Yun Huang

In the search for low-carbon alternatives to natural gas, Ammonia has received considerable attention, although its low reactivity often requires hydrogen enrichment to achieve stable lean combustion. This benefit comes with a tradeoff, as greater hydrogen use reintroduces storage, handling, and cost burdens that Ammonia is intended to alleviate. This work examines trace carbon nanoparticle seeding as a complementary pathway for extending lean Ammonia combustion and investigates the associated flame-front restructuring. Premixed 100% Ammonia and 80% Ammonia 20% Hydrogen flames were stabilized on a low-swirl burner with and without Acetylene Black seeding at 0.01 mg/s. Non-dimensional characterization, lean-blowoff measurements, high-speed imaging, and OH planar laser-induced fluorescence were combined to resolve changes from global stability to local flame geometry. Seeding shifted the stable lean limit of pure Ammonia from an equivalence ratio of 0.85 to 0.81 and that of the Ammonia Hydrogen blend from 0.49 to 0.46, while hydrogen enrichment remained the dominant stabilizing influence. Curvature probability density function peaks increased by 6.9–15.8%, exceeding the estimated uncertainty across all investigated conditions, while curvature variance decreased by up to 11.9%, with the larger reductions clearly resolved. Changes in average absolute radius of curvature were smaller, indicating that particle seeding primarily altered the distribution and variability of flame-front geometry rather than imposing a large shift in characteristic curvature. Collectively, the results support nanoparticle seeding as a complementary stabilization approach accompanied by measurable flame-front restructuring, with potential to reduce reliance on hydrogen enrichment.

Thermal energy storage based on phase change material (PCM) is recognised as a promising technology for efficient waste heat recovery and carbon emission reduction. However, achieving high-performance thermal discharging remains a key challenge for practical deployment. The complex nonlinear dynamics of PCM storage systems and the strong dependence on high-quality data limit the reliability and industrial applicability of model predictive control and machine learning–based control approaches. This study proposed an industry-oriented control solution based on an advanced Proportional–Integral (PI) controller tuned using nature-inspired Artificial Intelligence (AI) algorithms. A 200 kW-level distributed energy system integrated with PCM thermal energy storage was developed and experimentally tested to validate and compare the proposed control methods. The results showed that the PI controller tuned by Artificial Rabbit Optimisation (ARO) achieved superior temperature and power regulation performance compared with the empirical Ziegler-Nichols method and the conventional evolutionary search of Genetic Algorithm approach. The ARO-based framework provided a more adaptive exploration-exploitation balance while directly minimising the closed-loop tracking error under nonlinear thermal dynamics and actuator constraints. Based on the validated control results, a comprehensive techno-economic analysis was conducted to evaluate the influence of control strategies and operating setpoints on system performance. The results indicated that constant power regulation maintained a higher exergy-to-energy ratio for comparable thermal energy output, making it suitable for power generation applications. Meanwhile, the constant temperature strategy exhibited an optimal setpoint that maximised net profit and minimised payback period by balancing economic gains, capital investment, and operating expenditure. Overall, this study provided an AI-enhanced and industrially viable control strategy for improving the discharging performance of PCM thermal storage systems and offered valuable insights for their large-scale deployment in waste heat recovery and low-carbon energy systems.

The melting performance of spherical particles with core-shell structure containing sodium carbonate as high temperature phase change material is systematically investigated through a fully resolved lattice Boltzmann simulation. Three types of highly conductive shell materials are considered to facilitate the melting of PCM. The numerical simulation shows that the complete melting time of a single particle decreases linearly with the increase of shell thickness. It is suggested that the thermal conductivity of the shell material is at least 10 times of that of PCM to realize significant improvement of melting performance. For randomly packed particle bed, the melting time decreases exponentially with the increase of packing density due to the contact network with high thermal conductivity. Therefore, the optimized packing density is expected to be ϕ=0.5~0.6, which is close to the densest state of a random packing of particles.

High-temperature thermal energy storage shows promise for advancing industrial decarbonization. Within this field, earth-based particulates can be attractive for widespread adoption due to their scalability and abundant resource availability.

The health state of DC-DC power supply is the key factor to determine whether the electronic equipment can operate normally. The failure and deterioration of the power supply system will lead to the collapse of the entire electronic system. The research in this paper is based on the long-term high temperature degradation test data of a certain type of DC-DC power supply. The degradation law of power supply is studied by data preprocessing and noise reduction of sensitive parameters such as input current, output current, input voltage and output voltage. On this basis, we use the method of deep learning to model the efficiency of power supply in the process of degradation test. The experimental results show that the efficiency time series modeling of power supply degradation using LSTM method can effectively reflect the law of power supply efficiency degradation. Based on the DC-DC power health management technology combining degradation test and deep learning, the advanced fault prediction model is used to reflect the change law of power supply in the real degradation process. This method has certain theoretical and engineering value for power PHM modeling and application.

The negative bias temperature instability (NBTI) mechanisms for Core and input/output (I/O) devices from a 130 nm partially-depleted silicon on insulator (PDSOI) technology are investigated. The I/O device degrades more than the Core device under the same stress electric field due to the different gate oxide processes in these two types of devices. Both the oxide trap charge and interface trap lead to the transfer characteristics degradations of the device after NBTI. While the near interfacial traps result in the increase of low frequency noise (LFN). The trap densities near the silicon/gate oxide interface introduced by stress are extracted using the LFN method. NBTI-induced gate current increase is observed for Core device, but not for I/O device. It is result from the enhanced tunneling process, which is induced by the increase of electric field in the gate oxide after charge trapping at or near the channel interface. The gate width and length dependences of NBTI are observed. The enhanced NBTI degradation observed in short channel and narrow channel device is result from the enhanced NBTI effect at channel edge regions. The larger hole concentration is a main cause of the more serious NBTI degradation at the channel edge regions, including the nearby region of STI sidewall along the channel width direction and the gate edge region along the channel length direction. This conclusion is also verified by the TCAD simulations.