The transportation sector significantly contributes to global CO2 emissions, thereby there exists a requirement for the sustainable alternatives. The work compares the total cost of ownership (TCO) of sport utility vehicles with different powertrain technologies, including internal combustion engine vehicles (ICEVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and battery electric vehicles (BEVs), in the Thai market. Using a detailed TCO model, the study considers purchase price, depreciation, fuel/electricity costs, maintenance, insurance, annual tax, and government incentives. Data from literature and market reports assess the financial viability of each vehicle type, structured into four comparison groups based on model and popularity. Findings indicate that while ICEVs have lower initial costs, BEVs and HEVs achieve competitive TCO with subsidies and better battery technology. Results show significant energy and maintenance savings for EVs over their lifecycle compared to ICEVs. However, high initial costs and inadequate charging infrastructure hinder EV adoption in Thailand. The study concludes that targeted policies and incentives are essential to promote EV adoption, reduce emissions, and advance sustainable transportation. These insights guide consumers and policymakers in supporting Thailand’s electric mobility transition.

Fused deposition modeling (FDM) parts show condition-dependent variability in ultimate tensile strength (UTS), yet most machine learning (ML) studies report only deterministic point estimates, limiting use in safety-critical design. This work presents a probabilistic framework mapping FDM-PETG process parameters to orientation-specific conditional quantile estimates with pooled uncertainty, covering model selection, deployment, and process optimization. A controlled experiment varied 12 parameters across 111 printing conditions from a combined Taguchi and Latin Hypercube Sampling (LHS) design, yielding 333 ASTM D3039 tensile measurements. Six probabilistic models were trained on raw replicate data and ranked on point accuracy and calibration, weighted 75% toward uncertainty quantification (UQ). The mixture density network (MDN) showed the lowest composite rank. Orientation-specific (MDN-OR) and unified (MDN-UF) variants were compared; MDN-OR gave more conservative lower-quantile estimates for safety-critical design. Lower-quantile (q = 0.05) SHAP analysis identified filament consumption and wall loops as the strongest predictive associations with worst-case strength. For each orientation, the configuration with minimum filament consumption at maximum strength was selected, with its post hoc q = 0.05 lower bound confirmed by physical tensile testing. Within the studied single-material, single-printer setting, the framework shows how probabilistic process–property modeling can support uncertainty-aware parameter selection toward autonomous additive manufacturing.

Submicron particulate matter in the 0.1–1.0 µm range is difficult to remove using conventional air pollution control devices because of its low capture efficiency. Condensation-induced particle enlargement has therefore been proposed as a preconditioning method to increase particle size before collection. This study aims to numerically investigate the condensation-induced growth of submicron particles in a cylindrical tube under different pressure-recovery conditions and to clarify how pressure-controlled supersaturation affects droplet-growth kinetics. A three-dimensional computational fluid dynamics (CFD) model was developed in ANSYS Fluent by coupling the Discrete Phase Model (DPM) with a custom User-Defined Function (UDF) growth law to predict droplet growth, condensation time, and associated heat and mass transfer characteristics. Initial particle diameters of 0.1–1.0 µm were examined for growth to a target diameter of 5 µm under initial pressure conditions of 0.5–0.9 bar followed by recovery to 1 atm, corresponding to calculated nominal supersaturated RH values of 202.65–112.58%, respectively. The results show that pressure-induced supersaturation is the dominant factor controlling condensation kinetics. Lower initial pressures resulted in shorter condensation times and higher mass and heat transfer rates. For an initial diameter of 0.5 µm, the condensation time decreased from approximately 0.1434 s at 0.9 bar to 0.0167 s at 0.5 bar, corresponding to an 88.35% reduction. These findings indicate that pressure-controlled supersaturation can significantly accelerate submicron particle enlargement and provide design guidance for condensation-assisted fine-particle removal technologies.

A very-high-order Multi-Moment (MM) framework is developed for the Eulerian convection--diffusion equation in which multiple moments are advanced as independent degrees of freedom rather than reconstructed from a single primary representation. Pointwise and integral moments distributed over complementary geometric entities provide additional subcell information, extending the resolving capability attainable on a fixed grid. A systematic construction is presented up to twenty-fourth order, together with a coupled Fourier analysis that characterizes inter-moment coupling, dispersion, phase velocity, and amplitude behavior. Frontogenesis tests show that the proposed schemes achieve the target accuracy with fewer degrees of freedom than explicit finite-difference methods of equivalent order and provide an average 1.49X speedup across the tested error range. Their resolving efficiency is also competitive with compact finite-difference and discontinuous Galerkin schemes. For nonlinear problems, fourth-order flux correction generally preserves the accuracy advantage of the eighth- and tenth-order MM schemes, while sixth-order correction becomes necessary for the tenth-order scheme only at an error tolerance of 10-5. These results establish independently evolved multi-moment discretization as an efficient strategy for high-order, high-resolution transport simulation. To facilitate reproducibility, the open-source MM2D solver implementation for the Frontogenesis benchmark is made publicly available.