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An Intelligent Optimised Estimation of the Hydraulic Jump Roller Length

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Applications of Evolutionary Computation (EvoApplications 2023)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13989))

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Abstract

In this paper, we address a problem in the field of hydraulics which is also relevant in terms of sustainability. Hydraulic jump is a physical phenomenon that occurs both for natural and man-made reasons. Its importance relies on the exploitation of the intrinsic energy dissipation characteristics and on the other hand the danger that might produce on bridges and river structures as a consequence of the interaction with the large vortex structures that are generated. In the present work, we try to address the problem of estimating the hydraulic jump roller length, whose evaluation is inherently affected by empirical errors related to its dissipative nature. The problem is approached using a regression model and exploiting a dataset of observations. Regression is performed by minimising the loss function using ten different black-box optimisers. In particular, we selected some of the most used metaheuristics, such as Evolution Strategies, Particle Swarm Optimisation, Differential Evolution and others. Furthermore, an experimental analysis has been conducted to validate the proposed approach and compare the effectiveness of the metaheuristics.

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Notes

  1. 1.

    The data set is available with the supplementary data at https://doi.org/10.5281/zenodo.7595510.

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Acknowledgement

This work was partially supported by the following research grants from “Università per Stranieri di Perugia”: (i) “Finanziamento Dipartimentale alla Ricerca FDR 2022”, (ii) “Artificial Intelligence for Education, Social and Human Sciences”, (iii) “Progettazione e sviluppo di strumenti digitali per la formazione a distanza”.

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Authors and Affiliations

  1. University for Foreigners of Perugia, Perugia, Italy

    Antonio Agresta, Chiara Biscarini & Valentino Santucci

  2. Department of Computer Science, Swansea University, Computational Foundry, Swansea, SA1 8EN, UK

    Fabio Caraffini

Authors
  1. Antonio Agresta
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  2. Chiara Biscarini
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  3. Fabio Caraffini
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  4. Valentino Santucci
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Corresponding author

Correspondence to Valentino Santucci .

Editor information

Editors and Affiliations

  1. University of Coimbra, Coimbra, Portugal

    João Correia

  2. University of York, York, UK

    Stephen Smith

  3. Al Hussein Technical University, Amman, Jordan

    Raneem Qaddoura

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Agresta, A., Biscarini, C., Caraffini, F., Santucci, V. (2023). An Intelligent Optimised Estimation of the Hydraulic Jump Roller Length. In: Correia, J., Smith, S., Qaddoura, R. (eds) Applications of Evolutionary Computation. EvoApplications 2023. Lecture Notes in Computer Science, vol 13989. Springer, Cham. https://doi.org/10.1007/978-3-031-30229-9_31

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  • DOI: https://doi.org/10.1007/978-3-031-30229-9_31

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  • Online ISBN: 978-3-031-30229-9

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