Method for obtaining prediction model of oriented heat conductivity coefficient of phyteral concrete considering influence of multiple factors

Abstract

The invention discloses a method for obtaining a phyteral concrete directional heat conductivity coefficient prediction model considering multi-factor influence. The method mainly comprises the following steps: S1, measuring the heat conductivity of different phyteral concrete in a steady state; s2, observing the 3D form of the sample through microscopic tomography, and observing the surface form of the selected sample by using an environmental scanning electron microscope to obtain a binder phase related to the actual phase volume; s3, calculating a thermal conductivity actual value and a thermal conductivity measured value based on an extreme value method; s4, according to the influence of the porosity on the phase morphology, drawing fitting linear correlation according to the conductivity in the two directions; s5, using linear fitting between the porosity and the series model fraction to predict thermal conductivity in two directions; and S6, fitting precision is supplemented. The method is clear in design thought, high in theoretical property and clear in steps, and has important significance on development of directional heat-conducting property research of the plant concrete.

Description

Technical Field

[0001] The invention relates to a method for obtaining a directional thermal conductivity prediction model of plant-based concrete taking into account the influence of multiple factors, and belongs to the field of plant-based concrete. Background Art

[0002] Bio-based materials play an important role in the energy and ecological transition due to their ability to store carbon and their low embodied energy. Of the 27% of global greenhouse gas emissions from the construction industry, one third will be attributed to building materials. In fact, this sector accounts for more than one third of the world's consumption of non-biological resources. Although the performance of bio-based materials is not easy to predict, they are likely to fulfil all the functions required of building facades, including the structural and thermal insulation aspects of the shell.

[0003] Among them, vegetable concrete has attracted great interest in the construction sector due to its low environmental impact, good mechanical properties and outstanding hygrothermal performance, especially due to the low density of plant fibers. It is used as a biogenic material for construction due to its good integration between different functions. In particular, the improvement of the combination of thermal insulation and compressive strength will allow it to be used as a single wall in buildings with less environmental impact. The thermal inertia provided by this material will increase the resistance of houses to global warming in terms of summer comfort. Therefore, it is a very important task to accurately obtain the thermal conductivity of vegetable concrete. Summary of the invention

[0004] Purpose of the invention: In order to overcome the shortcomings of the prior art, the present invention provides a method for obtaining a directional thermal conductivity prediction model for plant-based concrete taking into account the influence of multiple factors. Based on the theoretical thermal conductivity in two directions parallel and perpendicular to the compaction degree, linear fitting is used to obtain the relationship between each parameter and the weight factor. The prediction accuracy can be quantitatively judged according to the correlation coefficient, which comprehensively reflects the key influencing parameters of the thermal conductivity of plant-based concrete with different materials and different densities.

[0005] Technical solution: To solve the above technical problems, the present invention provides a method for obtaining a directional thermal conductivity prediction model of plant-based concrete considering the influence of multiple factors, comprising the following steps:

[0006] Step 1: Measure the thermal conductivity values ​​of plant-based concrete with different parameters at steady state as a fitting database;

[0007] Step 2: Measure the density of the sample’s binder, aggregate, and pores;

[0008] Step 3: Use scanning electron microscopy to observe the surface morphology of the selected samples and obtain the volume ratios of binder, aggregate, and pore space for each specimen;

[0009] Step 4: Calculate the theoretical thermal conductivity λ of the plant fiber perpendicular and parallel to the compaction direction during sample preparation⊥ and λ || ; Step 5: The thermal conductivity of the actual sample is expressed as:

[0010] When the plant fibers are mainly in the vertical direction: λ across =f across λ ⊥ +(1-f across ) ||

[0011] When the plant fibers are mainly parallel: along =f along λ ⊥ +(1-f along ) ||

[0012] f across and f along are the weight factors of thermal conductivity in the vertical compaction direction in different models;

[0013] Step 6: According to the thermal conductivity of the samples in the database, the least square method is used to fit the correlation between the weight factor and the density, porosity, binder, and aggregate mass ratio parameters;

[0014] Step 7: Select the parameter fitting result with the largest correlation coefficient as the weight factor to obtain λ across and λ along .

[0015] Preferably, in step 3, the volume ratio of binder, aggregate and pores is expressed as:

[0016]

[0017] ε a =1-ε p -ε b

[0018] Where: ε p is the volume fraction of plant particles, ε b is the volume fraction of binder, ε a is the porosity, ρ a is the air density, ρ p is the particle density, ρ b is the density of the binder, ρ sample is the sample density, R BP is the binder / particle mass ratio.

[0019] Preferably, in step 4,

[0020]

[0021] λ || =εa +ε p λ p +ε b λ b

[0022] Where: a is the thermal conductivity of air, λ p is the particle thermal conductivity, λ b is the thermal conductivity of the binder.

[0023] In the present invention, in step 1, two directions of heat flux are defined during the thermal conductivity measurement process, when the applied temperature gradient is parallel to the compaction direction, the direction is "cross", and when the applied temperature gradient is orthogonal to the compaction direction, the direction is "same".

[0024] Preferably, in step 5, the direction of the fibers is determined according to the compaction pressure during the preparation of the concrete. When the compaction pressure is small, a model with mainly vertical plant fibers is used. When the compaction pressure is large, a model with mainly vertical plant fibers is used. At this time, the compaction pressure is large and small with half of the maximum compaction pressure as the dividing line, and the compaction pressure less than 0.5 times the maximum compaction pressure is small.

[0025] Beneficial effects: Compared with the prior art, the present invention has the following advantages:

[0026] (1) The present invention can be applied to plant-based concrete with various characteristics. By analyzing the influence of parameters such as particle size, density, binder / particle ratio on the thermal conductivity of plant-based concrete, the results obtained are more comprehensive and rigorous.

[0027] (2) The present invention is based on the theoretical thermal conductivity in two directions parallel and perpendicular to the compaction degree, and uses linear fitting to obtain the relationship between each parameter and the weight factor. The prediction accuracy can be quantitatively determined based on the correlation coefficient, which fully reflects the key influencing parameters of the thermal conductivity of plant concrete with different materials and different densities. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] Figure 1 Scatter plot of hemp fiber particles and distribution of width and length weighted by particle area.

[0029] Figure 2 It is the ternary diagram of voids, plant fibers and binder.

[0030] Figure 3 A global view of thermal conductivity measurements along and through the particle.

[0031] Figure 4 The relationship between the measured thermal conductivity and density.

[0032] Figure 5This is the thermal conductivity data after classification and density fitting diagram.

[0033] Figure 6 The figure shows the proportion of parallel models and the corresponding fitted linear correlation diagram.

[0034] Figure 7 are the predicted and measured values ​​of thermal conductivity.

[0035] Figure 8 Plots of measured and predicted thermal conductivity as a function of density. DETAILED DESCRIPTION

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] The present invention provides a method for obtaining a directional thermal conductivity prediction model of plant-based concrete taking into account the influence of multiple factors, which is further described below in conjunction with specific examples. The embodiments are only used to more clearly illustrate the technical solution of the present invention and cannot be used to limit the scope of protection of the present invention.

[0038] Example 1

[0039] The plant materials used in the experiment are hemp fibers, including unprocessed fibers, slender fibers, large fibers, medium fibers and small fibers. The fiber sizes are shown in Table 1.

[0040] Table 1 Mean and standard deviation of fiber size distribution

[0041]

[0042] Using scanning electron microscopy, the geometric characteristics of hemp fibers, such as Figure 1 The raw material characteristics are that the fiber apparent density is 300kg m -3 . Thermal conductivity is 0.08W m-1K-1 . Slurry density is 1300kg m-3 , thermal conductivity is 0.7W m-1K-1 The air density is 1.2kg / m 3 , the thermal conductivity is 0.024W / (m·K). That is, ρ a =1.2kgm -3 ,λ a =0.024Wm-1k-1,ρ p =300kgm-3,λ p =0.08Wm-1k-1,ρ b =1300kgm-3,λ b =0.7Wm-1k-1, use central composite design to select mix proportion, see Table 2, the size of the concrete is 15×15×15cm 3 .

[0043] Table 2 Experimental design

[0044]

[0045] Measure the thermal conductivity of different samples, store them as a fitting database, and draw a ternary diagram, see Figure 2 and Figure 3 .

[0046] Plot thermal conductivity versus density, see Figure 4 , which has a large discreteness, indicating that density alone cannot correctly predict thermal conductivity. The measured thermal conductivity is divided into parallel and perpendicular directions, and the results show a good correlation. The data graph and fitting results are shown in Figure 5 and Figure 6 shown. Figure 5 This shows that the binder ratio has a significant effect on the thermal conductivity prediction model. At a constant density, the thermal conductivity increases with the increase of the binder ratio. The correlation between porosity and thermal conductivity is analyzed in Figure 6 , the correlation coefficient is low in the parallel direction, which is mainly due to the obvious deviation of the samples of small and medium-sized fibers. The parameter fitting result with the largest correlation coefficient is selected as the weight factor to obtain λ across and λ along The fitting model obtained according to the test results is λ across =0.01+0.77ε a ,λ along =0.32+0.53ε a .

[0047] Compare the above model prediction results with the measured results, as shown in the following figure. Figure 7 and Figure 8 As shown in the figure, the predicted values ​​are basically consistent with the measured values, and the model has high accuracy.

[0048] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for obtaining a directional thermal conductivity prediction model of plant-based concrete considering the influence of multiple factors, characterized in that: The steps include: Step 1: Measure the thermal conductivity values ​​of plant-based concrete with different parameters at steady state as a fitting database; Step 2: Measure the density of the sample’s binder, aggregate, and pores; Step 3: Use scanning electron microscopy to observe the surface morphology of the selected samples and obtain the volume ratios of binder, aggregate, and pore space for each specimen; Step 4: Calculate the theoretical thermal conductivity λ of the plant fiber perpendicular and parallel to the compaction direction during sample preparation ⊥ and λ || ; Step 5: The thermal conductivity of the actual sample is expressed as: When the plant fibers are mainly in the vertical direction: λ across =f across λ ⊥ +(1-f across ) || When the plant fibers are mainly parallel: along =f along λ ⊥ +(1-f along ) || f across and f along are the weight factors of thermal conductivity in the vertical compaction direction in different models; Step 6: According to the thermal conductivity of the samples in the database, the least square method is used to fit the correlation between the weight factor and the density, porosity, binder, and aggregate mass ratio parameters; Step 7: Select the parameter fitting result with the largest correlation coefficient as the weight factor to obtain λ across and λ along .

2. The method for obtaining a directional thermal conductivity prediction model of plant-based concrete considering the influence of multiple factors according to claim 1, characterized in that: In step 3, the volume ratio of binder, aggregate and voids is expressed as: e a =1-e p -e b Where: ε p is the volume fraction of plant particles, ε b is the volume fraction of binder, ε a is the porosity, ρ a is the air density, ρ p is the particle density, ρ b is the density of the binder, ρ sample is the sample density, R BP is the binder / particle mass ratio.

3. The method for obtaining a directional thermal conductivity prediction model of plant-based concrete considering the influence of multiple factors according to claim 1, characterized in that: In step 4, l || =e a l a +e p l p +e b l b Where: a is the thermal conductivity of air, λ p is the particle thermal conductivity, λ b is the thermal conductivity of the binder.

4. The method for obtaining a directional thermal conductivity prediction model of plant-based concrete considering the influence of multiple factors according to claim 1, characterized in that: In step 5, the direction of the fibers is determined according to the compaction pressure during the concrete preparation process. When the compaction pressure is small, a model based on vertical plant fibers is used. When the compaction pressure is large, a model based on vertical plant fibers is used.

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