scholarly journals Design and characterization of effective solar cells

2021 ◽  
Author(s):  
Varun Ojha ◽  
Giorgio Jansen ◽  
Andrea Patanè ◽  
Antonino La Magna ◽  
Vittorio Romano ◽  
...  
Keyword(s):  
Solar Cells ◽  
Quantum Efficiency ◽  
Cost Benefit Analysis ◽  
Cost Minimization ◽  
Cell Structure ◽  
Cost Benefit ◽  
Structure Design ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

AbstractWe propose a two-stage multi-objective optimization framework for full scheme solar cell structure design and characterization, cost minimization and quantum efficiency maximization. We evaluated structures of 15 different cell designs simulated by varying material types and photodiode doping strategies. At first, non-dominated sorting genetic algorithm II (NSGA-II) produced Pareto-optimal-solutions sets for respective cell designs. Then, on investigating quantum efficiencies of all cell designs produced by NSGA-II, we applied a new multi-objective optimization algorithm II (OptIA-II) to discover the Pareto fronts of select (three) best cell designs. Our designed OptIA-II algorithm improved the quantum efficiencies of all select cell designs and reduced their fabrication costs. We observed that the cell design comprising an optimally doped zinc-oxide-based transparent conductive oxide (TCO) layer and rough silver back reflector (BR) offered a quantum efficiency ($$Q_e$$ Q e ) of 0.6031. Overall, this paper provides a full characterization of cell structure designs. It derives relationship between quantum efficiency, $$Q_e$$ Q e of a cell with its TCO layer’s doping methods and TCO and BR layer’s material types. Our solar cells design characterization enables us to perform a cost-benefit analysis of solar cells usage in real-world applications.

Multi-objective optimization design for a hydrodynamic retarder based on CFD simulation considering cavitation effect

2021 ◽  
pp. 095440622110145
Author(s):  
Konghua Yang ◽  
Chunbao Liu ◽  
Qingtao Wu ◽  
Xuesong Li
Keyword(s):  
Flow Field ◽  
Optimization Design ◽  
Cfd Simulation ◽  
Internal Flow ◽  
Structure Design ◽  
Multi Objective Optimization ◽  
Hexahedral Mesh ◽  
Nsga Ii ◽  
Multi Objective ◽  
Cavitation Effect

It is important to suppress cavitation phenomenon for lower vibration and noise, which can be realized by structure optimization to reduce cavitation bubbles of flow field. Nonetheless, performance factors in hydrodynamic retarder are usually conflicted when conducting a structure design, it is hard to simultaneously restrain cavitation and improve the retarding performance. In our study, a combination of comprehensive CFD simulation and multi-objective optimization is developed to improve the retarding torque ([Formula: see text]), lessen the volume of Retarder ([Formula: see text]) and reduce the volume of bubbles ([Formula: see text]) in the internal flow field. First, the elaborate CFD simulation calculation, included a refined hexahedral mesh and the stress-blended eddy simulation (SBES), is proposed to investigate the unsteady flow field considering the cavitation, and its accuracy is validated by experimental data. Then, the RSM (Respond Surface Method) approximation model is constructed by combination of DOE (Design of Method) and CFD methods. The NSGA-II (Non-Dominated Sorting Genetic Algorithm) is selected as multi-objective optimization algorithm, and the weight and scale factor of each sub objective are specified. The optimization results, verified by theoretical calculation, show that [Formula: see text] is increased by 22%–24%, [Formula: see text] is reduced by 32%–45% and [Formula: see text] is reduced by 1%. Furthermore, the comparison of the vortex distributions before and after optimization demonstrates that the optimization improves the flow field impact and pressure loss in the retarder and reduces the number of bubbles resulting in the increasing vortex. Additionally, parameters’ effect on the cavitation and the braking performance are analyzed to efficiently achieve the best comprehensive performance of the retarder design. The newly-developed optimization method, which can understand the optimization principle and guide a balance between the cavitation and the retarding performance improvement, will reduce huge trial cost and time cost in the manufacture.

scholarly journals PROJECT MANAGEMENT BY MULTIMOORA AS AN INSTRUMENT FOR TRANSITION ECONOMIES / PROJEKTŲ VADYBA SU MULTIMOORA KAIP PRIEMONĖ PEREINAMOJO LAIKOTARPIO ŪKIAMS

2010 ◽  
Vol 16 (1) ◽  
pp. 5-24 ◽  
Author(s):  
Willem Karel M. Brauers ◽  
Edmundas Kazimieras Zavadskas
Keyword(s):  
Project Management ◽  
Transition Economy ◽  
Cost Benefit Analysis ◽  
Cost Benefit ◽  
Nominal Group ◽  
Benefit Analysis ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Delphi Techniques ◽  
Specific Project

The countries of Central and Eastern Europe moved from a previously centrally planned economy to a modern transition economy with strong market aspects. This paper proposes project management as an answer to this transition. Traditional Cost‐Benefit analysis does not respond to this purpose. Indeed Cost‐Benefit analysis is only interested in one specific project and not in a competition between projects. In addition all goals (objectives) have to be translated into money terms, leading sometimes to immoral consequences. On the contrary Multi‐Objective Optimization takes care of different objectives, whereas the objectives keep their own units. However different methods exist for the application of Multi‐Objective Optimization. The authors tested them after their robustness resulting in seven necessary conditions. MOORA (Multi‐Objective Optimization by Ratio analysis) and MULTIMOORA (MOORA plus Full Multiplicative Form), assisted by Ameliorated Nominal Group and Delphi Techniques, satisfy the seven conditions, although in a theoretical way. A simulation exercise illustrates the use of these methods, ideals to be strived for as much as possible. Santrauka Centrinės ir rytų Europos šalys perėjo iš anksčiau centralizuotai planuojamos ūkinės sistemos į šiuolaikinę pereinamąją ūkinę sistemą, kuriai būdingi ryškūs rinkos požymiai. Šiame straipsnyje siūloma projektų vadyba kaip atsakas į perėjimą. Įprastinė kainos ir naudos analizė tam tikslui yra netinkama. Be viso to, kainos ir naudos analizėje neatsižvelgiama į kiekvieną atskirai paimtą projektą bei į konkurenciją tarp tų projektų. Visi tikslai turi būti pakeisti piniginėmis vertėmis. Del to kartais kyla nepageidaujamų pasėkmių. Priešingai tam daugiatikslė optimizacija atsižvelgia į skirtingus tikslus, išsaugant tikslams būdingus mato vienetus. Yra daug įvairių daugiatikslės optimizacijos metodų. Autoriai patikrino jų stipriasias savybęs pagal septynias būtinasias sąlygas. MOORA (daugiatikslė optimizacija santykių dydžių analizės pagrindu) ir MULTIMOORA (MOORA plius pilnoji sandaugos forma), apimanti patobulintą normaliųjų grupių ir Delphi būdus, geriausiai atitinka septynias būtinąsiąs sąlygas taip pat ir teoriniu lygmeniu. Pavyzdžio modelis iliustruoja šiu metodu taikymą, idealai buvo pasiekti tiek, kiek galima.

Synchronous R-NSGA-II: An Extended Preference-Based Evolutionary Algorithm for Multi-Objective Optimization

Informatica ◽  
2015 ◽  
Vol 26 (1) ◽  
pp. 33-50 ◽  
Author(s):  
Ernestas Filatovas ◽  
Olga Kurasova ◽  
Karthik Sindhya
Keyword(s):  
Evolutionary Algorithm ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective

Microcrystalline (Si,Ge):H Solar Cells

2003 ◽  
Vol 762 ◽  
Author(s):  
Jianhua Zhu ◽  
Vikram L. Dalal
Keyword(s):  
Solar Cells ◽  
Buffer Layer ◽  
Quantum Efficiency ◽  
Fill Factor ◽  
Open Circuit Voltage ◽  
Defect Density ◽  
Cell Structure ◽  
Open Circuit ◽  
Base Layer ◽  
Ecr Plasma

AbstractWe report on the growth and properties of microcrystalline Si:H and (Si,Ge):H solar cells on stainless steel substrates. The solar cells were grown using a remote, low pressure ECR plasma system. In order to crystallize (Si,Ge), much higher hydrogen dilution (∼40:1) had to be used compared to the case for mc-Si:H, where a dilution of 10:1 was adequate for crystallization. The solar cell structure was of the p+nn+ type, with light entering the p+ layer. It was found that it was advantageous to use a thin a-Si:H buffer layer at the back of the cells in order to reduce shunt density and improve the performance of the cells. A graded gap buffer layer was used at the p+n interface so as to improve the open-circuit voltage and fill factor. The open circuit voltage and fill factor decreased as the Ge content increased. Quantum efficiency measurements indicated that the device was indeed microcrystalline and followed the absorption characteristics of crystalline ( Si,Ge). As the Ge content increased, quantum efficiency in the infrared increased. X-ray measurements of films indicated grain sizes of ∼ 10nm. EDAX measurements were used to measure the Ge content in the films and devices. Capacitance measurements at low frequencies ( ~100 Hz and 1 kHz) indicated that the base layer was indeed behaving as a crystalline material, with classical C(V) curves. The defect density varied between 1x1016 to 2x1017/cm3, with higher defects indicated as the Ge concentration increased.

scholarly journals Multi-Objective Lightweight Optimization of Parameterized Suspension Components Based on NSGA-II Algorithm Coupling with Surrogate Model

Machines ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 107
Author(s):  
Rongchao Jiang ◽  
Zhenchao Jin ◽  
Dawei Liu ◽  
Dengfeng Wang
Keyword(s):  
Ride Comfort ◽  
Lightweight Design ◽  
Dynamic Performance ◽  
Vehicle Dynamic ◽  
Multi Objective Optimization ◽  
Original Design ◽  
Dynamic Simulations ◽  
Nsga Ii ◽  
Torsion Beam ◽  
Multi Objective

In order to reduce the negative effect of lightweighting of suspension components on vehicle dynamic performance, the control arm and torsion beam widely used in front and rear suspensions were taken as research objects for studying the lightweight design method of suspension components. Mesh morphing technology was employed to define design variables. Meanwhile, the rigid–flexible coupling vehicle model with flexible control arm and torsion beam was built for vehicle dynamic simulations. The total weight of control arm and torsion beam was taken as optimization objective, as well as ride comfort and handling stability performance indexes. In addition, the fatigue life, stiffness, and modal frequency of control arm and torsion beam were taken as the constraints. Then, Kriging model and NSGA-II were adopted to perform the multi-objective optimization of control arm and torsion beam for determining the lightweight scheme. By comparing the optimized and original design, it indicates that the weight of the optimized control arm and torsion beam are reduced 0.505 kg and 1.189 kg, respectively, while structural performance and vehicle performance satisfy the design requirement. The proposed multi-objective optimization method achieves a remarkable mass reduction, and proves to be feasible and effective for lightweight design of suspension components.

Multi-objective optimization of squirrel cage fan for range hood based on Kriging model

2021 ◽  
pp. 095440622199586
Author(s):  
Qianhao Xiao ◽  
Jun Wang ◽  
Boyan Jiang ◽  
Weigang Yang ◽  
Xiaopei Yang
Keyword(s):  
Flow Rate ◽  
Optimization Design ◽  
Volume Flow Rate ◽  
Kriging Model ◽  
Volume Flow ◽  
Design Parameters ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Multi Objective ◽  
Squirrel Cage

In view of the multi-objective optimization design of the squirrel cage fan for the range hood, a blade parameterization method based on the quadratic non-uniform B-spline (NUBS) determined by four control points was proposed to control the outlet angle, chord length and maximum camber of the blade. Morris-Mitchell criteria were used to obtain the optimal Latin hypercube sample based on the evolutionary operation, and different subsets of sample numbers were created to study the influence of sample numbers on the multi-objective optimization results. The Kriging model, which can accurately reflect the response relationship between design variables and optimization objectives, was established. The second-generation Non-dominated Sorting Genetic algorithm (NSGA-II) was used to optimize the volume flow rate at the best efficiency point (BEP) and the maximum volume flow rate point (MVP). The results show that the design parameters corresponding to the optimization results under different sample numbers are not the same, and the fluctuation range of the optimal design parameters is related to the influence of the design parameters on the optimization objectives. Compared with the prototype, the optimized impeller increases the radial velocity of the impeller outlet, reduces the flow loss in the volute, and increases the diffusion capacity, which improves the volume flow rate, and efficiency of the range hood system under multiple working conditions.

Multi-objective optimization of weld geometry in hybrid fiber laser-arc butt welding using Kriging model and NSGA-II

2016 ◽  
Vol 122 (6) ◽  
Author(s):  
Zhongmei Gao ◽  
Xinyu Shao ◽  
Ping Jiang ◽  
Chunming Wang ◽  
Qi Zhou ◽  
...  
Keyword(s):  
Fiber Laser ◽  
Kriging Model ◽  
Multi Objective Optimization ◽  
Hybrid Fiber ◽  
Nsga Ii ◽  
Butt Welding ◽  
Multi Objective ◽  
Weld Geometry

scholarly journals Multi-Objective Optimization of Autonomous Microgrids with Reliability Consideration

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4466
Author(s):  
Maël Riou ◽  
Florian Dupriez-Robin ◽  
Dominique Grondin ◽  
Christophe Le Loup ◽  
Michel Benne ◽  
...  
Keyword(s):  
Power Systems ◽  
Rural Areas ◽  
Energy System ◽  
Design Stage ◽  
Multi Objective Optimization ◽  
Renewable Energy Resources ◽  
Nsga Ii ◽  
Renewable Integration ◽  
Multi Objective ◽  
Trade Offs

Microgrids operating on renewable energy resources have potential for powering rural areas located far from existing grid infrastructures. These small power systems typically host a hybrid energy system of diverse architecture and size. An effective integration of renewable energies resources requires careful design. Sizing methodologies often lack the consideration for reliability and this aspect is limited to power adequacy. There exists an inherent trade-off between renewable integration, cost, and reliability. To bridge this gap, a sizing methodology has been developed to perform multi-objective optimization, considering the three design objectives mentioned above. This method is based on the non-dominated sorting genetic algorithm (NSGA-II) that returns the set of optimal solutions under all objectives. This method aims to identify the trade-offs between renewable integration, reliability, and cost allowing to choose the adequate architecture and sizing accordingly. As a case study, we consider an autonomous microgrid, currently being installed in a rural area in Mali. The results show that increasing system reliability can be done at the least cost if carried out in the initial design stage.

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