Mechanical engineer Misagh Daraei chatted about advancing mechanical engineering efficiency in New York City and across the United States.

He opened up about his use of technology in his daily routine as an engineer, and how AI (artificial intelligence) is impacting the contemporary engineering sector.

In New York City, where engineering innovation meets large scale infrastructure and industry, mechanical engineer Misagh Daraei is developing new methods to improve efficiency, sustainability, and system performance.

His research and applied work in mechanical engineering optimization combine computational simulations, artificial intelligence, and data driven design to redefine how engineers approach complex mechanical systems in both New York and the broader United States.

Through his ongoing research and engineering projects in New York City and across the United States, Misagh Daraei is contributing to a more efficient and sustainable future for mechanical systems.

By integrating computational modeling, AI, and scientific rigor, his work demonstrates how advanced mechanical engineering can balance performance, cost, and environmental responsibility, setting a thoughtful example for the next generation of engineers.

How do you define efficiency in the context of modern mechanical engineering?

Efficiency in mechanical engineering is about achieving the maximum possible performance with minimal resource consumption.

It means designing systems that use less energy, waste fewer materials, and last longer without compromising safety or function.

In cities like New York, where space, cost, and energy are critical considerations, efficiency becomes both a technical and an environmental necessity.

Your work often integrates advanced simulations. How do these tools improve engineering design?

Simulation is a cornerstone of modern engineering. I use Finite Element Analysis (FEA) and Computational Fluid Dynamics (CFD) to understand how materials and structures behave under different conditions before physical prototypes are made.

These simulations allow for highly accurate modeling of stress, heat transfer, and fluid flow.

By doing so, we can identify weaknesses, improve geometry, and enhance performance early in the design process -- which leads to more efficient and sustainable engineering outcomes.

Artificial intelligence and machine learning are becoming increasingly important in the field. How are you applying these technologies?

Artificial intelligence enables engineers to move from reactive to predictive design. In my work, machine learning algorithms analyze large datasets from simulations and real-world measurements to predict performance trends, energy losses, and potential failure points.

This data-driven approach allows for continuous optimization of mechanical systems, whether it's improving the aerodynamics of a vehicle or increasing the output efficiency of a wind turbine.

How is AI impacting the mechanical engineering sector?

Artificial intelligence (AI) is helping mechanical engineers make faster and more informed decisions based on evidence rather than trial and error.

What are some of the key applications of your research and engineering methods in New York and the United States?

There are several active areas of application:

* Aerospace Engineering (United States): Reducing drag and improving aerodynamic performance for greater fuel efficiency.

* Automotive Engineering (New York and nationwide): Enhancing chassis and body designs using CFD and optimization models to increase fuel savings.

* Renewable Energy Systems: Applying predictive simulations to extend the lifespan and efficiency of wind turbines and thermal systems.

All of these applications reflect a shared goal, improving mechanical performance while minimizing environmental and material costs.

Looking ahead, what do you see as the next major step in mechanical engineering optimization?

We are moving toward autonomous optimization systems that combine AI, IoT (Internet of Things), and potentially quantum computing.

These systems could monitor and adjust mechanical components in real time, learning from data and adapting to changing conditions automatically.

In places like New York City, where infrastructure operates under constant demand, such technology could drastically improve maintenance efficiency, safety, and energy use. This direction represents the next evolution of mechanical engineering, intelligent systems that optimize themselves continuously.

How does sustainability influence your approach to mechanical engineering?

Sustainability is no longer an optional consideration; it's a fundamental design requirement.

My work focuses on reducing waste through simulation-based design and ensuring that every engineering decision considers both performance and environmental impact.

Whether developing lighter structures for aerospace or improving the energy efficiency of thermal systems, the objective is always the same: to engineer smarter, cleaner, and more responsible systems for long-term use.