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Analysis of Dynamic Mode Decomposition Outputs of Unsteady Pressure-Sensitive Paint Measurements in the NASA Wind Tunnel Tests
This paper discusses the Dynamic Mode Decomposition (DMD) outputs of the Unsteady Pressure-Sensitive Paint (uPSP) measurements, which were collected with four Phantom high-speed cameras at a constant sample frequency in the Ascent Transient Aerodynamics Test (ATAT) of the Space Launch System (SLS) Block 1 cargo vehicle in the 11-by-11-foot transonic test section of the Unitary Plan Wind Tunnel (UPWT) at NASA Ames Research Center in September 2019. In this paper, the effectiveness to use the DMD outputs of uPSP measurements in the diagnosis and analysis of the aerodynamic and acoustic phenomena in the SLS ATAT is demonstrated. The work described in the paper is a part of NASA’s development of a new state-of-the-art uPSP capability in production wind tunnels. Funding was provided by the NASA Aerosciences Evaluation and Test Capabilities (AETC) Project.
Self-Aligned Focusing Schlieren and OH Planar Laser-Induced Fluorescence Flow Visualization in a Dual-Mode Scramjet
Ahigh-speed self-aligned focusing schlieren (SAFS) system was used to visualize density gradients in and around the cavity flameholder of the combustor section of the University of Virginia Supersonic Combustion Facility (UVASCF). Images with this system were acquired at a framing rate of 110 kHz with no fuel injection, with fuel injection but no flame, and for fuel injection with combustion corresponding to a global equivalence ratio of 𝜙 = 0.18. Images with an air throttle in operation to modify the shock train location with fuel injection and with flame were also acquired. Simultaneous OH planar laser-induced fluorescence (PLIF) images were also acquired at a framing rate of 20 Hz. Results obtained with both visualization techniques are compared to one another to highlight how SAFS can complement more advanced flow visualization techniques and resolve dynamic behavior that may not otherwise be captured. Both proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD) analysis techniques are applied to the SAFS image sequences to identify coherent periodic structures for the runs with fuel injection and combustion.
Investigation of methods for targeted search of dominant higher modes in subcritical systems
The reactor physics modeling of subcritical systems requires a substantially different approach from that of a critical reactor due to the presence of the so-called higher modes. Earlier investigations showed that the dominant higher modes lie in the inner part of the eigenvalue spectrum, making the conventional eigenvalue searches unfeasible and calling for a targeted search. This paper investigates the possibility of targeted and multitudinous eigenvalue calculation by testing the Krylov-Schur and the Dynamic Modes Decomposition (DMD) method for simple, analytically tractable problems. While the DMD method clearly showed its efficiency in a one-group homogeneous problem, modal analysis of a three-dimensional reflected reactor in two-groups approximation demonstrated that the increasing complexity poses challenges for both methods. (authors)
Dynamic flux surrogate-based partitioned methods for interface problems
Loosely coupled partitioned methods for multiphysics problems treat each subproblem as a separate entity and advance them independently in time. In so doing these methods enable code reuse, increase concurrency and provide a convenient framework for plug-and-play multiphysics simulations. However, mathematically loosely coupled schemes are equivalent to a single step of an iterative solution method, which can compromise their accuracy and stability. We present a new data-driven partitioned method for coupled parametric PDEs that can improve upon the accuracy of traditional loosely coupled methods without incurring a performance penalty. To that end, we replace conventional field transfers across the interface by a surrogate for the dynamics of the interface flux exchanged between the subdomains. To develop this surrogate we apply dynamic mode decomposition to a non-standard staggered-in-time state, comprising the interface flux and small solution patches near the interface. The new approach shifts the main computational burden to an offline training phase, whereas application of the surrogate in the online phase amounts to a single matrix–vector multiplication. In conclusion, we provide stability analysis of the surrogate-based partitioned scheme and include numerical results that demonstrate its potential.
Characterization of Acoustic Emissions From Analogue Rocks Using Sparse Regression‐DMDc
Abstract Moisture loss in rock is known to generate acoustic emissions (AE). Phenomena that result in AE during drying are related to the movement of fluids through the pores and induced‐cracks that arise from differential mineral shrinkage, especially in clay‐bearing rock. AE from the movement of fluids occurs from the reconfiguration of fluid interfaces during drying, while AE from mineral shrinkage involves the debonding within or between minerals. Here, analogue rock samples were used to examine the differences in the AE signatures when one or both AE source‐types are present. An unsupervised sparse regression model, Dynamic Mode Decomposition with control, that extends Dynamic Mode Decomposition is used to characterize the AE signals recorded during the drying of porous analogue rock samples fabricated with ordinary Portland cement, with and without clay. This method can effectively and accurately reconstruct acoustic signals emitted from samples that only experience moisture loss without cracking. However, the method struggles to reconstruct signals from samples with intricate crack networks that formed during drying because AE generating mechanisms can emit contemporaneously, and the resulting waves propagate through drying‐induced cracks that can lead to multiple internal reflections. Thus, the differential reconstruction accuracy of time series generated by different underlying physical processes provides a robust filter for reducing large data catalogs. In general, both dynamics and sparse initiating events are learned directly from data and this method exposes a data hierarchy based on the complexity of the intrinsic dynamics.