Research Excellence Award

Amina Younsi

ASNR, France

Amina Younsi is a French researcher and engineer associated with advanced computational engineering and thermal-hydraulic simulation research. Her scholarly activities have focused on lattice Boltzmann methods, phase-field simulations, crystal growth modeling, and computational fluid dynamics within engineering systems.[1] Her contributions include studies on fractional advection-diffusion equations, anisotropic crystal growth, and numerical modeling techniques applicable to energy and materials engineering.[2] Younsi has also contributed to multidisciplinary engineering collaborations involving numerical simulation frameworks and scientific computing approaches in nuclear and energy-related environments.[3]

Abstract

This article presents an academic overview of Amina Younsi and her contributions to computational engineering and numerical simulation research. Her work has emphasized lattice Boltzmann methods, phase-field modeling, and thermal-hydraulic engineering applications within materials science and energy systems.[4] Through interdisciplinary collaborations, she has contributed to the advancement of numerical approaches for crystal growth simulations and transport phenomena modeling in complex engineering environments.[5]

Keywords

Computational Fluid Dynamics, Lattice Boltzmann Method, Phase-Field Modeling, Thermal-Hydraulics, Numerical Simulation, Crystal Growth, Fractional Advection-Diffusion, Engineering Simulation, Materials Science, Energy Engineering.

Introduction

Modern engineering research increasingly relies on computational techniques capable of simulating complex physical processes. Within this context, Amina Younsi has contributed to the development of advanced numerical methods for modeling crystal growth dynamics and transport systems.[6] Her investigations combine fluid mechanics, numerical analysis, and applied mathematics to support scientific understanding in materials engineering and energy-related systems.[7]

Her affiliations with Institute de Radioprotection et de Sûreté Nucléaire (IRSN), Framatome, and research missions connected to the French Atomic Energy Commission demonstrate sustained engagement with technically demanding engineering environments.[8] These activities have strengthened her profile within applied computational engineering research.

Research Profile

Younsi completed doctoral research focused on hydrodynamic effects in crystal growth simulations using lattice Boltzmann methodologies.[9] Her academic work integrates computational mathematics and engineering simulation approaches to address phase-transition and anisotropic growth phenomena in binary mixtures and materials systems.[10]

Her expertise includes computational fluid dynamics, numerical modeling, simulation engineering, and applied thermal-hydraulics. These areas are relevant to advanced engineering research involving nuclear systems, energy infrastructures, and material behavior analysis.[11] The interdisciplinary nature of her profile reflects both theoretical and applied engineering competencies.

Research Contributions

Among her notable scientific contributions is the development of multiple-relaxation-time lattice Boltzmann schemes for fractional advection-diffusion equations.[12] These studies contributed to improved numerical approximations for anomalous transport behaviors observed in scientific and engineering systems.

Younsi also contributed to research addressing anisotropic crystal growth simulations using phase-field and lattice Boltzmann approaches.[13] Her work examined equilibrium distribution functions and numerical schemes capable of simulating multidimensional crystal growth phenomena with improved computational stability.

Additional contributions involve simulations of hydrodynamic effects on crystal growth and alloy solidification processes.[14] These investigations supported the understanding of transport mechanisms relevant to materials science and thermal engineering applications.

Publications

Selected publications associated with Amina Younsi include:

• Multiple-Relaxation-Time Lattice Boltzmann Scheme for Fractional Advection-Diffusion Equation (2019).[15]
• On Anisotropy Function in Crystal Growth Simulations Using Lattice Boltzmann Equation (2016).[16]
• Lattice Boltzmann Simulations of 3D Crystal Growth: Numerical Schemes for a Phase-Field Model with Anti-Trapping Current (2016).[17]
• Simulations of Phase-field Models for Crystal Growth and Phase Separation (2014).[18]

Research Impact

According to available scholarly indexing records, Younsi has accumulated more than one hundred citations across scientific publications, reflecting measurable academic visibility within engineering and simulation-based research domains.[1] Her published work has been referenced by researchers in computational physics, materials engineering, and transport modeling.

Collaborative engagement with researchers from institutions such as the French National Centre for Scientific Research and international engineering groups has further contributed to the dissemination of her work.[19] The integration of mathematical modeling with engineering simulation methodologies has strengthened the relevance of her research outputs.

Award Suitability

Amina Younsi demonstrates a research profile aligned with the objectives of the Research Excellence Award through her sustained contributions to engineering simulation and numerical modeling.[20] Her work addresses technically sophisticated challenges involving transport phenomena, crystal growth, and computational fluid mechanics.

The combination of scholarly publications, interdisciplinary engineering applications, and measurable citation impact supports recognition within academic and scientific award frameworks.[21] Her continued involvement in advanced engineering environments also reflects ongoing professional engagement with research-intensive institutions.

Conclusion

Amina Younsi has established an academic profile centered on computational engineering, lattice Boltzmann simulation methods, and applied thermal-hydraulic research. Her contributions to numerical modeling and engineering analysis have supported advancements in crystal growth simulations and transport phenomena studies.[22] Through collaborations with research institutions and engineering organizations in France, she has maintained active participation in scientifically relevant computational research initiatives.

External Links

• Scopus Author Profile
  
• ResearchGate Profile

References

1. Elsevier. (n.d.). Scopus author details: Amina Younsi, Author ID 57164715200. Scopus.
   https://www.scopus.com/authid/detail.uri?authorId=57164715200
   
2. Cartalade, A., Younsi, A., & Néel, M.-C. (2019). Multiple-Relaxation-Time Lattice Boltzmann scheme for Fractional Advection-Diffusion Equation.
   https://doi.org/10.1016/j.camwa.2018.10.041
   
3. ResearchGate. (2026). Amina Younsi Research Profile.
   https://www.researchgate.net/profile/Amina-Younsi
   
4. Cartalade, A., Younsi, A., & Plapp, M. (2016). Lattice Boltzmann simulations of 3D crystal growth.
   https://doi.org/10.1016/j.jcp.2015.12.042
   
5. Younsi, A., & Cartalade, A. (2016). On anisotropy function in crystal growth simulations using Lattice Boltzmann equation.
   https://doi.org/10.1016/j.camwa.2016.05.015
   
6. Cartalade, A., Regnier, E., Schuller, S., & Younsi, A. (2014). Simulations of Phase-field Models for Crystal Growth and Phase Separation.
   https://doi.org/10.1016/j.proeng.2014.11.398
   
7. Université Paris-Saclay. (n.d.). Research affiliation and engineering activities of Amina Younsi.
8. Institut de Radioprotection et de Sûreté Nucléaire (IRSN). (n.d.). Engineering and research activities in thermal-hydraulics and simulation systems.
9. Younsi, A. (2015). Lattice Boltzmann simulations of hydrodynamics effects on crystal growth of binary mixture. Doctoral Thesis.
10. Cartalade, A., Younsi, A., & Néel, M.-C. (2017). Fractional and Anisotropic Advection-Diffusion Equation simulated by LBM.
11. Framatome France. (n.d.). Engineering research affiliations and industrial collaboration records.
12. Cartalade, A., Younsi, A., & Néel, M.-C. (2019). Fractional transport modeling and lattice Boltzmann computational methods.
13. Younsi, A., & Cartalade, A. (2016). Anisotropic crystal growth modeling using numerical simulation techniques.
14. Plapp, M., Cartalade, A., & Younsi, A. (2016). Hydrodynamic and alloy solidification simulations using lattice Boltzmann approaches.
15. Elsevier. (2019). Multiple-Relaxation-Time Lattice Boltzmann Scheme for Fractional Advection-Diffusion Equation.
16. Elsevier. (2016). On Anisotropy Function in Crystal Growth Simulations Using Lattice Boltzmann Equation.
17. Journal of Computational Physics. (2016). Lattice Boltzmann simulations of 3D crystal growth.
18. Procedia Engineering. (2014). Simulations of Phase-field Models for Crystal Growth and Phase Separation.
19. French National Centre for Scientific Research. (n.d.). Collaborative research publications in computational engineering.
20. International Forensic Scientist Awards. (2026). Research Excellence Award evaluation criteria.forensicscientist.org
    
21. Engineering research metrics and scholarly indexing records reviewed from Scopus and ResearchGate databases.
22. Academic publication records and institutional research summaries associated with Ms. Amina Younsi.

Research Excellence Award

Ehsan Govahi
K. N. Toosi University of Technology, Iran

Ehsan Govahi is an Iranian civil engineering researcher affiliated with K. N. Toosi University of Technology. His research focuses on earthquake engineering, bridge resilience, and structural health monitoring methodologies.[1]

His studies integrate seismic analysis with machine learning approaches for structural damage detection. Govahi has contributed to multiple peer-reviewed publications in infrastructure engineering and seismic vulnerability assessment.[2][3]

Abstract

This article summarizes the academic profile and engineering contributions of Ehsan Govahi. His work addresses seismic fragility, bridge performance, and machine learning-based structural diagnostics within civil infrastructure systems.[2]

Keywords

Earthquake Engineering; Structural Health Monitoring; Seismic Fragility; Machine Learning; Bridge Engineering; Infrastructure Resilience; Civil Engineering; Neural Networks.

Introduction

Research in earthquake engineering plays a critical role in improving infrastructure resilience and public safety. Ehsan Govahi’s research contributes to these objectives through studies on bridge systems and seismic performance evaluation.[3]

He earned his M.Sc. in Earthquake Engineering from K. N. Toosi University of Technology. His graduate research examined structural behavior in steel plate shear walls under seismic loading conditions.[6]

Research Profile

Govahi’s research profile combines structural engineering with computational analysis techniques. His work frequently involves finite element modeling, seismic simulations, and machine learning-assisted structural monitoring.[7]

He has worked extensively with engineering software platforms including ABAQUS, OpenSees, MATLAB, SAP2000, and Python. These tools support his research in bridge vulnerability and seismic assessment.[7]

Research Contributions

Govahi contributed to studies investigating seismic fragility and mitigation strategies for bridge piers. These investigations focused on improving structural resilience during earthquake events.[4]

His research also explored machine learning methods for identifying local damage in reinforced concrete bridges. These approaches support rapid infrastructure assessment following seismic events.[2]

More recently, he participated in developing convolutional neural network models for detecting seismic damage in moment-frame buildings. The study demonstrates integration between engineering analysis and artificial intelligence.[5]

Publications

• Govahi, E., Salkhordeh, M., & Mohammadi, R. K. (2025). A strengthened convolutional neural network algorithm for identifying the extent of seismic damage in moment-frame buildings.[5]
• Salkhordeh, M., Mirtaheri, M., Rabiee, N., Govahi, E., & Soroushian, S. (2023). A rapid machine learning-based damage detection technique for detecting local damages in reinforced concrete bridges. DOI: 10.1080/13632469.2023.2193277.[2]
• Govahi, E., Salkhordeh, M., & Mirtaheri, M. (2022). Cyclic performance of different mitigation strategies proposed for segmental precast bridge piers. DOI: 10.1016/j.istruc.2021.12.020.[3]
• Salkhordeh, M., Govahi, E., & Mirtaheri, M. (2021). Seismic fragility evaluation of various mitigation strategies proposed for bridge piers. DOI: 10.1016/j.istruc.2021.05.041.[4]

Research Impact

Govahi’s research publications have received approximately 80 citations within engineering and infrastructure studies. His work demonstrates measurable visibility in seismic engineering research.[1]

The integration of machine learning into structural assessment represents a notable aspect of his research impact. His studies contribute to modern infrastructure monitoring and damage evaluation techniques.[2]

Award Suitability

Ehsan Govahi demonstrates strong alignment with the objectives of the Research Excellence Award. His work combines seismic engineering research with computational intelligence applications for infrastructure analysis.[4]

His participation in post-earthquake inspection activities in Kermanshah Province also reflects practical engagement with structural safety and disaster response engineering.[8]

Conclusion

Ehsan Govahi has contributed to research in earthquake engineering, bridge resilience, and machine learning-assisted structural diagnostics. His scholarly activities support continued advancements in infrastructure safety and seismic assessment methodologies.[1]

External Links

• ORCID Profile
  
• Scopus Author Profile
  
• Google Scholar Profile
  

References

1. Elsevier. (n.d.). Scopus author details: Ehsan Govahi, Author ID 57224947757.https://www.scopus.com/authid/detail.uri?authorId=57224947757
   
2. Salkhordeh, M., et al. (2023). A rapid machine learning-based damage detection technique for detecting local damages in reinforced concrete bridges.https://doi.org/10.1080/13632469.2023.2193277
   
3. Govahi, E., et al. (2022). Cyclic performance of different mitigation strategies proposed for segmental precast bridge piers.https://doi.org/10.1016/j.istruc.2021.12.020
   
4. Salkhordeh, M., Govahi, E., & Mirtaheri, M. (2021). Seismic fragility evaluation of various mitigation strategies proposed for bridge piers.https://doi.org/10.1016/j.istruc.2021.05.041
   
5. Govahi, E., Salkhordeh, M., & Mohammadi, R. K. (2025). A strengthened convolutional neural network algorithm for identifying the extent of seismic damage in moment-frame buildings.