Volume 38, Issue 3

A particle tracking method based on face fluxes data calculated using finite volume methods is developed for unstructured three-dimensional polyhedral grids. The flow velocity field reconstruction on grid cells using a mixed finite element method is proposed. Cases of sinks and sources in cells as well as different cell partitionings are considered. Algorithms for streamlines and time of flight calculation are provided. Performance and convergence of the method are demonstrated on a set of reference problems.

A new parallel numerical framework CarNum is presented for efficient coupling of mathematical models in multiphysics problems such as computational cardiac electromechanics. This framework is based on open source projects, which provide the core functionality of the platform. Computational cardiac electromechanics requires a complex pipeline of solving different types of ordinary and partial differential equations. Our framework allows one to implement different numerical schemes and provides more control in multiphysics coupling. This paper outlines a concept of the new platform and details of numerical modelling of cardiac electromechanics. First experiments with well-known cardiac electromechanics benchmarks show good agreement with other groups and decent parallel scalability.

The paper presents the dynamical core of the new sea ice model SIMUG (Sea Ice Model on Unstructured Grid) on the A- and CD-types of unstructured triangular grids in the local-element basis on sphere. Three standardized box tests to reproduce the Linear Kinematic Features (LKFs), and the short-term forecast in the real Arctic Ocean geometry with the realistic atmosphere and ocean forcing demonstrate the model quality compared to other sea ice models like CICE, FESOM, MITgcm, and ICON-O. The distinctive features of the model presented are a wide choice of transport schemes, and the new numerical implementation with the serial and parallel C++ coding and INMOST, Ani2D, and Ani3D packages to deal with unstructured grids. Code profiling and scalability assessment are carried out. In general, the A-version of the ice drift model works faster, but has fewer degrees of freedom on the same grid. Due to the increase in the degrees of freedom, the model on the CD grid gives ultra-resolution of LKFs, but requires more strict conditions for stability.

In the present work, we construct a model of coronary flow, which utilizes both CT scans of large coronary arteries and coronary CT perfusion. The model describes pulsatile flow in the patient’s network of coronary vessels and takes into account a number of physiological effects: myocardium contractions, stenoses, impairment of microvascular perfusion. The main novelty of this model is the new smooth boundary conditions that have not been used before in patient-specific simulations of coronary circulation. New boundary conditions use 0D lumped model approach and provide asymptotic convergence of the solution for the cases of one-to-one vascular connection and bifurcation with a very thin child vessel. The new boundary conditions make it possible to estimate the fractional flow margin more accurately. We also studied sensitivity of haemodynamic indices (fractional flow reserve, coronary flow reserve, instantaneous wave-free ratio) to the variations of microcirculation impairment. No substantial difference in sensitivity was observed between new model and original approach. The advantage of the presented approach is the availability of the required data in everyday clinical practice and, thus, improved personalization of the model.

Modern numerical models of the Earth system are complex and inherit its natural chaotic behaviour. The numerical results depend on various specifications of the simulation process, including computing systems, compilers, etc. Due to the chaotic behaviour, these minor differences lead to significant and unpredictable deviations. Therefore, some procedure verifying that simulation results describe the behaviour of the same physical system is of practical importance. The present paper proposes a statistical verification algorithm developed for the INM RAS Earth system model. Different ensemble generation techniques and statistical estimators are evaluated for verification suitability. The ability of the method to detect the deviations in the simulation results is demonstrated on a series of cases. Practical guidelines on how to choose the perturbation amplitude for the ensemble generation are provided for various verification cases.

The present study promotes a new algorithm for estimating the water pollution propagation with the primary goal of providing more reliable and high quality estimates to decision makers. To date, the widely used variational method suffers from the large computational burden, which limits its application in practice. Moreover, this method, considering the initial state as a control variable, is very sensitive in specifying initial error, especially for unstable dynamical systems. The Neural Network Filter (NNF), proposed in the present paper, is aimed at overcoming these two drawbacks in the variational method: by its nature, the NNF is sequential (no batch large assimilation window used) and stable even for unstable dynamics, with the gain parameters as control variables. The NNF, developed in the present paper, is a Neural Network Filter (NNF) version of the Singular Evolutive Interpolated Kalman Filter (SEIKF). One of the new versions of this NNF is that it uses structure of the gain of SEIKF0 taken by the SEIKF at the first time moment of correction process. To deal with the uncertainty of the system parameters and of the noise covariance, the proposed Neural Network SEIKF0 named by NNSEIKF0 makes use of the covariance of a reduced rank iterated during assimilation process and of some pertinent gain parameters tuned adaptively to yield the minimum prediction error for the system output. The computational burden in implementation of the NNSEIKF0 is reduced drastically due to applying the optimization tool known as a simultaneous perturbation stochastic approximation (SPSA) algorithm, which requires only two integrations of the numerical model. No iterative loop is required at each assimilation instant as usually happens with the standard gradient descent optimization algorithms. Data assimilation experiment, carried out by the SEIKF0 and NNSEIKF0, is implemented for the Thanh Nhan Lake in Hanoi and the performance comparison between the NNSEIKF0 and SEIKF0 is given to show the high efficiency of the proposed NNSEIKF0.