Reliability-Based Design Optimization Applied to a Rotor Supported by Hydrodynamic Bearings

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The authors optimized the shaft diameter and oil temperature of a rotor supported by hydrodynamic bearings, and considered the materials’ elastic module, density, and bearing clearance as uncertain parameters. I think the uncertain optimization is very important in the engineering and the results, simulation method is interesting in this paper. Thus, this paper can be accepted. In order to improve the quality of this paper, some comments are list are following.

1 Please give a reason for choose an offset rotor model.

2 More detail should add in table 1. Like the mass of disk, critical speed, stiffness. For the optimization, it’s better to compare the initial and optimization status.

3 I checked the parameter; it is monotonic relationship between the shaft Diameter and Temperature. Can we just choose the left and right boundary of shaft Diameter to calculate the Temperature?

4 Broaden and update the literature review to better connect to the current effort in the field in the context of mechanical sciences; for papers like this one we expect no less than 40 journal papers including 15-20 recent ones to be critically discussed. I don’t suggest to cite text books or manuals. Thus, new literature is suggested like “Dynamic analysis of Jeffcott rotor under uncertainty based on Chebyshev convex method. Mechanical Systems and Signal Processing, 2022, 167: 108603.”

5 Figure is not very clear to see in the PDF document.

Comments on the Quality of English Language

Nothing.

Author Response

Dear reviewer,

Thank you for the valuable comments. We followed all of your requests. All changes are highlighted in blue. Here we comment and answer the questions you have asked.

The authors optimized the shaft diameter and oil temperature of a rotor supported by hydrodynamic bearings, and considered the materials’ elastic module, density, and bearing clearance as uncertain parameters. I think the uncertain optimization is very important in the engineering and the results, simulation method is interesting in this paper. Thus, this paper can be accepted. In order to improve the quality of this paper, some comments are list are following.

We thank you for the comment. We followed all of the recommendations in the list.

1 Please give a reason for choose an offset rotor model.

The model that was used in simulations considered a central disc. We made it clearer. However, it is possible to use non-central disc. In that case, more anisotropy is included in the system model.

This answer is highlighted on page 8.

2 More detail should add in table 1. Like the mass of disk, critical speed, stiffness. For the optimization, it’s better to compare the initial and optimization status.

More details were included in Table 1. Some information about inertia and mass was added. Regarding stiffness, a journal bearing model was applied based on references [52, 53]. In the text, the answer for this point is on page [11].

3 I checked the parameter; it is monotonic relationship between the shaft Diameter and Temperature. Can we just choose the left and right boundary of shaft Diameter to calculate the Temperature?

The answer of this question is highlighted on page 10.

4 Broaden and update the literature review to better connect to the current effort in the field in the context of mechanical sciences; for papers like this one we expect no less than 40 journal papers including 15-20 recent ones to be critically discussed. I don’t suggest to cite text books or manuals. Thus, new literature is suggested like “Dynamic analysis of Jeffcott rotor under uncertainty based on Chebyshev convex method. Mechanical Systems and Signal Processing, 2022, 167: 108603.”

We changed all introduction section, including more references as indicated by this reviewer.

5 Figure is not very clear to see in the PDF document.

Figure quality and size were improved.

Reviewer 2 Report

Comments and Suggestions for Authors

In this paper the authors focus on optimizing the shaft diameter and oil temperature of a rotor supported by hydrodynamic bearings, considering as uncertain parameters the elastic modulus of the materials, the density and the bearing clearance. The machine is modeled using the finite element method and the bearings are represented by stiffness and damping coefficients, considering the linear short bearing model but surrogate models are employed to solve the reliability-based optimization problem. The optimization problem was solved using Kriging, Polynomial Chaos Expansion and Pynomial Chaos-Kriging.

The paper is interesting research, with a right methodology and the manuscript is clear, well organized and structured and the authors have worked exhaustively, taking care of the technical details.

It is opinion of the reviewer that the manuscript is very well, but some suggestions could improve it:

- It would be advisable to make a list of acronyms (the reviewer counted more than ten acronyms).

- The text frequently refers to 'these figures' without specifying their corresponding figure numbers.

- The introduction could be improved by incorporating relevant studies to provide a comprehensive scientific framework.

- The conclusions should be more concise.

For these reasons, the reviewer suggests the manuscript for the publication after minor revisions.

Author Response

Dear reviewer,

Thank you for the valuable comments. We followed all of your requests. All changes are highlighted in blue. Here we comment and answer the questions you have asked.

In this paper the authors focus on optimizing the shaft diameter and oil temperature of a rotor supported by hydrodynamic bearings, considering as uncertain parameters the elastic modulus of the materials, the density and the bearing clearance. The machine is modeled using the finite element method and the bearings are represented by stiffness and damping coefficients, considering the linear short bearing model but surrogate models are employed to solve the reliability-based optimization problem. The optimization problem was solved using Kriging, Polynomial Chaos Expansion and Pynomial Chaos-Kriging.

The paper is interesting research, with a right methodology and the manuscript is clear, well organized and structured and the authors have worked exhaustively, taking care of the technical details.

It is opinion of the reviewer that the manuscript is very well, but some suggestions could improve it:

We thank the reviewer for the comment. We followed all recommendations and explained each improvement in the points below.

- It would be advisable to make a list of acronyms (the reviewer counted more than ten acronyms).

We include the list at the end of the manuscript (page 30).

- The text frequently refers to 'these figures' without specifying their corresponding figure numbers.

We correct all figures' references.

- The introduction could be improved by incorporating relevant studies to provide a comprehensive scientific framework.

The introduction was entirely changed, including more references.

- The conclusions should be more concise.

We improved the conclusion as requested.

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

The authors had done a good work now. I think it can be accepted at this situation.