English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Chemically reacting flows are common in engineering, such as hypersonic flow,combustion, explosions, manufacturing processes and environmental assessments.For combustion, the number of reactions can be significant (over 100) and dueto the very large CPU requirements of chemical reactions (over 99%) a largenumber of flow and combustion problems are presently beyond the capabilities ofeven the largest supercomputers. Motivated by this, novel Deep Neural Networks(DNNs) are introduced to approximate stiff ODEs. Two approaches are compared,i.e., either learn the solution or the derivative of the solution to theseODEs. These DNNs are applied to multiple species and reactions common inchemically reacting flows. Experimental results show that it is helpful toaccount for the physical properties of species while designing DNNs. Theproposed approach is shown to generalize well.

Novel DNNs for Stiff ODEs with Applications to Chemically Reacting Flows