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: 2010
• 资助国家: 加拿大
• 起止时间: 2010-01-01 至 2011-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=450965
• 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.

多年来，预测非线性航空弹性行为的能力一直是激烈研究的主题，因为在航空航天工程行业中，飞机结构的航空弹性响应中的不稳定性的能力非常重要。当前的大多数预测方法基于确定性方法，在该方法中，得出了数学模型（具有初始条件和边界条件的管理系统方程），并且通过数值模拟计算解决方案。在现实世界中，确实存在不确定性，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的值，实验测量中的噪声等。其他潜在的不确定性源可能是由数值错误引起的，例如离散化。因此，对于现实的建模和模拟，由于不确定性而产生的影响不可忽视。拟议的研究将集中于将不确定性纳入气弹性响应预测中。这是一个具有挑战性的任务，因为现在必须在航空弹性模型中引入随机配方，并且必须开发一种新型的仿真方法，以避免使用常规但效率低下的蒙特卡洛方法。