Unlocking Hydrogen’s Potential: Leveraging U-M Advanced Research Computing for Enhanced Vehicle Fuel Usability

By | News

How do we efficiently store hydrogen for use as fuel when it behaves differently under certain conditions? Alauddin Ahmed, an assistant research scientist in mechanical engineering, has been using resources provided by ITS’s Advanced Research Computing (ARC) team to answer this exact question in an effort to increase hydrogen’s usability as a promising vehicular fuel.

Alauddin Ahmed is shown from the shoulders up wearing a suite, shirt, and tie.

Alauddin Ahmed, assistant research scientist in mechanical engineering

The density of hydrogen gas is very low per unit volume; this presents a challenge when trying to store large amounts of the gas in a car to be used as fuel leading to more frequent trips to refuel and ultimately a higher reliance on cars that burn gasoline. Ahmed theorized that using computational analysis, he could identify which materials would be able to store the most amount of hydrogen gas under variable conditions. In order to complete the computational analysis, he was in need of high performance computing resources: enter ARC.

With the help of ARC’s Lighthouse service, a high-performance computing (HPC) cluster that allows researchers to place their own hardware within ARC’s HPC environment, Ahmed was able to run calculations for 100,000 different materials to learn how efficient they would be when storing hydrogen. Using machine learning, he went on to run over one million calculations on the same 100,000 materials to understand how their efficiency is affected by changes in temperature and pressure. Completing such a large number of calculations by hand or even using the average computer would be nearly impossible. Using ARC’s resources, Ahmed has been able to discover the most efficient materials for storing hydrogen at different temperatures and pressures, initially identifying the top three materials for hydrogen storage at the time (there have since been additional discoveries, and Ahmed’s identified materials currently rank at two, three, and four in terms of storage efficiency). Ahmed and his co-authors have published their findings in several high-profile environmental science journals, including Nature Communications and Energy & Environmental Science.

“Without Advanced Research Computing services, it would not be possible to complete my work.” – Alauddin Ahmed, assistant research scientist in mechanical engineering

Alauddin Ahmed’s use of Advanced Research Computing resources is a perfect example of the importance of offering high-performance computing services to researchers across campus. His work in identifying the most efficient materials for storing and managing hydrogen, as well as his work with carbon capture and management, materials discovery and design, and artificial intelligence recently earned him a Research Faculty Recognition Award from the Office of Vice President for Research.

2024 ARC Summer Maintenance happening in June

By | News

Summer maintenance will be happening from June 3-5. Updates will be made to software, hardware, and operating systems to improve the performance and stability of services. ARC works to complete these tasks quickly to minimize the impact of the maintenance on research.

The dates listed below are the days the work will be occurring.

HPC clusters and storage systems (/home and /scratch) will be unavailable:

  • June 3-4: for Great Lakes
  • June 3-5: for Armis2 and Lighthouse

No downtime for ARC storage systems maintenance:

  • Turbo, Locker, and Data Den will not see any interruption in service

Queued jobs and maintenance reminders

Jobs will remain queued, and will automatically begin after the maintenance is completed. The command “maxwalltime” will show the amount of time remaining until maintenance begins for each cluster, so you can size your jobs appropriately. 

Status updates

  • Status updates will be posted to the ITS website and the ARC Twitter feed
  • ARC will send an email when the maintenance has been completed.

How can we help you?

For assistance or questions, contact ARC at arc-support@umich.edu.

Secure Enclave Service (SES) outage due to maintenance on March 23

By | News, Systems and Services, Uncategorized

Beginning at 6:30 a.m. on Saturday, March 23, through 5 p.m. on March 23, the Secure Enclave Services cluster will be shut down to allow for emergency maintenance on the underlying power distribution unit in the data center. This includes shutdowns for virtual machines hosted on the cluster.  To perform the upgrade, the entire cluster and all virtual machines will be offline on March 23.  

Status updates will be posted on the ITS Service Status page

How can we help you?

For assistance or questions, please contact ARC at arc-support@umich.edu.

For other topics, contact the ITS Service Center:

Using GenAI to design floor plans and buildings

By | Data, Data sets, Educational, Feature, General Interest, HPC, News, Research, Systems and Services

There is a lot to consider when designing places where humans live and work. How will the space be used? Who’s using the space? What are budget considerations? It is painstaking and time consuming to develop all of those details into something usable. 

What if Generative AI (GenAI) could help? We already know that it can be used to create text, music, and images. Did you know that it can also create building designs and floor plans? 

Dr. Matias del Campo, associate professor of architecture in the Taubman College for Architecture and Urban Planning, has been working to make architectural generative models more robust. He aims to expand on the patterns, structures, and features from the available input data to create architectural works. Himself a registered architect, designer, and educator, del Campo conducts research on advanced design methods in architecture using artificial intelligence techniques.

He leverages something called neural networks for two projects: 

  • Common House: A project that focuses on floor plan analysis and generation.
  • Model Mine: A large-scale, 3D model housing database for architecture design using Graph Convolutional Neural Networks and 3D Generative Adversarial Networks.

This is an example from the annotated data created from the Common House research project. The main obstacle that has emerged in creating more real-life plans is the lack of databases that are tailored for these architecture applications. The Common House project aims at creating a large-scale dataset for plans with semantic information. Precisely, our data creation pipeline consists of annotating different components of a floor plan, for e.g., Dining Room, Kitchen, Bed Room, etc.

 

Four quadrants showing 9 models each of chairs, laptops, benches, and airplanes

A large scale 3D model housing database for Architecture design using Graph Convolutional Neural Networks and 3D Generative Adversarial Networks.

What exactly are neural networks? The name itself takes inspiration from the human brain and the way that biological neurons signal to one another. In the GenAI world, neural networks are a subset of machine learning and are at the heart of deep learning algorithms. This image of AI hierarchy may be helpful in understanding how they are connected.

Dr. del Campo’s research uses GenAI for every step of the design process including 2D models for things like floors and exteriors, and 3D models for shapes of the rooms, buildings, and volume of the room. The analysis informs design decisions. 

DEI considerations

del Campo notes that there are some DEI implications for the tools he’s developing. “One of the observations that brought us to develop the ‘Common House’ (Plangenerator) project is that the existing apartment and house plan datasets are heavily biased towards European and U.S. housing. They do not contain plans from other regions of the world; thus, most cultures are underrepresented.” 

To counterbalance that, del Campo and his team made a global data collection effort, collecting plans and having them labeled by local architects and architecture students. “This not only ensured a more diverse dataset but also increased the quality of the semantic information in the dataset.”

How technology supports del Campo’s work

A number of services from Information Technology & Services are used in these projects, including: Google at U-M collaboration tools, GenAI, Amazon Web Services at U-M (AWS), and GitHub at U-M

Also from ITS, the Advanced Research Computing (ARC) team provides support to del Campo’s work. 

“We requested allocations from the U-M Research Computing Package for high-performance computing (HPC) services in order to train two models. One focuses on the ‘Common House’ plan generator, and the other focuses on the ‘Model Mine’ dataset to create 3D models based,” said del Campo. 

Additionally, they used HPC allocations from the UMRPC in the creation of a large-scale artwork called MOSAIK which consists of over 20,000 AI-generated images, organized in a color gradient. 

A large scale 3D model housing database for Architecture design using Graph Convolutional Neural Networks and 3D Generative Adversarial Networks.

“We used HPC to run the algorithm that organized the images. Due to the necessary high resolution of the image, this was only possible using HPC.”

“Dr. del Campo’s work is really novel, and it is different from the type of research that is usually processed on Great Lakes. I am impressed by the creative ways Dr. del Campo is applying ITS resources in a way that we did not think was possible,” said Brock Palen, director of the ITS Advanced Research Computing. 

Related: Learn about The Architecture + Artificial Intelligence Laboratory (AR2IL)