Computational aeroelastic modelling of airframes and turbomachinery: progress and challenges

Area of Science:

• Aerospace Engineering
• Computational Mechanics
• Multidisciplinary Design Optimization

Background:

• Computational fluid dynamics (CFD) and computational structural dynamics (CSD) are maturing engineering tools.
• Computational aeroelasticity (CAE) integrates CFD and CSD, playing an increasing role in aerospace vehicle design.
• The progressive integration of CAE into multidisciplinary aerospace design presents both successes and challenges.

Purpose of the Study:

• To survey the current state of computational aeroelasticity (CAE).
• To discuss recent research, successes, and challenges in CAE integration into aerospace design.
• To provide an overview of prediction methods and nonlinear modeling in airframe and turbomachinery design.

Main Methods:

• Review of advanced linear and nonlinear prediction methods in CAE.
• Discussion of nonlinear flow models and order reduction techniques.
• Exploration of future trends in incorporating structural nonlinearity.

Main Results:

• CAE is increasingly utilized in airframe and turbomachinery design.
• Different levels of nonlinear modeling offer trade-offs between accuracy, complexity, and computational cost.
• Advanced methods and order reduction techniques are key to efficient CAE.

Conclusions:

• CAE is a vital and evolving tool for modern aerospace engineering.
• Continued research is needed to address challenges in nonlinear modeling and integration.
• Successful examples demonstrate the practical application of CAE in aerospace design.