Lensless Particle Image Velocimetry
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Engineering topics
Publications and source records attributed to Jenna Eppink.
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The application of lensless imaging to particle image velocimetry (PIV) is demonstrated. Lensless PIV eliminates the need for imaging lenses to measure flow fields near a surface. Only the camera sensor, a thin mask, and computations are required to image particles in a flow field and to compute the velocity field. The small form factor could enable embedded sensors for near-wall measurements. Flow field measurements are obtained simultaneously for a lensless system and lens-based 2D PIV system, and several different reconstruction techniques are demonstrated. The reconstructed particle images and computed velocity fields compare well for both a uniform and shear flow. The potential for stereo and 3D volumetric PIV with a single camera sensor is demonstrated through different image reconstruction approaches.
A simple method for detecting boundary-layer transition using only mean static pressure port data is presented. The method can be applied to most existing models with pressure taps, and only requires that a fine angle-of-attack sweep be performed. A small but abrupt change in the static pressure is visible when the transition front passes over the pressure tap. Results from a recent Juncture Flow test entry are used to illustrate the technique. Infrared thermography measurements of the transition front compare very well to the transition locations obtained from the static pressure ports. Some differences in behavior occur depending on the dominant transition mechanism. While the technique is somewhat qualitative, it can be an excellent tool for estimating the transition location when other tools are not readily available. Given the simplicity of the technique, and the fact that most wind-tunnel models are already designed with numerous static pressure taps, this method can be applied with very little overhead.
What are some challenges associated with a mission to Mars? - Trip time - round trip human expedition to Mars: 2-3 years (incl. time spent on the Red Planet) - Astronauts lose muscle and bone mass due to microgravity - Radiation exposure - How to provide enough supplies - How to store enough fuel
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This study examines the effect of random micron-sized distributed roughness on stationary crossflow instabilities. The roughness parameters are varied by creating nanoparticle coatings of various formulations and applying them to inserts that cover approximately the first 14% of the model. In addition to the baseline configuration (no added roughness, root-mean-square (RMS) ≈ 0.42 𝜇m), panels with RMS roughness values of 4.8 and 8.6 𝜇m were tested, with correlation lengths of 1029 and 385 𝜇m, respectively. Despite the significant roughness levels tested, the transition location was found to be only mildly impacted by the additional roughness, and the roughness panel with lower RMS amplitude caused a larger upstream movement of transition, on average. However, the stationary crossflow amplitudes and wavelength content were found to vary substantially depending on the roughness input. In particular, the panel with higher RMS roughness amplitude resulted in significantly larger amplitudes in the 7.5-9 mm wavelength range at the farthest upstream measurement station, while the lower roughness panel resulted in mildly larger amplitudes at 10 mm and wavelengths larger than 15 mm. Nonlinear Parabolized Stability Equations (PSE) computations were performed to attempt to estimate the initial amplitudes of the stationary crossflow instabilities. Wavelength spectra were matched at the most upstream measurement location, but large discrepancies exist between the predicted and measured growth behavior farther downstream, thus, more work is required to improve confidence in initial amplitude estimates.
This study examines the effect of random micron-sized distributed roughness on stationary crossflow instabilities. The roughness parameters are varied by creating nanoparticle coatings of various formulations and applying them to inserts that cover approximately the first 14% of the model. In addition to the baseline configuration (no added roughness, root-mean-square (RMS) ≈ 0.42 𝜇m), panels with RMS roughness values of 4.8 and 8.6 𝜇m were tested, with correlation lengths of 1029 and 385 𝜇m, respectively. Despite the significant roughness levels tested, the transition location was found to be only mildly impacted by the additional roughness, and the roughness panel with lower RMS amplitude caused a larger upstream movement of transition, on average. However, the stationary crossflow amplitudes and wavelength content were found to vary substantially depending on the roughness input. In particular, the panel with higher RMS roughness amplitude resulted in significantly larger amplitudes in the 7.5-9 mm wavelength range at the farthest upstream measurement station, while the lower roughness panel resulted in mildly larger amplitudes at 10 mm and wavelengths larger than 15 mm. Nonlinear Parabolized Stability Equations (PSE) computations were performed to attempt to estimate the initial amplitudes of the stationary crossflow instabilities. Wavelength spectra were matched at the most upstream measurement location, but large discrepancies exist between the predicted and measured growth behavior farther downstream, thus, more work is required to improve confidence in initial amplitude estimates.
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An in-depth investigation was conducted at the NASA Langley Research Center 14- by 22-Foot Subsonic Tunnel to evaluate flow state switching in the liftoff flow environment of the Space Launch System Block 2 Crew launch vehicle using force measurements, two-component particle image velocimetry, and tuft flow visualization. Multimodal flow states were observed in the gap flow between the centerbody and solid rocket boosters at a range of incoming flow angles and were characterized for the relative strength of state switches. When flow switches occur, the flow is predominantly bimodal, but trimodal flow states are observed with the launch tower downstream of the vehicle. Tuft visualization indicates three-dimensionality during flow state switching, which initiates at one part of the gap and quickly transitions throughout the length of the booster. Due to the long time scales between switches, statistics such as frequency of flow state switches and converged state probabilities cannot be established without significantly increased acquisition times.
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