PORTALE DELLA DIDATTICA

Ricerca CERCA
  KEYWORD

Shape-sensing based on strain-data and video-data

estero Thesis abroad


keywords EXPERIMENTAL TESTS, FINITE ELEMENT METHOD, SHAPE-SENSING, STRAINS

Reference persons MARCO GHERLONE, CECILIA SURACE

External reference persons Marco Esposito, Rinto Roy

Research Groups 02-AESDO

Thesis type NUMERICAL AND EXPERIMENTAL

Description Premise:
Shape-sensing is the reconstruction of the deformed configuration of a structure starting from some discrete measurements

Technical Problem:
Video data can provide dense 2D displacement measurements, but these measurements are noisy (uncertain) and sensitive to algorithmic hyperparameters. Sensor installations (ie. strain gauges) have lower noise, but require dense installations to sufficiently quantify displacements

Research Question:
How can we combine image (video) based displacement measurements with traditional sensor installations to better quantify strain and displacement fields?

Proposed Technical Approach:
Evaluate two forms of data fusion to combine these sensor measurements
- Gaussian process interpolants: kriging, Bayesian filtering
- Machine learning: shallow and deep neural networks
Two rounds of experiments
- Simple structures with well constrained 1D load-displacement relationships
- More complex structures with 2D load-displacement relationships

See also  gmu-polito.pdf  https://www.dimeas.polito.it/en/research/research_groups/aesdo_aircraft_and_engine_structural_design_and_optimization

Required skills Structural analysis, programming (MATLAB), FEM commercial codes


Deadline 12/04/2023      PROPONI LA TUA CANDIDATURA




© Politecnico di Torino
Corso Duca degli Abruzzi, 24 - 10129 Torino, ITALY
Contatti