Dalian Minzu University

Purpose Low-velocity impacts may cause barely visible internal damage in carbon fiber-reinforced polymer (CFRP) laminates, which can significantly reduce the load-carrying capacity of Type IV hydrogen storage cylinders. This study aims to propose an acoustic-emission (AE) sensing framework to quantitatively evaluate low-velocity impact damage in CFRP laminates relevant to Type IV cylinder structures, and to establish severity grading criteria based on an interpretable scalar indicator. Design/methodology/approach Drop-weight impact tests were conducted from 10–80 J while AE signals were continuously recorded. AE features were extracted using empirical mode decomposition (EMD) and principal component analysis (PCA). A hybrid Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) classifier was trained to identify damage-related AE signals, and wavelet entropy was calculated to quantify impact damage severity. Nonlinear ultrasonic testing was additionally used to validate damage progression. Findings The CNN–LSTM model achieved 93.3% classification accuracy for AE signals associated with matrix cracking, delamination and fiber fracture. Wavelet entropy showed strong correlation with impact damage severity and identified three damage stages. Two critical thresholds were found: wavelet entropy 0.275 (transition to moderate damage) and 0.55 (transition to severe damage), corresponding to impact energies of 40 and 60 J, respectively. Originality/value This study links AE signal characteristics to impact damage severity through an interpretable scalar metric (wavelet entropy) and provides quantitative thresholds (0.275 and 0.55) for three-stage damage grading. It further combines EMD–PCA feature extraction with a CNN–LSTM classifier (93.3% accuracy) to identify damage-related AE signals, and validates damage progression using nonlinear ultrasonic testing. The framework offers a practical sensing strategy for composite pressure-vessel monitoring.

Dual-source heat pump systems combining photovoltaic-thermal (PVT) and air-source technologies have attracted considerable research interest due to their energy complementarity. Based on the climatic characteristics of the Dalian region, this study conducted field measurements and data analysis on a developed dual-source heat pump system incorporating three adaptive operational modes: (1) PVT mode, (2) PVT/air dual-source mode, and (3) photovoltaic (PV)/air-source mode. Compared to Mode (3), Mode (1) achieves a 5.76% higher heating capacity and an 11.56% greater electrical efficiency. Meanwhile, Mode (2) demonstrates a 12.23% increase in heating capacity, and a 9.14% improvement in electrical efficiency relative to Mode (3). A data-driven methodology is provided to quantify the system’s evaporation temperature, the thermal efficiency of PVT mode, and the coefficient of performance (COP) of the PVT heat pump. The economic assessment demonstrates that the proposed dual-source heat pump system achieves a heating cost as low as RMB 0.1125/kWh and a payback period of 6.4 years, indicating favorable economic benefits. This study provides fundamental data and computational methods for the optimized operation of the PVT/air dual-source heat pump.