Search Results (205)

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

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

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

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

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

Telemetry systems are increasingly finding applications in agriculture for a variety of tasks. These systems assist farmers in optimizing farm processes. By leveraging these technologies, energy resources can be used more efficiently, leading to reduced environmental pollution. The primary objective of this research is to analyze telemetry data and explore ways to enhance the efficiency of combine harvesters (CHs). For this study, data from the Lexion 750 TT CH equipped with a crawler chassis was selected. Harvesting operations were conducted across fields growing popular plant types in Lithuania, including wheat, barley, rapeseed, oats, corn, and beans. The selected CH was also equipped with a remote monitoring system for tracking machine parameters. During the research, the structure of the time distribution of the work and the consumed fuel was analyzed. The highest operational efficiency—defined as the proportion of time spent on productive harvesting tasks—was 78%, observed during the oat harvest, when the unloading while harvesting, unloading while idle, harvesting, and headland turns were 3%, 2%, 64%, and 9%, respectively. The lowest efficiency, 56%, occurred during wheat harvesting. It was found that harvesting 899.32 ha of six different plant species with the tested CH produces 46.11 t of GHG emissions in CO_2eq. The largest part of the emission in CO_2eq was released during direct harvesting, with the engine operating at 1800–1900 min⁻¹. However, as much as 30% of the time and 11.2% of fuel was consumed by the CH for non-harvesting activities. In conclusion, attention should be paid to reducing the inefficient use of CH time. In this way, technological operations would not only be carried out more rationally, but also environmental pollution would be reduced, and in the case of this study, we could potentially reduce CO_2eq emissions by more than 10%. Full article

Thermal energy storage (TES) is likely to play a significant role in the decarbonisation of domestic heat, allowing consumers to shift their energy consumption away from peak demand periods and reducing overall strain on the grid. Phase change materials (PCMs) are a promising option for TES, in which energy can be stored in the latent heat of the melting of the PCM; these offer greater storage densities than sensible heat TES and have the benefit of releasing stored heat at a consistent temperature (the crystallisation temperature of the PCM). One of the key difficulties for PCM-based TES is state of charge (SoC) estimation (the estimation of the proportion of energy stored in the TES unit up to its maximum capacity), particularly during idle periods while the unit is storing heat. SoC estimation is key to the implementation of TES, as it enables the effective control of the units. The use of a resonator within the PCM for SoC estimation could potentially provide a global estimate of the SoC, since the resonator passes through the full depth of the PCM in the unit. The SoC could be inferred by measuring the vibrational response of the resonator under excitation, which varies depending on the melt state of the PCM. This paper presents findings from a test rig investigating this proposal, including discussions on the features required from the resonator response for SoC inference. Full article

Currently, in countries with warm climatic conditions, the average height of corn stalks during silage harvesting reaches 2.5 m. However, trailed forage harvesters are designed for mowing stalks not more than 1.5 m. That is why there is a demand to develop a compact forage harvester that can mow and shred tall stalks. This research proposes a two-level mowing method for tall stalks. According to the obtained analytical expressions, the technological distance between the vertical axes of the mowing rotors is determined to be 700 mm. A forage harvester is developed, equipped with a device that orientates itself by the length of stalks at the entrance to the shredding chamber and two mowing rotors designed for mowing tall stalks. Analytical expressions are obtained, which determine the required power for the mass transportation processes by the screw working device and the mass supply by the orienting drum into the chamber of the radial-knife blade shredding rotor. The required powers of the screw conveyor and the orienting drum are determined based on the obtained expressions; they are, respectively, equal to 0.42 kW and 0.11 kW. As a result of the conducted laboratory field tests, the required powers for mowing and for the general processes of mowing, shredding, and transporting the mass through the deflector are determined. The power balance of the harvester units is compiled. It is established that 75% of the consumed power is used for effective work, while 25% is for used for idling the harvester. During the mowing and shredding of corn stalks with a height of 2.9 m, the harvester’s productivity reaches 16.3 tons per hour, with a required power of 12.5 kW. During the tests, the harvester functioned without losses and without any violation of the technological process. The test proves the performance of the design and technological scheme and the accuracy of the obtained analytical expression determining the technological distance between the vertical axes of mowing rotors. Full article

Broiler gait score (GS) evaluation relies on manual assessments by experts, which can be laborious, hindering timely welfare management. Deep learning (DL) models, conversely, may serve as a cost-effective solution in evaluating GS via automated detection of broiler mobility. This study aimed to develop a vision-based YOLOv8 model to detect the locations of individual broilers, allowing for continuous tracking of birds within a pen and determining bird walking distances, speeds, idleness and movement ratios, and time at the feeder and drinker ratios. Then, Machine Learning (ML) models were developed to estimate the GS level from the mobility indicators in a lab setting. Ten broilers were color-coded and recorded via a top-view camera for 41 days. Their GS were assessed manually twice per week. The YOLOv8 model was trained, validated, and tested with 600, 150, and 50 images, respectively, and subsequently applied to the dataset yielding each broiler’s mobility indicators. The GS levels and mobility indicators were correlated through Ordinal Logistics (OL), Random Forest (RF), and Support Vector Machine (SVM) ML models. The YOLOv8 model was developed with 91% training, 89% testing, and 87% validation mean average precision (mAP) accuracies in identifying color-coded broilers. After tracking, the model estimated an average of 472.26 ± 234.18 cm hourly distance traveled and 0.13 ± 0.07 cm/s speed by a broiler. It was found that with deteriorated GS levels (i.e., worse walking ability), broilers walked shorter distances (p = 0.001), had lower speeds (p = 0.001), were increasingly idle and less mobile, and were increasingly stationed near or around the feeder. The movement ratio, average hourly walking distance, hourly average speed, and age variables were found to be the most significant variables (p < 0.005) in predicting GS levels. These variables were further reduced to one, the average hourly walking distance, because of high collinearity and were used to predict the GS with ML models. The RF model, outperforming others, was able to predict GS with a generalized R² of 0.62, root mean squared error (RMSE) of 0.54, and 65% classification accuracy. Full article

A novel artificial intelligence (AI) application was proposed in the current study to predict CTF’s compressive strength (CS). The database contained six input parameters: the age of curing for specimens (AS), cement–sand ratio (C/S), maintenance temperature (T), additives (EA), additive type (AT), additive concentration (AC), and one output parameter: CS. Then, adaptive boosting (AdaBoost) was applied to existing AI and soft computing techniques, using AdaBoost, random forest (RF), SVM, and ANN. Data were arbitrarily separated into training (70%) and test (30%) sets. Results confirm that AdaBoost and RF have the best prediction accuracy, with a correlation coefficient (R²) of 0.957 between these sets for AdaBoost. Using Python 3.9 (64-bit), IDLE (Python 3.9 64-bit), and PyQt5 to achieve the machine learning model computation and software function interface development, the application of this software can quickly predict the strength property of CTF specimens, which saves time and costs efficiently for backfill researchers and developing new eco-efficient components. Full article

The ongoing adoption of Robot Operating Systems (ROSs) not only for research-oriented projects but also for industrial applications demands a more thorough assessment of its security than in the past. This paper highlights that a key ROS component—the ROS Master—is indeed vulnerable to a novel kind of Slow Denial of Service (slow DoS) attack, the root reason of this vulnerability being an extremely high idle connection timeout. The effects of vulnerability exploitation have been evaluated in detail by means of a realistic test bed, showing how it leads to a systemwide and potentially dangerous disruption of ROS system operations. Moreover, it has been shown how some basic forms of built-in protection of the Linux kernel can be easily circumvented, and are therefore ineffective against this kind of threat. Full article

In the age of hardware accelerators, increasing pressure is applied on computer architects and hardware engineers to improve the balance between the cost and benefits of specialized computing units, in contrast to more general-purpose architectures. The first part of this study presents the embedded Streaming Hardware Accelerator (eSAC) architecture. This architecture can reduce the idle time of specialized logic. The remainder of this paper explores the integration of an eSAC into a Central Processing Unit (CPU) core embedded inside a System-on-Chip (SoC) design, using the AXI-Stream protocol specification. The three evaluated architectures are the Tightly Coupled Streaming, Protocol Adapter FIFO, and Direct Memory Access (DMA) Streaming architectures. When comparing the tightly coupled architecture with the one including the DMA, the experiments in this paper show an almost 3× decrease in frame latency when using the DMA. Nevertheless, this comes at the price of an increase in FPGA resource utilization as follows: LUT (2.5×), LUTRAM (3×), FF (3.4×), and BRAM (1.2×). Four different test scenarios were run for the DMA architecture, showcasing the best and worst practices for data organization. The evaluation results highlight that poor data organization can lead to a more than 7× increase in latency. The CPU model was selected as the newly released MicroBlaze-V softcore processor. The designs presented herein successfully operate on a popular low-cost Field-Programmable Gate Array (FPGA) development board at 100 MHz. Block diagrams, FPGA resource utilization, and latency metrics are presented. Finally, based on the evaluation results, possible improvements are discussed. Full article

This study examines the impact of alcohol blends on the performance and emissions of aviation micro-turbojet engines. Thus, propanol, butanol, pentanol, hexanol, heptanol, and octanol were tested at 10%, 20%, and 30% concentrations and mixed with Jet A, as well as with an additional 5% heptanol blend to preserve base fuel properties, to fuel a JetCat P80 micro-turbojet. Physicochemical properties such as density, viscosity, and elemental composition were analyzed before engine testing. Carbon dioxide (CO₂) emissions from 1 kg of fuel combustion varied, with propanol yielding the lowest at 3.02 kg CO₂ per kg of fuel and octanol yielding the highest at 3.22 kg CO₂ per kg of fuel. The following results were obtained: alcohol blends lowered exhaust gas temperature by up to 7.5% at idle and intermediate thrust but slightly increased it at maximum power; fuel mass flow increased with alcohol concentration, peaking at 20.4% above Jet A for 30% propanol; and thrust varied from −4.92% to +7.4%. While specific fuel consumption increased by up to 12.8% for propanol, thermal efficiency declined by 1.8–5.6% and combustion efficiency remained within ±2% of Jet A. Butanol and octanol emerged as viable alternatives, balancing emissions reduction and efficiency. The results emphasize the need for an optimal trade-off between environmental impact and engine performance. Full article

The Mixed No-Idle Permutation Flow Shop Scheduling Problem (MNPFSSP) represents a specific case within regular flow scheduling problems. In this problem, some machines allow idle times between consecutive jobs or operations while other machines do not. Traditionally, the MNPFSSP has been addressed using the metaheuristics and exact methods. This work proposes an Evolutionary Strategy Based on the Generalized Mallows Model (ES-GMM) to solve the issue. Additionally, its advanced version, ES-GMMc, is developed, incorporating operating conditions to improve execution times without compromising solution quality. The proposed approaches are compared with algorithms previously used for the problem under study. Statistical tests of the experimental results show that the ES-GMMc achieved reductions in execution time, especially standing out in large instances, where the shortest computing times were obtained in 23 of 30 instances, without affecting the quality of the solutions. Full article

Trend forecasting and early anomaly warnings are important for avoiding aircraft engine failures or accidents. This study proposes a trend forecasting method based on enhanced Slice-level Adaptive Normalization (SAN) using a Long Short-Term Memory (LSTM) neural network under multi-operating conditions. Firstly, a condition recognition technology is constructed to automatically identify the operating conditions based on the predetermined judgment conditions, and vibration signal features are adaptively divided into three typical operating conditions, namely, the idling operating condition, the starting operating condition and the utmost operating condition. The features of original signals are extracted to reduce the impacts of signal fluctuations and noise preliminarily. Secondly, enhanced SAN is used to normalize and denormalize the features to alleviate non-stationary factors. To improve prediction accuracy, an L₁ filter is adopted to extract the trend term of the features, which can effectively reduce the overfitting of SAN to local information. Moreover, the slice length is quantitatively estimated by the fixed points in L₁ filtering, and a tail amendment technology is added to expand the applicable range of enhanced SAN. Finally, an LSTM-based forecasting model is constructed to forecast the normalized data from enhanced SAN, serving as input during denormalization. The final results under different operating conditions are the output from denormalization. The validity of the proposed method is verified using the test data of an aircraft engine. The results show that the proposed method can achieve higher forecasting accuracy compared to other methods. Full article