New approach for normalization and photon-number distributions of photon-added (-subtracted) squeezed thermal states

Using the thermal field dynamics theory to convert the thermal state to a "pure" state in doubled Fock space, it is found that the average value of e^{fa^{{\dag}}a} under squeezed thermal state (STS) is just the generating function of Legendre polynomials, a remarkable result. Based on this point, the normalization and photon-number distributions of m-photon added (or subtracted) STS are conviently obtained as the Legendre polynomials. This new concise method can be expanded to the entangled case.Comment: 5 pages, no figur

Mechanism, Model, and Upscaling of the Gas Flow in Shale Matrix: Revisit

Shale gas accounts for an increasing proportion in the world’s oil and gas supply, with the properties of low carbon, clean production, and huge potential for the compensation for the gradually depleted conventional resources. Due to the ubiquitous nanopores in shale matrix, the nanoscale gas flow becomes one of the most vital themes that are directly related to the formulation of shale gas development schemes, including the optimization of hydraulic fracturing, horizontal well spacing, etc. With regard to the gas flow in shale matrix, no commonly accepted consensus has been reached about the flow mechanisms to be considered, the coupled flow model in nanopores, and the upscaling method for its macroscopic form. In this chapter, the propositions of wall-associated diffusion, a physically sound flow mechanism scheme, a new coupled flow model in nanopores, the upscaling form of the proposed model, and the translation of lab-scale results into field-scale ones aim to solve the aforementioned issues. It is expected that this work will contribute to a deeper understanding of the intrinsic relationship among various flow mechanisms and the extension of the flow model to full flow regimes and to upscaling shale matrix, thus establishing a unified model for better guiding shale gas development

A Micromechanics Study on Ferroelectrics and Multiferroic Composites

Two kinds of modern composites are studied in this thesis. One is ferroelectric materials, and the other is the multiferroic composites. Based upon the essential properties of these two types materials, several issues need to be considered in order to study their effective properties. For example, the microstructures of the system including the shape, size and the distribution; the phase connectivity; and the loading conditions will impact the results. First, we focus on the investigation of ferroelectrics and its composites. Based on the experimental observations, the nonlinear electromechanical coupling responses of ferroelectrics are strongly depending on the frequency of the applied electric field. In order to interpret the influence of a frequency, an exponential function is introduced to express the remanent polarization and coercive field in terms of the frequency. We incorporate the frequency-dependent exponential functions into the micromechanics-based model to establish the nonlinear constitutive relations of a ferroelectric material. Then, for the two-phase ferroelectric composites, the Mori-Tanaka Method can be applied to analyze and solve the overall effective electro-mechanical coupling behavior of the system. We demonstrate how the overall nonlinear effective physical properties depend on the phase volume concentration and inclusions’ shape and the loading frequency. Our predictions are shown to be in a good agreement with experimental data. Second, for multiferroic composites, both linear and nonlinear effective physical properties are studied. For the linear case, a popular BaTiO3-CoFe2O4 system is studied with different connectivity and aspect ratio. The effective physical properties of overall composites analyzed by the Mori-Tanaka method. The results shown that the magnetoelectric coupling coefficients 11 and 33 are highly depending on the volume fraction and aspect ratio. For the case of nonlinear, a two-level micromechanics model is developed to study the nonlinear magnetoelectric (ME) effects of 2-2 multiferroic composites consisting both ferromagnetic and ferroelectric phases. At the first level, similar to a ferroelectric material, a ferromagnetic phase can also be studied using a model involving a thermodynamically based evolution of the product domain from the parent one. Once the physical properties of ferroelectric and/or ferromagnetic phase can be obtained at the first level, their multiferroic composites can be studied as a two-phase composite at the second level. Then the two-level model is applied to evaluate the nonlinear magnetoelectric effects of Terfenol-D/PZT/Terfenol-D laminated system under the applied magnetic field. To verify the model, the voltage coefficient E33 of the system is calculated and compared with the experimental data. The comparison between the theory and experiment is in a good agreement

Research On Low Frequency Vibration Of Rotary Compressor

The abnormal noise of an outdoor domestic air-conditioner operating at low speed is experimentally analyzed. The structure-borne noise which passes through the mounting system is confirmed to be the main source of the abnormal noise due to the large low frequency vibration on compressor foot. Then the characteristic of low frequency vibration of rotary compressor including the dynamic model, exciting forces and dynamic response is researched. Based on this, mounting system including compressor foot and rubber grommet is optimized to solve this problem, more than 8dB reduced

Modeling Viscosity of High Titania Slag

TiO2-FeO-Ti2O3 slag system is the dominant system for industrial high-titania slag production. In the present work, viscosities of TiO2-FeO and TiO2-FeO-Ti2O3 systems were experimentally determined using the concentric rotating cylinder method under argon atmosphere. A viscosity model suitable for the TiO2-FeO-Ti2O3 slag system was then established based on the modification of the Vogel-Fulcher-Tammann (VFT) equation. The experimental results indicate that completely melted high-titania slags exhibit very low viscosity of around 0.8 dPa s with negligible dependence on temperature and compositions. However, it increases dramatically with decreasing temperature slightly below the critical temperature. Besides, the increase in FeO content was found to remarkably lower the critical temperature, while the addition of Ti2O3 increases it. The developed model can predict the viscosities of the TiO2-FeO-Ti2O3 and TiO2-FeO systems over wide ranges of compositions and temperatures within experimental uncertainties. The average relative error for the present model calculation is < 18.82 pct, which is better than the previously developed models for silicate slags reported in the literature. An iso-viscosity distribution diagram was made for the TiO2-FeO-Ti2O3 slag system, which can serve as a roadmap for the Ilmenite smelting reduction process as well as the high titania slags tapping process.publishedVersio

Exploring Natural Language Processing Methods for Interactive Behaviour Modelling

Analysing and modelling interactive behaviour is an important topic in human-computer interaction (HCI) and a key requirement for the development of intelligent interactive systems. Interactive behaviour has a sequential (actions happen one after another) and hierarchical (a sequence of actions forms an activity driven by interaction goals) structure, which may be similar to the structure of natural language. Designed based on such a structure, natural language processing (NLP) methods have achieved groundbreaking success in various downstream tasks. However, few works linked interactive behaviour with natural language. In this paper, we explore the similarity between interactive behaviour and natural language by applying an NLP method, byte pair encoding (BPE), to encode mouse and keyboard behaviour. We then analyse the vocabulary, i.e., the set of action sequences, learnt by BPE, as well as use the vocabulary to encode the input behaviour for interactive task recognition. An existing dataset collected in constrained lab settings and our novel out-of-the-lab dataset were used for evaluation. Results show that this natural language-inspired approach not only learns action sequences that reflect specific interaction goals, but also achieves higher F1 scores on task recognition than other methods. Our work reveals the similarity between interactive behaviour and natural language, and presents the potential of applying the new pack of methods that leverage insights from NLP to model interactive behaviour in HCI