Imperial College London

DrPranaySeshadri

Faculty of Natural SciencesDepartment of Mathematics

Research Fellow
 
 
 
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Contact

 

p.seshadri Website

 
 
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Location

 

Huxley BuildingSouth Kensington Campus

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Summary

 

Publications

Publication Type
Year
to

9 results found

Wong CY, Seshadri P, Parks G, 2021, Extremum sensitivity analysis with polynomial Monte Carlo filtering, RELIABILITY ENGINEERING & SYSTEM SAFETY, Vol: 212, ISSN: 0951-8320

Journal article

Scillitoe A, Seshadri P, Girolami M, 2021, Uncertainty quantification for data-driven turbulence modelling with Mondrian forests, JOURNAL OF COMPUTATIONAL PHYSICS, Vol: 430, ISSN: 0021-9991

Journal article

Wong CY, Seshadri P, Parks GT, Girolami Met al., 2020, Embedded ridge approximations, COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, Vol: 372, ISSN: 0045-7825

Journal article

Seshadri P, Yuchi S, Parks GT, Shahpar Set al., 2020, Supporting multi-point fan design with dimension reduction, AERONAUTICAL JOURNAL, Vol: 124, Pages: 1371-1398, ISSN: 0001-9240

Journal article

Seshadri P, Simpson D, Thorne G, Duncan A, Parks GTet al., 2020, Spatial flow-field approximation using few thermodynamic measurements Part I: formulation and area averaging, Journal of Turbomachinery, ISSN: 0889-504X

Our investigation raises an important question that is of relevance to the wider turbomachinery community: howdo we estimate the spatial average of a flow quantity given finite (and sparse) measurements? This paper seeks toadvance efforts to answer this question rigorously. In this paper, we develop a regularized multivariate linear regressionframework for studying engine temperature measurements. As part of this investigation, we study the temperaturemeasurements obtained from the same axial plane across five different engines yielding a total of 82 data-sets. Thefive different engines have similar architectures and therefore similar temperature spatial harmonics are expected. Ourproblem is to estimate the spatial field in engine temperature given a few measurements obtained from thermocouplespositioned on a set of rakes. Our motivation for doing so is to understand key engine temperature modes that cannotbe captured in a rig or in computational simulations, as the cause of these modes may not be replicated in thesesimpler environments. To this end, we develop a multivariate linear least squares model with Tikhonov regularizationto estimate the 2D temperature spatial field. Our model uses a Fourier expansion in the circumferential direction anda quadratic polynomial expansion in the radial direction. One important component of our modeling framework isthe selection of model parameters, i.e. the harmonics in the circumferential direction. A training-testing paradigm isproposed and applied to quantify the harmonics.

Journal article

Seshadri P, Duncan A, Simpson D, Thorne G, Parks GTet al., 2020, Spatial flow-field approximation using few thermodynamic measurements Part II: Uncertainty assessments, Journal of Turbomachinery

Journal article

Seshadri P, Constantine P, Iaccarino G, Parks Get al., 2016, A density-matching approach for optimization under uncertainty, Computer Methods in Applied Mechanics and Engineering, Vol: 305, Pages: 562-578, ISSN: 0045-7825

Journal article

Seshadri P, Duncan A, Thorne G, Parks G, Diaz RV, Girolami Met al., Bayesian Assessments of Aeroengine Performance with Transfer Learning

Aeroengine performance is determined by temperature and pressure profilesalong various axial stations within an engine. Given limited sensormeasurements both along and between axial stations, we require a statisticallyprincipled approach to inferring these profiles. In this paper we detail aBayesian methodology for interpolating the spatial temperature or pressureprofile at axial stations within an aeroengine. The profile at any given axialstation is represented as a spatial Gaussian random field on an annulus, withcircumferential variations modelled using a Fourier basis and radial variationsmodelled with a squared exponential kernel. This Gaussian random field isextended to ingest data from multiple axial measurement planes, with the aim oftransferring information across the planes. To facilitate this type of transferlearning, a novel planar covariance kernel is proposed, with hyperparametersthat characterise the correlation between any two measurement planes. In thescenario where precise frequencies comprising the temperature field areunknown, we utilise a sparsity-promoting prior on the frequencies to encouragesparse representations. This easily extends to cases with multiple engineplanes whilst accommodating frequency variations between the planes. The mainquantity of interest, the spatial area average is readily obtained in closedform. We term this the Bayesian area average and demonstrate how this metricoffers far more precise averages than a sector area average -- a widely usedarea averaging approach. Furthermore, the Bayesian area average naturallydecomposes the posterior uncertainty into terms characterising insufficientsampling and sensor measurement error respectively. This too provides asignificant improvement over prior standard deviation based uncertaintybreakdowns.

Journal article

Seshadri P, Parks G, Shahpar S, An Aerodynamic Analysis of a Robustly Redesigned Modern Aero-Engine Fan

This paper documents results from a recent computational study aimed atde-sensitizing fan stage aerodynamics---in a modern, high bypass ratioaero-engine---to the effects of rear-seal leakage flows. These flows are theresult of seal erosion between a rotor and stator disk in an engine, anddeterioration over the life of an engine. The density-matching technique foroptimization under uncertainty was applied to this problem. This involved RANSand adjoint flow solves of a full fan stage carried out at two differentleakage conditions. Here a detailed analysis of the fan stage aerodynamics iscarried out to determine why exactly the new design is more insensitive to theeffects of leakage flows. Specifically, it is shown that this insensitivity isattributed to three main factors: a slight rearward shift in loading, and thusa reduction in incidence; a reduction in the cross-passage pressure gradient;and a re-acceleration of the flow towards the trailing edge, which preventedany corner separation.

Journal article

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