The structure of methane/air tubular diffusion flames with 65 % fuel dilution by either CO2 or N2 is numerically investigated as a function of pressure. As pressure is increased, the reaction zone thickness reduces due to decrease in diffusivities with pressure. The flame with CO2-diluted fuel exhibits much lower nitrogen radicals (N, NH, HCN, NCO) and lower temperature than its N2-diluted counterpart. In addition to flame structure, NO emission characteristics are studied using analysis of reaction rates and quantitative reaction pathway diagrams (QRPDs). Four different routes, namely the thermal route, Fenimore prompt route, N2O route, and NNH route, are examined and it is observed that the Fenimore prompt route is the most dominant for both CO2- and N2-diuted cases at all values of pressure followed by NNH route, thermal route, and N2O route. This is due to low temperatures (below 1900 K) found in these highly diluted, stretched, and curved flames. Further, due to lower availability of N2 and nitrogen bearing radicals for the CO2-diluted cases, the reaction rates are orders of magnitude lower than their N2-diluted counterparts. This results in lower NO production for the CO2-diluted flame cases.

Surrogate models often provide an effective tradeoff between accuracy and efficiency during reliability analysis with expensive physics models. In snap-through buckling reliability analysis, a surrogate model could be built for the critical buckling load, as a function of loading, material properties, geometry, and boundary conditions. However, in the presence of spatiotemporal variability, the response surface of the critical buckling load is often highly nonlinear and irregular, thus rendering commonly used response surface-type surrogate modeling strategies ineffective. This paper proposes a new buckling reliability analysis method based on support vector machines for structures subjected to spatiotemporal variability and in the presence of epistemic uncertainty regarding model inputs and parameters. Bayesian calibration is first used to quantify the epistemic uncertainty in the modeling of spatiotemporal variability under limited data. Upon the modeling of spatiotemporal variability and epistemic uncertainty, a time-dependent reliability analysis method is developed for the snap-through buckling failure by constructing a nonlinear support vector machine classifier. Considering that the computer simulation is computationally expensive and the support vector machine classifier may not be well trained due to limited computational resources, a method is also developed to quantify the uncertainty in the reliability estimate due to classification uncertainty. A curved beam with an uncertain boundary condition, spatially varying cross-section geometry, and spatiotemporally varying loading is used to demonstrate the effectiveness of the proposed method.