Projects per year
Personal profile
Academic Biography
Foad Faraji is a Lecturer in Engineering in the School of Computing, Engineering and Digital Technologies (SCEDT), Teesside University.
Before coming back to Teesside to complete his PhD in "gas-condensate reservoir performance modeling" and provide teaching support, Foad was part of quality control team at British Steel Special Profile Skinningrove providing support for the descaling project. Foad completed MEng Mechanical Engineering at Teesside University in 2016 with First-Class Honours. Foad has published in peer-reviewed journals about mathematical modeling, engineering application of Artificial Intelligence for characterization of fluid properties, and mathematical modeling of multiphase flow.
Summary of Research Interests
Ford's main research area focuses on developing new methods using Artificial Intelligence & Machine Learning techniques for various engineering applications.
Ford's expertise and research interests include:-
- Fluid flow characteriztion in porous media (subsurface energy storage)
- Engineering application of Machine Learning and Artificial Intelligence
- Finite Element Methods (Ansys and NX Siemnse)
- Fluid Mechanics (Multiphase flow)
- Statistical modelling
Research Projects & External Funding
Foad is involved in the design and optimization of formula student car chassis using Finite Element Method techniques. In this project CAD and FEM software package (Siemens NX) is used to model and optimize formula student car chassis with respect to the different materials (Steel, carbon fibre and etc.,).
Foad is involved in the modeling multiphase flow in various media using large-scale experimental databank. Different machine learning methods including Neural Networks, fuzzy logic and adaptive neuro-fuzzy inference system (ANFIS) are utilized for the project.
Learning and Teaching Interests and Activities
- Computer Aided Analysis
- Finite Element Methods (FEM)
- Mechatronic Principles
- Manufacturing Process
- Final Year Engineering Projects (BEng & MSc)
Academic Achievements
Fellow of the Higher Education Academy (FHEA)
Academic Responsibilities
- Teaching and project supervision in Engineering department
- Research and enterprise activities
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Network
Projects
- 2 Active
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Hy-DTLD: Digital Tools and Machine Learning for early Leak Detection in Hydrogen Pipeline
Rezaei Gomari, S., Hughes, D. & Faraji, F.
16/01/23 → 15/05/23
Project: Research
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Stress Analysis of Various Designs of Centrifugal Pump Impellers Using Finite Element Method
Mohamed, N., Chong, P. L., Faraji, F., Moey, L. K., Ibrahim, A., Yazdi, M. H. & Mardji, A., 11 Aug 2022, In: Journal of Engineering & Technological Advances (JETA). 7, 1, p. 36-46 10 p.Research output: Contribution to journal › Article › peer-review
Open AccessFile92 Downloads (Pure) -
Two-phase flow pressure drop modelling in horizontal pipes with different diameters
Faraji, F., Santim, C., Chong, P. L. & Hamad, F., 1 Jul 2022, In: Nuclear Engineering and Design. 395, 16 p., 111863.Research output: Contribution to journal › Article › peer-review
Open AccessFile28 Downloads (Pure) -
Development of new gas condensate viscosity model using artificial intelligence
Faraji, F., Ugwu, J. & Chong, P. L., 17 Nov 2021, (E-pub ahead of print) In: Journal of King Saud University, Engineering Sciences. JKSUES581.Research output: Contribution to journal › Article › peer-review
Open AccessFile85 Downloads (Pure) -
Modelling two-phase Z factor of gas condensate reservoirs: Application of artificial intelligence
Faraji, F., Ugwu, J. & Chong, P. L., 8 Nov 2021, (E-pub ahead of print) In: Journal of Petroleum Science and Engineering. 109787.Research output: Contribution to journal › Article › peer-review
Open AccessFile6 Downloads (Pure) -
Experimental and numerical study of pressure drop in pipes packed with large particles
Hamad, F., Santim, C. G. S., Faraji, F., Al-Dulaimi, M. J. & Ganesan, P. B., 26 Aug 2020, In: Heat and Mass Transfer.Research output: Contribution to journal › Article › peer-review
Open AccessFile97 Downloads (Pure)
Datasets
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Data for: Modelling viscosity of liquid dropout near wellbore region in gas-condensate reservoirs using modern numerical approaches.
Faraji, F. (Contributor), Mendeley Data, 31 Mar 2020
DOI: 10.17632/wtrc6p938g.1, https://data.mendeley.com/datasets/wtrc6p938g
Dataset
Thesis
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Gas-condensate Reservoir Performance Modelling.
Author: Faraji, F., 19 Feb 2021Supervisor: Ugwu, J. (Supervisor), Chong, P. L. (Supervisor) & Nabhani, F. (Supervisor)
Student thesis: Doctoral Thesis
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