A material proportioning design and performance optimization method based on multi-entropy cooperation

Abstract

The application discloses a kind of material proportioning design and performance optimization method based on multi-entropy cooperation. The method determines a plurality of candidate material systems and the proportioning parameters of each candidate material system, and establishes a sample set;Obtain the physical distribution data, chemical composition data and structure distribution data corresponding to each candidate material system, calculate the physical entropy parameter, chemical entropy parameter and structure entropy parameter;Three kinds of entropy parameters are normalized, weighted and coupled to obtain a comprehensive entropy index;Establish the corresponding relationship between the comprehensive entropy index and the target performance, and determine the target interval of the comprehensive entropy index according to the preset target performance requirement;Under the constraint of the preferred parameter range corresponding to the target interval, screen the selected proportioning combination, and obtain a material proportioning scheme that meets the target performance requirement. The application can integrate material proportioning, micro-distribution characteristics and macro-performance response into a unified evaluation framework, improving the efficiency of multi-component material system proportioning design and performance optimization.

Description

Technical Field

[0001] This invention relates to the field of engineering materials technology, and more specifically, to a method for material proportioning design and performance optimization based on multi-entropy synergy. Background Technology

[0002] Material properties are typically influenced by a combination of factors, including composition, distribution, and structure. In multi-component material systems, the proportional relationships, physical distribution, chemical composition, and structural distribution characteristics of different components are coupled together to determine the material's macroscopic properties, such as mechanical properties, performance, volume stability, durability, transport properties, and reactivity.

[0003] Existing material composition design methods mostly rely on empirical trial mixing, single-factor adjustments, or local parameter optimization. They often establish a correspondence between material properties and a single type of parameter, making it difficult to simultaneously reflect the synergistic effects of physical, chemical, and structural factors within the material system. Therefore, when dealing with material systems with complex compositions and numerous control variables, traditional methods suffer from low design efficiency, reliance on experience in the optimization process, and difficulty in uniformly characterizing parameters at different scales.

[0004] Entropy, as a crucial parameter characterizing the distribution state, compositional complexity, and structural order of a system, offers a novel approach for the unified description of multi-component material systems. In recent years, research has emerged based on the concept of high entropy in material design, aiming to improve material performance by increasing the system's compositional, distributional, or structural complexity. However, a high-entropy state does not necessarily correspond to a high-performance state; the impact of different entropy levels on material performance varies depending on both the system and the target. For material design, the key is not simply to pursue higher entropy values, but to clarify the correspondence between entropy parameters and target performance, and to regulate the entropy state of the material system accordingly to optimize material performance. If entropy parameters can be established from three dimensions—physical distribution, chemical composition, and structural distribution—and further developed into a comprehensive entropy index, it is hoped that the material formulation design and performance optimization processes can be unified within a single quantifiable framework, thereby enabling targeted regulation of material performance.

[0005] Therefore, it is necessary to propose a material proportioning design and performance optimization method based on multi-entropy synergy to solve the problems that existing material design methods are difficult to take into account the synergy of multiple factors and the difficulty to achieve reverse performance optimization. Summary of the Invention

[0006] The purpose of this invention is to provide a material proportioning design and performance optimization method based on multi-entropy synergy. By constructing physical entropy parameters, chemical entropy parameters, structural entropy parameters, and a comprehensive entropy index E, a unified correlation between material proportioning parameters and target performance is established, thereby realizing material performance optimization and proportioning reverse design based on entropy regulation.

[0007] To achieve the above objectives, the present invention adopts the following technical solution: A material proportioning design and performance optimization method based on multi-entropy synergy includes the following steps: S1. Determine multiple candidate material systems and the proportioning parameters of each candidate material system, and establish a sample set containing multiple candidate material systems; S2. For each candidate material system in the sample set, obtain the corresponding physical distribution data, chemical composition data, and structural distribution data; S3. Calculate the physical entropy parameter, chemical entropy parameter, and structural entropy parameter based on the physical distribution data, chemical composition data, and structural distribution data, respectively. S4. Normalize, weight, and couple the physical entropy parameters, chemical entropy parameters, and structural entropy parameters to obtain the comprehensive entropy index corresponding to each candidate material system. S5. Obtain the target performance data corresponding to each candidate material system in the sample set, and establish the correspondence between the comprehensive entropy index and the target performance; S6. Determine the target range of the comprehensive entropy index based on the preset target performance requirements; S7. Determine the preferred parameter range based on the target range of the comprehensive entropy index, and screen the candidate ratio combinations under the constraints of the preferred parameter range to obtain a material ratio scheme that meets the target performance requirements.

[0008] Furthermore, the proportioning parameters include one or more of the following: the amount, proportion, dosage, substitution relationship, and composition relationship of each component in the candidate material system.

[0009] Furthermore, the physical distribution data includes one or more of the following: size distribution data, specific surface area distribution data, morphology distribution data, thickness distribution data, and length distribution data.

[0010] Furthermore, the chemical composition data includes one or more of the following: elemental composition data, compound composition data, component composition data, ionic composition data, and proportioning composition data.

[0011] Furthermore, the structural distribution data includes one or more of the following: pore structure distribution data, phase region distribution data, interface region distribution data, and structural unit distribution data.

[0012] Furthermore, the comprehensive entropy index is obtained by weighted coupling of the normalized physical entropy parameter, chemical entropy parameter, and structural entropy parameter, and the calculation formula is: in, , , These are the normalized physical entropy parameter, chemical entropy parameter, and structural entropy parameter, respectively. , , These are the weighting coefficients, and ; is the coupling coefficient.

[0013] Furthermore, the target performance includes one or more of mechanical properties, working performance, volume stability, durability, transport performance, reactivity, and uniformity, wherein the mechanical properties include compressive strength.

[0014] Furthermore, the target range of the comprehensive entropy index is obtained by screening candidate material systems whose target performance data meet the preset target performance requirements from the sample set, and determining the target range of the comprehensive entropy index based on the minimum and maximum values ​​of the comprehensive entropy index corresponding to the screened candidate material systems.

[0015] Furthermore, in S7, the material proportioning scheme is obtained by: screening candidate material systems whose comprehensive entropy index falls within the target range of the comprehensive entropy index from the sample set; determining the optimal parameter range based on the proportioning parameters, physical distribution data, chemical composition data, and structural distribution data corresponding to the screened candidate material systems; generating at least one set of candidate proportioning combinations under the constraints of the optimal parameter range; calculating the comprehensive entropy index corresponding to the candidate proportioning combinations; and determining the candidate proportioning combinations whose comprehensive entropy index falls within the target range of the comprehensive entropy index as the material proportioning scheme.

[0016] Furthermore, the physical entropy parameter H P Chemical entropy parameter H C and structural entropy parameter H S Calculate according to the following formulas respectively: ; ; ; in, is the normalized proportion of the i-th physical distribution interval, and n is the number of physical distribution intervals; is the normalized proportion of the j-th chemical unit, and m is the number of chemical units; denoted as the normalized proportion of the k-th structural distribution interval, and r is the number of structural distribution intervals.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This invention establishes entropy parameters from three dimensions: physical distribution, chemical composition, and structural distribution, which can more comprehensively characterize the state of a material system than a single index.

[0018] 2. By constructing a comprehensive entropy index E, this invention incorporates material proportioning parameters, microscopic state parameters, and macroscopic performance responses into the same framework, which can more clearly reflect the synergistic effect of multiple factors.

[0019] 3. This invention emphasizes the regulation of material properties through entropy parameters and the comprehensive entropy index E. Its core lies in regulating performance through entropy, rather than simply conducting empirical matching or local parameter correction. Therefore, it is more conducive to achieving performance-oriented design.

[0020] 4. This invention can adjust the proportioning parameters in reverse according to the target performance requirements, thereby realizing the integration of material proportioning design and performance optimization, improving design efficiency and reducing trial mixing costs.

[0021] 5. This invention is applicable to the proportioning design and performance optimization of multi-component material systems, and has good versatility and scalability. Detailed Implementation

[0022] To make the usage and features of this invention more apparent and understandable, specific embodiments are provided below for detailed explanation. However, the scope of protection of this invention is not limited to the following embodiments. Equivalent substitutions or modifications made by those skilled in the art without departing from the concept of this invention should fall within the scope of protection of this invention.

[0023] This invention provides a multi-entropy synergistic control method for material proportioning design and performance optimization, comprising the following steps: First, multiple candidate material systems and their proportioning parameters are determined. The proportioning parameters can be one or more of the following: the amount, proportion, dosage, substitution relationship, and compositional relationship of each component in the candidate material system.

[0024] Secondly, for each candidate material system, corresponding physical distribution data, chemical composition data, and structural distribution data are obtained. The physical distribution data may include size distribution data, specific surface area distribution data, morphology distribution data, thickness distribution data, length distribution data, etc.; the chemical composition data may include elemental composition data, compound composition data, component composition data, ionic composition data, proportioning composition data, etc.; the structural distribution data may include pore structure distribution data, phase region distribution data, interface region distribution data, structural unit distribution data, etc.

[0025] Furthermore, the physical entropy parameter H is calculated based on the physical distribution data. P The chemical entropy parameter H is calculated based on the chemical composition data. C The structural entropy parameter H is calculated based on the structural distribution data. S Among them, the physical entropy parameter H P Chemical entropy parameter H Cand structural entropy parameter H S These are used to characterize the complexity, discreteness, or orderliness of a material system at the physical distribution level, chemical composition level, and structural distribution level, respectively.

[0026] Furthermore, regarding the physical entropy parameter H... P Chemical entropy parameter H C and structural entropy parameter H S After normalization, weighting, and coupling processing, the comprehensive entropy index E is obtained. Preferably, the comprehensive entropy index E is used to characterize the synergistic state of multiple entropy factors in the candidate material system.

[0027] Furthermore, by adjusting the physical entropy parameter H P Chemical entropy parameter H C and structural entropy parameter H S And the comprehensive entropy index E, to achieve material performance regulation. The material properties include, but are not limited to, one or more of the following: mechanical properties, working performance, volume stability, durability, transport properties, reactivity, and uniformity.

[0028] The correspondence between the comprehensive entropy index and the target performance can be a sample comparison relationship, an interval mapping relationship, or a regression fitting relationship. When the number of samples is small, the target interval of the comprehensive entropy index can be determined based on the comprehensive entropy index range corresponding to the samples that meet the target performance requirements.

[0029] Furthermore, the target range of the comprehensive entropy index E is determined according to the target performance requirements, and the proportioning parameters are adjusted accordingly to obtain a material proportioning scheme that meets the target performance requirements.

[0030] As a preferred method, the physical entropy parameter H P Calculate using the following formula: in, is the normalized proportion of the i-th physical distribution interval, and n is the number of physical distribution intervals.

[0031] The chemical entropy parameter H C Calculate using the following formula: in, is the normalized proportion of the j-th chemical unit, and m is the number of chemical units.

[0032] The structural entropy parameter H S Calculate using the following formula: in, denoted as the normalized proportion of the k-th structural distribution interval, and r is the number of structural distribution intervals.

[0033] As a preferred method, the comprehensive entropy index E is obtained after processing the three types of entropy parameters, and the calculation formula is: in, , , These are the normalized physical entropy parameter, chemical entropy parameter, and structural entropy parameter, respectively. , , These are the weighting coefficients, and ; is the coupling coefficient.

[0034] The present invention will be further described below with reference to the embodiments, but the present invention is not limited to the following embodiments.

[0035] The following examples all use four intervals for distribution division, therefore the theoretical maximum entropy normalization method is preferred: in, .

[0036] In the following embodiments, the comprehensive entropy index The following formula is preferred for calculation: In the following embodiments, for ease of explanation, the 28-day compressive strength value is directly used as the target performance data T; in other embodiments, one or more performance indicators can be normalized and weighted to obtain the comprehensive performance score T.

[0037] All different embodiments are fully calculated , , and However, different main variables are changed to characterize physical entropy, chemical entropy, structural entropy and their coupling effects.

[0038] Example 1: A candidate cementitious material system M1 was selected, consisting of S95 grade granulated blast furnace slag powder, Grade II fly ash, and metakaolin, with a mass ratio of slag powder, fly ash, and metakaolin of 50:30:20. A composite activator of water glass and NaOH with a modulus of 1.2 was used, with a liquid-to-solid ratio of 0.42. Curing conditions included sealed curing at 20℃.

[0039] The precursor particle size distribution in this system was divided into four ranges: 0-10 μm, 10-20 μm, 20-45 μm, and greater than 45 μm. The proportions of each physical distribution range were as follows: The effective chemical components in this system are divided into four categories: SiO2, Al2O3, CaO, and other active oxides, with their normalized proportions as follows: The pore structure distribution of its hardened body was divided into four intervals: less than 10 nm, 10-50 nm, 50-100 nm, and greater than 100 nm. The proportions of each interval were as follows: Calculated: After normalization: The comprehensive entropy index is then: The compressive strength of the system was measured to be 58 MPa.

[0040] This embodiment serves as a baseline embodiment to illustrate that the method of the present invention can perform complete multientropy calculation and performance mapping for candidate material systems.

[0041] Example 2: Based on Example 1, keeping the types of raw materials, the mass ratio of raw materials, the composition of activators and the curing regime unchanged, the particle distribution of the precursor was adjusted only by graded grinding and particle size compounding, so that the overall particle size distribution of slag powder, fly ash and metakaolin was more balanced, and candidate material system M2 was obtained.

[0042] The proportions of their physical distribution ranges are as follows: The chemical composition data and structural distribution data remain the same as in Example 1, that is: Calculated: After normalization: but: The measured compressive strength is 72 MPa.

[0043] This embodiment demonstrates that, under otherwise essentially unchanged conditions, optimizing the particle size distribution of slag powder, fly ash, and metakaolin can improve the physical entropy and comprehensive entropy index, thereby correspondingly enhancing the target performance.

[0044] Example 3: Based on Example 1, keeping the particle size distribution, activator system, and curing regime unchanged, only the ratio of the three precursors was adjusted to increase the complexity of the chemical composition, resulting in candidate material system M3. The mass ratio of slag powder, fly ash, and metakaolin was adjusted to 40:30:30.

[0045] The normalized proportions of their chemical constituent units are as follows: The physical distribution data and structural distribution data remain the same as in Example 1, that is: Calculated: After normalization: but: The measured compressive strength is 83 MPa.

[0046] This embodiment demonstrates that by adjusting the compounding ratio of slag powder, fly ash, and metakaolin, the complexity of the chemical composition can be increased, thereby improving the chemical entropy and the comprehensive entropy index, which has a positive effect on the target performance.

[0047] Example 4: Based on Example 1, while keeping the precursor type, precursor ratio, and particle size distribution unchanged, the uniformity of the structure distribution of the hardened body was improved only by adjusting the amount of activator and curing conditions, resulting in candidate material system M4. Specifically, this involved appropriately reducing the early water loss rate and delaying excessively rapid local reactions, thus making the pore structure distribution more balanced.

[0048] The proportions of their structural distribution intervals are as follows: The physical distribution data and composition data remain the same as in Example 1, that is: Calculated: After normalization: but: The measured compressive strength is 69 MPa.

[0049] This embodiment demonstrates that by optimizing the excitation reaction conditions and curing regime, the pore structure distribution of the hardened body can be made more balanced, thereby increasing the structural entropy and the overall entropy index.

[0050] Example 5: Simultaneously, the precursor particle size distribution, precursor blending ratio, and activation and curing conditions were synergistically optimized to obtain the candidate material system M5. The mass ratio of slag powder, fly ash, and metakaolin was 45:30:25, and combined with the optimized particle size distribution and curing regime, the physical distribution, chemical composition, and structural distribution were all in a relatively optimal state.

[0051] The percentages of each interval are as follows: Calculated: After normalization: but: The measured compressive strength is 91 MPa.

[0052] This embodiment shows that when the particle size distribution, compositional complexity, and hardened structure distribution of the slag powder, fly ash, and metakaolin system are optimized simultaneously, the target performance is significantly better than that of the single main variable optimization case, indicating that the method of the present invention can characterize and utilize the multi-entropy synergistic effect.

[0053] Example 6: Based on Examples 1 to 5, a sample library of candidate material systems composed of slag powder-fly ash-metakaolin cementing system was established, and the comprehensive entropy index corresponding to each sample was determined. With target performance data Perform correlation analysis.

[0054] As can be seen from Examples 1 to 5, the candidate material systems... and They are respectively: Example 1: , Example 2: , Example 3: , Example 4: , Example 5: , In this embodiment, the target performance data T is the 28-day compressive strength, in MPa, and the preset target performance requirement is T≥83 MPa; Samples with T≥83 are selected from the sample set. The minimum and maximum values ​​of E corresponding to these samples are used as the target interval for the comprehensive entropy index. The target interval for the comprehensive entropy index corresponding to the target performance requirement is as follows: Furthermore, for the physical distribution parameters that satisfy the above conditions... Chemical composition parameters and structural distribution parameters Statistical analysis yielded the following optimal value ranges for each parameter: Physical distribution parameters The preferred range is: Chemical composition parameters The preferred range is: Structural distribution parameters The preferred range is: Among them, the physical distribution parameter p, chemical composition parameter x, and structural distribution parameter q are not directly equivalent to the proportioning parameters, but rather serve as constraint parameters in the selection process of candidate proportioning combinations. Based on the target range of the comprehensive entropy index and the optimal range of each parameter, several sets of slag powder-fly ash-metakaolin candidate proportioning combinations are further generated, and the physical distribution parameter p, chemical composition parameter x, and structural distribution parameter q corresponding to each candidate proportioning combination are obtained or calculated. Then, the corresponding physical entropy parameter, chemical entropy parameter, structural entropy parameter, and comprehensive entropy index E are calculated. Candidate proportioning combinations whose comprehensive entropy index E falls within 0.971 to 1.007, and whose physical distribution parameter p, chemical composition parameter x, and structural distribution parameter q are all within the above-mentioned optimal range, are selected as candidate material systems.

[0055] In one specific screening result, candidate material system M6 with a mass ratio of slag powder, fly ash, and metakaolin of 43:29:28 was obtained, and its corresponding parameters are as follows: Calculated: After normalization: The comprehensive entropy index is then: This value is within the preset preferred range. Inside.

[0056] The measured 28-day compressive strength T of the candidate material system was 87 MPa, which meets the preset target performance requirements. .

[0057] This embodiment demonstrates that the multi-entropy synergistic regulation method of the present invention can first establish a comprehensive entropy index based on existing samples of the slag powder-fly ash-metakaolin cementing system. With target performance data The mapping relationship between them is then used to reverse-select the optimal range of the comprehensive entropy index based on the target performance requirements, and further determine the physical distribution parameters in the candidate material system. Chemical composition parameters and structural distribution parameters The preferred range is determined, and the selection of proportioning parameters is completed based on the preferred range, thereby realizing the optimized design of candidate material systems oriented towards target performance.

[0058] As can be seen from Examples 1-6, the multi-entropy synergistic regulation method of the present invention can fully characterize the physical distribution characteristics, chemical composition complexity and structural distribution characteristics of the candidate material system, and establish a correspondence between the comprehensive entropy index E and the target performance.

[0059] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for material proportioning design and performance optimization based on multi-entropy synergy, characterized in that, Includes the following steps: S1. Determine multiple candidate material systems and the proportioning parameters of each candidate material system, and establish a sample set containing multiple candidate material systems; S2. For each candidate material system in the sample set, obtain the corresponding physical distribution data, chemical composition data, and structural distribution data; S3. Calculate the physical entropy parameter, chemical entropy parameter, and structural entropy parameter based on the physical distribution data, chemical composition data, and structural distribution data, respectively. S4. Normalize, weight, and couple the physical entropy parameters, chemical entropy parameters, and structural entropy parameters to obtain the comprehensive entropy index corresponding to each candidate material system. S5. Obtain the target performance data corresponding to each candidate material system in the sample set, and establish the correspondence between the comprehensive entropy index and the target performance; S6. Determine the target range of the comprehensive entropy index based on the preset target performance requirements; S7. Determine the preferred parameter range based on the target range of the comprehensive entropy index, and screen the candidate ratio combinations under the constraints of the preferred parameter range to obtain a material ratio scheme that meets the target performance requirements.

2. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, The proportioning parameters include one or more of the following: dosage, ratio, doping amount, substitution relationship, and composition relationship of each component in the candidate material system.

3. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, The physical distribution data includes one or more of the following: size distribution data, specific surface area distribution data, morphology distribution data, thickness distribution data, and length distribution data.

4. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, The chemical composition data includes one or more of the following: elemental composition data, compound composition data, component composition data, ionic composition data, and proportioning composition data.

5. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, The structural distribution data includes one or more of the following: pore structure distribution data, phase region distribution data, interface region distribution data, and structural unit distribution data.

6. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, The comprehensive entropy index is obtained by weighted coupling of the normalized physical entropy parameter, chemical entropy parameter, and structural entropy parameter, and the calculation formula is as follows: in, , , These are the normalized physical entropy parameter, chemical entropy parameter, and structural entropy parameter, respectively. , , These are the weighting coefficients, and ; is the coupling coefficient.

7. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, The target performance includes one or more of the following: mechanical properties, working performance, volume stability, durability, transport performance, reactivity, and uniformity, wherein the mechanical properties include compressive strength.

8. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, The target range of the comprehensive entropy index is obtained by screening candidate material systems whose target performance data meet the preset target performance requirements from the sample set, and determining the target range of the comprehensive entropy index based on the minimum and maximum values ​​of the comprehensive entropy index corresponding to the screened candidate material systems.

9. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, In S7, the material proportioning scheme is obtained by: screening candidate material systems whose comprehensive entropy index falls within the target range of the comprehensive entropy index from the sample set; determining the preferred parameter range based on the proportioning parameters, physical distribution data, chemical composition data, and structural distribution data corresponding to the screened candidate material systems; generating at least one set of candidate proportioning combinations under the constraints of the preferred parameter range; calculating the comprehensive entropy index corresponding to the candidate proportioning combinations; and determining the candidate proportioning combinations whose comprehensive entropy index falls within the target range of the comprehensive entropy index as the material proportioning scheme.

10. The material proportioning design and performance optimization method based on multi-entropy synergy according to claim 1, characterized in that, The physical entropy parameter H P Chemical entropy parameter H C and structural entropy parameter H S Calculate according to the following formulas respectively: ； ； ； Where, p i x represents the normalized proportion of the i-th physical distribution interval, where n is the number of physical distribution intervals; j q represents the normalized proportion of the j-th chemical unit, where m is the number of chemical units; k denoted as the normalized proportion of the k-th structural distribution interval, and r is the number of structural distribution intervals.

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