D. DOMIRI GANJIProfessor of Mechanical EngineeringGet in touch
Research/Python & scientific computing
Research in focus

Python, in service
of engineering.

A closer look at Ganji and collaborators’ use of Python in mathematical modeling, analytical approximation and thermal-fluid research.

12selected coauthored studies
with documented Python use
A SCIENTIFIC COMPUTING WORKFLOW
01
FORMULATEA physical question.

Equations · assumptions · conditions

02
COMPUTEA solution in Python.

Symbolic algebra · numerical calculation

03
EXAMINEResults to examine.

Comparison · error · physical meaning

MODEL → COMPUTATION → UNDERSTANDING
From equations to computation

A tool for solving.
A way to investigate.

In this research, Python provides a bridge between mathematical methods and calculations that can be compared, visualized and checked. Some studies build symbolic approximations; others combine analytical methods with numerical calculations for engineering models.

The contribution is collaborative. These are coauthored studies associated with Ganji’s research; software development and other individual contributions vary by paper.

Read with a question

What is being modeled? What does Python do? How is the result checked?

Selected research · 12 studies

The work,
in closer detail.

Each study includes a plain-language introduction and a link to its scholarly source.

012024
Case Studies in Thermal Engineering · 2024

Python approach for using homotopy perturbation method to investigate heat transfer problems

Payam Jalili · Bahram Jalili · Irshad Ahmad · Ahmed S. Hendy · Mohamed R. Ali · Davood Domiri Ganji

The engineering question

How can the equations for cooling, fins and heat conduction become calculations that a student can inspect? This study applies homotopy perturbation to three heat-transfer examples, connecting approximate mathematics with executable symbolic work.

What Python contributes

Python with SymPy implements the homotopy perturbation method symbolically for convective–radiative lumped cooling, a rectangular fin and a Laplace heat-transfer problem.

Read the studyView supporting sourceDOI 10.1016/j.csite.2024.104049
022023
Results in Physics · 2023

Analytical analyzing mixed convection flow of nanofluid in a vertical channel using python approach

Payam Jalili · Amirali Shateri · Ali Mirzagoli Ganji · Bahram Jalili · Davood Domiri Ganji

The engineering question

In a vertical passage, an imposed flow and the buoyancy of warmer fluid act together. The study examines how these effects, alongside nanoparticle transport, shape fluid velocity, temperature and concentration.

What Python contributes

Python supports implementations of the Akbari–Ganji and homotopy perturbation methods for the transformed nanofluid equations.

Read the studyView supporting sourceDOI 10.1016/j.rinp.2023.106908
032024
Case Studies in Thermal Engineering · 2024

Heat transfer analysis of magnetized fluid flow through a vertical channel with thin porous surfaces: Python approach

Davood Domiri Ganji · Mehdi Mahboobtosi · Bahram Jalili · Payam Jalili

The engineering question

What changes when fluid moves through a vertical channel whose porous walls admit suction or injection? The model examines how magnetic forcing, heat sources, radiation and reaction alter flow and transport.

What Python contributes

AGM and HPM are implemented in Python to solve the reduced equations and investigate velocity, temperature and concentration profiles.

Read the studyView supporting sourceDOI 10.1016/j.csite.2024.104643
042024
International Journal of Thermofluids · 2024

Thermal study of magnetohydrodynamic nanofluid flow and brownian motion between parallel sheets

Pooriya Majidi Zar · Bahram Jalili · Payam Jalili · Davood Domiri Ganji

The engineering question

Between two parallel sheets, magnetic forcing interacts with the random motion of nanoparticles and their response to temperature gradients. The study compares ways of calculating the resulting heat and mass transfer.

What Python contributes

Python and SymPy implement AGM and HPM. The authors compare these results with a fourth-order Runge–Kutta numerical calculation.

Read the studyView supporting sourceDOI 10.1016/j.ijft.2024.100806
052024
South African Journal of Chemical Engineering · 2024

Heat and mass transfer conduct in an unsteady two- dimensional stream between parallel sheets

Pooriya Majidi Zar · Payam Jalili · Bahram Jalili · Davood Domiri Ganji

The engineering question

When the distance between parallel sheets changes, the fluid is squeezed and its thermal and concentration fields evolve together. This paper studies that coupled transport in a viscous nanofluid.

What Python contributes

Python with SymPy symbolically implements HPM and AGM for the flow, heat and mass-transfer equations.

Read the studyView supporting sourceDOI 10.1016/j.sajce.2024.07.011
062025
Results in Engineering · 2025

Utilizing Python for numerical analysis of bioconvection in magnetized Casson-Maxwell nanofluid systems with gyrotactic microorganisms: An investigation of dominant factors

Amirali Shateri · Ali Mirzagoli Ganji · Payam Jalili · Bahram Jalili · Davood Domiri Ganji

The engineering question

Swimming microorganisms, nanoparticles and a magnetic field can influence the same fluid motion. This study models their interaction in a non-Newtonian Casson–Maxwell fluid, including heat and species transport.

What Python contributes

Python numerically solves the dimensionless boundary-value equations. The publisher-deposited abstract specifies the solve_bvp function.

Read the studyView supporting sourceDOI 10.1016/j.rineng.2024.103760
072025
International Journal of Thermofluids · 2025

Semi-analytical modeling of compact flow with mass and heat exchange: a python-based approach

Davood Domiri Ganji · Fateme Nadalinia Chari · Mehdi Mahboobtosi

The engineering question

This study follows velocity, temperature and concentration as a viscous fluid is squeezed between two plates. It also examines which parameters influence wall friction and heat and mass exchange.

What Python contributes

The abstract states that the AGM and differential transform implementations are entirely in Python. ANOVA is used to examine transport quantities and parameter effects.

Read the studyView supporting sourceDOI 10.1016/j.ijft.2025.101336
082025
Multiscale and Multidisciplinary Modeling, Experiments and Design · 2025

Investigating fluid flow and heat transfer in porous media: a python-based approach for medical applications and thermal regulation

Davood Domiri Ganji · Mehdi Mahboobtosi · Fateme Nadalinia Chari

The engineering question

How do permeability, suction or injection, magnetic forcing and heat sources change transport through a porous material? The paper investigates velocity and temperature in a mathematical model motivated by thermal regulation.

What Python contributes

Python is used to solve the reduced ordinary differential equations with AGM and HPM.

Read the studyView supporting sourceDOI 10.1007/s41939-025-00930-z
092025
ZAMM - Journal of Applied Mathematics and Mechanics / Zeitschrift für Angewandte Mathematik und Mechanik · 2025

Radiative effects on 2D unsteady MHD Al₂O₃-water nanofluid flow between squeezing plates: A comparative study using AGM and HPM in Python

Pooriya Majidi Zar · Amirali Shateri · Payam Jalili · Fuad A. M. Al‐Yarimi · Bahram Jalili · Davood Domiri Ganji · Nidhal Ben Khedher

The engineering question

This model combines plate motion, magnetic forcing and radiative effects in an alumina–water nanofluid. It compares two approximate approaches for predicting the fluid motion and thermal response.

What Python contributes

The publisher abstract states that both AGM and HPM are implemented through Python to solve the dimensionless differential equations.

Read the studyView supporting sourceDOI 10.1002/zamm.202400546
102026
International Journal of Thermofluids · 2026

Computational enhancement of radiative and reactive magneto-thermal performance with CPHNF: A python-assisted analytical framework for biomedical applications

Fateme Nadalinia Chari · Davood Domiri Ganji · Mehdi Mahboobtosi

The engineering question

The paper models a Casson fluid containing five nanoparticle materials between squeezing plates, asking how radiation, chemical reaction and magnetic forcing change momentum, heat and mass transport.

What Python contributes

The publisher identifies a Python-assisted analytical framework; the abstract specifies AGM applied after transforming the governing partial differential equations into ordinary differential equations.

Read the studyView supporting sourceDOI 10.1016/j.ijft.2026.101590
112026
The European Physical Journal Special Topics · 2026

Innovative Python-based numerical and semi-analytical study of Fe₃O₄/Al₂O₃ nanofluid performance in a parabolic trough solar collector

Shahryar Hajizadeh · Zahra Poolaei Moziraji · Bahram Jalili · Payam Jalili · Davood Domiri Ganji

The engineering question

Which modeled nanofluid transfers heat more effectively in a parabolic-trough solar collector? The study compares iron-oxide and alumina particles in engine oil while examining porous-medium and flow parameters.

What Python contributes

The publisher abstract specifies Python implementations of AGM for approximate analytical solutions and the finite element method for numerical solutions.

Read the studyView supporting sourceDOI 10.1140/epjs/s11734-026-02244-8
122026
Engineering Science and Technology, an International Journal · 2026

Data-driven prediction of thermo-solutal transport in sodium alginate-based Casson radiative magnetohydrodynamics ternary nanofluid squeeze flow

Ali Mirzagoli Ganji · Amirali Shateri · Hesam Ehsani · Mehdi Mahboobtosi · Davood Domiri Ganji · Bahram Jalili · Payam Jalili

The engineering question

Can a trained model predict the flow, temperature and concentration fields without repeatedly solving the full boundary-value problem? This study explores that question for a radiating, reactive Casson ternary nanofluid under magnetic forcing.

What Python contributes

A Python collocation solver generates numerical training data. A compact artificial neural network then maps the physical parameters and position to velocity, temperature and concentration predictions.

Read the studyView supporting sourceDOI 10.1016/j.jestch.2026.102363
A selected reading collection of 12 studies, including 9 in Elsevier journals, reviewed on 28 September 2026. It includes studies whose use of Python is documented in the title, abstract or accessible paper text. It is not a complete publication count. See research selection and evidence notes.
Continue the idea

Understand the method.
Then examine the code.

The learning notebook explains the mathematical ideas behind homotopy perturbation, the Akbari–Ganji method and related techniques. Start there, then follow the original papers for each model’s implementation and assumptions.

Homotopy perturbationThe Akbari–Ganji methodCompare approximation & referenceExplore the full indexed archive