Lacustrine facies sedimentary deformation dynamic numerical simulation system

Abstract

The invention discloses a dynamic numerical simulation system for lacustrine facies sedimentary deformation. The dynamic numerical simulation system comprises a sedimentary layer model building module, a seismic wave processing module, a numerical simulation calculation module and a result output and analysis module, the sedimentary layer model building module is used for building a multilayer sedimentary combination model containing clay and sandy soil according to the characteristics of the lacustrine facies sedimentary environment and setting physical parameters such as saturation density, porosity and viscosity of the clay and the sandy soil; and the seismic wave processing module is used for acquiring and processing original seismic wave data to obtain a continuous velocity function. The system architecture covers the whole process of sedimentary layer model construction, seismic wave processing, numerical simulation calculation and result analysis, standardization and systematization of lacustrine facies sedimentary deformation dynamic simulation are achieved, limitation of fragmentation simulation is avoided, and the integrity and operability of the simulation process are improved.

Description

TECHNICAL FIELD

[0001] The application belongs to the technical field of earthquake research simulation, and particularly relates to a lacustrine sediment deformation dynamic numerical simulation system. BACKGROUND

[0002] Paleoseismology is the core support for long-term earthquake disaster risk prediction and regional seismicity assessment. Soft sediment deformation structures can directly reflect the traces of paleoearthquake events, and have become one of the key indicators for identifying paleoearthquakes. At present, the research methods for lacustrine sediment soft sediment deformation in the industry mainly include field geological survey, shaking table simulation experiment and numerical simulation technology. Among them, numerical simulation gradually becomes an important tool for exploring the mechanism of soft sediment deformation due to its repeatability and controllability. The commonly used computational fluid dynamics (CFD) software (such as Fluent) and multiphase flow model (such as VOF model) are used to carry out related simulation, combined with seismic wave data processing technology (such as filtering and numerical integration), to preliminarily realize the simulation and analysis of the deformation process of part of the sediment layer, and provide basic technical support for understanding the correlation between soft sediment deformation and seismic action.

[0003] Although the existing technology has made certain progress in the research of soft sediment deformation, it is still difficult to meet the precision and systematization needs of paleoseismology. The existing numerical simulation is fragmented, and lacks a complete system covering model construction, seismic wave processing, simulation calculation and result analysis, resulting in poor consistency and operability of the simulation process. The size, thickness combination and physical parameters of the sediment layer model are empirically set, and the adaptability to the real lacustrine sediment environment is limited, which affects the reliability of the simulation results. The suppression effect of noise and data drift in the seismic wave processing process is not good, and it is difficult to accurately restore the real propagation characteristics of the seismic wave. At the same time, the existing simulation lacks sufficient precision in capturing the interaction between clay and sand interface and the evolution of liquefied deformation structure, and cannot clearly reproduce the complete mechanism of soft sediment deformation. Moreover, the explicit quantitative correlation between deformation characteristics and seismic peak acceleration and sediment layer thickness is not established, which leads to the lack of direct and effective technical support for the intensity determination of paleoearthquake events and the identification of soft sediment deformation earthquake causes, limiting the depth and breadth of paleoseismology research. SUMMARY

[0004] In order to overcome the above-mentioned defects of the prior art, the present application provides a lacustrine sediment deformation dynamic numerical simulation system, which solves the problems of incomplete simulation system, poor model adaptation, inaccurate wave processing and lack of quantitative correlation between deformation and earthquake and soil layer in the prior art.

[0005] To achieve the above-mentioned purpose, the present application provides the following technical scheme: The lacustrine sediment deformation dynamic numerical simulation system comprises a sediment layer model construction module, a seismic wave processing module, a numerical simulation calculation module and a result output and analysis module. The sedimentary layer model construction module is used to construct a multi-layer sedimentary composite model containing clay and sand based on the characteristics of lacustrine sedimentary environments, and to set the physical parameters of saturated density, porosity and viscosity of clay and sand. The seismic wave processing module is used to acquire and process raw seismic wave data to obtain the continuous velocity function of the seismic wave. The numerical simulation module, based on the VOF multiphase flow model of Fluent software, constructs the multilayer sedimentary composite model, imports the processed seismic wave continuous velocity function, sets relevant calculation parameters, solves the fluid motion state based on the principle of incompressible fluid, tracks the dynamic changes of soil interface through geometric reconstruction algorithm, and simulates the liquefaction and thixotropic deformation process of lacustrine sedimentary layers under seismic action. The results output and analysis module is used to output the results related to the liquefaction and thixotropic deformation of the sedimentary layer, and to establish the correlation between deformation characteristics and peak ground acceleration and sedimentary layer thickness.

[0006] Preferably, the sedimentary assemblage model includes three sizes: 2m × 0.2m, 2m × 0.4m, and 2m × 1m. The thickness combinations of clay and sand in each type of model are: 10mm clay + 10mm sand, 20mm clay + 20mm sand, 30mm clay + 10mm sand, 50mm clay + 50mm sand, 80mm clay + 20mm sand, and 60mm sand + 40mm clay. The size of the sedimentary assemblage model and the thickness of each soil layer are adjusted according to the characteristics of the lacustrine sedimentary environment.

[0007] The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 1 is characterized in that the saturated density of clay is 1.664 g / cm³, the porosity is 43.44%, and the viscosity is 0.1 Pa·s; the saturated density of sand is 1.927 g / cm³, the porosity is 61.44%, and the viscosity is 0.02 Pa·s; the physical parameters are set based on lacustrine sedimentary field survey data and shaking table experimental results.

[0008] Preferably, the processing procedure of the seismic wave processing module includes: scaling the original seismic wave data by calculating a scaling factor to obtain a seismic wave with a preset peak acceleration; using high-pass filtering to remove low-frequency noise from the scaled seismic wave, obtaining the velocity at each moment through numerical integration and performing velocity baseline correction; and using the Fourier series fitting method to convert the discrete seismic wave data into a continuous seismic wave velocity function.

[0009] Preferably, the preset peak acceleration ranges from 0.125g to 1.0g, with key node values ​​of 0.25g, 0.5g, and 0.8g; scaling processing matches the original seismic wave peak acceleration with the preset peak acceleration by calculating a scaling factor.

[0010] Preferably, Fourier series fitting decomposes the function into a linear combination of sine and cosine functions of different frequencies, determines the optimal coefficients using the least squares method and the orthogonality of trigonometric functions, and obtains the fitted data after periodic expansion of the data; seismic wave velocity is obtained through numerical integration, which adopts the trapezoidal method, and forms a trapezoidal approximate integral by connecting adjacent discrete data points to capture the change of acceleration within the time step.

[0011] Preferably, the relevant equations for the incompressible fluid principle include the Navier-Stokes equation, the continuity equation, Newton's law of internal friction, the momentum equation, and the mixture continuity equation; among them, the Navier-Stokes equation is expressed as follows: v is the vector of fluid velocity at time t, P is the pressure, μ is the viscosity coefficient, f is gravity or centrifugal force, and ρ is the fluid density; the continuity equation includes and The expression for Newton's law of internal friction is: τ is the shear stress, u is the velocity in the flow direction, and y is the distance perpendicular to the flow direction; the momentum equation is expressed as follows: The expression for the continuity equation of a mixture is: .

[0012] Preferably, the phase density ρ and dynamic viscosity μ are calculated from the volume fraction of each phase, and the expressions are as follows: α1 is the clay volume fraction, α2 is the sand volume fraction, ρ1 is the clay density, ρ2 is the sand density, μ1 is the clay dynamic viscosity, and μ2 is the sand dynamic viscosity. .

[0013] Preferably, in the numerical simulation calculation module, the grid edge length is determined to be one-fiftieth to one-hundredth of the model characteristic length; the grid edge length in both the x and y directions of the 2m × 0.2m model is 0.2 cm, the grid edge length in both the x and y directions of the 2m × 0.4m model is 0.4 cm, and the grid edge length in both the x and y directions of the 2m × 1m model is 1 cm; the boundary conditions are set as the bottom of the model is a sliding boundary, the input is a seismic wave velocity function, and the other boundaries are set as fixed walls; the phase properties are set as clay as the main phase and sand as the secondary phase, and the main phase and the secondary phase share the same velocity field and pressure field.

[0014] Preferably, the geometric reconstruction algorithm is a piecewise linear interface construction algorithm or a Geo-Reconstruct algorithm, used to reconstruct the sharp interface between clay and sand, capturing soil layer interactions, mixing, intrusion, and encapsulation phenomena; the VOF multiphase flow model describes the distribution of different phases in the computational domain through phase functions, with phase function values ​​ranging from 0 to 1, where 0 represents no target fluid, 1 represents the mesh being completely occupied by the target fluid, and values ​​between 0 and 1 represent a two-phase mixing interface in that region; transient calculation parameters include a simulation time of 25 seconds, 500 time steps, and a time step size of 0.05 seconds. The pressure-based implicit calculation method was adopted. Deformation-related results include dynamic simulation results, deformation structure types, and characteristic parameters. Deformation structure types include liquefied diapirs, liquefied sand laminar flow, liquefied coiling, load structures, and liquefaction veins. Characteristic parameters include deformation initiation time, maximum deformation height, lateral extension distance, and structural development degree. The results output and analysis module outputs results in the form of cloud maps, dynamic animations, and data reports. The correlation is that under the same peak acceleration, the smaller the sedimentary layer thickness, the more significant the deformation. In the same sedimentary layer model, the higher the peak acceleration, the richer the deformation structure types and the greater the deformation intensity.

[0015] The technical effects and advantages of the lacustrine sedimentary deformation dynamic numerical simulation system of this invention are as follows: 1. The system architecture of this invention covers the entire process of sedimentary layer model construction, seismic wave processing, numerical simulation calculation and result analysis, realizing the standardization and systematization of dynamic simulation of lacustrine sedimentary deformation, avoiding the limitations of fragmented simulation, and improving the integrity and operability of the simulation process.

[0016] 2. The sedimentary layer model of this invention sets physical parameters based on actual lacustrine sedimentary surveys and experimental data, and the size and soil layer thickness can be flexibly adapted to different sedimentary environments, ensuring the consistency between the model and real lacustrine sedimentary conditions, and improving the pertinence and reliability of the simulation results.

[0017] 3. This invention effectively restores the true propagation characteristics of seismic waves by combining scaling, filtering, Fourier series fitting, and trapezoidal method numerical integration, thereby reducing noise and data drift interference and providing accurate and stable input conditions for numerical simulation.

[0018] 4. Based on the VOF multiphase flow model and the core equation of incompressible fluid, combined with the geometric reconstruction algorithm, this invention can accurately capture the interface interaction, mixing and intrusion process of clay and sand, clearly reproduce the formation and evolution of various liquefaction deformation structures, and solve the technical problem of the difficulty in accurately reproducing the soft sediment deformation mechanism.

[0019] 5. This invention clearly establishes a quantitative correlation between deformation characteristics and peak ground acceleration and sedimentary layer thickness, providing direct quantitative support for determining the intensity of paleoseismic events and identifying the causes of earthquakes caused by soft sediment deformation. It makes up for the lack of systematic numerical simulation basis in traditional paleoseismic research and expands the technical path and scientific depth of paleoseismic research. Attached Figure Description

[0020] Figure 1 This is a flowchart of the dynamic numerical simulation system for lacustrine sedimentary deformation proposed in this invention. Detailed Implementation

[0021] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0022] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include," "contain," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "includes..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0023] refer to Figure 1This invention discloses a dynamic numerical simulation system for lacustrine sedimentary deformation, aiming to accurately reproduce the liquefaction and thixotropic deformation processes of lacustrine sedimentary layers under seismic loading, providing quantitative support for paleoseismic research. The system comprises four main modules: sedimentary layer model construction, seismic wave processing, numerical simulation calculation, and result output and analysis. The sedimentary layer model construction module, based on lacustrine sedimentary field surveys and shaking table experimental data, constructs a multi-layer clay-sand model with dimensions and soil layer thickness adaptable and sets physical parameters. The seismic wave processing module transforms the original discrete seismic waves into accurate continuous velocity functions through scaling, high-pass filtering, trapezoidal method numerical integration, and Fourier series fitting. The numerical simulation calculation module, relying on Fluent's VOF multiphase flow model, imports the model and seismic wave data, and, based on core equations such as the Navier-Stokes equation and the continuity equation, combines a geometric reconstruction algorithm to track the dynamics of the soil interface and simulate the deformation process. The result output and analysis module outputs dynamic simulation results and characteristic parameters, establishing a quantitative correlation between deformation and peak ground acceleration (PGA) and sedimentary layer thickness. The system as a whole achieves standardized and precise simulation processes, solving the technical problem of the difficulty in reproducing soft sedimentary deformation mechanisms.

[0024] Principle of Fluent multiphase flow simulation method: The flow of all objects in nature can be described by the Navier-Stokes equations (NSE), which are a set of differential equations describing the motion of fluid substances such as liquids or gases. The most general form of these equations is the conservation of mass, momentum, and energy.

[0025] (1) Where v is the vector of fluid velocity at time t, and P is the pressure. It represents viscosity, and f represents gravity or centrifugal force.

[0026] In the entire numerical model, only incompressible fluids are considered. It is assumed that the fluid density remains constant, and the continuity equations (representing mass conservation) (2) and (3) hold, thus ensuring that the total volume of the fluid remains constant during the motion.

[0027] (2) (3) Viscosity is a measure of the resistance to shear forces generated by the attractive forces between fluid molecules. All fluids have a certain shear resistance; fluids without shear resistance are called ideal fluids or inviscid fluids. Isaac Newton proposed that in laminar flow (i.e., fluid particles move along parallel lines), the shear stress at the tangential interface in the flow direction is proportional to the velocity change perpendicular to the interface (e.g., in the xy plane), which can be expressed as: (4) Where µ is the viscosity coefficient, its dimensions are [kg m -1 s -1 The correlation coefficient is the kinematic viscosity ν, with dimensions [m² s]. -1 Since all gases and most liquids are Newtonian fluids, their behavior follows the equations above, meaning that fluid flow is closely related to density and viscosity.

[0028] Fluid flow exists in two modes: laminar and turbulent. At low velocities, the fluid remains stable and maintains this stability over time; this is the laminar stage. As the flow velocity increases, the fluid becomes unstable and highly distorted, reaching the turbulent stage. The turbulent properties of the fluid influence its velocity distribution. This study treats saturated sand and clay as fluids, whose deformation structures conform to the nonlinear stage of turbulence. Computational fluid dynamics (CFD) simulation is well-suited for this nonlinear stage. Based on this, this study uses Fluent software and a VOF model to investigate the liquefaction and thixotropic deformation patterns of different sedimentary layer combinations under varying seismic vibration intensities.

[0029] Main models for multiphase flow simulation: Fluent is a commercial CFD software that solves the Navier-Stokes equations numerically. Users provide the necessary boundary conditions and mesh settings, and configure a series of physical parameters such as fluid viscosity and density. Users can also insert a time-dependent density or velocity field along the boundary using user-defined functions (UDFs). When the fluid flow is unstable, the software can generate flow animations and display fields such as velocity, pressure, and density. Based on this, we can clearly observe the soil deformation process under seismic loading.

[0030] Fluent offers a variety of methods for simulating multiphase flow, the choice of which depends primarily on the distribution characteristics of the dispersed phases and the interphase coupling mechanism. These methods are all described within the Eulerian framework, which discretizes the computational domain into a fixed grid and solves the integral form of a series of conservation equations (mass, momentum, energy) to track the fluid state within different control volumes.

[0031] The main models include: Euler-Lagrange model (DPM): Suitable for scenarios with low dispersed phase volume fraction (typically <10%) and can be considered as discrete particles / bubbles, such as particle separation and spraying.

[0032] The Euler-Euler model treats all phases as interpenetrating continuous media, suitable for tightly coupled systems where the volume fraction of each phase is not negligible. This model is further divided into: Mixture model: Each phase is considered as an interpenetrating continuum. The mixing momentum equation is solved, and the phases are described by their relative velocities.

[0033] The Eulerian model is the most complex model, where each phase is considered an interpenetrating continuum. Each phase has its own continuity and momentum equations, and the phases are coupled through pressure and interphase models.

[0034] VOF model: a surface tracking technique primarily used in cases where the phases are immiscible. The momentum equation is shared by all phases, and each phase is described by its phase fraction.

[0035] Given that the large deformation characteristics of soil after earthquake liquefaction exhibit clear and transient evolution of free surfaces (such as water jetting and sand eruption, lateral flow), this study selects the VOF model as the core model.

[0036] The basic principle of the VOF model: Model Overview: The VOF (Volume of Fluid) model is a numerical method for handling two-phase or multiphase flows in fluid dynamics. The main idea behind this model is to track the phase interfaces of the fluid by constructing a phase function (usually denoted as α) to describe the distribution of different phases within the computational domain. The phase function α takes values ​​between 0 and 1, where 0 represents no target fluid, 1 represents the mesh being completely occupied by the target fluid, and values ​​between 0 and 1 indicate that the region is a two-phase mixing interface.

[0037] The VOF model obtains fluid motion by solving the continuity and momentum equations. Since the phase interface is determined by the phase function α, this method can effectively track the dynamic changes of the interface while maintaining its clarity. The VOF model is well-suited for simulating the fluid behavior of complex interfaces, continuously capturing the transient behavior of two-phase interfaces and clearly showing the motion changes of various fluids.

[0038] Governing equations: The VOF model shares the same set of momentum and continuity equations for all phases, which are solved based on the volume fraction α of the fluid phase.

[0039] (1) Momentum equation: The entire multiphase fluid system shares the same velocity and pressure fields within the computational domain, requiring the solution of a single momentum equation: (5) For a two-phase system, the density ρ and dynamic viscosity μ are averaged values ​​calculated from the volume fractions of each phase: (6) in, (2) Continuity equation: The VOF model solves the continuity equation for mixtures, and its form is the same as that for single-phase flow: (7) (3) Interface reconstruction: VOF models typically employ geometric reconstruction algorithms to improve the accuracy of interface capture and correctly track fluid phase interfaces. Common geometric reconstruction algorithms include planar reconstruction and piecewise linear interface construction (PLIC). Therefore, after each time step calculation, Fluent uses a specific geometric reconstruction algorithm based on the calculated volume fraction field to reconstruct the sharp interfaces between phases, clearly capturing the interactions, mixing, intrusion, and encapsulation phenomena of different soil masses during seismic vibration.

[0040] Calculation settings: The VOF model requires defining appropriate solution algorithms and time steps. In Fuent software, finite volume methods and multigrid techniques can be used to accelerate computational convergence. Furthermore, to ensure computational stability and accuracy, the convergence criteria and time step size must be set appropriately. Typically, the time step needs to be small enough to capture rapid changes in the interface, but not too small to avoid excessive computation time.

[0041] Deposition model design scheme: The correct definition of fluid parameters (viscosity and density) is a fundamental and crucial factor in every computational fluid dynamics model. Due to the nonlinear relationship between the Navier-Stokes equations and these factors, it significantly affects the simulation results.

[0042] Basic physical properties: According to the shaking table experiment, the saturated densities of sand and silty clay are 1.927 g / cm3 and 1.664 g / cm3, respectively; the porosities are 43.44% and 61.44%, respectively; and the viscosities are 0.02 Pa•s and 0.1 Pa•s, respectively. See Table 1 for details.

[0043] Table 1. Basic physical parameters of soil samples: Model building: This study was divided into three models, setting up multiple combinations of sedimentary layers that conform to the depositional environment and are capable of liquefaction or thixotropy. The models were set as 0.20m×2m, 0.4m×2m, and 1m×2m. Since different seismic accelerations have different effects on sedimentary layers, accelerations of 0.125g, 0.25g, 0.5g, 0.8g, and 1.0g were set to verify the degree of influence of different sedimentary layer thicknesses on deformation under different accelerations. The three models were compared to verify the influence of sand layer thickness on deformation under the same environment, and to verify the minimum acceleration required for different soft sedimentary structures. At the same time, the relationship between deformation thickness or soft sediment type and acceleration was explored. Details are shown in Table 2.

[0044] Table 2 Numerical simulation scheme for soft deposition deformation Seismic wave function: In Python, the current peak acceleration is found, and scaling factors are calculated to scale the peak accelerations to 0.125g, 0.25g, 0.5g, 0.8g, and 1.0g. The velocity is then obtained by numerically integrating these scaled accelerations. Numerical integration utilizes the trapezoidal rule, one of the most fundamental and widely used methods in numerical integration. For earthquake engineering applications, it provides sufficient accuracy, and compared to the simple rectangular method, it more accurately approximates continuous integration, precisely capturing the changes in acceleration within the time step. Most importantly, it is applicable to discrete data points, approximating integration by connecting adjacent points to form a trapezoid, thus making it suitable for discrete data such as El Centro waves.

[0045] Because seismic wave data may contain noise and baseline drift, and drift may occur during integration, the scaled acceleration data needs to be filtered and baseline adjusted. Filtering typically uses a high-pass filter to remove low-frequency noise, and baseline correction is achieved by removing the trend term after integrating the acceleration and velocity. However, since El Centro waves are usually already processed, baseline correction for acceleration is omitted, and only filtering and velocity baseline correction are performed.

[0046] Because seismic wave data is discrete and complex, a simple analytical function cannot accurately represent the seismic wave velocity function. Therefore, interpolation or fitting methods are used for approximation. A finite-term Fourier series is used to approximate the original function. Fourier series fitting decomposes the function into a linear combination of sine and cosine functions of different frequencies, and uses the least squares method and the orthogonality of trigonometric functions to determine the optimal coefficients, thus achieving an approximation of the original function with a finite-term series. Seismic waves are the evolution and propagation of source vibration over time. The Fast Fourier Transform (FFT) is used to convert the time-domain data into frequency-domain data. Then, the main frequency components are selected and approximated using a finite-term Fourier series. Because Fourier series fits periodic signals well, and seismic waves are non-periodic, the data is periodically extended to obtain the fitted data. Finally, the seismic wave function is represented as a series of sine and cosine functions using Fourier series.

[0047] Numerical simulation steps: Model building: Based on the model scheme, models of different sizes were built in DesignModeler, and then meshed using Meshching. The mesh digitally defines the model environment, allowing for the definition of boundary types (walls, vents, pressure inlets, velocity inlets, etc.) and characterizing the region type (solid or fluid). The model was set as a fluid. Within the model, the mesh size was set according to the minimum model length as shown in the table below (it is generally recommended to use 1 / 50 to 1 / 100 of the model's characteristic length as the initial mesh size). Since this study focuses on the deformation process of saturated sand and clay under seismic loading, a seismic function was input at the bottom. Therefore, the bottom was set to wall-bottom, and the other edges were set to wall. Details are shown in Table 3.

[0048] Table 3 Grid Size Primary and secondary aspects: Main phase: It can usually be considered as a continuous medium, which occupies the main part in the flow region. The main phase is also called the basic phase.

[0049] Secondary phase: This can be considered as a phase dispersed within the primary phase. In multiphase flow, all materials other than the primary phase are secondary phases. There can be secondary phases with particles of various sizes, and secondary phases are sometimes also called subordinate phases.

[0050] In this study, the primary phase was set as a lighter layer, namely clay, and the secondary phase was set as a heavier layer, namely sand.

[0051] 2.4.3 Setting parameters: In Fluent, select transient simulation. Transient simulation refers to computational fluid dynamics (CFD) simulation that considers the time factor. Transient simulation solves for how physical quantities (such as velocity, pressure, and temperature) change with time. The governing equations include partial derivatives with respect to time (e.g.,...). ρ / t, (ρv) / The simulation results are closely related to the time history. The model is set to pressure basis, which is suitable for low-speed incompressible flow and supports multiphase flow models, while density basis is suitable for high-speed compressible flow but does not support multiphase flow.

[0052] Set the model to V0F model, select implicit calculation, and set the viscosity and density of sand and clay in the fluid. Set clay as the first phase and sand as the second phase. Select wall-bottom for boundary conditions, slip motion for motion, and component motion for the mode. Finally, input the velocity function in the x-axis, set the sand region, initialize, set the contour plot, and perform the calculation. Since the strong earthquake lasts approximately 20 seconds, the simulation time is set to 25 seconds, the time step is set to 500 steps, and the time step size is set to 0.05 seconds (total simulation time = number of time steps × time step size).

[0053] Results and Analysis: Variation characteristics under different peak accelerations: Peak acceleration 0.25g: (1) Model 1: At 4 seconds into the simulation, the sand on both sides began to deform initially; at 8 seconds, the sand in this area underwent vertical migration and eventually pierced through the overlying clay layer, forming a liquefied diapir about 2 cm high. Peak acceleration 0.5g: (1) Model 1: Two seconds after the simulation began, initial disturbances appeared in the sand layers on both sides of the model. By four seconds, significant vertical migration of the sand in this area began. By five seconds, the sand had completely penetrated the top of the overlying clay layer, forming a liquefied sand laminar flow. As the seismic activity continued, it further induced liquefaction curling structures, which in turn caused adjacent sand to penetrate the clay layer upwards, ultimately forming a liquefied diapiric structure.

[0054] (2) Model 2 The two sets of simulation results showed significant differences in deformation time and process. In the first set, there were no obvious changes after the simulation started, until liquefaction diapirs gradually appeared after about 8 seconds; while the tectonic activity in the second set was more intense than in the first set: the sand penetrated the clay layer upwards in 2 seconds, reaching a maximum height of 10 cm. Subsequently, the lateral compression generated after part of the sand body fell back further induced the adjacent sand to penetrate upwards, eventually forming liquefaction diapirs.

[0055] Peak acceleration 0.8g: (1) Model 1 In the first group, after 1 second, the sand on both sides begins to move upward. After 4.5 seconds, the sand on the right side reaches the top of the clay and begins to move to the left, undergoing liquefaction and curling, which engulfs some of the clay into the sand. At this time, the sand on the left side also begins to liquefy and curl. Due to the high density of the sand, after curling, the sand gradually moves downward, and some of the sand liquefies and continues to intrude upward, forming a liquefied diapir.

[0056] (2) Model 2 In the first group, the sand began to move upwards noticeably after about 4 seconds and penetrated the clay layer after 5 seconds, forming liquefied curls. In contrast, the deformation in the second group started significantly earlier than in the first group. The sand began to move upwards after about 1 second, but fell back under the influence of gravity, and did not completely penetrate the clay layer and form liquefied curls until 7 seconds later.

[0057] (3) Model 3 Under the action of 0.8g, the deformation of Model 3 is relatively small compared to that of Models 1 and 2. Only the sand below slightly intrudes upward into the clay. This is because the overlying clay layer is too thick, which restricts the movement of the sand layer.

[0058] Peak acceleration 1.0g: (1) Model 1 Upon simulation initiation, the sand immediately began vertical movement, intruding into and penetrating the overlying clay layer. By the 4th second, liquefaction-curved structures simultaneously developed on both sides of the model, with a maximum lateral extension of approximately 25 cm. Within the curled structures, some sand continued to intrude upwards, forming localized liquefaction diapirs. Subsequently, some sand fell back under gravity, forming a load-bearing structure approximately 10 cm long and 3 cm high.

[0059] The simulation at time T=8s revealed the maximum deformation characteristics under different accelerations, exhibiting structures such as liquefied diapirs, liquefied sand laminar flow, liquefied curling, and loading. The simulation results indicate that the development of liquefaction-related structures is closely related to the input acceleration and soil layer thickness. Generally, under the same acceleration conditions, thinner soil layers result in more significant deformation; and for the same model, higher accelerations not only induce more types of liquefaction structures but also significantly enhance their deformation intensity. Liquefied diapirs form at a peak acceleration of 0.5g, while liquefied sand laminar flow, liquefied curling, and loading structures form around 0.8g. Simulation snapshots at 8 seconds were calculated under different ground accelerations and different soil layer thicknesses.

[0060] This study has certain limitations. The simulation process did not directly employ a two-phase flow non-Newtonian constitutive model that can reflect solid-fluid coupling, which may lead to insufficient accuracy in characterizing key phenomena such as the accumulation of excess pore water pressure and the formation of finite-thickness shear bands during liquefaction. In addition, the sediment viscosity parameters used were borrowed from the literature and may differ from the rheological properties of actual liquefied or thixotropic sediments.

[0061] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of protection of the claims.

[0062] In conclusion, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A numerical simulation system for dynamic deformation of lacustrine sediments, characterized in that, It includes a sedimentary layer model building module, a seismic wave processing module, a numerical simulation calculation module, and a result output and analysis module; The sedimentary layer model construction module is used to construct a multi-layer sedimentary composite model containing clay and sand based on the characteristics of lacustrine sedimentary environments, and to set the physical parameters of saturated density, porosity and viscosity of clay and sand. The seismic wave processing module is used to acquire and process raw seismic wave data to obtain the continuous velocity function of the seismic wave. The numerical simulation module, based on the VOF multiphase flow model of Fluent software, constructs the multilayer sedimentary composite model, imports the processed seismic wave continuous velocity function, sets relevant calculation parameters, solves the fluid motion state based on the principle of incompressible fluid, tracks the dynamic changes of soil interface through geometric reconstruction algorithm, and simulates the liquefaction and thixotropic deformation process of lacustrine sedimentary layers under seismic action. The results output and analysis module is used to output the results related to the liquefaction and thixotropic deformation of the sedimentary layer, and to establish the correlation between deformation characteristics and peak ground acceleration and sedimentary layer thickness.

2. The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 1, characterized in that, The sedimentary assemblage models come in three sizes: 2m × 0.2m, 2m × 0.4m, and 2m × 1m. The clay and sand thickness combinations in each model are: 10mm clay + 10mm sand, 20mm clay + 20mm sand, 30mm clay + 10mm sand, 50mm clay + 50mm sand, 80mm clay + 20mm sand, and 60mm sand + 40mm clay. The size of the sedimentary assemblage models and the thickness of each soil layer are adjusted according to the characteristics of the lacustrine sedimentary environment.

3. The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 1, characterized in that, The saturated density of clay was 1.664 g / cm³, the porosity was 43.44%, and the viscosity was 0.1 Pa·s; the saturated density of sand was 1.927 g / cm³, the porosity was 61.44%, and the viscosity was 0.02 Pa·s. The physical parameters were set based on lacustrine sedimentary field survey data and shaking table experimental results.

4. The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 1, characterized in that, The processing steps of the seismic wave processing module include: scaling the original seismic wave data by calculating the scaling factor to obtain the seismic wave with the preset peak acceleration; using high-pass filtering to remove low-frequency noise from the scaled seismic wave, obtaining the velocity at each moment through numerical integration and performing velocity baseline correction; and using the Fourier series fitting method to transform the discrete seismic wave data into a continuous seismic wave velocity function.

5. The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 4, characterized in that, The preset peak acceleration ranges from 0.125g to 1.0g, with key node values ​​of 0.25g, 0.5g, and 0.8g. Scaling processing matches the original seismic wave peak acceleration with the preset peak acceleration by calculating a scaling factor.

6. The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 4, characterized in that, Fourier series fitting decomposes the function into a linear combination of sine and cosine functions of different frequencies, uses the least squares method and the orthogonality of trigonometric functions to determine the optimal coefficients, and obtains the fitted data after periodic expansion of the data; seismic wave velocity is obtained by numerical integration, which adopts the trapezoidal method, and forms a trapezoidal approximate integral by connecting adjacent discrete data points to capture the change of acceleration within the time step.

7. The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 1, characterized in that, The relevant equations for the principle of incompressible fluids include the Navier-Stokes equation, the continuity equation, Newton's law of internal friction, the momentum equation, and the continuity equation for mixtures; among them, the Navier-Stokes equation is expressed as follows: v is the vector of fluid velocity at time t, P is the pressure, μ is the viscosity coefficient, f is gravity or centrifugal force, and ρ is the fluid density; the continuity equation includes and The expression for Newton's law of internal friction is: τ is the shear stress, u is the velocity in the flow direction, and y is the distance perpendicular to the flow direction; the momentum equation is expressed as follows: The expression for the continuity equation of a mixture is: .

8. The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 7, characterized in that, Phase density ρ and dynamic viscosity μ are calculated from the volume fraction of each phase, and their expressions are as follows: α1 is the clay volume fraction, α2 is the sand volume fraction, ρ1 is the clay density, ρ2 is the sand density, μ1 is the clay dynamic viscosity, and μ2 is the sand dynamic viscosity. .

9. The lacustrine sedimentary deformation dynamic numerical simulation system as described in claim 1, characterized in that, In the numerical simulation module, the grid edge length is determined to be between one-fiftieth and one-hundredth of the model's characteristic length; the grid edge length in both the x and y directions is 0.2 cm for the 2 m × 0.2 m model, 0.4 cm for both the x and y directions for the 2 m × 0.4 m model, and 1 cm for both the x and y directions for the 2 m × 1 m model; the boundary conditions are set as follows: the bottom of the model is a sliding boundary, the seismic wave velocity function is input, and the other boundaries are set as fixed walls; the phase properties are set as follows: clay is the main phase, sand is the secondary phase, and the main and secondary phases share the same velocity and pressure fields.

10. The dynamic numerical simulation system for lacustrine sedimentary deformation as described in claim 1, characterized in that, The geometric reconstruction algorithm, a piecewise linear interface construction algorithm or Geo-Reconstruct algorithm, is used to reconstruct the sharp interface between clay and sand, capturing soil layer interactions, mixing, intrusion, and encapsulation phenomena. The VOF multiphase flow model describes the distribution of different phases within the computational domain through phase functions. Phase function values ​​range from 0 to 1, where 0 represents no target fluid, 1 represents the mesh being completely occupied by the target fluid, and values ​​between 0 and 1 represent a two-phase mixing interface. Transient calculation parameters include a simulation time of 25 seconds, 500 time steps, and a time step size of 0.05 seconds. The pressure-based implicit calculation method is used; deformation-related results include dynamic simulation results, deformation structure types, and characteristic parameters. Deformation structure types include liquefied diapirs, liquefied sand laminar flow, liquefied coiling, load structures, and liquefaction veins. Characteristic parameters include deformation initiation time, maximum deformation height, lateral extension distance, and structural development degree. The results output and analysis module outputs results in the form of cloud maps, dynamic animations, and data reports. The correlation is that under the same peak acceleration, the smaller the sedimentary layer thickness, the more significant the deformation; within the same sedimentary layer model, the higher the peak acceleration, the richer the deformation structure types and the greater the deformation intensity.

Images