Development of Gasoline Direct Injector Using Giant Magnetostrictive Materials

Ghodsi, Mojtaba; Hosseinzadeh, Nasser; Özer, Abdullah; Dizaj, Hamid Rajabzadeh; Hojjat, Yousef; Varzeghani, Nader Garjasi; Sheykholeslami, Mohammad Reza; Talebian, Soheil; Ghodsi, M; Al-Yahmadi, Amer S.

doi:10.1109/tia.2016.2606591

Cited by 24 publications

(6 citation statements)

References 16 publications

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“…The values presented for empirical models' parameters have been identified using the data collected from experiments on diesel fuel/carbon dioxide mixtures, reported in [12]. Advantageously, aforementioned mixtures are similar to petroleum fluids [13], dissimilar to air/water mixtures, which have also been used in experiments to develop ESP empirical models [14][15][16]. Latter models have been excluded from this paper due to significant difference of their tested fluids and GPFs.…”

Section: Currently Prevalent Modelsmentioning

confidence: 99%

Estimate of the Head Produced by Electrical Submersible Pumps on Gaseous Petroleum Fluids, A Radial Basis Function Network Approach

zaheri¹,

Ghodsi²,

Alqallaf³

2018

IJAIA

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This paper reports successful development of an exact and an efficient radial basis function network (RBFN) model to estimate the head of gaseous petroleum fluids (GPFs) in electrical submersible pumps (ESPs). Head of GPFs in ESPs is now often estimated using empirical models. Overfitting and its consequent lack of model generality data is a potentially serious issue. In addition, available data series is fairly small, including the results of 110 experiments.All these limits were considered in RBFN design process, and highly accurate RBFNs were developed and cross validated.

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Section: Currently Prevalent Modelsmentioning

confidence: 99%

Estimate of the Head Produced by Electrical Submersible Pumps on Gaseous Petroleum Fluids, A Radial Basis Function Network Approach

zaheri¹,

Ghodsi²,

Alqallaf³

2018

IJAIA

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“…Models (1-3) are converted to linear equations after taking logarithm. As a result, the parameters can be identified with use of experimental data and non-iterative least square of error algorithm [17] (or alternatively with response surface method (RSM) [18] or full factorial experiments [19,20]…”

Section: Heuristic Empirical Modelsmentioning

confidence: 99%

A Critical Review on Empirical Head-Predicting Models of Two-phase Petroleum Fluids in Electrical Submersible Pumps

Mohammadzaheri¹

2018

PPEJ

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This paper critically reviews empirical models, which predict head of two-phase petroleum fluids in electrical submersible pumps. The article categorises empirical models in terms of mathematical structure and parameter identification algorithm. Categories are heuristic and artificial intelligence models. Models of the former category have fairly low accuracy and 4 or fewer parameters identifiable using non-iterative methods; conversely, models the of latter category have high accuracy and tens or even hundreds of parameters. These models require complex iterative identification algorithms. Due to availability of inexpensive digital processors, use of accurate artificial intelligence models is anticipated to broaden.

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“…The parameters of the presented empirical models have been identified using the data collected from experiments on diesel fuel/carbon dioxide mixtures, reported in [12]. Aforementioned mixtures are similar to petroleum fluids [13]. A number of other empirical models have been also reported in the literature in which their parameters have been identified based on the data of experiments on air/water mixtures [14][15][16].…”

Section: Empirical Modelsmentioning

confidence: 99%

Development of a Radial Basis Function Network to Estimate the Head Generated by Electrical Submersible Pumps on Gaseous Petroleum Fluids

Mohammadzaher¹,

Ghodsi²,

Alqallaf³

2017

Computer Science &Amp; Information Technology

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Development of Gasoline Direct Injector Using Giant Magnetostrictive Materials

Cited by 24 publications

References 16 publications

Estimate of the Head Produced by Electrical Submersible Pumps on Gaseous Petroleum Fluids, A Radial Basis Function Network Approach

Estimate of the Head Produced by Electrical Submersible Pumps on Gaseous Petroleum Fluids, A Radial Basis Function Network Approach

A Critical Review on Empirical Head-Predicting Models of Two-phase Petroleum Fluids in Electrical Submersible Pumps

Development of a Radial Basis Function Network to Estimate the Head Generated by Electrical Submersible Pumps on Gaseous Petroleum Fluids

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