In this paper, we present the design of the software-in-the-loop simulation framework for a quadcopter that is incorporated in our hybrid aircraft. The hybrid aircraft comprises a quad-copter and a fixed wing with one forward thrust rotor. We need to develop a split control system that utilizes a typical quadcopter controller to control four motors/propellers and a supervisor controller to control a forward thrust rotor. The supervisor controller shall take feedback signals from the quadcopter and will command the fifth rotor for stabilizing the hybrid aircraft and resolves problems like thrust saturation. The simulation simulates the control algorithm and verifies the quadcopter's behavior using MATLAB and Simulink together. Achieving these results, we come to know how our hybrid controller will be implemented, what results to expect once the forward thrust rotor is attached to the quadcopter. The software-in-the-loop simulation of a quadcopter is one of the most effective methods for verifying overall control performance and safety of the hybrid aircraft before actual hardware implementation and flight test.

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.

Upper Northern Thailand continues to face a protracted structural crisis from fine-particulate matter (PM_2.5), primarily driven by biomass burning and wildfires. Conventional mechanical capture systems, such as cyclones, often suffer a drastic efficiency drop when treating sub-micron particles. This study introduces an innovative Evaporative Condensation Growth Scrubber (ECGS) designed to bridge this technological gap by promoting the growth of fine particles through heterogeneous nucleation. Experimental testing across 10 different nozzle configurations was conducted to optimize the system’s performance. The results revealed that the ECGS system significantly outperformed the dry cyclone (Baseline) across all nine testing configurations. While the Baseline showed inherent limitations in capturing sub-micron particles, the ECGS demonstrated relative efficiency improvements ranging from 39.53% to 83.23% for PM_2.5, and 26.10% to 61.50% for PM₁₀ compared to the baseline. Optimal performance was achieved using a 90-degree injection angle and a 10 cm distance, which created a complete spray curtain and maximized collision probability. Under these conditions, the outlet PM_2.5 concentration stabilized at 11.81 µg/m³ within 180 s of water injection. Crucially, despite sensor interference caused by high relative humidity, the system’s effectiveness was confirmed by a significant difference in performance in PM₁₀ and PM_2.5 removal. The PM₁₀ collection efficiency outperformed that of PM_2.5 by 28.82%, providing empirical evidence that PM_2.5 particles successfully acted as nuclei for condensation and grew into the larger PM₁₀ size range. This particle growth enabled more effective centrifugal separation, demonstrating that the ECGS system offers a viable and efficient solution for fine particle removal in highly polluted environments.

Fine particulate matter PM_2.5 continues to pose a critical public health risk in Northern Thailand, particularly in Chiang Mai, where traditional filtration methods often face limitations in cost and efficiency for large-scale applications. This study introduces a novel “Evaporator Condensation Growth Particle Scrubber (ECGP)” designed to enhance the collection efficiency of sub-micron particles by enlarging their physical size through a pressure-driven growth mechanism. The ECGP system utilizes synergistic effects between solid nuclei, high relative humidity, and mechanical pressure modulation. The ECGP system integrates solid nuclei, ~95% relative humidity and mechanical pressure modulation within a single chamber. Using incense smoke as a PM surrogate, the process utilizes controlled adiabatic cycles to induce stable heterogeneous condensation. The results indicate that the integrated process effectively shifts particle size distribution, reducing the PM_2.5/PM₁₀ mass ratio from 1.00 to 0.83. This indicates that approximately 17.5% (with a standard deviation < 1% across 10 trials, p < 0.05) of the fine mass successfully transitioned into the larger, more filterable PM₁₀ fraction and exhibited high physical stability and resistance to re-evaporation, effectively overcoming the low-efficiency threshold (typically <10%) of standard mechanical scrubbers and cyclones for sub-micron dust. This study concludes that ECGP technology offers a promising, cost-effective alternative for improving indoor air quality in large public infrastructures by leveraging particle inertia for enhanced removal, providing a scalable solution to the persistent smog crisis.