Dynamic Analysis of Deepwater Multi-Segment Mooring Lines Using Modal Superposition

This thesis starts from the construction of a mathematical model of the multi-segment mooring line, based on the work-energy variational method. The equations of motion in both Cartesian and Lagrange local coordinate systems are derived. Meanwhile, with the catenary theory applied, the static equilibrium configuration of the multi-segment mooring line is determined. Furthermore, Galerkin’s finite element method is used to generate mass, stiffness and damping coefficient matrices of a single mooring line. The coefficient matrices in the Lagrange local coordinate system are shown to be diagonal, which means the motions in the three directions of this coordinate system are uncoupled. With this information, the eigenvalue problem is solved to obtain the natural frequencies and associated mode shapes of a mooring line in both coordinate systems. By approximating the mooring line as a linear system, the modal superposition approach allows computationally efficient modeling of dynamics in the frequency domain, including estimation of extreme value statistics using Rice’s theory for Gaussian processes. The accuracy of the modal superposition approach is demonstrated through comparison with results from nonlinear time domain simulations using OrcaFlex. This approximate modeling approach is useful for optimizing the design of a mooring system in the preliminary phases of design

A Method to Optimize Geometric Errors of Machine Tool based on SNR Quality Loss Function and Correlation Analysis

Instead improving the accuracy of machine tool by increasing the precision of key components level blindly in the production process, the method of combination of SNR quality loss function and machine tool geometric error correlation analysis to optimize five-axis machine tool geometric errors will be adopted. Firstly, the homogeneous transformation matrix method will be used to build five-axis machine tool geometric error modeling. Secondly, the SNR quality loss function will be used for cost modeling. And then, machine tool accuracy optimal objective function will be established based on the correlation analysis. Finally, ISIGHT combined with MATLAB will be applied to optimize each error. The results show that this method is reasonable and appropriate to relax the range of tolerance values, so as to reduce the manufacturing cost of machine tools

Remarkable nucleation and growth of ultrafine particles from vehicular exhaust

High levels of ultrafine particles (UFPs; diameter of less than 50 nm) are frequently produced from new particle formation under urban conditions, with profound implications on human health, weather, and climate. However, the fundamental mechanisms of new particle formation remain elusive, and few experimental studies have realistically replicated the relevant atmospheric conditions. Previous experimental studies simulated oxidation of one compound or a mixture of a few compounds, and extrapolation of the laboratory results to chemically complex air was uncertain. Here, we show striking formation of UFPs in urban air from combining ambient and chamber measurements. By capturing the ambient conditions (i.e., temperature, relative humidity, sunlight, and the types and abundances of chemical species), we elucidate the roles of existing particles, photochemistry, and synergy of multipollutants in new particle formation. Aerosol nucleation in urban air is limited by existing particles but negligibly by nitrogen oxides. Photooxidation of vehicular exhaust yields abundant precursors, and organics, rather than sulfuric acid or base species, dominate formation of UFPs under urban conditions. Recognition of this source of UFPs is essential to assessing their impacts and developing mitigation policies. Our results imply that reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions is ineffective and can even exacerbate this problem

Remarkable nucleation and growth of ultrafine particles from vehicular exhaust

High levels of ultrafine particles (UFPs; diameter of less than 50 nm) are frequently produced from new particle formation under urban conditions, with profound implications on human health, weather, and climate. However, the fundamental mechanisms of new particle formation remain elusive, and few experimental studies have realistically replicated the relevant atmospheric conditions. Previous experimental studies simulated oxidation of one compound or a mixture of a few compounds, and extrapolation of the laboratory results to chemically complex air was uncertain. Here, we show striking formation of UFPs in urban air from combining ambient and chamber measurements. By capturing the ambient conditions (i.e., temperature, relative humidity, sunlight, and the types and abundances of chemical species), we elucidate the roles of existing particles, photochemistry, and synergy of multipollutants in new particle formation. Aerosol nucleation in urban air is limited by existing particles but negligibly by nitrogen oxides. Photooxidation of vehicular exhaust yields abundant precursors, and organics, rather than sulfuric acid or base species, dominate formation of UFPs under urban conditions. Recognition of this source of UFPs is essential to assessing their impacts and developing mitigation policies. Our results imply that reduction of primary particles or removal of existing particles without simultaneously limiting organics from automobile emissions is ineffective and can even exacerbate this problem

A Novel NADP(H)-Dependent 7alpha-HSDH: Discovery and Construction of Substrate Selectivity Mutant by C-Terminal Truncation

7α-Hydroxysteroid dehydrogenase (7α-HSDH) plays an important role in the biosynthesis of tauroursodeoxycholic acid (TUDCA) using complex substrate chicken bile powder as raw material. However, chicken bile powder contains 4.74% taurocholic acid (TCA), and a new by-product tauroursocholic acid (TUCA) will be produced, having the risk of causing colorectal cancer. Here, we obtained a novel NADP(H)-dependent 7α-HSDH with good thermostability from Ursus thibetanus gut microbiota (named St-2-2). St-2-2 could catalyze taurochenodeoxycholic acid (TCDCA) and TCA with the catalytic activity of 128.13 and 269.39 U/mg, respectively. Interestingly, by a structure-based C-terminal truncation strategy, St-2-2△C10 only remained catalytic activity on TCDCA (14.19 U/mg) and had no activity on TCA. As a result, it can selectively catalyze TCDCA in waste chicken bile powder. MD simulation and structural analysis indicated that enhanced surface hydrophilicity and improved C-terminal rigidity affected the entry and exit of substrates. Hydrogen bond interactions between different subunits and interaction changes in Phe249 of the C-terminal loop inverted the substrate catalytic activity. This is the first report on substrate selectivity of 7α-HSDH by C-terminal truncation strategy and it can be extended to other 7α-HSDHs (J-1-1, S1-a-1)

Bayesian Filtering Multi-Baseline Phase Unwrapping Method Based on a Two-Stage Programming Approach

Phase unwrapping (PU) has been a key step in the processing of interferometric synthetic aperture radar (InSAR) data, and its processing accuracy will directly affect the reconstruction results of digital elevation models (DEMs). The traditional single-baseline (SB) PU must be calculated under continuity assumptions. However, multi-baseline (MB) PU can get rid of the limitation of continuity assumption, so reasonable results can be obtained in regions with large gradient changes. However, the poor noise robustness of MBPU has always been a key problem. To address this issue, we transplant three Bayesian filtering methods with a two-stage programming approach (TSPA), and propose corresponding MBPU models. First, we propose a gradient-estimation method based on the first step of TSPA, and then the corresponding PU model is determined according to different Bayesian filtering. Finally, the wrapped phase can be obtained by unwrapping, one by one, using an effective quality map based on heapsort. These methods can improve the robustness of the MBPU methods. More significantly, this paper establishes a novel TSPA-based Bayesian filtering MBPU framework for the first time. This is of great significance for broadening the research of MBPU. The proposed methods experiments on simulated and real MB InSAR datasets. From the results, we can see that the TSPA-based Bayesian filtering MBPU framework can significantly improve the robustness of the MBPU method

The Effect of Boarding on the Mental Health of Primary School Students in Western Rural China

Based on the panel data of 20,594 fourth- and fifth-grade students in the western provinces A and B in China, this paper analyzed the effect of boarding at school on the mental health of students using a combination of the propensity score matching (PSM) and difference-in-differences (DID) methods. The results showed that boarding had no significant effect on the mental health of students, but the tendency of loneliness among boarding school students was increased. Heterogeneity analysis found that fifth-grade students whose parents had both left home to work were more likely to have poorer mental health when boarding. This paper has essential policy significance for guiding rural primary schools to improve the mental health of boarding school students, especially left-behind children