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A rectangular single-expansion ramp nozzle (SERN) with an aft deck is used to model the exhaust from a three-stream engine. At the nozzle exit two unmixed streams exist. The first is assumed to be ...

Time-resolved schlieren measurements were conducted on a supersonic rectangular multi-stream nozzle used for three-stream engines. The jet utilizes a single expansion ramp nozzle (SERN) configuration along with an bypass stream, operating at an ideally expanded bulk flow of M j , 1 = 1.6 and bypass stream of M j , 3 = 1.0 . Experiments were conducted with and without an aft deck to model airframe integration. Schlieren imaging was acquired up to 100 kHz in two different orientations. Proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) were applied to the schlieren data to identify flow structures. Comparisons were made between the two decomposition methods in the form of spatial correlations.

Time-resolved schlieren measurements were conducted on a supersonic rectangular multi-stream nozzle used for three-stream engines. The jet utilizes a single expansion ramp nozzle (SERN) configuration along with an bypass stream, operating at an ideally expanded bulk flow of M j , 1 = 1.6 and bypass stream of M j , 3 = 1.0 . Experiments were conducted with and without an aft deck to model airframe integration. Schlieren imaging was acquired up to 100 kHz in two different orientations. Proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) were applied to the schlieren data to identify flow structures. Comparisons were made between the two decomposition methods in the form of spatial correlations.

A jet from an axisymmetric convergent nozzle is studied at ideal and underexpanded conditions using velocity and acoustic data. Two particle imaging velocimetry setups, a 10 kHz system and a multi-camera configuration, were used to capture near-field velocities while simultaneously sampled with far-field microphones. Proper orthogonal decomposition is performed on the velocity data to extract modes representative of physical processes in the flow. The decomposed velocity fields are then correlated with acoustic data to identify modes related to specific noise spectra. Specifically, four modes are associated with noise production in the sonic plume. Selective flow-field reconstruction is carried out, revealing interesting dynamics associated with loud flow states. In the supersonic case, screech-containing and turbulent mixing modes are isolated. The spatial modes of each data set are then compared for similarities in structures.

Low-dimensional models of experimental and simulation data for a complex supersonic jet are fused to reconstruct time-dependent coefficients. Data fusion techniques use the strengths of experimental statistical ensembles and the time resolution of large-eddy simulation to provide a more complete view of the flow.

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.

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.

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.

The effects of elastic compliance in dynamically pitching wind turbine blades have been experimentally investigated via unsteady surface pressure measurements and phase-locked Particle Imaging Velocimetry. Using a torsionally compliant member, aeroelastic effects on the unsteady aerodynamics were compared against the results from a corresponding noncompliant case to isolate the effects of compliance. The presence of compliance has been found i) to change the details of dynamic stall, ii) to alter the dynamic loading as measured by the sectional lift and moment coefficients, and iii) to be highly sensitive to small changes in the aeroelastic system. These results demonstrate the potential for aerodynamic control as a means to mitigate adverse loading effects and improve wind turbine efficiency.

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.

An experimental capability developed for testing two-dimensional aerofoils while dynamically pitching is discussed. Key to the approach are a dynamic pitch system, the rapid prototyping of aerofoils, inexpensive time-resolved pressure measurements, the ability to capture flow-field structure, and the ability to add compliance to the system. In addition to describing the system components, examples of typical results for characterization and control studies are given. Use of the data is also demonstrated through comparison of the results from a simulation with those from an experiment under the same conditions. Future uses of this experimental capability are also discussed.