FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3047754677> ?p ?o ?g. }

• W3047754677 abstract "Fine-scale models that represent first-principles physics are challenging to represent at larger scales of interest in many application areas. In nanoporous media such as tight-shale formations, where the typical pore size is less than 50 nm, confinement effects play a significant role in how fluids behave. At these scales, fluids are under confinement, affecting key properties such as density, viscosity, adsorption, etc. Pore-scale Lattice Boltzmann Methods (LBM) can simulate flow in complex pore structures relevant to predicting hydrocarbon production, but must be corrected to account for confinement effects. Molecular dynamics (MD) can model confinement effects but is computationally expensive in comparison. The hurdle to bridging MD with LBM is the computational expense of MD simulations needed to perform this correction. Here, we build a Machine Learning (ML) surrogate model that captures adsorption effects across a wide range of parameter space and bridges the MD and LBM scales using a relatively small number of MD calculations. The model computes upscaled adsorption parameters across varying density, temperature, and pore width. The ML model is 7 orders of magnitude faster than brute force MD. This workflow is agnostic to the physical system and could be generalized to further scale-bridging applications." @default.
• W3047754677 created "2020-08-13" @default.
• W3047754677 creator A5003104055 @default.
• W3047754677 creator A5010192859 @default.
• W3047754677 creator A5021344986 @default.
• W3047754677 creator A5040722494 @default.
• W3047754677 creator A5040783568 @default.
• W3047754677 creator A5043046558 @default.
• W3047754677 creator A5052676364 @default.
• W3047754677 creator A5059810716 @default.
• W3047754677 creator A5072591335 @default.
• W3047754677 creator A5073783714 @default.
• W3047754677 date "2020-08-07" @default.
• W3047754677 modified "2023-10-18" @default.
• W3047754677 title "Modeling and scale-bridging using machine learning: nanoconfinement effects in porous media" @default.
• W3047754677 cites W1975021623 @default.
• W3047754677 cites W1980381105 @default.
• W3047754677 cites W1981136290 @default.
• W3047754677 cites W1992773065 @default.
• W3047754677 cites W1993675820 @default.
• W3047754677 cites W2001664729 @default.
• W3047754677 cites W2005490958 @default.
• W3047754677 cites W2007599238 @default.
• W3047754677 cites W2012893865 @default.
• W3047754677 cites W2017742898 @default.
• W3047754677 cites W2019465613 @default.
• W3047754677 cites W2021035369 @default.
• W3047754677 cites W2023167709 @default.
• W3047754677 cites W2026466407 @default.
• W3047754677 cites W2026632806 @default.
• W3047754677 cites W2033642666 @default.
• W3047754677 cites W2060138396 @default.
• W3047754677 cites W2077508082 @default.
• W3047754677 cites W2078402834 @default.
• W3047754677 cites W2086634221 @default.
• W3047754677 cites W2117242079 @default.
• W3047754677 cites W2120900954 @default.
• W3047754677 cites W2128873947 @default.
• W3047754677 cites W2144486764 @default.
• W3047754677 cites W2149397149 @default.
• W3047754677 cites W2154706897 @default.
• W3047754677 cites W2165067994 @default.
• W3047754677 cites W2167355155 @default.
• W3047754677 cites W2170843318 @default.
• W3047754677 cites W2283733733 @default.
• W3047754677 cites W2329048608 @default.
• W3047754677 cites W2507757066 @default.
• W3047754677 cites W2511063201 @default.
• W3047754677 cites W2532042304 @default.
• W3047754677 cites W2541404351 @default.
• W3047754677 cites W2585519079 @default.
• W3047754677 cites W2738606079 @default.
• W3047754677 cites W2751364937 @default.
• W3047754677 cites W2757244286 @default.
• W3047754677 cites W2769182252 @default.
• W3047754677 cites W2802633111 @default.
• W3047754677 cites W2807980588 @default.
• W3047754677 cites W2887006546 @default.
• W3047754677 cites W2887253643 @default.
• W3047754677 cites W2893991825 @default.
• W3047754677 cites W2898069542 @default.
• W3047754677 cites W2899650154 @default.
• W3047754677 cites W2906138391 @default.
• W3047754677 cites W2946875900 @default.
• W3047754677 cites W2949915428 @default.
• W3047754677 cites W2954088480 @default.
• W3047754677 cites W2962403043 @default.
• W3047754677 cites W2963127029 @default.
• W3047754677 cites W2966332045 @default.
• W3047754677 cites W2982353498 @default.
• W3047754677 cites W3007221501 @default.
• W3047754677 cites W3008518994 @default.
• W3047754677 cites W3099661197 @default.
• W3047754677 cites W3105469151 @default.
• W3047754677 cites W324280150 @default.
• W3047754677 cites W4250981202 @default.
• W3047754677 cites W4252556951 @default.
• W3047754677 cites W653188449 @default.
• W3047754677 doi "https://doi.org/10.1038/s41598-020-69661-0" @default.
• W3047754677 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7414857" @default.
• W3047754677 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/32770012" @default.
• W3047754677 hasPublicationYear "2020" @default.
• W3047754677 type Work @default.
• W3047754677 sameAs 3047754677 @default.
• W3047754677 citedByCount "20" @default.
• W3047754677 countsByYear W30477546772020 @default.
• W3047754677 countsByYear W30477546772021 @default.
• W3047754677 countsByYear W30477546772022 @default.
• W3047754677 countsByYear W30477546772023 @default.
• W3047754677 crossrefType "journal-article" @default.
• W3047754677 hasAuthorship W3047754677A5003104055 @default.
• W3047754677 hasAuthorship W3047754677A5010192859 @default.
• W3047754677 hasAuthorship W3047754677A5021344986 @default.
• W3047754677 hasAuthorship W3047754677A5040722494 @default.
• W3047754677 hasAuthorship W3047754677A5040783568 @default.
• W3047754677 hasAuthorship W3047754677A5043046558 @default.
• W3047754677 hasAuthorship W3047754677A5052676364 @default.
• W3047754677 hasAuthorship W3047754677A5059810716 @default.
• W3047754677 hasAuthorship W3047754677A5072591335 @default.
• W3047754677 hasAuthorship W3047754677A5073783714 @default.

• next