一种“KooN”结构的马尔可夫简并建模和简化计算的方法

IEC61508等标准提出了几种计算安全仪表系统需求时平均失效概率的方法,但是,对于具有冗余配置的复杂系统,随着组件数量的增加,系统的中间状态数量快速增长,用户难以构建马尔可夫模型,即便借助计算机来建模运算也较为耗时。提出了一种同型“K oo N”简并状态的马尔可夫建模的通用方法,首先是根据降级状态进行判断,将符合条件的状态进行简并,然后对标记为危险失效状态的概率进行计算。通过严格的理论推导,该简并状态方法可以在不损失精度的前提下简化马尔可夫建模。     

MHD mixed convection of a Cu–water nanofluid flow through a channel with an open trapezoidal cavity and an elliptical obstacle

In the current work, numerical simulations are achieved to study the properties and the characteristics of fluid flow and heat transfer of (Cu–water) nanofluid under the magnetohydrodynamic effects in a horizontal rectangular canal with an open trapezoidal enclosure and an elliptical obstacle. The cavity lower wall is grooved and represents the heat source while the obstacle represents a stationary cold wall. On the other hand, the rest of the walls are considered adiabatic. The governing equations for this investigation are formulated, nondimensionalized, and then solved by Galerkin finite element approach. The numerical findings were examined across a wide range of Richardson number (0.1 ≤ Ri ≤ 10), Reynolds number (1 ≤ Re ≤ 125), Hartmann number (0 ≤ Ha ≤ 100), and volume fraction of nanofluid (0 ≤ φ ≤ 0.05). The current study's findings demonstrate that the flow strength increases inversely as the Reynolds number rises, which pushes the isotherms down to the lower part of the trapezoidal cavity. The Nu_avg rises as the Ri rise, the maximum Nu_avg = 10.345 at Ri = 10, Re = 50, ϕ = 0.05, and Ha = 0; however, it reduces with increasing Hartmann number. Also, it increase by increasing ϕ, at Ri = 10, the Nu_avg increased by 8.44% when the volume fraction of nanofluid increased from (ϕ = 0–0.05).     

Combined effect of channel to rib width ratio and gas diffusion layer deformation on high temperature – Polymer electrolyte membrane fuel cell performance

The present study investigates the combined influence of Channel to Rib Width (CRW) ratio and clamping pressure on the structure and performance of High Temperature-Polymer Electrolyte Membrane Fuel Cell (HT-PEMFC) using a three-dimensional numerical model developed previously. It also considers the impact of interfacial contact resistance between the Gas Diffusion Layer (GDL) and Bipolar Plate (BPP). The structural analysis of the single straight channel HT-PEMFC geometry shows that the von-Mises stress greatly increases in the GDL under the ribs as the CRW ratio increases resulting in considerably high deformation. The cell performance analysis depicts the significance of ohmic resistance and concentration polarization for different CRW ratios, particularly at higher operating current densities. However, in low to medium current density regions, the CRW ratio has little influence on cell performance. A substantial impact on the species, overpotential, and current distributions is observed. The findings also reveal that the CRW ratio significantly affects the temperature distribution in the cell.     

A comparison of shear stress estimation methods for a single geobag on a rough bed

There are several methods for estimating bed shear stress in the literature, but comprehensive comparisons among them are limited and under specific conditions. This study compared these methods first on a bare smooth bed, and then for a single geobag on a rough bed in the interest of determining the stability of geobags used in riverbank protection structures. The geobag was filled with cement or sand and tested under different open channel flow conditions. The turbulent kinetic energy method appeared to best represent the local bed shear stress on the geobag when using the newly calibrated proportionality constants. The Reynolds stress method via extrapolation was relatively unaffected by changes to the geobags shape and measurement locations, suggesting this method inadequately represents the local bed shear stress. The Patel method and the universal law of the wall method failed to represent local bed shear stress in the rough bed cases due to instrument limitations and the breakdown of the law of the wall. This study highlights the impact of different methods on the bed shear stress estimation.     

Convolutional Neural Network Auto Encoder Channel Estimation Algorithm in MIMO-OFDM System

Higher transmission rate is one of the technological features of prominently used wireless communication namely Multiple Input Multiple Output-Orthogonal Frequency Division Multiplexing (MIMO–OFDM). One among an effective solution for channel estimation in wireless communication system, specifically in different environments is Deep Learning (DL) method. This research greatly utilizes channel estimator on the basis of Convolutional Neural Network Auto Encoder (CNNAE) classifier for MIMO-OFDM systems. A CNNAE classifier is one among Deep Learning (DL) algorithm, in which video signal is fed as input by allotting significant learnable weights and biases in various aspects/objects for video signal and capable of differentiating from one another. Improved performances are achieved by using CNNAE based channel estimation, in which extension is done for channel selection as well as achieve enhanced performances numerically, when compared with conventional estimators in quite a lot of scenarios. Considering reduction in number of parameters involved and re-usability of weights, CNNAE based channel estimation is quite suitable and properly fits to the video signal. CNNAE classifier weights updation are done with minimized Signal to Noise Ratio (SNR), Bit Error Rate (BER) and Mean Square Error (MSE).     

Acidification of the electrolyte during the galvanic corrosion of AA7075: A numerical and experimental study

The electrochemical interactions between aluminum alloy 7075 and low-carbon steels under gelled electrolytes were studied. Such electrolytes provided the opportunity to investigate both thick and thin electrolyte systems. The electrolyte was chemically modified to visually track the acidic fronts during the anodic reaction and the subsequent hydrolysis process. Two mathematical models were validated for both thick and ultrathin electrolytes. The acidification of thick electrolytes was extended some millimeters beyond the aluminum alloy surface, whereas the acidic front was localized next to the metallic joint using ultrathin electrolytes. The combination of both numerical and experimental results allows proving (and explaining why) that the acidification process is more aggressive under dilute than under concentrated electrolytes.     

Form selection of concomitant polymorphs: A case study informed by crystallization kinetics modeling

Molecular mechanisms and process kinetics of crystallizing concomitant polymorphs remain poorly understood. Solvent-mediated phase transformation and concomitant crystallization are difficult to be distinguished in practice, as multiple forms can be detected at the same time. Herein, we developed a population balance model to simulate a concomitant crystallization process of two polymorphs of tolfenamic acid. Our kinetic modeling aims to understand concomitant crystallization and help guide form selection of such a molecular system. Crystallization kinetics of ethanolic solutions were uncovered from induction time measurements, as well as seeded and unseeded crystallization experiments. Experimental and simulation results demonstrate that the stable form I crystallizes concomitantly with the metastable form II. The faster growing form II results in an intermediate decline in the composition of form I in crystallized samples, a characteristic feature of the concomitantly crystallized system. A four-quadrant scheme of attainable polymorph outcome was simulated under various crystallization conditions.     

A novel adaptive sampling based methodology for feasible region identification of compute intensive models using artificial neural network

Identification of feasible region of operations in multivariate processes is a problem of interest in several fields. This is particularly challenging when the process model is black-box in nature and/or is computationally expensive, as analytical solutions are not available and the number of possible model evaluations is limited. An efficient methodology is required to identify samples where the model is evaluated for developing a computationally efficient surrogate model. In this work, an artificial neural network based surrogate model is proposed which is integrated with a statistical-based approach (Jack-knifing) to estimate the variance of the surrogate model prediction. This allows implementation of an adaptive sampling approach where new samples are identified close to the feasible region boundary or in regions of high prediction uncertainty. The proposed approach performs better than a previously published kriging based method for different dimensionality case studies.     

Mathematical Modeling of Drying Processes of Selected Fruits and Vegetables

This study investigates the behavior of fruit and vegetable samples during drying. The experimental data are fitted to several different thin-layer drying models. Regression analysis is used to determine model parameters, while statistical indicators serve to evaluate the goodness of fit. The power function model gives the best fit for all examined samples. Based on this model, different drying and heat storage technologies can be combined to ensure that the required residual moisture content of an agricultural product is reached. It is demonstrated on the case of a specific Togolese processing plant that under favorable conditions, fossil fuel consumption can be decreased by 33 %.