Compressor performance modelling method based on support vector machine nonlinear regression algorithm

To overcome the difficulty of having only part of compressor characteristic maps including on-design operating point, and accurately calculate compressor thermodynamic performance under variable working conditions, this paper proposes a novel compressor performance modelling method based on support vector machine nonlinear regression algorithm. It is compared with the other three neural network algorithms (i.e. back propagation (BP), radial basis function (RBF) and Elman neural networks) from the perspective of interpolation and extrapolation accuracy as well as calculation time, to prove the validity of the proposed method. Application analyses indicate that the proposed method has better interpolation and extrapolation performance than the other three neural networks. In terms of flow characteristic map representation, the root mean square error (RMSE) of the extrapolation performance at higher and lower speed operating area by the proposed method is 0.89% and 2.57%, respectively. And the total RMSE by the proposed method is 2.72%, which is more accurate by 47% than the Elman algorithm. For efficiency characteristic map representation, the RMSE of the extrapolation performance at higher and lower speed operating area by the proposed method is 2.85% and 1.22%, respectively. And the total RMSE by the proposed method is 1.81%, which is more accurate by 35% than the BP algorithm. Moreover, the proposed method has better real-time performance compared with the other three neural network algorithms.

thermodynamic modeling, only part of compressor characteristic lines containing design condition points can be obtained by test bed or flow analysis scheme. Therefore, it is necessary to propose a method for expressing the compressor characteristic map with prefect interpolation and generalization performance in order to accurately calculate the thermal calculation of the compressor under variable working conditions." should not be the part of abstract, it better suited in introduction section. While elaborative quantitative and qualitative advantages of the proposed procedure should be provided in the abstract of the manuscript. 3. Introduction section is too short and mainly based on old reference (Only two to three from last five years). However, introduction can be made appropriate by segmented in the introduction into three separate subsections, (1 Introduction, 1.1 related work, 1.2 Innovative contribution, 1.3 organization. Moreover, in the introduction section salient feature of the proposed methodology should be listed in bullet form. 4. Literature review regarding the journal applications of neural networks in diversified field in lacking in the introduction section. Authors are advice to see the recent paper of Prof. Dumitru Baleanu [r1-r3] and Prof A. M. Wazwaz [r4] and see how in different applications ANN is applied such as astrophysics, plasma physics, atomic physics, thermodynamics, electromagnetic, machines, nanotechnology, fluid mechanics, electrohydrodynamics, signal processing, power, energy, bioinformatics, economic and finance are provided there. [r1] A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head. The European Physical Journal Plus, 2018 133 (9) The editors assigned to your paper ("Compressor characteristic modeling method based on Support Vector Machine Regression Algorithm") have now received comments from reviewers. We would like you to revise your paper in accordance with the referee and Associate Editor suggestions which can be found below (not including confidential reports to the Editor). Please note this decision does not guarantee eventual acceptance.
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• Funding statement Please list the source of funding for each author. Comments to the Author(s) 1) The abstract should be condensed and reorganized, The abstract should point out the contribution of the paper over some qualitative and quantitative results obtained by the proposed approach.
2) The quality of the table 2 could be improved, please edit it.
3) The written English should be modified carefully to avoid grammatical errors.

Reviewer: 2
Comments to the Author(s) Review Comments Manuscript Number: RSOS-191596 Title: Compressor characteristic modeling method based on Support Vector Machine Regression Algorithm In this work, author proposed a novel method for representing the compressor characteristic line based on support vector machine (SVM) nonlinear regression and provide the comparative analysis with three neural network algorithm, i.e., BP, RBF and Elman neural networks to verify and validate the performance in terms of perspective of interpolation and generalization accuracy nd computational time. Results show that that the SVM regression algorithm has better interpolation and extrapolation performance than the other three neural networks. Additionally, the SVM regression method proposed in the article has better computational real-time performance while ensuring the required accuracy. Generally, topic is interesting and contribution has some merits but before publication in the journal I have few suggestions: 1. The title " Compressor characteristic modeling method based on Support Vector Machine Regression Algorithm " have no reflection of novelty and clarity. Well-known problem and its analysis with well-known method. Please revise if possible. 2 These sentences in abstract "The development of gas turbine industry is of essential strategic significance for promoting the adjustment, transformation and upgrading of national industrial structure and improving the quality and efficiency of economic growth. In the process of actual thermodynamic modeling, only part of compressor characteristic lines containing design condition points can be obtained by test bed or flow analysis scheme. Therefore, it is necessary to propose a method for expressing the compressor characteristic map with prefect interpolation and generalization performance in order to accurately calculate the thermal calculation of the compressor under variable working conditions." should not be the part of abstract, it better suited in introduction section. While elaborative quantitative and qualitative advantages of the proposed procedure should be provided in the abstract of the manuscript. 3. Introduction section is too short and mainly based on old reference (Only two to three from last five years). However, introduction can be made appropriate by segmented in the introduction into three separate subsections, (1 Introduction, 1.1 related work, 1.2 Innovative contribution, 1.3 organization. Moreover, in the introduction section salient feature of the proposed methodology should be listed in bullet form. 4. Literature review regarding the journal applications of neural networks in diversified field in lacking in the introduction section. Authors are advice to see the recent paper of Prof. Dumitru Baleanu [r1-r3] and Prof A. M. Wazwaz [r4] and see how in different applications ANN is applied such as astrophysics, plasma physics, atomic physics, thermodynamics, electromagnetic, machines, nanotechnology, fluid mechanics, electrohydrodynamics, signal processing, power, energy, bioinformatics, economic and finance are provided there. It is a pleasure to accept your manuscript entitled "Compressor performance modeling method based on support vector machine nonlinear regression algorithm" in its current form for publication in Royal Society Open Science.
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For Reviewer 1
Recommendation: Accept (minor edits) Comments: 1) The abstract should be condensed and reorganized. The abstract should point out the contribution of the paper over some qualitative and quantitative results obtained by the proposed approach.
Answer: We have rewritten the abstract and added some simple analyses, which points out the contribution of the paper over some qualitative and quantitative results obtained by the proposed approach.
Thanks for your advice.
2) The quality of the table 2 could be improved, please edit it.
Answer: We have modified this part in the paper and made each table to keep a consistent number of significant digits.
Thanks for your advice.
3) The written English should be modified carefully to avoid grammatical errors.