scholarly journals Effect of Hydrogen Content and Dilution on Laminar Burning Velocity and Stability Characteristics of Producer Gas-Air Mixtures

2008 ◽  
Vol 2008 ◽  
pp. 1-8 ◽  
Author(s):  
V. Ratna Kishore ◽  
M. R. Ravi ◽  
Anjan Ray
Keyword(s):  
Hydrogen Content ◽  
Alternative Fuels ◽  
Burning Velocity ◽  
Operating Conditions ◽  
Producer Gas ◽  
Laminar Burning Velocity ◽  
High Hydrogen ◽  
Promising Alternative ◽  
Preferential Diffusion ◽  
Wide Range

Producer gas is one of the promising alternative fuels with typical constituents of H2, CO, CH4, N2, and CO2. The laminar burning velocity of producer gas was computed for a wide range of operating conditions. Flame stability due to preferential diffusional effects was also investigated. Computations were carried out for spherical outwardly propagating flames and planar flames. Different reaction mechanisms were assessed for the prediction of laminar burning velocities of CH4, H2, H2-CO, and CO-CH4and results showed that the Warnatz reaction mechanism with C1 chemistry was the smallest among the tested mechanisms with reasonably accurate predictions for all fuels at 1 bar, 300 K. To study the effect of variation in the producer gas composition, each of the fuel constituents in ternary CH4-H2-CO mixtures was varied between 0 to 48%, while keeping diluents fixed at 10% CO2and 42% N2by volume. Peak burning velocity shifted fromϕ=1.6to 1.1 as the combined volumetric percentage of hydrogen and CO varied from 48% to 0%. Unstable flames due to preferential diffusion effects were observed for lean mixtures of fuel with high hydrogen content. The present results indicate that H2has a strong influence on the combustion of producer gas.

scholarly journals Production of JET fuel containing molecules of high hydrogen content

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Sz. Tomasek ◽  
Z. Varga ◽  
A. Holló ◽  
N. Miskolczi ◽  
J. Hancsók
Keyword(s):  
Hydrogen Content ◽  
Alternative Fuels ◽  
Jet Fuel ◽  
Plastic Waste ◽  
High Hydrogen ◽  
High Hydrogen Content ◽  
Waste Polyethylene ◽  
Harmful Effects

AbstractThe harmful effects of aviation can only be reduced by using alternative fuels with excellent burning properties and a high hydrogen content in the constituent molecules. Due to increasing plastic consumption the amount of the plastic waste is also higher. Despite the fact that landfill plastic waste has been steadily reduced, the present scenario is not satisfactory. Therefore, the aim of this study is to produce JET fuel containing an alternative component made from straight-run kerosene and the waste polyethylene cracking fraction. We carried out our experiments on a commercial NiMo/Al

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2021 ◽  
Vol 143 (3) ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract Conventional physics-based or experimental-based approaches for gas turbine combustion tuning are time consuming and cost intensive. Recent advances in data analytics provide an alternative method. In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor vibrational acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

Analysis of Combustion Flame of Syngas-Air Mixtures

2010 ◽  
Author(s):  
Eliseu Monteiro ◽  
Abel Rouboa
Keyword(s):  
Energy Source ◽  
Burning Velocity ◽  
Biomass Gasification ◽  
Producer Gas ◽  
Laminar Burning Velocity ◽  
Volume Percentage ◽  
Elevated Pressures ◽  
Syngas Combustion ◽  
Fundamental Understanding ◽  
Heat Value

In the proposed paper for this conference, three typical mixtures of H2, CO, CH4, CO2 and N2 have been considered as representative of the producer gas (syngas) resulting from biomass gasification. Syngas is being recognized worldwide as a viable energy source, particularly for stationary power generation. However, there are gaps in the fundamental understanding of syngas combustion characteristics, particularly at elevated pressures that are relevant to practical combustors. In this work, constant volume spherical expanding flames of three typical syngas compositions have been employed to measure the laminar burning velocity for pressures ranges between 1.0 and 20 bar. Over the ranges studied, the burning velocities are fitted by the functional formula of Metghalchi and Keck. Conclusion can be drawn that the burning velocity decreases with the increase of pressure. In opposite, the increase of temperature induces the increase of burning velocity. The higher burning velocity value is obtained for the downdraft syngas. This result is endorsed to the higher heat value, lower dilution and higher volume percentage of hydrogen in the downdraft syngas.

A predictive combustion model for one-dimensional gasoline engine simulation

2020 ◽  
pp. 146808742094590
Author(s):  
Yoshihiro Nomura ◽  
Seiji Yamamoto ◽  
Makoto Nagaoka ◽  
Stephan Diel ◽  
Kenta Kurihara ◽  
...  
Keyword(s):  
Gasoline Engine ◽  
Early Stage ◽  
Burning Velocity ◽  
Turbulent Flame ◽  
Operating Conditions ◽  
Combustion Model ◽  
One Dimensional ◽  
Turbulent Reynolds Number ◽  
Proposed Model ◽  
Wide Range

A new predictive combustion model for a one-dimensional computational fluid dynamics tool in the multibody dynamics processes of gasoline engines was developed and validated. The model consists of (1) a turbulent burning velocity model featuring a flame radius–based transitional function, steady burning velocity that considers local quenching using the Karlovitz number and laminarization by turbulent Reynolds number, as well as turbulent flame thickness and its quenching model near the liner wall, and (2) a knock model featuring auto-ignition by the Livengood–Wu integration and ignition delay time obtained using a full-kinetic model. The proposed model and previous models were verified under a wide range of operating conditions using engines with widely different specifications. Good agreement was only obtained for combustion characteristics by the proposed model without requiring individual calibration of model constants. The model was also evaluated for utilization after prototyping. Improved accuracy, especially of ignition timing, was obtained after further calibration using a small amount of engine data. It was confirmed that the proposed model is highly accurate at the early stage of the engine development process, and is also applicable for engine calibration models that require higher accuracy.

Experimental and Computational Determination of LBV of Hydrogen Enriched Methane Mixtures

2015 ◽  
Vol 5 (1) ◽  
Author(s):  
Vinod Kumar Yadav ◽  
Ranjeet Singha ◽  
Abhishek Kumar Pandey ◽  
Saumya   ◽  
Ashish Kumar Singh ◽  
...  
Keyword(s):  
Coming Out ◽  
Burning Velocity ◽  
Laminar Burning Velocity ◽  
Flammability Limits ◽  
Ambient Temperatures ◽  
Gaseous Fuels ◽  
Wide Range ◽  
Hydrogen Enrichment ◽  
Institute Of Technology ◽  
Set Up

One of the major causes of environmental pollution and ozone layer depletion is the emissions coming out of the combustion devices including industrial burners, automobile vehicles and household appliances. Most of the conventional fuels used now days have high GWP and ODP. So the greatest challenges among the combustion researchers and scientists are to develop some sustainable and non conventional sources of energy that possesses capability to replace the conventional ones. One of the important gaseous fuels in non conventional category is hydrogen, which is a cleaner fuel and reduces pollution enormously. In the present work, experimental & computational analysis of laminar burning velocity (LBV) of premixed gaseous fuels (primary focus on Hydrogen enrichment) was carried out. For experimental investigation the experimental set up available in Fuel and pollution lab of Indian Institute of Technology Delhi is used. Experiments were carried out on mixtures of methane- Air and Methane-Hydrogen-Air for wide range of equivalence ratios and compared with the computational results of PREMIX with full GRI-Mech 3.0 mechanism. Most of the experiments available in literature were carried out at 298 K. In the present work it has been tried to relate the effect of low temperatures on laminar burning velocity of mixtures. The experiments have been conducted at 1 bar pressure and around 292 Kelvin with equivalence ratio ranging from 0.8 to 1.2. Methane gas is enriched with hydrogen in varying proportions and the effect of hydrogen enrichment on its laminar burning velocity studied. The objective of the addition of hydrogen to methane was to increase its laminar burning velocity as well as to extend its lean flammability limits at lower ambient temperatures.

Combustion Tuning for a Gas Turbine Power Plant Using Data-Driven and Machine Learning Approach

2020 ◽  
Author(s):  
Suhui Li ◽  
Huaxin Zhu ◽  
Min Zhu ◽  
Gang Zhao ◽  
Xiaofeng Wei
Keyword(s):  
Machine Learning ◽  
Power Plant ◽  
Gas Turbine ◽  
Operating Conditions ◽  
Data Driven ◽  
Ann Model ◽  
Promising Alternative ◽  
Combustion Performance ◽  
Wide Range ◽  
Gas Turbine Power Plant

Abstract In this paper, we present a cross-disciplinary study on the combustion tuning of an F-class gas turbine that combines machine learning with physics understanding. An artificial-neural-network-based (ANN) model is developed to predict the combustion performance (outputs), including NOx emissions, combustion dynamics, combustor acceleration, and turbine exhaust temperature. The inputs of the ANN model are identified by analyzing the key operating variables that impact the combustion performance, such as the pilot and the premixed fuel flow, and the inlet guide vane angle. The ANN model is trained by field data from an F-class gas turbine power plant. The trained model is able to describe the combustion performance at an acceptable accuracy in a wide range of operating conditions. In combination with the genetic algorithm, the model is applied to optimize the combustion performance of the gas turbine. Results demonstrate that the data-driven method offers a promising alternative for combustion tuning at a low cost and fast turn-around.

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