Computational engineering

Faculty in the Department of Mechanical Engineering are creating computer-aided design tools for process simulations and novel algorithms for the biomodeling of molecules using computational methods.

People

Learn who in Mechanical Engineering is working in computational engineering.

Computational engineering

Computational geometry, CAD, and CAE

Our faculty investigate computational approaches and computer-aided design to create tools for visualization, modeling, simulation, sketched-based user interfaces, and human-computer problem solving. These can impact areas ranging from product design to medical imaging.

Faculty involved: Jonathan Cagan, Burak Kara, Kenji Shimada, Yongjie Jessica Zhang

Computational tools for manufacturing

Our experts create and apply computational design tools and computer-aided manufacturing tools to advance manufacturing processes and technologies. The work includes design optimization for manufacturability and analysis software for process simulations.

Faculty involved: Jack Beuth, Jonathan Cagan, Burak Kara, Kenji Shimada

Computation in energy

From improving energy conversion and storage efficiency to reducing energy consumption, our experts explore micro/nano-scale phenomena through atomic- and meso-scale simulations and machine learning.

Faculty involved: Amir Barati Farimani, Shawn Litster, Alan McGaughey, Venkat Viswanathan, Ding Zhao

Computational biology and medicine

Our researchers seek to improve human health by exploring the biomechanics of cells and molecules, developing better medical imaging technologies, and developing novel algorithms to biomodel molecules, cells, tissue, and organs.

They are combining molecular dynamic simulations, machine learning, and statistical learning to understand and predict the properties and interactions of bio-molecules such as DNA and proteins.

Faculty involved: Amir Barati Farimani, Jonathan Cagan, Noelia Grande Gutiérrez, Eni Halilaj, Burak Kara, Philip LeDuc, Yoed Rabin, Kenji Shimada, Yongjie Jessica Zhang

Computational fluid dynamics (CFD) and multiphysics simulations

Our faculty are developing the computational tools to simulate engineering applications and the phenomena that impact them in their full-scale complexity.

They perform multiphase flow simulations, direct numerical simulations (DNS), large-eddy simulations (LES), porous media flow simulations, electrokinetics, and multiphysics simulations.

Faculty involved: Amir Barati Farimani, Shawn Litster, Satbir Singh

Related News

Automating consistent product design Opens in new window

Researchers develop AI-driven method to automate the discovery of brand related features in product design.

Research paper Opens in new window

In a world run by catalysts, why is optimizing them still so tough?

CatBERTa, an energy prediction Transformer model, was developed by researchers in Carnegie Mellon University’s College of Engineering in an approach to tackle molecular property prediction using machine learning.

Branching out: Modeling neurons in new ways Opens in new window

Predicting neuron cell growth just got a little bit easier, thanks to CMU researchers.

Research paper Opens in new window

Additive manufacturing in focus Opens in new window

Researchers introduce an experimental method to measure melt pool temperature using a single commercial color camera during additive manufacturing.

Jon Cagan named head of Mechanical Engineering Opens in new window

Jon Cagan will be the new head of the Department of Mechanical Engineering at Carnegie Mellon University, effective November 1, 2022.

Modeling the growth process of neurons Opens in new window

Jessica Zhang’s lab has furthered its research with IGA analysis and accurately modeled the growth of a neuron.

NIH awards Halilaj $2.7M

Eni Halilaj will collaborate with UPMC to predict the onset of post-traumatic osteoarthritis after anterior cruciate ligament reconstruction surgery.

Modeling neuron traffic jams in the brain Opens in new window

MechE’s Jessica Zhang and Angran Li have developed a new way to model material transport regulation in neurons using cutting-edge parametrization technology and isogeometric analysis. This new, much more detailed modeling will help provide insights on “neural traffic jams,” which contribute to neurodegenerative diseases such as Alzheimer's, Huntington’s, and Parkinson’s disease.

See More Stories

Machine learning gets smarter to speed up drug discovery Opens in new window

This self-supervised learning framework is better at predicting molecular properties than other machine learning models because it can use large amounts of unlabeled data that other models can’t.

Simulating fluid flow to study disease

New MechE faculty member Noelia Grande Gutiérrez uses computational modeling to study the fluid mechanics of blood flow to design personalized therapies for thrombosis, earlier diagnosis of coronary artery disease, and precise, targeted drug delivery for cancer.

Using deep learning to research material transport in the brain

Understanding the causes of degenerative diseases like Alzheimer's, Huntington's, and Parkinson's will require the meticulous investigation into the complex, branch-like neurite networks of the brain. Machine learning can be an efficient, highly accurate part of this process.

Pushing through nanopores: Genetic sequencing with MXene Opens in new window

MXene—a single layer, two-dimensional nanomaterial—shows potential for solid-state, nanopore-based DNA sequencing. This could lead to efficient, rapid diagnostics and personalized medicine.

Research paper Opens in new window

Using DNA for tiny tech Opens in new window

Tito Babatunde and her advisors Rebecca Taylor and Jon Cagan are combining their expertise to optimize designs for DNA origami nanostructures.

Research paper Opens in new window

This is how we roll Opens in new window

From an academic perspective, Buggy symbolizes education in motion. The sleek, three-wheeled vessels are the byproduct of classroom and hands-on learning that’s reinforced with legacy knowledge.

Watch the Video Opens in new window

Virtually awarded

MechE graduate students earned accolades at the department’s annual research symposium poster sessions. This year's events were virtual through Gather Town, an interactive, video-chat world.

The electric future of autonomous vehicles Opens in new window

A team of CMU Engineering researchers has shown that autonomous vehicles can be electric—despite the decreased driving range.

Research paper Opens in new window

Zhang receives NSF funding for brain research

Yongjie Jessica Zhang is researching brain biomechanics with funding from the National Science Foundation's Engineering for America's Prosperity, Health, and Infrastructure (LEAP HI) program.

First real-time physics engine for soft robotics Opens in new window

Collaborators have adapted the sophisticated computer graphics technology used in blockbuster films and video games to simulate the movements of soft, limbed robots for the first time.

Research paper Opens in new window

Solid-ion conductors for safer batteries

Venkat Viswanathan and collaborators at Lawrence Berkeley National Laboratory investigate how a solid-ion conductor—a component that can be used as a separator between the anode and the electrolyte in a battery—can prevent dendrites.

A more efficient way to turn saltwater into drinking water Opens in new window

Researchers are working on a way to transform seawater into fresh drinking water with a new, honeycombed-patterned membrane—only a few atoms thick—that uses less energy than existing methods.

Research paper Opens in new window

AI learns to design Opens in new window

AI agents imitate engineers to construct effective new designs using visual cues like humans do.

Related Article Opens in new window

Zhang and Yao earn MFI award for 4D printing

Jessica Zhang and Lining Yao earn a Manufacturing Futures Initiative (MFI) award to develop fiber-reinforced composite structures through a process called 4D printing.

College of Engineering launches “Arjuna,” a multi-departmental GPU cluster

Carnegie Mellon University’s College of Engineering has installed a new, multi-departmental GPU cluster at a Pittsburgh Supercomputing Center facility in Monroeville, Pennsylvania.

Explore Other Research Topics

CAPS

Center for Atmospheric Particle Studies

featured center

Learn more about CAPS