NASA’s Unsteady Pressure-Sensitive Paint Research and Operational Capability Developments

Nettie H. Roozeboom; David D. Murakami; Jie Li; Marc A. Shaw-Lecerf; E. Lara Lash; Nicholas W. Califano; Kenneth R. Lyons; Paul M. Stremel; Jennifer K. Baerny; Christopher E. Barreras; Jack J. Ortega; Lawrence A. Hand

NASA NTRS · 20220018047

NASA’s Unsteady Pressure-Sensitive Paint Research and Operational Capability Developments

Nettie H. Roozeboom·David D. Murakami·Jie Li·Marc A. Shaw-Lecerf·E. Lara Lash·Nicholas W. Califano·Kenneth R. Lyons·Paul M. Stremel·Jennifer K. Baerny·Christopher E. Barreras·Jack J. Ortega·Lawrence A. Hand

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Abstract

To address challenges in the field of unsteady aerodynamics, NASA has developed a new state-of-the-art capability called Unsteady Pressure-Sensitive Paint (uPSP). It has been developed as an operational surface-pressure measurement capability for deployment in NASA’s AETC ground test facilities. Offering unprecedented spatiotemporal resolution, uPSP is an extremely powerful tool for investigating unsteady separated flows. To accelerate scientific discovery, uPSP data is to be processed and distributed as rapidly as it is acquired. Several demonstrations of the technology collected valuable data that has been used to develop data products, a robust processing pipeline, and other computational capabilities. A collection of papers documents the most recent research and development work on the uPSP technology and capability. This paper summarizes the current state of this effort at NASA.

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Nettie H. Roozeboom, David D. Murakami, Jie Li, Marc A. Shaw-Lecerf, E. Lara Lash, Nicholas W. Califano, Kenneth R. Lyons, Paul M. Stremel, Jennifer K. Baerny, Christopher E. Barreras, Jack J. Ortega, Lawrence A. Hand. NASA’s Unsteady Pressure-Sensitive Paint Research and Operational Capability Developments. https://ntrs.nasa.gov/citations/20220018047

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Spectral Analysis of Integrated Pressures on Patches with Unsteady Pressure-Sensitive Paint Measurements

This paper describes the spectral analysis of integrated pressures on patches of the scale model of the Space Launch System (SLS) Block 1B crew vehicle with the Unsteady Pressure-Sensitive Paint (uPSP) measurements, which were collected in the ascent transient aerodynamics tests with the Unitary Plan Wind Tunnel 11-by-11-foot Transonic Wind Tunnel in September 2019 at NASA Ames Research Center. Recent research has demonstrated that uPSP can be an essential tool for the assessment of the unsteady, aerodynamic phenomena. The work described in this paper is a part of NASA’s development of a new state-of-the-art uPSP capability in production wind tunnels. In this paper, 108 patches are defined with x station values and azimuth angles of the scale model. For each patch, the polygons are determined from the surface cells of the grid of the model, clipped with the edges of the patch, and each of the polygons is divided into triangles. The inputs of the pressure integration algorithm are the time series of pressure coefficients on the vertices of the grid of the model, generated by the uPSP data processing program from the videos taken with 4 Phantom high-speed cameras during the wind tunnel tests. The integrated pressure of the patch is determined as the ratio of the sum of the forces on the triangles over the sum of the areas of the triangles. For each of the test cases, the Cross Power Spectral Density (CPSD) and Magnitude-Squared Coherence (msCohere) are computed for frequencies over 1/3 octave bands from the time series of the integrated pressures on the patches. The spectral analysis outputs of different cases of the wind tunnel tests are analyzed and the coherence of patches is investigated over frequencies, x station values, azimuth angles, Mach numbers and SLS vehicle attitudes. The pressure integration and spectral analysis described in this paper were executed on the NASA Pleiades supercomputer. Funding for this research was provided by the NASA Aeroscience Evaluation and Test Capabilities (AETC) Project.

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NASA NTRSDate not supplied

Spectral Analysis of Integrated Pressures on Patches with Unsteady Pressure-Sensitive Paint Measurements

This paper describes the spectral analysis of integrated pressures on patches of the scale model of the Space Launch System Block 1 cargo vehicle with the Unsteady Pressure-Sensitive Paint measurements, which were collected in the ascent transient aerodynamics test with the Unitary Plan Wind Tunnel 11-by-11-foot Transonic Wind Tunnel in September 2019 at NASA Ames Research Center. The patches are defined with x station values, indicating the position along length of the vehicle, and azimuth angles of the scale model. For each patch, the polygons are determined from the surface cells of the grid of the model, clipped with the edges of the patch, and each of the polygons is divided into triangles. The integrated pressure of the patch is determined as the ratio of the sum of the forces on the triangles over the sum of the areas of the triangles. For each run of the test, the cross power spectral density and magnitude squared coherence are computed from the time series of the integrated pressures on the patches. The results of pressure integration and spectral analysis are presented, and the data consistency of the test is demonstrated. The work described in this paper is a part of NASA’s development of a new state-of-the-art uPSP capability in production wind tunnels. Funding for this research was provided by the NASA Aeroscience Evaluation and Test Capabilities Project.

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Dynamic Mode Decomposition of Unsteady Pressure-Sensitive Paint Measurements for the NASA Unitary Plan Wind Tunnel Tests

This paper describes the Dynamic Mode Decomposition (DMD) of the pressures on the scale model of the Space Launch System (SLS) Block 1 cargo vehicle with the Unsteady Pressure-Sensitive Paint (uPSP) measurements, which were collected in the Ascent Transient Aerodynamics Tests with the Unitary Plan Wind Tunnel 11-by-11-foot Transonic Wind Tunnel in September 2019 at NASA Ames Research Center. The work described in this paper is a part of NASA’s development of a new state-of-the-art uPSP capability in production wind tunnels. The conventional DMD algorithm is based on the Singular Value Decomposition (SVD) of the data matrix. For the matrix of the uPSP measurements of the SLS ATAT, the number of rows is equal to the number of nodes in the grid of the scale model, and the number of columns is equal to the number of frames in the videos taken with 4 Phantom high-speed cameras. In this paper, it is verified that, for the time series with zero mean value, the DMD is equivalent to the decomposition with the Discrete Fourier Transform (DFT). Considering the uPSP is mainly used in the assessment of the unsteady, aerodynamic phenomena, the DMD of the uPSP measurements can be implemented in two steps: (1) subtract the mean value from the uPSP measurement on each of the grid nodes; (2) apply the Fast Fourier Transform (FFT) on the resulting zero-mean time series. The DMD of the uPSP measurements with FFT has two advantages: (1) the computational complexity of FFT is O(N*logN), where N is the length of the time series; (2) compared to the SVD-based DMD algorithm, the DMD with FFT can be easily implemented in parallel processing. A sample matrix of uPSP measurements, at the size of 341 grid nodes and 128 frames, is generated. Figures 1 and 2 show the eigenvalues and the ratios of the eigenvectors, respectively, of the sample matrix, without and with the mean value removed on each of the grid nodes, computed with the SVD-based DMD and the FFT. The figures demonstrate the equivalence of the SVD-based DMD and the decomposition with DFT/FFT for the time series with zero mean value. The results of DMD of the uPSP measurements of the SLS ATAT in September 2019 are presented in the paper. The DMD modes at different frequencies are shown, the aerodynamic phenomena (e.g. shockwave and vortex shedding) are demonstrated and the correlation of the DMD modes with the test configuration parameter (e.g., the Mach Number) is discussed. Figure 3 shows a software tool to visualize the DMD modes. The code to implement the algorithm described in this paper was written in C, with libraries of FFTW for FFT and MPI/OpenMP for parallel processing, and executed on the NASA Pleiades supercomputer. Funding for this research was provided by the NASA Aerosciences Evaluation and Test Capabilities Project.

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