Experimental Fluid Mechanics Research Articles

Preface

2024 4th International Conference on Fluid and Chemical Engineering (ICFCE 2024) has been successfully held on July 26-27, 2024, in Wuhan China as a virtual event. Organized by Hubei Zhongke Institute of Geology and Environment Technology, ICFCE 2024 intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations. ICFCE 2024 proceeding is a collection of outstanding submissions in the field of biofluid mechanics, chemically reacting fluids and combustion, computational fluid dynamics (CFD), experimental fluid mechanics, flow through porous media, fluid–solid interactions (FSI), geophysical fluid dynamics, granular/suspension flows, heat and mass transfer, hydrodynamics, physical, theoretical and computational chemistry, chemical engineering fundamentals, chemical reaction engineering, chemical engineering equipment design and process design, thermodynamics, catalysis & reaction engineering, particulate systems, crystallization, etc. Around 65 articles were selected from 146 submissions after peer-review, and authors and guests participated in ICFCE 2024 with keynote speeches, oral presentations and posters, promoting the communication among researchers and experts from institutes and universities, including University of Sharjah, The University of Manchester, Harbin Engineering University, China University of Petroleum (East China), Shanghai Maritime University, Jiangxi University of Science and Technology, Nagoya University, Marine Design & Research Institute of China, Tianjin Transportation Technical College, Okayama University, etc. Cordial gratitude is delivered to Technical Program Committee of ICFCE 2024 for the professional work on guarantying the high standard of conference as well as the proceedings. We believe the proceedings will enhance the exchange of ideas and state-of-art knowledge on Fluid and Chemical Engineering. Finally, we thank all the staff members at IOP Publishing for their valuable editorial support. Professor Omid Mahian Xian Jiaotong University, China Associate Professor Sébastien Déon University of Bourgogne-Franche-Comté, France List of Technical Committee is available in the pdf

Particle Shadow Velocimetry And Its Potential Applications And Advantages Vis-à-Vis Particle Image Velocimetry

Particle image velocimetry (PIV) is an established standard velocimetry technique in experimental fluid mechanics that involves determining a fluid flow velocity field from the motion of tracer particles illuminated by a laser sheet. The necessity of laser illumination poses challenges in certain applications, and is a potential entry barrier due to its complexity and high cost. A laser-free alternative to PIV is particle shadow velocimetry (PSV), which uses images of the shadows cast by the particles on the camera sensor under back-illumination, instead of the Mie scattering signal produced by laser illumination. This study aims to compare various aspects of PSV such as depth of field, seeding density, type of illumination required, particle size, image filtering, cost effectiveness, and limitations with those of PIV. PSV and PIV measurements are conducted in the wake of a flow past a cylinder and in a boundary layer developing over a flat plate. It is found that PSV is capable of achieving equivalent accuracy to PIV and is a viable alternative to PIV in certain applications where light sheet illumination creates experimental challenges.

Flow field reconstruction from sparse sensor measurements with physics-informed neural networks

In the realm of experimental fluid mechanics, accurately reconstructing high-resolution flow fields is notably challenging due to often sparse and incomplete data across time and space domains. This is exacerbated by the limitations of current experimental tools and methods, which leave critical areas without measurable data. This research suggests a feasible solution to this problem by employing an inverse physics-informed neural network (PINN) to merge available sparse data with physical laws. The method's efficacy is demonstrated using flow around a cylinder as a case study, with three distinct training sets. One was the sparse velocity data from a domain, and the other two datasets were limited velocity data obtained from the domain boundaries and sensors around the cylinder wall. The coefficient of determination (R2) coefficient and mean squared error (RMSE) metrics, indicative of model performance, have been determined for the velocity components of all models. For the 28 sensors model, the R2 value stands at 0.996 with an associated RMSE of 0.0251 for the u component, while for the v component, the R2 value registers at 0.969, accompanied by an RMSE of 0.0169. The outcomes indicate that the method can successfully recreate the actual velocity field with considerable precision with more than 28 sensors around the cylinder, highlighting PINN's potential as an effective data assimilation technique for experimental fluid mechanics.

Virtual physical framework reveals vortex-induced vibration “Soar” and “Death” for a bluff body at high Reynolds numbers

Vortex-induced vibration (VIV) of a bluff body is well known as a canonical phenomenon in complex fluid–structure interactions (FSI). However, our understanding and physical theories of this phenomenon are still restricted to a lower Reynolds number (Re) range owing to the limitations of experimental fluid mechanics. Herein, we describe a virtual physical framework (VPF) capable of editing and controlling the physical parameters of an actual physical system with high fidelity by combining numerical disciplinary and mechanical actuators. We demonstrated the effectiveness of applying a VPF to address the experimental fluid mechanical challenges of vortex-induced vibration (VIV) for a bluff body at a high Re by virtualizing a large, flexibly mounted rigid cylindrical system. We observed the VIV evolution of the bluff body at different Re values. Notably, the VIV appears in its “soar” stages with unexpected amplitudes of 2.4 D, nearly 200% higher than the traditionally acknowledged value at approximately Re = 2.0E5, while suddenly entering into its “death” stages with no vibrations over the critical Re region. The dissynchrony changes in the energy dissipation and input with Re caused by drag and excitation forces in the fluid system were further found to result in these VIV phenomena. This discovery upsets previous perceptions of VIVs and raises profound questions regarding related engineering projects. The VPF also opens a promising paradigm in experimental research for accelerating scientific discovery and investigating unaddressed classical physical phenomena.

Physics-informed deep-learning applications to experimental fluid mechanics

High-resolution reconstruction of flow-field data from low-resolution and noisy measurements is of interest due to the prevalence of such problems in experimental fluid mechanics, where the measurement data are in general sparse, incomplete and noisy. Deep-learning approaches have been shown suitable for such super-resolution tasks. However, a high number of high-resolution examples is needed, which may not be available for many cases. Moreover, the obtained predictions may lack in complying with the physical principles, e.g. mass and momentum conservation. Physics-informed deep learning provides frameworks for integrating data and physical laws for learning. In this study, we apply physics-informed neural networks (PINNs) for super-resolution of flow-field data both in time and space from a limited set of noisy measurements without having any high-resolution reference data. Our objective is to obtain a continuous solution of the problem, providing a physically-consistent prediction at any point in the solution domain. We demonstrate the applicability of PINNs for the super-resolution of flow-field data in time and space through three canonical cases: Burgers’ equation, two-dimensional vortex shedding behind a circular cylinder and the minimal turbulent channel flow. The robustness of the models is also investigated by adding synthetic Gaussian noise. Furthermore, we show the capabilities of PINNs to improve the resolution and reduce the noise in a real experimental dataset consisting of hot-wire-anemometry measurements. Our results show the adequate capabilities of PINNs in the context of data augmentation for experiments in fluid mechanics.

ECR Spotlight – Kristian Maar

ECR Spotlight is a series of interviews with early-career authors from a selection of papers published in Journal of Experimental Biology and aims to promote not only the diversity of early-career researchers (ECRs) working in experimental biology but also the huge variety of animals and physiological systems that are essential for the ‘comparative’ approach. Kristian Maar is an author on ‘ The fluid dynamics of barnacle feeding’, published in JEB. Kristian is a PhD student in the lab of Professor Thomas Kiørboe at Centre for Ocean Life, DTU Aqua, Kgs. Lyngby, Denmark, investigating microscale ocean biophysics and experimental fluid mechanics of marine organisms.

Artifact noise suppression of particle-field computed tomography based on lightweight residual and enhanced convergence neural network

<sec>The realization of fast and high-quality three-dimensional particle-field image characterization is always highly desired in the areas, such as experimental fluid mechanics and biomedicine, for the micro-particle distribution status in a flow-field can characterize the field properties well. In the particle-field image reconstruction and characterization, a wildly used approach at present is the computed tomography. The great advantage of the computed tomography for particle-field image reconstruction lies in the fact that the full particle spatial distribution can be obtained and presented due to multi-angle sampling.</sec><sec>Recently, with the development and application of deep learning technique in computed tomography, the image quality has been greatly improved by the powerful learning ability of a deep learning network. In addition, the deep learning application also makes it possible to speed up the computed tomographic imaging process from sparse-sampling due to the ability of the network to strongly extract image feature. However, sparse-sampling will lead to insufficient acquirement of the object information during sampling for the computed tomography. Therefore, a sort of artifact noise will emerge and be accompanied with the reconstructed images, and thus severely affecting the image quality. As there is no universal network approach that can be applied to all types of objects in the suppression of artifact noise, it is still a challenge in removing the sparse-sampling-induced artifact noise in the computed tomography now.</sec><sec>Therefore, we propose and develop a specific lightweight residual and enhanced convergence neural network (LREC-net) approach for suppressing the artifact noise in the particle-field computed tomography. In this method, the network input dataset is also optimized in signal-to-noise ratio (SNR) in order to reduce the input noise and ensure the effective particle image feature extraction of the network in the imaging process.</sec><sec>In the design of LREC-net architecture, a five-layer lightweight and dual-residual down-sampling is constructed on the basis of typical U-net and Resnet50, making the LREC-net more suitable for the particle-field image reconstruction. Moreover, a fast feature convergence module for rapid particle-field feature acquirement is added to up-sampling process of the network to further promote the network processing efficiency. Apart from the design of LREC-net network itself, the optimization of network input dataset in SNR of images is achieved by finding a fit image reconstruction algorithm that can produce higher-SNR particle images in the computed tomography. This achievement reduces the input noise as much as possible and ensures effective particle-field feature extraction by the network.</sec><sec>The simulation analysis and experimental test demonstrate the effectiveness of the proposed LREC-net method, which involves the evaluations of SNR changes of the input-output images through the network, the proportion of residual artifact noise as ghost-particles (GPP) in the reconstructed images, and the valid-particle loss proportion (PLP). In contrast to the performances of U-net and Resnet50 under the same imaging conditions, all the data in SNR, GPP and PLP show the great improvement of the image quality due to the application of LREC-net method. Meanwhile, the designed LREC-net method also enhances the network running efficiency to a large extent due to the remarkable reduction of training time. Therefore, this work provides a new and effective approach for developing sparse-sampling-based fast and high-quality particle-field computed tomography.</sec>

Three-dimensional image reconstruction of opaque particle swarms by lightweight dual-residual deep learning computed tomography

High quality of three-dimensional particle swarm images is always highly desired in some important areas like experimental fluid mechanics and combustion field measurement, etc. as particle distributions in these fields can characterize the field properties well. Recently, deep learning techniques have been applied for the computed tomography to improve three-dimensional image reconstruction quality of an object. However, how to promote the network processing efficiency and quality to reduce artifact noise, missing-particles and ghost-particles in the reconstruction image is still a challenge at present. For this purpose, we propose a lightweight dual-residual deep learning approach for improvement of tomographic image reconstruction of particle swarms in this paper. It includes the innovative designs in lightweight dual-residual down-sampling, fast feature convergence module and high-SNR network input datasets to acquire image features more effectively. Numerical analysis and experimental test have demonstrated that the proposed method can effectively reduce not only the missing particles and ghost-particles in the reconstructed images under sparse-sampling, but also the network training time, in contrast to the performances of some typical deep learning networks under the same conditions.

Preface

2023 3rd International Conference on Fluid and Chemical Engineering (ICFCE 2023) has been successfully held on July 20-21, 2023, in Wuhan China. Organized by Hubei Zhongke Institute of Geology and Environment Technology, ICFCE 2023 intends to invite worldwide famous scientists, experts, scholars, and researchers for academic presentations.ICFCE 2023 proceeding is a collection of outstanding submissions in the field of biofluid mechanics, chemically reacting fluids and combustion, computational fluid dynamics (CFD), experimental fluid mechanics, flow through porous media, fluid–solid interactions (FSI), geophysical fluid dynamics, granular/suspension flows, heat and mass transfer, hydrodynamics, physical, theoretical and computational chemistry, chemical engineering fundamentals, chemical reaction engineering, chemical engineering equipment design and process design, thermodynamics, catalysis & reaction engineering, particulate systems, crystallization, etc. Around 70 articles were selected from more than 150 submissions after peer-review, and authors and guests participated in ICFCE 2023 with keynote speeches, oral presentations and posters, promoting the communication among researchers and experts from institutes and universities, including Ningbo University, University of Sharjah, China Association for Science and Technology, Tianjin University, China Agricultural University, South China University of Technology, Nanjing University of Aeronautics and Astronautics, etc.Cordial gratitude is delivered to Technical Program Committee of ICFCE 2023 for the professional work on guarantying the high standard of conference as well as the proceedings. We believe the proceedings will enhance the exchange of ideas and state-of-art knowledge on Fluid and Chemical Engineering.Professor Omid MahianXian Jiaotong University, ChinaAssociate Professor Sébastien DéonUniversity of Bourgogne-Franche-Comté, FranceList of Technical Committee, Chair, Editor, International Committee Members are available in this pdf.

A Fourier-based methodology without numerical diffusion for conducting dye simulations and particle residence time calculations

Dye experimentation is a widely used method in experimental fluid mechanics for flow analysis or for the study of the transport of particles within a fluid. This technique is particularly useful in biomedical diagnostic applications ranging from hemodynamic analysis of cardiovascular systems to ocular circulation. However, simulating dyes governed by convection-diffusion partial differential equations (PDEs) can also be a useful post-processing analysis approach for computational fluid dynamics (CFD) applications. Such simulations can be used to identify the relative significance of different spatial subregions in particular time intervals of interest in an unsteady flow field. Additionally, dye evolution is closely related to non-discrete particle residence time (PRT) calculations that are governed by similar PDEs. This contribution introduces a pseudo-spectral method based on Fourier continuation (FC) for conducting dye simulations and non-discrete particle residence time calculations without numerical diffusion errors. Convergence and error analyses are performed with both manufactured and analytical solutions. The methodology is applied to three distinct physical/physiological cases: 1) flow over a two-dimensional (2D) cavity; 2) pulsatile flow in a simplified partially-grafted aortic dissection model; and 3) non-Newtonian blood flow in a Fontan graft. Although velocity data is provided in this work by numerical simulation, the proposed approach can also be applied to velocity data collected through experimental techniques such as from particle image velocimetry.

An attention-mechanism incorporated deep recurrent optical flow network for particle image velocimetry

Particle image velocimetry (PIV), as a key technique in experimental fluid mechanics, is able to estimate complex velocity field through consecutive input particle images. In this study, an attention-mechanism incorporated deep recurrent network called ARaft-FlowNet has been proposed, on the basis of a previously established Recurrent All-Pairs Field Transforms optical flow model. The attention module is added to improve the network's capability of recognizing tracer particles' motion. Moreover, a parameterized dataset, ParaPIV-Dataset, is generated to explore the influence of particle parameters on deep learning networks, including particle diameter, image particle density, Gaussian noise, and peak intensity. The accuracy and generalizability of the newly proposed model has been evaluated and analyzed comprehensively. The results indicate that ARaft-FlowNet achieves state-of-the-art performance. Compared to previous methods, ARaft-FlowNet shows an accuracy improvement of 62.9%, 10.9%, and 9.4% in cylindrical flow, surface quasi-geostrophic flow, and DNS-turbulence flow. Meanwhile, the proposed model shows the strongest generalization and best capability to deal with complex flow fields with small-scale vortices. Additionally, tests on experimental turbulent jet data reveal that ARaft-FlowNet is able to deal with real PIV images with brightness variations and noise.

Reconstructing turbulent velocity and pressure fields from under-resolved noisy particle tracks using physics-informed neural networks

Volume-resolving imaging techniques are rapidly advancing progress in experimental fluid mechanics. However, reconstructing the full and structured Eulerian velocity and pressure fields from sparse and noisy particle tracks obtained experimentally remains a significant challenge. We introduce a new method for this reconstruction, based on Physics-Informed Neural Networks (PINNs). The method uses a Neural Network regularized by the Navier-Stokes equations to interpolate the velocity data and simultaneously determine the pressure field. We compare this approach to the state-of-the-art Constrained Cost Minimization method [1]. Using data from direct numerical simulations and various types of synthetically generated particle tracks, we show that PINNs are able to accurately reconstruct both velocity and pressure even in regions with low particle density and small accelerations. PINNs are also robust against increasing the distance between particles and the noise in the measurements, when studied under synthetic and experimental conditions.

The rangeomorph Pectinifrons abyssalis: Hydrodynamic function at the dawn of animal life

Rangeomorphs are among the oldest putative eumetazoans known from the fossil record. Establishing how they fed is thus key to understanding the structure and function of the earliest animal ecosystems. Here, we use computational fluid dynamics to test hypothesized feeding modes for the fence-like rangeomorph Pectinifrons abyssalis, comparing this to the morphologically similar extant carnivorous sponge Chondrocladia lyra. Our results reveal complex patterns of flow around P.abyssalis unlike those previously reconstructed for any other Ediacaran taxon. Comparisons with C. lyra reveal substantial differences between the two organisms, suggesting they converged on a similar fence-like morphology for different functions. We argue that the flow patterns recovered for P.abyssalis do not support either a suspension feeding or osmotrophic feeding habit. Instead, our results indicate that rangeomorph fronds may represent organs adapted for gas exchange. If correct, this interpretation could require a dramatic reinterpretation of the oldest macroscopic animals.

A two-experiment finite similitude approach to experimental fluid mechanics

Fluid dynamics experimentation at scale provides an important means of investigation that is presently underpinned by dimensional analysis and computational fluid dynamics (CFD). Unfortunately, despite the obvious success of this twin approach, dimensional analysis provides no solution if scale effects are prevalent and CFD offers reduced practical advantage in the absence of a theory that links information across scaled experiments. This issue is addressed in this paper with the introduction of a new approach to scaled experimentation founded on the theory of finite similitude. Finite similitude is founded on the metaphysical concept of space scaling, where experimental apparatuses, facilities, prototypes, systems, wind tunnels etc., are scaled by the means of space contraction or expansion. There exists no practical means to contract or expand space but what is possible is a mathematical description of space scaling and its impact on governing physics, so providing a means for comparison with scaled systems and real-world experimental behaviours. This paper examines an adaptation of the finite-similitude theory, where two scaled experiments are involved but a second-order version of the theory is applied. All previous work has been limited to zeroth and first-order applications, but in the case of fluid dynamics, second order is shown necessary to capture more accurately convective terms. It is demonstrated in the paper by means of two scaled experiments (designed using second-order theory) that the approach is able to represent behaviours at the full scale that hitherto would have been deemed impossible with traditional dimensional analysis. Numerical and analytical case studies on gravity impacted flows are trialled to illustrate the benefits of the approach with percentage errors reduced from double to single digits.

High Spatial Resolution 2C-2D PIV Measurements Using A 47 MPx Sensor Of High Reynolds Number Turbulent Boundary Layer Flow

In the past decade, advances in electronics technology have made larger imaging sensors available to the experimental fluid mechanics community. These advancements have enabled the measurement of 2-component 2-dimensional (2C-2D) velocity fields using particle image velocimetry (PIV) with much higher spatial resolution than previously possible using a single camera. Although previously reported experiments have incorporated multiple-camera arrays to acquire high spatial resolution PIV, using a single large camera can greatly reduce the complexity of the experimental setup as well as the error introduced by the calibration between the cameras. In this paper, the ability of a single large sensor for high spatial resolution PIV is demonstrated by performing the measurement of a zero-pressure-gradient turbulent boundary layer (ZPG-TBL). In post-processing the PIV images, the lens distortion error is of particular importance, as the lens distortion error increases with the size of the imaging sensor. The third-order polynomial functions are used to model the lens distortion in this study, and the correction is performed on the PIV vectors to save computational cost. The first- and second-order statistics are calculated and compared with the profiles captured by small camera arrays, and the result shows that the corrected profiles agree well with the previously acquired data, therefore, the lens distortion error can be corrected.

Generalization of deep recurrent optical flow estimation for particle-image velocimetry data

Particle-image velocimetry (PIV) is one of the key techniques in modern experimental fluid mechanics to determine the velocity components of flow fields in a wide range of complex engineering problems. Current PIV processing tools are mainly handcrafted models based on cross-correlations computed across interrogation windows. Although widely used, these existing tools have a number of well-known shortcomings, including limited spatial output resolution and peak-locking biases. Recently, new approaches for PIV processing leveraging a novel neural network architecture for optical flow estimation called recurrent all-pairs field transforms (RAFT) have been developed. These have matched or exceeded the performance of classical, handcrafted models. While the RAFT-PIV method is a promising approach, it is important for the broader fluids community to more completely understand its empirical behavior and performance. To this end, in this study, we thoroughly investigate the performance of RAFT-PIV under varying image and lighting conditions. We consider applications spanning synthetic and experimental data, with a breadth and depth going far beyond currently available empirical results. The results for the wide variation of experiments shed new light on the capabilities of deep learning for PIV processing.

Method for utilizing PIV to investigate high curvature and acceleration boundary layer flows around the compressor blade leading edge

Particle Image Velocimetry (PIV) is a well-developed and contactless technique in experimental fluid mechanics, but the strong velocity gradient and streamline curvature near the wall substantially limits its accuracy improvement. This paper presents a data processing procedure combining conventional PIV and newly developed Mirror Interchange (MI) based Interface-PIV for the measurement of the boundary layer parameter development in the blade leading edge region. The synthetic particle images are used to analyze the measurement errors in the entire procedure. Overall, three types of errors, namely the errors caused by the Window Deformation Iterative Multigrid (WIDIM) algorithm, the discrete data interpolation and integration, and the wall offset uncertainty, comprise the main measurement error. Specifically, the errors due to the discrete data interpolation and integration and the WIDIM algorithm comprise the mean bias, which can be corrected through the error analysis method proposed in the present work. Meanwhile, the errors due to the WIDIM algorithm and the wall offset uncertainty contribute to the measurement uncertainty. Computational fluid dynamics-based synthetic particle flows were generated to verify the newly developed PIV data processing procedure and the corresponding error analysis method. Results showed that the data processing method could improve the accuracy of PIV measurements for boundary layer flows with high curvature and acceleration and even with significant flow separation bubbles. Finally, the data processing method is also applied in a PIV experiment to investigate the boundary layer flows around a compressor blade leading edge, and several credible boundary flow parameters were obtained.

Comparison of fibre-based phase Doppler analysers

Laser diagnostics techniques are widely used in experimental fluid mechanics. By far the most widely used systems for getting spatial velocity fields and turbulence data are laser Doppler anemometers (LDA). Further, in the cases of two-phase flows phase Doppler analysers (PDA) are typically chosen to measure the size and velocity of the droplets or bubbles. The PDA system is non-intrusive laser technique with high spatial and temporal resolution. Moreover, the PDA system does not require additional calibration as, for example, hot-wire anemometers. However, with a growing number of PDA users, there is a need for verification of the results among the workplaces and systems themselves. The current paper deals with the comparison of two fibre-based PDA systems. The main scope of the investigation is an evaluation of the system's age and the influence of lasers type. One of the systems is older, operated with Argon-Ion laser and the second one is brand new equipped with Diode-pumped solid-state lasers. Both have the same optics and were manufactured by the same company. Various properties of the PDA system are tested to show particular influence on the quality of results when measuring in a spray generated by a small air-blast atomizer.

Application of oil and dye flow visualization in incompressible turbomachinery flows

Flow visualization is one of many available tools in experimental fluid mechanics and is used from the primary stages of fluid mechanics research in order to identify the physical sizes and locations of the flow features under consideration. Most of the fluids used in engineering applications are transparent (water, air, etc) and flow visualization techniques are used in order to make their flow patterns visible. Simple flow visualization experiments are relatively inexpensive and they can be easily implemented providing with a first feeling of the characteristics of the flow domain. Subsequently, flow visualization techniques are of great applicability in complex flow fields and especially in turbomachinery applications where the flow is characterized by three dimensional and secondary flow patterns. In general, fluid motion can be visualized by surface flow visualization, by particle tracer methods or by optical methods. The former flow visualization technique reveals the streamlines of fluid flows around a solid surface. In this paper flow visualization techniques applied in two different cases of experimental testing (fans in crossflow and cascade experiment) are presented. In both cases, the mixture of paint was prepared using a highly volatile light mineral or heavy machine oil of viscosities of approximately100cPand200cP, respectively, together with very fine pigments of Titanium Dioxide (TiO2) or fluorescein sodium in various colors. After the preparation of the mixture, a homogenous thin film was applied onto the whole plate surface by painting it with a soft brush. The air stream which flows over the surface of the plate, modifies the concentration and the homogeneity of the oil film, according to the flow conditions very close to the wall. The film was dried by the airflow and photographed for further consideration while the time taken for drying depended on the wind tunnel velocity as well as, on the pigmentation of the mixture. Successful and un-successful flow visualization tests are herein presented while each case is respectively commented as far as the mixtures, the proportions used and the application onto the rigs are concerned.

Application of PIV Technique in Teaching Experiment of Solitary Wave Flow Field Measurement

Particle Image Velocimetry (PIV), as an advanced experimental technique of fluid mechanics, has been widely used in many fields such as experimental fluid mechanics and practical engineering applications. The solitary wave is a kind of advanced wave that has only one peak or trough and only appears in shallow water. This kind of wave is quasi-particle, and its theory is used to study the water depth of wave breaking, which has a guiding effect on the design of offshore structures. To let students understand PIV technology visually, and at the same time, learn the transmission and evolution process of solitary waves, this paper puts forward a PIV measurement platform for the fine flow field of solitary waves, which measures the velocity vector distribution, velocity scalar distribution and vorticity distribution of the flow field in the transmission, climbing deformation and crushing process of the two-dimensional solitary wave. Through this experiment, students have a more direct impression of the fluid phenomenon of transmission and deformation of solidarity waves and understand the principle and use of PIV technology. This experiment deepens students’ understanding of the knowledge, stimulates students’ interest in learning, cultivates their practical ability, improves their innovation ability, and lays a good foundation for future scientific research and exploration.