Interview Questions for Propeller Wake Analysis

A propeller wake is the disturbed flow field left behind a propeller as it rotates. It’s a complex pattern of swirling water with varying velocities and pressures. This wake significantly impacts vessel performance because it affects the propulsive efficiency and maneuverability of the ship. Imagine throwing a pebble into a still pond; the ripples spreading outward are analogous to the wake’s influence. A poorly designed propeller or an inefficient hull form can lead to a large, energetic wake, resulting in wasted energy and reduced fuel efficiency. Conversely, a well-designed system creates a smaller, less intense wake, optimizing performance.

The impact manifests in several ways: Increased resistance: The wake adds to the hull’s resistance, requiring more power to maintain speed. Reduced propulsive efficiency: The non-uniform flow in the wake reduces the propeller’s ability to generate thrust effectively. Cavitation: In extreme cases, the low pressure regions in the wake can cause cavitation (formation of vapor bubbles), leading to noise, vibration, and even propeller damage. Maneuverability issues: The wake’s influence on the surrounding flow can affect a vessel’s steering and turning characteristics.

Propeller wake analysis employs various methods, each with its strengths and weaknesses:

• Experimental methods: These involve using physical models in towing tanks or open water basins. Velocity measurements are made using various instruments like Particle Image Velocimetry (PIV) or Laser Doppler Velocimetry (LDV). This offers highly accurate data but is expensive and time-consuming.
• Computational Fluid Dynamics (CFD): This numerical approach solves the Navier-Stokes equations to simulate the flow around the propeller and hull. It allows for detailed investigation of the flow field under various operating conditions and is increasingly popular due to improved computing power. However, accurate CFD requires significant expertise and computational resources and its accuracy depends on the quality of the numerical model and the turbulence model used.
• Analytical methods: These involve simplified mathematical models that predict certain aspects of the wake. While less computationally expensive than CFD, they often lack the detail and accuracy of experimental or advanced CFD simulations. They are useful for preliminary design stages or for understanding basic wake characteristics.

Key parameters considered in propeller wake analysis include:

• Wake fraction (w): Represents the reduction in axial velocity behind the propeller relative to the freestream velocity. A higher wake fraction indicates a stronger wake.
• Wake velocity profiles: The distribution of velocity in both the axial and radial directions behind the propeller. These profiles define the wake’s intensity and extent.
• Wake momentum flux: Measures the amount of momentum carried away in the wake, indicating the energy lost to the wake.
• Turbulence intensity: Characterizes the level of chaotic fluctuations in the wake flow. High turbulence intensity can lead to increased resistance and cavitation.
• Pressure field: The distribution of pressure in the wake. Low pressure regions are prone to cavitation.
• Propeller geometry: The propeller’s diameter, pitch, number of blades, and blade section shape all influence the wake.
• Advance coefficient (J): A dimensionless parameter relating the propeller’s advance speed to its rotational speed.

Propeller geometry significantly influences wake characteristics. For instance:

• Number of blades: A higher number of blades generally results in a more uniform and less intense wake, but may introduce more noise and vibration. Fewer blades create a more intense and less uniform wake.
• Pitch: The pitch (distance the propeller would advance in one revolution if operating in a solid medium) dictates the thrust and the axial velocity distribution in the wake. A higher pitch generally leads to a more focused wake.
• Blade section shape: The airfoil shape of each blade section impacts the lift and drag characteristics, thereby influencing the pressure and velocity distribution in the wake. Optimized blade sections can reduce wake intensity and increase propeller efficiency.
• Diameter: Larger diameter propellers, for a given thrust, will generally create a larger but less intense wake. Conversely, smaller propellers create a more concentrated and intense wake.

These geometric factors interact in complex ways, necessitating careful design optimization to achieve desired wake characteristics and maximize propulsive efficiency.

Contra-rotating propellers (CRP) consist of two propellers rotating in opposite directions on the same shaft. This design aims to reduce the wake’s intensity and improve propulsive efficiency. The forward propeller generates a wake with a certain velocity and swirl pattern. The aft propeller interacts with this wake, recovering some of the energy lost in the wake generated by the first propeller. This leads to a significantly reduced overall wake compared to a single propeller system.

The interaction between the two propellers’ wakes is complex and depends on the distance between them, their geometries, and the operating conditions. Properly designed CRP systems exhibit significantly improved propulsion efficiency and reduced noise and vibration. However, they are more complex to design and manufacture, and their performance is highly sensitive to alignment and balance.

Limitations of wake analysis methods:

• Experimental methods: High cost, time-consuming, scaling issues (extrapolating results from model tests to full-scale vessels), limited ability to explore a wide range of operating conditions.
• Computational Fluid Dynamics (CFD): High computational cost, accuracy dependent on mesh resolution and turbulence model choice, requires skilled expertise for model setup and result interpretation, challenges in accurately modeling complex phenomena like cavitation.
• Analytical methods: Significant simplifications are made, resulting in limited accuracy, especially for complex geometries or high-speed flows.

The choice of method depends on the project’s specific needs, budget, and timescale. Often, a hybrid approach combining different methods is employed, for example, using analytical or simplified CFD simulations for initial design and experimental validation for final design stages.

Validation of propeller wake analysis results is crucial to ensure accuracy and reliability. This is usually done by comparing the predicted results with experimental measurements.

Methods for validation include:

• Comparison with experimental data from towing tank tests: This involves comparing predicted wake velocity profiles, wake fractions, and turbulence intensities with measurements obtained from model tests in a towing tank. The comparison should show good agreement between the predicted and measured values.
• Full-scale measurements: In some cases, wake measurements can be made on a full-scale vessel. This provides the most direct validation but is expensive and difficult to conduct.
• Comparison with other numerical methods: The results from one CFD simulation can be compared with results from a different CFD code or a different turbulence model to check for consistency and identify potential errors.

Discrepancies between predicted and measured values should be investigated to identify potential sources of error in the analysis. This could involve refining the computational mesh, using a more sophisticated turbulence model, or reassessing the accuracy of experimental measurements.

My experience with CFD (Computational Fluid Dynamics) software for propeller wake simulation is extensive. I’ve used several industry-standard packages, including ANSYS Fluent and OpenFOAM, to model the complex flow field around propellers and predict their wake characteristics. This involves setting up the computational domain, defining boundary conditions (like inflow velocity and propeller rotation), choosing an appropriate turbulence model, meshing the geometry (crucial for accuracy, particularly near the propeller blades), and running the simulation. I’m proficient in post-processing the results to visualize velocity profiles, pressure distributions, and turbulent kinetic energy within the wake. For instance, in a recent project involving a high-speed vessel, I used ANSYS Fluent to optimize the propeller design for minimal cavitation and improved efficiency. The simulation results provided valuable insights into the wake’s velocity gradients and helped us identify areas for design improvement.

A typical workflow involves a process of iterative refinement: starting with a coarse mesh for initial results, progressively refining the mesh in areas of high gradients (like the blade tips) to increase accuracy. Validation against experimental data or existing models is crucial, requiring careful selection of appropriate turbulence models and numerical schemes to ensure reliable predictions.

Propeller wake analysis presents several challenges. One major hurdle is accurately capturing the unsteady nature of the flow. Propeller blades create a highly unsteady wake due to their rotation, leading to complex vortex shedding and interactions. Another challenge is the complex three-dimensional geometry involved, especially considering the hull-propeller interaction. Accurately resolving the flow near the propeller blades and hull requires very fine meshing, significantly increasing computational cost and time. Turbulence modeling adds another layer of complexity. Selecting the appropriate turbulence model is vital but often involves trade-offs between accuracy and computational feasibility. Furthermore, accurately representing cavitation – the formation and collapse of vapor bubbles – is challenging and requires specialized modeling techniques.

Finally, validating simulation results can be difficult due to the difficulty in obtaining highly accurate experimental data for the complex flow around propellers. Comparison with experimental data is essential for ensuring that the simulations are giving meaningful results.

Accounting for unsteady effects in propeller wake simulations is critical for accurate results. Several methods exist. One common approach is using unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations, which resolve the unsteady fluctuations of the mean flow. This involves solving the governing equations at each time step, allowing the capture of the periodic variations caused by the propeller’s rotation. However, URANS can be computationally expensive, especially for high-resolution simulations. Alternatively, Detached Eddy Simulation (DES) or Large Eddy Simulation (LES) can be used to better capture the unsteady flow features, particularly the turbulent structures in the wake. These methods are more computationally intensive but offer greater accuracy. The choice depends on the specific application, the computational resources available, and the desired level of accuracy.

For example, in simulating the wake of a marine propeller, a URANS approach might be suitable for capturing the overall wake structure, while LES could be used to study the fine-scale turbulence and vortex dynamics for a more detailed analysis. Often, a hybrid approach is used, employing URANS for the bulk flow and LES in regions of interest to balance accuracy and computational cost.

Turbulence modeling is crucial in propeller wake analysis because the flow around a propeller is highly turbulent. The propeller blades generate intense turbulence, which significantly impacts the wake’s characteristics and its interaction with the hull. Without accurate turbulence modeling, the simulation results would be unrealistic and unreliable. Common turbulence models include the k-ε model, k-ω SST model, and Reynolds Stress Models (RSM). Each model has its strengths and weaknesses, and the choice depends on the specific application and the desired level of accuracy. For instance, the k-ω SST model is often preferred for its ability to handle both near-wall and free-stream turbulence accurately. However, RSMs, while more computationally expensive, offer a more detailed description of turbulence anisotropy which can be important in complex flows.

Imagine trying to predict the wake without accounting for turbulence; you’d only get a smooth, idealized flow, completely missing the chaotic, swirling nature of the actual wake, affecting downstream performance prediction significantly.

The hull form significantly influences the propeller wake. The hull’s shape affects the inflow conditions experienced by the propeller, influencing the velocity and pressure distributions. A streamlined hull will generally produce a more uniform inflow, leading to a cleaner, more predictable wake. Conversely, a blunt hull can create non-uniform inflow, resulting in a more complex and potentially unsteady wake. The hull’s proximity to the propeller also plays a crucial role. A close-proximity hull can block a significant portion of the propeller’s flow, altering the wake pattern and potentially causing increased hull pressure drag. This interaction creates complex, three-dimensional flow patterns that are difficult to model accurately, often necessitating high-fidelity CFD simulations.

For example, a displacement hull with a bulbous bow will affect the propeller’s inflow differently than a planing hull, resulting in distinct wake characteristics. The presence of appendages, such as rudders or struts, further complicates the flow and significantly affects the resulting wake.

My experience with experimental techniques for measuring propeller wake includes using Particle Image Velocimetry (PIV) and Laser Doppler Velocimetry (LDV). PIV involves seeding the flow with small particles and illuminating them with a laser sheet. A high-speed camera captures the particles’ movement, enabling the calculation of velocity vectors throughout the flow field. LDV, on the other hand, measures velocity at specific points in the flow using laser beams. While LDV provides highly accurate point measurements, PIV provides a more comprehensive two-dimensional or even three-dimensional view of the velocity field. Both techniques are invaluable for validating CFD simulations and improving our understanding of propeller wake behavior. However, experimental measurements can be challenging, especially in the complex, high-velocity flow near the propeller blades. Careful planning, instrumentation, and data processing are vital for reliable results. I’ve used these techniques in numerous projects, involving both model scale testing in towing tanks and full-scale measurements in open water conditions.

For propeller wake analysis, I’m proficient in several software and tools. My primary CFD packages are ANSYS Fluent and OpenFOAM. These are industry-standard software packages offering a wide range of turbulence models and solver options for complex flow simulations. I also have experience using specialized propeller design and analysis software, such as PROPELLER, which streamlines the process of generating propeller geometries and predicting their performance characteristics. For post-processing and visualization, I utilize Tecplot and ParaView, allowing for detailed analysis of the flow field and wake characteristics. Finally, I’m familiar with various data acquisition and processing tools for experimental techniques like PIV and LDV. This combination of software and tools allows me to perform comprehensive propeller wake analysis, combining both numerical simulations and experimental validation for accurate and reliable results.

A propeller’s wake, the swirling flow of water behind it, significantly impacts the performance of other marine appendages like rudders, stabilisers, and sea chests. The wake’s uneven velocity and pressure fields create fluctuating forces and moments on these appendages, affecting their efficiency and control.

For instance, a rudder operating within a propeller’s wake experiences varying angles of attack, leading to reduced steering effectiveness and increased drag. Similarly, a stabiliser encountering a turbulent wake might experience increased vibrations and reduced damping capability, making the vessel more susceptible to roll motion. Sea chests, responsible for drawing water for various ship systems, might experience fluctuating flow rates due to the non-uniformity of the wake, potentially leading to reduced efficiency or even cavitation issues within the intake.

Understanding the interaction between the propeller wake and other appendages is crucial for optimal vessel design and performance. CFD (Computational Fluid Dynamics) simulations are often used to predict and mitigate these adverse effects, allowing naval architects to optimize the placement and design of appendages to minimize interference and improve overall vessel efficiency.

The interaction of propeller wake with the free surface (the water’s surface) is complex and highly influential. The rotating propeller generates a system of unsteady waves and vortices that interact with the surface, creating a highly three-dimensional flow field.

Several key phenomena occur: the formation of a trailing vortex system behind the propeller, the generation of free surface waves whose amplitude and wavelength are related to the propeller’s characteristics and operating conditions, and the development of surface piercing vortices near the propeller. These interactions alter the overall wake structure, significantly impacting its velocity and pressure distributions and influencing both propulsive efficiency and the forces exerted on other appendages. For example, the generation of waves can lead to increased resistance, while the surface piercing vortices can create regions of intense turbulence and potentially increase cavitation risk.

Accurate prediction of these free surface effects requires sophisticated numerical methods like Volume of Fluid (VOF) or Reynolds Averaged Navier-Stokes (RANS) models that can capture the intricate interaction between the fluid and the free surface.

Cavitation is the formation of vapor-filled cavities (bubbles) within a liquid due to localized pressure drops. In propeller wake analysis, cavitation is a significant concern because it can significantly alter the wake’s structure and severely impact propeller performance and efficiency.

When the pressure in the water drops below the vapor pressure, cavitation bubbles form. As these bubbles collapse near areas of higher pressure, they can cause intense localized pressure pulses and erosion damage to the propeller blades. This erosion can significantly alter the propeller’s geometry over time, further affecting the wake pattern and reducing propulsion efficiency. Moreover, the collapsing bubbles create noise and vibrations that can impact the overall vessel operation and potentially lead to structural fatigue.

Cavitation’s effect on the wake is complex: the presence of vapor bubbles changes the fluid’s density and viscosity, leading to non-uniformities in the wake’s velocity and pressure distribution. This can exacerbate the effects of the wake on other appendages, amplifying the forces and moments exerted on them.

Interpreting and presenting the results of a propeller wake analysis involves a multi-faceted approach that combines quantitative data with visual representations to effectively convey the key findings.

The analysis typically starts with a review of velocity and pressure fields around the propeller and the surrounding appendages. This involves examining velocity profiles, pressure contours, and streamlines to understand the flow patterns and identify regions of high turbulence or unusual flow behavior.

Results are often presented using:

• Contour plots: Showing the spatial distribution of velocity, pressure, and turbulence intensity.
• Velocity vectors: Illustrating the magnitude and direction of the flow.
• Streamlines: Visualizing the path of fluid particles.
• Graphs: Presenting key metrics such as thrust, torque, efficiency, and wake fraction along the propeller axis.

A thorough analysis also considers potential issues such as cavitation, turbulence intensity, and flow separation to provide a complete picture of the wake characteristics. This information is crucial for evaluating the hydrodynamic performance of the propeller and its impact on other marine appendages.

Key Performance Indicators (KPIs) for evaluating propeller wake are designed to quantify its characteristics and impact on vessel performance. These include:

• Wake Fraction: Represents the reduction in flow velocity behind the propeller relative to the free stream velocity.
• Thrust Deduction Fraction: Indicates the loss of thrust due to the propeller’s own wake.
• Taylor Expansion Coefficients: Describe the spatial distribution of the wake’s axial and radial velocity components.
• Turbulence Intensity: Measures the level of turbulence in the wake, influencing the forces on appendages.
• Cavitation Number: Indicates the likelihood of cavitation occurring.
• Pressure Fluctuations: Quantify the unsteady pressure field within the wake.

The choice of KPIs depends on the specific objective of the analysis. For instance, in assessing the interaction with a rudder, focusing on wake velocity profiles and turbulence intensity in the rudder plane is crucial. While for propeller optimization itself, thrust deduction and efficiency are critical metrics. The goal is to select KPIs that directly address the design or operational challenges at hand.

Wake fraction is a dimensionless parameter that quantifies the reduction in water velocity in the propeller’s wake relative to the incoming flow velocity. It’s a crucial indicator of propeller performance and its impact on other appendages.

It’s calculated as: Wake Fraction = (V∞ - Vw) / V∞, where V∞ is the free stream velocity and Vw is the velocity in the wake. A higher wake fraction signifies a stronger wake, indicating higher thrust deduction and potential increased influence on other appendages.

Its significance lies in its ability to predict the forces and moments experienced by appendages operating in the propeller wake. A higher wake fraction will generally mean increased drag and unsteady forces on the rudder or other appendages. In ship design, a lower wake fraction is generally desirable, as it implies better propulsive efficiency and reduced impact on other parts of the hull.

Handling complex geometries in propeller wake simulations requires sophisticated computational techniques. The complexity arises from the intricate shapes of propellers and appendages, coupled with the unsteady and turbulent nature of the flow.

Several strategies are employed:

• Body-Fitted Grids: Creating meshes that conform to the complex shapes of the propeller and appendages is essential for accurate resolution of the flow field around these surfaces. This often involves using advanced mesh generation techniques like block-structured or unstructured grids.
• Overset Grids: When dealing with moving parts like propellers, overset grids allow independent meshing of the propeller and the hull, reducing computational cost while accurately capturing the interaction between them. This technique involves creating multiple meshes that overlap and exchange information during the simulation.
• Advanced Turbulence Models: Accurately capturing the turbulent nature of the wake requires advanced turbulence models like Reynolds Stress Models (RSM) or Detached Eddy Simulation (DES). These models offer superior accuracy compared to simpler models, particularly in highly turbulent regions.
• High-Performance Computing (HPC): Propeller wake simulations are computationally intensive. Employing HPC resources like clusters of computers is essential for solving large-scale simulations within a reasonable time frame.

The choice of method depends on the specific geometry, computational resources, and desired accuracy. Often, a combination of these techniques is used to achieve optimal results.

Optimizing propeller design to minimize wake involves a multifaceted approach, combining computational fluid dynamics (CFD), experimental testing, and a deep understanding of hydrodynamics. My experience centers around iterative design processes. We start by defining target wake characteristics – for instance, minimizing the size and intensity of the tip vortices, reducing the overall wake volume, or controlling the wake’s downstream influence. This is often driven by requirements related to cavitation avoidance, minimizing the impact on following vessels or underwater structures, or enhancing the vessel’s maneuverability.

I’ve extensively used CFD software to simulate various propeller designs and analyze their respective wake fields. By adjusting parameters like blade geometry (number of blades, pitch, chord, skew, rake), cavitation performance, and hub design, we can systematically reduce wake strength and size. For instance, modifying the blade tip geometry to reduce pressure gradients can significantly lessen the intensity of tip vortices. Experimentation involves using cavitation tunnels or towing tanks to validate CFD simulations and obtain precise measurements of the wake characteristics.

A successful example involved designing a propeller for a high-speed ferry. Through CFD analysis and refinement, we reduced the tip vortex strength by 15%, leading to improved fuel efficiency and reduced cavitation erosion. The process involved multiple iterations, closely monitoring wake parameters, and making adjustments based on simulations and experimental data. This iterative optimization is crucial for achieving substantial wake reduction.

Propeller wake analysis plays a critical role in the design of marine vehicles, impacting almost every aspect of their performance and functionality. Accurate wake analysis is essential for predicting the hydrodynamic forces and moments acting on the vessel, which directly influence its stability, maneuverability, and propulsion efficiency. The wake created by the propeller interacts with the hull, generating additional drag and impacting the vessel’s overall resistance. Understanding this interaction allows for optimized hull design to minimize this induced drag.

Beyond the hull-propeller interaction, wake analysis is crucial for considering the effects of the propeller wake on other components or nearby vessels. For example, in a multi-propeller system or a closely spaced vessel formation, understanding the wake interaction is crucial to prevent damage from excessive turbulence or to optimize the propulsive performance of the whole system. In underwater applications, such as autonomous underwater vehicles (AUVs), the propeller wake can affect the vehicle’s stability and guidance system. Essentially, accurate wake analysis provides critical information for a safe and efficient design across all facets.

Propeller wake analysis significantly informs propeller selection by providing a quantitative assessment of the wake characteristics generated by various propeller designs. This analysis allows for the selection of the propeller that best meets the specific requirements of the application while minimizing negative consequences. The key parameters derived from wake analysis are the size and intensity of the wake, the extent of cavitation, and the distribution of velocities within the wake. These parameters are closely tied to the vessel’s performance requirements, such as speed, efficiency, and maneuverability.

For instance, a high-speed vessel requires a propeller design that produces a relatively small and well-defined wake to minimize drag and improve handling. In contrast, a vessel designed for efficient low-speed operation might favor a propeller that generates a larger, slower-moving wake, even if it means a slight reduction in efficiency, if this produces a more robust and stable wake. By comparing the wake characteristics of different propeller designs, engineers can make well-informed decisions that optimize the vessel’s overall performance, taking into account aspects like propulsion efficiency, cavitation avoidance, and structural integrity.

Advancements in propeller wake analysis are driven by improvements in computational power and numerical methods. High-fidelity CFD simulations using advanced turbulence models are now capable of capturing finer details of the wake structure, including the complex interactions between the propeller and the hull. Furthermore, the increasing integration of experimental data with numerical simulations enhances the accuracy and reliability of the predictions. This combined approach, sometimes referred to as hybrid methods, is a significant advancement.

Future trends point toward the use of machine learning techniques to optimize propeller design for reduced wake. Machine learning algorithms can analyze vast datasets generated from CFD simulations and experimental tests, identifying optimal propeller designs more efficiently than traditional methods. Furthermore, research into advanced computational techniques, such as large-eddy simulations (LES), promises even greater accuracy in capturing the turbulent nature of the propeller wake, leading to improved design optimization.

Another key trend is the incorporation of real-time wake monitoring systems. These systems use advanced sensors and data processing techniques to provide continuous feedback on the wake generated by the propeller, allowing for dynamic adjustments to the propeller operation and improving overall efficiency and safety. This will become crucial as autonomous vessels become more prevalent.

Potential flow and viscous flow approaches represent different levels of complexity in modeling propeller wake. Potential flow theory simplifies the fluid as inviscid (frictionless) and incompressible, neglecting the effects of viscosity and turbulence. This approach is computationally efficient but sacrifices accuracy, particularly in capturing the detailed near-field wake structure and the viscous effects near the propeller blades (critical for accurate cavitation prediction). It’s often used for initial design exploration or to obtain a first-order approximation of the wake.

Viscous flow approaches, primarily using Computational Fluid Dynamics (CFD) simulations such as RANS or LES, account for the effects of viscosity and turbulence. These methods provide a significantly more accurate representation of the propeller wake, capturing the details of the tip vortices, boundary layer separation, and the complex three-dimensional flow structures. However, these simulations are computationally intensive and require significant processing power and expertise.

In practice, a combined approach is frequently employed. Potential flow methods might be used in the initial design stages to quickly assess several propeller configurations, followed by detailed viscous flow simulations to refine the design and optimize performance for the chosen candidate. The choice depends on the available resources, the required accuracy, and the specific design objectives.

Accounting for environmental factors like waves and currents significantly increases the complexity of propeller wake analysis. These factors introduce unsteady and non-uniform flow fields that interact with the propeller wake in complex ways. The presence of waves can cause variations in the propeller’s inflow velocity and angle of attack, affecting the wake characteristics dynamically. Likewise, currents can introduce shear flows and modify the distribution of velocities within the wake.

Incorporating these effects requires advanced computational techniques. For example, CFD simulations can include a moving mesh to account for wave motion, and can incorporate detailed models of turbulence and shear flows to account for currents. More sophisticated approaches include using Reynolds-Averaged Navier-Stokes (RANS) simulations with appropriate turbulence models which account for the added complexities of the unsteady wake structure caused by wave-propeller interactions. Experimental methods may also incorporate wave tanks or towing tanks to generate realistic environmental conditions for the testing phase. The level of detail needed depends on the specific application and tolerance for uncertainty in the predictions. For critical applications, the additional effort for this level of detail is usually worthwhile.

In a recent project involving the design of a new class of research AUV, the optimization of the propeller design for minimal wake disturbance was critical. The AUV’s primary mission involved precise navigation and close-range sensing. Excessive turbulence from the propeller wake could have introduced significant errors in the sensor data and negatively impacted the vehicle’s stability.

Our analysis began with CFD simulations of several candidate propeller designs using viscous flow methods. We used RANS simulations with a k-ω SST turbulence model to accurately capture the wake structure. We meticulously examined the size, intensity, and downstream extent of the tip vortices. We then optimized the blade geometry—modifying the number of blades, pitch, and tip shape—to minimize the near-field wake turbulence. Furthermore, simulations included the AUV’s hull to accurately capture the interaction between the propeller wake and the vehicle’s body.

The outcome was a propeller design with significantly reduced wake turbulence compared to the initial designs, resulting in improved sensor accuracy and enhanced AUV stability. The selection of the best propeller design was based on quantitative metrics derived from the CFD simulations, carefully comparing various candidate propeller designs. The successful integration of this optimized propeller significantly improved the operational capabilities of the AUV.