Search Results (345)

To prevent marine biofouling in seawater lift pumps, electrolyzed seawater containing Cu²⁺ needs to be injected into the pumps. This study employs Computational Fluid Dynamics (CFD) to simulate the variation in Cu²⁺ injection concentration required to achieve a Cu²⁺ concentration of 3 ppb within a 10 cm range around the pump under different operating conditions, including the installation of baffles and varying seawater flow rates. The simulation results demonstrate that CFD can accurately predict the distribution of Cu2+ concentration in electrolyzed seawater, with the distribution significantly influenced by seawater flow direction, necessitating reference to upstream data. When the lift pumps are idle, the required Cu²⁺ injection concentration increases with rising seawater flow rates, reaching 41.9 μg/L at the maximum flow rate of 1.9 m/s. During alternating pump operation, the required Cu²⁺ injection concentration also increases with the flow rate, significantly affected by the pump’s operational position: lower concentrations are required when the upstream pump is active compared to the downstream pump. Additionally, installing baffles around the pumps effectively mitigates the impact of seawater flow on Cu²⁺ distribution, significantly reducing the required injection concentration. This study provides theoretical and data-driven insights for optimising marine biofouling prevention in seawater lift pumps. Full article

Electric buses (EBs) are increasingly adopted for their environmental and operational benefits, yet their real-world efficiency is influenced by climate, route characteristics, and auxiliary energy demands. While most existing research identifies winter as the most energy-intensive season due to cabin heating and reduced battery performance, this study presents a contrasting perspective based on a three-year longitudinal analysis of the LYMMO fleet in Orlando, Florida—a subtropical U.S. region. The findings reveal that summer is the most energy-intensive season, primarily due to sustained HVAC usage driven by high ambient temperatures—a seasonal pattern rarely reported in the current literature and a key regional contribution. Additionally, idling time exceeds driving time across all seasons, with HVAC usage during idling emerging as the dominant contributor to total energy consumption. To mitigate these inefficiencies, a proxy-based HVAC energy estimation method and an optimization model were developed, incorporating ambient temperature and peak passenger load. This approach achieved up to 24% energy savings without compromising thermal comfort. Results validated through non-parametric statistical testing support operational strategies such as idling reduction, HVAC control, and seasonally adaptive scheduling, offering practical pathways to improve EB efficiency in warm-weather transit systems. Full article

Operations management personnel emphasize the maintenance of workforce empowerment on the shop floor. This is made possible by implementing effective operations and human resource management practices. However, organizations are adept at controlling the workforce empowerment domain within operational scenarios. In the current industry revolution scenario, industry personnel often face failure due to a laggard mindset in the face of industry revolutions. There are higher possibilities of failure because of standardized operations controlling the shop floor. Organizations utilize well-established human resource concepts, including McClelland’s acquired needs theory, Herzberg’s two-factor theory, and Maslow’s hierarchy of needs, in order to enhance the workforce’s performance on the shop floor. Current SME individuals require fast-paced approaches for tracking the performance and idleness of a workforce in order to control them more efficiently in both flexible and transformational stages. The present study focuses on investigating the parameters and factors that contribute to workforce empowerment in an industrial revolution scenario. The present research is used to develop a framework utilizing operations and human resource management approaches in order to identify and address the issues responsible for deteriorating workforce contributions. The framework includes HRM and operations management practices, including Herzberg’s two-factor theory, Maslow’s theory, and lean and smart approaches. The developed framework contains four phases for achieving desired outcomes on the shop floor. The developed framework is validated by implementing it in a real-life electric vehicle manufacturing organization, where the human resources and operations team were exhausted and looking to resolve employee-related issues instantly and establish a sustainable work environment. The current industry is transforming from Industry 3.0 to Industry 4.0, and seeks future-ready innovations in operations, control, and monitoring of shop floor setups. The operations management and human resource management practices teams reviewed the results over the next three months after the implementation of the developed framework. The results revealed an improvement in workforce empowerment within the existing work environment, as evidenced by reductions in the number of absentees, resignations, transfer requests, and medical issues, by 30.35%, 94.44%, 95.65%, and 93.33%, respectively. A few studies have been conducted on workforce empowerment by controlling shop floor scenarios through modifications in operations and human resource management strategies. The results of this study can be used to fulfil manufacturers’ needs within confined constraints and provide guidelines for efficiently controlling workforce performance on the shop floor. Constraints refer to barriers that have been decided, including production time, working time, asset availability, resource availability, and organizational policy. The study proposes a decision-making plan for enhancing shop floor performance by providing suitable guidelines and an action plan, taking into account both workforce and operational performance. Full article

This study examines the performance of two diffuser configurations—a trumpet-shaped and a semi-diagonal design—for application in micro gas turbine engines, aiming to assess their suitability in terms of efficiency and operational flexibility. Both diffusers were initially evaluated using steady-state CFD simulations with the k-omega SST turbulence model, followed by experimental testing on an actual engine across the start-up sequence from idle to 70% of nominal speed. Performance was mapped over four constant-speed lines for each configuration. Results showed that the trumpet-shaped diffuser offered a greater choke margin but suffered from increased aerodynamic losses, whereas the semi-diagonal diffuser demonstrated higher efficiency but required closer alignment with the target operating point. The k-omega SST model showed strong predictive accuracy, with 5.13% agreement across all instrumented parameters for all investigated speed lines. These findings suggest that while the trumpet diffuser provides better stability, the semi-diagonal design is more efficient when properly targeted. Future work will focus on extending the analysis to higher speed ranges and transient regimes using harmonic balance CFD methods and enhanced data acquisition techniques. Full article

Electric buses are key in the strategy towards a greenhouse-gas-neutral fleet. However, their restrictions in terms of range and refueling as well as their increased price point present new challenges for public transport companies. This study aims to address, based on real-world operational data, how energy consumption and charging behavior affect battery aging and how operational strategies can be optimized to extend battery life under realistic conditions. This article presents an energy consumption analysis with respect to ambient temperatures and average vehicle speed based exclusively on real-world data of an urban bus fleet, providing a data foundation for range forecasting and infrastructure planning optimized for public transport needs. Additionally, the State of Charge (SOC) window during operation and vehicle idle time as well as the charging power were analyzed in this case study to formulate recommendations towards a more battery-friendly treatment. The central research question is whether battery-friendly operational strategies—such as reduced charging power and lower SOC windows—can realistically be implemented in daily public transport operations. The impact of the recommendations on battery lifetime is estimated using a battery aging model on drive cycles. Finally, the reduction in CO₂ emissions compared to diesel buses is estimated. Full article

This study investigates the use of a custom-built 10 GHz continuous wave micro-Doppler radar system to analyze external vibrations of idling vehicles under various conditions. Scenarios included different gear engagements with one occupant and parked gear with up to four occupants. Motivated by security concerns, such as the threat posed by idling vehicles with multiple occupants, the research explores how micro-Doppler signatures can indicate vehicle readiness to move. Experiments focused on a mid-size SUV, with similar trends seen in other vehicles. Radar data were compared to in situ accelerometer measurements, confirming that the radar system can detect subtle frequency changes, especially during gear shifts. The system’s sensitivity enables it to distinguish variations tied to gear state and passenger load. Extracted features like frequency and magnitude show strong potential for use in machine learning models, offering a non-invasive, remote sensing method for reliably identifying vehicle operational states and occupancy levels in security or monitoring contexts. Spectrogram and PSD analyses reveal consistent tonal vibrations around 30 Hz, tied to engine activity, with harmonics at 60 Hz and 90 Hz. Gear shifts produce impulse signatures primarily below 20 Hz, and transient data show distinct peaks at 50, 80, and 100 Hz. Key features at 23 Hz and 45 Hz effectively indicate engine and gear states. Radar and accelerometer data align well, supporting the potential for remote sensing and machine learning-based classification. Full article

In the field of intralogistics, new systems are continuously being studied to increase flexibility and adaptability while striving to maintain high handling capabilities and performance. Among these new systems, this article focuses on a novel under-actuated intelligent surface capable of performing various handling tasks with a simplified design and without employing motors. The technology behind the device involves idle rotors, i.e., without motor-driven spinning, whose axis of rotation can be controlled in a few discrete positions. The system’s operation and digital model have already been tested and validated; however, a control system that makes the surface “smart” has not yet been developed. In this context, the following work analyzes control methodologies for the concept. Specifically, in a first phase, a threshold-based method is introduced and tested on a prototype of the surface for sorting and orientation operations. This basic technique involves actuating the surface modules according to pre-assigned rules once a chosen threshold condition is reached. In a second phase, instead, a decentralizd PID control is described and simulated based on real and potential industrial applications. Unlike the first method, in this case, it is the control law that defines the actuation and, through the dynamic description of the device, determines the best combination to achieve the goal. Additionally, the article illustrates how the difficulties introduced by the numerous nonlinearities, due to the under-actuation and the simplifications of the physical system, were overcome. For both control methods, promising results were obtained in terms of handling capability and errors in achieving the desired movement. Full article

Due to the limitations of current battery technologies—such as lower energy density and high cost compared to fossil fuels—electric vehicles (EVs) face constraints in applications requiring extended range or heavy payloads, such as refuse trucks. As a midterm solution, hybrid electric vehicles (HEVs) combine internal combustion engines (ICEs) and electric powertrains to enable flexible energy usage, particularly in urban duty cycles characterized by frequent stopping and idling. This study introduces a model-based energy management strategy using the Equivalent Consumption Minimization Strategy (ECMS), tailored for a retrofitted series hybrid refuse truck. A conventional ISUZU NPR 10 truck was instrumented to collect real-world driving and operational data, which guided the development of a vehicle-specific ECMS controller. The proposed strategy was evaluated over five driving cycles—including both standardized and measured urban scenarios—under varying load conditions: Tare Mass (TM) and Gross Vehicle Mass (GVM). Compared with a rule-based control approach, ECMS demonstrated up to 14% improvement in driving range and significant reductions in exhaust gas emissions (CO, NO_x, and CO₂). The inclusion of auxiliary load modeling further enhances the realism of the simulation results. These findings validate ECMS as a viable strategy for optimizing fuel economy and reducing emissions in hybrid refuse truck applications. Full article

Pulse and Glide (PnG), as a fuel-saving technique, has primarily been applied to manual transmission vehicles. So, its effectiveness when integrated with a novel vehicle type like connected and automated vehicles (CAVs) remains largely unexplored. On the other hand, CAVs have evidently received less attention regarding energy conservation, and their prominent perception capabilities clearly exhibit individual variations. In light of this, this study investigates the impacts of PnG combined with CAVs on energy conservation and safety within the emerging mixed traffic flow composed of CAVs with varying sensing accuracies. The results indicate the following: (i) compared to the traditional driving modes, the PnG can achieve a maximum fuel-saving rate of 39.53% at Fuel Consumption with Idle (FCI), reducing conflicts by approximately 30% on average; (ii) CAVs, equipped with sensors boasting a greater detection range, markedly enhance safety during vehicle operation and contribute to a more uniform distribution of individual fuel consumption; (iii) PnG modes with moderate acceleration, such as 1–2 m/s², can achieve excellent fuel consumption while ensuring safety and may even slightly enhance the operational efficiency of the intersection. The findings could provide a theoretical reference for the transition of transportation systems toward sustainability. Full article

Sustainable traffic management relies on accurate traffic flow prediction to reduce congestion, fuel consumption, and emissions and minimise the external environmental impacts of traffic operations. This study contributes to this objective by developing and evaluating advanced machine learning models that leverage multisource data to predict traffic patterns more effectively, allowing for the deployment of proactive measures to prevent or reduce traffic congestion and idling times, leading to enhanced eco-friendly mobility. Specifically, this paper evaluates the impact of multisource sensor inputs and spatial detector interactions on machine learning-based traffic flow prediction. Using a dataset of 839,377 observations from 14 detector stations along Melbourne’s Eastern Freeway, Bidirectional Long Short-Term Memory (BiLSTM) models were developed to assess predictive accuracy under different input configurations. The results demonstrated that incorporating speed and occupancy inputs alongside traffic flow improves prediction accuracy by up to 16% across all detector stations. This study also investigated the role of spatial flow input interactions from upstream and downstream detectors in enhancing prediction performance. The findings confirm that including neighbouring detectors improves prediction accuracy, increasing performance from 96% to 98% for eastbound and westbound directions. These findings highlight the benefits of optimised sensor deployment, data integration, and advanced machine-learning techniques for smart and eco-friendly traffic systems. Additionally, this study provides a foundation for data-driven, adaptive traffic management strategies that contribute to sustainable road network planning, reducing vehicle idling, fuel consumption, and emissions while enhancing urban mobility and supporting sustainability goals. Furthermore, the proposed framework aligns with key United Nations Sustainable Development Goals (SDGs), particularly those promoting sustainable cities, resilient infrastructure, and climate-responsive planning. Full article

This paper proposes a non-invasive, data-driven methodology for monitoring and optimising machine utilisation in manufacturing environments. By analysing high-resolution power consumption data, the system automatically classifies machine states (off, idling, and working, and segments operational periods into discrete production events. Unsupervised learning techniques enable the identification of production patterns, product typologies, and anomalies, supporting improvements in operational efficiency and quality control. The approach also estimates energy consumption and cost using time-of-use tariffs, offering insights into both performance and sustainability. Experimental evaluations across multiple industrial settings demonstrate the method’s robustness, with high agreement with production records and significant potential for reducing idle time, improving scheduling, and enhancing resource allocation. This work presents a scalable and interpretable analytics framework to support data-driven decision-making in modern manufacturing operations. Full article

Indonesia’s high logistics costs—approximately 14.6% of its GDP—pose a significant challenge to national economic competitiveness. Key contributing factors include complex geography, fragmented multimodal transport systems and inefficient container terminal operations, particularly concerning the handling of empty containers. This study investigates operational optimization in a container terminal using Agile and Lean principles, without additional investment or infrastructure expansion. It compares throughput before and after optimization, focusing on equipment productivity and reduction in idle time, especially related to equipment and human resources. Field implementation began in 2015, followed by simulation-based validation using system dynamics modeling. The terminal demonstrated a sustained increase in capacity beginning in 2016, eventually exceeding its original design capacity while maintaining acceptable berth and Yard Occupancy Ratios (BOR and YOR). Agile practices improved empty container handling, while Lean methods enhanced berthing process efficiency. The findings confirm that significant reductions in port operational costs, shipping operational costs, voyage turnover time, and logistics costs can be achieved through strategic operational reforms and better resource utilization, rather than through capital-intensive expansion. The study provides a replicable model for improving terminal efficiency in ports facing similar constraints. Full article

Torque and velocity fluctuations in internal combustion engines (ICEs), particularly during idle and low-speed operation, can reduce efficiency, increase vibration, and impose mechanical stress on coupled systems. This work presents the design, simulation, and experimental validation of a passive balancing cam mechanism developed to mitigate fluctuations in single-cylinder internal combustion engines (ICEs). The system consists of a cam and a spring-loaded follower that synchronizes with the engine cycle to store and release energy, generating a compensatory torque that stabilizes rotational speed. The mechanism was implemented on a single-cylinder Honda^® engine and evaluated through simulations and laboratory tests under idle conditions. Results demonstrate a reduction in torque ripple amplitude of approximately 54% and standard deviation of 50%, as well as a decrease in angular speed fluctuation amplitude of about 43% and standard deviation of 42%, resulting in significantly smoother engine behavior. These improvements also address longstanding limitations in traditional powertrains, which often rely on heavy flywheels or electronically controlled dampers to manage rotational irregularities. Such solutions increase system complexity, weight, and energy losses. In contrast, the proposed passive mechanism offers a simpler, more efficient alternative, requiring no external control or energy input. Its effectiveness in stabilizing engine output makes it especially suited for integration into hybrid electric systems, where consistent generator performance and low mechanical noise are critical for efficient battery charging and protection of sensitive electronic components. Full article

The component characteristics of an aeroengine below idle speed are fundamental for start-up process simulations. However, due to experimental limitations, these characteristics must be extrapolated from data above idle speed. Existing extrapolation methods often suffer from insufficient utilization of available data, reliance on specific prior conditions, and an inability to capture unique operating modes (e.g., the stirring mode and turbine mode of compressor). To address these limitations, this study proposes a novel curve-and-surface fitting-based extrapolation method. The key innovations include: (1) extrapolating sub-idle characteristics through constrained curve/surface fitting of limited above-idle data, preserving their continuous and smooth nature; (2) transforming discontinuous isentropic efficiency into a continuous specific enthalpy change coefficient (SECC), ensuring physically meaningful extrapolation across all operating modes; (3) applying constraints during fitting to guarantee reasonable and smooth extrapolation results. Validation on a micro-turbojet engine demonstrates that the proposed method requires only conventional performance parameters (corrected flow, pressure/expansion ratio, and isentropic efficiency) above idle speed, yet successfully supports ground-starting simulations under varying inlet conditions. The results confirm that the proposed method not only overcomes the limitations of existing approaches but also demonstrates broader applicability in practical aeroengine simulations. Full article

Additive manufacturing (AM) is revolutionizing industrial production by enabling the fabrication of complex, customized components with reduced material waste. However, the scheduling of AM machines presents significant challenges in terms of optimizing both time-related performance and energy consumption. This paper introduces a novel multi-objective mixed-integer linear programming (MILP) model for scheduling AM machines with the dual objectives of minimizing makespan and energy consumption. We address the single-machine environment with detailed mathematical formulation that accounts for machine-specific parameters such as power consumption rates during different operational states, including printing, setup, and idle modes. Additionally, we consider part-specific characteristics including height, area requirements, and volume, ensuring practical feasibility constraints are met. The proposed model is validated using a comprehensive set of test problems, with optimal solutions reported for small to medium-sized instances. For larger problem instances, where computational complexity prevents finding optimal solutions within reasonable time limits, we report the best solutions obtained under specified time constraints. Computational experiments demonstrate that our approach effectively balances the trade-off between makespan and energy consumption, providing valuable insights for production planning in AM facilities. The results indicate potential energy savings of up to 18% compared to makespan-only optimization approaches, with minimal impact on overall completion times. Full article