Hasil untuk "Engineering design"

Wenbin Ye, YanZhou Duan, Jun Yuan et al.

Abstract In the field of engineering, the utilization of surrogate models to replace computationally intensive simulation software has become a widely adopted approach. However, when addressing complex engineering problems, the costs of simulations can escalate significantly, making it challenging for simulation data to fulfill the training requirements of surrogate models. Recognizing that designers accumulate valuable design knowledge throughout the design process, this knowledge inherently governs the mapping rules between design parameters and performance metrics. This paper introduces a novel method for constructing surrogate models by integrating limited simulation data with engineering knowledge through Bayesian neural networks (B-DaKnow). In B-DaKnow, neural networks employ variational inference and automatic differentiation to amalgamate simulation data and engineering knowledge while optimizing weights and biases via evolutionary algorithms. The proposed methodology is validated using ten benchmark functions and three engineering cases. The experimental results demonstrate that: (1) the incorporation of diverse engineering knowledge enhances prediction accuracy in B-DaKnow to varying degrees; (2) in tackling complex engineering challenges, B-DaKnow exhibits superior performance compared to alternative algorithms; (3) B-DaKnow demonstrates commendable robustness, as evidenced by only slight fluctuations in prediction results across different problems.

Roberto López-Chila, Henry Abad-Reyna, Joao Morocho-Cajas et al.

Standardized driving cycles such as the FTP-75 fail to represent traffic conditions in cities like Guayaquil, where high congestion and varied driving behaviors are not captured by external models. This study aimed to develop representative driving cycles for the city’s most congested urban routes, covering the north, south, center, and west zones. Using the direct method, real-world trips were conducted with an M1-category vehicle equipped with an OBDLINK MX+ device, allowing real-time data collection. Driving data were processed through OBDWIZ software Version 4.30.1 and statistically analyzed using Minitab. From pilot tests, the appropriate sample size was estimated, and normality tests were applied to determine the correct measures of central tendency. The final representative cycles were constructed using a weighting criteria method. The results provided quantified evidence of variations in average speed, idle time, and acceleration patterns across the routes, which were transformed into representative driving cycles. These cycles provide a more accurate basis for emission modeling, vehicle certification, and transport policy design in congested cities such as Guayaquil, and this is the applied impact that is highlighted in our contribution. Furthermore, the developed cycles provide a foundation for future research on emission modeling and the design of sustainable transport strategies in Latin American cities.

Luis Mariano Bibbo, Alejandro Fernandez, José Manuel Suarez et al.

This annotated bibliography delves into the field of quantum computing, focusing specifically on the resources used throughout the software life cycle. It examines several published works that analyze quantum software modelling in the context of the various phases of the life cycle, from analysis/requirements to testing and maintenance. Each annotation provides an analysis of software engineering resources applicable to quantum software development and their applicability to different phases of the software development process. By synthesizing these diverse perspectives, this bibliography illuminates the evolving landscape of quantum software development and underscores the critical role of modelling in the context of software engineering. The result provides a valuable starting point for researchers and practitioners who wish to deepen the interplay between quantum computing and software engineering, fostering innovation and advances in this evolving field.

Dongqiao Shao, Wenlun Zhang, Zhiming You

Numerous manually excavated loess caves are present within a cultural relic protection zone in the northwestern region of China. The collapse of these caves frequently leads to the cracking, tilting, and even collapse of ancient buildings above, posing a severe threat to the safety of cultural architectural relics. Investigating the stability and characteristics of deformation and failure in loess caves is essential for effectively reinforcing and protecting cultural relics. A two-dimensional model of a loess underground cavern was developed using OptumG2. The stability and modes of deformation and failure in the underground cavern were analyzed through the augmentation of soil gravity and the strength reduction method. This analysis determined the cavern's safety factor, force, deformation and damage mode, and the plastic zone's progression. Numerical simulations analyzed the force characteristics of the support structure under different stress release ratios. The findings revealed that, with the implementation of an anchor rod concrete lining support scheme, the most probable failure mode is a shear failure, initiating at the arch foot. The ground's stress release rate does not influence the safety factor of the cavern but rather the material, design, and strength of the support structure. However, the magnitude of the internal forces acting on the supporting structure by the soil in the cavern is related to the degree of ground stress release. When applied during significant stress release, support structures may experience reduced internal forces, albeit with more substantial stratum displacement; opting for an appropriate stress release when applying support structures is crucial for achieving optimal stratum displacement and lining internal forces.

Karl Gummesson, Karin Forsell, Stefan Johansson et al.

The aim of this study was to explore how people with impairments perceived the accessibility of information regarding the COVID-19 pandemic in Sweden and what improvements they suggest to ensure accessibility of information in future societal crises. The study had a descriptive design, involving interviews and focus group discussions with people with impairments and their representative organisations, alongside analysis of public crisis information websites. The results showed that while many people with impairments could use their usual information channels, other found that the adapted information they needed was missing and that the government agencies, regional healthcare organisations and local municipalities were unprepared to produce accessible information. In conclusion, society exhibited shortcomings in providing accessible information to people with impairments during the COVID-19 pandemic. The responsible authorities seemed unprepared to provide accessible information. Proactive planning and training are imperative to ensure the provision of accessible information in future crises.

Xinjing Qi, Huan Wang, Yubo Ji et al.

As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.

Qin Haoting, Li Tianzhi, Li Jianhua et al.

With the undergoing adjustment of the national energy structure of China, developing cascaded hydropower plants in hydro-solar complementary systems becomes an important way to optimize the energy structure. This study focuses on the risk that solar energy disturbances induce large load transfers between hydropower units that can cause hydropower units to frequently cross their vibration zones. Vibration zone crossing can further cause wear of mechanical components of hydropower units. To this end, considering the requirement of avoiding vibration zone crossing, the concept of the vibration zone crossing risk coefficient is introduced and a cascaded hydropower plant optimal operation model considering the risk of vibration zone crossing is proposed. Moreover, this is two-layer algorithm. This layer of algorithms includes improved migration model and migration operator is proposed to solve this mean a cascaded hydropower plant optimal operation model considering the risk of vibration zone crossing. Numerical simulation results based on the data of an actual hydropower plant are used to validate the proposed model and (IBBO-DP) algorithm.

Susana Aberturas, José Luis Olazagoitia, Miguel Ángel García et al.

In the automobile sector, energy recovery and sustainability are becoming more and more important, and energy-harvesting suspension systems (EHSAs) have a lot of promise to improve vehicle efficiency. This investigation expands on prior work that investigated the viability of an EHSA that uses permanent magnets and amorphous core coils. The performance of the proposed system is demonstrated and enhanced in the current study through the development and optimization of a prototype. A thorough testing of the prototype is performed to determine design improvements for boosting the system’s overall performance and to quantify the recovered energy. In previous work, a method was proposed to find the dependence of the magnetic flux with the relative position between the primary and secondary elements to obtain the optimal position for the system. This method is applied to optimize the energy harvesting coil by testing different configurations in terms of the placement and type of amorphous or nonamorphous core inside the energy harvesting coil. This is a crucial area of attention in order to maximize energy recovery while solving the low-frequency problem that suspension systems have (on the order of 10 Hz).

Panagiotis Stavropoulos, Vasiliki Christina Panagiotopoulou

Additive Manufacturing (AM), or else Smart Manufacturing, has been an intrinsic concept in Industry 4.0, offering flexibility and material efficiency. Certain limitations prevent AM from being used in the industrial setting extensively, despite its advantages. Therefore, a literature review on the process modelling approaches, their advantages and limitations was performed. The most frequently used process modelling approaches were reviewed and summarized with respect to the process modelling approach, scale and limitations. The different categories of process modelling approaches were compared, with molecular dynamics being a promising modelling technique that can be used in software applications. A new framework for modelling additive manufacturing processes based on molecular dynamics was proposed in this work, combining previously published manufacturing methodologies for the AM process, such as manufacturability, design and planning of the AM. A validation plan followed, with the main parameters and details highlighted. The proposed framework is offering a unique approach for modelling the AM process, based on parameters from the manufacturing design, planning and process. This framework will be used in software platforms for predicting temperature distributions and for optimizing shape and AM process.

Margarita Panagiotopoulou, Sofia Papadaki, Magdalini Krokida

The objective of this work is to investigate the nanoencapsulation of valuable bioactive compounds with the innovative electrohydrodynamic technology. More specifically, electrospraying, was applied for the encapsulation of three different active agents with lipolytic, anti-aging and antioxidant activity: Deoxycholic acid (DCA), olive leaves’ enzymatically modified extract and a hybrid of Deoxycholic acid-Hydroxytyrosol (DCA-HXT) in zein (ZN) biodegradable polymer. The resulting powders were evaluated regarding the system's: compositional and chemical characteristics, thermal stability and humidity effect, morphology, shape and size, encapsulation efficiency and release kinetics from the wall material. Encapsulation led to stable nanosystems, regarding the moisture and temperature effect, with high glass Transition Temperatures (Tg between 130-139 °C), and a mean particle size of 400-600 nm. Finally, high encapsulation efficiencies (DCA= 84.01%, DCA-HXT = 79.34% and modified extract =80.75%) and long-term release profiles (DCA=192 h, DCA-HXT=228 h and mod. extract=100 h) were measured for all three systems. Based on these results, we conclude that electrospraying process is an effective methodology for the development of bioactive agents’ nanosystems with excellent physicochemical properties and increased bioavailability; a crucial factor for the formation of high added-value products such as cosmetics.

Katja Andrina Kravanja, Matjaž Finšgar, Željko Knez et al.

The development of drug-eluting bioactive coatings for orthopedic implants has gained increased interest in recent years with an intent to reduce postoperative complications and improve tissue regeneration at the implant interface. Due to the remarkable benefits of natural polyphenolic components, such as antioxidant, antimicrobial, anti-inflammatory, anti-cancer and bioactive activity, and their ubiquitous availability in nature, they are promising candidates for incorporation into bioactive coatings of advanced medical devices in future clinical applications. However, further research is needed to address all challenges. This review aims to highlight the prosperity of natural compounds widely available in nature loaded in implantable devices, summarize the “state of the art” in this field, identify the challenges, and accordingly suggest the optimal preparation methods and characterization.

Kuchkarova Dilarom, Achilova Dilnoza

Natural relief adjustment for functional purpose - the task of vertical planning - is one of the main complexes of engineering preparation problems of urban areas, industrial sites, reclamation of irrigated land. Numerous methods of designing vertical levelling are aimed at building algorithms that would allow us to obtain an optimal solution in an automated mode. In the set of problems of engineering preparation of urban areas and industrial sites, vertical planning is defined as: a set of engineering and aesthetic measures aimed at adapting the natural relief for the needs of development and subsequent operation, taking into account the functional characteristics of the site a part of engineering preparation, which consists in providing a height arrangement of buildings and structures necessary for the best technological connection between individual objects, as well as a quick collection of atmospheric waters Reclamation of irrigated lands has specific requirements to design surface - maximum preservation of fertile layer, the satisfaction of cultivation technology conditions of different crops and irrigation technique. The main disadvantage of all known methods of designing vertical levelling is a cumbersome solution - a very labor-intensive way of successive approximation to an acceptable solution requires a large expenditure of computer time and subsequent manual revision. The article proposes one of the approaches to the design of levelling using geometric estimates.

Jian Song, Wenzhao Zhou, Yuejiao Wang et al.

Optimization method has been widely acknowledged as an effective approach to design engineering structures, and yet few studies adopt this method to design cellular materials. Here, we firstly adopted a Kriging assisted Multi-objective Genetic Algorithm to guideline the design of octet-truss (OCT) cellular materials with the maximum specific modulus. Subsequently, additional struts were artificially introduced into the optimized OCT to further mechanically reinforce performances. All the cellular materials were precisely fabricated using a Stereolithography 3D printing technique. By reasonably optimizing the sizes of OCT, the optimized OCT with a 1.112 mm diameter and 8.282 mm cell length was achieved, which displays a superior modulus-to-mass ratio. The highest modulus and strength of as-designed cellular materials achieved 34.12 MPa and 2.64 MPa, reinforced by ~3.11 and 4.81 times, respectively. Additionally, the absorbed energy efficiencies of them improved from 74.75% to 90.80%, which are significantly higher than other cellular materials. By in situ tests and fracture analyses, the high recoverability is attributed to the comprehensive effect of net-shaped architecture and elastic-plastic deformation. Keywords: Cellular materials, Optimization design, 3D printing technique, In situ tests, Deformation mechanism, Recoverability