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UID:7@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20230821T100000
DTEND;TZID=Asia/Kolkata:20230821T110000
DTSTAMP:20231102T045936Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-colloquium-cds-a-
 co-kurtosis-tensor-based-featurization-for-scalable-combustion-simulations
 /
SUMMARY:M.Tech Research Thesis {Colloquium}: CDS : “A co-kurtosis tensor 
 based featurization for scalable combustion simulations.”
DESCRIPTION:DEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\nM.Tech Research 
 Thesis Colloquium\n\n_____________________________________________________
 _____________________________________\n\nSpeaker: Mr. Dibya Jyoti Nayak\n\
 nS.R. Number: 06-18-01-10-22-21-1-19747\n\nTitle: “A co-kurtosis tensor 
 based featurization for scalable combustion simulations.“\n\nResearch Su
 pervisor: Dr. Konduri Aditya\n\nDate &amp\; Time: August 21\, 2023 (Monday
 ) at 10:00 AM\n\nVenue: # 102 CDS Seminar Hall\n__________________________
 ________________________________________________________________\nAbstract
 \nIdentifying low-dimensional representations of the thermo-chemical state
  space for turbulent reacting flow systems is vitally important\, primaril
 y to significantly reduce the computational cost of device-scale combustio
 n simulations. Moreover\, these simulations are often performed to gain fu
 ndamental insights into the inception of extreme/anomalous events such as 
 flashbacks\, flame extinction\, blow-offs\, thermoacoustic instabilities\,
  etc.\, which can have detrimental effects on combustion efficiency and en
 gine performance. With the scale of scientific investigations ever increas
 ing\, the need for robust anomaly detection methods becomes increasingly c
 ritical for judicious steering of these simulations and also aiding smooth
  operations of practical engines. Recent studies have shown that the fourt
 h-order joint statistical moment tensor\, i.e.\, co-kurtosis\, effectively
  captures anomalies/outliers in scientific data. Accordingly\, the primary
  objective of this work centers around leveraging the unique properties of
  the co-kurtosis tensor to drive low-cost and scalable combustion simulati
 ons and build robust algorithms for extreme event detection. Particularly\
 , the first part of this work develops tools for dimensionality reduction 
 for chemistry\, while the second part focuses on employing a co-kurtosis b
 ased detection algorithm for capturing extreme events such as flame instab
 ilities in hydrogen-fired reheat burners relevant to sequential gas turbin
 e engines.\n\nTo obtain low-dimensional manifolds (LDMs) that describe the
  original thermo-chemical state\, principal component analysis (PCA) and i
 ts variants are widely employed. An alternative dimensionality reduction t
 echnique that focuses on higher order statistics\, co-kurtosis PCA (CoK-PC
 A)\, has been shown to provide an optimal LDM for effectively capturing th
 e stiff chemical dynamics associated with spatiotemporally localized react
 ion zones. While its effectiveness has only been demonstrated based on a p
 riori analyses with linear reconstruction\, in this work\, we employ nonli
 near techniques to reconstruct the full thermo-chemical state and evaluate
  the efficacy of CoK-PCA compared to PCA. Specifically\, we combine a CoK-
 PCA-/PCA-based dimensionality reduction (encoding) with an artificial neur
 al network (ANN) based reconstruction (decoding) and examine\, a priori\, 
 the reconstruction errors of the thermo-chemical state. We employ three co
 mbustion test cases representing varying degrees of complexity in the geom
 etrical domain\, combustion regimes\, ignition kinetics\, etc.\, to assess
  CoK-PCA/PCA coupled with ANN-based reconstruction. Results from the analy
 ses demonstrate the robustness of the CoK-PCA based LDM with ANN reconstru
 ction in accurately capturing the data\, specifically from the reaction zo
 nes.\n\nHydrogen’s highly reactive and diffusive nature towards decarbon
 ization is prone to flashbacks\, flame instabilities\, and thermoacoustic 
 instabilities. For example\, in the case of reheat burners of hydrogen-fir
 ed sequential gas turbine engines\, intermittent temperature and pressure 
 fluctuations result in flame instabilities\, such as intermittent autoigni
 tion events at off-design locations that can adversely impact the engine
 ’s performance. To address this issue\, we develop an unsupervised learn
 ing methodology based on the co-kurtosis tensor to detect the early onset 
 of spontaneous ignition kernels in lean premixed hydrogen combustion at vi
 tiated conditions. The accuracy of the model is evaluated for various igni
 tion test cases.\n\n======================================================
 =========================\n\nALL ARE WELCOME
CATEGORIES:Events,Ph.D. Thesis Colloquium
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