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The study of external flow is essential for creating structures and objects that interact efficiently and safely with moving fluids, such as air or water. When a body is immersed in a flowing fluid, it experiences two primary forces: drag, which opposes motion along the flow direction, and lift, which acts perpendicular to the flow. The shape, size, and orientation of the object influence these forces.Streamlined and Blunt Bodies in External FlowObjects in fluid flow are classified as...

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Related Experiment Video

Updated: Jun 14, 2025

A Rapid Method for Modeling a Variable Cycle Engine

A Rapid Method for Modeling a Variable Cycle Engine

Published on: August 13, 2019

Building-block-flow computational model for large-eddy simulation of external aerodynamic applications.

Gonzalo Arranz¹, Yuenong Ling², Sam Costa²

¹Department of Aeronautics and Astronautics, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA. garranz@mit.edu.

Communications Engineering

|September 7, 2024

Summary

This summary is machine-generated.

A new Building-block Flow Model (BFM) enhances computational fluid dynamics simulations. This model improves accuracy for complex flow phenomena by incorporating essential physics from simpler flow cases.

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Last Updated: Jun 14, 2025

A Rapid Method for Modeling a Variable Cycle Engine

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Area of Science:

Engineering
Computational Fluid Dynamics
Fluid Mechanics

Background:

Computational fluid dynamics (CFD) is crucial for engineering design but lacks a universal model for all flow phenomena.
Current models face limitations due to inherent assumptions, affecting accuracy across diverse flow conditions.

Purpose of the Study:

Introduce a novel closure model for wall-modeled large-eddy simulation (LES) to overcome existing limitations.
Develop a more accurate and versatile CFD tool applicable to complex engineering challenges.

Main Methods:

The Building-block Flow Model (BFM) integrates physics from simple flow cases to predict complex scenarios.
BFM unifies boundary and bulk flow modeling, accounts for numerical errors, and handles complex geometries.

Main Results:

BFM demonstrated comparable or superior predictive capabilities to state-of-the-art models in five test cases.
Key quantities were accurately predicted, including simulations of an aircraft in landing configuration.

Conclusions:

The BFM offers a promising new approach for developing accurate and adaptable CFD closure models.
This model facilitates the accurate representation of diverse flow physics across various engineering applications.