Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm II (NSGA-II)

Lee, Ungki; Park, Su-Dong; Lee, Ikjin

doi:10.1016/j.energy.2020.117090

Cited by 57 publications

(16 citation statements)

References 41 publications

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“…Recently, the NSGA-II algorithm has been used extensively in the area of NN optimization [ 33 ], Design optimization [ 34 , 35 ], Network optimization [ 36 ] and so forth. Considering the strong precedence and use cases, we use NSGA-II as a tool for optimizing our multi-objective problem in this research work.…”

Section: Resultsmentioning

confidence: 99%

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

Pathmakumar

Rayguru

Ghanta

et al. 2021

Sensors

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The hydro blasting of metallic surfaces is an essential maintenance task in various industrial sites. Its requirement of a considerable labour force and time, calls for automating the hydro blasting jobs through mobile robots. A hydro blasting robot should be able to cover the required area for a successful implementation. If a conventional robot footprint is chosen, the blasting may become inefficient, even though the concerned area is completely covered. In this work, the blasting arm’s sweeping angle is chosen as the robot’s footprint for hydro blasting task, and a multi-objective optimization-based framework is proposed to compute the optimal sweeping arc. The genetic algorithm (GA) methodology is exploited to compute the optimal footprint, which minimizes the blasting time and energy simultaneously. Multiple numerical simulations are performed to show the effectiveness of the proposed approach. Moreover, the strategy is successfully implemented on our hydro blasting robot named Hornbill, and the efficacy of the proposed approach is validated through experimental trials.

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Section: Resultsmentioning

confidence: 99%

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

Pathmakumar

Rayguru

Ghanta

et al. 2021

Sensors

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“…Figure 7 shows the complete process of NSGA-II [ 47 ]. Compared with the previous generation MOO evolutionary algorithm NSGA, the NSGA-II mainly makes the following three improvements: A new algorithm for fast non-dominant sorting is added, which greatly reduces the computational complexity.…”

Section: Multi-objective Optimizationmentioning

confidence: 99%

“…As the number of performance indicators of heat engines increases, it is necessary to obtain global optimization solutions of several objective functions when optimizing the performance of the heat engines. Compared with the NSGA, the improved multi-objective optimization (MOO) algorithm (NSGA-II) has a faster running speed and better solution set, so it is the first choice of the MOO algorithm [ 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 ]. Many scholars have applied NSGA-II to the performance optimizations of heat engines and then used several MOO decision-making methods to choose the optimal solution.…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis and Four-Objective Optimization of an Irreversible Rectangular Cycle

Gong

Chen

et al. 2021

Entropy

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Based on the established model of the irreversible rectangular cycle in the previous literature, in this paper, finite time thermodynamics theory is applied to analyze the performance characteristics of an irreversible rectangular cycle by firstly taking power density and effective power as the objective functions. Then, four performance indicators of the cycle, that is, the thermal efficiency, dimensionless power output, dimensionless effective power, and dimensionless power density, are optimized with the cycle expansion ratio as the optimization variable by applying the nondominated sorting genetic algorithm II (NSGA-II) and considering four-objective, three-objective, and two-objective optimization combinations. Finally, optimal results are selected through three decision-making methods. The results show that although the efficiency of the irreversible rectangular cycle under the maximum power density point is less than that at the maximum power output point, the cycle under the maximum power density point can acquire a smaller size parameter. The efficiency at the maximum effective power point is always larger than that at the maximum power output point. When multi-objective optimization is performed on dimensionless power output, dimensionless effective power, and dimensionless power density, the deviation index obtained from the technique for order preference by similarity to an ideal solution (TOPSIS) decision-making method is the smallest value, which means the result is the best.

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“…Here, the temperature loss between TEM leg-1 and TEM leg-2 is equivalent to Ktem/Kp for the materials (Bi2Te3, Al2O3, and Graphite). The loss to thermal conductance of 1/Ktem and 1/Kp is shown in the [7,11,12,33,35,38,44,54]. Likewise for 2 nd stage of TEG are optimized by decision making methods such as fuzzy, LINMAP and TOPSIS [9,18,40,43].…”

Section: Matlab Optimization In Thermoelectric Devicementioning

confidence: 99%

A review on recent opportunities in MATLAB software based modelling for thermoelectric applications

Sankar

Moorthy

Ramasamy

et al. 2021

International Journal of Energy Applications and Technologies

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The thermoelectric application is one of most popular energy harvesting application from waste heat. The thermoelectric generators, thermoelectric coolers, and thermoelectric modular devices criterion comes in both sustainable energy as well as renewability of electrical energy from waste heat sinks. The recovery operations are optimized with the help of modeling using MATLAB software. The material science based thermoelectric applications can be modeled with the help of MATLAB simulink modeling. The numerical and algorithmic method of MATLAB modeling is for the development of hybrid thermoelectric coolers and generators (solar thermoelectric generators, radiative cooler, heat sinks). Later, the use of MATLAB software gives opportunities to develop the cost effective and high power thermoelectric generators. The emerging commercial device making is also discussed for thermoelectric generator using MATLAB optimization.

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Robust design optimization (RDO) of thermoelectric generator system using non-dominated sorting genetic algorithm II (NSGA-II)

Cited by 57 publications

References 41 publications

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

An Optimal Footprint Based Coverage Planning for Hydro Blasting Robots

Performance Analysis and Four-Objective Optimization of an Irreversible Rectangular Cycle

A review on recent opportunities in MATLAB software based modelling for thermoelectric applications

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