Model scale cavitation noise spectra prediction: Combining physical knowledge with data science

Miglianti, Fabiana; Cipollini, Francesca; Oneto, Luca; Tani, Giorgio; Viviani, Michele

doi:10.1016/j.oceaneng.2019.02.002

Cited by 11 publications

(32 citation statements)

References 15 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For example, in Coraddu et al (2017) authors show that it is possible to effectively predict fuel consumption with HMs. Another example is the one reported in Miglianti et al (2019); authors showed that it is possible to predict the propeller cavitation noise characteristics via HMs. Finally, in our preliminary work Coraddu et al (2018), we attempt to model the engine exhaust gas temperature with a naive HM.…”

Section: Hmsmentioning

confidence: 99%

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Coraddu

Oneto

Cipollini

et al. 2021

Ships and Offshore Structures

Self Cite

View full text Add to dashboard Cite

Fast diesel engine models for real-time prediction in dynamic conditions are required to predict engine performance parameters, to identify emerging failures early on and to establish trends in performance reduction. In order to address these issues, two main alternatives exist: one is to exploit the physical knowledge of the problem, the other one is to exploit the historical data produced by the modern automation system. Unfortunately, the first approach often results in hard-to-tune and very computationally demanding models that are not suited for real-time prediction, while the second approach is often not trusted because of its questionable physical grounds. In this paper, the authors propose a novel hybrid model, which combines physical and data-driven models, to model diesel engine exhaust gas temperatures in operational conditions. Thanks to the combination of these two techniques, the authors were able to build a fast, accurate and physically grounded model that bridges the gap between the physical and data driven approaches. In order to support the proposal, the authors will show the performance of the different methods on real-world data collected from the Holland Class Oceangoing Patrol Vessel.

show abstract

Section: Hmsmentioning

confidence: 99%

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Coraddu

Oneto

Cipollini

et al. 2021

Ships and Offshore Structures

Self Cite

View full text Add to dashboard Cite

show abstract

“…• Noise Spectra Parametrization 1 (NSP1), consisting in the noise frequency and level of the central peak characterizing the spectra; • Noise Spectra Parametrization 3 (NSP2), consisting in the series of relevant parameters characterizing the spectra proposed in [19]; • Noise Spectra Parametrization 2 (NSP3), consisting in a new series of relevant parameters characterizing the spectra; • Noise Spectra Parametrization 4 (NSP4), consisting in the source levels in one-third octave band representation; • Noise Spectra Parametrization 5 (NSP5), consisting in the rsdited noise levels in one-third octave band representation.…”

Section: Problem Definitionmentioning

confidence: 99%

“…This idea is the basis of Hybrid Models (HMs), which are developed to take advantage of the best characteristics of both PMs and DDMs by combining them together. The application of HMs to the problem of propeller noise modelling based on data obtained through MSTs has been presented in [19] with satisfactory results.…”

Section: Introductionmentioning

confidence: 99%

“…These models have been compared with the conventional models previously presented in [19], where the 1D representation was manually selected based on the theories on cavitation noise. It is demonstrated that the advanced models outstand the performance of the conventional ones if a high dimensional and complex dataset is considered.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, with respect to [19], the whole feature set has been rearranged in order to include only data easily obtainable at the early stage of propeller design. Different combinations of input variables and targets have been considered, allowing to analyse the relative merits of each.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the cavitating marine propeller noise at design stage: A deep learning based approach

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

Coraddu

Kalikatzarakis

Theotokatos

et al. 2021

Energy, Environment, and Sustainability

Self Cite

View full text Add to dashboard Cite

Accurate, reliable, and computationally inexpensive models of the dynamic state of combustion engines are a fundamental tool to investigate new engine designs, develop optimal control strategies, and monitor their performance. The use of those models would allow to improve the engine cost-efficiency trade-off, operational robustness, and environmental impact. To address this challenge, two state-of-the-art alternatives in literature exist. The first one is to develop high fidelity physical models (e.g., mean value engine, zero-dimensional, and one-dimensional models) exploiting the physical principles that regulate engine behaviour. The second one is to exploit historical data produced by the modern engine control and automation systems or by high-fidelity simulators to feed data-driven models (e.g., shallow and deep machine learning models) able to learn an accurate digital twin of the system without any prior knowledge. The main issues of the former approach are its complexity and the high (in some case prohibitive) computational require-

show abstract

Model scale cavitation noise spectra prediction: Combining physical knowledge with data science

Cited by 11 publications

References 15 publications

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Physical, data-driven and hybrid approaches to model engine exhaust gas temperatures in operational conditions

Predicting the cavitating marine propeller noise at design stage: A deep learning based approach

Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

Contact Info

Product

Resources

About