Dual-view flow field imaging tracking apparatus, method and system based on chemical fluid signals

Abstract

The present disclosure relates to a double-view flow field imaging tracking device, method and system based on chemical fluid signal, comprising: an active motion unit, comprising an active sheet with catalytic active asymmetric structure, the active sheet is placed on the gas-liquid contact surface of the fluid environment; a fluid environment unit for containing fluid medium containing tracer particles; a signal tracking unit, comprising an LED cold face light source, a top view camera and a side view camera, the LED cold face light source irradiates the tracer particles to collect the distribution image of the surrounding flow field; the top view camera and the side view camera collect the motion trajectory of the active sheet and the distribution image of the surrounding flow field; a data processing unit is used to extract the motion parameters of the active sheet and the fluid dynamics signal from the motion trajectory and the distribution image of the surrounding flow field, calculate the fluid driving force according to the flow field velocity distribution, and establish the space-time correspondence between the fluid dynamics signal, the motion parameters and the fluid driving force. The present disclosure realizes the synchronous tracking of the fluid dynamics signal and the active substance motion.

Description

Technical Field

[0001] This disclosure relates to the field of fluid measurement technology, and in particular to a dual-view flow field imaging tracking device, method and system based on chemical fluid signals. Background Technology

[0002] Active motion refers to the phenomenon where an active carrier converts chemical energy or biological energy into mechanical motion, achieving autonomous propulsion or behavioral response. Chemically signal-driven active motion achieves energy conversion and autonomous propulsion through the consumption of chemical fuels. Its motion behavior is dominated by local chemical concentration gradients, chemical mechanical force feedback, or fluid dynamics feedback, and can respond to environmental changes without external intervention. However, the driving mechanism of chemical fluid signal-driven active motion is currently unclear, mainly because the correspondence between fluid signals and motion behavior has not yet been established, including the relationship between fluid direction and velocity and the direction, velocity, and driving force of the active substance, as well as the correspondence between reaction kinetics and fluid dynamics and motion laws.

[0003] Current research on chemical fluid dynamics-driven active motion mostly records the trajectory of active carriers or tracks flow field velocity from a single perspective. Existing tracking systems generally suffer from the following technical shortcomings: there is a gap in the analysis and identification of chemical fluid signals, and the tracking of active carriers remains at the level of trajectory and velocity analysis; flow field capture technologies such as 3D particle image velocimetry (3D-PIV) are mostly used for macroscopic engineering applications, while micro-particle image velocimetry (Micro-PIV) and digital holographic microscopy, although capable of capturing microscopic fluid-solid contact structures, are difficult to track the continuous macroscopic motion of carriers and are also very expensive; the capture of fluid dynamics focuses only on single parameters such as flow velocity and pressure, lacking signal analysis and motion characteristic analysis, and cannot achieve linkage analysis and quantitative characterization between signals, motion, and driving forces.

[0004] In summary, current technologies still lack a deep understanding of the coupling mechanism between chemical fluid dynamics signals and moving entities. Specifically, this manifests in several ways: poor spatiotemporal signal synchronization and acquisition, making it difficult to simultaneously acquire motion and chemical fluid signals under low flow conditions; inadequate fluid signal analysis, failing to achieve full-domain synchronous tracking of the three-dimensional motion state of the active carrier and surrounding fluid dynamics signals; a lack of an integrated signal and motion analysis module, hindering the coordinated quantitative analysis and visualization of signal characteristics, motion characteristics, and driving forces; and the fact that existing systems are often designed for specific active carriers, exhibiting poor compatibility, and that the lasers used in conventional PIV systems can damage active materials, causing photothermal flow effects and limiting their application scope. Summary of the Invention

[0005] To address the problem that existing systems mostly focus on measuring single signals and lack systematic tracking and analysis of the bidirectional coupling relationship between signals and motion, this disclosure proposes a dual-view flow field imaging and tracking device based on chemical fluid signals to solve the above problems.

[0006] According to one aspect of this disclosure, a dual-view flow field imaging and tracking device based on chemical fluid signals is provided, comprising: An active motion unit includes an active sheet having a catalytically active asymmetric structure, the active sheet being placed at a gas-liquid contact surface in a fluid environment; A fluid environment unit is used to contain a fluid medium and provide a motion environment for the active sheet, wherein the fluid medium contains tracer particles; The signal tracking unit includes an LED cold surface light source, a top-view camera, and a side-view camera. The LED cold surface light source is used to illuminate tracer particles in the fluid medium to acquire images of the distribution of the surrounding flow field. The top-view camera and the side-view camera are used to simultaneously acquire the motion trajectory of the active sheet and the distribution images of the surrounding flow field, respectively. The data processing unit, connected to the signal tracking unit, is used to extract the motion parameters and fluid dynamics signals of the active sheet from the motion trajectory of the active sheet and the distribution image of the surrounding flow field, respectively, calculate the fluid driving force on the active sheet based on the flow field velocity distribution in the fluid dynamics signal, and establish the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet, and the fluid driving force.

[0007] Preferably, the active sheet is an MXene material sheet, half of which is covered with catalase and the other half is uncovered, forming an asymmetric structure of catalytic activity.

[0008] Preferably, the top-view camera and the side-view camera are a first CCD camera and a second CCD camera, respectively, and the second CCD camera is equipped with a zoom lens.

[0009] According to one aspect of this disclosure, a dual-view flow field imaging and tracking method based on chemical fluid signals is provided, comprising: S10. Acquire the motion trajectory of the active sheet and the distribution image of the surrounding flow field. The motion trajectory of the active sheet and the distribution image of the surrounding flow field are acquired simultaneously by a top-view camera and a side-view camera, respectively. S20. Extract the motion parameters and fluid dynamics signals of the active sheet from the motion trajectory and the distribution images of the surrounding flow field of the active sheet, respectively; S30. Based on the fluid-structure interaction mechanics model, calculate the fluid driving force on the active sheet according to the flow field velocity distribution in the fluid dynamics signal; S40. Establish the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet, and the fluid driving force, and generate a visual output result.

[0010] Preferably, extracting the fluid dynamics signal from the distribution image of the surrounding flow field includes: Obtain the velocity distribution of the flow field from the surrounding flow field distribution image; Based on the flow field velocity distribution, a fluid dynamic signal propagating along the direction of motion is identified, and the fluid dynamic signal is a pulse wave signal.

[0011] Preferably, the fluid driving force is expressed as: , In the formula, Driven by fluid force, p For pressure, I For unit tensors, For dynamic viscosity, The gradient of the velocity field, This is the transpose of the gradient of the velocity field. n It is the unit normal vector.

[0012] Preferably, establishing the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet, and the fluid driving force includes: The fluid dynamics signal at the same moment is time-aligned with the motion parameters of the active sheet to obtain the correspondence between the fluid dynamics signal and the motion parameters in the time dimension; By spatially matching the spatial distribution characteristics of the fluid dynamics signal with the spatial location of the active sheet, the correspondence between the fluid dynamics signal and the motion parameters in the spatial dimension is obtained. The curve of the fluid driving force changing over time is compared with the fluid dynamics signal to analyze the periodic variation characteristics and phase relationship between the fluid driving force and the fluid dynamics signal.

[0013] According to one aspect of this disclosure, a dual-view flow field imaging and tracking system based on chemical fluid signals is provided, comprising: The image acquisition module acquires images of the motion trajectory of the active sheet and the distribution of the surrounding flow field. The motion trajectory of the active sheet and the distribution of the surrounding flow field are acquired simultaneously by a top-view camera and a side-view camera, respectively. The signal extraction module extracts the motion parameters and fluid dynamics signals of the active sheet from the motion trajectory of the active sheet and the distribution image of the surrounding flow field, respectively. The driving force calculation module, based on the fluid-structure interaction mechanics model, calculates the fluid driving force on the active sheet according to the flow field velocity distribution in the fluid dynamics signal; The visualization output module establishes the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet, and the fluid driving force, and generates visualization output results.

[0014] According to one aspect of this disclosure, an electronic device is provided, comprising: a processor; a memory for storing processor-executable instructions; wherein the processor is configured to execute the above-described dual-view flow field imaging and tracking method based on chemical fluid signals.

[0015] According to one aspect of this disclosure, a computer-readable storage medium is provided that stores a computer program / instructions and a bit stream thereon, wherein the computer program / instructions, when executed by a processor, implement the above-described dual-view flow field imaging and tracking method based on chemical fluid signals to generate the bit stream.

[0016] Compared to the prior art, the beneficial effects of this disclosure are as follows: 1) This disclosure uses a dual-optical-path imaging system to simultaneously acquire images of the motion trajectory of the active sheet and the distribution of the surrounding flow field at different scales, establishes the correspondence between fluid dynamics signals and motion parameters, and realizes the synchronous capture and correlation analysis of fluid dynamics signals and the motion of the active sheet.

[0017] 2) This disclosure can accurately calculate the fluid driving force on the active sheet by measuring the flow field signal of the fluid-structure interaction interface, overcoming the shortcomings of the prior art that cannot perform linkage quantitative analysis of signal characteristics, motion characteristics and driving force.

[0018] 3) This disclosure uses an LED cold surface light source, which is suitable for capturing one-dimensional pulse signals and has the advantages of low cost, high reliability and strong scalability.

[0019] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0020] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0021] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this disclosure and, together with the specification, serve to illustrate the technical solutions of this disclosure.

[0022] Figure 1 A schematic diagram of the experimental setup for the synchronous dual-optical-path imaging active motion tracking system in this embodiment is shown. Figure 2 A flowchart of a dual-view flow field imaging and tracking method based on chemical fluid signals is shown in an embodiment of this disclosure; Figure 3 A flowchart illustrating the verification method for the driving effect of chemical fluid signals on MXene active sheets is shown. Figure 4 The chemical fluid dynamics signal and the resulting motion curve of the active sheet are shown in the example of this disclosure; Figure 5 A schematic diagram illustrating the correlation analysis results between chemical fluid pulse signals and active motion in an example of this disclosure is shown; Figure 6 A schematic diagram of the viscous shear-driven resultant force of an active thin sheet quantized by chemical fluid signal is shown in an example of this disclosure; Figure 7 A block diagram of a dual-view flow field imaging and tracking system based on chemical fluid signals according to an embodiment of this disclosure is shown. Detailed Implementation

[0023] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0024] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0025] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0026] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0027] To make the objectives, technical solutions, and advantages of the embodiments of this disclosure clearer, the technical solutions of the embodiments of this disclosure will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are some embodiments of this disclosure, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of this disclosure without creative effort are within the scope of protection of this disclosure.

[0028] Based on the above ideas, this disclosure proposes a dual-view flow field imaging and tracking device based on chemical fluid signals. Figure 1 A schematic diagram of an experimental setup for a synchronous dual-optical-path imaging active motion tracking system is shown. The setup includes: The active motion unit includes an active sheet 40 with a catalytically active asymmetric structure, the active sheet 40 being placed at a gas-liquid contact surface in a fluid environment; A fluid environment unit is used to contain a fluid medium and provide a motion environment for the active sheet 40, wherein the fluid medium contains tracer particles; The signal tracking unit includes an LED cold surface light source 30, a top-view camera, and a side-view camera. The LED cold surface light source 30 is used to illuminate tracer particles in the fluid medium to acquire images of the distribution of the surrounding flow field. The top-view camera and the side-view camera are used to simultaneously acquire the motion trajectory of the active sheet 40 and the distribution images of the surrounding flow field, respectively. The data processing unit, connected to the signal tracking unit, is used to extract the motion parameters and fluid dynamics signals of the active sheet 40 from the motion trajectory of the active sheet 40 and the distribution image of the surrounding flow field, respectively, calculate the fluid driving force on the active sheet 40 based on the flow field velocity distribution in the fluid dynamics signal, and establish the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet 40, and the fluid driving force.

[0029] The active motion unit includes an active sheet 40 with a catalytically active asymmetric structure, the active sheet 40 being placed at a gas-liquid contact surface in a fluid environment.

[0030] In one embodiment, an asymmetrically modified MXene rectangular sheet is used as the active sheet 40, with half of its area covered by catalase and the other half uncovered, forming an asymmetric structure for catalytic activity. This active sheet 40 is prepared by a mask-assisted vacuum filtration method, with dimensions of 2 × 5 mm² and a thickness of approximately 10 μm.

[0031] A fluid environment unit is provided for containing a fluid medium and for providing a motion environment for the active sheet 40, wherein the fluid medium contains tracer particles.

[0032] In this embodiment, the active sheet 40 is placed at the gas-liquid contact surface in a reaction tank containing an H2O2 solution with a concentration of 20 mM. The reaction tank dimensions are 76 × 76 × 5 mm³, ensuring minimal boundary effects. Due to its thin thickness and large specific surface area, the sheet can be stably suspended at the interface under the balance of buoyancy and gravity. The catalytic reaction occurring in the region of the active sheet 40 causes a gradient change in local solution density, which in turn triggers fluid flow. This flow acts on the active sheet 40 and drives its linear motion.

[0033] The signal tracking unit includes an LED cold surface light source 30, a top-view camera, and a side-view camera. The LED cold surface light source 30 is used to illuminate tracer particles in the fluid medium to acquire images of the distribution of the surrounding flow field. The top-view camera and the side-view camera are used to simultaneously acquire the motion trajectory of the active sheet 40 and the distribution images of the surrounding flow field, respectively.

[0034] In this embodiment, a dual-path CCD camera imaging system is used to simultaneously record the motion trajectory of the active sheet 40 and the distribution images of the surrounding flow field. The dual-path CCD camera imaging system includes a top-view camera and a side-view camera. The top-view camera and the side-view camera simultaneously acquire images of the motion trajectory of the active sheet 40 and the distribution images of the surrounding flow field. The top-view camera and the side-view camera are respectively a first CCD camera 10 and a second CCD camera 20. The first CCD camera 10 and the second CCD camera 20 are required to have a frame rate ≥30 fps and a resolution ≥1920*1280 to ensure imaging accuracy. The side-view camera (second CCD camera 20) uses a zoom lens to capture the microscopic fluid motion profile, with a magnification of 0.1X-1.8X. At the same time, an LED cold surface light source 30 is used to enhance particle contrast, requiring an illumination intensity ≥1 mW / cm², no heat radiation, and to avoid fluid disturbance. The tracer particles are polystyrene microspheres with an optimal particle size of 10 μm. The propagation of velocity signals is observed by analyzing the velocity distribution near the active sheet 40.

[0035] The data processing unit, connected to the signal tracking unit, is used to extract the motion parameters and fluid dynamics signals of the active sheet 40 from the motion trajectory of the active sheet 40 and the distribution image of the surrounding flow field, respectively, calculate the fluid driving force on the active sheet 40 based on the flow field velocity distribution in the fluid dynamics signal, and establish the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet 40, and the fluid driving force.

[0036] In this embodiment, various signals such as pressure, temperature, and flow rate are ubiquitous in the complex fluid environment. The fluid dynamics signal (chemical fluid signal) is represented by the velocity distribution of the flow field in the region adjacent to the active sheet 40 along the motion profile of the active sheet 40, i.e., the velocity distribution on the horizontal plane where the lower surface of the active sheet 40 is located in the XZ plane during one-dimensional linear motion. The motion trajectory data acquired by the first CCD camera 10 is processed using Fiji software, and the distribution image information of the surrounding flow field acquired by the second CCD camera 20 is analyzed using PIVlab software. The chemical fluid signal and the motion of the active sheet 40 are visualized synchronously, and the fluid driving force is quantified. The correspondence between the direction of the chemical fluid signal and the direction of motion of the active sheet 40, the numerical relationship between the velocity of the chemical fluid signal and the velocity of the active sheet 40, and the correspondence between dynamics and the resultant force of fluid dynamics and driving motion are further analyzed.

[0037] Based on the above ideas, this disclosure proposes a dual-view flow field imaging and tracking method based on chemical fluid signals. Figure 2 A flowchart illustrating a dual-view flow field imaging and tracking method based on chemical fluid signals is shown. The method includes: S10. Acquire the motion trajectory of the active sheet 40 and the distribution image of the surrounding flow field. The motion trajectory of the active sheet 40 and the distribution image of the surrounding flow field are acquired simultaneously by a top-view camera and a side-view camera, respectively. S20. Extract the motion parameters and fluid dynamics signals of the active sheet 40 from the motion trajectory and the distribution images of the surrounding flow field of the active sheet 40, respectively. S30. Based on the fluid-structure interaction mechanics model, calculate the fluid driving force on the active sheet 40 according to the flow field velocity distribution in the fluid dynamics signal; S40. Establish the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet 40, and the fluid driving force, and generate a visual output result.

[0038] This disclosure further extends the above-described method with detailed possible implementations, specifically including: S10. Acquire the motion trajectory of the active sheet 40 and the distribution image of the surrounding flow field. The motion trajectory of the active sheet 40 and the distribution image of the surrounding flow field are acquired simultaneously by a top-view camera and a side-view camera, respectively.

[0039] In one embodiment, such as Figure 1As shown, MXene sheets with asymmetric catalase coating were prepared: MXene sheets of 2×5 mm² and 10 μm thickness were prepared by vacuum filtration, and the enzyme was programmatically coated using a masking method. For the one-dimensional motion design, half of the area of ​​a rectangular actuator was covered with catalase, while the other half was uncovered. A 10 mM hydrogen peroxide solution was injected into the experimental chamber module, and polystyrene microsphere tracer particles with a particle size of 10 μm were added. The MXene sheet was placed at the gas-liquid interface of the chamber. The experiment was conducted in a square container (76 mm × 76 mm × 5 mm) filled with a 10 mM H₂O₂ aqueous solution.

[0040] The synchronous control module sets the shooting frame rate of two CCD cameras (first CCD camera 10 and second CCD camera 20) to 30 fps. One camera captures the top-view (XY) trajectory, while the other zoom lens (0.1X-1.8X) camera records the side-view (XZ) profile for flow field analysis. Its focal plane is set near the active sheet 40 on the XZ and XY planes. The spatial position and scale are pre-calibrated before the experiment, and then synchronous monitoring is performed. LED cold surface light source 30 (1 mW / cm²) is used. 2 This is used for side illumination to enhance particle contrast. This configuration also provides background illumination for the top-view high-speed camera, thus avoiding the use of lasers and preventing photothermal effects.

[0041] S20. Extract the motion parameters and fluid dynamics signals of the active sheet 40 from the motion trajectory and the distribution image of the surrounding flow field of the active sheet 40, respectively.

[0042] Extracting the fluid dynamics signal from the distribution image of the surrounding flow field includes: obtaining the flow field velocity distribution from the distribution image of the surrounding flow field; and identifying the fluid dynamics signal propagating along the direction of motion based on the flow field velocity distribution, wherein the fluid dynamics signal is a pulse signal.

[0043] In this embodiment, the imaging data is processed to extract the centroid coordinates of the active sheet 40 and calculate its centroid displacement and velocity to obtain the directional motion characteristics of the active sheet 40 from the enzyme-coated side to the uncoated side; the signal data is processed to extract the XZ plane flow field data of the one-dimensional linear motion and find that the one-dimensional motion signal propagates in the form of pulse waves.

[0044] S30. Based on the fluid-structure interaction mechanics model, calculate the fluid driving force on the active sheet 40 according to the flow field velocity distribution in the fluid dynamics signal.

[0045] In this embodiment, the fluid driving force is driven by the viscous shear force of the fluid, and the fluid driving force is expressed as: , In the formula, Driven by fluid force, p For pressure, I For unit tensors, For dynamic viscosity, The gradient of the velocity field, This is the transpose of the gradient of the velocity field. n It is the unit normal vector.

[0046] Considering the force characteristics of the active sheet, the above fluid driving force formula is simplified. The total fluid driving force on the active sheet is the integral of the viscous shear force at its fluid-solid contact surface, specifically expressed as: , In the formula, for t Time Driver x The total fluid force acting in the direction (i.e., the core force driving the actuator). A For fluid-solid contact surface area, W For the width of the driver, , The driver is in x The coordinates of the left and right boundaries of the direction for t time x The viscous shear force component of the fluid acting on the actuator at the location (is) (core component in the shear direction) L This is the length of the driver.

[0047] The length of the driver is expressed as: , The viscous shear force component is expressed as: , In the formula, , Fluids in space x direction, z The velocity component in the direction (acquired and output by the data processing unit of the second CCD camera 20). for x directional flow velocity at z Gradient of direction, for z directional flow velocity at x Gradient of direction.

[0048] Based on boundary layer theory, The value of the item is much smaller than The term can be ignored, and further forward difference method is adopted, utilizing different zActual measurement at height Value calculated Combined with measured dynamic viscosity Finally, the viscous shear force was obtained. By integrating the shear force over the contact surface of the actuator, the total hydrodynamic force driving the actuator can be obtained. The relevant numerical calculation process is implemented using MATLAB code.

[0049] S40. Establish the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet 40, and the fluid driving force, and generate a visual output result.

[0050] Establishing the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet 40, and the fluid driving force includes: aligning the fluid dynamics signal at the same moment with the motion parameters of the active sheet 40 in time to obtain the correspondence between the fluid dynamics signal and the motion parameters in the time dimension; spatially matching the spatial distribution characteristics of the fluid dynamics signal with the spatial position of the active sheet 40 to obtain the correspondence between the fluid dynamics signal and the motion parameters in the spatial dimension; and comparing the curve of the fluid driving force changing with time with the fluid dynamics signal to analyze the periodic change characteristics and phase relationship between the fluid driving force and the fluid dynamics signal.

[0051] In this embodiment, the method for verifying the driving effect of the chemical fluid signal on the MXene active sheet 40 is as follows: The method involves matching the position of the chemical fluid signal at the same moment with the position of the centroid of the active sheet and the relationship with the driving force. Figure 3As shown in the diagram, firstly, the MXene active sheet 40 is driven by a chemical fluid signal to generate active motion, and dual-view data is collected simultaneously. One type of data is the chemical fluid signal data, including fluid direction, flow velocity, and signal velocity; the other type is the MXene active sheet 40 data, including motion behavior, centroid position, and instantaneous velocity. Subsequently, the two types of data are aligned and normalized over time, and spatiotemporal correlation matching is performed to establish the correspondence between the fluid pulse position and the sheet centroid motion position. Then, the correspondence is verified from multiple dimensions, including verifying the co-directionality of the chemical fluid signal and the sheet motion direction, the correlation between the signal velocity and the sheet motion velocity and fluid driving force, and the consistency between the experimental fluid driving force and the simulated fluid driving force. If the verification is successful, the chemical fluid signal has a driving effect on the MXene active sheet 40. If the verification fails, the experimental conditions are adjusted, and the experiment and analysis are repeated from the time axis alignment and normalization steps until the verification is successful. Through dual-view synchronous acquisition, spatiotemporal correlation matching, and multi-dimensional verification, the precise quantification and scientific verification of the driving effect of chemical fluid signals on MXene active sheet 40 were achieved, providing reliable technical methods and theoretical basis for the application of active materials in micro-nano drive, flexible actuators and other fields.

[0052] The visualization output module displays in real time the spatiotemporal development of the three-dimensional directional motion trajectory of the active MXene sheet and the flow field velocity. After the experiment, the imaging and signal data are played back simultaneously to analyze the correspondence between the pulse wave and the motion of the active sheet, and to obtain motion characteristic parameters, signal characteristic parameters, correlation analysis results, and model fitting comparison results.

[0053] The tracking system of this disclosure successfully captured the drive signal and directional motion trajectory of the active sheet actuator. Based on the active motion induced by the asymmetric design, a high-speed camera was used to track the linear motion of the one-dimensional system on the focal plane. Figure 4 As shown, the spatial distribution of chemical fluid signals at different times evolves over time, with both the chemical signals and the active thin films shifting in the negative direction. Figure 4 (a) and Figure 4 (b) shows the experimental and simulation results of the chemical fluid dynamics signal and the resulting active sheet motion, respectively. In the one-dimensional system, the tracking system captured the pulse signal moving with the active sheet 40 in space quite well, and the results were consistent with the simulation results.

[0054] Furthermore, the spatial relationship between the one-dimensional pulse signal and the motion of the center of mass was quantified. Figure 5 ), Figure 5 In the diagram, (a) represents the co-current motion of the experimental pulse signal (red) and the centroid motion (black); Figure 5 In the diagram, (c) represents the co-current motion of the simulated pulse signal (red) and the centroid motion (black); Figure 5(b) and Figure 5 (d) in the figure represents the experimental and simulation results of the corresponding centroid movement velocity. It was found that the motion of the MXene sheet driven by the chemical fluid dynamics signal is a type of wave motion, driving its directional motion. During this process, the motion velocity of the active sheet 40 exhibits oscillations, which is due to the coupling effect of the chemical fluid signal and the active motion, and the changes in signal and centroid velocity are synchronized within each driving cycle.

[0055] The fluid driving force for this directional motion is also quantified, such as... Figure 6 As shown, the changes in fluid driving force and the signal also exhibit periodic changes. Figure 6 (a) and Figure 6 (b) shows the experimental and simulation results of the viscous shear fluid driving force of the active sheet, respectively. Due to the spatial distance delay, the signal and the fluid driving force are out of phase. The correspondence above realizes a joint quantitative analysis of chemical fluid signals and active motion and driving force.

[0056] The MATLAB code for the experimental quantitative solution of the fluid-driven force on the active sheet 40 is as follows: clear; clc % Parameter settings h = 0.0002; % u / The solution for the height variation (m) is as follows: mu = 1.789e-3; % Dynamic viscosity (Pa·s) W = 0.002; % Active tablet width (m) dx = 0.0001; % Integral step size in the x-direction (m) L_half = 0.0025; % Driver half length (m) % File path path_centroid = 'centroid.txt'; path_velocity = 'speed.txt'; path_u1 = 'Export u1 with a fixed height of z=h using PIVlab'; path_u2 = 'PIVlab exports fixed height u2 at z=2h'; % Read centroid and velocity data centroid_data = load(path_centroid); % Two columns: Time (s) and Coordinates (mm) velocity_data = load(path_velocity); % Two columns: time (s) velocity (m / s) Ensure the time columns are consistent and merge them (assuming strict time correspondence). times = centroid_data(:,1); % Get centroid time xc_mm = centroid_data(:,2); % Centroid coordinates (mm) Find the velocity at the corresponding time from the velocity data, and use linear interpolation (if the time does not match perfectly). u0 = interp1(velocity_data(:,1), velocity_data(:,2), times, 'linear','extrap'); % Pre-allocated result array Fx_results = zeros(length(times), 1); % Prepare a cell array to store detailed data (for final Excel output) % Header line detail_header = {'Time_s', 'Integration point x_m', 'Searchable u1_m_s', 'Searchable u2_m_s', ... 'gradient_1_s', 'shear force_Pa'}; detail_data = {}; % Initialization, subsequent appending % Loop through each time step for i = 1:length(times) t = times(i); xc = xc_mm(i) / 1000; % Convert to meters u0_t = u0(i); % Integral range xL = xc - L_half; xR = xc + L_half; % Generate integration points (51 points in total, 50 intervals) `x_points = linspace(xL, xR, 51)'; % Column vector` % Find the corresponding filenames u1 and u2 based on time t file_name_u1 = sprintf('PIVlab_Extr_Magnitude in m per s_%.1f.xls', t); file_name_u2 = sprintf('PIVlab_Extr_Magnitude in m per s_%.1f.xls', t); fullfile_u1 = fullfile(path_u1, file_name_u1); fullfile_u2 = fullfile(path_u2, file_name_u2); % File robustness (allowable error 0.1s) if ~exist(fullfile_u1, 'file') files_u1 = dir(fullfile(path_u1, 'PIVlab_Extr_Magnitude in mper s_*.xls')); times_avail = zeros(length(files_u1),1); for k = 1:length(files_u1) fname = files_u1(k).name; tokens = regexp(fname, '(\d+\.?\d*)', 'match'); if ~isempty(tokens) times_avail(k) = str2double(tokens{1}); end end [~, idx] = min(abs(times_avail - t)); file_name_u1 = files_u1(idx).name; fullfile_u1 = fullfile(path_u1, file_name_u1); fprintf('Time%.2f Not found exact u1 file, using the closest %.2f\n', t,times_avail(idx)); end if ~exist(fullfile_u2, 'file') files_u2 = dir(fullfile(path_u2, 'PIVlab_Extr_Magnitude in mper s_*.xls')); times_avail = zeros(length(files_u2),1); for k = 1:length(files_u2) fname = files_u2(k).name; tokens = regexp(fname, '(\d+\.?\d*)', 'match'); if ~isempty(tokens) times_avail(k) = str2double(tokens{1}); end end [~, idx] = min(abs(times_avail - t)); file_name_u2 = files_u2(idx).name; fullfile_u2 = fullfile(path_u2, file_name_u2); fprintf('Time%.2f Not found exact u2 file, using the closest %.2f\n', t,times_avail(idx)); end % Read Excel files u1 and u2 try data_u1 = readtable(fullfile_u1); data_u2 = readtable(fullfile_u2); catch warning('Failed to read file at time %.2f, skip this time', t); Fx_results(i) = NaN; continue? end % Extract x and velocity (assuming the first column is the x-coordinate and the fourth column is the velocity value) x1 = data_u1{:,1}; u1_vals = data_u1{:,4}; x2 = data_u2{:,1}; u2_vals = data_u2{:,4}; For each integration point x_i, find the closest u1 and u2. tau = zeros(size(x_points)); for j = 1:length(x_points) xi = x_points(j); % Find the closest value in x1 [~, idx1] = min(abs(x1 - xi)); u1_ij = u1_vals(idx1); [~, idx2] = min(abs(x2 - xi)); u2_ij = u2_vals(idx2); % Calculate gradient grad = (-3*u0_t + 4*u1_ij - u2_ij) / (2*h); tau(j) = mu * grad; % Store the detailed data of the current point into detail_data (store the numerical value directly to preserve precision). detail_data = [detail_data; ... {t, xi, u1_ij, u2_ij, grad, tau(j)}]; end Trapezoid method for integration I = (dx / 2) * (tau(1) + 2*sum(tau(2:end-1)) + tau(end)); Fx = W * I; Fx_results(i) = Fx; % Outputs the resultant force at this moment to the command window. fprintf('Time%.2f s, Resultant force Fx = %.6e N\n', t, Fx); end Save the summary results to Excel (store values ​​directly). summary_header = {'time_s', 'resultant force_N'}; summary_data = {}; for i = 1:length(times) summary_data = [summary_data; {times(i), Fx_results(i)}]; end summary_output = [summary_header; summary_data]; writecell(summary_output, 'Result of resultant force calculation.xlsx'); fprintf('The summary results have been saved to the resultant force calculation results.xlsx\n'); Save detailed process to Excel detail_output = [detail_header; detail_data]; writecell(detail_output, 'detailed calculation process.xlsx'); fprintf('Detailed calculation process has been saved to detailed calculation process.xlsx\n'); disp('Calculation complete!'); The dual-view flow field imaging and tracking method based on chemical fluid signals proposed in this disclosure simultaneously acquires images of the motion trajectory of the active sheet 40 and the distribution of the surrounding flow field using a top-view camera and a side-view camera. Motion parameters and fluid dynamics signals are extracted respectively, and the fluid driving force on the active sheet 40 is calculated based on the flow field velocity distribution using a fluid-structure interaction mechanics model. This establishes the spatiotemporal correspondence between the fluid dynamics signal, motion parameters, and fluid driving force, generating a visualized output. Without requiring complex fluid parameter pre-calibration, the method of this disclosure enables the spatiotemporal synchronous analysis of the center-of-mass motion of the active sheet 40, the fluid velocity distribution, and the resultant driving force in a microscale active motion scenario. It quantifies the evolution law of the driving force dominated by viscous shear force, and the experimental and simulation results show a high degree of agreement. Compared with traditional methods, the method of this disclosure has a significant advantage in the synchronous capture accuracy of the signal-motion coupling relationship, providing a more systematic, reliable, and low-cost technical approach for the study of chemical signal-driven mechanisms.

[0057] As another aspect of this disclosure, a dual-view flow field imaging and tracking system 100 based on chemical fluid signals is also provided, such as... Figure 7 As shown, it includes: Image acquisition module 1 acquires images of the motion trajectory of the active sheet 40 and the distribution of the surrounding flow field. The motion trajectory of the active sheet 40 and the distribution of the surrounding flow field are acquired simultaneously by a top-view camera and a side-view camera, respectively. The signal extraction module 2 extracts the motion parameters and fluid dynamics signals of the active sheet 40 from the motion trajectory of the active sheet 40 and the distribution image of the surrounding flow field, respectively. The driving force calculation module 3, based on the fluid-structure interaction mechanics model, calculates the fluid driving force on the active sheet 40 according to the flow field velocity distribution in the fluid dynamics signal; The visualization output module 4 establishes the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet 40, and the fluid driving force, and generates visualization output results.

[0058] Without causing contradictions, the above-described modules in the system of the present disclosure embodiments can implement any of the above-described methods.

[0059] Based on the description of the above embodiments, it can be seen that the embodiments of this disclosure can achieve the following technical effects: 1) In this embodiment of the present disclosure, the motion trajectory of the active sheet 40 and the distribution images of the surrounding flow field are simultaneously acquired at different scales by a dual-optical-path imaging system, and the correspondence between fluid dynamics signals and motion parameters is established, thereby realizing the synchronous capture and correlation analysis of fluid dynamics signals and the motion of the active sheet 40.

[0060] 2) By measuring the flow field signal of the fluid-structure interaction surface, this embodiment of the present disclosure can accurately calculate the fluid driving force on the active sheet 40, overcoming the defect of the prior art that cannot perform linkage quantitative analysis of signal characteristics, motion characteristics and driving force.

[0061] 3) The embodiments disclosed herein use LED cold surface light source 30, which is suitable for capturing one-dimensional pulse signals and has the advantages of low cost, high reliability and strong scalability.

[0062] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured for the aforementioned dual-view flow field imaging and tracking method based on chemical fluid signals. The electronic device can be provided as a terminal, a server, or other form of device.

[0063] This disclosure also proposes a computer-readable storage medium storing a computer program / instructions and a bitstream thereon. When the computer program / instructions are executed by a processor, they implement the aforementioned dual-view flow field imaging and tracking method based on chemical fluid signals to generate the bitstream. The computer-readable storage medium can be a non-volatile computer-readable storage medium.

[0064] Those skilled in the art will understand that, in the above-described dual-view flow field imaging and tracking method and system based on chemical fluid signals in specific embodiments, the order in which each step is written does not imply a strict execution order and does not constitute any limitation on the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0065] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of an instruction containing one or more executable instructions for implementing a specified logical function. In some alternative implementations, the functions marked in the blocks may occur in a different order than those shown in the drawings. For example, two consecutive blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, may be implemented using a dedicated hardware-based system that performs the specified function or action, or using a combination of dedicated hardware and computer instructions.

[0066] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or technical improvements to the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A dual-view flow field imaging and tracking device based on chemical fluid signals, characterized in that, include: An active motion unit includes an active sheet having a catalytically active asymmetric structure, the active sheet being placed at a gas-liquid contact surface in a fluid environment; A fluid environment unit is used to contain a fluid medium and provide a motion environment for the active sheet, wherein the fluid medium contains tracer particles; The signal tracking unit includes an LED cold surface light source, a top-view camera, and a side-view camera. The LED cold surface light source is used to illuminate tracer particles in the fluid medium to acquire images of the distribution of the surrounding flow field. The top-view camera and the side-view camera are used to simultaneously acquire the motion trajectory of the active sheet and the distribution images of the surrounding flow field, respectively. The data processing unit, connected to the signal tracking unit, is used to extract the motion parameters and fluid dynamics signals of the active sheet from the motion trajectory of the active sheet and the distribution image of the surrounding flow field, respectively, calculate the fluid driving force on the active sheet based on the flow field velocity distribution in the fluid dynamics signal, and establish the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet, and the fluid driving force.

2. The apparatus according to claim 1, characterized in that, The active sheet is a thin sheet of MXene material, with one half of its surface covered with catalase and the other half uncovered, forming an asymmetric structure of catalytic activity.

3. The apparatus according to claim 1, characterized in that, The top-view camera and the side-view camera are respectively a first CCD camera and a second CCD camera, and the second CCD camera is equipped with a zoom lens.

4. A dual-view flow field imaging and tracking method based on chemical fluid signals, characterized in that, include: S10. Acquire the motion trajectory of the active sheet and the distribution image of the surrounding flow field. The motion trajectory of the active sheet and the distribution image of the surrounding flow field are acquired simultaneously by a top-view camera and a side-view camera, respectively. S20. Extract the motion parameters and fluid dynamics signals of the active sheet from the motion trajectory and the distribution images of the surrounding flow field of the active sheet, respectively; S30. Based on the fluid-structure interaction mechanics model, calculate the fluid driving force on the active sheet according to the flow field velocity distribution in the fluid dynamics signal; S40. Establish the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet, and the fluid driving force, and generate a visual output result.

5. The method according to claim 4, characterized in that, Extracting the fluid dynamics signal from the distribution image of the surrounding flow field includes: Obtain the velocity distribution of the flow field from the surrounding flow field distribution image; Based on the flow field velocity distribution, a fluid dynamic signal propagating along the direction of motion is identified, and the fluid dynamic signal is a pulse wave signal.

6. The method according to any one of claims 4 or 5, characterized in that, The fluid driving force is expressed as: ， In the formula, Driven by fluid force, p For pressure, I For unit tensors, For dynamic viscosity, The gradient of the velocity field, This is the transpose of the gradient of the velocity field. n It is the unit normal vector.

7. The method according to any one of claims 4 or 5, characterized in that, Establishing the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet, and the fluid driving force includes: The fluid dynamics signal at the same moment is time-aligned with the motion parameters of the active sheet to obtain the correspondence between the fluid dynamics signal and the motion parameters in the time dimension; By spatially matching the spatial distribution characteristics of the fluid dynamics signal with the spatial location of the active sheet, the correspondence between the fluid dynamics signal and the motion parameters in the spatial dimension is obtained. The curve of the fluid driving force changing over time is compared with the fluid dynamics signal to analyze the periodic variation characteristics and phase relationship between the fluid driving force and the fluid dynamics signal.

8. A dual-view flow field imaging and tracking system based on chemical fluid signals, characterized in that, include: The image acquisition module acquires images of the motion trajectory of the active sheet and the distribution of the surrounding flow field. The motion trajectory of the active sheet and the distribution of the surrounding flow field are acquired simultaneously by a top-view camera and a side-view camera, respectively. The signal extraction module extracts the motion parameters and fluid dynamics signals of the active sheet from the motion trajectory of the active sheet and the distribution image of the surrounding flow field, respectively. The driving force calculation module, based on the fluid-structure interaction mechanics model, calculates the fluid driving force on the active sheet according to the flow field velocity distribution in the fluid dynamics signal; The visualization output module establishes the spatiotemporal correspondence between the fluid dynamics signal, the motion parameters of the active sheet, and the fluid driving force, and generates visualization output results.

9. An electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the dual-view flow field imaging and tracking method based on chemical fluid signals as described in any one of claims 5 to 7.

10. A computer-readable storage medium storing a computer program / instructions and a bit stream thereon, characterized in that, When the computer program / instructions are executed by the processor, they implement the dual-view flow field imaging and tracking method based on chemical fluid signals as described in any one of claims 5 to 7 to generate the bit stream.

Images