@article {2022, title = {Model order reduction for bifurcating phenomena in fluid-structure interaction problems}, journal = {International Journal for Numerical Methods in FluidsInternational Journal for Numerical Methods in FluidsInt J Numer Meth Fluids}, volume = {n/a}, year = {2022}, month = {2022/05/23}, abstract = {

Abstract This work explores the development and the analysis of an efficient reduced order model for the study of a bifurcating phenomenon, known as the Coand? effect, in a multi-physics setting involving fluid and solid media. Taking into consideration a fluid-structure interaction problem, we aim at generalizing previous works towards a more reliable description of the physics involved. In particular, we provide several insights on how the introduction of an elastic structure influences the bifurcating behavior. We have addressed the computational burden by developing a reduced order branch-wise algorithm based on a monolithic proper orthogonal decomposition. We compared different constitutive relations for the solid, and we observed that a nonlinear hyper-elastic law delays the bifurcation w.r.t.\ the standard model, while the same effect is even magnified when considering linear elastic solid.

}, keywords = {Bifurcation theory, Coand{\u a} effect, continuum mechanics, fluid dynamics, monolithic method, parametrized fluid-structure interaction problem, Proper orthogonal decomposition, reduced order modeling}, isbn = {0271-2091}, url = {https://doi.org/10.1002/fld.5118}, author = {Moaad Khamlich and Federico Pichi and Gianluigi Rozza} } @unpublished {2022, title = {Model Reduction Using Sparse Polynomial Interpolation for the Incompressible Navier-Stokes Equations}, year = {2022}, author = {Martin W. Hess and Gianluigi Rozza} } @article {2021, title = {A Monolithic and a Partitioned, Reduced Basis Method for Fluid{\textendash}Structure Interaction Problems}, journal = {Fluids}, volume = {6}, year = {2021}, pages = {229}, abstract = {

The aim of this work is to present an overview about the combination of the Reduced Basis Method (RBM) with two different approaches for Fluid{\textendash}Structure Interaction (FSI) problems, namely a monolithic and a partitioned approach. We provide the details of implementation of two reduction procedures, and we then apply them to the same test case of interest. We first implement a reduction technique that is based on a monolithic procedure where we solve the fluid and the solid problems all at once. We then present another reduction technique that is based on a partitioned (or segregated) procedure: the fluid and the solid problems are solved separately and then coupled using a fixed point strategy. The toy problem that we consider is based on the Turek{\textendash}Hron benchmark test case, with a fluid Reynolds number Re=100.

}, issn = {2311-5521}, doi = {10.3390/fluids6060229}, url = {https://www.mdpi.com/2311-5521/6/6/229}, author = {Monica Nonino and F. Ballarin and Gianluigi Rozza} } @conference {romor2020pamm, title = {Multi-fidelity data fusion for the approximation of scalar functions with low intrinsic dimensionality through active subspaces}, booktitle = {Proceedings in Applied Mathematics \& Mechanics}, volume = {20}, number = {S1}, year = {2021}, publisher = {Wiley Online Library}, organization = {Wiley Online Library}, doi = {10.1002/pamm.202000349}, author = {Francesco Romor and Marco Tezzele and Gianluigi Rozza} } @article {14318, title = {Multi-fidelity data fusion through parameter space reduction with applications to automotive engineering}, journal = {arXiv preprint arXiv:2110.14396}, year = {2021}, author = {Francesco Romor and Marco Tezzele and Markus Mrosek and Carsten Othmer and Gianluigi Rozza} } @booklet {2020, title = {MicroROM: An Efficient and Accurate Reduced Order Method to Solve Many-Query Problems in Micro-Motility}, year = {2020}, keywords = {FOS: Mathematics, Numerical Analysis (math.NA)}, doi = {10.48550/ARXIV.2006.13836}, url = {https://arxiv.org/abs/2006.13836}, author = {Nicola Giuliani and Martin W. Hess and Antonio DeSimone and Gianluigi Rozza} } @conference {tezzele2018model, title = {Model Order Reduction by means of Active Subspaces and Dynamic Mode Decomposition for Parametric Hull Shape Design Hydrodynamics}, booktitle = {Technology and Science for the Ships of the Future: Proceedings of NAV 2018: 19th International Conference on Ship \& Maritime Research}, year = {2018}, publisher = {IOS Press}, organization = {IOS Press}, address = {Trieste, Italy}, abstract = {We present the results of the application of a parameter space reduction methodology based on active subspaces (AS) to the hull hydrodynamic design problem. Several parametric deformations of an initial hull shape are considered to assess the influence of the shape parameters on the hull wave resistance. Such problem is relevant at the preliminary stages of the ship design, when several flow simulations are carried out by the engineers to establish a certain sensibility with respect to the parameters, which might result in a high number of time consuming hydrodynamic simulations. The main idea of this work is to employ the AS to identify possible lower dimensional structures in the parameter space. The complete pipeline involves the use of free form deformation to parametrize and deform the hull shape, the full order solver based on unsteady potential flow theory with fully nonlinear free surface treatment directly interfaced with CAD, the use of dynamic mode decomposition to reconstruct the final steady state given only few snapshots of the simulation, and the reduction of the parameter space by AS, and shared subspace. Response surface method is used to minimize the total drag.}, doi = {10.3233/978-1-61499-870-9-569}, url = {http://ebooks.iospress.nl/publication/49270}, author = {Marco Tezzele and Nicola Demo and Mahmoud Gadalla and Andrea Mola and Gianluigi Rozza} } @article {doi:10.1137/17M1150591, title = {Model Reduction for Parametrized Optimal Control Problems in Environmental Marine Sciences and Engineering}, journal = {SIAM Journal on Scientific Computing}, volume = {40}, number = {4}, year = {2018}, pages = {B1055-B1079}, doi = {10.1137/17M1150591}, url = {https://doi.org/10.1137/17M1150591}, author = {Maria Strazzullo and F. Ballarin and Mosetti, R. and Gianluigi Rozza} } @inbook {ChinestaHuertaRozzaWillcox2017, title = {Model Reduction Methods}, booktitle = {Encyclopedia of Computational Mechanics Second Edition}, year = {2017}, pages = {1-36}, publisher = {John Wiley \& Sons}, organization = {John Wiley \& Sons}, chapter = {Model Reduction Methods}, abstract = {

This chapter presents an overview of model order reduction {\textendash} a new paradigm in the field of simulation-based engineering sciences, and one that can tackle the challenges and leverage the opportunities of modern ICT technologies. Despite the impressive progress attained by simulation capabilities and techniques, a number of challenging problems remain intractable. These problems are of different nature, but are common to many branches of science and engineering. Among them are those related to high-dimensional problems, problems involving very different time scales, models defined in degenerate domains with at least one of the characteristic dimensions much smaller than the others, model requiring real-time simulation, and parametric models. All these problems represent a challenge for standard mesh-based discretization techniques; yet the ability to solve these problems efficiently would open unexplored routes for real-time simulation, inverse analysis, uncertainty quantification and propagation, real-time optimization, and simulation-based control {\textendash} critical needs in many branches of science and engineering. Model order reduction offers new simulation alternatives by circumventing, or at least alleviating, otherwise intractable computational challenges. In the present chapter, we revisit three of these model reduction techniques: proper orthogonal decomposition, proper generalized decomposition, and reduced basis methodologies.} preprint = {http://preprints.sissa.it/xmlui/bitstream/handle/1963/35194/ECM_MOR.pdf?sequence=1\&isAllowed=y

}, doi = {10.1002/9781119176817.ecm2110}, author = {Francisco Chinesta and Antonio Huerta and Gianluigi Rozza and Karen Willcox} } @inbook {2016, title = {Model Order Reduction: a survey}, booktitle = {Wiley Encyclopedia of Computational Mechanics, 2016}, year = {2016}, publisher = {Wiley}, organization = {Wiley}, url = {http://urania.sissa.it/xmlui/handle/1963/35194}, author = {Francisco Chinesta and Antonio Huerta and Gianluigi Rozza and Karen Willcox} } @article {11966, title = {A multi-physics reduced order model for the analysis of Lead Fast Reactor single channel}, journal = {Annals of Nuclear Energy, 87, 2 (2016): pp. 198-208}, volume = {87}, number = {Annals of Nuclear Energy;87}, year = {2016}, pages = {208}, publisher = {Elsevier}, chapter = {198}, abstract = {In this work, a Reduced Basis method, with basis functions sampled by a Proper Orthogonal Decomposition technique, has been employed to develop a reduced order model of a multi-physics parametrized Lead-cooled Fast Reactor single-channel. Being the first time that a reduced order model is developed in this context, the work focused on a methodological approach and the coupling between the neutronics and the heat transfer, where the thermal feedbacks on neutronics are explicitly taken into account, in time-invariant settings. In order to address the potential of such approach, two different kinds of varying parameters have been considered, namely one related to a geometric quantity (i.e., the inner radius of the fuel pellet) and one related to a physical quantity (i.e., the inlet lead velocity). The capabilities of the presented reduced order model (ROM) have been tested and compared with a high-fidelity finite element model (upon which the ROM has been constructed) on different aspects. In particular, the comparison focused on the system reactivity prediction (with and without thermal feedbacks on neutronics), the neutron flux and temperature field reconstruction, and on the computational time. The outcomes provided by the reduced order model are in good agreement with the high-fidelity finite element ones, and a computational speed-up of at least three orders of magnitude is achieved as well.}, doi = {doi:10.1016/j.anucene.2015.09.002}, url = {http://urania.sissa.it/xmlui/handle/1963/35191}, author = {Alberto Sartori and Antonio Cammi and Lelio Luzzi and Gianluigi Rozza} } @article {BennerOhlbergerPateraRozzaSorensenUrban2015, title = {Model order reduction of parameterized systems ({MoRePaS}): Preface to the special issue of advances in computational mathematics}, journal = {Advances in Computational Mathematics}, volume = {41}, number = {5}, year = {2015}, pages = {955{\textendash}960}, doi = {10.1007/s10444-015-9443-y}, author = {Peter Benner and Mario Ohlberger and Anthony Patera and Gianluigi Rozza and Sorensen, D.C. and Karsten Urban} } @article {2015, title = {Multilevel and weighted reduced basis method for stochastic optimal control problems constrained by Stokes equations}, journal = {Numerische Mathematik, (2015), 36 p. Article in Press}, number = {Numerische Mathematik;}, year = {2015}, publisher = {Springer}, abstract = {In this paper we develop and analyze a multilevel weighted reduced basis method for solving stochastic optimal control problems constrained by Stokes equations. We prove the analytic regularity of the optimal solution in the probability space under certain assumptions on the random input data. The finite element method and the stochastic collocation method are employed for the numerical approximation of the problem in the deterministic space and the probability space, respectively, resulting in many large-scale optimality systems to solve. In order to reduce the unaffordable computational effort, we propose a reduced basis method using a multilevel greedy algorithm in combination with isotropic and anisotropic sparse-grid techniques. A weighted a posteriori error bound highlights the contribution stemming from each method. Numerical tests on stochastic dimensions ranging from 10 to 100 demonstrate that our method is very efficient, especially for solving high-dimensional and large-scale optimization problems.}, doi = {10.1007/s00211-015-0743-4}, url = {http://urania.sissa.it/xmlui/handle/1963/34491}, author = {Gianluigi Rozza and Peng Chen and Alfio Quarteroni} } @article {2014, title = {Model Order Reduction in Fluid Dynamics: Challenges and Perspectives}, number = {Modeling simulation and applications;volume 9; pages 235-273;}, year = {2014}, publisher = {Springer}, abstract = {This chapter reviews techniques of model reduction of fluid dynamics systems. Fluid systems are known to be difficult to reduce efficiently due to several reasons. First of all, they exhibit strong nonlinearities - which are mainly related either to nonlinear convection terms and/or some geometric variability - that often cannot be treated by simple linearization. Additional difficulties arise when attempting model reduction of unsteady flows, especially when long-term transient behavior needs to be accurately predicted using reduced order models and more complex features, such as turbulence or multiphysics phenomena, have to be taken into consideration. We first discuss some general principles that apply to many parametric model order reduction problems, then we apply them on steady and unsteady viscous flows modelled by the incompressible Navier-Stokes equations. We address questions of inf-sup stability, certification through error estimation, computational issues and-in the unsteady case - long-time stability of the reduced model. Moreover, we provide an extensive list of literature references.}, doi = {10.1007/978-3-319-02090-7_9}, author = {Toni Lassila and Andrea Manzoni and Alfio Quarteroni and Gianluigi Rozza} }