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/

Global: Zendy Plus annual

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

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Energy production from natural gas has considerably increased worldwide. The composition of natural gas is diverse, and each blend has a different reactivity. Understanding the reactivities of these fuels enables the designer to develop fuel-source independent combustors. Also, future engines such as scramjet and homogeneous charge compression ignition (HCCI) will rely heavily on the reactivity of the fuel. Ignition delay time (IDT) is a direct measure of a fuel’s reactivity. In the current study, an artificial neural network (ANN) based model is developed to predict the IDT of different natural gas blends. The model has 13 inputs and three hidden layers and is trained using a back-propagation approach. The developed model is superior compared to a multiple linear regression approach and is validated with shock tube experiments. Furthermore, the model is used to predict the IDT of six different liquified natural gas blends (LNG), and the predicted results match the experimental data accurately. Additionally, the IDTs of four different commercial natural gas blends are predicted using the ANN model, showcasing the application of the tool in a real-world scenario.

Artificial Neural Networks-Based Ignition Delay Time Prediction for Natural Gas Blends