Department of Mechanical engineering

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    Optimization of heat transfer in shell-and-tube heat exchangers using MOGA algorithm: adding nanofluid and changing the tube arrangement
    (Taylor & Francis, 2021-10) Bhattacharyya, Suvanjan
    The purpose of this study is to assess the impact of a wide variety of parameters to maximize the heat transfer rate using nanofluid, baffles, different Reynolds numbers (Re), different tube arrangements, and various geometry dimensions using the multi-objective genetic algorithm (MOGA) algorithm. The ANSYS FLUENT software, the SIMPLE algorithm as well as single-phase approach are employed for simulations. The study was performed for volume fractions (φ) of 0% to 4% and 10,000 < Re < 20,000. The results are presented for rectangular and triangular arrangements of tubes. It is demonstrated that in the rectangular configuration, the average Nusselt number (Nuave) is 34.38 when number of baffles (NB) of 10, φ = 4%, Re = 20,000. For the same values of φ and Re, when NB = 10, Nuave is enhanced by 7.4% and 10.4% compared to the cases in which NB = 6 and 8, respectively. However, for the triangular arrangement of tubes, Nuave=35.15. For the same values of φ and Re, when NB = 10, Nuave is enhanced by 5.7% and 11.4% compared to the cases in which NB = 6 and 8, respectively. Also, the triangular arrangement has about 2.1% more thermal efficiency than the rectangular one when NB, φ, and Re are maximum. Unlike the smaller figure for tubes mounted in the heat exchanger to transfer heat compared to other studies, the addition of nanofluid and using baffles lead to employing the heat exchanger for practical applications. However, a larger number of baffles causes a higher pressure drop. Hence, the optimization is performed using MOGA to reduce the pressure drop
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    Fuzzy-TOPSIS based multi-objective optimization of machining parameters for improving energy consumption and productivity
    (Elsevier, 2021) Garg, Girish Kant; Routroy, Srikanta
    Due to the increasingly global market and environmental challenges, there is a lot of pressure on manufacturing industries to reduce the energy consumption of the machining process without compromising productivity. The objective of this work is to develop a multi-objective optimization model for the selection of optimal cutting parameters during the turning of an Aluminum workpiece using carbide inserts. Two performance characteristics: energy consumption and productivity were simultaneously optimized. The Taguchi full factorial orthogonal array L27 was used to obtain the experimental plan. The Fuzzy based Technique for Order Preference by Similarity to Ideal Solution (Fuzzy-TOPSIS) was applied to determine the optimal cutting parameters for multi-objective optimization. The optimal results obtained by Fuzzy-TOPSIS were further validated by using the Taguchi method. ANOVA results show that all the considered cutting parameters were statistically significant. Further, the depth of cut was found the most influencing cutting parameter on the energy consumption and productivity.
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    Anaerobic co-digestion of food waste, algae, and cow dung for biogas yield enhancement as a prospective approach for environmental sustainability
    (Elsevier, 2022-08) Soni, Manoj Kumar
    The enduring economic and environmental concerns have prompted extensive research in bioenergy in recent decades. Biogas is an effective carbon-free, sustainable energy source generated by the anaerobic digestion of biological wastes. Biogas production is promoted globally to decrease carbon emissions and maximize resource recycling from various wastes. The extant work examines biogas production in an anaerobic digester using co-digestion, which uses food wastes, algae, chicken, and fish mixed with cow manure. A physicochemical pre-treatment is used to change the lignocellulosic structure of the mixture of the wastes prior to the anaerobic co-digestion. The response surface technique is used to optimize the co-digestion factors, like pH, F/I ratio, organic loading rate, temperature, and concentration of the wastes. The optimal values of cumulative CO2, methane, and biogas have been obtained as 30.18 ml, 1345.97 ml, and 2244.58 ml, respectively.
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    Numerical Simulation of Compliance Variation for a Topology-Optimized Structure
    (IEEE, 2011) Rout, Bijay Kumar
    Topology optimization is a well-developed tool in the domain of structural design. It generates the optimized topology over a predefined material space, subject to desired objective function and amount of material-reduction. In the obtained "optimized structure", reliability plays a vital role. The ongoing research in this area, explored the possibilities of dealing with uncertainties and errors involved in the design and manufacturing process, statistically. In the present work, few sources of uncertainty like modulus of elasticity, yield stress and applied force are chosen to examine their effects on optimized structure. The variation and uncertainty in these parameters, affect compliance value of the structure, and may cause design failure too. Hence, it is important to simulate the compliance-variation and various possible consequences of failure. In order to achieve these objectives, a methodology is designed, based on Monte Carlo Simulation (MCS), which simulates the realistic conditions numerically. This methodology is implemented on MATLAB, and an MBB- beam problem is simulated for realistic conditions. The results show the variation of compliance from the deterministic value and the strength-reliability is estimated. Presented work will be an aid to the design and analysis phase of topology optimization process, which incorporates the realistic conditions. In addition, it will be very helpful to evaluate the various reliability based topology optimization techniques.
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    Investigation on parametric sensitivity of topologically optimized structures
    (Sage, 2012-02) Rout, Bijay Kumar
    Topology optimization is a powerful method of material minimization in structural design problems. The obtained topology and the compliance values by this method are very sensitive to each of the input parameters such as, applied force, volume fraction, dimensions, and support-rigidity. In real-life situations, these parameters may vary due to material uncertainty, manufacturing imperfections, and operating conditions. Hence, the topology obtained during the conceptual design phase may not suffice the actual working condition. Thus, it is desirable to explore individual and the combined effects of the parametric variations and uncertainties. This study describes a systematic approach utilized to investigate the effect of different input parameters on compliance values along with material and load uncertainties for a topologically optimized structure. In this paper, applied force, volume fraction, and aspect ratio of the domain are treated as input parameters and their effects are analyzed. Proposed work modifies the solid isotropic microstructure with penalization method to incorporate the effect of uncertainties and uses design of experiments approach to investigate statistically significant input parameters. Four different benchmark problems available in the literature are analyzed and the results are obtained for aforesaid input parameters along with uncertainties. Results obtained from this investigation will help designers/practitioners to select suitable input parameters combination to achieve targeted compliance.
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    Energy and advanced exergoeconomic analysis of a novel ejector-based CO2 refrigeration system and its optimization for supermarket application in warm climates
    (Elsevier, 2023-09) Dasgupta, Mani Sankar
    Supermarkets' high refrigeration and air-conditioning energy use increases their carbon footprint. Thus, this sector must be encouraged to innovate to increase efficiency, reduce emissions, and support UN Sustainable Development Goals 11 and 13. In this study, we present a novel dual-ejector based CO2 refrigeration configuration. Real component data from manufacturers are utilised to make the theoretical evidence proximate to a controlled experiment. Mathematical model of the ejector is validated using published experimental data with a maximum deviation of 9.23%. Energetic performance of the proposed system is contrasted with a dedicated mechanical subcooling based CO2 system (DMS) and is found to be superior by 41.97% to 35.38% for operation within ambient temperature 28 °C − 40 °C. The year-round performance of the proposed system for various warm ambient locations in India, UAE and Spain is evaluated. Compared to a conventional R404A direct expansion system, a substantial annual energy savings, upto 11.35% is observed. Advanced exergy and exergoeconomic analysis, carried out at 40 °C ambient, provides an estimate of the limits up to which the irreversibilities and associated costs can be avoided for high ambient operation. The high-stage compressor in the configuration is found to have the highest potential of reducing the irreversibility by 36.08% and cost rate by 23.24%. Extent of mutual interactions among various components are also investigated using mexogenous analysis. A multi-objective optimization using genetic algorithm is employed to optimize exergoeconomic performance. The exergetic performance of the optimized system is found to be 6% higher than DMS system.
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    Development of a Multi-criteria Optimization Model for Minimizing Carbon Emissions and Processing Time During Machining
    (Elsevier, 2018) Sangwan, Kuldip Singh
    Manufacturing activities consume large amount energy and thus the carbon emissions are also high. Recent environmental policies in many countries have laid an additional financial burden on the manufacturers for high carbon emissions. Optimization of cutting parameters has a direct impact on the efficiency of the machining process and carbon emissions. This paper presents a systematic methodology to quantify the carbon emissions of CNC machine tools. A multi-objective mathematical optimization model is presented for optimizing carbon emissions and processing time during a turning process. The proposed model is validated by using experimental studies. This model can be useful for manufacturing organizations to select optimum cutting parameters for reduction of the carbon emissions and improvement of machining efficiency.
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    Optimization of Machining Parameters for Improving Energy Efficiency using Integrated Response Surface Methodology and Genetic Algorithm Approach
    (Elsevier, 2017) Sangwan, Kuldip Singh; Garg, Girish Kant
    Machine tools consume enormous amount of energy during machining, build-up to machining, post machining and idling condition to drive motors and auxiliary equipments in the manufacturing system. Reduction of energy consumption during the machining phase is extremely important to improve the environmental performance over the entire life cycle. This paper presents a predictive and optimization model based on integrated response surface methodology and genetic algorithm approach to predict the energy consumption and the corresponding machining parameters during the turning of AISI 1045 steel with a tungsten carbide tool. Experiments using Taguchi design are performed to develop the predictive model. The developed predictive model is used to formulate the objective function for genetic algorithm. The confirmation experiments are performed to validate the developed model and the results are found within 4% error. The statistical significance of the developed model has been tested by the analysis of variance test. This research will be beneficial for a number of manufacturing industries for selection of machine tools on the basis of energy consumption. The reduction of peak load through optimization will results in lowering the energy consumption of the machine tools during non-cutting time
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    Predictive Modelling and Optimization of Machining Parameters to Minimize Surface Roughness using Artificial Neural Network Coupled with Genetic Algorithm
    (Elsevier, 2015) Sangwan, Kuldip Singh; Garg, Girish Kant
    This paper develops a predictive and optimization model by coupling the two artificial intelligence approaches – artificial neural network and genetic algorithm – as an alternative to conventional approaches in predicting the optimal value of machining parameters leading to minimum surface roughness. A real machining experiment has been referred in this study to check the capability of the proposed model for prediction and optimization of surface roughness. The results predicted by the proposed model indicate good agreement between the predicted values and experimental values. The analysis of this study proves that the proposed approach is capable of determining the optimum machining parameters.
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    Multi-objective optimization of machining parameters to minimize surface roughness and power consumption using TOPSIS
    (Elsevier, 2019) Routroy, Srikanta; Garg, Girish Kant
    Energy saving in the industrial sector is mandatory for emerging countries to reduce negative environmental impact. Manufacturing consumes a significant amount of energy and releases a large amount of waste (solid, liquid and gas), resulting in the substantial stress on the environment. Negative environmental impact is due to a large amount of energy consumption by the machine tools in discrete manufacturing processes like turning, milling and drilling etc. This paper presents a multi-objective optimization model to optimize the machining parameters in turning process. Two objectives, surface roughness and power consumption are simultaneously optimized. The machining parameters are cutting speed, feed rate and depth of cut. Technique for order preference by similarity to ideal solution (TOPSIS) is used to identify the optimal turning parameters and the obtained results indicate that depth of cut is the most significant factor followed by the feed rate and cutting speed. The results obtained by the TOPSIS approach are compared with the existing grey relational analysis approach results. It is found that both optimization techniques show different optimal values. The confirmations experiments are necessary to select the best optimization approach.