Christopher J. Ruscher

Identifying coherent structures in a 3-stream supersonic jet flow using time-resolved schlieren imaging

We analyze time-resolved schlieren images of the near-field of a 3-stream supersonic jet. The primary jet operates in the vicinity of Ma = 1.6, and the images are collected at the rate of 50 to 400 kfps. We analyze transverse-axial images by constructing time series from more than 400 points selected for their possible significance, based on a qualitative analysis of the schlieren images. The points are grouped along the various shear layers and in the near-field outside the jet. We examine in turn the power spectra and cross-correlations between points. Overall qualitative and quantitative trends in both spectra and correlation are noted, revealing a strong dependence of both on transverse and axial location in the flow field. Defining features in the spectra give insight into the frequency bands which will be more closely analyzed in future phases of this study. The results from this preliminary study point to the validity of using time-resolved schlieren imaging as a non-intrusive experimental method to generate time series, to which a range of analysis methods is applicable.

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.

Application of Snapshot POD on a Varying Grid

Snapshot Proper Orthogonal Decomposition (POD) is a useful tool applied to many different flows. This technique is used mainly for turbulent flows to find the most energetic structures, but it can also be used to extract dominant structures in any type of data. The method usually involves using multiple snapshots in time at one spatial location. However, a problem that can arise is when some of the data is not available in some of the snapshots due to obstructions. Several techniques have been investigated for creating data to fill in where it is missing. An example test case of a plunging NACA 0012 airfoil is used to access the efficiency and accuracy of the various fill-in techniques.

Repairing Occluded Data for a Mach 0.6 Jet via Data Fusion

Particle image velocimetry and near-field pressure were collected for an axisymmetric, Mach 0.6 jet. Some of the pressure sensors were in between the laser sheet and camera, causing occlusions in the particle image velocimetry data. Using ideas from the data fusion community, these occluded regions could be repaired. In this case, the particle image velocimetry data could be fused with the knowledge that the velocity field was symmetric about the center axis using a new technique called fused proper orthogonal decomposition, which is inspired by gappy proper orthogonal decomposition and image/wavelet fusion. Using this technique, 90% of the missing data could be estimated with 10% error.

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.

Noise sources in a commercial supersonic jet

Stringent noise regulations currently limit commercial aviation. These regulations make supersonic commercial flight impractical. The development of an engine that can meet these strict rules is paramount to making supersonic commercial flight a reality. One method of noise reduction is to add additional streams to an engine. As such, the three-stream jet has potential to help reduce exhaust noise. Understanding the noise sources in the jet plume can help to design nozzles that are quieter. To accomplish this, high-fidelity, high-speed data are required. Data for an axisymmetric and offset three-stream nozzle were generated using the LES code JENRE developed by the Naval Research Laboratory. The simulation data has been shown to match well with experimental data. Advanced analyses methods that are based on Proper Orthogonal Decomposition (POD), wavelet decomposition, and Stochastic estimation have been applied to extract noise sources in the jet plume.Stringent noise regulations currently limit commercial aviation. These regulations make supersonic commercial flight impractical. The development of an engine that can meet these strict rules is paramount to making supersonic commercial flight a reality. One method of noise reduction is to add additional streams to an engine. As such, the three-stream jet has potential to help reduce exhaust noise. Understanding the noise sources in the jet plume can help to design nozzles that are quieter. To accomplish this, high-fidelity, high-speed data are required. Data for an axisymmetric and offset three-stream nozzle were generated using the LES code JENRE developed by the Naval Research Laboratory. The simulation data has been shown to match well with experimental data. Advanced analyses methods that are based on Proper Orthogonal Decomposition (POD), wavelet decomposition, and Stochastic estimation have been applied to extract noise sources in the jet plume.

Modeling Flow Through a Perforated Plate for a New Combustion Rig

Combustion modeling is an important field that is imperative for studying jet engines. A new combustion rig has been designed to create combustion models for advanced engine technology. The rig consists of two streams; one for fresh air and another stream for vitiated air. Knowing the inlet conditions of the test section is important when doing high-resolution simulations such as large eddy simulations (LES). A simulation of the rig upstream of the test section has been performed in order to obtain the boundary conditions, which would be difficult and costly to obtain experimentally.