Hydroxylammonium nitrate (HAN) is a promising green propellant because of its low toxicity, high volumetric specific impulse, and reduced development cost. Electrolytic decomposition of HAN is an efficient approach to prepare it for further ignition and combustion. This paper describes the investigation of a co-electrolysis effect on electrolytic decomposition of HAN-fuel mixtures using stainless steel-platinum (SS-Pt) electrodes. For the first time, different materials were utilized as electrodes to alter the cathodic reaction, which eliminated the inhibition effect and achieved a repeatable and consistent electrolytic decomposition of HAN solution. Urea and methanol were added as fuel components in the HAN-fuel mixtures. When the mass ratio of added urea ≥20%, the electrolytic decomposition of a HAN-urea ternary mixture achieved 67% increment in maximum gas temperature (Tgmax) and 185% increment in overall temperature increasing rate over the benchmark case of HAN solution. The co-electrolysis of urea released additional electrons into the mixtures and enhanced the overall electrolytic decomposition of HAN. In contrast, the addition of methanol did not improve the Tgmax but only increased the overall temperature increasing rate. This work has important implications in the development of an efficient and reliable electrolytic decomposition system of HAN and its mixtures for propulsion applications.
Emotion is a crucial aspect of human health, and emotion recognition systems serve important roles in the development of neurofeedback applications. Most of the emotion recognition methods proposed in previous research take predefined EEG features as input to the classification algorithms. This paper investigates the less studied method of using plain EEG signals as the classifier input, with the residual networks (ResNet) as the classifier of interest. ResNet having excelled in the automated hierarchical feature extraction in raw data domains with vast number of samples (e.g., image processing) is potentially promising in the future as the amount of publicly available EEG databases has been increasing. Architecture of the original ResNet designed for image processing is restructured for optimal performance on EEG signals. The arrangement of convolutional kernel dimension is demonstrated to largely affect the model's performance on EEG signal processing. The study is conducted on the Shanghai Jiao Tong University Emotion EEG Dataset (SEED), with our proposed ResNet18 architecture achieving 93.42% accuracy on the 3-class emotion classification, compared to the original ResNet18 at 87.06% accuracy. Our proposed ResNet18 architecture has also achieved a model parameter reduction of 52.22% from the original ResNet18. We have also compared the importance of different subsets of EEG channels from a total of 62 channels for emotion recognition. The channels placed near the anterior pole of the temporal lobes appeared to be most emotionally relevant. This agrees with the location of emotion-processing brain structures like the insular cortex and amygdala.