The interfacial interactions between inorganics and polymer matrix have important effects on the properties of nano-composites. A commercially available nano-SiO₂ was modified by surface pretreatment with sulfonated polyethersulfone, which is a typical polyethersulfone (PES) derivative, and served as a macromolecular modifier in this paper to fabricate PES/SiO₂ nano-composite films. The modified SiO₂ phase was well dispersed in polymer matrix due to its unique structure and the satisfactory interfacial interaction between nano-particles and the PES matrix. Compared with pristine SiO₂ as a ceramic filler, there was noticeable improvement in the transmission of light when modified SiO₂ was used. Effects of surface modification on thermal stability were also studied by thermo-gravimetric analysis.

The casting quality of the coolant pump casing of the nuclear power plant reactor is directly related to the operational reliability and safety of the nuclear main pump, and plays a key role in the integrity of the pressure-bearing boundary of the reactor primary loop. In this paper, aiming at the low impact performance of the sample during the casting process of the main pump casing of a nuclear power plant, through using failure analysis tools like fishbone diagram from multiple dimensions such as material selection, design and technology, melting analysis, pouring process, riser design, and heat treatment process, and combining with metal macro-fracture analysis and micro-electron microscopy scanning methods for cause analysis, finally, it was found that the basic reason for the low impact performance of the pump shell is that the secondary inclusions appear on the fracture of the sample during the solidification of the molten steel. Using test-retest inspection and finite element mechanics simulation analysis, the comprehensive evaluation of the impact performance of the sample was obtain, which provides an effective solution for the analysis and evaluation of casting inclusions in water pumps of nuclear power plants, and also provides an important reference for the structural optimization and equipment research and development of water pump equipment of nuclear power plants.

Silicone rubber is extensively used in engineering applications due to its toughness and impact resistance; however, traditional characterisation methods fail to capture its nonlinear deformation characterisation and triaxial mechanical behaviour. To address this, we derived a constitutive model within the framework of continuum mechanics that assumes a condition of near incompressibility and conducted uniaxial, planar, and equibiaxial tension tests to fit the model parameters. Through systematic analysis of triaxial mechanical responses under these three loading modes, we determined the material’s nonlinear large-deformation behaviour and sensitivity to the biaxiality ratio. Comparative analyses with classical hyperelastic models show that the proposed model achieves a good balance between the number of parameters and fitting accuracy. After the parameter-fitting process, we performed finite element simulations of the three loading modes. The simulation results show good agreement with experimental data in terms of deformation patterns and stress–strain curves. This study provides a novel theoretical tool for evaluating the mechanical properties and structural designs of soft materials.

Accurate calibration of the Preston–Tonks–Wallace (PTW) model is a nonlinear, multimodal optimization problem. Root mean square error (RMSE) is the sole optimization objective, with average absolute relative error (AARE) used only as a diagnostic. The primary comparison jointly calibrates one seven-parameter vector across all temperatures within each fixed strain-rate group; the 0.1, 3000, and 5000 s⁻¹ groups are calibrated separately. A secondary transfer assessment imposes the stricter constraint of one unchanged vector across all 11 temperature-strain-rate conditions. The adaptive GA-BO strategy combines diversity-dependent mutation, periodic and stagnation safeguards, elite-envelope Bayesian optimization (BO), and BO-to-population reinjection. Genetic algorithm (GA), BO, and GA-BO each receive exactly 1000 constitutive-model evaluations and 20 paired seeds. In the primary comparison, GA-BO gives median RMSE values of 8.06, 7.94, and 21.69 MPa and significantly improves on GA at all three rates, while remaining statistically comparable to BO and requiring substantially less serial time in the present implementation. The shared-vector assessment gives a GA-BO median global RMSE of 45.54 MPa and quantifies the accuracy-transfer trade-off created by enforcing one vector over the full rate range. Bootstrap, held-out-temperature, ablation, and one-factor setting analyses further quantify parameter coupling, calibration-transfer uncertainty, component contributions, and setting robustness. The resulting workflow therefore improves the accuracy-efficiency balance of reproducible PTW calibration while explicitly reporting the parameter-transfer cost across rate regimes.