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: 470140627
• 负责人: Dr.-Ing. Sparsh Sharma, Ph.D.
• 金额: --
• 依托单位: Institut für Flugführung (FL)
• 依托单位国家: 德国
• 项目类别: WBP Position
• 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/470140627?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.

喷气式发动机以不同的方式产生噪音，但主要是这种噪音来自高速排气流，离开发动机后部的喷嘴。飞机在缓慢移动时声音最大，比如在起飞或降落时。当废气流遇到相对静止的空气时，它会产生巨大的剪切力，很快变得不稳定。新的降噪方案的性能评价关键取决于在相关马赫数流态下对声学过程的源机制进行建模的稳健但经济的数值方法。本研究提案的目的是更好地了解高频范围内的缺失噪声，该噪声具有较高的烦恼惩罚和有限的频率带宽预测，并开发一种模拟工具来预测相同的噪声。这一建议有助于后者通过基于地图的随机降阶建模方法。模拟像射流湍流这样的复杂现象需要使用极高分辨率的网格来表示所涉及的动力学。一个典型的模拟可能有5亿个网格点。将其乘以5，以说明压力，密度和速度的三个分量，以描述每个网格点处的流动。这相当于数十亿个自由度或计算机用来模拟单个理想喷气机噪音的变量数量。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现状是否可以进一步发展，以回答有关喷流噪声的剩余问题。在拟议的研究中，我提出了一个降阶建模方法，有一个独特的可能性，将详细和解决的物理，目的是增加仿真结果的保真度在该level.The预期的研究结果将是一个喷气噪声预测模型，这是科学上有充分的基础，涉及合成压力场降阶模型产生的。更广泛地说，这项研究的目的是证明可靠的预测湍流射流的压力场，其影响远远超出了本研究的范围。