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CN-121997685-A - Deep sea mining transportation elbow region abrasion prediction method considering ore grain morphology

CN121997685ACN 121997685 ACN121997685 ACN 121997685ACN-121997685-A

Abstract

The invention discloses a deep sea mining transportation elbow region abrasion prediction method considering ore grain morphology, which belongs to the technical field of deep sea mining, and comprises the steps of constructing an irregular three-dimensional ore grain morphology database and an elbow high-precision geometric model; the method comprises the steps of simulating transient interaction of ore grain groups and seawater in an elbow by adopting a numerical method of coupling smooth particle fluid dynamics and discrete element method to obtain collision information, calculating accumulated abrasion depth and positioning a high risk area based on an improved abrasion equation introducing form factors, deploying a sensor monitoring system to collect real-time data, dynamically correcting the form factors by utilizing a machine learning algorithm, realizing model self-adaptive optimization, further predicting abrasion trend and evaluating the residual life of a pipeline. According to the invention, by combining quantitative mineral grain real form influence, high-fidelity coupling simulation and data driving correction, abrasion prediction precision and working condition adaptability are remarkably improved, and closed-loop intelligent management from prediction to maintenance is realized.

Inventors

  • Sun Jiazhao
  • ZOU LI
  • FAN XIANGQIAN
  • HU YINGJIE
  • MA XINYU
  • Liu Jueding

Assignees

  • 南方海洋科学与工程广东省实验室(广州)

Dates

Publication Date
20260508
Application Date
20260212

Claims (10)

  1. 1. The deep sea mining transportation bend zone abrasion prediction method considering the ore grain morphology is characterized by comprising the following steps of: Step 1, constructing an irregular three-dimensional ore grain form database and a high-precision three-dimensional geometric model of a deep sea transportation bent pipe; Step 2, simulating a solid-liquid two-phase transient interaction process of ore grain groups in the irregular three-dimensional ore grain morphology database and a seawater medium in a bent pipe runner by adopting a numerical method of coupling smooth particle fluid dynamics with a discrete element method, and acquiring collision information of the ore grains and a pipe wall; Step 3, calculating the accumulated wear depth of each position of the pipe wall according to the collision information based on an improved wear calculation equation introducing the form factor, and generating a wear distribution map to locate a high risk area; And 4, deploying a sensor monitoring system in a key area of the bent pipe, collecting wall thickness reduction and impact vibration data in real time, comparing the real-time monitoring data with the prediction result in the step 3 by using a machine learning algorithm, dynamically and reversely correcting the form factor to realize self-adaptive optimization of a wear prediction model, predicting the wear development trend based on the corrected model, evaluating the residual service life of the pipeline, and establishing a wear management database.
  2. 2. The method according to claim 1, wherein in the step 1, the irregular three-dimensional ore morphology database is constructed by obtaining three-dimensional geometric data of real deep sea ore particles through three-dimensional laser scanning or CT scanning image reconstruction technology, and dispersing the ore particles into DEM units, and calculating the volume, the surface area, the mass center and the inertia tensor of the ore particles.
  3. 3. The method according to claim 1, wherein in the step 1, when the high-precision three-dimensional geometric model of the deep sea transportation elbow is built, the initial velocity of particles and fluid, the boundary condition of the inlet velocity of the pipeline, the boundary condition of the outlet pressure of the pipeline and the boundary condition of no slip of the pipe wall are set.
  4. 4. The method of claim 1, wherein in the step 2, the fluid motion is solved by adopting a smooth particle fluid dynamics method, comprising a continuity equation and a momentum equation, the motion track of each irregular ore particle under the action of fluid drag force, pressure gradient force, gravity force, buoyancy force and contact force is solved by adopting a discrete element method, and the force and motion interaction between the fluid domain and the particle is realized by a bidirectional coupling mechanism.
  5. 5. The method according to claim 4, wherein the contact force model between the ore particles and the wall surface in the discrete element method comprises a normal contact force and a tangential contact force, and the expressions are respectively: ; ; Wherein, the In order for the normal contact force to be a normal contact force, For the normal spring rate, As a normal damping coefficient, The volume of the stack is such that, For a relative normal velocity, n is the contact normal, In order for the tangential contact force to be a force, In order to achieve a tangential spring rate, In order to be able to achieve a relative tangential velocity, For the tangential damping coefficient, dt is the time step and L T is the tangential spring displacement.
  6. 6. The method according to claim 1, wherein in the step 3, all the collision data output in the step 2 are taken as input, a single collision abrasion amount is calculated by using an improved abrasion calculation equation, and abrasion contributions of all collisions are accumulated on the inner wall surface of the bent pipe through numerical integration, so that an abrasion depth distribution cloud image is generated; The improved wear calculation equation is: ; wherein V is the abrasion volume, Is a form factor, W is an initial wear constant, In order to hit the normal force of the bump, Is the tangential sliding distance.
  7. 7. The method of claim 1, wherein in step 4, the sensor monitoring system comprises an ultrasonic thickness sensor and a vibration acceleration sensor deployed on the outer arch wall of the elbow.
  8. 8. The method according to claim 1, wherein in the step 4, the morphological factor is dynamically and reversely corrected by using a machine learning algorithm in a specific manner that the collision characteristic parameters obtained by the simulation in the step 2 and the predicted wear data calculated in the step 3 are taken as input characteristics, the actual wear data obtained by the real-time monitoring of the sensor is taken as a training target, and the morphological factor is automatically corrected by using an iterative optimization algorithm.
  9. 9. The method according to claim 1, wherein in step 4, numerical simulation is performed on different mining conditions based on the corrected wear prediction model, so as to dynamically predict the time-space evolution law of the wear of the pipe wall, and the remaining service life of the pipe is estimated according to a preset critical wear thickness.
  10. 10. The method according to claim 1, wherein the wear management database established in the step 4 is used for storing time-series monitoring data, simulation parameters and model correction records, and performing retrospective analysis and long-term prediction on the wear trend of the pipeline full life cycle by adopting a data mining or prediction algorithm based on the database.

Description

Deep sea mining transportation elbow region abrasion prediction method considering ore grain morphology Technical Field The invention belongs to the technical field of deep sea mining, and particularly relates to a deep sea mining transportation elbow region abrasion prediction method considering the form of ore particles. Background In deep sea mining operations, the mined minerals are typically transported through long distance pipelines to a surface platform in the form of a solid-liquid two-phase flow. Currently, predictive studies on pipe wear are mostly based on idealized spherical particle assumptions and are estimated using classical wear models (such as the Archard model or its derivatives). The method simplifies ore particles into regular spheres, and predicts the wear rate and distribution of pipe wall materials by calculating parameters such as collision frequency, normal force, sliding distance and the like of the particles and the pipe wall. The model has the advantages of simple form and high calculation efficiency, is applied to the primary engineering design and the abrasion trend analysis to a certain extent, and provides a reference basis for pipeline material selection and structural layout. However, the above-described wear prediction method based on the spherical particle hypothesis has significant limitations. In the actual deep sea mining process, the crushed minerals are in irregular polygonal angles, flaky or rod-shaped forms, and the motion track, rotation characteristic, contact area with the pipe wall and collision energy of the minerals in water flow are obviously different from those of spherical particles. Particularly in a conveying bent pipe area, because the flow direction is changed sharply, secondary flow, vortex shedding and other complex flow structures are easy to generate, irregular ore particles are easy to generate severe collision and scraping under the action of centrifugal force, and the local abrasion is aggravated. The existing model fails to fully consider the influence of ore grain morphology on collision behavior, and also does not effectively incorporate the actual working conditions of deep sea high-pressure, low-temperature environment and high-speed high-concentration turbulent flow, so that the deviation between a predicted result and an actual abrasion condition is large, an abrasion high-risk area is difficult to accurately identify, and the establishment of a pipeline fine design and predictive maintenance strategy is restricted. Disclosure of Invention In order to solve the technical problems, the invention provides a deep sea mining transportation bend area abrasion prediction method considering the form of ore particles, so as to solve the problems in the prior art. In order to achieve the above object, the present invention provides a deep sea mining transportation elbow region abrasion prediction method considering the form of ore particles, comprising: Step 1, constructing an irregular three-dimensional ore grain form database and a high-precision three-dimensional geometric model of a deep sea transportation bent pipe; Step 2, simulating a solid-liquid two-phase transient interaction process of ore grain groups in the irregular three-dimensional ore grain morphology database and a seawater medium in a bent pipe runner by adopting a numerical method of coupling smooth particle fluid dynamics with a discrete element method, and acquiring collision information of the ore grains and a pipe wall; Step 3, calculating the accumulated wear depth of each position of the pipe wall according to the collision information based on an improved wear calculation equation introducing the form factor, and generating a wear distribution map to locate a high risk area; And 4, deploying a sensor monitoring system in a key area of the bent pipe, collecting wall thickness reduction and impact vibration data in real time, comparing the real-time monitoring data with the prediction result in the step 3 by using a machine learning algorithm, dynamically and reversely correcting the form factor to realize self-adaptive optimization of a wear prediction model, predicting the wear development trend based on the corrected model, evaluating the residual service life of the pipeline, and establishing a wear management database. Preferably, in the step 1, the specific way of constructing the irregular three-dimensional ore morphology database is to obtain three-dimensional geometric data of the real deep sea ore particles through a three-dimensional laser scanning or CT scanning image reconstruction technology, and to disperse the ore particles into DEM units, and calculate the volume, the surface area, the mass center and the inertia tensor of the ore particles. Preferably, in the step 1, when the high-precision three-dimensional geometric model of the deep sea transportation elbow is built, the initial speed of particles and fluid, the boundary condition of the inlet speed