It is economical and efficient to use existing natural gas pipelines to transport hydrogen. The fast and accurate prediction of mixing uniformity of hydrogen injection in natural gas pipelines is important for the safety of pipeline transportation and downstream end users. In this study, the computational fluid dynamics (CFD) method was used to investigate the hydrogen injection process in a T-junction natural gas pipeline. The coefficient of variation (COV) of a hydrogen concentration on a pipeline cross section was used to quantitatively characterize the mixing uniformity of hydrogen and natural gas. To quickly and accurately predict the COV, a deep neural network (DNN) model was constructed based on CFD simulation data, and the main influencing factors of the COV including flow velocity, hydrogen blending ratio, gas temperature, flow distance, and pipeline diameter ratio were taken as input nodes of the DNN model. In the model training process, the effects of various parameters on the prediction accuracy of the DNN model were studied, and an accurate DNN architecture was constructed with an average error of 4.53% for predicting the COV. The computational efficiency of the established DNN model was also at least two orders of magnitude faster than that of the CFD simulations for predicting the COV.

This work carried out liquid–solid two-phase jet experiments and simulations to study the erosion behavior of 304 stainless steel at 30° impingement. The single-phase impinging jet was simulated using dense grid by one-way coupling of solid phase due to its dilute distribution. The simulation results agreed well with experiments. It was found that after impinging particle attrition occurred and particles became round with decreasing length-ratio and particle breakage occurred along the “long” direction. Both experiment and simulations found that the erosion generated on the sample could be divided into three regions that were nominated as stagnant region, cutting transition region and wall jet region. Most particle–wall impacts were found to occur in the cutting transition region and the wall jet region. In the cutting transition region, holes and lip-shaped hogbacks were generated in the same direction as the flow imping. In the wall jet region, furrows and grooves were generated. The averaged grooves depth tended to become constant with the progress of impinging and reach the steady state of erosion in the end. In addition, it was found that impinging effect increased erosion and anti-wear rate. Graphic abstract

On-line monitoring of crystal size in the crystallization process is crucial to many pharmaceutical and fine-chemical industrial applications. In this paper, a novel method is proposed for the on-line monitoring of the cooling crystallization process of L-glutamic acid (LGA) using electrical impedance spectroscopy (EIS). The EIS method can be used to monitor the growth of crystal particles relying on the presence of an electrical double layer on the charged particle surface and the polarization of double layer under the excitation of alternating electrical field. The electrical impedance spectra and crystal size were measured on-line simultaneously by an impedance analyzer and focused beam reflectance measurement (FBRM), respectively. The impedance spectra were analyzed using the equivalent circuit model and the equivalent circuit elements in the model can be obtained by fitting the experimental data. Two equivalent circuit elements, including capacitance ( C ₂ ) and resistance ( R ₂ ) from the dielectric polarization of the LGA solution and crystal particle/solution interface, are in relation with the crystal size. The mathematical relationship between the crystal size and the equivalent circuit elements can be obtained by a non-linear fitting method. The function can be used to predict the change of crystal size during the crystallization process.

As the prevalence of respiratory diseases continues to rise, inhalation therapy has emerged as a crucial method for their treatment. The effective transmission of medications within the respiratory tract is vital to achieve therapeutic outcomes. Given that most inhaled particles carry electrostatic charges, understanding the electrostatic effect on particle behavior in bifurcated tubes is of significant importance. This work combined Large Eddy Simulation-Lagrangian particle tracking (LES-LPT) technology to simulate particle behavior with three particle sizes (10, 20, and 50 μm) from G2 to G3 (“G” stands for generation) in bifurcated tubes, either with or without electrostatics, under typical human physiological conditions (Re = 1036). The results indicate that the electrostatic force has a significant effect on particle behavior in bifurcated tubes, which increases with particle size. Within the bifurcated tubes, the electrostatic force enhances particle movement in alignment with the secondary flow as well as intensifies the interaction of particles with local turbulent vortices and promotes particle dispersion rather than agglomeration. On the other hand, the distribution of the electrostatic field is influenced by particle behavior. Higher particle concentration presents stronger electrostatic strength, which increases with particle size. Therefore, it can be concluded that the electrostatic interactions among particles can prevent particles from aggregating and enhance the efficiency of inhalation therapy.