National Center for Combustion Research and Development

Engineering, United States

National Center for Combustion Research and Development

Engineering, United States
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Gnanaprakash K.,National Center for Combustion Research and Development | Chakravarthy S.R.,National Center for Combustion Research and Development | Sarathi R.,Indian Institute of Technology Madras
Combustion and Flame | Year: 2017

Combustion of sandwiches made of alternating laminae of ammonium perchlorate (AP) and a matrix of hydrocarbon binder mixed with fine AP and nano-aluminium particles is considered in the 1–12 MPa pressure range. Companion non-aluminized sandwiches are studied for comparison. The nano-Al particles are produced from the electrical wire explosion process in the size range ∼45 nm, and added to the matrix containing either 5 or 45 µm AP particles. Three categories of middle lamina thicknesses—thin (100–170 µm), intermediate (200–280 µm), and thick (370–480 µm)—are examined in all the sandwiches. High-speed high-magnification imaging of the combustion is performed, and surface profiles and features of quenched sandwiches are investigated, besides burning rate measurements. The sandwich burning rates are compared with those of the corresponding matrices and AP laminae burning alone as well. The results imply the close location of lamina leading edge flames (L-LEFs) over the lamina interface edges, leaving the outer AP laminae inclined steeply relative to the matrix middle lamina, in the nano-aluminized cases relative to the non-aluminized versions. This observation suggests that the L-LEFs control the burning rates of the sandwiches even in the case of the fast burning matrix that contains nano-Al. The non-aluminized sandwiches containing the 5 µm AP show higher burning rates than their 45 µm AP counterparts because of the enhanced heat feedback available to the matrix burning surface from the extended fuel-rich branches of adjacent L-LEFs in the former case. Therefore, the enhancement in burning rate is minimal with the inclusion of nano-Al in sandwiches with smaller fine AP size, whereas the nano-aluminized sandwiches containing larger fine AP particles exhibit such enhancement. These effects affirm previous results on propellants with smaller fine and coarse AP sizes, where the burning rate is not enhanced by nano-Al. © 2017 The Combustion Institute


Mukhopadhyay A.,National Center for Combustion Research and Development | Mukhopadhyay A.,Jadavpur University | Chaudhari R.R.,Jadavpur University | Paul T.,Jadavpur University | And 2 more authors.
Journal of Propulsion and Power | Year: 2013

This paper develops a novel strategy for prediction of lean blowout in gas turbine combustors based on symbolic analysis of time series data from optical sensors, where the range of instantaneous data is partitioned into a finite number of cells and a symbol is assigned to each cell. Depending on the cell to which a data point belongs, a symbolic value is assigned to the data point. Thus, the set of time series data is converted to a symbol string. The (estimated) state probability vector is computed based on the number of occurrence of each symbol over a given time span. For the purpose of detecting lean blowout in gas turbine combustors, the state probability vector obtained at a condition sufficiently away from lean blowout (reference state) is considered as the reference vector. The deviation of the current state vector from the reference state vector is used as an anomaly measure for early detection of lean blowout. The results showed that the rate of change of the anomaly measure with equivalence ratio changed significantly as the system approached lean blowout. This change in slope of the curve was observed approximately at a similar proximity to lean blowout for different operating conditions and, hence, could be used as an early lean blowout precursor. The actual location of the change of slope depended primarily on the choice of reference state. This technique performed satisfactorily over a wide range of premixing. Copyright © 2013 by Achintya Mukhopadhyay, Rajandra R. Chaudhari, Tanoy Paul, Swarnendu Sen and Asok Ray.

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