Effects of Ce concentrations on ignition temperature and surface tension of Mg-9wt.%Al alloy

Magnesium alloys are well known for their excellent properties, but the potential issues with oxidation and burning during melting and casting largely limit its industrial applications. The addition of Ce in magnesium alloys can significantly raise ignition-proof performance and change the structure of the oxide film on the surface of the molten metal as well as the surface tension values. Surface tension is an important physical parameter of the metal melts, and it plays an important role in the formation of surface oxide film. In this present work, the ignition temperature and the surface tension of Mg-9wt.%Al alloy with different Ce concentrations were studied. Surface tensions was measured using the maximum bubble pressure method (MBPM). Ignition temperature was measured using NiCr-NiSi type thermocouples and was monitored and recorded via a WXT-604 desk recording device. The results show that the ignition point of Mg-9wt.%Al alloy can be effectively elevated by adding Ce. The ignition temperature reaches its highest point of 720 ℃ when the addition of Ce is 1wt.%. The surface tension of the molten Mg-9wt.%Al alloy decreases exponentially with the increase of Ce addition at the same temperature. Similarly, the experiment also shows that the surface tension of Mg-9wt.%Al alloy decreases exponentially with the increase of temperature

Cyber-physical system based optimization framework for intelligent powertrain control

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented. Simulation-based parameter optimizations are carried out according to the objective functions. Simulation results show that an electric powertrain with intelligent controller can perform its tasks well under sport, eco, and normal driving modes. The vehicle further improves overall performance in vehicle dynamics, ride comfort, and energy efficiency. The results validate the feasibility and effectiveness of the proposed CPS-based optimization framework, and demonstrate its advantages over a baseline benchmark

Attitude Controller Design with State Constraints for Kinetic Kill Vehicle Based on Barrier Lyapunov Function

An adaptive attitude controller is designed based on Barrier Lyapunov Function (BLF) to meet the state constraints caused by side window detection. Firstly, the attitude controller is designed based on the BLF, but the stabilization function is complex and its time derivative will cause “differential explosion”. Therefore, Finite-time-convergent Differentiator (FD) is used to estimate the first derivative of the stabilization function. If the tracking error is outside the BLF's convergence domain, BLF controller cannot guarantee the error global convergence. Sliding mode controller (SMC) is used to make the system's error converge to set domain, and then the BLF controller could be used to ensure that the output constraint is not violated. Uncertainties and unknown time-varying disturbances usually make the control precision worse and Nonlinear Disturbance Observer (NDO) is designed for estimation and compensation uncertainties and disturbances. The pseudo rate modulator (PSR) is used to shape the continuous control command to pulse or on-off signals to meet the requirements of the thruster. Numerical simulations show that the proposed method can achieve state constraints, pseudo-linear operation, and high accuracy

Identifying the Mechanisms behind the Positive Feedback Loop between Nitrogen Cycling and Algal Blooms in a Shallow Eutrophic Lake

Algal blooms have increased in frequency, intensity, and duration in response to nitrogen (N) cycling in freshwater ecosystems. We conducted a high-resolution sedimentary study of N transformation and its associated microbial activity in Lake Taihu to assess the accumulation rates of the different N fractions in response to algal blooms, aiming to understand the mechanisms of N cycling in lacustrine environments. Downcore nitrification and denitrification processes were measured simultaneously in situ via diffusive gradients in thin-films technique, peeper, and microelectrode devices in a region of intensified algal blooms of shallow lake. The decomposition of different biomasses of algal blooms did not change the main controlling factor on different N fractions in profundal sediment. However, the decomposition of different algal biomasses led to significant differences in the nitrification and denitrification processes at the sediment–water interface (SWI). Low algal biomasses facilitated the classic process of N cycling, with the balanced interaction between nitrification and denitrification. However, the extreme hypoxia under high algal biomasses significantly limited nitrification at the SWI, which in turn, restricted denitrification due to the lack of available substrates. Our high-resolution results combined with estimates of apparent diffusion fluxes of the different N fractions inferred that the lack of substrates for denitrification was the main factor influencing the positive feedback loop between N and eutrophication in freshwater ecosystems. Moreover, this positive feedback can become irreversible without technological intervention

ORDERED POROUS ANODIC ALUMINUM OXIDE FILMS MADE BY TWO-STEP ANODIZATION

Porous Anodic Aluminum Oxide (AAO) films were prepared by two-step anodizing in sulfuric and oxalic acid solutions and observed by transmission electron microscope (TEM) and X-ray diffraction. The results show that the form of AAO film is affected by the varieties and concentrations of electrolyte, anodizing voltage, and the anodizing time; the formation and evolution processes of the AAO film are relative with the anodizing voltage severely, and the appropriate voltage is helpful to the ordering of the holes. The formation of the AAO film could be explained based on the present experiment and some former models.Porous, anodic aluminum oxide, anodizing, two-step anodization

Developmental characteristics of fractures in deep tight sandstone reservoirs in the second Member of the Xujiahe Formation of Zhongjiang Gas Field

The development characteristics, scale and control factors of fractures are the core subjects of reservoir sweet spot prediction. The sandstone reservoir of the TX2 gas reservoir in the Zhongjiang Gas Field is a typical low porosity and low permeability tight reservoir with strong heterogeneity, but relatively high-quality reservoirs can be found in different well areas and well segments. In this paper, taking the second Member of the Xujiahe Formation (TX2) as an example, the control factors of fractures were systemically investigated via core observation, thin section, logging data, and fracture logging identifications. The results show that shear fractures are mainly developed in the cores, and they generally have high filling rate and poor effectiveness; microfractures can be found based on the vitrinite and cast thin section results. The intersection diagram (semi-quantitative) and the principal component and BP comprehensive identification (quantitative) methods can effectively identify different types of fractures. The combined application of principal component and BP comprehensive identification methods results in an 83.3 % fracture identification probability. Finally, we found that the development of fractures in TX2 is comprehensively affected by lithology, rock thickness, porosity, and faults

A Temporal Pool Learning Algorithm Based on Location Awareness

Hierarchical Temporal Memory is a new type of artificial neural network model, which imitates the structure and information processing flow of the human brain. Hierarchical Temporal Memory has strong adaptability and fast learning ability and becomes a hot spot in current research. Hierarchical Temporal Memory obtains and saves the temporal characteristics of input sequences by the temporal pool learning algorithm. However, the current algorithm has some problems such as low learning efficiency and poor learning effect when learning time series data. In this paper, a temporal pool learning algorithm based on location awareness is proposed. The cell selection rules based on location awareness and the dendritic updating rules based on adjacent inputs are designed to improve the learning efficiency and effect of the algorithm. Through the algorithm prototype, three different datasets are used to test and analyze the algorithm performance. The experimental results verify that the algorithm can quickly obtain the complete characteristics of the input sequence. No matter whether there are similar segments in the sequence, the proposed algorithm has higher prediction recall and precision than the existing algorithms

Design Mechanism and Property of the Novel Fluorescent Probes for the Identification of Microthrix Parvicella In Situ

In this study, two novel fluorescent probes, probe A and probe B were designed, synthesized and characterized, based on Microthrix parvicella (M. parvicella) preferring to utilize long-chain fatty acid (LCFA), for the labeling of M. parvicella in activated sludge. The molecular structure of probe A and probe B include long-chain alkane and LCFA, respectively. The results indicated that probe A and probe B had a large stokes shift of 118 nm and 120 nm and high quantum yield of 0.1043 and 0.1058, respectively, which were significantly helpful for the fluorescent labeling. As probe A was more stable than probe B in activated sludge, and the fluorescence intensity keep stable during 24 h, probe A was more suitable for labeling M. parvicella in situ. In addition, through the Image Pro Plus 6 (IPP 6) analysis, a quantitative relationship was established between sludge volume index (SVI) and integral optical density (IOD) of the labeled M. parvicella in activated sludge samples. The relationship between IOD and SVI conforms to Logistic curve (R2 = 0.94)