Design method of ship repairing and building welding workshop dust removal system based on computational fluid mechanics
By using CFD simulation technology, the problems of blind and over-design in the dust removal system design of shipbuilding and repair welding workshops were solved, achieving precise and economical optimization of the dust removal system and ensuring occupational health and safety.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-12-03
- Publication Date
- 2026-04-03
AI Technical Summary
The existing dust removal system design in ship repair and welding workshops lacks precise analysis, resulting in dust removal blind spots, over-design and high modification costs, and the design effect is difficult to verify before installation.
By employing computational fluid dynamics (CFD) simulation technology, through data acquisition, 3D modeling of the workshop, initial diffusion simulation, and scheme comparison, the diffusion and concentration distribution of smoke and dust are accurately predicted, and the optimal dust removal system design scheme is determined.
It enables accurate assessment during the design phase, avoids dust blind spots and over-design, reduces energy consumption and investment costs, improves design success rate, and ensures occupational health and safety.
Smart Images
Figure CN121787307A_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of industrial ventilation and pollution control technology, and in particular to a design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics (CFD). Background Technology
[0002] Welding workshops in shipyards are typically spacious, with multiple dispersed welding stations. The resulting welding fumes permeate and spread throughout the workshop, seriously endangering worker health and production safety. Traditional dust collection system designs often rely on the designer's experience, employing principles of "uniform distribution" or "local reinforcement," lacking precise analysis of airflow organization and fume diffusion paths within the workshop. This experience-based design method has significant drawbacks: First, it easily leads to dust collection blind spots, with some areas exceeding fume concentration standards; second, it may result in over-design, placing unnecessary dust collection equipment in certain areas, increasing initial investment and long-term operating energy consumption; finally, the effectiveness of the design can only be verified through on-site testing after system installation, and if the results are unsatisfactory, the cost of modification is exorbitant. Summary of the Invention
[0003] The purpose of this invention is to provide a scientific, accurate, and quantifiable design method for dust removal systems in shipbuilding and repair welding workshops based on computational fluid dynamics.
[0004] The technical solution adopted by this invention to achieve the above objectives is: a design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics, comprising the following steps: a. Data acquisition: Through on-site measurement and data collection, obtain the building structure data, welding condition data, and average meteorological data of the target welding workshop. b. Workshop 3D modeling: Using 3D modeling software, based on the building structure data, construct a 3D geometric model of the target workshop and export it in a format recognizable by CFD software. c. Initial diffusion simulation: Import the three-dimensional geometric model, welding condition data and average meteorological data into the computational fluid dynamics (CFD) software, set the dust source terms and boundary conditions, and run the simulation to obtain the natural diffusion cloud map of welding dust in the target workshop. d. Compare the solutions and propose at least two dust removal system design schemes. Each scheme includes dust removal equipment of a specific type, quantity and location. Import the three-dimensional model of the dust removal equipment into the three-dimensional geometric model of the target workshop, set its performance parameters in CFD software, and run simulation to predict the concentration distribution of smoke and dust in the workshop after purification under the scheme. e. Compare and determine the optimal solution: Compare and analyze the dust concentration distribution obtained from the simulation of each solution in step d. Taking the dust concentration in most areas of the workshop being lower than the preset safety threshold as the core indicator, and combining equipment energy consumption and cost, determine the optimal dust removal system design solution.
[0005] In step a, the target welding workshop is a workshop that is used for shipbuilding or ship repair, has a complete roof structure, and is planned for indoor welding processes.
[0006] In step a, the building structure data includes the building's length, width, height, column positions, and door and window openings.
[0007] In step a, the welding condition data includes the number of welding stations, their spatial distribution, the simultaneous working rate, and the amount of smoke and dust generated per unit time at each station.
[0008] In step a, the average meteorological data includes the prevailing wind direction and average wind speed for the whole year, which are used to set the ingress boundary conditions for the CFD simulation.
[0009] In step b, the three-dimensional geometric model of the target workshop is modeled at a 1:1 scale with the actual object.
[0010] In steps c and d, the turbulence model used in the CFD software is the k-epsilon model, and the multiphase flow model is the Eulerian-Lagrange discrete phase model (DPM).
[0011] In step d, the dust removal equipment is at least one of a mechanical dust collector, a filter dust collector, a wet dust collector, or an electrostatic precipitator.
[0012] In step e, the preset safety threshold is no higher than 4 mg / m³.
[0013] This invention presents a design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics. By utilizing CFD simulation technology, the effects of different schemes can be predicted during the design phase, thereby finding the optimal solution and realizing the transformation from "experience-based design" to "simulation-driven design," achieving multiple goals such as ensuring occupational health, saving investment, and reducing energy consumption. Attached Figure Description
[0014] Figure 1 This is a general flowchart of the design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics, according to the present invention.
[0015] Figure 2 This is a schematic diagram of the three-dimensional geometric model of the target workshop of the present invention, which is a design method for a dust removal system in a shipbuilding and welding workshop based on computational fluid dynamics.
[0016] Figure 3This is a cloud map of dust concentration distribution obtained from the initial diffusion simulation step of a design method for a dust removal system in a shipbuilding and welding workshop based on computational fluid dynamics, according to the present invention.
[0017] Figure 4 This is a cloud map showing the dust concentration distribution after implementing one of the schemes in the scheme comparison steps of the design method of a dust removal system for a shipbuilding and welding workshop based on computational fluid dynamics in this invention. Detailed Implementation
[0018] like Figures 1 to 4As shown, the design method for a dust removal system in a shipbuilding or repair welding workshop based on computational fluid dynamics includes the following steps: a) Data acquisition: Through on-site measurements and data collection, obtain the building structure data, welding condition data, and average meteorological data of the target welding workshop. The target welding workshop is a workshop for shipbuilding or repair purposes, with a complete roof structure, and planned for indoor welding operations. The building structure data is obtained from architectural design drawings, including the building's length, width, height, column positions, and door and window openings. The welding condition data includes the number of welding stations, their spatial distribution, simultaneous operation rate, and the amount of dust generated per unit time at each station. According to the survey, not all 60 stations operate simultaneously; the simultaneous operation rate is approximately 60%. Each station uses CO2 gas shielded welding. Based on the welding materials and technical parameters, the dust generation per unit time is estimated. The dust generation rate is 400 mg / s. Average meteorological data, including the prevailing wind direction and average wind speed, are used to set the inlet boundary conditions for the CFD simulation. For example, data obtained from the local meteorological bureau shows that the prevailing wind direction in this area is southeast, with an average wind speed of 1.1 m / s. b) 3D modeling of the workshop: Using 3D modeling software such as SolidWorks and AutoCAD, a 1:1 scale 3D geometric model of the target workshop is constructed based on the building structure data. Using SolidWorks software, the 3D model is meticulously built to the required dimensions, including all doors, windows, and ventilation openings, and saved in .stp format. The model is then exported to a format recognizable by the CFD software. c) Initial diffusion simulation: The 3D geometric model, welding condition data, and average meteorological data are imported into computational fluid dynamics (CFD) software such as ANSYS. Fluent, COMSOL, and other software are used to set dust source terms and boundary conditions based on welding condition data. For example, the inlet wind speed is based on meteorological data, and the outlet is a pressure outlet. Simulations are run to obtain natural diffusion cloud maps of welding fumes within the target workshop. Steady-state or transient simulations are run to visually obtain the natural diffusion patterns and concentration distribution of fumes within the workshop without the intervention of a dust removal system. For example, the model is imported into ANSYS Fluent software, setting the southeast-facing wall and windows as velocity inlets with a wind speed of 1.1 m / s, and the corresponding wall openings as pressure outlets. A geometric point is set at each welding station as a dust source term, with a temperature of 2000 K and a particulate release rate of 400 mg / s. The k-epsilon turbulence model and the Euler-Lagrange DPM discrete phase model are selected for simulation calculations. The simulation results are as follows: Figure 3As shown, a high-concentration dust accumulation zone has formed in the upper-middle area of the workshop, with the highest concentration exceeding 4 mg / m³. d. Scheme comparison is conducted. Based on the initial simulation results, at least two dust removal system design schemes are proposed. Each scheme includes dust removal equipment of a specific type, quantity, and location. The dust removal equipment is at least one of mechanical dust collectors, filter dust collectors, wet dust collectors, or electrostatic precipitators. The three-dimensional model of the dust removal equipment is imported into the three-dimensional geometric model of the target workshop. Its performance parameters are set in CFD software, and simulations are run to predict the performance of the vehicles under the proposed scheme. The concentration distribution of smoke and dust in the workshop after purification was determined. Realistic performance parameters, such as rated airflow and pressure loss, were set for these devices in CFD software. The simulation was run again to obtain the flow field and smoke and dust concentration field within the workshop after each scheme was implemented. For example, Scheme 1: Horizontal flow supply and return air ducts are arranged at a height of 12.0m on both sides of the target area, with a supply air volume of 96,000 m³ / h; diffused flow supply air ducts are arranged at a height of 1.5m on both sides of the target area, with a supply air volume of 224,000 m³ / h; and a return air duct is arranged at a height of 12.0m on the opposite side of the supply air ducts, with a return air volume of 320,000 m³ / h. m³ / h; Option 2: Arrange horizontal flow supply and return air ducts at a height of 9.0m on both sides of the target area, with a supply air volume of 96,000 m³ / h; arrange diffused flow supply air ducts at a height of 1.5m on both sides of the target area, with a supply air volume of 224,000 m³ / h; arrange return air ducts at a height of 7.5m on the opposite side of the supply air ducts, with a return air volume of 160,000 m³ / h; arrange return air ducts at a height of 12.0m on the opposite side of the supply air ducts, with a return air volume of 160,000 m³ / h; input the equipment models and parameters of each option into CFD software for simulation; e. Compare and determine the optimal option: compare and analyze the dust concentration distribution obtained by each option in step d, taking the dust concentration in most areas of the workshop being lower than the preset safety threshold as the core indicator. The preset safety threshold is no higher than 4mg / m³. Combined with equipment energy consumption and cost, determine the optimal dust removal system design scheme, and compare the simulation result cloud maps of the two schemes, such as Figure 4 The report presents the effects and numerical results of Scheme 1: After applying Scheme 1, the average particulate matter concentration in the workshop decreased by 38.5%; the total ventilation volume was 320,000 m³ / h; and the total equipment power was 296 kW. After applying Scheme 2, the average particulate matter concentration in the workshop decreased by 46.7%; the total ventilation volume was 320,000 m³ / h; and the total equipment power was 296 kW. Scheme 2 showed better overall performance, therefore it was ultimately selected as the engineering implementation scheme. The simulation results of each scheme were quantitatively compared. The core evaluation indicator was whether the dust concentration in most areas of the workshop's breathing zone was lower than the national occupational health standard, such as 4 mg / m³. At the same time, the total equipment power, estimated energy consumption, and total investment cost of each scheme were comprehensively compared, and the optimal design scheme with high purification efficiency and good economic performance was finally selected.
[0019] The present invention provides a design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics. This method is precise and scientific, using CFD simulation to accurately predict the diffusion path and concentration distribution of dust, providing a basis for dust removal design and avoiding the blind spots of experience-based design, thus fundamentally eliminating dust removal blind spots. It is also economical and efficient, enabling virtual testing of multiple solutions before physical installation, allowing for the selection of the most cost-effective solution through comparison, avoiding waste from "over-design" and losses from secondary modifications due to "under-design." Furthermore, it is forward-looking and reliable, anticipating and resolving potential problems during the design phase, significantly improving the first-time success rate of the design and shortening the design cycle, providing reliable technical protection for occupational health and safety in shipbuilding and repair welding workshops.
Claims
1. A design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics, characterized in that, Includes the following steps: a. Data acquisition: Through on-site measurement and data collection, obtain the building structure data, welding condition data, and average meteorological data of the target welding workshop location; b. Workshop 3D modeling: Using 3D modeling software, based on the building structure data, construct a 3D geometric model of the target workshop and export it in a format recognizable by CFD software. c. Initial diffusion simulation: Import the three-dimensional geometric model, welding condition data and average meteorological data into the computational fluid dynamics (CFD) software, set the dust source terms and boundary conditions, and run the simulation to obtain the natural diffusion cloud map of welding dust in the target workshop. d. Compare the solutions and propose at least two dust removal system design schemes. Each scheme includes dust removal equipment of a specific type, quantity and location. Import the three-dimensional model of the dust removal equipment into the three-dimensional geometric model of the target workshop, set its performance parameters in CFD software, and run simulation to predict the concentration distribution of smoke and dust in the workshop after purification under the scheme. e. Compare and determine the optimal solution: Compare and analyze the dust concentration distribution obtained from the simulation of each solution in step d. Taking the dust concentration in most areas of the workshop being lower than the preset safety threshold as the core indicator, and combining equipment energy consumption and cost, determine the optimal dust removal system design solution.
2. The design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics as described in claim 1, characterized in that: In step a, the target welding workshop is a workshop that is used for shipbuilding or ship repair, has a complete roof structure, and is planned for indoor welding processes.
3. The design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics as described in claim 1, characterized in that: In step a, the building structure data includes the building's length, width, height, column positions, and door and window openings.
4. The design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics as described in claim 1, characterized in that: In step a, the welding condition data includes the number of welding stations, their spatial distribution, the simultaneous working rate, and the amount of smoke and dust generated per unit time at each station.
5. The design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics according to claim 1, characterized in that: In step a, the average meteorological data includes the prevailing wind direction and average wind speed for the whole year, which are used to set the inlet boundary conditions for the CFD simulation.
6. The design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics according to claim 1, characterized in that: In step b, the three-dimensional geometric model of the target workshop is modeled at a 1:1 scale with the actual object.
7. The design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics according to claim 1, characterized in that: In steps c and d, the turbulence model used in the CFD software is the k-epsilon model, and the multiphase flow model is the Eulerian-Lagrange discrete phase model (DPM).
8. The design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics according to claim 1, characterized in that: In step d, the dust removal equipment is at least one of a mechanical dust collector, a filter dust collector, a wet dust collector, or an electrostatic precipitator.
9. The design method for a dust removal system in a shipbuilding and repair welding workshop based on computational fluid dynamics according to claim 1, characterized in that: In step e, the preset safety threshold is no higher than 4 mg / m³.