Neural network modeling for dynamic pulsed GTAW process with wire filler based on MATLAB

Double-sided weld pool shapes were determined by multiple welding parameters and wire feed parameters during pulsed GTAW with wire filler. Aiming at such a system with multiple inputs and outputs, an effective modeling method, consisting of the impulse signal design, model structure and parameter identification and verification, was developed based on MATLAB software. Then, dynamic neural network models, TDNNM (Topside dynamic neural network model) and BHDNNM (Backside width and topside height dynamic neural network model), were established to predict double-sided shape parameters of the weld pool. The characteristic relationship of the welding process was simulated and analyzed with the models.

Numerical modeling and dynamic analysis of the 2017 Xinmo landslide in Maoxian County, China

A catastrophic landslide occurred at Xinmo village in Maoxian County, Sichuan Province,China, on June 24, 2017. A 2.87×106 m3 rock mass collapsed and entrained the surface soil layer along the landslide path. Eighty-three people were killed or went missing and more than 103 houses were destroyed. In this paper, the geological conditions of the landslide are analyzed via field investigation and high-resolution imagery. The dynamic process and runout characteristics of the landslide are numerically analyzed using a depth-integrated continuum method and Mac Cormack-TVD finite difference algorithm.Computational results show that the evaluated area of the danger zone matchs well with the results of field investigation. It is worth noting that soil sprayed by the high-speed blast needs to be taken into account for such kind of large high-locality landslide. The maximum velocity is about 55 m/s, which is consistent with most cases. In addition, the potential danger zone of an unstable block is evaluated. The potential risk area evaluated by the efficient depthintegrated continuum method could play a significant role in disaster prevention and secondary hazard avoidance during rescue operations.

Dynamic soft sensor development based on Gaussian mixture regression for fermentation processes

The dynamic soft sensor based on a single Gaussian process regression(GPR) model has been developed in fermentation processes.However,limitations of single regression models,for multiphase/multimode fermentation processes,may result in large prediction errors and complexity of the soft sensor.Therefore,a dynamic soft sensor based on Gaussian mixture regression(GMR) was proposed to overcome the problems.Two structure parameters,the number of Gaussian components and the order of the model,are crucial to the soft sensor model.To achieve a simple and effective soft sensor,an iterative strategy was proposed to optimize the two structure parameters synchronously.For the aim of comparisons,the proposed dynamic GMR soft sensor and the existing dynamic GPR soft sensor were both investigated to estimate biomass concentration in a Penicillin simulation process and an industrial Erythromycin fermentation process.Results show that the proposed dynamic GMR soft sensor has higher prediction accuracy and is more suitable for dynamic multiphase/multimode fermentation processes.

Influence of core sand properties on flow dynamics of core shooting process based on experiment and multiphase simulation

The influence of core sand properties on flow dynamics was investigated synchronously with various core sands, transparent core-box and high-speed camera. To confirm whether the core shooting process has significant turbulence, the flow pattern of sand particles in the shooting head and core box was reproduced with colored core sands. By incorporating the kinetic theory of granular flow(KTGF), kinetic-frictional constitutive correlation and turbulence model, a two-fluid model(TFM) was established to study the flow dynamics of the core shooting process. Two-fluid model(TFM) simulations were then performed and a areasonable agreement was achieved between the simulation and experimental results. Based on the experimental and simulation results, the effects of turbulence, sand density, sand diameter and binder ratio were analyzed in terms of filling process, sand volume fraction(αs) and sand velocity(Vs).

Hot deformation characteristics of as-cast high-Cr ultra-super-critical rotor steel with columnar grains

Isothermal hot compression tests of as-cast high-Cr ultra-super-critical（USC） rotor steel with columnar grains perpendicular to the compression direction were carried out in the temperature range from 950 to 1250°C at strain rates ranging from 0.001 to 1 s^（-1）. The softening mechanism was dynamic recovery（DRV） at 950°C and the strain rate of 1 s^（-1）, whereas it was dynamic recrystallization（DRX） under the other conditions. A modified constitutive equation based on the Arrhenius model with strain compensation reasonably predicted the flow stress under various deformation conditions, and the activation energy was calculated to be 643.92 kJ ×mol^（-1）. The critical stresses of dynamic recrystallization under different conditions were determined from the work-hardening rate（θ）–flow stress（σ） and-θ/σ–σ curves. The optimum processing parameters via analysis of the processing map and the softening mechanism were determined to be a deformation temperature range from 1100 to 1200°C and a strain-rate range from 0.001 to 0.08 s^（-1）, with a power dissipation efficiency η greater than 31%.