چکیده مقاله
Progress in computational capabilities has enabled the numerical modeling of extensive fluid mechanical and particulate systems, which are essential to many fundamental industrial operations Of the various numerical techniques, the discrete element method DEM offers one of the most precise depictions of diverse physical systems featuring granular and fragmented materials As a result, DEM has gained broad acceptance for addressing engineering issues related to granular flows and powder dynamics Moreover, DEM can be combined with grid based computational fluid dynamics CFD approaches to model chemical reactions, such as those in fluidized beds Nevertheless, DEM's high computational demands arise from the inherent multiscale characteristics of particulate systems, limiting simulation length or particle quantity To overcome this, NeuralDEM offers a comprehensive framework that substitutes sluggish DEM computations with rapid, flexible deep learning proxies NeuralDEM can simulate extended transport phenomena across various flow regimes using only macroscopic variables, independent of microscopic parameters Initially, it reinterprets DEM's Lagrangian mesh as a foundational continuous field, while also directly representing macroscopic traits as supplementary fields Additionally, NeuralDEM employs multi branch neural operators that scale to real time simulation of industrial scale setups, spanning from gradual pseudo steady states to rapid transients—challenges that have historically eluded deep learning solutions Impressively, NeuralDEM accurately replicates coupled CFD DEM fluidized bed reactors involving 160,000 CFD cells and 500,000 DEM particles over 28 second paths NeuralDEM promises to unlock novel avenues in engineering innovation and accelerate process workflows significantly
کلیدواژهها
نویسندگان
Ali Pouladi
Master of Structural Engineering & Member of Young and Elite Research Club, IAU, Iran
Anahita Ghavami
Lecturer, University of Applied Sciences and Technology, Tehran Branch
شیوه ارجاع
Ghavami, Anahita,1404,NeuralDEM: A Real-Time Deep Learning Surrogate for Industrial Granular Flow Simulation,19th International Conference on Mechanical, Construction Industrial & Civil Engineering,Tehran
ارائهشده در
مجموعه مقالات نوزدهمین کنفرانس بین المللی مکانیک، ساخت، صنایع و مهندسی عمران26 مهر 1404 · تهران