Method for analyzing erosion and deposition evolution of corridor-square cabin combined type seabed data center

Abstract

The invention provides an analysis method for erosion and deposition evolution of a corridor-square cabin combined type seabed data center, and is suitable for the field of underwater data center design. The method comprises the steps of data collection, model parameter calculation, model establishment and verification, result prediction and structure optimization, modeling analysis is carried out on the erosion and deposition evolution process of the seabed data center by selecting proper parameters, and the seabed topography after sediment erosion and deposition evolution with time lapse in the service cycle of the seabed data center is obtained. The pile foundation can be specifically reinforced and the structure can be optimized according to a submarine topography evolution result, data of different sea areas can be collected, and a sea area with smaller submarine topography change after sediment scour and deposition change is selected to arrange a submarine data center according to submarine topography evolution simulation results of different sea areas; and the stability and durability of the structural pile foundation are improved to the maximum extent, and the maintenance cost for cleaning deposited silt is reduced.

Description

Technical Field

[0001] The present invention relates to the field of underwater data center design, and in particular to an analysis method for the scouring and silting evolution of a corridor-cabin combined submarine data center. Background Art

[0002] The corridor-cabin combined submarine data center is an innovative data center construction solution. By setting up submarine corridors to connect multiple data cabins, it realizes convenient maintenance and repair of submarine data cabins, improves the concentration level of submarine data centers, and effectively reduces construction and operation costs. However, the design and deployment of this new type of data center also faces a series of technical and environmental challenges.

[0003] Due to the layout of submarine data centers, the water-blocking area in some areas of the seabed has increased significantly, resulting in faster water flow and the formation of eddies in some areas. This change in flow field will not only affect the submarine ecosystem, but also threaten the stability of the data center itself. The acceleration of water flow and the formation of eddies will lead to the scouring of the bottom foundation of the submarine data center, especially for data centers with pile foundations. The scouring of the seabed will reduce the bearing capacity of the pile foundation and increase the risk of structural instability.

[0004] At the same time, the sedimentation of the seabed may also cause the data center to be covered with sediment, especially the cabins that store data servers. Due to the operation of the servers, the interior needs to be cooled and dissipated by flowing seawater. If the cabins are covered with sediment, the risk of server overheating increases, seriously affecting the normal operation of the data center. The regular cleaning costs caused by sedimentation are high. Summary of the invention

[0005] The purpose of the present invention is to improve the stability and durability of the structure and reduce the maintenance cost. An analysis method for the scouring and silting evolution of a corridor-cabin combined submarine data center is proposed to ensure the foundation stability of the submarine data center and optimize the structural size and layout of the submarine data center, thereby minimizing the impact of siltation on the data center. The method can be widely used in the field of underwater data center design.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] S1. Collect data.

[0008] The data collection includes planning more than two corridor-cabin combined submarine data center construction areas, arranging n measuring points in the construction area at intervals of ΔT to collect sea area characteristic data, hydrodynamic parameters, meteorological data and bottom composition parameters within a time period of T; the sea area characteristic data includes soil type and high-precision seabed topography data, the hydrodynamic parameters include sea surface wave height, period and seabed water flow velocity and direction, the meteorological data includes wind speed and wind direction, and the bottom composition parameters include sediment particle size, fluid density, and sediment mixed density;

[0009] S2, calculation of model parameters;

[0010] The calculation of model parameters includes calculating the critical Shields parameter according to the Soulsby-Whitehouse formula,

[0011]

[0012] In the formula, R * is the dimensionless parameter of the median particle size of sediment; the dimensionless parameter of the median particle size of sediment is calculated according to the following formula:

[0013]

[0014] Where, d 50 is the median particle size of sediment, ρ f is the fluid density, ρ s is the density of sediment mixture, g is the acceleration due to gravity, μ f is the fluid dynamic viscosity; further considering the effect of the seafloor slope angle on sediment deposition, the modified critical Shields parameter is obtained:

[0015]

[0016] In the formula, is the sediment repose angle, α is the angle between the velocity vector and the seabed surface, and β is the angle between the seabed surface and gravity;

[0017] It also includes the use of the Mastbergen formula to calculate the conversion of seabed sediment into suspended sediment by water flow.

[0018]

[0019] Where k is the sediment narrow band coefficient, n s is the normal vector of the seabed surface, d * is the dimensionless particle size of the sediment particles, d s is the sediment particle size, θ s is the local Shields number of the seabed sediment; the dimensionless particle size of the sediment particles is calculated according to the following formula:

[0020]

[0021] The local Shields number of the seabed sediment is calculated according to the following formula:

[0022]

[0023] Where τ is the local shear stress of the seabed; part of the suspended sediment settles under the action of gravity and turns into deposited sediment. The sediment settling velocity is calculated as follows:

[0024]

[0025] Where, ν f is the kinematic viscosity of the fluid;

[0026] It also includes the calculation of bed load movement of sediment particles in water flow based on the bed load transport velocity single width volume sediment transport formula.

[0027] φ=β MPM (θ i -θ cr ) 1.5 c b (8)

[0028] Where φ is the bed load transport rate per unit width, β MPM is the bed load coefficient, c b is the sediment volume fraction;

[0029] S3, model building and verification;

[0030] The model establishment and verification includes obtaining the structural dimension data of the corridor-cabin combined submarine data center, establishing a CFD model according to the structural dimension data and submarine topography data, inputting the submarine roughness and the model parameters into the CFD model, and then performing grid division, and comparing the measured value and the simulated value of the flow velocity and flow direction under the influence of the submarine data center, and using the Pearson correlation coefficient to characterize the accuracy of the model;

[0031] S4, result prediction and structure optimization;

[0032] The result prediction and structural optimization include simulating the silt deposition evolution process during the service life of the corridor-cabin combined submarine data center according to the CFD model, comparing the evolution simulation results of each construction area, selecting an area where the seabed topography changes less after silt scouring and silt deposition as the target construction area, and according to the evolution simulation results of the target construction area, for areas where the seabed terrain becomes lower after silt scouring, it is necessary to increase the pile length when designing the pile foundation, and for areas where silt silt buries the cabin, it is necessary to optimize the structural size and the structural form of the corridor-cabin combined submarine data center to obtain a smaller silt silt burial depth, and for areas where the cabin burial depth is still large after optimization, a high-rise is set at the bottom of the cabin, and the height of the high-rise is the burial depth.

[0033] As a preferred technical solution of the present invention, in step S2, the sediment particle size is obtained by using a sediment particle analysis device, and the sediment particle analysis device includes a particle size distribution detector, a particle data processor and a memory. A seabed sediment sample is placed in the particle size distribution detector to obtain a particle size distribution curve of the sediment. The particle data processor reads the sediment particle size distribution curve, automatically analyzes to obtain the sediment particle size, dimensionless parameters of the sediment median particle size and dimensionless particle size of the sediment particles, and stores them in the memory.

[0034] As a preferred technical solution of the present invention, in step S2, the value range of the bed load coefficient is 8 to 13. The specific value-taking process includes establishing a CFD three-dimensional model of a non-seabed data center structure based on the seabed topography data, setting the initial bed load coefficient to 8, and implementing step S3 with other conditions unchanged, and then running the model to simulate the evolution process of seabed scouring and silting within a certain time scale, comparing the evolution simulation results with the changes in the measured seabed topography data during the T / ΔT period, and adjusting the bed load coefficient to make the model more accurately reflect the actual changes in sediment scouring and silting.

[0035] As a preferred technical solution of the present invention, in step S2, the sediment volume fraction is obtained by measuring the weight and volume of the sediment by on-site sampling and then calculating its volume proportion in the water body.

[0036] The beneficial effects of the present invention are as follows: by selecting appropriate parameters to model and analyze the scouring and silting evolution process of the submarine data center, the submarine topography after the evolution of silt scouring and siltation over time during the use period of the submarine data center can be obtained, and the pile foundation can be strengthened and the structure optimized according to the results of the submarine topography evolution. Data from different sea areas can also be collected, and according to the simulation results of the submarine topography evolution in different sea areas, the sea area with smaller changes in the submarine topography after silt scouring and siltation can be selected to deploy the submarine data center. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1It is a flow chart of the analysis method of the corridor-cabin combined submarine data center scouring and silting evolution of the present invention. DETAILED DESCRIPTION

[0038] The specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. It should be understood that the specific embodiments given here are only used to illustrate and explain the present invention and cannot be used to limit the present invention. It should be noted that many specific details are set forth in the following description to facilitate a full understanding of the present invention, but the present invention may also have other embodiments and variations thereof, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below.

[0039] Example 1 The analysis method for the evolution of scouring and silting of the corridor-cabin combined submarine data center is as follows:

[0040] S1. Collect data.

[0041] Two corridor-cabin combined submarine data center construction areas are planned. In the construction area, about 3000m 2 Within the scope, 30 measuring points are arranged, and the interval time ΔT=3h is to collect the sea area characteristic data, hydrodynamic parameters, meteorological data and bottom composition parameters within T=365*24h; the sea area characteristic data includes soil type and high-precision seabed topography data, the hydrodynamic parameters include sea surface wave height, period and seabed water flow velocity and direction, the meteorological data includes wind speed and wind direction, and the bottom composition parameters include sediment particle size, fluid density, and sediment mixed density;

[0042] The sediment particle size is obtained by using a sediment particle analysis device, which includes a particle size distribution detector, a particle data processor and a memory. A seabed sediment sample is placed in the particle size distribution detector to obtain a sediment particle size distribution curve. The particle data processor reads the sediment particle size distribution curve, automatically analyzes the sediment particle size, dimensionless parameters of the sediment median particle size and dimensionless particle size of the sediment particles, and stores them in the memory.

[0043] S2, calculation of model parameters;

[0044] According to the Navier-Stokes equations, the flow of incompressible fluid is described as follows, and the continuity equation and momentum equation are constructed:

[0045]

[0046] Where u, v, and w are the instantaneous velocity components of the water flow in the x, y, and z directions, respectively, and V F is the flowable volume fraction, A x , A y , A zis the flowable area fraction in the x, y, and z directions, ρ is the fluid density, p is the instantaneous pressure, G x , G y , G z is the gravitational acceleration component in the x, y, and z directions, f x 、f y 、f z are the viscous acceleration components in the x, y, and z directions;

[0047] The RNG k-ε turbulence model is constructed for numerical solution, and its control equation is as follows:

[0048]

[0049] In the formula, k is the turbulent kinetic energy, ε is the turbulent kinetic energy dissipation rate, P T is the term for turbulent kinetic energy generation, G T is the term for the turbulent kinetic energy generated by buoyancy. For incompressible fluids, it is taken as 0. k , D ε is the diffusion term, C ε1 , C ε2 , C ε3 is the correlation coefficient;

[0050] Construct a sediment transport model and calculate the critical Shields parameter according to the Soulsby-Whitehouse formula.

[0051]

[0052] In the formula, R * is the dimensionless parameter of the median particle size of sediment; the dimensionless parameter of the median particle size of sediment is calculated according to the following formula:

[0053]

[0054] Where, d 50 is the median particle size of sediment, ρ f is the fluid density, ρ s is the density of sediment mixture, g is the acceleration due to gravity, μ f is the fluid dynamic viscosity; further considering the effect of the seafloor slope angle on sediment deposition, the modified critical Shields parameter is obtained:

[0055]

[0056] In the formula, is the sediment repose angle, which is 32° in this embodiment, α is the angle between the velocity vector and the seabed surface, and β is the angle between the seabed surface and gravity;

[0057] It also includes the use of the Mastbergen formula to calculate the conversion of seabed sediment into suspended sediment by water flow.

[0058]

[0059] Where k is the sediment narrow band coefficient, which is generally taken as 0.018, n s is the normal vector of the seabed surface, d * is the dimensionless particle size of the sediment particles, θ s is the local Shields number of the seabed sediment; the dimensionless particle size of the sediment particles is calculated according to the following formula:

[0060]

[0061] The local Shields number of the seabed sediment is calculated according to the following formula:

[0062]

[0063] Where τ is the local shear stress of the seabed; part of the suspended sediment settles under the action of gravity and turns into deposited sediment. The sediment settling velocity is calculated as follows:

[0064]

[0065] Where, ν f is the kinematic viscosity of the fluid;

[0066] It also includes the calculation of bed load movement of sediment particles in water flow based on the bed load transport velocity single width volume sediment transport formula.

[0067] φ=β MPM (θ i -θ cr ) 1.5 c b (11)

[0068] Where φ is the bed load transport rate per unit width, β MPM is the bed load coefficient, c b is the sediment volume fraction;

[0069] The value range of the bed load coefficient is 8 to 13. The specific value selection process includes: establishing a CFD three-dimensional model without a seabed data center structure according to the seabed topography data, setting the initial bed load coefficient to 8, and implementing step S3 with other conditions unchanged, and then running the model to simulate the evolution process of seabed scouring and silting within a certain time scale, comparing the evolution simulation results with the changes in the measured seabed topography data during T / ΔT=2920, and adjusting the bed load coefficient so that the model can more accurately reflect the actual changes in sediment scouring and silting;

[0070] The sediment volume fraction is obtained by measuring the weight and volume of the sediment by on-site sampling and then calculating its volume proportion in the water body. In this embodiment, the sediment volume fraction is 0.6;

[0071] S3, model building and verification;

[0072] The structural dimension data of the corridor-cabin combined submarine data center is obtained. The corridor is a rectangular parallelepiped structure with a length of 100m, a width of 6m, and a height of 5m. The two upper corners of the corridor cross section are chamfered with a radius of 1m. Cabins with a length of 12m, a width of 5m, and a height of 5m are arranged at intervals of 10m on both sides. The length direction of the cabin is perpendicular to the length direction of the corridor. The two upper corners of the cabin cross section are chamfered with a radius of 1m. A CFD model is established based on the structural dimension data and the submarine topography data. The model parameters are input into the CFD model, and then meshing is performed. The flow velocity and flow direction under the influence of the submarine data center are compared with the measured values ​​and the simulated values. The Pearson correlation coefficient is used to characterize the accuracy of the model.

[0073] The measured and simulated values ​​of water flow velocity at some measuring points are shown in the following table.

[0074] Table 1 Measured and simulated values ​​of water flow velocity at measuring point 12

[0075] Time t(h) 0 3 6 9 … 768 771 774 … Measured value (m / s) 1.19 1.25 1.33 1.45 … 2.01 2.07 2.02 … Analog value (m / s) 1.16 1.2 1.29 1.39 … 2.05 2.06 2.06 …

[0076] According to the Pearson correlation coefficient calculation formula,

[0077]

[0078] In the formula, r is the correlation coefficient, X is the measured value, and Y is the simulated value; the average correlation coefficient value of each measuring point is 0.868, which has strong correlation and good fitting degree;

[0079] S4, result prediction and structure optimization;

[0080] The result prediction and structural optimization include simulating the sedimentation evolution process of the corridor-cabin combined submarine data center during the service life according to the CFD model, comparing the evolution simulation results of the two construction areas, selecting an area where the seabed topography changes less after sediment scouring and sedimentation as the target construction area, and according to the evolution simulation results of the target construction area, for areas where the seabed terrain becomes lower after sediment scouring, it is necessary to increase the pile length when designing the pile foundation. For areas where sediment siltation buries the cabins, part of the cabins with the original length direction perpendicular to the corridor are adjusted and arranged at an angle of 45°, with the tail of the cabin biased toward the downstream direction of the main flow direction of seawater. In areas with a large burial depth, a high-rise building is set under the cabin.

[0081] In summary, the analysis method of the scouring and silting evolution of the corridor-cabin combined submarine data center of the present invention can improve the stability and durability of the structural pile foundation and reduce the maintenance cost of clearing silt in the field of underwater data center design.

[0082] It should be understood that the above-mentioned embodiments are one or more embodiments of the present invention, and there are many other embodiments and variations thereof based on the present invention; the variations and modifications made by ordinary technicians in this industry through the present invention without making pioneering innovations all fall within the scope of protection of the present invention.

Claims

1. A method for analyzing the evolution of erosion and siltation of a corridor-cabin combined submarine data center, characterized in that The specific steps include: S1. Collect data. The data collection includes planning more than two corridor-cabin combined submarine data center construction areas, arranging n measuring points in the construction area at intervals of ΔT to collect sea area characteristic data, hydrodynamic parameters, meteorological data and bottom composition parameters within a time period of T; the sea area characteristic data includes soil type and high-precision seabed topography data, the hydrodynamic parameters include sea surface wave height, period and seabed water flow velocity and direction, the meteorological data includes wind speed and wind direction, and the bottom composition parameters include sediment particle size, fluid density, and sediment mixed density; S2, calculation of model parameters; The calculation of model parameters includes calculating the critical Shields parameter according to the Soulsby-Whitehouse formula, In the formula, R * is the dimensionless parameter of the median particle size of sediment; the dimensionless parameter of the median particle size of sediment is calculated according to the following formula: Where, d 50 is the median particle size of sediment, ρ f is the fluid density, ρ s is the density of sediment mixture, g is the acceleration due to gravity, μ f is the fluid dynamic viscosity; further considering the effect of the seafloor slope angle on sediment deposition, the modified critical Shields parameter is obtained: In the formula, is the sediment repose angle, α is the angle between the velocity vector and the seabed surface, and β is the angle between the seabed surface and gravity; It also includes the use of the Mastbergen formula to calculate the conversion of seabed sediment into suspended sediment by water flow. Where k is the sediment narrow band coefficient, n s is the normal vector of the seabed surface, d * is the dimensionless particle size of the sediment particles, d s is the sediment particle size, θ s is the local Shields number of the seabed sediment; the dimensionless particle size of the sediment particles is calculated according to the following formula: The local Shields number of the seabed sediment is calculated according to the following formula: Where τ is the local shear stress of the seabed; part of the suspended sediment settles under the action of gravity and turns into deposited sediment. The sediment settling velocity is calculated as follows: Where, ν f is the kinematic viscosity of the fluid; It also includes the calculation of bed load movement of sediment particles in water flow based on the bed load transport velocity single width volume sediment transport formula. φ=β MPM (i i -θ cr ) 1.5 c b (8) Where φ is the bed load transport rate per unit width, β MPM is the bed load coefficient, c b is the sediment volume fraction; S3, model building and verification; The model establishment and verification includes obtaining the structural dimension data of the corridor-cabin combined submarine data center, establishing a CFD model according to the structural dimension data and submarine topography data, inputting the submarine roughness and the model parameters into the CFD model, and then performing grid division, and comparing the measured value and the simulated value of the flow velocity and flow direction under the influence of the submarine data center, and using the Pearson correlation coefficient to characterize the accuracy of the model; S4, result prediction and structure optimization; The result prediction and structural optimization include simulating the silt deposition evolution process during the service life of the corridor-cabin combined submarine data center according to the CFD model, comparing the evolution simulation results of each construction area, selecting an area where the seabed topography changes less after silt scouring and silt deposition as the target construction area, and according to the evolution simulation results of the target construction area, for areas where the seabed topography becomes lower after silt scouring, it is necessary to increase the pile length when designing the pile foundation, and for areas where silt silt buries the cabin, it is necessary to optimize the structural size and the structural form of the corridor-cabin combined submarine data center to obtain a smaller silt silt burial depth, and for areas where the cabin burial depth is still large after optimization, a high-rise is set at the bottom of the cabin, and the height of the high-rise is the burial depth.

2. The method for analyzing the evolution of scouring and silting of a corridor-cabin combined submarine data center according to claim 1 is characterized by: In step S2, the sediment particle size is obtained by using a sediment particle analysis device, which includes a particle size distribution detector, a particle data processor and a memory. A seabed sediment sample is placed in the particle size distribution detector to obtain a particle size distribution curve of the sediment. The particle data processor reads the sediment particle size distribution curve, automatically analyzes and obtains the sediment median particle size, the dimensionless parameters of the sediment median particle size and the dimensionless particle size of the sediment particles, and stores them in the memory.

3. The method for analyzing the evolution of scouring and silting of a corridor-cabin combined submarine data center according to claim 1 is characterized by: In step S2, the value range of the bed load coefficient is 8 to 13. The specific value-taking process includes establishing a CFD three-dimensional model without a seabed data center structure based on the seabed topography data, setting the initial bed load coefficient to 8, and implementing step S3 with other conditions unchanged. Then, the model is run to simulate the evolution process of seabed scouring and silting within a certain time scale, and the evolution simulation results are compared with the changes in the measured seabed topography data during the T / ΔT period. The bed load coefficient is adjusted to make the model more accurately reflect the actual changes in sediment scouring and silting.

4. The method for analyzing the evolution of scouring and silting of a corridor-cabin combined submarine data center according to claim 1 is characterized by: In step S2, the sediment volume fraction is obtained by measuring the weight and volume of the sediment by on-site sampling and then calculating its volume percentage in the water body.

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