{"id":{"repo_id":"vt","oai_identifier":"oai:vtechworks.lib.vt.edu:10919/140931"},"canonical_url":"https://search.dev.ndltd.org/etd/vt/oai:vtechworks.lib.vt.edu:10919/140931","repository":{"repo_id":"vt","name":"Virginia Tech","base_url":"https://vtechworks.lib.vt.edu/oai/request"},"display":{"title":"A Study of Instability Mechanisms Driving Cloud Cavitation: Experimental Analysis Using New Advanced Velocimetry and Evaluation of Numerical Models","abstract":"Hydrodynamic cloud cavitation, characterized by the periodic shedding and violent collapse of large vapor structures, poses significant risks to the integrity and performance of hydraulic machinery. Despite extensive research, the precise interaction between competing shedding mechanisms, specifically re-entrant jets and condensation shocks, remains a subject of debate, largely due to the limitations of experimental diagnostics in resolving flow within opaque, dense multiphase regions. This dissertation addresses these challenges by integrating advanced deep learning-based diagnostics with rigorous experimental characterization and computational fluid dynamics (CFD) to elucidate the physics of cloud shedding in a converging-diverging Venturi geometry. To overcome the limitations of traditional Particle Image Velocimetry (PIV) in unseeded, high-void-fraction flows, a domain-specific optical flow framework was developed. A Recurrent All-Pairs Field Transforms (RAFT) network was fine-tuned on a novel synthetic dataset derived from CFD simulations, explicitly designed to bridge the domain gap between computer vision benchmarks and multiphase flow textures. This fine-tuned model (RAFT-CloudCav) demonstrated a 37% reduction in end-point error compared to pre-trained baselines and was the only method capable of qualitatively resolving complex flow features such as the re-entrant jet and the Kelvin-Helmholtz (K-H) roll-up in high-speed experimental footage (130,000 fps). Sensitivity analysis established that a temporal resolution exceeding 32,500 fps is a prerequisite for accurate feature tracking in this regime. Leveraging this high-fidelity velocimetry and a novel pixel-intensity \"Source Term\" analysis, the experimental campaign identified the K-H instability as a primary trigger for shedding, challenging the conventional view that shear instabilities are secondary in macro-scale flows. The analysis revealed a coupled mechanism where shear-induced vortex roll-up initiates local collapse events, which subsequently generate the acoustic perturbations necessary to drive upstream-propagating condensation shocks. To predict the transition between shedding topologies, a new dimensionless criterion based on the product of the Cavitation number and Reynolds number ($Gamma = sigma cdot Re$) was proposed. Validation across multiple scales confirmed that regimes where $Gamma < 10^5$ exhibit multi-cloud shedding, while regimes where $Gamma > 1.5 times 10^5$ transition to single-cloud shedding. Finally, the experimental observations were used to validate an incompressible Homogeneous Mixture Model (HMM) simulation using OpenFOAM. The study confirmed that the standard Merkle cavitation model, when coupled with Scale-Adaptive Simulation (SAS) turbulence modeling, accurately captures the shedding frequency and the condensation front propagation speeds (3.4-5.7 m/s) observed experimentally. This work establishes a robust, data-driven framework for analyzing multiphase flows, offering new predictive criteria for the design of robust hydraulic systems.","abstract_html":"Hydrodynamic cloud cavitation, characterized by the periodic shedding and violent collapse of large vapor structures, poses significant risks to the integrity and performance of hydraulic machinery. Despite extensive research, the precise interaction between competing shedding mechanisms, specifically re-entrant jets and condensation shocks, remains a subject of debate, largely due to the limitations of experimental diagnostics in resolving flow within opaque, dense multiphase regions. This dissertation addresses these challenges by integrating advanced deep learning-based diagnostics with rigorous experimental characterization and computational fluid dynamics (CFD) to elucidate the physics of cloud shedding in a converging-diverging Venturi geometry. To overcome the limitations of traditional Particle Image Velocimetry (PIV) in unseeded, high-void-fraction flows, a domain-specific optical flow framework was developed. A Recurrent All-Pairs Field Transforms (RAFT) network was fine-tuned on a novel synthetic dataset derived from CFD simulations, explicitly designed to bridge the domain gap between computer vision benchmarks and multiphase flow textures. This fine-tuned model (RAFT-CloudCav) demonstrated a 37% reduction in end-point error compared to pre-trained baselines and was the only method capable of qualitatively resolving complex flow features such as the re-entrant jet and the Kelvin-Helmholtz (K-H) roll-up in high-speed experimental footage (130,000 fps). Sensitivity analysis established that a temporal resolution exceeding 32,500 fps is a prerequisite for accurate feature tracking in this regime. Leveraging this high-fidelity velocimetry and a novel pixel-intensity &quot;Source Term&quot; analysis, the experimental campaign identified the K-H instability as a primary trigger for shedding, challenging the conventional view that shear instabilities are secondary in macro-scale flows. The analysis revealed a coupled mechanism where shear-induced vortex roll-up initiates local collapse events, which subsequently generate the acoustic perturbations necessary to drive upstream-propagating condensation shocks. To predict the transition between shedding topologies, a new dimensionless criterion based on the product of the Cavitation number and Reynolds number ($Gamma = sigma cdot Re$) was proposed. Validation across multiple scales confirmed that regimes where <span class=\"etd-inline-math\">Gamma &lt; 10<sup>5</sup></span> exhibit multi-cloud shedding, while regimes where <span class=\"etd-inline-math\">Gamma &gt; 1.5 times 10<sup>5</sup></span> transition to single-cloud shedding. Finally, the experimental observations were used to validate an incompressible Homogeneous Mixture Model (HMM) simulation using OpenFOAM. The study confirmed that the standard Merkle cavitation model, when coupled with Scale-Adaptive Simulation (SAS) turbulence modeling, accurately captures the shedding frequency and the condensation front propagation speeds (3.4-5.7 m/s) observed experimentally. This work establishes a robust, data-driven framework for analyzing multiphase flows, offering new predictive criteria for the design of robust hydraulic systems.","abstract_has_math":true,"creators":["Chamala, Naga Nitish"],"institution":"Virginia Tech","degree_name":"Doctor of Philosophy","degree_level":"doctoral","degree_discipline":"Aerospace Engineering","degree_department":"Aerospace and Ocean Engineering","school":null,"contributors":[],"advisors":[],"committee_chairs":["Coutier-Delgosha, Olivier"],"committee_members":["Wang, Kevin Guanyuan","Roy, Christopher John","Devenport, William J."],"year":2026,"date_issued":"2026-01-21","date_published":"2026-01-21","updated_at":"2026-07-22T22:19:24Z","subjects":["Multi-phase flows","Cloud Cavitation","Computational Fluid Dynamics"],"languages":["en"],"rights":["Creative Commons Attribution 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by/4.0/"],"identifier_entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45573"],"render_values":[{"text":"vt_gsexam:45573","href":null,"code":true}]}]},"links":{"outbound_url":"https://hdl.handle.net/10919/140931","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.committeechair","label":"Committee Chair","values":["Coutier-Delgosha, Olivier"]},{"key":"dc:contributor.committeemember","label":"Committee Member","values":["Wang, Kevin Guanyuan","Roy, Christopher John","Devenport, William J."]},{"key":"dc:contributor.department","label":"Department","values":["Aerospace and Ocean Engineering"]},{"key":"dc:creator","label":"Author","values":["Chamala, Naga Nitish"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2026-01-22T09:00:54Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2026-01-22T09:00:54Z"]},{"key":"dc:date.issued","label":"Date","values":["2026-01-21"]},{"key":"dc:publisher","label":"Institution","values":["Virginia Tech"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Aerospace Engineering"]},{"key":"thesis:degree_level","label":"Degree Level","values":["doctoral"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Doctor of Philosophy"]},{"key":"thesis:institution_name","label":"Thesis Institution Name","values":["Virginia Polytechnic Institute and State University"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Multi-phase flows","Cloud Cavitation","Computational Fluid Dynamics"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language.iso","label":"Language (ISO)","values":["en"]},{"key":"dc:rights","label":"Dc Rights","values":["Creative Commons Attribution 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.other","label":"Dc Identifier Other","values":["vt_gsexam:45573"]},{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://hdl.handle.net/10919/140931"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Hydrodynamic cloud cavitation, characterized by the periodic shedding and violent collapse of large vapor structures, poses significant risks to the integrity and performance of hydraulic machinery. Despite extensive research, the precise interaction between competing shedding mechanisms, specifically re-entrant jets and condensation shocks, remains a subject of debate, largely due to the limitations of experimental diagnostics in resolving flow within opaque, dense multiphase regions. This dissertation addresses these challenges by integrating advanced deep learning-based diagnostics with rigorous experimental characterization and computational fluid dynamics (CFD) to elucidate the physics of cloud shedding in a converging-diverging Venturi geometry. To overcome the limitations of traditional Particle Image Velocimetry (PIV) in unseeded, high-void-fraction flows, a domain-specific optical flow framework was developed. A Recurrent All-Pairs Field Transforms (RAFT) network was fine-tuned on a novel synthetic dataset derived from CFD simulations, explicitly designed to bridge the domain gap between computer vision benchmarks and multiphase flow textures. This fine-tuned model (RAFT-CloudCav) demonstrated a 37% reduction in end-point error compared to pre-trained baselines and was the only method capable of qualitatively resolving complex flow features such as the re-entrant jet and the Kelvin-Helmholtz (K-H) roll-up in high-speed experimental footage (130,000 fps). Sensitivity analysis established that a temporal resolution exceeding 32,500 fps is a prerequisite for accurate feature tracking in this regime. Leveraging this high-fidelity velocimetry and a novel pixel-intensity \"Source Term\" analysis, the experimental campaign identified the K-H instability as a primary trigger for shedding, challenging the conventional view that shear instabilities are secondary in macro-scale flows. The analysis revealed a coupled mechanism where shear-induced vortex roll-up initiates local collapse events, which subsequently generate the acoustic perturbations necessary to drive upstream-propagating condensation shocks. To predict the transition between shedding topologies, a new dimensionless criterion based on the product of the Cavitation number and Reynolds number ($Gamma = sigma cdot Re$) was proposed. Validation across multiple scales confirmed that regimes where $Gamma < 10^5$ exhibit multi-cloud shedding, while regimes where $Gamma > 1.5 times 10^5$ transition to single-cloud shedding. Finally, the experimental observations were used to validate an incompressible Homogeneous Mixture Model (HMM) simulation using OpenFOAM. The study confirmed that the standard Merkle cavitation model, when coupled with Scale-Adaptive Simulation (SAS) turbulence modeling, accurately captures the shedding frequency and the condensation front propagation speeds (3.4-5.7 m/s) observed experimentally. This work establishes a robust, data-driven framework for analyzing multiphase flows, offering new predictive criteria for the design of robust hydraulic systems."]},{"key":"dc:description.abstractgeneral","label":"General Abstract","values":["When liquids move at high speeds inside pumps, propellers, or fuel injectors, the pressure can drop low enough to rip the liquid apart, forming vapor cavities or \"clouds\" of bubbles. This phenomenon is called cavitation. When these clouds collapse, they release shockwaves intense enough to eat through metal and destroy machinery. For decades, engineers have struggled to stop this because it is incredibly difficult to see what happens inside these opaque, chaotic clouds of bubbles—it is like trying to see the wind inside a tornado. This dissertation solves this visibility problem by using Artificial Intelligence. Standard measurement tools fail in these \"frothy\" mixtures, so we adapted a deep learning computer vision model—originally designed for tracking moving objects in movies—and \"taught\" it fluid dynamics. By training this AI on thousands of computer-generated simulations of bubbles, we created a tool capable of seeing the invisible velocity patterns inside cavitation clouds recorded with high-speed cameras at 130,000 frames per second. Using this new AI tool, we discovered that the violent shedding of these clouds is often triggered by a specific instability caused by friction between the fast-moving liquid and the slow-moving vapor, similar to how wind creates waves on the ocean. We developed a new mathematical formula to predict exactly when this instability will occur. Finally, we used these discoveries to prove that computer simulations can accurately replicate these explosive events, provided they are set up correctly. This research provides engineers with better tools and predictive rules to design safer, quieter, and longer-lasting hydraulic machines."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Doctor of Philosophy"]},{"key":"dc:format.medium","label":"Dc Format Medium","values":["ETD"]},{"key":"dc:title","label":"Title","values":["A Study of Instability Mechanisms Driving Cloud Cavitation: Experimental Analysis Using New Advanced Velocimetry and Evaluation of Numerical Models"]}]}],"canonical_facts":{"dc:contributor.committeechair":["Coutier-Delgosha, Olivier"],"dc:contributor.committeemember":["Wang, Kevin Guanyuan","Roy, Christopher John","Devenport, William J."],"dc:contributor.department":["Aerospace and Ocean Engineering"],"dc:creator":["Chamala, Naga Nitish"],"dc:date.accessioned":["2026-01-22T09:00:54Z"],"dc:date.available":["2026-01-22T09:00:54Z"],"dc:date.issued":["2026-01-21"],"dc:description.abstract":["Hydrodynamic cloud cavitation, characterized by the periodic shedding and violent collapse of large vapor structures, poses significant risks to the integrity and performance of hydraulic machinery. Despite extensive research, the precise interaction between competing shedding mechanisms, specifically re-entrant jets and condensation shocks, remains a subject of debate, largely due to the limitations of experimental diagnostics in resolving flow within opaque, dense multiphase regions. This dissertation addresses these challenges by integrating advanced deep learning-based diagnostics with rigorous experimental characterization and computational fluid dynamics (CFD) to elucidate the physics of cloud shedding in a converging-diverging Venturi geometry. To overcome the limitations of traditional Particle Image Velocimetry (PIV) in unseeded, high-void-fraction flows, a domain-specific optical flow framework was developed. A Recurrent All-Pairs Field Transforms (RAFT) network was fine-tuned on a novel synthetic dataset derived from CFD simulations, explicitly designed to bridge the domain gap between computer vision benchmarks and multiphase flow textures. This fine-tuned model (RAFT-CloudCav) demonstrated a 37% reduction in end-point error compared to pre-trained baselines and was the only method capable of qualitatively resolving complex flow features such as the re-entrant jet and the Kelvin-Helmholtz (K-H) roll-up in high-speed experimental footage (130,000 fps). Sensitivity analysis established that a temporal resolution exceeding 32,500 fps is a prerequisite for accurate feature tracking in this regime. Leveraging this high-fidelity velocimetry and a novel pixel-intensity \"Source Term\" analysis, the experimental campaign identified the K-H instability as a primary trigger for shedding, challenging the conventional view that shear instabilities are secondary in macro-scale flows. The analysis revealed a coupled mechanism where shear-induced vortex roll-up initiates local collapse events, which subsequently generate the acoustic perturbations necessary to drive upstream-propagating condensation shocks. To predict the transition between shedding topologies, a new dimensionless criterion based on the product of the Cavitation number and Reynolds number ($Gamma = sigma cdot Re$) was proposed. Validation across multiple scales confirmed that regimes where $Gamma < 10^5$ exhibit multi-cloud shedding, while regimes where $Gamma > 1.5 times 10^5$ transition to single-cloud shedding. Finally, the experimental observations were used to validate an incompressible Homogeneous Mixture Model (HMM) simulation using OpenFOAM. The study confirmed that the standard Merkle cavitation model, when coupled with Scale-Adaptive Simulation (SAS) turbulence modeling, accurately captures the shedding frequency and the condensation front propagation speeds (3.4-5.7 m/s) observed experimentally. This work establishes a robust, data-driven framework for analyzing multiphase flows, offering new predictive criteria for the design of robust hydraulic systems."],"dc:description.abstractgeneral":["When liquids move at high speeds inside pumps, propellers, or fuel injectors, the pressure can drop low enough to rip the liquid apart, forming vapor cavities or \"clouds\" of bubbles. This phenomenon is called cavitation. When these clouds collapse, they release shockwaves intense enough to eat through metal and destroy machinery. For decades, engineers have struggled to stop this because it is incredibly difficult to see what happens inside these opaque, chaotic clouds of bubbles—it is like trying to see the wind inside a tornado. This dissertation solves this visibility problem by using Artificial Intelligence. Standard measurement tools fail in these \"frothy\" mixtures, so we adapted a deep learning computer vision model—originally designed for tracking moving objects in movies—and \"taught\" it fluid dynamics. By training this AI on thousands of computer-generated simulations of bubbles, we created a tool capable of seeing the invisible velocity patterns inside cavitation clouds recorded with high-speed cameras at 130,000 frames per second. Using this new AI tool, we discovered that the violent shedding of these clouds is often triggered by a specific instability caused by friction between the fast-moving liquid and the slow-moving vapor, similar to how wind creates waves on the ocean. We developed a new mathematical formula to predict exactly when this instability will occur. Finally, we used these discoveries to prove that computer simulations can accurately replicate these explosive events, provided they are set up correctly. This research provides engineers with better tools and predictive rules to design safer, quieter, and longer-lasting hydraulic machines."],"dc:description.degree":["Doctor of Philosophy"],"dc:format.medium":["ETD"],"dc:identifier.other":["vt_gsexam:45573"],"dc:identifier.uri":["https://hdl.handle.net/10919/140931"],"dc:language.iso":["en"],"dc:publisher":["Virginia Tech"],"dc:rights":["Creative Commons Attribution 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by/4.0/"],"dc:subject":["Multi-phase flows","Cloud Cavitation","Computational Fluid Dynamics"],"dc:title":["A Study of Instability Mechanisms Driving Cloud Cavitation: Experimental Analysis Using New Advanced Velocimetry and Evaluation of Numerical Models"],"dc:type":["Dissertation"],"thesis:degree_discipline":["Aerospace Engineering"],"thesis:degree_level":["doctoral"],"thesis:degree_name":["Doctor of Philosophy"],"thesis:institution_name":["Virginia Polytechnic Institute and State University"]},"updated_at":"2026-07-22T22:19:24Z"}