A method and system for designing and simulating optimization of a photonic structure of temperature-adaptive radiation-controlled glass

Abstract

The application relates to the technical field of building envelope energy-saving, and discloses a photonic structure design and simulation optimization method and system of temperature self-adaptive radiation temperature control glass, wherein the method comprises the following steps: extracting multiple-phase optical constants and inverting two-phase equivalent surface impedance; constructing a joint optimization objective function; obtaining optimal interval layer thickness and refractive index; combining building thermal performance simulation and Monte Carlo tolerance verification to generate a photonic structure scheme. Compared with the prior art which usually only matches impedance for a single phase, especially under the condition that the real part and the imaginary part of the refractive index of vanadium dioxide both undergo phase transition, the technical problem that the interval layer cannot realize differential impedance matching between the two phase states. Due to the transmission line analogy analysis and conjugate matching differential quantification, the application realizes the coordination of high-temperature strong coupling absorption and low-temperature residual cavity suppression, and improves the long-wave infrared high-low temperature emissivity difference and engineering feasibility.

Description

Technical Field

[0001] This invention relates to the field of energy-saving technology for building envelopes, and in particular to a method and system for photonic structure design and simulation optimization of temperature-adaptive radiation-controlled glass. Background Technology

[0002] Currently, temperature-adaptive radiation-controlled glass mainly utilizes thermotropic phase-change materials such as vanadium dioxide to dynamically regulate the absorption and emission characteristics of infrared thermal radiation through temperature-induced insulating-metal phase transitions, thereby achieving adaptive energy saving of the building envelope throughout the year. In the typical photonic structure design of this type of glass, an asymmetric Fabry-Perot cavity structure is often employed, consisting of a vanadium dioxide metasurface array, an infrared transparent dielectric spacer layer, and a selective infrared reflective layer. The cavity resonance conditions are adjusted by selecting the thickness and refractive index of the spacer layer. Existing processing methods are mostly based on thin-film optical admittance matching or the transfer matrix method, typically assuming that the optical constants of vanadium dioxide are fixed in the target phase state, and using impedance matching in the high-temperature metallic phase as the sole optimization objective in designing the spacer layer parameters. This approach fails to fully consider the impact of the simultaneous and drastic changes in the real and imaginary parts of the material's complex refractive index before and after the phase transition on the cavity mode coupling behavior.

[0003] For example, in this asymmetric Fabry-Perot cavity, if the physical thickness and refractive index of the spacer layer are optimized only for the conjugate impedance matching condition between the metasurface and the reflective layer in the high-temperature metallic phase, then in the low-temperature insulating phase, the complex refractive index of vanadium dioxide undergoes a sudden change, leading to a significant alteration in the dielectric environment. This causes the cavity input impedance to deviate significantly from the conjugate value of the metasurface's equivalent impedance, unexpectedly exciting a residual cavity mode absorption peak in the 8-13 micrometer atmospheric window band. This residual cavity effect causes undesirable infrared absorption in the insulating phase, which should maintain low emissivity, resulting in an abnormally high emissivity. This severely compresses the emissivity control range between the high and low temperature phases, weakening the passive temperature control capability and annual energy-saving benefits of the glass window.

[0004] Therefore, there is an urgent need for a spacer optical thickness design method that can simultaneously quantify the difference in complex optical constants between the two phases of vanadium dioxide and assess the risk of residual cavity absorption in the insulating phase. This method would allow for an optimal combination of physical thickness and refractive index of the spacer layer, even when the phase transitions of the real and imaginary parts of the material's refractive index cannot be avoided. This would enable strong coupling absorption between the metasurface and the reflective layer in the high-temperature metallic phase, while ensuring that the cavity coupling effect in the low-temperature insulating phase is effectively suppressed and the emissivity is maintained at a low level. This would improve the depth of control over the high and low temperature emissivity difference in the long-wave infrared band and the reliability of building thermal performance. Summary of the Invention

[0005] To address the aforementioned technical shortcomings, the purpose of this invention is to propose a photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass. This method aims to solve the problem that existing technologies typically only perform impedance matching for a single phase state, especially when both the real and imaginary parts of the vanadium dioxide refractive index undergo phase transitions, making it impossible to achieve differentiated impedance matching of the spacer layer between the two phase states.

[0006] To solve the above-mentioned technical problems, the present invention adopts the following technical solution: The present invention provides a method for designing and simulating the photonic structure of temperature-adaptive radiation-controlled glass.

[0007] The photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass includes:

[0008] Step S10: Obtain the set of optical constants of the temperature-controlled glass, and perform the equivalent surface impedance extraction task based on the set of optical constants using the Floquet mode S-parameter inversion method, and output the two-phase equivalent surface impedance set.

[0009] Step S20: Based on the two-phase equivalent surface impedance set, perform the Fabry-Perot cavity input impedance analysis task using the transmission line equivalent analytical method, and output the cavity impedance analysis result;

[0010] Step S30: Based on the cavity impedance analysis results, the joint objective construction task is performed using the impedance matching difference quantization rule, and the joint optimization objective function is output;

[0011] Step S40: Based on the joint optimization objective function, the Gaussian process Bayesian optimization method is used to perform the task of solving the interval layer parameters, and the optimal combination of interval layer parameters is output.

[0012] Step S50: Perform continuous thermal performance simulation and Monte Carlo tolerance verification based on the optimal combination of spacer layer parameters, and output the verified photonic structure scheme.

[0013] Preferably, step S10, which involves obtaining the set of optical constants for the temperature-controlled glass, and performing the equivalent surface impedance extraction task based on the set of optical constants using the Floquet mode S-parameter inversion method to output a two-phase equivalent surface impedance set, specifically includes:

[0014] Step S101: Establish a three-dimensional model of the periodic unit of the temperature-controlled glass in the electromagnetic simulation platform. The three-dimensional model of the periodic unit includes a selective infrared reflective layer, a vanadium dioxide tuning layer, a transparent dielectric spacer layer and a glass substrate arranged sequentially from the incident side to the exit side. Import a set of optical constants into the three-dimensional model of the periodic unit. The set of optical constants includes the complex refractive index of the vanadium dioxide insulating phase, the complex refractive index of the vanadium dioxide metallic phase, the complex refractive index of the selective infrared reflective layer material, the complex refractive index of the transparent dielectric spacer layer and the complex refractive index of the glass substrate.

[0015] Step S102: Set Floquet periodic boundary conditions in the transverse direction of the periodic unit three-dimensional model, set port boundary conditions at the incident port and the exit port respectively, and perform frequency scanning on TE polarized light and TM polarized light in the 8-micron to 13-micron band respectively to obtain the reflection coefficient corresponding to vanadium dioxide insulation and the reflection coefficient corresponding to vanadium dioxide metal.

[0016] Step S103: Define the interface where the surface layer to be equivalent is located as the reference plane T, and convert the surface layer to be equivalent at the reference plane T into a lumped impedance element, and determine the normalized equivalent surface impedance according to the following formula:

[0017]

[0018] in, wavelength Normalized equivalent surface impedance at the location, This is the reflection coefficient converted to the reference plane T after phase compensation; from Obtain the equivalent surface impedance , The free-space wave impedance is given, and the corresponding equivalent surface impedance of vanadium dioxide insulation is obtained. Equivalent surface impedance corresponding to vanadium dioxide metal This forms the equivalent surface impedance set of the two phase states.

[0019] Preferably, step S20, which involves performing the Fabry-Perot cavity input impedance analysis task based on the two-phase equivalent surface impedance set using a transmission line equivalent analytical method and outputting the cavity impedance analysis result, specifically includes:

[0020] Step S201: The vanadium dioxide tuning layer corresponding to the two-phase equivalent surface impedance set is equivalent to a lumped impedance unit, the transparent dielectric spacer layer in the temperature-controlled glass is equivalent to a lossy transmission line, and the selective infrared reflection layer in the temperature-controlled glass is equivalent to a terminal load, thus establishing a one-dimensional transmission line equivalent model of the Fabry-Perot cavity.

[0021] Step S202: Based on the one-dimensional transmission line equivalent model, calculate the cavity input impedance under different phase states according to the following formula:

[0022]

[0023] in, This is a phase identifier for vanadium dioxide, and , It represents the insulating phase of vanadium dioxide. Indicates the metallic phase of vanadium dioxide; phase state At wavelength The cavity input impedance at that location. The characteristic impedance of the transparent dielectric spacer layer. The refractive index of the transparent dielectric spacer layer, phase state The corresponding terminal load impedance, The thickness of the transparent dielectric spacer layer, The propagation constant of the transparent medium spacer layer is... It is the attenuation constant. The phase constant, The extinction coefficient of the transparent dielectric spacer layer. The imaginary unit;

[0024] Step S203: Insulate the cavity input impedance corresponding to vanadium dioxide insulation. The cavity input impedance corresponding to vanadium dioxide metal The coupling factor, which is converted from the port reflection coefficient, is written into the cavity impedance analysis result.

[0025] Preferably, in step S202, the terminating load impedance The terminal load impedance is determined by the complex permittivity, layer thickness, and back-side load impedance of the selective infrared reflective layer; when the layer thickness of the selective infrared reflective layer is greater than the skin depth at the corresponding wavelength, the terminal load impedance... Take an approximate load value ,in, phase state The corresponding equivalent complex permittivity of the selective infrared reflective layer; when the thickness of the selective infrared reflective layer is not greater than the skin depth at the corresponding wavelength, the terminal load impedance It is calculated recursively from a transmission line of finite thickness.

[0026] Preferably, step S30, which involves executing a joint objective construction task based on the cavity impedance analysis results using impedance matching difference quantization rules and outputting a joint optimization objective function, specifically includes:

[0027] Step S301: Based on the cavity impedance analysis results and the two-phase equivalent surface impedance set, calculate the impedance matching difference factor according to the following formula:

[0028]

[0029] in, wavelength Impedance matching difference factor at the location, This represents the cavity input impedance corresponding to vanadium dioxide metal. This represents the equivalent surface impedance corresponding to vanadium dioxide metal. This is the cavity input impedance corresponding to vanadium dioxide insulation. The equivalent surface impedance corresponding to vanadium dioxide insulation. To prevent positive numbers with a denominator of zero;

[0030] Step S302: Calculate the average emissivity of vanadium dioxide metal in the 8-micron to 13-micron wavelength band based on the cavity impedance analysis results. The average emissivity corresponding to vanadium dioxide insulation The average value of impedance matching difference factor and cavity inhibitors The cavity inhibition factor This represents the non-target absorption integral value of the vanadium dioxide insulating phase under blackbody spectral radiative force weighting.

[0031] Step S303: Based on the average emissivity The average emissivity The average value and the cavity inhibition factor Construct a joint optimization objective function:

[0032]

[0033] in, The thickness of the transparent dielectric spacer layer and the refractive index of the transparent medium spacer layer Let the joint optimization objective function be the variables. , , and All are preset weighting coefficients.

[0034] Preferably, step S40, which involves performing the interval layer parameter solution task based on the joint optimization objective function using a Gaussian process Bayesian optimization method and outputting the optimal interval layer parameter combination, specifically includes:

[0035] Step S401: Based on the refractive index range, fabricable thickness range, and the joint optimization objective function of the candidate transparent medium material, establish the spacer layer parameter search space and generate an initial sample set using the Latin hypercube sampling method;

[0036] Step S402: Input the initial sample set into the Gaussian process surrogate model, use the value of the joint optimization objective function as the sample response value, and select the candidate parameter combination to be simulated from the interval layer parameter search space through the expected improvement acquisition function;

[0037] Step S403: Perform full-wave electromagnetic simulation on the candidate parameter combination to obtain the emissivity distribution and joint optimization objective function value corresponding to the candidate parameter combination. Write the joint optimization objective function value back to the Gaussian process surrogate model until the optimal value change between two adjacent rounds is less than the preset convergence threshold or the preset number of iterations is reached. Output the optimal thickness of the transparent dielectric spacer layer. Optimal refractive index of transparent dielectric spacer layer This forms the optimal combination of interval layer parameters.

[0038] Preferably, step S50, which involves performing continuous thermal performance simulation and Monte Carlo tolerance verification based on the optimal spacer layer parameter combination, and outputting the verified photonic structure scheme, specifically includes:

[0039] Step S501: Read the optimal thickness of the transparent dielectric spacer from the optimal spacer parameter combination. Optimal refractive index of transparent dielectric spacer layer Select the optimal refractive index of the spacer layer with the transparent medium. Matched candidate materials, combined with the period, unit diameter and height parameters of the vanadium dioxide tuning layer, generate a table of photonic structure parameters for the temperature-controlled glass;

[0040] Step S502: Establish a continuous thermal performance calculation model for temperature-controlled glass under a multi-physics coupling environment. Input the hourly outdoor temperature, solar irradiance, equivalent sky temperature and indoor set temperature throughout the year into the continuous thermal performance calculation model to obtain the net radiative heat flow during the heating season and the net radiative heat flow during the cooling season of the window.

[0041] Step S503: Perform Monte Carlo tolerance verification on the photonic structure parameter table, set the thickness deviation of the transparent medium spacer layer, the thickness deviation of the vanadium dioxide tuning layer and the refractive index fluctuation range, generate the emissivity distribution after tolerance perturbation, and output the verified photonic structure scheme when the emissivity distribution meets the preset tolerance threshold.

[0042] This invention also provides a photonic structure design and simulation optimization system for temperature-adaptive radiation-controlled glass, comprising:

[0043] The equivalent surface impedance extraction module is used to obtain the set of optical constants of the temperature-controlled glass. Based on the set of optical constants, the equivalent surface impedance extraction task is performed using the Floquet mode S-parameter inversion method, and the two-phase equivalent surface impedance set is output.

[0044] The cavity input impedance analysis module is used to perform the Fabry-Perot cavity input impedance analysis task based on the two-phase equivalent surface impedance set using the transmission line equivalent analysis method, and output the cavity impedance analysis result.

[0045] The joint objective construction module is used to perform a joint objective construction task based on the cavity impedance analysis results and using impedance matching difference quantization rules, and output a joint optimization objective function.

[0046] The interval layer parameter solving module is used to perform the interval layer parameter solving task based on the joint optimization objective function using a Gaussian process Bayesian optimization method, and output the optimal interval layer parameter combination.

[0047] The scheme generation and verification module is used to perform continuous thermal performance simulation and Monte Carlo tolerance verification based on the optimal combination of spacer layer parameters, and output the verified photonic structure scheme.

[0048] The present invention also provides a photonic structure design and simulation optimization device for temperature-adaptive radiation temperature-controlled glass. The photonic structure design and simulation optimization device for temperature-adaptive radiation temperature-controlled glass includes: a memory, a processor, and a photonic structure design and simulation optimization program for temperature-adaptive radiation temperature-controlled glass stored in the memory and executable on the processor. When the photonic structure design and simulation optimization program for temperature-adaptive radiation temperature-controlled glass is executed by the processor, the above-mentioned method is implemented.

[0049] The present invention also provides a computer program product, which includes a photonic structure design and simulation optimization program for temperature-adaptive radiation-controlled glass. When the photonic structure design and simulation optimization program for temperature-adaptive radiation-controlled glass is executed by a processor, it implements the above-mentioned method.

[0050] The beneficial effects of this invention are as follows:

[0051] This invention establishes a transmission line equivalent model for the vanadium dioxide asymmetric Fabry-Perot cavity, introduces a differential impedance matching factor and a cavity mode suppression factor, and incorporates the impedance matching difference under the two phase states and the suppression requirement of residual cavity absorption in the insulating phase into the joint optimization objective function. This allows the solution of the spacer layer parameters to quantitatively take into account both the emissivity contrast at high and low temperatures and the cavity mode suppression effect.

[0052] This invention combines rapid pre-screening of transmission line equivalent models with accurate verification of full-wave electromagnetic simulation, and embeds a self-consistent correction iterative loop. While ensuring the accuracy of the solution, it reduces the computational overhead of direct full-wave scanning of the full-dimensional parameter space, enabling the optimization process to stably converge to the globally optimal spacer layer combination under the premise of drastic phase transition of vanadium dioxide complex refractive index. Attached Figure Description

[0053] Figure 1 This is a flowchart illustrating the first embodiment of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass according to the present invention.

[0054] Figure 2 This is a schematic diagram of the geometric model of the TARC glass micro / nano structure unit, representing the first embodiment of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass according to the present invention.

[0055] Figure 3 This is a schematic diagram of the simulation input of the complex refractive index curves of vanadium dioxide before and after the phase transition in the infrared band, representing the first embodiment of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass of the present invention.

[0056] Figure 4 This is a schematic diagram of the electric field intensity distribution and absorption spectrum curve from COMSOL simulation of the first embodiment of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass of the present invention.

[0057] Figure 5 This diagram illustrates the emissivity comparison between TARC glass and conventional glass at different temperatures, representing the first embodiment of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass according to the present invention. Detailed Implementation

[0058] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments.

[0059] Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0060] Example 1: As Figure 1 The diagram shown is a flowchart illustrating the first embodiment of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass according to the present invention.

[0061] In the first embodiment, the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass includes:

[0062] Step S10: Obtain the multiphase optical constants of the temperature-controlled glass, and perform the equivalent surface impedance extraction task based on the multiphase optical constants using the Floquet mode S-parameter inversion mechanism to output the two-phase equivalent surface impedance.

[0063] The multiphase optical constants mentioned in this step refer to the complex refractive index dispersion data of vanadium dioxide thermally induced phase change materials obtained through ellipsometric measurements or spectral reflectance methods in both the insulating and metallic phases, covering the 8-13 micrometer long-wave infrared atmospheric window band, specifically including the real part of the refractive index. and extinction coefficient The wavelength-dependent curves serve as the fundamental material inputs for constructing the electromagnetic model of the asymmetric Fabry-Perot cavity. The Floquet mode S-parameter inversion mechanism involves applying transverse Floquet periodic boundary conditions to the three-dimensional model of the temperature-controlled glass periodic unit in an electromagnetic simulation platform, performing frequency scanning to solve Maxwell's equations for both TE and TM polarization states, and extracting the complex reflection coefficients containing phase information. and complex transmission coefficient After de-embedding and phase compensation processing, the complex reflection coefficient on the reference plane T at the interface between the metasurface layer and the spacer layer is converted into a normalized equivalent surface impedance. This equivalent surface impedance integrates the complex electromagnetic responses such as the geometry, array period, and material dispersion of the vanadium dioxide micro / nano array into a wavelength-varying complex impedance parameter, outputting the equivalent surface impedance Zs under the insulating phase. And the equivalent surface impedance Zs under the metallic phase, These two sets of complex impedance curves completely preserve the influence of large-angle diffraction, polarization dependence, and near-field mutual coupling on the metasurface reflection characteristics. They provide a quantitative terminal description of the metasurface layer in two phases for calculating the cavity input impedance using the transmission line model in step S20, and constitute the key parameter interface for transferring from three-dimensional full-wave simulation to one-dimensional analytical model.

[0064] After this step, the originally discrete and strongly dispersed vanadium dioxide multiphase complex refractive index curves and the vector field distribution of the three-dimensional micro / nano structure are condensed into two concise equivalent surface impedance dispersion curves, significantly compressing the information dimension characterizing the metasurface's electromagnetic response. Since the S-parameter inversion of the Floquet mode strictly includes the contributions of higher-order diffraction modes and the attenuation of surface wave modes, the obtained equivalent surface impedance accurately reflects the anomalous reflection and absorption enhancement effects that may occur under oblique incidence or large-period conditions, avoiding the polarization selectivity and mode resonance details lost due to homogenization in conventional equivalent medium approximations. When these impedance parameters are incorporated into the subsequent transmission line model, the calculation of the cavity input impedance from the metasurface to the reflector layer can be connected with the spacer layer transmission line equations and the terminal reflector layer impedance using a highly accurate numerical interface. This ensures that the difference in surface impedance between the insulating and metallic phases is not smoothed or obscured in the model chain, providing a reliable data boundary for the starting point of differentiated impedance matching design.

[0065] In traditional multilayer film or metasurface design, to reduce modeling complexity, periodic micro / nanostructures are often equated to a uniform thin film and their equivalent refractive index is calculated using effective medium theory, or they are directly treated as a thickness-free impedance boundary. This approach introduces significant errors in the vanadium dioxide asymmetric Fabry-Perot cavity scenario because it fails to reflect the significant differences in equivalent surface impedance under TE and TM polarization, and neglects the influence of array near-field coupling and diffraction lobe effects on the reflection phase. When such systematically biased equivalent parameters are used in subsequent cavity mode analysis, the calculation of the matching relationship between the surface impedance in the insulating phase and the input impedance of the spacer layer is distorted, leading to inaccurate predictions of the wavelength position and intensity of the residual cavity absorption peak. Consequently, it becomes impossible to accurately assess the degree to which the optical thickness of the spacer layer suppresses residual absorption in the insulating phase. This step utilizes the Floquet port to decompose the Floquet modes of each order in the transverse wave vector domain, incorporating the contributions of higher-order diffraction and near-field coupling into the reflection coefficient and inverting it through impedance relationships. This ensures that the electromagnetic properties of the metasurface layer and the longitudinal cavity resonance effect of the spacer layer maintain consistent physical interface accuracy, laying a reliable foundation for quantitative optimization in subsequent steps.

[0066] Taking a metasurface composed of a square array of vanadium dioxide nanosheets as an example, with a nanosheet diameter of 800 nm and an array period of 1.2 μm, vanadium dioxide transforms into a metallic phase when the ambient temperature rises to approximately 75°C and reverts to an insulating phase when the temperature drops to 25°C. A periodic element model was established in a finite element simulation tool, including a semi-infinite air domain, a visible light antireflection layer, the aforementioned metasurface array, a zinc sulfide spacer layer with an initial thickness of 1 μm, and a silver reflective layer on the back. Measured complex refractive index and dispersion data at two temperature points were imported. After setting Floquet periodic conditions at the transverse boundaries of the elements, frequency scans under TE and TM polarization were performed at the incident and exit ports to obtain the complex reflection coefficients in the 8 μm to 13 μm wavelength range. The complex reflection coefficient at the reference plane T is determined after de-embedding. Normalized impedance relationships are applied to the insulating and metallic phases respectively, yielding two sets of equivalent surface impedance curves varying with wavelength. Under the metallic phase, the real part of the impedance is smaller and the imaginary part is inductive, while under the insulating phase, the real part of the impedance increases and the imaginary part shifts towards the capacitive region. These two sets of curves are directly read by the subsequent transmission line model to calculate the behavior of the input impedance from the metasurface interface to the reflective layer as a function of the spacer layer thickness.

[0067] Specifically, for example, such as Figure 2 As shown, the three-dimensional model of the periodic unit uses a single vanadium dioxide tuning unit as a repeating primitive, including a semi-infinite air domain, a visible light antireflection protective layer, a vanadium dioxide tuning layer, a transparent dielectric spacer layer, a selective infrared reflective layer, and a glass substrate. Floquet periodic boundaries are set on both sides of the model. The incident port is used to apply TE and TM polarization scans in the 8-13 micrometer band, and the exit port is used to acquire transmission response and phase information. By defining the interface of the equivalent surface layer as the reference plane T and performing phase de-embedding processing on the port reflection coefficients, the complex scattering response of the three-dimensional periodic unit can be converted into equivalent surface impedance dispersion curves corresponding to the vanadium dioxide insulating phase and the metal, providing a unified reference plane for subsequent cavity input impedance analysis.

[0068] Step S20: Based on the two-phase equivalent surface impedance, the cavity input impedance analysis task is performed using the transmission line analogy equivalent mechanism, and the Fabry-Perot cavity input impedance is output.

[0069] The Fabry-Perot cavity input impedance in this step This refers to the equivalent terminating impedance viewed from the interface between the metasurface layer and the infrared transparent dielectric spacer towards the selective infrared reflective layer. It describes the overall load characteristics of the electromagnetic wave after it propagates back and forth within the spacer and interacts with the reflective layer. The core of the transmission line analogy equivalence mechanism is to abstract the infrared transparent dielectric spacer as a characteristic impedance. Complex propagation constant A lossy transmission line, wherein the phase constant is The attenuation constant is determined by the refractive index nd of the spacer layer. The physical thickness is d, determined by the extinction coefficient kd. The selective infrared reflective layer is equivalent to the terminal load impedance ZL, the value of which is determined by the complex dielectric function of the reflective layer and the physical thickness. When the thickness of the reflective layer is greater than the skin depth, ZL tends to a finite complex value. At the interface between the metasurface and the spacer layer, the transmission line impedance transformation formula is used. ·(ZL+Zd· ) / (Zd+ZL· The input impedance is obtained by transforming the terminating load using a lossy transmission line. This analytical expression is simultaneously modulated by the physical thickness of the spacer layer, its refractive index, its extinction coefficient, and the terminating impedance of the reflective layer. It is the core intermediate variable connecting the equivalent surface impedance of the two-phase state of the metasurface with the structural parameters of the spacer layer. It directly determines the strength of cavity coupling and the position of absorption peak, and provides the necessary cavity side parameters for defining the differential impedance matching factor and cavity mode suppression factor in step S30.

[0070] Through this analogy, the longitudinal electromagnetic energy transfer and phase coherence relationship between the metasurface and the reflective layer are expressed in a closed analytical form in the complex input impedance. In this process, the cavity frequency response, which previously required frequent full-wave simulations to obtain, is transformed into a complex numerical sequence directly calculated from the spacer layer parameters. This makes the control of the cavity resonance conditions by the spacer layer thickness and refractive index explicit; as the spacer layer thickness increases, it affects the phase constant. When the input impedance changes, it produces an analytically traceable rotational trajectory along the transmission line circle diagram, while the attenuation term... The presence of this component causes the magnitude and phase of the input impedance to be gradually compressed as the thickness increases, thus reflecting the damping characteristics of the non-ideal cavity. By employing a complex variable form including the hyperbolic tangent function, this step fully considers the amplitude attenuation and phase shift caused by the intrinsic absorption of the spacer material. This allows for the sensitive capture of the residual cavity mode response attenuated due to the weak absorption of the spacer layer when calculating the cavity mode suppression factor for the insulating phase in subsequent steps. This improves the accuracy of the joint optimization objective function in characterizing the cavity effect and makes the gradient information of the objective function in the parameter space more closely resemble the changing trend of the actual electromagnetic response.

[0071] In traditional thin-film optics design, when analyzing asymmetric Fabry-Perot cavities, the spacer layer is often treated as a lossless ideal dielectric. In transmission line analogies, the propagation constant is directly simplified to a purely imaginary phase term, or the absorptivity is calculated using optical admittance recursive formulas. This approach physically ignores the small but not negligible extinction coefficient of most infrared transparent media in the infrared band. When vanadium dioxide is in the insulating phase, the real and imaginary parts of the refractive index change significantly, and the position of the metasurface's equivalent surface impedance also shifts considerably. If the calculation of the cavity input impedance at this time underestimates the imaginary part shift due to neglecting losses, it will incorrectly determine the degree of conjugate matching between the input impedance and the metasurface impedance in the insulating phase state. This will cause the subsequent differential impedance matching factor to fail to accurately reflect the residual absorption risk of the cavity mode. This step introduces the complex propagation constant and uses the complete transmission line impedance transformation formula to incorporate the weak absorption characteristics of the spacer layer and its resulting non-ideal cavity effects into the input impedance. Without adding additional fitting parameters, the influence of actual losses is incorporated into the optimization chain, making the optimized spacer layer thickness more reliable in suppressing unexpected absorption peaks in the insulating phase.

[0072] For example, taking a cavity configuration combining a vanadium dioxide metasurface with a zinc sulfide spacer layer and a silver reflective layer, the refractive index of zinc sulfide is approximately 2.2 near a wavelength of 10 micrometers, and its extinction coefficient is approximately 0.0006. The thickness of the back silver reflective layer is much greater than the skin depth, and its terminal load impedance ZL is approximately equal to the free-space wave impedance divided by the square root of the complex permittivity of silver at that wavelength. Using the transmission line formula to calculate Zin at different thicknesses, it can be seen that as the zinc sulfide thickness gradually increases from 0.5 micrometers to 2.5 micrometers, the input impedance traces a spiral-shaped attenuation trajectory on the complex plane that gradually contracts inwards, with the degree of envelope contraction dominated by the extinction coefficient. When the thickness makes Zin and Zs in the metallic phase... When their conjugate values ​​are close, strong coupling absorption in the cavity is excited; however, near the same thickness under the insulating phase, due to the spiral decay characteristic of the input impedance, Zin and Zs... The conjugate points are relatively far apart, and the attenuation reduces the Q value, naturally suppressing cavity mode absorption. This characteristic is quantitatively captured in subsequent steps using differential matching factors and cavity mode suppression factors.

[0073] Specifically, for example, such as Figure 3As shown, the vanadium dioxide tuning layer is abstracted as a lumped impedance unit located at the reference plane T, the transparent dielectric spacer layer is abstracted as a lossy transmission line with length d, characteristic impedance Zd, and propagation constant γ, and the silver reflective layer or selective infrared reflective layer is abstracted as the terminal load ZL. When the incident electromagnetic wave propagates back and forth within the spacer layer, the phase accumulation and amplitude attenuation of the transparent dielectric spacer layer jointly determine the trajectory of the cavity input impedance Zin in the complex plane. When Zin under the metallic phase is close to the conjugate matching position of the equivalent surface impedance of vanadium dioxide, while Zin under the insulating phase is far away from the corresponding conjugate matching position, a temperature adaptive response of high-temperature strong radiation heat dissipation and low-temperature weak radiation heat preservation can be formed.

[0074] Step S30: Based on the input impedance of the Fabry-Perot cavity, a conjugate matched differential quantization mechanism is used to perform the joint optimization objective function definition task, and the joint optimization objective function is output;

[0075] The joint optimization objective function in this step It is a comprehensive evaluation index constructed based on the physical thickness d and refractive index nd of the spacer layer. Among them, the differential impedance matching factor... Quantitatively characterizing the ratio of the distance between the cavity input impedance and the conjugate value of the metasurface metallic phase impedance at each wavelength to the distance between the conjugate value of the insulating phase impedance, when When the value is smaller in the metallic phase and larger in the insulating phase, it indicates that the conjugate matching state of the two phases at that wavelength has achieved the required spatial separation. The cavity mode suppression factor (CSF) is specifically used to measure the absorption component contributed by residual cavity resonance in the insulating phase state. Specifically, it is the value obtained by integrating the portion of the insulating phase emissivity attributable to enhanced cavity coupling in the 8-13 micrometer wavelength range after weighting by the radiative power of the 300K blackbody spectrum. The joint optimization objective function uses the high-temperature average emissivity... With low temperature average emissivity The difference The self-value, the weighted average of DIMF, and CSF are linearly combined through preset weight coefficients W1 to W4, mapping the two requirements of improving emissivity contrast and suppressing residual cavity absorption in the insulating phase into a single scalar, so that the iterative solution in step S40 can perform a directional parameter search around the extremum of the function.

[0076] After adopting this mechanism, the objective function is no longer simply based on and Instead of using maximizing this difference as the sole criterion, penalties are imposed on potential unexpected absorptions in the insulating phase by introducing the average DIMF and CSF term. This is because a certain combination of spacer layer parameters leads to high absorption in the metallic phase but increases in the insulating phase due to insufficient suppression of the cavity mode. When the DIMF average value is too small, these terms will lower the joint objective function value, thus naturally filtering out parameter points that, although the nominal emissivity difference is large, have unacceptable absorption by the insulating phase during the optimization process. Simultaneously, the DIMF average value integrates the relative separation of the two-phase impedance matching in the spectral dimension. It is directly related to the trajectory on the impedance circle diagram in the transmission line model, providing an additional gradient structure for the objective function in the parameter space. This helps the optimizer distinguish between the illusion of high emissivity contrast caused by coincidental matching of a single wavelength and the parameter range that truly achieves impedance differentiation across the entire wavelength band. This design explicitly embeds the physical relationship between the optical thickness of the spacer layer and the impedance matching state of the two phases into the optimization loop, making the optimization results more in line with the stringent requirements of temperature-controlled glass for low emissivity of the insulating phase.

[0077] In existing optimization methods based on thin-film optics or circuit equivalence, the algebraic difference between high and low temperature absorbance or emissivity is typically used as the objective function. The optimizer may converge in the parameter space to a parameter combination where the metallic phase absorption is nearly perfect and the insulating phase is simultaneously excited to a strong cavity resonance. While the difference index may appear good, it actually provides a switch from one high absorption state to another, rather than the large emissivity span required between high and low temperatures, failing to realize the adaptive adjustment function of radiation-controlled temperature glass between high-temperature heat dissipation and low-temperature insulation. Furthermore, this single difference objective lacks constraints on the physical causes of impedance matching, resulting in high sensitivity to process fluctuations in the refractive index or thickness of the spacer layer. This step incorporates the conjugate matching difference and cavity mode suppression factor into the joint optimization objective function, inherently requiring the cavity input impedance under the insulating phase to actively deviate from the conjugate state of the metasurface surface impedance, making cavity mode mismatch an inherent characteristic of the insulating phase rather than an accidental phenomenon. The optimal combination of spacer parameters obtained in this way maintains the strong coupling absorption of the metallic phase while suppressing the residual absorption of the cavity mode of the insulating phase to a low level. Furthermore, because the objective function includes a smooth measure of impedance matching differences, it improves the robustness of the optimization results to structural parameter drift caused by phase difference.

[0078] Step S40: Based on the joint optimization objective function, an iterative self-consistent coupling optimization mechanism is used to perform the task of solving the interval layer parameters, and the optimal combination of interval layer parameters is output;

[0079] The optimal spacer layer parameter combination mentioned in this step refers to the spacer layer physical thickness *dopt* and refractive index *nd,opt* that simultaneously satisfy multiple design objectives: sufficiently high high-temperature emissivity, sufficiently low low-temperature emissivity, and controllable absorption of the residual cavity in the insulating phase. The iterative self-consistent coupled optimization mechanism is a closed-loop solution framework that couples rapid pre-screening of the transmission line equivalent model with accurate verification using three-dimensional full-wave electromagnetic simulation. In its initial stage, a coarse mesh is scanned within the two-dimensional parameter space composed of the physical thickness and refractive index at a preset step size. The transmission line model is used to quickly calculate the average differential impedance matching factor at each mesh point and the estimated absorption rate based on the port impedance matching principle. Based on the estimated value of the joint optimization objective function, promising candidate parameter combinations are selected. Subsequently, these candidate parameters are substituted one by one into the three-dimensional full-wave model, which includes the true geometry of the metasurface and the full diffraction effect, to calculate the accurate high-temperature average emissivity and low-temperature average emissivity. The S-parameters obtained from the full-wave simulation are then used to back-calculate and correct the metasurface equivalent impedance or terminal load parameters in the transmission line model. The corrected equivalent parameters are reloaded into the transmission line model for a new round of pre-screening and full-wave verification. This process is repeated until the relative change in the objective function value of the optimal solution obtained in two adjacent iterations is lower than the preset convergence threshold or the maximum number of iterations is reached. Finally, the globally optimal dot and nd,opt are output.

[0080] Through this mechanism, the transmission line equivalent model delineates a significantly smaller candidate region for parameters in the early stages of iteration, avoiding indiscriminate full-wave scanning across a vast parameter space and thus saving computational resources. As iteration progresses, the precise reflection phase and absorptivity data obtained from the full-wave simulation are used to correct the dispersion curve of the metasurface equivalent impedance and possible terminal load values ​​in the transmission line model. This allows the prediction accuracy of the transmission line model in subsequent pre-screening to gradually approach the full-wave level, and the candidate points retained in the pre-screening become increasingly concentrated near the true global optimum. Two models with different fidelity are mutually calibrated in this way: the transmission line model provides efficient exploration capabilities, while the full-wave simulation ensures the accuracy of the final parameter evaluation. Through their synergistic effect, the resulting optimal spacer layer parameter combination has been tested against all the real electromagnetic complexities, including three-dimensional structure diffraction effects, polarization dependence, material dispersion, and near-field coupling. This reduces dependence on empirical initial values ​​or search ranges while maximizing the joint optimization objective function value.

[0081] In traditional photonic structure optimization processes, equivalent transmission line or equivalent circuit models are typically used only for rough estimations in the initial conceptual design phase. Once a coarse range is obtained, the model is discarded, and full-wave simulation or heuristic algorithms are used for parameter scanning or optimization. This approach carries risks when vanadium dioxide optical constants undergo drastic phase transitions and surface impedance shifts significantly: the parameters used in the initial equivalent model are often estimated based on a fixed phase state or idealized conditions, and their systematic biases may cause potential optimal solutions located at the edge of the parameter space to be missed during the pre-screening phase. Even if full-wave optimization finds local extrema within the initial screening range, the impedance information obtained from precise simulation cannot be used to correct the original simplified model, resulting in model errors persisting throughout the design process. The self-consistent coupling mechanism in this step re-extracts or corrects the equivalent impedance parameters using full-wave simulation results at the end of each iteration, making the transmission line model used in the next round of pre-screening closer to the actual physical response than the previous generation. This reduces the solution space distortion caused by model simplification and improves the certainty of the global optimal solution without adding external data post-processing steps.

[0082] For example, the search range for the zinc sulfide spacer layer thickness is set to 0.5 μm to 2.5 μm, and the search range for the refractive index is set to 1.8 to 2.4. In the first iteration, the transmission line model performs a mesh scan with a thickness step of 50 nm and a refractive index step of 0.05 to quickly estimate the joint objective function value of each parameter combination, from which the top 20 candidate parameters with the highest objective function values ​​are selected. These candidate thicknesses and refractive indices are then substituted into the complete full-wave model containing the vanadium dioxide disk array to calculate the accurate... and Find the optimal candidate point 0.87 The value was 0.10, but the surface impedance extracted by the full-wave simulation differed somewhat from the impedance curve used in the transmission line model. After correcting the equivalent impedance data accordingly, the second round of transmission line pre-screening showed that the optimal solution was concentrated in a region with a thickness of 1.2 micrometers and a refractive index of 2.05. At this point, the full-wave simulation performed more detailed sampling in this local region, ultimately determining dot to be 1.18 micrometers and nd,opt to be 2.04. These parameters maintain strong coupling absorption in the metallic phase while keeping the cavity mode absorption in the insulating phase at an extremely low level. After two rounds of iteration, the change in the objective function value of the optimal solution was less than 1%, satisfying the convergence condition, and the optimal spacer layer parameters for this set were output.

[0083] Specifically, for example, such as Figure 4As shown, the iterative optimization process for spacer layer parameters first reads the set of equivalent surface impedances of the two-phase states and the analytical results of the cavity input impedance, and establishes a candidate parameter space using the thickness d and refractive index nd of the transparent dielectric spacer layer as design variables. Then, for each set of candidate parameters, the average emissivity of the metallic phase, the average emissivity of the insulating phase, the average impedance matching difference factor, and the cavity suppression factor are calculated to form a joint optimization objective function value. A Gaussian process surrogate model or a modified transmission line equivalent model selects the next batch of candidate points for full-wave simulation based on the current sample response, and the full-wave simulation results are then written back to update the surrogate model. This process is repeated until the change in the optimal objective function of adjacent iterations is lower than a preset convergence threshold or the preset number of iterations is reached, at which point the optimal spacer layer parameter combination is output.

[0084] Step S50: Based on the optimal combination of spacer layer parameters, the scheme generation and verification task is performed using building thermal performance simulation and Monte Carlo tolerance verification mechanism, and the verified photonic structure scheme is output.

[0085] The validated photonic structure scheme used in this step is a complete design document for temperature-controlled glass. It includes at least the optimal physical thickness of the spacer layer (dopt) determined in step S40, the specific type of medium material matching its refractive index (nd,opt), and geometric parameters such as the period, unit diameter, and unit height of the vanadium dioxide metasurface. The building thermal performance simulation and Monte Carlo tolerance verification mechanism involves establishing a building thermal model of the temperature-controlled glass window under a multiphysics coupling environment. Inputting typical annual meteorological data, including hourly outdoor air temperature, solar irradiance, equivalent sky temperature, and indoor set temperature, the model calculates the net radiative heat flux of the window during the heating and cooling seasons and compares it with ordinary low-emissivity glass or unoptimized temperature-controlled glass to quantitatively evaluate its radiative temperature control benefits under real dynamic conditions. Simultaneously, to address potential random deviations in spacer layer thickness and refractive index during actual manufacturing, Monte Carlo process tolerance analysis was performed. The thickness and refractive index were set to follow a normal distribution centered on the optimal value and with the standard deviation of process capability as the variance. A large number of random samples were generated and substituted into a three-dimensional full-wave model to recalculate the high- and low-temperature emissivity difference. The distribution location, dispersion, and quantiles within a pre-set confidence interval of this difference were statistically analyzed to determine whether the design scheme could still stably maintain the required emissivity control range under the expected manufacturing precision. Finally, the design parameters that meet the engineering feasibility threshold, along with the verification results, were output as a scheme document.

[0086] By incorporating this verification step, the emissivity contrast advantage, which was originally only valid under nominal parameter combinations, is re-evaluated under dynamic boundary conditions and manufacturing random disturbances that more closely resemble actual application environments. The hourly meteorological conditions introduced in the building thermal performance simulation consider the actual spectral absorption and radiative heat transfer processes of the window under different seasons, different solar incidence angles, and varying sky background radiation. This reveals parameter combinations that only exhibit good emissivity differences under specific steady-state conditions but contribute little or even negatively in the dynamic thermal environment throughout the year, thus ensuring that the output photonic structure scheme has genuine building energy-saving value. Monte Carlo tolerance analysis statistically characterizes the flatness of the objective function plateau near the optimal point: if most samples of the emissivity difference are still higher than the preset lower limit within the 90% confidence interval, it indicates that the design is not sensitive to manufacturing deviations and can be directly transferred to trial production; conversely, it indicates that the scheme relies too heavily on precision machining and requires returning to previous steps to adjust the parameter range or process constraints. The prediction and closure of manufacturability and application effectiveness are completed during the simulation phase.

[0087] After the conventional photonic structure or film system design is completed, it is often only based on spectral data under nominal parameters as the verification basis, with little simultaneous system-level evaluation based on dynamic simulation of building heat load throughout the year and manufacturing tolerance. This disconnect can lead to some designs that show ideal emissivity control range in laboratory prototypes failing in actual building curtain walls or insulated glass products. Due to differences between real weather conditions and standard irradiance assumptions, or thickness deviations of tens of nanometers and slight refractive index fluctuations caused by coating and etching processes, residual cavity models of the insulating phase reappear, and the final improvement effect of net radiative heat flow in winter and summer is far lower than the design expectation. This step makes building thermal performance simulation and Monte Carlo tolerance verification a necessary step before the scheme is released, establishing a complete pathway from electromagnetic optimization to engineering performance prediction and manufacturing feasibility assessment. The statistical distribution of emissivity contrast under all operating conditions and all tolerances is directly fed back into the decision rules for design acceptance or rejection. Without additional prototype hardware, parameter combinations that are overly sensitive to the process can be screened out in digital space, reducing the probability of performance degradation of the finished product on the building site and enhancing the engineering implementation credibility of the overall design process.

[0088] For example, for the optimal parameter combination of 1.18 micrometers thickness and 2.04 refractive index of the zinc sulfide spacer layer obtained in step S40, typical meteorological data from northern Chinese cities were loaded into the building thermal performance simulation. The calculation results show that, compared with conventional double-silver Low and E insulating glass, this temperature-controlled glass reduces net radiative heat loss from indoors to outdoors by about 14% during the winter heating season due to the lower emissivity of the insulating phase, and reduces solar infrared heat gain by about 11% during the summer cooling season due to the synergistic effect of the high emissivity of the metallic phase and the infrared reflective layer. The overall net radiative heat load reduction effect throughout the year is obvious. In the Monte Carlo tolerance analysis, assuming the coating process results in a standard deviation of 10 nm for the spacer layer thickness and a standard deviation of 0.02 for the refractive index, 2000 sets of randomly selected samples following a normal distribution were drawn. After performing full-wave simulations on each set, the median average emissivity of the metallic phase remained at 0.86, the median average emissivity of the insulating phase remained at 0.12, and the difference in emissivity between high and low temperatures ranged from 0.73 to 0.76 in the 5% to 95% quantile range. All the differences in these samples were significantly higher than the 0.65 threshold required for engineering feasibility. This indicates that the spacer layer parameter scheme is insensitive to process fluctuations within the expected range, possesses the technical conditions for transitioning to mask fabrication and pilot production, and has been successfully verified, resulting in a complete photonic structure scheme.

[0089] Specifically, for example, such as Figure 5 As shown, the scheme verification process uses the optimal photonic structure parameters as input. On one hand, it loads the hourly outdoor temperature, solar irradiance, equivalent sky temperature, and indoor set temperature throughout the year into the building thermal performance model, and calculates the net radiative heat flux during the heating season and the net radiative heat flux during the cooling season, respectively. On the other hand, it applies random perturbations to the thickness of the transparent dielectric spacer layer, the thickness of the vanadium dioxide tuning layer, the array period, and the refractive index of the materials, and conducts Monte Carlo sampling verification. If the difference in average emissivity between high and low temperatures, the intensity of the residual absorption peak of the insulating phase, and the improvement in net heat load throughout the year in the perturbation sample all meet the preset thresholds, then the parameter set is confirmed as the verified photonic structure scheme, and a structural parameter table is generated that can be used for subsequent mask fabrication, coating process setting, and sample prototyping.

[0090] Example 2: Furthermore, the present invention provides a photonic structure design and simulation optimization system for temperature-adaptive radiation-controlled glass, employing a photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass as described in the above embodiments, which can solve the technical problem of photonic structure design and simulation optimization for temperature-adaptive radiation-controlled glass. The beneficial effects of the photonic structure design and simulation optimization system for temperature-adaptive radiation-controlled glass provided by the present invention are the same as those of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass provided in the above embodiments, and other technical features of the photonic structure design and simulation optimization system for temperature-adaptive radiation-controlled glass are the same as those disclosed in the methods of the above embodiments, and will not be repeated here.

[0091] Example 3: This invention provides a photonic structure design and simulation optimization device for temperature-adaptive radiation-controlled glass. The device includes at least one processor and a memory communicatively connected to the processor. The memory stores instructions executable by the processor, which are then executed to enable the processor to perform the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass described in Example 1. This device can be used, but is not limited to, mobile terminals such as mobile phones, laptops, digital radio receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Description), PMPs (Portable Media Players), and in-vehicle terminals (e.g., in-vehicle navigation terminals), as well as fixed terminals such as digital TVs and desktop computers. This device is merely an example and should not limit the functionality or scope of the invention. A photonic structure design and simulation optimization device for temperature-adaptive radiation-controlled glass may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) that can perform various appropriate actions and processes based on a program stored in a read-only memory (ROM) or a program loaded from a storage device into a random access memory (RAM). The RAM also stores various programs and data required for the operation of the photonic structure design and simulation optimization device. The processing unit, ROM, and RAM are interconnected via a bus. An I / O interface is also connected to the bus. Typically, the following systems can be connected to the I / O interface: input devices including, for example, touchscreens, touchpads, keyboards, mice, image sensors, microphones, accelerometers, gyroscopes, etc.; output devices including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices including, for example, magnetic tapes, hard disks, etc.; and communication devices. The communication device allows the photonic structure design and simulation optimization device for temperature-adaptive radiation-controlled glass to communicate wirelessly or wiredly with other devices to exchange data. While a photonic structure design and simulation optimization device for temperature-adaptive radiation-controlled glass with various systems has been described, it should be understood that implementation of all the described systems is not required. Alternatively, more or fewer systems may be implemented.

[0092] Example 4: This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass as described above. The computer program product provided by this invention can solve the technical problem of photonic structure design and simulation optimization for temperature-adaptive radiation-controlled glass. Compared with the prior art, the beneficial effects of the computer program product provided by this invention are the same as those of the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass provided in the above embodiments, and will not be repeated here.

[0093] In particular, according to the embodiments disclosed in this invention, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this invention include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device, or installed from a storage device, or installed from a read-only memory. When the computer program is executed by a processing device, it performs the functions defined in the methods of the embodiments disclosed in this invention.

[0094] It should be understood that the various parts disclosed in this invention can be implemented using hardware, software, firmware, or a combination thereof. In the description of the above embodiments, specific features, structures, materials, or characteristics may be combined in any suitable manner in one or more embodiments or examples.

[0095] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the present invention and its equivalents, the present invention also intends to include these modifications and variations.

Claims

1. A method for designing and simulating the photonic structure of temperature-adaptive radiation-controlled glass, characterized in that, The methods include: Step S10: Obtain the set of optical constants of the temperature-controlled glass, and perform the equivalent surface impedance extraction task based on the set of optical constants using the Floquet mode S-parameter inversion method, and output the two-phase equivalent surface impedance set. Step S20: Based on the two-phase equivalent surface impedance set, perform the Fabry-Perot cavity input impedance analysis task using the transmission line equivalent analytical method, and output the cavity impedance analysis result; Step S30: Based on the cavity impedance analysis results, the joint objective construction task is performed using the impedance matching difference quantization rule, and the joint optimization objective function is output; Step S40: Based on the joint optimization objective function, the Gaussian process Bayesian optimization method is used to perform the task of solving the interval layer parameters, and the optimal combination of interval layer parameters is output. Step S50: Perform continuous thermal performance simulation and Monte Carlo tolerance verification based on the optimal combination of spacer layer parameters, and output the verified photonic structure scheme.

2. The photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass as described in claim 1, characterized in that, Step S10 involves obtaining the set of optical constants for the temperature-controlled glass, and then performing an equivalent surface impedance extraction task based on the set of optical constants using the Floquet mode S-parameter inversion method to output a two-phase equivalent surface impedance set. This step specifically includes: Step S101: Establish a three-dimensional model of the periodic unit of the temperature-controlled glass in the electromagnetic simulation platform. The three-dimensional model of the periodic unit includes a selective infrared reflective layer, a vanadium dioxide tuning layer, a transparent dielectric spacer layer and a glass substrate arranged sequentially from the incident side to the exit side. Import a set of optical constants into the three-dimensional model of the periodic unit. The set of optical constants includes the complex refractive index of the vanadium dioxide insulating phase, the complex refractive index of the vanadium dioxide metallic phase, the complex refractive index of the selective infrared reflective layer material, the complex refractive index of the transparent dielectric spacer layer and the complex refractive index of the glass substrate. Step S102: Set Floquet periodic boundary conditions in the transverse direction of the periodic unit three-dimensional model, set port boundary conditions at the incident port and the exit port respectively, and perform frequency scanning on TE polarized light and TM polarized light in the 8-micron to 13-micron band respectively to obtain the reflection coefficient corresponding to vanadium dioxide insulation and the reflection coefficient corresponding to vanadium dioxide metal. Step S103: Define the interface where the surface layer to be equivalent is located as the reference plane T, and convert the surface layer to be equivalent at the reference plane T into a lumped impedance element, and determine the normalized equivalent surface impedance according to the following formula: ； in, wavelength Normalized equivalent surface impedance at the location, This is the reflection coefficient converted to the reference plane T after phase compensation; from Obtain the equivalent surface impedance , The free-space wave impedance is given, and the corresponding equivalent surface impedance of vanadium dioxide insulation is obtained. Equivalent surface impedance corresponding to vanadium dioxide metal This forms the equivalent surface impedance set of the two phase states.

3. The photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass as described in claim 1, characterized in that, Step S20, which involves performing the Fabry-Perot cavity input impedance analysis task based on the two-phase equivalent surface impedance set using the transmission line equivalent analytical method, and outputting the cavity impedance analysis result, specifically includes: Step S201: The vanadium dioxide tuning layer corresponding to the two-phase equivalent surface impedance set is equivalent to a lumped impedance unit, the transparent dielectric spacer layer in the temperature-controlled glass is equivalent to a lossy transmission line, and the selective infrared reflection layer in the temperature-controlled glass is equivalent to a terminal load, thus establishing a one-dimensional transmission line equivalent model of the Fabry-Perot cavity. Step S202: Based on the one-dimensional transmission line equivalent model, calculate the cavity input impedance under different phase states according to the following formula: ； in, This is a phase identifier for vanadium dioxide, and , It represents the insulating phase of vanadium dioxide. Indicates the metallic phase of vanadium dioxide; phase state At wavelength The cavity input impedance at that location. The characteristic impedance of the transparent dielectric spacer layer. The refractive index of the transparent dielectric spacer layer, phase state The corresponding terminating load impedance, The thickness of the transparent dielectric spacer layer, The propagation constant of the transparent medium spacer layer is... The attenuation constant is The phase constant, The extinction coefficient of the transparent dielectric spacer layer. The imaginary unit; Step S203: Insulate the cavity input impedance corresponding to vanadium dioxide insulation. The cavity input impedance corresponding to vanadium dioxide metal The coupling factor, which is converted from the port reflection coefficient, is written into the cavity impedance analysis result.

4. The photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass as described in claim 3, characterized in that, In step S202, the terminating load impedance The terminal load impedance is determined by the complex permittivity, layer thickness, and back-side load impedance of the selective infrared reflective layer; when the layer thickness of the selective infrared reflective layer is greater than the skin depth at the corresponding wavelength, the terminal load impedance... Take an approximate load value ,in, phase state The corresponding equivalent complex permittivity of the selective infrared reflective layer; when the thickness of the selective infrared reflective layer is not greater than the skin depth at the corresponding wavelength, the terminal load impedance It is obtained by recursive calculation from a transmission line of finite thickness.

5. The photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass as described in claim 1, characterized in that, Step S30, which involves using impedance matching difference quantization rules to perform a joint objective construction task based on the cavity impedance analysis results and outputting a joint optimization objective function, specifically includes: Step S301: Based on the cavity impedance analysis results and the two-phase equivalent surface impedance set, calculate the impedance matching difference factor according to the following formula: ； in, wavelength Impedance matching difference factor at the location, This represents the cavity input impedance corresponding to vanadium dioxide metal. This represents the equivalent surface impedance corresponding to vanadium dioxide metal. This is the cavity input impedance corresponding to vanadium dioxide insulation. The equivalent surface impedance corresponding to vanadium dioxide insulation. To prevent positive numbers with a denominator of zero; Step S302: Calculate the average emissivity of vanadium dioxide metal in the 8-micron to 13-micron wavelength band based on the cavity impedance analysis results. The average emissivity corresponding to vanadium dioxide insulation The average value of impedance matching difference factor and cavity inhibitors The cavity inhibition factor This represents the non-target absorption integral value of the vanadium dioxide insulating phase under blackbody spectral radiative force weighting. Step S303: Based on the average emissivity The average emissivity The average value and the cavity inhibition factor Construct a joint optimization objective function.

6. The photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass as described in claim 1, characterized in that, Step S40, which involves performing the interval layer parameter solution task based on the joint optimization objective function using a Gaussian process Bayesian optimization method and outputting the optimal interval layer parameter combination, specifically includes: Step S401: Based on the refractive index range, fabricable thickness range, and the joint optimization objective function of the candidate transparent medium material, establish the spacer layer parameter search space and generate an initial sample set using the Latin hypercube sampling method; Step S402: Input the initial sample set into the Gaussian process surrogate model, use the value of the joint optimization objective function as the sample response value, and select the candidate parameter combination to be simulated from the interval layer parameter search space through the expected improvement acquisition function; Step S403: Perform full-wave electromagnetic simulation on the candidate parameter combination to obtain the emissivity distribution and joint optimization objective function value corresponding to the candidate parameter combination. Write the joint optimization objective function value back to the Gaussian process surrogate model until the optimal value change between two adjacent rounds is less than the preset convergence threshold or the preset number of iterations is reached. Output the optimal thickness of the transparent dielectric spacer layer. Optimal refractive index of transparent dielectric spacer layer This forms the optimal combination of interval layer parameters.

7. The photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass as described in claim 1, characterized in that, Step S50, which involves performing continuous thermal performance simulation and Monte Carlo tolerance verification based on the optimal spacer layer parameter combination, and outputting the verified photonic structure scheme, specifically includes: Step S501: Read the optimal thickness of the transparent dielectric spacer from the optimal spacer parameter combination. Optimal refractive index of transparent dielectric spacer layer Select the optimal refractive index of the spacer layer with the transparent medium. Matched candidate materials, combined with the period, unit diameter and height parameters of the vanadium dioxide tuning layer, generate a table of photonic structure parameters for the temperature-controlled glass; Step S502: Establish a continuous thermal performance calculation model for temperature-controlled glass under a multi-physics coupling environment. Input the hourly outdoor temperature, solar irradiance, equivalent sky temperature and indoor set temperature throughout the year into the continuous thermal performance calculation model to obtain the net radiative heat flow during the heating season and the net radiative heat flow during the cooling season of the window. Step S503: Perform Monte Carlo tolerance verification on the photonic structure parameter table, set the thickness deviation of the transparent medium spacer layer, the thickness deviation of the vanadium dioxide tuning layer and the refractive index fluctuation range, generate the emissivity distribution after tolerance perturbation, and output the verified photonic structure scheme when the emissivity distribution meets the preset tolerance threshold.

8. A photonic structure design and simulation optimization system for temperature-adaptive radiation-controlled glass, applied to the photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass according to any one of claims 1 to 7, characterized in that, The photonic structure design and simulation optimization system includes: The equivalent surface impedance extraction module is used to obtain the set of optical constants of the temperature-controlled glass. Based on the set of optical constants, the equivalent surface impedance extraction task is performed using the Floquet mode S-parameter inversion method, and the two-phase equivalent surface impedance set is output. The cavity input impedance analysis module is used to perform the Fabry-Perot cavity input impedance analysis task based on the two-phase equivalent surface impedance set using the transmission line equivalent analysis method, and output the cavity impedance analysis result. The joint objective construction module is used to perform a joint objective construction task based on the cavity impedance analysis results and using impedance matching difference quantization rules, and output a joint optimization objective function. The interval layer parameter solving module is used to perform the interval layer parameter solving task based on the joint optimization objective function using a Gaussian process Bayesian optimization method, and output the optimal interval layer parameter combination. The scheme generation and verification module is used to perform continuous thermal performance simulation and Monte Carlo tolerance verification based on the optimal combination of spacer layer parameters, and output the verified photonic structure scheme.

9. A device for designing and simulating the photonic structure of temperature-adaptive radiation-controlled glass, characterized in that, The photonic structure design and simulation optimization device for temperature-adaptive radiation temperature-controlled glass includes: a memory, a processor, and a photonic structure design and simulation optimization program for temperature-adaptive radiation temperature-controlled glass stored in the memory and executable on the processor. When the photonic structure design and simulation optimization program for temperature-adaptive radiation temperature-controlled glass is executed by the processor, it implements a photonic structure design and simulation optimization method for temperature-adaptive radiation temperature-controlled glass according to any one of claims 1 to 7.

10. A computer program product, characterized in that, The computer program product includes a photonic structure design and simulation optimization program for temperature-adaptive radiation-controlled glass. When the photonic structure design and simulation optimization program for temperature-adaptive radiation-controlled glass is executed by the processor, it implements a photonic structure design and simulation optimization method for temperature-adaptive radiation-controlled glass according to any one of claims 1 to 7.

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