ETFE membrane structure simulation method based on optimized energy efficiency

Abstract

This invention presents a simulation method for ETFE membrane structures based on optimized energy efficiency. First, a multi-level energy model is established by collecting data on the thermal performance, light transmittance, and mechanical properties of ETFE materials. This model is designed to be hierarchical, dividing the energy demand of the ETFE membrane structure into three main parts: lighting, heating, and cooling. Each part is dynamically adjusted based on different environmental variables, including temperature, light intensity, and usage frequency. Support vector machines (SVM) or neural networks are used to optimize the model parameters. Next, a high-precision simulation algorithm based on physical simulation is developed to simulate the impact of complex environmental influences on the energy efficiency of the ETFE membrane structure. Multi-parameter optimization is performed using genetic algorithms or particle swarm optimization (PSO) techniques to ensure the accuracy and efficiency of the simulation results. Finally, a real-time data-driven dynamic adjustment system is employed, integrating temperature, humidity, and light intensity sensors. A developed real-time feedback control algorithm adjusts internal environmental settings, including the operating status of the HVAC system, based on sensor data.

Description

Technical Field

[0001] This invention relates to a method for simulating ETFE membrane structures, specifically a method for simulating ETFE membrane structures based on optimizing energy efficiency. Background Technology

[0002] While current methods for simulating ETFE membrane structures offer technical support in several aspects, they still suffer from several significant shortcomings and drawbacks in practical applications. These shortcomings typically involve simulation accuracy, cost-effectiveness, environmental adaptability, and the complexity of actual operation. Firstly, many existing simulation methods lack sufficient precision and flexibility to accurately simulate the complex physical and chemical behavior of ETFE membranes. The performance of ETFE membranes is influenced by a variety of factors, such as the nonlinear properties of the material, the variability of environmental conditions, and the unique shape of the structure. Existing simulation tools often employ simplified physical models, neglecting the effects of factors such as material aging, ultraviolet radiation, and temperature variations on material properties. This simplification can lead to discrepancies between design and reality, causing the structure to fail to achieve the expected performance in applications.

[0003] Secondly, existing simulation methods are often inflexible or inaccurate when dealing with complex boundary conditions and dynamic environmental factors. For example, when ETFE membrane structures are exposed to changing climatic conditions, the effects of factors such as wind pressure, snow load, and temperature changes need to be accurately simulated. However, many simulation software programs still rely on static or outdated environmental data, failing to reflect environmental changes in real time, resulting in simulation results that cannot accurately predict the structure's performance in real-world environments. Furthermore, cost is a significant drawback of existing simulation methods. Advanced simulation software typically requires expensive licenses and specialized operating skills, which can be a burden for many design teams and research institutions. In addition, the simulation process often requires substantial computational resources and time, which may not be optimal in terms of economic efficiency and project timelines. Therefore, the low cost-effectiveness of simulation methods limits their application in small or low-budget projects. Moreover, operational complexity also limits the widespread adoption of existing simulation methods. Effective simulations typically require users to possess advanced technical knowledge and professional experience, including a deep understanding of ETFE material behavior and proficiency in operating the simulation software. This requirement limits the user base of these tools, especially when educational and training resources are limited.

[0004] Furthermore, existing simulation methods often lack the ability to integrate with other building systems. ETFE membrane structures typically need to work in conjunction with other parts of the building, such as HVAC systems, lighting fixtures, and security systems. The lack of simulation tools for system integration can lead to information silos during the design process, affecting the final architectural effect and operational efficiency. Moreover, from a sustainability and environmental perspective, existing simulation methods often do not adequately consider the recycling of materials and their environmental impact. Few tools are available for assessing the life-cycle costs and environmental footprint of materials when designing and simulating ETFE membrane structures. This is particularly important in the current context of increasing demand for sustainable buildings and green technologies. Summary of the Invention

[0005] The purpose of this invention is to provide a method for simulating ETFE membrane structures based on optimized energy efficiency, thereby addressing some of the drawbacks and shortcomings pointed out in the background art.

[0006] The technical solution adopted by the present invention to solve the above-mentioned technical problems includes the following steps: First, a multi-level energy model is established. By collecting data on the thermal performance, light transmittance and mechanical properties of ETFE material and designing it to be hierarchical, the energy demand of the ETFE membrane structure is divided into three main parts: lighting, heating and cooling. Each part is dynamically adjusted according to different environmental variables, including temperature, light intensity and usage frequency. The model parameters are optimized using support vector machine (SVM) or neural network.

[0007] Next, we developed a high-precision simulation algorithm based on physical simulation to simulate the impact of complex environmental influences on the energy efficiency of ETFE membrane structures. We then performed multi-parameter optimization using genetic algorithms or particle swarm optimization (PSO) techniques to ensure the accuracy and efficiency of the simulation results.

[0008] Then, a real-time data-driven dynamic adjustment system is adopted, which integrates temperature, humidity and light intensity sensors. The developed real-time feedback control algorithm adjusts the internal environmental settings, including the operating status of the HVAC system, based on the sensor data, and tests the system's response speed and adjustment efficiency in a simulated environment.

[0009] Finally, an energy efficiency verification method was established. By defining and calculating the energy efficiency benchmark of the traditional ETFE membrane structure, and using the developed simulation method and algorithm, the energy consumption of the new and old systems under the same conditions was compared. Statistical analysis methods, including analysis of variance (ANOVA), were applied to verify the statistical significance of the optimization effect.

[0010] Furthermore, the aforementioned multi-level energy model is constructed using nonlinear functions:

[0011]

[0012] Where x, y, and z represent temperature, light intensity, and frequency of use, respectively, a i ,b i ,c i p is a coefficient extracted from the thermal, optical transmittance, and mechanical properties data of ETFE materials. i The nonlinear exponent is a variable;

[0013] Next, a high-precision simulation algorithm based on physical simulation was developed, using calculus formulas:

[0014] g(u,v,w)=∫(∫(au 2 +bv 2 +cw 2 +duvw)du)dv

[0015] Where u, v, w represent temperature changes, humidity, and wind speed in the environment, respectively, and a, b, c, d are adjustment coefficients. Multi-parameter optimization is performed using genetic algorithms or particle swarm optimization (PSO) techniques to ensure the accuracy and efficiency of the simulation results.

[0016] Finally, a real-time data-driven dynamic adjustment system is introduced, employing a dynamic adjustment function:

[0017]

[0018] Where t represents the real-time data collected from the sensor, and λ j ,γ j ,k j To adjust parameters, the internal environmental settings, including the operating status of the HVAC system, are dynamically adjusted based on sensor data to simulate the system's response speed and adjustment efficiency in a test environment.

[0019] Furthermore, the construction of high-precision simulation algorithms based on physical simulation includes:

[0020] S1. First, a model based on in-depth physical understanding is adopted, including the thermodynamic, optical and hydrodynamic behavior of ETFE materials. At the same time, multiple environmental factors, including temperature fluctuations, wind load, humidity and ultraviolet radiation, are integrated. Boundary conditions and initial conditions are used to simulate the influence of the actual environment on the ETFE membrane structure.

[0021] S2. Next, simulation algorithms are developed and applied, using numerical methods including finite element analysis (FEA) combined with advanced mathematical techniques, such as solving nonlinear partial differential equations, and integrating real-time environmental monitoring data to enable the model to reflect real-time environmental changes, thereby enhancing the model's adaptability and prediction accuracy.

[0022] S3. Finally, multi-parameter optimization is performed using genetic algorithms and particle swarm optimization (PSO) techniques. An adaptive mechanism is introduced to dynamically adjust algorithm parameters, including crossover rate, mutation rate, particle velocity, and position update rules, according to the optimization progress, in order to improve convergence speed and optimization efficiency.

[0023] Furthermore, the model based on deep physical understanding includes:

[0024] First, a thermodynamic model is used, in which the heat transfer of ETFE material is achieved through the following formula:

[0025] f(T) = k·T n ·e -αT

[0026] The expression is given by T, where T is the material temperature, and k, n, and α are material-specific constants obtained based on experimental data, taking into account the nonlinear response of convection and radiation exchange.

[0027] Secondly, the light transmission and reflection properties of ETFE material are simulated using an optical model, employing the formula:

[0028] g(λ,I)=β·sin(λ·ω)·I p

[0029] Where λ is the wavelength of light, I is the intensity of incident light, and β, ω and p are parameters for adjusting the optical response;

[0030] The impact of wind load on the ETFE membrane structure is considered again through fluid dynamics analysis, using the following formula:

[0031] h(v,A)=ρ·v m ·A q

[0032] Where v is the wind speed, A is the wind-receiving area of ​​the membrane structure, and ρ, m and q are coefficients adjusted according to material properties and structural design;

[0033] Finally, environmental factors, including temperature fluctuations, wind load, humidity, and ultraviolet radiation, are integrated and applied using dynamic boundary conditions, along with the initial conditions:

[0034] I(t) = σ·t r ·e -γt

[0035] The simulation is performed, where t represents time, and σ, r, and γ are parameters set based on the specific environment.

[0036] Furthermore, the simulation algorithm construction steps include:

[0037] S1. First, finite element analysis (FEA) is used to process the geometric structure and nonlinear physical behavior of ETFE material, using nonlinear partial differential equations:

[0038]

[0039] To simulate heat transfer, κ(T) is the temperature-dependent thermal conductivity, σ and λ are material property parameters, T is the temperature field, and Q is the heat source term related to environmental conditions, so as to express the influence of ambient temperature, wind load and radiation on the thermal properties of ETFE materials.

[0040] S2. Then, combining real-time environmental monitoring data, dynamic boundary conditions are applied:

[0041]

[0042] Where T0 is the initial temperature, γ(t) is a time function reflecting real-time environmental parameters including changes in temperature and humidity, and L x ,L y ,L z For the spatial scale of the membrane structure, a n The amplitude parameter is used to indicate the temperature distribution pattern of the ETFE membrane structure in multidimensional space.

[0043] S3. Utilizing adaptive mesh technology:

[0044]

[0045] The simulation accuracy is optimized, where h(x,y,z,t) is the mesh size, h0 is the initial mesh size, and η is the adjustment coefficient. The mesh density is dynamically adjusted according to the temperature gradient to meet the detailed simulation requirements of high temperature gradient regions.

[0046] Furthermore, multi-parameter optimization methods using genetic algorithms and particle swarm optimization (PSO) techniques include:

[0047] First, dynamic crossover rate is used:

[0048]

[0049] Where χ0 is the initial crossover rate, t is the current iteration number, T is the maximum iteration number, and α is an adjustment parameter to gradually reduce the frequency of crossover operations and promote local fine-grained search of the population;

[0050] Then the mutation rate is calculated as follows:

[0051]

[0052] The adjustment is made, where μ0 is the initial mutation rate and β is the decay rate, to control population diversity and prevent premature convergence.

[0053] The final position update uses:

[0054] xnew =x old +γ(t)·v new γ(t)

[0055] This is an adjustment factor for position updates to ensure that particles adapt their paths based on optimization progress and frontier discovery.

[0056] Furthermore, the aforementioned real-time data-driven dynamic adjustment system includes using data fusion technology to calculate an environmental state assessment based on sensor data:

[0057] E(t)=ω1T(t)+ω2H(t)+ω3L(t)

[0058] Where Tt, Ht, and Lt represent real-time temperature, humidity, and light intensity readings, respectively, and ω1, ω2, and ω3 are weighting coefficients to ensure a comprehensive environmental condition assessment.

[0059] Then, an adaptive control algorithm based on Model Predictive Control (MPC) is adopted, through:

[0060] u(t)=K(r(t)-∫0 t e -λ(t-s) E(s)ds)

[0061] Adjust the HVAC system, where u(t) is the control signal, K is the predetermined control gain, r(t) is the target environmental state, and λ is the attenuation factor, to express the influence of historical environmental states on the current control decision and optimize the generation of the control signal;

[0062] Finally, the system's response speed and tuning efficiency were tested in a simulation environment using:

[0063]

[0064] The evaluation system's adjustment response, where E target For the target environmental state, E max E min Let Δu(t) represent the extreme value of the environmental state, and let Δu(t) represent the rate of change of the control signal to reflect the system's response speed and adjustment efficiency.

[0065] Furthermore, the energy efficiency verification method includes the following steps:

[0066] S1. First, define an energy efficiency benchmark based on dynamic adjustments to environmental parameters, through:

[0067]

[0068] The calculation is performed, where B0 is the initial baseline energy efficiency, γ(t) represents the temperature adjustment coefficient, λ is the attenuation factor, and T(t) is the real-time temperature. refHere, ρ is the reference temperature, ρ is the humidity adjustment factor, and H(t) is the real-time humidity.

[0069] S2. Next, a simulation model is used to simulate the energy consumption of the old and new systems. The energy consumption of the new system is determined by:

[0070] E new (s)=∫0 ∞ κ(s,Θ)·s (-1 / θ(s)) ds

[0071] Define κ(s,Θ) as an energy density function dependent on system parameter Θ, and θ(s) as a function characterizing the energy response curvature.

[0072] S3. Finally, the advanced statistical analysis method ANOVA is applied to verify the statistical significance of the optimization effect. The method used is:

[0073]

[0074] Calculate the F-statistic, where n i It is the number of samples in the i-th group. It is the sample mean of the i-th group. It is the population mean, x ij It is the j-th sample in the i-th group, N is the total number of samples, and k is the number of groups.

[0075] This invention provides a number of beneficial effects, giving it significant practical value and technical advantages in the fields of architectural design and materials science:

[0076] 1. Improved Energy Efficiency: Through detailed simulation of the ETFE membrane structure, this invention optimizes the design of materials and structure to achieve higher energy utilization efficiency. This includes precise analysis of heat transfer, light transmittance, and reflection characteristics, enabling the ETFE membrane to provide necessary illumination and insulation while reducing energy consumption.

[0077] 2. Dynamic environmental adaptability: By combining real-time environmental monitoring data and the use of dynamic boundary conditions, this invention enables the ETFE membrane structure to respond in real time to changes in the external environment, such as temperature fluctuations, wind load, humidity and ultraviolet radiation, ensuring that the structure can maintain optimal performance under different environments.

[0078] 3. Enhanced structural durability and reliability: Through combined simulations of fluid dynamics and structural mechanics, this invention helps designers assess and predict the durability and reliability of ETFE membrane structures under long-term environmental influences, thereby optimizing designs, reducing maintenance costs, and extending service life.

[0079] 4. Optimized Design and Cost-Effectiveness: This invention improves the accuracy and efficiency of the design through simulation algorithms and multi-parameter optimization techniques, such as genetic algorithms and particle swarm optimization. This not only reduces the number and cost of physical experiments but also allows for the prediction and resolution of potential problems during the design phase.

[0080] 5. Implementation of advanced control strategies: Through model predictive control (MPC) and other advanced control algorithms, this invention provides an efficient control strategy for the environmental control system of ETFE membrane structures, which can automatically adjust according to predetermined environmental standards, thereby improving user comfort and system energy management efficiency. Attached Figure Description

[0081] Figure 1 This is a flowchart of the ETFE membrane structure simulation method based on optimized energy efficiency according to the present invention.

[0082] Figure 2 This is a flowchart illustrating the construction of a high-precision simulation algorithm based on physical simulation, as described in this invention. Detailed Implementation

[0083] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0084] The energy efficiency-optimized simulation method for ETFE membrane structures involves first establishing a multi-level energy model. This model collects data on the thermal properties, light transmittance, and mechanical characteristics of ETFE materials and designs this data into a hierarchical structure to more precisely address energy demands at different levels. Next, the energy requirements of the ETFE membrane structure are divided into three main components: lighting, heating, and cooling. Each component is dynamically adjusted based on a series of environmental variables, including but not limited to temperature, light intensity, and usage frequency, to ensure maximum energy efficiency. Finally, to optimize model parameters, advanced machine learning techniques such as Support Vector Machines (SVM) or neural networks are employed. These techniques can effectively learn from large amounts of data and predict the energy efficiency of the ETFE membrane structure under different environmental conditions, thereby significantly reducing energy consumption while maintaining indoor environmental comfort. This method, through precise simulation and intelligent adjustment, improves the energy utilization efficiency of ETFE membrane structures and provides an innovative solution for building energy management.

[0085] Next, a high-precision simulation algorithm based on physical simulation was developed. This algorithm aims to simulate the impact of complex environmental factors on the energy efficiency of ETFE membrane structures. By using advanced optimization techniques such as genetic algorithms or particle swarm optimization (PSO), the algorithm can handle the adjustment of multiple parameters, thereby testing and optimizing the performance of ETFE membrane structures under different environmental conditions. By precisely controlling and adjusting various parameters in the model, such as the material's heat transfer rate, light transmittance, and interaction with environmental factors such as temperature, humidity, and wind speed, the algorithm can ensure that the simulation results are not only accurate but also quickly reflect the energy efficiency under different design and environmental settings. The core advantage of this method is that it can provide detailed data support in the design stage, helping designers and engineers to make more reasonable structural and material choices, ultimately achieving the goal of improving energy efficiency. At the same time, it also significantly reduces energy waste caused by improper design, providing a scientific and practical technical path for the design and optimization of ETFE membrane structures.

[0086] A real-time data-driven dynamic adjustment system is then employed, integrating sensors for temperature, humidity, and light intensity to monitor these key environmental parameters in real time. Through a developed real-time feedback control algorithm, the system dynamically adjusts the internal environmental settings, including the operating status of the HVAC system, based on the data collected by the sensors. This adjustment is not only based on current environmental conditions but also predicts impending changes, allowing for proactive adjustments to environmental settings and ensuring optimal indoor comfort and energy efficiency. Testing these adjustments in a simulated environment verifies the system's responsiveness and adjustment efficiency, ensuring a rapid and accurate response to environmental changes in real-world applications, optimizing energy use, and reducing energy consumption—crucial for energy conservation, emission reduction, and improving building sustainability. Through this highly automated and intelligent control system, ETFE membrane structures have achieved a new level of energy management, providing innovative solutions for modern building technology.

[0087] Finally, an energy efficiency verification method is established. By defining and calculating the energy efficiency benchmark of the traditional ETFE membrane structure, a reference point is established for the existing system. Then, using developed simulation methods and algorithms, the energy consumption of the old and new systems is compared under the same environmental conditions. This clarifies the improvement effect of the new system in practical applications. Specific operations include detailed data collection and model building to calculate the energy consumption of the traditional ETFE membrane structure under different conditions, generating a benchmark dataset. Next, the newly developed simulation methods and optimization algorithms are applied to the new system, recording its energy consumption data under the same environmental conditions to ensure the consistency and fairness of the comparison conditions. Finally, advanced statistical analysis methods such as analysis of variance (ANOVA) are applied to compare and analyze the energy consumption data of the old and new systems, verifying whether the optimization effect is statistically significant. ANOVA can effectively determine whether the differences between different systems are due to optimization measures, rather than random errors or environmental noise. This rigorous verification method not only provides scientific evidence to prove the superiority of the new system but also provides reliable data support for further optimization and improvement, thereby ensuring that the energy efficiency of the ETFE membrane structure is truly improved.

[0088] Example 1:

[0089] A company called "Green Energy Building" is dedicated to optimizing building energy through advanced technologies. To achieve this goal, the company decided to use ETFE membrane structures in its newly built high-efficiency office building and utilize simulation methods to optimize energy efficiency to maximize energy savings.

[0090] First, the green building team needs to construct a multi-layered energy model, which will utilize coefficients extracted from data on the thermal performance, light transmittance, and mechanical properties of ETFE materials. Modeling will be performed using nonlinear functions.

[0091]

[0092] Where x, y, and z represent temperature, light intensity, and frequency of use, respectively, and a i b i c i p is a coefficient extracted from ETFE material property data. i The nonlinear exponent is a variable.

[0093] Substitute actual data

[0094] 1. Temperature (x): Set the current average temperature range to 15°C to 30°C.

[0095] 2. Illumination intensity (y): The average illuminance ranges from 200 to 1000 Lux.

[0096] 3. Usage frequency (z): Usage frequency is measured in hours, ranging from 0 to 24 hours.

[0097] Specific range of coefficient values

[0098] a0: The value ranges from 0.5 to 2.0.

[0099] a i The value ranges from 0.1 to 1.5.

[0100] b i The value ranges from 0.01 to 0.05.

[0101] c i The value ranges from 0.5 to 3.0.

[0102] p i The value range is from 1 to 3.

[0103] Let n = 3, and choose the following coefficients:

[0104] a0 = 1.2

[0105] a1 = 0.8, a2 = 1.1, a3 = 0.6

[0106] b1 = 0.02, b2 = 0.03, b3 = 0.04

[0107] c1 = 1.5, c2 = 2.0, c3 = 2.5

[0108] p1 = 2, p2 = 3, p3 = 1

[0109] For a temperature of x = 25°C, a light intensity of y = 600°, and a light intensity of z = 12, the frequency of use is:

[0110] f(25,600,12)=1.2+0.8·25 2 ·e 0.02·600 ·sin(1.5·12)

[0111] +1.1·25 3 ·e 0.03·600 sin(2.0·12)+0.6·25·e 0.04·600 ·sin(2.5·12)

[0112] Through calculation:

[0113] 0.8·625·e 12 ·sin(18)≈0.8·625·162754·0.3≈24.4×10 6

[0114] 1.1·15625·e 18·sin(24)≈1.1·15625·6.57×10 7 -0.91 ≈ -10.4 × 10 9

[0115] 0.6·25·e 24 sin(30)≈0.6·25·2.78×10 10 0.5 ≈ 2.1 × 10 11

[0116] In summary:

[0117] f(25,600,12)≈1.2+24.4×10 6 -10.4×10 9 +2.1×10 11 ≈2.1×10 11

[0118] Through this multi-layered model, green building companies can accurately simulate the energy demands of ETFE membrane structures under different environmental conditions, specifically down to the three main components: lighting, heating, and cooling. Each component is dynamically adjusted based on temperature, light intensity, and usage frequency to maximize energy efficiency. The coefficients and exponents in the above formula can be further optimized and corrected using experimental data and machine learning methods to ensure the model's accuracy and practicality. In practical applications, green building has installed a real-time monitoring system in its newly built office building. Combining the above model, the system adjusts the HVAC system's operating status in real time using sensor data, and the system's response speed and adjustment efficiency have been verified in actual operation, significantly improving the building's energy efficiency.

[0119] In this embodiment, after establishing a multi-level energy model, the green building company then developed a high-precision simulation algorithm based on physical simulation. This algorithm uses calculus formulas to accurately simulate the impact of complex environmental factors on the energy efficiency of ETFE membrane structures. The formula is as follows:

[0120] g(u,v,w)=∫(∫(au 2 +bv 2 +cw 2 +duvw)du)dv

[0121] Where u, v, and w represent the temperature, humidity, and wind speed changes in the environment, respectively, and a, b, c, and d are adjustment coefficients. To make the simulation results more accurate and efficient, the green building team used genetic algorithm (GA) and particle swarm optimization (PSO) techniques for multi-parameter optimization.

[0122] In the actual data, the ambient temperature variation u ranges from -5℃ to 5℃, the humidity v ranges from 20% to 80%, and the wind speed w ranges from 0 m / s to 10 m / s. The specific value ranges of the adjustment coefficients are as follows:

[0123] a: 0.01 to 0.1

[0124] b: 0.02 to 0.15

[0125] c: 0.01 to 0.05

[0126] d: 0.001 to 0.01

[0127] The selected coefficients are set as follows:

[0128] a = 0.05

[0129] b = 0.1

[0130] c = 0.03

[0131] d = 0.005

[0132] Calculation example:

[0133] Temperature change u = 2℃

[0134] Humidity v = 50%

[0135] Wind speed w = 5 m / s

[0136] Substitute these values ​​into the calculus formula:

[0137] g(2,50,5)=∫0 50 (∫0 2 (0.05u 2 +0.1v 2 +0.03w 2 +0.005uvw)du)dv

[0138] First, calculate the internal integral:

[0139]

[0140]

[0141] Then calculate the external integral:

[0142]

[0143] To ensure the accuracy and efficiency of the simulation results, the green building team employed genetic algorithms and particle swarm optimization (PSO) techniques for multi-parameter optimization. First, the genetic algorithm generates new coefficient combinations through selection, crossover, and mutation operations, calculates their fitness values, and iteratively optimizes the coefficients to maximize energy efficiency. Second, PSO techniques adjust the coefficient combinations by simulating swarm intelligence behavior, updating the formulas based on velocity and position.

[0144] v new =ωv old +c1r1(p best -x current )+c2r2(g best -x current )

[0145] x new =x old +v new

[0146] The optimal solution is finally obtained.

[0147] Thanks to the efforts of the green building team, and through high-precision simulation algorithms and multi-parameter optimization technology, the new ETFE membrane structure model has been successfully applied to the office building. Actual operation data shows that the new system reduces energy consumption by 30% compared to traditional systems, saving the company significant energy costs.

[0148] This embodiment further introduces a real-time data-driven dynamic adjustment system to better manage and optimize building energy use. This system relies on temperature, humidity, and light intensity sensors to monitor environmental data in real time and uses a dynamic adjustment function to intelligently adjust the HVAC system. The formula is as follows:

[0149]

[0150] Where t represents the real-time data collected from the sensor, and λ j γ j k j This parameter is used to dynamically adjust internal environment settings based on environmental data. The specific value range for the adjustment parameter is as follows:

[0151] λ j : 0.1 to 1.0

[0152] γ j : 1 to 10

[0153] k j : 0.5 to 2.0

[0154] The system is set to monitor the following data in real time:

[0155] The current time t is 5 minutes.

[0156] The monitored temperature was 22℃, humidity was 45%, and light intensity was 800 Lux.

[0157] Set m=3 and select the following coefficients:

[0158] λ1 = 0.8, λ2 = 0.5, λ3 = 0.3

[0159] γ1 = 5, γ2 = 7, γ3 = 9

[0160] k1 = 1.5, k2 = 1.0, k3 = 0.8

[0161] Substitute these values ​​into the dynamic adjustment function:

[0162] h(t) = 0.8·log(5+5) 1.5 )+0.5·log(7+5 1.0 )+0.3·log(9+5 0.8 )

[0163] First, calculate the internal value of each item:

[0164] 5 1.5 =11.18

[0165] 5 1.0 =5

[0166] 5 0.8 =3.68

[0167] Then substitute these values ​​into the calculation:

[0168] h(t)=0.8·log(5+11.18)+0.5·log(7+5)+0.3·log(9+3.68)

[0169] h(t)=0.8·log(16.18)+0.5·log(12)+0.3·log(12.68)

[0170] h(t)=0.8·2.78+0.5·2.48+0.3·2.54

[0171] h(t) = 2.22 + 1.24 + 0.76

[0172] h(t) = 4.22

[0173] To ensure the system's real-time response and adjustment efficiency, the green building team tested the system's reaction speed and adjustment effects in a simulated environment. In actual operation, when sensor data changes, the dynamic adjustment function immediately calculates new control parameters and adjusts the HVAC system's operating status. For example, when the temperature rises to 25°C, the dynamic adjustment function adjusts the cooling system's output to maintain a comfortable indoor temperature; when the humidity increases to 60%, the system automatically increases the operation of the dehumidifier. In the green building's new office building, the real-time data-driven dynamic adjustment system has been successfully applied. Through intelligent adjustment, the HVAC system can quickly respond to environmental changes, maintaining indoor comfort and optimizing energy use.

[0174] Example 2:

[0175] The ETFE membrane structure was further optimized using a model based on in-depth physical understanding. This model incorporates the thermodynamic, optical, and hydrodynamic behavior of the ETFE material and integrates various environmental factors such as temperature fluctuations, wind load, humidity, and ultraviolet radiation to simulate the impact of the real environment on the ETFE membrane structure.

[0176] First, the company applied a thermodynamic model to describe the heat transfer behavior of the ETFE material. The formula for this model is as follows:

[0177] f(T) = k·T n ·e -αT

[0178] Where T is the material temperature, and k, n, and α are material-specific constants obtained based on experimental data. In practical applications, k = 0.02, n = 1.5, and α = 0.003 are set.

[0179] 1. Application of thermodynamic models

[0180] The ambient temperature T is set to range from -10℃ to 40℃, and the material temperature at a certain moment is T = 25℃.

[0181] Substitute the temperature into the formula to calculate the heat transfer:

[0182] f(25) = 0.02·25 1.5 ·e -0.003·25

[0183] f(25) = 0.02·125·e -0.075

[0184] f(25) = 0.02·125·0.927

[0185] f(25) = 2.315

[0186] The calculation results show that the heat transfer efficiency is 2.315 at 25℃.

[0187] 2. Optical Behavior Simulation

[0188] The light transmittance of ETFE materials is described by the refractive index formula:

[0189]

[0190] Where λ is the wavelength, and R0, R1, and R2 are the optical constants of the material. We set R0 = 1.1, R1 = 0.01, and R2 = 0.0001.

[0191] Select a visible light wavelength λ = 550 nm:

[0192]

[0193] R(550)≈1.1+3.3×10 -8 +1.09×10 -12

[0194] R(550)≈1.100000033

[0195] The optical transmittance calculation results show that at a wavelength of 550 nm, the refractive index of the ETFE film is 1.100000033, which is close to the actual optical behavior of the material.

[0196] 3. Simulation of fluid dynamics behavior

[0197] The effect of wind load on ETFE membrane structures is described using the Navier-Stokes equations:

[0198]

[0199] Where u is the fluid velocity, t is the time, ρ is the fluid density, p is the pressure, ν is the kinematic viscosity, and f is the external force.

[0200] Set the wind speed u = 10 m / s and the fluid density ρ = 1.225 kg / m³. 3 Pressure p = 101325 Pa, kinematic viscosity coefficient ν = 1.48 × 10⁻⁶ -5 m 2 / s, external force f=0.

[0201] These values ​​were used to simulate the effect of wind load on the ETFE membrane structure, and the velocity changes of the fluid at different time points were calculated:

[0202]

[0203] because and The complexity of the operation is only shown in equation form here.

[0204] A green building company installed an ETFE membrane structure system in its experimental building and uses sensors to monitor environmental data in real time, including temperature, humidity, and light intensity. Through a real-time data-driven dynamic adjustment system, the system dynamically adjusts the operation of the HVAC system based on the collected sensor data to maintain optimal energy efficiency. For example, at a given moment, the sensors detect a temperature of 30°C, humidity of 60%, and light intensity of 800 lux. The system then uses a dynamic adjustment function:

[0205]

[0206] Where the parameters are set as λ1=0.5, γ1=2, k1=1.2, and t is the total sensor data (30+60+800=890), then:

[0207] h(890)=0.5·log(2+890 1.2 )

[0208] h(890)=0.5·log(2+225265.58)

[0209] h(890) = 0.5·log225267.58

[0210] h(890)=0.5·5.352

[0211] h(890)=2.676

[0212] The calculated result is 2.676, and the system adjusts its operating status accordingly to improve energy efficiency. By testing the system's response speed and adjustment efficiency in a simulated environment, the system demonstrates its ability to quickly respond to environmental changes and achieve high energy efficiency.

[0213] In this embodiment, to further optimize the energy efficiency of the ETFE membrane structure, the green building company decided to use an optical model to simulate the light transmission and reflection characteristics of the ETFE material. They used the following formula to describe this process:

[0214] g(λ,I)=β·sin(λ·ω)·I p

[0215] Where λ is the wavelength of light, I is the intensity of incident light, and β, ω, and p are parameters for adjusting the optical response. The specific value range of the parameters is set as follows: β = 0.8, ω = 0.05, p = 1.3.

[0216] 1. Application of optical models

[0217] On a sunny midday, the main wavelength of sunlight is about 550 nm, and the intensity of incident light is 1000 lux.

[0218] Substitute these values ​​into the formula to calculate the light transmission characteristics:

[0219] g(550,1000)=0.8·sin(550·0.05)·1000 1.3

[0220] g(550,1000)=0.8·sin(27.5)·1000 1.3

[0221] g(550,1000)=0.8·0.998·15849.95

[0222] g(550,1000)=12678.24

[0223] The calculation results show that the transmittance of the ETFE film structure is 12678.24 at a wavelength of 550 nm and an incident light intensity of 1000 lux.

[0224] A green building company installed an ETFE membrane structure in a newly constructed office building. They equipped it with sophisticated sensors and a control system to monitor and adjust light transmittance and reflection characteristics. On a sunny morning, the light sensors detected a wavelength of 550 nm and a light intensity of 1000 lux. The control system used the aforementioned formula to calculate a light transmittance value of 12678.24. Based on this value, the system dynamically adjusts the angle and transmittance of the ETFE membrane to ensure sufficient and uniform indoor light distribution, reducing the need for artificial lighting and further saving energy.

[0225] Over the next few days, the system continued to monitor and adjust the optical properties of the film structure. For example, at 3 PM, the light wavelength changed to 600 nm, and the light intensity dropped to 700 lux.

[0226] g(600,700)=0.8·sin(600·0.05)·700 1.3

[0227] g(600,700)=0.8·sin(30)·700 1.3

[0228] g(600,700)=0.8·0.5·11941.34

[0229] g(600, 700) = 4776.54

[0230] At this point, the light transmittance value is 4776.54. Based on this, the system adjusts the membrane structure to optimize the indoor lighting environment and maintain efficient energy utilization. Through this process, the green building company verified the effectiveness and adaptability of their optical model under different conditions, ensuring that the ETFE membrane structure can provide optimal energy efficiency in various environments.

[0231] In this embodiment, the team decided to further investigate the impact of wind load on the ETFE membrane structure through fluid dynamics analysis. To achieve this goal, they used the following formula:

[0232] h(v,A)=ρ·v m ·A q

[0233] Where v is the wind speed, A is the wind-receiving area of ​​the membrane structure, and ρ, m, and q are coefficients adjusted according to material properties and structural design. The specific value range of the parameters is set as follows: ρ = 1.225 kg / m 3 (Air density), m = 2, q = 1.5.

[0234] 1. Fluid Dynamics Analysis

[0235] In an environment with a wind speed of 10 m / s, the wind-receiving area of ​​the ETFE membrane structure is 50 m². 2 .

[0236] Substitute these values ​​into the formula to calculate the wind load effect:

[0237] h(10,50)=1.225·10 2 ·50 1.5

[0238] h(10,50)=1.225·100·353.55

[0239] h(10,50)=43393.75

[0240] Calculation results show that at a wind speed of 10 m / s and a windward area of ​​50 m², 2 Under these conditions, the wind load influence value is 43393.75 N.

[0241] In the newly constructed office building, the green energy building company not only had to consider the optical properties on sunny days, but also had to cope with strong wind conditions. During a storm with wind speeds reaching 10 m / s, the sensors detected a wind speed of 10 m / s, and the membrane structure had a wind-receiving area of ​​50 m². 2 The control system used the aforementioned formula to calculate the wind load impact value as 43393.75 N. Based on this value, the system dynamically adjusted the tension of the ETFE membrane and the support structure to ensure the stability and safety of the structure in strong winds.

[0242] To further optimize energy efficiency, the team also considered the integration of environmental factors, including temperature fluctuations, wind load, humidity, and ultraviolet radiation. They used the following initial condition formula:

[0243] I(t) = σ·t r ·e -γt

[0244] Where t represents time, and σ, r, and γ are parameters set based on the specific environment. The specific value range for the parameters is: σ = 5, r = 2, γ = 0.1.

[0245] 2. Integration of environmental factors

[0246] The initial conditions for temperature, wind speed, humidity, and ultraviolet radiation over a 24-hour period are as follows:

[0247] Substitute these values ​​into the formula to calculate the environmental impact:

[0248] I(t) = 5·t 2 ·e -0.1t

[0249] Calculate hourly over a 24-hour period:

[0250] I(1) = 5·1 2 ·e -0.1·1 =5·e -0.1 =4.524

[0251] I(12)=5·12 2 ·e -0.1·12 =720·e -1.2 =196.212

[0252] I(24)=5·24 2 ·e -0.1·24 =2880·e -2.4 =284.536

[0253] The calculations show that environmental factors have a significant impact on the ETFE membrane structure over time. The dynamic changes in temperature, wind speed, humidity, and ultraviolet radiation over a 24-hour period were accurately simulated. Using this simulation method that comprehensively considers optical properties, wind load effects, and environmental factors, the green building company successfully achieved higher energy efficiency and structural stability in its new office building.

[0254] Example 3:

[0255] In this embodiment, the team continued to optimize the ETFE membrane structure, focusing on developing and applying a simulation algorithm based on finite element analysis (FEA) combined with advanced mathematical techniques. This algorithm integrates real-time environmental monitoring data to enhance the model's adaptability and predictive accuracy. The algorithm's construction process is as follows:

[0256] 1. Finite Element Analysis (FEA) Application: The team first used finite element analysis to address the geometry and nonlinear physical behavior of ETFE materials. FEA allows for the accurate calculation of stress and thermal effects caused by environmental variations, which is crucial for understanding how materials behave under real-world conditions.

[0257] 2. Solving the Nonlinear Partial Differential Equation: Next, a nonlinear partial differential equation is used to simulate the heat transfer process. The expression of this equation is as follows:

[0258]

[0259] Where κ(T) is the temperature-dependent thermal conductivity, σ and λ are material property parameters, T is the temperature field, and Q is a heat source term related to environmental conditions. This equation considers the effects of ambient temperature, wind load, and radiation on the thermal properties of ETFE materials.

[0260] Parameter settings: To apply this equation in practice, the following settings are made:

[0261] κ(T) = 0.02 + 0.01T (temperature-dependent thermal conductivity as a function of temperature)

[0262] σ = 0.5 (the material's sensitivity to temperature changes)

[0263] λ = 0.03 (Environmental adaptability factor of the material)

[0264] Q = 500 (set as a fixed ambient heat source intensity)

[0265] 3. Real-time environmental data integration: By integrating real-time monitored environmental data (temperature, humidity, wind speed, etc.), this data is directly input into the simulation model, updating the model's boundary conditions and initial conditions in real time, enabling the model to reflect the current environmental state and predict future changes.

[0266] Example of real-time data application: If real-time monitoring shows that the external temperature suddenly rises to 35°C and the wind speed increases to 15 m / s, these data will be used to adjust the values ​​of Q and κ(T) in real time. Specifically, the adjusted Q will be increased to 550 to reflect the increased environmental heat load.

[0267] Through these detailed steps, the green building company was able to achieve more advanced energy management in the ETFE membrane structure of its new office building. For example, when external conditions change during the high temperatures of summer, this simulation model helps the building automatically adjust the operation of its internal HVAC system to optimize energy use and maintain comfortable indoor temperatures. The integration of real-time data ensures that the model is always adjusted based on the latest environmental information, improving the accuracy of predictions and the speed of system response.

[0268] In this embodiment, the team is now working on improving the energy efficiency of the ETFE membrane structure using advanced simulation technology. The next steps involve integrating real-time environmental data and applying dynamic boundary conditions. The core of this stage is adjusting the model using real-time environmental monitoring data to achieve more accurate and responsive temperature distribution simulations.

[0269] The team used dynamic boundary conditions to simulate the temperature response of the ETFE membrane structure under different environmental conditions. The expression for this temperature field is as follows:

[0270]

[0271] in:

[0272] T0 is the initial temperature, set to 25℃.

[0273] γ(t) is a time function that is adjusted based on real-time monitored environmental data (such as temperature and humidity). For example, on hot days, γ(t) is a positive value, reflecting the increase in temperature.

[0274] L x ,L y ,L z These are the dimensions of the ETFE membrane structure in the x, y, and z directions, for example, 20 meters long, 15 meters wide, and 10 meters high.

[0275] a n It is an amplitude parameter that expresses the degree of response of the membrane structure in different modes. The specific value ranges from 0.1℃ to 2℃, depending on the specific simulation requirements and structural characteristics.

[0276] Imagine an environment where the ambient temperature gradually increases from 15°C to 35°C on a given morning, while the humidity decreases from 90% in the morning to 50% in the afternoon. In this scenario, γ(t) is defined as γ(t) = 0.05t, where t is the number of hours since sunrise. In this way, the team can dynamically simulate the real-time effects of temperature on the ETFE membrane structure, predicting its thermal behavior under different times and environmental conditions.

[0277] This dynamic boundary condition not only reflects changes in external temperature and humidity but also takes into account the geometry and unique physical properties of the membrane structure. This makes the simulation results more accurate and practical, providing a scientific basis for building design and energy management. It also enables green building companies to consider how to optimize energy efficiency through material selection and structural design during the design phase.

[0278] This embodiment then utilizes adaptive meshing technology to improve simulation accuracy, especially under conditions of high temperature gradients. The core of this step is adjusting the density of the simulation mesh to ensure that highly variable regions are processed more finely.

[0279] The team implemented an adaptive mesh adjustment algorithm to optimize the simulation accuracy of temperature fields for ETFE membrane structures under complex environments. The expression for the mesh size is:

[0280]

[0281] in:

[0282] h(x,y,z,t) represents the grid size at point (x,y,z) and time t.

[0283] h0 is the initial grid size, for example, 0.5 meters.

[0284] η is an adjustment coefficient, with a specific value ranging from 0.01 to 0.1, which is adjusted according to the simulation requirements and accuracy.

[0285] The scenario involves a hot summer day where the ETFE membrane structure experiences dramatic temperature changes from morning to afternoon. Under these conditions, the temperature gradient will vary significantly across different parts of the membrane, especially near windows or other heat sources. By monitoring data in real time, such as a morning temperature rising from 20°C to 35°C, the algorithm can adjust the mesh size accordingly, particularly in areas of greatest temperature variation.

[0286] When the temperature gradient is large, such as in an area near a window, To achieve a temperature of 0.1℃ / m, using η = 0.05 and an initial grid size of h0 = 0.5 meters, the grid size in these areas will be adjusted as follows:

[0287] h(x,y,z,t)=0.5(1+0.05×0.1) -1 ≈0.476 meters

[0288] This sophisticated mesh adjustment strategy enables more accurate simulations in regions with drastic temperature changes, better capturing and predicting the response of ETFE materials to environmental variations. Through this technology, green building companies can design more energy-efficient and environmentally responsive building solutions for their clients, optimizing the design and material selection of ETFE membrane structures to improve the overall energy efficiency and user comfort of the structure.

[0289] Example 4:

[0290] A key step in this embodiment is multi-parameter optimization using genetic algorithms and particle swarm optimization (PSO) techniques. This method introduces an adaptive mechanism to dynamically adjust algorithm parameters, including crossover rate, mutation rate, particle velocity, and position update rules, based on the optimization progress, in order to improve convergence speed and optimization efficiency.

[0291] In the early stages of the project, the green building company's engineering team began applying genetic algorithms (GA) and particle swarm optimization (PSO) techniques to optimize the design parameters of the ETFE membrane structure. They needed to find the optimal solution in a multidimensional parameter space to maximize the energy efficiency of the membrane structure under different environmental conditions.

[0292] To achieve dynamic crossover rate in the genetic algorithm, the team used the following formula:

[0293]

[0294] in:

[0295] χ0 is the initial crossover rate, ranging from 0.6 to 0.9.

[0296] t represents the current iteration number.

[0297] T represents the maximum number of iterations, for example, 1000.

[0298] α is an adjustment parameter, with a value ranging from 1 to 3.

[0299] In practical applications, the initial crossover rate χ0 is set to 0.8, the maximum number of iterations T is set to 1000, and the adjustment parameter α is set to 2. If the current iteration number t is set to 500, then the crossover rate χ(t) at this time is:

[0300]

[0301] By dynamically adjusting the crossover rate from an initial 0.8 to 0.2, the genetic algorithm can perform extensive searches in the early stages and local fine-tuning in the later stages, thus improving convergence efficiency.

[0302] The team used the following formula to adjust the mutation rate:

[0303]

[0304] in:

[0305] μ_0 is the initial mutation rate, ranging from 0.01 to 0.05.

[0306] t represents the current iteration number.

[0307] T represents the maximum number of iterations, for example, 1000.

[0308] β is an adjustment parameter, with a value range of 2 to 5.

[0309] With the initial mutation rate μ0 set to 0.03 and the adjustment parameter β set to 3, the mutation rate μ(t) at the iteration number t is:

[0310]

[0311] The gradual decrease in the mutation rate helps to explore a wider solution space in the early stages of optimization and to focus on searching for local optima in the later stages of optimization.

[0312] In PSO technology, the team used the following formula to update particle velocity and position:

[0313] v i (t+1)=ωv i (t)+c1r1(p best,i -x i (t))+c2r2(g best -x i (t))

[0314] x i (t+1)=x i (t)+v i (t+1)

[0315] in:

[0316] ω is the inertia weight, with a value ranging from 0.5 to 0.9.

[0317] c1 and c2 are learning factors, typically with a value of 2.

[0318] r1 and r2 are random numbers, with values ​​ranging from 0 to 1.

[0319] v i (t) represents the velocity of particle i at time t.

[0320] x i (t) represents the position of particle i at time t.

[0321] p best,i This represents the historical best position of particle i.

[0322] g best This is the globally optimal position.

[0323] Set the current particle velocity v i (t) = 0.5, position x i (t) = 1.0, inertial weight ω = 0.7, learning factors c1 = c2 = 2, random numbers r1 = 0.5, r2 = 0.3, and the particle's historical best position p. best,i =1.2, global optimal position g best =1.5, then the particle velocity and position are updated in the next time step as follows:

[0324] v i (t+1)=0.7×0.5+2×0.5×(1.2-1.0)+2×0.3×(1.5-1.0)

[0325] =0.35 + 0.2 + 0.3 = 0.85

[0326] x i (t+1) = 1.0 + 0.85 = 1.85

[0327] Through this dynamic adjustment, the particle swarm optimization algorithm can more effectively search for the global optimum throughout the optimization process, while avoiding getting trapped in local optima. By combining genetic algorithms and particle swarm optimization techniques, the green building company successfully optimized the design of the ETFE membrane structure, improving its energy efficiency and achieving the project goals.

[0328] Example 5:

[0329] The real-time data-driven dynamic adjustment system is a key component of this embodiment. It utilizes data fusion technology to calculate environmental state assessments based on sensor data. This system is designed to monitor and adjust the environmental response of the membrane structure in real time to ensure maximum energy efficiency. The following details each step, using real-world data and a scenario to illustrate its feasibility.

[0330] At this stage, the team utilized data fusion technology to comprehensively consider multiple environmental factors, including temperature, humidity, and light intensity. They used the following formula to assess the environmental condition:

[0331] E(t)=ω1T(t)+ω2H(t)+ω3L(t)

[0332] in:

[0333] T(t) is the real-time temperature reading.

[0334] H(t) represents the real-time humidity reading.

[0335] L(t) is the real-time light intensity reading.

[0336] ω1, ω2, and ω3 are weighting coefficients to ensure a comprehensive environmental status assessment. The weighting coefficients range from ω1 = 0.3, ω2 = 0.4, to ω3 = 0.3, and can be adjusted according to project requirements and environmental sensitivity.

[0337] Imagine this scenario: a green building company is exhibiting their ETFE membrane structure technology. The exhibition is located in a region with a variable climate, making the monitoring and adjustment of environmental parameters crucial. Here are the sensor readings during the exhibition:

[0338] Temperature T(t) = 25℃

[0339] Humidity H(t) = 50%

[0340] Light intensity L(t) = 800 lux

[0341] Calculate the environmental status assessment value using the formula above:

[0342] E(t)=0.3×25+0.4×50+0.3×800

[0343] The specific value is calculated as follows:

[0344] E(t) = 7.5 + 20 + 240 = 267.5

[0345] This environmental condition assessment value, E(t) = 267.5, is used to dynamically adjust the control system of the ETFE membrane structure, such as adjusting the operating parameters of the HVAC system to optimize temperature, humidity, and lighting conditions, ensuring maximum energy efficiency and providing a comfortable environment. By combining real-time data, employing data fusion technology, and dynamically adjusting the system, green building companies can effectively address the challenges of different environmental conditions, ensuring the performance and efficiency of ETFE membrane structures in practical applications.

[0346] This embodiment further employs an adaptive control algorithm based on model predictive control (MPC) to optimize the response of the HVAC system. This approach not only considers current environmental data but also integrates historical data to predict and adjust future control strategies, thereby ensuring that the ETFE membrane structure maintains optimal energy efficiency in a constantly changing environment.

[0347] At this stage, the team used the following control formula to adjust the operation of the HVAC system:

[0348] u(t)=K(r(t)-∫0 t e -λ(t-s) E(s)ds)

[0349] in:

[0350] u(t) is a control signal that indicates the adjustment amount of the HVAC system.

[0351] K is the predetermined control gain, with a value of 0.1, which is adjusted according to the system's response characteristics.

[0352] r(t) represents the target environmental conditions, such as a temperature of 22°C and a humidity of 45%.

[0353] λ is the attenuation factor, with a value of 0.05, representing the impact of historical environmental conditions on current control decisions.

[0354] E(s) is the environmental state assessment value obtained from data fusion technology.

[0355] Considering a specific time period during the exhibition, the target environmental state r(t) is set to maintain a temperature of 22°C and a humidity of 45%. Using the above control formula, the team can calculate the adjustment signals required for the HVAC system and further optimize the environmental conditions.

[0356] For example, if E(t) is evaluated as 267.5 at a certain moment (the previously calculated value), then the control signal u(t) can be calculated by interpolating the actual data:

[0357] u(t)=0.1(22-∫0 t e -0.05(t-s) 267.5ds)

[0358] Once the control signal u(t) is received, it is used to adjust the operating state of the HVAC system to adapt to environmental changes and maintain temperature and humidity at the set target environmental conditions. This not only ensures comfort but also optimizes energy consumption. Through this model-based predictive control method, the green building company is able to ensure that its ETFE membrane structure maintains optimized energy efficiency and comfort in dynamic environments, fully demonstrating the importance and effectiveness of combining advanced control technology with real-time data monitoring.

[0359] Next, to test and optimize the responsiveness and adjustment efficiency of the HVAC system, the team introduced a mathematical model to evaluate the dynamic performance of the control system. This step is a crucial evaluation of the entire simulation process, ensuring that the system can respond quickly and accurately to environmental changes in actual operation.

[0360] The system's dynamic adjustment capability is evaluated by the rate of change of the control signal, Δu(t), which is calculated using the following formula:

[0361]

[0362] E target The values ​​are assessments of the target environmental conditions, such as a temperature of 22°C and a humidity of 45%.

[0363] E(t) is the environmental state assessment value obtained in real time through sensors.

[0364] E max and E min These are the maximum and minimum values ​​of the environmental conditions recorded during the simulation, for example, the maximum temperature is 35℃ and the minimum temperature is 15℃.

[0365] To ensure the practical feasibility of this control model, the team conducted a series of tests. At a certain moment, the real-time reading of the environmental state E(t) was set to 25℃, while the target state E... target The temperature is 22℃. In this case, if E... max =35℃ and E min =15℃, then the rate of change of the control signal is calculated as follows:

[0366]

[0367] This calculation shows that if the change of E(t) is a function of time t, such as E(t) = 25 - 0.1t, then Δu(t) will be displayed as:

[0368]

[0369] This value represents the change in the control signal per unit time, thus providing a measure of the system's dynamic response.

[0370] Through the above calculations and assessments, Green Energy Building Company is able to ensure that its ETFE membrane structure HVAC system has a high responsiveness and adaptability in actual operation.

[0371] Example 6:

[0372] In this embodiment, the team decided to apply an energy efficiency benchmark model based on dynamic adjustments to environmental parameters, specifically for evaluating and optimizing the energy efficiency of ETFE membrane structures. This model incorporates real-time temperature and humidity data to dynamically adjust the energy efficiency benchmark, ensuring maximum energy efficiency across the entire system. This process not only supports environmental sustainability goals but also provides real-time data-driven decision support for building management systems.

[0373] The energy efficiency benchmark B(t) is calculated using the following formula:

[0374]

[0375] B0 represents the initial baseline energy efficiency, which can be set to 0.75, indicating an initial energy efficiency level of 75%.

[0376] γ(t ′The temperature adjustment coefficient (T) depends on the time variable and can vary between 0.01 and 0.05 to reflect temperature sensitivity over different time periods.

[0377] λ is the attenuation factor, used to adjust the intensity of the effect when the temperature deviates from the reference temperature. The constant value is, for example, 0.1.

[0378] T(t ′ (T) is the real-time temperature. ref This is a reference temperature, for example, set to a comfortable indoor temperature of 22℃.

[0379] ρ is the humidity adjustment factor, which can be set to 0.02, representing the weight of humidity in energy efficiency assessment.

[0380] H(t ′ () is the real-time humidity.

[0381] Assuming an ambient temperature gradually rises from 20°C to 24°C on a given day, while humidity increases from 30% to 50%, the above formula can be used to assess energy efficiency. This model allows the team to observe in real-time how the energy efficiency benchmark adjusts as external temperature and humidity change.

[0382] For example, during the period from 8:00 AM to 12:00 PM, γ(t) ′ The value gradually decreases from 0.03 to ·, reflecting a higher demand for temperature adjustment in the morning and a gradual decrease in the afternoon. Using integral calculations, the energy efficiency baseline change over these four hours can be obtained. This helps adjust the operating strategies of the HVAC system, such as adjusting temperature settings or humidity control, to ensure optimal energy efficiency.

[0383] The next important step in this embodiment is to use simulation models to compare and simulate the energy consumption of the old and new systems, especially the energy consumption model for the new system. This step aims to visually demonstrate the advantages and potential of the new system in terms of energy efficiency, as well as to accurately predict its response to environmental factors.

[0384] The new system's energy consumption E new (s) is defined by the following formula:

[0385] E new (s)=∫0 ∞ κ(s,Θ)·s -1 / θ(s) ds

[0386] κ(s,Θ) represents the energy density function that depends on the system parameter Θ. These parameters may include the performance indicators of temperature control equipment, the thermal conductivity of building materials, or the degree of automation of the system.

[0387] θ(s) is a function characterizing the curvature of the energy consumption response, reflecting the efficiency change of the system under different energy inputs. For example, θ(s) can be a function that increases with increasing energy input, indicating that the system's efficiency gradually decreases under high energy consumption conditions.

[0388] Located in a commercial building, the new ETFE membrane structure design incorporates the latest environmentally responsive technologies, where Θ includes the membrane's photothermal properties and the implementation of an automated shading system. κ(s,Θ) is set to 0.05s. 0.8 The coefficient 0.05 represents the basic energy consumption per unit area per hour, and the exponent 0.8 indicates that energy consumption increases non-linearly with the intensity of system operation. Meanwhile, θ(s) = 1 + 0.01s is set to indicate that energy efficiency gradually decreases as the system operating time increases.

[0389] Using this model, the team can simulate the energy consumption of the new ETFE membrane structure at different times of the day, such as morning, noon, and evening, and simulate the energy consumption response under different external environmental conditions. Such simulations not only help to understand the energy efficiency performance of the new system, but also allow for the adjustment of system parameters based on the simulation results to achieve optimal energy use and environmental adaptability.

[0390] In this embodiment, to scientifically verify whether the energy efficiency improvement brought by the new system compared to the traditional system is statistically significant, the team decided to use the advanced statistical analysis method ANOVA (Analysis of Variance). This step is to determine whether the performance differences between the new and old systems are significant under different environmental and operating conditions, thereby providing solid data support for the final system design and implementation.

[0391] The purpose of ANOVA is to assess whether the differences between the means of multiple groups are statistically significant. In this example, the groups represent energy consumption data of different systems under the same environmental conditions. The formula for calculating the F-statistic is as follows:

[0392]

[0393] n i is the number of samples in the i-th group, representing the number of repeated measurements of the system under specific conditions.

[0394] It is the sample mean of the i-th group, that is, the average energy consumption of the system under specific conditions.

[0395] It is the population mean of all samples, representing the average energy consumption of all systems under all conditions.

[0396] x ij It is the j-th sample in the i-th group, that is, the energy consumption data of a single measurement.

[0397] N is the total number of samples, and k is the number of groups (the number of different systems plus the combination of different environmental conditions).

[0398] The study includes two configurations: a new system and an old system. Each configuration is tested under three different environmental conditions, with five replicate experiments performed under each condition. For example, for the five tests of the new system under specific environmental conditions, if the energy consumption data obtained are 100, 102, 98, 101, and 99 kWh, the sample mean and population mean of this group can be calculated, and then the F-statistic can be calculated. By comparing the calculated F-value with the critical value in the corresponding F-distribution table, it can be determined whether the difference in energy consumption between groups is statistically significant. If the F-value is greater than the critical value, it indicates that the energy consumption difference between different systems or under different conditions is significant, meaning that the energy efficiency improvement of the new system is effective. This statistical verification is a crucial step in the optimization process, providing reliable data support for technological improvements and decision-making.

[0399] Through this detailed data analysis, the research team was able to accurately assess the energy efficiency performance of the new ETFE membrane structure system, thus providing a scientific basis for future design directions and optimization strategies. This not only enhances the practical application value of the project but also provides important reference for research in related fields.

[0400] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of this invention is defined by the appended claims and their equivalents.

Claims

1. A method for simulating ETFE membrane structures based on optimized energy efficiency, characterized in that... Includes the following steps: First, a multi-level energy model is established. By collecting data on the thermal performance, light transmittance, and mechanical properties of ETFE materials and designing it in a hierarchical manner, the energy demand of the ETFE membrane structure is divided into three main parts: lighting, heating, and cooling. Each part is dynamically adjusted according to different environmental variables, including temperature, light intensity, and usage frequency. The model parameters are optimized using support vector machine (SVM) or neural network. Next, we developed a high-precision simulation algorithm based on physical simulation to simulate the impact of complex environmental influences on the energy efficiency of ETFE membrane structures. We then performed multi-parameter optimization using genetic algorithms or particle swarm optimization (PSO) techniques to ensure the accuracy and efficiency of the simulation results. Then, a real-time data-driven dynamic adjustment system is adopted, which integrates temperature, humidity and light intensity sensors. The developed real-time feedback control algorithm adjusts the internal environmental settings, including the operating status of the HVAC system, based on the sensor data, and tests the system's response speed and adjustment efficiency in a simulated environment. Finally, an energy efficiency verification method was established. By defining and calculating the energy efficiency benchmark of the traditional ETFE membrane structure, and using the developed simulation method and algorithm, the energy consumption of the new and old systems under the same conditions was compared. Statistical analysis methods, including analysis of variance (ANOVA), were applied to verify the statistical significance of the optimization effect.

2. The ETFE membrane structure simulation method based on optimized energy efficiency according to claim 1, characterized in that... The multi-level energy model is constructed using nonlinear functions: Where x, y, and z represent temperature, light intensity, and frequency of use, respectively, a i ,b i ,c i p is a coefficient extracted from the thermal, optical transmittance, and mechanical properties data of ETFE materials. i The nonlinear exponent is a variable; Next, a high-precision simulation algorithm based on physical simulation was developed, using calculus formulas: g(u,v,w)=∫(∫(au 2 +bv 2 +cw 2 +duvw)du)dv Where u, v, w represent temperature changes, humidity, and wind speed in the environment, respectively, and a, b, c, d are adjustment coefficients. Multi-parameter optimization is performed using genetic algorithms or particle swarm optimization (PSO) techniques to ensure the accuracy and efficiency of the simulation results. Finally, a real-time data-driven dynamic adjustment system is introduced, employing a dynamic adjustment function: Where t represents the real-time data collected from the sensor, and λ j ,γ j ,k j To adjust parameters, the internal environmental settings, including the operating status of the HVAC system, are dynamically adjusted based on sensor data to simulate the system's response speed and adjustment efficiency in a test environment.

3. The ETFE membrane structure simulation method based on optimized energy efficiency according to claim 1, characterized in that... The construction of the high-precision simulation algorithm based on physical simulation includes: S1. First, a model based on in-depth physical understanding is adopted, including the thermodynamic, optical and hydrodynamic behavior of ETFE materials. At the same time, multiple environmental factors, including temperature fluctuations, wind load, humidity and ultraviolet radiation, are integrated. Boundary conditions and initial conditions are used to simulate the influence of the actual environment on the ETFE membrane structure. S2. Next, simulation algorithms are developed and applied, using numerical methods including finite element analysis (FEA) combined with advanced mathematical techniques, such as solving nonlinear partial differential equations, and integrating real-time environmental monitoring data to enable the model to reflect real-time environmental changes, thereby enhancing the model's adaptability and prediction accuracy. S3. Finally, multi-parameter optimization is performed using genetic algorithms and particle swarm optimization (PSO) techniques. An adaptive mechanism is introduced to dynamically adjust algorithm parameters, including crossover rate, mutation rate, particle velocity, and position update rules, according to the optimization progress, in order to improve convergence speed and optimization efficiency.

4. The ETFE membrane structure simulation method based on optimized energy efficiency according to claim 3, characterized in that... The models based on deep physical understanding include: First, a thermodynamic model is used, in which the heat transfer of ETFE material is achieved through the following formula: f(T)=k·T n ·e -αT The expression is given by T, where T is the material temperature, and k, n, and α are material-specific constants obtained based on experimental data, taking into account the nonlinear response of convection and radiation exchange. Secondly, the light transmission and reflection properties of ETFE material are simulated using an optical model, employing the formula: g(λ,I)=β·sin(λ·ω)·I p Where λ is the wavelength of light, I is the intensity of incident light, and β, ω and p are parameters for adjusting the optical response; The impact of wind load on the ETFE membrane structure is considered again through fluid dynamics analysis, using the following formula: h(v,A)=ρ·v m ·AND q Where v is the wind speed, A is the wind-receiving area of ​​the membrane structure, and ρ, m and q are coefficients adjusted according to material properties and structural design; Finally, environmental factors, including temperature fluctuations, wind load, humidity, and ultraviolet radiation, are integrated and applied using dynamic boundary conditions, along with the initial conditions: I(t)=σ·t r ·e -γt The simulation is performed, where t represents time, and σ, r, and γ are parameters set based on the specific environment.

5. The ETFE membrane structure simulation method based on optimized energy efficiency according to claim 3, characterized in that... The simulation algorithm construction steps include: S1. First, finite element analysis (FEA) is used to process the geometric structure and nonlinear physical behavior of ETFE material, using nonlinear partial differential equations: To simulate heat transfer, κ(T) is the temperature-dependent thermal conductivity, σ and λ are material property parameters, T is the temperature field, and Q is the heat source term related to environmental conditions, so as to express the influence of ambient temperature, wind load and radiation on the thermal properties of ETFE materials. S2. Then, combining real-time environmental monitoring data, dynamic boundary conditions are applied: Where T0 is the initial temperature, γ(t) is a time function reflecting real-time environmental parameters including changes in temperature and humidity, and L x ,L y ,L z For the spatial scale of the membrane structure, a n The amplitude parameter is used to indicate the temperature distribution pattern of the ETFE membrane structure in multidimensional space. S3. Utilizing adaptive mesh technology: The simulation accuracy is optimized, where h(x,y,z,t) is the mesh size, h0 is the initial mesh size, and η is the adjustment coefficient. The mesh density is dynamically adjusted according to the temperature gradient to meet the detailed simulation requirements of high temperature gradient regions.

6. The ETFE membrane structure simulation method based on optimized energy efficiency according to claim 3, characterized in that... The multi-parameter optimization method using genetic algorithms and particle swarm optimization (PSO) techniques includes: First, dynamic crossover rate is used: Where χ0 is the initial crossover rate, t is the current iteration number, T is the maximum iteration number, and α is an adjustment parameter to gradually reduce the frequency of crossover operations and promote local fine-grained search of the population; Then the mutation rate is calculated as follows: The adjustment is made, where μ0 is the initial mutation rate and β is the decay rate, to control population diversity and prevent premature convergence. The final position update uses: x new =x old +γ(t)·v new γ(t) This is an adjustment factor for position updates to ensure that particles adapt their paths based on optimization progress and frontier discovery.

7. The ETFE membrane structure simulation method based on optimized energy efficiency according to claim 1, characterized in that... The real-time data-driven dynamic adjustment system includes calculating an environmental state assessment based on sensor data using data fusion technology: E(t)=ω1T(t)+ω2H(t)+ω3L(t) Where Tt, Ht, and Lt represent real-time temperature, humidity, and light intensity readings, respectively, and ω1, ω2, and ω3 are weighting coefficients to ensure a comprehensive environmental condition assessment. Then, an adaptive control algorithm based on Model Predictive Control (MPC) is adopted, through: Adjust the HVAC system, where u(t) is the control signal, K is the predetermined control gain, r(t) is the target environmental state, and λ is the attenuation factor, to express the influence of historical environmental states on the current control decision and optimize the generation of the control signal; Finally, the system's response speed and tuning efficiency were tested in a simulation environment using: The evaluation system's adjustment response, where E target For the target environmental state, E max E min Let Δu(t) represent the extreme value of the environmental state, and let Δu(t) represent the rate of change of the control signal to reflect the system's response speed and adjustment efficiency.

8. The ETFE membrane structure simulation method based on optimized energy efficiency according to claim 1, characterized in that... The energy efficiency verification method includes the following steps: S1. First, define an energy efficiency benchmark based on dynamic adjustments to environmental parameters, through: The calculation is performed, where B0 is the initial baseline energy efficiency, γ(t) represents the temperature adjustment coefficient, λ is the attenuation factor, and T(t) is the real-time temperature. ref Here, ρ is the reference temperature, ρ is the humidity adjustment factor, and H(t) is the real-time humidity. S2. Next, a simulation model is used to simulate the energy consumption of the old and new systems. The energy consumption of the new system is determined by: Define κ(s,Θ) as an energy density function dependent on system parameter Θ, and θ(s) as a function characterizing the energy response curvature. S3. Finally, the advanced statistical analysis method ANOVA is applied to verify the statistical significance of the optimization effect. The method used is: Calculate the F-statistic, where n i It is the number of samples in the i-th group. It is the sample mean of the i-th group. It is the population mean, x ij It is the j-th sample in the i-th group, N is the total number of samples, and k is the number of groups.

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