10 results about "Scale structure" patented technology

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 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 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 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 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 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.