Numerical Studies on the Induced Ignition Process of Various Fuels

Diese Arbeit präsentiert eine detaillierte numerische Untersuchung der erzwungenen Zündung und der frühen Flammenausbreitung in sowohl kohlenwasserstoffbasierten Brennstoffen (Methan und Primärreferenzbrennstoffe, PRF) als auch kohlenstofffreien Brennstoffen (Ammoniak und Ammoniak/Wasserstoff-Gemischen) unter Verwendung eindimensionaler Simulationen, detaillierter chemischer Kinetik und detaillierter Transportmodelle. Die Studie konzentriert sich auf die Abhängigkeit der minimalen Zündenergie (MIE) von verschiedenen Zündparametern, die entscheidend für die Verbesserung der Verbrennungseffizienz und die Gewährleistung der Sicherheit durch Vermeidung unerwünschter Zündungen ist. Durch die Analyse verschiedener Zündparameter, darunter Gemischzusammensetzung, Druck und Zündgeometrie, deckt die Forschung wichtige Erkenntnisse über das Zündverhalten unterschiedlicher Brennstoffgemische auf. Die Simulationen replizieren experimentelle Beobachtungen genau und zeigen signifikante Abhängigkeiten der MIE von Faktoren wie dem Äquivalenzverhältnis und der Zündgeometrie. Diese Studie untersucht auch die stochastische Natur der Zündradien, die als ein entscheidender Parameter identifiziert wurde, der zum stochastischen Zündverhalten in Experimenten beiträgt. Die Ergebnisse tragen zum Forschungsfeld bei, indem sie eine umfassende quantitative Analyse der MIE-Abhängigkeiten liefern und die Simulationsergebnisse mit experimentellen Daten validieren. Die Forschung unterstreicht die Bedeutung des Verständnisses der komplexen Wechselwirkung zwischen chemischer Kinetik und Transportvorgängen während des Zündprozesses. Diese Arbeit liefert wertvolle Erkenntnisse für die Entwicklung effizienterer und sichererer Verbrennungssysteme

Numerical Studies on Minimum Ignition Energies in Methane/Air and Isooctane/Air Mixtures

In this study, the dependence of minimum ignition energies (MIE) on ignition geometry, ignition source radius and mixture composition is investigated numerically for methane/air and isooctane/air mixtures. Methane and isooctane are both important hydrocarbon fuels, but differ strongly with respect to their Lewis numbers. Lean isooctane air mixtures have particularly large Lewis numbers. The results show that within the flammability limits, the MIE for both mixtures stays almost constant, and increases rapidly at the limits. The MIEs for both fuels are also similar within the flammability limits. Furthermore, the MIEs of isooctane/air mixtures with a small spherical ignition source increase rapidly for lean mixtures. Here the Lewis number is above unity, and thus, the flame may quench because of flame curvature effects. The observations show a distinct difference between ignition and flame propagation for iso-octane. The minimum energy required for initiating a successful flame propagation can be considerably higher than that required for initiating an ignition in the ignition volume. For iso-octane with a small spherical ignition source, this effect was observed at all equivalence ratios. For iso-octane with cylindrical ignition sources, the phenomenon appeared at lower equivalence ratios only, where the mixture’s Lewis number is large. For methane fuel, the effect was negligible. The results highlight the significance of molecular transport properties on the decision whether or not an ignitable mixture can evolve into a propagating flame

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design.

BACKGROUND: Root systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions. RESULTS: Our results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass ( R 2 = 0.82 ) and root surface area ( R 2 = 0.8 ). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits. CONCLUSION: Our findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods

Enhancing Representation Learning for Periodic Time Series with Floss: A Frequency Domain Regularization Approach

Time series analysis is a fundamental task in various application domains, and deep learning approaches have demonstrated remarkable performance in this area. However, many real-world time series data exhibit significant periodic or quasi-periodic dynamics that are often not adequately captured by existing deep learning-based solutions. This results in an incomplete representation of the underlying dynamic behaviors of interest. To address this gap, we propose an unsupervised method called Floss that automatically regularizes learned representations in the frequency domain. The Floss method first automatically detects major periodicities from the time series. It then employs periodic shift and spectral density similarity measures to learn meaningful representations with periodic consistency. In addition, Floss can be easily incorporated into both supervised, semi-supervised, and unsupervised learning frameworks. We conduct extensive experiments on common time series classification, forecasting, and anomaly detection tasks to demonstrate the effectiveness of Floss. We incorporate Floss into several representative deep learning solutions to justify our design choices and demonstrate that it is capable of automatically discovering periodic dynamics and improving state-of-the-art deep learning models.Comment: 12 page

When Source-Free Domain Adaptation Meets Label Propagation

Source-free domain adaptation, where only a pre-trained source model is used to adapt to the target distribution, is a more general approach to achieving domain adaptation. However, it can be challenging to capture the inherent structure of the target features accurately due to the lack of supervised information on the target domain. To tackle this problem, we propose a novel approach called Adaptive Local Transfer (ALT) that tries to achieve efficient feature clustering from the perspective of label propagation. ALT divides the target data into inner and outlier samples based on the adaptive threshold of the learning state, and applies a customized learning strategy to best fits the data property. Specifically, inner samples are utilized for learning intra-class structure thanks to their relatively well-clustered properties. The low-density outlier samples are regularized by input consistency to achieve high accuracy with respect to the ground truth labels. In this way, local clustering can be prevented from forming spurious clusters while effectively propagating label information among subpopulations. Empirical evidence demonstrates that ALT outperforms the state of the arts on three public benchmarks: Office-31, Office-Home, and VisDA

Characterization of pore systems in fine-grained carbonate rocks using digital core technology

The characterization of pore systems in fine-grained carbonate rocks faces numerous challenges due to the significant complexity of microscopic features, including a variation of micro- and nanoscale pore sizes and the complex pore-throat distribution. In this work, digital core technology was adopted to characterize the pore systems of lacustrine fine-grained carbonate rocks in the Yingxi area of Qaidam Basin. The simulated results indicated that the pore types predominantly contain intercrystalline and dissolution pores. The former exhibit high porosity but extremely low permeability and are primarily developed in bedded dolostones. Conversely, the latter show relatively higher permeability, predominantly developed in bedded calcareous dolostones. The elevated dolomite content provides the material basis for the development of intercrystalline pores, while the extremely small throat radius constrains the fluidity of this pore system. In addition, the dissolution has a great impact on improving the permeable capability of intercrystalline pore system via increasing the radius and specific surface area of pores and throats.Document Type: Research highlightCited as: Hu, C., Zhao, Z., Gao, S., Liu, C., Wu, K., Pang, P. Characterization of pore systems in fine-grained carbonate rocks using digital core technology. Advances in Geo-Energy Research, 2024, 12(1): 77-80. https://doi.org/10.46690/ager.2024.04.0

The microbiota continuum along the female reproductive tract and its relation to uterine-related diseases

Reports on bacteria detected in maternal fluids during pregnancy are typically associated with adverse consequences, and whether the female reproductive tract harbours distinct microbial communities beyond the vagina has been a matter of debate. Here we systematically sample the microbiota within the female reproductive tract in 110 women of reproductive age, and examine the nature of colonisation by 16S rRNA gene amplicon sequencing and cultivation. We find distinct microbial communities in cervical canal, uterus, fallopian tubes and peritoneal fluid, differing from that of the vagina. The results reflect a microbiota continuum along the female reproductive tract, indicative of a non-sterile environment. We also identify microbial taxa and potential functions that correlate with the menstrual cycle or are over-represented in subjects with adenomyosis or infertility due to endometriosis. The study provides insight into the nature of the vagino-uterine microbiome, and suggests that surveying the vaginal or cervical microbiota might be useful for detection of common diseases in the upper reproductive tract.Shenzhen Municipal Government of China [JCYJ20160229172757249, JCYJ20150601090833370]; Danish Strategic Research Council [2106-07-0021]; Ole Romer grant from Danish Natural Science Research Council; Solexa project [272-07-0196]SCI(E)ARTICLE

Endothelial Cell-Specific Molecule 2 (Ecsm2) Localizes To Cell-Cell Junctions And Modulates Bfgf-Directed Cell Migration Via The Erk-Fak Pathway

Background: Despite its first discovery by in silico cloning of novel endothelial cell-specific genes a decade ago, the biological functions of endothelial cell-specific molecule 2 (ECSM2) have only recently begun to be understood. Limited data suggest its involvement in cell migration and apoptosis. However, the underlying signaling mechanisms and novel functions of ECSM2 remain to be explored. Methodology/Principal Findings: A rabbit anti-ECSM2 monoclonal antibody (RabMAb) was generated and used to characterize the endogenous ECSM2 protein. Immunoblotting, immunoprecipitation, deglycosylation, immunostaining and confocal microscopy validated that endogenous ECSM2 is a plasma membrane glycoprotein preferentially expressed in vascular endothelial cells (ECs). Expression patterns of heterologously expressed and endogenous ECSM2 identified that ECSM2 was particularly concentrated at cell-cell contacts. Cell aggregation and transwell assays showed that ECSM2 promoted cell-cell adhesion and attenuated basic fibroblast growth factor (bFGF)-driven EC migration. Gain or loss of function assays by overexpression or knockdown of ECSM2 in ECs demonstrated that ECSM2 modulated bFGF-directed EC motility via the FGF receptor (FGFR)-extracellular regulated kinase (ERK)-focal adhesion kinase (FAK) pathway. The counterbalance between FAK tyrosine phosphorylation (activation) and ERK-dependent serine phosphorylation of FAK was critically involved. A model of how ECSM2 signals to impact bFGF/FGFR-driven EC migration was proposed. Conclusions/Significance: ECSM2 is likely a novel EC junctional protein. It can promote cell-cell adhesion and inhibit bFGF-mediated cell migration. Mechanistically, ECSM2 attenuates EC motility through the FGFR-ERK-FAK pathway. The findings suggest that ECSM2 could be a key player in coordinating receptor tyrosine kinase (RTK)-, integrin-, and EC junctional component-mediated signaling and may have important implications in disorders related to endothelial dysfunction and impaired EC junction signaling. © 2011 Shi et al