arXiv subjects

Xingcai Lu

Publications and source records attributed to Xingcai Lu.

2 recordsLinked to original sources

arXiv2026

Experimental measurements and modeling of characteristic time scales in single iron particle ignition

Recyclable metal fuels such as iron are promising carbon-free energy carriers for heat and power. In such systems, particle ignition characteristics strongly affect combustion efficiency and combustor stability, making them critical for burner and reactor design. However, predictive ignition modelling remains limited by the lack of time-resolved data for single-particle solid-phase oxidation and phase transitions. In this work, digital in-line holography combined with ultra-high-speed single-color pyrometry is used to resolve characteristic solid-phase oxidation times of spherical micron-sized iron particles burning in well-defined hot oxidizing environments. Three temperature plateaus are identified, corresponding to FeO melting, the {\gamma}-Fe to {\delta}-Fe transition, and Fe melting, from which pre-melting oxidation times and melting durations are extracted. An ignition model based on solid-phase iron oxidation kinetics following a parabolic rate law, coupled with external-oxygen-transport-limited description, is used to simulate these characteristic times. The model accurately captures the FeO-scale pre-melting oxidation time, which is nearly independent of oxygen concentration, while the FeO, {\gamma}-Fe to {\delta}-Fe, and Fe melting stages show strong oxygen-concentration dependence consistent with external-oxygen-transport-limited reaction rates. These measurements and simulations provide the first diameter-resolved dataset for FeO and Fe melting processes and show that this modelling framework can quantitatively predict characteristic times for single iron particles in metal-fuel applications.

cond-mat.mtrl-sci↗

arXiv2026

RealPDEBench: A Benchmark for Complex Physical Systems with Real-World Data

Predicting the evolution of complex physical systems remains a central problem in science and engineering. Despite rapid progress in scientific Machine Learning (ML) models, a critical bottleneck is the lack of expensive real-world data, resulting in most current models being trained and validated on simulated data. Beyond limiting the development and evaluation of scientific ML, this gap also hinders research into essential tasks such as sim-to-real transfer. We introduce RealPDEBench, the first benchmark for scientific ML that integrates real-world measurements with paired numerical simulations. RealPDEBench consists of five datasets, three tasks, eight metrics, and ten baselines. We first present five real-world measured datasets with paired simulated datasets across different complex physical systems. We further define three tasks, which allow comparisons between real-world and simulated data, and facilitate the development of methods to bridge the two. Moreover, we design eight evaluation metrics, spanning data-oriented and physics-oriented metrics, and finally benchmark ten representative baselines, including state-of-the-art models, pretrained PDE foundation models, and a traditional method. Experiments reveal significant discrepancies between simulated and real-world data, while showing that pretraining with simulated data consistently improves both accuracy and convergence. In this work, we hope to provide insights from real-world data, advancing scientific ML toward bridging the sim-to-real gap and real-world deployment. Our benchmark, datasets, and instructions are available at https://realpdebench.github.io/.

cs.LG↗