J. Exp. Theor. Anal., Volume 3, Issue 1 (March 2025) – 10 articles

Pillar-to-pillar dashboards have become common in modern electric vehicles. These dashboards are made of liquid crystal displays (LCDs), of which backlight units (BLUs) are an integral part. Particulate contamination inside BLUs can lead to either an aesthetic or functional failure and is in consequence a part of quality control. Automatic optical inspection (AOI) was used to detect particulate matter to enable a process chain analysis to be carried out. The investigation showed that a high percentage of all contaminants originated from the assembly of the edge/side lightguide. The implementation of an additional cleaning process was the favored countermeasure to reduce the contaminants. The objective (cleanliness requirement) was to remove all contaminants larger than 100 µm from the lightguide with contactless (non-destructive) cleaning methods. The preferred cleaning methods of choice were compressed air and CO₂ snow jet cleaning. This work investigates the cleaning efficacy of both cleaning methods under consideration of the following impact factors: distance, orientation (inclination) and speed. The central question of this paper was as follows: would cleaning with compressed air be sufficient to meet the cleanliness requirements? In order to answer this question, a cleaning validation was carried out, based on a Box–Behnken design of experiments (DoE). To do so, representative test contaminants had to be selected in step one, followed by the selection of an appropriate measurement technology to be able to count the contaminants on the lightguide. In the third step, a test rig had to be designed and built to finally carry out the experiments. The data revealed that CO₂ was able to achieve a cleaning efficacy of 100% in five of the experiments, while the best cleaning efficacy of compressed air was 89.87%. The cleaning efficacy of compressed air could be improved by a parameter optimization to 94.19%. In contrast, a 100% cleaning efficacy is achievable with CO₂ after parameter optimization, which is what is needed to meet the cleanliness requirements. Full article

The vibration response of a component, particularly the frequency response of the component, can be used in the determination of the loss factor damping, η, due to energy dissipation and the elastic modulus (E). The ASTM E756-04 standard provides the methodology and the guidance for the determination of the loss factor damping and elastic modulus experimentally. This standard specifically calls for the use of a beam with a rectangular cross-section. Also, the theoretical formulation developed there is based on such a beam cross-section. Here, in this paper, the theoretical formulation and numerical simulation for determining the loss factor damping and elastic modulus are a derivation of the methodology used in the ASTM standard and other R&D work, but for a circular plate configuration. The delta change derivation, both theoretically and numerically, is proven to be accurate and validated here. This method is useful in the characterization of materials that have applications in structural vibration, aerospace subcomponents, micro and mini sensory devices, medical devices, and many other areas. Similar to the ASTM standard, the materials could include metals, ceramics, rubbers, plastics, reinforced epoxy matrices, composites, and woods. This paper mainly formulates the technique via numerical and computational methods. It is the intention of the author to also, as a future research agenda, experimentally produce data that can be correlated with this theoretical and numerical methodology. Full article

Precise flow measurement is crucial in fluid power systems. Especially in combination with pressure, hydraulic power can be particularly beneficial for predictive maintenance and control applications. However, conventional flow sensors in fluid power systems are often invasive, thus disrupting the flow and yielding unreliable measurements, especially under transient conditions. A common alternative is to estimate the flow rate using pressure differentials along a pipe and the Hagen–Poiseuille law, which is limited to steady, laminar, and incompressible flows. This study advances a previously introduced analytical soft sensor, demonstrating its ability to accurately determine the transient pipe flow beyond laminar conditions, without requiring a dedicated flow rate sensor. This method provides a robust and computationally efficient solution for real-world hydraulic systems by applying two pressure transducers. A key contribution of this work is the investigation of signal filtering, revealing that even a simple first-order low-pass filter with a 100 Hz cutoff frequency significantly improves accuracy, which is demonstrated for pulsation frequencies of 5, 10, and 15 Hz, where the filtered results closely match experimental data from a test rig. These findings underscore the soft sensor’s potential as a reliable alternative to traditional flow sensors, offering high accuracy with minimal computational overhead for a wide range of flow conditions. Full article

Stress is a natural response of the organism to challenging situations, but its accurate detection is challenging due to its subjective nature. This study proposes a model based on depth-separable convolutional neural networks (DSCNN) to analyze heart rate variability (HRV) and detect stress. Electrocardiogram (ECG) signals are pre-processed to remove noise and ensure data quality. The signals are then transformed into two-dimensional images using the continuous wavelet transform (CWT) to identify pattern recognition in the time–frequency domain. These representations are classified using the DSCNN model to determine the presence of stress. The methodology has been validated using the SWELL-KW dataset, achieving an accuracy of 99.9% by analyzing the variability in three states (neutral, time pressure, and interruptions) of the 25 samples in the experiment, scanning the acquired signal every 5 s for 45 min per state. The proposed approach is characterized by its ability to transform ECG signals into time–frequency representations by means of short duration sampling, achieving an accurate classification of stress states without the need for complex feature extraction processes. This model is an efficient and accurate tool for stress analysis from biomedical signals. Full article

Accurate flow measurement is critical for hydraulic systems because it represents a crucial parameter in the control of fluid power systems and enables the calculation of hydraulic power when combined with pressure data, which is valuable for applications such as predictive maintenance. Existing flow sensors in fluid power systems typically operate invasively, disturbing the flow and providing inaccurate results, especially under transient conditions. A conventional method involves calculating the flow rate using the pressure difference along a pipe via the Hagen–Poiseuille law, which is limited to steady, laminar, incompressible flow. This paper presents a novel soft sensor with an analytical model for transient pipe flow based on two pressure signals, thus eliminating the need for an actual volumetric flow sensor. The soft sensor was derived in previous research and validated with a distributed parameter simulation. This work uses a constructed test rig to validate the soft sensor with real-world experiments. The results highlight the potential of the soft sensor to accurately and computationally efficiently measure transient pipe volumetric flow based on two pressure signals. Full article

Regular aircraft engine inspections play a crucial role in aviation safety. However, traditional inspections are often performed manually, relying heavily on the judgment and experience of operators. This paper presents a data-driven deep learning framework capable of automatically detecting defects on reactor blades. Specifically, this study develops Deep Neural Network models to detect defects in borescope images using various datasets, based on Computer Vision and YOLOv8n object detection techniques. Firstly, reactor blade images are collected from public resources and then annotated and preprocessed into different groups based on Computer Vision techniques. In addition, synthetic images are generated using Deep Convolutional Generative Adversarial Networks and a manual data augmentation approach by randomly pasting defects onto reactor blade images. YOLOv8n-based deep learning models are subsequently fine-tuned and trained on these dataset groups. The results indicate that the model trained on wide-shot blade images performs better overall at detecting defects on blades compared to the model trained on zoomed-in images. The comparison of multiple models’ results reveals inherent uncertainties in model performance that while some models trained on data enhanced by Computer Vision techniques may appear more reliable in some types of defect detection, the relationship between these techniques and subsequent results cannot be generalized. The impact of epochs and optimizers on the model’s performance indicates that incorporating rotated images and selecting an appropriate optimizer are key factors for effective model training. Furthermore, models trained solely on artificially generated images from collages perform poorly at detecting defects in real images. A potential solution is to train the model on both synthetic and real images. Future work will focus on improving the framework’s performance and conducting a more comprehensive uncertainty analysis by utilizing larger and more diverse datasets, supported by enhanced computational power. Full article

The climate crisis represents one of the greatest contemporary global challenges, requiring actions to mitigate its impacts and sustainable solutions to meet the growing demands for clean energy and coastal protection. Therefore, the study of devices such as the submerged plate (SP), which simultaneously acts as a breakwater (BW) and wave energy converter (WEC), is especially relevant. In this context, the present numerical study compares the efficiency of an SP device under regular waves across different geometric configurations considering inclination angles. To achieve this, a horizontal SP was adopted as a reference. Its thickness and total material volume were kept constant while ten alternative geometries, each with a different inclination for the SP, were proposed and investigated. The computational domain was modeled as a full-scale regular wave channel with each SP positioned below the free surface. The volume of fluid (VOF) multiphase model was employed to represent the interaction between water and air. The finite volume method (FVM) was applied to solve the transport equations for volume fraction, momentum, and mass. The SP’s efficiency as a BW was evaluated by assessing the free surface elevation upstream and downstream of the SP, while its efficiency as a WEC was measured by evaluating the axial velocity below the SP. Results indicated that the efficiency of the SP can vary significantly depending on its inclination, with the optimal case at θ = 15° showing improvements of 11.95% and 16.59%, respectively, as BW and WEC. Full article

Tunneling in loose soil and urban areas presents numerous challenges. One effective solution is the use of Earth Pressure Balance Shields (EPBSs). Maintaining the correct balance of pressure at the tunnel face is critical, as applying too little pressure can cause a collapse, while excessive pressure may result in a blow-out. Therefore, a key aspect of using EPBSs in urban environments is determining the optimal pressure required to stabilize the tunnel face, taking into account the existing soil in the excavation chamber and controlling the screw conveyor’s rotation rate. This study focuses on a section of the second line of the Tabriz subway to evaluate the minimum pressure needed for tunnel face stability using empirical, analytical, and numerical approaches. The analytical methods involve evaluating the limit equilibrium of forces and considering soil buckling due to overburden, while the numerical methods employ 3D finite element analysis. Additionally, a sensitivity analysis of the parameters affecting the required pressure was conducted and compared across the three approaches. The results revealed that the formation of a pressure arch mitigates the full impact of overburden pressure on the tunnel face. For soil cohesion values below 20 kPa, the numerical results aligned well with the empirical and analytical findings. For a tunnel depth of 22.5 m and a water table 2 m below the surface, the estimated minimum pressure ranged from 150 to 180 kPa. Moreover, the analytical methods were deemed more suitable for determining the required support pressure at the tunnel face. These methods considered wedge and semi-circular mechanisms as the most probable failure modes. Also, for cohesive ground, the pressure from the finite element analysis was found to be almost always equal to or greater than the values obtained with the analytical solutions. Full article

Nowadays, electromobility has a significant role in transportation; different electrically driven vehicles are spreading continuously. Due to this form of drivetrain, fire safety hazards have also changed when compared to those of conventional vehicles. Lately, electric vehicle fires have become more common; thus, we have chosen to investigate the negative impacts of these fires on humans and the environment, in addition to the toxic properties of the resulting combustion products. In our research work, we conducted a full-scale fire experiment on an electric passenger car. Fire extinguishing was executed with fire-fighting foam, and its efficiency was examined. After extinguishing the fire, we took samples from the combustion gases and soil. Samples were subjected to laboratory investigations. Our results and experiences are presented in this article. Full article