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A recently developed spray Flamelet Generated Manifolds (SFGM) approach has been extended to include turbulence–chemistry interactions for studies of turbulent spray flames based on Eulerian–Lagrangian multiphase Large Eddy Simulation (LES). The Eulerian–Eulerian two-fluid formulation of the SFGM was extended to include the mixture fraction variance to construct a 4-D manifold library. The effect of heat loss due to droplet evaporation was considered in the SFGM tabulation, and the SFGM approach was evaluated using the Sydney ethanol spray flame experimental database. The two-phase interaction was investigated from complete analyses of the continuous- and dispersed-phase dynamics. Comparisons between the numerical results and the experimental data indicate that the SFGM formulated from counterflow spray flames can produce a good agreement with the gaseous flow field and the dispersed-phase statistics. By comparing the results in terms of the temperature field with other existing models, it can be inferred that the discrepancies between the flamelet-based results and the experimental data may not depend on the choice of the flamelet approach but on the modeling of the turbulence/chemistry interaction model. A Beta-function of the normalized progress variable can be an alternative to improve the temperature predictions. Moreover, using a well-designed definition of the progress variable by finding the optimal weights of several appropriate species is another possible path to improve the predictions. [ABSTRACT FROM AUTHOR]

Feedforward control with model inversion is a widely-used solution for high-precision output tracking. However, because inverting a non-minimum phase model generates unbounded control input, model-inversion only applies to limited types of systems. This paper presents a new non-parametric pseudo-inversion approach to design bounded optimal control input with desirable properties for arbitrary types of systems. Closed-form equations are presented for the batch (full preview) and recursive (limited preview) implementations of this approach, and its performance is compared against existing pseudo-inversion methods in benchmark numerical examples. Furthermore, the practical implementation of the proposed method is demonstrated by designing a feedforward controller for a commercial 3-Dimensional (3D) printer. The results show that the proposed approach effectively compensates for the structural vibrations of the printer, preventing layer-shifting errors that usually happen during high-speed printing. [ABSTRACT FROM AUTHOR]

We use the spatial map of the logarithm of past estimated released earthquake energies as input of fully convolutional networks (FCN) to forecast future earthquakes. This model is applied to California and compared with an elaborated version of the epidemic type aftershock sequence (ETAS) model. Our long-term earthquake forecast simulations show that the FCN model is close to the ETAS model in forecasting earthquakes with |$M \ge 3.0,\,\,4.0,\,\,{\rm{and\,\,}}5.0$| according to the Molchan diagram. Moreover, training and implementing the FCN model is 2000–4000 times faster than calibrating the ETAS model and generating its probabilistic forecasts. The FCN model is straightforward in terms of its neural network structure and feature engineering. It does not require extensive knowledge of statistical seismology or the analysis of earthquake catalogue completeness. Using the earthquake catalogue with |$M \ge 0$| as FCN input can enhance the model's performance in some time–magnitude forecasting windows. [ABSTRACT FROM AUTHOR]

We study a family of structure-preserving deterministic numerical schemes for Lindblad equations. This family of schemes has a simple form and can systemically achieve arbitrary high-order accuracy in theory. Moreover, these schemes can also overcome the non-physical issues that arise from many traditional numerical schemes. Due to their preservation of physical nature, these schemes can be straightforwardly used as backbones for further developing randomized and quantum algorithms in simulating Lindblad equations. In this work, we systematically study this family of structure-preserving deterministic schemes and perform a detailed error analysis, which is validated through numerical examples. [ABSTRACT FROM AUTHOR]