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Investigating Intrinsic Instabilitie...

Nawaz, Behlol,

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Investigating Intrinsic Instabilities of Spherically Expanding Flames in a Constant Volume Combustion Chamber Using Image Processing and Machine Learning /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Investigating Intrinsic Instabilities of Spherically Expanding Flames in a Constant Volume Combustion Chamber Using Image Processing and Machine Learning // Behlol Nawaz.
Author:	Nawaz, Behlol,
Description:	1 electronic resource (159 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-12, Section: B.
Contained By:	Dissertations Abstracts International86-12B.
Subject:	Alternative energy. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31933575
ISBN:	9798280723474

Investigating Intrinsic Instabilities of Spherically Expanding Flames in a Constant Volume Combustion Chamber Using Image Processing and Machine Learning /
Nawaz, Behlol,

Investigating Intrinsic Instabilities of Spherically Expanding Flames in a Constant Volume Combustion Chamber Using Image Processing and Machine Learning /Behlol Nawaz. - 1 electronic resource (159 pages)

Source: Dissertations Abstracts International, Volume: 86-12, Section: B.

Energy systems are a core component of most aspects of civilization and have a bearing on several key global issues. Fossil fuels are currently the dominant energy sources, but they create environmental pollution, contribute to climate change, and consume finite natural resources. In response to this, renewable energy systems are being added to the global portfolio of energy sources at an increasing pace. This has led to an increase in the diversification of energy sources worldwide. Not only are the newer sources more sustainable, but the diversification of the energy mix also makes the overall energy production infrastructure more robust and resilient to certain shock events. However, as many of the more promising renewable sources are intermittent and non-dispatchable, the storage and transportation of the energy they produce is critical and continues to be a challenge. Sustainable fuels can address these challenges and would be especially suitable for areas that require higher energy densities or scale, such as aviation, marine propulsion, heavy transportation, heating, or utility-scale storage. Conventional fuels such as gasoline, diesel, kerosene, and fuel oils have high energy densities and are easy to handle, transport, and refuel. Therefore, renewable fuels should not only provide reduced, net-zero, or zero carbon emissions, but also meet or exceed the requirements for storage and transportation.It is important to understand the fundamental combustion properties of fuels as they dictate the performance, emissions, safety, and design of combustion systems that use them. One such property is the intrinsic instability of flames for a given combustible mixture. These instabilities cause wrinkles (or cracks) on flame fronts, forming cellular structures that can impact performance and safety in some cases due to their effect on burning velocity, explosion risk, and emissions. Intrinsic instabilities are often studied in a constant volume combustion chamber (CVCC) with optical access, which is an experimental device that allows the study of certain combustion properties and flame structures in a controlled environment. This dissertation focuses on methods for the detection and characterization of intrinsic instabilities for several fuels including ammonia (NH3), hydrogen (H2), and methane (CH4) with multiple combinations of diluents and under varied initial conditions. Several methods are explored for detecting wrinkling observed in high-speed images of spherical CVCC flames due to intrinsic instabilities. The use of pressure traces for detecting the instabilities using machine learning methods is also explored. This could be a potential alternative for CVCC systems that may not have optical access.This work improves our understanding of intrinsic instabilities for novel fuel and diluent combinations. It also helps expand the methods used to study them, using image processing as well time series classification with pressure traces. Both of these contributions could make calculations and simulations of combustion systems more accurate by helping them account for the effects of intrinsic instabilities. That in turn would help with the design processes, ensuring future combustion systems can be developed in a cost-effective manner while improving their performance, reliability, and emissions.

English

ISBN: 9798280723474Subjects--Topical Terms:

3436775
Alternative energy.
Subjects--Index Terms:

Alternative fuels

Investigating Intrinsic Instabilities of Spherically Expanding Flames in a Constant Volume Combustion Chamber Using Image Processing and Machine Learning /
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520 $a Energy systems are a core component of most aspects of civilization and have a bearing on several key global issues. Fossil fuels are currently the dominant energy sources, but they create environmental pollution, contribute to climate change, and consume finite natural resources. In response to this, renewable energy systems are being added to the global portfolio of energy sources at an increasing pace. This has led to an increase in the diversification of energy sources worldwide. Not only are the newer sources more sustainable, but the diversification of the energy mix also makes the overall energy production infrastructure more robust and resilient to certain shock events. However, as many of the more promising renewable sources are intermittent and non-dispatchable, the storage and transportation of the energy they produce is critical and continues to be a challenge. Sustainable fuels can address these challenges and would be especially suitable for areas that require higher energy densities or scale, such as aviation, marine propulsion, heavy transportation, heating, or utility-scale storage. Conventional fuels such as gasoline, diesel, kerosene, and fuel oils have high energy densities and are easy to handle, transport, and refuel. Therefore, renewable fuels should not only provide reduced, net-zero, or zero carbon emissions, but also meet or exceed the requirements for storage and transportation.It is important to understand the fundamental combustion properties of fuels as they dictate the performance, emissions, safety, and design of combustion systems that use them. One such property is the intrinsic instability of flames for a given combustible mixture. These instabilities cause wrinkles (or cracks) on flame fronts, forming cellular structures that can impact performance and safety in some cases due to their effect on burning velocity, explosion risk, and emissions. Intrinsic instabilities are often studied in a constant volume combustion chamber (CVCC) with optical access, which is an experimental device that allows the study of certain combustion properties and flame structures in a controlled environment. This dissertation focuses on methods for the detection and characterization of intrinsic instabilities for several fuels including ammonia (NH3), hydrogen (H2), and methane (CH4) with multiple combinations of diluents and under varied initial conditions. Several methods are explored for detecting wrinkling observed in high-speed images of spherical CVCC flames due to intrinsic instabilities. The use of pressure traces for detecting the instabilities using machine learning methods is also explored. This could be a potential alternative for CVCC systems that may not have optical access.This work improves our understanding of intrinsic instabilities for novel fuel and diluent combinations. It also helps expand the methods used to study them, using image processing as well time series classification with pressure traces. Both of these contributions could make calculations and simulations of combustion systems more accurate by helping them account for the effects of intrinsic instabilities. That in turn would help with the design processes, ensuring future combustion systems can be developed in a cost-effective manner while improving their performance, reliability, and emissions.
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650 4 $a Mechanical engineering. $3 649730
650 4 $a Computer science. $3 523869
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653 $a Intrinsic instabilities
653 $a Machine learning
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