Generalized differential quadrature analysis of MHD mixed convective motion of Casson fluids past an isothermal irregular slender geometry via Cattaneo–Christov's approach

Classical Fourier's theory is well-known in continuum physics and thermal sciences. However, the primary drawback of this law is that it contradicts the principle of causality. To explore the thermal relaxation time characteristic, Cattaneo–Christov's theory is adopted thermally. In this regard, the features of magnetohydrodynamic (MHD) mixed convective flows of Casson fluids over an impermeable irregular sheet are revealed numerically. In addition, the resulting system of partial differential equations is altered via practical transformations into nonlinear ordinary differential equations. An advanced numerical algorithm is developed in this respect to get higher approximations for temperature and velocity fields, as well as their corresponding wall gradients. For validating our numerical code, the current outcomes are compared with the available literature results. Moreover, it is revealed that the velocity field is more prominent in the suction flow situation as compared with the injection flow case. It is also found that the Casson fluid is hastened in the case of lower yield stress. Larger values of thermal relaxation parameters create a lessening trend in the temperature distribution and its related boundary layer breadth.     

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).     

“电路分析”课程混合式教学探索与实践

本文以“电路分析”课程为例，借助云班课平台，采用“SPOC”与“BOPPPS”相结合的混合式教学模式，开展探究式、个性化、参与式教学。形成了“学生中心，产出导向，持续改进”闭环。经过近年来的不断改进，课程建设已初步完成。通过对三届学生的教学实践，发现采用“SPOC+BOPPPS”的线上线下混合式教学模式，学生学习积极性、主动性明显增强，同时学习效果也得到了显著提升。     

An iterative approach for the discrete‐time dynamic control of Markov jump linear systems with partial information

The , and mixed dynamic output feedback control of Markov jump linear systems in a partial observation context is studied through an iterative approach. By partial information, we mean that neither the state variable x(k) nor the Markov chain θ(k) are available to the controller. Instead, we assume that the controller relies only on an output y(k) and a measured variable coming from a detector that provides the only information of the Markov chain θ(k). To solve the problem, we resort to an iterative method that starts with a state‐feedback controller and solves at each iteration a linear matrix inequality optimization problem. It is shown that this iterative algorithm yields to a nonincreasing sequence of upper bound costs so that it converges to a minimum value. The effectiveness of the iterative procedure is illustrated by means of two examples in which the conservatism between the upper bounds and actual costs is significantly reduced.     

Mathematical formulation and heuristic algorithms for optimisation of auto-part milk-run logistics network considering forward and reverse flow of pallets

The operational planning of distribution network for automotive industry is complex with many conditions to consider, including heterogeneous fleet, enforcing the feasibility of 3D-packing of pallets into vehicles to address the vehicle's capacity in terms of weight and volume, compatibility of orders in a vehicle, returning empty pallets from assembly-plants backwards to suppliers, and delivery time windows. A mathematical model (MILP) is proposed that takes account of these conditions to minimise total transportation costs. The network structure can be a combination of direct shipment and milk-run for both forward and reverse flow of pallets. The model is solved optimally for small-size problems. For solving larger problems, a heuristic algorithm (in two versions) is proposed that uses a similarity measure to generate a reasonable list of orders. Best/first-fit strategies are employed to generate a feasible solution with the aid of a relaxed version of the proposed MILP. Improvement heuristics are also designed. Unlike most of existing constructive heuristics, our aim for developing the heuristic approach is to force routing decision, with all of its considerations, being made optimal. We also use the proposed best-fit strategy in the body of grouping evolution strategy (GES) algorithm to attain an effective meta-heuristic approach. The effectiveness of heuristics is tested on generated instances which demonstrates they are optimal for small-size problems. They are also tested on the data of daily auto-parts shipments gathered from the largest Iranian automobile company. Results demonstrate there exists a significant potential for cost saving through milk-run strategy compared with the direct shipping strategy.     

Computing approximate blocking probabilities for transparent waveband switching based WDM networks using hierarchical cross-connects

An analytical model is presented to study the dynamics of wavelength division multiplexing (WDM) networks with waveband switching (WBS). The reduced load approximation method is considered to compute approximated network blocking probabilities in WBS-based WDM networks. The analytical model considers the link blocking probability due to insufficient link capacity and an impact of the waveband granularity (G). The analytical model also considers the node blocking probability due to unavailability of a switch port at the wavelength cross connect (WXC) layer of an Hierarchical cross connect (HXC) switch node. The set of nonlinear equations is obtained with the link independence assumption and solved using repeated substitutions. The accuracy of the analytical model is examined by comparing with simulation results considering the random-fit algorithm for waveband and wavelength assignments in different network scenarios. Lightpaths are routed between source and destination (s-d) HXC switch nodes using shortest path first (SPF) routing. An impact of the switch parameter to limit the input and the output WXC switch ports of an HXC switching node is also being investigated using the analytical model as well as through simulation results.     

小尺度交通信号灯的检测与状态识别

交通信号灯识别包括检测和状态识别，在智能交通系统中发挥重要作用。基于YOLOv3算法提出了一种交通信号灯检测与状态识别模型。针对交通信号灯相较于交通场景中其他目标具有尺度小的特性进行了算法的设计：降低骨干网络的下采样倍率以增加小尺度目标的特征描述能力；通过增大特征图的尺度来改进多尺度特征融合；引入广义交并比作为检测任务的损失函数来改进目标边界框的回归效果。同时，根据交通信号灯本身的特性，使用颜色和形状约束的方法对信号灯进行状态识别和类别验证。最后在公开的Bosch交通信号灯数据集上和实际的城区道路进行了实验验证。实验结果表明，所提出的算法能够提升交通信号灯识别的精度和召回率，识别准确率可以达到90%左右。     

钢铁企业日际煤气最优混合配比的算法研究

钢铁企业根据日际生产计划,可以确定日际生产的副产煤气总体积、各煤气消耗设备的热量需求。为进一步确定各设备消耗的混合煤气中的煤气配比,使煤气产耗平衡,提出混合煤气逆向分解方法,将各设备消耗的混合煤气中所包含的单一煤气成分体积,表示为各设备获得热量与混合煤气热值的函数,在此基础上建立日际煤气最优混合配比算法模型。该模型以各设备的混合煤气热值及热量作为决策变量,以各煤气消耗设备的热量偏差最小为目标,综合考虑各煤气设备的热值要求、自备电厂的热值、热量要求及煤气体积守恒等约束条件。采用遗传算法求解,并利用遗传算法基因初始化的范围区间控制混合煤气的热值范围。算例结果表明:建立的日际煤气最优混合配比算法模型,在优化煤气分配的同时,显著减少了约束方程及决策变量的数量,为钢铁企业日际煤气平衡调度提供了理论支撑。     

The effect of traffic data source on deterioration rates of heavy-duty flexible pavements

The purpose of this study is to assess the effect of traffic data source (estimated vs. actual) on predicted progression rates of roughness and rutting for heavy-duty flexible pavements of rural freeways. Progression rates are predicted using calibrated HDM-4 models. The assessment is performed in terms of variations in maintenance intervention timing associated with the variations in progression rates. Time series pavement condition data (covering 3–5 years) have been collected for 7 sections of rural freeways for use in calibrating HDM-4 deterioration models. They range in length from 10 to 60.8 km and cover different traffic volumes, climate zones and subgrade soil types. For these sections, estimated annual average daily traffic (AADT), growth factors and assumed loading have been extracted from relevant database. Only six segments of these sections have Weigh-in-Motion (WIM) sites so relevant actual AADT, growth factors and axle load distributions have been extracted from WIM reports. The results of running the calibrated HDM-4 deterioration models using different traffic data show that actual traffic data from WIM sites result in higher rates of deterioration to that of estimated data for four sites, resulting in earlier intervention timing and higher present value agency cost. The other two sites have lower rates with actual data due to lower traffic loading than estimated.