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The problem of modeling interaction between processes with different time scales is very important in geoscience. In this report, we propose a new form of empirical evolution operator model based on the analysis of multiple time series representing processes with different time scales. We assume that the time series are given on the same time interval.To construct the model, we extend the previously developed general form of nonlinear stochastic model based on artificial neural networks and designed for the case of time series with constant sampling interval [1]. This sampling interval is related to the main time scale of the process under consideration, which is described by the deterministic component of the model, while the faster time scales are modeled by its stochastic component, possibly depending on the system’s state. This model also includes slower processes in the form of weak time-dependence, as well as external forcing. The structure of the model is optimized using Bayesian approach [1]. The model has proven its efficiency in a number of applications [2-4].The idea of modeling time series with different time scales is to formulate the above-described model individually for each time scale, and then to include the parameterized influence of the other time scales in it. Particularly, the influence of “slower” time series is included in the form of parameter trends, and the influence of “faster” time series is included by time-averaging their statistics. The algorithm and first results of comparison between the new model and the model without cross-interactions will be discussed.The work was supported by the Russian Science Foundation (Grant No. 20-62-46056).1. Gavrilov, A., Loskutov, E., & Mukhin, D. (2017). Bayesian optimization of empirical model with state-dependent stochastic forcing. Chaos, Solitons & Fractals, 104, 327–337. http://doi.org/10.1016/j.chaos.2017.08.0322. Mukhin, D., Kondrashov, D., Loskutov, E., Gavrilov, A., Feigin, A., & Ghil, M. (2015). Predicting Critical Transitions in ENSO models. Part II: Spatially Dependent Models. Journal of Climate, 28(5), 1962–1976. http://doi.org/10.1175/JCLI-D-14-00240.13. Gavrilov, A., Seleznev, A., Mukhin, D., Loskutov, E., Feigin, A., & Kurths, J. (2019). Linear dynamical modes as new variables for data-driven ENSO forecast. Climate Dynamics, 52(3–4), 2199–2216. http://doi.org/10.1007/s00382-018-4255-74. Mukhin, D., Gavrilov, A., Loskutov, E., Kurths, J., & Feigin, A. (2019). Bayesian Data Analysis for Revealing Causes of the Middle Pleistocene Transition. Scientific Reports, 9(1), 7328. http://doi.org/10.1038/s41598-019-43867-3

We suggest a new method for building data-driven dynamical models from observed multidimensional time series. The method is based on a recurrent neural network with specific structure, which allows for the joint reconstruction of both a low-dimensional embedding for dynamical components in the data and an evolution operator. The key link of the method is a Bayesian optimization of both model structure and the hypothesis about the data generating law, which is needed for constructing the cost function for model learning. First, the performance of the method is successfully tested in the situation when a signal from a low-dimensional dynamical system is hidden in noisy multidimensional observations. Second, the method is used for building the data-driven model of the low frequency variability (LFV) in the quasigeostrophic model of the Earth's midlatitude atmosphere-a high-dimensional chaotic system. It is demonstrated that the key regimes of the atmospheric LFV are reproduced correctly in data simulations by means of the obtained model.

The paper reveals the capabilities of SigmaFlow CFD code to predict wind conditions in terms of pedestrian comfort as illustrated by a model problem. The proposed numerical model was verified by comparing with experimental data. A group of buildings consisting of low-rise buildings and a high-rise building was considered. A comparative analysis of five computational variants with different grid saturation was performed. The results of mathematical modeling allow observing the vortex flow structure that is generated when streamlining buildings.

The present paper is the second part of a two-part study on empirical modeling and prediction of climate variability. This paper deals with spatially distributed data, as opposed to the univariate data of Part I. The choice of a basis for effective data compression becomes of the essence. In many applications, it is the set of spatial empirical orthogonal functions that provides the uncorrelated time series of principal components (PCs) used in the learning set. In this paper, the basis of the learning set is obtained instead by applying multichannel singular-spectrum analysis to climatic time series and using the leading spatiotemporal PCs to construct a reduced stochastic model. The effectiveness of this approach is illustrated by predicting the behavior of the Jin–Neelin–Ghil (JNG) hybrid seasonally forced coupled ocean–atmosphere model of El Niño–Southern Oscillation. The JNG model produces spatially distributed and weakly nonstationary time series to which the model reduction and prediction methodology is applied. Critical transitions in the hybrid periodically forced coupled model are successfully predicted on time scales that are substantially longer than the duration of the learning sample.

This paper presents the implementation of the immersed boundary method (IBM) for grids consisting of arbitrary cells. This approach allows to combine the IBM with the body-fitted method (BFM). The implementation is based on the combination of the “ghost-cell” method and penalization method. The paper presents the IBM to determine the basic design parameters and the results of the verification of the method for laminar and turbulent flow regimes for both Newtonian and viscoplastic fluids. In the last test, the method was used to simulate a flow in an annular channel with the orbital motion of the inner cylinder.

The paper presents the results of the implementation of computational algorithms of hydrodynamics for using graphics processor units. The implementation was carried out on the basis of the in-house CFD code SigmaFlow. Numerical simulations were based on the solution of the Navier-Stokes equations using SIMPLE-like procedure. The discretization of the differential equations was based on the control volume method on unstructured mesh. In the case of multiple CPU/GPU, parallel calculations were performed by means of domain decomposition. In the GPU-version of the code, basic computational functions were implemented as CUDA kernels to perform on GPUs. The code has been verified using several test cases. The computational efficiencies of several GPUs were compared with each other and that of modern CPUs. A modern GPU can increase the calculation performance of CFD problems by more than two times compared to a modern six-core CPU.

A model is proposed, which describes the work of the viscometer sensor of the physical pendulum type. The model enables the obtaining of data on fluid viscosity directly from the measurement of the settling frequency of sensor oscillations or the amplitude of these oscillations. To describe the sensor operation a numerical computational algorithm is developed. This method enables the solution of a wide class of three-dimensional laminar fluid flow problems involving moving solids of arbitrary geometry. The results of testing the proposed numerical technique are presented.

We suggest a new nonlinear expansion of space-distributed observational time series. The expansion allows constructing principal nonlinear manifolds holding essential part of observed variability. It yields low-dimensional hidden time series interpreted as internal modes driving observed multivariate dynamics as well as their mapping to a geographic grid. Bayesian optimality is used for selecting relevant structure of nonlinear transformation, including both the number of principal modes and degree of nonlinearity. Furthermore, the optimal characteristic time scale of the reconstructed modes is also found. The technique is applied to monthly sea surface temperature (SST) time series having a duration of 33 years and covering the globe. Three dominant nonlinear modes were extracted from the time series: the first efficiently separates the annual cycle, the second is responsible for ENSO variability and combinations of the second and the third modes explain substantial parts of Pacific and Atlantic dynamics. A relation of the obtained modes to decadal natural climate variability including current hiatus in global warming is exhibited and discussed.

This paper overviews and analyzes progressive trends in modern data center, and existing solutions to build the distributed cloud data center. Authors present the hierarchical distributed agent-based control plane architecture to build a web-scale control layer based on software-defined domains. The goal of this approach is to design the simple extensible agent that could be used for any management purposes, just by adding some specific code. Using of this approach makes easier the scalability and increases the efficiency of the management of a multi-site environment. There are five main use-cases of using this approach: distributed cloud, hybrid cloud, hyperscale data center, IoT, Continuous Integration.

Results of unsteady two-dimensional numerical simulations for the turbulent flow over the semi-cylindrical profile at Reynolds number 45000 are presented. Three different turbulence RANS eddy viscosity models are used for the simulation. The calculations show the formation of alternating large-scale vortices shed in the wake of the body. The calculated averaged pressure distribution on the contour demonstrate a good agreement with the experimental data. A series of calculations have been performed for the estimation of influence of mesh resolution in the wake of streamlined body on the unsteady solution accuracy. It was found that a mesh size ~0.04 of chord length is sufficient to resolve the vortex street.

Modern software development is currently seeking new paths to improve quality and meet time and cost constraints. Reuse of existing software components is considered one of these paths. However, this process experiences significant problems related to efficiently maintaining component repositories, and, moreover, providing the means to discover and retrieve the most suitable ones. This paper aims to provide a methodology to improve the component-based software development process. Specifically, its objective is to introduce an approach that reduces the time to locate suitable software components. The suggested methodology meets the requirements for the efficient searching of components in repositories and also addresses the need for adequate retrieval of the most suitable software components based on the needs of developers. To achieve this we employ a combination of partitional clustering algorithms borrowed from the field of computational intelligence and fuzzy logic thus creating a subset of the available components that are most suitable to the developers' preferences.