Pinqing Kan

Mach-number dependence of acoustic source properties in high speed jets -- Part I: ensemble statistics of active regions

We present ensemble statistics connecting the near-field and far-field of a cold circular jet at Mach numbers 0.6, 0.85 and 1. The far-field data consist of pressure measurements, specifically in the cone dominated by noise generated by coherent structures. The near-field data consists of pressure measurements just outside the shear layer, and of 10 kHz TR-PIV measurements in various windows of the near-field. We extract diagnostic time-series from the PIV data, providing spatial and temporal resolution. Band-pass filtering is performed by using continuous wavelet transforms. We calculate frequency-specific cross-correlations between nearand far-field data, thereby locating and characterizing the near-field regions most active in the production of coherent noise. The topological diagnostic Q in particular seems to be active in a very narrow range of axial, transverse and spectral values. Possible changes in active regions below, at and above Ma = 0.85 are noted for further study.

Investigation of "Loud" Modes in a High-Speed Jet to Identify Noise-Producing Events

The current investigation focuses on a fully compressible, axisymmetric jet operating at high subsonic conditions. The test bed of interest includes 10 kHz time-resolved particle image velocimetry coupled with simultaneously sampled near and far-field pressure measurements. The experimental results to be presented have been conducted in the Syracuse University anechoic chamber at the Skytop campus. This study focuses on identifying possible noise-producing events in the flow field by implementing reduced-order modeling techniques to extract “loud” modes in the flow. These concepts are coupled with waveletbased diagnostic tracking techniques to examine the spatial and temporal nature of the “loud” modes. For this work, Mach 0.6 and Mach 0.85 will be the focus, in an effort to understand the noise-producing structures in a subsonic jet. The overall goal of this work is to efficiently link near-field velocity with far-field acoustics to identify the interactions of the flow field responsible for far-field noise generation. Low-dimensional “loud” modes can then be implemented into closed-loop control algorithms in real-time for far-field noise suppression. This paper will focus on these “loud” modes, primarily linking the flow physics directly to the acoustics.

Turbulent Flow Physics and Control: The Role of Big Data Analyses Tools

We are studying several problems involving turbulence and big data that range from more efficient and lower noise in next generation jet propulsion systems to bio-inspired concepts for energy production. Specific examples include flows over airfoils (flapping and stationary) and other complex bodies such as turrets and high-speed jet flows. These research activities involve the collection of massive amounts of data from multi-scale computer simulations and/or large-scale experiments. Such experiments/simulations routinely produce terabytes of multi-modal data (velocity, pressure, acoustics, etc.) in fractions of a second. Time-resolved particle image velocimetry data, for example, has requirements of 10 kHz or higher sampling rates in time along with spatial resolution requirements over a broad range of spatial scales observed in high Reynolds and Mach number turbulent flows. Common questions that arise include: How do we compare and contrast data that have different levels of granularity, density (or sparseness), and distribution (e.g. uniform, checkered, lattice, random, etc.)? Can we combine such fields that span in space and time to develop a holistic systems-level understanding? This is important in linking numerical simulations that apply lenses with varying magnifications to the same system, as well as integrating qualitative and quantitative experimental observations with computer simulations. We will discuss our general efforts to apply big data analyses/modeling tools (the “right filters”) to identify patterns and predictive models rather than just a posteriori trends, statistics, and distributions. Our advanced tools include proper orthogonal decomposition, stochastic estimation, optimal inferred decomposition, wavelet analysis, and Lagrangian coherent structure methods which we are using for understanding, modeling, and controlling such flows. In this paper we focus on high Reynolds and Mach number jets, both axisymmetric and more complex. We have also utilized compressive sensing to examine high-dimensional airfoil data but will not discuss those results here and instead refer the reader to other papers in this volume which focus on this approach in some detail.