789 results about "Model test" patented technology

The invention relates to the technical field of underground engineering groove wall stability physical simulation, and discloses a model test device and method for stratum excavation and grouting reinforcement, and the model test device comprises a grooving construction module, a grouting support module and a dynamic monitoring module. The device adopts a pressing plate method to simulate a grooving excavation process, so that the device conforms to an actual excavation working condition from top to bottom; a servo motor drives a power lead screw to drive a model groove to move downwards, so that a free face is formed in a sand layer, and the actual excavation working condition is simulated; a water tank for storing fresh slurry is pressurized by using an air compressor, so that the slurry overflows from the hollow model groove section, and the permeation and invasion process of the slurry is simulated; and a water and soil pressure gauge and a matched acquisition device are arranged in the sand layer, and a soil displacement field is obtained by utilizing a particle image velocity measurement system, so that the visualization of the whole process of excavation-grouting-invasion is realized. It is ensured that simulation results are highly matched with actual working conditions, and accuracy and reliability are improved.

The invention discloses a method and a system for predicting the lumpiness of open-air throwing blasting, and relates to the technical field of rock blasting engineering. The invention discloses a new calculation relation about a lumpiness similarity proportion, relates to the technical field of open-air loose blasting mining, and can predict blasting lumpiness of projects such as mines, tunnels and the like by scaling a lumpiness distribution proportion. The method comprises the following steps: firstly, acquiring a rock sample from a blasting site, measuring parameters such as rock density and fracture toughness, and acquiring blasting parameter information of a to-be-blasted area; and then, a numerical simulation model under an open-air throwing blasting similar material is constructed by using a numerical simulation method, the change rule of blasting fragmentation lumpiness along with explosive unit consumption in a scale model test is studied, and the blasting lumpiness in a field blasting area is predicted according to the blasting lumpiness similarity ratio. According to the method, the change rule along with the unit consumption of the explosive is analyzed, the utilization rate of crushing energy of the explosive in surface blasting engineering is improved, reference is provided for blasting lumpiness control, and prediction of field blasting lumpiness before blasting is achieved.

The invention discloses a multivariate monitoring data fusion method and system for a slope tunnel model test, particularly relates to the technical field of geotechnical engineering physical model test, and aims to solve the problem that dynamic stress waves in an existing model test integrating static and dynamic measurement cause static monitoring signal distortion and further influence the model state evaluation accuracy. Coupling interference in static signals is identified by synchronously collecting dynamic excitation and static response signals; obtaining signal samples before and after a model state sudden change event to perform frequency domain comparative analysis, identifying characteristic frequency components related to sudden change, and evaluating a coupling interference dominant frequency band; separating an effective static response component from the original static signal based on the frequency band; and finally, fusing the effective component with a soil dynamic parameter derived from dynamic excitation to generate comprehensive stability evaluation data. Dynamic interference is effectively suppressed, the authenticity of static signals is improved, and the reliability of model stability state evaluation is improved.

The invention provides a method and device for an intelligent computing center cloud platform to adjust model training parameters according to computing power operation states, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructures. The first working component is a component deployed in a node in the intelligent computing center cloud platform, the target link comprises a data preparation link, a model training link, a model evaluation link and / or a model test link in the first working component, and the target link performs data processing or model training based on preset hyper-parameters; s2, calculating target correction parameters corresponding to the operation related parameters in a preset period based on a preset regulation and control model; and S3, adjusting the preset hyper-parameter based on the target correction parameter to obtain a target hyper-parameter. The computing power utilization rate of the intelligent computing center cloud platform can be greatly improved, and then the model training efficiency is greatly improved.

The invention discloses a wave-current combined simulation system and method in a low-pressure environment, and relates to the technical field of underwater vehicle hydrodynamic model tests. The defects that in the prior art, sealing and strength are difficult to guarantee in a low-air-pressure environment, and accurate flow velocity and shear gradient control of a multi-layer flow channel cannot be achieved are overcome. According to the technical scheme, the device comprises a low-pressure pool, a plurality of layers of layered water inlet galleries, a plurality of layers of layered water outlet galleries, a rectifying wall, a rectifying baffle, a flow guide grid, a wave absorbing bank, an in-cabin rocking plate type wave maker, a plurality of bottom flow making pump flow making systems, an electric butterfly valve, a high-voltage frequency conversion cabinet, a three-phase asynchronous motor, a gear box, an impeller shaft system and the like. All the devices work cooperatively, and accurate coupling simulation of a multi-layer flow field and high sea condition waves under low air pressure is achieved. The device is suitable for wave-current combined hydrodynamic characteristic simulation and test research of deep-sea navigation bodies and ocean engineering equipment in a low-pressure environment.

The invention discloses a multi-parameter distortion compensation method for a fractured rock mass seepage similar model test, and relates to the technical field of rock mass engineering physical model tests. The method comprises the following steps of: firstly, determining two constraint conditions that the fluid property is invariable and the gravitational acceleration is invariable, correspondingly selecting a basic physical quantity and extracting a core criterion; then analyzing three types of distortion sources of geometric dimensions, dielectric materials and boundary conditions, and establishing a distortion function according to result parameters of seepage flow velocity, seepage flow and seepage pressure; secondly, parameters difficult to control are selected as distortion items, parameters easy to regulate and control are selected as compensation items, the distortion items are substituted into a distortion function to obtain a distortion ratio, the optimal distortion ratio of the compensation items is solved based on a compensation balance formula, and the parameters are adjusted; and finally verifying the interval range of the actually measured distortion ratio. According to the method, the multi-source distortion coupling influence is accurately quantified, the compensation pertinence and precision are improved, operation is easy and convenient, universality is high, and reliable support is provided for underground engineering seepage research.

The invention relates to a locomotive energy consumption prediction model training method and device. The method comprises the following steps: performing correlation analysis on a plurality of locomotive energy consumption influence indexes and sample locomotive energy consumption data, and constructing an acquired sample data sequence based on a correlation analysis result; the method comprises the following steps: performing data preprocessing on an acquired sample data sequence to obtain a model training data set and a model test data set; the whale optimization algorithm is adopted to optimize the initial model for locomotive energy consumption prediction, an optimized model is obtained, the optimized model is trained according to the model training data set, and a locomotive energy consumption prediction model is obtained; the initial model is constructed by combining a bidirectional long-short-term memory network of an attention mechanism; and obtaining an output prediction result based on the model test data set and the locomotive energy consumption prediction model, and carrying out reverse normalization processing on the output prediction result to obtain a test prediction result so as to verify the effectiveness of the locomotive energy consumption prediction model. By adopting the method, the accuracy and effectiveness of the locomotive energy consumption prediction model can be effectively improved.

The invention provides a WaveNet-BiLSTM-ATA oil well yield prediction method based on PSO optimization, and the method is characterized in that the method comprises the following steps: 1) collecting oil well time sequence characteristic data; 2) data cleaning and preprocessing; 3) executing data formatting and standardization operation to adapt to model test requirements; 4) dividing a training set and a test set according to oil well models; 5) screening important features of the oil well by adopting an isolated forest and a Pearson's correlation coefficient method, and constructing a daily oil production data set; 6) sequentially inputting the oil well characteristics in the step 5) into a WaveNet module and a BiLSTM module, introducing an ATA mechanism at the same time, and building a prediction model; 7) optimizing model hyper-parameters by using a PSO algorithm; and 8) carrying out grouping performance verification by adopting an independent test, completing model evaluation by calculating MAE, RMSE and MAPE indexes, and outputting a prediction result at the same time. According to the method, the WaveNet local feature extraction capability, the BiISTM bidirectional time sequence modeling advantage and the PSO efficient parameter optimization characteristic are fused, so that a relatively good prediction effect is obtained in a specific oil well test.

The invention discloses a boundary layer cushion block leveling method suitable for a wind tunnel half-mode test, relates to the field of experimental aerodynamics, and aims to reduce interference of wind tunnel wallboards in the half-mode test and improve assembly efficiency. The method comprises the following steps: S1, measuring corresponding data according to an established three-dimensional model of a boundary layer cushion block and a balance adapter; s2, based on the data in the step S1, the sizes of the first tool, the second tool, the third tool and the fourth tool are determined; s3, a corresponding first tool, a second tool, a third tool and a fourth tool are manufactured; s4, a balance adapter is installed in the wind tunnel test section, and a balance is measured; and S5, a leveling tool is used for assisting in leveling of the boundary layer cushion block. By adopting the method, the leveling tool is simple and convenient to apply, measurement parameters are few, and the mounting and leveling operation of the boundary layer base plate is effectively simplified; meanwhile, the leveling precision is high enough, the requirement for the installation precision of the boundary layer cushion block of the half-mold test can be met, and labyrinth groove collision is avoided.

The invention discloses a tunnel surrounding rock comprehensive elastic modulus testing method and system based on a surface wave method, and relates to the technical field of tunnel surrounding rock elastic model testing, and the method comprises the steps: collecting surface wave signals through detectors in a plurality of detection holes preset in a tunnel wall on the premise of exciting a seismic source signal; performing signal conversion on each preprocessed surface wave signal to obtain a plurality of first signals of a frequency domain type; performing smooth summation processing on the first signals subjected to the linear dynamic correction processing to obtain an output result; obtaining corresponding surface wave energy data through the output result, and obtaining a corresponding surface wave frequency dispersion curve based on extraction of the surface wave energy data; according to the surface wave dispersion curve, surface wave velocities of different depths in the surrounding rock are obtained through inversion, and the comprehensive elastic modulus of the tunnel surrounding rock is obtained through calculation based on the surface wave velocities; testing is carried out based on a surface wave method, drilling and coring are not needed, and the method has the advantages of high testing efficiency, low cost, small disturbance to surrounding rock and the like.

The invention discloses a ground penetrating radar pipeline intelligent inversion method based on multi-scale deep learning, and belongs to the technical field of ground penetrating radar pipeline data inversion. The method aims at solving the problems that a traditional inversion method is low in resolution, high in calculation complexity, depends on prior information and the like. The method comprises the specific steps that underground simulation model data sets are constructed in batches; forward modeling calculation is carried out based on time domain finite difference, and a matching data pair of the radar B-scan image and dielectric constant distribution is generated; constructing a multi-scale deep neural network comprising a residual attention module, a dual-path space attention module, a multi-scale feature fusion module and a reconstruction module; through end-to-end training and model test optimization, an optimal model is finally stored to realize inversion output from original radar data to dielectric constant distribution. According to the method, through a multi-scale feature extraction and fusion mechanism, the recognition precision of pipelines of different sizes is effectively improved, the inversion efficiency is greatly improved, and an efficient and accurate technical scheme is provided for underground pipeline detection.

The invention relates to the technical field of air temperature risk assessment, in particular to an AdaBoost-based urban air temperature inference and thermal exposure risk assessment method. The method comprises the following steps: acquiring original city form data and city meteorological data of a city; performing data preprocessing on the original city form and the city meteorological data of the city to generate processed original city form data and city meteorological data; integrating the processed original city form data and city meteorological data into a model training set and a model test set; and designing an artificial neural network architecture by adopting an AdaBoost algorithm, and performing model training on the model training set by utilizing the artificial neural network architecture to generate an urban air temperature inference pre-model. According to the method, by fusing multi-source data, applying an advanced machine learning algorithm and enhancing sensitivity analysis and Monte Carlo simulation, the accuracy and reliability of urban air temperature inference and thermal exposure risk assessment are improved.

The invention discloses a grouting splitting pressure simulation device under high-ground and high-water-pressure conditions, and relates to the technical field of tunnel and underground engineering model tests. A first water permeable unit controlled by the water injection unit and a second water permeable unit controlled by the confining pressure unit are respectively arranged at two ends in the length direction, so that water pressure and stratum confining pressure are independently applied. The integrated grouting unit of the device is used for grouting soil in the box and is provided with a comprehensive detection unit, the comprehensive detection unit monitors various parameters such as grouting pressure, water pressure, confining pressure, flow, soil resistance and wave velocity in real time, and independent loading and accurate dynamic control of the grouting pressure, formation water pressure and formation stress can be achieved; therefore, the grouting process of the deep-buried tunnel under the conditions of different burial depths, high water head differences and complex grout characteristics can be simulated in a laboratory with high precision, and the technical bottleneck that the complex environment of high ground stress and high water pressure coupling is difficult to reproduce in a traditional test method is effectively solved.

The invention relates to an oil and gas pipeline magnetic flux leakage detection defect quantification method based on a composite convolutional neural network. The method comprises the following steps: 1) a data acquisition stage: carrying out saturation magnetization detection on a pipeline by adopting a pipeline internal detector to obtain magnetic flux leakage internal detection data containing defects; 2) a data preprocessing stage: performing normalization processing on the magnetic flux leakage data, and constructing a defect quantification data set; 3) a model construction stage: constructing a composite convolutional neural network model; 4) a model training stage: inputting a defect quantification data set sample into the model for training; and 5) a model test stage: performing feedback and iterative optimization for multiple times through a back propagation optimization algorithm of the model, finally outputting optimal model parameters in the training process, and exporting and storing the model parameters. Based on the pipeline magnetic flux leakage detection data, high-precision quantification of pipeline defect size parameters can be realized, and the accuracy and reliability of defect evaluation can be improved.

The invention belongs to the technical field of surface collapse group simulation, and discloses a split mounting type surface collapse group simulation experiment system. The system comprises a supporting steel frame, a rainfall simulator, a split mounting type surface collapse model test box, a water level control box, an overflow device, a split mounting type karst pipeline, a split mounting type karst water cavity simulation box, a water supply box, a peristaltic pump and various monitoring devices. Through the split mounting type structural design, the number of collapse points, the distance, the cavity shape, the karst pipeline connectivity, the underground water level, the rainfall intensity and other key parameters can be flexibly adjusted, and multi-working-condition simulation of a surface collapse group is achieved; meanwhile, through cooperative work of various monitoring devices such as a laser displacement sensor and a pore water pressure sensor, multi-physical-quantity data in the collapse process are collected in real time. According to the invention, the problems of lack of surface collapse group simulation capability and poor parameter adjustability in the prior art are solved, and experimental support can be provided for research and prediction of the development mechanism of the surface collapse group.

The invention discloses a hyperspectral image classification method based on double-flow heterogeneous spatial feature collaboration. The method comprises the following steps: step 1, obtaining a hyperspectral image classification data set; step 2, designing an overall structure of a double-flow heterogeneous spatial feature collaborative modeling method (DHSFCM); step 3, setting experimental parameters; according to the hyperspectral image classification method based on double-flow heterogeneous spatial feature collaboration, noise reduction and feature enhancement are carried out through a spectrum transformation layer, then complementary features are extracted through a non-Euclidean feature extraction network (NEFEN) and an Euclidean feature extraction network (EFEN), and the hyperspectral image classification method based on double-flow heterogeneous spatial feature collaboration is obtained. And effective feature integration and interaction are realized by means of a cross attention fusion module (CAFM), and finally a classification result is output. According to the technology, accurate classification support can be provided for intelligent agriculture, resource exploration and ecological monitoring, and a new thought with engineering landing value is provided for intelligent analysis of hyperspectral images.

The invention provides a lightweight design method, device and equipment for an intelligent robot with a body and a storage medium, and relates to the technical field of artificial intelligence. According to the method, the scene simulation environment is constructed by collecting the task scene parameters, the robot three-dimensional model is constructed based on the lightweight design scheme, the test cases are generated according to the task requirements, the model test parameters are collected, and comprehensive evaluation and diagnosis of the initial lightweight design scheme of the robot are achieved. Through collection, evaluation and diagnosis of model test parameters, a design scheme is continuously optimized, and the reliability of robot performance is improved; through repeated testing and optimization evaluation, iterative optimization of a lightweight design scheme is realized, so that an optimal optimization design scheme is obtained, the lightweight design level of the intelligent robot with the body is remarkably improved, the convenience and adaptability of the specific intelligent robot are enhanced, and the intelligent robot with the body is more intelligent. And powerful technical support is provided for efficient application of the robot in complex task scenes in the financial and medical fields.

The invention discloses a water level automatic control device for a hydraulic model test, which comprises control units respectively arranged at the water level upstream and the water level downstream of a hydraulic model water tank, and is characterized in that each control unit comprises a door mounting frame, a gate, a threaded rotating rod, a height sensor, a control box, a water level meter and a track, the gate is rotationally connected with a threaded rotating rod, the threaded rotating rod penetrates through the mounting plate and is connected with the electric knob, the height sensor and the control box are mounted on the mounting plate, and the control box is connected with the water level meter through a signal transmission line. The control method comprises the following steps: S1, equipment installation; s2, debugging the device; s3, creating a test item; s4, water level closed-loop control; s5, confirming the water level; s6, collecting and recording data; and S7, completing test cycle monitoring. By adjusting the opening degree of the gate, it is ensured that upstream and downstream water levels are stabilized within the set range, and the problems that in the prior art, manual water level control is low in efficiency and poor in precision in the low-pressure environment are solved.

The invention relates to a tunnel state evaluation method based on a defective lining model test. The method comprises the following steps: constructing a test device, manufacturing a lining model and a surrounding rock model, starting a servo hydraulic loading device to simulate a surrounding rock load, simulating a train load by using an excitation device, and recording development conditions of diseases at different moments. And analyzing the collected sample data, constructing an analysis model based on a result obtained through analysis, judging the danger degree of lining diseases through the analysis model, speculating the residual life of the lining, and evaluating the state of the tunnel by adopting the residual life / reference life. The model test is closer to a complex disease scene of a real tunnel, the influence of different disease coupling effects on the lining life can be revealed, and a new method is provided for tunnel life prediction and state evaluation.

The invention discloses a pile sinking soil body response prediction regulation and control method based on a physical model, and relates to the technical field of geotechnical mechanics pile foundation engineering, and the method comprises the following steps: constructing an experimental model, arranging a sensor, and operating a loading device to enable the experimental model to sink a pile; according to the model pile sinking process, the soil body response is monitored in real time, and monitoring data are obtained; inverting the dynamic response of the soil body through the PINN inversion model, and coupling the DEM and the FEM to simulate and predict the response of the soil body; and risk control is carried out through a CEI early warning decision-making method. The device and the method are used for solving the problems of incomplete data acquisition, inaccurate simulation and lack of early warning in a traditional open tubular pile soil squeezing model test.

The invention relates to the technical field of soil engineering model tests, and discloses a test system and method for simulating water and mud bursting of a subsea tunnel under the action of vibration, and the system comprises a multi-degree-of-freedom servo feedback vibration module which is used for providing a multidirectional vibration loading platform and feeding back a motor current signal in real time; the visual rock-soil water inrush simulation and multi-field loading module is mounted above the vibration table and is used for accommodating a sample and applying confining pressure and water pressure load; the hierarchical multi-source data acquisition module is communicated with the discharge port and the observation window, and is used for acquiring the outburst mixture and monitoring physical field data; and the intelligent control and risk assessment analysis module is connected with each module to regulate and control loading parameters and invert a catastrophe state based on feedback data. According to the method, a self-adaptive optimization loading mechanism based on the improved particle swarm optimization algorithm is constructed, so that collaborative progressive regulation and control of pore water pressure and vibration frequency parameters are realized, and a critical threshold point of water inrush and mud inrush catastrophe can be accurately captured.

The invention relates to a development type single-phase earth fault real-model test device and a control method, the development type single-phase earth fault real-model test device is connected in a whole power grid and comprises a fault intelligent generation module, and the fault intelligent generation module comprises a dynamic resistance simulation unit and an intelligent switching control system. And the intelligent switching control system is used for controlling the access resistance value and the access time of the resistor in the dynamic resistance simulation unit. The dynamic resistance simulation unit structure is of a three-level resistance topology and is combined with a mixed switching matrix of a vacuum relay and a solid-state relay. The intelligent switching control system is used for realizing the switching of the resistor at a current zero crossing point, the intelligent switching control system comprises a low-power-consumption control circuit and a signal acquisition circuit, the structure is matched with the control method, the continuous or step dynamic change of the grounding resistor can be accurately simulated, the problem of transient impact in the resistor switching process is solved, and the stability of the grounding resistor is improved. And a standardized and quantifiable test platform is provided for performance assessment of power distribution network fault handling equipment.

A steel sleeve system structure for researching a shield receiving model test comprises lower barrels and upper barrels, the multiple sections of lower barrels and upper barrels are in butt joint and buckled together, arc-shaped butt joint edges are arranged on the edges of the adjacent lower barrels and upper barrels, horizontal supporting transverse rods and vertical supporting vertical rods are arranged in the lower barrels and the upper barrels respectively, and the arc-shaped butt joint edges are connected with the arc-shaped butt joint edges. The two ends of the supporting transverse rod are fixedly connected with the inner side wall of the corresponding lower barrel or the upper barrel respectively, one end of the supporting vertical rod is perpendicularly and fixedly connected with the middle of the supporting transverse rod, the other end of the supporting vertical rod is fixedly connected with the inner side wall of the corresponding lower barrel or the upper barrel, and the supporting bases are fixed to the side faces of the bottoms of the sections of lower barrels respectively. According to the utility model, through a model test, the steel sleeve is controlled to receive parameter control and matters needing attention at each stage, and the influence on an adjacent bridge pile structure and normal traffic in a shield receiving and tunneling process is reduced, so that the construction safety of shield receiving under complex conditions is ensured.

The invention discloses a dynamic random language input generation method based on large model architecture and training data features, and relates to the technical field of large model testing and natural language processing crossing. Comprising the steps of obtaining a target large model type, training data and a test scene demand, and performing large model-classical feature collaborative preparation; the noise tolerance is corrected by training data field concentration degree and grammar specification degree quantification in combination with a model type coefficient, and the confusion degree is finely adjusted according to a trigger rate; effective conflicts are screened by means of cross-modal consistency loss, and input effectiveness is verified in a three-dimensional mode through the trigger rate, the strength and the influence degree; calculating power and resource efficiency quantitative feedback iteration is carried out, stage adaptive input is generated based on time sequence features, and cross-task feature multiplexing is realized through task similarity; performing low-sensitive word disturbance and confrontation-random balance enhancement as required, verifying a result, and returning to a parameter adaptation link for readjustment if the result does not reach the standard; finally, high input adaptation and high-efficiency generation are realized, and the test is ensured to be accurate and efficient.

The invention relates to the technical field of ship manipulation and control, and discloses a self-propelled model test system for multi-tug collaborative operation simulation, and the system comprises at least one tug self-propelled model which is provided with a propulsion system and an inertial navigation integrated positioning system; the cargo ship self-propelled model is used as a guided target; and a shore-based control system. The shore-based control system exchanges data with each ship model in real time through a wireless communication network, receives ship model state information, calculates required total control force and torque based on expected movement of the cargo ship self-propelled model and a mathematical model, executes a self-adaptive collaborative thrust distribution strategy, distributes the total control requirement to each tug self-propelled model, and controls the ship model to move. And determining the target thrust magnitude and direction of each tug. According to the invention, high-precision cargo ship guidance can be realized in a simulated environment, the adaptability to the change of the simulated environment is enhanced, the potential of energy conservation and consumption reduction is explored, and an effective technical research and verification means is provided for actual complex port operation.

The application provides a local surge-induced adjacent reef seabed topography evolution model test device and test method, and the device comprises a soil box, a soil layer, a runner, a handle, a support, a fixing ring, a hose, an adjusting pipe, a pore pressure gauge, a displacement gauge and a partition plate. A constant pressure water head is set at one end to simulate a far-field water pressure, a hose fixed on the runner periodically rises and falls at the other end, a periodic surge pressure is formed in the soil layer on the right side of the partition plate, and the local scouring and far-field pressure seepage migration effects of the local surge on the adjacent reef seabed are simulated. The pore pressure gauge is used to determine the pore pressure change of the stratum to analyze the local scouring force and the far-field stratum seepage force change of the adjacent reef seabed, and the displacement gauge is used to determine the stratum displacement to analyze the local scouring deformation and the far-field pressure seepage migration law of the adjacent reef seabed. The influence of the local surge cycle, amplitude and pressure sudden increase point position on the surface morphology of the adjacent reef seabed is researched, and analysis basis is provided for the adjacent reef seabed topography evolution, far-sea island reef construction and erosion protection.

The application relates to the technical field of bridge vibration control, in particular to a bridge excitation and damping integrated device, a bridge and a laying method. The bridge excitation and damping integrated device comprises a mass block, a spring assembly, an actuator and two groups of lever assemblies. The middle part of the mass block is provided with a mounting hole, and a flywheel is arranged on the mass block. The spring assembly is arranged below the mass block and connected with the mass block, and the other end is connected with a structure to be damped. The actuator is arranged in the range of the mounting hole. The two groups of lever assemblies are respectively connected with the mass blocks on the two sides of the mounting hole through power arm ends, and the resistance arm ends are connected with the end parts of the telescopic rods of the actuator. When the telescopic rods of the actuator are telescopically extended or retracted, the mass blocks are driven to move up and down through the lever assemblies. The problem that the damping effect is only predicted through numerical simulation or simplified model test in the prior art and it is difficult to adjust parameters after installation can be solved. In addition, the nesting layout of the actuator and the mass block can reduce the requirement for the installation space by using the lever displacement amplification principle.

The application belongs to the technical field of model test equipment, and discloses a model pile calibration tank testing device and a testing method thereof, which comprises a loading frame, a calibration tank, a model pile and an actuator. The calibration tank is placed on the lower side of the loading frame, the inner side of the calibration tank is provided with a top plate, the middle of the upper end of the top plate is connected with the loading frame through a load transmission rod, the actuator is arranged on one end of the upper side of the calibration tank, the model pile is arranged on the side of the actuator, the model pile is driven to move up and down by the actuator, and the bottom of the calibration tank and the top plate are respectively provided with openings corresponding to the model pile. The indoor calibration tank model pile test has good parallel test effect and good consistency with the field test result.

The present invention discloses a cross-physics-field model test device and test method for floating wind turbines, relating to the field of offshore wind power engineering technology. The device comprises a water tank test subsystem, a wind tunnel test subsystem, and a real-time interactive system. The water tank test subsystem comprises an aerodynamic load reproducer and a tower-floating body-mooring physical model; the wind tunnel test subsystem comprises a floating body motion simulator and a wind rotor-tower physical model; and the real-time interactive system is capable of controlling the motion of the aerodynamic load reproducer and the floating body motion simulator. The present invention simultaneously utilizes multifunctional wind tunnel and marine engineering water tank test conditions to reproduce the floating body motion characteristics and the aerodynamic characteristics of the wind turbine in real time. By dividing the floating wind turbine system into two test subsystems, the problem of the inability to simultaneously match the aerodynamic similarity criteria and the hydrodynamic similarity criteria in scaled model tests is avoided, thereby improving the accuracy and reliability of integrated floating wind turbine model tests.

The invention provides a device and a method for testing a local scouring model below a pipeline. The device comprises an organic glass plate cuboid model box, a cover plate, a limiting plate, a rubber ring, the pipeline, a side baffle, a water tank A, a water tank B, a deposition tank, a balance, a computer and a camera. Through collaborative design of a limiting plate and a cover plate, water flow only acts on a contact interface between the lower portion of the pipeline and a soil layer, disturbance of an upper streaming vortex-induced effect is effectively avoided, and precise simulation of local scouring below the pipeline is achieved; a water flow pipe winding phenomenon, a sediment migration and deposition process and pit flushing time evolution are observed in real time by virtue of a camera, and an evolution process of initial micro-pit formation, pit body expansion, gap generation, gap extension and pit flushing form stability is completely captured in combination with quantitative weighing of a balance and a computer; and accurate test data and theoretical support are provided for the anti-scouring design of the pipeline foundation.