Designs

The unpredictability in time of seismic activities and the dependence of tectonic movements on a multitude of factors challenges specialists to identify the most accurate related methods to avoid catastrophes associated with hazards. Early warning systems are critical in reducing negative effects in the case of an earthquake with a magnitude above 5 MW. Their precision is all the better as they corroborate and transmit more information collected from the regional or on-site sensory nodes to a central unit that discloses events and estimates the epicentral location, earthquake magnitude, or ground shaking amplitude. The shaking table is the proper instrument for evaluating an early warning systems’ dynamic response and performance under specific vibration conditions. To this issue, the paper presents a laboratory single-axis shaking table with a small-scale, low-cost design and an accurate displacement control. Experiments based on a suite of 12 real earthquakes provided results with very small errors related to similar models, bearing out the designed shaking table is suitable for early earthquake warning system response testing for high magnitude earthquakes. Full article

The high demand for energy resources due to the increasing number of electronic devices has prompted the constant search for different or alternative energy sources to reduce energy consumption, aiming to meet the high demand for energy without exceeding the consumption of natural sources. In this context, the objective of this study was to examine research trends in the machine-learning-based design of electrical and electronic devices. The methodological approach was based on the analysis of 152 academic documents on this topic selected from Scopus and Web of Science in accordance with the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement. Quantity, quality, and structural indicators were calculated to contextualize its thematic evolution. The results showed a growing interest in the subject since 2019, mainly in the United States and China, which stand out as world powers in the information and communication technology industry. Moreover, most studies focused on developing devices for controlling, monitoring and reducing energy consumption, mainly in 5G and thermal comfort devices, primarily using deep-learning techniques. Full article

The American lobster (Homarus americanus) is the most valuable seafood on Canada’s Atlantic coast, generating over CAD 800 million in export revenue alone for New Brunswick. However, labor shortages plague the lobster industry, and lobsters must be processed quickly to maintain food safety and quality assurance standards. This paper proposes a lobster estimation orientation approach using a convolutional neural network model, with the aim of guiding the FANUC LR Mate 200 iD robotic arm for lobster manipulation. To validate this technique, four state-of-the-art object detection algorithms were evaluated on an American lobster images dataset: YOLOv7, YOLOv7-tiny, YOLOV4, and YOLOv3. In comparison to other versions, YOLOv7 demonstrated a superior performance with an F1-score of 95.2%, a mean average precision (mAP) of 95.3%, a recall rate of 95.1%, and 111 frames per second (fps). Object detection models were deployed on the NVIDIA Jetson Xavier NX, with YOLOv7-tiny achieving the highest fps rate of 25.6 on this platform. Due to its outstanding performance, YOLOv7 was selected for developing lobster orientation estimation. This approach has the potential to improve efficiency in lobster processing and address the challenges faced by the industry, including labor shortages and compliance with food safety and quality standards. Full article

Coupling is a key concept in the field of embodied interaction with digital products and systems, describing how digital phenomena relate to the physical world. In this paper, we present a Research through Design process in which the concept of coupling is explored and deepened. The use case that we employed to conduct our research is an industrial workplace proposed by Audi Brussels and Kuka. Our aim was to enrich this workplace with projection, or Spatial Augmented Reality, while focusing on operator interaction. We went through three successive design iterations, each of which resulted in a demonstrator. We present each of the three demonstrators, focusing on how they propelled our understanding of coupling. We establish a framework in which coupling between different events, be they physical or digital, emerges on four different aspects: time, location, direction, and expression. We bring the first three aspects together under one heading—coupling of meaning—and relate it to ease of use and pragmatic usability. We uncover the characteristics of the fourth aspect—coupling of expression—and link it to the psychological wellbeing of the operator in the workplace. We conclude this paper by highlighting its contribution to the embodied interaction research agenda. Full article

Challenges caused by design complexities during the design stages of a product must be coordinated and overcome by the selection of a suitable manufacturing approach. Additive manufacturing (AM) is capable of fabricating complex shapes, yet there are limiting aspects to surface integrity, dimensional accuracy, and, in some instances, design restrictions. Therefore, the goal is essentially to establish the complex areas of a tool during the design stage to achieve the desired quality levels for the corresponding injection moulding tool insert. When adopting a manufacturing approach, it is essential to acknowledge limitations and restrictions. This paper presents the development of a feature-based manufacturability assessment system (FBMAS) to demonstrate the feasibility of integrating selective laser melting (SLM), a metal-based AM technology, with subtractive manufacturing for any given part. The areas on the tool inserts that hold the most geometrical complexities to manufacture are focused on the FBMAS and the design features that are critical for the FBMAS are defined. Furthermore, the structural approach used for developing the FBMAS graphical user interface is defined while explaining how it can be operated effectively and in a user-friendly approach. The systematic approach established is successful in capturing the benefits of SLM and subtractive methods of manufacturing, whilst defining design limitations of each manufacturing method. Finally, the FBMAS developed was validated and verified against the criteria set by experts in the field, and the system’s logic was proven to be accurate when tested. The decision recommendations proved to correlate with the determined recommendations of the field experts in evaluating the feature manufacturability of the tool inserts. Full article

The desalination of saltwater is a viable option to produce freshwater. All the desalination processes are energy-intensive and can be carried out on a large scale. Therefore, producing freshwater using renewable energy sources is the most desirable option considering the current energy crisis and the effect that fossil-fuel-based energy has on our carbon footprint. In this respect, the tray-type still, one of several solar power desalination still varieties, is popular owing to its straightforward design, economic materials of construction, and minimal maintenance requirements, especially in isolated island regions with restricted energy and natural water supplies. The traditional tray-type solar power has a few drawbacks, such as the inability to recover latent heat from condensation, reduced thermal convection, a large heat capacity, and comparatively minimal driving power through evaporation. Therefore, the improvement of heat and mass transfer capabilities in tray-type stills has been the subject of many studies. However, there is a lack of a comprehensive review in the open literature that covers the design and operational details of multistage solar stills. The purpose of this paper is to present a thorough overview of the past research on multistage solar stills, in terms of configurations, capabilities, and cutting-edge options. In comparison to a unit without a salt-blocking formation, the review indicates that a multistage distillation unit may run continuously at high radiation and generate pure water that is around 1.7 times higher than a unit without a salt-blocking formation. The most effective deign is found to be “V”-shaped solar still trays that attach to four-stage stills, since they are less expensive and more economical than the “floor” (Λ-shape) design, which requires two collectors. Additionally, it can be stated that the unit thermal efficiency, solar percentage, and collected solar energy (over the course of a year) increase by 23%, 18%, and 24%, respectively, when the solar collectors are increased by 26% (at the constant inflow velocity of the water). Full article

The efficient utilization of solar energy technology is significantly enhanced by the application of energy storage, which plays an essential role. Nowadays, a wide variety of applications deal with energy storage. Due to the intermittent nature of solar radiation, phase change materials are excellent options for use in several types of solar energy systems. This overview of the relevant literature thoroughly discusses the applications of phase change materials, including solar collectors, solar stills, solar ponds, solar air heaters, and solar chimneys. Despite the complexity of their availability and high costs, phase change materials are utilized in the majority of solar energy techniques because of the considerable technical improvements they provide. While numerous studies have investigated the progress of phase change materials used in solar energy applications such as photovoltaic systems, it is vital to understand the conceptual knowledge of employing phase change materials in various types of solar thermal energy systems. Investigations into the use of phase change materials in solar applications for the purpose of storing thermal energy are still being carried out to upgrade the overall performance. This paper briefly reviews recently published studies between 2016 and 2023 that utilized phase change materials as thermal energy storage in different solar energy systems by collecting more than 74 examples from the open literature. This study focuses on demonstrating the maturity of phase change materials and their integration into solar energy applications. Based on the findings, proposals for new research projects are made. Full article

Minimum lap-time planning (MLTP) is a well-established problem in the race car industry to provide guidelines for drivers and optimize the vehicle’s setup. In this paper, we tackle the 3D nature of the problem in its full extension, making no simplifying assumptions on the mechanics of the system. We propose a multibody vehicle model, described by rigorous dynamical equations. To effectively handle the resulting complexity, we devised an efficient direct dynamics computational method based on Featherstone’s articulated-body algorithm (ABA). To solve the MLTP, we employed a direct-collocation technique, discretizing the problem so that all information of the 3D track is pre-processed and directly embedded into the discrete problem. This discretization approach turns out to be perfectly compatible with our vehicle model, leading to a solution in accessible computational time frames. The high level of detail of the model makes the proposed approach most useful for in-depth vehicle dynamics analyses on complex tracks. To substantiate the analysis, we provide a comparison with the results obtained by a double-track model on the Nürburgring Nordschleife circuit. Consistently with the average trend defined by the double track, the proposed model features a more dynamically rich behavior, realistically capturing the higher-order effects elicited by the sharp corners and the highly variable slope of the track. Full article

Containers are fundamental elements for the development of international trade; however, it is estimated that there are more than 17 million retired containers stacked in ports around the world. Considering the high costs involved in the process of storing, transporting, or destroying these materials, in addition to their non-degradable nature, it is urgent to develop strategies for the sustainable use of these decommissioned containers. In this context, repurposing these containers into permanent structures is becoming a predominant trend. One solution is converting steel shipping structures into habitable spaces. However, due to the urgency with which Container Houses (CHs) are demanded in case of disasters, they are usually planned to be built as quickly as possible, serving as many people as possible, and do not consider the basic principles of energy efficiency. The performance of the CHs is, then, impaired, including risks of overheating, corrosion, and rust, among others, during service, making them an even more stressful experience for their users who are already in a vulnerable situation. Therefore, the objective of this study is to compare the performance of two thermal insulators applied to a temporary shelter container designed to promptly serve vulnerable populations. The model was developed in Building Information Modeling (BIM) software and simulated in Building Energy Simulation (BES) software, aiming to obtain subsidies for its technical and economic viability analysis. The results indicated that thermal insulators are able to generate significant savings in energy consumption, with mineral wool presenting better long-term performance. Full article

DC–DC converters are used in many power electronics applications, such as switching power supply design, photovoltaic, power management systems, and electric and hybrid vehicles. Traditionally, DC–DC converters are linearly modeled using a typical operating point for their control design. Some recent works use nonlinear models for DC–DC converters, due to the inherent nonlinearity of the switching process. In this sense, a standout modeling technique is the Takagi–Sugeno fuzzy exact method due to its ability to represent nonlinear systems over the entire operating range. It is more faithful to system behavior modeling, and allows a nonlinear closed-loop control design. The use of nonlinear models allows the testing of controllers obtained by linear methods to operate outside their linearization point, corroborating with robust controllers for specific applications. This work aims to perform the exact fuzzy Takagi–Sugeno modeling of a buck–boost converter with non-ideal components, and to design a discrete proportional–integral–derivative (PID) controller from the pole cancellation technique, obtained linearly, to test the controller at different operating points. The PID control ensured a satisfactory result compared with the stationary value of the different operating points, but it did not reach the desired transient response. Since the proposed model closely represents the operation of the buck–boost converter by considering the components’ non-idealities, other control techniques that consider the system’s nonlinearities can be applied and optimized later. Full article

The authors propose a technique for reactive power compensation using a powerful regenerative controlled-speed synchronous motor drive (SMD) based on a three-level (3L) neutral point clamped (NPC) active front-end rectifier (AFE) and a voltage source inverter (VSI). The review of technical solutions for reactive power compensation showed that the limitations on the transmitted reactive power in the system under consideration still have not been studied. The paper provides a mathematical description and proposes synthesis-friendly block diagrams of the mathematical 3L-NPC-AFE-VSI and SMD models. The developed models allow defining the instantaneous values of the total 3L-NPC-AFE power consumed from the grid depending on the SMD load diagram. It is noted that the 3L-NPC-AFE-VSI-SMD system is designed without considering the opportunities for reactive power generation. It was determined that the limit value of reactive power generated by a 3L-NPC-AFE depends on the DC link voltage, the grid current consumption and the modulation index. The possibility of reactive power compensation by the SMD system through a 3L-NPC-AFE was experimentally tested on the main drive of a metal plate hot rolling mill. The analysis of the results obtained showed that during the breakdown, an SMD can generate reactive power equal to 16% of the total rated power using a 3L-NPC-AFE at a rated DC link voltage and without overcurrent. It was shown that generating reactive power is expedient in low-load SMD operation modes or at idle. Research in this area is promising due to the widespread use of high-power SMD based on a 3L-NPC-AFE-VSI and the tightening of requirements for energy saving and efficiency and supply voltage quality. The proposed reactive power control technique can be used as part of an industrial smart grid. Full article

Centrifugal pumps have a wide range of applications in the aviation field. The present work focuses on the optimal design of aviation fuel pump impellers by means of an inverse method. The fuel pump impeller is designed here by solving an inverse problem, in which the impeller geometry is found by imposing a target blade loading. As the inverse procedure is inviscid, an iterative process based on RANS is then applied to finally converge to a fully viscous solution. Three representative loading distributions have been investigated, and the final performances are evaluated by RANS computations. Since flow variables, rather than the blade geometry, are imposed on the target flow field, it is found that the impellers designed by way of the inverse method have high efficiency under the conditions without cavitation; among them, the pump impeller with a higher loading at the hub maintains a high efficiency for a wide range of flow conditions and also has better anti-cavitation performances under low inlet pressure conditions. Moreover, cavitation resistance can be improved by adjusting the loading distribution near the blade leading edge using the inverse design tool. Full article

With the qualitative development of DC microgrids, the usage of different loads with unique conditions and features is now possible in electric power grids. Due to the negative impedance features of some loads, which are called constant power loads (CPLs), the control of DC power converters faces huge challenges from a stability point of view. Despite the significant advances in semiconductors, there is no upgrade in the control of gate drivers to exploit all potential of power electronic systems. In this paper, quantum computations are incorporated into artificial intelligence (AI) to stabilize a full-bridge (FB) DC-DC boost converter feeding CPL. Aiming to improve the bus voltage stabilization of the FB DC-DC boost converter, a quantum deep reinforcement learning (QDRL) control methodology is developed. By defining a reward function according to the specification of the FB power converter, the desired performance and control objectives are fulfilled. The main task of QDRL is to adjust the control coefficients embedded in the feedback controller to suppress the negative impedance effect resulting from deploying the CPLs. By deploying the potential advantages of quantum fundamentals, the deep reinforcement learning improved by quantum specifications will not only enhance the performance of the DRL algorithm on conventional processes but also advance related research areas such as quantum computing and AI. Unlike the basic quantum theory, which requires real quantum hardware, QDRL can be executed on classic computers. To examine the feasibility of the QDRL scheme, hardware-in-the-loop (HiL) examinations are conducted using the OPAL-RT. The comparison of the proposed controller with the classic state-of-the-art methodologies reveals the superiority and feasibility of QDRL-based control schemes in both the transient and steady-state conditions to other schemes. Analysis using various performance criteria, including the integral absolute error (IAE), integral time absolute error (ITAE), mean absolute error (MAE), and root mean square error (RMSE), demonstrates the dynamic improvement of the proposed scheme over sliding mode control (approximately 50%) and proportional integral control (approximately 100%). Full article

The current prioritisation of road safety enhancement in the automotive sector is leading toward the near future implementation of Advanced Driver Assistance Systems (ADASs), aiming at the simultaneous intervention of braking and steering for impact avoidance in case of an impending collision. However, it is partially unclear how new technologies for controlling the steering will actually behave in the case of inevitable collision states; the need consequently emerges to propose and tune efficient ADAS strategies to handle the complexity of critical road scenarios. An adaptive intervention logic on braking and steering for highly automated vehicles is applied in the context of a “lane departure”, two-vehicle critical road scenario; the ADAS implementing the logic activates to minimise the injury risk for the ego vehicle’s occupants at each time step, adapting to the eventual scenario evolution consequent to actions by other road users. The performance of the adaptive logic is investigated by a software-in-the-loop approach, varying the mutual position of the involved vehicles at the beginning of the criticality and comparing the injury risk outcomes of the eventual impacts with those connected to the Autonomous Emergency Braking (AEB). The results highlight a twofold benefit from the adaptive logic application in terms of road safety: (1) it decreases the frequency of impacts compared to the AEB function; (2) in inevitable collision states, it decreases injury risk for the vehicles’ occupants down to 40% compared to the AEB. This latter condition is achieved thanks to the possibility of reaching highly eccentric impact conditions (low impact forces and occupants’ injury risk as a consequence). The obtained highlights expand the literature regarding the adaptive logic by considering a diverse critical road scenario and investigating how fine variations on the vehicles’ mutual position at the beginning of the criticality reflect on the injury outcomes for different types of intervention logic. Full article

Microgrids are energy systems that can operate independently or in conjunction with the main electricity grid. Their purpose is to link different energy sources, enhance customer participation in energy markets, and improve energy system efficiency and flexibility. However, regulatory, technical, and financial obstacles hinder their deployment. To comprehend the current state of the field, this study utilized citation network analysis (CNA) methodology to examine over 1500 scholarly publications on microgrid research and development (R&D). The study employed modularity-based clustering analysis, which identified seven distinct research clusters, each related to a specific area of study. Cluster 1, focused on control strategies for microgrids, had the highest proportion of publications (23%) and the maximum citation link count (151), while Cluster 4, which examined microgrid stability, had the lowest proportion of papers (10%). On average, each publication within each cluster had four citation links. The citation network of microgrid research was partitioned using cluster analysis, which aided in identifying the main evolutionary paths of each subfield. This allowed for the precise tracing of their evolution, ultimately pinpointing emerging fronts and challenges. The identification of key pathways led to the discovery of significant studies and emerging patterns, highlighting research priorities in the field of microgrids. The study also revealed several research gaps and concerns, such as the need for further investigation into technical and economic feasibility, legislation, and standardization of microgrid technology. Overall, this study provides a comprehensive understanding of the evolution of microgrid research and identifies potential directions for future research. Full article

Breast cancer poses the greatest long-term health risk to women worldwide, in both industrialized and developing nations. Early detection of breast cancer allows for treatment to begin before the disease has a chance to spread to other parts of the body. The Internet of Things (IoT) allows for automated analysis and classification of medical pictures, allowing for quicker and more effective data processing. Nevertheless, Fog computing principles should be used instead of Cloud computing concepts alone to provide rapid responses while still meeting the requirements for low latency, energy consumption, security, and privacy. In this paper, we present CanDiag, an approach to cancer diagnosis based on Transfer Deep Learning (TDL) that makes use of Fog computing. This paper details an automated, real-time approach to diagnosing breast cancer using deep learning (DL) and mammography pictures from the Mammographic Image Analysis Society (MIAS) library. To obtain better prediction results, transfer learning (TL) techniques such as GoogleNet, ResNet50, ResNet101, InceptionV3, AlexNet, VGG16, and VGG19 were combined with the well-known DL approach of the convolutional neural network (CNN). The feature reduction technique principal component analysis (PCA) and the classifier support vector machine (SVM) were also applied with these TDLs. Detailed simulations were run to assess seven performance and seven network metrics to prove the viability of the proposed approach. This study on an enormous dataset of mammography images categorized as normal and abnormal, respectively, achieved an accuracy, MCR, precision, sensitivity, specificity, f1-score, and MCC of 99.01%, 0.99%, 98.89%, 99.86%, 95.85%, 99.37%, and 97.02%, outperforming some previous studies based on mammography images. It can be shown from the trials that the inclusion of the Fog computing concepts empowers the system by reducing the load on centralized servers, increasing productivity, and maintaining the security and integrity of patient data. Full article

The gear manufacturing method is an important determinant of their performance and service life. Surface hardness and dimensional accuracy play a significant influence in determining wear and contact fatigue in gears. This study’s goal was to measure the gear profile dimensions and surface behavior of nodular cast iron made using the chill casting technique. Chill plates made of 304 stainless steel with thicknesses of 0.2, 0.4, and 0.6 mm were used to provide good surface cooling rates during the chill casting of gears performed using open molds of silica sand. Chill plates are plated onto the walls of the mold, and then the molten material is poured at 1400 °C. The obtained gears were tested using photographs, microstructures, SEM-EDX, microhardness, wear, and dimensional measurements. The thickness of the chill plate can affect the hardening process of the gear surface. Thicker chill plates result in slower cooling rates, resulting in a more homogeneous microstructure and increasing the hardness level of the hardened layer. Whereas thinner chill plates result in a faster cooling rate, which results in a higher hardness and wear resistance of the hardened layer. Reducing the thickness of the chill plate from 0.6 mm to 0.2 mm increases the cooling rate and increases the amount of diffusion that can occur. The results showed that M₇C₃ and the (FeCrC)₇C₃ matrices were formed, with an average hardness within a range of 700–994.96 HV. A chill plate with a thickness of 0.4 mm produces gear with the best accuracy and precision. Full article

This work intends to perform technical and 2E (economic & environmental) analysis for the proposed hybrid energy generating system for a part load at SRM IST at the Delhi-NCR campus, India. The investigation has been done for electricity generation and hydrogen production through renewable energy sources, mainly solar energy. It is in line with the Indian Government’s initiatives. The proposed hybrid system has to meet the electric load demand of 400 kWh/day with a peak load of 74.27 kW and hydrogen load demand of 10 kg/day with a peak demand of 1.86 kg/h. The analysis has been performed for both on-grid and off-grid conditions. As a result, optimum results have been obtained off-grid condition, with $0.408 per kWh cost of energy, $16.6 per kg cost of hydrogen, low O&M cost ($21,955 per year), a high renewable fraction (99.8%), and low greenhouse emissions (247 kg/year). In addition, sensitivity analysis has been performed between—(1) the solar PV array size & the number of battery strings, with NPC, renewable fraction & CO₂ emissions as sensitivity variables, and (2) reformer capacity & hydrogen tank capacity, with NPC as sensitivity variable. Full article

The use of stress–strain analysis in structural design or mechanical components is critical for avoiding or investigating structural failures. In the case of complicated designs, mathematical full-field stress modeling produces imprecise predictions. Experimental analysis can be used as a replacement for mathematical modeling, but with the use of currently available strain gauges, it is cumbersome and impossible in the case of moving parts. Mechanoluminescent materials transform mechanical energy into visible light and can be used as a replacement for strain gauges to monitor strain/stress. Three-dimensional printing technology has made major advances in terms of additive manufacturing. In this article, we describe a method to produce an ML 3D print. The fabricated samples are precise and versatile and satisfy the need for easy and non-destructible spatial stress analysis. A 3D printed photopolymer sample with SrAl₂O₄: Eu, Dy particle addition only to the final layers was tested, and the number of layers was optimized. It was determined that the optimal number of layers for easy detection is in the range of 10 to 20 layers. It opens the possibility for the real-time evaluation of complex uneven forces on complex parts, thus having a good potential for commercialization. Full article