Matthew Bonanni

Throwback to yesterday, with our very own @MatthewBonanni in his PhD Defense! We congratulate him for this tremendous achievement and wish him all the best for the future!

4k60 version is on YouTube: youtu.be/56frwqtaS2M?si…

With more than 54 million cells, this simulation merited a pretty video :)

My other publication from the #40thISOC is now online! We studied the distribution of residence time in a scramjet cavity and developed some low-order models. Using these, we showed that the skewed shape of the distribution improves combustion stability. authors.elsevier.com/a/1jcDA_Z25REl…

I'll be presenting this work at the #40thISOC this Friday at 2pm in the PPP session. Please stop by and chat if you're interested to learn more!

Happy to share that my recent work on rotating detonation engines is now online! We studied how the inhomogeneous composition in the chamber distorts the detonation and how this distortion couples back to the inhomogeneity ahead of the next passing wave. authors.elsevier.com/a/1jTuO5UKsGJQ…

@wt_chung @FxLabStanford @Combustion_Intl Congratulations!

My work with @FxLabStanford in ML for Rocket Science is now published in @Combustion_Intl's proceedings! By combining a U-Net with a stochastic model, we can generate spatially resolved rocket ignition probability maps from sparse measurements. Link: authors.elsevier.com/a/1jLGL5UKsGJP…

Yesterday I had the pleasure of presenting at ICNC 2024 in Kyoto, Japan, continuing my work on adaptive combustion modeling in scramjets. @Combustion_Intl

Excited to have our lab members present our research at ICNC in Kyoto, Japan! Here’s our PI, Matthias, in his presentation on Machine Learning for Combustion. @Combustion_Intl

Rotating detonation engines are next-gen propulsion systems that promise better thermo efficiency 🚀 Check out our new paper (coauthored by @MatthewBonanni) where we performed high-fidelity sims and developed a wavenumber analysis method. Link: doi.org/10.1016/j.comb…

I'm proud to have been a part of this study on rotating detonation engines recently published in Combustion and Flame! authors.elsevier.com/c/1ihTy2KiHiDt8

Celebrating the new @Stanford school year with a lab trip to @GoldenGateNPS Marin Headlands! Abstract: 💡->🚶🚶‍♀️->🌉->🌮

Check out this @GoogleAI article featuring our PI, Matthias Ihme!

Introducing a new simulation framework that leverages advances on @TensorFlow and TPU architecture to enable efficient large-scale simulations of turbulent flows (e.g., for cloud formation, wildfires, and more) at unprecedented scale. Learn more at goo.gle/45WU93Q

Recurrent Convolutional #DeepNeuralNetworks for Modeling Time-Resolved #Wildfire Spread Behavior by John Burge, @MatthewBonanni, R. Lily Hu & Matthias Ihme ➡ bitly.ws/RbYA @FxLabStanford @StanfordEng

Wildfires are increasingly relevant in a world with changing climate. In this collab with @Google, we developed predictive models for wildfire spread with ML approaches Check out this @FireTechnology paper co-authored by PhD student @MatthewBonanni: link.springer.com/article/10.100…

During my PhD I've also had the opportunity to work on modeling wildfires. I'm happy to share that this work was recently published, in which we used recurrent neural networks to model wildfire spread behavior at fine time scales: link.springer.com/article/10.100…

I also was a co-author on a third study analyzing secondary counter-rotating acoustic waves in our RDRE simulation

In the second, we presented an update on our development of adaptive combustion models for scramjets

I had a great time attending ICDERS 2023 this year in Seoul! I presented two works, both based on LES: In the first, we used a Lagrangian analysis technique to characterize the inhomogeneities that occur due to the nonpremixed injection systems in RDREs