Strojniški vestnik - Journal of Mechanical Engineering

Study on the Trade-off Mechanism Between Flow Field Uniformity and Turbulent Pressure Pulsations in Back-Pressure Matching for Supersonic Wind Tunnels

Peng Liu — 2026-07-01

Supersonic wind tunnel testing is critical for aircraft aerodynamic configuration validation, where test chamber flow uniformity and turbulent pressure pulsations are core determinants of test data reliability. Existing back-pressure matching studies only optimize flow uniformity as a single objective, ignoring its coupling effect on pressure pulsations. This study proposes a rapid Reynolds-averaged Navier–Stokes (RANS)-based back-pressure matching method with the shear stress transport (SST) k−ω model to determine the optimal back-pressure Pm (ideal expansion at <5 % jet centerline Mach number deviation). Multiscale Large Eddy Simulations (LES) show ideal expansion extends the uniform core by 23 % with 3.4 % velocity pulsation standard deviation, while back-pressure mismatch causes up to 18 % total pressure loss via periodic shocks. Critically, ideal expansion yields the strongest pressure pulsations as unimpeded shear layer turbulence develops fully, uncovering a key trade-off: shock-based turbulence suppression reduces pulsations but sacrifices 7 % to 15 % flow uniformity. This work fills the research gap of coupled uniformity-pulsation analysis and proposes scenario-specific back-pressure strategies for supersonic wind tunnel tests.

Exploration of Erosion Characteristics in Multi-step Machining by CFD-Assisted Abrasive Waterjet

Yemin Yuan — 2026-07-01

Abrasive water jet (AWJ) technology, predicated on the high-velocity mixing of air, water, and abrasive particles, is a critical technique for precision material removal. This study presents a comprehensive investigation into AWJ erosion mechanisms by integrating experimental observations with advanced Computational fluid dynamics (CFD) simulations. The inherent complexity of the three-phase erosion field, particularly regarding the evolution of stagnation zones at increased erosion depths, presents significant challenges for direct experimental observation. To overcome these limitations, an initial erosion channel profile obtained under controlled experimental conditions was employed as a boundary condition in CFD simulations to model the trajectory of abrasive particles accurately. The simulations facilitate the prediction of successive erosion channel profiles by elucidating the influence of stagnation zones on abrasive particle refraction during both normal and inclined multi-step erosion processes. Comparative analysis between the CFD results and experimental data confirms that stagnation zones play a pivotal role in modulating AWJ erosion energy. This work not only refines the predictive modeling of AWJ-induced erosion but also deepens the fundamental understanding of the erosion process through detailed examination of stagnation zone dynamics.

An Integrated ANN–GA-Based Framework for Multi-Parameter Design Optimization of a Large-Scale 3D Concrete Printer Frame in a Discrete Design Space

Duc Hai Ta — 2026-07-01

This paper presents an integrated artificial neural network-genetic algorithm (ANN-GA)-based framework for multi-parameter design optimization of a large-scale concrete 3-dimensional (3D) printer frame in a discrete design space. Based on practical design requirements and operating conditions, a finite element (FE) model of the printer frame is developed using APDL® scripting, enabling automated evaluation of printhead deflection and natural frequencies. Using the FE-generated dataset, a multilayer feed-forward neural network (MLFFNN) is trained as a surrogate model to predict the structural responses of the frame. Parametric investigations demonstrate that the artificial neural network (ANN) surrogate model substantially reduces computational time while maintaining high prediction accuracy, with errors ranging from 1 % to 4 % compared to direct FE analysis. A mass-minimization optimization model is then formulated with ten design variables and four constraints related to printhead deflection and natural frequencies. Genetic algorithms are employed to solve the optimization problem using two different approaches: direct optimization coupled with FE analysis and surrogate-based optimization using the ANN model. Notably, the optimization is conducted in a discrete design domain consistent with the standard dimensions of commercially available steel box sections. The optimal solutions obtained from different optimization strategies, including continuous and discrete FE-based models, the ANN surrogate model, and an experience-based design, are systematically compared. The optimization results demonstrate that the proposed framework achieves a structural weight reduction of 27 % to 38 % compared to the initial experience-based design. Furthermore, the ANN-based surrogate optimization reduces the total computational time from approximately 38 hours to about 200 seconds, clearly demonstrating the efficiency and practical applicability of the proposed approach for real-world large-scale machine design.

Business and Technological Processes Optimization in Automotive Manufacturing: Continuous Improvement through Process Capability, Operational Effectiveness, and Return on Investment

Robert Pavlin — 2026-07-01

This study develops and empirically validates an integrated monitoring framework linking technological, operational, and financial performance indicators in automotive manufacturing. While process capability (Cpk), overall equipment effectiveness (OEE), and return on investment (ROI) are widely applied, their interrelationships are rarely examined within a unified empirical framework in real production environments. The proposed model is implemented within a business and technological processes monitoring system for the production of automotive daytime running lights (DRL), combining real-time measurement, automated data acquisition, and structured process optimization. A multi-phase implementation strategy enabled the transition from manual to fully automated monitoring, supported by more than 1,400 measurements collected across key technological operations in accordance with international standards. A longitudinal case study design was applied, and statistical analyses, including correlation and regression methods, were used to examine relationships between process capability, operational performance, and financial outcomes. The results show that systematic optimization increased equipment effectiveness from 78.36 % to 85.41 % and financial return from €2.9 million to €7.98 million, while achieving process capability levels above the required thresholds (Cpk > 2). A strong statistical relationship was identified between OEE and ROI, whereas the relationship between process capability Cpk and OEE was not statistically confirmed as a direct effect. The findings indicate that technological, operational, and financial indicators are interconnected but not strictly linear, highlighting the importance of integrated monitoring for understanding performance dynamics in manufacturing systems. The proposed framework provides an empirically grounded approach for linking process stability, operational efficiency, and financial outcomes, supporting performance evaluation and continuous improvement in automotive manufacturing.

Numerical Analysis of Nonuniform Flow Around a Large Wind Turbine Operating at Variable Speeds

Vidosava Vilotijević — 2026-07-01

The increasing reliance on wind energy necessitates a deeper understanding of the unsteady aerodynamic behavior of wind turbines operating under variable speed conditions. This study employs computational fluid dynamics (CFD) simulations to investigate the flow dynamics around a large wind turbine subjected to height-dependent wind speed variations common within the atmospheric boundary layer (ABL). Utilizing a fine computational mesh and the k-ω shear stress transport (SST) turbulence model, we explored the interaction between wind turbine rotor aerodynamics, wake structures formation and evolution, and power performance under nonuniform operational conditions. The obtained numerical results highlight the nonlinear relationship between power output and angular velocity, demonstrating peak power generation at an optimal rotational speed (1.25 rad/s) before aerodynamic losses reduce performance. Additionally, the study examines variations in power and thrust coefficients, emphasizing their dependence on tip speed ratio and wind inflow characteristics. The computed power coefficient curve matches closely the corresponding experimental one, thus validating the adopted numerical setup as well as the assigned boundary conditions. Velocity contour analyses reveal critical regions of flow deceleration and separation, intensive turbulence, and wake interactions, providing insights for optimizing turbine design and control strategies. The findings underscore the importance of accurate inflow conditions and turbulence modeling in improving the reliability of estimated wind turbine.