Electric Vehicles

​​​​What Are Nanofluids and Why Use Them?

Nanofluids are engineered coolants created by dispersing nanoparticles into a base fluid. These nanoparticles can significantly enhance heat transfer properties, making them promising candidates for high-performance cooling applications.

In this study, we investigate nanofluids containing:

• Gold (Au)

• Silver (Ag)

• Copper Oxide (CuO)

• Aluminum Oxide (Al₂O₃)

Each nanoparticle type has unique thermal characteristics, and understanding their behavior inside a BTMS is essential for real-world application.

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Research Approach and Methodology

To ensure realistic and reliable results, we combine experimental testing with CFD-based thermal modeling.

Our methodology includes:

• Controlled laboratory experiments

• Computational Fluid Dynamics (CFD) simulations

• Analysis across varying Battery Surface Temperatures (B.S.T.)

• Evaluation under different Cooler Surface Temperatures (C.S.T.)

This combined approach allows us to capture both physical behavior and system-level thermal dynamics.

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Key Findings and Insights

Our results reveal that nanofluids do not behave uniformly — their performance depends strongly on temperature and nanoparticle type:

• Au, CuO, and Al₂O₃ nanofluids provide stable cooling at 30°C B.S.T.

• Ag and Al₂O₃ nanofluids perform better at 35°C B.S.T.

• Compared to plain coolant, nanofluids significantly improve cooling stability at higher temperatures

• At elevated B.S.T., nanofluids exhibit nonlinear behavior, influenced by:

  • Local temperature dips

  • Sudden thermal fluctuations within the cooling network

• At lower temperatures, nanofluids maintain near-linear and predictable behavior

These findings highlight the importance of temperature-specific coolant selection rather than a one-solution-fits-all approach.

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Why This Research Is Important

This work bridges the gap between laboratory-scale nanofluid research and real-world EV thermal management systems. Instead of assuming ideal conditions, we study how nanofluids interact with the complex and dynamic thermal environment of an actual BTMS.

The outcomes provide:

• Design guidelines for EV thermal engineers

• Insight into nanofluid stability under realistic conditions

• A foundation for optimizing cooling systems in future EV platforms

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Opportunities for Researchers and Collaboration

This research is part of a broader effort to develop smarter, safer, and more efficient electric vehicles. We actively welcome researchers interested in:

• Battery thermal management

• Nanofluids and advanced heat transfer

• CFD modeling and experimental validation

• EV systems engineering and energy storage

By joining this research initiative, collaborators can contribute to cutting-edge EV technology with real industrial relevance and global impact.

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Eligibility:

• UG: B.Tech / B.E. in Energy and Power Engineering, Automobile Engineering, Environmental Engineering, Aviation/Aeronautical/Aerospace Engineering, Marine Engineering, Mechanical Engineering (pursuing/completed)

• PG: M.Tech in Energy, Automobile, Environmental, Aviation, Marine, Mechanical (pursuing/completed)

• Doctorate: Any Doctorate (pursuing/completed)

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​​​​​Hardware and Software Requirements:

• Operating System: Windows 10 or above

• Software: ANSYS Student Version (Mechanical / Fluent / Workbench as required)

• System Configuration: Minimum 8 GB RAM

• Storage: At least 2 GB free space for simulation files

• Internet Connection: Required for downloading resources, submitting results, and communication

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​​​​​Work Description:

As a CAE Engineer Intern, you will work on simulation-based engineering problems using industry-standard tools. You will be provided with:

• A structured workflow for geometry creation, meshing, and simulation

• Step-by-step setup and execution manuals

Your Responsibilities

• Create or modify geometry based on given design parameters

• Generate and refine computational mesh for accurate simulations

• Set up and run simulations using appropriate models and boundary conditions

• Analyze results such as flow behavior, pressure distribution, forces, or thermal effects

• Perform parametric studies by changing input conditions

• Document observations and compare results across different cases

Workflow and Tools

All project data and submissions will be managed through:

• ZOHO Workspace (file sharing and documentation)

• ZOHO People (attendance and progress tracking)

Support and Guidance

• Continuous technical support will be provided by our team

• Assistance available via phone or email

• Support Hours: 11:00 AM to 5:00 PM (IST)​​​

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Benefits for Intern Future:

This research provides exposure to modern computational engineering methodologies increasingly adopted across simulation, product development, and engineering consulting industries.

Traditional mechanical engineering roles have focused heavily on design, manufacturing, and conventional analysis methods. However, modern engineering industries are rapidly shifting toward:

• Simulation-Driven Product Development

• Digital Engineering and Virtual Testing

• Computational Fluid Dynamics (CFD) Analysis

• Numerical and Performance-Based Engineering Evaluation

• Design Optimization through Computational Analysis

• Simulation-Based Research and Validation

By working on practical CAE and simulation workflows, mechanical engineering students can develop hands-on understanding of modern engineering methodologies used in advanced industrial R&D and engineering consulting environments.

This experience can support future opportunities in domains such as:

• CAE and CFD Analysis

• Engineering Simulation Consulting

• Aerospace and Automotive Simulation

• Thermal and Fluid System Analysis

• Product Development and Optimization

• Computational Engineering Research

Organizations and consulting companies actively working in these areas include:

• Tata Technologies

• L&T Technology Services

• Quest Global

• Capgemini Engineering

• KPIT Technologies

• Altair Engineering

• ANSYS

As industries increasingly adopt simulation-driven engineering and digital product development methodologies, gaining early exposure to these computational workflows can help participants build stronger industry-relevant engineering and analysis-oriented profiles.