Aeroelastic response predictions under uncertainty and computational acoustics

不确定性和计算声学下的气动弹性响应预测

• 批准号: 43474-2008
• 负责人: Wong, YauShu
• 金额: $ 1.95万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2011
• 资助国家: 加拿大
• 起止时间: 2011-01-01 至 2012-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=474997
• 关键词: Aeroelastic; response; predictions; under; uncertainty

The capability to predict nonlinear aeroelastic behavior has been the subject of intense research for many years, since the ability to forecast instabilities in the aeroelastic response of aircraft structures is of great importance in the aerospace engineering industry. The majority of the current prediction methodologies are based on a deterministic approach, in which a mathematical model (governing system equations with initial and boundary conditions) is derived, and a solution is computed by numerical simulations. In real world problems, uncertainty does exist and this could be due to the lack of information in the modeling, errors resulting from imprecise material properties or values of system parameters, noises in experimental measurements, etc. The other potential source for uncertainty could arise from numerical errors, such as discretization. Hence, for realistic modeling and simulation, the effects due to uncertainty can not be ignored. The proposed research will focus on incorporating uncertainty in aeroelastic response prediction. This is a challenging task because stochastic formulation must now be introduced in the aeroelastic model, and a novel simulation methodology has to be developed to avoid using the conventional but very inefficient Monte Carlo method.

预测非线性气动弹性行为的能力多年来一直是深入研究的主题，因为预测飞机结构气动弹性响应的不稳定性的能力在航空航天工程行业中非常重要。当前的大多数预测方法都基于确定性方法，其中导出数学模型（具有初始条件和边界条件的控制系统方程），并通过数值模拟计算解。在现实世界的问题中，不确定性确实存在，这可能是由于建模中缺乏信息、不精确的材料属性或系统参数值导致的误差、实验测量中的噪声等造成的。不确定性的另一个潜在来源可能来自于数值误差，例如离散化。因此，对于现实建模和仿真来说，不确定性带来的影响不容忽视。拟议的研究将侧重于将不确定性纳入气动弹性响应预测。这是一项具有挑战性的任务，因为现在必须在气动弹性模型中引入随机公式，并且必须开发一种新颖的模拟方法以避免使用传统但效率非常低的蒙特卡罗方法。