676 results about "Electromagnetic simulation" patented technology

The invention relates to the technical field of electromagnetic compatibility optimization design methods, in particular to a system intelligent electromagnetic compatibility optimization design method and system, and the method comprises the following steps: building an electromagnetic simulation unit model of a component, and constructing a component model library; performing interference path analysis on the component model through a path optimization algorithm, identifying key nodes and interference source nodes, and constructing an electromagnetic interference path model; according to the electromagnetic interference path model, carrying out adaptive adjustment on electromagnetic radiation and immunity parameters of system components so as to optimize electromagnetic compatibility; carrying out dynamic prediction and parameter optimization adjustment on electromagnetic compatibility parameters of the system through a deep learning model; the electromagnetic compatibility of the system and the sub-modules thereof is integrally optimized by adopting a progressive feedback adjustment mechanism, and the technical contradiction in the traditional method is effectively solved by introducing innovative mechanisms such as path weight calculation, nonlinear adaptive adjustment and multi-level non-uniform optimization.

The invention relates to a radio frequency module electromagnetic compatibility analysis method and system, and the method comprises the steps: obtaining the structure data and electromagnetic parameter information of a radio frequency module, carrying out the electromagnetic simulation of the structure data based on the electromagnetic parameter information, and obtaining the corresponding simulation data of the radio frequency module; acquiring near-field scanning data of the radio frequency module, and performing correlation calculation on the near-field scanning data and the radio frequency module simulation data through a preset fully implicit frequency domain finite volume numerical algorithm to obtain corresponding far-field electromagnetic distribution characteristic data; obtaining interference emission data of the radio frequency module, and performing zero eigenvalue entropy correction to obtain corresponding interference propagation feature data; performing optimization analysis on the far-field electromagnetic distribution characteristic data and the interference propagation characteristic data to obtain corresponding initial optimization information; and performing scheme analysis on the initial optimization information and an EMC performance evaluation result to obtain a corresponding EMC optimization scheme. According to the invention, the electromagnetic characteristics of the radio frequency module can be evaluated more comprehensively, so that the accuracy of electromagnetic compatibility analysis is improved.

The invention relates to the technical field of metasurface antennas, and provides a metasurface antenna parameter optimization method and system based on a convolutional neural network, and the method comprises the steps: collecting metasurface sample data; extracting a comprehensive electromagnetic feature set, and establishing a nonlinear mapping relation model; constructing an antenna performance comprehensive evaluation function, inputting the nonlinear mapping relation model and the antenna performance comprehensive evaluation function into a hybrid optimization algorithm to generate a parameter candidate set, and performing local optimization on the parameter candidate set by using a particle swarm algorithm to obtain a metasurface antenna parameter combination; and extracting electromagnetic characteristics of the metasurface antenna parameter combination, iteratively adjusting the height parameter of the resonant cavity until the height parameter meets a threshold value to obtain an electromagnetic simulation verification result, and feeding back the electromagnetic simulation verification result to the deep Q neural network model for parameter updating to obtain an optimal metasurface antenna parameter combination. According to the method, the optimization of antenna parameters is realized, the design efficiency of the metasurface antenna is improved, and the consumption of electromagnetic simulation calculation resources is reduced.

The invention provides an electromagnetic compatibility test method and system for PCB design, a PCB comprises a schematic circuit diagram and a structural model, and the method comprises the following steps: extracting high-frequency signal characteristics in the schematic circuit diagram to obtain an electromagnetic simulation parameter set; performing multi-physical field coupling modeling according to the electromagnetic simulation parameter set and the structure model to obtain an electromagnetic field-circuit model; performing frequency-time domain hybrid excitation loading on the electromagnetic field-circuit model to obtain an electromagnetic noise distribution diagram; performing gradient descent optimization on layout parameters of the structure model according to the electromagnetic noise distribution diagram to obtain an anti-interference layout scheme; and performing multi-working-condition Monte Carlo simulation and residual error calibration on the anti-interference layout scheme to generate a correction guidance library.

The present invention discloses a radar scattering field simulation method for a three-dimensional moving target group combined with a neural network, and belongs to the field of electromagnetic simulation technology. The method of the present invention converts the electromagnetic response of a three-dimensional moving target group into the response of the target when it is distributed at different positions, and extracts the three-dimensional numerical Green's function of the randomly distributed target group in the solution domain by changing the spatial distribution of the target group; the three-dimensional numerical Green's function is split into scattering terms and coupling terms, and the artificial neural network is trained separately; when training the artificial neural network, according to the characteristics of the moving target group, its position distribution and scattering terms / coupling terms are input into the artificial neural network as the main features; the scattering terms and coupling terms output by the artificial neural network model are combined into numerical Green's function terms, which can be applied to integral equations to achieve fast and accurate solution of the scattering field of the three-dimensional moving target group.

The invention provides an artificial intelligence auxiliary antenna design method based on a Gaussian regression process, and the method comprises the steps: employing a script interface to interact with a simulation platform, automatically completing the parameterized modeling process of an antenna model, and setting an excitation condition and a radiation boundary; a multi-dimensional space stratified sampling method is adopted to generate a uniformly distributed sample point set in the multi-dimensional parameter space, and electromagnetic simulation is carried out; preprocessing the data output by simulation to form a data structure suitable for machine learning model processing; integrating the trained MLNN model into a grey wolf optimization algorithm, and optimizing antenna design parameters; and performing electromagnetic simulation on the optimal parameter obtained by optimization, comparing a prediction result with actual simulation data, and verifying the performance of the optimization engine in the target frequency band. According to the method, the problems of insufficient sample generation efficiency, insufficient parameter sensitivity analysis, limited model generalization ability, low convergence speed of an optimization algorithm and the like in the prior art can be solved.

The invention relates to the technical field of transformer simulation, in particular to a simulation system and method for a temperature rise field of a dry-type transformer. The method comprises the following steps: acquiring geometric parameters of an iron core lamination of the dry-type transformer to be simulated, spatial arrangement information of a winding and a positioning relation of an insulating component to form structural design data; the method comprises the following steps: constructing three-dimensional geometric modeling data based on structural design data, importing the three-dimensional geometric modeling data into a finite element electromagnetic simulation platform, setting a power-on condition of an excitation winding, and applying rated current or voltage, so as to obtain local magnetic flux density distribution data at each iron core lamination position in a whole operation period. According to the method, on the basis of three-dimensional structure data, three physical processes of an electromagnetic field, a thermal field and a fluid field are fused, a high-precision local heat source model and a dynamic temperature rise prediction model are constructed, and geometry-material-boundary all-parameter modeling is achieved from the whole structure of an iron core lamination, a winding and an insulation assembly.

The present invention discloses a neural network accelerated simulation method for the radar scattering field of a wide-band target group, belonging to the technical field of electromagnetic simulation. The method of the present invention constructs a KAN neural network model at the central frequency of the wide band, and constructs KAN neural network models at other target frequencies through transfer learning or direct training; uses the trained KAN neural network model to generate a multi-frequency point numerical Green's function data set, and then constructs a wide-band training sample to train the multi-task learning model to realize the global prediction of the numerical Green's function of the entire target wide band; uses the numerical Green's function output by the multi-task learning model to realize the fast simulation of the scattering field of the wide-band target group. The method of the present invention can realize the fast and accurate simulation of the radar scattering field of the wide-band target group.

The invention relates to the technical field of terahertz, in particular to a terahertz sensor array design optimization method based on metasurface enhancement, which improves the performance of the terahertz sensor array in the aspects of sensitivity, resolution, frequency response and the like through multi-dimensional optimization design, obtains initial parameters of the array based on a physical model, and improves the accuracy of the design of the terahertz sensor array. By optimizing the structure and the arrangement mode of the metasurface unit and combining electromagnetic simulation analysis, the transmission efficiency and the phase regulation and control capability of terahertz waves are improved, the performance change of the sensor under high-temperature and high-stress conditions is evaluated through thermal effect and stress field simulation, the stability and the reliability of the sensor in a complex environment are ensured, and the application range of the sensor is widened. Based on laboratory testing, data of key performance indexes such as sensitivity, resolution and response time are fed back, design parameters are adjusted, and iterative optimization is completed; according to the invention, through combination of multi-dimensional simulation and experimental feedback, the performance and environmental adaptability of the terahertz sensor array are significantly improved, and the terahertz sensor array is suitable for wide terahertz detection application.

The invention provides an RCS prediction method based on deep learning, computer equipment and a readable storage medium. The method comprises the following steps: firstly, constructing an electromagnetic simulation model of a target, and resolving to obtain noise-free RCS sample data corresponding to different incident wave angles; and then simulation errors are introduced to form noise sample data. Then, constructing a convolutional auto-encoder network as an RCS denoising model, and training the RCS denoising model by using noise-containing and noise-free sample data; and then a deep neural network model is built based on a grey wolf algorithm to serve as an RCS prediction model, the trained denoising model is used for preprocessing the training data set, then the prediction model is trained, and finally prediction of a target RCS value is achieved. According to the method, the GWO-DNN deep learning model is constructed for prediction, so that the dependence on a physical mechanism and the manual parameter adjustment cost are reduced. Meanwhile, noise reduction preprocessing is carried out on the actually measured RCS result through CDAE, the too high influence of measurement errors on the prediction result is overcome, and the RCS prediction precision is improved.

The invention discloses a broadband wave-absorbing material based on an MAX-phase metasurface structure and an impedance matching optimization method of the broadband wave-absorbing material. The wave-absorbing material comprises an MAX-phase ceramic substrate and periodic metasurface structures which are arranged on the surface of the MAX-phase ceramic substrate in an array mode and composed of conductive resonance units. The resonance units are isolated through gaps, an equivalent R1L1C1 series resonance circuit is formed in an electromagnetic field, and a base body is equivalent to a parallel resistor R2. By establishing an equivalent circuit model and optimizing parameters R1, L1, C1 and R2, impedance matching of the wave-absorbing material in a target frequency band can be regulated and controlled, so that the wave-absorbing material at least has an absorption peak of which the reflection loss is less than-10 dB. According to the optimization method, electromagnetic simulation and circuit simulation are combined, optimized circuit parameters are reversely mapped into structure geometric parameters, material preparation is guided, and accurate design and broadband absorption of the wave absorbing performance are achieved.

The invention discloses a reciprocity theorem-based array antenna multi-beam pattern synthesis method, which comprises the following steps of: establishing an array antenna model in electromagnetic simulation software, and extracting a radiation pattern of each antenna unit; defining the number and direction of to-be-formed beams and the gain ratio between the beams; establishing an array antenna energy transmission efficiency transfer function, calculating scattering parameters of each antenna unit of the array antenna through a reciprocal theorem and coordinate transformation, and constructing a scattering parameter matrix of a multi-antenna system; automatically adjusting parameters by using a particle swarm algorithm, and dynamically regulating and controlling the gain proportion between the beams; the optimal excitation value of each antenna unit is obtained, and a target beam is generated after actual loading. According to the invention, the multi-beam synthesis result of the array antenna is more convenient, more stable and more flexible.

The invention discloses a plasma sheath electromagnetic characteristic simulation method and system for a hypersonic flight vehicle, and the method comprises the steps: carrying out the fluid simulation according to the geometric shape and flight parameters of the hypersonic flight vehicle, so as to obtain the information of a flow field containing a plasma sheath region; according to the flow field information, simulation parameters of all positions of the plasma sheath space are determined, and the simulation parameters at least comprise electron density distribution and electron collision frequency distribution; based on the simulation parameters, a three-dimensional model of the plasma sheath is established, the three-dimensional model is layered according to electron density distribution, a layered three-dimensional model composed of a plurality of plasma layers is formed, and each plasma layer has the electron density within a preset range; based on the layered three-dimensional model, establishing a non-uniformly distributed electromagnetic simulation model of the plasma sheath; and carrying out grid division on the non-uniformly distributed electromagnetic simulation model, and carrying out numerical calculation by adopting a numerical solver so as to obtain the electromagnetic characteristics of the plasma sheath. According to the method, the problem of deviation between a simulation result and an actual situation of a traditional simulation model can be solved.

The invention discloses a TR assembly gold wire bonding process parameter prediction method based on a multilayer perceptron neural network, and belongs to the technical field of microwave device intelligent manufacturing. According to the method, an intelligent mapping model of gold wire bonding geometric parameters and radio frequency performance is constructed by fusing a multi-layer perceptron neural network and parameterized electromagnetic simulation. The method specifically comprises the following steps: generating 45 groups of samples in a process parameter space by adopting Latin hypercube sampling; obtaining an S parameter data set through batch processing electromagnetic simulation; box-Cox conversion and normalization preprocessing are carried out on the data; the method comprises the following steps: constructing an MLP neural network model of a 3-32-16-2 structure, and determining hyper-parameters by using Bayesian optimization; and after training is completed, rapid reverse mapping from target performance to process parameters is realized. According to the method, the number of traditional tests is reduced from more than 200 to 45, the predicted root-mean-square error of S21 is smaller than or equal to 0.12 dB, the determination coefficient is larger than or equal to 0.96, and the parameter backstepping time lt is obtained; according to the method, full-process automation from simulation, training, optimization to production and issuing is realized, and the development efficiency of the TR component is remarkably improved.

The invention provides an electromagnetic thermal multi-physical field simulation method based on an FDTD / FVTD algorithm, and belongs to the technical field of electromagnetic simulation. Performing time domain finite difference discretization on the established spatial discrete model by adopting a Yee cellular structure, and calculating electromagnetic field distribution according to a Maxwell rotation equation; constructing an adaptive steady-state judgment criterion based on the instantaneous total energy change rate, and updating electromagnetic field distribution of the spatial discrete model by adopting an FDTD explicit method; calculating the electromagnetic loss power density of the spatial discrete model, and transmitting the electromagnetic loss power density into a thermal field for calculation; based on an FVTD method, thermal field distribution of the discrete model is calculated through an explicit or implicit format, and thermal field distribution information is obtained. The FDTD method is based on explicit time domain iteration, transient behaviors such as electromagnetic wave propagation and scattering can be visually captured, and the FDTD method has broadband response capability and complex medium adaptability and is especially suitable for the high-frequency electromagnetic problem. The FVTD method strictly ensures energy conservation, directly treats boundary heat flux and is suitable for non-uniform materials and complex heat transfer boundary conditions.

The invention provides a radio frequency circuit schematic diagram-to-layout collaborative optimization method and system based on an AI model, and the method comprises the steps: building a mapping database from the geometric parameters of a passive device to corresponding electromagnetic characteristics through obtaining the geometric parameters of the passive device; based on a database, a corresponding AI agent model is trained for each passive device, through the AI agent models, millisecond-level model prediction is used for replacing time-consuming electromagnetic simulation to serve as design guidance, the calling frequency of calculation-intensive electromagnetic simulation is remarkably reduced, the layout parasitic effect is further considered in the schematic diagram design stage, and the design efficiency is improved. And a performance gap between a schematic diagram and a layout is effectively bridged. Then a target schematic diagram is obtained, passive devices in the target schematic diagram are automatically replaced based on a Bayesian optimization algorithm and an AI agent model, and the design period from the schematic diagram to a qualified layout is greatly shortened; by optimizing the layout effect in the schematic diagram design stage, it can be ensured that the simulation performance of the finally generated physical layout is consistent with a schematic diagram simulation result.

The invention discloses a tight-pitch antenna decoupling method and device based on deep learning and a storage medium. The method comprises the following steps: acquiring tight-pitch antenna data; inputting the tight-spacing antenna data into the antenna decoupling structure prediction model to obtain decoupling structure parameters; performing tight-spacing antenna decoupling processing according to the decoupling structure parameters; the antenna decoupling structure prediction model is obtained through the following steps: obtaining rectangular microstrip patch antenna information; the patch length and the patch width are calculated; establishing a binary antenna model; performing electromagnetic field simulation processing by utilizing a preset script program and electromagnetic simulation software to obtain a simulation result data set; constructing an initial convolutional neural network model; and inputting the simulation result data set into the initial convolutional neural network model according to the mean square error, so that the initial convolutional neural network model is trained to obtain an antenna decoupling structure prediction model. According to the invention, antenna decoupling is realized, and efficiency and accuracy are improved. The method can be widely applied to the technical field of wireless communication.

The invention discloses an electromagnetic simulation multi-scale region segmentation method based on deep learning, and the method comprises the steps: constructing and training a deep learning model which is used for recognizing a key region and a non-key region in an electromagnetic simulation region; in the training process, a data set is labeled, and the model performance is optimized by using a cross validation method; simulating the key area by adopting a full-wave solution method to obtain field distribution of the key area; simulating the non-critical area by adopting a high-frequency approximation method, and simulating an electromagnetic field generated after an incident wave is reflected by the surface of an object; dynamic boundary conditions are generated at the boundary of the key area and the non-key area, and continuity and consistency of field values are ensured. According to the method, in a large-scale electromagnetic simulation scene, through combination of deep learning and a mixed solution strategy, optimal configuration of computing resources is achieved, limited computing force is focused on a high-precision demand area, and simulation efficiency and overall performance are remarkably improved.

The invention discloses a three-dimensional moving target group radar scattering field simulation method combined with a neural network, and belongs to the technical field of electromagnetic simulation. The method comprises the following steps of: converting electromagnetic response of a three-dimensional moving target group into response of targets distributed at different positions, and extracting a three-dimensional numerical green function of a randomly distributed target group in a solution domain by changing spatial distribution of the target group; splitting the three-dimensional numerical green function into a scattering term and a coupling term, and training an artificial neural network respectively; when the artificial neural network is trained, position distribution and scattering terms / coupling terms of the moving target group are used as main characteristics to be input into the artificial neural network according to the characteristics of the moving target group; scattering terms and coupling terms output by the artificial neural network model are combined into numerical Green function terms which can be suitable for integral equations, and rapid and accurate solving of a three-dimensional moving target group scattering field is achieved.

The invention relates to a multi-modal multifunctional composite metasurface design method and device and a medium, and the method comprises the steps: carrying out the parameterized sampling of the key size of a unit structure through Latin hypercube sampling, and generating a training sample set; electromagnetic simulation is carried out on the sampled structure to obtain two-state electromagnetic response data, and a mixed variable database is obtained; according to the target response, generating a plurality of groups of candidate structures through a generator in the improved conditional variation auto-encoder generative adversarial network, screening the candidate structures through a predictor, calculating a prediction comprehensive error, and selecting a preset number of candidate structures with the minimum prediction comprehensive error to perform full-wave simulation verification; and obtaining a simulation double-state response, calculating a simulation comprehensive error, and judging the current candidate structure according to a preset comprehensive error threshold value. Compared with the prior art, the method has the advantages of multi-state collaboration, low calculation cost, high degree of freedom and the like.

The invention relates to the technical field of electrical equipment detection, and discloses a terahertz time-domain spectroscopy-based GIS insulating material nondestructive testing method, which comprises the following steps: acquiring GIS insulating material time-domain signals by using a reflection-type terahertz time-domain spectroscopy (THz-TDS), and separating and reconstructing defect echoes based on electromagnetic simulation modeling in combination with wavelet transform, STFT and a transmission matrix method; multi-dimensional features are extracted, SVM and RBFNN are trained after dimensionality reduction, and a classification and quantitative regression model fusing physical constraints is constructed; and inputting a signal of a sample to be detected into the model to realize rapid, non-contact and accurate detection on the type, size and depth of the defect. The method overcomes the defects that traditional detection is low in efficiency, has damage or is difficult to quantify, the signal-to-noise ratio, the sensitivity and the automation level are remarkably improved, the method can be widely applied to GIS production quality control, installation acceptance, operation and maintenance monitoring, health assessment and life prediction, and powerful support is provided for safe and reliable operation of an electric power system.

The invention discloses a circuit board printing control method and system based on signal transmission loss. And obtaining a target differential transmission line parameter and a circuit board lamination parameter. And establishing an electromagnetic simulation model, and simulating insertion loss, return loss and impedance discontinuous point distribution. The line width, the line spacing and the interlayer hollowed-out area of the differential transmission line are dynamically adjusted through the reinforcement learning model, the proportion of the line width to the shielding layer spacing is limited by a preset critical value, and the line spacing is configured based on the multiple relation of the shielding layer spacing. And a sectional optimization strategy is adopted for the corners of the transmission line. The ratio of the aperture to the diameter of the anti-pad is optimized, and impedance mutation is reduced. According to the method, the key parameters of the target differential transmission line and the circuit board lamination parameters are acquired, and the electromagnetic simulation model is combined with the multi-physical field coupling algorithm, so that the insertion loss, the return loss and the impedance discontinuous point distribution can be accurately simulated. The method improves the adjustment efficiency, guarantees the accuracy of an adjustment result, and effectively reduces the signal transmission loss.

The invention discloses a hybrid electromagnetic simulation method suitable for a multi-scale fine-structure antenna, and the method comprises the steps: firstly creating a multi-scale fine-structure antenna model which needs to be simulated, and determining the range of the size of the multi-scale fine-structure antenna model in a calculation space; constructing a convolution complete matching layer suitable for the hybrid implicit-explicit time domain finite difference method, determining a whole calculation space range including the convolution complete matching layer, then setting a non-uniform grid, and determining a fine region and a transition region from fine to rough; combining the three, adding an excitation source of an electric field, and then obtaining a convolution complete matching layer absorption item, a magnetic field value and an electric field value at the next moment; and finally updating the excitation source in the fine structure direction and updating the time step. According to the method, the hybrid implicit-explicit finite difference time domain method is efficiently combined with the non-uniform grid technology and the convolution complete matching layer, the calculation precision is improved, the implementation process is remarkably simplified, and the calculation efficiency is improved.

The invention discloses an electromagnetic transient simulation and mapping method and system oriented to multi-physics field coupling, and belongs to the technical field of substation digital operation and maintenance and electromagnetic simulation. Deploying multiple types of sensors in a transformer substation, preprocessing the acquired multi-physical field data, and fusing with the three-dimensional twinborn model; a coupling algorithm is adopted to carry out multi-physical field coupling analysis, multi-physical quantity display and thermodynamic diagram superposition are realized, and scene evolution change simulation under various typical working conditions is supported; a micro-service architecture is adopted to construct a digital twin system, double closed-loop verification of simulation and actual measurement is performed through actual measurement data and simulation data of a real-model test field, model optimization is completed, and real-time mapping of electromagnetic transient dynamic simulation and a transformer substation physical entity is realized. The method improves the precision of model and entity mapping, meets the requirements of real-time tracking and response of the electromagnetic transient change of the transformer substation, adapts to the digital transformation of different transformer substations, and provides a basis for the operation and maintenance of the transformer substations.

The invention discloses an electromagnetic field simulation optimization method and system based on deep learning. The method comprises the following steps: discretizing a calculation area, and dividing the calculation area into a limited number of units; constructing a finite element matrix for the electric field of each unit; the finite element matrix is subjected to block processing, and two sub-block matrixes describing the electric field gradient and the electric field rotation of the triangular surface area are decomposed into an upper triangular matrix and a lower triangular matrix through an incomplete-like Cholesky decomposition model; inversing the upper triangular matrix and the lower triangular matrix to obtain approximate inverse of the finite element matrix; substituting the approximate inverse of the finite element matrix into a conjugate gradient algorithm, and solving to obtain an electric field of each unit; and obtaining a corresponding magnetic field according to the electric field of each unit so as to obtain an electromagnetic simulation result. According to the method, through a technical path of block dimension reduction-graph structure learning-physical constraint enhancement, the bottleneck problem that preprocessing efficiency and adaptability in electromagnetic field simulation are difficult to consider at the same time is solved.

The invention provides a tensor completion method based on low-rank and sparse combined constraint, and the method is applied to radar high-resolution two-dimensional sparse reconstruction imaging so as to enhance sparse imaging performance. Radar high-resolution two-dimensional imaging is realized through the following steps: firstly, constructing a tensor model of radar data through a sliding window method to capture an internal structure of high-dimensional data and mine low-rank characteristics of the data; secondly, representing enhanced low-rank and sparse characteristics of the constructed radar tensor data by using a tensor low-rank and sparse combined constraint model represented based on a Kronecker basis; and then an optimal solution of the constraint model is efficiently solved in an iteration mode by using an alternating direction multiplier method, and related parameters are updated through a closed-form solution method. According to the method, the superiority of the tensor completion method based on the low-rank and sparse joint constraint in radar high-resolution two-dimensional imaging is verified through comparison experiments on electromagnetic simulation and actually measured radar data.

The invention discloses an antenna size optimization method, an antenna and a household electrical appliance product, and the method comprises the steps: carrying out the multi-time iteration of a size parameter combination of an ultrahigh frequency antenna, and evaluating the performance of the antenna corresponding to the size parameter combination obtained through each iteration through a pre-trained performance estimation model, so as to screen out the optimal antenna size of the ultrahigh frequency antenna. Electromagnetic simulation software does not need to be used in the antenna size automatic optimization process, the computing power cost and the time cost in the antenna design process are effectively reduced, and the risk of insufficient memory is reduced.

The invention belongs to the technical field of three-dimensional electromagnetic field numerical solution, and particularly provides a curved surface adaptive fitting method based on anisotropic grids, which comprises the following steps: firstly, establishing a geometric structure of a coaxial transmission line to be calculated, setting simulation frequency, excitation and boundary conditions, and establishing an electromagnetic simulation model; then identifying a curved surface on the model, applying an anisotropic grid on the curved surface, reducing the size of a tangential grid so as to improve a grid curved surface fitting effect, and subdividing a computational domain; carrying out finite element electromagnetic simulation solution and posterior error estimation to obtain a unit mark needing to be encrypted and an encryption size; twice encryption is carried out in the self-adaptive grid encryption stage, simulation calculation is carried out again after twice encryption is completed, and the process is repeated until the solution meets the precision requirement. In conclusion, according to the method, through two-step encryption, the anisotropy of the grids on the curved surface is not damaged in the self-adaptive encryption process, and good solving precision and calculation efficiency are both considered.

The invention provides a DCIM-based method for rapidly calculating a green function of a uniaxial anisotropic layered medium and an electromagnetic simulation system. The method comprises the following steps: modeling a uniaxial anisotropic layered medium to be simulated; determining a uniaxial anisotropic dielectric material of each layer, and assigning transverse parameters and longitudinal parameters; determining longitudinal coordinates of field point and source point distribution required to be solved, fitting a uniaxial anisotropic medium Green function spectral domain expression under the field source distribution by using a DCIM method, and solving an analytical expression in a spatial domain by using Somerfeld identical equation properties; solving the size of the electromagnetic field on the plane according to the fitted analytical expression; compared with a direct numerical integration method or a finite element method numerical solution using the Green function, the method has the advantages that the airspace analytical expression corresponding to the field source distributed on the plane is directly fitted, so that the solution of the airspace Green function is greatly accelerated, and the calculation efficiency and the calculation precision are both good in an actual example.

The invention relates to a high-efficiency super lens based on refractive index matching and a design method thereof. The super lens comprises a super atom structure layer and a bonding substrate layer on any surface, the difference value between the refractive index of any substrate layer and the average effective refractive index of the super-atomic structure layer is smaller than a preset value; when the super lens is designed, the relation among the average effective refractive index, the impedance and the S parameter of the super-atom structure layer is constructed, the structure parameter of the super-atom structure layer is determined based on the requirement parameter of the super lens, the preset S parameter of the super-atom structure layer is obtained based on electromagnetic simulation, and the average effective refractive index of the super-atom structure layer is calculated. And the substrate layer is used as an expected refractive index matching material and is bonded on one surface or two surfaces of the super-atomic structure layer. According to the invention, the high-efficiency super lens is obtained by using the principle of refractive index matching and processing methods such as large-area bonding, the focusing efficiency of the super lens is greatly improved, the application of the super lens is promoted, and the leap-type upgrading of optical devices in the dimensions of light weight and high performance is realized.