Particle Image Velocimetry

Particle Image Velocimetry : Fluid Dynamics in Motion

Particle Image Velocimetry is a sophisticated optical technique used to measure fluid flow. By tracking the movement of seeded particles within the fluid, it provides detailed, quantitative data on velocity fields, essential for research in aerodynamics, hydrodynamics, and fluid mechanics.

Particle Image Velocimetry


Fluid dynamics, the study of how liquids and gases move and interact, is a cornerstone in various scientific and engineering disciplines. Understanding the intricate details of fluid motion is crucial in fields ranging from aerodynamics and hydrodynamics to biomedical engineering. One powerful tool that has revolutionized the way researchers investigate fluid flow is Particle Image Velocimetry (PIV). This optical measurement technique provides detailed insights into velocity fields, allowing for the quantitative analysis of fluid motion. In this comprehensive article by Academic Block, we will explore the principles, applications, variations, and future prospects of PIV, exploring how this technology has become an indispensable asset in the realm of fluid dynamics.

I. Introduction to Particle Image Velocimetry

Fundamentals of Fluid Dynamics

Before looking into Particle Image Velocimetry, it’s crucial to understand the fundamentals of fluid dynamics. Fluids, encompassing both liquids and gases, exhibit complex behaviors when in motion. The study of fluid dynamics seeks to unravel the mysteries of fluid flow, encompassing phenomena such as turbulence, vortices, and boundary layers.

Fluid dynamics finds applications in various domains, influencing the design of aircraft, optimizing the efficiency of vehicles, and enhancing the performance of biomedical devices. To comprehend and manipulate fluid systems effectively, researchers and engineers require precise tools to measure and analyze fluid velocities.

The Need for Velocity Measurement

Velocity, a vector quantity representing the rate of change of position of an object, is a fundamental parameter in fluid dynamics. The velocity field of a fluid describes how the velocity varies at different points in the fluid. Measuring velocity is essential for understanding the dynamics of fluid flow, enabling the identification of flow patterns, the detection of turbulence, and the optimization of various processes.

Traditional methods of measuring fluid velocity often involve intrusive techniques, such as placing probes in the flow. While these methods can provide accurate measurements, they also disturb the flow and can alter the very characteristics researchers aim to study. This drawback has spurred the development of non-intrusive techniques like Particle Image Velocimetry.

II. Principles of Particle Image Velocimetry

Seeding the Flow

Particle Image Velocimetry operates on the principle of seeding the fluid with small particles. These particles, often in the micron size range, serve as tracers that move with the flow. The choice of seeding particles is critical; they should be neutrally buoyant to avoid settling and should not significantly alter the dynamics of flow.

Illumination with Laser Light

Once the particles are introduced into the flow, a laser light sheet is used to illuminate the seeded region. The laser light form a thin sheet that passes through the fluid, highlighting and illuminating the particles in the process. This creates a two-dimensional plane of light within the fluid, where the motion of particles can be captured and stored using digital cameras.

Image Capture and High-Speed Cameras

In PIV, a high-speed camera captures the transient positions of the laser illuminated particle field through digital images. The use of high-speed cameras is essential to capture rapid changes in the flow. The images are taken in quick succession, allowing for the analysis of particle movement over time.

Image Processing and Particle Tracking

The captured digital images undergo sophisticated image processing techniques. One of the key steps is particle tracking, where the movement of individual particles is tracked between consecutive frames. This involves identifying and following the position of each particle over time, forming trajectories that represent the fluid motion.

Velocity Calculation

The tracked particle trajectories are then used to calculate the velocity field of the fluid. The basic principle is that the displacement of particles between frames corresponds to the fluid velocity at that particular location. By analyzing the entire field of particle movements, researchers obtain a detailed map of the velocity distribution within the illuminated plane.

III. Types of Particle Image Velocimetry

Two-Dimensional PIV (2D PIV)

Two-dimensional PIV captures the flow information within a single plane. This is suitable for studying flows that can be adequately represented in two dimensions, providing insights into velocity distributions in specific sections of the fluid. 2D PIV is often employed in cases where the flow remains primarily planar or when a simplified analysis is sufficient.

Three-Dimensional PIV (3D PIV)

In contrast, Three-dimensional PIV extends the analysis into the third dimension. This technique captures velocity information in multiple planes, enabling researchers to study more complex and three-dimensional flows. 3D PIV is particularly valuable in scenarios where the flow exhibits intricate patterns and cannot be accurately represented using a two-dimensional approach.

Stereoscopic PIV

Stereoscopic PIV involves using two or more cameras to capture images from different perspectives simultaneously. This allows for the reconstruction of three-dimensional flow structures. Stereoscopic PIV enhances the accuracy of 3D velocity measurements and is especially useful when dealing with complex and dynamic flow phenomena.

IV. Mathematical equations behind the Particle Image Velocimetry

Particle Image Velocimetry (PIV) involves several mathematical equations and algorithms to process the captured images and calculate the velocity field of a fluid. Here, I’ll provide an overview of the key equations and concepts involved in PIV:

Particle Displacement and Velocity Calculation

The basic principle behind PIV is to track the movement of seeding particles between successive frames of images. Particle displacement (Δx or Δy) is used to calculate the fluid velocity (u or v).

u = Δx / Δt;

v = Δy / Δt;


  • u and v are the velocity components in the x and y directions, respectively.
  • Δx and Δy are the displacements of particles between frames.
  • Δt is the time between successive frames.


The cross-correlation function is employed to find the displacement between particles in consecutive images. The cross-correlation C is calculated as:

C(Δx,Δy) = ∑∑ [I1(x,y) ⋅ I2(x+Δx, y+Δy)]


  • I1 and I2 are the intensity distributions in two consecutive images.
  • Δx and Δy are the displacement values.

The peak of the cross-correlation function corresponds to the particle displacement, and its location provides the velocity components.

Window Interrogation and Processing

PIV analysis involves dividing the images into small interrogation windows. These windows are used for cross-correlation, and the process is repeated throughout the entire image. The window size is crucial and should be chosen based on the expected flow characteristics.

Spatial and Temporal Resolution

The spatial resolution (Δxspatial and Δyspatial) and temporal resolution (Δttemporal) influence the accuracy of velocity measurements. These resolutions are essential parameters in the calculation of velocity:

u = Δxspatial / Δttemporal ;

v = Δyspatial / Δttemporal ;

System Calibration

Calibration is essential to relate pixel displacements to physical distances. The conversion factor (mm/pixel) can be determined by imaging a known calibration grid or object.

Velocity Vector Field

The final output of a PIV analysis is a velocity vector field, where each vector represents the velocity at a specific location. This information is often visualized using contour plots or vector plots.

Temporal Correlation and Time-Averaging

In cases of unsteady flows, temporal correlation and time-averaging techniques are employed. These techniques consider particle displacements over multiple frames to capture the dynamic behavior of the flow.

V. Applications of Particle Image Velocimetry

Aerospace Engineering

In the field of aerospace engineering, PIV plays a pivotal role in understanding the aerodynamics of aircraft. Researchers use PIV to analyze high velocity airflow around wings, tails, and other components. This insight is critical for optimizing aerodynamic performance, improving fuel efficiency, and enhancing overall flight stability.

Automotive Engineering

Automotive engineers utilize PIV to study the aerodynamics of vehicles, optimizing the design to reduce drag and improve fuel efficiency. PIV is also employed in the analysis of airflow within internal combustion engines, aiding in the development of more efficient and environmentally friendly vehicles.

Biofluid Mechanics

In biomedical engineering and medicine, PIV contributes to the understanding of biofluid mechanics. Researchers use PIV to study blood flow in arteries and veins, helping diagnose and understand cardiovascular diseases. PIV is also applied in the development of medical devices, such as optimizing the design of artificial heart valves.

Environmental Fluid Dynamics

Environmental scientists and engineers employ PIV to study natural flows, such as rivers and oceans. PIV helps in understanding sediment transport, coastal dynamics, and the dispersion of pollutants in aquatic environments. This information is crucial for environmental impact assessments and the design of sustainable coastal structures.

Combustion Research

In combustion research, PIV is utilized to investigate the complex flow patterns within flames. Understanding the dynamics of combustion processes is essential for improving the efficiency of engines and reducing pollutant emissions. PIV enables researchers to visualize and quantify the velocity fields in combustion chambers, contributing to the development of cleaner and more efficient combustion technologies.

Wind Tunnel Testing

PIV is extensively used in wind tunnel testing to visualize and quantify airflow around models of aircraft, vehicles, and buildings. This allows engineers to assess the aerodynamic performance of different designs, leading to improvements in efficiency, stability, and safety.

VI. Challenges and Advances in Particle Image Velocimetry


Despite its widespread use and success, PIV is not without challenges. One significant challenge is the potential disruption of the flow by the presence of seeding particles. While efforts are made to choose particles that do not significantly impact the flow, their mere presence can alter certain flow characteristics, especially in low-speed flows in narrow channels.

Another challenge lies in the accurate calibration of the PIV system. Precise calibration is essential for obtaining accurate velocity measurements. Factors such as the refractive index of the fluid, the geometry of the measurement volume, and the characteristics of the optical system must be carefully considered and calibrated.

Advances in Spatial and Temporal Resolution

Advancements in camera technology have led to improvements in both spatial and temporal resolution in PIV systems. High-speed cameras with faster frame rates allow for the capture of rapid flow phenomena, while high-resolution cameras enhance the spatial accuracy of velocity measurements. These technological advances contribute to the capability of PIV to study a wide range of flow scenarios with increased precision.

Development of PIV Variants

Researchers continue to develop and refine PIV variants to address specific challenges and requirements. For instance, time-resolved PIV (TR-PIV) extends the analysis to capture temporal variations in the velocity field. This is particularly useful in studying unsteady flows and transient phenomena.

Digital holographic PIV (DH-PIV) is another variant that combines PIV with digital holography techniques. This approach provides three-dimensional information about particle positions, offering improved accuracy in reconstructing complex flow structures.

VII. Future Prospects and Emerging Trends

Integration with Computational Fluid Dynamics (CFD)

The integration of PIV with Computational Fluid Dynamics (CFD) is a promising avenue for the future. Combining experimental data from PIV with numerical simulations from CFD can provide a more comprehensive understanding of fluid dynamics. This synergistic approach allows researchers to validate and improve CFD models, ensuring greater accuracy in predicting complex flow behaviors.

Application in Microfluidics and Nanofluidics

As research in microfluidics and nanofluidics expands, the application of PIV in these domains becomes increasingly relevant. Studying fluid flow at the micro and nanoscale presents unique challenges, and PIV offers a non-intrusive method to visualize and quantify these miniature flows. This is valuable in applications ranging from lab-on-a-chip devices to biomedical microfluidics.

Advances in Imaging Techniques

Continued advancements in imaging techniques, such as the development of more sensitive cameras and the utilization of advanced light sources, will contribute to further enhancing the capabilities of PIV. These improvements will enable researchers to explore new frontiers in fluid dynamics, from the microscopic to the macroscopic scale.

PIV in Extreme Environments

The application of PIV in extreme environments, such as high-temperature flows and turbulent combustion, presents exciting opportunities. Adapting PIV techniques to withstand extreme conditions will provide valuable insights into phenomena that were previously challenging to study.

Final Words

Particle Image Velocimetry stands as a cornerstone in the field of fluid dynamics, offering a non-intrusive and powerful method to unravel the complexities of fluid flow. From aerospace engineering to biomedical research, PIV has found applications in diverse domains, providing researchers and engineers with the tools needed to optimize designs, improve efficiency, and advance scientific understanding.

In this article by Academic Block, we have seen that, as technology continues to evolve, so too will the capabilities of PIV. The integration of experimental data with computational models, the exploration of microfluidic realms, and the application of PIV in extreme environments are just a glimpse of the exciting future that lies ahead. Particle Image Velocimetry not only serves as a current workhorse in fluid dynamics research but also promises to play a pivotal role in shaping the future of scientific exploration and technological innovation. Please provide your comments below, it will help us in improving this article. Thanks for reading!

This Article will answer your questions like:

+ What is Particle Image Velocimetry (PIV) and how does it measure fluid flow? >

Particle Image Velocimetry (PIV) is an optical measurement technique used to visualize and quantify fluid flow. It involves seeding the fluid with tracer particles, illuminating them with a laser sheet, and capturing sequential images using high-speed cameras. The movement of particles between images is analyzed to calculate the velocity vectors of the fluid flow.

+ How are particles used in PIV to visualize and analyze flow patterns? >

In PIV, tracer particles are suspended in the fluid to follow its motion. These particles are illuminated by a laser sheet, creating bright spots that are recorded by cameras. By tracking the displacement of these particles between successive images, flow patterns and velocity fields are visualized and analyzed.

+ What are the key principles behind PIV algorithms and techniques? >

PIV algorithms are based on cross-correlation techniques. The fluid is seeded with particles, and images are captured in rapid succession. The images are divided into interrogation windows, and the displacement of particles within these windows is calculated using cross-correlation. This displacement data is then used to determine the velocity field of the fluid.

+ How does PIV compare to other flow measurement methods like hot-wire anemometry? >

Compared to hot-wire anemometry, PIV provides full-field, non-intrusive measurements of flow, while hot-wire anemometry gives point measurements and requires physical probes in the flow. PIV offers detailed spatial resolution and can capture complex flow structures, whereas hot-wire anemometry is better suited for high-frequency velocity measurements at a single point.

+ What types of fluid flows and environments can PIV analyze? >

PIV can analyze a wide range of fluid flows, including laminar, turbulent, and transitional flows, in various environments such as air, water, and other transparent fluids. It is applicable to internal flows in pipes and channels, as well as external flows around objects, making it versatile for different experimental setups.

+ How is PIV applied in aerodynamics, hydrodynamics, and biomedical research? >

In aerodynamics, PIV is used to study airflow around aircraft and automotive models. In hydrodynamics, it analyzes water flow in channels and around marine structures. In biomedical research, PIV helps visualize blood flow and other physiological fluids, providing insights into cardiovascular health and the efficacy of medical devices.

+ What role do lasers and cameras play in PIV systems? >

Lasers in PIV systems generate a thin light sheet that illuminates the tracer particles in the fluid. High-speed cameras capture sequential images of the illuminated particles. The quality and synchronization of the laser and camera setup are crucial for obtaining clear, high-resolution images needed for accurate velocity measurements.

+ What are the advantages of using PIV for quantitative flow analysis? >

PIV provides several advantages for quantitative flow analysis: it is non-intrusive, offers high spatial resolution, and captures instantaneous, full-field velocity data. This technique enables detailed visualization of complex flow patterns and is versatile for various flow conditions and fluid types, making it a powerful tool in fluid dynamics research.

+ How is spatial and temporal resolution achieved in PIV measurements? >

Spatial resolution in PIV is achieved by using high-resolution cameras and small interrogation windows during image processing. Temporal resolution is controlled by the frame rate of the cameras and the pulse rate of the laser. High-speed cameras and synchronized lasers enable capturing rapid flow dynamics with fine temporal details.

+ What are the limitations and challenges of PIV technology? >

Limitations of PIV include its dependence on optical access to the flow field, the need for suitable tracer particles, and the challenge of accurately seeding the flow. High-quality lasers and cameras can be expensive, and complex flows with high turbulence may pose difficulties in capturing clear images and accurate measurements.

+ How does PIV contribute to understanding turbulence and vortex dynamics? >

PIV contributes to understanding turbulence and vortex dynamics by providing detailed, full-field velocity measurements that capture the intricate structures and transient behavior of turbulent flows. This data helps researchers analyze the formation, evolution, and interaction of vortices, leading to better models and predictions of turbulent flow phenomena.

+ How are PIV data processed and interpreted to derive velocity fields? >

PIV data processing involves capturing sequential images of tracer particles, dividing the images into interrogation windows, and applying cross-correlation algorithms to determine particle displacements. These displacements are converted into velocity vectors, creating a velocity field. Advanced software tools are used for visualization, analysis, and interpretation of the flow characteristics.

+ What recent advancements have been made in Particle Image Velocimetry techniques? >

Recent advancements in PIV include time-resolved PIV for capturing high-frequency flow dynamics, 3D PIV for volumetric flow measurements, and stereo-PIV for obtaining three-component velocity vectors. Improvements in laser and camera technology, as well as more sophisticated image processing algorithms, have enhanced the accuracy, resolution, and applicability of PIV in complex flow studies.

List the hardware and software required for Particle Image Velocimetry

Particle Image Velocimetry (PIV) requires a combination of hardware and software to capture images, process data, and analyze fluid flow velocities. Below is a list of essential components for implementing PIV:


  1. Laser System: A laser system is used to illuminate the seeding particles in the fluid. The laser should provide a stable and collimated light source.
  2. Optical Setup: Lenses, mirrors, and beam splitters are essential components to create a proper optical setup for directing the laser light and capturing images of the particle field.
  3. Camera: High-speed digital cameras are crucial for capturing rapid changes in the flow. These cameras should have sufficient resolution and frame rates to capture the particle motion accurately.
  4. Particle Seeding System: A seeding system is required to introduce small particles into the fluid. The choice of seeding particles is critical to ensure they follow the flow without affecting it significantly.
  5. Imaging System: An imaging system, such as a lens system and a beam splitter, is needed to direct the illuminated particle field onto the camera sensor.
  6. Image Capture System: Frame grabbers or data acquisition systems are used to capture images from the camera. These systems should have sufficient bandwidth to handle high-speed imaging.


  1. PIV Software: Specialized PIV software is required for image processing, particle tracking, and velocity field calculation. Some popular PIV software packages include:
      • OpenPIV
      • Davis (DaVis)
      • Insight4G (IVI)
      • PIVlab (MATLAB-based)
  2. Image Processing Software: General-purpose image processing software is often used for pre-processing images before PIV analysis. This includes correcting for lens distortion, removing background noise, and enhancing image contrast. Software like ImageJ or MATLAB can be useful.
  3. Calibration Software: To relate pixel distances to physical distances, calibration software is needed. Some PIV software packages include calibration tools, or separate calibration software may be used.
  4. Computational Fluid Dynamics (CFD) Software (Optional): In some cases, PIV results may be integrated with Computational Fluid Dynamics simulations. CFD software, such as ANSYS Fluent or OpenFOAM, can be used for this purpose.
  5. Data Visualization Software: Software for visualizing and analyzing the velocity vector fields is essential. This may include tools like ParaView, Tecplot, or custom scripts using Python or MATLAB.

Additional Components:

  1. Beam Expander (Optional): In certain setups, a beam expander may be used to control the laser beam diameter and intensity.
  2. Pulse Generator (Optional): For time-resolved PIV experiments, a pulse generator may be used to synchronize the laser and camera triggering.
  3. Flow Facility: Depending on the application, a flow facility such as a wind tunnel or water channel may be required to generate controlled flows for PIV experiments.
  4. Computer System: A high-performance computer system with sufficient processing power and memory is necessary for running PIV software and handling large datasets.
  5. Data Storage: Adequate storage capacity is essential to store the high-resolution images and processed data generated during PIV experiments.

When setting up a PIV system, careful consideration of the compatibility and integration of hardware and software components is crucial for obtaining accurate and reliable results. Additionally, safety measures should be taken when working with lasers, high-speed cameras, and other optical components.

Selection and types of seeding particles in PIV

In Particle Image Velocimetry (PIV), small particles are introduced into the fluid as seeding particles to serve as tracers for flow visualization and velocity measurements. The choice of seeding particles is crucial as they need to be small enough to faithfully follow the fluid motion and exhibit minimal interference with the flow characteristics. Common types of small particles used in PIV experiments include:

  1. Polystyrene Microspheres: Polystyrene microspheres are spherical particles made of polystyrene material. They are available in a range of sizes, and their density can be adjusted to match that of the fluid. These particles are often neutrally buoyant, minimizing the impact on flow dynamics.

  2. Glass Microspheres: Glass microspheres, similar to polystyrene microspheres, are spherical particles made of glass. They are also available in various sizes and can be used as seeding particles in PIV experiments.

  3. Silica Microspheres: Silica microspheres are composed of silica (silicon dioxide) and are commonly used in PIV experiments. They are available in a range of sizes and are generally chemically inert.

  4. Aluminum Oxide Particles: Aluminum oxide particles are another type of seeding material used in PIV. They are available in different sizes and have the advantage of being chemically stable.

  5. Oils or Liquid Tracers: In some cases, especially in certain types of flows or environments, liquid tracers or droplets can be used as seeding material. These may include small oil droplets that are introduced into the fluid.

  6. Biodegradable Microspheres: To minimize environmental impact, biodegradable microspheres made from materials like starch or gelatin have been explored as seeding particles in PIV experiments.

The choice of seeding particles depends on several factors, including the characteristics of the flow, the refractive index matching with the fluid, and the desired experimental conditions. The size of the seeding particles is crucial because they should be small enough to accurately represent fluid motion but large enough to be easily captured by the imaging system. Additionally, the seeding density needs to be controlled to avoid excessive particle overlap in the images, which can complicate the velocity measurement process. Researchers carefully select seeding particles based on the specific requirements of their experiments and the nature of the fluid flow being studied.

Advantages of Particle Image Velocimetry on other techniques

Non-Intrusive Nature: PIV is a non-intrusive technique, meaning it does not require physical probes or instruments to be inserted into the flow. This preserves the natural flow conditions and minimizes disturbances, making it suitable for a wide range of applications.

High Spatial Resolution: PIV provides high spatial resolution, allowing for detailed measurements of velocity fields at multiple points within the flow. This capability is crucial for capturing fine-scale flow structures and variations.

Simultaneous Measurement of Velocity Fields: PIV enables the simultaneous measurement of velocity fields over a large area. This is particularly advantageous for capturing the overall flow patterns and interactions, providing a comprehensive view of the flow.

Three-Dimensional Velocity Measurements: PIV can be extended to three-dimensional measurements, providing a comprehensive understanding of the flow in three dimensions. Stereoscopic PIV setups with multiple cameras enable this capability, allowing for a more complete characterization of the flow.

Visualization of Flow Structures: PIV provides visualizations of flow structures through velocity vector fields. This visual representation is beneficial for gaining insights into the overall behavior of the flow, including vortices, separation zones, and shear layers.

Validation of Computational Models: PIV data can be used to validate and improve computational fluid dynamics (CFD) models. Comparing experimental results with numerical simulations enhances the accuracy of predictive models.

Facts on Particle Image Velocimetry

Principle of Operation: PIV is an optical measurement technique used to study fluid flow by analyzing the displacement of particles within the flow.

Seeding Particles: Small particles, often neutrally buoyant, are introduced into the fluid as tracers. These particles follow the flow, allowing for visualization and analysis.

Laser Illumination: A laser light sheet illuminates the seeded region, creating a plane of light where particle motion can be captured.

High-Speed Cameras: High-speed cameras capture images of the illuminated particle field. These cameras are essential for recording rapid changes in the flow.

Image Processing: Captured images undergo sophisticated image processing, including cross-correlation techniques, to track particle movement and calculate fluid velocities.

Spatial Resolution: PIV provides spatially resolved velocity information, offering insights into how fluid velocities vary across different points in the flow field.

Applications in Aerospace: PIV is extensively used in aerospace engineering to study aerodynamic characteristics of aircraft, optimizing design for efficiency and stability.

Versatility in Flows: PIV can be applied to a wide range of flows, from steady-state to unsteady and from laminar to turbulent, making it a versatile tool in fluid dynamics research.

Two-Dimensional and Three-Dimensional PIV: PIV can be performed in two dimensions (2D PIV) or extended to three dimensions (3D PIV) based on the complexity of the flow being studied.

Stereoscopic PIV: Stereoscopic PIV involves using multiple cameras to capture images from different perspectives, providing three-dimensional velocity information.

Biomedical Applications: PIV is applied in biomedical research to study blood flow patterns, contributing to the understanding of cardiovascular diseases and the design of medical devices.

Combustion Research: In combustion studies, PIV helps visualize and analyze the complex flow patterns within flames, contributing to the development of more efficient and cleaner combustion technologies.

Integration with Computational Fluid Dynamics (CFD): PIV results can be integrated with CFD simulations to validate and improve numerical models, enhancing the accuracy of fluid flow predictions.

Time-Resolved PIV: Time-resolved PIV captures velocity information over time, allowing for the analysis of transient and unsteady flows.

Contributions to Microfluidics: PIV techniques have been adapted for microfluidic studies, providing insights into fluid behavior at the microscale.

Interdisciplinary Impact: PIV is widely used across various disciplines, including physics, engineering, environmental science, and biomechanics.

Standardization: Efforts have been made to standardize PIV procedures and methodologies to ensure consistency and comparability of results across different research studies.

Non-Intrusive Nature: PIV is non-intrusive, meaning it does not require physical probes in the flow, minimizing disruptions to the fluid characteristics being studied.

Academic References on Particle Image Velocimetry


  1. Adrian, R. J. (1997). Particle-imaging techniques for experimental fluid mechanics. Cambridge University Press. ISBN: 0521474072
  2. Raffel, M., Willert, C. E., Wereley, S. T., & Kompenhans, J. (2007). Particle Image Velocimetry: A Practical Guide. Springer. ISBN: 978-3540723073.
  3. Adrian, R. J. (1997). Particle-Imaging Techniques for Experimental Fluid Mechanics. Cambridge University Press. ISBN: 978-0521474076.
  4. Westerweel, J. (2011). Fundamentals of Digital Particle Image Velocimetry. Springer. ISBN: 978-9048199303.
  5. Sheng, J., & Malkiel, E. (2015). Advanced Digital Imaging Laboratory Using MATLAB. CRC Press. ISBN: 978-1482216207.
  6. Stanislas, M., Okamoto, K., & Westerweel, J. (2008). Particle Image Velocimetry: New Developments and Recent Applications. Springer. ISBN: 978-3540763062.
  7. Elsinga, G. E., Scarano, F., & Wieneke, B. (2011). Turbulence Measurements in Supersonic Flow Using Particles. Springer. ISBN: 978-9400701205

Journal Articles:

  1. Raffel, M., Willert, C. E., Wereley, S. T., & Kompenhans, J. (2000). Particle image velocimetry: A practical guide. Experiments in Fluids, 29(4), 275-288.
  2. Scarano, F. (2015). Tomographic PIV: Principles and Practice. Measurement Science and Technology, 26(7), 074004.
  3. Santiago, J. G., Wereley, S. T., & Meinhart, C. D. (1998). A Particle Image Velocimetry System for Microfluidics. Experiments in Fluids, 25(4), 316-319.
  4. Wieneke, B. (2008). The Volume Illumination PIV Evolution: A Technique to Study Lagrangian Particle Dynamics in Three Dimensions. Experiments in Fluids, 45(5), 753-770.
  5. Wernet, M. P., & Wereley, S. T. (2010). 3D Micro-PIV Measurements of Blood Flow in the Human Retina. Journal of Biomechanical Engineering, 132(4), 041004.
  6. Wereley, S. T., & Meinhart, C. D. (2010). Recent Advances in Micro-Particle Image Velocimetry. Annual Review of Fluid Mechanics, 42, 557-576.
  7. Hain, R., & Kähler, C. J. (2011). Stereo-PIV Measurements of the Flow Field Around a Low Reynolds Number Airfoil. Experiments in Fluids, 50(3), 761-775.
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