A recent mathematical technique of homotopy perturbation method (HPM) for solving nonlinear differential equations has been applied in this paper for the analysis of steady-state heat transfer in an annular fin with temperature-dependent thermal conductivity and with the variation of thermogeometric fin parameters. Excellent benchmark agreement indicates that this method is a very simple but powerful technique and practical for solving nonlinear heat transfer equations and does not require large memory space that arises out of discretization of equations in numerical computations, particularly for multidimensional problems. Three conditions of heat transfer, namely, convection, radiation, and combined convection and radiation, are considered. Dimensionless parameters pertinent to design optimization are identified and their effects on fin heat transfer and efficiency are studied. Results indicate that the heat dissipation under combined mode from the fin surface is a convection-dominant phenomenon. However, it is also found that, at relatively high base temperature, radiation heat transfer becomes comparable to pure convection. It is worth noting that, for pure radiation condition, the dimensionless parameter of aspect ratio (AR) of a fin is a more desirable controlling parameter compared to other parameters in augmenting heat transfer rate without much compromise on fin efficiency.

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Swirl-assisted distributed combustion was examined using a deep learning framework. High intensity distributed combustion was fostered from a 5.72 MW/m3-atm thermal intensity swirl combustor (with methane fuel at equivalence ratio 0.9) by diluting the flowfield with carbon dioxide. Dilution of the flowfield caused reduction of global oxygen (%) content of the inlet mixture from 21% to 16% (in distributed combustion). The adiabatic flame temperature gradually reduced, resulting in decreased flame luminosity and increased flame thermal field uniformity. Gradual reduction of flame chemiluminescence was captured using high-speed imaging without any spectral filtering at different oxygen concentration (%) levels to gather the data input. Convolutional neural network (CNN) was developed from these images (with 85% of total data used for training and 15% for testing) for flames at O2 = 16, 18, 19, and 21%. Hyperparameters were varied to optimize the model. New flame images at O2 = 20% and 17% were introduced to verify the image recognition capability of the trained model in terms of training image data. The results showed good promise of developed deep learning-based convolutional neural network or machine learning neural network for efficient and effective recognition of distributed combustion regime.

One of the major challenges in the development of micro-combustors is heat losses that results in flame quenching, and reduced combustion efficiency and performance. In this work, a novel thermal barrier coating (TBC) using hexagonal boron nitride (h-BN) nanosheets as building blocks was developed and applied to a Swiss roll micro-combustor for determining its heat losses with increased temperatures inside the combustor that contributes to improved performance. It was found that by using the h-BN TBC, the combustion temperature of the micro-combustor increased from 850K to 970K under the same thermal loading and operational conditions. This remarkable temperature increase using the BN TBC originated from its low cross-plane thermal conductivity of 0.4 W m-1 K-1to mitigate the heat loss from the micro-combustor plates. Such a low thermal conductivity in the h-BN TBC is attributed to its interfacial resistance between the nanosheets. The development of h-BN TBC provides an effective approach to improve thermal management for performance improvements of gas turbine engines, rocket engines and all various kinds of micro-combustors.