21 results about "Scale structure" patented technology

The invention belongs to the technical field of microstructure and multi-scale structure design, and particularly relates to a microstructure rapid design and filling method and system based on macroscopic strain. Constructing a microstructure topological optimization model based on the macroscopic strain, sampling the macroscopic strain and the volume fraction to generate a corresponding optimized microstructure, and establishing a microstructure training data sample library with labels; carrying out equivalence analysis on a strain energy expression, solving the problem of variable inconsistency by utilizing an intermediate variable, and converting the strain in the training sample into the intermediate variable; establishing a deep neural network with an intermediate variable and a volume fraction as input and a microstructure topological configuration as output, and training the deep neural network by using a data sample to obtain a microstructure fast reverse design network; and carrying out staged multi-scale structure filling design by using the microstructure fast reverse design network. According to the method, the online calculation cost of microstructure and multi-scale structure design can be reduced, and the design efficiency is improved.

The present application belongs to the technical field of structure optimization design, and discloses a macro-micro double-scale structure topology optimization method and equipment capable of controlling the minimum size of microstructure, comprising the following steps: (1) interpolating the elastic matrix and the corresponding density of the actual microstructure in the offline database by using radial basis function interpolation, and then constructing a mapping model; (2) constructing a compliance minimization optimization model, wherein the design variables of the optimization model are the cutting height of two virtual microstructures in each unit and the topology variable, and the constraint functions are the volume fraction of the overall structure and the topology variable constraint; (3) calculating the sensitivity of the objective function and the constraint function to the design variable, and then updating the design variable; (4) when the compliance error of the updated double-scale structure meets the optimization termination condition, the optimization is completed and the double-scale structure meeting the requirements is obtained; otherwise, go to step (3). The present application solves the problems of low calculation efficiency and poor connectivity.

The invention relates to the technical field of structure fatigue evaluation, in particular to a pipe truss structure fatigue analysis parameter optimization method and system based on deep learning, and the method comprises the following steps: constructing a deep learning proxy model fusing a multi-scale structure diagram encoder, a working condition parameter encoder and a damage dimension curve decoder; training the model by using a high-fidelity training data set; for the target truss structure and the load working condition, predicting a damage dimension relation curve through the model; determining the optimal dimension of a rain flow counting matrix by analyzing the convergence characteristic of the curve; and predicting a multi-working-condition damage value through a model, and screening out a dominant working condition based on a damage contribution percentage. According to the method, the deep learning agent model is used for replacing traditional iterative simulation, rapid optimization of key parameters in fatigue analysis is achieved, the efficiency and accuracy of fatigue evaluation of the large truss structure are remarkably improved, and the generalization ability of the model is improved by fusing the physical effect of a multi-scale structure chart encoder to capture key hotspots.

The invention discloses a cross-scale structure-material rheology detection system, which belongs to the technical field of biomedical engineering, and realizes cross-scale geometric imaging by combining optical coherence tomography and laser scanning confocal or fluorescence imaging common optical path design. Geometric-mechanical synchronous observation and analysis of in-situ real-time monitoring and cross-scale synchronous monitoring of stress and strain of a multi-scale structure are supported; the biological 3D printing based on the six-axis robot supports in-situ printing on a rheology detection platform, so that a standardized rheology detection model of a bionic tissue inherent structure is constructed, and automatic and high-throughput bio-ink geometry-mechanics synchronous observation is supported; a multi-physics field coupling measurement model in a dynamic microenvironment is constructed, and a printing structure-material mechanics coupling mechanism is analyzed based on a rheology-structure data quantitative mapping model of deep learning. The system is based on rheology detection of the structure, and the theoretical and technical limitations of multi-scale structural mechanical property monitoring are solved.

The invention discloses a weak wake turbine guide vane based on a trailing edge discrete multi-scale structure. The weak wake turbine guide vane comprises a turbine guide vane body and the discrete multi-scale structure. The discrete multi-scale structure is arranged on the tail edge of the turbine guide vane body and used for enhancing the interaction of vortexes of different scales in the wake and the interaction of the wake and the main flow so as to weaken the wake of the turbine guide vane and reduce the pneumatic excitation amplitude of the wake. Structures of different scales are discretely arranged on the tail edge of the turbine guide vane body, the interaction of vortexes of different scales in the wake and the interaction of the wake and the main flow are enhanced, and therefore the wake of the turbine guide vane is weakened, the excitation amplitude of the wake of the turbine guide vane is reduced, and the design of the weak wake excitation turbine guide vane is achieved.

The application provides a life prediction method and system for airtight multi-cell steel structure, and relates to the technical field of structure finite element analysis, which comprises the following steps: taking the airtight multi-cell steel structure as a prototype, determining a scale reduction ratio, and establishing a scaled structure of the airtight multi-cell steel structure; performing laboratory accelerated environmental testing and mechanical testing on the scaled structure under the equivalent service environment of the airtight multi-cell steel structure, obtaining mechanical response test data of the scaled structure, and determining test targets of the scaled structure at different times; taking scaled plate material data as input to establish a simulation model of the scaled structure and obtain a transfer coefficient; taking original plate material data as input to establish a full-size structure simulation model of the airtight multi-cell steel structure; and using the full-size structure simulation model to output crack propagation size and residual life of a target welded structure at different times under the full-size structure. The application effectively improves the accuracy of life prediction of the airtight multi-cell steel structure.

The invention relates to a high-ice-grade controllable-pitch propeller hub assembly test model design method, which comprises the steps of S1, designing a scaling test model structure based on structural characteristics of a high-ice-grade controllable-pitch propeller hub assembly prototype; s2, determining a similarity criterion of the prototype machine and the test model, and calculating a similarity coefficient on the basis of geometric dimensions, material performance and load parameters of the prototype machine and the test model; s3, scaling the structural size of the prototype machine according to the similarity coefficient, and determining geometric parameters of each part of the test model; s4, selecting a test model material meeting similar criteria with the prototype material performance, and ensuring that the stress-strain characteristics of the test model are consistent with those of the prototype; s5, reproducing an anti-loose structure of the prototype machine in the test model, wherein the anti-loose structure comprises an arrangement mode of anti-loose stainless steel wires and anti-loose strips; and S6, based on the zoomed structure and the selected material, assembling a test model, carrying out a static strength and fatigue life reliability test, and verifying the mechanical similarity between the test model and a prototype.

The utility model relates to a movable scaleplate structure which comprises a forklift carriage, a forklift gantry, a fixed scaleplate, a movable scaleplate assembly and a linkage mechanism, the forklift carriage is installed on the outer side of the forklift gantry, the forklift carriage is in sliding connection with the forklift gantry, and the forklift gantry comprises a fixed outer gantry and a telescopic inner gantry; the telescopic inner door frame is directly embedded in the fixed outer door frame, the movable scale assembly is installed on the lower portion of the forklift sliding frame and the lower portion of the telescopic inner door frame, the movement of the forklift sliding frame and the telescopic inner door frame drives the movable scale assembly to move through the linkage mechanism, and the linkage mechanism is located on the movable scale assembly. The linkage mechanism is detachably connected with the movable scale assembly, and the fixed scale is installed on the fixed outer door frame and detachably connected with the fixed outer door frame. The goods shelf has the technical effect that the specific goods shelf layer height can be indicated layer by layer.

The application provides a method for measuring damping ratio of a building by using solid sound waves, which comprises calculating an attenuation coefficient α of sound waves in the building; obtaining an undamped natural angular frequency ω n of the building according to physical parameters of the building; and calculating a damping ratio ζ by bringing the attenuation coefficient α and the undamped natural angular frequency ω n into a formula. The method has the advantages of simple testing device, easy operation, simple operation process, greatly reduced testing cost and time, and suitability for on-site testing and large-scale structure application, especially for structures difficult to test in a laboratory environment. Through reasonable data analysis method, the damping ratio of the building can be relatively accurately measured.

The application relates to the technical field of data processing, and discloses a surrounding rock structure surface model generation method and system based on three-dimensional machine vision, aiming to solve the problems of single scale, poor spatial continuity and disconnection of engineering semantics of the existing method, and mainly comprising the following steps: multi-source point cloud collection and fusion, bottom-up aggregation path, top-down segmentation path, double-path feature fusion and structure surface parameterization output. Through the hierarchical processing framework of the bottom-up aggregation and top-down segmentation double paths, the multi-scale geometric features in the point cloud data are extracted, full-scale accurate identification from large-scale structure surfaces to microstructure surfaces is automatically realized, the problems of single scale, poor spatial continuity and disconnection of engineering semantics in the prior art are solved, the surrounding rock structure surface model generation efficiency and accuracy are improved, and the application is suitable for rock mass stability analysis.

The invention discloses a large-scale structure reanalysis method and system based on a separation-reconstruction framework, and the method comprises the steps: dividing a truss structure into a plurality of sub-domains, and constructing an extraction matrix; according to the total stiffness matrix and the extraction matrix, constructing a flexibility matrix corresponding to the sub-domain, and according to structural design modification, correcting the stiffness of the corresponding sub-domain, and constructing a maintenance reduction positive equation of the sub-domain; carrying out iterative solution on the descending maintenance positive equation of the subdomains according to a stable biconjugate gradient method to obtain displacement correction of each subdomain; mapping the displacement correction of each sub-domain to the overall structure, and reconstructing a displacement response vector of the overall structure; and calculating internal and external force residual vectors of the displacement response vector, judging whether the internal and external force residual vectors meet a preset convergence condition, and if so, outputting a structure reanalysis result. A local correction structure reanalysis method based on dimension reduction iteration is combined with a high-dimensional model characterization method and a residual error correction mechanism so as to improve the reanalysis efficiency of a large-scale or overall modification structure.

The present application relates to the technical field of structural fatigue evaluation, in particular to a pipe truss structure fatigue analysis parameter optimization method and system based on deep learning, the steps are as follows: a deep learning agent model is constructed by fusing a multi-scale structure graph encoder, a working condition parameter encoder and a damage dimension curve decoder; the model is trained by using a high-fidelity training data set; for a target truss structure and a load working condition, the damage dimension relationship curve of the target truss structure is predicted by the model; the optimal dimension of the rain flow counting matrix is determined by analyzing the convergence characteristics of the curve; and the multi-working condition damage values are predicted by the model, and the dominant working condition is screened out based on the damage contribution percentage. The present application replaces the traditional iterative simulation with the deep learning agent model, realizes the rapid optimization of the key parameters in the fatigue analysis, significantly improves the efficiency and accuracy of the large truss structure fatigue evaluation, and improves the generalization ability of the model by fusing the multi-scale structure graph encoder to capture the physical effects of the key hot spots.

The application discloses a large-scale structure reanalysis method and system based on a separation-reconstruction framework, which comprises the following steps: dividing a truss structure into multiple subdomains and constructing an extraction matrix; constructing a flexibility matrix corresponding to the subdomains according to a total stiffness matrix and the extraction matrix, and modifying the stiffness of the corresponding subdomains according to structure design modification, thereby constructing a dimension-reduced modification equation of the subdomains; iteratively solving the dimension-reduced modification equation of the subdomains according to a stable double-conjugate gradient method to obtain displacement modification amounts of the subdomains; mapping the displacement modification amounts of the subdomains to the whole structure to reconstruct a displacement response vector of the whole structure; calculating an internal and external force residual vector of the displacement response vector, and judging whether the internal and external force residual vector meets a preset convergence condition; and if yes, outputting a structure reanalysis result. The local modification structure reanalysis method based on dimension reduction iteration, in combination with a high-dimensional model representation method and a residual modification mechanism, can improve the reanalysis efficiency of large-scale or whole modified structures.

The application discloses a method for selecting an optimal cutting direction of a cross-scale structure feature surface, changes the spectrum component of the cross-section profile of the target cross-scale structure feature surface by changing the nominal cutting direction, thereby changing the error between the machinable cross-scale structure feature surface and the target cross-scale structure feature surface. α Within the selection range of the nominal cutting direction, the nominal cutting direction is changed with an angle resolution of ξ α The average overall relative error between the machinable cross-scale structure feature surface and the target cross-scale structure feature surface under all the nominal cutting directions is calculated, the nominal cutting direction with the minimum average overall relative error is selected as the optimal cutting direction of the cross-scale structure feature surface, thereby the error between the machinable cross-scale structure feature surface and the target cross-scale structure feature surface during cutting machining can be reduced, the error between the finally machined cross-scale structure feature surface and the given cross-scale structure feature surface is further reduced, and the machining precision is improved.

The invention discloses a BESO-based multi-scale structure integrated topological optimization design method. The method comprises the steps of 1, constructing and initializing a multi-scale finite element model; 2, solving the multi-scale finite element model, and calculating to obtain the density and strain energy of the microscopic unit; 3, summing the strain energy of the microcosmic unit to obtain the summed strain energy of the microcosmic unit; filtering the summed strain energy of the microcosmic unit; step 4, calculating to obtain sensitivity; 5, updating design variables of the multi-scale finite element model based on a bidirectional progressive structure optimization method BESO; and 6, judging whether an iteration stop condition is met, if so, stopping iteration to obtain the density of the microscopic unit, and otherwise, executing the step 2. And step 7, smoothing and modeling a design result obtained by optimization, and filling the obtained unit cell model into the corresponding macroscopic unit. And the structural model is manufactured through 3D printing.

The application discloses a double-scale connectable topology optimization method based on material field series expansion, and belongs to the field of double-scale design of aerospace structures. In the aspect of double-scale structure representation, two material field functions are introduced to represent the distribution of microstructures on the macro level and the topology configuration of different microstructures, respectively. Different double-scale topology configurations can be obtained by adjusting the correlation length of different material fields, and the microstructures can continuously evolve in the optimization process. Compared with the traditional double-scale optimization method of pre-defined microstructure layout form, the optimization result is not dependent on the initial guess, and the double-scale topology optimization design with connectivity can be obtained. The application does not have any limitation on the objective function and the constraint function, can process complex double-scale structure design problems, and the optimization efficiency is significantly improved due to the dimension reduction of the design variable of the material field series expansion. The application is expected to become a double-scale structure design method with strong innovation and applicability in the field of aerospace, and can be applied to complex problems such as buckling and energy absorption.

The invention provides a simulation material for a lithosphere scale model and application of the simulation material. The lithosphere scale model comprises an upper crust, a lower crust, a lithosphere ground curtain and a soft flow circle ground curtain. The simulation material of the supercrust comprises the following raw materials: silica powder, quartz sand and carborundum; the simulation materials of the lower earth crust and the lithosphere mantle comprise the following raw materials: silica gel, quartz sand and carborundum; the simulation material of the soft flow ring mantle comprises any one of syrup, honey or a sodium iodide solution. The simulation material provided by the invention can quantitatively complete a lithosphere scale structure physical simulation experiment, and scans a lithosphere scale model through an industrial CT technology to obtain a structure evolution process image, so that the structure evolution characteristics are analyzed, the rheological structure and layered stretching deformation characteristics of the lithosphere can be better simulated, and the simulation material can be applied to the field of lithosphere engineering. And an effective technical means is provided for researching development and evolution of lithosphere structural deformation.

The utility model relates to the technical field of weighing equipment, and discloses a mobile electronic scale structure which comprises a scale pan, and the upper surface of the scale pan is fixedly connected with a mounting box. Through the arrangement of the movable scale panel, the connecting rod, the movable blocks, the driving rod and the driven rods, when the first motor starts to operate, the circular shaft and the driving rod start to rotate, the rotation of the driving rod enables the two driven rods to also start to rotate, and meanwhile, the other ends of the two driven rods drive the two movable blocks respectively; the two movable blocks start to move back to back under the limitation of the limiting sleeves, meanwhile, the movement of the movable blocks drives the connecting rods to rotate, the rotation of the connecting rods drives the movable scale panels, and the two movable scale panels start to rotate with the left side and the right side of the fixed scale panel as the axis respectively; finally, along with the rotation of the two movable scale panels, the upper surface of the fixed scale panel is flush with the upper surfaces of the two movable scale panels.

The application belongs to the technical field of oil and gas reservoir exploration and development, and discloses a digital core cross-scale structure generation method based on GAN. In view of the problems of poor structure restoration of traditional interpolation methods and poor generation effect of single GAN model, the method collects 480*480*480 and 256*256*256 resolution core data pairs, and constructs a data set after preprocessing; a 3D residual cycle GAN model containing double generators and double discriminators is constructed, and training is completed by matching a combined loss function and a gradient optimization strategy, so that cross-scale bidirectional generation is realized; the reliability of the result is verified through multi-dimensional quantitative evaluation and visualization. Experiments show that the average SSIM of the generated core is 0.8599, the average porosity error is 3.86%, and the structure and physical property consistency is high. The method supports batch generation and automatic evaluation, guarantees the generation precision and engineering applicability, and provides a new technical path for digital core cross-scale characterization.

The invention provides an electron beam welding deformation simulation control method based on a mapping algorithm, and aims to solve the problems that traditional thermal-elastic-plastic simulation is low in efficiency, large in shell unit algorithm error and poor in complex structure adaptability. According to the method, rapid and accurate deformation prediction is realized by constructing a local-overall plastic strain mapping system: a coordinate system conversion relation between a local model and an overall model is established based on a second-order tensor format, and the mapping precision is ensured through a plastic strain force and torque conservation criterion; dynamic coordinate system conversion and 2D-3D and 3D-3D multi-dimensional mapping algorithms are designed for the curve type welding seam; the bounding box unit query and algebraic multi-grid technology is adopted to improve the plastic strain loading efficiency and convergence speed of the large-scale structure; and the simulation precision is verified by combining surface structured light measurement. According to the invention, rapid simulation and accurate control of welding deformation of a large-size complex structure can be realized, and support is provided for welding process optimization of complex components in the fields of weaponry and the like.