Effects of Swirl on Early Flame Development and Late Combustion Characteristic in a High-Speed Single-Shot Visualized SI Engine.

Single-shot tests in a single-cylinder, optical SI engine operated by a rapid compression and expansion machine were performed in order to investigate the combined effects of engine speed, ignition position and swirl on early combustion and overall performance. For central ignition, swirl showed consistently favourable effects on combustion-related performance. However, at half-radius ignition the desirable swirl effect persisted at low engine speeds but faded away as the speed increased. This reversing of trends can be partially explained by differences in the maximum cylinder pressure, flame growth rate and flame front wrinkling.

Download Full-text

High-speed 3D shape measurement by one pitch phase analysis method using brightness values in small square area of single-shot image

Optics and Lasers in Engineering ◽

10.1016/j.optlaseng.2018.08.023 ◽

2019 ◽

Vol 113 ◽

pp. 38-46 ◽

Cited By ~ 5

Author(s):

Yoshiharu Morimoto ◽

Akihiro Masaya ◽

Masaki Ueki

Keyword(s):

Phase Analysis ◽

High Speed ◽

Shape Measurement ◽

Single Shot ◽

Analysis Method ◽

3D Shape ◽

3D Shape Measurement

Download Full-text

Experimental Investigation Into the High Altitude Relight of a Three-Cup Combustor Sector

Volume 4B: Combustion, Fuels, and Emissions ◽

10.1115/gt2018-77146 ◽

2018 ◽

Cited By ~ 1

Author(s):

Michael J. Denton ◽

Samir B. Tambe ◽

San-Mou Jeng

Keyword(s):

Pressure Drop ◽

High Altitude ◽

High Speed ◽

Development Process ◽

Recirculation Zone ◽

Operating Conditions ◽

Pressure Drops ◽

High Speed Video ◽

Air Jet ◽

Flame Development

The altitude relight of a gas turbine combustor is an FAA and EASA regulation which dictates the successful re-ignition of an engine and its proper spool-up after an in-flight shutdown. Combustor pressure loss, ambient pressure, ambient temperature, and equivalence ratio were all studied on a full-scale, 3-cup, single-annular aviation combustor sector to create an ignition map. The flame development process was studied through the implementation of high-speed video. Testing was conducted by placing the sector horizontally upstream of an air jet ejector in a high altitude relight testing facility. Air was maintained at room temperature for varying pressure, and then the cryogenic heat exchanger was fed with liquid nitrogen to chill the air down to a limit of −50 deg F, corresponding with an altitude of 30,000 feet. Fuel was injected at constant equivalence ratios across multiple operating conditions, giving insight into the ignition map of the combustor sector. Results of testing indicated difficulty in achieving ignition at high altitudes for pressure drops greater than 2%, while low pressure drops show adequate performance. Introducing low temperatures to simulate the ambient conditions yielded a worse outcome, with all conditions having poor results except for 1%. High-speed video of the flame development process during the relight conditions across all altitudes yielded a substantial effect of the pressure drop on ignitability of the combustor. An increase in pressure drop was associated with a decrease in the likelihood of ignition success, especially at increasing altitudes. The introduction of the reduced temperature effect exacerbated this effect, further hurting ignition. High velocity regions in the combustor were detrimental to the ignition, and high area, low velocity regions aided greatly. The flame tended to settle into the corner recirculation zone and recirculate back into the center-toroidal recirculation zone (CTRZ), spreading downstream and likewise into adjacent swirl cups. These tests demonstrate the need for new combustor designs to consider adding large recirculation zones for combustor flame stability that will aid in relight requirements.

Download Full-text

Spark Ignition of SPP Injector Under Sub-Atmospheric Conditions

10.1115/gt2021-58699 ◽

2021 ◽

Author(s):

Qianpeng Zhao ◽

Yong Mu ◽

Jinhu Yang ◽

Yulan Wang ◽

Gang Xu

Keyword(s):

High Speed ◽

Spark Ignition ◽

Test Facility ◽

Propagation Mode ◽

Inlet Temperature ◽

Reacting Flow ◽

Atmospheric Conditions ◽

Flame Kernel ◽

Flame Development ◽

Initial Flame

Abstract The sub-atmospheric ignition performance of an SPP (Stratified Partially Premixed) injector and combustor is investigated experimentally on the high-altitude test facility. In order to explore the influence of sub-atmospheric pressure on reignition performance and flame propagation mode, experiments are conducted under different pressures ranging from 19 kPa to 101 kPa. The inlet temperature and pressure drop of the injector (ΔPsw/P3t) are kept constant at 303 K and 3% respectively. The transparent quartz window mounted on the sidewall of the model combustor provides optical access of flame signals. Ignition fuel-air ratio (FAR) under different inlet pressures are experimentally acquired. The spark ignition processes, including the formation of flame kernel, the flame development and stabilization are recorded by a high-speed camera at a rate of 5kHz. Experimental results indicate that the minimum ignition FAR grows rapidly as the inlet air pressure decreases. An algorithm is developed to track the trajectory of flame kernels within 25ms following the spark during its breakup and motion processes. Results show that the calculated trajectory provides a clear description of the flame evolution process. Under different inlet air pressures, the propagation trajectories of flame kernels share similarities in initial phase. It is pivotal for a successful ignition that the initial flame kernel keeps enough intensity and moves into CTRZ (Center-Toroidal Recirculation Zone) along radial direction. Finally, the time-averaged non-reacting flow field under inlet pressure of 54kPa and fuel mass flow of 8kg/h is simulated. The effects of flow structure and fuel spatial distribution on kernel propagation and flame evolution are analyzed.

Download Full-text

High-Speed Railway Clearance Intrusion Detection with Improved SSD Network

Applied Sciences ◽

10.3390/app9152981 ◽

2019 ◽

Vol 9 (15) ◽

pp. 2981 ◽

Cited By ~ 2

Author(s):

Baoqing Guo ◽

Jiafeng Shi ◽

Liqiang Zhu ◽

Zujun Yu

Keyword(s):

Intrusion Detection ◽

High Speed ◽

Rapid Development ◽

Single Shot ◽

L1 Norm ◽

Low Level ◽

Model Size ◽

Public Dataset ◽

The Mean ◽

High Level

With the rapid development of high-speed railways, any objects intruding railway clearance will do great threat to railway operations. Accurate and effective intrusion detection is very important. An original Single Shot multibox Detector (SSD) can be used to detect intruding objects except small ones. In this paper, high-level features are deconvolved to low-level and fused with original low-level features to enhance their semantic information. By this way, the mean average precision (mAP) of the improved SSD algorithm is increased. In order to decrease the parameters of the improved SSD network, the L1 norm of convolution kernel is used to prune the network. Under this criterion, both the model size and calculation load are greatly reduced within the permitted precision loss. Experiments show that the mAP of our method on PASCAL VOC public dataset and our railway datasets have increased by 2.52% and 4.74% respectively, when compared to the original SSD. With our method, the elapsed time of each frame is only 31 ms on GeForce GTX1060.

Download Full-text

Dense-RefineDet for Traffic Sign Detection and Classification

Sensors ◽

10.3390/s20226570 ◽

2020 ◽

Vol 20 (22) ◽

pp. 6570

Author(s):

Chang Sun ◽

Yibo Ai ◽

Sheng Wang ◽

Weidong Zhang

Keyword(s):

High Speed ◽

Large Scale ◽

Design Method ◽

Contextual Information ◽

Real Life ◽

Single Shot ◽

Traffic Sign ◽

Traffic Signs ◽

High Level ◽

Anchor Design

Detecting and classifying real-life small traffic signs from large input images is difficult due to their occupying fewer pixels relative to larger targets. To address this challenge, we proposed a deep-learning-based model (Dense-RefineDet) that applies a single-shot, object-detection framework (RefineDet) to maintain a suitable accuracy–speed trade-off. We constructed a dense connection-related transfer-connection block to combine high-level feature layers with low-level feature layers to optimize the use of the higher layers to obtain additional contextual information. Additionally, we presented an anchor-design method to provide suitable anchors for detecting small traffic signs. Experiments using the Tsinghua-Tencent 100K dataset demonstrated that Dense-RefineDet achieved competitive accuracy at high-speed detection (0.13 s/frame) of small-, medium-, and large-scale traffic signs (recall: 84.3%, 95.2%, and 92.6%; precision: 83.9%, 95.6%, and 94.0%). Moreover, experiments using the Caltech pedestrian dataset indicated that the miss rate of Dense-RefineDet was 54.03% (pedestrian height > 20 pixels), which outperformed other state-of-the-art methods.

Download Full-text

The Assisted Positioning Technology for High Speed Train Based on Deep Learning

Applied Sciences ◽

10.3390/app10238625 ◽

2020 ◽

Vol 10 (23) ◽

pp. 8625

Author(s):

Yali Song ◽

Yinghong Wen

Keyword(s):

Deep Learning ◽

High Speed ◽

Detection Method ◽

Generative Adversarial Networks ◽

Single Shot ◽

High Speed Train ◽

Data Set ◽

Detection Model ◽

Model Based ◽

Cumulative Error

In the positioning process of a high-speed train, cumulative error may result in a reduction in the positioning accuracy. The assisted positioning technology based on kilometer posts can be used as an effective method to correct the cumulative error. However, the traditional detection method of kilometer posts is time-consuming and complex, which greatly affects the correction efficiency. Therefore, in this paper, a kilometer post detection model based on deep learning is proposed. Firstly, the Deep Convolutional Generative Adversarial Networks (DCGAN) algorithm is introduced to construct an effective kilometer post data set. This greatly reduces the cost of real data acquisition and provides a prerequisite for the construction of the detection model. Then, by using the existing optimization as a reference and further simplifying the design of the Single Shot multibox Detector (SSD) model according to the specific application scenario of this paper, the kilometer post detection model based on an improved SSD algorithm is established. Finally, from the analysis of the experimental results, we know that the detection model established in this paper ensures both detection accuracy and efficiency. The accuracy of our model reached 98.92%, while the detection time was only 35.43 ms. Thus, our model realizes the rapid and accurate detection of kilometer posts and improves the assisted positioning technology based on kilometer posts by optimizing the detection method.

Download Full-text