Browse Topic: Mathematical models
According to the working characteristics of the tire changer, the movement characteristics of its rim clamping mechanism are analyzed, and the complex movement structure is abstracted and simplified into four identical six-bar mechanism subunits. One of the subunits is taken as the research object, and the mathematical model of kinematic analysis is established. Using MATLAB software to simulate and analyze the motion law of each component, the mechanical characteristics of the component are analyzed. The optimization of the design parameters of the “six-bar mechanism subunit” is realized, the rim clamping mechanism becomes more stable, and the clamping force follows the diameter of the rim more closely.
Air Traffic Management (ATM) must be familiar with the exact Aircraft Take-off Weights (ATOWs) of airplanes to make the most use of runways, maintain safety margins high, and keep utilization and resources in balance. This paper aims to present a dependable ATOW forecasting methodology that can assist the air transport industry in enhancing operational decision-making. This research used datasets acquired from the EUROCONTROL Performance Review Commission (PRC) 2024 Aircraft Take-Off Weight Estimation dataset featuring 527,000 flights over Europe containing aircraft details, air trips and flight conditions. Technique comprises structured data input, inspection of missing data, timestamp aggregation to identify demand cycles over time, and domain-specific feature engineering using distance_per_minute, block_minutes, taxiout_ratio, and a strong wake turbulence metric The two supervised learning models used were Linear Regression (LR) for understanding and XGBoost for performance prediction In comparison to LR's 4,409 kg MAE (mean absolute error), 7,061 kg RMSE (root mean square error), and 0.9825 R2 value, XGBoost significantly excelled with validation results showing an R2 value of 0.9992 and an RMSE of 1,514 kg In the absence of labelled test targets, cross-validation nevertheless showed a constant degree of generalizability The residual diagnostics showed that the model was reliable for practical execution with low-variance deviations that were unbiased An accurate ATOW estimate improves the demand-capacity balance and On-Time Performance (OTP) in ATM, which in turn affects the runway schedule, wake turbulence diversion, slot allocation, and fuel planning The results highlight the need to include ATOW predictions in both tactical and strategic planning to reduce delays, increase airspace usage, and promote sustainable aviation operation and possesses significant improvements will consist of weather and runway conditions, stochastic ambiguity computation, and drift monitoring to keep up with ever-changing operating variables while maintaining accurate forecasts.
This study presents a data-driven approach for strengthening aviation safety by integrating human factors assessment with modern predictive modeling techniques. The work focuses on understanding how human performance, operational conditions, and system-level interactions collectively influence safety risk, and how these interactions can be quantified to support improved design and decision-making. Unlike previous studies that address human factors or predictive modeling in isolation, this research offers a unified framework that links causal human factors indicators with statistical modeling, feature extraction, and machine learning based risk estimation. The novelty of this work lies in the structured pipeline that transforms raw categorical and narrative human factors information into measurable predictors that can be analyzed using structural modeling and machine learning. The methodology includes data preparation, dimensionality reduction, latent pattern discovery, dependence modeling, model training, and interpretability analysis. The study demonstrates how this pipeline uncovers hidden relationships among operational errors, environmental influences, maintenance actions, design considerations, and crew behavior. The findings show that the integrated approach improves the accuracy and stability of risk prediction and highlights specific human factors patterns that consistently contribute to elevated risk levels. These insights support targeted mitigation strategies, inform design improvements, and help prioritize safety interventions. The work concludes that a combined human factors and predictive modeling framework enhances the ability of organizations to identify vulnerabilities earlier, allocate resources more effectively, and strengthen system resilience. This approach is adaptable to diverse aviation contexts and offers a practical path for transforming human factors data into actionable safety intelligence.
Soft robot systems demonstrate exceptional load-bearing capacity and spatial compliance during operation, with transformative potential in disaster response scenarios requiring adaptive morphology and hazardous material manipulation. By integrating the complementary advantages of soft robotics and particle jamming mechanisms, this study proposes a real-time variable-stiffness soft actuator, while systematically investigating its mathematical modeling framework and stiffness modulation principles. A deformation model for the variable stiffness soft actuator is established, followed by static analysis of the variable-stiffness members using particle jamming theory, with theoretical investigation of their stress distributions. Subsequently, a variable-stiffness driver was fabricated via additive manufacturing (3D printing), resulting in a flexible mechanical digit capable of stiffness tuning, A soft mechanical hand grasping test platform was built, and grasping experiments of objects of different shapes and sizes were conducted. Experimental validation confirms the influence of actuator dimensions, particle characteristics, and granule size distribution on both stress states and bending angles at the soft robotic digit’s distal segment. The obtained results establish theoretical foundations and advance variable-stiffness soft robotics research and associated stiffness regulation methodologies.
The design, testing, and analysis of a Guided Autorotative Delivery System (GADS) for suppression of incipient wildfires is described. The GADS consists of an unpowered 1 m diameter rotor, a control unit, and a payload of 2.2 kg of fire suppressant powder. On release from a fixed-wing UAV, the rotor passively deploys and enters autorotation, decelerating the payload and allowing precise delivery of the suppressant using cyclic pitch control. A numerical model of the system was developed to calculate the trajectory of the GADS during rotor deployment and descent, in the presence of ambient wind and cyclic pitch inputs. A reduced-scale model of the rotor was tested in a wind tunnel, and an uncontrolled full-scale, 1.5 kg prototype of the GADS was fabricated and tested by dropping from a hovering quadcopter as well as a fixed-wing UAV. The full-scale drop experiments validated the deployment and autorotation stability of the system, and demonstrated that the GADS maintains descent velocities suitable for incipient fire suppression (≈ 5 m/s). Numerical predictions indicate that the GADS descent trajectory can be controlled with cyclic pitch in an ambient crosswind of at least 5 m/s (10 kts). Measurements captured during the drop tests using onboard instrumentation show good qualitative agreement with numerical predictions. Future work will include drop tests with remotely controlled cyclic pitch, followed by fully autonomous controlled descent. The study establishes design guidelines for guided autorotative systems and illustrates their potential for scalable UAV-based wildfire suppression or emergency response.
The bird strike performance of rotorcraft components must be demonstrated to the airworthiness authority in accordance with the certification requirements of CS 29.631. This necessitates continuous efforts to design and validate birdstrike-resistant structures through a combination of experiments and simulations. In this study, an integrated experimental and numerical investigation is conducted to evaluate the structural response and failure characteristics of the main rotor pitch link subjected to bird impact. In the experimental program, high-speed imaging and strain measurements were used to capture the transient deformation and impact force history. In parallel, a highly nonlinear finite element model was developed using the LS-DYNA solver. The numerical model was validated against experimental results. Results demonstrate that localized plastic deformation and stress concentrations occur near the impact region, consistent with damage patterns observed in real-world incidents. This research provides a validated methodology supporting CS 29.631 bird strike certification for rotorcraft flight-control components.
Ammonia has emerged as a viable hydrogen energy carrier owing to its superior hydrogen density and mature industrial utilization. However, ammonia faces critical challenges including inadequate ignition characteristics and sluggish combustion kinetics, necessitating supplementary high-reactivity fuels for optimizing combustion. Onboard ammonia decomposition technology resolves this problem through on-demand hydrogen real-time production. Among existing ammonia decomposition methods, gliding arc plasma (GAP) demonstrates exceptional promise for onboard hydrogen production given its high processing flow rate，decent hydrogen conversion rate, and transient response capability. Prevailing research predominantly relies on experimental approaches, with insufficient understanding of the effects of specific electrical field parameters and inlet pressure on system performance. This study established a quasi-one-dimensional numerical model for GAP-assisted ammonia decomposition. A comprehensive analysis was conducted to examine the influence of key electric field parameters, such as reduced electric field strength (REFS) and electron density (De), on ammonia conversion rate and energy efficiency. Furthermore, the study explored the synergistic effects of inlet pressure and electric field parameters on system performance under constant mass flow rate conditions. The results indicate that increasing REFS and De significantly substantially elevates ammonia conversion rate, but energy efficiency decreases as these parameters increase. Keeping a constant NH3 inlet mass flow rate, the gas velocity decreases when the inlet pressure increases and then extends the residence time. Consequently, the ammonia conversion rate significantly improves while the energy efficiency slightly decreases. By increasing inlet pressure and simultaneously reducing REFS or De, system energy efficiency can be effectively enhanced without altering ammonia conversion rates. This study demonstrates the synergistic regulation mechanism of electric field parameters and inlet pressure on hydrogen production performance, providing optimization strategies for GAP reactor design.
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