Probabilistic structural and thermal analysis of a gasketed flange

Performance of a flange joint is characterized mainly by its ‘strength’ and ‘sealing capability’. A number of analytical and experimental studies have been conducted to study these characteristics under internal pressure loading. However, with the advent of new technological trends for high temperature and pressure applications, an increased demand for analysis is recognized. The effect of steady-state thermal loading makes the problem more complex as it leads to combined application of internal pressure and temperature. Structural and thermal analysis of a gasketed flange was computationally simulated by a finite element method and probabilistically evaluated in view of the several uncertainties in the performance parameters. Cumulative distribution functions and sensitivity factors were computed for Stress Intensities and Von Mises Stresses due to the structural and thermodynamic random variables. These results can be used to quickly identify the most critical design variables in order to optimize the design and make it cost effective. The analysis leads to the selection of the appropriate measurements to be used in structural and heat transfer analysis and to the identification of both the most critical measurements and parameters. Conventional engineering design methods are generally deterministic. But in reality, many engineering systems are stochastic in nature where a probability assessment of the results becomes a necessity. This probabilistic engineering design analysis assumes probability distributions of design parameters, instead of mean values only. This enables the designer to design for a specific reliability and hence maximize safety and quality and cost. In the present work, thermal and structural analysis on the flange was performed to obtain the areas of maximum stress under the given boundary conditions. The product was modeled and then simulated using the Finite Element Analysis (FEA). The results obtained were probabilistically evaluated for optimum design.