System and method for determining the properties of composite materials

Abstract

The embodiment determines the properties of the composite material. [Solution] One such embodiment acquires an image of the composite material in memory. The acquired image is segmented to identify multiple phases within the composite material. Based on the acquired image and the identified multiple phases, at least one transformed image of the composite material is generated showing the changes in the identified multiple phases. For each of the generated at least one transformed image, a finite element (FE) model is constructed. Simulation of the composite material is performed using each FE model constructed to determine at least one property of the composite material.

Description

Technical Field

[0001] The present disclosure relates to systems and methods for determining properties of composite materials.

Background Art

[0002] Composite materials, such as cement and concrete, are widely used across many industries. Among other examples, composite materials have become essential in construction. In view of the wide use of composite materials, accurately determining properties of composite materials, for example mechanical properties, is important from engineering and safety perspectives. Some existing technologies particularly rely on imaging methodologies and / or techniques for predicting properties from one or more three-dimensional (3D) microstructures of a composite material.

Summary of Invention

[0003] A problem with existing technologies for predicting properties of composite materials is that they cannot take into account changes in both 3D microstructure and material properties, for example caused by carbonation reaction. For example, to predict the overall properties of cement, several conventional approaches perform a homogenization process based on pre-established proportions for the different materials constituting cement. For example, 50% is material A, 20% is material B, and the remaining 30% is material C. While these approaches can be used to predict properties of cement, they have low accuracy. Another disadvantage of these conventional approaches is that they only consider the proportions of different materials in the composite material, without considering the spatial distribution of the material and / or specific aspects, such as the rate at which the carbonation reaction proceeds. Some other conventional approaches rely on image segmentation techniques with poor performance. Accordingly, there is a need for functionality with improved accuracy, performance, and effectiveness for determining properties of composite materials, such as concrete or cement. Embodiments provide such functionality.

[0004] Exemplary embodiments for determining the properties of composite materials may extract phase segmentation of the material from images, such as microcomputed tomography (micro-CT) images of the material. Other exemplary embodiments may perform image processing, such as segmentation, to simulate or mimic chemical reactions, such as carbonation reactions. Yet another exemplary embodiment may create a finite element (FE) model in, for example, Abaqus® (Dassault Systemes Americas Corp., Waltham, Massachusetts), assign material properties to different phases in the model, and set stress / strain boundary conditions in the model. Exemplary embodiments may be configured to determine the properties of the composite material, such as mechanical properties, according to different carbonation of the material.

[0005] Furthermore, the simulation results of the exemplary embodiments can be verified using laboratory experimental data.

[0006] It should be noted that the embodiments can perform n-phase segmentation of composite materials. In other words, the embodiments are not limited to segmenting a specific number of phases (or types of phases) of a composite material. For example, if a new material is developed for cement, the embodiments can successfully segment the new material in addition to the existing material for cement.

[0007] In exemplary embodiments, for non-limiting examples, the properties of a composite material can be predicted under chemical reaction based on inputs including the properties of the phase materials and the 3D microstructure. In other exemplary embodiments, the 3D microstructure can be obtained from images, e.g., micro-CT images, or virtual microstructures created by, for example, a hydration model.

[0008] Furthermore, some embodiments relate to simulations.

[0009] One exemplary embodiment relates to a computer implementation method for determining the properties of a composite material. The method begins by acquiring an image of the composite material in memory. The acquired image is then segmented to identify multiple phases of the composite material. Based on the acquired image and the identified multiple phases, the method then generates at least one transformed image of the composite material. The at least one transformed image shows the changes in the identified multiple phases. For each of the generated at least one transformed image, the method constructs an FE model. The composite material is then simulated using each FE model constructed to determine at least one property of the composite material. According to the exemplary embodiment, the composite material is concrete or cement.

[0010] In an exemplary embodiment, the changes in the identified phases may be the result of a chemical reaction. According to one such exemplary embodiment, the chemical reaction may be a carbonation reaction.

[0011] In exemplary embodiments, generating a given transformed image from at least one transformed image based on an acquired image and identified phases includes, until the chemical reaction is complete, repeatedly: (1) using the acquired image to identify the contact surface between the first and second phases of the identified phases; (2) determining, as a result of the progress of the chemical reaction, the substitution between (i) at least one of the first and second phases and (ii) the product of the chemical reaction; and (3) updating the acquired image by propagating the transformation from the identified contact surface to the first and second phases based on the determined substitution until a threshold is met. To continue, in such embodiments, in response to determining that the chemical reaction is complete, a given transformed image is generated based on the updated acquired image. In such embodiments, the first and second phases are the reactants in the chemical reaction. According to other such embodiments, in the first iteration, the contact surface may be identified using an acquired image, and in each iteration following the first iteration, the contact surface may be identified using an acquired image updated from the previous iteration. In other such embodiments, the first and second phases may comprise resolution-treated pores and at least one mineral. According to yet another such embodiment, the replacement may be a volume ratio. In one such embodiment, the replacement may be determined based on any combination of density, porosity, and stoichiometric ratio. According to another such embodiment, the transformation may be propagated based on a 3D voxel-based growth model. In yet another such embodiment, the method may further include performing a post-distribution characterization of the composite material based on an updated acquired image. According to one such embodiment, performing a post-distribution characterization of the composite material may include determining at least one of the elastic modulus and effective diffusivity based on the microstructure of the composite material shown by the updated acquired image.

[0012] According to other exemplary embodiments, segmentation may include determining each label corresponding to each of the identified phases. In one such embodiment, each given label determined may include one of the following: a resolving pore, calcium silicate hydrate (CSH), portlandite (i.e., calcium hydroxide (CH)), and clinker (i.e., an unhydrated phase).

[0013] In exemplary embodiments, each constructed FE model may be configured to correspond to the respective porosities of the composite material. According to one such embodiment, performing the simulation may be configured to determine at least one property of the composite material as a function of porosity using each FE model constructed to correspond to the respective porosity.

[0014] According to other exemplary embodiments, the determined at least one property may include at least one of Young's modulus and shear coefficient.

[0015] In exemplary embodiments, constructing an FE model may include configuring at least one of stress boundary conditions and strain boundary conditions for the FE model.

[0016] Other exemplary embodiments relate to a computer-based system for determining the properties of composite materials. The system includes a processor and memory storing computer code instructions. The processor and memory are configured to use the computer code instructions to cause the system to implement any embodiment or combination of embodiments described herein.

[0017] Further embodiments relate to a computer program product for determining the properties of composite materials. The computer program product includes a non-temporary computer-readable medium on which computer code instructions are stored. When executed by a processor, the computer code instructions are configured to cause a device associated with the processor to implement any embodiment or combination of embodiments described herein.

[0018] It should be noted that embodiments of this method, system, and computer program product may be configured to implement any embodiment or combination of embodiments described herein.

[0019] A patent or application file must include at least one drawing in color. A copy of the published patent or patent application containing one or more color drawings will be provided by the Patent and Trademark Office upon request and payment of the required fees.

[0020] The foregoing will become clear from the following more specific description of the exemplary embodiments, as similar reference letters throughout the different figures are illustrated in the accompanying drawings to refer to the same parts. The drawings are not necessarily to exact scale and are intended to emphasize that they illustrate embodiments. [Brief explanation of the drawing]

[0021] [Figure 1A] Figure 1A is an illustrative image of a composite material according to one embodiment. [Figure 1B] Figure 1B shows an exemplary segmentation of the image in Figure 1A according to one embodiment. [Figure 2] Figure 2 shows an exemplary cross-sectional view illustrating the change in the microstructure of cement according to one embodiment. [Figure 3A] Figure 3A shows an exemplary FE model with different mineral phases according to one embodiment. [Figure 3B]FIG. 3B shows an exemplary simulation result of stress distribution in the FE model of FIG. 3A, according to one embodiment. [Figure 4A] FIG. 4A is an exemplary graph of simulation results of changes in Young's modulus and shear coefficient according to an embodiment. [Figure 4B] FIG. 4B is an exemplary graph of simulation results of changes in Young's modulus and shear coefficient according to an embodiment. [Figure 5] FIG. 5 is an example graph illustrating validation of an exemplary embodiment using laboratory experimental results. [Figure 6] FIG. 6 is an example graph showing changes in relative diffusivity of cement according to one embodiment. [Figure 7] FIG. 7 is a flowchart of a method for determining properties of a composite material according to an exemplary embodiment. [Figure 8] FIG. 8 is a schematic diagram of a computer network in which embodiments may be implemented. [Figure 9] FIG. 9 is a block diagram showing an exemplary embodiment of a computer node in the computer network of FIG. 8. DESCRIPTION OF EMBODIMENTS FOR CARRYING OUT THE INVENTION

[0022] Description of exemplary embodiments is provided below.

[0023] Composite materials are widely used across many industries. For example, one non-limiting example of a composite material, cement, is widely used in construction. In view of the extensive use of composite materials, it has become increasingly important to know the properties, for example mechanical properties, of composite materials. Over the past few decades, advances in computing resources, algorithms, and imaging techniques have made it possible to predict the mechanical properties of composite materials based on the three-dimensional microstructure of the composite material. However, there are no existing methods that focus on both the change of the three-dimensional microstructure of composite materials caused by chemical reactions and the mechanical properties thereof.

[0024] In the case of cement, carbon dioxide (CO2) from the air or underground storage can react with the hydrating material in the cement. This chemical process, called carbonation, significantly affects the properties and durability of the cement. The mechanical properties of cement are regulated by its 3D microstructure. As mentioned above, advances in computing resources, algorithms, and imaging techniques have made it possible to predict the mechanical properties of cement from its 3D microstructure, but there has been no work that focuses on both the changes in the 3D microstructure and mechanical properties of cement due to carbonation (which causes changes in the 3D microstructure). The embodiment provides this functionality. Furthermore, it should be noted that the embodiment is not limited to determining the mechanical properties of cement, but rather can be used to determine any type of properties for any type of composite material.

[0025] A non-limiting exemplary application of the embodiment is a borehole below the surface. The borehole may be configured to have cement injected below the surface. Furthermore, CO2 may be injected below the surface for storage, for example, for the purpose of CO2 capture. The stored CO2 reacts with the borehole cement in a carbonation reaction, thereby changing or altering the properties of the cement. Such reactions and the resulting changes in cement properties may jeopardize the integrity of the borehole. Then, for example, if the integrity of the borehole is compromised by cement decomposition, CO2 leakage may occur, impairing the effective storage of CO2. The embodiment can determine changes in the properties of cement in a borehole due to carbonation and other reactions. By utilizing the embodiment, cement decomposition of a borehole can be analyzed or predicted, and therefore such decomposition can be completely mitigated or avoided. For example, the embodiment can be used to design a borehole that withstands decomposition and successfully isolates CO2 over time. Similarly, the embodiment can be used to determine the properties of an existing borehole, identify the current properties of a borehole, and predict the future properties of a borehole. These determined characteristics can be used to determine the need for design changes, such as modifications, to the borehole to prevent problems, such as CO2 leakage.

[0026] However, it should be noted that the embodiments are not limited to, for example, determining the mechanical properties of cement in boreholes. For example, cement is used in a number of different applications other than boreholes. Natural environment or atmospheric CO2 can also react with cement under a wide variety of conditions. The embodiments are also useful for these other types of cement applications and other types of reaction conditions. More generally, the embodiments are not limited to determining the properties of cement or its mechanical properties. Rather, the embodiments can also determine other types of properties for other types of composite materials.

[0027] Example of a simulation workflow Figure 1A is an exemplary grayscale image 100a of cement 102 according to one embodiment. Figure 1B is an example of a segmented image 100b of cement 102 from Figure 1A, showing various mineral phases including resolving pores 104a, CSH 104b, CH 104c, and clinker 104d according to one embodiment. As described below, embodiments can take an image of a composite material, for example, image 102, and determine the phases of the composite material from the image of the composite material, for example, as shown in image 100b.

[0028] Figure 2 shows exemplary two-dimensional (2D) cross-sectional views 212a–212j of the cement microstructure. Specifically, Figure 212a shows non-carbonated cement, Figures 212b–212e show dry-carbonated cement, and Figures 212f–212j show wet-carbonated cement according to embodiments. As shown in Figure 2, in exemplary embodiments, Figures 212a–212j show various mineral phases, including resolving pores 204a, CSH 204b, CH 204c, clinker 204d, and / or calcium carbonate 204e. More specifically, Figures 212a–212j show the changes in cement phases 204a–204e as the cement progresses from non-carbonated (Figure 212a) to dry-carbonated (Figures 212b–212e) and wet-carbonated (Figures 212f–212j). Figures 212a to 212j in 2D are illustrative images, and it should be noted that embodiments may also use, for example, 3D blocks or 3D microstructures to determine the properties of the composite material.

[0029] Continuing with Figure 2, in the different phases 204a-204e, CH204c (i.e., Ca(OH)2) can rapidly react with CO2 (not shown) during dry carbonation (shown across Figures 212b-212e) to form calcium carbonate 204e (i.e., CaCO3), as well as water (not shown). As CO2 moves through pores 204a, it can react at any location where pores 204a are adjacent to CH204c. These changes are shown across Figures 212b-212e, where portions of pores 204a interacting with CH204c are converted to calcium carbonate 204e. Embodiments may follow given reaction rules for the gradual conversion of CH204c to calcium carbonate 204e. In this way, embodiments can determine how the structure of the cement, e.g., its microstructure, changes over time. These changes in the structure can be used to determine the properties of the cement.

[0030] Continuing with Figure 2, the dry carbonation reaction shown in Figures 212b-212e may deplete all CH204c when it reaches the point shown in Figure 212e in the upper right corner. However, if CO2 continues to be introduced, the CO2 can react with and dissolve solid calcium carbonate 204e. This result may also be additionally resolving pores 204a. These changes are shown in Figures 212f-212j, where calcium carbonate 204e decreases and additional pores 204a appear.

[0031] In one embodiment, an exemplary workflow for determining the properties of a composite material may take a voxelized image of the composite material, e.g., a 3D voxelized image, e.g., an image 100a of cement 102, as input (Figure 1A). According to another exemplary embodiment, image 100a may capture representative elemental volumes of cement 102 and have sufficient resolution to distinguish different phases. In yet another exemplary embodiment, real-world microstructures obtained from micro-CT images of the National Institute of Standards and Technology (NIST) Visible Cement Dataset may be used as input. The dataset is publicly available and has been used to extract various effective properties of cement. However, it should be noted that other types of known images and / or image sources are also suitable.

[0032] To continue, in one embodiment, the workflow for determining the properties of the composite material may be configured to include the following exemplary steps.

[0033] Firstly, the input image, for example, a 3D voxelized image 100a (Figure 1A), may be segmented so that individual voxels are labeled as belonging to different phases, for example, the mineral phases of the resolving pores 104a, CSH 104b, CH 104c, and clinker 104d (Figure 1B). According to an exemplary embodiment, image segmentation may be performed by thresholding both the gray level and its gradient, which tends to avoid the unrealistic segmentation of one mineral surrounding other minerals that is typically produced by conventional approaches.

[0034] Secondly, exemplary image processing techniques can be used to simulate carbonation reactions, including, for example, dry carbonation (i.e., bicarbonation) shown in Figures 212b-212e of Figure 2, and / or wet carbonation (i.e., bicarbonation) shown in Figures 212f-212j of Figure 2, which tend to have the opposite effect or influence on the mechanical properties of the cement, as illustrated in Figure 2, for example. In one embodiment, exemplary image processing for dry carbonation may include identifying voxels having intersections of resolving pores 204a (Figure 2) and CH204c (Figure 2), where the intersections can be used as seeding points for growing the reaction products. Growing the products may include not only replacing CH204c (Figure 2) with calcium carbonate 204e (Figure 2), but also simulating the penetration of resolving pores 204a by calcium carbonate 204e. In this way, the material (i.e., phases 204a-204e) may be transformed at appropriate locations. The process for dry carbonation may be continued in a systematic manner until CH204c is completely consumed. The exemplary embodiment may also faithfully follow the stoichiometry of the dry carbonation reaction (shown over Figures 212b-212e) and the wet carbonation reaction (shown over Figures 212f-212j). Subsequently, for wet carbonation, calcium carbonate 204e in contact with the resolving pores 204a may be removed. The embodiment may utilize information on density and / or volume as part of exemplary image processing techniques.

[0035] Thirdly, in these exemplary embodiments, the labeled voxelized 3D image can be converted to a structured mesh representation identical to the image voxels, for example, via a Python script or other suitable known software programming technique. The meshed model may then be input into an FE solver, for example, as part of an Abaqus® application by Applicant-Assignee Dassault Systemes Americas Corporation, and strain / stress boundary conditions may also be configured, for example, by utilizing the Abaqus® micromechanics plugin. While embodiments are described in this disclosure as utilizing tools and platforms provided by Applicant-Assignee Dassault Systemes Americas Corporation and Dassault Systemes, it should be noted that embodiments are not limited to these tools and platforms, and rather similar known tools and platforms are also preferred.

[0036] Fourth and lastly, a simulation process for homogenization can be carried out, and the stiffness matrix can be calculated for cement under various carbonation conditions, for example.

[0037] Example simulation results Figure 3A shows an exemplary mesh model 300a having different colors representing different mineral phases 304a to 304d according to one embodiment. Figure 3B shows an exemplary simulation result 300b of the mesh model 300a of Figure 3A according to one embodiment.

[0038] To generate the exemplary test results shown in Figures 3A and 3B, one embodiment utilized a subvolume of the entire cement microstructure and imported the resulting mesh model 300a into Abaqus® to achieve the desired balance between computational cost and accuracy. Furthermore, one embodiment applied six exemplary load cases, including three-directional compression and three-directional shear, to obtain a symmetric 6×6 stiffness matrix of cement. Figure 3A shows the meshed model 300a, and Figure 3B shows the simulation result 300b of the stress distribution 332 under an exemplary compression load case. In legend 332, the notation "S11" refers to stress in one direction (i.e., the x-direction), which is one of the horizontal directions, as indicated by index 342 in Figure 3B. The aforementioned stiffness matrix (not shown) includes two exemplary mechanical properties of cement: Young's modulus and shear modulus.

[0039] Figure 4A is an exemplary graph 400a of the simulation results of the change in Young's modulus 414, e.g., gigapascals (GPa), and Figure 4B is an exemplary graph 400b of the simulation results of the change in the shear coefficient 416 of uncarbonated 418, dry-carbonated 406, and wet-carbonated 408 cement as the carbonation reaction progresses, according to an embodiment. Graphs 400a and 400b also include Figures 434a-434c showing the cement and associated phases between uncarbonated 418, dry-carbonated 406, and wet-carbonated 408, respectively.

[0040] As shown in Figures 4A and 4B, in exemplary embodiments, dry carbonation 406 generally tends to increase mechanical properties 414 and 416, while wet carbonation 408 tends to decrease mechanical properties 414 and 416.

[0041] Figure 5 is an exemplary graph 500 of Young's modulus 514 versus porosity 522 according to one embodiment, and shows the verification of simulation results for carbonation 524a and bicarbonation 524b having laboratory experimental results 526. In plot 500, arrows 536 and 538 show the time evolution of the carbonation 524a and bicarbonation 524b reactions, respectively. For example, at the start of the carbonation reaction 524a, Young's modulus 514 is approximately 15 GPa, and at the end of the carbonation reaction 524a, Young's modulus 514 is approximately 25 GPa. At the start of the bicarbonation reaction 524b, Young's modulus 514 is approximately 25 GPa, and at the end of the bicarbonation reaction 524b, Young's modulus 514 is approximately 5 GPa.

[0042] The simulation results of the embodiment were validated with laboratory experimental data 526. In the experiment, ordinary Portland cement mixed with water was used under conditions very similar to those of the simulation. The samples were completely dry carbonated, and the Young's modulus 514 was measured for both the uncarbonated and dry carbonated samples. Figure 5 shows a comparison of the simulation results 524a and 524b of the embodiment with the laboratory experimental results 526, demonstrating the accuracy of the embodiment. The simulation results show that the dry 524a and wet 524b carbonation reactions result in constitutive relationships, i.e., a non-simple evolution of how composite materials such as cement respond or behave as a result of one or more chemical reactions.

[0043] Figure 6 is an example of a graph 600 showing the change in relative diffusivity 628 with respect to the porosity 622 of the cement for the carbonation 624a and bicarbonation 624b reactions according to one embodiment. In plot 600, arrows 636 and 638 indicate the time evolution of the carbonation 624a and bicarbonation 624b reactions, respectively. For example, at the start of the carbonation reaction 624a, the relative diffusivity 628 is approximately 0.05, and at the end of the carbonation reaction 624a, the relative diffusivity 628 is approximately 0.005. At the start of the bicarbonation reaction 624b, the relative diffusivity 628 is approximately 0.005, and at the end of the bicarbonation reaction 624b, the relative diffusivity 628 is approximately 0.1.

[0044] In addition to elastic properties, the exemplary workflow of the embodiment can also be applied to obtain properties of other composite materials of cement, for example. One non-limiting example is cement diffusivity. Figure 6 shows an exemplary change in relative diffusivity 628 as the carbonation reactions 624a and 624b proceed according to one embodiment. In the exemplary embodiment, relative diffusivity 628 may be defined as the ratio of the effective diffusivity of the microstructure to the bulk diffusivity. The exemplary results shown in Graph 600 show that the dry carbonation section 624a tends to decrease the relative diffusivity 628, while the wet carbonation section 624b tends to increase the relative diffusivity 628. Hysteresis of carbonation in dry 624a and wet 624b can also be observed, similar to the evolution of Young's modulus 514 in Figure 5.

[0045] Exemplary Method Embodiments Figure 7 is a flowchart of a method 700 for determining the properties of a composite material according to one embodiment. The method 700 may be implemented using a computer or any computing device or combination of computing devices known to those skilled in the art, such as a processor.

[0046] Method 700 begins in step 701 by acquiring an image, e.g., 100a (Figure 1A), and a composite material, e.g., cement 102 (Figure 1A), in memory. Next, in step 702, Method 700 segments the acquired image to identify multiple phases in the composite material, e.g., 104a-104d (Figure 1B) or 204a-204e (Figure 2). In step 703, at least one transformed image of the composite material is generated based on the acquired image and the identified multiple phases. The transformed image shows the changes in the identified multiple phases. In step 704, an FE model, e.g., 300a (Figure 3A), is constructed for each generated at least one transformed image. Next, in step 705, a simulation of the composite material is performed using each constructed FE model to determine at least one property of the composite material.

[0047] As described, Method 700 is computer-implemented, and therefore its functions and effective operation, e.g., acquisition (701), segmentation (702), generation (703), construction (704), and execution (705), are automatically implemented by one or more digital processors. Method 700 can also be implemented using any computer device or combination of computing devices known in the art. In particular among other embodiments, Method 700 may be implemented using the computer / device 50 and / or 60 described below in connection with Figures 8 and 9.

[0048] Embodiments of Method 700 may acquire an image in step 701 from any source, such as computer storage or an image acquisition device, that is communicably coupled to or can be communicably coupled to a computing device implementing Method 700. Furthermore, the image may be any image known to those skilled in the art. In particular, among other embodiments, the image acquired in step 701 may be a 2D or 3D voxelized image.

[0049] In embodiments of Method 700, the image acquired in step 701 may be any composite material known to those skilled in the art. Among other embodiments, in one embodiment of Method 700, the composite material may be concrete or cement, e.g., 102 (Figure 1A). Furthermore, the image acquired in step 701 may be any real-world composite material. For example, the acquired image may be an image of concrete in a real-world borehole, or a concrete bridge. In such applications, embodiments of Method 700 may be used to determine the properties of the real-world composite material and one or more real-world uses of the composite material. These properties can then be used to identify changes, e.g., repairs to the real-world composite material, or repairs to a larger structure incorporating the composite material. In further embodiments, the acquired image may be a composite material not yet used in a real-world application. For example, the acquired image may be a candidate composite material. Furthermore, in such exemplary implementations, Method 700 can be used to evaluate candidate composite materials and determine which materials meet the requirements.

[0050] In one embodiment, the acquired image is segmented in step 702 by calculating an intensity-vs-gradient graph and using a threshold to define different regions of minerals for seeding and growing connecting regions within the acquired image. In contrast to the embodiment, conventional segmentation methods that use only intensity thresholds determined from a histogram result in inaccurate segmentation of, for example, CH and CSH. The innovative technique of the embodiment helps to avoid, for example, the unrealistic segmentation of one mineral surrounding other minerals that are typically produced by conventional approaches. The embodiment can also perform n-phase segmentation of composite materials. In other words, the embodiment is not limited to segmenting a specific number of phases (or types of multiple phases) of a composite material. For example, if a new material is developed for cement, the embodiment can successfully segment the new material in addition to existing materials for cement. According to another exemplary embodiment of method 700, the segmentation in step 702 may include determining the respective labels corresponding to each of the identified multiple phases. In one such embodiment of Method 700, each of the labels determined may include one of the resolving pores, for example, 104a (Figure 1B) or 204a (Figure 2), CSH, for example, 104b (Figure 1B) or 204b (Figure 2), CH, for example, 104c (Figure 1B) or 204c (Figure 2), clinker, for example, 104d (Figure 1B) or 204d (Figure 2), and calcium carbonate, for example, 204e (Figure 2).

[0051] According to other exemplary embodiments of Method 700, the identified phase changes may be the result of a chemical reaction. In one such exemplary embodiment of Method 700, the chemical reaction may be a carbonation reaction.

[0052] In exemplary embodiments of Method 700, generating a given transformed image from at least one transformed image in step 703 based on an acquired image and identified phases includes repeating: (1) using the acquired image to identify the contact surface between a first phase and a second phase of the identified phases until the chemical reaction is complete; (2) determining, as a result of the progress of the chemical reaction, the substitution between (i) at least one of the first and second phases and (ii) the product of the chemical reaction; and (3) updating the acquired image by propagating the transformation from the identified contact surface to the first and second phases based on the determined substitution until a threshold is met. According to such embodiments, the first and second phases may be reactants in the chemical reaction. To illustrate this functionality, refer to Figure 2, in such exemplary embodiments, an image is acquired in step 701 and segmented in step 702 to generate a segmented image 212a. In step 703, one or more of the following repetitions may occur until one or more chemical reactions are complete. a) Using image 212a, contact surfaces can be identified between the resolving porous 204a phase and the CH204c phase. Replacement can be determined between the CH204c phase and the dry carbonation reaction products in the form of calcium carbonate 204e, for example, by using their density, microporosity, and / or probabilistic ratios in the reaction. Based on replacement, image 212a may be updated by propagating the conversion from the identified contact surface to the resolving porous 204a phase and the CH204c phase, resulting in the updated image 212b. b) Using image 212b, the contact surface between the resolving porous 204a phase and the CH204c phase can be identified. Replacement can be determined between the CH204c phase and the product of the dry carbonation reaction in the form of calcium carbonate 204e. Based on the replacement, image 212b may be updated by propagating the conversion from the contact surface to the resolving porous 204a phase and the CH204c phase, resulting in the updated image 212c. c) Using image 212c, the contact surface between the resolving porous 204a phase and the CH204c phase can be identified. Replacement may be determined between the CH204c phase and the product of the dry carbonation reaction in the form of calcium carbonate 204e. Based on the replacement, image 212c may be updated by propagating the conversion from the contact surface to the resolving porous 204a phase and the CH204c phase, resulting in the updated image 212d. d) Using image 212d, the contact surface between the resolving porous 204a phase and the CH204c phase can be identified. Replacement can be determined between the CH204c phase and the product of the dry carbonation reaction in the form of calcium carbonate 204e. Based on the replacement, image 212d may be updated by propagating the conversion from the contact surface to the resolving porous 204a phase and the CH204c phase, resulting in the updated image 212e. e) Using image 212e, the contact surface between the resolving porous 204a phase and the calcium carbonate 204e phase can be identified. Replacement between the calcium carbonate 204e phase and the product of the wet carbonation reaction in the form of the resolving porous 204a can be determined. Based on the replacement, image 212e may be updated by propagating the conversion from the contact surface to the resolving porous 204a phase and the calcium carbonate 204e phase, resulting in the updated image 212f. f) Using image 212f, the contact surface between the resolving porous 204a phase and the calcium carbonate 204e phase can be identified. Replacement between the calcium carbonate 204e phase and the product of the wet carbonation reaction in the form of the resolving porous 204a may be determined. Based on the replacement, image 212f may be updated by propagating the conversion from the contact surface to the resolving porous 204a phase and the calcium carbonate 204e phase, resulting in the updated image 212g. g) Using image 212g, the contact surface between the resolving porous 204a phase and the calcium carbonate 204e phase can be identified. Replacement between the calcium carbonate 204e phase and the product of the wet carbonation reaction in the form of the resolving porous 204a may be determined. Based on the replacement, image 212g may be updated by propagating the conversion from the contact surface to the resolving porous 204a phase and the calcium carbonate 204e phase, resulting in the updated image 212h. h) Using image 212h, the contact surface between the resolving porous 204a phase and the calcium carbonate 204e phase can be identified. Replacement between the calcium carbonate 204e phase and the product of the wet carbonation reaction in the form of the resolving porous 204a may be determined. Based on the replacement, image 212h may be updated by propagating the conversion from the contact surface to the resolving porous 204a phase and the calcium carbonate 204e phase, resulting in the updated image 212i. i) Using image 212i, the contact surface between the resolving porous 204a phase and the calcium carbonate 204e phase can be identified. Replacement between the calcium carbonate 204e phase and the product of the wet carbonation reaction in the form of the resolving porous 204a may be determined. Based on the replacement, image 212i may be updated by propagating the conversion from the contact surface to the resolving porous 204a phase and the calcium carbonate 204e phase, resulting in the updated image 212j.

[0053] According to one such embodiment of Method 700, in the first iteration, the contact surface may be identified using an acquired image, and in each iteration following the first iteration, the contact surface may be identified using an acquired image updated from the previous iteration. In another such embodiment of Method 700, the first and second phases may comprise resolution-treated pores and at least one mineral. According to yet another such embodiment of Method 700, the replacement may be a volume ratio. In one such embodiment of Method 700, the replacement may be determined based on any combination of density, porosity, and probabilistic ratio. According to yet another such embodiment of Method 700, the transformation may propagate based on a 3D voxel-based growth model. In yet another such embodiment, Method 700 may further include performing a post-distribution characterization of the composite material based on an updated acquired image. According to one such embodiment of Method 700, performing a post-distribution characterization of the composite material may include determining at least one of the elastic modulus and effective diffusivity based on the microstructure of the composite material shown by the updated acquired image.

[0054] According to one embodiment, the FE model is constructed in step 704 by, for example, converting at least one transformed image into a mesh model. For example, the FE model may employ the same hexahedral elements as the image voxels. In an exemplary embodiment of Method 700, each constructed FE model may correspond to a respective porosity of the composite material, e.g., 522 (Figure 5) or 622 (Figure 6). Returning to the embodiment in Figure 2, each of Figures 212b-212j is a transformed image resulting from step 703 (where each of the composite materials in Figures 212b-212j has a respective porosity), and in step 704, each FE model is generated based on each transformed image, and therefore each FE model corresponds to a respective porosity. According to one such embodiment of Method 700, performing the simulation may be configured to determine at least one property of the composite material as a function of porosity using each FE model constructed corresponding to a respective porosity. In an exemplary embodiment of Method 700, constructing the FE model in step 704 may include configuring at least one of stress boundary conditions and strain boundary conditions for the FE model. For example, the strain and / or stress boundary conditions may be configured, for example, by utilizing the Abaqus® micromechanics plug-in, which can facilitate, for example, the assignment of small displacement / loads in six directions (such as three vertical and three shear directions) and the calculation of the elastic properties of the composite material from the measured stress / strain.

[0055] According to one embodiment, the simulation is performed in step 705 by employing a linear perturbation analysis that implements six loading conditions for homogenization in one step using, for example, Abaqus®. The simulation can also be performed using other known FE solvers or numerical solvers. According to another exemplary embodiment of method 700, the at least one property to be determined may include at least one of Young's modulus, e.g., 414 (Figure 4A) or 514 (Figure 5), and shear coefficient, e.g., 416 (Figure 4B).

[0056] Embodiments, such as Method 700, can be used as part of a design or development process. For example, Method 700 can be used to determine the properties of real-world composite materials, such as cement structures, for non-limiting examples. In such embodiments, alternative design scenarios for cement structures can be identified based on the determined properties, which may indicate that the cement structure is deteriorating, for example, due to pore expansion over time. Furthermore, embodiments can be used in the development process of real-world composite materials to identify potential formulations of materials having different volume fractions of constituent components.

[0057] Examples of advantages The embodiments can predict the properties of composite materials under carbonation reactions from 3D microstructures, including, for example, non-carbonated cement samples to carbonated cement samples. Test results for cement samples and comparisons with laboratory-measured moduli are presented in this disclosure. Existing simulations in the patent literature do not include the effects of carbonation reactions and lack a quantitative approach to evaluate how chemical processes alter composite material properties. As a result, conventional methodologies cannot accurately determine the properties of composite materials and cannot accurately evaluate and design real-world structures. The embodiments overcome these and other shortcomings of conventional approaches.

[0058] Computer support The embodiments can be implemented on existing software and computer-aided design (CAD) and computer-aided engineering (CAE) platforms. For example, the embodiments can be implemented using the features and functions of 3DS SIMULIA® software, including, among other examples, the Abaqus® and DigitalROCK® applications by Applicant-Assignee Dassault Systemes Americas Corporation.

[0059] Figure 8 is a schematic diagram of a computer network in which an embodiment may be implemented. The client computer / device 50 and server computer 60 provide processing, storage, and input / output (I / O) devices for running application programs and the like. The client computer / device 50 can also link to other computing devices, including other client devices / processors 50 and server computers 60, via a communication network 70. The communication network 70 can be part of a remote access network, a global network (e.g., the Internet), a collection of computers worldwide, a local area or wide area network, and a gateway that communicates with each other using its respective protocol (e.g., TCP / IP, Bluetooth®, etc.). Other electronic device / computer network architectures are also suitable.

[0060] Figure 9 is a block diagram illustrating an exemplary embodiment of computer nodes (e.g., client processor / device 50 or server computer 60) within the computer network 70 of Figure 8. Each computer node 50, 60 encompasses a system bus 79, which is a set of hardware lines used for data transfer between components of a computer or processing system. The system bus 79 is essentially a shared conduit that connects various different elements of a computer system (e.g., processor, disk storage, memory, I / O ports, network ports, etc.) and enables information transfer between these elements. An I / O device interface 82 is connected to the system bus 79 for connecting various input / output devices (e.g., keyboard, mouse, display, printer, speaker, etc.) to the computer nodes 50, 60. A network interface 86 allows the computer nodes to connect to various other devices connected to the network (e.g., network 70 in Figure 8). Memory 90 provides volatile storage for computer software instructions 92a and data 94a used to implement embodiments of the present invention (e.g., method 700 in Figure 7). The disk storage 95 provides non-volatile storage for computer software instructions 92b and data 94b used to implement embodiments of the present disclosure. The central processing unit 84 is also connected to the system bus 79 and provided for executing computer instructions.

[0061] In one embodiment, the processor routines 92a-92b and data 94a-94b are a computer program product (generally referred to as 92) comprising a computer-readable medium (e.g., a removable storage medium such as a DVD-ROM, CD-ROM, diskette, or tape) that provides at least a portion of the software instructions for the disclosed system. The computer program product 92 can be installed by any preferred software installation procedure, as is well known in the art. In other embodiments, at least a portion of the software instructions may also be downloaded via cable, communications, and / or wireless connections. In other embodiments, the program of the Disclosure is a computer program propagated signal product embodied in a propagated signal on a propagated medium (e.g., radio waves, infrared waves, laser waves, sound waves, or electrical waves propagated over a global network such as the Internet or other networks). Such carrier medium or signal provides at least a portion of the software instructions for the routines / programs 92 of the Disclosure.

[0062] In alternative embodiments, the propagated signal is an analog carrier wave or digital signal carried on a propagation medium. For example, the propagated signal may be a digital signal propagated over a global network (e.g., the Internet), a communication network, or another network (such as network 70 in Figure 8). In one embodiment, the propagated signal is a signal transmitted over a period of time via the propagation medium, such as instructions for a software application sent in a packet over a network over a period of milliseconds, seconds, minutes, or more. In another embodiment, the computer-readable medium of the computer program product 92 is a propagation medium that the computer system 50 can receive and read, for example, by receiving the propagation medium and identifying the propagated signal embodied within the propagation medium, as described above for the computer program propagated signal product.

[0063] Generally, the terms "carrier medium" or "transient carrier" encompass the aforementioned transient signals, propagated signals, propagation mediums, storage mediums, and so on.

[0064] In other embodiments, the program product 92 may be implemented as so-called Software as a Service (SaaS), or as other installations or communications that support the end user.

[0065] Embodiments or aspects thereof may be implemented in the form of hardware, including but not limited to hardware circuits, firmware, or software. When implemented in software, the software may be stored on any non-temporary computer-readable medium configured to allow a processor to read the software or a subset of its instructions. The processor is then configured to execute instructions and operate a device, or to cause a device to operate in the manner described herein.

[0066] Furthermore, hardware, firmware, software, routines, or instructions may be described herein as performing specific operations and / or functions of a data processor. However, naturally, such descriptions included herein are merely for convenience, and such operations are actually the responsibility of the computing device, processor, controller, or other device that performs firmware, software, routines, instructions, etc.

[0067] Naturally, flowcharts, block diagrams, and network diagrams may contain more or fewer elements, be arranged differently, or be represented differently. However, even more naturally, a particular implementation may carry out in a particular way the number of block diagrams and network diagrams, as well as the number of block diagrams and network diagrams illustrating the execution of the embodiment, are determined.

[0068] Therefore, further embodiments may also be implemented in various computer architectures, physical computers, virtual computers, cloud computers, and / or some combination thereof, and thus the data processors described herein are for illustrative purposes only and not to limit the embodiments.

[0069] All patents, published applications, and references cited herein are incorporated in their entirety by reference.

[0070] While exemplary embodiments have been specifically shown and described, those skilled in the art will understand that various modifications of form and detail can be made therein without departing from the scope of embodiments included in the appended claims.

[0071] For example, the foregoing description and details of the embodiments shown in the figures refer to, but are not limited to, the tools and platforms of the applicant-assignee (Dassault Systemes Americas Corporation) and Dassault Systemes for illustrative purposes. Other similar tools and platforms are also preferred.

[0072] References

[0073] Bentz, D. P. (1997). Three-dimensional computer simulation of Portland cement hydration and microstructure development. J.Am. Ceram. Soc., 80(1), 3-21.

[0074] Bentz, D. P., Mizell, S., Satterfield, S., Devaney, J., George, W., Ketcham, P., Graham, J., Porterfield, J., Quenard, D., & Vallee, F. (2002)。The visible cement data set. Journal of Research of the National Institute of Standards and Technology,107(2),137.

[0075] Huang, J., Krabbenhoft, K., & Lyamin, A. V. (2013)。Statistical homogenization of elastic properties of cement paste based on X-ray microtomography images. International Journal of Solids and Structures, 50(5), 699-709.

[0076] Kim, J.-S., Lim, J.-H., Stephan, D., Park, K., & Han, T.-S. (2022)。Mechanical behavior comparison of single and multiple phase models for cement paste using micro-CT images and nanoindentation. Construction and Building Materials, 342, 127938.

[0077] Qin, S., McLendon, R., Oancea, V., & Beese, A. M. (2018)。Micromechanics of multiaxial plasticity of DP600: Experiments and microstructural deformation modeling. Materials Science and Engineering: A, 721, 168-178.

[0078] Sun, Z., Salazar-Tio, R., Duranti, L., Crouse, B., Fager, A., & Balasubramanian, G. (2021)。Prediction of rock elastic moduli based on a micromechanical finite element model. Computers and Geotechnics, 135, 104149.

[0079] Zhang, H., Romero Rodriguez, C., Dong, H., Gan, Y., Schlangen, E., & Savija, B. (2020). Elucidating the effect of accelerated carbonation on porosity and mechanical properties of hydrated portland cement paste using X-ray tomography and advanced micromechanical testing. Micromachines, 11(5), 471.

Claims

1. A computer implementation method for determining the properties of composite materials, wherein a processor is used. In memory, to acquire an image of the composite material, The acquired image is segmented to identify multiple phases in the composite material, Based on the acquired image and the identified plurality of phases, generate at least one transformed image of the composite material showing the changes in the identified plurality of phases, For each generated, at least one transformed image, a finite element (FE) model is constructed, A computer implementation method comprising performing a simulation of the composite material using each FE model constructed to determine at least one property of the composite material.

2. The computer mounting method according to claim 1, wherein the composite material is concrete or cement.

3. The computer implementation method according to claim 1, wherein the changes in the identified plurality of phases are the result of a chemical reaction.

4. The computer implementation method according to claim 3, wherein the chemical reaction is a carbonation reaction.

5. A computer implementation method according to claim 1, wherein a given transformed image is generated based on the acquired image and a plurality of identified phases of the at least one transformed image, Until the chemical reaction is complete, Using the acquired image, identify the contact surface between the first phase and the second phase among the identified plurality of phases, and identify that the first phase and the second phase are reactants in the chemical reaction. As a result of the progress of the chemical reaction, (i) to determine the substitution between at least one of the first phase and the second phase, and (ii) the product of the chemical reaction, Based on the determined replacement, the acquired image is updated by propagating the conversion from the identified contact surface to the first phase and the second phase until a threshold is met. A computer implementation method comprising: repeatedly generating a given transformed image based on the updated acquired image in response to determining that the chemical reaction is complete.

6. The computer implementation method according to claim 5, wherein in the first iteration, the contact surface is identified using the acquired image, and in each iteration following the first iteration, the contact surface is identified using the updated acquired image from the previous iteration.

7. The computer mounting method according to claim 5, wherein the first phase and the second phase each comprise (i) a resolution-treated pore and (ii) at least one mineral.

8. The computer mounting method according to claim 5, wherein the replacement is by volume ratio.

9. The computer implementation method according to claim 5, wherein the substitution is determined based on any combination of (i) density, (ii) porosity, and (iii) probabilistic ratio.

10. The computer implementation method according to claim 5, wherein the transformation is propagated based on a three-dimensional (3D) voxel-based growth model.

11. A computer implementation method according to claim 5, A computer implementation method further comprising performing a post-distribution characteristics evaluation of the composite material based on the updated acquired image.

12. A computer mounting method according to claim 11, wherein the post-distribution characteristics evaluation of the composite material is performed, A computer implementation method comprising determining at least one of (i) the elastic modulus and (ii) the effective diffusivity based on the microstructure of the composite material shown by the updated acquired image.

13. A computer implementation method according to claim 1, wherein the segmentation is performed A computer implementation method comprising determining the respective indicators corresponding to each phase of the identified plurality of phases.

14. The computer mounting method according to claim 13, wherein each of the determined given labels comprises one of (i) a resolution-treated pore, (ii) calcium silicate hydrate (CSH), (iii) portlandite (CH), and (iv) clinker.

15. The computer implementation method according to claim 1, wherein each constructed FE model corresponds to the respective porosity of the composite material.

16. A computer implementation method according to claim 15, wherein the simulation is performed A computer implementation method comprising the step of determining the at least one property of the composite material as a function of porosity using each FE model constructed corresponding to each of the aforementioned porosities.

17. A computer-aided mounting method according to claim 1, wherein the at least one determined characteristic includes at least one of Young's modulus and shear coefficient.

18. A computer implementation method according to claim 1, wherein the FE model is constructed A computer implementation method for the FE model, comprising configuring at least one of (i) stress boundary conditions and (ii) strain boundary conditions.

19. A computer-based system for determining the properties of composite materials, Processor and A processor and a memory storing computer code instructions are provided, and the processor and the memory use the computer code instructions to operate the computer-based system. Within the aforementioned memory, an image of the composite material is acquired, The acquired image is segmented to identify multiple phases within the composite material, Based on the acquired image and the identified plurality of phases, generate at least one transformed image of the composite material showing the changes in the identified plurality of phases, For each generated, at least one transformed image, a finite element (FE) model is constructed, A computer-based system configured to perform simulations of the composite material using each FE model constructed to determine at least one property of the composite material.

20. A computer program product for determining the properties of a composite material, wherein the computer program product comprises a non-temporary computer-readable medium storing computer code instructions, and when the computer code instructions are executed by a processor, the device associated with the processor, In memory, to acquire an image of the composite material, The acquired image is segmented to identify multiple phases within the composite material, Based on the acquired image and the identified plurality of phases, generate at least one transformed image of the composite material showing the changes in the identified plurality of phases, For each generated, at least one transformed image, a finite element (FE) model is constructed, A computer program product configured to perform a simulation of the composite material using each FE model constructed to determine at least one property of the composite material.

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