Experimental Tests Research Articles

Fluid-filled lattices for responsive orthotic insoles

Abstract Orthotic insoles are essential for alleviating discomfort and preventing injuries in the foot caused by high peak pressures in the plantar tissue. Traditional orthotic insoles, often prescribed to address these issues, are designed based on the static foot shape and pressure measurements, lacking responsiveness to dynamic movements. This study explores the behaviour of fluid-filled lattices for improving the functionality of orthotic insoles, focusing on energy dissipation and pressure redistribution capabilities. Using numerical homogenisation, the research integrates hyperelastic and permeability models to simulate the behaviour of Solid–Liquid Composites. Experimental tests validated these models, examining the influence of fluid viscosity and structural variations on energy dissipation and pressure distribution. Results show that fluid-filled lattices provide enhanced energy dissipation and reduce peak pressures by evening out the pressure distribution compared to non-fluid-filled samples. These findings highlight the potential of fluid-filled lattices to improve the performance and comfort of orthotic insoles.

Neurotoxic mixture effects of chemicals extracted from blood of pregnant women.

Human biomonitoring studies typically capture only a small and unknown fraction of the entire chemical universe. We combined chemical analysis with a high-throughput in vitro assay for neurotoxicity to capture complex mixtures of organic chemicals in blood. Plasma samples of 624 pregnant women from the German LiNA cohort were extracted with a nonselective extraction method for organic chemicals. 294 of >1000 target analytes were detected and quantified. Many of the detected chemicals as well as the whole extracts interfered with neurite development. Experimental testing of simulated complex mixtures of detected chemicals in the neurotoxicity assay confirmed additive mixture effects at concentrations less than individual chemicals' effect thresholds. The use of high-throughput target screening combined with bioassays has the potential to improve human biomonitoring and provide a new approach to including mixture effects in epidemiological studies.

Impact of Nano-TiO2 Combination with Biodiesel on Diesel Engine Performance and Emissions Under Fuel Magnetism Conditioning

AbstractProblems of atomization, spray, and lower output power are due to the biodiesel’s higher viscosity. All of these aim to encourage fuel magnetism and nanoparticles addition to reduce fuel consumption. Waste cooking oil was converted to methyl ester by transesterification. To make methyl ester blend, diesel and biodiesel were mixed at volume ratio of 20%. TiO2 nanoparticles were added to biodiesel blend B20 at doses of 25 and 50 mg/L. TEM and XRD were used to characterize the nanomaterials. A magnetic coil was placed before the fuel injector to apply a magnetic field on the line of fuel. South pole of the magnetic field is located near to the fuel line, whereas the north pole is located further away. To examine the impact of these nanomaterials with fuel magnetism on engine performance and emissions using WCO biodiesel mixture, an experimental test rig was built connected to diesel engine. During testing, diesel engine operates at 1500 rpm with load variation. The average increases in BTE were 1, 1.5, 3.5, 5.5, and 6.5% but the decreases in BSFC were 1.2, 2, 4, 5, and 6% for B20 + magnet, B20 + 25 TiO2, B20 + 25 TiO2 + magnet, B20 + 50 TiO2, and B20 + 50 TiO2 + magnet, respectively, at engine load range. The average drops in CO, NOx, and HC concentrations were 16, 22, and 33%, respectively, at load range for B20 + 50 TiO2 + magnet. To improve engine performance and reduce emissions, biodiesel blend B20 from waste cooking oil with nanoTiO2 concentration of 50 ppm under magnetic field effect was recommended as a substitute fuel in diesel engine.

Boiling heat transfer of water on smooth and square pin fins surfaces in minichannel

In this investigation, flow boiling heat transfer and hydrodynamic instability using deionized water in a minichannel with smooth and square pin fin surfaces was experimentally examined. The study is aimed to assess local and overall heat transfer coefficients, heat transfer coefficients, pressure drop and Nusselt number across various liquid mass flux ranges (50–93 kg m−2 s−1) and a constant heat flux of 46.91 kW/m2. The minichannel, with a hydraulic diameter of 2.85 mm, features a rectangular shape. Experimental testing was conducted on aluminum surface within a minichannel of dimensions 270 mm in length, 30 mm in width, and 1.5 mm height. The square pin fins are integrated at the base of the boiling surfaces, arranged in staggered form. The results revealed that the boiling heat transfer coefficient on square pin fin surfaces was approximately 88 % higher than that on smooth surfaces. Additionally, a notable increase in local heat transfer coefficient was observed for square pin fin surfaces (i.e., 34.64 kW/m2.K) compared to smooth surfaces (8.09 kW/m2.K) at the same mass flux of 50 kg/m2.s. Besides, the square pin fins surface exhibited a lower and stable pressure drop, particularly near annular flow regimes, compared to the smooth surface. Notably, the highest pressure drop observed was 4.8 kPa for the square pin fins surface, while the smooth surface experienced a higher pressure drop of 6.36 kPa, accompanied by more pronounced pressure fluctuations during annular flow.

Piezoelectric-electromagnetic wearable harvester for energy harvesting and motion monitoring

This paper proposes a piezoelectric-electromagnetic wearable harvester (PEWH). The device is used to harvest the energy generated when the upper limb swings and can perform the function of motion monitoring. The main structure of PEWH consists of the piezoelectric power generation module and electromagnetic sensing module. Among them, the piezoelectric sheet in the piezoelectric module deforms and outputs electric energy, and the magnetic ball in the electromagnetic module moves to generate induced electromotive force to achieve sensing and energy supply. Through experimental testing, PEWH output performance is optimal when the spring wire diameter is 0.8 mm and the distance between the spring connector where the spring located and the position of the top of the shaft is 65 mm. At a vibration excitation frequency of 2 Hz, the device’s output voltage reaches a peak-to-peak value of 57.84Vpp, accompanied by a maximum power of 115.52mW. A range of application experiments were conducted to confirm the output performance of the device, which can power 82 LEDs and the temperature and humidity sensor. The prototype can be worn on the arm to enable monitoring of the current movement status. PEWH can effectively capture the energy generated by human movement for self-powered and self-sensing motion detection.

Polymer Shaped Punches Produces with Fused Filament Fabrication to Improve Cup Accuracy in Sheet Metal Forming

Rapid tooling has become an effective solution for reducing time and costs in tool production. In sheet metal forming, polymer tools produced via additive manufacturing offer performance comparable to traditional tools. However, a key challenge in this area is compensating for the radial expansion of polymer tools during the forming process, which leads to reduced accuracy in the produced parts and limits the achievable forming depth. To address this issue, the authors of this study proposed a novel punch design aimed at containing radial expansion, thereby enabling greater drawing depth and improved part accuracy. Different punch geometries were designed with a re-entrant angle varying between 150° and 180°. Numerical simulations were conducted to evaluate the optimal geometry, identifying the 160° angle as the best option to compensate for radial expansion and reduce punch load. Experimental tests were then performed to verify the numerical results, demonstrating the potential of this new design producing cups with higher drawing depth and best radial accuracy.

Efficient Data Collection Strategy for Modeling the Dynamics of Floating Robotic Vehicles Using Neural Networks

As the hydrodynamic model of a surface vehicle is highly nonlinear and of high dimension, an extensive set of experiments is required to estimate the parameters of the model. Typically, the dynamic behavior of a surface vehicle is investigated through experimental tests such as planar motion mechanisms or free running tests in specialized test facilities. However, the cost of such tests is very high, and it is challenging to design effective and efficient test conditions, especially when the model is represented by numerous parameters such as neural networks. In this paper, we propose an efficient data collection strategy for modeling the dynamics of a floating robotic vehicle using neural networks. For generalized modeling, it is important to collect diverse data by exploring all the reachable state space of the vehicle. To achieve this, a data collection policy is built based on sampling-based planning toward reducing the uncertainty of the neural network-based model. The proposed method allows for collecting more comprehensive data than other commonly used random-based data collection policies. We show experimentally that the proposed method enables more accurate modeling of both fully actuated and under-actuated floating robotic vehicles, as demonstrated by the effective execution of various tasks.

Effect of cenospheres on the proprieties of plain concrete exposed to elevated temperature

AbstractThe main objective of the study was to determine the effect of the use of cenospheres for C30/37 class concretes used in construction and road construction. As part of the research, four concrete mixtures were designed: three with the addition of cenospheres and one reference mixture, without the additive. In the three modified mixtures, 20, 40 and 60% of fine aggregate were replaced by cenospheres, respectively. Selected features of the mixtures were determined—consistency and density, as well as the strength properties of concrete. Strength tests were carried out after 28 and 56 days of maturation. Experimental tests of concrete after initial thermal treatment were also carried out at three selected temperatures: 300, 500 and 700 °C, with loading times ranging from 60 to 180 min. These times were selected in such a way that the samples could be heated in their entire volume. A separate group of experimental studies was microstructure studies, the aim of which was to assess the changes occurring in concrete under the influence of high temperatures and to try to justify why CS-I concrete containing cenospheres in the amount of 20% of concrete subjected to annealing at 300 °C has a higher compressive strength than the corresponding reference concrete. The negative influence of cenospheres on the strength of concrete after initial thermal treatment was unequivocally demonstrated. An increase in the amount of cenospheres causes a decrease in compressive strength. A similar negative relationship was also demonstrated in the case of non-heat-treated samples. Graphical Abstract

Evaluating the Performance of Ensemble Machine Learning Algorithms Over Traditional Machine Learning Algorithms for Predicting Fire Resistance in FRP Strengthened Concrete Beams

In recent years, fiber-reinforced polymers (FRP) have emerged as a highly effective solution for strengthening reinforced concrete (RC) structures. However, accurately assessing the fire resistance of FRP-strengthened members remains a significant challenge due to the limited guidance available in current building codes, often leading to conservative and cost-intensive evaluations. Experimental testing and numerical analysis required for such assessments are resource-demanding, highlighting the need for more efficient methods. This study investigates the application of machine learning (ML) techniques to predict the fire resistance of FRP-strengthened RC beams. Twelve ML models, including eight ensemble methods and four traditional approaches, were employed. The models were trained using a comprehensive dataset comprising over 21,000 data points obtained from numerical simulations and experimental tests. The dataset captured variations in geometric configurations, insulation strategies, loading conditions, and material properties. To enhance predictive accuracy, Bayesian optimization and k-fold cross-validation were applied for model tuning, while the Shapley Additive Explanations (SHAP) method was utilized to assess the relative importance of features influencing fire resistance. Among the models tested, Extreme Gradient Boosting (XGBoost), Categorical Boosting (CatBoost), Light Gradient Boosting (LGBoost), and Gradient Boosting (GRB) demonstrated superior performance, achieving accuracy rates exceeding 92%. Key factors identified as significantly affecting fire resistance included loading ratio, area of tensile reinforcement, insulation depth, concrete cover thickness, and FRP area. The findings underscore the potential of ensemble ML techniques over traditional methods for accurately predicting the fire resistance of FRP-strengthened RC beams, offering critical insights for optimizing design practices and enhancing structural fire safety.

Analysis and experiment of structural geometry for improved strain sensitivity of FBG sensors

AbstractThis paper presents analysis and experimental studies to significantly enhance the strain sensitivity of fiber Bragg grating (FBG) sensors by suitably modifying the host structure used for mounting the FBG. The proposed host structure is a novel, compact flexure beam-based design, specially engineered to amplify and convert horizontal strain into vertical strain more effectively. Its unique geometry includes circular sections for hinge connections, resulting in improved displacement amplification and reduced stress across the structure. Using ANSYS calculations and finite element analysis, simulations were conducted to evaluate the vertical deformation, stress, and longevity of the sensor's mechanical structure. Results from these simulations indicate an enhanced strain sensitivity of approximately 15.633 pm/με, a significant improvement over the 1.191 pm/με sensitivity observed with bare FBGs. Experimental tests were carried out on fabricated sensor structures to validate the enhancement in strain sensitivity. FBGs utilized in the experiments were inscribed using a 255 nm UV beam generated from a second harmonic copper vapour laser. The strain sensitivity of FBGs mounted on the optimized structure was found to increase up to 9.95 pm/με. The difference between simulation and experimental results are attributed to the partial absorption of strain by the adhesive used to affix FBGs.

Experimental and numerical research on the thermal performance of a vertical earth-to-air heat exchanger system

The Earth-to-Air Heat Exchanger (EAHE) system is an efficient and clean geothermal application technology that can be used for pre-cooling in summer and heating in winter. This paper proposes a novel Vertical Earth-to-Air Heat Exchanger (VEAHE) system that uses baffles to divide the vertical duct into two ventilation tunnels with a hollow area at the bottom for air circulation. This system occupies a small land area and has a relatively high geothermal energy utilization efficiency. This study evaluates the thermal performance of the system through experimental tests under various operating conditions. Additionally, a numerical model of the system was established to explore the influence of baffles length, thickness, and duct depth on its thermal performance. The experimental results show that the 2.5-meter deep VEAHE system achieves an average air pre-cooling temperature reduction of 5.42 °C, with a maximum temperature reduction of up to 7.58 °C. Below the 1.2-meter mark of the system, the cooling capacity of the descending pipe is 1.52 times that of the ascending pipe. The simulation showed a Maximum Absolute Relative Error (MARE) of 3.15 % compared to the experimental results. As the length and thickness of the baffles, duct length, and soil thermal conductivity increase, the average outlet air temperature gradually decreases, while the system's heat exchange capacity significantly improves, in contrast to the duct diameter. Among the influencing factors, the duct length has the greatest impact on the system. Under the recommended configuration, the system's maximum pre-cooling potential is 915.90 W, with the outlet air temperature ranging from 12.05 °C to 14.79 °C.

Development of a novel crack-resistance technique for concrete slab in the hogging moment region of continuous composite beam

Composite steel-concrete beams offer distinct mechanical advantages. However, concrete cracking in the hogging moment region limits the composite performance of the material. This study presents an innovative technique for enhancing the crack resistance of continuous steel-concrete composite beams, which involves the prestressing of the concrete slab in the hogging moment region through post-tensioning. Following the completion of the concrete slab in the sagging moment region, the prestress is released, thereby preserving pre-compression stress in the hogging moment region without the need for long-term prestressing. This technique is enhanced using uplift-restricted and slip-free connectors, which improve the efficiency of pre-stress introduction. Experimental tests were conducted on composite beams employing this novel technique, alongside numerical simulations. The results indicated that the proposed technique significantly mitigated concrete cracking in the hogging moment region. Specifically, the cracking load of the specimen using the new technique increased by 467 % compared to the traditional composite beam specimen, accompanied by a substantial reduction in both the quantity and width of cracks. Other primary mechanical performance indicators remained relatively similar; however, the new technique exhibited a 14 % increase in the ductility coefficient compared with the traditional method. Additionally, this technique influenced interface slip, moment redistribution, and strain response along the cross-section in both the hogging and sagging moment regions. The numerical results aligned closely with the experimental data, confirming their accuracy and effectiveness. The simulations provided solutions for various construction stages by incorporating different connection constitutive models with adequate precision. A parametric analysis was conducted, revealing a design method for the innovative technique.

Sustainable separation of molybdenum from mixed mineral acids generated as semiconductor industry waste streams using tributyl phosphate (TBP) by effects of hybrid machine learning models

This study explores the separation and optimization of molybdenum (Mo) from mixed mineral acids derived from semiconductor industry waste streams with tributyl phosphate (TBP) by implementing machine learning (ML) models. Considerable experimental tests were performed to evaluate the impact of various operational variables on the effectiveness of Mo extraction and stripping. The support vector regression (SVR) paired with harmony search algorithm (HSA), genetic algorithm (GA), and shuffled frog leaping algorithm (SFLA) were employed for enhancement in the separation process and structural optimization. The SVR-SFLA model yielded the most meticulous predictions, identifying optimal extraction conditions with a TBP concentration, mixing time, temperature, and O/A ratio of 50%, 30 min, 25 °C, and 1, respectively, achieving 77.8% efficiency. The derived results from the SVR-SFLA model, in tandem with the McCabe-Theil diagram, indicated a four-stage counter-current extraction process required to achieve a yield exceeding 99%. For the stripping process, the hybrid model indicated optimal conditions with 3 M NH4OH and an A/O ratio of 0.5 at 50 °C for 20 min, requiring two counter-current stages for nearly complete stripping. Feature importance analysis using a random forest algorithm (RFA) highlighted the NH4OH concentration and phase ratio as the most significant factors, contributing 40.3% and 29.1%, respectively, to the stripping from the loaded TBP phase. The final product, obtained after crystallization and thermal decomposition of the strip solutions, was characterized by X-ray diffraction (XRD), thermogravimetric analysis (TGA), scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS), revealing 99.74% purity for molybdenum trioxide.

Experimental cyclic testing of masonry pier-spandrel substructures reinforced with engineered cementitious composites overlay

Experimental cyclic testing of masonry pier-spandrel substructures reinforced with engineered cementitious composites overlay

Axial capacity of cold-formed steel channel sections with slits

Cold-formed steel (CFS) studs often include slits in their webs to improve thermal performance. However, the inclusion of such slits reduces their axial capacity. The literature contains limited experimental studies on the axial capacity of these studs with slits, and no non-linear elasto-plastic finite element studies have been reported. This research aims to address this gap. Non-linear elasto-plastic finite element models were developed and validated against experimental test results. A parametric study consisting of 960 finite element analysis (FEA) models was then conducted, covering the effects of different channel dimensions, slit sizes, section thicknesses, and stud lengths. Ultimate axial capacities obtained from these analyses were compared to the current codified direct strength method (DSM) predictions as per the Australian/New Zealand Standard AS/NZS 4600. Finally, design recommendations in the form of strength reduction factors are proposed. Based on these recommendations, modified DSM design equations with a reliability index above 2.5 are presented.

The effects of neighborhood disadvantage and adverse childhood experiences on conditioned pain modulation in adults with chronic low back pain

Chronic low back pain (cLBP) remains a major health crisis worldwide. Current conceptualizations of cLBP utilize the biopsychosocial model, yet research on social factors remains limited. Adverse childhood experiences (ACEs) are a risk factor for a variety of chronic health problems, including cLBP. However, the extent to which socioeconomic context might influence associations between ACEs and cLBP remains unclear. Socioeconomic factors such as healthcare access and living conditions, which cluster at the neighborhood level, may affect how ACEs relate to cLBP in adulthood. This study examined (1) the relationship between ACEs and conditioned pain modulation (CPM), and (2) the moderating effect of area-level deprivation index (ADI) in a sample of community-dwelling adults with cLBP. 183 adults with cLBP (53% female, 62.8% non-Hispanic Black) reported on ACEs, ADI, sociodemographics, and completed experimental testing of conditioned pain modulation (CPM). Greater ACEs were associated with a less efficient CPM response for individuals residing in low neighborhood deprivation (p < 0.01). ACEs were not significantly associated with CPM for those residing in average (p = 0.31) or high deprivation (p = 0.15). Our findings suggest that a history of ACEs is associated with diminished ability to inhibit pain, especially among individuals living in less deprived neighborhoods. The association between ACEs and CPM was weakest for the portion of our sample residing in neighborhoods with the most deprivation. People from disadvantaged backgrounds may experience numerous psychosocial stressors that hinder CPM, making it difficult to assess the specific impact of ACEs on CPM. Trial registrationThis study utilized baseline data collected as part of a parent trial titled “Examining Racial and SocioEconomic Disparities in Chronic Low Back Pain” (ERASED - ClinicalTrials.gov ID: NCT03338192). PerspectiveThis study demonstrates that early life adversity is associated with abnormal endogenous pain modulation, particularly for participants who live in neighborhoods characterized by less deprivation.

Lower limb exoskeleton for gait rehabilitation with adaptive nonsingular sliding mode control

Abstract This paper introduces a lower limb exoskeleton for gait rehabilitation, which has been designed to be adjustable to a wide range of patients by incorporating an extension mechanism and series elastic actuators (SEAs). This configuration adapts better to the user’s anatomy and the natural movements of the user’s joints. However, the inclusion of SEAs increases actuator mass and size, while also introducing nonlinearities and changes in the dynamic response of the exoskeletons. To address the challenges related to the human–exoskeleton dynamic interaction, a nonsingular terminal sliding mode control that integrates an adaptive parameter adjustment strategy is proposed, offering a practical solution for trajectory tracking with uncertain exoskeleton dynamics. Simulation results demonstrate the algorithm’s ability to estimate unknown parameters. Experimental tests analyze the performance of the controller against uncertainties and external disturbances.

Development of Flexible Rotor Balancing Procedure Using Response Matching Technique

Abstract Purpose The two main methods under use for balancing of flexible rotor know as Influence Coefficient Method (ICM) and Modal Balancing Method (MBM). It needs few trials to arrive at predicting the unbalance mass. In presence of initial bow along with unbalance these methods will fail to give accurate predictions. It becomes further complex when rotors are mounted on flexible supports. To overcome the limitations of these two methods, a new method called response matching method is disused here for flexible rotor balancing. Method The objective of present work is to develop a balancing procedure for flexible rotor balancing, wherein unbalance response of Finite element model (FEM) is matched with experimental response using iterative technique. Unbalance masses, moments and shaft stiffness of FEM model was iterated to match experimental unbalance responses at three distinct speeds below its first flexural critical speed. Further, unbalances mass was estimated iteratively to cross the bending critical speed with lower residual amplitudes at critical speeds. Results Experimental test setup was developed to validate the proposed method. Results show that present method predicts the unbalance masses and moments to compensate the bow more accurately. Conclusion Results shows that with single run, rotor unbalance masses and movements are predicted accurately. Corrected masses are used in experimental test setup to check the rotor amplitudes during its bending critical speeds. It shows rotor passes first bending critical speed without excessive amplitudes.

Thermal control of a hydrogen-powered uncrewed aerial vehicle for crossing the Atlantic Ocean

The Drone Mermoz project aims to evaluate the feasibility of an uncrewed aircraft system powered by a hydrogen fuel cell, designed to traverse the Atlantic Ocean. The aircraft must complete a journey of 3000 km with a minimum endurance of 36 hours. In addition to ensuring sufficient onboard energy, a critical requirement is the stable operation of the entire propulsion system throughout the journey.This paper outlines the developmental stages, theoretical modeling, and experimental testing of a thermal management system designed for a long-range uncrewed aircraft system equipped with hydrogen fuel cell-based propulsion. The 4-meter, sub-25 kg aircraft is engineered to undertake a 3000 km journey from Dakar, Senegal to Natal, Brazil.A critical challenge in developing this hydrogen-powered drone is designing an efficient thermal management system to ensure continuous ventilation of the fuselage. While one side of the system involves the fuel cell generating electricity for aircraft propulsion, the other side must effectively dissipate a substantial amount of heat to ensure the stable operation of the entire system.

Enhancing SRM Performance through Multi-Platform Optimization and FPGA-Based Real-Time Simulation

This paper proposes a multi-platform algorithm methodology in order to define firing angles of torque sharing functions (TSFs) for the indirect torque control of switched reluctance motors (SRM) through a hardware-in-the-loop (HIL) system. This proposal is used to achieve optimal levels of torque ripple and losses with accuracy and reliability, while taking advantage of real-time simulation. The analysis is performed assuming steady-state conditions of speed and torque reference for levels below the base speed, aiming to obtain firing angles ensuring optimal tracking performance. A novel methodology is proposed by using a grid search algorithm that handles the communication between Python, Code Composer, and the Typhoon HIL device. For that, an experimental data FPGA-based model with 500~ns of simulation time step is used to ensure highly accurate dynamic responses of current and electromagnetic torque. Moreover, controller-HIL (C-HIL) is used to have a safe and high fidelity testing environment, allowing rapid testing before transitioning to an experimental test bench. The simulation results are experimentally validated, demonstrating that the proposed strategy is effective and ensures optimal performance, taking into account peripheral systems of A/D, signal conditioning, PWM, and sensor emulation.