Papers by Keyword: Multi-Objective Optimization

Abstract: In order to improve the head and efficiency of centrifugal pumps and reduce shaft power under gas-liquid two-phase(GLTP) conditions, a high-speed centrifugal pump model Q5H26 was taken as the research object. Studies have shown that when the gas content of the centrifugal pump is between 0 % and 25 %, its performance decreases significantly: the shaft power increases, while the head and efficiency decrease, indicating that the gas has a negative impact on the effective operation of the pump. In order to improve the performance of centrifugal pump, the sensitivity of blade inlet and outlet angle, cover plate thickness and blade number was analyzed by multi-objective programming (MOP) model, and these key parameters were optimized by genetic algorithm. The optimized centrifugal pump shows better performance under various operating conditions. This study effectively fills the gap in the performance optimization of centrifugal pumps in the field of gas-containing liquid treatment. Through numerical simulation and experimental comparison, under the condition of rated working condition and 10 % gas content, the optimized model pump efficiency is increased by 7.8 %, the head is increased by 1.9 m, and the shaft power is reduced by 37 W. These results not only prove that the method can significantly improve the performance of centrifugal pumps under gas-liquid two-phase conditions, but also provide valuable reference for design and optimization in this field. This study is especially suitable for industrial applications where efficient and energy-saving operations are required. It provides a practical solution for the treatment of gas-containing liquids, thereby making up for the shortcomings of existing technologies.

Abstract: Several advancements in the field of parallel manipulators have taken place in recent days as they offer many advantages over serial manipulators in terms of accuracy, agility, stiffness, speed, etc. The Parallel Kinematic Machines (PKMs) with lower Degree of Freedom (DoF) joints are being explored for a variety of industrial applications and, in particular, machining applications as these offer more accuracy, high machining capability, and more stiffness. This research work focuses on the modeling, kinematics, workspace and dexterity analyses of a 3DoF Translational PKM having coplanar rails along the Cartesian axes: -X, +X, +Y. Actuation of sliders, independently along the respective rails, offer the tool platform pure translational motion. Fixed length links are used to connect the sliders and tool platform. The PKM under study is modeled in CATIA. Inverse kinematics and workspace analysis are carried out using the performance indices, namely, Workspace Volume Index (WVI) and Global Condition Index (GCI). Attempts are also made to find the optimal dimensions of the PKM through multi-objective optimization using Genetic Algorithms in MATLAB. The methodology presented is helpful to predict the PKM's performance capability while the results obtained are helpful for the development of a physical prototype necessary for further experimental investigations.

Abstract: In the HVAC system, the required energy consumption is very large so that energy saving processes are needed. One effective method in this energy saving process is the heat recovery process using a Heat Pipe Heat Exchanger. Various studies have been carried out regarding the application of HPHE in HVAC systems to reduce temperature and maintain air humidity. In its application, there are many factors that affect the effectiveness of using this HPHE. Therefore, in this research, an optimization process will be carried out by considering the number of HPHE modules, inlet air temperature, and inlet air velocity. The modelling is performed to predict energy recovery and payback period by using Response Surface Methodology (RSM). Optimization study was conducted to investigate the optimum energy recovery and payback period by varying HPHE parameters experiment.

Abstract: In this study, the Jaya optimization algorithm is used to address the micro-grid energy management optimization problem using a hybrid PV-wind-microturbine-storage energy system. The main goals of this study are to reduce environmental pollution, increase microturbine operating efficiency, and minimize the cost of power generated. The overall objective of the proposed optimization method employed in the PV-WECS system is to run the PV-WECS systems at full capacity while running the microturbine when the PV-WECS systems are unable to produce all of the required power. The amount of emissions and costs of generated energy are reduced when BESS is used in the microgrid system. Furthermore, it is observed from the results that there is about 61.39% cost saving in the micro-grid operational costs and 38% carbon emissions reductions using the proposed optimization algorithm compared to the other metaheuristic algorithms used in this study. To demonstrate the appropriateness and supremacy of the proposed algorithm over the various optimization techniques for energy management of the proposed micro-grid systems, simulation results from the proposed algorithm are compared with those from other population-based metaheuristic algorithms, such as Particle Swarm Optimization (PSO), Differential Evolution (DE), Teaching Learning Based Optimization (TLBO), and Genetic Algorithms (GA). It is clear that the proposed algorithm outperforms and produces better results than the existing metaheuristic optimization techniques. More importantly, it illustrates the viability and efficacy of the proposed JAYA optimization approach in addressing the issue of energy management for large-scale power systems.

Abstract: Aluminum extrusion is an efficient industrial process. However, one of the main problems is related to the temperatures developed during the process that can detrimentally affect the achievable productivity, profile quality and/or die life. Cooling of the die with liquid nitrogen represents an efficient solution to overcome this limit but a further issue arises lying in the number of process and design variables that need to be managed in order to set-up of an efficient system. In this context, a 3D FE model of the extrusion process, coupled with a 1D model of the cooling channel, previously proposed by the authors, has been integrated in an optimization platform in order to iteratively and automatically adjusts the channel geometry and the process variables gaining to a final optimal solution in terms of thermal balance, cooling efficiency and nitrogen consumption. The original channel design used during the extrusion of industrial hollow AA6060 profile guaranteed an efficient but unbalanced cooling with a maximum temperature deviation of 60 °C registered by the thermocouple positioned around the bearings. The optimized designs showed temperature deviations below the 16 °C as well as the reduction of 50% in terms of nitrogen consuming.

Abstract: This study aims to minimize electrode wear (EW) and maximize material removal rate (MRR) in powder mixed electrical discharge machining (PMEDM) process of 9CrSi alloy steel with silicon carbide powder. To achieve these objectives, Taguchi method and Grey Relational Analysis (GRA) are applied to optimize one two-level and four three-level PMEDM process parameters, including T_on, T_off, CP, IP and SV in eighteen experiments based on an orthogonal array L₁₈ (2¹ and 4³). Results have provided a set of optimal PMEDM process parameters in which T_on has the strongest effect on SW and MRR while that of CP is insignificant. The obtained minimum EW and maximum MRR have been verified and proven by a PMEDM experiment using optimal process parameters. The proposed method can be further applied to optimize other PMEDM process parameters for different objectives.

Abstract: The purpose of this work is to find an optimal combination of the input parameters when electrical discharge machining (EDM) cylindrical shaped parts so that both the surface roughness and the electrode wear are minimum. In this study, four input parameters including the pulse on time, the pulse off time, the current, and the serve voltage were taken into account. Experimental plan was designed based on L9 orthogonal array. Also, Taguchi method and Grey Relational Analysis (GRA) were joined for solving the multiobjective optimization problem and to find optimum input parameters. Experiments with optimal input parameters were performed for proving the predicted model. The experimental results of the surface roughness and the electrode wear matched with the calculated model. This indicates the proposed models can be used for practice.

Abstract: Surface finish accrued extra-production cost, reduced effective sheet thickness, stir zone galling, undesirable flash-root stress concentration and fatigue cracks are consequences of bulk expulsion of flash during friction stir spot welding of aluminum alloys. This paper attempts to cutback the abovementioned challenges and improves the weld strength (shear failure load) of friction stir spot welded joints of an Al alloy by adopting an integrated Grey relational analysis-entropy measurement method as a multi-objective optimization tool. Shear failure load, and expelled flash properties (pushed out length and thickness) are the three examined quality characteristics of the joint while tool rotational speed (600-1400 rpm), dwell time (3-6 s) and plunge depth (1.5-1.7 mm) are the studied process parameters. The experiment was planned via the use of Taguchi method whereas the entropy measurement method facilitated the identification of the precise weighting values required for the estimation of the unified grey relational grade. The failure load of the joint was maximized while both flash height and pushed-out length were minimized. The optimized shear failure load and flash properties were attained at a parameter setting of 1400 rpm rotational speed, 6 s dwell time and 1.5 mm plunge depth. The tool rotational speed was found to have the most significant effect and percentage contribution on the combined responses with 67.75%, followed by plunge depth (12.88 %) and dwell time (11.94 %) respectively. The validation results confirm the robustness of the entropy measurement-based multi-objective optimization as a tool for improving the quality responses of friction stir spot welds.

Abstract: Fabricating three dimensional shaped surfaces from flat sheet metals by laser forming, both out-of-plane and in-plane deformations are required. This article presents the modeling of coupling mechanism activated laser forming of sheet metals based on experimental data for prediction and optimization of bending and thickening deformations. Experiments were performed based on a central composite design of experiments on coupling mechanism based laser metal forming process considering the input process parameters like laser power, scan speed and spot diameter, bending and thickening were taken as the outputs. Neural network and neuro-fuzzy system-based models were developed to carry out both forward and inverse modeling of the laser metal forming process under the coupling mechanism. Multi-objective optimization based on the non-dominated sorting genetic algorithm was used to obtain multiple optimal solutions to achieve different amounts of out-of-plane and in-plane deformations. The proposed method could guide for a suitable selection of the process parameters to produce three-dimensional shapes utilizing coupling mechanism-based laser forming using multiple laser line heating.

Abstract: Excellent mechanical behavior and low density of composite materials make them candidates to replace metals for many underwater applications. This paper presents a comprehensive study about the multi-objective optimization of composite pressure hull subjected to hydrostatic pressure to minimize the weight of the pressure hull and maximize the buckling load capacity according to the design requirements. Two models were constructed, one model constructed from Carbon/Epoxy composite (USN-150), the other model is metallic pressure hull constructed from HY100. The analysis and the optimization process were completely performed using ANSYS Parametric Design Language (APDL). Tsai-Wu failure criterion was incorporated in the optimization process. The results obtained emphasize that, the submarine constructed from Carbon/Epoxy composite (USN-150) is better than the submarine constructed from HY100. Finally, an optimized model with an optimum pattern of fiber orientations was presented. Hopefully, the results may provide a valuable insight for the future of designing composite underwater vehicles.