81 research outputs found

    Experimental and Modeling Investigations on Soot Formation of Ethanol, <i>n</i>‑Butanol, 2,5-Dimethylfuran, and Biodiesel in Diesel Engines

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    The soot formation in the combustion of four oxygenated fuels on a single-cylinder engine has been investigated experimentally and numerically. To accomplish this objective, a reduced combustion mechanism was proposed for the modeling studies, which has been extensively validated. Then direct injection compression ignition experiments fueled with diesel, biodiesel, and its blends with 20% volume fraction of ethanol (E20), <i>n</i>-butanol (B20), and 2,5-dimethylfuran (D20) have been conducted. In the three-dimensional (3-D) modeling studies, the reduced mechanism can well predict the experimental combustion and soot emission results. In contrast to the combustion phasing, the soot emissions for the five fuels were sequenced as diesel > biodiesel > B20 > D20 > E20, which was mainly due to the different oxygen content and fuel reactivity in the spray-combustion processes. Furthermore, 0-D modeling investigations were conducted as well to clarify the effects of the different oxygenated structures on the polycyclic aromatic hydrocarbons (PAHs) formation under homogeneous condition. It was shown that, for the five pure fuel cases, the sooting tendencies were sequenced as ethanol < <i>n</i>-butanol < methyl-decanoate < <i>n</i>-heptane < 2,5-dimethylfuran. According to the reaction pathway analyses, oxygenated fuels with a longer carbon chain molecular structure could produce more intermediate species, which favor the PAHs and soot formation. However, much more phenol and cyclopentadienyl radical can be produced due to the special cyclic structure of 2,5-dimethylfuran, resulting in the highest soot formation among these investigated fuels

    Prediction Accuracy and Efficiency of the <i>n</i>‑Heptane Mechanism at Different Reduction Levels

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    The detailed combustion mechanism can be coupled with the computational fluid dynamics (CFD) software to simulate the combustion process in a practical engine. However, the computing time may be unfeasibly long. To improve the efficiency of the simulations, a reduced mechanism is preferable. However, the combustion characteristics and prediction accuracy will be influenced by the reduction of the combustion mechanism. In this work, the effects of the reduction of the detailed <i>n</i>-heptane mechanism on the prediction accuracy and efficiency were investigated theoretically. The reduction was based on the directed relation graph method without revising the original kinetic parameters. The results indicated that the reduced combustion mechanism at <sup>1</sup>/<sub>2</sub> size (<sup>1</sup>/<sub>2</sub> mechanism) of the detailed mechanism performed well and the <sup>1</sup>/<sub>4</sub> mechanism showed some deviation from the experimental data as a result of the removal of some low-temperature reactions. The <sup>1</sup>/<sub>8</sub> mechanism performed even worse. An ideal combustion mechanism for coupling with CFD simulations should be of the size between that of the <sup>1</sup>/<sub>2</sub> mechanism and <sup>1</sup>/<sub>4</sub> mechanism

    Template patterns.

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    <p>Each template pattern consists of two representative within-group patterns, shown in a grid with the central sagittal, coronal, and axial slices. In each grid, the upper and the lower slices are from the representative patterns of the Alzheimer's disease group and the normal control group, respectively.</p

    Correspondences of the twelve most stable template patterns.

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    <p>They are illustrated in subject images with the sagittal, coronal, and axial slices. The squares indicate 3D regions for the descriptors of the correspondences. L = left; R = right.</p

    Comparison of correspondences between 2L-SIFT and SIFT.

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    <p>The twelve most stable template patterns were used for the comparison. The corresponding rates (CR) for both the Alzheimer's disease group (AD) and the normal control group (NC) demonstrate the improvement of the effectiveness of the 2L-SIFT. The appearance distances (DIST) in terms of mean ± standard deviations demonstrate the improvement of accuracy of the 2L-SIFT.</p

    SRCIN1 inhibited epithelial–mesenchymal transition of the osteosarcoma cell.

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    <p>(A) The mRNA expression of E-cadherin, N-cadherin, Vimentin and Snail was measured by using qRT-PCR in the MG-63 cells after treated SRCIN1 vector. (B) The protein expression of E-cadherin, N-cadherin, Vimentin and Snail was detected by using western blot in the MG-63 cells after treated SRCIN1 vector.</p

    ROC curves for three methods on three different divisions.

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    <p>M1, M2, and M3 denote the proposed method, Toews' method, and the proposed method without the secondary set, respectively. M1 outperforms M2 a little, and performs much better than M3. Including wider range of age (B) or severity of clinical diagnosis (C) both result in reduced classification performance.</p

    SRCIN1 suppressed the osteosarcoma cell proliferation.

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    <p>(A) The protein expression of SRCIN1 was measured by using western blot in the MG-63 cells after treated SRCIN1 vector. (B) The mRNA expression of SRCIN1 was measured by using qRT-PCR in the MG-63 cells after treated SRCIN1 vector. (C) The cell proliferation was measured by CCK-8 analysis in the MG-63 cells. (D) The mRNA expression of ki-67 was measured by using qRT-PCR. (E) The protein expression of ki-67 was measured by using western blot in the MG-63 cells after treated SRCIN1 vector.(F) The mRNA expression of PCNA was measured by using qRT-PCR. (G) The protein expression of PCNA was measured by using western blot. *p<0.05, **p<0.01 and ***p<0.001.</p

    The expression of SRCIN1 was downregulated in the osteosarcoma cells.

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    <p>(A) One representive patient was diagnosed as osteosarcoma using HE staining. (B) The mRNA expression of SRCIN1 was measured by using qRT-PCR. (C) The protein expression of SRCIN1 was measured by using western blot.</p

    More accurate correspondences.

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    <p>The squares indicate 3D regions for the descriptors of localized patterns. A is a template pattern shown in subject image. A1 and A2 show two local features extracted from the same training subject, both arising from the same underlying anatomical structure as A. A2 is a more accurate correspondence than A1 according to the location and the scale.</p
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