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Comparative Aerodynamic Analysis and parallel Performance of 2D cfd Simulations of a VAWT Using Sliding Mesh Interface Method

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2025年第3期37卷

作者： Onel, Hueseyin Can Adam, Ali Ata Uzol, Nilay Sezer Adana Sci & Technol Univ Dept Aerosp Engn Adana Turkiye Univ Oxford Dept Engn Sci Oxford England Middle East Tech Univ Dept Aerosp Engn Ankara Turkiye

With rapid advancements in computer hardware and numerical modeling methods, computational fluid dynamics (cfd) has gained prominence in simulating complex flows. As parallel computation becomes an industry standard, the computational efficiency of simulations has become critical. The flow around a Vertical Axis Wind Turbine (VAWT), characterized by complex dynamics and challenging rotating geometry, serves as an intriguing case for cfd studies. This study employs the open-source cfd solvers SU2 and OpenFOAM to simulate the incompressible, unsteady, and turbulent flow around an H-type Darrieus VAWT in two dimensions. Spatial and temporal discretization parameters are examined to balance computational cost and accuracy, revealing notable effects on power predictions. Simulations conducted under identical conditions allow for a comparison of the predictions and parallel performances of SU2 and OpenFOAM across three distinct tip speed ratios (TSRs). The findings show that discretization parameters behave differently at various TSRs. While power predictions from SU2 and OpenFOAM generally align with experimental data and with each other, discrepancies arise at lower TSRs, with thrust predictions showing better consistency. Although OpenFOAM provides a faster solution across all parallel configurations, SU2 demonstrates superior parallel scalability, achieving higher speedup and efficiency.

关键词： dynamic mesh OpenFOAM SU2 parallel performance Vertical Axis Wind Turbine (VAWT) computational fluid dynamics (cfd)

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A distributed parallel computing-based cfd analysis of plate-type fuel assemblies in CARR reactors

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INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER 2024年 153卷

作者： Yin, Xinli Chen, Guangliang Qian, Hao Li, Lei Su, Shaomin Jiang, Hongwei Zhang, Hanqi Harbin Engn Univ Fundamental Sci Nucl Safety & Simulat Technol Lab Harbin 150001 Peoples R China

Fine flow and heat transfer characteristics are critical to nuclear reactor safety. However, a comprehensive cfd calculation of the entire reactor core requires substantial computing resources, restricting the research domain's scope. To address this issue, we propose a Distributed parallel (DP) computing scheme, tailored specifically for reactor cores using plate-type fuel assemblies. This study focuses on the thermal-hydraulic calculation of the large domain of a reactor core utilizing plate-type fuel assembly, based on the computational fluid dynamics (cfd) method. This innovative solution enables the completion of extensive domain cfd calculations using modestly equipped personal workstations (8 cores, 128G RAM), which traditionally would require supercomputing platforms. To verify the distributed-parallel computing technique, it is successfully applied to all standard, full-length fuel assemblies in the China Advanced Research Reactor (CARR). In the result analysis, it was observed that detailed results could be obtained with reduced computational resources, demonstrating a significant breakthrough for cfd engineering analysis.

关键词： Plate-type fuel cfd Scheme design Thermal hydraulic Distributed parallel computing

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Comparison between Compartment and computational fluid dynamics Models for Simulating Reactive Crystallization Processes

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2024年第50期63卷 21991-22004页

作者： Querio, Andrea Shiea, Mohsen Buffo, Antonio Marchisio, Daniele Luca Politecn Torino Dipartimento Sci Applicata & Tecnol Corso Duca Abruzzi 24 I-10129 Turin Italy IFP Energies Nouvelles F-69360 Solaize France

This work compares two different computational approaches aimed at describing the reactive crystallization or precipitation process in stirred tanks. The first approach is a full computational fluid dynamics (cfd) model coupled with population balance modeling, which is accelerated by the operator-splitting method and hybrid MPI-OpenMP parallelization. Here, emphasis is given to the hybrid MPI-OpenMP parallelization that improves parallel scalability, when the operator-splitting method is used to take relatively large time steps, despite the large separation of time-scales in such processes. The second approach is a compartment model (CM) enhanced by an automatic tool for the generation of compartments based on some relevant features of the system. The two models are compared for a case study of particular interest: the reactive coprecipitation of Ni-Mn-Co hydroxide in a continuous stirred tank, main precursor to produce cathode active materials of lithium-ion batteries. The obtained results demonstrate the effectiveness of hybrid parallelization in improving the parallel scalability of the cfd model. In addition, it is shown that the CM can produce less accurate but still relevant predictions with relatively small computational cost.

关键词： computational fluid dynamics

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cfd Code parallelization on GPU and the Code Portability

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ADVANCED THEORY AND SIMULATIONS 2025年第3期8卷

作者： Tomanovic, Ivan Belosevic, Srdjan Milicevic, Aleksandar Crnomarkovic, Nenad Stojanovic, Andrijana Deng, Lei Che, Defu Univ Belgrade VINCA Inst Nucl Sci Natl Inst Republ Serbia Dept Thermal Engn & Energy Mike Petrovica Alasa 12-1411351 VincaPOB 522 Belgrade 11001 Serbia Xi An Jiao Tong Univ Sch Energy & Power Engn State Key Lab Multiphase Flow Power Engn Xian 710049 Peoples R China

Goal of this paper is to develop a fully functional parallel computational fluid dynamics (cfd) code that is optimized to run on a single Graphics Processing Unit (GPU). This is achieved by writing the code in FORTRAN and OpenACC (Open Accelerators), providing them with an easily portable, platform independent code. Existing cfd code is significantly modified to allow for parallel asynchronous execution. Also, due to strong recursive dependencies in Tridiagonal Matrix Algorithm (TDMA) solver, it is replaced with Jacobi, which provides fast execution in environments with large number of parallel cores. In this research a computer code for simulation of 2D flow of water through the axisymmetric channel is used as a base for development. The parallel code is executed on GPU, single, and multicore Central Processing Unit (CPU), and the execution times are compared between platforms. Even though that Jacobi solver performs worse on single core computers, compared to its Gauss-seidel counterpart, it is used to provide a baseline for comparison. In this work, it is shown that computation on finer grids takes less time on GPU than on CPU. The computation time increase with the number of cells in grid on GPU should follow the observed linear trend until the GPUs physical limitations are reached depending on memory size and core count.

关键词： computational fluid dynamics graphics processing units jacobi multicore processors OpenACC parallelization

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GenSDF: An MPI-Fortran based signed-distance-field generator for computational fluid dynamics applications

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SOFTWAREX 2025年 30卷

作者： Patil, Akshay Paranjothi, Udhaya Chandiran Krishnan Garcia-Sanchez, Clara Delft Univ Technol Fac Architecture & Built Environm Geoinformat Res Grp 3D Delft Netherlands Delft Univ Technol Fac Aerosp Engn Flow Phys & Technol Dept Wind Energy Sect Delft Netherlands

This paper presents a highly efficient signed-distance field (SDF) generator designed specifically for computational fluid dynamics (cfd) workflows. Our approach integrates the Message Passing Interface (MPI) for parallel computing with the performance benefits of modern Fortran, enabling efficient and scalable signed distance field (SDF) computations for complex geometries. The algorithm focuses on localized distance calculations to minimize computational overhead, ensuring efficiency across multiple processors. An adjustable stencil width allows users to balance computational cost with the desired level of accuracy in the distance approximation. Additionally, GenSDF supports the widely used Wavefront OBJ format, utilizing its encoded outward normal information to achieve accurate boundary definitions. Performance benchmarks demonstrate the tool's ability to handle large-scale 3D models (similar to O(107) triangulation faces) and computational grid points similar to O(109) with high fidelity and reduced computational demands. This makes it a practical and effective solution for cfd applications that require fast, reliable distance field computations while accommodating diverse geometric complexities.

关键词： Signed-distance-field computational fluid dynamics MPI

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Novel reduced-order framework combining proper orthogonal decomposition and multi-parallel Gaussian process regression: Multi-physics prediction of ethylene cracking furnaces

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CHEMICAL ENGINEERING SCIENCE 2025年 305卷

作者： Hu, Guihua Chen, Mimi Long, Jian East China Univ Sci & Technol Key Lab Smart Mfg Energy Chem Proc Minist Educ Shanghai 200237 Peoples R China East China Univ Sci & Technol Engn Res Ctr Proc Syst Engn Minist Educ Shanghai 200237 Peoples R China

Accurate multi-physics field prediction is important in the design and optimization for ethylene cracking furnaces. However, traditional computational fluid dynamics (cfd) simulations are time-consuming and traditional surrogate models lack information about the physical field. The proposed novel reduced-order model (ROM) framework integrates proper orthogonal decomposition (POD) and multiple-parallel Gaussian Process Regression (mGPR) to predict the multi-physics of an industrial ethylene cracking furnace while significantly reducing computational time and resource requirements. cfd simulations are first conducted to obtain multi-physics data, which are then compared to industrial values. A dataset covering various operating conditions is generated through pairwise experimental design methods. POD is employed to extract modes and coefficients of the physical fields, and mGPR is used to model the nonlinear relationship between the POD coefficients and operating parameters. The results show that the relative error of the outer wall temperature of reactor tube between the POD simulation results and the industrial values is 4.13%. The proposed ROM achieves a global error on the order of 10-3, with minimal truncation degrees of 5, 3, and 6 for flue gas temperature, pressure, and mass fraction of H2O, respectively. The mGPR model outperforms GPR model, demonstrating lower mean squared errors (MSE) (609.667 versus 3718.822) and higher R2 (0.9907 versus 0.9433). In comparison to cfd, the ROM improves computational efficiency by a factor of at least 900 and reduces storage space by approximately 96.3%. The proposed ROM provides reliable technical support for the design and optimization of ethylene cracking furnaces.

关键词： Ethylene cracking furnaces computational fluid dynamics Reduced-order model Proper orthogonal decomposition Multiple-parallel Gaussian process regression

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Numerical simulation of direct methanol fuel cells using computational fluid dynamics

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2025年 108卷 87-98页

作者： Blanco-Cocom, Luis Botello-Rionda, Salvador Ordonez, L. C. Valdez, S. Ivvan Ctr Invest Matemat AC Jalisco S-NCol Valenciana 36023Apartado Postal 4 Guanajuato 36000 Gto Mexico Ctr Invest Cient Yucatan Parque Cient Tecnol Yucat Unidad Energia Renovable Merida 97302 Yucatan Mexico CENTROGEO AC CONACYT Ctr Invest Ciencias Informac Geoespacial Queretaro 76703 Mexico

Recently, there has been a research trend towards clean energy sources such as fuel cells, owing to their high efficiency and close-to-zero emissions. However, the efficiency level depends on the design, and physical experiments are time-consuming requiring expensive materials;therefore, a realistic numerical simulation is crucial to test different designs and conditions. In this paper, the fluid dynamics and electrochemical reactions in direct methanol fuel cells (DMFC) are mathematically modeled and numerically simulated using computational fluid dynamics (cfd) techniques within the OpenFOAM software. The profiles of temperature, reactants, and products flow through the anodic and cathodic chambers of DMFC are derived from the equations of continuity, momentum, species transport, and electrochemical reactions are simulated to study two flow field geometries of a DMFC, parallel channels, and a serpentine channel. A methodology to obtain the crossover current density is presented, and its effect is evaluated on the DMFC behavior. The accuracy and confidence of the results are validated with a case reported in the literature.

关键词： Direct alcohol fuel cells computational fluid dynamics method (cfd) Polarization curve Crossover current density Numerical simulation OpenFOAM

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computational fluid dynamics-Deep Neural Network (cfd-DNN) Surrogate Model with Graphical User Interface (GUI) for Predicting Hydrodynamic Parameters in Three-Phase Bubble Column Reactors

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INDUSTRIAL & ENGINEERING CHEMISTRY RESEARCH 2024年第26期63卷 11670-11685页

作者： Dhakane, Vishal Yadav, Ashutosh Indian Inst Technol Dept Chem Engn Jammu 181221 Jammu & Kashmir India

Bubble column reactors (BCRs) find extensive application in the chemical industry for facilitating three-phase reactions where most often solid-phase catalysts are employed alongside gaseous and liquid reactants. In these reactors, accurately identifying the prevailing hydrodynamic behavior is crucial for both design optimization and successful scale-up efforts. This research investigates the flow characteristics of solid-liquid-gas mixtures in cylindrical bubble columns through a comprehensive approach combining computational fluid dynamics (cfd) simulations and a cfd-based machine learning (ML) model. The three-phase Eulerian-Eulerian cfd simulations explored the effects of 5% and 20% solid loading with varying superficial gas velocities (0.1 and 0.2 m/s) on solid distribution. The obtained results align qualitatively with the experimental findings from the literature. A cfd-deep neural network (DNN) model is presented in a parallel study of a three-phase bubble column situation. By using a large data set obtained from cfd simulations, the deep neural network model is trained to accurately predict fluid dynamics closely resembling the cfd simulations. The ML architecture incorporates input data obtained from cfd results, thereby allowing the deep neural network to acquire knowledge and identify patterns in different fluid flow characteristics, such as velocity, pressure, and holdup. The tuning of hyperparameters is crucial for successful ML predictions (with R-2 values 0.97 to 0.99) in the current cfd-DNN model. Additionally, a graphical user interface (GUI) on the basis of cfd-DNN has been developed for easy model handling. The cfd-DNN model showcases its capacity to forecast hydrodynamic parameters that were not used in the training process, hence diminishing computing expenses and simulation duration in comparison with conventional cfd techniques.

关键词： Bubbles Hydrodynamics Layers Liquids Neural networks

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Application of parallel neural networks (PNN) in predicting and analyzing vehicle attitude during the water exit process

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OCEAN ENGINEERING 2025年 323卷

作者： Huang, Xin Li, Zhuangzhi Dai, Yu Zhu, Xiang Cent South Univ Coll Mech & Elect Engn Changsha Peoples R China

This study proposes a parallel neural network (PNN) model that integrates numerical simulation with machine learning to investigate the hydrodynamic behavior of a deep-sea mining vehicle (DSMV) during the water exit process. The PNN is composed of multiple fully connected neural networks (FCNN) that run in parallel. The PNN model was trained using computational fluid dynamics (cfd) simulation data across various wave phases and successfully predicted the position drift and attitude changes of the DSMV in level 4 sea conditions. Compared to conventional cfd methods, the PNN model achieves a significant improvement in computational efficiency. It takes about 70 h for traditional cfd to calculate the hydrodynamic parameters of a wave phase, while the training of the model only takes about 1 h, and then it takes only a few seconds to predict the hydrodynamic parameters of each wave phase. Meanwhile, the PNN model maintains high accuracy, with a mean squared error (MSE) below 0.01%. The model enables long-time and high-precision prediction of hydrodynamic parameters on small data sets. Additionally, the model demonstrates strong generalization performance. The prediction results provide valuable insights into the dynamic characteristics of the DSMV during water exit, offering a reliable tool for optimizing the design and operation of deep-sea mining vehicles.

关键词： Machine learning Deep-sea mining vehicle Water-exit Hydrodynamic characteristics

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Unsteady three-dimensional multiphase modelling of anode flow distributor in PEM electrolysis cell

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PROGRESS IN computational fluid dynamics 2024年第4期24卷 240-253页

作者： Ozdemir, Safiye Nur Taymaz, Imdat Univ Sakarya Dept Mech Engn TR-54187 Adapazari Turkiye

A polymer electrolyte membrane electrolysis cell (PEMEC) is a system in which water is oxidised at an oxygen electrode, producing oxygen gas on the anode electrode and hydrogen gas on the cathode electrode, respectively. In this study, we aim to develop a three-dimensional, unsteady numerical model of the anode flow field plate (AFFP) for the PEMEC system that focuses on two main objectives: the first objective is to characterise the two-phase flow distribution. The second purpose is to change the channels in the AFFP, analyse their effect on pressure drop, velocity, and oxygen gas distribution, and determine the appropriate number of channels. The three-dimensional, single-domain, and isothermal model of an AFFP was applied in the commercial computational fluid dynamics (cfd) code;it was well-validated with the experimental results of a PEMEC with a parallel flow field. Numerical results show that the pressure gradient decreases diagonally from the inlet port to the outlet port along with the AFFP. It was recorded that when the number of electrode channels was increased from 4 to 16, the pressure drop decreased by 33% but also increased by approximately 73 Pa when it was increased from 16 to 20.

关键词： green hydrogen production PEM electrolysis cell anode flow field plate AFFP computational fluid dynamics cfd modelling two-phase flow

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