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Documents published in Scipedia

• A reduced order modeling approach for optimal allocation of Distributed Generation in power distribution systems

  R. García-Blanco, P. Diez, D. Borzacchiello, F. Chinesta

  International Energy Conference (ENERGYCON) (2016). (preprint) pp, 1-6

  
  

  Abstract

  This paper presents an “offline-online” strategy for optimal allocation and sizing of Distributed Generation. In traditional optimization approaches, each function evaluation requires the solution of a power flow problem, which makes global optimality a computationally challenging goal. In the proposed strategy the power flow solver is invoked only once and a parametric solution is constructed with a monolithic solver. Despite the fact that the parametrized power flow equations result in a high-dimensional problem, the proposed algorithm is specifically designed to circumvent the curse of dimensionality. This is achieved through the application of Model Reduction, in particular the Proper Generalized Decomposition combined with a nonlinear solver. Numerical examples are carried out for showing the validity of the proposed method.

  Abstract
  This paper presents an “offline-online” strategy for optimal allocation and sizing of Distributed Generation. In traditional optimization approaches, each function [...]

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• Unified formulation of a family of iterative solvers for power system analysis

  F. Chinesta, P. Diez, D. Borzacchiello, M. Malik, R. García-Blanco

  Electric Power Systems Research (2016). (Preprint) Vol. 140, pp. 201-208

  
  

  Abstract

  This paper illustrates the construction of a new class of iterative solvers for power flow calculations based on the method of Alternating Search Directions. This method is fit to the particular algebraic structure of the power flow problem resulting from the combination of a globally linear set of equations and nonlinear local relations imposed by power conversion devices, such as loads and generators. The choice of the search directions is shown to be crucial for improving the overall robustness of the solver. A noteworthy advantage is that constant search directions yield stationary methods that, in contrast with Newton or Quasi-Newton methods, do not require the evaluation of the Jacobian matrix. Such directions can be elected to enforce the convergence to the high voltage operative solution. The method is explained through an intuitive example illustrating how the proposed generalized formulation is able to include other nonlinear solvers that are classically used for power flow analysis, thus offering a unified view on the topic. Numerical experiments are performed on publicly available benchmarks for large distribution and transmission systems.

  Abstract
  This paper illustrates the construction of a new class of iterative solvers for power flow calculations based on the method of Alternating Search Directions. This method is [...]

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• Numerical modeling of undersea acoustics using a partition of unity method with plane waves enrichment

  R. Hospital-Bravo, J. Sarrate, P. Diez

  Computational Mechanics (2016). (Preprint) Vol. 57 (5), pp. 717-732

  
  

  Abstract

  A new 2D numerical model to predict the underwater acoustic propagation is obtained by exploring the potential of the Partition of Unity Method (PUM) enriched with plane waves. The aim of the work is to obtain sound pressure level distributions when multiple operational noise sources are present, in order to assess the acoustic impact over the marine fauna. The model takes advantage of the suitability of the PUM for solving the Helmholtz equation, especially for the practical case of large domains and medium frequencies. The seawater acoustic absorption and the acoustic reflectance of the sea surface and sea bottom are explicitly considered, and perfectly matched layers (PML) are placed at the lateral artificial boundaries to avoid spurious reflexions. The model includes semi-analytical integration rules which are adapted to highly oscillatory integrands with the aim of reducing the computational cost of the integration step. In addition, we develop a novel strategy to mitigate the ill-conditioning of the elemental and global system matrices. Specifically, we compute a low-rank approximation of the local space of solutions, which in turn reduces the number of degrees of freedom, the CPU time and the memory footprint. Numerical examples are presented to illustrate the capabilities of the model and to assess its accuracy.

  Abstract
  A new 2D numerical model to predict the underwater acoustic propagation is obtained by exploring the potential of the Partition of Unity Method (PUM) enriched with plane waves. [...]

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• Adaptividade e estimativas de erro orientadas por metas aplicadas a um benchmark test de propagação de onda

  L. Steffens, P. Diez, M. Alves

  Tendências em Matemática Aplicada e Computacional (2016). (preprint) Vol. 17 (1), pp. 35-53

  
  

  Abstract

  O objetivo deste artigo é estudar a eficiência e a robustez de técnicas adaptativas e estimativas de erro orientadas por metas para um benchmark test. As técnicas utilizadas aqui são baseadas em um simples pós-processo das aproximações de elementos finitos. As estimativas de erro orientadas por metas são obtidas por analisar o problema direto e um problema auxiliar, o qual está relacionado com a quantidade de interesse específico. O procedimento proposto é válido para quantidades lineares e não-lineares. Além disso, são discutidas diferentes representações para o erro e é analisada a influência do erro de dispersão. Os resultados numéricos mostram que as estimativas de erro fornecem boas aproximações ao erro real e que a técnica de refino adaptativo proposta conduz a uma redução mais rápida do erro.

  Abstract
  O objetivo deste artigo é estudar a eficiência e a robustez de técnicas adaptativas e estimativas de erro orientadas por metas para um benchmark test. [...]

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• An error estimator for real-time simulators based on model order reduction

  I. Alfaro, D. Gonzalez, S. Zlotnik, P. Diez, E. Cueto, F. Chinesta

  Adv. Model. and Simul. in Eng. Sci. (2015). Vol. 2 (1), pp. 1-30

  
  

  Abstract

  Model order reduction (MOR) is one of the most appealing choices for real-time simulation of non-linear solids. In this work a method is presented in which real time performance is achieved by means of the o-line solution of a (high dimensional) parametric problem that provides a sort of response surface or computational vademecum. This solution is then evaluated in real-time at feedback rates compatible with haptic devices, for instance (i.e., more than 1kHz). This high dimensional problem can be solved without the limitations imposed by the curse of dimensionality by employing Proper Generalized Decomposition (PGD) methods. Essentially, PGD assumes a separated representation for the essential eld of the problem. Here, an error estimator is proposed for this type of solutions that takes into account the non-linear character of the studied problems. This error estimator allows to compute the necessary number of modes employed to obtain an approximation to the solution within a prescribed error tolerance in a given quantity of interest.

  Abstract
  Model order reduction (MOR) is one of the most appealing choices for real-time simulation of non-linear solids. In this work a method is presented in which real time performance [...]

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• Effect of the separated approximation of input data in the accuracy of the resulting PGD solution

  S. Zlotnik, P. Diez, D. Gonzalez, E. Cueto, A. Huerta

  Adv. Model. and Simul. in Eng. Sci. (2015). Vol. 2, art. 28

  
  

  Abstract

  The Proper Generalized Decomposition (PGD) requires separability of the input data (e.g. physical properties, source term, boundary conditions, initial state). In many cases the input data is not expressed in a separated form and it has to be replaced by some separable approximation. These approximations constitute a new error source that, in some cases, may dominate the standard ones (discretization, truncation…) and control the nal accuracy of the PGD solution. In this work the relation between errors in the separated input data and the errors induced in the PGD solution is discussed. Error estimators proposed for homogenized problems and oscillation terms are adapted to asses the behaviour of the PGD errors resulting from approximated input data. The PGD is stable with respect to error in the separated data, with no critical amplification of the perturbations. Interestingly, we identified a high sensitiveness of the resulting accuracy on the selection of the sampling grid used to compute the separated data. The separation has to be performed on the basis of values sampled at integration points: sampling at the nodes defining the functional interpolation results in an important loss of accuracy. For the case of a Poisson problem separated in the spatial coordinates (a complex diffusivity function requires a separable approximation), the final PGD error is linear with the truncation error of the separated data. This relation is used to estimate the number of terms required in the separated data, that has to be in good agreement with the truncation error accepted in the PGD truncation (tolerance for the stoping criteria in the enrichment procedure). A sensible choice or the prescribed accuracy of the PGD solution has to be kept within the limits set by the errors in the separated input data.

  Abstract
  The Proper Generalized Decomposition (PGD) requires separability of the input data (e.g. physical properties, source term, boundary conditions, initial state). In many cases [...]

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• Real time parameter identification and solution reconstruction from experimental data using the Proper Generalized Decomposition

  E. Nadal Soriano, F. Chinesta, P. Diez, F. Fuenmayor, F. Denia

  Comput. Methods Appl. Mech. Engrg., (2015). (preprint) Vol. 296 (1), pp. 113-128

  
  

  Abstract

  Some industrial processes are modelled by parametric partial differential equations. Integrating computational modelling and data assimilation into the control process requires obtaining a solution of the numerical model at the characteristic frequency of the process (real-time). This paper introduces a computational strategy allowing to efficiently exploit measurements of those industrial processes, providing the solution of the model at the required frequency. This is particularly interesting in the framework of control algorithms that rely on a model involving a set of parameters. For instance, the curing process of a composite material is modelled as a thermo-mechanical problem whose corresponding parameters describe the thermal and mechanical behaviours. In this context, the information available (measurements) is used to update the parameters of the model and to produce new values of the control variables (data assimilation). The methodology presented here is devised to ensure the possibility of having a response in real-time of the problem and therefore the capability of integrating it in the control scheme. The Proper Generalized Decomposition is used to describe the solution in the multi-parametric space. The real-time data assimilation requires a further simplification of the solution representation that better fits the data (reconstructed solution) and it provides an implicit parameter identification. Moreover, the analysis of the assimilated data sensibility with respect to the points where the measurements are taken suggests a criterion to locate the sensors.

  Abstract
  Some industrial processes are modelled by parametric partial differential equations. Integrating computational modelling and data assimilation into the control process [...]

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• An efficient and general approach for implementing thermodynamic phase equilibria information in geophysical and geodynamic studies

  J. Alfonso, S. Zlotnik, P. Diez

  Geochmistry, geophysics, geosystems (2015). (preprint) Vol. 16 (10), pp. 3767-3777

  
  

  Abstract

  We present a flexible, general, and efficient approach for implementing thermodynamic phase equilibria information (in the form of sets of physical parameters) into geophysical and geodynamic studies. The approach is based on Tensor Rank Decomposition methods, which transform the original multidimensional discrete information into a separated representation that contains significantly fewer terms, thus drastically reducing the amount of information to be stored in memory during a numerical simulation or geophysical inversion. Accordingly, the amount and resolution of the thermodynamic information that can be used in a simulation or inversion increases substantially. In addition, the method is independent of the actual software used to obtain the primary thermodynamic information, and therefore, it can be used in conjunction with any thermodynamic modeling program and/or database. Also, the errors associated with the decomposition procedure are readily controlled by the user, depending on her/his actual needs (e.g., preliminary runs versus full resolution runs). We illustrate the benefits, generality, and applicability of our approach with several examples of practical interest for both geodynamic modeling and geophysical inversion/modeling. Our results demonstrate that the proposed method is a competitive and attractive candidate for implementing thermodynamic constraints into a broad range of geophysical and geodynamic studies. MATLAB implementations of the method and examples are provided as supporting information and can be downloaded from the journal's website.

  Abstract
  We present a flexible, general, and efficient approach for implementing thermodynamic phase equilibria information (in the form of sets of physical parameters) into geophysical [...]

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• Fast r-adaptivity for multiple queries of heterogeneous stochastic material fields

  R. Cottereau, P. Diez

  Computational Mechanics (2015). (preprint) Vol. 56 (4), pp. 601-612

  
  

  Abstract

  We present an r-adaptivity approach for boundary value problems with randomly fluctuating material parameters solved through the Monte Carlo or stochastic collocation methods. This approach tailors a specific mesh for each sample of the problem. It only requires the computation of the solution of a single deterministic problem with the same geometry and the average parameter, whose numerical cost becomes marginal for large number of samples. Starting from the mesh used to solve that deterministic problem, the nodes are moved depending on the particular sample of mechanical parameter field. The reduction in the error is small for each sample but sums up to reduce the overall bias on the statistics estimated through the Monte Carlo scheme. Several numerical examples in 2D are presented.

  Abstract
  We present an r-adaptivity approach for boundary value problems with randomly fluctuating material parameters solved through the Monte Carlo or stochastic collocation [...]

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• Proper generalized decomposition of a geometrically parametrized heat problem with geophysical applications

  S. Zlotnik, P. Diez, D. Modesto, A. Huerta

  Int. J. Numer. Meth. Engng. (2015). (preprint) Vol. 103 (10), pp. 737-758

  
  

  Abstract

  The solution of a steady thermal multiphase problem is assumed to be dependent on a set of parameters describing the geometry of the domain, the internal interfaces and the material properties. These parameters are considered as new independent variables. The problem is therefore stated in a multidimensional setup. The proper generalized decomposition (PGD) provides an approximation scheme especially well suited to preclude dramatically increasing the computational complexity with the number of dimensions. The PGD strategy is reviewed for the standard case dealing only with material parameters. Then, the ideas presented in [Ammar et al., “Parametric solutions involving geometry: A step towards efficient shape optimization.” Comput. Methods Appl. Mech. Eng., 2014; 268:178–193] to deal with parameters describing the domain geometry are adapted to a more general case including parametrization of the location of internal interfaces. Finally, the formulation is extended to combine the two types of parameters. The proposed strategy is used to solve a problem in applied geophysics studying the temperature field in a cross section of the Earth crust subsurface. The resulting problem is in a 10‐dimensional space, but the PGD solution provides a fairly accurate approximation (error ≤1%) using less that 150 terms in the PGD expansion.

  Abstract
  The solution of a steady thermal multiphase problem is assumed to be dependent on a set of parameters describing the geometry of the domain, the internal interfaces and the [...]

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