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Combustion Energy: A Measure of Stability in Alkanes and Cycloalkanes

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The low reactivity in alkanes can be attributed to the non-polar nature of C–C and C–H σ bonds. Alkanes, therefore, were initially termed as “paraffins,” derived from the Latin words: parum, meaning “too little,” and affinis, meaning “affinity.”
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Related Experiment Video

Updated: Jun 10, 2026

Combustion Chemistry of Fuels: Quantitative Speciation Data Obtained from an Atmospheric High-temperature Flow Reactor with Coupled Molecular-beam Mass Spectrometer

Combustion Chemistry of Fuels: Quantitative Speciation Data Obtained from an Atmospheric High-temperature Flow Reactor with Coupled Molecular-beam Mass Spectrometer

Published on: February 19, 2018

In situ visualization for large-scale combustion simulations.

Hongfeng Yu¹, Chaoli Wang, Ray W Grout

¹Sandia National Laboratories, CA, USA. hyu@sandia.gov

IEEE Computer Graphics and Applications

|July 24, 2010

Summary

This summary is machine-generated.

In situ visualization is key for analyzing massive datasets from scientific supercomputing. This method helps scientists understand complex, short-lived events in simulations like turbulent combustion.

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In Situ Visualization of the Phase Behavior of Oil Samples Under Refinery Process Conditions

In Situ Visualization of the Phase Behavior of Oil Samples Under Refinery Process Conditions

Published on: February 21, 2017

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Last Updated: Jun 10, 2026

Combustion Chemistry of Fuels: Quantitative Speciation Data Obtained from an Atmospheric High-temperature Flow Reactor with Coupled Molecular-beam Mass Spectrometer

Combustion Chemistry of Fuels: Quantitative Speciation Data Obtained from an Atmospheric High-temperature Flow Reactor with Coupled Molecular-beam Mass Spectrometer

Published on: February 19, 2018

In Situ Visualization of the Phase Behavior of Oil Samples Under Refinery Process Conditions

In Situ Visualization of the Phase Behavior of Oil Samples Under Refinery Process Conditions

Published on: February 21, 2017

Area of Science:

Scientific visualization
High-performance computing
Computational science

Background:

Scientific simulations generate massive datasets at petascale and exascale levels.
Traditional post-hoc analysis struggles with the scale and complexity of this data.
Understanding transient phenomena requires efficient data exploration methods.

Purpose of the Study:

To highlight the importance of in situ visualization for scientific discovery.
To emphasize its role in analyzing large-scale simulation data.
To showcase its application in understanding intermittent events.

Main Methods:

Leveraging in situ visualization techniques within simulation workflows.
Developing scalable methods for real-time data analysis.
Focusing on capturing transient phenomena during simulation execution.

Main Results:

In situ visualization provides a scalable approach to data analysis.
It enables comprehensive understanding of simulation outputs.
Effective for capturing intermittent transient phenomena.

Conclusions:

In situ visualization is essential for modern scientific supercomputing.
It facilitates the study of complex events like turbulent combustion.
Scalable data analysis is crucial for future scientific advancements.