Genetic Optimisation of a Free-Stream Water Wheel Using 2D Computational Fluid Dynamics Simulations Points towards Design with Fully Immersed Blades
Round 1
Reviewer 1 Report
The reviewed article presents in detail the practical application of two-dimensional hydraulic modeling in the context of technical solutions used in hydrotechnical structures - in this case, in a free-jet water wheel. We should praise both the meticulous description of the methodological assumptions, the way of introducing the assumed topics together with the relevant literature on the subject, as well as the description of the results, drawing conclusions and presenting the continuity of the research. The practical aspect of the presented results and their potential for further research were also emphasized. I enjoyed reading the submitted work, I have only a few minor comments that the Authors may consider before publishing the manuscript in the Energies journal:
1) Please provide methodological limitations of the conducted research - to what extent they can be used and in which they are not recommended, and what is the measurement error of the presented methods.
2) The topic was described in an interesting way, but I miss more examples in this field, especially in the context of the comparison with the obtained results. I mean comparing the obtained results with the results of other researchers, if similar studies were carried out earlier.
The manuscript shows a lot of work that researchers put into its preparation and I think it is suitable for publication in its current form (possibly the above comments may be considered).
Author Response
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Reviewer 2 Report
The manuscript has both a clear descriptive form, and a numerical study on the design and optimization of the fully immersed blade is also clearly presented. The design parameters were optimized by using a genetic algorithm. As described in this manuscript, satisfactory results are obtained. This is a valuable and innovative method in the field of the free-stream water wheel research. The manuscript is recommended to be accepted until the authors respond to the following comments.
1. Section 3. The detail grid seems unclear.
2. Section 3. A more detailed description is required by the boundary conditions.
3. Section 3. Other parameters like grid quality, especially the y plus value, are used as guidance for mesh configuration and the selection of the most.
Author Response
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Reviewer 3 Report
Free flow wheel is a kind of power generation device with wide application prospect. Its biggest characteristic is that it will not block river connectivity and aquatic organisms. This paper uses numerical simulation and genetic optimization algorithm, through a lot of calculation research, put forward an optimized free flow wheel design scheme, the new scheme reduces the blade material consumption, and greatly improves the power generation output, has a high prospect of popularization and application.
Author Response
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Reviewer 4 Report
I read deeply the manuscript. The manuscript reports a detailed introduction to the aim of the work, with a broad look at the results in the literature. Also, the manuscript focuses on the deep description of the materials and methods adopted in this study. I can judge this analysis part is extremely accurate and detailed with reference. The results obtained in this work are very interesting and their description is very well organized. The conclusions and abstract fully hit the target and the results obtained in the work reflect the overall idea of the manuscript. Thus, this manuscript can be accepted after the authors addressed my following minor concerns.
The authors are suggested to apply why they used the k-w SST turbulence model compared to its counterparts to provide a better overview to interested researchers.
The authors mentioned that the three-dimensional simulations can be conducted in the follow-up attempts. As they already stated that those will be time-consuming, in my opinion, different optimization techniques may be considered to overcome computational expense. For instance, they may take different metaheuristics, i.e., particle swarm optimization, grey wolf optimization, etc., into account, since those optimization approaches are almost as robust as the genetic algorithm which generally result in a relatively lower computational burden. Perhaps, this could be added as a future research direction by the authors.
Author Response
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