HIGH INJECTION PRESSURE IMPINGING DIESEL SPRAY CHARACTERISTICS AND SUBSEQUENT SOOT FORMATION IN REACTING CONDITIONS

The spray impingement in diesel engines attracts the attention of engine researchers in recent decades as the physical size of the engine is reduced. Due to the spray impingement, the atomization, vaporizing and air-fuel mixing quality is altered compared to a free spray. For emission control, soot is one of the major particulate emissions from diesel combustion and its formation in an impinged spray is worthy to be investigated. Firstly, to understand the impinged spray characteristics, the experiments for both non-vaporizing and reacting conditions were conducted in a constant volume combustion vessel. The impinged spray was captured by a high-speed camera and the instantaneous spray propagation distance and rate were obtained. For a better understanding, the microscopic behavior of the spray propagation, the curvature of the impinged spray was calculated and a relationship between local fuel distribution and soot formation was found. After that, the apparent heat release rate from an impinge spray combustion and the heat flux through the impingement were analyzed. The apparent heat release rate was obtained by the internal chamber pressure and the heat flux was measured by heat flux probes embedded in the impinging plate. Then, the soot formation of an impinged spray was both studied from experiments and simulations. In the experiments, the natural luminosity mainly due to the incandescence of soot particles was captured by the high-speed camera. A computational fluid dynamics (CFD) approach was adopted to quantitatively study the soot formation in terms of absolute soot mass and soot mass fractions in the vicinity of the wall. In the last, the film formation under different ambient temperatures, impinging distances, and oxygen concentration was investigated in terms of film area and thickness. The impact of film formation on the soot outcomes was then investigated by comparing the rate of film vaporization and soot formation. To summarize, the main goal of this dissertation is going to benefit the understanding of the impinged spray in reacting diesel-relevant engine conditions. From experiments, a global view of soot formation in an impinged spray was analyzed and the mechanism of soot formation was further revealed by the CFD simulations

Semi-stable and splitting models for unitary Shimura varieties over ramified places. I

We consider Shimura varieties associated to a unitary group of signature $(n-s,s)$ where $n$ is even. For these varieties, we construct smooth $p$-adic integral models for $s=1$ and regular $p$-adic integral models for $s=2$ and $s=3$ over odd primes $p$ which ramify in the imaginary quadratic field with level subgroup at $p$ given by the stabilizer of a $\pi$-modular lattice in the hermitian space. Our construction, which has an explicit moduli-theoretic description, is given by an explicit resolution of a corresponding local model.Comment: 35 pp. In this version we added section 7 where we give an explicit moduli theoretic description of our construction

Behavior-Driven Model Design: A Deep Learning Recommendation Model Jointing Users and Products Reviews

Data-driven is widely mentioned, but the data is generated by user behavior. Our work aims to utilize a behavior-driven model design pattern to improve accuracy and provide explanations in review-based recommendations. Review-based recommendation introduces review text to overcome the sparseness and unexplainably of rating or scores-based model. Driven by users rating behavior and human cognitive abilities, we proposed a deep learning recommendation model jointing users and products reviews (DLRM-UPR) to learn user preferences and product characteristics adaptively. The DLRM-UPR consists of word, text, and context co-attention layers considering the interaction between each user-product-context pair. Extensive experiments on real datasets demonstrate that DLRM-UPR outperforms existing state-of-the-art models. In addition, the relevant information in the reviews and the suggestion for improving the user experience can be highlighted to explain the recommendation results

Enhanced Capsule-based Networks and Their Applications

Current deep models have achieved human-like accuracy in many computer vision tasks, even defeating humans sometimes. However, these deep models still suffer from significant weaknesses. To name a few, it is hard to interpret how they reach decisions, and it is easy to attack them with tiny perturbations. A capsule, usually implemented as a vector, represents an object or object part. Capsule networks and GLOM consist of classic and generalized capsules respectively, where the difference is whether the capsule is limited to representing a fixed thing. Both models are designed to parse their input into a part-whole hierarchy as humans do, where each capsule corresponds to an entity of the hierarchy. That is, the first layer finds the lowest-level vision patterns, and the following layers assemble the larger patterns till the entire object, e.g., from nostril to nose, face, and person. This design enables capsule networks and GLOM the potential of solving the above problems of current deep models, by mimicking how humans overcome these problems with the part-whole hierarchy. However, their current implementations are not perfect on fulfilling their potentials and require further improvements, including intrinsic interpretability, guaranteed equivariance, robustness to adversarial attacks, a more efficient routing algorithm, compatibility with other models, etc. In this dissertation, I first briefly introduce the motivations, essential ideas, and existing implementations of capsule networks and GLOM, then focus on addressing some limitations of these implementations. The improvements are briefly summarized as follows. First, a fast non-iterative routing algorithm is proposed for capsule networks, which facilitates their applications in many tasks such as image classification and segmentation. Second, a new architecture, named Twin-Islands, is proposed based on GLOM, which achieves the many desired properties such as equivariance, model interpretability, and adversarial robustness. Lastly, the essential idea of capsule networks and GLOM is re-implemented in a small group ensemble block, which can also be used along with other types of neural networks, e.g., CNNs, on various tasks such as image classification, segmentation, and retrieval

Influence of green financing, technology innovation, and trade openness on consumptionbased carbon emissions in BRICS countries

The study explores the dynamic effects of renewable energy investment (green financing), green technology, and trade openness on consumption-based (trade-adjusted) carbon emissions in BRICS economies from 2000 to 2020. The study employs the cross-section autoregressive distributed lag method for empirical estimation to address slope heterogeneity and cross-sectional dependency issues in panel data. The findings exhibit that green financing and sustainable technologies mitigate consumption-based carbon emissions in the long-run, while trade openness contributes to emissions in BRICS countries. The short-run outcomes are compatible with long-run; however, the magnitude of long-run estimates is larger than the short-run. Moreover, the error correction term reveals a significant negative coefficient value, endorsing the conversion towards steady-state equilibrium with a 37% yearly adjustment rate in case of any deviation from equilibrium. The robustness of results is confirmed through augmented mean group and common correlated effect mean group. These findings imply that BRICS countries should encourage financing in renewable energy projects and allocate R&D investment to promote the adaptation of sustainable technologies. In addition, sustainable and green trade policies would help to curb trade-adjusted pollution

An Experimental Study of Diesel Spray Impingement on a Flat Plate: Effects of Injection Conditions

[EN] Advanced injection strategies for internal combustion engines have been extensively studied although there still exists a significant fundamental knowledge gap on the mechanism for high-pressure spray interaction with the piston surface and chamber wall in the internal combustion engine. The current study focuses on providing qualitative and quantitative information on spray-wall impingement and its characteristics by expanding the range of operating parameters under engine-like conditions. Parameters considered in the experiment are ambient gas and fuel injection conditions. The test included the non-vaporizing spray at the different ambient density (14.8, 22.8 and 30 kg/m3 ) and injection pressure (1200, 1500 and 1800 bar) with the isothermal condition (ambient, and plate temperatures of 423 K). The test was conducted in the constant-volume vessel with the 60-degree impinging spray angle relative to the plate. The free spray and impinged spray properties were qualitatively analysed based on Mie and schlieren images. The results showed that the lower ambient density and higher injection pressure tended to result in relatively higher impinged spray height. The expanding shape of the impinged spray on the wall showed the oval shape.This material is based upon work supported by the Department of Energy, Office of Energy Efficiency and Renewable Energy (EERE) and the Department of Defense, Tank and Automotive Research, Development, and Engineering Center (TARDEC), under Award Number DE‐EE0007292.Zhu, X.; Zhao, L.; Zhao, Z.; Ahuja, N.; Naber, J.; Lee, S. (2017). An Experimental Study of Diesel Spray Impingement on a Flat Plate: Effects of Injection Conditions. En Ilass Europe. 28th european conference on Liquid Atomization and Spray Systems. Editorial Universitat Politècnica de València. 208-215. https://doi.org/10.4995/ILASS2017.2017.4733OCS20821

Measurement method of torsional vibration signal to extract gear meshing characteristics

A technique in measuring torsional vibration signal based on an optical encoder and a discrete wavelet transform is proposed for the extraction of gear meshing characteristics. The method measures the rotation angles of the input and output shafts of a gear pair by using two optical encoders and obtains the time interval sequences of the two shafts. By spline interpolation, the time interval sequences based on uniform angle sampling can be converted into angle interval sequences on the basis of uniform time sampling. The curve of the relative displacement of the gear pair on the meshing line (initial torsional vibration signal) can then be obtained by comparing the rotation angles of the input and output shafts at the interpolated time series. The initial torsional vibration signal is often disturbed by noise. Therefore, a discrete wavelet transform is used to decompose the signal at certain scales; the torsional vibration signal of the gear can then be obtained after filtering. The proposed method was verified by simulation and experimentation, and the results showed that the method could successfully obtain the torsional vibration signal of the gear at a high frequency. The waveforms of the torsional vibration could reflect the meshing characteristics of the teeth. These findings could provide a basis for fault diagnosis of gears