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SUMMARY:Data-Driven Engineering Design: Aerodynamic design insight in the 
 age of data
DTSTART:20190213T090000
DTEND:20190213T100000
DTSTAMP:20260428T074550Z
UID:87feb0d1923e7e54ba274f2c1a8d04da45915f840bd0fbc2d8161493
CATEGORIES:Conferences - Seminars
DESCRIPTION:Pranay Seshadri\, Department of Engineering\, University of C
 ambridge\, \nand Alan Turing Institute\, UK\nBroadly\, this talk is about
  the capitalization of data guided by principles of aerodynamics\, dimensi
 on reduction and uncertainty quantification. This data—acquired from com
 puter models\, instrumentation sensors\, or both—is likely to be noisy\,
  high-dimensional and sparse. The objective of my research is to identify 
 salient aerodynamic relationships in this data to empower improved aerodyn
 amic design insight\, analysis\, and manufacturing strategies. In this tal
 k\, I will present two case studies that encapsulate these ideas. \nThe f
 irst case study concerns the way one designs and manufactures blade\, wher
 e data arises from computational flow simulations. Here ideas from the fie
 ld of subspace-based dimension reduction can play a powerful role in ensur
 ing more efficient design workflows\, while simultaneously cutting down th
 e costs associated with manufacturing to certain tolerances. The identific
 ation and exploitation of these subspaces—weighted linear combinations o
 f all the design variables—facilitate detailed quantification of aerodyn
 amic design trade-offs. In other words\, instead of simply stating that ce
 rtain blades are more sensitive to manufacturing variations\, or that they
  lie on a hypothetical “efficiency cliff”\, I will demonstrate how we 
 can visualize\, quantify and make precise such notions of design.\nThe sec
 ond case study revolves around how one can reconstruct an aerodynamic flow
 -field given sparse (and noisy) measurements from thermocouples. We are co
 ncerned here with approximating the temperature field (and its area averag
 e) given data from a few circumferentially scattered rakes in an engine. B
 y developing new ideas within uncertainty quantification\, specifically Ba
 yesian statistics\, I will demonstrate how we can estimate the spatial flo
 w-field (and its uncertainty)\, permitting a greater understanding of key 
 loss mechanisms and aerothermal harmonics. Overall\, this work has broad r
 amifications on the way we book-keep uncertainties in experiments\, and ar
 ticulate how existing metrics in international uncertainty standards (e.g.
 \, ASME\, ISO GUM) fall short.\nI will close this talk by offering a glimp
 se of ongoing and future avenues for my research.\n\nBio of speaker: Prana
 y Seshadri is a postdoctoral fellow at the Department of Engineering\, Uni
 versity of Cambridge. He is also a Group Leader within the Data-Centric En
 gineering Programme\, largely focused on aerospace. He obtained his Ph.D. 
 in turbomachinery and computational engineering at the University of Cambr
 idge. \n 
LOCATION:MEB1 B10 https://plan.epfl.ch/?room==ME%20B1%20B10
STATUS:CONFIRMED
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