康明斯

The impact of mild hydrothermal aging (HTA) on low-temperature (150-200 degrees C) standard-SCR is investigated using transient response methods and transient kinetic analysis. We decouple the reduction and the oxidation half-cycles (RHC and OHC) of the standard-SCR redox mechanism to study them independently. While the RHC rates are essentially unaffected, OHC is inhibited by mild HTA. By equating the estimated rate expressions (both 2nd order in Cu sites, OHC with O2 as the sole oxidant), we predict exactly the steady-state low-temperature standard-SCR performance in terms of both NO conversion and bed-average Cu redox state, as well as the detrimental effect of mild HTA on the DeNOx efficiency. We also demonstrate that the DeNOx activity of the aged catalyst can be precisely restored by incrementing the oxygen partial pressure in proportion to the drop of the OHC rate constant, as predicted by our simple two-reaction model. These findings offer valuable insights into the design of next-generation urea-SCR exhaust gas aftertreatment (EGA) systems featuring enhanced cold-start performance and durability under aging conditions.

Improving diesel engine efficiency, reducing emissions, and enabling robust health monitoring have been critical research topics in engine modelling. While recent advancements in the use of neural networks for system monitoring have shown promising results, such methods often focus on component-level analysis, lack generalizability, and physical interpretability. In this study, we propose a novel hybrid framework that combines physics-informed neural networks (PINNs) with deep operator networks (DeepONet) to enable accurate and computationally efficient parameter identification in mean-value diesel engine models. Our method leverages physics-based system knowledge in combination with data-driven training of neural networks to enhance model applicability. Incorporating offline-trained DeepONets to predict actuator dynamics significantly lowers the online computation cost when compared to the existing PINN framework. To address the re-training burden typical of PINNs under varying input conditions, we propose two transfer learning (TL) strategies: (i) a multi-stage TL scheme offering better runtime efficiency than full online training of the PINN model and (ii) a few-shot TL scheme that freezes a shared multi-head network body and computes physics-based derivatives required for model training outside the training loop. The second strategy offers a computationally inexpensive and physics-based approach for predicting engine dynamics and parameter identification, improving computational efficiency over the existing PINN framework. Compared to existing health monitoring methods, our framework combines the interpretability of physics-based models with the flexibility of deep learning, offering substantial gains in generalization, accuracy, and deployment efficiency for diesel engine diagnostics.

Emerging power generation technologies such as solid oxide fuel cells (SOFCs) offer promising pathways for clean and efficient energy conversion. However, their material instability accelerates unexpected degradation, which remains a major barrier to large-scale commercialization. As a practical solution, control and diagnosis systems are integral to optimizing SOFCs' lifetime and efficiency in real-world operations. Degradation mechanisms induce nonlinear, time-varying patterns in running fuel cells, affecting the effectiveness of control and diagnosis strategies. This study introduces a data-driven machine learning framework for predicting SOFC performance under degradation. Four lab-scale SOFCs were subjected to accelerated degradation tests, generating diverse run-to-failure datasets. To capture the complex, nonstationary dynamics in these data, a dynamic neural network based on Kolmogorov-Arnold approximation theory (DKAN) is developed. DKAN employs univariate splines as learnable activation functions, hierarchically adapting low-dimensional functions to diverse nonlinearities and temporal patterns. Comparative experiments against state-of-the-art sequence models, including LSTM, TCN, WaveNet, DGRU, Informer, and ConvRec, show that DKAN achieves on average 30% lower prediction error (across RMSE, WAPE, and MASE) and 55% faster inference relative to the baselines, while demonstrating superior generalization to unseen degradation patterns. Furthermore, statistical analyses using the Friedman-Nemenyi and ANOVA-Tukey tests confirm the significance of DKAN's performance improvements across multiple datasets and metrics. These results highlight DKAN's potential as a lightweight and scalable solution for real-time SOFC diagnostics and control.

This study presents a cradle-to-grave lifecycle analysis of energy use and greenhouse gas (GHG) emissions for U.S. medium- and heavy-duty vehicles across current (2021) and future (2035) technologies using the Greenhouse gas, Regulated Emissions, and Energy use in Technologies (GREET) model with industry-vetted assumptions. Results vary across vehicle classes but point to common trends: today, battery electric vehicles (BEVs) offer significant (10-60%) GHG emissions reduction compared to diesel internal combustion engine vehicles and are the lowest emissions option per ton-mile of cargo movement, followed by hydrogen fuel cell electric vehicles (FCEVs) (5-50% emissions reduction). Emissions savings depend largely on the duty cycle and fuel economy of the vehicle type. Future vehicle technology advancements result in comparable emission reductions associated with BEVs and hydrogen FCEVs. Weight-limited BEV trucks see less per-ton-mile emissions reduction due to the impact of battery weight on increased vehicle weight and reduced payload capacity. By 2035, improvements in vehicle efficiency can reduce emissions across all powertrains. However, very low levels of emissions require switching vehicles' use-phase fuel/energy to low-carbon fuels and electricity. Renewable diesel, e-fuels, hydrogen produced from natural gas with carbon capture and storage or renewables, and use of low-carbon electricity can all achieve over 70% reduction in GHG emissions from the current day diesel-based internal combustion engine vehicle.

The research focuses on the modification of the microstructure in high-pressure diesel engines through improvements in the austempering process. Unstable conditions during the austempering treatment led to the formation of undesirable microstructural features, including non-uniform subsurface decarburization, excessive carbide precipitation, and regions of pearlitic phases due to inadequate cooling. These defects contributed to surface peeling and fatigue cracking under high cyclic pressure, leading to fuel leakage. A comprehensive root cause analysis was performed to examine the heat treatment and post-processing methods. The study highlights modifications to the austempering quenching process to achieve a more consistent lower bainitic microstructure, which enhances fatigue strength and wear resistance. Studies were performed on precise salt bath control, optimized racking configurations, and refined heat treatment parameters to ensure uniform quenching conditions and promote complete bainitic transformation. Post-processing shot peening treatment was also introduced to mitigate the effects of hot corrosion, further enhancing component durability in cyclic high-pressure environments.