In today’s power system landscape, renewable energy (RE) resources play a pivotal role, particularly within the residential sector. Despite the significance of these resources, the intermittent nature of RE resources, influenced by variable weather conditions, poses challenges to their reliability as energy resources. Addressing this challenge, the integration of an energy storage system (ESS) emerges as a viable solution, enabling the storage of surplus energy during peak-generation periods and subsequent release during shortages. One of the great challenges of ESSs is how to design ESSs efficiently. This paper focuses on a distributed power-flow system within a smart home environment, comprising uncontrollable power generators, uncontrollable loads, and multiple energy storage units. To address the challenge of minimizing energy loss in ESSs, this paper proposes a novel approach, called energy-efficient storage capacity with loss reduction (SCALE) scheme, that combines multiple-load power-flow assignment with a load-shifting algorithm to minimize energy loss and determine the optimal energy storage capacity. The optimization problem for optimal energy storage capacity is formalized using linear programming techniques. To validate the proposed scheme, real experimental data from a smart home environment during winter and summer seasons are employed. The results demonstrate the efficacy of the proposed algorithm in significantly reducing energy loss, particularly under winter conditions, and determining optimal energy storage capacity, with reductions of up to 11.4% in energy loss and up to 62.1% in optimal energy storage capacity.

To reduce the carbon footprint of manufacturing processes, it is necessary to reduce the number of stages in the development process. To this end, integrating additive manufacturing processes with three-dimensional (3D) printing makes it possible to eliminate the need to use tooling for component manufacturing. Furthermore, using 3D printing allows the generation of complex models to optimize different components, reducing the development time and realizing lightweight structures that can be applied in different industries, such as the mobility industry. Printing process parameters have been studied to improve the mechanical properties of printed items. In this regard, although the failure of most structural components occurs under dynamic load, the majority of the evaluations are quasistatic. This work highlights an improvement in fatigue strength under dynamic loads in 3D-printed components through heat treatment. The fatigue resistance was improved regarding the number of cycles and the dispersion of results. This allows 3D-printed polylactic acid components to be structurally used, and increasing their reliability allows their evolution from a prototype to a functional component.

Constant pressure variable flow reciprocating axial pumps (CPAP) are used in various applications, where a constant output pressure is maintained when the flow rate changes. When the hydraulic system is at rated pressure or less, the swash plate has maximum tilt, and the pump delivers maximum flow. The swash plate comes into this position thanks to the action of a reactive piston in which there are two springs. However, when the pressure rises above the nominal pressure value, the piston of the hydraulic pressure transducer (HPT) distributes the fluid under pressure to the hydraulic cylinder (HC), which causes a decrease in the tilt angle of the swash plate and a decrease in flow. The CPAP was selected as a component of the hydraulic system of the aircraft for the experimental tests in this paper. The experimental tests covered the structural and working parameters of the pump and analyzed their performance, efficiency and reliability. Experimental tests of structural and operating parameters of the CPAP were carried out in the Laboratory for Hydraulics and Pneumatics “PPT-Namenska” Trstenik on the hydraulic system, which simulated the real conditions prevailing in the hydraulic system of the aircraft. A system was used for data acquisition and recording of pump characteristics, which were obtained during experimental testing. The results of the measurement and testing of the structural parameters of the CPAP are shown in tabular form, and the experimental tests of static characteristics and dynamic behavior are shown diagrammatically.

This study addresses the pressing energy constraints in nations like Bangladesh by proposing the implementation of photovoltaic (PV) microgrids. Given concerns about environmental degradation, limited fossil fuel reserves, and volatile product costs, renewable energy sources are gaining momentum globally. Our research focuses on a grid-connected solar PV system model at Char Jazira, Lalpur, Natore, Rajshahi, Bangladesh. Through PVsyst 7.1 simulation software, we assess the performance ratio (PR) and system losses, revealing an annual solar energy potential of 3375 MWh at standard test condition (STC) efficiency. After considering losses, the system generates 2815.2 MWh annually, with 2774 MWh exported to the grid. We analyze an average PR of 78.63% and calculate a levelized cost of energy (LCOE) of 2.82 BDT/kWh [1 USD = 110 BDT]. The financial assessment indicates a cost-effective LCOE for the grid-connected PV system, with an annual gross income of 27,744 kBDT from selling energy to the grid and operating costs of 64,060.60 BDT/year. Remarkably, this initiative can prevent 37,647.82 tCO2 emissions over the project’s 25-year lifespan.

Because of its numerous advantages, 3D printing is widely employed for a variety of purposes. The mechanical characteristics of 3D-printed items are quite important. 3D-printed polylactic acid (PLA) is a common thermoplastic polymer due to its excellent characteristics and affordable cost. Because of its enhanced characteristics, polyethylene terephthalate glycol (PETG) has recently received a lot of attention. Despite PETG’s potential appeal in the 3D-printing field, little research has been conducted to explore its qualities, such as the impacts of raster angle on elasticity, which could lead to the development of more accurate guidelines for inspection and assessment. In this regard, this study examines the mechanical characteristics of polylactic acid (PLA) and polyethylene terephthalate glycol (PETG) 3D-printing specimens with different raster angles. Test specimens with raster angles of 15° and 30° were printed, and the stress–strain responses were recorded and compared with the simulated profiles generated using ANSYS software. The results showed that the raster angle significantly affected the mechanical properties of both types of materials. The simulated profile matched well with the experimental profile only in the case of PLA printed with a raster angle of 15°. These findings imply that extra effort should be made to ensure that the raster angle is tailored to yield the optimal mechanical properties of 3D-printed products.

In recent years, people have been buying custom-built PCs based on the performance they want and what they will use them for. However, there are many challenges for non-technical users when purchasing a custom-built PC. Not only is the terminology of computer devices unfamiliar to non-experts, but there are many specifications for different computer devices that need to be considered. Therefore, this paper proposes a method for recommending appropriate device models when purchasing custom-built PCs using a skyline. Because different computer devices have different specifications, we need a method that takes into account multiple attributes. Skyline querying is a technique that considers multiple attributes of an object and indexes them in order of user satisfaction. A grid skyline is a technique that uses a grid-based partitioning technique to reduce the number of calculations of the dominance relationship between objects in the existing skyline technique, thus reducing the index construction time. We measured the similarity between the results of the grid skyline and the leaderboard for each model of computer device. As a result of this experiment, compared to the leaderboard categorized by model of computer device, the average score was 88 out of 100, which was similar to the actual leaderboard.

Traffic management systems play a vital role in ensuring safe and efficient transportation on roads. However, the use of advanced technologies in traffic management systems has introduced new safety challenges. Therefore, it is important to ensure the safety of these systems to prevent accidents and minimize their impact on road users. In this survey, we provide a comprehensive review of the literature on safety in traffic management systems. Specifically, we discuss the different safety issues that arise in traffic management systems, the current state of research on safety in these systems, and the techniques and methods proposed to ensure the safety of these systems. We also identify the limitations of the existing research and suggest future research directions.

The low order Taylor’s series expansion was employed in this study to estimate the reliability indices of the failure criteria for reliability-based design optimization of a linear static structure subjected to random loads and boundary conditions. By taking the advantage of the linear superposition principle, only a few analyses of the structure subjected to unit-loads are needed through the entire optimization process to produce acceptable results. Two structural examples are presented in this study to illustrate the effectiveness of the proposed approach for reliability-based design optimization: one deals with a truss structure subjected to random multiple point constraints, and the other conducts shape design optimization of a plane stress problem subjected to random point loads. Both examples were formulated and solved by the finite element method. The first example used the penalty method to reformulate the multiple point constraints as external loads, while the second example introduced an approach to propagate the uncertainty linearly from the nodal displacement vector to the nodal von Mises stress vector. The final designs obtained from the reliability-based design optimization were validated through Monte Carlo simulation. This validation process was completed with only four unit-load analyses for the first example and two for the second example.

To investigate the performance of transcritical CO2 quasi-secondary compression cycle with ejector (TCIEJ) for heat pump water heaters, the thermodynamic model of TCIEJ is established based on the pinch point, and TCEX, TCEJ, and TCI are selected as comparisons. The effects of changing high pressure and ambient temperature on the heating COP and compressor exhaust temperature are analyzed, and the influence of cooling water inlet and outlet temperature and vapor injection pressure on TCIEJ is further analyzed. The results show that there are optimal high pressures that make the heating COP of the four heat pump cycles reach the maximum value, of which TCIEJ has the best performance. At an ambient temperature of −15 °C, the maximum heating COP of TCIEJ increased by about 20.5%, 14.9%, and 7.9% compared with TCEX, TCEJ, and TCI. With the increase in ambient temperature, the optimal high pressure continues to increase, and the corresponding maximum heating COP gradually increases. Selecting the geometric mean of high pressure and evaporation pressure as the optimal vapor injection pressure for TCIEJ, the error is small compared to the actual optimal vapor injection pressure. With the increase in ambient temperature and cooling water outlet temperature, the optimal high pressure of TCIEJ continues to increase, and the correlation formula of optimal high pressure is fitted according to the simulation results.

Solar energy can function as a supplementary power supply for other renewable energy sources. On average, Vellore region experiences approximately six hours of daily sunshine throughout the year. Solar photovoltaic (PV) modules are necessary to monitor and fulfill the energy requirements of a given day. An artificial neural network (ANN) based maximum power point tracking (MPPT) controller is utilised to regulate the solar photovoltaic (PV) array and enhance its output. The utilisation of this controller can enhance the efficiency of the module even in severe circumstances, where reduced current and torque ripples will be observed on the opposite end. The motorised vehicle has the capability to function at its highest torque level in different load scenarios as a result. The proposed method is expected to provide advantages in various electric vehicle (EV) applications that require consistent velocity and optimal torque to satisfy the load conditions. The study employs a solar battery that is linked to an SVPWM inverter and subsequently a DC-DC boost converter to supply power to the load. An Artificial Neural Network (ANN) based Maximum Power Point Tracking (MPPT) control system is proposed for a solar battery powered Electric Vehicle (EV) and the system’s performance is evaluated by collecting and analysing data under adjustable load conditions to obtain constant parameters such as speed and torque. The MATLAB® Simulink® model was utilised for this purpose.