Search Results (365)

This paper presents a structured and experimentally validated approach to the parameter identification, modeling, and real-time speed control of a brushless DC (BLDC) motor. Electrical parameters, including resistance and inductance, were measured through DC and AC testing under controlled conditions, respectively, while mechanical and electromagnetic parameters such as the back electromotive force (EMF) constant and rotor inertia were determined experimentally using an AVL dynamometer. The back EMF was obtained by operating the motor as a generator under varying speeds, and inertia was identified using a deceleration method based on the relationship between angular acceleration and torque. The identified parameters were used to construct a transfer function model of the motor, which was implemented in MATLAB/Simulink R2024b and validated against real-time experimental data using sinusoidal and exponential input signals. The comparison between simulated and measured speed responses showed strong agreement, confirming the accuracy of the model. A proportional–integral (PI) controller was developed and implemented for speed regulation, using a low-cost National Instruments (NI) USB-6009 data acquisition (DAQ) and a Kelly controller. A first-order low-pass filter was integrated into the control loop to suppress high-frequency disturbances and improve transient performance. Experimental tests using a stepwise reference speed profile demonstrated accurate tracking, minimal overshoot, and robust operation. Although the modeling and control techniques applied are well known, the novelty of this work lies in its integration of experimental parameter identification, real-time validation, and practical hardware implementation within a unified and replicable framework. This approach provides a solid foundation for further studies involving more advanced or adaptive control strategies for BLDC motors. Full article

Objectives: To identify clinical, functional, laboratory, and patient-reported parameters associated with medium-term risk of hospitalization or death among older adults attending a multidisciplinary outpatient clinic, and to assess the predictive performance of these measures for individual risk stratification. Methods: In this cohort study, 350 adults aged ≥65 years were assessed at baseline and followed for an average of 8 months. The primary outcome was a composite of hospitalization or all-cause mortality. Parameters assessed included frailty and comorbidity measures, functional parameters, such as gait speed and grip strength, laboratory biomarkers, and patient-reported measures, such as quality of life (QoL, assessed on a Likert scale) and the presence of depressive symptoms. Predictive performance was evaluated using univariable logistic regression and multivariable modeling. Discriminative ability was assessed via area under the ROC curve (AUC), and selected models were internally validated using repeated k-fold cross-validation. Results: Overall, 40 participants (11.4%) experienced hospitalization or death. Traditional clinical risk indicators, including frailty and comorbidity scores, were significantly associated with the outcome. Patient-reported QoL (AUC = 0.74) and Geriatric Depression Scale (GDS) scores (AUC = 0.67) demonstrated useful overall discriminatory ability, with high specificities at optimal cut-offs, suggesting they could act as “red flags” for adverse outcomes. However, the limited sensitivities of individual predictors underscore the need for more comprehensive screening instruments with improved ability to identify at-risk individuals earlier. A multivariable model that incorporated several predictors did not outperform QoL alone (AUC = 0.79), with cross-validation confirming comparable discriminative performance. Conclusions: Patient-reported measures—particularly quality of life and depressive symptoms—are valuable predictors of hospitalization or death and may enhance traditional frailty and comorbidity assessments in outpatient geriatric care. Future work should focus on developing or integrating screening tools with greater sensitivity to optimize early risk detection and guide preventive interventions. Full article

With the advancement of modern fan architectures, dedicated experimental benchmarks are becoming fundamental to improving the knowledge of flow physics, validating novel CFD methods, and fine-tuning existing methods. In this context the open test case ECL5/CATANA, representative of a modern Ultra High Bypass Ratio (UHBR) architecture, has been designed and experimentally investigated at École Centrale de Lyon (ECL) in a novel test facility with multi-physical instrumentation, providing a large database of high-quality aerodynamic and aeromechanic measurements. In this paper, a thorough numerical study of the fan stage aerodynamics was performed using the CFD TRAF code developed at the University of Florence. Fan stage performance was studied at design speed over the entire operating range. The results were discussed and compared with datasets provided by ECL. Detailed sensitivity on numerical schemes and state-of-the-art turbulence/transition models allowed for the selection of the best numerical setup to perform UHBR fan simulations. Moreover, to have a deeper understanding of the fan stall margin, unsteady simulations were also carried out. The results showed the appearance of blade tip instability, precursor of a rotating stall condition, which may generate non-synchronous blade vibrations. Full article

This work aims to assess the droplets produced by a novel evaporation process, proposed as an alternative for managing tequila vinasses, using a spectral camera with three spectral bands and a thermal camera mounted on an unmanned aerial vehicle (UAV). High-resolution satellite images with seven spectral bands complemented this characterization. The spectral characterization was conducted by comparing three experimental conditions: the background of the study area without droplets, the droplets generated from purified water, and the droplets produced from tequila vinasses. Two monitoring campaigns, conducted in November 2024 and January 2025, revealed that the tequila vinasse droplets exhibited a maximum influence radius of 16 m, primarily regulated by wind speed conditions (6–16 km/h). Thermal analysis identified the droplet plume as a zone with a lower temperature, creating a thermal contrast of up to 6.6 °C against the average background temperature of 36.6 °C. No significant difference was observed in the influence radius between the droplets generated from vinasse and those from potable water. Spectral analysis of the UAV and satellite images showed significant (p < 0.05) differences in reflectance when the droplets were present (e.g., the coastal blue band increased from an average of 14.43 to 95.59 when vinasse droplets were present). This suggests that the presence of chemical compounds altered light absorption and reflection. However, the instrument’s sensitivity limited the detection of organic compounds at concentrations below its detection limit. The monitoring data presented in this manuscript is crucial for developing strategies to mitigate the potential environmental impacts of the droplets emitted by this novel process. Full article

The worldwide air pollution situation reveals significant environmental challenges. In addition to being a major contributor to the deterioration of air quality, particulate matter (PM) is also an important factor affecting the performance of solar energy systems given its ability to decrease light transmission to solar panels. As part of our research, the present investigation involves monitoring concentrations of PM using a high-performance optical instrument, the in situ calibration protocol of which is described in detail. For the city of Rabat, observations revealed significant variations in concentrations between day and night, with peaks observed around 8 p.m. correlating with high relative humidity and low wind speeds, and the highest levels recorded in February with a monthly average value reaching 75 µm/m³. In addition, an experimental protocol was set up for an analysis of the elemental composition of particles in the same city using SEM/EDS, providing a better understanding of their morphology. To assess the impact of meteorological variables on PM concentrations in two distinct climatic environments, a database from the city of Marrakech for the year 2024 was utilized. Overall, the distribution of PM values during this period did not fluctuate significantly, with a monthly average value not exceeding 45 µm/m³. The random forest method identified the most influential variables on these concentrations, highlighting the strong influence of the type of environment. The findings provide crucial information for the modeling of solar installations’ soiling and for improving understanding of local air quality. Full article

The aerial measurement of wind speed and direction is important for the development of the low-altitude economy, meteorology, climate research, and renewable energy systems. Existing UAV-based wind measurements, whether instrument-based or flight-dynamic-based, consistently exhibit bias and significant errors, limiting their reliability for precise wind estimation. This study introduces a machine learning (ML) approach to improve the accuracy of the wind speed and direction estimation using UAVs. The proposed method leverages data from sensors onboard UAV platforms, combined with advanced ML algorithms trained on ground-truth measurements obtained through high-resolution LiDAR systems. The experiments reveal that incorporating a 10 s smoothing window yields a root mean square error (RMSE) value of 0.39 m/s for the wind speed (horizontal) and an even lower bias (≤0.069 m/s) when using a 60 s smoothing window, representing a marked improvement over traditional techniques. These results are particularly promising at longer smoothing windows (>50 s), where the ML-based approach achieves superior accuracy compared to LiDAR measurements. The findings underscore the potential of integrating machine learning with UAV-based wind measurement systems to achieve higher precision and reliability in wind characterization. Full article

This work—mixing an original experimental approach, as well as numerical simulations—proposes to study film boiling modes around a small nickel sphere. While dealing with a simple looking phenomenon that is found in many industrial processes and has been solved for basic quenching regimes, we focus on describing precisely how vapor formation and film thicknesses, as well as vapor bubble evacuation, affect cooling kinetics. As instrumenting small spheres may lead to experimental inaccuracies, we optically captured, using a high-speed camera, the vapor film thickness at mid height, the vapor bubble volume, and the bubble detachment frequency, along with the heat flux. More precisely, an estimation of the instant sphere temperature, in different conditions, was obtained through cooling time measurement before the end of the film boiling mode, subsequently facilitating heat flux evaluation. We encountered a nearly linear decrease in both the vapor film thickness and vapor bubble volume as the sphere temperature decreased. Notably, the detachment frequency remained constant across the whole temperature range. The estimation of the heat fluxes confirmed the prevalence of conduction as the primary heat transfer mode; a major portion of the energy was spent increasing the liquid temperature. The results were then compared to finite element simulations using an in-house multiphysics solver, including thermic phase changes (liquid to vapor) and their hydrodynamics, and we also captured the interfaces. While presenting a challenge due to the contrast in densities and viscosities between phases, the importance of the small circulations along them, which improve the heat removal in the liquid phase, was highlighted; we also assessed the suitability of the model and the numerical code for the simulation of such quenching cases when subcooling in the vicinity of a saturation temperature. Full article

In this study, a series of comprehensive experimental tests were conducted to assess the impact of permanent displacements observed during the construction of the Cho’ponota L1 Bridge in Uzbekistan and to evaluate the bridge’s structural suitability for service. The investigation included Operational Modal Analysis and static and dynamic vehicular load tests, conducted using two trucks with different weights under varying loading scenarios and speeds. A total of 28 static and 24 dynamic load cases were tested across the bridge’s four spans. Displacement measurements were acquired using geodetic instruments during the static tests, while acceleration data were recorded during dynamic tests using high-sensitivity accelerometers, from which Dynamic Amplification Factors were calculated. The results indicated that all displacement values remained within permissible safety limits, and no visible damage or cracking was detected. Beyond conventional analysis, the study proposed a test-assisted digital twin framework in which high-fidelity field data were integrated into a finite-element model. The initial numerical model was calibrated using modal properties obtained from OMA, and discrepancies were minimized through iterative updates to material parameters, especially concrete stiffness. The resulting validated digital twin accurately reflects the bridge’s current structural condition and can be used for future predictive simulations and performance-based evaluations. The findings underscore the effectiveness of combining non-destructive testing with digital twin methodology in diagnosing structural behavior and offer a replicable model for assessing bridges experiencing construction-related anomalies. Full article

Computational fluid dynamics (CFD) simulations play a strong role in the design and development of aerospace and defense vehicles, including high-speed applications where testing under the correct operational conditions is not yet viable. In this study, metrics for the onset of isolator unstart are identified. An assessment of the variance of operating variables and their impact on metrics for the onset of isolator unstart and operability metrics was performed, utilizing a nozzle–isolator assembly from NASA Langley Research Center as a demonstration case. The effects of increasing backpressure ratio and decreasing inflow Mach number on these metrics and the underlying contributions of shock physics were investigated in detail. A major conclusion from this study is that both inflow Mach number and backpressure ratio can strongly impact pseudo shock train and shock–boundary layer interactions inside the isolator, but inflow Mach number has a stronger impact than the backpressure ratio. The research presented in this paper demonstrates that the isolator performance can shift from start to unstarted and operable to inoperable with a small variance in operating conditions. Another important insight presented in this research is that the length of the pseudo shock train and the Mach stem height change discontinuously with both the backpressure ratio and the inflow Mach number. Therefore, the length of the pseudo shock train and height of the Mach stem are strong indicators of the onset of unstart, which is an important consequence for instrumentation and closed-loop adaptive feedback control system design for scramjet flight operations. Full article

Magnetic pulse welding (MPW) is a promising solid-state joining process that utilizes electromagnetic forces to create high-speed, impact-like collisions between two metal components. This welding technique is widely known for its ability to join dissimilar metals, including aluminum, steel, and copper, without the need for additional filler materials or fluxes. MPW offers several advantages, such as minimal heat input, no distortion or warping, and excellent joint strength and integrity. The process is highly efficient, with welding times typically ranging from microseconds to milliseconds, making it suitable for high-volume production applications in sectors including automotive, aerospace, electronics, and various other industries where strong and reliable joints are required. It provides a cost-effective solution for joining lightweight materials, reducing weight and improving fuel efficiency in transportation systems. This contribution concerns an application for the automotive sector (body-in-white) and specifically examines the welding of AA6082-T6 aluminum alloy with HC420LA cold-rolled micro-alloyed steel. One of the main aspects for MPW optimization is the determination of the process window that does not depend on the equipment used but rather on the parameters associated with the physical mechanisms of the process. It was demonstrated that process windows based on contact angle versus output voltage diagrams can be of interest for production use for a given component (shock absorbers, suspension struts, chassis components, instrument panel beams, next-generation crash boxes, etc.). The process window based on impact pressures versus impact velocity for different impact angles, in addition to not depending on the equipment, allows highlighting other factors such as the pressure welding threshold for different temperatures in the impact zone, critical transition speeds for straight or wavy interface formation, and the jetting/no jetting effect transition. Experimental results demonstrated that optimal welding conditions are achieved with impact velocities between 900 and 1200 m/s, impact pressures of 3000–4000 MPa, and impact angles ranging from 18–35°. These conditions correspond to optimal technological parameters including gaps of 1.5–2 mm and output voltages between 7.5 and 8.5 kV. Successful welds require mean energy values above 20 kJ and weld specific energy values exceeding 150 kJ/m². The study establishes critical failure thresholds: welds consistently failed when gap distances exceeded 3 mm, output voltage dropped below 5.5 kV, or impact pressures fell below 2000 MPa. To determine these impact parameters, relationships based on Buckingham’s π theorem provide a viable solution closely aligned with experimental reality. Additionally, shear tests were conducted to determine weld cohesion, enabling the integration of mechanical resistance isovalues into the process window. The findings reveal an inverse relationship between impact angle and weld specific energy, with higher impact velocities producing thicker intermetallic compounds (IMCs), emphasizing the need for careful parameter optimization to balance weld strength and IMC formation. Full article

Optimization algorithms serve as a powerful instrument for tackling optimization issues and are highly valuable in the context of engineering design. The coati optimization algorithm (COA) is a novel meta-heuristic algorithm known for its robust search capabilities and rapid convergence rate. However, the effectiveness of the COA is compromised by the homogeneity of its initial population and its reliance on random strategies for prey hunting. To address these issues, a multi-strategy adaptive coati optimization algorithm (MACOA) is presented in this paper. Firstly, Lévy flights are incorporated into the initialization phase to produce high-quality initial solutions. Subsequently, a nonlinear inertia weight factor is integrated into the exploration phase to bolster the algorithm’s global search capabilities and accelerate convergence. Finally, the coati vigilante mechanism is introduced in the exploitation phase to improve the algorithm’s capacity to escape local optima. Comparative experiments with many existing algorithms are conducted using the CEC2017 test functions, and the proposed algorithm is applied to seven representative engineering design problems. MACOA’s average rankings in the three dimensions (30, 50, and 100) were 2.172, 1.897, and 1.759, respectively. The results show improved optimization speed and better performance. Full article

Methane is the second largest greenhouse gas. The detection of methane super-emitters and the quantification of their emission rates are necessary for the implementation of methane emission reduction policies to mitigate global warming. High-spectral-resolution satellites such as Gaofen-5 (GF-5), EMIT, GHGSat, and MethaneSAT have been successfully employed to detect and quantify methane point source leaks. In this study, a matched filter (MF) algorithm is improved using data from the EMIT instrument and applied to data from the Advanced Hyperspectral Imager (AHSI) onboard the Ziyuan-1 (ZY-1) satellite. Validation by comparison with EMIT′s L2 XCH₄ products shows the good performance of the improved MF algorithm, in spite of the lower spectral resolution of AHSI/ZY-1 in comparison with other point source imagers. The improved MF algorithm applied to AHSI/ZY-1 data was used to detect and quantify methane super-emitters in global methane hotspot regions. The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. Sixteen methane plumes were detected in global hotspot regions, originating from coal mines, oil and gas fields, and landfills, with emission rates ranging from 0.57 to 78.85 t/h. The largest plume was located at an offshore oil and gas field in the Gulf of Mexico, with instantaneous emissions nearly equal to the combined total of the other 15 plumes. The findings demonstrate that AHSI, despite its lower spectral resolution, can detect sources with emission rates as small as 571 kg/h and achieve faster retrieval speeds, showing significant potential for global methane monitoring. Additionally, this study highlights the need to focus on methane emissions from marine sources, alongside terrestrial sources, to efficiently implement reduction strategies. Full article

Accurate mechanical property parameters constitute an indispensable guarantee for the accuracy of finite element simulations. Traditionally, uniaxial tensile tests are instrumental in acquiring the stress–strain data of materials during elongation, thereby facilitating the determination of the materials’ mechanical property parameters. By capitalizing on the digital image correlation (DIC) non-contact optical measurement technique, the entire test can be comprehensively documented using high-speed cameras. Subsequently, through in-depth analysis and meticulous numerical computations enabled by computer vision technology, the complete strain evolution of the specimen throughout the test can be precisely obtained. In this study, a comparison was made between the application of strain gauges and DIC testing systems for measuring the strain alterations during the tensile testing of 316L stainless steel, which serves as the material for the primary circuit pipelines of pressurized water reactor (PWR) nuclear power plants (NPPs). The data procured from these two methods were utilized as material mechanical parameters for finite element simulations, and a numerical simulation of the uniaxial tensile test was executed. The results reveal that, within the measuring range of the strain gauge, the DIC method generates measurement outcomes that are virtually identical to those obtained by strain gauges. Given its wider measurement range, the DIC method can be effectively adopted in the process of obtaining material mechanical parameters for finite element simulations. Full article

Ground reaction force (GRF) and center of pressure (COP) during walking are two important measures that could be used in a range of applications, from the control of devices such as exoskeletons to clinical assessments. Recording these measures requires fixed laboratory equipment such as force plates or expensive portable insoles. We present an alternative approach by developing a 3D-printed insole that uses pneumatic chambers and pressure sensors to estimate the net GRF and the anterior–posterior COP position. The intentionally simple design, using just two pneumatic chambers, can be fabricated using standard 3D printing technology and readily available soft materials. We used experimentally recorded data from a motion capture system along with parameter identification techniques to characterize and validate the insole while walking at different speeds. Our results showed that the insole was capable of withstanding repeated loading during walking—up to 1.2 times the body weight—and possessed a bandwidth high enough to capture gait dynamics. The identified models could estimate the GRF and the anterior–posterior COP position with less than 9% error. These results compare favourably with those of commercially available instrumented insoles and can be obtained at a fraction of their cost. This low-cost yet effective solution could assist in applications where it is important to record gait outside of laboratory conditions, but the cost of commercial solutions is prohibitive. Full article

Accurate vertical load measurement through intelligent tire technology is crucial for vehicle stability, handling, and safety. Existing studies have mainly focused on modeling and bench experiments, overlooking a detailed comparative analysis of real sensor performance and validation under actual driving conditions. This study addresses this gap by performing sensor comparisons and extensive real-road validation to ensure the accuracy and reliability of the proposed methods. First, finite element modeling (FEM) is used to assess the feasibility of accelerometer and strain-based sensors for vertical load prediction. High-precision bench tests quantitatively compare the performance of multiple triaxial Integrated Electronics Piezoelectric (IEPE) accelerometers and Polyvinylidene Fluoride (PVDF) sensors, identifying accelerometers as the superior choice due to their better stability and linearity. Vertical load prediction algorithms are developed using Support Vector Machine (SVM) and linear regression, considering variables like contact length, vehicle speed, and tire pressure. The algorithms are validated under real-road conditions using high-performance instruments across constant speed, acceleration, braking, and cornering, and a self-designed compact Intelligent Tire Test Unit (ITTU) is deployed for product-level implementation, confirming its effectiveness in real-world driving scenarios. The findings provide a validated framework for accurate vertical load estimation and real-time tire parameter prediction, offering practical insights for improving intelligent tire technology in dynamic driving conditions. Full article