Modelling Energy Dissipation Over Stepped-gabion Weirs by Artificial Intelligence

The hydraulics of energy dissipation over stepped-gabion weirs is investigated by carrying out a series of laboratory experiments, building models to explain the experimental data, and testing their robustness by using the data reported by other researchers. The experiments comprise: six different stepped-gabion weirs tested in a horizontal laboratory flume, a wide range of discharge values, two weir slopes (V:H): 1:1 and 1:2, and gabion filling material gravel size (porosity equal to 38 %, 40 % and 42 %). These experimental setups were selected to ensure the development of both the nappe and skimming flow regimes within the measured dataset. The models developed for computing energy dissipation over stepped-gabion weirs comprise: multiple regression equations based on dimensional analysis theory, Artificial Neural Network (ANN) and Gene Expression Programming (GEP). The analysis shows that the measured data capture both flow regimes and the transition in between them and above all, and by using all of the data, it may be possible to identify the range of each regime. Energy dissipation modelled by the ANN formulation is successful and may be recommended for reliable estimates but those by GEP and regression analysis can still serve for rough-and-ready estimates in engineering applications.     

Quality Evaluation of Groundwater Resources using Geostatistical Methods (Case Study: Central Lorestan Plain,Iran)

In this research, based on the qualitative data of 40 wells, variations of water quality parameters of the Central Plain Aquifer were evaluated using kriging and IDW (Inverse Distance Weighting) methods. Owing to the normal distribution of the studied parameters (except Na⁺, SO₄^2?, and TH: total hardness), ordinary kriging was used for modeling. The analysis of the data trends indicated that all the variables were influenced by in two general trends, i.e., NW–SE and NE–SW. In fact, these trends were a result of the effect of the structural conditions on aquifer properties such as transmissivity and flow direction. Variogram analysis (based on C₀ near zero and C₀/σ² ratio between 0.0–0.5) showed that the Na⁺, TDS (total dissolved solids), Ca²⁺, and TH variables have a good spatial structure and the BOD (biochemical oxygen demand), COD (chemical oxygen demand), NO₃^?, and EC variables have poor spatial structure. The BOD, COD, NO₃^?, and EC (electrical conductivity) variables have the smallest range and isotropic distribution. On the other hand, the Ca²⁺, Mg²⁺, Na⁺, SO₄^2?, Cl^?, HCO₃^?, pH, TDS, TH and Alk (alkalinity) parameters are characterized by anisotropic distributions. The Na⁺, TDS, Ca²⁺, and TH variables have the largest range. The results showed that both the IDW and kriging methods have close estimates to one another. The pH variable decreases toward the outlet whereas the EC and TDS variables increase along the direction of water flow and toward the outlet. The distributions of the BOD and COD variables do not perfectly match with the aggregation of industrial activities in the central part as well as the agricultural activities in the southeastern and central parts of the aquifer. The distributions of the Ca²⁺, Mg²⁺, and Alk variables completely follow the geology condition and regional spread of carbonate formations. The Na⁺ concentration increases from the center toward the outlet. The concentration of the Cl^? variable is the highest in the central part of the plain due to the concentration of agricultural and industrial activities. The distribution of the SO₄^2? variable is influenced by a natural factor (lithology), especially in the southeastern parts and the outlet as well as artificial factors (agricultural and industrial activities) in the central and southeastern parts of the aquifer. The NO₃^? variable, which is directly influenced by agricultural and livestock-farming activities, has its maximum concentration in the southeastern areas.     

Mechanics based model for predicting structure-induced rolling resistance (SRR) of the tire-pavement system

The structure-induced rolling resistance of pavements, and its impact on vehicle fuel consumption, is investigated in this study. The structural response of pavement causes additional rolling resistance and fuel consumption of vehicles through deformation of pavement and various dissipation mechanisms associated with inelastic material properties and damping. Accurate and computationally efficient models are required to capture these mechanisms and obtain realistic estimates of changes in vehicle fuel consumption. Two mechanistic-based approaches are currently used to calculate vehicle fuel consumption as related to structural rolling resistance: dissipation-induced and deflection-induced methods. The deflection-induced approach is adopted in this study, and realistic representation of pavement–vehicle interactions (PVIs) is incorporated. In addition to considering viscoelastic behavior of asphalt concrete layers, the realistic representation of PVIs in this study includes non-uniform three-dimensional tire contact stresses and dynamic analysis in pavement simulations. The effects of analysis type, tire contact stresses, pavement viscoelastic properties, pavement damping coefficients, vehicle speed, and pavement temperature are then investigated.     

Numerical Investigation of Convective Heat Transfer in Unsteady Laminar Flow Over a Square Cylinder in a Channel

Unsteady laminar flow past a heated square cylinder mounted inside a plane channel was investigated numerically. The blockage ratio was chosen as 1/8 and the Reynolds number based on the mean flow velocity and chord length of the square cylinder was selected as less than 200, for which the two-dimensional behavior of the flow is assured. The time-averaged Nusselt number as well as some integral parameters such as drag coefficient, recirculation length, and Strouhal number were obtained and compared with literature. Results show a nearly linear increase in recirculation length and decrease in drag coefficient with increasing Reynolds number for the steady flow regime. There is an increase in the total Nusselt number and drag coefficient with a Reynolds number for unsteady flow regime, where vortex shedding is observed from the cylinder. A correlation was obtained for the variation of the total Nusselt number with the Reynolds number.     

Efficient surrogate method for predicting pavement response to various tire configurations

A computationally efficient surrogate model was developed based on artificial neural networks (ANN) to investigate the effect of the new generation of wide-base tires on pavement responses. Non-uniform tire contact stress measurements were obtained using a stress-in-motion instrument. The measured 3-D contact stresses were applied on two extreme 3-D flexible pavement finite element models representing low-volume (thin) and high-volume (thick) roads. Eleven critical pavement responses were modeled at two different material properties input levels—detailed and simplified—depending on data availability. The results rendered by the ANN surrogate models were highly accurate with average prediction error less than 5 % and R-square values higher than 0.95. In addition, two sensitivity analyses were performed to investigate the variables effect on pavement responses. It was found that the type of tire (wide-base vs. dual tire assembly) is more influential than the inflation pressure on pavement responses. However, the tire inflation pressure seemed to have a significant effect on near-surface responses. The developed models were incorporated into a tool to assist designers and engineers in investigating the effect of the pavement responses of wide-base versus dual tire assembly under typical loading conditions and pavement structures.

     

Use of artificial neural networks to determine parameters controlling the nanofibers diameter in electrospinning of nylon‐6,6

This study aimed to find out the primary factors influencing the diameter of electrospun nanofibers of nylon‐6,6 using artificial neural networks (ANNs). Four variables, namely, polymer concentration, working distance, injection rate, and applied voltage were considered as input parameters and the nanofibers diameter measured by scanning electron microscopy was taken as the output. The data were modeled and validated against a set of unseen data. The generated model was used to study the interactions occurring between the input variables and their effect on the diameter. Results show that the injection rate and the polymer concentration are major factors affecting the nanofibers diameter with inverse and direct relations with the diameter, respectively, while the working distance and the applied voltage have direct but minor effects on nanofibers diameter. © 2011 Wiley Periodicals, Inc. J Appl Polym Sci 124:1589–1597, 2011     

Predicting thermal degradation kinetics of nylon6/feather keratin blends using artificial intelligence techniques

A multi-structured architecture of artificial intelligence techniques including artificial neural network (ANN), adaptive-neuro-fuzzy-interference system (ANFIS) and radial basis function (RBF) were developed to predict thermal degradation kinetics (TDK) of nylon6 (NY6)/feather keratin (FK) blend films. By simultaneous implementation of back-propagation ANN and feed-forward ANFIS modeling on the experimental data obtained from thermogravimetric analysis (TGA) method, thermal degradation behavior of various compositions of NY6/FK blends was successfully predicted with minimum mean square errors (MSE). RBF networks were then trained on the TGA data at one heating rate for predicting analogs information at different heating rates, providing sufficient feed for TDK modeling. According to the comparison made between experimental and predicted kinetic parameters of thermal degradation process calculated from Friedman and Kissinger methods, the proposed prediction effort could effectively contribute to the estimation of precise activation energy (E_a) and reaction order (n) values with least amount of experimental work and most accuracy.     

Microfluidic Synthesis of Ultrasmall Chitosan/Graphene Quantum Dots Particles for Intranasal Delivery in Alzheimer's Disease Treatment

Nanoparticles (NPs) based therapies for Alzheimer's disease (AD) attract interest due to their ability to pass across or bypass the blood-brain barrier. Chitosan (CS) NPs or graphene quantum dots (GQDs) are promising drug carriers with excellent physicochemical and electrical properties. The current study proposes the combination of CS and GQDs in ultrasmall NP form not as drug carriers but as theranostic agents for AD. The microfluidic-based synthesis of the CS/GQD NPs with optimized characteristics makes them ideal for transcellular transfer and brain targeting after intranasal (IN) delivery. The NPs have the ability to enter the cytoplasm of C6 glioma cells in vitro and show dose and time-dependent effects on the viability of the cells. IN administration of the NPs to streptozotocin (STZ) induced AD-like models lead to a significant number of entrances of the treated rats to the target arm in the radial arm water maze (RAWM) test. It shows the positive effect of the NPs on the memory recovery of the treated rats. The NPs are detectable in the brain via in vivo bioimaging due to GQDs as diagnostic markers. The noncytotoxic NPs localize in the myelinated axons of hippocampal neurons. They do not affect the clearance of amyloid β (Aβ) plaques at intercellular space. Moreover, they showed no positive impact on the enhancement of MAP2 and NeuN expression as markers of neural regeneration. The memory improvement in treated AD rats may be due to neuroprotection via the anti-inflammation effect and regulation of the brain tissue microenvironment that needs to be studied.     

Thermal quantum correlation and entanglement in the Bose–Hubbard Hamiltonian

We study a two-qutrit system which is described by the Bose–Hubbard Hamiltonian with two external magnetic fields. The entanglement (through the negativity) and quantum correlation (through the geometric discord) between the qutrits are calculated as functions of the magnetic field (B), the temperature (T), the linear and nonlinear coupling constants among two spins (J and K). Then, we compare the effect of these parameters on entanglement and quantum correlation of this system. For some values of system parameters, we show that the negativity is zero while, the geometric discord is nonzero. Moreover, we investigate the effect of finite external magnetic fields direction on these measures. This study leads to some new and interesting results as well.