Study of Quasi‐Dimensional Combustion Model of Hydrogen‐ Enriched Compressed Natural Gas (HCNG) Engines

The reserves of the petroleum‐based fuels are directly correlated with the increasing demand of human mankind for energy production. With the growing world populations, industries, vehicles, and equipment, energy demand leads to the search for the substitute of petroleum fuels, which can cater for the need of people today. Considering the current global economic crisis, the interest in alternative fuels is extremely high. It is known that there is a limited amount of fossil‐based fuels as a sustainable energy source. The majority of the energy used today is obtained from the fossil fuels. Due to the continuing increase over the cost of fossil fuels, demands for clean energy have also been increasing. With this increasing demand for energy very soon the petroleum fuels will be depleted so researchers are focusing on to find the ways and means to generate cheap and abundant renewable and clean energy sources. Moving ahead with these plans, hydrogen‐enriched compressed natural gas (HCNG) engines have emerged as a future energy carrier for an internal combustion engine. Several countries are striving hard to bring down the pollution level by promoting hydrogen‐enriched compressed natural gas‐fueled vehicles in general by powering heavy vehicles like transportation buses as well as passenger cars. In general, under certain conditions, the indicated thermal efficiency of the HCNG engine is much better than CNG engines without compromising the high level of pollutant emissions. Even so, the hydrogen addition to CNG increases the NOx emission, due to high heat generated inside combustion chamber. This can be minimized by application of lean‐burn combustion or with three‐way catalyst

A Unified Approximation Framework for Compressing and Accelerating Deep Neural Networks

Deep neural networks (DNNs) have achieved significant success in a variety of real world applications, i.e., image classification. However, tons of parameters in the networks restrict the efficiency of neural networks due to the large model size and the intensive computation. To address this issue, various approximation techniques have been investigated, which seek for a light weighted network with little performance degradation in exchange of smaller model size or faster inference. Both low-rankness and sparsity are appealing properties for the network approximation. In this paper we propose a unified framework to compress the convolutional neural networks (CNNs) by combining these two properties, while taking the nonlinear activation into consideration. Each layer in the network is approximated by the sum of a structured sparse component and a low-rank component, which is formulated as an optimization problem. Then, an extended version of alternating direction method of multipliers (ADMM) with guaranteed convergence is presented to solve the relaxed optimization problem. Experiments are carried out on VGG-16, AlexNet and GoogLeNet with large image classification datasets. The results outperform previous work in terms of accuracy degradation, compression rate and speedup ratio. The proposed method is able to remarkably compress the model (with up to 4.9x reduction of parameters) at a cost of little loss or without loss on accuracy.Comment: 8 pages, 5 figures, 6 table

Life cycle analysis of HCNG light-duty vehicle demonstration project

The demonstration is an effective initiative to bridge the gap between a premature technology and its largescalecommercialization. A systematic method of LCA was used to perform the well-to-wheel analysis of aCNG light-duty truck operated with 0%, 15%, and 30% blends of hydrogen with compressed natural gas fuelunder the system boundary of “per vehicle km”. The GREET simulation was performed on GREET_1 (ExcelSeries_2017) to evaluate hydrogen production pathways with numerous parametric assumptions adopted tobase the study in the context of China. Resource use, fossil energy use, GHG emission, and major air pollutantsnamely and PM were studied. The idea was to demonstrate the effects of hydrogen additionthroughout the entire fuel cycle of end-use of HCNG in an LDV. The hydrogen blend of 30% withconventional CNG decreased the well-to-wheel GHG emission compared with 0%HCNG by 32.982%,29.275%, and 9.694% with hydrogen pathways such as solar, biomass, and coke oven gas, respectively.Moreover, for 30%HCNG (Conv.NG), the well-to-wheel total energy consumption was increased by15.176%, and 15.719% for solar and biomass-based pathways, respectively. However, although the energyconsumption was increased for solar and biomass-based 15%HCNG and 30%HCNG pathways comparedwith 0%HCNG, the feedstock used was renewable and qualitatively cleaner. The worst scenario was found inthe form of 30%HCNG (Conv.NG) with grid electrolysis pathway which showed 60.648% increment inWTW GHG and 75.479% increment in WTW total energy compared with baseline 0%HCNG (Conv.NG).The booming renewable electricity generation and availability of a tremendous amount of coke oven gas asby-products from coking industries in China can establish a prospective platform for sustainable hydrogeneconomy in China and is supposed to promote the commercialization of HCNG vehicles in future.Keywords: demonstration, life cycle analysis, , well-to-wheel, LDV, GREE

Comparative miRNA expression profile analysis of porcine ovarian follicles: new insights into the initiation mechanism of follicular atresia

Follicular atresia occurs in every stage of ovarian development, which is relevant to female fertility. In the past decade, increasing studies have confirmed that miRNAs, a class of short non-coding RNAs, play an important role in follicular atresia by post-transcription regulation of their target genes. However, the function of miRNAs on follicular atresia initiation is unknown. In the present study, high-throughput small RNA sequencing was performed to analyze differential miRNA expression profiles between healthy (HF) follicles and early atretic (EAF) follicles. A total of 237 conserved miRNA were detected, and the miR-143 is the highest expressed in follicles. Meanwhile, we also found wide sequence variations (isomiRs) in porcine ovarian miRNA, including in 5′un-translation region, core seed sequences and 3′untranslation region. Furthermore, we identified 22 differentially expressed miRNAs in EAF groups compared to HF group, of which 3 miRNAs were upregulated, as well as 19 miRNAs were downregulated, and then the RT-PCR was performed to validate these profiles. The target genes of these differentially expressed miRNAs were predicted by using miRwalk, miRDB, and Targetscan database, respectively. Moreover, the gene ontology and KEGG pathway enrichment established that the regulating functions and signaling pathways of these miRNAs contribute to follicular atresia initiation and cell fate. In conclusion, this study provides new insights into the changes of miRNAs in early atretic follicles to demonstrate their molecular regulation in ovarian follicular atretic initiation

The well-to-wheel analysis of hydrogen enriched compressed natural gas for heavy-duty vehicles using life cycle approach to a fuel cycle

Increasing urban air pollution, greenhouse gases, and declining fossil energy sources are the three major problems of transportation sector which drive the use of alternative vehicular fuels to prevent energy shortage, reduce oil dependency and decrease tailpipe emissions including air pollutants and greenhouse gas emissions. This research work focused on life cycle analysis of HCNG (R) heavy-duty vehicle in which 20% gaseous hydrogen blended with compressed natural gas has been investigated in terms of net energy ratio, GHG value, and cost-effectiveness over a scale of 'per MJ energy output' in two fuel options, i.e. 0%HCNG and 20%HCNG for an entire well-to-wheel cycle. An engineering economic approach has been used to evaluate cost-effectiveness ratio of CNG and 20%HCNG pathways derived from fuel economy improvement. It has been shown that at pump-to-wheel stage, 7% reduction in fuel consumption can be achieved together with 11% reduction in GHGs, 7% reduction in energy consumption at operation and 7% reduction in total costs (RMB/MJ) for 20%HCNG compared with CNG. Rank (1 means 'best and 10 means 'worst') showed that renewable-based hydrogen pathways such as solar, wind and biomass showed dual benefits of lower energy consumption and lower GHG emissions whereas grid electricity-to-hydrogen displayed the worst case in both scenarios. Usually, biomass-based HCNG pathways may have higher net energy ratio, but the sources are cleaner, and renewable in nature. The energy efficiency of fossil-based pathways such as natural gas, coal, etc., is higher than biomass gasification pathway

In-cylinder combustion analysis of a SI engine fuelled with hydrogen enriched compressed natural gas (HCNG): engine performance, efficiency and emissions

The main objective of this study was to investigate the effect of hydrogen addition on spark ignition (SI) engine’s performance, thermal efficiency, and emission using variable composition hydrogen/CNG mixtures. The hydrogen was used in amounts of 0%, 20%, 40% by volume fraction at each engine speed and load. Experimental analysis was performed at engine speed of 1200 rpm, load of 120 Nm corresponding BMEP = 0.24 MPa, spark timing 26 CAD BTDC, and at engine speed of 2000 rpm, load of 350 Nm corresponding BMEP = 0.71 MPa, spark timing 22 CAD BTDC. The investigation results show that increasing amounts of hydrogen volume fraction contribute to shorten ignition delay time and decrease of the combustion duration, that also affect main combustion phase. The combustion duration analysis of mass fraction burned (MFB) was presented in the article. Decrease of CO2 in the exhaust gases was observed with increase of hydrogen amounts to the engine. However, nitrogen oxides (NOX) were found to increase with hydrogen addition if spark timing was not optimized according to hydrogen’s higher burning speed

Effects of chamber geometry, hydrogen ratio and EGR ratio on the combustion process and knocking characters of a HCNG engine at the stoichiometric condition

Stoichiometric operation natural gas engine can achieve low emissions and high-power output by combining EGR and three-way catalytic converter. However, under high load conditions, engine performance will be deteriorated due to the knocking limits. The main methods to solve the knocking problems of spark ignition engines are shortening the flame propagation time and reducing the temperature and pressure rise of the end mixture. This paper firstly established a 3D model based on a natural gas engine, and then conducted the numerical simulations to investigate the effects of chamber geometry, hydrogen ratio and EGR ratio on engine knocking and combustion. The validation indicates a satisfied numerical results of chamber pressure and heat release rate. The turbine chamber exhibits relatively better swirl and tumble, thus achieving the highest turbulence intensity. HCNG fuel will increase the knocking tendency of the engine. In the meantime, the addition of hydrogen will also reduce the IMEP of the engine due to the low volume heat value of hydrogen. However, the addition of hydrogen may accelerate the combustion rate at the end of combustion to a certain extent. EGR can significantly reduce the knocking tendency of HCNG engine by reducing the combustion temperature in the cylinder. Compared with the case of 14% EGR rate, the ignition angle of 30% EGR case is advanced by 19 °CA, and the indicated thermal efficiency is increased by 2.06%

Lightweight Super-Resolution with Self-Calibrated Convolution for Panoramic Videos

Panoramic videos are shot by an omnidirectional camera or a collection of cameras, and can display a view in every direction. They can provide viewers with an immersive feeling. The study of super-resolution of panoramic videos has attracted much attention, and many methods have been proposed, especially deep learning-based methods. However, due to complex architectures of all the methods, they always result in a large number of hyperparameters. To address this issue, we propose the first lightweight super-resolution method with self-calibrated convolution for panoramic videos. A new deformable convolution module is designed first, with self-calibration convolution, which can learn more accurate offset and enhance feature alignment. Moreover, we present a new residual dense block for feature reconstruction, which can significantly reduce the parameters while maintaining performance. The performance of the proposed method is compared to those of the state-of-the-art methods, and is verified on the MiG panoramic video dataset