Reproducibility, accuracy and performance of the Feltor code and library on parallel computer architectures

Feltor is a modular and free scientific software package. It allows developing platform independent code that runs on a variety of parallel computer architectures ranging from laptop CPUs to multi-GPU distributed memory systems. Feltor consists of both a numerical library and a collection of application codes built on top of the library. Its main target are two- and three-dimensional drift- and gyro-fluid simulations with discontinuous Galerkin methods as the main numerical discretization technique. We observe that numerical simulations of a recently developed gyro-fluid model produce non-deterministic results in parallel computations. First, we show how we restore accuracy and bitwise reproducibility algorithmically and programmatically. In particular, we adopt an implementation of the exactly rounded dot product based on long accumulators, which avoids accuracy losses especially in parallel applications. However, reproducibility and accuracy alone fail to indicate correct simulation behaviour. In fact, in the physical model slightly different initial conditions lead to vastly different end states. This behaviour translates to its numerical representation. Pointwise convergence, even in principle, becomes impossible for long simulation times. In a second part, we explore important performance tuning considerations. We identify latency and memory bandwidth as the main performance indicators of our routines. Based on these, we propose a parallel performance model that predicts the execution time of algorithms implemented in Feltor and test our model on a selection of parallel hardware architectures. We are able to predict the execution time with a relative error of less than 25% for problem sizes between 0.1 and 1000 MB. Finally, we find that the product of latency and bandwidth gives a minimum array size per compute node to achieve a scaling efficiency above 50% (both strong and weak)

Overview of the JET preparation for deuterium-tritium operation with the ITER like-wall

For the past several years, the JET scientific programme (Pamela et al 2007 Fusion Eng. Des.82 590) has been engaged in a multi-campaign effort, including experiments in D, H and T, leading up to 2020 and the first experiments with 50%/50% D–T mixtures since 1997 and the first ever D–T plasmas with the ITER mix of plasma-facing component materials. For this purpose, a concerted physics and technology programme was launched with a view to prepare the D–T campaign (DTE2). This paper addresses the key elements developed by the JET programme directly contributing to the D–T preparation. This intense preparation includes the review of the physics basis for the D–T operational scenarios, including the fusion power predictions through first principle and integrated modelling, and the impact of isotopes in the operation and physics of D–T plasmas (thermal and particle transport, high confinement mode (H-mode) access, Be and W erosion, fuel recovery, etc). This effort also requires improving several aspects of plasma operation for DTE2, such as real time control schemes, heat load control, disruption avoidance and a mitigation system (including the installation of a new shattered pellet injector), novel ion cyclotron resonance heating schemes (such as the three-ions scheme), new diagnostics (neutron camera and spectrometer, active Alfvèn eigenmode antennas, neutral gauges, radiation hard imaging systems...) and the calibration of the JET neutron diagnostics at 14 MeV for accurate fusion power measurement. The active preparation of JET for the 2020 D–T campaign provides an incomparable source of information and a basis for the future D–T operation of ITER, and it is also foreseen that a large number of key physics issues will be addressed in support of burning plasmas.This work has been carried out within the framework of the EUROfusion Consortium and has received funding from the Euratom research and training programme 2014–2018 and 2019–2020 under grant agreement No. 633053Postprint (published version

Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning

Multi-objective optimisation design procedures have shown to be a valuable tool for control engineers. They enable the designer having a close embedment of the tuning process for a wide variety of applica- tions. In such procedures, evolutionary multi-objective optimisation has been extensively used for PI and PID controller tuning; one reason for this is due to their flexibility to include mechanisms in order to en- hance convergence and diversity. Although its usability, when dealing with multi-variable processes, the resulting Pareto front approximation might not be useful, due to the number of design objectives stated. That is, a vast region of the objective space might be impractical or useless a priori, due to the strong degradation in some of the design objectives. In this paper preference handling techniques are incorpo- rated into the optimisation process, seeking to improve the pertinency of the approximated Pareto front for multi-variable PI controller tuning. That is, the inclusion of preferences into the optimisation process, in order to seek actively for a pertinent Pareto front approximation. With such approach, it is possible to tune a multi-variable PI controller, fulfilling several design objectives, using previous knowledge from the designer on the expected trade-off performance. This is validated with a well-known benchmark exam- ple in multi-variable control. Control tests show the usefulness of the proposed approach when compared with other tuning techniques.This work was partially supported by the fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) and by EVO-CONTROL project (ref. PROMETEO/2012/028, Generalitat Valenciana - Spain).Reynoso Meza, G.; Sanchís Saez, J.; Blasco, X.; Freire, RZ. (2016). Evolutionary multi-objective optimisation with preferences for multivariable PI controller tuning. Expert Systems with Applications. 51:120-133. doi:10.1016/j.eswa.2015.11.028S1201335

Valorització de dissolvents

This project explains about solvents valuation, doing the appropriate calculation to carry through a flash distillation. To carry out this calculation, we have to measure a flash separator. We have realized this calculation from calculate feed temperature. We have calculated the outgoing current, vapour current and liquid current, as well as we have calculated the outgoing compositions, the temperature and the pressure operation. In other hand, we have to measure a heat exchanger, to bring the feed from environment temperature to operation temperature. In this sense, the project is characterized by be an open project, given that there are different ways to investigate. This ways of investigation will give us a good form to realize an economical solvent recovery

Controller tuning by means of multi-objective optimization algorithms: a global tuning framework

© 2013 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.A holistic multi-objective optimization design technique for controller tuning is presented. This approach gives control engineers greater flexibility to select a controller that matches their specifications. Furthermore, for a given controller it is simple to analyze the tradeoff achieved between conflicting objectives. By using the multi-objective design technique it is also possible to perform a global comparison between different control strategies in a simple and robust way. This approach thereby enables an analysis to be made of whether a preference for a certain control technique is justified. This proposal is evaluated and validated in a nonlinear multiple-input multiple-output system using two control strategies: a classical proportional- integral-derivative control scheme and a feedback state controller.This work was supported in part by the FPI-2010/19 Grant and the Project PAID-06-11 from the Universitat Politecnica de Valencia and in part by the Projects DPI2008-02133, TIN2011-28082, and ENE2011-25900 from the Spanish Ministry of Science and Innovation.Reynoso Meza, G.; García-Nieto Rodríguez, S.; Sanchís Saez, J.; Blasco, X. (2013). Controller tuning by means of multi-objective optimization algorithms: a global tuning framework. IEEE Transactions on Control Systems Technology. 21(2):445-458. https://doi.org/10.1109/TCST.2012.2185698S44545821

A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization

[EN] New challenges in engineering design lead to multiobjective (multicriteria) problems. In this context, the Pareto front supplies a set of solutions where the designer (decision-maker) has to look for the best choice according to his preferences. Visualization techniques often play a key role in helping decision-makers, but they have important restrictions for more than two-dimensional Pareto fronts. In this work, a new graphical representation, called Level Diagrams, for n-dimensional Pareto front analysis is proposed. Level Diagrams consists of representing each objective and design parameter on separate diagrams. This new technique is based on two key points: classification of Pareto front points according to their proximity to ideal points measured with a specific norm of normalized objectives (several norms can be used); and synchronization of objective and parameter diagrams. Some of the new possibilities for analyzing Pareto fronts are shown. Additionally, in order to introduce designer preferences, Level Diagrams can be coloured, so establishing a visual representation of preferences that can help the decision-maker. Finally, an example of a robust control design is presented - a benchmark proposed at the American Control Conference. This design is set as a six-dimensional multiobjective problem. (c) 2008 Elsevier Inc. All rights reserved.Partially supported by MEC (Spanish Government) and FEDER funds: Projects DPI2005-07835, DPI2004-8383-C03-02 and GVA-026.Blasco, X.; Herrero Durá, JM.; Sanchís Saez, J.; Martínez Iranzo, MA. (2008). A new graphical visualization of n-dimensional Pareto front for decision-making in multiobjective optimization. Information Sciences. 178(20):3908-3928. https://doi.org/10.1016/j.ins.2008.06.010S390839281782

Preference driven multi-objective optimization design procedure for industrial controller tuning

Multi-objective optimization design procedures have shown to be a valuable tool for con- trol engineers. These procedures could be used by designers when (1) it is difficult to find a reasonable trade-off for a controller tuning fulfilling several requirements; and (2) if it is worthwhile to analyze design objectives exchange among design alternatives. Despite the usefulness of such methods for describing trade-offs among design alterna- tives (tuning proposals) with the so called Pareto front, for some control problems finding a pertinent set of solutions could be a challenge. That is, some control problems are com- plex in the sense of finding the required trade-off among design objectives. In order to improve the performance of MOOD procedures for such situations, preference handling mechanisms could be used to improve pertinency of solutions in the approximated Pareto front. In this paper an overall MOOD procedure focusing in controller tuning applications using designer s preferences is proposed. In order to validate such procedure, a bench- mark control problem is used and reformulated into a multi-objective problem statement, where different preference handling mechanisms in the optimization process are evalu- ated and compared. The obtained results validate the overall proposal as a potential tool for industrial controller tuning.This work was partially supported by projects TIN2011-28082, ENE2011-25900 from the Spanish Ministry of Economy and Competitiveness. First author gratefully acknowledges the partial support provided by the postdoctoral fellowship BJT-304804/2014-2 from the National Council of Scientific and Technologic Development of Brazil (CNPq) for the development of this work.Reynoso Meza, G.; Sanchís Saez, J.; Blasco Ferragud, FX.; Martínez Iranzo, MA. (2016). Preference driven multi-objective optimization design procedure for industrial controller tuning. Information Sciences. 339:108-131. doi:10.1016/j.ins.2015.12.002S10813133

Evidence of functional declining and global comorbidity measured at baseline proved to be the strongest predictors for long-term death in elderly community residents aged 85 years: a 5-year follow-up evaluation, the OCTABAIX study

Objective: To investigate the predictive value of functional impairment, chronic conditions, and laboratory biomarkers of aging for predicting 5-year mortality in the elderly aged 85 years. Methods: Predictive value for mortality of different geriatric assessments carried out during the OCTABAIX study was evaluated after 5 years of follow-up in 328 subjects aged 85 years. Measurements included assessment of functional status comorbidity, along with laboratory tests on vitamin D, cholesterol, CD4/CD8 ratio, hemoglobin, and serum thyrotropin. Results: Overall, the mortality rate after 5 years of follow-up was 42.07%. Bivariate analysis showed that patients who survived were predominantly female (P=0.02), and they showed a significantly better baseline functional status for both basic (P<0.001) and instrumental (P<0.001) activities of daily living (Barthel and Lawton index), better cognitive performance (Spanish version of the Mini-Mental State Examination) (P<0.001), lower comorbidity conditions (Charlson) (P<0.001), lower nutritional risk (Mini Nutritional Assessment) (P<0.001), lower risk of falls (Tinetti gait scale) (P<0.001), less percentage of heart failure (P=0.03) and chronic obstructive pulmonary disease (P=0.03), and took less chronic prescription drugs (P=0.002) than nonsurvivors. Multivariate Cox regression analysis identified a decreased score in the Lawton index (hazard ratio 0.86, 95% confidence interval: 0.78-0.91) and higher comorbidity conditions (hazard ratio 1.20, 95% confidence interval: 1.08-1.33) as independent predictors of mortality at 5 years in the studied population. Conclusion: The ability to perform instrumental activities of daily living and the global comorbidity assessed at baseline were the predictors of death, identified in our 85-year-old community-dwelling subjects after 5 years of follow-up

Nonlinear Robust Identification using Evolutionary Algorithms. Application to a Biomedical Process

[EN] This work describes a new methodology for robust identification (RI), meaning the identification of the parameters of a model and the characterization of uncertainties. The alternative proposed handles non-linear models and can take into account the different properties demanded by the model. The indicator that leads the identification process is the identification error (IE), that is, the difference between experimental data and model response. In particular, the methodology obtains the feasible parameter set (FPS, set of parameter values which satisfy a bounded IE) and a nominal model in a non-linear identification problem. To impose different properties on the model, several norms of the IE are used and bounded simultaneously. This improves the model quality, but increases the problem complexity. The methodology proposes that the RI problem is transformed into a multimodal optimization problem with an infinite number of global minima which constitute the FPS. For the optimization task, a special genetic algorithm (epsilon-GA), inspired by Multiobjective Evolutionary Algorithms, is presented. This algorithm characterizes the FPS by means of a discrete set of models well distributed along the FPS. Finally, an application for a biomedical model which shows the blockage that a given drug produces on the ionic currents of a cardiac cell is presented to illustrate the methodology. (C) 2008 Elsevier Ltd. All rights reserved.Partially supported by MEC (Spanish government) and FEDER funds: Projects DP12005-07835, DP12004-8383-CO3-02 and Generalitat Valenciana (Spain) Project GVA-026.Herrero Durá, JM.; Blasco, X.; Martínez Iranzo, MA.; Ramos Fernández, C.; Sanchís Saez, J. (2008). Nonlinear Robust Identification using Evolutionary Algorithms. Application to a Biomedical Process. Engineering Applications of Artificial Intelligence. 21(8):1397-1408. https://doi.org/10.1016/j.engappai.2008.05.001S1397140821

Robust identification of non-linear greenhouse model using evolutionary algorithms

[EN] This paper presents the non-linear modelling, based oil first principle equations, for a climatic model of a greenhouse and the estimation of the feasible parameter set (FPS) when the identification error is bounded simultaneously by several norms. The robust identification problem is transformed into a multimodal optimization problem with an infinite number of global minima that constitute the FPS. For the optimization task, a special evolutionary algorithm (epsilon-GA) is presented, which characterizes the FPS by means of a discrete set of models that are well distributed along the FPS. A procedure for determining the norm bounds, such that FPS not equal 0, is (c) 2007 Elsevier Ltd. All rights reserved.Partially supported by MEC (Spanish government) and FEDER funds: projects DPI2005-07835 and DPI2004- 8383-C03-02.Herrero Durá, JM.; Blasco, X.; Martínez Iranzo, MA.; Ramos Fernández, C.; Sanchís Saez, J. (2008). Robust identification of non-linear greenhouse model using evolutionary algorithms. Control Engineering Practice. 16(5):515-530. https://doi.org/10.1016/j.conengprac.2007.06.001S51553016