Numerical solution of stokes flow in a circular cavity using mesh-free local RBF-DQ

This work reports the results of a numerical investigation of Stokes flow problem in a circular cavity as an irregular geometry using mesh-free local radial basis function-based differential quadrature (RBF-DQ) method. This method is the combination of differential quadrature approximation of derivatives and function approximation of Radial Basis Function. As a result, the method can be used to directly approximate the derivatives of dependent variables on a scattered set of knots. In this study knots were distributed irregularly in the solution domain using the Halton sequences. The method is applied on a two-dimensional geometry. The obtained results from the numerical simulations are compared with those gained by previous works. Outcomes prove that the current technique is in very good agreement with previous investigations and this fact that RBF-DQ method is an accurate and flexible method in solution of partial differential equations (PDEs).     

Shape memory response of porous NiTi shape memory alloys fabricated by selective laser melting

Porous NiTi scaffolds display unique bone-like properties including low stiffness and superelastic behavior which makes them promising for biomedical applications. The present article focuses on the techniques to enhance superelasticity of porous NiTi structures. Selective Laser Melting (SLM) method was employed to fabricate the dense and porous (32–58%) NiTi parts. The fabricated samples were subsequently heat-treated (solution annealing?+?aging at 350?°C for 15?min) and their thermo-mechanical properties were determined as functions of temperature and stress. Additionally, the mechanical behaviors of the samples were simulated and compared to the experimental results. It is shown that SLM NiTi with up to 58% porosity can display shape memory effect with full recovery under 100?MPa nominal stress. Dense SLM NiTi could show almost perfect superelasticity with strain recovery of 5.65 after 6% deformation at body temperatures. The strain recoveries were 3.5, 3.6, and 2.7% for samples with porosity levels of 32%, 45%, and 58%, respectively. Furthermore, it was shown that Young’s modulus (i.e., stiffness) of NiTi parts can be tuned by adjusting the porosity levels to match the properties of the bones.

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Performance evaluation of EWMA and CUSUM control charts to detect anomalies in social networks using average and standard deviation of degree measures

Detection of communication outbreak among members of a network or a subgroup of a network has been a topic of interest in the literature of social network analysis. One approach to monitoring changes in a social network is to monitor graph measures related to the network representation in each period and detecting anomalies by applying a control chart. In this paper, we compare the performance of average degree and standard deviation of degree measures of a network for detecting outbreaks on a weighted undirected network using exponentially weighted moving average and cumulative sum control charts. Evaluation results indicate that average degree measure is better in detecting small changes than standard deviation of degree measure. Whereas for greater changes and outbreaks consisting of more members of the network, the opposite is true. In addition, exponentially weighted moving average control charts perform better than cumulative sum in detecting smaller changes and outbreaks consisting of less members of the network.     

Microscale 3D Printing of Nanotwinned Copper

Nanotwinned (nt)‐metals exhibit superior mechanical and electrical properties compared to their coarse‐grained and nanograined counterparts. nt‐metals in film and bulk forms are obtained using physical and chemical processes including pulsed electrodeposition (PED), plastic deformation, recrystallization, phase transformation, and sputter deposition. However, currently, there is no process for 3D printing (additive manufacturing) of nt‐metals. Microscale 3D printing of nt‐Cu is demonstrated with high density of coherent twin boundaries using a new room temperature process based on localized PED (L‐PED). The 3D printed nt‐Cu is fully dense, with low to none impurities, and low microstructural defects, and without obvious interface between printed layers, which overall result in good mechanical and electrical properties, without any postprocessing steps. The L‐PED process enables direct 3D printing of layer‐by‐layer and complex 3D microscale nt‐Cu structures, which may find applications for fabrication of metamaterials, sensors, plasmonics, and micro/nanoelectromechanical systems.     

A mathematical model for order splitting in a multiple supplier single-item inventory system

In this paper we develop a mathematical model which considers multiple-supplier single-item inventory systems. The lead times of the suppliers and demand arrival rate are random variables. All shortages are backordered. Continuous review (s, Q) policy has been assumed. When the inventory level hits the reorder level, the total order is split among n suppliers. The problem is to determine the reorder level and order quantity for each supplier so that the expected total cost per time unit, including ordering cost, procurement cost, inventory holding cost and shortage cost is minimized. We also conduct extensive numerical experiments to show the advantages of our model compared to the relevant models in the literature. In addition, some managerial insights are observed.     

Formation of homogenous nanofibers using silicon microneedle spinnerets

We report the use of a silicon microfabricated device as a new spinneret for electrospinning purposes. This device has been realized on silicon substrates using a deep reactive ion etching process. To make proper holes in the center of microneedles, a rotating angle deposition method followed by vertical etching of silicon is employed. By using these needles as fluid nozzles in the electrospinning process, poly vinyl alcohol solution with a concentration of 7?% has been converted into nanofibers. The formation of nanofibers has been investigated using field emission scanning electron microscopy. Using this process, nanofibers with a diameter of 100–200?nm are realized where the dispersion is less than 50?nm. Finally, the effects of needle size and the applied voltage have been investigated on the diameter of nanofibers.     

Molecular tracking: A concept for side-draw distillation column design

A simple approach is introduced to locate a side-draw tray for ternary and multi-component mixtures with middle boiling component(s) present in the system at trace levels. The concept is based on a probability function defined by the thermodynamic properties of the system. The advantage of this method over existing methods is the ability to quickly and efficiently provide a feasible configuration of the distillation unit without relying on rigorous optimization or trial and error approaches. Moreover, it provides an intuitive understanding of the movements of the middle boiling components in the column.     

Integrated design and control of reactive distillation processes using the driving force approach

Superior controllability of reactive distillation (RD) systems, designed at the maximum driving force (design-control solution) is demonstrated in this article. Binary or multielement single or double feed RD systems are considered. Reactive phase equilibrium data, needed for driving force analysis and design of the RD system, is generated through an in-house property prediction tool. Rigorous steady-state simulation is carried out in ASPEN plus in order to verify that the predefined design targets and dynamics are met. A multiobjective performance function is employed to evaluate the performance of the RD system in terms of energy consumption, sustainability metrics (total CO₂ footprint), and control performance. Controllability of the designed system is evaluated using indices like the relative gain array (RGA) and Niederlinski index (N_I ), to evaluate the degree of loop interaction, as well as through dynamic simulations using proportional-integral (PI) controllers and model predictive controllers (MPC). The design-control of the RD systems corresponding to other alternative designs that do not take advantage of the maximum driving force is also investigated. The analysis shows that the RD designs at the maximum driving force exhibit enhanced controllability and lower carbon footprint than the alternative RD designs.     

Quick integrative optimizers for minimizing the error of neural computing in pan evaporation modeling

To achieve an efficient methodology for approximating pan evaporation (E_P), this study offers two metaheuristic-integrated predictors. Shuffled complex evolution (SCE) and electromagnetic field optimization (EFO) are two of the fastest metaheuristic algorithms that are synthesized with artificial neural network (ANN). By doing this, the ANN is optimized in a noticeably shorter time compared to its integration with other metaheuristic techniques. Five-year climatic data of the Bakersfield station (California, USA) with an 80:20 ratio are used for developing and testing the methods. The proposed hybrids are implemented with appropriate population sizes (20 and 35 for the SCE and EFO, respectively) and their results are compared to a single ANN. Accuracy evaluation (correlation coefficients > 0.99) professed that the neural network with both conventional and sophisticated trainers is a competent approach for the E_P simulation. Besides, it was observed that the error of prediction by the ANN-SCE and ANN-EFO is 6.02 and 9.27% lower than the single ANN, respectively. Therefore, the used strategies can enhance the applicability of the ANN. The time elapsed in the optimization using SCE and EFO was 479.0 and 281.9 s, respectively. A comparison between these algorithms revealed that the EFO is both a faster and more accurate optimizer. The ANN-EFO is accordingly recommended as a new efficient model for predicting the E_P.