Electrification and lightweighting technologies are important components of greenhouse gas (GHG) emission reduction strategies for light-duty vehicles. Assessments of GHG emissions from light-duty vehicles should take a cradle-to-grave life cycle perspective and capture important regional effects. We report the first regionally explicit (county-level) life cycle assessment of the use of lightweighting and electrification for light-duty vehicles in the U.S. Regional differences in climate, electric grid burdens, and driving patterns compound to produce significant regional heterogeneity in the GHG benefits of electrification. We show that lightweighting further accentuates these regional differences. In fact, for the midsized cars considered in our analysis, model results suggest that aluminum lightweight vehicles with a combustion engine would have similar emissions to hybrid electric vehicles (HEVs) in about 25% of the counties in the US and lower than battery electric vehicles (BEVs) in 20% of counties. The results highlight the need for a portfolio of fuel efficient offerings to recognize the heterogeneity of regional climate, electric grid burdens, and driving patterns.

The critical heat flux (CHF) corresponding to the departure from nucleate boiling (DNB) crisis is essential to the design and safety of a two-phase flow boiling system. Despite the abundance of predictive tools available to the thermal engineering community, the path for an accurate, robust CHF model remains elusive due to lack of consensus on the DNB triggering mechanism. This work aims to apply a physics-informed, machine learning (ML)-aided hybrid framework to achieve superior predictive capabilities. Such a hybrid approach takes advantage of existing understanding in the field of interest (i.e., domain knowledge) and uses ML to capture undiscovered information from the mismatch between the actual and domain knowledge-predicted target. A detailed case study is carried out with an extensive DNB-specific CHF database to demonstrate (1) the improved performance of the hybrid approach as compared to traditional domain knowledge-based models, and (2) the hybrid model's superior generalization capabilities over standalone ML methods across a wide range of flow conditions. The hybrid framework could also readily extend its applicability domain and complexity on the fly, showing an elevated level of flexibility and robustness. Based on the case study conclusions, the window-type extrapolation mapping methodology is further proposed to better inform high-cost experimental work. Keywords: critical heat flux, departure from nucleate boiling, hybrid framework, machine learning, domain knowledge. API application programming interface CHF critical heat flux DK domain knowledge DNB departure from nucleate boiling EPRI Electric Power Research Institute LUT look-up table MAE mean absolute error ML machine learning MSE mean squared error NN (feed-forward) neural network PWR pressurized water reactor ReLU rectified linear unit RF random forest rRMSE relative root-mean-square error

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