EAGER: CFD-based Adjoint Methods for Design Applications with Unsteady Separated Flows

EAGER：用于非定常分离流设计应用的基于 CFD 的伴随方法

• 批准号: 2110095
• 负责人: Douglas Bohl
• 金额: $ 22.15万
• 依托单位: Clarkson University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2110095&HistoricalAwards=false
• 关键词: EAGER; CFD; based; Adjoint; Methods

A computational method called adjoint based optimization is used in vehicle design to find optimal shapes for desired performance, such enhanced lift or reduced drag of aerodynamic shapes. Historically, this method has been used on streamlined shapes in simple flow fields, but more recently it has been applied to more complex shapes such as automobiles in complex, turbulent flow fields. However, the accuracy of the flow simulations and the effectiveness of the design process are unclear. An application where the accuracy of aerodynamic optimization is critical is the Winter Olympic sport of Luge. History has shown that Olympic medals have been determined by time differences of only a few thousandths of a second. The aerodynamics are complicated because the geometry itself is complex and the flow behind the rider/sled involves large scale unsteady turbulent motion (similar to a vehicle). Traditionally luge equipment has been developed by former athletes and was hand shaped based on hunches, feel, and experience. The flow conditions, and historic lack of science based design optimization, make luge sleds an ideal choice for investigation of the effectiveness of adjoint based optimization to perform aerodynamic optimization The successful design, build, and experimentally validating adjoint designs for unsteady flows in this project can potentially have a significant benefit to future designs of cars/trucks, boat/ships, and advanced air vehicles for drag minimization, increased performance during turns/maneuvering, or the prevention of flow separation while accelerating/decelerating.The goal of this work is to investigate the effectiveness of adjoint methods to reach an optimal shape in turbulent, unsteady, separated flows using experimental validation. An adjoint based method will be developed and applied to a luge sled/rider model that targets modification of the sled shape to minimize drag. Experimental testing will be performed to provide performance data of the intermediate shapes throughout the process to ascertain if the adjoint process is accurately predicting the actual performance and converging towards an optimal solution. The experimental validation will be accomplished by a combination of surface pressure measurement, drag measurement and measurement of the flow fields around the model. Beyond these technical goals is the high-level goal of transferring scientifically optimized race technology to the US Luge Association for use in Olympic competition.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

在飞行器设计中使用称为基于伴随的优化的计算方法，以找到满足期望性能的最佳形状，例如空气动力学形状的增强升力或减小阻力。 从历史上看，该方法已用于简单流场中的流线型形状，但最近已应用于更复杂的形状，如复杂湍流场中的汽车。 然而，流动模拟的准确性和设计过程的有效性尚不清楚。冬季奥运会无舵雪橇运动是空气动力学优化精度至关重要的一个应用。历史表明，奥运会奖牌的决定因素仅为千分之几秒的时差。空气动力学是复杂的，因为几何形状本身是复杂的，并且骑手/雪橇后面的流动涉及大规模的不稳定湍流运动（类似于车辆）。传统的无舵雪橇设备是由以前的运动员开发的，并根据预感，感觉和经验手工成形。流动条件和历史上缺乏基于科学的设计优化，使无舵雪橇成为研究基于伴随优化的空气动力学优化有效性的理想选择。本项目中非定常流的成功设计、建造和实验验证伴随设计可能对未来汽车/卡车、船/船和先进飞行器的阻力最小化设计有显著的好处，在转弯/机动过程中提高性能，或在加速/减速时防止流动分离。这项工作的目的是研究伴随方法的有效性，以达到最佳形状的湍流，非定常，分离流使用实验验证。将开发一种基于伴随的方法，并将其应用于无舵雪橇/骑手模型，该模型的目标是修改雪橇形状以最小化阻力。将进行实验测试，以提供整个过程中中间形状的性能数据，以确定伴随过程是否准确预测实际性能并收敛到最优解。实验验证将通过表面压力测量、阻力测量和模型周围流场测量的组合来完成。除了这些技术目标之外，还有一个高层次的目标，即向美国无舵雪橇协会转让科学优化的比赛技术，用于奥运会比赛。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。