Flutter analysis for bridge decks using Lattice Boltzmann Method

Aiming at using the Lattice Boltzmann Method for flutter analysis of the bridge decks, a fluid- structure interaction algorithm is developed  within the framework of multiple –relaxation- time Lattice Boltzmann  Method. In the present algorithm, the unsteady fluid dynamics  is computed by the extended two-dimensional Lattice Boltzmann Method by incorporating the dynamic Smagorinsky subgrid scale  model, while the structure is modelled by an elastically suspended rigid body and its dynamic analysis is performed by using a Runge–Kutta method. A staggered coupling strategy is adopted to couple the fluid solver and the structure solver. To demonstrate the applicability of the presented algorithm, flutter analyses of the Second Forth Road Bridge and the Guamá River Bridge are employed. The numerical results are compared with wind tunnel measurements. It is shown that the  presented algorithm has a good prediction for the flutter onset  velocities of the Forth Road Bridge and the Guamá River Bridge and thus indicates, to a certain extent, the applicability of the presented algorithm

Modeling and Performance Evaluation of Multistage Serial Manufacturing Systems with Rework Loops and Product Polymorphism

This paper studies multistage serial manufacturing systems with the integrated consideration of machine failures, process defects, multiple rework loops, etc. In particular, multiple rework loops and product polymorphism lead to a more complex conversion of internal material flows, and therefore it's difficult to model and analyse such manufacturing systems. A modular modeling method based on Generalized Stochastic Petri Nets (GSPN) is presented to characterize the material flows, it is capable of representing the processing differences resulting from product polymorphism comparing with traditional Markov model or Queuing network model. By analysing the model, the processing ratio of each workstation is inferred. Using 2M1B (two-machine and one-buffer) Markov cell model as the building blocks, which is obtained based on the GSPN models for their isomorphism, an overlapping decomposition method is then developed for evaluating the performance of the multistage serial systems with rework loops. Numerical experiments and a case study of a powertrain assembly line illustrate the efficiency of the proposed method

A Heuristic Approach to Solve an Industrial Scalability Problem

In recent years, the rapid change of market demand is increasing the need for scalability as a key characteristic of manufacturing systems. Scalability allows production capacity to be rapidly and cost-effectively reconfigured in different situation with different requirements and constraints. Our industrial partners are facing quarterly scalability problems involving a multi-unit and multi-product manufacturing system. In this paper, an original approach is presented to solve this kind of problems. Starting from the original manufacturing system configuration and process plan, a set of practical principles are introduced to seek for the feasible configurations; a GA is designed to search in the global solution space. A balancing objective function is defined and used to rank the proposed configurations. A real case study with 4-unit / 4-product situation demonstrates both the validity and efficiency of the proposed approach

Systematic elucidation of the traditional Chinese medicine prescription Danxiong particles via network pharmacology and molecular docking

Purpose: To investigate the pharmacological effect of the traditional Chinese medicine (TCM) prescription Danxiong particles (TDX105) and its mechanism of action.Methods: The active compound and targets of TDX105 were investigated via network pharmacology. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were enriched, and protein-protein interaction network (PPI) was constructed. A network of ‘components-targets-pathways’ was developed with Cytoscape 3.8.0 software, while the formation of molecular docking analysis was conducted using Autodock vina software.Results: There were 304 compounds and 482 targets identified in total. Genes with degree ≥ mean node values were selected as the crucial targets, and string database was to be combined to 64 targets identified with cytoscape so as to draw a protein interaction map. A total of 137 pathways were enriched from 64 targets involving mainly 10 pathways, for example, PI3K-Akt signaling pathway, pathways in cancer, human cytomegalovirus infection and focal adhesion. Then, compound-target and compoundtarget- pathways were constructed using cytoscape (3.8.0). Finally, the five most active compounds, viz, quercetin, myricetin, luteolin, ellagic acid and kaempferol, and the top ten targets AKT1, GAPDH, TP53, ALB, EGFR, MAPK3, JUN, MAPK1, SRC and ESR1 were selected for molecular docking. These targets and compounds had strong interactions through a combination of hydrogen bonds and hydrophobic forces.Conclusion: The mechanism of action of TDX105 has been successfully explained using the combination of network pharmacology and molecular docking. This may offer a solid foundation to the clinical use of TDX105, and further strengthen the prospects of its development for clinical use

NiO hollow microspheres interconnected by carbon nanotubes as an anode for lithium ion batteries

In this work, NiO hollow microspheres interconnected by multi-walled carbon nanotubes (MWCNTs) were prepared, characterized, and evaluated in terms of lithium ion storage properties. Characterization results showed that the NiO hollow microspheres were formed by self assembly of NiO nanoparticles promoted by MWCNTs, which connected the NiO microspheres to form a long-range network. Electrochemical measurement results showed a charge capacity as high as 597.2 mAh g when cycling at the rate 2 C and maintained 85.3% capacity of 0.1 C. After cycling for 100 times at 1 C, it maintained a capacity of 692.3 mAh g with retention 89.3% of the initial capacity. The observed excellent electrochemical performance is attributed to the presence of MWCNTs interconnecting the NiO microspheres of the composite material, of which electronic conductivity was improved, and the mesoporous hollow structure effectively alleviated the volume changes to maintain the structural stability during cycling

Production Line Layout Planning Based on Complexity Measurement

Mass customization production increases the difficulty of the production line layout planning. The material distribution process for variety of parts is very complex, which greatly increases the cost of material handling and logistics. In response to this problem, this paper presents an approach of production line layout planning based on complexity measurement. Firstly, by analyzing the influencing factors of equipment layout, the complexity model of production line is established by using information entropy theory. Then, the cost of the part logistics is derived considering different variety of parts. Furthermore, the function of optimization including two objectives of the lowest cost, and the least configuration complexity is built. Finally, the validity of the function is verified in a case study. The results show that the proposed approach may find the layout scheme with the lowest logistics cost and the least complexity. Optimized production line layout planning can effectively improve production efficiency and equipment utilization with lowest cost and complexity

Generative Adversarial Mapping Networks

Generative Adversarial Networks (GANs) have shown impressive performance in generating photo-realistic images. They fit generative models by minimizing certain distance measure between the real image distribution and the generated data distribution. Several distance measures have been used, such as Jensen-Shannon divergence, $f$-divergence, and Wasserstein distance, and choosing an appropriate distance measure is very important for training the generative network. In this paper, we choose to use the maximum mean discrepancy (MMD) as the distance metric, which has several nice theoretical guarantees. In fact, generative moment matching network (GMMN) (Li, Swersky, and Zemel 2015) is such a generative model which contains only one generator network $G$ trained by directly minimizing MMD between the real and generated distributions. However, it fails to generate meaningful samples on challenging benchmark datasets, such as CIFAR-10 and LSUN. To improve on GMMN, we propose to add an extra network $F$, called mapper. $F$ maps both real data distribution and generated data distribution from the original data space to a feature representation space $\mathcal{R}$, and it is trained to maximize MMD between the two mapped distributions in $\mathcal{R}$, while the generator $G$ tries to minimize the MMD. We call the new model generative adversarial mapping networks (GAMNs). We demonstrate that the adversarial mapper $F$ can help $G$ to better capture the underlying data distribution. We also show that GAMN significantly outperforms GMMN, and is also superior to or comparable with other state-of-the-art GAN based methods on MNIST, CIFAR-10 and LSUN-Bedrooms datasets.Comment: 9 pages, 7 figure

Effect of Surcharge on the Stability of Rock Slope under Complex Conditions

In this paper, a general analytical expression for the factor of safety of the rock slope against plane failure is proposed, incorporating most of the practically occurring under complex conditions such as depth of tension crack, depth of water in tension crack, seismic loads and surcharge. Several special cases of this expression are established, which can be found similarly to those reported in the literature. A detailed parametric analysis is presented to study the effect of surcharge on the stability of the rock slope for practical ranges of main parameters such as depth of tension crack, depth of water in tension crack, the horizontal seismic coefficient and the vertical seismic coefficient. The parametric analysis has shown that the factor of safety of the rock slope decreases with increase in surcharge for the range of those parameters in this paper. It is also shown that the horizontal seismic coefficient is the most important factor which effects on the factor of safety in the above four influence factors. The general analytical expression proposed in this paper and the results of the parametric analysis can be used to carry out a quantitative assessment of the stability of the rock slopes by engineers and researchers