Carlo Janna si è laureato in Ingegneria Civile il 2 ottobre 2003 presso l'Università degli Studi di Padova con votazione 109/110 e nella stessa Università ha conseguito il titolo di Dottore di Ricerca con la tesi “Modellazione numerica del comportamento meccanico delle faglie regionali per il confinamento geologico della CO2 antropica" con il Prof. Giuseppe Gambolati come relatore. Dal dicembre 2011 è Ricercatore di Analisi Numerica presso il Dip. ICEA. I principali interessi scientifici di Carlo Janna si collocano nell'ambito dello studio numerico-matematico della meccanica dei mezzi porosi, con applicazioni specifiche relative al settore dell'idraulica sotterranea e dell'industria petrolifera, e nell’ambito dell’algebra lineare numerica. L'attività prevalente svolta fino ad oggi consiste nello sviluppo ed implementazione di modelli numerici FEM per la simulazione dei principali processi geomeccanici e fluidodinamici del sottosuolo. Per quanto concerne l’algebra lineare, ha studiato e approfondito le tecniche numeriche per la soluzione di sistemi lineari e la ricerca di autovalori che scaturiscono in problemi strutturali e fluidodinamici di grandi dimensioni, in particolare metodi iterativi precondizionati. Nell'ambito del calcolo sequenziale ha studiato e implementato precondizionatori ad hoc per la soluzione ottimale di determinati problemi legati alla modellazione del sottosuolo. Dal 2010 al 2012, Carlo Janna ha partecipato ai programmi di ricerca PARPSEA (PARallel Preconditioners for large Size Engineering Applications), SCALPREC (SCALable PREConditioners), OPTIDAS (OPTImization and Data ASSimilation) e SPREAD (Scalable PREconditioners for Advanced Discretizations) in ambito HPC nel corso dei quali ha sviluppato e implementato su architetture massicciamente parallele precondizionatori di nuova concezione.

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1. V. A. Paludetto Magri, A. Franceschini, M. Ferronato, and C. Janna (2018), Multilevel Approaches for FSAI Preconditioning, Numerical Linear Algebra with Applications, available online (SJR-Scopus 1.104).

2. S. Ye, A. Franceschini, Y. Zhang, C. Janna, X. Gong, J. Yu and P. Teatini (2018), Earth fissure development caused by extensive aquifer exploitation. A novel modelling approach applied to the Wuxi case study, China, Water Resources Research, 54, pp. 2249–2269 (SJR-Scopus 2.296).

3. H. T. Honorio, C. R. Maliska, M. Ferronato, and C. Janna (2018), A stabilized element-based finite volume method for poroelastic problems, Journal of Computational Physics, 364, pp. 49–72 (SJR-Scopus 2.047).

4. Franceschini, V. A. Paludetto Magri, M. Ferronato, and C. Janna (2018), A Robust Multilevel Approximate Inverse Preconditioner for Symmetric Positive Definite Matrices, SIAM Journal on Matrix Analysis and Applications, 39, pp. 123–147 (SJR-Scopus 1.739).

5. N. Spiezia, M. Ferronato, C. Janna and P. Teatini (2017), A two-invariant pseudo-elastic model for reservoir compaction, International Journal for Numerical and Analytical Methods in Geomechanics, 41, pp. 1870–1893 (SJR-Scopus 1.452).

6. Zanette, M. Ferronato, and C. Janna (2017), Enriching the finite element method with meshfree techniques in structural mechanics, International Journal for Numerical Methods in Engineering, 110, pp. 675–700 (SJR-Scopus 1.623).

7. R. Baggio, A. Franceschini, N. Spiezia, and C. Janna (2017), Rigid body modes deflation of the preconditioned conjugate gradient in the solution of discretized structural problems, Computers & Structures, 18, pp. 15–26 (SJR-Scopus 1.630).

8. Franceschini, M. Ferronato, C. Janna, and P. Teatini (2016), A novel Lagrangian approach for a stable numerical simulation of fault and fracture mechanics, Journal of Computational Physics, 314, pp. 503–521 (SJR-Scopus 2.047).

9. M. Bernaschi, M. Bisson, C. Fantozzi, and C. Janna (2016), A FSAI preconditioned conjugate gradient solver on GPUs, SIAM Journal on Scientific Computing, 38, pp. C53–C72 (SJR-Scopus 1.973).

10. C. Janna, M. Ferronato and G. Gambolati (2015), The use of supernodes in factored sparse approximate inverse preconditioning, SIAM Journal on Scientific Computing, 37, pp. C72–C94 (SJR-Scopus 1.973).

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1- Analisi Numerica

2- Algebra Lineare Numerica

3- Calcolo parallelo

4- Geomeccanica

5- Modellistica Ambientale

1- Defining a better heuristic for the Newton method in reservoir simulation

Typical reservoir simulation involves the repeated solution in time of highly non-linear systems of equations as several quantities, such as relative permeability, capillary pressure, elastic storage, depend on the unknown solution at the current time. The Newton method, on its side, is an
iterative scheme requiring the solution of a sequence of linearized system at each step, which are typically solved through preconditioned iterative methods, e.g., algebraic multigrid (AMG).

In principle, since the systems change at every Newton step, the most conservative strategy is that of setting-up a new preconditioner for every non-linear solve. However, since the preconditioner set-up stage could be quite expensive, a more efficient alternative could be that of computing a new preconditioner or updating an existing one only when the system matrix changes significantly.

The present proposal focuses on studying optimal choices for preconditioner reset or update in reservoir simulation. The thesis will be carried out in collaboration with ENI and will involve an extensive use of Echelon, the ENI-proprietary reservoir simulation model, on modern parallel computers. The student will learn how to run an up-to-date reservoir simulator on real or realistic field, will learn how to work on a parallel computer using a load-leveler and finally will learn the basis of parallel programming and numerical methods.

External co-supervisors:
Alberto Cominelli, Mario Tacconi, ENI E&P
Leonardo Patacchini, Stone Ridge Technology

Improving the iterative solution of linear systems arising from reservoir simulation models

Reservoir simulation software requires the solution in time of several non-linear systems of the equations, which are typically solved with the Newton method. Newton, in turn, involves the solution of a sequence of large and sparse linear systems which are very often solved through algebraic multigrid (AMG). Though being extremely efficient, the time spent for the AMG solve accounts for up to 90% of the total simulation time in real world problems, so that it is of paramount importance increasing its efficiency.

For instance, Echelon, the reservoir simulation software used in ENI, relies on the linear solver in the GAMPACK library which guarantees excellent performance. However, other solver packages are available offering promising improvements in the context of AMG, such as aggressive coarsening techniques, extended and long-range prolongations, filtering or advanced smoothers.

The aim of this thesis is exploring the effectiveness of these approaches, currently implemented in other packages such as Hypre (https://computing.llnl.gov/projects/hypre-scalable-linear-solvers-multigrid-methods) or Chronos (https://www.m3eweb.it/chronos) and evaluate their impact on real world reservoir and geomechanical models.

The thesis will be carried out in collaboration with ENI and will involve the use of GAMPACK and other linear solvers on modern parallel computers. The student will learn how to use and customize state-of-the-art parallel linear solvers, will learn how to work on a parallel computer using a load-leveler and, finally, will learn the basis of parallel programming.

External co-supervisors:
Alberto Cominelli, Silvia Monaco, Mario Tacconi, ENI E&P
Leonardo Patacchini, Stone Ridge Technology