Javier Blasco

Modelling the Temporal Evolution of a Reduced Combustion Chemical System With an Artificial Neural Network

The present work introduces a way of embedding a combustion chemical system in a neural network, in such a way that it can be used, with considerable CPU time and RAM memory savings, in fluid-flow-simulation codes. The system is composed of four neural networks, with three of them simulating the evolution of the reactive species and one providing density and temperature as a function of composition. The performance in terms of accuracy of the networks is assessed by comparison with the results of the direct integration of the thermochemical system for a large number of random samples. Error measurements are reported, and sample evolutions of the chemical system with both methods are compared. It can be summarized that the results of this exercise are satisfactory, and the CPU-time and memory savings encouraging.

An economical strategy for storage of chemical kinetics: Fitting in situ adaptive tabulation with artificial neural networks

Reducing the computational time of chemical kinetics is essential for implementation of realistic chemistry into large-scale numerical simulations. Among the storage-based techniques, the in situ adaptive tabulation (ISAT) method features storing and retrieving data during the simulation: therefore, only the needed data are stored. As ISAT is based on linear approximation, the required storage can grow rapidly when a wide range of chemical states is involved, such as occurs in turbulent flames. An economical strategy for storing chemical kinetics data is proposed here by fitting results obtained from ISAT with artificial neural networks (ANN). This concept is explored in this study using a partially stirred reactor (PaSR) with two reduced chemical mechanisms of 9 and 17 reactive scalars. The performance of the ANN fitting is assessed on the basis of accuracy, memory, and CPU time. Test results based on PaSR demonstrate that a significant saving in memory can be realized with the ANN. Both the accuracy and CPU time with the ANN are found comparable with those of ISAT, suggesting great promise for use of ANN in large-scale computations.

High resolution x-ray absorption near edge structure at the Mn K edge of manganites

We report a systematic study of manganese perovskites, RE1-xDxMnO3 (RE = rare earth; D = Ca, Sr), by means of x-ray absorption near edge structure (XANES) spectroscopy at the Mn K edge. High resolution has been achieved by recording the intensity of the Mn Kβ fluorescence spectrum. All the samples show a unique resonance at the Mn K edge. This common feature does not depend on small changes in the local structure around the Mn atom. The XANES spectra of mixed (0<x<1) composition compounds cannot be obtained by linear combination of REMnO3 and DMnO3 spectra. Therefore, Mn3+ and Mn4+ ions cannot be distinguished in intermediate RE1-xDxMnO3 compounds, either spatially or temporally. The possibility of a charge disproportionation is also considered and discussed in the paper.

Raman and x-ray study of perovskite solid solutions

La and Nd gallates are currently being used as substrate materials for high temperature superconductor thin film deposition. In this work we have synthesized and characterized by means of XRD and Raman spectroscopy solid solutions of the type . Polarized Raman study of and single crystals is also performed. Our results confirm the Pbnm space group for both La and Nd perovskites at room temperature and the centrosymmetric for in the high temperature phase. The orthorhombic to rhombohedral first order phase transition ( in ) is studied as a function of Nd substitution. increases quadratically with x for . Beyond x = 0.17 the phase transition, if present, is above the limit of our experimental setup (870 K). The detection of a soft mode in the rhombohedral phase is interpreted as due to the existence of a virtual second order transition to cubic symmetry at high temperature, analogous to that observed in rare earth aluminates.

Analysis of the x-ray resonant scattering at the Mn K edge in half-doped mixed valence manganites

We reconsider the interpretation of x-ray resonant scattering experiments performed on the so-called charge-ordered manganites with Mn3+/Mn4+ = 1. The comparison between these experiments and our x-ray absorption data shows that no real Mn3+/Mn4+ charge ordering occurs in these compounds. However, these experiments demonstrate the presence of two different types of manganese atom with a different local geometrical structure. We propose a structural model which accounts for these resonant scattering experiments. Within this model, the charge-ordering phase transition can be explained as a structural phase transition driven by the softening of a phonon mode. This produces a periodic arrangement of local distortions, which is responsible for the observed resonances. Furthermore, electronic localization in these materials would occur on a length scale larger than the atomic one.

A self-organizing-map approach to chemistry representation in combustion applications

Several alternative techniques have been proposed in the literature in order to avoid the CPU-intensive numerical integration of the thermochemical equations in the simulation of combustion processes. The present paper introduces a new approach, which is based on two artificial neural-network (ANN) paradigms, namely the self-organizing map (SOM) and the multilayer perceptron (MLP). The SOM is first employed for the automatic partitioning of the thermochemical space into subdomains. Then, a specialized MLP is trained in order to fit the thermochemical points belonging to a given subdomain. The presented strategy is tested on a partially stirred reactor (PaSR) with a reduced methane-air mechanism, and encouraging results are reported. The relatively modest CPU-time and memory requirements of the method make the SOM-MLP approach a promising technique for the inclusion of large chemical mechanisms in the context of complex applications, such as the multidimensional simulation of combustion.

A single-step time-integrator of a methane–air chemical system using artificial neural networks

Abstract The present paper reports a novel method for embedding a reduced chemical system, suitable for the simulation of methane–air combustion, in an artificial neural network (ANN). The use of ANNs as a means of storing in a compact manner the chemical kinetics of a system is an emerging alternative to other methods, the full potential of which remains to be exploited. The current contribution introduces two novelties: firstly, the compositional domain is split into subdomains, for each of which an ANN fitting is attempted; and secondly, the timestep is introduced as an additional input to the network, thus increasing the accuracy and speed of the method. The paper introduces three alternative types of network, and describes in detail the methodology used for their construction and validation, as well as the validation results. The level of accuracy attained is at least one order of magnitude better than with previously-published ANN approaches.

Valence transition in (Pr,Ca)CoO3 cobaltites: Charge migration at the metal-insulator transition

6 paginas, 5 figuras, 1 tabla.-- PACS number(s): 71.30.+h, 75.25.Dk, 75.30.Wx, 78.70.Dm.-- et al.

An efficient particle-locating algorithm for application in arbitrary 2D and 3D grids

Abstract Several alternative techniques have been proposed in the literature to efficiently locate particles within unstructured grids. The present paper reviews two recently published, particle-locating algorithms, and introduces a new approach which improves the performance of the previous methods. The proposed algorithm is valid for arbitrary two-dimensional (2D) or three-dimensional (3D) grids, it is simple to implement, and results in fairly small CPU-time requirements. Furthermore, the directed-search feature of the present method offers shorter search paths and allows the detection of walls during the tracking of the particle trajectory. The performance of the proposed particle-locating strategy is compared with existing ones, and is evaluated on two tests, namely a 2D/3D random particle-locating test and a 2D Lagrangian simulation of a premixed turbulent flame.

Valence change of praseodymium in Pr0.5Ca0.5CoO3 investigated by x-ray absorption spectroscopy

X-ray absorption spectroscopy measurements in Pr0.5Ca0.5CoO3 were performed at the Pr M4,5, Pr L3, and Ca L2,3 absorption edges as a function of temperature below 300 K. Ca spectra show no changes down to 10 K while a noticeable thermally dependent evolution takes place at the Pr edges across the metal-insulator transition. Spectral changes are analyzed by different methods, including multiple scattering simulations, which provide quantitative details on an electron loss at Pr 4f orbitals. We conclude that in the insulating phase a fraction [15(+5)%] of Pr3+ undergoes a further oxidation to adopt a hybridized configuration composed of an admixture of atomic-like 4f1 states (Pr4+) and f- symmetry states on the O 2p valence band (Pr3+L states) indicative of a strong 4f- 2p interaction.