Search Results (153)

This research introduces an innovative passive vibration control methodology employing acoustic black hole (ABH) structures to mitigate vibration transmission in electric automotive steering machines—a prevalent issue adversely affecting driving comfort and vehicle safety. Leveraging the inherent bending wave manipulation properties of ABH configurations, we conceive an integrated vibration suppression framework synergizing advanced computational modeling with intelligent optimization algorithms. A high-fidelity finite element (FEM) model integrating ABH-attached steering machine system was developed and subjected to experimental validation via rigorous modal testing. To address computational challenges in design optimization, a hybrid modeling strategy integrating parametric design (using Latin Hypercube Sampling, LHS) with Kriging surrogate modeling is proposed. Systematic parameterization of ABH geometry and damping layer dimensions generated 40 training datasets and 12 validation datasets. Surrogate model verification confirms the model’s precise mapping of vibration characteristics across the design space. Subsequent multi-objective genetic algorithm optimization targeting RMS velocity suppression achieved substantial vibration attenuation (29.2%) compared to baseline parameters. The developed methodology provides automotive researchers and engineers with an efficient suitable design tool for vibration-sensitive automotive component design. Full article

The development of a robust and efficient electric vehicle (EV) charging infrastructure is essential for accelerating the transition to sustainable transportation. This systematic review analyzes recent research on EV charging network planning, with a particular focus on optimization techniques, machine learning applications, and sustainability integration. Using bibliometric methods and Principal Component Analysis (PCA), we identify key thematic clusters, including smart grid integration, strategic station placement, renewable energy integration, and public policy impacts. This study reveals a growing trend toward hybrid models that combine artificial intelligence and optimization methods to address challenges such as grid constraints, range anxiety, and economic feasibility. We provide a taxonomy of computational approaches—ranging from classical optimization to deep reinforcement learning—and synthesize practical insights for researchers, policymakers, and urban planners. The findings highlight the critical role of coordinated strategies and data-driven tools in designing scalable and resilient EV charging infrastructures, and point to future research directions involving intelligent, adaptive, and sustainable charging solutions. Full article

First demonstrated in 1942, subcritical and zero-power critical assemblies, also known as piles, are a fundamental tool for research and education at universities. Traditionally, their role has been primarily instructional and for measuring the fundamental properties of neutron diffusion and transport. However, these assemblies could hold potential for modern applications and nuclear research. The Purdue University subcritical pile previously lacked a substantial testing volume, limiting its utility to simple neutron activation experiments for the purpose of undergraduate education. Following the design and addition of a mechanical and electrical testbed, this paper aims to provide an overview of the testbed design and characterize the neutron flux of the rearranged Purdue subcritical pile, justifying its use as a modern scientific instrument. The newly installed 1.5 × 10⁵ cubic-centimeter volume testbed enables a systematic investigation of neutron and gamma effects on materials and the generation of a comprehensive data set with the potential for machine learning applications. The neutron flux throughout the pile is measured using gold-197 and indium-115 foil activation alongside cadmium-covered foils for two-group neutron energy classification. The neutron flux measurements are then used to benchmark a detailed geometrically and materialistically accurate Monte Carlo model using OpenMC 0.15.0 and MCNP 6.3. The experimental measurements reveal that the testbed has a neutron environment with a total neutron flux approaching 9.5 × 10³ n/cm² × s and a thermal flux of 6.5 × 10³ n/cm² × s. This work establishes that the modified Purdue subcritical pile can provide fair neutron and gamma fluxes within a large volume to enable the radiation testing of integral electronic components and can be a versatile research instrument with the potential to support material testing and limited isotope activation, while generating valuable training data sets for machine learning algorithms in nuclear applications. Full article

Silicon is the most commonly used material in the electronic industries due to its unique properties, which also make it very difficult to machine using conventional machining. Electrical discharge machining (EDM) is a non-traditional process that is gaining popularity for machining silicon, although a slower machining rate is one of its limitations. This study investigates two electrode design strategies to enhance the efficiency of EDM by improving the material removal rates, reducing tool wear, and refining the quality of machined features. The first approach involves using graphite electrodes in various array configurations (1 × 4 to 6 × 4) and leg heights (0.2″ and 0.3″). The second approach employs hollow electrodes with differing wall thicknesses (0.04″, 0.08″, and 0.12″). The effects of these variables on performance were evaluated by maintaining constant EDM parameters. The results indicate that increasing the number of electrode legs improves the flushing conditions, resulting in shorter machining times. Meanwhile, the shorter electrode height outperforms the taller electrode, providing a higher machining speed. The thinnest wall thickness for hollow electrodes yielded the best performance due to the increased energy distribution. Both electrode design methodologies can be used for the mass fabrication of features with targeted profiles on silicon using the die-sinking EDM process. Full article

Electric discharge machining (EDM) is widely employed for machining hard, conductive materials. Tool rotation has emerged as an effective strategy to enhance debris flushing and improve stability during deep-hole EDM drilling. This study proposes a machine learning-based approach to evaluate the influence of tool rotation and predict the unstable machining conditions in EDM of ultrafine grained tungsten carbide. A structured analytical workflow, combining Taguchi–Grey optimization, regression analysis, and classification models, was designed to capture discharge dynamics across macro- and micro-timescales. Classification models trained on raw and processed electrical signal features achieved over 88% accuracy and 90% recall. SHAP analysis revealed that the relationship between key discharge events such as sparks and short circuits varied significantly across stable and unstable machining phases, underscoring the importance of phase-specific modeling. While correlation analysis showed weak global associations, phase-dependent SHAP values revealed opposing feature effects, allowing the context-informed interpretation of model behavior. Phase segmentation revealed that, compared to 1000 RPM, short circuits were reduced by about 40% during stable machining at 8000–9000 RPM. Conversely, during unstable phases, spark effectiveness dropped by nearly 45%, and secondary discharges increased throughout this range. These insights support the design of adaptive control strategies that adjust the rotation rate in response to detected phase changes, aiming to sustain machining stability. The findings support the development of dynamic control frameworks to improve EDM performance, particularly for mold fabrication using tungsten carbide. Full article

Heterogeneous photocatalysis has emerged as a versatile and sustainable technology for the degradation of emerging contaminants in water. This review highlights recent advancements in photocatalysts design, including band gap engineering, heterojunction formation, and plasmonic enhancement to enable visible-light activation. Various reactor configurations, such as slurry, immobilized, annular, flat plate, and membrane-based systems, are examined in terms of their efficiency, scalability, and operational challenges. Hybrid systems combining photocatalysis with membrane filtration, adsorption, Fenton processes, and biological treatments demonstrate improved removal efficiency and broader applicability. Energy performance metrics such as quantum yield and electrical energy per order are discussed as essential tools for evaluating system feasibility. Special attention is given to solar-driven reactors and smart responsive materials, which enhance adaptability and sustainability. Additionally, artificial intelligence and machine learning approaches are explored as accelerators for catalyst discovery and process optimization. Altogether, these advances position photocatalysis as a key component in future water treatment strategies, particularly in decentralized and low-resource contexts. The integration of material innovation, system design, and data-driven optimization underlines the potential of photocatalysis to contribute to global efforts in environmental protection and sustainable development. Full article

9Cr18MoV stainless steel is widely employed in cutting-tool applications owing to its exceptional hardness and corrosion resistance. In this study, we systematically optimized the wire electrical discharge machining (WEDM) process parameters for 9Cr18MoV stainless steel through an L₁₆ (4⁴) orthogonal experimental design. The key parameters investigated include pulse width (T_on), pulse interval (T_off), peak current (IP), and wire feed speed (WS), with cutting efficiency (CE) and surface roughness (R_a) serving as the primary optimization objectives. A signal-to-noise ratio (SNR) analysis was applied to assess the effects of the individual parameters and derive single-objective optimal configurations. Subsequently, grey relational analysis (GRA) integrated with analytic hierarchy process (AHP)-based weighting was employed to establish a multi-objective optimal parameter set, which was experimentally validated. The results reveal that the optimal multi-objective performance was attained at T_on = 28 μs, T_off = 3 μs, IP = 9 A, and WS = level 3. SEM characterization confirmed that this parameter combination yields a more uniform surface morphology, with diminished oxidation and molten debris deposition, thereby significantly enhancing surface integrity. The adoption of this optimized parameter set not only ensures superior machining efficiency but also results in improved surface quality. Full article

The transition to a decarbonized energy sector, driven by the integration of Renewable Energy Sources (RESs), smart building technology, and the rise of Electric Vehicles (EVs), has highlighted the need for optimized energy system planning. Increasing EV adoption creates additional challenges for charging infrastructure and grid demand, while proactive and informed decisions by residential EV users can help mitigate such challenges. Our work develops a smart residential charging framework that assists residents in making informed decisions about optimal EV charging. The framework integrates a machine-learning-based forecasting engine that consists of two components: a stacking and voting meta-ensemble regressor for predicting EV charging load and a bidirectional LSTM for forecasting national net energy exchange using real-world data from local road traffic, residential charging sessions, and grid net energy exchange flow. The combined forecasting outputs are passed through a data-driven weighting mechanism to generate probabilistic recommendations that identify optimal charging periods, aiming to alleviate grid stress and ensure efficient operation of local charging infrastructure. The framework’s modular design ensures adaptability to local charging infrastructure within or nearby building complexes, making it a versatile tool for enhancing energy efficiency in residential settings. Full article

Car styling is crucial for consumer acceptance and market success. Since vehicle manufacturers produce electric vehicles, they have faced the challenge of maintaining the typicality of their original products and presenting the innovation of new technologies. We propose a method that integrates artificial intelligence (AI)-generated images and image classification technology to help designers effectively balance between typicality and novelty. We collected 118 pictures of electric vehicles and 122 pictures of fuel vehicles in 2024 from the BMW official website. Focusing on seven key visual features of the vehicles, we used the Waikato environment for knowledge analysis (WEKA) to train an image classification model on the dataset through three separate training and testing sessions. First, we used the prompts that described typical BMW design to generate images of new BMW electric vehicles in Stable Diffusion. The images consisted of 21 front views, 20 side views, and 20 rear views. The accuracy of the model of front views trained with the pyramid histogram of oriented gradients filter (PHOG)-Filter and random forest classifier was 78.5%, and the test accuracy reached 95%. The accuracy of the model of rear views trained with BinaryPatternsPyramid-Filter and random forest classifier was 80.5%, and the test accuracy was 90%. However, the accuracy of the model of side views did not reach 70%. That implies the distinction between BMW fuel vehicles and its electric vehicles is mainly based on the front and rear views, rather than the side view. The results of this study showed that integrating image classification and AI-generated images can be used to examine the balance between product typicality and novelty, and the application of machine learning and AI tools to study car style. Full article

Current approaches in the process of evaluating the quality of the machined surface during wire electrical discharge machining (WEDM) generally do not include the assessment of micro- and macro-geometric indicators of both parts of the cut. In practice, however, there are specific cases when it is necessary to use both halves of the cut. In such cases, it is necessary to choose a special approach not only in the machining process but also when evaluating the quality indicators of the machined surface. Therefore, experimental measurements were aimed at the identification of these micro- and macro-geometrical indicators in symmetrical WEDM. Within them, qualitative indicators of flat and curved surfaces were assessed. The identification of individual characteristics was carried out using Suftes, Roundtest Mitutoyo, and a 3D coordinate measuring device. The design of the experiment followed the full DoE factorial design method, and the obtained results were processed using the Taguchi method. Based on the obtained results, the response of macro and micro-geometric parameters was characterized by means of multiple regression models (MRM) in symmetrically machined surfaces of tool steel EN X37CrMoV5-1 (Bohdan Bolzano, Kladno, ČR) by WEDM technology. They revealed the mutual dependence of the output qualitative indicators of the eroded area on the input variables’ main technological parameters (MTP). Subsequent multi-parameter optimization resulted in a suitable level of setting of the MTP input variable parameters I, t_on, U, and t_off (9 A, 32 μs, 15 μs, and 70 V), through which the greatest agreement of macro and micro-geometric output indicators of symmetrically machined surfaces can be achieved. By applying the optimized levels of MTP settings for symmetrical WEDM of tool steel EN X37CrMoV5-1, their agreement was achieved at the level of 95%. Full article

The thermal aging of insulation systems in electrical machines is a critical factor influencing their reliability and lifetime, particularly in modern high-performance electrical equipment. However, evaluating and predicting insulation lifetime under thermal aging poses significant challenges due to the complex aging mechanisms. Thermal aging not only leads to the degradation of macroscopic properties such as dielectric strength and breakdown voltage but also causes progressive changes in the microstructure, making the correlation between aging stress and aging indicators fundamental for lifetime evaluation and prediction. This review first summarizes the performance indicators reflecting insulation thermal aging. Subsequently, it systematically reviews current methods for reliability assessment and lifetime prediction in the thermal aging process of electrical machine insulation, with a focus on the application of different modeling approaches such as physics-of-failure (PoF) models, data-driven models, and stochastic process models in insulation lifetime modeling. The theoretical foundations, modeling processes, advantages, and limitations of each method are discussed. In particular, PoF-based models provide an in-depth understanding of degradation mechanisms to predict lifetime, but the major challenge remains in dealing with complex failure mechanisms that are not well understood. Data-driven methods, such as artificial intelligence or curve-fitting techniques, offer precise predictions of complex nonlinear relationships. However, their dependence on high-quality data and the lack of interpretability remain limiting factors. Stochastic process models, based on Wiener or Gamma processes, exhibit clear advantages in addressing the randomness and uncertainty in degradation processes, but their applicability in real-world complex operating conditions requires further research and validation. Furthermore, the potential applications of thermal lifetime models, such as electrical machine design optimization, fault prognosis, health management, and standard development are reviewed. Finally, future research directions are proposed, highlighting opportunities for breakthroughs in model coupling, multi-physical field analysis, and digital twin technology. These insights aim to provide a scientific basis for insulation reliability studies and lay the groundwork for developing efficient lifetime prediction tools. Full article

As battery electric vehicles (BEVs) gain significance in the automotive industry, manufacturers must diversify their vehicle portfolios with a wide range of electric vehicle models. Electric powertrains must be designed to meet the unique requirements and boundary conditions of different vehicle concepts to provide satisfying solutions for their customers. During the early development phases, it is crucial to establish an initial powertrain component design that allows the respective divisions to develop their components independently and minimize interdependencies, avoiding time- and cost-intensive iterations. This study presents a holistic electric powertrain component design model, including the high-voltage battery, power electronics, electric machine, and transmission, which is meant to be used as a foundation for further development. This model’s simulation results and performance characteristics are validated against a reference vehicle, which was torn down and tested on a vehicle dynamometer. This tool is applicable for an optimization approach, focusing on achieving optimal energy consumption, which is crucial for the design of battery electric vehicles. Full article

Power transformers are fundamental components in electrical grids, requiring robust insulation to operate reliably under various abnormal conditions, including overvoltages caused by lightning or switching. As defined by existing standards, the Basic Insulation Level (BIL) or Switching Insulation Level (SIL) of a transformer validates its reliability through impulse testing. These tests presume linearity in the overall system and equipment being tested. They compare waveforms at reduced and full impulse levels to detect or enhance insulation failures. Traditionally, this relies on visual inspection due to subjective acceptance criteria. This article presents a historical background review of the practices involving the use of analogue instruments evolved into digital oscilloscopes and digitizers, and the ways in which they enhance waveform acquisition and analysis capabilities. Despite advances in digital processing, including analyses on the frequency domain rather than only on time, such as transfer function analysis and coherence functions, and other signal transformations, such as wavelet calculation, interpreting differences in waveform records remains subjective. This article presents the development of a tool designed to emulate traditional photographic methods for waveform comparison. Moreover, the TRIMP software used enables multiple comparisons using various similarity and dissimilarity metrics in both the time and frequency domains, providing a robust system for identifying significant differences. The developed methodology and implemented metrics can form the basis for future machine learning or artificial intelligence (AI) applications. While digital tools offer significant advantages in impulse testing, improve reliability, reduce subjectivity, and provide robust decision-making metrics, their test approval remains based on visual comparisons due to consolidated engineering practices. Regardless of the metrics or indications obtained, the developed tool is a powerful graphic visualizer. Full article

In this study, we address the critical issue of accurately detecting series AC arc faults, which are often challenging to identify due to their small fault currents and can lead to devastating electrical fires. We propose an intelligent diagnosis method based on the extreme learning machine (ELM) model to enhance detection accuracy and real-time monitoring capabilities. Our approach involves collecting high-frequency current signals from 23 types of loads using a self-developed AC series arc fault data acquisition device. We then extract 14 features from both the time and frequency domains as candidates for arc fault diagnosis, employing a random forest to select the most significantly changed features. Finally, we design an ELM classifier for series arc fault diagnosis, achieving an identification accuracy of 99.00% ± 0.26%. Compared to existing series arc fault diagnosis methods, our ELM-based method demonstrates superior recognition performance. This study contributes to the field by providing a more accurate and efficient diagnostic tool for series AC arc faults, with broad implications for electrical safety and fire prevention. Full article

Numerous studies address the estimation of energy consumption at intermodal terminals, with a primary focus on existing facilities. However, a significant research gap lies in the lack of reliable methods and tools for the ex ante estimation of energy consumption in transshipment systems. Such tools are essential for assessing the energy demand and intensity of intermodal terminals during the design phase. This gap presents a challenge for intermodal terminal designers, power grid operators, and other stakeholders, particularly in an era of growing energy needs. The authors of this paper identified this issue in the context of a real business case while planning potential intermodal terminal locations along new railway lines. The need became apparent when power grid designers requested energy consumption forecasts for the proposed terminals, highlighting the necessity to formulate and mathematically solve this problem. To address this challenge, a three-stage model was developed based on a pre-designed intermodal terminal. Stage I focused on establishing the fundamental assumptions for intermodal terminal operations. Key parameters influencing energy intensity were identified, such as the size of the transshipment yard, the types of loading operations, the number of containers handled, and the selection of handling equipment. These parameters formed the foundation for further analysis and modeling. Stage II focused on determining the optimal number of machines required to handle a given throughput. This included determining the specific parameters of the equipment, such as speed, span, and efficiency coefficients, as well as ensuring compliance with installation constraints dictated by the terminal layout. Stage III focused on estimating the energy consumption of both individual handling cycles and the total consumption of all handling equipment installed at the terminal. This required obtaining detailed information about the operational parameters of the handling equipment, which directly influence energy consumption. Using these parameters and the equations outlined in Stage III, the energy consumption for a single loading cycle was calculated for each type of handling equipment. Based on the total number of loading operations and model constraints, the total energy consumption of the terminal was estimated for various workload scenarios. In this phase of the study, numerous test calculations were performed. The analysis of testing parameters and the specified terminal layout revealed that energy consumption per cycle varies by equipment type: rail-mounted gantry cranes consume between 5.23 and 8.62 kWh, rubber-tired gantry cranes consume between 3.86 and 7.5 kWh, and automated guided vehicles consume approximately 0.8 kWh per cycle. All handling equipment, based on the adopted assumptions, will consume between 2200 and 13,470 kWh per day. Based on the testing results, a methodology was developed to aid intermodal terminal designers in estimating energy consumption based on variations in input parameters. The results closely align with those reported in the global literature, demonstrating that the methodology proposed in this article provides an accurate approach for estimating energy consumption at intermodal terminals. This method is also suited for use in ex ante cost–benefit analysis. A sensitivity analysis revealed the key variables and parameters that have the greatest impact on unit energy consumption per handling cycle. These included the transshipment yard’s dimensions, the mass of the equipment and cargo, and the nominal specifications of machinery engines. This research is significant for present-day economies heavily reliant on electricity, particularly during the energy transition phase, where efficient management of energy resources and infrastructure is essential. In the case of Poland, where this analysis was conducted, the energy transition involves not only switching handling equipment from combustion to electric power but, more importantly, decarbonizing the energy system. This study is the first to provide a methodology fully based on the design parameters of a planned intermodal terminal, validated with empirical data, enabling the calculation of future energy consumption directly from terminal technical designs. It also fills a critical research gap by enabling ex ante comparisons of energy intensity across transport chains, an area previously constrained by the lack of reliable tools for estimating energy consumption within transshipment terminals. Full article