Soot formation modeling, when incorporated into computational fluid dynamics of industrial devices, can be numerically prohibitive. Nonetheless, there remains a significant push to predict soot formation, so as to aid in environmentally sustainable design. The present work features a redesign of an inexpensive soot estimator, that has been developed and applied to laminar flames with great success. It is much more accurate and efficient than previous versions. The soot estimator consists of a library generated from validated sooting flame models, in which the Lagrangian histories of soot-containing fluid parcels are stored. The library is used in post-processing to estimate soot concentrations. For the first time, the estimator framework is used to predict the entire soot field. Also, important parameters to the estimator technique are analyzed. This work is conducted for nine different sooting ethylene/air coflow diffusion flames. The framework successfully predicts the entire soot field. When the data from many flames were combined into one library based on mixture fraction, temperature, and H 2 histories, it could predict all flames with high accuracy. Finally, two scenarios were considered to assess the framework with an independent set of data, and the predictor presented very good accuracy in capturing soot formation.

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Fetal supraventricular tachyarrithmias are rare in the general population. Nevertheless, the fetus may present with severe heart failure and death. Considering the satisfactory therapeutic response, accurate diagnosis and early treatment of these conditions are extremely important.

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