Geometric optimization of thermoelectric generator using genetic algorithm considering contact resistance and Thomson effect

2021 ◽  
Author(s):  
Aminu Yusuf ◽  
Nevra Bayhan ◽  
Abdullahi A. Ibrahim ◽  
Hasan Tiryaki ◽  
Sedat Ballikaya
Keyword(s):  
Genetic Algorithm ◽  
Contact Resistance ◽  
Thermoelectric Generator ◽  
Geometric Optimization ◽  
Thomson Effect

scholarly journals Theoretical investigation on the influence of Seebeck and Thomson effects in a thermoelectric generator

2021 ◽  
Author(s):  
Chika Calistus Maduabuchi ◽  
Yuri G. Gurevich
Keyword(s):  
Temperature Gradient ◽  
Seebeck Coefficient ◽  
System Performance ◽  
Thermoelectric Generator ◽  
Operating Temperature ◽  
Geometric Optimization ◽  
Thomson Effect ◽  
Performance Parameters ◽  
Cold Junction ◽  
The Impact

Abstract Four possible cases of modelling a thermoelectric generator (TEG) are presented in this paper. Case 1 models equal hot and cold junction Seebeck coefficient (SC) ignoring Thomson effect (TE), case 2 adopts equal hot and cold junction SC including TE, case 3 defines unequal hot and cold junction SC neglecting TE while case 4 represents unequal hot and cold junction SC with TE. The modified expressions for these cases are derived and employed in a comparative electric and geometric optimization of the TEG. The impact of the harmonious relationship between TE and SC on the optimum and maximum system performance parameters is studied and discussed. The results show that the systems performance can be enhanced or reduced depending on the operating temperature gradient and prevalent relationship between SC and TE. The results obtained from this work should be able to accurately predict the suitable TE and SC configuration for optimum and maximum system performance.

INFLUENCE OF THOMSON EFFECT ON EFFICIENCY OF THERMOELECTRIC GENERATOR

2018 ◽  
Author(s):  
Yuanyuan Wang ◽  
Mengjun Zhang ◽  
Yuanyuan Tian ◽  
Zihua Wu ◽  
Jiaojiao Xing ◽  
...  
Keyword(s):  
Thermoelectric Generator ◽  
Thomson Effect

scholarly journals CONSTRUCTAL DESIGN AND SIMULATED ANNEALING EMPLOYED FOR GEOMETRIC OPTIMIZATION OF A Y-SHAPED CAVITY INTRUDED INTO CONDUCTIVE WALL

2015 ◽  
Vol 14 (1) ◽  
pp. 79
Author(s):  
G. V. Gonzales ◽  
E. D. Dos Santos ◽  
L. R. Emmendorfer ◽  
L. A. Isoldi ◽  
E. S. D. Estrada ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Simulated Annealing ◽  
Solid Wall ◽  
Internal Heat ◽  
Optimization Methods ◽  
Optimization Method ◽  
Internal Heat Generation ◽  
Geometric Optimization ◽  
Constructal Design ◽  
Optimal Shapes

he problem study here is concerned with the geometrical evaluation of an isothermal Y-shaped cavity intruded into conducting solid wall with internal heat generation. The cavity acts as a sink of the heat generated into the solid. The main purpose here is to minimize the maximal excess of temperature (θmax) in the solid. Constructal Design, which is based on the objective and constraints principle, is employed to evaluate the geometries of Y-shaped cavity. Meanwhile, Simulated Annealing (SA) algorithm is employed as optimization method to seek for the best shapes. To validate the SA methodology, the results obtained with SA are compared with those achieved with Genetic Algorithm (GA) and Exaustive Search (ES) in recent studies of literature. The comparison between the optimization methods (SA, GA and ES) showed that Simulated Annealing is highly effective in the search for the optimal shapes of the studied case.

Multi-Objective Geometric Optimization of Elliptical-Toothed Gerotor Pumps for Kinematics and Wear by Genetic Algorithm

2018 ◽  
Author(s):  
Andrew J. Robison ◽  
Andrea Vacca
Keyword(s):  
Genetic Algorithm ◽  
Pareto Front ◽  
Adhesive Wear ◽  
Geometric Optimization ◽  
Objective Functions ◽  
Computationally Efficient ◽  
Nsga Ii ◽  
Generation Algorithm ◽  
Flow Ripple ◽  
Gerotor Pumps

A computationally efficient gerotor gear generation algorithm has been developed that creates elliptical-toothed gerotor gear profiles, identifies conditions to guarantee a feasible geometry, evaluates several performance objectives, and is suitable to use for geometric optimization. Five objective functions are used in the optimization: minimize pump size, flow ripple, adhesive wear, subsurface fatigue (pitting), and tooth tip leakage. The gear generation algorithm is paired with the NSGA-II optimization algorithm to minimize each of the objective functions subject to the constraints to define a feasible geometry. The genetic algorithm is run with a population size of 1000 for a total of 500 generations, after which a clear Pareto front is established and displayed. A design has been selected from the Pareto front which is a good compromise between each of the design objectives and can be scaled to any desired displacement. The results of the optimization are also compared to two profile geometries found in literature. Two alternative geometries are proposed that offer much lower adhesive wear while respecting the size constraints of the published profiles and are thought to be an improvement in design.

scholarly journals Comparison of Genetic Algorithm and Harmony Search Method for 2D Geometry Optimization.

2018 ◽  
Vol 159 ◽  
pp. 01009 ◽  
Author(s):  
Mohammad Ghozi ◽  
Anik Budiati
Keyword(s):  
Genetic Algorithm ◽  
Harmony Search ◽  
Geometry Optimization ◽  
Steel Structure ◽  
Search Method ◽  
Geometric Optimization ◽  
Computational Time ◽  
Time Required ◽  
Average Computational Time ◽  
Better Than

There are many applications of Genetic Algorithm (GA) and Harmony Search (HS) Method for solving problems in civil engineering design. The question is, still, which method is better for geometry optimization of a steel structure. The purpose of this paper is to compare GA and HS performance for geometric optimization of a steel structure. This problem is solved by optimizing a steel structure using GA and HS and then comparing the structure’s weight as well as the time required for the calculation. In this study, GA produced a structural weight of 2308.00 kg to 2387.00 kg and HS scored 2193.12 kg to 2239.48 kg. The average computational time required by GA is 607 seconds and HS needed 278 seconds. It concludes that HS is faster and better than GA for geometry optimization of a steel structure.

Influence of Thomson effect on the thermoelectric generator

2019 ◽  
Vol 137 ◽  
pp. 1183-1190 ◽  
Author(s):  
Mengjun Zhang ◽  
Yuanyuan Tian ◽  
Huaqing Xie ◽  
Zihua Wu ◽  
Yuanyuan Wang
Keyword(s):  
Thermoelectric Generator ◽  
Thomson Effect

scholarly journals Hydro-Acoustic and Hydrodynamic Optimization of a Marine Propeller Using Genetic Algorithm, Boundary Element Method, and FW-H Equations

2019 ◽  
Vol 7 (9) ◽  
pp. 321 ◽  
Author(s):  
Abouzar Ebrahimi ◽  
Mohammad Saeed Seif ◽  
Ali Nouri-Borujerdi
Keyword(s):  
Genetic Algorithm ◽  
Boundary Element Method ◽  
Boundary Element ◽  
Rake Angle ◽  
Geometric Optimization ◽  
Hydrodynamic Performance ◽  
Numerical Code ◽  
Skew Angle ◽  
Propeller Noise ◽  
Element Method

Noise generated by ships is one of the most significant noises in seas, and the propeller has a significant impact on the noise of ships, which reducing it can significantly lower the noise of vessels. In this study, a genetic algorithm was used to optimize the hydro-acoustic and hydrodynamic performance of propellers. The main objectives of this optimization were to reduce the propeller noise and increase its hydrodynamic efficiency. Modifying the propeller geometry is one of the most effective methods for optimizing a propeller performance. One of the numerical methods for calculating propeller noise is the Ffowcs Williams and Hawkings (FW-H) Model. A numerical code was developed by authors which solved these equations using the velocity and pressure distribution around the propeller and calculated its noise. To obtain flow quantities and to investigate the hydrodynamic performance of the propeller, a code was developed using a Boundary Element Method, the panel method. The geometry of DTMB 4119 propeller was selected for optimization, where geometric modifications included skew angle, rake angle, pitch to diameter (P/D) distribution, and chord to diameter (c/D) distribution. Finally, the results of geometric optimization were presented as Pareto optimal solutions. The results indicated that the optimum geometries had rake angles between 8.14 and 12.05 degrees and skew angles between 31.52 and 39.74 degrees. It was also observed that the increase in the chord up to a specific limit enhanced the efficiency and reduced the noise of the propeller.

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