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pages 837-865
Dimensional Analysis and Similitude
Publication type: Book Chapter
Publication date: 2024-11-02
SJR: —
CiteScore: 0.2
Impact factor: —
ISSN: 21905193, 21905207
Abstract
Dimensional analysis is a powerful tool in designing, ordering, and analyzing the experiment results and also synthesizing them. The important theorem in dimensional analysis is known as theBuckingham, E., Buckingham Π theorem[aut]Buckingham Π theorem, so called since it involves nondimensional groups[aut]Dimensional analysisBuckingham Π theoremnondimensional group of the products of the quantities. In this chapter, BuckinghamBuckingham, E., Π theorem and its uses are thoroughly discussed. Physical models for hydraulic structures or river courses are usually built to carry out experimental studies under controlled laboratory conditions. The main purposes of the physical models are to replicate a small-scale hydraulic structure or a flow phenomenon in a river and to investigate the model performance under different flow and sediment conditions. The concept of similitude is commonly used so that the measurements made in laboratory model studies can be used to describe the characteristics of similar systems in the practical field situations. This chapter describes hydraulic similitude in terms of geometric, kinematic, and dynamic similitudes. Two categories of hydraulic models are discussed: immobile-bed model[aut]Similitudemodelimmobile bed model and mobile-bed model. The analysis leads to the definition of model-scale ratios. A number of illustrative examples are presented.
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TY - GENERIC
DO - 10.1007/978-3-031-26038-4_11
UR - https://link.springer.com/10.1007/978-3-031-26038-4_11
TI - Dimensional Analysis and Similitude
T2 - GeoPlanet: Earth and Planetary Sciences
AU - Dey, Subhasish
PY - 2024
DA - 2024/11/02
PB - Springer Nature
SP - 837-865
SN - 2190-5193
SN - 2190-5207
ER -
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@incollection{2024_Dey,
author = {Subhasish Dey},
title = {Dimensional Analysis and Similitude},
publisher = {Springer Nature},
year = {2024},
pages = {837--865},
month = {nov}
}