Anisotropic Diffusion for Aircraft Fluid Flow Simulation
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Solution Overview
Problem
Current computer-generated fluid flow simulations for aircraft surfaces are computationally expensive due to the need for complex CFD models that account for viscous effects, especially in regions near the surface, which can slow down the design iteration process and increase resource usage.
Innovation Solution
The method employs anisotropic diffusion techniques to propagate boundary-layer fluid properties from known regions to unknown regions using variable diffusion rates based on pressure gradients, allowing for a hybrid approach that combines inviscid and viscous simulations, reducing computational burden by performing viscous simulations only in critical areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a complex CFD simulation module that accounts for viscous effects is used, then accuracy of fluid property prediction is improved, but computational time and resource usage increase
Solution Approach 1:
The patent divides the fluid flow region into multiple zones based on viscosity importance: a first region near the aircraft surface where viscous effects are significant and requires accurate simulation, and a second region farther from the surface where viscous effects are negligible and can use simplified simulation. This segmentation allows the system to apply different simulation complexities to different regions, improving overall computational efficiency while maintaining accuracy where needed.
Solution Approach 2:
The patent applies different simulation approaches to different spatial locations: complex viscous CFD simulation is applied locally in the first region near the surface where viscosity matters, while simplified inviscid simulation is applied in the second region where it does not matter. This local differentiation of simulation quality matches the physical reality that viscous effects are localized near surfaces, thereby reducing computational burden without sacrificing accuracy in critical areas.
2Productivity
If a simplified simulation that ignores viscous effects is used, then processing time is improved, but accuracy in regions affected by viscosity deteriorates
Solution Approach 1:
The patent segments the simulation domain into regions where simplified simulation is adequate (second region) and regions where it is not (first region). By performing simplified simulation in the second region, the patent achieves faster processing speeds for the majority of the fluid domain where viscous effects are negligible, while maintaining the option to use accurate viscous simulation only where necessary.
Solution Approach 2:
The patent applies viscous simulation only partially - specifically only in the first region near the surface where it is needed - rather than applying it excessively throughout the entire fluid domain. This partial application of the more computationally intensive viscous simulation model achieves the necessary accuracy in critical areas while avoiding the excessive computational cost of applying it everywhere.
3Use of energy by moving object
If a hybrid simulation approach is used, then resource efficiency is improved, but simulation complexity increases
Solution Approach 1:
The patent manages simulation complexity by segmenting the problem into distinct regions with clearly defined simulation requirements. The system complexity is justified and organized by the physical segmentation of the flow field, where each segment has a predetermined simulation approach based on the local importance of viscous effects. This structured segmentation makes the hybrid approach more manageable and less chaotic than a fully uniform complex simulation.
Solution Approach 2:
The patent changes the simulation parameters (specifically, the inclusion or exclusion of viscous terms in the Navier-Stokes equations) based on spatial location. In the first region, the full viscous Navier-Stokes equations are solved, while in the second region, simplified inviscid equations are used. This parameter change approach allows the system to adapt computational complexity to local physical conditions, improving resource efficiency while maintaining a systematic and manageable simulation framework.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enables accurate fluid property prediction across the entire fluid flow region without the full computational load of a Navier-Stokes-based simulation, improving processing time and resource efficiency while maintaining accuracy.
Implementation Method 1
A minimum diffusion rate is defined along the direction of the pressure gradient vector of the selected polygon. A maximum diffusion rate is also defined in a direction perpendicular to the direction of the pressure gradient vector of the selected polygon.
Data Source
AI summary
A fluid-flow simulation over a computer-generated surface is generated using a diffusion technique. The surface is comprised of a surface mesh of polygons. A boundary-layer fluid property is obtained for a subset of the polygons of the surface mesh. A gradient vector is determined for a selected polygon, the selected polygon belonging to the surface mesh but not one of the subset of polygons. A maximum and minimum diffusion rate is determined along directions determined using the gradient vector corresponding to the selected polygon. A diffusion-path vector is defined between a point in the selected polygon and a neighboring point in a neighboring polygon. An updated fluid property is determined for the selected polygon using a variable diffusion rate, the variable diffusion rate based on the minimum diffusion rate, maximum diffusion rate, and the gradient vector.