Chenzhao Li

Current airframe health monitoring generally relies on deterministic physics models and ground inspections. This paper uses the concept of a dynamic Bayesian network to build a versatile probabilistic model for diagnosis and prognosis in order to realize the digital twin vision, and it illustrates the proposed method by an aircraft wing fatigue crack growth example. The dynamic Bayesian network integrates physics models and various aleatory (random) and epistemic (lack of knowledge) uncertainty sources in crack growth prediction. In diagnosis, the dynamic Bayesian network is used to track the evolution of the time-dependent variables and calibrate the time-independent variables; in prognosis, the dynamic Bayesian network is used for probabilistic prediction of crack growth in the future. This paper also proposes a modification to the dynamic Bayesian network structure, which does not affect the diagnosis results but reduces the time cost significantly by avoiding Bayesian updating with load data. By using a particle filter as the Bayesian inference algorithm for the dynamic Bayesian network, the proposed approach handles both discrete and continuous variables of various distribution types, as well as nonlinear relationships between nodes. Challenges in implementing the particle filter in the dynamic Bayesian network, where 1) both dynamic and static nodes exist and 2) a state variable may have parent nodes across two adjacent networks, are also resolved.

Integrating the atomically dispersed and nitrogen coordinated single Fe site-rich carbon support and the ordered PtCo intermetallic nanoparticles is an effective approach to designing high-performance low-PGM fuel cell catalysts for transportation.

Developing low platinum-group-metal (PGM) catalysts for the oxygen reduction reaction (ORR) in proton-exchange membrane fuel cells (PEMFCs) for heavy-duty vehicles (HDVs) remains a great challenge due to the highly demanded power density and long-term durability. This work explores the possible synergistic effect between single Mn site-rich carbon (MnSA-NC) and Pt nanoparticles, aiming to improve intrinsic activity and stability of PGM catalysts. Density functional theory (DFT) calculations predicted a strong coupling effect between Pt and MnN4 sites in the carbon support, strengthening their interactions to immobilize Pt nanoparticles during the ORR. The adjacent MnN4 sites weaken oxygen adsorption at Pt to enhance intrinsic activity. Well-dispersed Pt (2.1 nm) and ordered L12-Pt3Co nanoparticles (3.3 nm) were retained on the MnSA-NC support after indispensable high-temperature annealing up to 800 °C, suggesting enhanced thermal stability. Both PGM catalysts were thoroughly studied in membrane electrode assemblies (MEAs), showing compelling performance and durability. The Pt@MnSA-NC catalyst achieved a mass activity (MA) of 0.63 A mgPt–1 at 0.9 ViR-free and maintained 78% of its initial performance after a 30,000-cycle accelerated stress test (AST). The L12-Pt3Co@MnSA-NC catalyst accomplished a much higher MA of 0.91 A mgPt–1 and a current density of 1.63 A cm–2 at 0.7 V under traditional light-duty vehicle (LDV) H2–air conditions (150 kPaabs and 0.10 mgPt cm–2). Furthermore, the same catalyst in an HDV MEA (250 kPaabs and 0.20 mgPt cm–2) delivered 1.75 A cm–2 at 0.7 V, only losing 18% performance after 90,000 cycles of the AST, demonstrating great potential to meet the DOE targets.