Herein, a 2D, axisymmetric microcombustor with a backward‐facing step and triangular wall fin is considered for enhancement of combustor performance. The outer wall temperature, combustor efficiency, and pressure characteristics of the microcombustor are numerically investigated with a detailed chemical kinetic mechanism and conjugate heat transfer. The proposed microcombustors with variable blockage ratios, wall fin shapes, and locations are analyzed with respect to flow and combustion interaction, along with heat flux exchange between the flame and combustor wall. Combustion performance is also evaluated by means of the amount of heat transferred through the combustor wall, conversion ratio of input chemical energy to exploitable heat, and OH mass fraction distributions. Standard k–ε turbulence along with 9 species and the 19‐step combustion reaction scheme is implemented using the eddy dissipation concept (EDC) model in the numerical study. The results indicate that the wall fin blockage ratio alters the reaction zone and improves combustor performance significantly at lower inlet velocities (4–20 m s−1). The fin location and shape are observed to influence the temperature distribution. However, the higher blockage ratio adversely impacts the combustion characteristics at the higher inlet velocity, that is, 48 m s−1.
Many engineering and industrial constrained optimization problems can be modeled as mixed integer nonlinear programming (MINLP) problems e.g. heat and mass exchange networks, reactor-separator networks, batch plant design and scheduling, flow sheeting etc. The global optima in such problems are ill-conditioned due to the involvement of continuous and discrete variables, nonlinearities and non-convexities.This research work concerns the development of a modified GA and to apply it to find the solutions of several difficult MINLP problems. The modified GA utilizes tournament selection, SBX cross-over, polynomial mutation and variable elitism operators, along with distance based dynamic penalty with anti-distortion. The algorithm has been programmed in MATLAB. Six MINLP problems, which emerged from the optimal design of sequential multi-product batch plants, and considered as difficult ones in literature, were successfully solved. The solutions thus obtained are either comparable or better than those available in literature. The above combination of various schemes in modified GA helps in achieving faster convergence to global optimum with comparatively less violation of constraints; population size required is also less. The effect of various parameters on the convergence to global optimum has also been studied along with setting of various parameters. In future, efforts may be devoted to search proper merging strategy of quality operators for the design of a general purpose and robust GA so as to use it for a variety of engineering, specifically process engineering problems, more effectively.
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