The thermal conditions of Venus

Models of Venus' thermal evolution are examined. The following subject areas are covered: (1) modified approximation of parameterized convection; (2) description of the model; (3) numerical results and asymptotic solution of the MAPC equations; (4) magnetism and the thermal regime of the cores of Earth and Venus; and (5) the thermal regime of the Venusian crust

Electron-positron pair production in the Aharonov-Bohm potential

In the framework of QED we evaluate the cross section for electron-positron pair production by a single photon in the presence of the external Aharonov-Bohm potential in first order of perturbation theory. We analyse energy, angular and polarization distributions at different energy regimes: near the threshold and at high photon energies.Comment: LaTeX file, 13 page

Thermokinetic Study of Aluminum-Induced Crystallization of a-Si: The Effect of Al Layer Thickness

The effect of the aluminum layer on the kinetics and mechanism of aluminum-induced crystallization (AIC) of amorphous silicon (a-Si) in (Al/a-Si)n multilayered films was studied using a complex of in situ methods (simultaneous thermal analysis, transmission electron microscopy, electron diffraction, and four-point probe resistance measurement) and ex situ methods (X-ray diffraction and optical microscopy). An increase in the thickness of the aluminum layer from 10 to 80 nm was found to result in a decrease in the value of the apparent activation energy Ea of silicon crystallization from 137 to 117 kJ/mol (as estimated by the Kissinger method) as well as an increase in the crystallization heat from 12.3 to 16.0 kJ/(mol Si). The detailed kinetic analysis showed that the change in the thickness of an individual Al layer could lead to a qualitative change in the mechanism of aluminum-induced silicon crystallization: with the thickness of Al ≤ 20 nm. The process followed two parallel routes described by the n-th order reaction equation with autocatalysis (Cn-X) and the Avrami–Erofeev equation (An): with an increase in the thickness of Al ≥ 40 nm, the process occurred in two consecutive steps. The first one can be described by the n-th order reaction equation with autocatalysis (Cn-X), and the second one can be described by the n-th order reaction equation (Fn). The change in the mechanism of amorphous silicon crystallization was assumed to be due to the influence of the degree of Al defects at the initial state on the kinetics of the crystallization process

Моделирование непрерывной затравочной кристаллизации гиббсита методом машинного обучения

Continuous seeded crystallization is characterized by oscillations of particle size distribution (PSD) and liquor productivity. To describe these oscillations using analytical methods is a complicated task due to non-linearity and slow response of the process. This paper uses a statistical approach to the preparation of initial data, determination of the significant factors and arrangement of the said factors by their impact on the dynamics of crystal population development. Various methods of machine learning were analyzed to develop a model capable of forecasting the time series of particle size distribution and composition of the final solution. The paper proposes to use deep learning methods for predicting the distribution of crystals by grades and liquor productivity. Such approach has never been used for these purposes before. The study shows that models based on long short-term memory (LSTM) cells provide for better accuracy with less trainable parameters as compared with other multilayer neural networks. Training of the models and the assessment of their quality are performed using the historical data collected in the hydrate crystallization area at the operating alumina refineryНепрерывной затравочной кристаллизации характерны осцилляции фракционного состава и продуктивности раствора, которые трудно описать аналитическими методами из-за существенной нелинейности и высокой инерционности процесса. В работе использован статистический подход к подготовке исходных данных, определению значимых факторов и их ранжированию по степени влияния на динамику развития популяции кристаллов. Выполнен анализ эффективности различных методов машинного обучения для построения модели, прогнозирующей временные ряды классов крупности частиц и состав конечного раствора. Предложен способ прогнозирования распределения популяции кристаллов по размерам и продуктивности раствора с использованием методов глубокого обучения, который для решения этой задачи в мировой практике еще не применялся. Показано, что модели на основе ячеек с долгой краткосрочной памятью (LSTM) обеспечивают более высокую точность при меньшем числе обучаемых параметров в сравнении с другими архитектурами многослойных нейронных сетей. Обучение моделей и оценка их качества выполнены на основе архива исторических данных, собранных на участках кристаллизации гидроксида алюминия на действующем глиноземном завод

On the Detectability and Use of Normal Modes for Determining Interior Structure of Mars

ISSN:1572-9672ISSN:0038-630