An analytical method for the evolution of erosion and siltation in a corridor-shelter combined submarine data center

Abstract

This invention provides a method for analyzing the evolution of scouring and silting in a combined corridor-cabin submarine data center, applicable to the field of underwater data center design. The method involves data collection, model parameter calculation, model establishment and verification, result prediction, and structural optimization. By selecting appropriate parameters to model and analyze the evolution of scouring and silting in a submarine data center, the method obtains the seabed topography that evolves over time due to scouring and silting during the submarine data center's lifespan. This allows targeted reinforcement of pile foundations and structural optimization based on these results. Furthermore, data from different sea areas can be collected. Based on the simulation results of the seabed topography evolution in these different sea areas, the method selects areas where changes in seabed topography after scouring and silting are minimal for submarine data center deployment. This method maximizes the stability and durability of the pile foundation and reduces the maintenance costs of clearing silt.

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-shelter combination submarine data center is an innovative data center construction solution. By connecting multiple data shelters with a submarine corridor, it facilitates maintenance and repair of the shelters, improves the concentration of submarine data centers, and effectively reduces construction and operating costs. However, the design and deployment of this new type of data center also faces a series of technical and environmental challenges.

[0003] The deployment of submarine data centers significantly increases the water-blocking surface area in certain areas of the seabed, accelerating currents and potentially forming eddies in certain areas. This flow field change not only impacts the submarine ecosystem but also threatens the stability of the data centers themselves. Accelerated currents and eddies can cause scouring of the foundations of submarine data centers, especially those with pile foundations. Scouring the seabed can reduce the bearing capacity of the piles and increase the risk of structural instability.

[0004] At the same time, sediment accumulation on the seabed can also cause data centers to become covered in silt, particularly the shelters that house data servers. These servers require constant cooling and dissipation from flowing seawater. If these shelters are covered in silt, the risk of server overheating increases, seriously impacting the normal operation of the data center. Regular cleaning costs associated with silt accumulation are high. Summary of the Invention

[0005] The purpose of this invention is to improve the stability and durability of the structure and reduce maintenance costs. 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 silt deposition on the data center. The method can be widely applied 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 the construction of two or more corridor-shelter combined submarine data centers, arranging n measuring points within the construction area at intervals ΔT to collect sea area characteristic data, hydrodynamic parameters, meteorological data, and bottom sediment composition parameters within a time period T; the sea area characteristic data includes soil type and high-precision seabed topography data, the hydrodynamic parameters include sea surface wave height and period, and seabed water flow velocity and direction, the meteorological data includes wind speed and direction, and the bottom sediment composition parameters include sediment particle size, fluid density, and sediment mixed density;

[0009] S2, calculate model parameters;

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

[0011]

[0012] Where 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 of gravity, μ f is the fluid dynamic viscosity; further considering the influence of the seabed slope angle on sediment deposition, the modified critical Shields parameter is obtained,

[0015]

[0016] Where, 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 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 to the seabed surface, d * is the dimensionless particle size of sediment particles, d s is the sediment particle size, θ s is the local Shields number of 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 validation;

[0030] The model establishment and verification includes obtaining structural dimension data of the corridor-shelter combined submarine data center, establishing a CFD model based on the structural dimension data and submarine topography data, inputting the submarine roughness and the model parameters into the CFD model, and then performing meshing. The flow velocity and flow direction under the influence of the submarine data center are compared with the measured and simulated values, and the Pearson correlation coefficient is used to characterize the accuracy of the model.

[0031] S4, result prediction and structure optimization;

[0032] The result prediction and structural optimization include simulating the sedimentation evolution process of the corridor-cubic-cabin combined submarine data center during its service life according to the CFD model, comparing the evolution simulation results of each construction area, 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 accumulation buries the cubicles, it is necessary to optimize the structural dimensions and the structural form of the corridor-cubic-cabin combined submarine data center to obtain a smaller sediment accumulation burial depth; for areas where the cubicles still have a larger burial depth after optimization, a raised floor is set at the bottom of the cubicles, and the height of the raised floor is the burial depth.

[0033] As a preferred technical solution of the present invention, in step S2, the sediment particle size is obtained 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 the sediment 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.

[0034] As a preferred technical solution of the present invention, in step S2, the bed load coefficient has a value range of 8 to 13. The specific value selection process includes establishing a CFD three-dimensional model of 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 deposition 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 deposition.

[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 through 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 evolution process of scouring and silting of the submarine data center, the submarine topography after the evolution of silt scouring and silting over time during the service life of the submarine data center is obtained, and the pile foundation can be strengthened and the structure optimized in a targeted manner 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 silting changes 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 according to the present invention. DETAILED DESCRIPTION

[0038] The following describes in detail specific embodiments of the present invention in conjunction with the accompanying drawings. It should be understood that the specific embodiments provided herein are intended only to illustrate and explain the present invention and are not intended 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. However, the present invention may also have other embodiments and variations thereof. Therefore, the scope of protection 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 a combined corridor-shelter 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 with an interval of ΔT=3h to collect sea area characteristic data, hydrodynamic parameters, meteorological data and bottom sediment composition parameters within T=365*24h; the sea area characteristic data include soil type and high-precision seabed topography data, the hydrodynamic parameters include sea surface wave height and period, and seabed water flow velocity and direction, the meteorological data include wind speed and direction, and the bottom sediment composition parameters include sediment particle size, fluid density, and sediment mixed density;

[0042] The sediment particle size is obtained 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, the dimensionless parameters of the sediment median particle size and the dimensionless particle size of the sediment particles, and stores them in the memory.

[0043] S2, calculate model parameters;

[0044] The flow of incompressible fluid is described as follows based on the Navier-Stokes equations, 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. 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 acceleration due to gravity 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 governing equation is as follows:

[0048]

[0049] Where 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. D 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 based on the Soulsby-Whitehouse formula.

[0051]

[0052] Where 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 of gravity, μ f is the fluid dynamic viscosity; further considering the influence of the seabed slope angle on sediment deposition, the modified critical Shields parameter is obtained,

[0055]

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

[0057] It also includes the use of 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 to the seabed surface, d * is the dimensionless particle size of sediment particles, θ s is the local Shields number of 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 bed load coefficient has a value range of 8 to 13. The specific value selection 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 of seabed scouring and deposition within a certain time scale. The evolution simulation results are compared with the changes in the measured seabed topography data for T / ΔT=2920 periods, and the bed load coefficient is adjusted to make the model more accurately reflect the actual changes in sediment scouring and deposition.

[0070] The sediment volume fraction is obtained by measuring the weight and volume of sediment through 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 validation;

[0072] Obtain structural dimension data for a combined corridor-cabin submarine data center. The corridor is a rectangular parallelepiped structure measuring 100 meters in length, 6 meters in width, and 5 meters in height. The two upper corners of the corridor's cross section are chamfered with a radius of 1 meter. Cabins measuring 12 meters in length, 5 meters in width, and 5 meters in height are arranged 10 meters apart on either side. The length of the cabins is perpendicular to the length of the corridor, and the two upper corners of the cabin cross section are chamfered with a radius of 1 meter. A CFD model is established based on the structural dimension data and submarine topography data. The model parameters are input into the CFD model, and then meshing is performed. The measured and simulated values ​​of the flow velocity and flow direction under the influence of the submarine data center are compared, and 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] Where r is the correlation coefficient, X is the measured value, and Y is the simulated value. The average correlation coefficient of each measuring point is 0.868, which indicates strong correlation and good fitting degree.

[0079] S4, result prediction and structure optimization;

[0080] The result prediction and structural optimization include simulating the evolution process of silt deposition during the service life of the corridor-shelter combined submarine data center based on a CFD model, comparing the evolution simulation results of the two construction areas, 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. For areas where silt deposition buries the cabins, some of the cabins whose original length is perpendicular to the corridor are adjusted and arranged at an angle of 45 degrees, with the tail of the cabin tilted toward the downstream direction of the main flow direction of seawater. In some areas with a large burial depth, a high-rise building is set under the cabin.

[0081] In summary, the analysis method for the evolution of scouring and silting in a combined corridor-cabin 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 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 groundbreaking innovations all fall within the scope of protection of the present invention.

Claims

1. A method for analyzing the evolution of erosion and siltation in a corridor-shelter combined submarine data center, characterized in that The specific steps include: S1. Collect data; The data collection includes planning the construction of two or more corridor-shelter combined submarine data centers, arranging n measuring points within the construction area at intervals ΔT to collect sea area characteristic data, hydrodynamic parameters, meteorological data, and bottom sediment composition parameters within a time period T; the sea area characteristic data includes soil type and high-precision seabed topography data, the hydrodynamic parameters include sea surface wave height and period, and seabed water flow velocity and direction, the meteorological data includes wind speed and direction, and the bottom sediment composition parameters include sediment particle size, fluid density, and sediment mixed density; S2, calculate model parameters; The calculation of model parameters includes calculating the critical Shields parameter according to the Soulsby-Whitehouse formula, Where 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 of gravity, μ f is the fluid dynamic viscosity; further considering the influence of the seabed slope angle on sediment deposition, the modified critical Shields parameter is obtained, Where, 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 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 to the seabed surface, d * is the dimensionless particle size of sediment particles, d s is the sediment particle size, θ s is the local Shields number of 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 validation; The model establishment and verification includes obtaining structural dimension data of the corridor-shelter combined submarine data center, establishing a CFD model based on the structural dimension data and submarine topography data, inputting the submarine roughness and the model parameters into the CFD model, and then performing meshing. The flow velocity and flow direction under the influence of the submarine data center are compared with the measured and simulated values, and the Pearson correlation coefficient is used to characterize the accuracy of the model. S4, result prediction and structure optimization; The result prediction and structural optimization include simulating the sedimentation evolution process of the corridor-cubic-cabin combined submarine data center during its service life according to the CFD model, comparing the evolution simulation results of each construction area, 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 topography becomes lower after sediment scouring, it is necessary to increase the pile length when designing the pile foundation; for areas where sediment accumulation buries the cubicles, it is necessary to optimize the structural dimensions and the structural form of the corridor-cubic-cabin combined submarine data center to obtain a smaller sediment accumulation burial depth; for areas where the cubicles still have a larger burial depth after optimization, a raised floor is set at the bottom of the cubicles, and the height of the raised floor is the burial depth.

2. The method for analyzing the evolution of erosion and siltation in a combined corridor-shelter submarine data center according to claim 1 is characterized by: In step S2, the sediment particle size is obtained 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 parameter 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 erosion and siltation in a combined corridor-shelter submarine data center according to claim 1 is characterized by: In step S2, the bed load coefficient has a value range of 8 to 13. The specific value selection 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 deposition 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 deposition.

4. The method for analyzing the evolution of erosion and siltation in a combined corridor-shelter 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 through on-site sampling and then calculating its volume ratio in the water body.

Images