Direct, inverse and mirror wave modes of coupled displacements and microrotations monochromatic plane waves in hemitropic micropolar media

В статье обсуждаются вопросы распространения монохроматических волн в гемитропном микрополярном континууме. Сформулированы уравнения динамики гемитропного микрополярного упругого тела в терминах псевдотензоров с 9-ю определяющими псевдоскалярами. Рассмотрены преобразования указанных уравнений в случаях инверсии пространства и зеркального отражения относительно заданной плоскости. Показано наличие инверсных волновых мод (наряду с прямыми) в распространяющейся плоской волне. Получены формулы преобразования прямых волновых мод перемещений и микровращений в инверсные и зеркально отраженные моды. Приводятся соответствующие формулы. The paper deals with the propagation of monochromatic plane waves in a hemitropic micropolar continuum. The dynamics equations of a hemitropic micropolar elastic solid in terms of pseudotensors with 9 constitutive pseudoscalars are derived and discussed. Formulae for the cases of space inversion and mirror reflection relative to a given plane are obtained and considered. The simultaneous existence of the direct, inverse and mirror reflected wave modes in propagating plane waves is established. Formulae for direct wave modes of displacements and microrotations in inverse and mirror modes are given.

Data-Space Inversion With a Recurrent Autoencoder for Naturally Fractured Systems

Data-space inversion (DSI) is a data assimilation procedure that directly generates posterior flow predictions, for time series of interest, without calibrating model parameters. No forward flow simulation is performed in the data assimilation process. DSI instead uses the prior data generated by performing O(1000) simulations on prior geomodel realizations. Data parameterization is useful in the DSI framework as it enables representation of the correlated time-series data quantities in terms of low-dimensional latent-space variables. In this work, a recently developed parameterization based on a recurrent autoencoder (RAE) is applied with DSI for a real naturally fractured reservoir. The parameterization, involving the use of a recurrent neural network and an autoencoder, is able to capture important correlations in the time-series data. RAE training is accomplished using flow simulation results for 1,350 prior model realizations. An ensemble smoother with multiple data assimilation (ESMDA) is applied to provide posterior DSI data samples. The modeling in this work is much more complex than that considered in previous DSI studies as it includes multiple 3D discrete fracture realizations, three-phase flow, tracer injection and production, and complicated field-management logic leading to frequent well shut-in and reopening. Results for the reconstruction of new simulation data (not seen in training), using both the RAE-based parameterization and a simpler approach based on principal component analysis (PCA) with histogram transformation, are presented. The RAE-based procedure is shown to provide better accuracy for these data reconstructions. Detailed posterior DSI results are then presented for a particular “true” model (which is outside the prior ensemble), and summary results are provided for five additional “true” models that are consistent with the prior ensemble. These results again demonstrate the advantages of DSI with RAE-based parameterization for this challenging fractured reservoir case.

History Matching and Production Prediction of Steam Drive Reservoir Based on Data-Space Inversion Method

Recently, a data-space inversion (DSI) method has been proposed and successfully applied for the history matching and production optimization for conventional waterflooding reservoir. Under Bayesian framework, DSI can directly and effectively obtain posterior flow predictions without inverting any geological parameters of reservoir model. In this paper, we integrate the numerical simulation model with DSI method for rapid history matching and production prediction for steam flooding reservoir. Based on the finite volume method, a numerical simulation model is established and it is used to provide production data samples for DSI by the simulation of ensemble geological models. DSI-based production prediction model is then established and get trained by the historical data through the random maximum likelihood principle. The posterior production estimation can be obtained fast by training the DSI-based model with history data, but without any posterior geological parameters. The proposed method is applied for history matching and estimating production performance prediction in some numerical examples and a field case, and the results prove its effectiveness and reliability.

Nonreciprocity of spin waves in the conical helix state

Nonreciprocity emerges in nature and in artificial objects from various physical origins, being widely utilized in contemporary technologies as exemplified by diode elements in electronics. While most of the nonreciprocal phenomena are realized by employing interfaces where the inversion symmetry is trivially lifted, nonreciprocal transport of photons, electrons, magnons, and possibly phonons also emerge in bulk crystals with broken space inversion and time reversal symmetries. Among them, directional propagation of bulk magnons (i.e., quanta of spin wave excitation) is attracting much attention nowadays for its potentially large nonreciprocity suitable for spintronic and spin-caloritronic applications. Here, we demonstrate nonreciprocal propagation of spin waves for the conical spin helix state in Cu2OSeO3 due to a combination of dipole and Dzyaloshinskii–Moriya interactions. The observed nonreciprocal spin dispersion smoothly connects to the hitherto known magnetochiral nonreciprocity in the field-induced collinear spin state; thus, all the spin phases show diode characteristics in this chiral insulator.