An experimental investigation was carried out on the influence of equivalence ratio on gasoline-air mixture deflagration in a closed vessel. High-speed flame images, flame illuminance, illumination duration, overpressure and reaction produces were recorded for equivalence ratios ranging between the flammable limits. Efforts were made to explore the potential relationships between the visual character presented on the flame images and the deflagration parameters, e.g., the equivalence ratio, the flame duration, and the maximum overpressure. Results show that the gasoline-air mixture presents a smooth spherical flame with lean fuel, a spherical cellular flame with rich fuel, and a curled flocculent flame with very rich fuel. The three flame patterns could be quantitatively identified by calculating the edge pixel amounts and the average b* value (in a Lab color space) of the flame images. The maximum flame illuminance, illumination duration, reaction products, rate of pressure rise, and maximum pressure showed a discriminating value and variation trend versus equivalence ratio under different flame patterns. The rich flame could be considered as the most dangerous flame regime among the three because of its fastest pressure rising speed and highest maximum overpressure. The result is of value to the fundamental research as well as the video-based flame detecting technology.

To overcome the temperature effect of NaI(Tl) detectors for energy spectrometry without additional hardware, a new correction method was put forward based on pulse deconvolution, trapezoidal shaping and amplitude correction, named DTSAC. To verify this method, actual pulses from a NaI(Tl)-PMT detector were measured at various temperatures from −20 °C to 50 °C. Pulse processing and spectrum synthesis showed that the position drift of the ¹³⁷Cs 662 keV peak was less than 3 keV, and the corresponding resolution at 662 keV of the sum spectra ranged from 6.91% to 10.60% with the trapezoidal width set from 1000 ns to 100 ns. The DTSAC method corrects the temperature effect via pulse processing, and needs no reference peak, reference spectrum or additional circuits. The method solves the problem of correction of pulse shape and pulse amplitude at the same time, and can be used even at a high counting rate.

To overcome the temperature effect of NaI(Tl) detectors for energy spectrometry without additional hardware, a new correction method was put forward based on pulse deconvolution, trapezoidal shaping and amplitude correction, named DTSAC. To verify this method, actual pulses from a NaI(Tl)-PMT detector were measured at different temperatures from -20℃ to 50℃. Pulse processing and spectrum synthesis showed that the position drift of the 662 keV peak was less than 3 keV, and the corresponding resolution at 662 keV of the sum spectra ranges from 6.9% to 10.60% with trapezoidal width set as 1000 ns to 100 ns. The DTSAC method corrects temperature effect by pulse processing, and need no reference peak, reference spectrum and additional circuits. The method solves the problem of correction of pulse shape and pulse amplitude at the same time, and can be used even at high counting rate.

Accurate radionuclide identification is critical for nuclear safety and environmental radiation monitoring. To integrate the guiding effect of peak morphology features and the nonlinear fitting capability of data-driven models, this paper proposes a Physics-Enhanced Module (PEM). This module adopts the Laplacian of Gaussian (LoG) operator to decompose input gamma spectra and generate auxiliary features representing peak responses, which are concatenated with the original spectrum as an additional channel to inject peak position information into neural networks. Small-sample comparative experiments are conducted on a multi-label nuclide identification dataset covering 16 radionuclides, where PEM-ResNet and PEM-DenseNet are compared against vanilla ResNet and DenseNet baseline models. Experimental results demonstrate that all physics-enhanced networks outperform their corresponding baselines across all evaluation metrics. Consistent performance improvements observed on two mainstream convolutional architectures verify that the PEM effectively mitigates performance degradation induced by limited training samples. Further analysis reveals that the PEM provides the model with clearly identifiable peak response features for discrimination tasks. The integrated framework synergizes the fitting strengths of data-driven learning and the regularization merits of the fixed preprocessing module, thereby substantially improving the reliability and robustness of nuclide identification for nuclear energy applications.