Numerical investigation of the energy separation effect and flow mechanism inside convergent,straight, and divergent double‐sleeve RHVT

This study provides analysis of a cooled Ranque–Hilsch vortex tube (RHVT) with various specifications. It shows how cooling influences energy conversion inside the RHVT and improves performance of the device in separation of hot gas from the cold stream within the fluid by presenting the temperature detachment (the temperature diminution of cold air (ΔT_c = T_i ? T_c), isentropic efficiency (η_is), and coefficient of performance (COP) of divergent, convergent, and straight VTs. Two key parameters including hot tube length and number of nozzles for cooling and insulated cases are investigated to find out how the performance of the VT is affected by different geometry configurations under cooling conditions. These influences were researched for straight, convergent, and divergent VT separators under different flow characteristics. The optimum geometrical conditions for the cooling cases were identified. Results are indicative of positive influence of cooling for energy separation inside a VT. The quantities of ΔT_c, η_is, and COP for the cooled RHVT are greater than uncooled RHVT for various types of VTs. Cooling the VTs leads to an increase of 12.5% in ΔT_c, 14.4% in η_is, and 15.1% in COP when the base case was an uncooled VT.     

A study of capillary flow from a pendant droplet

Surface tension driven capillary flow from a pendant droplet into a horizontal glass capillary is investigated in this paper. Effect of the droplet surface on dynamic behavior of such capillary flow is examined and compared with surface tension driven capillary flow from an infinite reservoir. In the experiment, capillaries of 300–700 μm in diameter were used with glycerol–DI water mixture solutions having viscosities ranging from 80 to 934 mPa s. It is observed that compared to the capillary flow from an infinite reservoir, the capillary flow from a droplet exhibits higher rates of meniscus displacement. This is due to an additional driving force resulted from change in droplet surface area (or curvature). The two main parameters influencing the flow are the dimensionless droplet geometry parameter (k) and the dynamic contact angle (θ _D). The molecular kinetics theory of Blake and De Coninck’s model [Adv Colloid Interface Sci 96(1–3):21–36, 2002] is used to interpret the dynamic contact angle. This theory considers a molecular friction coefficient (ζ) at the liquid front flowing over a solid surface. Moreover, three models are proposed to describe the shape of the pendant droplet during capillary action. It is found that the egg-shaped model provides a more realistic model to compute the shape of the pendant droplet deformed during the capillary action. Thus the predictions by the egg-shaped model are in good agreement with the experimental data.     

Processing of Parboiled Wheat Noodles Fortified with Pulsed Ultrasound Pomegranate (<Emphasis Type="Italic">Punica granatum</Emphasis> L. var. Malas) Peel Extract

The main objective of this study was to improve the antioxidant properties of parboiled wheat noodles (salted and yellow alkaline noodles) by adding different concentrations of pomegranate peel extract (PPE) into the noodle formulations (0, 0.75, and 1.50%) in order to produce the acceptable new product. The total phenolic contents and 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity varied from 4.07 to 8.61 mg GAE/g and from 23.20 to 95.16%, respectively. The white salted noodle fortified with 1.50% PPE had the highest antioxidant activity among all prepared noodles. The fortified noodles showed significant (p ≤ 0.05) differences in terms of color and textural properties as compared to the control noodle. The fortified white salted noodles had a light brown color with a soft texture, whereas the fortified yellow alkaline noodles were substantially darker and harder than the white salted noodles. The fortified white noodles had a shorter optimum cooking time than the yellow noodles. The addition of PPE to the formulation of both noodles led to a reduction of pH. The current study revealed that the fortified noodles had more homogeneous and compact microstructure than the control noodle. There was no significant difference (p > 0.05) between the overall acceptability of both fortified noodles.     

On the Mutual Effect of Viscoplasticity and Interfacial Damage Progression in Interfacial Fracture of Lead-Free Solder Joints

The main goal of this paper is to shed light on the effect of strain rate and viscoplastic deformation of bulk solder on the interfacial failure of lead-free solder joints. For this purpose, interfacial damage evolution and mode I fracture behavior of the joint were evaluated experimentally by performing stable fracture tests at different strain rates employing an optimized tapered double cantilever beam (TDCB) design. The viscoplastic behavior of the solder was characterized in shear, and the constitutive parameters related to the Anand model were determined. A rate-independent cohesive zone damage model was identified to best simulate the interfacial damage progression in the TDCB tests by developing a three-dimensional (3D) finite-element (FE) model and considering the viscoplastic response of the bulk solder. The influence of strain rate on the load capability and failure mode of the joint was clarified by analyzing the experimental and simulation results. It was shown how, at the lower strain rates, the normal stress generated at the interface is limited by the significant creep relaxation developed in the bulk solder and thus is not sufficiently high to initiate interfacial damage, whereas at higher rates, a large amount of the external energy is dissipated into interfacial damage development.     

Statistical process control using optimized neural networks: A case study

The most common statistical process control (SPC) tools employed for monitoring process changes are control charts. A control chart demonstrates that the process has altered by generating an out-of-control signal. This study investigates the design of an accurate system for the control chart patterns (CCPs) recognition in two aspects. First, an efficient system is introduced that includes two main modules: feature extraction module and classifier module. In the feature extraction module, a proper set of shape features and statistical feature are proposed as the efficient characteristics of the patterns. In the classifier module, several neural networks, such as multilayer perceptron, probabilistic neural network and radial basis function are investigated. Based on an experimental study, the best classifier is chosen in order to recognize the CCPs. Second, a hybrid heuristic recognition system is introduced based on cuckoo optimization algorithm (COA) algorithm to improve the generalization performance of the classifier. The simulation results show that the proposed algorithm has high recognition accuracy.     

Isothermal Ageing of SnAgCu Solder Alloys: Three-Dimensional Morphometry Analysis of Microstructural Evolution and Its Effects on Mechanical Response

Due to the high homologous temperature and fast cooling rates, the microstructures of SnAgCu (SAC) solders are in a meta-stable state in most applications, which is the cause of significant microstructural evolution and continuous variation in the mechanical behavior of the joints during service. The link between microstructures evolution and deformation behavior of Sn-4.0Ag-0.5Cu solder during isothermal ageing is investigated. The evolution of the microstructures in SAC solders are visualized at different scales in 3D by using a combination of synchrotron x-ray and focused ion beam/scanning electron microscopy tomography techniques at different states of ageing. The results show that, although the grain structure, morphology of dendrites, and overall volume fraction of intermetallics remain almost constant during ageing, considerable coarsening occurs in the Ag₃Sn and Cu₆Sn₅ phases to lower the interfacial energy. The change in the morphometrics of sub-micron intermetallics is quantified by 3D statistical analyses and the kinetic of coarsening is discussed. The mechanical behavior of SAC solders is experimentally measured and shows a continuous reduction in the yield resistance of solder during ageing. For comparison, the mechanical properties and grain structure of β-tin are evaluated at different annealing conditions. Finally, the strengthening effect due to the intermetallics at different ageing states is evaluated by comparing the deformation behaviors of SAC solder and β-tin with similar grain size and composition. The relationship between the morphology and the strengthening effect due to intermetallics particles is discussed and the causes for the strength degradation in SAC solder during ageing are identified.     

Damage Classification of Sandwich Composites Using Acoustic Emission Technique and k-means Genetic Algorithm

In this study acoustic emission (AE) technique was used for monitoring mode I delamination test of sandwich composites. Since, during mode I delamination test various damage mechanisms appear, their classification is of major importance. Hence, integration of \(k\) -means algorithm and genetic algorithm was applied as an efficient clustering method to discriminate different failure modes. Performing primary experiments to find the relationship between AE parameters and damage mechanisms, the AE signals of obtained clusters were assigned to distinct damage mechanisms. Also, the dominance of damage mechanisms was determined based on the distribution of AE signals in different clusters. Finally SEM observation was employed to verify obtained results. The results indicate the efficiency of the proposed method in damage classification of sandwich composites.     

Presentation of machine learning methods to determine the most important factors affecting road traffic accidents on rural roads

The purpose of this research was to develop statistical and intelligent models for predicting the severity of road traffic accidents (RTAs) on rural roads. Multiple Logistic Regression (MLR) was used to predict the likelihood of RTAs. For more accurate prediction, Multi-Layer Perceptron (MLP) and Radius Basis Function (RBF) neural networks were applied. Results indicated that in MLR, the model obtained from the backward method with the correct percent of 84.7% and R² value of 0.893 was the best method for predicting the likelihood of RTAs. Also, MLR showed that the variables of not paying attention to the front not paying attention to the frontroad ahead, followed byand then vehicle-motorcycle/bike accidents were the greatest problems. Among the models, MLP had a better performance, so that the prediction accuracy of MLR, MLP, and RBF were 84.7%, 96.7%, and 92.1%, respectively. MLP model, due to higher accuracy, showed that the variable of reason of accident had the highest effect on the prediction of accidents, and considering MLR results, the variables of not paying attention to the front and then vehicle-motorcycle/bike accidents had the most influence on the occurrence of accidents. Therefore, motorcyclists and cyclists are more prone to accidents, and appropriate solutions should be adopted to enhance their safety.