Multifidelit in Computational Fluid Dynamics (CFD) modelling
refers to the use of models of varying levels of complexity or fidelity to solve a given problem. The goal is to balance accuracy with computational cost, using simpler models where sufficient accuracy can be achieved and more complex models where higher fidelity is required. Multifidelity in CFD modelling offers a flexible and efficient way to balance accuracy and computational cost, allowing engineers to tackle a wide range of problems in fluid dynamics. Here are some key aspects of multifidelity in CFD modelling:

  1. Hierarchical Modelling: Multifidelity approaches often involve a hierarchy of models, ranging from simple empirical or analytical models to complex, physics-based simulations. The choice of model depends on the specific requirements of the problem and the available computational resources.
  2. Model Coupling: In some cases, different models in the hierarchy may be coupled together to combine their strengths. For example, a simplified model may be used to provide boundary conditions for a more detailed simulation.
  3. Adaptive Modelling: Multifidelity approaches can be adaptive, meaning that the fidelity of the model is adjusted based on the specific requirements of the problem. For example, a higher-fidelity model may be used in regions of interest while a lower-fidelity model is used elsewhere.
  4. Uncertainty Quantification: Multifidelity approaches can be used to quantify the uncertainty in CFD simulations. By comparing results from models of different fidelities, engineers can assess the reliability of their predictions.
  5. Optimization and Design: Multifidelity approaches can be used in optimization and design studies to explore a wider range of design options while keeping computational costs manageable.