Flame Color as a Lean Blowout Predictor

Abstract

The study characterizes the behavior of a premixed swirl stabilized dump plane combustor flame near its lean blow-out (LBO) limit in terms of CH* chemiluminiscence intensity and observable flame color variations for a wide range of equivalence ratio, flow rates and degree of premixing (characterized by premixing length, L fuel ). LPG and pure methane are used as fuel. We propose a novel LBO prediction strategy based solely on the flame color. It is observed that as the flame approaches LBO, its color changes from reddish to blue. This observation is found to be valid for different levels of fuel-air premixing achieved by changing the available mixing length of the air and the fuel upstream of the dump plane although the flame dynamics were significantly different. Based on this observation, the ratio of the intensities of red and blue components of the flame as captured by a color CCD camera was used as a metric for detecting the proximity of the flame to LBO. Tests were carried out for a wide range of air flow rates and using LPG and CH 4 as fuel. For all the operating conditions and both fuels tested, this ratio was found to monotonically decrease as LBO was approached. Moreover, the value of this ratio was within a small range close to LBO for all the cases investigated. This makes the ratio suitable as a metric for LBO detection at all levels of premixing.

Dynamic Data-Driven Prediction of Lean Blowout in a Swirl-Stabilized Combustor

“…Let the alphabet size be k = || and the method of partitioning the time series be maximum entropy partitioning [31], i.e., a uniform probability distribution on the symbols with Then, the information loss, represented by the negative of the entropy [21] of the symbol sequence, is given as (11) By representing the computational complexity as a function g(k) of the alphabet size k and choosing an appropriate scalar tradeoff weighting parameter a  (0, 1), the cost functional to be optimized becomes: (12) The optimal alphabet size || is obtained by solving for k in the equation J(k + 1) -J(k) = 0 along with additional constraints that may have to be imposed in the optimization procedure to realize the effects of critical issues such as any bounds on the alphabet size.…”

“…However, both Lieuwen et al and Gutmark demonstrated their techniques for LBO prediction in premixed combustors. On the other hand, Mukhopadhyay and coworkers [11], [12] developed a number of techniques for early detection of LBO, which worked satisfactorily over a wide range of fuel-air premixing. Chaudhari et al [11] used flame color to device a novel and inexpensive strategy for LBO detection.…”

“…On the other hand, Mukhopadhyay and coworkers [11], [12] developed a number of techniques for early detection of LBO, which worked satisfactorily over a wide range of fuel-air premixing. Chaudhari et al [11] used flame color to device a novel and inexpensive strategy for LBO detection. In this work, they used as an LBO detection metric, the ratio of red and blue intensities in the flame image obtained with a commercial color charge-coupled device (CCD) camera.…”

Investigation of flame behavior and dynamics prior to lean blowout in a combustor with varying mixedness of reactants for the early detection of lean blowout

“…Flame visualization is considered as one of the important approaches to explore the dynamical behavior of the flame. 45 We show the typical flame images (captured by the digital CMOS camera) at different operating conditions (Φ/ΦLBO) of a premixed flame (using port F1) in Figure 3. At the equivalence ratios which are far from that corresponding to blowout, we observe a stable flame as the flame is anchored at the dump plane inside the combustor (see Figure.…”

Trustworthy deep learning for cyber-physical systems

“…There has been a recent focus on LBO in partially premixed combustion (De et al ( , 2020c). Previously, researchers have used different techniques for LBO prediction (Chaudhari (2011); Muruganandam (2006); Chaudhari et al (2013); Unni and Sujith (2016)) As the operating limits to avoid the LBO regime cannot be easily determined during the design stage, it is important to develop online LBO detection frameworks which will be accurate and computationally faster. Some studies have focused on the computational time aspect of a lean blowout (Mukhopadhyay et al (2013); Sarkar et al (2015c); Dey et al (2015)).…”