Pravin M. Singru

A molecular simulation based computational intelligence study of a nano-machining process with implications on its environmental performance

Abstract Determining the optimum input parameter settings (temperature, rotational velocity and feed rate) in optimizing the properties (strength and time) of the nano-drilling process can result in an improvement in its environmental performance. This is because the rotational velocity is an essential component of power consumption during drilling and therefore by determining its appropriate value required in optimization of properties, the trial-and-error approach that normally results in loss of power and waste of resources can be avoided. However, an effective optimization of properties requires the formulation of the generalized and an explicit mathematical model. In the present work, the nano-drilling process of Boron Nitride Nanosheet (BNN) panels is studied using an explicit model formulated by a molecular dynamics (MD) based computational intelligence (CI) approach. The approach consists of nano scale modeling using MD simulation which is further fed into the paradigm of a CI cluster comprising genetic programming, which was specifically designed to formulate the explicit relationship of nano-machining properties of BNN panel with respect to process temperature, feed and rotational velocity of drill bit. Performance of the proposed model is evaluated against the actual results. We find that our proposed integrated CI model is able to model the nano-drilling process of BNN panel very well, which can be used to complement the analytical solution developed by MD simulation. Additionally, we also conducted sensitivity and parametric analysis and found that the temperature has the least influence, whereas the velocity has the highest influence on the properties of nano-drilling process of BNN panel.

Combined CI-MD approach in formulation of engineering moduli of single layer graphene sheet

Abstract An evolutionary approach of multi-gene genetic programming (GP) is used to study the effects of aspect ratio, temperature, number of atomic planes and vacancy defects on the engineering moduli viz. tensile and shear modulus of single layer graphene sheet. MD simulation based on REBO potential is used to obtain the engineering moduli. This data is then fed into the paradigm of a GP cluster comprising of genetic programming, which was specifically designed to formulate the explicit relationship of engineering moduli of graphene sheets loaded in armchair and zigzag directions with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. We find that our MGGP model is able to model the engineering moduli of armchair and zigzag oriented graphene sheets well in agreement with that of experimental results. We also conducted sensitivity and parametric analysis to find out specific influence and variation of each of the input system parameters on the engineering moduli of armchair and zigzag graphene sheets. It was found that the number of defects has the most dominating influence on the engineering moduli of graphene sheets.

Determination of the Fundamental Frequency of Perforated Rectangular Plates: Concentrated Negative Mass Approach for the Perforation

This paper is concerned with a vibration analysis of perforated rectangular plates with rectangular perforation pattern of circular holes. The study is particularly useful in the understanding of the vibration of sound absorbing screens, head plates, end covers, or supports for tube bundles typically including tube sheets and support plates used in the mechanical devices. An energy method is developed to obtain analytical frequencies of the perforated plates with clamped edge, support conditions. Perforated plate is considered as plate with uniformly distributed mass. Holes are considered as concentrated negative masses. The analytical procedure using the Galerkin method is adopted. The deflected surface of the plate is approximated by the cosine series which satisfies the boundary conditions. Finite element method (FEM) results have been used to illustrate the validity of the analytical model. The comparisons show that the analytical model predicts natural frequencies reasonably well for holes of small size.

An Analytical Model to Determine Fundamental Frequency of Free Vibration of Perforated Plate by Using Greatest Integer Functions to Express Non Homogeneity

This paper aims at determining the fundamental frequency of square perforated plate with square perforation pattern of square holes. Rayleigh’s method is used for the solution of this problem. Non homogeneity in Young’s modulus and density at the perforation is expressed by using greatest integer function i.e. floor function. Boundary condition considered is clamped on all edges. Perforated plate is considered as plate with uniformly distributed mass and holes are considered as non homogeneous patches. The deflected surface of the plate is approximated by a function which satisfies the boundary conditions. Finite Element Method (FEM) modal analysis is carried out to validate the results of the proposed approach.

Investigation of mechanical strength of 2D nanoscale structures using a molecular dynamics based computational intelligence approach

A molecular dynamics (MD) based computational intelligence (CI) approach is proposed to investigate the Young modulus of two graphene sheets: Armchair and Zigzag. In this approach, the effect of aspect ratio, the temperature, the number of atomic planes and the vacancy defects on the Young modulus of two graphene sheets are first analyzed using the MD simulation. The data obtained using the MD simulation is then fed into the paradigm of a CI cluster comprising of genetic programming, which was specifically designed to formulate the explicit relationship of Young modulus of two graphene structures. We find that the MD-based-CI model is able to model the Young modulus of two graphene structures very well, which compiles in good agreement with that of experimental results obtained from the literature. Additionally, we also conducted sensitivity and parametric analysis and found that the number of defects has the most dominating influence on the Young modulus of two graphene structures.

Numerical method for heat transfer and fouling analysis of a shell and tube heat exchanger using statistical analysis

Through proper monitoring, problems can be identified and isolated well before the economics of the process are threatened. In contrast to most conventional methods, fouling can be detected when the heat exchanger operates in transient states. Statistical analysis is used to develop a fouling growth model of a heat exchanger subjected to fouling. The statistical analysis was considered for four different types of distributions out of which the lognormal distribution was found to be most suitable. Experiments were conducted with a single pass shell and tube heat exchanger with water both as the hot and cold fluids. The results show that the proposed tool is very effective in detecting critical fouling in a heat exchanger, which can be utilized for predicting the optimal maintenance schedule. Hence, the results of this work can find application in predicting the reduction in heat transfer efficiency due to fouling in heat exchangers that are in operation and assist the exchanger operators to plan cleaning schedules.

Application of artificial intelligence technique for modelling elastic properties of 2D nanoscale material

A novel computational approach is proposed to investigate the shear modulus of graphene nanostructures. In this approach, the factors that affect the shear modulus of graphene structures are analysed using an integrated artificial intelligence (AI) cluster comprising molecular dynamics (MD) and gene expression programming. The MD-based-AI approach has the ability to formulate the explicit relationship of shear modulus graphene nanostructure with respect to aspect ratio, temperature, number of atomic planes and vacancy defects. In addition, the shear modulus of graphene predicted using an integrated MD-based-AI model is in good agreement with that of experimental results obtained from the literature. The sensitivity and parametric analysis were further conducted to find out specific influence and variation of each of the input system parameters on the shear modulus of two graphene structures. It was found that the number of defects has the most dominating influence on the shear modulus of graphene nanostructure.

Performance of lognormal probability distribution in crossover operator of NSGA-II algorithm

This paper presents an improvement in performance of elitist nondominated sorting genetic algorithm (NSGA-II) by modifying the probability distribution of crossover operator. The probability distribution of simulated binary crossover (SBX-A) operator, used in NSGA-II algorithm, is modified with lognormal distribution (SBX-LN). This algorithm is used to test twenty multiobjective functions. This NSGA-II (SBX-LN) algorithm performed well for different functions. This algorithm also performed well in optimizing a turboalternator design. It found more optimum solutions with better diversity in turbo-alternator design optimization.

Identifying the Optimum Design of Turbo-Alternator Using Different Multi-objective Optimization Algorithms

This paper presents the method of selecting an optimum design of turbo-alternator using Multi-objective Differential Evolution-I (MODE-I) algorithm and comparing the optimum design obtained using non-dominated sorting genetic algorithm (NSGA-II). In this paper a real-life problem of turbo-alternator design is considered. Initially the complete design of turbo-alternator is worked out by conventional procedure. In the next stage, the optimization is obtained by using the MODE-I algorithm. This optimum design is compared with the optimum design obtained by the NSGA-II algorithm with simulated binary crossover operator with lognormal distribution (SBX-LN). From the set of results, the suitable optimum design of turbo-alternator is chosen. These results are also verified with the actual parameters of the turbo-alternator design. The results obtained are near global optimum solutions.

An ensemble evolutionary approach in evaluation of surface finish reduction of vibratory finishing process

Purpose – The functioning of multi-gene genetic programming (MGGP) algorithm suffers from the problem of difficulty in model selection. During the preliminary analysis, it is observed that there are many models in the population whose performance is better than that of the model selected with a little compromise on training error. Therefore, an ensemble evolutionary (Ensemble-MGGP) approach is proposed and applied to the data obtained from the vibratory finishing process. The paper aims to discuss these issues. Design/methodology/approach – Unlike the standard GP, each model participating in Ensemble-MGGP approach is made by combining the set of genes. Predicted residual sum of squares criterion (PRESS) criterion is integrated to improve its evolutionary search. The parametric analysis and sensitivity analysis (SA) conducted on the proposed model validates its robustness by unveiling dominant input parameters and hidden non-linear relationships. Findings – The results indicate that the proposed Ensemble-M...