Výsledky vyhledávání - "AOWABIN RAHMAN"

DESIGNING AN IMPROVED INTERFACE IN GRAPHENE/POLYMER COMPOSITES THROUGH MACHINE LEARNING

Autor: PRATHAMESH P. DESHPANDE, KAREN J. DEMILLE, AOWABIN RAHMAN, SUSANTA GHOSH, ASHLEY D. SPEAR, GREGORY M. ODEGARD

Publikováno v: American Society for Composites 2022.

The matrix-reinforcement interface has been studied extensively to enhance the performance of polymer matrix composites (PMCs). One commonly practiced approach is functionalization of the reinforcement, which significantly improves the interfacial in

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::190987bb2b1288a16be49a4b4325076e
https://doi.org/10.12783/asc37/36458

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Investigating the efficacy of machine learning tools in modeling the continuous stabilization and carbonization process and predicting carbon fiber properties

Autor: Satish Kumar, Prachi Patwardhan, Aowabin Rahman, Narayan Shirolkar, Ashley D. Spear

Publikováno v: Carbon. 174:605-616

Carbon fiber reinforced composites are finding increased usage in a wide variety of applications, ranging from aerospace to energy harvesting to sporting goods and more. Carbon fiber manufacturing is an extremely time- and cost-intensive process with

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::59b3da66ce7fd970472a0d883fbc56dc
https://doi.org/10.1016/j.carbon.2020.12.044

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Multi-fidelity Bayesian Optimization for Co-design of Resilient Cyber-Physical Systems

Autor: Soumya Vasisht, Aowabin Rahman, Thiagarajan Ramachandran, Arnab Bhattacharya, Veronica Adetola

Publikováno v: 2022 ACM/IEEE 13th International Conference on Cyber-Physical Systems (ICCPS).

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::bfdfff0a0c4018d807553f22b8f68de8
https://doi.org/10.1109/iccps54341.2022.00040

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A void descriptor function to uniquely characterize pore networks and predict ductile-metal failure properties

Autor: Ashley D. Spear, John Erickson, Aowabin Rahman

Publikováno v: International Journal of Fracture. 225:47-67

Porosity, a commonly occurring void defect in casting and additive manufacturing, is known to affect the mechanical response of metals, making it difficult or impossible to predict response variability. We introduce a new method of uniquely character

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::da57e962a71b10bfacec8142ebc879d7
https://doi.org/10.1007/s10704-020-00463-1

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Copper: a performance curve generator for building energy simulation

Autor: Jérémy Lerond, Aowabin Rahman, Jian Zhang, Michael Rosenberg

Publikováno v: Journal of Open Source Software. 8:4876

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::dda436d96dbec1839ec88f94bf6c6812
https://doi.org/10.21105/joss.04876

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Predicting Electricity and Heat Demand in Buildings Over a Longer Time Horizon Using a Sequence-to-Sequence Recurrent Neural Network Model

Autor: Amanda Smith, Aowabin Rahman

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::837b34c5900b0917397ec964bf7a82fc
https://doi.org/10.1115/1.0001380v

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Multiscale effects masked the impact of the COVID-19 pandemic on electricity demand in the United States

Autor: Amanda D. Smith, Nathalie Voisin, Aowabin Rahman, J. Rice, Casey D. Burleyson

Publikováno v: Applied energy. 304

Shelter-in-place orders and business closures related to COVID-19 changed the hourly profile of electricity demand and created an unprecedented source of uncertainty for the grid. The potential for continued shifts in electricity profiles has implica

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b0d1fa2688eaed1f36362f177a3eceb
https://pubmed.ncbi.nlm.nih.gov/36568493

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Flexible Reinforcement Learning Framework for Building Control using EnergyPlus-Modelica Energy Models

Autor: Aowabin Rahman, Srinivas Katipamula, Amanda D. Smith, Joon-Yong Lee, Sen Huang

Publikováno v: Proceedings of the 1st International Workshop on Reinforcement Learning for Energy Management in Buildings & Cities.

In recent years, reinforcement learning (RL) methods have been greatly enhanced by leveraging deep learning approaches. RL methods applied to building control have shown potential in many applications because of their ability to complement or replace

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::9395caabf502a50ef18abf6ae814d772
https://doi.org/10.1145/3427773.3427873

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Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms

Autor: Aowabin Rahman, Amanda D. Smith

Publikováno v: Applied Energy. 228:108-121

This paper evaluates the performance of deep recurrent neural networks in predicting heating demand for a commercial building over a medium-to-long term time horizon ( ⩾ 1 week), and proposes a modeling framework to demonstrate how these longer-ter

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::4493982f58d27b7f912d76635402ab88
https://doi.org/10.1016/j.apenergy.2018.06.064

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