Experimental Study on Spectral Characteristics of Kerosene Swirl Combustion

AbstractAn experimentalstudy has been conducted to investigate characteristics of emission spectra from combustion of kerosene liquid fuel, i.e., jet A-1. Radicals of interest in hydrocarbon combustion are OH*, CH*, and C2*. An experimental study about chemiluminescence characteristics of liquid fuel combustion has been devised to investigate emission characteristics depending on various operating parameters. A swirl combustor is designed for providing similar environments to those of actual liquid rocket engines. The model combustor has a central fuel injector making a hollow cone spray, which is surrounded by swirling flow. Kerosene flame exhibited highly luminous characteristics being attributed to CO2* chemiluminescence.OH* and CH* chemiluminescence intensities show a very similar trend as a function of equivalence ratio. And their intensities decrease along with an increase in equivalence ratio. The chemiluminescence intensity ratios between these two radicals show very close values to one regardless of equivalence ratio.C2* chemiluminescence intensity reveals relatively strong relations with equivalence ratio compared to CH* and OH*. Its intensity values increase as mixture becomes rich and also an increase in inlet air temperature enhances its intensities. The ratios between C2* and CH* manifest a linear relation as a function of equivalence ratio

HeLiPR: Heterogeneous LiDAR Dataset for inter-LiDAR Place Recognition under Spatial and Temporal Variations

Place recognition is crucial for robotic localization and loop closure in simultaneous localization and mapping (SLAM). Recently, LiDARs have gained popularity due to their robust sensing capability and measurement consistency, even in the illumination-variant environment, offering an advantage over traditional imaging sensors. Spinning LiDARs are widely accepted among many types, while non-repetitive scanning patterns have recently been utilized in robotic applications. Beyond the range measurements, some LiDARs offer additional measurements, such as reflectivity, Near Infrared (NIR), and velocity (e.g., FMCW LiDARs). Despite these advancements, a noticeable dearth of datasets comprehensively reflects the broad spectrum of LiDAR configurations optimized for place recognition. To tackle this issue, our paper proposes the HeLiPR dataset, curated especially for place recognition with heterogeneous LiDAR systems, embodying spatial-temporal variations. To the best of our knowledge, the HeLiPR dataset is the first heterogeneous LiDAR dataset designed to support inter-LiDAR place recognition with both non-repetitive and spinning LiDARs, accommodating different field of view (FOV) and varying numbers of rays. Encompassing the distinct LiDAR configurations, it captures varied environments ranging from urban cityscapes to high-dynamic freeways over a month, designed to enhance the adaptability and robustness of place recognition across diverse scenarios. Notably, the HeLiPR dataset also includes trajectories that parallel sequences from MulRan, underscoring its utility for research in heterogeneous LiDAR place recognition and long-term studies. The dataset is accessible at https: //sites.google.com/view/heliprdataset.Comment: 9 pages, 9 figures, 5 table

RainSD: Rain Style Diversification Module for Image Synthesis Enhancement using Feature-Level Style Distribution

Autonomous driving technology nowadays targets to level 4 or beyond, but the researchers are faced with some limitations for developing reliable driving algorithms in diverse challenges. To promote the autonomous vehicles to spread widely, it is important to address safety issues on this technology. Among various safety concerns, the sensor blockage problem by severe weather conditions can be one of the most frequent threats for multi-task learning based perception algorithms during autonomous driving. To handle this problem, the importance of the generation of proper datasets is becoming more significant. In this paper, a synthetic road dataset with sensor blockage generated from real road dataset BDD100K is suggested in the format of BDD100K annotation. Rain streaks for each frame were made by an experimentally established equation and translated utilizing the image-to-image translation network based on style transfer. Using this dataset, the degradation of the diverse multi-task networks for autonomous driving, such as lane detection, driving area segmentation, and traffic object detection, has been thoroughly evaluated and analyzed. The tendency of the performance degradation of deep neural network-based perception systems for autonomous vehicle has been analyzed in depth. Finally, we discuss the limitation and the future directions of the deep neural network-based perception algorithms and autonomous driving dataset generation based on image-to-image translation.Comment: Under Revie

Improvement of Regional Climate Simulation of Tropical Cyclone over the Western North Pacific through Sensitivity Tests to Horizontal Resolution and Convection Scheme

Department of Urban and Environmental Engineering (Disaster Management Engineering)Sensitivity test of horizontal resolution in simulating western North Pacific (WNP) tropical cyclone (TC) activity using three RCMs (SNURCM, WRF, and RegCM) which are participated in Coordinated Regional Climate Downscaling Experiment (CORDEX) for Ea.st Asia phase ??? (EAS-44) and ??? (EAS- 22) are compared. The simulated climatological mean of TC activity for the analysis period (1989-2005) shows that the frequency and intensity of typhoons at EAS-22 increased and simulated much more similar observations than at EAS-44 as the horizontal resolution increased except for WRF. Analysis for large-scale environments indicates that increased relative vorticity and weaken vertical wind shear (VWS) can induce increasing TC genesis over the South China Sea and the Philippine Sea. Sensitivity test for two convection schemes (Kain-Fritsch and Betts-Miller-Janjic) and three spectral nudging settings were conducted using WRF test on three years (1994, 1998, 2001). The TC activities in KF experiments outperformed than BMJ experiments in all three years of simulation. Spectral nudging reduces produced TC genesis and intensity linearly by its nudging strength. TC distribution analysis indicates KF_SN2 is most appropriate to simulate TCs activities over WNP by horizontal resolution in 25km. the 2001 year 11th TC PABUK is reproduced by KF_SN2 and BMJ_SN2 to investigate the azimuthal mean structure and thermodynamic vertical profile. KF_SN2 constructed the solid vertical structure of azimuthal mean radial and tangential wind. The updraft motion at the 850hPa over a radius of maximum windspeed (RMW) of KF_SN2 is much greater than BMJ_SN2. Corresponding change over time of thermodynamic vertical profile during TC intensifying period indicates KF_SN2 simulated stronger TC intensification than BMJ_SN in most of the vertical layer.clos

Dispersion Mechanism and Mechanical Properties of SiC Reinforcement in Aluminum Matrix Composite through Stir- and Die-Casting Processes

In this study, different volume fractions of silicon-carbide-reinforced AA2024 matrix composites were successfully fabricated using stir-casting (SC) and die-casting (DC) processes. The microstructural difference and physical properties of the composites during the manufacturing process were investigated in detail. The microstructural analysis found that the composite produced by the SC process had some reinforcement clusters and pores; however, defects and clusters significantly decreased after the DC process. In particular, the degree of reinforcement dispersion was quantitatively analyzed and compared before and after the DC process using the dispersion-analysis method. As a result of quantitative evaluation, the degree of dispersion was improved 2.5, 4.6, and 4.0 times with 3 vol.%, 6 vol.%, and 9 vol.% SiC-reinforced composite after the DC process, respectively. The electron backscatter diffraction (EBSD) analysis showed that the grain size of the 9 vol.% SiC-reinforced DC composite (17.67 μm) was 75% smaller than that of the SC composite (68.06 μm). The average tensile strength and hardness of the 9 vol.% SiC-reinforced DC composite were 2 times higher than those of the AA2024 matrix. The superior mechanical properties of the DC-processed composite can be attributed to the increase in dispersivity of the SiC particles and to decreases in defects and grain size during the DC process

CARLA Simulator-Based Evaluation Framework Development of Lane Detection Accuracy Performance Under Sensor Blockage Caused by Heavy Rain for Autonomous Vehicle

As self-driving cars have been developed targeting level 4 and 5 autonomous driving, the capability of the vehicle to handle environmental effects has been considered importantly. The sensors installed on autonomous vehicles can be easily affected by blockages (e.g., rain, snow, dust, fog, and others) covering the surface of them. In a virtual environment, we can safely observe the behavior of the vehicle and the degradation of the sensors by blockages. In this paper, the CARLA simulator-based evaluation framework has been developed and the assessment of lane detection performance under sensor blockage by heavy rain, which was analyzed by using the experimental data. Thus, we thoroughly note that the accuracy of lane detection for the autonomous vehicle has been decreased as the rainfall rate increases, and the impact of the blockage is more critical to curved lanes than straight lanes. Finally, we have suggested a critical rainfall rate causing safety failures of the autonomous vehicles, based on reasonably established rainfall equation based on experimental rain datasets. IEEEFALS

Development of Artificial Neural Network System to Recommend Process Conditions of Injection Molding for Various Geometries

This study combines an artificial neural network (ANN) and a random search to develop a system to recommend process conditions for injection molding. Both simulation and experimental results are collected using a mixed sampling method that combines Taguchi and random sampling. The dataset consists of 3600 simulations and 476 experiments from 36 different molds. Each datum has five process and 15 geometry features as input and one weight feature as output. Hyper‐parameter tuning is conducted to find the optimal ANN model. Then, transfer learning is introduced, which allows the use of simultaneous experimental and simulation data to reduce the error. The final prediction model has a root mean‐square error of 0.846. To develop a recommender system, random search is conducted using the trained ANN forward model. As a result, the weight‐prediction model based on simulated data has a relative error (RE) of 0.73%, and the weight prediction using the transfer model has an RE of 0.662%. A user interface system is also developed, which can be used directly with the injection‐molding machine. This method enables the setting of process conditions that yield parts having weights close to the target, by considering only the geometry and target weight.11Yothe

DataSheet1_Fast field echo resembling CT using restricted echo-spacing (FRACTURE) MR sequence can provide craniocervical region images comparable to a CT in dogs.docx

Magnetic resonance imaging (MRI) is essential for evaluating cerebellar compression in patients with craniocervical junction abnormalities (CJA). However, it is limited in depicting cortical bone because of its short T2 relaxation times, low proton density, and organized structure. Fast field echo resembling a computed tomography (CT) scan using restricted echo-spacing (FRACTURE) MRI, is a new technique that offers CT-like bone contrast without radiation. This study aimed to assess the feasibility of using FRACTURE MRI for craniocervical junction (CCJ) assessment compared with CT and conventional MRI, potentially reducing the need for multiple scans and radiation exposure, and simplifying procedures in veterinary medicine. CT and MRI of the CCJ were obtained from five healthy beagles. MRI was performed using three-dimensional (3D) T1-weighted, T2-weighted, proton density-weighted (PDW), single echo-FRACTURE (sFRACTURE), and multiple echo-FRACTURE (mFRACTURE) sequences. For qualitative assessment, cortical delineation, trabecular bone visibility, joint space visibility, vertebral canal definition, overall quality, and artifacts were evaluated for each sequence. The geometrical accuracy, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR) were quantified. Both sFRACTURE and CT images provided significantly higher scores for cortical delineation and trabecular bone visibility than conventional MRI. Joint space visibility and vertebral canal definition were similar to those observed on CT images, regardless of the MR sequence. In the quantitative assessment, the distances measured on T2-weighted images differed significantly from those measured on CT. There were no significant differences between the distances taken using T1-weighted, PD-weighted, sFRACTURE, mFRACTURE and those taken using CT. T1-weighted and sFRACTURE had a higher SNR for trabecular bone than CT. The CNR between the cortical bone and muscle was high on CT and FRACTURE images. However, the CNR between the cortical and trabecular bones was low in mFRACTURE. Similar to CT, FRACTURE sequences showed higher cortical delineation and trabecular bone visibility than T2-weighted, T1-weighted, and PDW CCJ sequences. In particular, sFRACTURE provided a high signal-to-noise ratio (SNR) of the trabecular bone and a high CNR between the cortical bone and muscle and between the cortical and trabecular bones. FRACTURE sequences can complement conventional MR sequences for bone assessment of the CCJ in dogs.</p