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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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Flame photometry, also known as flame emission spectrometry, is a technique used for the qualitative and quantitative analysis of elements present in a sample using a flame as the source of excitation energy. The concept of flame photometry was realized in the early 1860s by Kirchhoff and Bunsen, who discovered that specific elements emit characteristic radiation when excited in flames. The first instrument developed for this purpose was used to measure sodium (Na) in plant ash using a Bunsen...
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Updated: May 28, 2026

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

Feature-based statistical analysis of combustion simulation data.

Janine C Bennett1, Vaidyanathan Krishnamoorthy, Shusen Liu

  • 1Sandia National Laboratories, USA. jcbenne@sandia.gov

IEEE Transactions on Visualization and Computer Graphics
|October 29, 2011
PubMed
Summary
This summary is machine-generated.

This study introduces a new framework for analyzing large scientific datasets, focusing on turbulent combustion features. The method efficiently extracts and visualizes flow characteristics, enabling deeper insights into complex fluid dynamics and mixing processes.

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Area of Science:

  • Fluid Dynamics and Combustion Science
  • High-Performance Scientific Computing
  • Data Analysis and Visualization

Background:

  • Turbulent flows are crucial in combustion, astrophysics, and climate modeling, characterized by coherent structures impacting mixing and reactions.
  • Traditional multi-point statistics lack nonlocal structural information, hindering mechanistic causality understanding between fluid motion and reactive processes.
  • There is a need to capture and track flow features alongside scalar quantities like temperature and species concentrations.

Purpose of the Study:

  • To develop and demonstrate a novel framework for feature-based statistical analysis of large-scale scientific data.
  • To enable interactive exploration and visualization of complex flow features and their correlation with scalar fields.
  • To provide a computationally efficient method for analyzing terabytes of simulation data.

Main Methods:

  • Developed a framework encoding flow features using pre-computed merge trees augmented with statistical moments and spectral length-scales.
  • Employed an efficient streaming computation in a pre-processing step to generate compact metadata.
  • Integrated metadata analysis with a linked-view browser for interactive exploration, visualization, and diagnostics (CDFs, histograms, time-series).

Main Results:

  • The framework significantly reduces data size (orders of magnitude) while retaining essential feature information for flexible analysis.
  • Demonstrated effectiveness in analyzing features from Direct Numerical Simulations (DNS) of turbulent combustion.
  • Enabled interactive exploration and visualization of large datasets (e.g., one terabyte) for scientific discovery.

Conclusions:

  • The new feature-based framework offers a powerful and efficient approach for analyzing complex scientific data, particularly in turbulent flows.
  • It overcomes limitations of traditional statistics by capturing nonlocal structural information and enabling mechanistic causality insights.
  • The framework is broadly applicable across various scientific domains beyond combustion science.