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JOURNAL OF SYSTEMS AND SOFTWARE 2024年 210卷

作者： Lienhardt, Michael Beek, Maurice H. ter Damiani, Ferruccio Off Natl Etud & Rech Aerosp Palaiseau France CNR ISTI Pisa Italy Univ Turin Turin Italy

Data -centric parallel programming models such as dataflows are well established to implement complex concurrent software. However, in a context of a configurable software, the dataflow used in its computation might vary with respect to the selected options: this happens in particular in fields such as computational fluid dynamics (cfd), where the shape of the domain in which the fluid flows and the equations used to simulate the flow are all options configuring the dataflow to execute. In this paper, we present an approach to implement product lines of dataflows, based on Delta -Oriented Programming (DOP) and term rewriting. This approach includes several analyses to check that all dataflows of a product line can be generated. Moreover, we discuss a prototype implementation of the approach and demonstrate its feasibility in practice.

关键词： computational fluid dynamics

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parallel Multifidelity Design of Experiment Strategy Considering Low-Fidelity Simulation Feasibility

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ASCE-ASME JOURNAL OF RISK AND UNCERTAINTY IN ENGINEERING SYSTEMS PART A-CIVIL ENGINEERING 2025年第2期11卷

作者： Ding, Fei Peng, Han Zhang, Jize Kareem, Ahsan Univ Notre Dame Dept Aerosp & Mech Engn South Bend IN 46556 USA Hong Kong Univ Sci & Technol Dept Civil & Environm Engn Hong Kong Peoples R China Univ Notre Dame Engn NatHaz Modeling Lab South Bend IN 46556 USA

For computationally demanding engineering applications with variable-fidelity numerical simulators, multifidelity surrogate modeling has emerged as a promising approach to mitigate the computational cost. This paper proposes a novel sequential design of experiments (DoE) approach to efficiently enhance the global predictive accuracy of multifidelity co-kriging surrogate models. To leverage the capabilities of high-performance computing platforms, a parallel updating scheme is proposed for simultaneously identifying multiple experiments. To balance high-fidelity and low-fidelity data acquisition, our proposed DoE simultaneously determines the optimal location and fidelity for each experiment. To address low-fidelity simulation failures due to factors such as coarse mesh and modeling errors, a probabilistic binary classifier is introduced to identify undesirable low-fidelity input regions. Through a series of academic benchmark examples and practical computational fluid dynamics (cfd)-enabled aerodynamic building shape design, the proposed sequential strategy significantly reduces the number of expensive model evaluations while maintaining accurate approximations.

关键词： computational fluid dynamics

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An efficient intrusive deep reinforcement learning framework for OpenFOAM

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MECCANICA 2024年 1-21页

作者： Salehi, Saeed Chalmers Univ Technol Dept Mech & Maritime Sci Div Fluid Dynam Gothenburg Sweden Chalmers Ind Tekn Gothenburg Sweden

Recent advancements in artificial intelligence and deep learning offer tremendous opportunities to tackle high-dimensional and challenging problems. Particularly, deep reinforcement learning (DRL) has been shown to be able to address optimal decision-making problems and control complex dynamical systems. DRL has received increased attention in the realm of computational fluid dynamics (cfd) due to its demonstrated ability to optimize complex flow control strategies. However, DRL algorithms often suffer from low sampling efficiency and require numerous interactions between the agent and the environment, necessitating frequent data exchanges. One significant bottleneck in coupled DRL-cfd algorithms is the extensive data communication between DRL and cfd codes. Non-intrusive algorithms where the DRL agent treats the cfd environment as a black box may come with the deficiency of increased computational cost due to overhead associated with the information exchange between the two DRL and cfd modules. In this article, a TensorFlow-based intrusive DRL-cfd framework is introduced where the agent model is integrated within the open-source cfd solver OpenFOAM. The integration eliminates the need for any external information exchange during DRL episodes. The framework is parallelized using the message passing interface to manage parallel environments for computationally intensive cfd cases through distributed computing. The performance and effectiveness of the framework are verified by controlling the vortex shedding behind two and three-dimensional cylinders, achieved as a result of minimizing drag and lift forces through an active flow control mechanism. The simulation results indicate that the trained controller can stabilize the flow and effectively mitigate the vortex shedding.

关键词： Deep reinforcement learning computational fluid dynamics OpenFOAM Intrusive Active flow control

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Linking computational fluid dynamics Modeling to Device-Induced Platelet Defects in Mechanically Assisted Circulation

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ASAIO JOURNAL 2024年第12期70卷 1085-1093页

作者： Zhang, Jiafeng Han, Dong Chen, Zengsheng Wang, Shigang Sun, Wenji Griffith, Bartley P. Wu, Zhongjun J. Univ Maryland Sch Med Dept Surg 10 South Pine StMSTF 436 Baltimore 21201 MD USA Univ Maryland A James Clark Sch Engn Fischell Dept Bioengn College Pk MD USA

Thrombotic and bleeding events are the most common hematologic complications in patients with mechanically assisted circulation and are closely related to device-induced platelet dysfunction. In this study, we sought to link computational fluid dynamics (cfd) modeling of blood pumps with device-induced platelet defects. Fresh human blood was circulated in circulatory loops with four pumps (CentriMag, HVAD, HeartMate II, and CH-VAD) operated under a total of six clinically representative conditions. Blood samples were collected and analyzed for glycoprotein (GP) IIb/IIIa activation and receptor shedding of GPIb alpha and GPVI. In parallel, cfd modeling was performed to characterize the blood flow in these pumps. Numerical indices of platelet defects were derived from cfd modeling incorporating previously derived power-law models under constant shear conditions. Numerical results were correlated with experimental results by regression analysis. The results suggested that a scalar shear stress of less than 75 Pa may have limited contribution to platelet damage. The platelet defect indices predicted by the cfd power-law models after excluding shear stress <75 Pa correlated excellently with experimentally measured indices. Although numerical prediction based on the power-law model cannot directly reproduce the experimental data. The power-law model has proven its effectiveness, especially for quantitative comparisons.

关键词： blood pumps computational fluid dynamics mechanically assisted circulation platelet activation platelet defects power law model

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Speculative anisotropic mesh adaptation on shared memory for cfd applications

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ENGINEERING WITH COMPUTERS 2024年第6期40卷 3801-3827页

作者： Tsolakis, Christos Chrisochoides, Nikos Old Dominion Univ Dept Comp Sci CRTC Norfolk VA 23529 USA

Efficient and robust anisotropic mesh adaptation is crucial for computational fluid dynamics (cfd) simulations. The cfd Vision 2030 Study highlights the pressing need for this technology, particularly for simulations targeting supercomputers. This work applies a fine-grained speculative approach to anisotropic mesh operations. Our implementation exhibits more than 90% parallel efficiency on a multi-core node. Additionally, we evaluate our method within an adaptive pipeline for a spectrum of publicly available test-cases that includes both analytically derived and error-based fields. For all test-cases, our results are in accordance with published results in the literature. Support for CAD-based data is introduced, and its effectiveness is demonstrated on one of NASA's High-Lift prediction workshop cases.

关键词： parallel Anisotropic Metric-based Mesh adaptation

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ParTransgrid: A scalable parallel preprocessing tool for unstructured-grid cell-centered computational fluid dynamics applications

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SOFTWARE-PRACTICE & EXPERIENCE 2023年第1期53卷 6-26页

作者： Zhang, Jian Liu, Jie Zhou, Naichun Tang, Jing He, Xie Chen, Jianqiang Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha Peoples R China China Aerodynam Res & Dev Ctr Computat Aerodynam Inst Mianyang Sichuan Peoples R China

The development of a basic scalable preprocessing tool is the key routine to accelerate the entire computational fluid dynamics (cfd) workflow toward the exascale computing era. In this work, a parallel preprocessing tool, called ParTransgrid, is developed to translate the general grid format like cfd General Notation System into an efficient distributed mesh data format for large-scale parallel computing. Through ParTransgrid, a flexible face-based parallel unstructured mesh data structure designed in Hierarchical Data Format can be obtained to support various cell-centered unstructured cfd solvers. The whole parallel preprocessing operations include parallel grid I/O, parallel mesh partition, and parallel mesh migration, which are linked together to resolve the run-time and memory consumption bottlenecks for increasingly large grid size problems. An inverted index search strategy combined with a multi-master-slave communication paradigm is proposed to improve the pairwise face matching efficiency and reduce the communication overhead when constructing the distributed sparse graph in the phase of parallel mesh partition. And we present a simplified owner update rule to fast the procedure of raw partition boundaries migration and the building of shared faces/nodes communication mapping list between new sub-meshes with an order of magnitude of speed-up. Experiment results reveal that ParTransgrid can be easily scaled to billion-level grid cfd applications, the preparation time for parallel computing with hundreds of thousands of cores is reduced to a few minutes.

关键词： cfd CGNS parallel computing preprocessing unstructured grid

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computational fluid dynamics simulations of the heat transfer properties of graphene-based nanolubricants and application to hydrodynamic lubrication

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PHYSICS OF fluidS 2024年第5期36卷 36, 053106-36, 053106页

作者： Guarino, Roberto Pugno, Nicola Maria Univ Trento Dept Civil Environm & Mech Engn Lab Bioinspired Bion Nano Meta Mat & Mech Trento Italy Queen Mary Univ London Sch Engn & Mat Sci London England Ecole Polytech Fed Lausanne EPFL Swiss Plasma Ctr SPC CH-5232 Villigen Switzerland

In this paper, we consider experimental data available for graphene-based nanolubricants to evaluate their convective heat transfer performance by means of computational fluid dynamics (cfd) simulations. Single-phase models with temperature-dependent properties are employed for this purpose. The base fluid is a polyalkylene glycol, and we show the effect of the addition of carbon nanohorns and graphene nanoplatelets (GNPs), in different volume fractions, on the convective heat transfer coefficient between two parallel plates. Then, an application to hydrodynamic lubrication is discussed. The extreme in-plane thermal conductivity of graphene allows a smaller temperature rise of the GNP-based nanolubricant, i.e., a more effective heat removal. To the best of our knowledge, this work represents the first application of single-phase nanofluid models to hydrodynamic lubrication.

关键词： Graphene

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A Computer Vision Approach toward Verifying cfd Models of Stirred Tank Reactors

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ORGANIC PROCESS RESEARCH & DEVELOPMENT 2024年第9期28卷 3661-3673页

作者： Fyfe, Calum Barrington, Henry Gordon, Charles M. Reid, Marc Univ Strathclyde Dept Pure & Appl Chem Glasgow G1 1XL Scotland Scale Up Syst Ltd Dublin 4 Ireland

Mixing is one of the most important nonchemical considerations in the design of scalable processes. While noninvasive imaging approaches to deliver a quantifiable understanding of mixing dynamics are well-known, the use of imaging to verify computational fluid dynamics (cfd) models remains in its infancy. Herein, we use colorimetric reactions and our kinetic imaging software, Kineticolor, to explore (i) the correlation of imaging kinetics with pH probe measurements, (ii) feed point sensitivity for Villermaux-Dushman-type competing parallel reactions, and (iii) the use of experimental imaging kinetic data to qualitatively assess cfd models. We report further evidence that the influences of the stirring rate, baffle presence, and feed position on mixing in a tank reactor can be informatively captured with a camcorder and help experimentally verify cfd models. Overall, this work advances scarce little precedent in demonstrating the use of computer vision to verify cfd models of fluid flow in tank reactors.

关键词： mixing computer vision cfd kinetics imaging

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Exploring wind flow dynamics in foredune notches using computational fluid dynamics (cfd)

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COASTAL ENGINEERING 2025年 195卷

作者： Smyth, Thomas A. G. Pagon, Thomas Walker, Ian J. Liverpool Hope Univ Sch Comp Sci & Environm Liverpool England Univ Huddersfield Sch Appl Sci Dept Phys & Life Sci Div Geog Huddersfield England Univ Calif Santa Barbara Dept Geog Santa Barbara CA USA

Coastal dunes offer a wide range of valuable ecosystem services such as protection from erosion, flooding, sea-level rise, and provision of specialised habitat for endangered, endemic, or migratory species. Foredune blowouts and landward migrating parabolic dunes play an important role in many coastal dune settings creating ecological heterogeneity associated with inland sand transport, nutrient supply, and geomorphic disturbance processes. However, as coastal dunes globally are being increasingly stabilised by vegetation and declining in their ecological resilience and functionality, anthropogenic interventions, such as the removal of invasive species and excavation of foredune notches, have emerged to simulate and restore critical aeolian processes required to maintain dune morphodynamics and onshore sediment transport between the beach and inland dunes. This study employed computational fluid dynamics (cfd) modelling to investigate key controls on the wind flow dynamics and sand transport potential within idealised foredune notches of varying widths, slopes, and planform shape (rectangular vs. trapezoidal) for perpendicular and oblique incident wind directions. Compared with empirical findings from similarly engineered notches, our results show that notch width significantly influences shear velocity in the excavated notch 'slot', with narrower notches (25 m wide) enhancing wind flow acceleration and inland sediment transport potential. Spatial patterns of shear velocity throughout notches were also sensitive to incident wind direction, with maximum shear velocities, and consequent inland sand transport potential, occurring when winds were parallel to the orientation of the notch. On the lobes of the notches, shear velocity and sand transport potential were greatest during oblique winds. Our results suggest that a relatively narrow notch (e.g. 25 m as opposed to 50 m or 100 m), aligned with the prevailing wind direction, creates the most favourable conditions for

关键词： computational fluid dynamics

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Graph and convolutional neural network coupling with a high-performance large-eddy simulation solver

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COMPUTERS & fluidS 2024年 278卷

作者： Serhani, Anass Xing, Victor Dupuy, Dorian Lapeyre, Corentin Staffelbach, Gabriel CERFACS 42 Ave Gaspard Coriolis F-31057 Toulouse France

computational fluid dynamics (cfd) traditionally relies on long-standing numerical simulation strategies for the Navier-Stokes equations. Recently, interest in data -driven hybrid cfd solvers has spiked, leveraging pre-computed datasets to enhance various weak links inside existing solvers, such as closure models, underresolved physics, or even to guide numerical resolution strategies. Running these hybrid solvers, notably in High Performance Computing (HPC) environments, presents specific challenges. In particular, context-aware deep learning ( e.g. Convolutional (CNN) and Graph (GNN) Neural Networks) is promising for this task, but requires passing data representations between the physics solver and the neural network. In relevant industrial configurations, cfd meshes can be Cartesian but highly irregular, or unstructured, both of which do not match the pixel/voxel structure needed to run CNNs. In addition, discrepancies in programming language and libraries are common between cfd and machine learning applications. This work explores the many challenges of running a parallel hybrid solver in an HPC context, through the coupling of the AVBP cfd solver with neural networks in turbulent combustion and wall friction modeling applications. The knowledge gained is showcased in this article, as well as assembled in an actionable open-source library.

关键词： High-performance computing computational fluid dynamics Deep learning Hybrid architectures Sub-grid scale model Wall model

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