Reduced-order modelling of jet noise using a map-based stochastic turbulence approach

使用基于地图的随机湍流方法对喷气噪声进行降阶建模

• 批准号: 470140694
• 负责人: Dr.-Ing. Sparsh Sharma, Ph.D.
• 金额: --
• 依托单位: Department of Applied Mathematics and Theoretical Physics (DAMTP)
• 依托单位国家: 德国
• 项目类别: WBP Fellowship
• 财政年份: 2021
• 资助国家: 德国
• 起止时间: 2020-12-31 至 2022-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/470140694?language=en
• 关键词: Reduced; order; modelling; jet; noise

Jet engines produce noise in different ways, but mainly this noise comes from the high-speed exhaust stream that leaves the nozzle at the rear of the engine. And planes are loudest when they move slowly, such as at takeoff or landing. As the exhaust stream meets relatively still air, it creates tremendous shear that quickly becomes unstable. The evaluation of the performance of new noise-reducing concepts crucially depends on robust but economical numerical methods for modelling of source mechanisms of the acoustic processes in the relevant Mach number flow regimes. The purpose of this research proposal is to gain a better understanding of the missing noise in the range of high-frequencies, which exhibits a high annoyance penalty, and limited frequency bandwidth predictions and to develop a simulation tool to predict the same. This proposal contributes to the later via a map-based stochastic reduced-order modelling approach. Simulating a complex phenomenon like jet turbulence requires the use of an extremely high-resolution mesh to represent the dynamics involved. A typical simulation could have 500 million grid points. Multiply that by five to account for pressure, density and three components of velocity to describe the flow at every grid point. That equates to billions of degrees of freedom or the number of variables a computer uses to simulate the noise from a single idealised jet. The important question that arises here is whether the current status of these affordable high-resolution numerical simulations can be evolved further to answer the remaining questions about jet noise. In the proposed research, I propose the development of a reduced-order modelling approach that has a unique possibility to incorporate detailed and resolved physics with the aim of increasing the fidelity of the simulation results on that level.The outcome of the intended research will be a jet noise prediction model that is scientifically well-grounded, involving a synthetic pressure field generated by the reduced-order model. More broadly, the research is intended to demonstrate reliable prediction of the pressure field of a turbulent jet, with implications well beyond the scope of the present study.

喷气发动机以不同的方式产生噪音，但主要是这种噪音来自使发动机后部喷嘴的高速排气流。当飞机缓慢移动时，例如起飞或着陆时，它们的声音最大。随着排气流相对静止的空气，它会产生巨大的剪切，并迅速变得不稳定。对新的降噪概念的性能的评估至关重要地取决于相关的马赫数流动方案中声学过程的源机制的强大但经济的数值方法。这项研究建议的目的是更好地了解高频范围内缺失的噪声，该噪声表现出高度烦恼的惩罚，并且频率带宽预测有限，并开发了一个模拟工具来预测相同的模拟工具。该提案通过基于地图的随机降低订单建模方法有助于后来。像喷气湍流一样模拟复杂的现象需要使用极高的高分辨率网格来表示所涉及的动力学。典型的模拟可能具有5亿个网格点。将其乘以五个，以说明压力，密度和三个速度成分，以描述每个网格点的流量。这相当于数十亿个自由度或计算机用于模拟单个理想化喷气机的噪声的变量数量。这里出现的重要问题是，这些负担得起的高分辨率数值模拟的当前状态是否可以进一步发展，以回答有关喷气噪声的其余问题。在拟议的研究中，我提出了一种降低阶的建模方法的开发，该方法具有融合详细和解决的物理学的独特可能性，目的是提高模拟结果在该水平上的忠诚度。预期研究的结果将是一种Quige Prediction预测模型，该模型是科学的，涉及由降低的订购生成的合成压力场模型。从更广泛的角度来看，该研究旨在证明对湍流射流压力场的可靠预测，其影响远远超出了本研究的范围。