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UID:42@cds.iisc.ac.in
DTSTART;TZID=Asia/Kolkata:20240306T103000
DTEND;TZID=Asia/Kolkata:20240306T113000
DTSTAMP:20240227T061300Z
URL:https://cds.iisc.ac.in/events/m-tech-research-thesis-defense-cds-06-ma
 rch-2024-a-co-kurtosis-tensor-based-featurization-of-chemistry-for-scalabl
 e-combustion-simulations/
SUMMARY:M.Tech Research: Thesis Defense: CDS: 06\, March 2024 "A co-kurtosi
 s tensor based featurization of chemistry for scalable combustion simulati
 ons"
DESCRIPTION:\n\nDEPARTMENT OF COMPUTATIONAL AND DATA SCIENCES\n\nM.Tech Res
 earch Thesis Defense\n\n\n\nSpeaker          : Mr. Dibya Jyoti Naya
 k\nS.R. Number  : 06-18-01-10-22-21-1-19747.\nTitle              
   : "A co-kurtosis tensor based featurization of chemistry for scalable c
 ombustion simulations."\nResearch Supervisor : Dr. Konduri Aditya\nDate &a
 mp\; Time  : March 06\, 2024 (Wednesday)\, 10:30 am\nVenue         
      : Room No. 102 (CDS Seminar Hall)\n\n\n\n\nABSTRACT\nIdentifying l
 ow-dimensional representations of the thermo-chemical state space for turb
 ulent reacting flow systems is vitally important\, primarily to significan
 tly reduce the computational cost of device-scale combustion simulations. 
 Moreover\, these simulations are often performed to gain fundamental insig
 hts into the inception of extreme/anomalous events such as flashbacks\, fl
 ame extinction\, blow-offs\, thermoacoustic instabilities\, etc.\, which c
 an have detrimental effects on combustion efficiency and engine performanc
 e. With the scale of scientific investigations ever increasing\, the need 
 for robust anomaly detection methods becomes increasingly critical for jud
 icious steering of these simulations and also aiding smooth operations of 
 practical engines. Recent studies have shown that the fourth-order joint s
 tatistical moment tensor\, i.e.\, co-kurtosis\, effectively captures anoma
 lies/outliers in scientific data. Accordingly\, the primary objective of t
 his work centers around leveraging the unique properties of the co-kurtosi
 s tensor to drive low-cost and scalable combustion simulations and build r
 obust algorithms for extreme event detection. Particularly\, the first par
 t of this work develops tools for dimensionality reduction for chemistry\,
  while the second part focuses on employing a co-kurtosis based detection 
 algorithm for capturing extreme events such as flame instabilities in hydr
 ogen-fired reheat burners relevant to sequential gas turbine engines.\n\nT
 o obtain low-dimensional manifolds (LDMs) that describe the original therm
 o-chemical state\, principal component analysis (PCA) and its variants are
  widely employed. An alternative dimensionality reduction technique that f
 ocuses on higher order statistics\, co-kurtosis PCA (CoK-PCA)\, has been s
 hown to provide an optimal LDM for effectively capturing the stiff chemica
 l dynamics associated with spatiotemporally localized reaction zones. Whil
 e its effectiveness has only been demonstrated based on a priori analyses 
 with linear reconstruction\, in this work\, we employ nonlinear techniques
  to reconstruct the full thermo-chemical state and evaluate the efficacy o
 f CoK-PCA compared to PCA. Specifically\, we combine a CoK-PCA-/PCA-based 
 dimensionality reduction (encoding) with an artificial neural network (ANN
 ) based reconstruction (decoding) and examine\, a priori\, the reconstruct
 ion errors of the thermo-chemical state. We employ three combustion test c
 ases representing varying degrees of complexity in the geometrical domain\
 , combustion regimes\, ignition kinetics\, etc.\, to assess CoK-PCA/PCA co
 upled with ANN-based reconstruction. Results from the analyses demonstrate
  the robustness of the CoK-PCA based LDM with ANN reconstruction in accura
 tely capturing the data\, specifically from the reaction zones.\n\nHydroge
 n's highly reactive and diffusive nature towards decarbonization is prone 
 to flashbacks\, flame instabilities\, and thermoacoustic instabilities. Fo
 r example\, in the case of reheat burners of hydrogen-fired sequential gas
  turbine engines\, intermittent temperature and pressure fluctuations resu
 lt in flame instabilities\, such as intermittent autoignition events at of
 f-design locations that can adversely impact the engine's performance. To 
 address this issue\, we develop an unsupervised learning methodology based
  on the co-kurtosis tensor to detect the early onset of spontaneous igniti
 on kernels in lean premixed hydrogen combustion at vitiated conditions. Th
 e accuracy of the model is evaluated for various ignition test cases.\n\n\
 n\nALL ARE WELCOME
CATEGORIES:Events,Thesis Defense
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