An Analysis and Design Framework for Extensive Natural Laminar Flow of Aircraft Wings

飞机机翼大范围自然层流分析与设计框架

• 批准号: 538870-2019
• 负责人: Nadarajah, Sivakumaran
• 金额: $ 5.1万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=699362
• 关键词: Analysis; Design; Framework; Extensive; Natural

The objective of this collaborative project is to develop the necessary algorithms and tools for the analysis and design of extended region of laminar flow over aircraft components via aerodynamic shape optimization. The framework will employ a Reynolds-averaged Navier-Stokes solver, and turbulence and correlation-based transition models to accurately predict the transition point and an adjoint-based design optimization approach to perform both shape and planform optimization. The ultimate objective is to develop the enabling technologies to design environmentally responsible aircraft. This project will contribute towards the development of advanced aircraft concepts that satisfy the N+3 (2025) or equivalent EU standards. Modern aircraft design involves a large number of design variables for a multitude of disciplines culminating in extremely large number of data sets. Obtaining high fidelity solutions for all combinations of such parameters is currently computationally intractable. To alleviate this issue, designers often rely on surrogate models to simulate the input-output behaviour of complex systems based on large data sets in order to obtain a continuous approximation of functions of interest across the parameter space. In summary, the collaborative research and development will develop the necessary novel algorithms through three objectives. Objective One, implement, verify, and validate a transition model for a RANS-based solver with crossflow models. Objective Two, develop a design framework for natural laminar flow and investigate sensitivity of transitional flow to external perturbations through uncertainty quantification. Objective Three, develop a novel adaptive derivative-enhanced surrogate model of large data sets for performance and load analysis.

该合作项目的目标是开发必要的算法和工具，通过空气动力学形状优化来分析和设计飞机部件上的层流扩展区域。该框架将采用雷诺平均纳维-斯托克斯求解器以及基于湍流和相关性的转变模型来准确预测转变点，并采用基于伴随的设计优化方法来执行形状和平面优化。最终目标是开发设计环保飞机的支持技术。该项目将有助于开发满足 N+3 (2025) 或同等欧盟标准的先进飞机概念。现代飞机设计涉及多个学科的大量设计变量，最终产生大量数据集。目前，为这些参数的所有组合获得高保真度解在计算上是困难的。为了缓解这个问题，设计人员经常依靠代理模型来模拟基于大数据集的复杂系统的输入输出行为，以获得跨参数空间的感兴趣函数的连续近似。综上所述，协作研发将通过三个目标开发必要的新颖算法。目标一，使用横流模型实现、验证和确认基于 RANS 的求解器的转换模型。目标二，开发自然层流的设计框架，并通过不确定性量化研究过渡流对外部扰动的敏感性。目标三，开发一种用于性能和负载分析的新型大数据集自适应导数增强代理模型。