Abstract

A three-dimensional direct numerical simulation (DNS) is performed for a turbulent hydrogen-air flame, represented with detailed chemistry, stabilized in a model gas-turbine combustor. The combustor geometry consists of a mixing duct followed by a sudden expansion and a combustion chamber, which represents a geometrically simplified version of Ansaldo Energia's GT26/GT36 sequential combustor design. In this configuration, a very lean blend of hydrogen and vitiated air is prepared in the mixing duct and convected into the combustion chamber, where the residence time from the inlet of the mixing duct to the combustion chamber is designed to coincide with the ignition delay time of the mixture. The results show that when the flame is stabilized at its design position, combustion occurs due to both autoignition and flame propagation (deflagration) modes at different locations within the combustion chamber. An unstable operating condition is also identified, wherein periodic auto-ignition events occur within the mixing duct. These extreme events appear upstream of the intended stabilization position, due to positive temperature fluctuations induced by pressure waves originating from within the combustion chamber. The present DNS investigation represents the initial step of a comprehensive research effort aimed at gaining detailed physical insight into the rate-limiting processes that govern the sequential combustor behavior and avoid the insurgence of the off-design auto-ignition events.

With the increasing availability of data, machine/deep learning methods are becoming an important tool for modeling, analysis and prediction of several phenomena. In the scientific domain relevant to turbulent flows, there is an extensive research in the use of these methods for model development, flow control and feature extraction, among others. In this talk, we will present a new anomaly detection method to identify anomalous/extreme events in scientific phenomena, which are often described by multi-scale, multi-variate, smoothly varying data. The method leverages the statistical signature of anomalies hidden in the higher order statistical moments. For multi-variate data, this translates to an examination of the higher order joint moments and their association with anomalies or extreme events. Specifically, we use the direction of the principal vectors obtained from the decomposition of the fourth order joint moment tensor, in characterizing the occurrence of anomalous events. We will use this method to identify the inception of auto-ignition kernels in turbulent premixed combustion.

Biography

Konduri Aditya works as an Assistant Professor and the Arcot Ramachandran Young Investigator in the Department of Computational and Data Science at the Indian Institute of Science, Bengaluru. Prior to this, he was a postdoctoral researcher at the Combustion Research Facility, Sandia National Laboratories. He obtained his PhD from the Department of Aerospace Engineering at the Texas A&M University, College Station. Prior to this, he obtained a master’s degree in aerospace engineering from the Georgia Institute of Technology. He also obtained a master’s degree from JNCASR, Bengaluru, where he was supervised by Prof. Roddam Narasimha for his thesis. Aditya has a Bachelor’s in mechanical engineering from the Osmania University, Hyderabad.