Matthew G. Berry

Analysis of high speed jet flow physics with time-resolved PIV

This work focuses on a Mach 0.6 turbulent, compressible jet flow field with simultaneously sampled near and far-field pressure, as well as 10 kHz time-resolved PIV. Experiments have been conducted in the fully anechoic chamber and jet facility at Syracuse University. The PIV measurements were taken in the streamwise plane of the jet along the center plane at various downstream locations. In addition, measurements were taken off of the center plane to obtain a three-dimensional view of the jet flow. Active flow control (both open and closed-loop) was performed in order to see the effects on the potential core length and overall sound pressure levels. Various reduced-order models have been used to analyze previous experimental data sets at Syracuse University. This paper will focus on the analysis of the flow physics, using the time-resolved velocity field coupled with the simultaneously sampled pressure. Novel modeling approaches such as observable inferred decomposition and cluster-based reduced-order modeling have been implemented in an effort to link the near-field velocity with the far-field acoustics.

Application of POD on time-resolved schlieren in supersonic multi-stream rectangular jets

In this paper, we present an experimental investigation of a supersonic rectangular nozzle with aft deck used for three-stream engines. The jet utilizes a single expansion ramp nozzle (SERN) configuration along with multiple streams, operating at a bulk flow Mj,1 = 1.6 and bypass stream Mj,3 = 1.0. This idealized representation consists of two canonical flows: a supersonic convergent-divergent (CD) jet and a sonic wall jet. Time-resolved schlieren experiments were performed up to 100 kHz. Proper orthogonal decomposition (POD), as suggested by Lumley for structure identification in turbulent flows, is applied to the schlieren images and the spatial eigenfunctions and time-dependent coefficients are related to the flow structures. This research seeks to lay a foundation for fundamental testing of multi-stream SERNs and the identification of the flow physics that dominate these modern military nozzles.

Comparison of spatial and temporal resolution on high speed axisymmetric jets

The current investigation examines a 2 inch, circular, high-speed jet with two separate PIV setups simultaneously sampled with far-field pressure. Subsonic and supersonic velocity measurements are performed in the streamwise (r-z) plane of the jet with both time-resolved PIV and large window PIV configurations, taken at different times. The 10 kHz time-resolved PIV captures 1.5 streamwise diameter windows at several downstream locations. The large window PIV utilizes 3 simultaneously captured cameras stitched together to view a single interrogation window of the flow field approximately 2.5-9 streamwise diameters from the nozzle lip. Both PIV setups have an approximately 1.5 diameter spatial window in the radial direction. In this paper, we will focus on the Mach 0.6 flow in the region of the potential core collapse (z/D = 6 7.5). Low-dimensional modeling techniques in the form of proper orthogonal decomposition, Lumley (1967) and Sirovich (1987), are implemented in order to help us understand the large scale, energetic events within the flow. In previous work, the time-dependent POD modes from the TRPIV have been correlated with the far-field acoustics to determine which low-dimensional structures best relate to the noise. These correlated events are deemed as “loud” modes, Low et al. (2013). One issue is that this approach is greatly influenced by the temporal and spatial nature of the PIV. By utilizing the differences in our PIV setups, we map the convergence of POD modes based on their spatial and temporal resolution.

Reduced Order Models for a High Speed Jet with Time-Resolved PIV

In the current investigation, we examine a turbulent, compressible jet flow field in the high subsonic range (Mach 0.85). Experiments take place in the fully anechoic jet facility at Syracuse University, where the flow field is probed in order to measure near-field hydrodynamic pressure, far-field acoustic sound, and near-field velocity. High resolution 10 kHz time-resolved particle image velocimetry (TRPIV) is implemented to gain better insight into the structures formed in the region of the collapse of the potential core of the jet. By exploring these structures in conjunction with the near and far-field pressure, low-dimensional modeling techniques are implemented. With such techniques as proper orthogonal decomposition (POD) and observable inferred decomposition (OID), we seek to gain a better understanding of how jet noise created in the near-field propagates downstream, and how control can be implemented accordingly using such approaches. It has been found that through low-dimensional modeling techniques, “loud” modes in the flow have been identified, which will be utilized through a closed-loop control methodology.

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.

Toward the development of a noise and performance tool for supersonic jet nozzles: Experimental and computational results

Modal decomposition of experimental and computational data for a range of two- and three-stream supersonic jet nozzles will be conducted to study the links between the near-field flow features and the far-field acoustics. This is accomplished by decomposing near-field velocity and pressure data using proper orthogonal decomposition (POD). The resultant POD modes are then used with the far-field sound to determine a relationship between the near-field modes and portions of the far-field spectra. A model will then be constructed for each of the fundamental modes, which can then be used to predict the entire far-field spectrum for any supersonic jet. The resultant jet noise model will then be combined with an existing engine performance code to allow parametric studies to optimize thrust, fuel consumption, and noise reduction.

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.