Advances in CFD-Driven Design for Fluid-Particle Separation and Filtration Systems in Engineering Applications

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@article{1708639,
      author = {Musa Adekunle Adewoyin, Enoch Oluwadunmininu Ogunnowo, Joyce Efekpogua Fiemotongha, Thompson Odion Igunma, Adeniyi K. Adeleke},
      title = {Advances in CFD-Driven Design for Fluid-Particle Separation and Filtration Systems in Engineering Applications},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {3},
      pages = {347-365},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708639.pdf},
      abstract = {Recent advances in Computational Fluid Dynamics (CFD) have revolutionized the design and optimization of fluid-particle separation and filtration systems across a broad spectrum of engineering applications. These systems, essential in industries such as chemical processing, wastewater treatment, oil and gas, and pharmaceuticals, rely heavily on precise modeling of multiphase flows, turbulence, and particulate dynamics. CFD-driven design approaches enable the prediction and analysis of complex flow behavior, offering engineers powerful insights into performance metrics such as pressure drop, separation efficiency, and particle trajectory without relying solely on costly and time-intensive physical experiments. Emerging CFD techniques incorporate turbulence models, discrete phase models (DPM), Eulerian-Lagrangian frameworks, and population balance models (PBM) to capture the interactions between fluid flow and particulate matter at both micro and macro scales. The integration of these models enhances the predictive capability of CFD tools, allowing for the development of high-efficiency separators, cyclones, membrane filters, and hydrocyclones with improved throughput, lower energy consumption, and enhanced pollutant capture. In addition, optimization algorithms combined with CFD simulations now allow for iterative design refinement, enabling the identification of ideal geometries and operating conditions under various boundary conditions. The development of advanced meshing techniques, GPU-accelerated solvers, and adaptive mesh refinement (AMR) has significantly reduced computational cost and turnaround time. Furthermore, the integration of artificial intelligence (AI) and machine learning (ML) with CFD workflows is emerging as a transformative approach, enabling real-time performance prediction and automated design iterations. These hybrid approaches are particularly effective in identifying nonlinear patterns and sensitivities that traditional methods may overlook. This review highlights key developments in CFD methodologies for fluid-particle systems, recent industrial applications, and ongoing research challenges such as modeling fine particle agglomeration, membrane fouling, and multiphysics interactions. The future of CFD-driven design in this area lies in the continued convergence of high-fidelity simulations, big data analytics, and sustainable engineering principles. As these technologies evolve, they promise to further streamline the design process, enhance filtration system reliability, and support the global demand for cleaner and more efficient separation technologies.},
      keywords = {Computational Fluid Dynamics (CFD), Fluid-Particle Separation, Filtration Systems, Multiphase Flow, Design Optimization, Discrete Phase Model, Membrane Fouling, AI-Augmented CFD, Engineering Applications.},
      month = {September},
  }