Anti-condensation method for closed cavity with ventilation valve

Abstract

The invention discloses an anti-condensation method for a closed cavity with a ventilation valve, and the method comprises the following steps: setting a plurality of measurement points in the closed cavity to obtain a moisture diffusion coefficient of each measurement point in the closed cavity, and fitting to obtain an influence function of temperature, humidity and a humidity adjusting material on the moisture diffusion coefficient; according to the moisture diffusion coefficient and each influence function, constructing a simulation model to simulate the distribution of moisture in the closed cavity; acquiring internal temperature and humidity and external environment information of the closed cavity; obtaining condensation conditions according to the internal temperature, humidity and external environment information of the closed cavity in combination with a moisture diffusion coefficient; and according to the moisture distribution and condensation conditions in combination with the simulation model, obtaining the use amount and position distribution of the anti-condensation humidity adjusting material. Condensation prevention of the closed cavity with the ventilation valve is achieved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of humidity adjustment, in particular to a method for preventing condensation in a closed cavity with a breather valve. BACKGROUND

[0002] There are many places that need to use equipment cavities with breather valves (such as base stations, motors, electric controls, vehicle lights, batteries, power cabinets, laser radars, etc.), and the insulation capability of these equipment cavities is an important factor for safe and stable operation. In addition to the design, material and quality factors of the equipment cavity itself, insulation accidents caused by adverse environmental factors such as temperature, humidity and condensation in the indoor operating environment often occur. Long-term operation experience of some power cabinets shows that the generation of condensation in the cabinet will lead to a decrease in internal insulation performance, and various insulation defects will gradually develop into breakdown, causing accidents, which has an important impact on the stability of the entire power system. Therefore, it is very important to solve the condensation of switch cabinets for the safe operation of switch cabinets and the stability of the power grid.

[0003] At present, the understanding of the condensation phenomenon in the switch cabinet is not deep enough, and the physical process of condensation, especially the energy and mass exchange mechanism in condensation, is not clear. The influence factors of condensation under different operating conditions also lack systematic analysis, and it is urgent to study the condensation process and influence factors and how to prevent condensation. SUMMARY

[0004] In view of the above-mentioned deficiencies existing at present, the present application provides a method for preventing condensation in a closed cavity with a breather valve, which can realize the prevention of condensation in a closed cavity with a breather valve.

[0005] To achieve the above-mentioned purpose, the embodiments of the present application adopt the following technical solutions:

[0006] A method for preventing condensation in a closed cavity with a breather valve, the method comprising the following steps:

[0007] By setting multiple measurement points in the closed cavity, the moisture diffusion coefficient of each measurement point in the closed cavity is obtained, and the influence function of temperature, humidity and humidity control material on the moisture diffusion coefficient is fitted;

[0008] According to the moisture diffusion coefficient and the influence function, a simulation model is constructed to simulate the distribution of moisture in the closed cavity;

[0009] The internal temperature, humidity of the closed cavity and the external environment information are obtained;

[0010] According to the internal temperature, humidity of the closed cavity and the external environment information, the condensation condition is obtained in combination with the moisture diffusion coefficient;

[0011] According to the moisture distribution, the condensation condition and the simulation model, the amount and position distribution of the condensation-preventing humidity control material are obtained.

[0012] According to one aspect of the present application, the method for obtaining the moisture diffusion coefficient of each measuring point in the closed cavity by setting multiple measuring points in the closed cavity, and fitting the influence function of temperature, humidity and humidity control material on the moisture diffusion coefficient comprises:

[0013] obtaining the diffusion distance from each measuring point to the air permeable valve;

[0014] obtaining the temperature and humidity of the external environment of the closed cavity within a preset time period;

[0015] obtaining the temperature and humidity of each measuring point inside the closed cavity within a preset time period;

[0016] obtaining the moisture diffusion coefficient by the half-life method according to the diffusion distance, the temperature and humidity of the external environment of the closed cavity, and the temperature and humidity of each measuring point;

[0017] obtaining the moisture diffusion coefficient under different temperature and humidity conditions, and fitting the influence function of temperature and humidity on the moisture diffusion coefficient;

[0018] obtaining the moisture diffusion coefficient under the condition of setting humidity control material at different positions, and fitting the influence function of humidity control material on the moisture diffusion coefficient.

[0019] According to one aspect of the present application, the method for obtaining the internal temperature, humidity and external environment information of the closed cavity comprises: obtaining the air temperature, air relative humidity and surface temperature inside the closed cavity, and obtaining the external air temperature and external air humidity of the closed cavity.

[0020] According to one aspect of the present application, the method for obtaining the condensation condition according to the internal temperature, humidity and external environment information of the closed cavity and the moisture diffusion coefficient comprises: calculating the dew point temperature of the current position of the closed cavity according to the air temperature and air relative humidity inside the closed cavity, and if the surface temperature of the current position is lower than the dew point temperature, there is a risk of condensation.

[0021] According to one aspect of the present application, the dew point temperature calculation method is:

[0022]

[0023] wherein, , T dew is the dew point temperature, T is the internal air temperature of the equipment, RH is the internal air humidity of the equipment, a and b are constants, and are determined based on the Magnus-Tetens formula and its improved formula.

[0024] According to one aspect of the present application, the simulation model for simulating the distribution of moisture in the closed cavity according to the moisture diffusion coefficient and the influence functions includes: the influence of temperature and humidity changes on moisture diffusion; the influence of the moisture absorption and desorption process of the humidity control material on moisture diffusion; and the amount and distribution position of the humidity control material are taken as input parameters of the simulation model to predict the diffusion behavior of moisture in the device.

[0025] According to one aspect of the present application, the simulation model for simulating the distribution of moisture in the closed cavity according to the moisture diffusion coefficient and the influence functions includes: the influence of temperature and humidity changes on moisture diffusion; the influence of the moisture absorption and desorption process of the humidity control material on moisture diffusion; and the amount and distribution position of the humidity control material are taken as input parameters of the simulation model to predict the diffusion behavior of moisture in the device.

[0026] According to one aspect of the present application, the simulation model for simulating the distribution of moisture in the closed cavity according to the moisture diffusion coefficient and the influence functions includes: the influence of temperature and humidity changes on moisture diffusion; the influence of the moisture absorption and desorption process of the humidity control material on moisture diffusion; and the amount and distribution position of the humidity control material are taken as input parameters of the simulation model to predict the diffusion behavior of moisture in the device.

[0027] According to one aspect of the present application, the simulation model for simulating the distribution of moisture in the closed cavity according to the moisture diffusion coefficient and the influence functions includes: the influence of temperature and humidity changes on moisture diffusion; the influence of the moisture absorption and desorption process of the humidity control material on moisture diffusion; and the amount and distribution position of the humidity control material are taken as input parameters of the simulation model to predict the diffusion behavior of moisture in the device.

[0028] According to one aspect of the present application, the simulation model for simulating the distribution of moisture in the closed cavity according to the moisture diffusion coefficient and the influence functions includes: the influence of temperature and humidity changes on moisture diffusion; the influence of the moisture absorption and desorption process of the humidity control material on moisture diffusion; and the amount and distribution position of the humidity control material are taken as input parameters of the simulation model to predict the diffusion behavior of moisture in the device.

[0029] Through the above technical solution, the intelligent dynamic adjustment of the humidity in the closed space can be realized, and the condensation risk in the existing device cavity can be improved: under the premise of ensuring that the existing device cavity structure remains unchanged, the condensation risk in the existing device cavity can be improved by deploying the humidity control material. By fusing the internal and external environment monitoring data to construct a prediction model, the material arrangement scheme is gradually optimized, the reliability of the device cavity is improved, and the cost is reduced. This data-driven optimization mechanism not only guarantees the condensation prevention requirements of the key equipment in the cavity, but also optimizes the use of materials to reduce the maintenance cost of the device by 40%.

[0030] As Figure 1 , Figure 2The structure design shown, combined with the distributed sensor network, can know the dew condensation occurrence conditions and duration of important position points in the device cavity under different working condition experimental conditions. Then, improvement is realized through the deployment and simulation calculation of the humidity control material. First, the temperature and humidity parameters of the environment inside and outside the sealed cavity are dynamically monitored, a multi-dimensional data model is established, a multi-objective optimization algorithm based on weight coefficients is combined, and the optimal configuration of the humidity control material amount and distribution is realized; secondly, the humidity stability inside the cavity can be maintained by minimizing the alpha1.humidity fluctuation term, the dew condensation risk coefficient can be reduced by alpha2, and the dew condensation phenomenon can be effectively prevented, and the alpha3.humidity control material amount term constraint ensures the economy of material use; at the same time, Figures 1-2 The structure design shown supports the modular layout of the humidity control assembly, cooperates with the real-time data acquisition system to form a closed-loop control, and finally verifies the reliability of the device cavity under various working condition conditions. BRIEF DESCRIPTION OF DRAWINGS

[0031] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed in the embodiments will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can also be obtained by those skilled in the art without creative labor.

[0032] Figure 1 A schematic diagram of a dew condensation prevention method for a sealed cavity with a breather valve according to the present application;

[0033] Figure 2 A schematic diagram of a blank group motor and an experimental group motor according to embodiment three of the present application;

[0034] Figure 3 A blank group motor measurement point deployment diagram during motor dew condensation test according to embodiment three of the present application;

[0035] Figure 4 An experimental group motor measurement point deployment diagram during motor dew condensation test according to embodiment three of the present application;

[0036] Figure 5 A breather valve measurement point 11# temperature and humidity change curve diagram of a blank group motor according to embodiment three of the present application;

[0037] Figure 6 A breather valve measurement point 01# temperature and humidity change curve diagram of an experimental group motor according to embodiment three of the present application;

[0038] Figure 7 A coil measurement point 03# temperature and humidity change curve diagram of a blank group motor according to embodiment three of the present application;

[0039] Figure 8The temperature and humidity change curve diagram of the experimental group motor coil measuring point 13# is shown in Embodiment Three of the present application.

[0040] Figure 9 The temperature and humidity change curve diagram of the blank group motor high-voltage three-phase position measuring point 14# is shown in Embodiment Three of the present application.

[0041] Figure 10 The temperature and humidity change curve diagram of the experimental group motor high-voltage three-phase position measuring point 04# is shown in Embodiment Three of the present application. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of the present application.

[0043] Embodiment One

[0044] As shown in the figure, a closed cavity anti-condensation method with a breathable valve, the method comprises the following steps: Figure 1

[0045] Step S1: by setting multiple measuring points in the closed cavity, obtaining the moisture diffusion coefficient of each measuring point in the closed cavity, and fitting to obtain the influence function of temperature, humidity and humidity control material on the moisture diffusion coefficient;

[0046] The step S1 obtains the moisture diffusion coefficient of each measuring point in the closed cavity by setting multiple measuring points in the closed cavity, and fits to obtain the influence function of temperature, humidity and humidity control material on the moisture diffusion coefficient, which includes the following steps in specific implementation:

[0047] Step S11: obtaining the diffusion distance of each measuring point to the breathable valve;

[0048] First, obtain the positions of multiple measuring points in the closed cavity and their diffusion distances, establish a three-dimensional coordinate system, obtain the coordinate points of the breathable valve and the coordinate points of each measuring point, set the position of the breathable valve as the reference position , set the coordinates of the measuring points as (x, y, z), calculate the straight-line distance from the coordinate point of the measuring point to the coordinate point of the breathable valve as the diffusion distance L of the measuring point to the breathable valve, and the calculation formula is:

[0049] .

[0050] ​In the process, the humidity control material is placed at each measuring point, and the mass W of the humidity control material at each measuring point is the same and is measured by an electronic scale .

[0051] Step S12: Obtain the temperature and humidity of the environment outside the sealed cavity within a preset time period.

[0052] The temperature and humidity sensors and the surface temperature sensors are deployed outside the sealed cavity of the device, and the temperature and humidity data of the environment outside the device are obtained by the deployed temperature and humidity sensors and the surface temperature sensors. The obtained temperature and humidity of the environment outside the device can be the actual temperature and humidity actually obtained by measurement, or can be the condition temperature and humidity set according to requirements. The preset time is t.

[0053] In this embodiment, according to the working scene, working state, start-stop state and the like of the actual device, a plurality of combinations of the temperature and humidity of the environment outside the device are set to form different conditions of the temperature and humidity of the environment outside the device, for example: group 1: T out = 15℃, RH out = 30%; group 2: T out = 25℃, RH out = 60%; group 3: T out = 35℃, RH out = 90%; group 4: T out = 50℃, RH out = 50%; and the like; wherein T out represents the temperature of the environment outside the device, and RH out represents the humidity of the environment outside the device.

[0054] Step S13: Obtain the temperature and humidity of each measuring point inside the sealed cavity within a preset time period.

[0055] The temperature and humidity sensors and the surface temperature sensors are deployed at different positions such as the air permeable valve and the measuring points inside the sealed cavity of the device, and the temperature and humidity data of the air permeable valve and the plurality of measuring points inside the device are obtained by the deployed temperature and humidity sensors and the surface temperature sensors.

[0056] Step S14: Obtain the moisture diffusion coefficient by the half-life time method according to the diffusion distance, the temperature and humidity of the environment outside the sealed cavity, and the temperature and humidity of each measuring point.

[0057] Step S15: Obtain the moisture diffusion coefficient under different temperature and humidity conditions, and fit the influence function of the temperature and humidity on the moisture diffusion coefficient.

[0058] Step S16: Obtain the moisture diffusion coefficient under the condition that the humidity control material is arranged at different positions, and fit the influence function of the humidity control material on the moisture diffusion coefficient.

[0059] Steps S14~S16 specifically include:

[0060] Step S01: From the one-dimensional error function solution, by setting the half-life condition w(L, t 1 / 2 )=w ext +(w0-w ext ) / 2, the relationship is obtained:

[0061] ;

[0062] Where D is the moisture diffusion coefficient, L is the diffusion distance, t 1 / 2 is the half-life, where k=erfc - ¹(1 / 2) is about 0.4769, and further:

[0063] ;

[0064] Thus, the standard moisture diffusion coefficient D L of each measurement point is obtained, and the standard moisture diffusion coefficient D0 at the air permeable valve is taken as the reference moisture diffusion coefficient.

[0065] Step S02: Through the influencing factors of humidity and temperature in multiple scenarios, there will be different diffusion coefficients, so the following calculation formula is needed:

[0066] ;

[0067] The moisture diffusion equation, AH(x, y, z, t) is the absolute humidity at position (x, y, z) and time t, with units of g / m 3 ;

[0068] The divergence term Describes the net effect of moisture flow or diffusion, i.e. the net amount of moisture flowing into or out of a volume element. This term reflects the process of moisture diffusing from areas of high concentration to areas of low concentration in space.

[0069] D(W, T, RH) is the diffusion coefficient under the influence of the humidifying material, which depends on the weight W of the humidifying material, the temperature T and the relative humidity RH, and changes dynamically over time. The diffusion coefficient reflects the rate of moisture diffusion: the larger the diffusion coefficient, the faster the moisture diffuses.

[0070] is the spatial gradient of moisture concentration, which describes how the moisture concentration changes in space. The direction of the gradient points to the direction where the moisture concentration increases the fastest, and the size represents the rate of change of moisture concentration per unit distance in that direction.

[0071] is the spatial gradient of moisture diffusion, representing the change of moisture concentration in space and the diffusion process. Specifically, the change of moisture concentration is not only related to the diffusion rate in space, but also closely related to the change in time. According to the diffusion equation, the change in time of moisture is reflected through spatial diffusion.

[0072] Through the moisture diffusion equation, the moisture diffusion coefficient of each point can be obtained, and the calculation method is as follows:

[0073] ;

[0074] D0 is the reference diffusion coefficient, the reference position point is selected as the position of the air valve, and the diffusion coefficient in the absence of humidifying material, with the unit of m 2 / s;

[0075] is the temperature influence function on the diffusion coefficient;

[0076] is the humidity influence function on the diffusion coefficient;

[0077] is the humidifying material influence function on the diffusion coefficient, which depends on the weight of the humidifying material and its moisture absorption / desorption characteristics.

[0078] wherein, , α is the temperature influence coefficient on the diffusion coefficient, T0 is the reference temperature (e.g. 25°C), ; , γ is the humidity influence coefficient on the diffusion coefficient, RH0 is the reference humidity (e.g. 60%), and ; , β is a constant, representing the performance factor of the humidifying material, which determines the degree of inhibition of moisture diffusion by the material. The larger the β value, the more significant the influence of the humidifying material on moisture diffusion.

[0079] wherein the temperature influence coefficient α on the diffusion coefficient and the humidity influence coefficient γ on the diffusion coefficient are obtained by fitting the measured AH gradient and the corresponding D (deduced and calculated).

[0080] ;

[0081] In order to finally obtain the humidifying material influence function on the diffusion coefficient, a multi-stage experiment can be designed. In the first stage, inside the equipment without humidifying material, the position deployment of multiple test points (in addition to the air temperature and humidity at different positions, the equipment surface temperature at different positions is also obtained), and then in the equipment working state and start-stop state, different external temperature and humidity data are combined to obtain real-time data, and the fitting the function of temperature and humidity. In the second stage, under the same conditions as in the first stage, place the same quality of humidity control material at a different location each time, and calculate the diffusion coefficient D from the experimental data. In this way, the difference in the diffusion coefficient at different locations under the same external temperature and humidity combination conditions, with and without humidity control material, can be obtained. Through the above experimental design, the final .

[0082] In this embodiment, the specific humidity diffusion coefficient calculation method is obtained through the following experimental process:

[0083] First stage

[0084] 1.1 Objective: Obtain the diffusion coefficient without humidity control material, and fit the influence function of temperature f(T) and humidity f(RH).

[0085] 1.2 Experimental conditions:

[0086] Equipment: Choose equipment cavity with air permeable valve (such as base station, motor, electric control, power cabinet, etc.), ensure multiple measurement points inside.

[0087] External temperature and humidity: Set different combinations of external temperature and humidity of the equipment, ensure different environmental conditions in the working state, dormant state and start-stop state of the equipment.

[0088] Internal temperature and humidity: Deploy temperature and humidity sensors at different locations inside the equipment to record air humidity and temperature at each location.

[0089] Equipment surface temperature: Use thermocouples to arrange at different locations on the inner surface of the equipment, focusing on low temperature areas.

[0090] Equipment working state, dormant state, and start-stop state: Simulate the actual working conditions of the equipment, including the change of temperature and humidity during the start-stop process of the equipment.

[0091] 1.3 Experimental steps:

[0092] Equipment preparation: Ensure that there is no humidity control material inside the equipment (consistent with the actual application scenario), and set temperature and humidity sensors and thermocouples at multiple key locations.

[0093] External environment control: Set and adjust the external environment temperature and humidity, record the temperature and humidity data inside and outside the equipment, and set the working state and start-stop state of the equipment.

[0094] Temperature and humidity data recording: Record the humidity and temperature data of the air inside the equipment and the temperature of the inner surface of the equipment at different locations, ensuring the time synchronization of the data.

[0095] Diffusion coefficient calculation: Calculate the reference diffusion coefficient D0 by the half-life method, and fit the temperature f(T) and humidity f(RH) influence functions by experimental data.

[0096] 1.4 Experimental parameters and measurement methods:

[0097] Internal temperature and humidity: Place temperature and humidity sensors and thermocouples at multiple key locations to ensure the acquisition of air temperature and humidity data inside the device, as well as the temperature of the device's internal surfaces.

[0098] External environment temperature and humidity: Use the environmental control system to adjust and record the external temperature and humidity in real time.

[0099] Humidity change rate: Calculate the half-life of humidity change at each location, and calculate the diffusion coefficient D.

[0100] Data synchronization: Use the data acquisition system to synchronize the recording of data from each sensor.

[0101] Fit f T (T)∙f RH (RH) :

[0102] Objective: Through the data of the first phase, fit the influence functions of temperature and humidity on the diffusion coefficient.

[0103] Method: Use regression analysis to fit the influence of temperature and humidity on the diffusion coefficient.

[0104] Phase 2: Experiment with humidity control materials:

[0105] 2.1 Objective: Through multiple tests, each time placing the same mass of humidity control materials at different locations, measure their effect on the moisture diffusion coefficient D, and fit the humidity control material influence coefficient.

[0106] 2.2 Experimental conditions

[0107] Humidity control materials: Select humidity control materials with known moisture absorption / desorption characteristics, and use the same weight of humidity control materials for each experiment. The initial amount is calculated based on 600 grams per cubic meter, which is also the amount to be optimized later.

[0108] Device operation and start-stop state: Repeat the experimental conditions of the first phase to ensure experiments under different external temperature and humidity conditions.

[0109] 2.3 Experimental steps:

[0110] Device preparation: In the same device as in the first phase, select different test locations and place the same weight of humidity control materials.

[0111] External environment control: Set and record the external temperature and humidity conditions in the working state, dormant state, and start-stop state of the device, ensuring consistency with Phase 1.

[0112] Data recording: Real-time record the temperature and humidity data at different positions inside the device, especially focusing on the positions where the humidity control materials are placed.

[0113] Diffusion coefficient calculation: Use the half-life method to calculate the diffusion coefficient with humidity control materials and compare the difference in diffusion coefficients with and without humidity control materials.

[0114] 2.4 Experimental parameters and measurement methods:

[0115] Quality of humidity control materials: Use the same weight of humidity control materials for each experiment.

[0116] Temperature and humidity data: Ensure that the temperature and humidity data at each position are collected synchronously, as in Phase 1.

[0117] Moisture diffusion coefficient: Calculate the diffusion coefficient using the half-life method and compare the results with the experimental data without humidity control materials.

[0118] Through the data of Phase 2, fit the influence function of humidity control materials to describe the influence of humidity control materials at different positions and weights on moisture diffusion. According to the difference in moisture diffusion coefficients with and without humidity control materials, fit the influence function of humidity control materials on the diffusion coefficient.

[0119] Step S2: Construct a simulation model to simulate the distribution of moisture inside the closed cavity according to the moisture diffusion coefficient and each influence function;

[0120] In specific implementation, it includes the following contents:

[0121] 3.1 Fitting f T (T)·f RH (RH):

[0122] Objective: Through the data of Phase 1, fit the influence function of temperature and humidity on the diffusion coefficient.

[0123] Method: Use regression analysis to fit the influence of temperature and humidity on the diffusion coefficient.

[0124] 3.2 Fitting humidity control material influence coefficient f W (W):

[0125] Objective: Through the data of Phase 2, fit the influence function of humidity control materials to describe the influence of humidity control materials at different positions and weights on moisture diffusion.

[0126] Method: According to the difference of moisture diffusion coefficient between the condition of having humidity control material and not having humidity control material, the influence function of humidity control material on diffusion coefficient is fitted. The model can be further optimized by replacing the humidity control material with different humidity absorption and release rate.

[0127] 3.3 Establishing simulation model:

[0128] Using the experimental data of the first and second stages, a simulation model is established to simulate the distribution of moisture inside the device. The model includes: the influence of temperature and humidity change on moisture diffusion; the influence of humidity control material humidity absorption and release process on moisture diffusion.

[0129] The amount and distribution of humidity control material are used as input parameters of the model to predict the diffusion behavior of moisture inside the device.

[0130] Step S3: Obtain the internal temperature, humidity and external environment information of the sealed cavity;

[0131] The internal temperature, humidity and external environment information of the sealed cavity includes: obtaining the internal air temperature , air relative humidity and surface temperature of the sealed cavity, obtaining the external air temperature and external air humidity of the sealed cavity.

[0132] Step S4: Obtain the condensation condition according to the internal temperature, humidity and external environment information of the sealed cavity combined with the moisture diffusion coefficient;

[0133] The condensation condition obtained according to the internal temperature, humidity and external environment information of the sealed cavity combined with the moisture diffusion coefficient includes: calculating the dew point temperature of the current position of the sealed cavity according to the internal air temperature and air relative humidity of the sealed cavity, if the surface temperature of the current position is lower than the dew point temperature , there is a risk of condensation, and it is judged as a risk area.

[0134] That is, the condensation risk area is determined: .

[0135] The dew point temperature calculation method is:

[0136] ;

[0137] Wherein, , T dew is the dew point temperature, T is the internal air temperature of the device, RH is the internal air humidity of the device, a and b are constants, which are determined based on the Magnus-Tetens formula and its improved formula.

[0138] How to determine the dew point temperature calculation parameters a and b above 60℃?

[0139] Between 0℃ and 60℃, we often use the Magnus-Tetens approximation formula to calculate the dew point temperature:

[0140] ;

[0141] Where, .

[0142] Common parameters (applicable to 0℃-60℃):

[0143] a=17.27, b=237.7, which is derived from the Magnus-Tetens empirical formula, which is accurate in the low to moderate temperature range (0~60℃).

[0144] The specific values of a and b are shown in the following table:

[0145]

[0146] Antoine equation basic form:

[0147] Antoine equation is an empirical fitting formula, A, B, C has no strict "physical constant meaning", A: affects the "overall height" of the vapor pressure curve; B: describes the "temperature rise of the driving force of vapor pressure" - the larger the evaporation is more difficult; lC: used to adjust the position of the temperature axis, so that the fitting function is more accurate.

[0148] They are obtained by regression fitting according to the actual material saturation vapor pressure data; each material needs a specific set of A, B, C parameters; the same material may have multiple sets of coefficients (multi-section fitting) in different temperature intervals; the specific values are shown in the following table:

[0149] Step S5: According to the humidity distribution, the condensation conditions and the simulation model, the amount and position distribution of the anti-condensation humidifying material are obtained.

[0150] According to the humidity distribution and the external environment information of the closed cavity, the amount and position distribution of the humidifying material are calculated using an optimization algorithm, so that the internal humidity of the equipment is uniform and condensation is prevented.

[0151] Calculate the surface temperature and dew point temperature of the equipment, if , adjust to improve the local moisture absorption capacity.

[0152] 4.1 Optimize the distribution and amount of humidity control materials:

[0153] Objective: Through simulation calculation, optimize the minimum amount and distribution of humidity control materials, make the humidity in the equipment uniform, and prevent condensation.

[0154] Method: Use optimization algorithms (such as particle swarm optimization, genetic algorithm, etc.), according to the humidity distribution and working conditions of the equipment, calculate the optimal amount and position distribution of humidity control materials. Simulation calculation can predict the humidity distribution under each configuration.

[0155] In this embodiment, the calculation of the amount and position distribution of humidity control materials includes: solving by minimizing a comprehensive objective function: min(α1∙humidity fluctuation + α2∙condensation risk + α3∙amount of humidity control materials), where α1, α2, α3 are weight coefficients.

[0156] Where: 1) Humidity fluctuation (the smaller the better), measure the deviation of the relative humidity RH of each measuring point in the target space from the target set RH within the optimization time window: mean square deviation, absolute deviation, peak-valley amplitude; 2) Condensation risk (the smaller the better), measure the "intensity × duration" of the surface temperature Ts and dew point Tdew, which can be expressed in the following three forms: condensation degree (degree·hour), saturation excess degree (in terms of absolute humidity / saturation ratio), condensation occurrence probability or duration.

[0157] 3) Amount of humidity control materials (the smaller the better), which can be represented in the following ways: material mass, weighted cost (considering material cost, volume occupation, assembly cost), engineering penalty (weight, volume, space conversion).

[0158] In order to eliminate the dimensional differences of different indicators, normalization processing is needed: Set the target threshold θ (from the specification or customer requirements) and the reference value b (use the no-humidity control and historical scheme), and convert each indicator to a dimensionless indicator between 0 and 1: Where, ; J: actual indicator value of the current scheme; The normalized objective function is:

[0159] Method for determining weight coefficients: Method A: monetization weighting; Convert the risk or cost of each indicator into economic loss, let α1, α2, α3 be the marginal cost of humidity fluctuation, condensation risk, and amount respectively, example: α2:α1:α3 ≈ 10:1:0.2.

[0160] Method B: AHP (Analytic Hierarchy Process); Construct a pair-wise comparison matrix through expert scoring, calculate consistency, and obtain weights.

[0161] Method C: Pareto Frontier + Kneepoint Method; Obtain the Pareto frontier through multi-objective optimization, select a compromise solution, and deduce the weights.

[0162] Method D: Regression / Bayesian Method Based on Historical Data; Learn the implicit weights from the optimal solutions of historical projects.

[0163] Constraints: In actual engineering, condensation is usually zero-tolerant. The condensation risk is taken as a constraint condition, and the objective function only balances between humidity fluctuation and the amount of humidity control material.

[0164] Implementation Steps: 1. Define the test conditions and time window; 2. Select the index caliber; 3. Set the threshold and baseline value; 4. Normalize the index; 5. Select the initial weight (Method A / B / C / D), usually select A; 6. Numerical solution and iterative optimization; 7. Measurement verification and weight adjustment.

[0165] Example Numerical Values: Assume the target: humidity fluctuation ≤ 2%RH, condensation risk ≈ 0, and the amount of existing scheme as the target. The initial weights can be taken as α2=10, α1=1, α3=0.2.

[0166] Example Two

[0167] As Figure 1 shown, a condensation prevention method for a sealed cavity with a breather valve, the method comprising the following steps:

[0168] Step S1: By setting multiple measurement points in the sealed cavity, the moisture diffusion coefficient of each measurement point in the sealed cavity is obtained, and the influence function of temperature, humidity and humidity control material on the moisture diffusion coefficient is fitted;

[0169] The step S1 by setting multiple measurement points in the sealed cavity, the moisture diffusion coefficient of each measurement point in the sealed cavity is obtained, and the influence function of temperature, humidity and humidity control material on the moisture diffusion coefficient is fitted, in the specific implementation includes the following steps:

[0170] Step S11: Obtain the diffusion distance of each measurement point to the breather valve;

[0171] Firstly, the positions of multiple measuring points and their diffusion distances in the closed cavity are acquired, a three-dimensional coordinate system is established, the coordinate point of the breather valve and the coordinate points of the measuring points are acquired, and the position of the breather valve is set as a reference position , the coordinates of the measuring points are set as (x, y, z), the straight-line distance from the coordinate point of the measuring point to the coordinate point of the breather valve is calculated as the diffusion distance L of the measuring point to the breather valve, and the calculation formula is:

[0172] .

[0173] In this process, the humidifying material is placed at each measuring point, the mass W of the humidifying material at each measuring point is the same, the position of the humidifying material is measured by an electronic scale .

[0174] Step S12: acquiring the temperature and humidity of the external environment of the closed cavity within a preset time period;

[0175] The temperature and humidity sensors and the surface temperature sensors are deployed outside the closed cavity of the equipment, the temperature and humidity data of the external environment of the equipment are acquired through the deployed temperature and humidity sensors and the surface temperature sensors. The acquired external environment temperature and humidity can be the actual temperature and humidity actually acquired by measurement, or can be the condition temperature and humidity set according to needs. The preset time is t.

[0176] In this embodiment, according to the working scene, the equipment working state, the equipment start-stop state and the like of the actual equipment, multiple external environment temperature and humidity combinations are set, different external temperature and humidity conditions are formed, for example: group 1: T out = 15℃, RH out = 30%; group 2: T out = 25℃, RH out = 60%; group 3: T out = 35℃, RH out = 90%; group 4: T out = 50℃, RH out = 50% and the like; wherein T out represents the external environment temperature, and RH out represents the external environment humidity.

[0177] Step S13: acquiring the temperature and humidity of each measuring point in the closed cavity within a preset time period;

[0178] The temperature and humidity sensors and the surface temperature sensors are deployed at different positions such as the breather valve and the measuring points in the closed cavity of the equipment, the temperature and humidity data of the breather valve position and the multiple measuring points in the equipment are acquired through the deployed temperature and humidity sensors and the surface temperature sensors.

[0179] Step S14: According to the diffusion distance, the temperature and humidity of the environment outside the sealed cavity, the temperature and humidity of each measurement point, the moisture diffusion coefficient is calculated by the half-life method.

[0180] Step S15: Obtain the moisture diffusion coefficient under different temperature and humidity conditions, and fit the influence function of temperature and humidity on the moisture diffusion coefficient.

[0181] Step S16: Obtain the moisture diffusion coefficient under the condition of setting the humidity control material at different positions, and fit the influence function of the humidity control material on the moisture diffusion coefficient.

[0182] Step S14 to Step S16 specifically includes:

[0183] Step S01: Starting from the error function solution of one-dimensional diffusion, the relationship is obtained by setting the half-life time condition w(L, t 1 / 2 )=w ext +(w0-w ext ) / 2.

[0184] ;

[0185] Where D is the moisture diffusion coefficient, L is the diffusion distance, t 1 / 2 is the half-life time, where k=erfc - ¹(1 / 2) is about 0.4769, and then:

[0186] ;

[0187] Thus, the standard moisture diffusion coefficient D L of each measurement point is obtained, and the standard moisture diffusion coefficient D0 at the air permeable valve is taken as the reference moisture diffusion coefficient.

[0188] Step S02: Through the influence factors of humidity and temperature in multiple scenarios, there will be different diffusion coefficients, so the following calculation formula is needed to calculate:

[0189] ;

[0190] The moisture diffusion equation, AH(x, y, z, t) is the absolute humidity at position (x, y, z) and time t, with the unit of g / m 3 ;

[0191] The divergence term Describes the flow or net effect of diffusion of moisture, i.e. the net amount of moisture flowing into or out of a volume element. This term reflects the process of moisture diffusing from high concentration areas to low concentration areas in space.

[0192] D(W, T, RH) is the diffusion coefficient under the influence of the humidity-controlling material, which depends on the weight W, temperature T, and relative humidity RH of the humidity-controlling material, and dynamically changes over time. The diffusion coefficient reflects the rate of moisture diffusion: the larger the diffusion coefficient, the faster the moisture diffuses.

[0193] is the spatial gradient of the moisture concentration, which describes how the moisture concentration changes in space. The direction of the gradient points to the direction in which the moisture concentration increases the fastest, and the magnitude indicates the rate of change of the moisture concentration per unit distance in that direction.

[0194] is the spatial gradient of the moisture diffusion, which represents the change of the moisture concentration in space and the diffusion process. Specifically, the change of the moisture concentration is not only related to the rate of diffusion in space, but also closely related to the change over time. According to the diffusion equation, the temporal change of the moisture is reflected through the spatial diffusion.

[0195] Through the moisture diffusion equation, the moisture diffusion coefficient at each point can be obtained, and the calculation method is as follows:

[0196] ;

[0197] D0 is the reference diffusion coefficient, the reference position point is selected at the position of the air valve, and the diffusion coefficient in the absence of the humidity-controlling material, with the unit of m 2 / s;

[0198] is the temperature influence function on the diffusion coefficient;

[0199] is the humidity influence function on the diffusion coefficient;

[0200] is the humidity-controlling material influence function on the diffusion coefficient, which depends on the weight of the humidity-controlling material and its moisture absorption / desorption characteristics.

[0201] wherein, , α is the temperature influence coefficient on the diffusion coefficient, T0 is the reference temperature (e.g., 25°C), ; , γ is the humidity influence coefficient on the diffusion coefficient, RH0 is the reference humidity (e.g., 60%), ; , β is a constant, representing the performance factor of the humidity-controlling material, which determines the degree of inhibition of the material on the moisture diffusion. The larger the β value, the more significant the influence of the humidity-controlling material on the moisture diffusion.

[0202] ;

[0203] To finally get the function of the influence of the humidification material on the diffusion coefficient, a multi-stage experiment can be designed. In the first stage, the positions of multiple test points are deployed inside the equipment without humidification materials (in addition to the air temperature and humidity at different positions, the surface temperature of the equipment at different positions is also obtained), and then different external temperature and humidity data are combined in the working state and start-stop state of the equipment to obtain real-time data and a fitted function of f(T) and f(RH). In the second stage, under the same conditions as in the first stage, a humidification material of the same mass is placed at a different position each time, and the diffusion coefficient D is calculated based on the experimental data. In this way, the difference in the diffusion coefficient at different positions with and without humidification materials under the same external temperature and humidity combination conditions in the working state and start-stop state of the equipment can be obtained. Through the above experimental design, the function of .

[0204] In this embodiment, the specific method for calculating the moisture diffusion coefficient is obtained through the following experimental process:

[0205] First stage:

[0206] 1.1 Objective: Obtain the diffusion coefficient without humidification materials and fit the influence functions of temperature f(T) and humidity f(RH).

[0207] 1.2 Experimental conditions:

[0208] Equipment: Choose equipment cavities with air permeable valves (such as base stations, motors, electrical controls, power cabinets, etc.) to ensure multiple measurement points inside.

[0209] External temperature and humidity: Set different combinations of external temperature and humidity of the equipment to ensure different environmental conditions in the working state, dormant state, and start-stop state of the equipment.

[0210] Internal temperature and humidity: Deploy temperature and humidity sensors at different positions inside the equipment to record the air humidity and temperature at each position.

[0211] Equipment surface temperature: Use thermocouples to arrange at different positions on the inner surface of the equipment, focusing on low-temperature areas.

[0212] Equipment working state, dormant state, and start-stop state: Simulate the actual working conditions of the equipment, including the changes in temperature and humidity during the start-stop process of the equipment.

[0213] 1.3 Experimental steps:

[0214] Equipment preparation: Ensure that there is no humidification material inside the equipment (consistent with the actual application scenario), and set temperature and humidity sensors and thermocouples at multiple key positions.

[0215] External environment control: Set and adjust the external environment temperature and humidity, record the temperature and humidity data of the equipment outside and inside, set the equipment working state and start-stop state.

[0216] Temperature and humidity data recording: Record the humidity and temperature data of the air inside the equipment at different positions, and the temperature of the surface inside the equipment, to ensure the time synchronization of the data.

[0217] Calculate the diffusion coefficient: Calculate the reference diffusion coefficient D0 by the half-life method, and fit the temperature f(T) and humidity f(RH) influence functions by experimental data.

[0218] 1.4 Experimental parameters and measurement methods:

[0219] Internal temperature and humidity of the equipment: Place temperature and humidity sensors and thermocouples at multiple key positions to ensure the acquisition of air humidity and temperature data inside the equipment, as well as the temperature of the surface inside the equipment.

[0220] External environment temperature and humidity: Use the environmental control system to adjust and record the external temperature and humidity in real time.

[0221] Humidity change rate: Calculate the half-life of humidity change at each position, and calculate the diffusion coefficient D.

[0222] Data synchronization: Use the data acquisition system to synchronize the data of each sensor.

[0223] Fit f T (T)∙f RH (RH) :

[0224] Objective: Through the data of the first stage, fit the influence function of temperature and humidity on the diffusion coefficient.

[0225] Method: Use regression analysis to fit the influence of temperature and humidity on the diffusion coefficient.

[0226] Phase 2: Experiment with humidity control material:

[0227] 2.1 Objective: Through multiple tests, each time placing the same mass of humidity control material at different positions, measure its effect on the moisture diffusion coefficient D, and fit the humidity control material influence coefficient.

[0228] 2.2 Experimental conditions:

[0229] Humidity control material: Select humidity control materials with known moisture absorption / desorption characteristics, and use the same weight of humidity control material for each experiment. The initial amount is calculated according to 600 grams per cubic meter, which is also the amount to be optimized later.

[0230] Equipment working state and start-stop state: Repeat the experimental conditions of the first stage to ensure that the experiment is conducted under different external temperature and humidity conditions.

[0231] 2.3 Experimental steps:

[0232] Device preparation: In the same equipment as in Phase 1, select different test positions and place the same weight of humidity control material.

[0233] External environment control: Set and record the external temperature and humidity conditions in the working, dormant, and start-stop states of the equipment, ensuring consistency with Phase 1.

[0234] Data recording: Record the temperature and humidity data in real time at different positions inside the equipment, especially at the positions where the humidity control material is placed.

[0235] Diffusion coefficient calculation: Use the half-life method to calculate the diffusion coefficient with humidity control material and compare the difference in diffusion coefficients with and without humidity control material.

[0236] 2.4 Experimental parameters and measurement methods:

[0237] Humidity control material weight: Use the same weight of humidity control material for each experiment.

[0238] Temperature and humidity data: Ensure that temperature and humidity data are collected synchronously at each position, as in Phase 1.

[0239] Moisture diffusion coefficient: Calculate the diffusion coefficient using the half-life method and compare the results with the experimental data without humidity control material.

[0240] Through the data of Phase 2, fit the influence function of humidity control material to describe the influence of humidity control material at different positions and weights on moisture diffusion. According to the difference in moisture diffusion coefficients with and without humidity control material, fit the influence function of humidity control material on the diffusion coefficient.

[0241] Step S2: Construct a simulation model to simulate the distribution of moisture inside the sealed cavity based on the moisture diffusion coefficient and the influence functions.

[0242] In specific implementation, the following contents are included:

[0243] 3.1 Fitting f T (T)·f RH (RH):

[0244] Objective: Through the data of Phase 1, fit the influence function of temperature and humidity on the diffusion coefficient.

[0245] Method: Use regression analysis to fit the influence of temperature and humidity on the diffusion coefficient.

[0246] 3.2 Fitting humidity control material influence coefficient f W (W):

[0247] Objective: To fit the function of the effect of the humidity control material on the humidity diffusion, which describes the effect of the humidity control material with different positions and weights on the humidity diffusion, by the data of the second stage.

[0248] Method: The function of the effect of the humidity control material on the humidity diffusion coefficient is fitted according to the difference between the humidity diffusion coefficients under the conditions of the humidity control material and no humidity control material. The model can be further optimized by replacing the humidity control material with different humidity absorption and release rates.

[0249] 3.3 Establishment of simulation model:

[0250] A simulation model is established to simulate the distribution of the humidity in the device by using the experimental data of the first and second stages. The model includes: the effect of the temperature and humidity changes on the humidity diffusion; the effect of the humidity absorption and release process of the humidity control material on the humidity diffusion.

[0251] The amount and distribution position of the humidity control material are used as the input parameters of the model to predict the diffusion behavior of the humidity in the device.

[0252] Step S3: Obtain the internal temperature, humidity of the closed cavity and external environment information;

[0253] The obtaining of the internal temperature, humidity of the closed cavity and external environment information includes: obtaining the air temperature , air relative humidity and surface temperature of the closed cavity, and obtaining the external air temperature and external air humidity .

[0254] Step S4: Obtain the condensation condition according to the internal temperature, humidity of the closed cavity and external environment information combined with the humidity diffusion coefficient;

[0255] The obtaining of the condensation condition according to the internal temperature, humidity of the closed cavity and external environment information combined with the humidity diffusion coefficient includes: calculating the dew point temperature of the current position of the closed cavity according to the air temperature and air relative humidity in the closed cavity, and if the surface temperature of the current position is lower than the dew point temperature , there is a risk of condensation, and it is judged as a risk area.

[0256] That is, the condensation risk area is determined: .

[0257] The dew point temperature calculation method is:

[0258] ;

[0259] wherein, , T dewT is the temperature of the air inside the device, RH is the humidity of the air inside the device, a and b are constants, and are determined based on the Magnus-Tetens formula and its improved formula.

[0260] How to determine the dew point temperature calculation parameters a and b above 60℃?

[0261] Between 0℃ and 60℃, we usually use the Magnus-Tetens approximate formula to calculate the dew point temperature:

[0262] ;

[0263] Where, .

[0264] Common parameters (applicable to 0℃-60℃):

[0265] a=17.27, b=237.7, which is derived from the Magnus-Tetens empirical formula, which is accurate in the low to medium temperature (0~60℃) range.

[0266] The specific values of a and b are shown in the following table:

[0267]

[0268] Antoine equation basic form:

[0269] Antoine equation is an empirical fitting formula, A, B, C have no strict "physical constant meaning", A: affects the "overall height" of the vapor pressure curve; B: describes the "temperature rise of the driving force of the vapor pressure" - the larger the evaporation is more difficult; C: used to adjust the position of the temperature axis, so that the fitting function is more accurate.

[0270] They are obtained by regression fitting according to the actual material saturation vapor pressure data; each material needs a specific set of A, B, C parameters; the same material may have multiple sets of coefficients (multi-section fitting) in different temperature intervals; the specific values are shown in the following table:

[0271] Step S5: According to the humidity distribution, the condensation conditions and the simulation model, the amount and position distribution of the anti-condensation humidifying material are obtained.

[0272] According to the humidity distribution, the condensation conditions and the simulation model, the amount and position distribution of the anti-condensation humidifying material are obtained, including: using an optimization algorithm according to the humidity distribution and the external environment information of the closed cavity, calculating the amount and position distribution of the humidifying material, making the humidity inside the device uniform, and preventing condensation.

[0273] The computing device surface temperature and dew point temperature, if then adjusting to increase the local moisture absorption capacity.

[0274] 4.1 Optimize the distribution and amount of humidity control material:

[0275] Objective: Through simulation calculation, optimize the minimum amount and distribution position of humidity control material, make the humidity in the device uniform, and prevent condensation.

[0276] Method: Use optimization algorithms (such as particle swarm optimization, genetic algorithm, etc.), according to the humidity distribution and working conditions of the device, calculate the optimal amount and position distribution of humidity control material. Simulation calculation can predict the humidity distribution under each configuration.

[0277] In this embodiment, the calculation of the amount and position distribution of the humidity control material includes: solving by minimizing a comprehensive objective function: min(α1∙humidity fluctuation + α2∙condensation risk + α3∙humidity control material amount), where α1, α2, α3 are weight coefficients.

[0278] Where: 1) Humidity fluctuation (the smaller the better), measure the deviation of the relative humidity RH of each measuring point in the target space from the target set RH within the optimization time window: mean square deviation, absolute deviation, peak-valley amplitude; 2) Condensation risk (the smaller the better), measure the "intensity × duration" with the relationship between surface temperature Ts and dew point Tdew: condensation degree (degree·hour), saturation excess degree (according to absolute humidity / saturation ratio), condensation occurrence probability or duration.

[0279] 3) Humidity control material amount (the smaller the better), which can be represented in the following ways: material mass, weighted cost (considering material cost, volume occupation, assembly cost), engineering penalty (weight, volume, space conversion).

[0280] In order to eliminate the dimensional differences of different indicators, normalization processing is needed: Set the target threshold θ (from the specification or customer requirements) and the reference value b (use the no-humidity control and historical scheme) to convert each indicator to a dimensionless indicator between 0 and 1: Where, ; J: Actual indicator value of the current scheme; The normalized objective function is:

[0281] Method for determining the weight coefficient: Method A: Monetary weighting; Convert the risk or cost of each indicator into economic loss, and let α1, α2, α3 be the marginal contribution of humidity fluctuation, condensation risk, and dosage, respectively. For example: α2: α1: α3 ≈ 10:1:0.2.

[0282] Method B: AHP (Analytic Hierarchy Process) Construct a pair-wise comparison matrix through expert scoring, calculate consistency, and obtain weights.

[0283] Method C: Pareto Frontier + Kneepoint Method Obtain the Pareto frontier through multi-objective optimization, select the compromise solution, and back-propagate the weights.

[0284] Method D: Regression / Bayesian Method Based on Historical Data Learn the implicit weights from the optimal solutions of historical projects.

[0285] Constraints: In practical engineering, condensation is usually zero-tolerant. Take condensation risk as a constraint, and the objective function only balances between humidity fluctuation and the amount of humidification material.

[0286] Implementation Steps: 1. Define test conditions and time window; 2. Select index caliber; 3. Set threshold and baseline value; 4. Normalize the index; 5. Select initial weight (Method A / B / C / D), usually choose A; 6. Numerical solution and iterative optimization; 7. Experimental verification and weight adjustment.

[0287] Example Numerical: Suppose the target: humidity fluctuation ≤ 2%RH, condensation risk ≈ 0, dosage based on the existing scheme. The initial weights can be taken as α2=10, α1=1, α3=0.2.

[0288] Step S17: Experimental verification and adjustment.

[0289] 4.2 Experimental Verification and Adjustment: Experimental Verification: Use experimental data to verify the simulation results to ensure the accuracy of the simulation optimization scheme.

[0290] Adjust the optimization scheme: Adjust the parameters in the simulation model according to the experimental results, and further optimize the amount and distribution of humidification materials. By improving the performance of the humidification materials, the condensation prevention scheme is optimized, and the condensation prevention algorithm for the application scenario of "equipment cavity with air permeable valve" is established.

[0291] Specifically includes: Objective: To verify the effectiveness of the optimized material distribution scheme in Phase 2 and ensure its anti-condensation effect.

[0292] Experimental steps: 1. Based on the simulation optimization results, the humidity-regulating material W(x,y,z) is placed in different positions inside the equipment.

[0293] 2. Record the humidity changes inside the equipment to confirm whether there is condensation.

[0294] 3. Verify the effect of the optimized distribution of humidity-regulating materials to ensure uniform humidity distribution and prevent condensation.

[0295] This solution combines experimental testing, data analysis, and numerical simulation to optimize the distribution strategy of humidity-regulating materials, ensuring uniform humidity inside the equipment and preventing condensation.

[0296] Example 3

[0297] In this embodiment, when the method for preventing condensation in a sealed cavity with a breathable valve described in this invention is applied to a motor, the experimental steps and data are as follows:

[0298] like Figure 2 As shown, blank group motor (HC-T02) and experimental group motor (HC-T01) are prepared.

[0299] Figure 3 This is a test deployment location diagram for the blank group of motors. Figure 4 This is a diagram showing the test deployment locations for the motors in the experimental group; according to... Figure 3 and Figure 4 Deploy test locations; Table 1 below shows the deployment locations and settings for each location.

[0300] Table 1:

[0301]

[0302] As shown in Table 2 below, the key analysis points are:

[0303] Table 2:

[0304]

[0305] like Figure 5 The figure shows the temperature and humidity variation curves at the location of the vent valve on the blank group motor; as shown... Figure 6 The figure shows the temperature and humidity change curves at the location of the motor vent valve in the experimental group. It can be seen that before 14:00 on August 4th, the temperature at the vent valve location remained approximately 40℃, the humidity in the control group continuously rose to 90%, while the humidity in the experimental group remained around 70%. The condensation test data are shown in Table 3 below.

[0306] Table III:

[0307]

[0308] Figure 7~Figure 8 As shown in Table IV, the humidity of the blank group was basically above 90% during the period from July 29 to August 4 when the temperature at the coil position was about 40 DEG C, and the humidity of the experimental group was maintained at about 60%.

[0309] Table IV:

[0310]

[0311] Figure 9~Figure 10 As shown in Table IV, the humidity of the blank group was basically above 90% during the period from July 29 to August 4 when the temperature at the coil position was about 40 DEG C, and the humidity of the experimental group was maintained at about 60%.

[0312] Table V:

[0313]

[0314] The conclusions are as follows:

[0315] In the period of condensation, the temperature and humidity of the experimental group (with humidity control material) fluctuate less than those of the blank group. In the same period of condensation (8.4 / 20:00-8.5 / 03:30):

[0316] The average temperature of the experimental group is 41 DEG C, which is lower than the average temperature of the blank group of 48 DEG C

[0317] The relative humidity of the experimental group is 71.9%, and in combination with the average temperature of 41 DEG C, the average absolute humidity is about 41.42 g / m 3 ; The relative humidity of the blank group is 68.3%, and in combination with the average temperature of 48 DEG C, the average absolute humidity is about 56.15 g / m 3 .

[0318] The advantages of the embodiment of the present application are as follows: Through the above scheme, the goal of uniform distribution of humidity in the device and effective prevention of condensation is achieved. The method combining experimental verification and simulation optimization not only improves the accuracy and reliability of the condensation prevention scheme, but also greatly shortens the optimization period and reduces the research and development cost. In addition, the condensation prevention algorithm established by the scheme provides strong technical support and reference for the condensation prevention design in similar application scenarios of "device cavity with air permeable valve", and has wide application prospect and important practical value.

[0319] The above merely illustrates the specific embodiments of the present application, but the protection scope of the present application is not limited thereto, any person skilled in the art can easily think of the changes or replacements within the technical scope disclosed by the present application, which should be covered in the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claims.

Claims

1. A method for preventing condensation in a closed cavity having a breather valve, characterized by, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

2. The closed cavity anti-condensation method with a breather valve according to claim 1, characterized in that, The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps: The method comprises the following steps:

3. The closed cavity anti-condensation method with a breather valve according to claim 1, characterized in that, The method comprises the following steps:

4. The closed cavity anti-condensation method with a breather valve according to claim 3, characterized in that, The method comprises the following steps:

5. The closed cavity anti-condensation method with a breather valve according to claim 4, characterized in that, The method comprises the following steps: wherein , T dew is the dew point temperature, T is the air temperature inside the device, RH is the air humidity inside the device, and a and b are constants determined on the basis of the Magnus-Tetens equation and its modifications.

6. The closed cavity anti-condensation method with a breather valve according to claim 1, characterized in that, The method comprises the following steps:

7. The closed cavity anti-condensation method with a breather valve according to claim 6, characterized in that, The method comprises the following steps:

8. The closed cavity anti-condensation method with a breather valve according to any one of claims 1 to 7, characterized in that, The method comprises the following steps:

9. 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The closed cavity anti-condensation method with a breather valve according to claim 8, characterized in that, The calculating the amount and position distribution of the humidity control material comprises: solving min(α1∙humidity fluctuation+α2∙condensation risk+α3∙amount of the humidity control material) by minimizing a comprehensive objective function, wherein α1, α2 and α3 are weight coefficients.

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