Anshuman Pandey

Frequency-Response Characteristics of Polymer/Ceramic Pressure-Sensitive Paint

Empirical and analytical means have been used in this work to explore the dynamic response characteristics of polymer/ceramic pressure-sensitive paint. An acoustic resonance tube was used to understand the effect of surface roughness on the frequency response of the polymer/ceramic pressure-sensitive paint coating. Steady-state lifetime measurements were also performed. A model based on diffusion, excited-state-luminophore population dynamics, and attenuation of excitation light was derived and numerically implemented. Inclusion of light attenuation in the model improved the accuracy of the simulation and, on comparison with experimental data, was used to obtain characteristic parameters of the paint coating.

Step Response Characteristics of Polymer/Ceramic Pressure-Sensitive Paint.

Experiments and numerical simulations have been used in this work to understand the step response characteristics of Polymer/Ceramic Pressure-Sensitive Paint (PC-PSP). A recently developed analytical model describing the essential physics in PC-PSP quenching kinetics is used, which includes the effect of both diffusion time scale and luminescent lifetime on the net response of PC-PSP. Step response simulations using this model enables an understanding of the effects of parameters, such as the diffusion coefficient of O2 in the polymer/ceramic coating, attenuation of excitation light, ambient luminescent lifetime, sensitivity, and the magnitude and direction of pressure change on the observed response time scales of PC-PSP. It was found that higher diffusion coefficient and greater light attenuation lead to faster response, whereas longer ambient lifetime and larger sensitivity lead to slower response characteristics. Due to the inherent non-linearity of the Stern-Volmer equation, response functions also change with magnitude and direction of the pressure change. Experimental results from a shock tube are presented where the effects of varying the roughness, pressure jump magnitude and luminophore probe have been studied. Model parameters have been varied to obtain a good fit to experimental results and this optimized model is then used to obtain the response time for a step decrease in pressure, an estimate of which is currently not obtainable from experiments.

Dynamic Mode Decomposition of Fast Pressure Sensitive Paint Data.

Fast-response pressure sensitive paint (PSP) is used in this work to measure and analyze the acoustic pressure field in a rectangular cavity. The high spatial resolution and fast frequency response of PSP effectively captures the spatial and temporal detail of surface pressure resulting in the acoustic pressure field. In this work, a high-speed camera is used to generate a continuous time record of the acoustic pressure fluctuations with PSP. Since the level of the acoustic pressure is near the resolution limit of the sensor system, advanced analysis techniques are used to extract the spatial modes of the pressure field. Both dynamic mode decomposition (DMD) and proper orthogonal decomposition (POD) are compared with phase averaging for data analysis. While all three techniques effectively extract the pressure field and reduce the impact of sensor noise, DMD and POD are more robust techniques that can be applied to aperiodic or multi-frequency signals. Furthermore, DMD is better than POD at suppressing noise in particular regions of the spectrum and at effectively separating spectral energy when multiple acoustic excitation frequencies are present.

Iterative Blind Deconvolution Algorithm for Deblurring a Single PSP/TSP Image of Rotating Surfaces

Imaging of pressure-sensitive paint (PSP) for pressure measurement on moving surfaces is problematic due to the movement of the object within the finite exposure time of the imager, resulting in the blurring of the blade edges. The blurring problem is particularly challenging when high-sensitivity PSP with a long lifetime is used, where the long luminescence time constant of exponential light decay following a burst of excitation light energy results in blurred images. One method to ameliorate this effect is image deconvolution using a point spread function (PSF) based on an estimation of the luminescent time constant. Prior implementations of image deconvolution for PSP deblurring have relied upon a spatially invariant time constant in order to reduce computational time. However, the use of an assumed value of time constant leads to errors in the point spread function, particularly when strong pressure gradients (which cause strong spatial gradients in the decay time constant) are involved. This work introduces an iterative method of image deconvolution, where a spatially variant PSF is used. The point-by-point PSF values are found in an iterative manner, since the time constant depends on the local pressure value, which can only be found from the reduced PSP data. The scheme estimates a super-resolved spatially varying blur kernel with sub-pixel resolution without filtering the blurred image, and then restores the image using classical iterative regularization tools. A kernel-free forward model has been used to generate test images with known pressure surface maps and a varying amount of noise to evaluate the applicability of this scheme in different experimental conditions. A spinning disk setup with a grazing nitrogen jet for producing strong pressure gradients has also been used to evaluate the scheme on a real-world problem. Results including the convergence history and the effect of a regularization-iteration count are shown, along with a comparison with the previous PSP deblurring method.