High-performance aerogel refrigeration house cold insulation structure optimization design method based on genetic algorithm

Abstract

The invention belongs to the technical field of heat preservation, and particularly relates to a high-performance aerogel refrigeration house cold insulation structure optimization design method based on a genetic algorithm, which comprises the steps of determining material categories, constructing an optimization model, setting constraint conditions, then adopting the genetic algorithm to solve the optimization model and the like. The optimization algorithm takes economical efficiency as an optimization target, achieves the lowest comprehensive cost on the premise of meeting each cold insulation performance index, and is suitable for the design of the building envelope of various low-temperature refrigerators (food refrigerators, medicine refrigerators and the like).

Description

Technical Field

[0001] This invention belongs to the field of thermal insulation technology, specifically a high-performance aerogel cold storage insulation structure optimization design method based on genetic algorithm. Background Technology

[0002] The cold storage insulation structure is the core of maintaining the low temperature environment inside the storage (usually -25~0℃). Traditional insulation materials (polyurethane, polystyrene board) have problems such as high thermal conductivity (0.024~0.030W / (m·K)), large thickness, and easy aging after long-term use.

[0003] High-performance aerogels (with thermal conductivity as low as 0.012~0.018 W / (m·K)) have advantages such as lightweight, high temperature resistance, and anti-aging, but they are expensive and direct large-scale application can easily lead to cost overruns. Summary of the Invention

[0004] To overcome the shortcomings of existing technologies, this invention proposes a high-performance aerogel cold storage insulation structure optimization design method based on genetic algorithm. This optimization algorithm takes economy as the optimization objective, and achieves the lowest comprehensive cost while meeting various insulation performance indicators. It is applicable to the enclosure structure design of various low-temperature cold storage (food cold storage, pharmaceutical cold storage, etc.).

[0005] To address the problems in the background art, this invention provides a high-performance aerogel cold storage insulation structure optimization design method based on genetic algorithms, comprising the following steps: S1 Defines the objective: to design the insulation layer structure of cold storage using one or more of the five commonly used thermal insulation materials: high-performance aerogel, rigid polyurethane foam, extruded polystyrene board, phenolic foam, and mineral wool board, and to provide the single-layer thickness value for each material. S2 Optimization Model Construction: Using the number of material layers as the design variable, and with the control of insulation layer thickness, the temperature difference between the surface temperature after insulation and the ambient temperature, and the dew point temperature as constraints, the following single-objective optimization model is constructed: ; Where C represents the total cost of the insulation layer. x i Let i be the number of layers of material i. t i The thickness of a single layer of material i, in mm; P i The unit cost of material i is expressed in yuan / m. 2 ; PR i The unit price for the construction cost of a single layer of material i is expressed in yuan / m. 2 ; The insulation layer thickness control constraints are set as follows: ; in, x i Let i be the number of layers of material i. t i The thickness of a single layer of material i, in mm. H The total thickness of the insulation structure is limited, in mm; The temperature difference control constraints between the surface temperature after insulation and the ambient temperature are set as follows: ; in, T am For ambient temperature, T out The surface temperature of the insulation layer. The temperature difference between the surface temperature of the insulation layer and the ambient temperature is required; The surface temperature after insulation is calculated using the following formula: Among them, T out Temperature of the insulation layer surface, in °C; T in The design temperature inside the cold storage is q; q is the heat flux density, and R is the heat flux density. total The total thermal resistance is calculated using the following formula: h is the surface convective coefficient; The heat flux density q is calculated using the following formula: ; Dew point temperature control is defined as follows: the outer surface temperature after insulation is greater than the dew point temperature, i.e. T dew This is the dew point temperature, which is determined by the ambient temperature. T am The relative humidity (RH) is obtained by consulting the enthalpy-humidity diagram. S3 uses a genetic algorithm to solve the optimization model, which yields a cold storage insulation layer structure that meets the design requirements.

[0006] Preferably, a heat flux density constraint condition is also provided. Q is the design limit for heat flux density, in W / m².

[0007] Preferably, when S3 uses the genetic algorithm autumn optimization model, the population size is set to be no less than 30 groups, the number of iterations to be no less than 800, the crossover probability to be 0.8, the mutation probability to be 0.05, and the number of layers in a single-layer material is set to a range of [value missing]. x i ∈ {0,1, 2, 3, 4, 5, 6, 7, 8}.

[0008] The beneficial effects of this invention are as follows: The proposed optimization model is a mixed integer nonlinear programming problem (MINLP). A genetic algorithm is used to solve the optimization model. The core advantages of the genetic algorithm in solving MINLP problems are global search capability, adaptability to discrete and continuous variables, no gradient dependence, and strong robustness. It perfectly meets the needs of solving complex MINLP problems in the engineering field and can quickly obtain the optimal cold storage insulation layer structure design scheme. Detailed Implementation

[0009] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0010] This embodiment provides a high-performance aerogel cold storage insulation structure optimization design method based on genetic algorithm. The reactor body adopts a three-dimensional cylindrical structure, and includes the following steps: S1 Defines the objective: to design the insulation layer structure of cold storage using one or more of the five commonly used thermal insulation materials: high-performance aerogel, rigid polyurethane foam, extruded polystyrene board, phenolic foam, and mineral wool board, and to provide the single-layer thickness value for each material. S2 Optimization Model Construction: Using the number of material layers as the design variable, and with the control of insulation layer thickness, the temperature difference between the surface temperature after insulation and the ambient temperature, and the dew point temperature as constraints, the following single-objective optimization model is constructed: ; Where C represents the total cost of the insulation layer. x i Let i be the number of layers of material i. t i The thickness of a single layer of material i, in mm; P i The unit cost of material i is expressed in yuan / m. 2 ; PR i The unit price for the construction cost of a single layer of material i is expressed in yuan / m. 2 ; The insulation layer thickness control constraints are set as follows: ; in, x i Let i be the number of layers of material i. t i The thickness of a single layer of material i, in mm. H The total thickness of the insulation structure is limited, in mm; The temperature difference control constraints between the surface temperature after insulation and the ambient temperature are set as follows: ; in, T am For ambient temperature, T out The surface temperature of the insulation layer. The temperature difference between the surface temperature of the insulation layer and the ambient temperature is required; The surface temperature after insulation is calculated using the following formula: Among them, T out Temperature of the insulation layer surface, in °C; T in The design temperature inside the cold storage is q; q is the heat flux density, and R is the heat flux density. total The total thermal resistance is calculated using the following formula: h is the surface convective coefficient; The heat flux density q is calculated using the following formula: ; Dew point temperature control is defined as follows: the outer surface temperature after insulation is greater than the dew point temperature, i.e. T dew This is the dew point temperature, which is determined by the ambient temperature. T am The relative humidity (RH) is obtained by consulting the enthalpy-humidity diagram. S3 uses a genetic algorithm to solve the optimization model, which yields a cold storage insulation layer structure that meets the design requirements.

[0011] In a preferred embodiment, a heat flux density constraint condition is also provided. Q is the design limit for heat flux density, in W / m².

[0012] In some preferred embodiments, when S3 uses the genetic algorithm autumn optimization model, the population size is set to be no less than 30 groups, the number of iterations is no less than 800, the crossover probability is set to 0.8, the mutation probability is set to 0.05, and the number of layers in a single-layer material is set to a range of [value missing]. x i ∈ {0, 1, 2, 3, 4, 5, 6, 7, 8}.

[0013] The invention will now be described using a specific case study from the initial design phase of a cold storage facility construction project.

[0014] The number of insulation material categories n=5, meaning there are 5 types of materials to choose from. The relevant parameters are shown in the table below.

[0015]

[0016] Determine the ambient temperature and equipment parameters: 1) Ambient temperature T am = 25 ℃.

[0017] 2) Ambient relative humidity RH = 80%.

[0018] 3) Equipment surface temperature T in = -20 ℃ (internal temperature of cold storage).

[0019] 4) External surface flow coefficient h = 10 W / (m·℃).

[0020] 5) Dew point temperature T dew :according to T am = 25 ℃ and RH = 80%, consult the enthalpy chart or calculate using the formula to get T dew ≈ 21.5 ℃.

[0021] Define design requirements: 1) Total thickness requirements of thermal insulation structure H ≤ 250 mm.

[0022] 2) Temperature difference requirement Δ between the surface temperature of the insulation layer and the ambient temperature T = 2 ℃.

[0023] 3) Heat flux density requirements Q = 10 W / m².

[0024] After constructing the optimization model, determine the relevant parameters of the genetic algorithm. 1) Population size: 50, with each population group corresponding to a set of design variables.

[0025] 2) Number of iterations: 1000, to ensure that the algorithm converges to the optimal solution.

[0026] 3) Crossover probability: 0.8, to promote diversity in variable combinations.

[0027] 4) Mutation probability: 0.05, to avoid local optima.

[0028] 5) Design variable: Number of material layers x 1, x 2, x 3, x 4, x 5. Take integer variables and their value range. x i ∈ {0, 1, 2, 3, 4, 5, 6, 7, 8}, the allowed number of layers is 0, 0 means that the material is not used.

[0029] Part of the solution process is as follows: (1) Initial population The initial population consists of randomly generated individuals. Below are five example individuals from the population, each represented as […]. x 1, x 2, x 3, x 4, x 5]: Individual 1: [1,2,1,3,0]; Individual 2: [0,4,2,1,2]; Individual 3: [3,1,0,2,3]; Individual 4: [2,0,3,1,4]; Individual 5: [1,3,2,0,1].

[0030] (2) Parent selection and crossover From the initial population, based on fitness (higher fitness means lower total cost), parent individuals are selected for crossover. The following is a set of parent individuals and their crossover operation: Parent individual A: [1,2,1,3,0]; Parent individual B: [0,4,2,1,2].

[0031] Single-point crossover was used, with the crossover point randomly selected as position 3 (i.e., the first 3 genes come from parent A, and the last 2 genes come from parent B): Offspring individual C: [1,2,1,1,2]; Offspring individual D:[0,4,2,3,0].

[0032] (3) Variation The offspring individuals are mutated with a probability of 0.05. For example, the offspring individual C[1,2,1,1,2] may mutate, and the third gene changes from 1 to 2. The mutated offspring individual C:[1,2,2,1,2].

[0033] (4) Offspring population After selection, crossover, and mutation, a progeny population is generated. Below are five example individuals from the progeny population: Offspring individual 1: [1,2,2,1,2][1,2,2,1,2]; Offspring individual 2: [0,4,2,3,0][0,4,2,3,0]; Offspring individual 3: [2,3,0,2,1][2,3,0,2,1]; Offspring individual 4: [1,3,1,0,2][1,3,1,0,2]; Offspring individual 5: [2,1,2,1,3][2,1,2,1,3].

[0034] Through multiple iterations, the optimized model gradually eliminates high-cost individuals and retains low-cost individuals, eventually converging to the optimal solution.

[0035] The final solution is as follows: 1) Number of aerogel layers x 1 = 2; 2) Number of layers in rigid polyurethane foam x 2 = 3; 3) Extruded polystyrene board x 3 = 0 (meaning the material is not used); 4) Number of phenolic foam layers x 4 = 2; 5) Mineral wool board x 5 = 1.

[0036] Total cost = (300+50)×2 + (120+30)×3 + (80+20)×0 + (150+40)×2 + (60+25)×1 = 700 + 450 + 0 + 380 + 85 = 1615 yuan / m².

[0037] The optimization results were verified through the following process: (1) Thickness constraint verification The total thickness H = 10×2 + 20×3 + 25×0 + 20×2 + 30×1 = 150 mm ≤ 250 mm, which satisfies the thickness constraint condition.

[0038] (2) Verification of heat flux density constraint Total thermal resistance of insulation layer R cond = (0.01×2) / 0.015 + (0.02×3) / 0.024 + (0.025×0) / 0.030 + (0.02×2) / 0.022 + (0.03×1) / 0.038 = 1.333 + 2.5 + 0 + 1.818 + 0.789 =6.44 m²·℃ / W; External surface flow thermal resistance R conv = 1 / 10 = 0.1 m²·℃ / W; Total thermal resistance R total = 6.44 + 0.1 = 6.54 m²·℃ / W; Heat flux density q= (25 - (-20)) / 6.54 ≈ 6.88 W / m² ≤ 10 W / m², which also satisfies the heat flux density constraint condition.

[0039] (3) Verification of temperature difference on the outer surface Surface temperature T after insulation out = 25 - 6.88 × 0.1 ≈ 24.31 ℃; |T out - T am | = |24.31 - 25| = 0.69 ℃ ≤ 2 ℃, which satisfies the temperature difference constraint condition.

[0040] (4) Verification of dew point temperature difference T out ≈ 24.31 ℃ ≥ T dew ≈ 21.5 ℃, which meets the dew point temperature constraint.

[0041] By verifying each constraint, all constraints are satisfied. Therefore, the optimization results obtained from this optimization model can be used as the design scheme for the cold storage insulation structure.

[0042] 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 the present invention is defined by the appended claims and their equivalents.

Claims

1. A high-performance aerogel cold storage insulation structure optimization design method based on genetic algorithm, characterized in that, Includes the following steps: S1 Defines the objective: to design the insulation layer structure of cold storage using one or more of the five commonly used thermal insulation materials: high-performance aerogel, rigid polyurethane foam, extruded polystyrene board, phenolic foam, and mineral wool board, and to provide the single-layer thickness value for each material. S2 Optimization Model Construction: Using the number of material layers as the design variable, and with the control of insulation layer thickness, the temperature difference between the surface temperature after insulation and the ambient temperature, and the dew point temperature as constraints, the following single-objective optimization model is constructed: ； Where C represents the total cost of the insulation layer. x i Let i be the number of layers of material i. t i The thickness of a single layer of material i, in mm; P i The unit cost of material i is expressed in yuan / m. 2 ; PR i The unit price for the construction cost of a single layer of material i is expressed in yuan / m. 2 ; The insulation layer thickness control constraints are set as follows: ； in, x i Let i be the number of layers of material i. t i The thickness of a single layer of material i, in mm. H The total thickness of the insulation structure is limited, in mm; The temperature difference control constraints between the surface temperature after insulation and the ambient temperature are set as follows: ； in, T am For ambient temperature, T out The surface temperature of the insulation layer. The temperature difference between the surface temperature of the insulation layer and the ambient temperature is required; The surface temperature after insulation is calculated using the following formula: Among them, T out The temperature of the insulation layer's surface is expressed in °C. T in The design temperature inside the cold storage is q; q is the heat flux density, and R is the heat flux density. total The total thermal resistance is calculated using the following formula: h is the surface convective coefficient; The heat flux density q is calculated using the following formula: ; Dew point temperature control is defined as follows: the outer surface temperature after insulation is greater than the dew point temperature, i.e. , T dew This is the dew point temperature, which is determined by the ambient temperature. T am The relative humidity (RH) is obtained by consulting the enthalpy-humidity diagram. S3 uses a genetic algorithm to solve the optimization model, which yields a cold storage insulation layer structure that meets the design requirements.

2. The method for optimizing the design of a high-performance aerogel cold storage insulation structure based on a genetic algorithm according to claim 1, characterized in that: It also includes heat flux density constraints. Q is the design limit for heat flux density, in W / m².

3. The method for optimizing the design of a high-performance aerogel cold storage insulation structure based on a genetic algorithm according to claim 1, characterized in that: When using the genetic algorithm for autumn optimization in S3, the population size is set to be no less than 30 groups, the number of iterations to be no less than 800, the crossover probability to be 0.8, the mutation probability to be 0.05, and the number of layers in a single material is within a certain range. x i ∈ {0,1, 2, 3, 4, 5, 6, 7, 8}.

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