Akour, S. N., Al-Heymari, M., Ahmed, T., & Khalil, K. A. (2018). Experimental and theoretical investigation of micro wind turbine for low wind speed regions.
Renewable Energy,
116(Part A), 215–223.
https://doi.org/10.1016/j.renene.2017.09.076
Araújo, F. R. P. d., Pereira, M. G., Freitas, M. A. V., da Silva, N. F., & Dantas, E. J. d. A. (2021). Bigger is not always better: Review of small wind in Brazil.
Energies,
14(4), 976.
https://doi.org/10.3390/en14040976
De Oliveira, U. W., Francescatto, M. B., & Roos, C. (2021). Viabilidade econômica de microgeradores eólicos para residências unifamiliares.
Brazilian Journal of Business,
3(4), 2838–2850.
https://doi.org/10.34140/bjbv3n4-006
Deb, K., & Jain, H. (2014). An evolutionary many-objective optimization algorithm using reference-point-based nondominated sorting approach, Part I: Solving problems with box constraints.
IEEE Transactions on Evolutionary Computation,
18(4), 577-601.
https://doi.org/10.1109/TEVC.2013.2281535
Deng, S., Cheng, X., Wu, H., & Hu, Y. (2024). Multi-objective optimization of bias current coefficient based on NSGA-III for active magnetic bearing with redundant electromagnetic actuators.
Engineering Computations,
41(6), 1549-1571.
https://doi.org/10.1108/EC-03-2023-0127
Gu, Q., Xu, Q., & Li, X. (2022). An improved NSGA-III algorithm based on distance dominance relation for many-objective optimization.
Expert Systems with Applications,
207, 117738.
https://doi.org/10.1016/j.eswa.2022.117738
Huda, S. M. A., Arafat, M. Y., & Moh, S. (2022). Wireless Power Transfer in Wirelessly Powered Sensor Networks: A Review of Recent Progress.
Sensors,
22(8), 2952.
https://doi.org/10.3390/s22082952
Leung, D. Y. C., Deng, Y., & Leung, M. K. H. (2011). Parametric study of a fan-bladed micro-wind turbine.
Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy,
225(8), 1120-1131.
https://doi.org/10.1177/0957650911413974
Li, Q., Wang, Y., Li, X., Li, X., Zhang, G., Du, Y., & Xu, W. (2024). Investigation and optimization of textured water-lubricated journal bearings using multi-objective optimization.
Journal of Applied Fluid Mechanics,
17(9), 1912-1928.
https://doi.org/ 10.47176/jafm.17.9.2581
Maleki, E., Unal, O., & Kashyzadeh, K. R. (2021). Influences of shot peening parameters on mechanical properties and fatigue behavior of 316 L steel: experimental, Taguchi method and response surface methodology.
Metals and Materials International,
27, 4418–4440.
https://doi.org/10.1007/s12540-021-01013-7
Marin, A., Kishore, R., Schaab, D., Vuckovic, D. & Priya, S. (2016). Micro Wind Turbine for Powering Wireless Sensor Nodes.
Energy Harvesting and Systems,
3(2), 139-152.
https://doi.org/10.1515/ehs-2013-0004
Mi, S., Liu, J., Cai, L., & Xu, C. (2024). Multi-objective optimization of two-phase ice slurry flow and heat transfer characteristics in helically coiled tubes with RSM and NSGA-II.
International Journal of Thermal Sciences,
199, 108942.
https://doi.org/10.1016/j.ijthermalsci.2024.108942
Minhas, N., Thakur, A., Mehlwal, S., Verma, R., Sharma, V. S., & Sharma, V. (2021). Multi-variable optimization of the shot blasting of additively manufactured AlSi10Mg plates for surface roughness using response surface methodology.
Arabian Journal for Science and Engineering,
46, 11671–11685.
https://doi.org/10.1007/s13369-021-05654-z
Miranda, C., Basso, A. D., Francucci, G. M., & Ludueña, L. N. (2022). Design of blades for household small wind turbines.
International Journal of Energy and Environmental Engineering,
13, 621–642.
https://doi.org/10.1007/s40095-021-00464-3
Ramírez-Elías, V. A., Damian-Escoto, N., Choo, K., Gómez-Martínez, M. A., Balvantín-García, A., & Diosdado-De la Peña, J. A. (2022). Structural analysis of carbon fiber 3D-printed ribs for small wind turbine blades.
Polymers,
14(22), 4925.
https://doi.org/10.3390/polym14224925
Rocha, P. A., de Araujo, J. W. C., Lima, R. J. P., da Silva, M. E. V., Albiero, D., de Andrade, C. F., & Carneiro, F. O. M. (2018). The effects of blade pitch angle on the performance of small-scale wind turbine in urban environments.
Energy,
148, 169–178.
https://doi.org/10.1016/j.energy.2018.01.096
Sant, T., Farrugia, R. N., Muscat, M., Caruana, C., Axisa, R., Borg, A., Cassar, C. M., Cassar, J., Cordina, C., Farrugia, A., & Schembri, S. (2020). Development and performance testing of a small, multi-bladed wind turbine.
Wind Engineering,
44(1), 3–20.
https://doi.org/10.1177/0309524X19849845
Sharma, S., Gupta, V., & Mudgal, D. (2023). Parametric experimental investigation of additive manufacturing-based distal ulna bone plate: a response surface methodology-based design approach.
Rapid Prototyping Journal,
29(5), 1080–1096.
https://doi.org/10.1108/RPJ-06-2022-0205
Shen, X., Yang, H., Chen, J., Zhu, X., & Du, Z. (2016). Aerodynamic shape optimization of non-straight small wind turbine blades.
Energy Conversion and Management,
119, 266–278.
https://doi.org/10.1016/j.enconman.2016.04.008
Sunderland, K. M., Narayana, M., Putrus, G., Conlon, M. F., & McDonald, S. (2016). The cost of energy associated with micro wind generation: international case studies of rural and urban installations.
Energy,
109, 818–829.
https://doi.org/10.1016/j.energy.2016.05.045
Suresh, A., Raja Kumar, S., Aljafaric, B., & Thanikanti, S. B. (2024). Investigations of the performance of 3D printed micro wind turbine composed of PLA material.
Heliyon,
10(3), e25356.
https://doi.org/10.1016/j.heliyon.2024.e25356
Tiam Kapen, P., Medjo Nouadje, B. A., Tchuen, G., & Tchinda, R. (2020). Numerical simulation of micro wind turbine performance and efficiency for low wind speed Cameroonian's cities.
International Journal of Ambient Energy,
43(1), 2727–2741.
https://doi.org/10.1080/01430750.2020.1768894
Vedovelli, M., Eltayesh, A., Natili, F., & Castellani, F. (2022). Experimental and numerical investigation of the effect of blades number on the dynamic response of a small horizontal-axis wind turbine.
Energies,
15(23), 9134.
https://doi.org/10.3390/en15239134
Wang, Y., Wang, G., Yao, G., Shen, Q., Yu, X., & He, S. (2023). Combining GA-SVM and NSGA-III multi-objective optimization to reduce the emission and fuel consumption of high-pressure common-rail diesel engine.
Energy,
278(Part A), 127965.
https://doi.org/10.1016/j.energy.2023.127965
Yu, J., Jin, Z., Yu, Y., Zhao, M., Ma, W., & Wu, J. (2024). Multi-objective optimization of variable-stiffness composite for CFRP-winding buckle arrestor by using NSGA-III.
Marine Structures,
96, 103633.
https://doi.org/10.1016/j.marstruc.2024.103633
Yu, Y., Pan, Y., Chen, Q., Zeng, D., Hu, Y., Goh, H.-H., Niu, S., & Zhao, Z. (2022). Cogging torque minimization of surface-mounted permanent magnet synchronous motor based on RSM and NSGA-II.
Actuators,
11(12), 379.
https://doi.org/10.3390/act11120379
Zawadzki, K., Kuzalski, C., Śmiechowicz, W., Tarkowski, M., Kądrowski, D., Stępień, M., Kulak, M., & Lipian, M. (2020). Assessment of blade strength for small wind turbine applications.
E3S Web of Conferences,
160, 01007.
https://doi.org/10.1051/e3sconf/202016001007
Zhang, Z., Yan, J., Lu, X., Zhang, T., & Wang, H. (2023). Optimization of porosity and surface roughness of CMT-P wire arc additive manufacturing of AA2024 using response surface methodology and NSGA-II.
Journal of Materials Research and Technology,
24, 6923–6941.
https://doi.org/10.1016/j.jmrt.2023.04.259
Zhao, Y., Cui, L., Sivalingam, V., & Sun, J. (2023). Understanding machining process parameters and optimization of high-speed turning of NiTi SMA using response surface method (RSM) and genetic algorithm (GA).
Materials,
16(17), 5786.
https://doi.org/10.3390/ma16175786
Zuo, W., Li, D., Li, Q., Cheng, Q., Zhou, K., & E, J. (2023). Multi-objective optimization of multi-channel cold plate under intermittent pulsating flow by RSM and NSGA-II for thermal management of electric vehicle lithium-ion battery pack.
Energy,
283, 129085.
https://doi.org/10.1016/j.energy.2023.129085