The integrity of the flange sealing face is fundamental to the sealing performance of a connection system. However, during long‑term industrial service, flange sealing faces inevitably suffer from various forms of damage, including media corrosion, mechanical scoring, and thermal cycling fatigue. For a long time, engineering practice has relied mainly on visual inspection and personal experience to judge sealing face damage, lacking systematic quantitative assessment methods. In recent years, with the introduction of probabilistic statistical methods and computational techniques, this situation is undergoing profound change.
The sources of flange sealing face damage are diverse. In petrochemical plants, solid particles entrained in the process medium can cause erosion wear on the sealing face; sulfur‑ or chlorine‑containing media may induce local pitting; improper handling during installation can lead to scratches; and repeated thermal cycles can produce fatigue micro‑cracks on the sealing face. Although these damage forms manifest differently at the macro level, they all ultimately alter the surface topography of the sealing face, increase the leakage pathways at the contact interface, and reduce sealing reliability.
Traditionally, assessment of flange sealing face damage has depended mainly on visual inspection and dimensional measurement by inspectors. While intuitive, this approach fails to quantify the relationship between the degree of damage and the leakage risk. More critically, most existing leakage rate prediction models assume the sealing face is in perfect condition and do not incorporate sealing face damage as a significant variable. As a result, even when visible damage exists, it is difficult to accurately predict its actual impact on sealing performance.
In recent years, researchers have attempted to combine seepage models with probabilistic statistical methods to establish a quantitative assessment framework for sealing face damage. By introducing geometric parameters such as dimensionless damage angle, average damage depth, and radial projection of damage, a leakage rate calculation formula under damaged conditions has been developed. On this basis, the Markov Chain Monte Carlo (MCMC) method is used to perform probabilistic assessment of the damage state, enabling a transition from qualitative judgement of “whether damage exists” to quantitative analysis of “damage extent and associated leakage risk.”
The core value of this approach lies in quantifying the uncertainty of sealing face damage. The development of sealing face damage is inherently stochastic and progressive—the location, propagation rate, and final morphology of corrosion are influenced by multiple interacting factors and cannot be accurately described by a deterministic model. The MCMC method, by constructing a state‑transition probability matrix, can effectively handle such uncertainty and provide a probability distribution of damage severity rather than a single numerical value.
From an engineering application perspective, quantitative assessment of sealing face damage provides a more scientific basis for maintenance decisions regarding flange connections. In areas such as turnaround scheduling, flange replacement decisions, and risk classification, data‑based evaluation methods are more persuasive than pure experience. With continued development of relevant technologies and accumulation of engineering validation, quantitative assessment of sealing face damage is expected to become an important technical tool in flange integrity management.
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