With rapid advancements in computer hardware and numerical modeling methods, computationalfluiddynamics (cfd) has gained prominence in simulating complex flows. As parallel computation becomes an industry standard, ...
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With rapid advancements in computer hardware and numerical modeling methods, computationalfluiddynamics (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.
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...
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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.
This paper presents a highly efficient signed-distance field (SDF) generator designed specifically for computationalfluiddynamics (cfd) workflows. Our approach integrates the Message Passing Interface (MPI) for para...
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This paper presents a highly efficient signed-distance field (SDF) generator designed specifically for computationalfluiddynamics (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.
Accurate multi-physics field prediction is important in the design and optimization for ethylene cracking furnaces. However, traditional computationalfluiddynamics (cfd) simulations are time-consuming and traditiona...
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Accurate multi-physics field prediction is important in the design and optimization for ethylene cracking furnaces. However, traditional computationalfluiddynamics (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.
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 ...
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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 fluiddynamics and electrochemical reactions in direct methanol fuel cells (DMFC) are mathematically modeled and numerically simulated using computationalfluiddynamics (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.
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 p...
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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 computationalfluiddynamics (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.
A novel parallel computing method for computationalfluiddynamics in engineering is presented. It makes three dimensional numerical simulations of fluidstructure interaction problems feasible for most engineers. Wind...
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A novel parallel computing method for computationalfluiddynamics in engineering is presented. It makes three dimensional numerical simulations of fluidstructure interaction problems feasible for most engineers. Wind-induced vibration problems of long-span bridges with three dimensional turbulent flow simulations are implemented on the clusters built with the present parallel computing method and get a direct benefit in engineering.
The effective management of ships' ballast water is critical for preventing the spread of invasive species. Despite advancements in UV-based ballast water treatment systems (BWTSs), achieving a uniform flow distri...
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The effective management of ships' ballast water is critical for preventing the spread of invasive species. Despite advancements in UV-based ballast water treatment systems (BWTSs), achieving a uniform flow distribution within UV reactors (UVRs) remains challenging due to the spatial constraints of ships. This study employs computationalfluiddynamics (cfd) to analyze turbulent seawater flow in a real-case BWTS installed on a self-discharging bulk carrier. The flow uniformity at UVR inlets and the volume flow rate (Q) distribution between parallel reactors are evaluated at nominal flow rates of 1000, 1900, and 2000 m3/h. The results indicate significant disparities at maximum capacity (2000 m3/h), with the starboard configuration exceeding the recommended Q per UVR by 4.95%, thus requiring operational adjustments. Six geometric modifications are assessed, revealing that optimized pipeline bends and T-junction designs (e.g., ST_3 and ST_4) improve velocity uniformity and maintain the relative Q distribution errors below 8.5%. This study identifies vortical structures generated by sharp geometrical transitions as primary contributors to flow instability. By bridging cfd insights with practical engineering constraints, this work provides feasible recommendations for retrofitting existing BWTSs and designing future systems, ultimately enhancing treatment efficacy, reducing UV lamp wear, and supporting compliance with International Maritime Organization (IMO) standards.
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