Study on the effect of fuel injection on combustion performance and NOx emission of RQL trapped-vortex combustor

As a combustor that can reduce pollutant emissions, the trapped-vortex combustor can not only reduce the length of the combustor because of its radial classification technology, but also its design concept is inseparable from RQL (Rich burn, Quench, and Lean burn). In this paper, the effects of cavity equivalence ratio and fuel injection cone angle on combustion performance and NOx emission were studied by numerical simulation. It is found that the trapped-vortex combustor achieves RQL combustion mode. The increase of the cavity equivalence ratio results in the non-uniform fuel distribution. However, the fuel injection cone angle in the primary region has little effect on the fuel distribution. With the increase of the cavity equivalence ratio, the combustion efficiency and NOx emission first decrease and then increase, and outlet temperature distribution factor is positively correlated with the cavity equivalence ratio. The difference is that the outlet temperature distribution factor and combustion efficiency show almost the same law with the increase of injection cone angle in the primary region, and the NOx emission decreases, but the reduction is not significant

Optimal Selection of Image Segmentation Algorithms Based on Performance Prediction

Using different algorithms to segment different images is a quite straightforward strategy for automated image segmentation. But the difficulty of the optimal algorithm selection has prevented it from being used for many years. In this paper, a framework of algorithm selection system is proposed to achieve automated image segmentation. Off-line learning scheme is adopted to make use of interactive segmentation evaluation. During training, both the performance ranks of candidate algorithms on every image and image features are used to train a predictor. Then, the performance ranks of all candidates will be predicted according to image features. Finally, the algorithm with the highest rank will be regarded as optimal and applied to the image. A simulation system is constructed to select optimal segmentation algorithm from four candidates for synthetic images. In this system, histogram is used as image feature, the number of misclassified pixels and computation expenses are used to facilitate interactive segmentation evaluation, and Principle Components Analysis (PCA) and Support Vector Machine (SVM) are used to construct the predictor. The system is tested on 9000 images by comparing with the manual selection. The best algorithms are selected for 84.90% of cases. If the second best algorithm is also regarded as acceptable, more than 97.5% of images can be properly segmented. The satisfied results demonstrate that this study has provided a promising approach to automated image segmentation

Segmentation of Images Using Wavelet Packet Based Feature Set and Clustering Algorithm Segmentation of Images Using Wavelet Packet Based Feature Set and Clustering Algorithm

The presence of speckle in Synthetic Aperture Radar (SAR) images makes the segmentation of such images difficult A novel method for automatic segmentation of SAR images is proposed. Firstly, a wavelet packet based texture feature set is derived. It consists of the energy of the feature subimages obtained by the overcomplete wavelet packet decomposition of local areas in SAR image, where the downsampling between wavelet levels is omitted. Then an improved unsupervised clustering algorithm is proposed for image segmentation, which can determine the number of classes automatically. Segmentation results on real SAR image demonstrate the effectiveness of the proposed method

Effects of Nb on Elevated-Temperature Properties of Fire-Resistant Steel

Objective: Two kinds of fire-resistant steel with different Nb content (Nb-free and 0.03 wt.%) were prepared for studying the effects of Nb addition on the elevated-temperature strength of fire-resistant steel. Methods: Two stages of heat treatment were carried out on the steels to obtain different microstructures. Typical microstructures, dislocation, and precipitates morphology of steels were observed by SEM and TEM. The dislocation density was calculated by the X-ray data from the microstructures. High temperature and room temperature mechanical properties of steels were determined by tensile testing. Results: The results showed that the YS of N2-HR steel (addition of 0.03 wt.% Nb) at RT and 600 °C was higher than N1-HR steel (Nb-free) by about 81 and 30 MPa, respectively. This indicates that Nb is an alloying element as effective as Mo in increasing the elevated-temperature strength of fire-resistant steel. The dominant strengthening mechanisms of Nb addition on elevated-temperature yield strength are precipitation strengthening and bainite strengthening. Conclusions: Theoretical analysis shows that there are two precipitation strengthening stages in fire-resistant steel: (1) increasing dislocation density during hot rolling, and (2) blocking dislocation movement and recovery in tensile testing. The results also show that the effect of fine grain strengthening is not obvious at high temperature, but is obvious at room temperature

Effects of Nb on Elevated-Temperature Properties of Fire-Resistant Steel

Objective: Two kinds of fire-resistant steel with different Nb content (Nb-free and 0.03 wt.%) were prepared for studying the effects of Nb addition on the elevated-temperature strength of fire-resistant steel. Methods: Two stages of heat treatment were carried out on the steels to obtain different microstructures. Typical microstructures, dislocation, and precipitates morphology of steels were observed by SEM and TEM. The dislocation density was calculated by the X-ray data from the microstructures. High temperature and room temperature mechanical properties of steels were determined by tensile testing. Results: The results showed that the YS of N2-HR steel (addition of 0.03 wt.% Nb) at RT and 600 °C was higher than N1-HR steel (Nb-free) by about 81 and 30 MPa, respectively. This indicates that Nb is an alloying element as effective as Mo in increasing the elevated-temperature strength of fire-resistant steel. The dominant strengthening mechanisms of Nb addition on elevated-temperature yield strength are precipitation strengthening and bainite strengthening. Conclusions: Theoretical analysis shows that there are two precipitation strengthening stages in fire-resistant steel: (1) increasing dislocation density during hot rolling, and (2) blocking dislocation movement and recovery in tensile testing. The results also show that the effect of fine grain strengthening is not obvious at high temperature, but is obvious at room temperature