Fuzzy logic controller and cascade inverter for direct torque control of IM

In this paper, a five-level cascaded H-bridge multilevel inverters topology is applied on induction motor control known as direct torque control (DTC) strategy. More inverter states can be generated by a five-level inverter which improves voltage selection capability. This paper also introduces two different control methods to select the appropriate output voltage vector for reducing the torque and flux error to zero. The first is based on the conventional DTC scheme using a pair of hysteresis comparators and look up table to select the output voltage vector for controlling the torque and flux. The second is based on a new fuzzy logic controller using Sugeno as the inference method to select the output voltage vector by replacing the hysteresis comparators and lookup table in the conventional DTC, to which the results show more reduction in torque ripple and feasibility of smooth stator current. By using Matlab/Simulink, it is verified that using five-level inverter in DTC drive can reduce the torque ripple in comparison with conventional DTC, and further torque ripple reduction is obtained by applying fuzzy logic controller. The simulation results have also verified that using a fuzzy controller instead of a hysteresis controller has resulted in reduction in the flux ripples significantly as well as reduces the total harmonic distortion of the stator current to below 4 %.     

THE EFFECT OF PRECIPITATION PARAMETERS ON PREPARATION OF LITHIUM FLUORIDE (LIF) NANO-POWDER

Lithium fluoride powder (LiF) is a white powder with a density of 2.64 gr/cm³ and a melting point of 848°C. This powder has several applications such as flux, glaze, soldering, and aluminum melting process, but one of the most important uses of this powder is its application in dosimetry. The commercial powders currently used for this purpose have average sizes of 5 to 10 micrometers; the objective of this research is to produce LiF powder with nano-metric particle size. In this study, the reaction of LiOH + HF → LiF + H₂O has been selected from among several reactions that were able to produce LiF powder, and some precipitation parameters such as temperature, time, agitation type, and supersaturation degree have been controlled. The morphology, phase analysis, and particle size distribution of the resulting powders were analyzed by SEM, XRD, and LPSA. Finally, lithium fluoride nano-powder was synthesized at a temperature of 25°C, pH about 2-3, reaction time less than 1 s, and agitation by ultrasonic bath.     

Transforming Vulnerability Indexing for Saltwater Intrusion into Risk Indexing through a Fuzzy Catastrophe Scheme

Mapping vulnerability to Saltwater Intrusion (SWI) in coastal aquifers is studied in this paper using the GALDIT framework but with a novelty of transforming the concept of vulnerability indexing to risk indexing. GALDIT is the acronym of 6 data layers, which are put consensually together to invoke a sense of vulnerability to the intrusion of saltwater against aquifers with freshwater. It is a scoring system of prescribed rates to account for local variations; and prescribed weights to account for relative importance of each data layer but these suffer from subjectivity. Another novelty of the paper is to use fuzzy logic to learn rate values and catastrophe theory to learn weight values and these together are implemented as a scheme and hence Fuzzy-Catastrophe Scheme (FCS). The GALDIT data layers are divided into two groups of Passive Vulnerability Indices (PVI) and Active Vulnerability Indices (AVI), where their sum is Total Vulnerability Index (TVI) and equivalent to GALDIT. Two additional data layers (Pumping and Water table decline) are also introduced to serve as Risk Actuation Index (RAI). The product of TVI and RAI yields Risk Indices. The paper applies these new concepts to a study area, subject to groundwater decline and a possible saltwater intrusion problem. The results provide a proof-of-concept for PVI, AVI, RAI and RI by studying their correlation with groundwater quality samples using the fraction of saltwater (f_sea), Groundwater Quality Indices (GQI) and Piper diagram. Significant correlations between the appropriate values are found and these provide a new insight for the study area.

     

A theory of mode of action of azolylalkylquinolines as DNA binding agents using automated flexible ligand docking

Azolylalkylquinolines (AAQs) are a family of quinolines with varying degrees of cytotoxic activity (comparable or moderately superior to adriamycin in some cases) developed in the past decade in our group where their exact mode of action is still unclear. In this study the most probable DNA binding mode of AAQs was investigated employing a novel flexible ligand docking approach by using AutoDock 3.0. Forty-nine AAQs with known experimental inhibitory activity were docked onto d(CGCAAATTTGCG)(2), d(CGATCG)(2) and d(CGCG)(2) oligonucleotides retrieved from the Protein Data Bank (PDB IDs: 102D, 1D12 and 1D32, respectively) as the representatives of the three plausible models of interactions between chemotherapeutic agents and DNA (groove binding, groove binding plus intercalation and bisintercalation, respectively). Good correlation (r(2)=0.64) between calculated binding energies and experimental inhibitory activities was obtained using groove binding plus intercalation model for phenyl-azolylalkylquinoline (PAAQ) series. Our findings show that the most probable mode of action of PAAQs as DNA binding agents is via intercalation of quinolinic moiety between CG base pairs with linker chain and azole moiety binding to the minor groove.     

Selection of regression models for predicting strength and deformability properties of rocks using GA

Recently, many regression models have been presented for prediction of mechanical parameters of rocks regarding to rock index properties. Although statistical analysis is a common method for developing regression models, but still selection of suitable transformation of the independent variables in a regression model is difficult. In this paper, a genetic algorithm (GA) has been employed as a heuristic search method for selection of best transformation of the independent variables (some index properties of rocks) in regression models for prediction of uniaxial compressive strength (UCS) and modulus of elasticity (E). Firstly, multiple linear regression (MLR) analysis was performed on a data set to establish predictive models. Then, two GA models were developed in which root mean squared error (RMSE) was defined as fitness function. Results have shown that GA models are more precise than MLR models and are able to explain the relation between the intrinsic strength/elasticity properties and index properties of rocks by simple formulation and accepted accuracy.     

An enhanced beam search algorithm for the Shortest Common Supersequence Problem

The Shortest Common Supersequence Problem asks to obtain a shortest string that is a supersequence of every member of a given set of strings. It has applications, among others, in data compression and oligonucleotide microarray production. The problem is NP-hard, and the existing exact solutions are impractical for large instances. In this paper, a new beam search algorithm is proposed for the problem, which employs a probabilistic heuristic and uses the dominance property to further prune the search space. The proposed algorithm is compared with three recent algorithms proposed for the problem on both random and biological sequences, outperforming them all by quickly providing solutions of higher average quality in all the experimental cases. The Java source and binary files of the proposed IBS_SCS algorithm and our implementation of the DR algorithm and all the random and real datasets used in this paper are freely available upon request.     

Friction stir welding of API X70 steel incorporating Ti-dioxide

In the present work, the microstructural evolution and hardness of HSLA X70 joined by friction stir welding were investigated. The FSW was applied to HSLA X70 with and without the addition of titanium dioxide (TiO₂) powders. To evaluate the microstructural features and hardness of different weld zones, optical microscopy and Vickers microhardness measurements were applied. The results show that the distribution of TiO₂ powders is strongly dependent on the applied friction stir processing, which in turn changed significantly the microstructure and hardness profile. In this regard, the optimum stirring action resulted in a homogeneous and fine dispersion of particles leading to the domination of an acicular ferrite phase with a hardness of 370 HV. On the other hand, the lower stirring action resulted in coarse particles as well as the development of the polygonal ferrite structure with a hardness of ～185 HV.     

A discrete multicomponent droplet evaporation model; effects of O2-enrichment,steam injection,and EGR on evaporation of diesel droplet

A discrete multicomponent (DMC) model for droplet evaporation in convective ambient is developed. Three different sets of correlations for Nusselt and Sherwood number are examined. The model is compared with experimental data for single and multicomponent droplet evaporation at different conditions and the most suitable set of correlations is selected. Having validated model, the diesel droplet evaporation under different ambient conditions and compositions is investigated. Increasing of oxygen mass fraction in N₂–O₂ mixture ambient from 0 to 1 first decreases and then increases the lifetime. Steam addition enhances the evaporation rate and it affects evaporation more significantly at higher temperatures. Exhaust gas recirculation (EGR) results in slight variations in droplet lifetime and its heating period.