<p dir="ltr">Proper development of hypersonic vehicles requires knowledge of thermal loads, which are primarily dictated by the turbulent kinetic energy [1]. Accurate measurements of these values require measurements of velocity fluctuations, which are difficult to obtain using conventional seeded velocimetry methods [2]. However, molecular tagging velocimetry methods such as femtosecond laser electronic excitation tagging (FLEET) have been shown to be capable of measuring mean velocity within highly varying flows [3].</p><p dir="ltr">This work extends the available measurements provided by FLEET, through combination with optical thermometry and novel analysis methods of the signal. By performing FLEET velocimetry alongside thermometry, this shows capability to make instantaneous measurements of Mach number within supersonic flows [4]. Additionally, by tracking multiple characteristics of FLEET image signals, the ability to both capture instantaneous velocity fluctuations and improve measurement of mean velocity are demonstrated.</p><p dir="ltr">Furthermore, the uncertainty intrinsic to the analysis of FLEET signals is investigated. This is done using a combination of both classical statistical methods and uncertainty calculation methods commonly used in particle imaging velocimetry [5]. This is necessary to provide the best possible estimate of velocity fluctuations for the validation of computational fluid dynamic (CFD) models of boundary layer heat transfer.</p><p dir="ltr">Beyond simply improving the quality of velocity measurements, frequency analysis tools are developed and extended to analysis of fluid dynamic problems. These tools have been used prior for detecting extreme transitions within a signal [6], but are applied here to demonstrate their ability to detect physics captured within flow fields. These tools show promise in the ability to detect frequency couplings in time and can potentially be implemented to improve current control strategies in the field of fluid dynamics.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/25769718 |
| Date | 08 May 2024 |
| Creators | Jonathan E Crosmer (18516999) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/High-Precision_Molecular_Tagging_Velocimetry_and_Quantitative_Spatio-Temporal_Analysis/25769718 |
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