Mechanical Properties and Microstructure of Bionic Non-Smooth Stainless Steel Surface by Laser Multiple Processing

Laser multiple processing, i.e. laser surface texturing and then Laser Shock Processing (LSP), is a new surface processing technology for the preparation of bionic non-smooth surfaces. Based on engineering bionics, samples of bionic non-smooth surfaces of stainless steel 0Crl 8Ni9 were manufactured in the form of reseau structure by laser multiple processing. The mechanical properties (including microhardness, residual stress, surface roughness) and microstructure of the samples treated by laser multiple processing were compared with those of the samples without LSP The results show that the mechanical properties of these samples by laser multiple processing were clearly improved in comparison with those of the samples without LSP The mechanisms underlying the improved surface microhardness and surface residual stress were analyzed, and the relations between hardness, comnressive residual stress and roughness were also presented.

Unique microstructure and property of a 2024 aluminum alloy subjected to upsetting extrusion multiple processing

The microstructure and hardness of a 2024 aluminum alloy subjected tomulti-pass upsetting extrusion at ambient temperature were studied. Experimental results indicatedthat with the number of upsetting extrusion passes increasing, the grains of the alloy are graduallyrefined and the hardness increases correspondingly. After ten passes of upsetting extrusionprocessing, the grain size decreases to less than 200 nm in diameter and the sample maintains itsoriginal shape, while the hardness is double owing to equal-axial ultrafine grains and workhardening effect caused by large plastic deformation.

CHANNEL ERROR CORRECTION FOR WIDEBAND SAR-A JOINT MULTIPLE SUBPULSES PROCESSING METHOD

A novel method,referred to as joint multiple subpulses processing,is developed to calibrate the nonideal transfer function of radio frequency front-end and I/Q imbalance in quadrature modulate/demodulate systems simultaneously,which frequently occur in wideband Synthetic Aperture Radar(SAR) systems.Based on the time-frequency relation of the chirp signal and the analyses of the channel errors in wideband SAR,joint multiple subpulses processing method is adopted to separate the image frequency component due to the I/Q channel error.Then,the complete description of the channel error is acquired for building the correction function,which is used to correct the radar raw echo in frequency domain.The validity and capability of this method are demonstrated by the experiments of the channel error correction on the high resolution SAR system with the effective bandwidth of 500 MHz.

Direct Observation of Carrier Transportation Process in InGaAs/GaAs Multiple Quantum Wells Used for Solar Cells and Photodetectors

The resonant excitation is used to generate photo-excited carriers in quantum wells to observe the process of the carriers transportation by comparing the photoluminescence results between quantum wells with and without a p-n junction. It is observed directly in experiment that most of the photo-excited carriers in quantum wells with a p-n junction escape from quantum wells and form photoeurrent rather than relax to the ground state of the quantum wells. The photo absorption coei^cient of multiple quantum wells is also enhanced by a p-n junction. The results pave a novel way for solar cells and photodetectors making use of low-dimensional structure.

RATE OF CONVERGENCE FOR MULTIPLE CHANGEPOINTS ESTIMATION OF MOVING-AVERAGE PROCESSES

In this paper, the least square estimator in the problem of multiple change points estimation is studied. Here, the moving-average processes of ALNQD sequence in the mean shifts are discussed. When the number of change points is known, the rate of convergence of change-points estimation is derived. The result is also true for p-mixing, φ-mixing, a-mixing, associated and negatively associated sequences under suitable conditions.

A smart productivity evaluation method for shale gas wells based on 3D fractal fracture network model

The generation method of three-dimensional fractal discrete fracture network(FDFN)based on multiplicative cascade process was developed.The complex multi-scale fracture system in shale after fracturing was characterized by coupling the artificial fracture model and the natural fracture model.Based on an assisted history matching(AHM)using multiple-proxy-based Markov chain Monte Carlo algorithm(MCMC),an embedded discrete fracture modeling(EDFM)incorporated with reservoir simulator was used to predict productivity of shale gas well.When using the natural fracture generation method,the distribution of natural fracture network can be controlled by fractal parameters,and the natural fracture network generated coupling with artificial fractures can characterize the complex system of different-scale fractures in shale after fracturing.The EDFM,with fewer grids and less computation time consumption,can characterize the attributes of natural fractures and artificial fractures flexibly,and simulate the details of mass transfer between matrix cells and fractures while reducing computation significantly.The combination of AMH and EDFM can lower the uncertainty of reservoir and fracture parameters,and realize effective inversion of key reservoir and fracture parameters and the productivity forecast of shale gas wells.Application demonstrates the results from the proposed productivity prediction model integrating FDFN,EDFM and AHM have high credibility.

Identification of nonlinear process described by neural fuzzy Hammerstein-Wiener model using multi-signal processing

In this study,a novel approach for nonlinear process identification via neural fuzzy-based Hammerstein-Wiener model with process disturbance by means of multi-signal processing is presented.The Hammerstein-Wiener model consists of three blocks where a dynamic linear block is sandwiched between two static nonlinear blocks.Multi-signal sources are designed for achieving identification separation of the Hammerstein-Wiener process.The correlation analysis theory is utilized for estimating unknown parameters of output nonlinearity and linear block using separable signals,thus the interference of process disturbance is solved.Furthermore,the immeasurable intermediate variable and immeasurable noise term in identification model is taken over by auxiliary model output and estimate residuals,and then auxiliary model-based recursive extended least squares parameter estimation algorithm is derived to calculate parameters of the input nonlinearity and noise model.Finally,convergence analysis of the suggested identification scheme is derived using stochastic process theory.The simulation results indicate that proposed identification approach yields high identification accuracy and has good robustness.

Multifractal modeling of the production of concentrated sugar syrup crystal

High quality, concentrated sugar syrup crystal is produced in a critical step in cane sugar production： the clarification process. It is characterized by two variables： the color of the produced sugar and its clarity degree. We show that the temporal variations of these variables follow power-law distributions and can be well modeled by multiplicative cascade multifractal processes. These interesting properties suggest that the degradation in color and clarity degree has a systemwide cause. In particular, the cascade multifractal model suggests that the degradation in color and clarity degree can be equivalently accounted for by the initial ＂impurities＂ in the sugarcane. Hence, more effective cleaning of the sugarcane before the clarification stage may lead to substantial improvement in the effect of clarification.

A Numerical Study of the Effect of Multiple Pouring on Macrosegregation in a 438-Ton Steel Ingot

In present paper, a ladle-tundish-mold CFD model and a macrosegregation model were utilized to investigate the effects of the multiple pouring （MP） process on the macrosegregation in a 438-ton steel ingot. Firstly, the model was partially proved as compared to the measured carbon distributions along the transverse sections in the riser of ingot. Then, the comparison between the single pouring （SP） and MP process has been carried out to study their influences on the macrosegregation in ingot. Besides, the predicted macrosegregation results in MP process which introduced the improved riser fixed with an insulating sleeve were compared with that in traditional MP process. The traditional MP process leads to certain favorable initial carbon distribution in the mold, which has some favorable influence on suppressing the positive segregation in ingot. The holding time of the low carbon in the riser is the main factor to suppress the positive segregation in ingot. Improved insulating sleeve can prolong the holding time of the low carbon in the riser and release the positive segregation in the riser of ingot. Improvement of the insulating effect of the riser is an efficient method to control macrosegregation in large steel ingot.

Increasing Momentum-Like Factors:A Method for Reducing Training Errors on Multiple GPUs

In distributed training,increasing batch size can improve parallelism,but it can also bring many difficulties to the training process and cause training errors.In this work,we investigate the occurrence of training errors in theory and train ResNet-50 on CIFAR-10 by using Stochastic Gradient Descent(SGD) and Adaptive moment estimation(Adam) while keeping the total batch size in the parameter server constant and lowering the batch size on each Graphics Processing Unit(GPU).A new method that considers momentum to eliminate training errors in distributed training is proposed.We define a Momentum-like Factor(MF) to represent the influence of former gradients on parameter updates in each iteration.Then,we modify the MF values and conduct experiments to explore how different MF values influence the training performance based on SGD,Adam,and Nesterov accelerated gradient.Experimental results reveal that increasing MFs is a reliable method for reducing training errors in distributed training.The analysis of convergent conditions in distributed training with consideration of a large batch size and multiple GPUs is presented in this paper.

A Multiple Feature Approach for Disorder Normalization in Clinical Notes

In this paper we propose a multiple feature approach for the normalization task which can map each disorder mention in the text to a unique unified medical language system（UMLS）concept unique identifier（CUI）. We develop a two-step method to acquire a list of candidate CUIs and their associated preferred names using UMLS API and to choose the closest CUI by calculating the similarity between the input disorder mention and each candidate. The similarity calculation step is formulated as a classification problem and multiple features（string features,ranking features,similarity features,and contextual features） are used to normalize the disorder mentions. The results show that the multiple feature approach improves the accuracy of the normalization task from 32.99% to 67.08% compared with the Meta Map baseline.

A compositional method to model dependent failure behavior based on PoF models

In this paper, a new method is developed to model dependent failure behavior among failure mechanisms. Unlike the existing methods, the developed method models the root cause of the dependency explicitly, so that a deterministic model, rather than a probabilistic one, can be established. Three steps comprise the developed method. First, physics-of-failure（PoF） models are utilized to model each failure mechanism. Then, interactions among failure mechanisms are modeled as a combination of three basic relations, competition, superposition and coupling. This is the reason why the method is referred to as ＂compositional method＂. Finally, the PoF models and the interaction model are combined to develop a deterministic model of the dependent failure behavior. As a demonstration, the method is applied on an actual spool and the developed failure behavior model is validated by a wear test. The result demonstrates that the compositional method is an effective way to model dependent failure behavior.

Quantitative simulation and prediction of extreme geological events

The systematic study of extreme geological events(such as plate collision and subduction, extreme cold and extreme hot events, biological extinction and revival, earthquakes, volcanoes, mineralization, and oil accumulation) that occurred during the evolution of the earth is essential not only for understanding the “abrupt changes in the evolution of the earth”, but also for an in-depth understanding of the co-evolution of material-life-environment of the livable earth. However, due to the temporal and spatial anomalies and complexity of extreme geological events, classical mathematical models cannot be effectively applied to quantitively describe such events. After comparative studies of many types of geological events, the author found that such extreme geological events often depict “singular” characteristics(abnormal accumulation or depletion of matter or massive release or absorption of energy in a small space or time interval). On this basis, the author proposes a unified definition of extreme geological events, a new concept of “fractal density” and a “local singularity analysis” method for quantitative description and modeling of extreme geological events. Applications of these methods to several types of extreme geological events have demonstrated that the singularity theory and methods developed in the current research can be used as general approaches for the characterization, simulation, and prediction of geological events.