Research Areas

SIGMALab is active in several research topics but conventionally we have created three main research programmes. Each programme develops original and advanced technology platforms at the state of the art in order to deliver the best solutions for challenging problems. The areas merge in several activities.

Structural integrity under extreme load

Fit for purpose solutions for: Large deformation - Ductile and brittle failure (large fragmentation) - Crack propagation (damage and flow tolerant) - Composite failure (fibre/matrix cracking, delamination) - Low/high velocity impact - Explosion - Penetration into rock - Technological process - Failure of wire rope - Wear - Innovatime methods for material calibration by means of microindentation - Sports Engineering etc

Highlights:

A deep investigation both in practical aspect and state of the art ;

Material calibration (metal, composites, rock);

Numerical and analytical modelling: virtual testing ;

Experimental testing (up to full scale tests);

Expertise in non linear numerical modeling and innovative methods (ALE, SPH, FSI, creation of dedicated mat-card, multiphisics, etc)

Validation of the modelling approaches;

Definition of a predictive methods;

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Structural Health Monitoring and prognosis

Identification of damages and fatigue cracks by means of sensor networks directly installed on the structure (DIAGNOSIS) and prediction of the residual lifetime of components (PROGNOSIS)

Highlights:

Investigation of different state of art sensor technologies for SHM ;

Numerical and analytical modelling for SHM system training;

Machine learning and pattern recognition for diagnosis ;

Bayesian filters and Monte-Carlo methods for prognosis;

Experimental SHM verification and performance qualification;

Experimental testing (up to full scale tests);

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ENERGY

A new entry for SIGMALab research topic: Energy. SIGMALab team is working in order to provide dedicated solutions both for O&G and sustainable energy

PROGNOSTIC AND HEALTH MANAGEMENT (PHM) AND RELIABILITY AND STATISTICAL APPROACHES FOR STRUCTURAL INTEGRITY

Highlights:

Signal acquisition and processing (vibrations, lamb-waves, strains, etc.)

Numerical and analytical modeling of structural behavior and damage evolution (n-degrees of freedom models, finite element models, etc.)

Bayesian estimation and prediction frameworks

Machine learning algorithms for feature extraction, classification and regression -> Deep neural networks, autoencoders, principal component analysis, support vector machines, Gaussian processes, etc.)

Efficient Monte-Carlo simulation schemes for uncertainty quantification

Advanced optimization schemes (evolutionary algorithms, etc.) for optimal design

Risk assessment