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Denys Baranovskyi on Muck Rack

Denys Baranovskyi

As seen in: Nature, MDPI
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Articles

Multi-objective optimization of an energy-efficient diaphragm spring for clutch applications

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By Denys Baranovskyi, Anton Sergienko, Pavel L. Kalinin, Nikolay Sergienko, Serhii Vladov, Nataliia Pavlova, Aleksandr Nazarov, Taras Hutsol, Magomediemin Gasanov, Vladyslav Tykhanovskyi
| Nature Verified
Abstract This study presents a deterministic multi-objective framework for the energy-efficient design of a diaphragm spring for automotive clutch applications. The proposed methodology is based on the direct feasible-set approach and similarity theory, enabling synthesis-level optimal-rational design within a closed dimensionless parameter space.
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Application of the Integral Energy Criterion and Neural Network Model for Helicopter Turboshaft Engines’ Vibration Characteristics Analysis

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By Serhii Vladov, Maryna Bulakh, Denys Baranovskyi, Eduard Kisiliuk
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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Determination of the Risk of Failures of Locomotive Diesel Engines in Maintenance

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By Sergey Myamlin, Denys Baranovskyi, Maryna Bulakh, Adam Michajłyszyn
| MDPI
5. Conclusions This article presents the results of a theoretical and experimental study to determine the risk of failures of LDEs during maintenance. The technical condition of LDEs affects the operational parameters, and it is rather difficult or almost impossible to take into account all operational parameters. In this work, hourly fuel consumption has been taken as the main operational parameter since the value of hourly fuel consumption of LDEs is a weighted average value.
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