibited a much lower mean average errors at both Reynolds numbers, while the physics-based PINN showed lower physics loss.

Adversarial image-to-image model to obtain highly detailed wind fields from mesoscale simulations in urban environments

We propose a conditional Generative Adversarial Network (cGAN) that can produce detailed local wind fields in urban areas, comparable in level of detail to those from Computational Fluid Dynamics (CFD) simulations, that are generated from coarser Numerical Weather Prediction (NWP) data.In our approach, the cGAN is trained using NWP data as input and CFD as targets. Both CFD and NWP data are presented to the network as images, using an image-to-image model based on Pix2Pix to transform coarse meteorological conditions into detailed local wind fields.The methodology is tested in a residential district in a large Spanish city, Zaragoza. The model predictions show significant agreement with the actual CFD results, while reducing the computational time from eight hours to seconds. Feature engineering of image channels effectively reduces the model error, especially in the wind direction, achieving a mean absolute error in the wind speed of 0.35m/s and a wind direction error of 27.0°.

Combining Computational Fluid Dynamics and Experimental Data to Understand Fish Schooling Behavior.

Understanding the flow physics behind fish schooling poses significant challenges due to the difficulties in directly measuring hydrodynamic performance and the three-dimensional, chaotic, and complex flow structures generated by collective moving organisms. Numerous previous simulations and experiments have utilized computational, mechanical, or robotic models to represent live fish. And existing studies of live fish schools have contributed significantly to dissecting the complexities of fish schooling. But the scarcity of combined approaches that include both computational and experimental studies, ideally of the same fish schools, has limited our ability to understand the physical factors that are involved in fish collective behavior. This underscores the necessity of developing new approaches to working directly with live fish schools. An integrated method that combines experiments on live fish schools with computational fluid dynamics (CFD) simulations represents an innovative method of studying the hydrodynamics of fish schooling. CFD techniques can deliver accurate performance measurements and high-fidelity flow characteristics for comprehensive analysis. Concurrently, experimental approaches can capture the precise locomotor kinematics of fish and offer additional flow information through particle image velocimetry (PIV) measurements, potentially enhancing the accuracy and efficiency of CFD studies via advanced data assimilation techniques. The flow patterns observed in PIV experiments with fish schools and the complex hydrodynamic interactions revealed by integrated analyses highlight the complexity of fish schooling, prompting a reevaluation of the classic Weihs model of school dynamics. The synergy between CFD models and experimental data grants us comprehensive insights into the flow dynamics of fish schools, facilitating the evaluation of their functional significance and enabling comparative studies of schooling behavior. In addition, we consider the challenges in developing integrated analytical methods and suggest promising directions for future research.

Computational fluid dynamics (CFD)- deep neural network (DNN) model to predict hydrodynamic parameters in rectangular and cylindrical bubble columns

Bubble columns are omnipresent in the chemical, bio-chemical, petrochemicals, petroleum industries, but their design and scale-up is complex owing to its complex hydrodynamics. Liquid velocity and gas holdup is one of the critical hydrodynamic parameters which effects the mixing, heat and mass transfer in bubble columns. CFD is widely recognized as a powerful tool for estimating critical hydrodynamic parameters but requires significant computational resources, time and expertise. These limitations restrict its practical use in hydrodynamic simulations that need real-time processing involving large-scale simulations of bubble columns. To overcome these limitations, CFD-DNN model is developed to predict the time averaged gas holdup and axial liquid velocity at various operating conditions. The DNN model was trained using CFD data that was produced for rectangular (with dimensions L=0.2 m, W=0.05 m, H=1.2 m) and cylindrical (with a diameter of 0.19 m) bubble columns. The data covers a range of operating conditions and various flow regimes. The superficial gas velocity for the rectangle column was selected at 1.33 and 7.3 mm/s, whereas for the cylindrical bubble column, it was fixed at 0.02 and 0.12 m/s. The CFD-DNN model was validated against the experimental and the CFD data from the literature. Further, the model was tested for new data that the CFD-DNN model has not seen with existing literature and showed good agreement with their data and it reflects the excellent generalization ability of the model. The proposed CFD-DNN approach improves current CFD models by providing shorter computing time, decreasing computational expenses, and reducing the expertise in CFD simulations. The accuracy of the developed CFD-DNN model was evaluated using different metrics for gas holdup and axial liquid velocity. For rectangular bubble columns, the model achieved MSE of 0.0001 for gas holdup and 0.0007 for axial liquid velocity. Similarly, for cylindrical bubble columns, the MSE values were 0.0009 for gas holdup and 0.0006 for axial liquid velocity.

Denoising graph neural network based on zero-shot learning for Gibbs phenomenon in high-order DG applications

With the availability of high-performance computing technology and the development of advanced numerical simulation methods, Computational Fluid Dynamics (CFD) is becoming more and more practical and efficient in engineering. As one of the high-precision representative algorithms, the high-order Discontinuous Galerkin Method (DGM) has not only attracted widespread attention from scholars in the CFD research community, but also received strong development. However, when DGM is extended to high-speed aerodynamic flow field calculations, non-physical numerical Gibbs oscillations near shock waves often significantly affect the numerical accuracy and even cause calculation failure. Data driven approaches based on machine learning techniques can be used to learn the characteristics of Gibbs noise, which motivates us to use it in high-speed DG applications. To achieve this goal, labeled data need to be generated in order to train the machine learning models. This paper proposes a new method for denoising modeling of Gibbs phenomenon using a machine learning technique, the zero-shot learning strategy, to eliminate acquiring large amounts of CFD data. The model adopts a graph convolutional network combined with graph attention mechanism to learn the denoising paradigm from synthetic Gibbs noise data and generalize to DGM numerical simulation data. Numerical simulation results show that the Gibbs denoising model proposed in this paper can suppress the numerical oscillation near shock waves in the high-order DGM. Our work automates the extension of DGM to high-speed aerodynamic flow field calculations with higher generalization and lower cost.

Kinetic model implementation in raceway pond reactors with hydrodynamic and radiation fields

Vertical mixing plays a critical role in solar-driven processes using raceway pond reactors (RPRs). However, incorporating the time history of radiation for each fluid element is still an open issue. This work aims to develop an effective methodology using Computational Fluid Dynamics (CFD) tools that couples hydrodynamics and radiation fields into kinetic models of biomass growth or cell lysis enhanced from radiation. First, three methodologies to assess vertical mixing were investigated. It was found that, under typical RPR flow conditions, the velocity in the direction of solar light incidence can maintain particles in constant motion and a near-homogenous particle distribution. In addition, two RPR applications were studied regarding radiation influence analysis: the production of microalgae and an innovative approach for waste activated sludge (WAS) pre-treatment, fostering biogas production. Regarding microalgae production, coupling the biokinetic models with CFD data enables the development of a cost-effective computational methodology to describe the growth of microalgae cultures accounting for hydrodynamics and radiation fields. This work was successful in introducing hydrodynamics and radiation conditions in models to design and optimise RPRs. Reduced geometries based in 2D and Periodic Boundary Conditions were used for CFD simulations to make it feasible for RPRs design purposes.

Towards a momentum potential theory for reacting flows

Mutual coupling of (thermofluiddynamic) modes of perturbations can affect the thermo-acoustic stability of combustors and contribute to combustion noise. For example, vortical or entropic perturbations can be transferred to acoustic perturbations if accelerated by the mean flow. The decomposition of perturbation fields into the respective modes and a linear description of their interactions in terms of fluctuating primitive variables is challenging. In contrast, Doak’s momentum potential theory promises an unambiguous decomposition in terms of momentum fluctuations, which is not limited to the linear regime. Whereas classical momentum potential theory takes into account hydrodynamic, acoustic and entropic modes in unconfined flows, the investigation of noise generation in combustion chambers requires the extension of the momentum potential theory to capture modes linked to the fluctuation of species mass fractions (“species mode”) arising from the change in chemical composition due to the reaction. Furthermore, a rigorous treatment of boundary conditions due to the confinement of the flow inside the combustor is required. The herein presented extension to reactive flows consists of two steps, (i) the formulation of a potential for momentum fluctuations related to species modes and (ii) identification of the total fluctuating enthalpy related to species modes. The extended theory is applied to post-process computational fluid dynamic simulation data of the propagation of entropy and species perturbations through one-dimensional ducts, nozzles and premixed flames. We find that although momentum potential theory offers a complete decomposition of momentum perturbations for reactive flows, the meaningful interpretation of this decomposition is rather challenging, even for non-reactive flows.

Reconstruction of the turbulent flow field with sparse measurements using physics-informed neural network

Accurate high-resolution flow field prediction based on limited experimental data is a complex task. This research introduces an innovative framework leveraging physics-informed neural network (PINN) to reconstruct high-resolution flow fields using sparse particle image velocimetry measurements for flow over a periodic hill and high-fidelity computational fluid dynamics data for flow over a curved backward-facing step. Model training utilized mean flow measurements, with increased measurement sparsity achieved through various curation strategies. The resulting flow field reconstruction demonstrated marginal error in both test cases, showcasing the ability of the framework to reconstruct the flow field with limited measurement data accurately. Additionally, the study successfully predicted flow fields under two different noise levels, closely aligning with experimental and high-fidelity results (experimental, direct numerical simulation, or large eddy simulation) for both cases. Hyperparameter tuning conducted on the periodic hill case has been applied to the curved backward-facing step case. This research underscores the potential of PINN as an emerging method for turbulent flow field prediction via data assimilation, offering reduced computational costs even with sparse, noisy measurements.

Fast dynamic prediction of consequences of heavy gas leakage accidents based on machine learning

The field of emergency risk management in chemical parks has been characterized by a lack of fast, precise and dynamic prediction methods. The application of computational fluid dynamics (CFD) models, which offer the potential for dynamic and precise prediction, has been hindered by high computational costs. Therefore, taking liquid benzene as a case study, this paper combined machine learning (ML) algorithms with a CFD-based precise prediction model, to develop an ML model for fast dynamic prediction of heavy gas leakage consequences in chemical parks. Employing the CFD data as the input, the prediction models were developed using ML algorithms, refined with Bayesian optimization for parameter tuning. This study utilized PHOENICS software to establish a dynamic prediction model for the diffusion of liquid benzene leakage, validated by Burro nine experiment data. Comparative analyses of models based on five ML algorithms were conducted to evaluate the reliability of their predictions using both CFD standard and noisy data. The results indicated that temperature had the most significant effect on the consequences of the leakage accidents among four key factors (wind speed, temperature, leakage aperture and atmospheric stability), followed by wind speed. These factors served as input variables for ML model training. Among the models evaluated, the eXtreme Gradient Boosting (XGBoost) model showed superior performance, irrespective of the presence of noise in the data. An XGBoost-based fast prediction model was ultimately developed for predicting the consequences of liquid benzene leakage. A case analysis was conducted to validate the feasibility of the model prediction. The relative errors between the predicted and actual values of the model for acute exposure guideline level-1 (AEGL-1), AEGL-2, and AEGL-3 distances were 2.70%, 2.58%, and 0.23%, respectively. Furthermore, the XGBoost model completed the prediction in only 0.218 s, a stark contrast to the hours necessitated by the CFD model, thus offering substantial computational time savings while maintaining high accuracy levels. This paper introduces an ML model for fast dynamic prediction of heavy gas leakage, enabling chemical parks to make more timely and accurate decisions in emergency risk management.

A study of methanol‐to‐olefins packed bed reactor performance using particle‐resolved CFD and machine learning

AbstractIn this study, particle‐resolved computational fluid dynamics (CFD) simulations were performed to analyze fluid flow, mass transport, and reaction phenomena in methanol‐to‐olefins packed bed reactors with diverse cylindrical configurations and operating conditions. Utilizing validated CFD data, data‐driven surrogate models were developed based on several representative machine learning (ML) techniques. Comprehensive training and optimization of ML model hyperparameters were performed, followed by a comparative assessment of their capabilities to predict reactor performance. Subsequently, data‐driven surrogate models together with CFD simulations were applied to optimize catalyst structure design and operating conditions. Finally, a hybrid approach was developed that couples the ML‐aided data‐driven model with a genetic algorithm‐based multi‐objective optimization. The resulting hybrid method was applied to find the Pareto‐optimal compromise between pressure drop and light olefins yield.

Parameter optimization of the classifying device of straw micro-crusher based on neural network and CFD

To improve the classification performance of the straw micro-crusher's classifying device, the characteristic parameters impacting classification performance are identified through an analysis of the forces acting on the straw particles between the blades of the rotor cage and the motion characteristics of the fluid. Taguchi's experimental design is used to ascertain the combinations of computational fluid dynamics (CFD) simulation parameters for cut size and classifying sharpness index, while a neural network (NN) model is developed using CFD data to predict the optimal feature parameter combination for classification performance. The results reveal that the rotor cage speed holds the most significant impact on both the cut size and classifying sharpness index. The optimal combination of feature parameters recommended by the neural network is v=8m/s, n=1200r/min, z=36, θ=40°, at which the cut size is 28.3 μm, a reduction of 9.0 % compared to the Taguchi experiment. In addition, the relative error between the cut size predicted by the neural network model and the CFD simulation is less than 4.5 %, indicating the reliability of the neural network model in predicting the optimal parameter combination of the classifying device.

Utilizing global-local neural networks for the analysis of non-linear aerodynamics

In addressing the computational challenges pervasive in engineering where time and cost limitations are key concerns, particularly within the Computational Fluid Dynamics (CFD) domain, Reduced Order Models (ROMs) have emerged as instrumental tools. Focused on reducing computational complexity without intrusively modifying the computational model, this study centres on the strategic application of aerodynamic ROMs, which provide efficient computation of distributed quantities and aerodynamic forces. This work presents ROMs for non-linear aerodynamic applications, integrating principal component analysis (PCA) with Global Local Neural Networks (GLNN). The effectiveness of the proposed methodology is demonstrated by leveraging dependency on the parameter space created with non-linear high-fidelity CFD data, incorporating viscous simulation for a comprehensive approach. Results are first presented for a two-dimensional airfoil case and then for a three-dimensional test case featuring a transonic wing-body-tail transport aircraft configuration (NASA Common Research Model). In transonic flows, the proposed ROMs demonstrate the ability to accurately capture both the location and strength of shocks, as well as forces and moments for unseen prediction points. This highlights the efficiency of the proposed method in navigating complex aerodynamic scenarios, achieving comparable accuracy to full-order modelling but at orders of magnitude less computational time, for unseen parameters outside the ROM training set within the parameter space.

Aircraft Wake Evolution Prediction Based on Parallel Hybrid Neural Network Model

To overcome the time-consuming drawbacks of Computational Fluid Dynamics (CFD) numerical simulations, this paper proposes a hybrid model named PA-TLA (parallel architecture combining a TCN, LSTM, and an attention mechanism) based on the concept of intelligent aerodynamics and a parallel architecture. This model utilizes CFD data to drive efficient predictions of aircraft wake evolution at different initial altitudes during the approach phase. Initially, CFD simulations of continuous initial altitudes during the approach phase are used to generate aircraft wake evolution data, which are then validated against real-world LIDAR data to verify their reliability. The PA-TLA model is designed based on a parallel architecture, combining Long Short-Term Memory (LSTM) networks, Temporal Convolutional Networks (TCNs), and a tensor concatenation module based on the attention mechanism, which ensures computational efficiency while fully leveraging the advantages of each component in a parallel processing framework. The study results show that the PA-TLA model outperforms both the LSTM and TCN models in predicting the three characteristic parameters of aircraft wake: vorticity, circulation, and Q-criterion. Compared to the serially structured TCN-LSTM, PA-TLA achieves an average reduction in mean squared error (MSE) of 6.80%, in mean absolute error (MAE) of 7.70%, and in root mean square error (RMSE) of 4.47%, with an average increase in the coefficient of determination (R2) of 0.36% and a 35% improvement in prediction efficiency. Lastly, this study combines numerical simulations and the PA-TLA deep learning architecture to analyze the near-ground wake vortex evolution. The results indicate that the ground effect increases air resistance and turbulence as vortices approach the ground, thereby slowing the decay rate of the wake vortex strength at lower altitudes. The ground effect also accelerates the dissipation and movement of vortex centers, causing more pronounced changes in vortex spacing at lower altitudes. Additionally, the vortex center height at lower altitudes initially decreases and then increases, unlike the continuous decrease observed at higher altitudes.

Deep neural network model for predicting thermal-hydraulic performance of a solar air heater with artificial roughness: Sensitivity, generalization capacity, and computational efficiency

A novel coupled computational fluid dynamics-deep neural network approach is proposed to accurately predict the performance of an artificially roughened solar air heater. Computational fluid dynamics data sets are used to develop an optimized neural network model for the prediction of thermal-hydraulic performance factor. The optimized neural network model with softmax transfer function in hidden layers has an architecture of 3‐37‐37‐1 and it predicts the thermal-hydraulic performance factor of the artificially roughened solar air heater with maximum, mean, and minimum error of 2.54 %, 0.245 %, and 0.0009 %, respectively. The concept of Shapley values is adopted to evaluate the global sensitivity analysis of the optimized deep neural network model and it is found that rib height is the most influential design parameter for the artificially roughened solar air heater. The generalization capacity of the neural network model is evaluated by comparing its predictions with experimental data, demonstrating superior predictive performance over traditional computational fluid dynamics simulations. The optimized artificial neural network model achieves a computational time gain of approximately 100 % against computational fluid dynamics.

A critical insight into the most effective CFD settings to model atmospheric stability in wind energy applications

An accurate reconstruction of the Atmospheric Boundary Layer (ABL) is key for the estimation of energy production and loads in modern wind turbines since, at current rotor heights, the vertical structure of the ABL is heavily influenced by thermal stratification effects. The wind power community usually accounts for these effects by semi-empirical modifications to the neutral ABL profile obtained using linear solvers such as WAsP; this approach, however, may not be suitable for sites with complex terrain, time-dependent thermal effects, or dense canopies. In these applications, methods with higher fidelity, such as Computational Fluid Dynamics (CFD) approaches based on steady or unsteady Reynolds Averaged Navier-Stokes (RANS) equations are needed. While CFD is recognized as providing more accurate results for these realistic cases, its use is still not a common best practice. In the current study, the WindModeller CFD tool by Ansys was employed to assess the effect of various inlet boundary conditions on the predicted wind speed profiles in unstable and stable atmospheric conditions. The test case is a wind farm in North Dakota, USA, characterized by a simple terrain but strong atmospheric stability effects and temperature fluctuations. The comparison of CFD results with experimental measurements and WAsP-CFD simulations reveals a high level of agreement between CFD and experimental data in stable conditions. In the case of unstable conditions, despite the good representation of the wind field, a high sensitivity to inlet boundary conditions is highlighted.

Prediction method of centrifugal pump running in turbine mode based on losses analysis

Interest of Pump as Turbine (PAT) is growing with diverse applications in engineering. Usually, the data of PATs are not available in the hands of pump manufacturers. Therefore, performance prediction methods appear as an important research area of PATs. The current prediction methods reposed on expensive, inaccurate and time consuming experimental methods. In the scope of this work, a generic and robust prediction method is built up for a centrifugal impeller PAT. The most significant hydraulic losses were derived in PAT mode, these are namely, the shock losses at the impeller inlet, the swirling losses at the impeller outlet and the impeller wall frictional losses. The Euler head, the available total head and the hydraulic efficiency were computed as well. The global efficiency was computed taking into account the machine mechanical and volumetric efficiencies, enabling therefore to perform comparison of the new prediction method with experimental, computational fluid dynamic (CFD), Rossi and Perez performances prediction methods. From where it resulted a good agreement between the given prediction methods for the entire range of operation, confirming the robustness and the applicability of the developed prediction method. The relative difference between the new prediction method and CFD data and between the new prediction method and experimental data remained higher for lower discharge conditions, notably for extreme part load conditions, where a small error could result in very high relative difference.

Aerodynamic flow field analysis of vortex interactions on multi-swept wing configurations

The underlying flow physics of vortex breakdown and vortex-vortex interactions at moderate to high angles of attack is not well understood. This lack of understanding is a challenge to the development of optimized wing designs for fighter aircraft. The goal of this study is to gain insight into the breakdown of vortices on multi-swept wing configurations. Two generic models were considered, one with a gradual vortex breakdown and one with a rapid (abrupt) breakdown. Aerodynamic forces were calculated from the US Air Force Academy (USAFA) subsonic wind tunnel to validate the experimental setup using computational fluid dynamics (CFD) and previous work. Stereoscopic particle imaging velocimetry (SPIV) was used to visualize vortex-vortex interactions, measure vortex strengths, and identify vortex breakdown locations. The measured lift and drag forces showed flow physics that agreed with previous experiments and CFD, supporting the validity of the new experimental setup. Analysis of SPIV and CFD data improved our understanding of the different types of vortex interactions observed for both models. The only difference between the models was the location of the secondary sweep angle on the chord. The C4L80 model, with a secondary sweep angle at 40% of the root chord, had a large shear layer along the leading edge, which led to vortex merging and gradual vortex breakdown at lower angles of attack than its counterpart. The C6L80 model, with a secondary sweep angle at 60% of the root chord, had no such shear layer and instead developed strong, isolated vortices that were characterized by vortex braiding and abrupt breakdown at high angles of attack. These vortices were entrained into the main vortex at the leading edge of the strake (inboard vortex), creating a balanced vortex system that enhanced lift and delayed the onset of gradual vortex breakdown seen in the C4L80 model.

Dynamic motion analysis of stepless and stepped planing hulls in random waves: A CFD model perspective

Predicting the dynamic responses of planing hulls in real sea conditions is important for identifying how basic design factors influence their seakeeping performance. Hence, there is a pressing need to provide high-fidelity models for predicting the motions of these hulls in random waves, representing actual seas. In this article, a computational-based model for solving viscous fluid flow around the vessel is built to address this problem. Three different planing hulls, denoted as C, C1, and C2, each distinguished by the number of steps incorporated on their bottom surfaces (1 and 2 indicating the respective step count, with case C being the stepless hull), are modeled in a Computational Fluid Dynamics (CFD) tank, allowing for analysis of the effects of steps on dynamic responses of a planing surface operating in random waves. CFD data is compared against those collected in towing tank tests, revealing a satisfactory level of accuracy. Extreme value and gamma distributions are shown to give probabilities of maxima/minima of displacements and vertical acceleration at the center of gravity (CG) for all three hulls. It is shown that the stepless boat may be exposed to lower vertical acceleration at an early planing speed, but at higher planing speeds, a double-stepped design mitigates the vertical acceleration. Nevertheless, the double-stepped hull would experience more significant extreme heave responses across all speeds and may be exposed to less significant extreme pitch responses during the ride at the highest speed compared to the stepless and one-stepped hulls. The skewness of heave and pitch is evaluated, and it is found that the heave response tends to skew toward positive values (upward). This skewness becomes more noticeable with increasing speed but remains insensitive to wave steepness. Additionally, the pitch response at lower planing speeds shows a partial skew towards negative values (bow-down), but eventually, they may also be partially skewed towards positive values at higher speeds. Moreover, a correlation is observed between the kurtosis of responses of different hulls and the occurrence of the 1/100 highest responses, indicating that a kurtosis greater than 3.0 would result in more extreme responses. Overall, this analysis offers practical insights into planing hull behavior in actual sea conditions from a CFD model perspective, highlighting the potential of CFD in simulating this complex problem.