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This paper is devoted to the study of bubble dissolution of the melted polymer in rotomoulding process. Two types of linear low-density polyethylene (LLDPE) having the same properties, that is, melt index, melting point, crystallinity, viscosity, comonomer type, but different lamellae thickness were investigated. The results showed that bubbles in rotomoulded parts impaired the low-temperature impact and LLDPE with thicker lamellae showed better bubble dissolution. Furthermore, as the annealing treatment was applied to the LLDPE powders, lamellae thickness distribution derived from Thomson-Gibbs equation coincided with bubble observation, suggesting that the improvement in bubble dissolution is reasonably attributable to annealing which makes the lamellae of treated LLDPE thick. In addition, the rheological measurements showed that modulus was built-up with time indicating intermediate disentanglements are formed. Correlation between lamellae thickness distribution derived from differential scanning calorimetry data and experimental rheological result suggests that thicker lamellae is related to enhancing bubble dissolution. Consequently, it is reasonable to consider that the thicker lamellae is quite benefit for bubble dissolution, which could be well explained by the free volume according to free volume theory. LLDPE having thicker lamellae contains more free volume resulting in better bubble dissolution. KEYWORDS bubble dissolution, disentanglement, lamellae thickness, rotomoulding, 1 | INTRODUCTION Rotational molding, also known as rotomoulding or rotocasting, is a process for producing hollow plastic products. It involves tumbling a thermoplastic powder and heating the mold in [...]

Wave particle interactions are very important to understand the intricate evolution of the Earth's radiation belt electrons. Kinetic simulations, in terms of solving the Fokker‐Planck equation based on the quasilinear theory, are usually used to simulate the radiation belt electron dynamic evolution. However, the global wave and plasma density distributions adopted in the kinetic simulations are very difficult to be directly obtained by satellites. Here we present a new model, by integrating the machine learning technique and kinetic simulations, to analyze the spatiotemporal evolution of radiation belt electrons scattered by lower band chorus (LBC). Compared to the observations, our integrated model produces effectively the global distribution of plasmapause location, plasma density, and LBC intensity, and assesses quantitatively the scattering effect driven by LBC waves at different magnetic local times (MLT), L‐shell (the Mcllwain L‐parameter), and time. Incorporating the effect of radiation electron drift, we further use the 2‐D Fokker‐Planck equation to simulate the variations of electron phase space density in different MLT sectors at a fixed L, and find that the integrated model replicates reasonably the multi‐MeV electron acceleration at L = 4.5 during the period from the main phase to the early recovery phase of the storm. Our results demonstrate that such an integrated model, on basis of a combination of the machine learning technique and kinetic simulations, provides valuable means for improved understanding of the global dynamic evolution of the Earth's radiation belt electrons. Plain Language Summary: Understanding the complex behavior of electrons in Earth's radiation belts, including acceleration, loss, and transport, is crucial for space weather research. This study introduces an integrated model that combines machine learning technique and kinetic simulation to quantitatively analyze how lower band chorus (LBC) waves influences electrons during the 17 March 2015 storm. Through comparisons with observations, our results indicate that the integrated model effectively reproduces global distribution of plasmapause location, plasma density, and LBC intensity, and enables a quantitative assessment of how LBC affects electrons over space and time, closely matching observations during the main phase to the early recovery phase of the storm. Incorporating electron drift motion improves the spatial resolution of simulated electron behavior, particularly impacting the simulation of the tens of keV electron behavior during periods of strong LBC intensity. This integrated approach can enhance our understanding of the radiation belt electron dynamics. Key Points: A numerical model integrating machine learning technique and kinetic simulations is developed to study the wave‐driven electron scatteringMachine learning technique provides inputs for kinetic simulations, allowing the wave‐driven evolution to be estimatedThe integrated model reproduces effectively the multi‐MeV electron acceleration during a strong geomagnetic storm [ABSTRACT FROM AUTHOR]

Solar wind is an intermediary in energy transfer from the Sun into the Earth's magnetosphere, and is considered as a decisive driver of energetic electron dynamics at the geosynchronous orbit (GEO). Based on machine learning technology, several models driven by solar wind parameters have been established to predict GEO electron fluxes. However, the relative contributions of different solar wind parameters on GEO electron fluxes are still unclear. Recently, a feature attribution method, Deep SHapley Additive exPlanations (Deep SHAP) is proposed to open black boxes of machine learning models. In this study, we use the Deep SHAP method to quantify contributions of different solar wind parameters with an artificial neural network (ANN) model. Backpropagating the prediction results of this ANN model from 2011 to 2020, SHAP values for four solar wind parameters (interplanetary magnetic field (IMF) BZ, solar wind speed, solar wind dynamic pressure, and proton density) are calculated and comprehensively analyzed. The results suggest that solar wind speed with a lag of 1 day is the most important driver. We further investigate relative roles of different parameters in three specific electron fluxes variation events (corresponding to electron fluxes reaching a local maximum, a local minimum, and unchanged, respectively). The results suggest that high solar wind speed and southward IMF BZ (high dynamic pressures) facilitate increases (decreases) of electron fluxes. These findings help reveal the underlying physical mechanisms of GEO electron dynamics and help develop more accurate prediction models for GEO electron fluxes. Plain Language Summary: Solar wind, which transfers energy from the Sun to the Earth's magnetosphere, plays a crucial role in the dynamics of energetic electrons at geosynchronous orbit (GEO). The process is such complex that hundreds of studies have tried to clarify the correlation between solar wind and the dynamics of the GEO electron fluxes but haven't gained a clear conclusion yet. In this study, we reveal the influences of solar wind parameters on electron fluxes by opening the black box of a machine learning model based on a feature attribution method. We analyze the importance of different solar wind parameters statistically. The results show that the solar wind speed on previous day is the most important parameter for variations of GEO electron fluxes. We also investigate the influence of different solar wind parameters on electron fluxes during three specific moments. Our results indicate that high solar wind speed and southward IMF BZ (high dynamic pressures) are favorable for the increases (decreases) of electron fluxes. These findings can help us understand the key factors that affect energetic electron behaviors and contribute to the development of more accurate and reliable prediction models in the future. Key Points: The Deep SHapley Additive exPlanations method is used to quantify contributions of different solar wind parameters on dynamics of energetic electrons at geosynchronous orbit (GEO)Solar wind speed with a lag of 1 day is the most important factor for >800 keV electron flux variations at GEOHigh solar wind speed and southward interplanetary magnetic field BZ favor increases of energetic electron fluxes while high dynamic pressure inhibits the fluxes [ABSTRACT FROM AUTHOR]