Impacts of Fuel Stage Ratio on the Morphological and Nanostructural Characteristics of Soot Emissions from a Twin Annular Premixing Swirler Combustor.

Soot particles emitted from aircraft engines constitute a major anthropogenic source of pollution in the vicinity of airports and at cruising altitudes. This emission poses a significant threat to human health and may alter the global climate. Understanding the characteristics of soot particles, particularly those generated from Twin Annular Premixing Swirler (TAPS) combustors, a mainstream combustor in civil aviation engines, is crucial for aviation environmental protection. In this study, a comprehensive characterization of soot particles emitted from TAPS combustors was conducted using scanning electron microscopy (SEM), high-resolution transmission electron microscopy (HRTEM), and Raman spectroscopy. The morphology and nanostructure of soot particles were examined across three distinct fuel stage ratios (FSR), at 10%, 15%, and 20%. The SEM analysis of soot particle morphology revealed that coated particles constitute over 90% of the total particle sample, with coating content increasing proportionally to the fuel stage ratio. The results obtained from HRTEM indicated that average primary particle sizes increase with the fuel stage ratio. The results of HRTEM and Raman spectroscopy suggest that the nanostructure of soot particles becomes more ordered and graphitized with an increasing fuel stage ratio, resulting in lower oxidation activity. Specifically, soot fringe length increased with the fuel stage ratio, while soot fringe tortuosity and separation distance decreased. In addition, there is a prevalent occurrence of defects in the graphitic lattice structure of soot particles, suggesting a high degree of elemental carbon disorder.

Reconstruction of two-dimensional velocity distribution in scramjet by laser absorption spectroscopy tomography.

We propose a method to reconstruct the two-dimensional (2D) velocity distribution of flow fields by laser absorption spectroscopy tomography. A mathematic model is established to reveal the dependence of spectral absorbance on line-of-sight velocity distribution. Then, with multiple laser beams from different angular views covering the region of interest, a nonlinear equation set of 2D velocity distribution is established according to the model. The integrated absorbance coefficient distribution is reconstructed using the Landweber iteration algorithm and substituted into the nonlinear equation set for further simplification. Finally, the velocity distribution is reconstructed by solving the simplified equation group via the interior point algorithm. The proposed method is validated numerically by reconstructing the velocity distribution of water molecules, as calculated by computational fluid dynamics, over a cross section of a double-mode scramjet combustor. The method does not require adding extra tracer particles and avoids issues arising from the short lifetime of molecular tags. It is suitable for diagnosis of high-speed flow fields.

Assessing the particulate matter emission reduction characteristics of small turbofan engine fueled with 100 % HEFA sustainable aviation fuel.

With the continuous increase in global air transportation, the impact of ultrafine particulate matter (PM) emissions from aviation on human health and environmental pollution is becoming increasingly severe. In addition to carbon reduction throughout the lifecycle, Sustainable Aviation Fuels (SAF) also represent a significant pathway for reducing PM emissions. However, due to issues such as airworthiness safety and adaptability, existing research has mostly focused on the emission performance of SAF when blended with traditional fuels at <50 %, leaving the emission characteristics of higher blending ratios to be explored. In this study, using measurement methods recommended by the International Civil Aviation Organization (ICAO), the PM emission reduction characteristics of small turbofan engines fueled with 100 % Hydroprocessed Esters and Fatty Acids (HEFA)-SAF were experimentally evaluated and compared with traditional fuels RP-3 and Diesel, while avoiding the interference of lubricant blending combustion. The results showed that the peak number concentration of particle size distribution (PSD), PM total number, as well as the number and mass concentration of non-volatile particulate matter (nvPM) decreased initially and then increased with rising thrust conditions. HEFA-SAF exhibits PSD with smaller diameters, and the Geometric Mean Diameter (GMD) ranges from 7.7 nm to 20.3 nm under all conditions. Both volatile particulates (vPM) and nvPM from HEFA-SAF are significantly reduced, with nvPM number emission index (EIn) being 92 % and 71 % lower than Diesel and RP-3, respectively. The nvPM mass emission index (EIm) also shows reductions of 96 % and 89 % compared to Diesel and RP-3. Microscopic characterization also indicated that using HEFA-SAF emitted fewer and smaller PMs. This study establishes a foundation for evaluating the effectiveness of 100 % SAF in reducing PM emissions within the aviation sector, and contributes to the airworthiness regulations development related to the use of SAF in a variety of application environments, alongside enhancing environmental protection measures.

A convolutional neural network prediction model for aviation nitrogen oxides emissions throughout all flight phases.

In recent years, there has been an increasing amount of research on nitrogen oxides (NOx) emissions, and the environmental impact of aviation NOx emissions at cruising altitudes has received widespread attention. NOx may play a crucial role in altering the composition of the atmosphere, particularly regarding ozone formation in the upper troposphere. At present, the ground emission database based on the landing and takeoff (LTO) cycle is more comprehensive, while high-altitude emission data is scarce due to the prohibitively high cost and the inevitable measurement uncertainty associated with in-flight sampling. Therefore, it is necessary to establish a comprehensive NOx emission database for the entire flight envelope, encompassing both ground and cruise phases. This will enable a thorough assessment of the impact of aviation NOx emissions on climate and air quality. In this study, a prediction model has been developed via convolutional neural network (CNN) technology. This model can predict the ground and cruise NOx emission index for turbofan engines and mixed turbofan engines fueled by either conventional aviation kerosene or sustainable aviation fuels (SAFs). The model utilizes data from the engine emission database (EEDB) released by the International Civil Aviation Organization (ICAO) and results obtained from several in-situ emission measurements conducted during ground and cruise phases. The model has been validated by comparing measured and predicted data, and the results demonstrate its high prediction accuracy for both the ground (R2 > 0.95) and cruise phases (R2 > 0.9). This surpasses traditional prediction models that rely on fuel flow rate, such as the Boeing Fuel Flow Method 2 (BFFM2). Furthermore, the model can predict NOx emissions from aircrafts burning SAFs with satisfactory accuracy, facilitating the development of a more complete and accurate aviation NOx emission inventory, which can serve as a basis for aviation environmental and climatic research. SYNOPSIS: The utilization of the ANOEPM-CNN offers a foundation for establishing more precise emission inventories, thereby reducing inaccuracies in assessing the impact of aviation NOx emissions on climate and air quality.