Engineering Design Research Articles

Design considerations for a continuous mussel farm in New England Offshore waters. Part I: Development of environmental conditions for engineering design

Sustainable aquaculture in nearshore waters faces challenges such as stakeholder conflicts, environmental pollution, and spatial constraints. Offshore aquaculture offers a promising solution but requires robust engineering design to withstand extreme weather conditions. This study develops the environmental conditions essential for engineering the design of a continuous mussel dropper system in New England offshore waters. A potential farm location was identified using criteria including water depth, federal boundaries, seafloor suitability, farm size, and proximity to ports, based on bathymetric and sedimentary maps. Historical data from five wave monitoring stations and two current velocity stations were analyzed to model extreme environmental conditions, as waves and currents pose primary threats. The extreme wave and current conditions are modeled using the Weibull distribution, on the annual maximum hourly significant wave height data and the largest 0.3% of current speeds. A newly proposed method combining Spalding’s wall function with a fourth-order polynomial is used to enhance the current profile analysis. Additionally, a joint probability density function was developed for wave height and current velocity at a specific depth, providing insights into wave height, period, and wavelength for various return periods such as 10, 25, 50, and 100 years. The results suggest a 10-yr wave of 8 m significant wave height and a current speed of 1.68 m/s, while a 50-yr values are 9.4 m and 1.96 m/s respectively. These findings offer critical data on extreme wave and current conditions in New England’s offshore waters, providing practical guidance for the engineering design of offshore mussel farms. This research advances offshore mussel farming and benefits the development of all types of offshore aquaculture systems.

Analyzing component failures in series-parallel systems with dependent components

This paper investigates a series-parallel system comprising N independent subsystems with interchangeable dependent components, a prevalent reliability structure in engineering and network design. The primary aim of this research is to derive the joint probability distribution of the number of failed components within these configurations, considering component dependence and varying distributions across subsystems. This approach reflects a more realistic scenario than previously explored in the literature. Initially, the analysis is conducted for systems with two subsystems and subsequently extended to encompass configurations with N subsystems. The study also evaluates key reliability metrics including the average number of failed components and the mean time to failure (MTTF) of the entire system, theoretically proving that the system’s MTTF increases with the number of components under certain sufficient conditions. In addition to probabilistic analysis, an optimization problem is addressed to determine the optimal allocation of components within each subsystem. The objective is to minimize the average cost associated with corrective maintenance, thereby enhancing the cost-effectiveness of system operation.

Engineering Design of 1 MJ HTS SMES System With kA-Level Current Capacity for NICA Accelerator

Engineering Design of 1 MJ HTS SMES System With kA-Level Current Capacity for NICA Accelerator

Research on Integrated Optimization of Design Parameters and Process Simulation for Throughput Efficiency

This thesis presents a comprehensive approach to enhance the production throughput of aluminium bumper beams, a critical component in automotive manufacturing. Using CATIA, an innovative bumper beam design was developed, focusing on simplicity of design and manufacturability. The design parameters considered included material selection, cross-sectional area optimization, and welding techniques to ensure both performance and ease of manufacturing. Transitioning to Tecnomatix Plant Simulation, a virtual environment was constructed to simulate the bumper beam production process. The simulation incorporated the identified design parameters, process routes, layout considerations, and machine arrangements to optimize throughput without inflating costs. By leveraging advanced simulation techniques, the study aimed to identify the most efficient production strategies while maintaining design integrity and minimizing resource consumption. Through systematic scenarios experimentation and analysis, the research yielded insights into the complex relationship between design decisions and manufacturing processes. The results highlight the potential for significant throughput enhancements achievable through collaborative optimization of design and production parameters. The optimization was able to achieve an increase in throughput of about 12.64% of its initial throughput. Moreover, the study underscores the importance of holistic approaches integrating design engineering with manufacturing simulation to drive continuous improvement in industrial operations. This thesis contributes to advancing automotive manufacturing practices by offering a methodology for enhancing production efficiency while preserving product quality and cost-effectiveness. The findings provide valuable guidance for engineers and practitioners seeking to optimize manufacturing processes through the use of simulation and design parameters

Substantiation of rational design parameters of a crusher with two movable jaws

Purpose. Design engineering of the mechanism of a two-jaw crusher with a compound motion of the jaws, which ensures a change in the angle of gripping of pieces of material within a very small range. Methodology. In the work, a theoretical study on the fourth-class lever mechanism, as the basis of a two-jaw crusher, was performed using the Mathcad 15 software. The study was performed on a vector representation of the links of the hinged mechanism. The development and analysis of constructive solutions were performed using the Autocad software product, which reduces the risk of errors and increases the quality of the design process. Findings. The paper theoretically determines the rational geometric parameters of the two-jaw crusher with the optimal value of the nip angle, whose crushing chamber is formed by the closed loop circuit of the flat fourth-class lever mechanism and proposes a new constructive solution of the mechanism for adjusting the crusher opening with two movable jaws and the bottom mounting of eccentric nodes. The developed device in the form of separate blocks is made with the possibility of their simple assembly and disassembly, which ensures the rate and quality of replacing lining plates or surfacing restoration of the worn working surface of the jaws. The relative movement of the jaws ensures the crushing of pieces of material with the required crushing coefficient. The geometric parameters were obtained by the method of kinematic synthesis. The geometric parameters of the crushing chamber of the two-jaw crusher were determined theoretically; the dependence was obtained of the change in the nip angle on the height of the movable jaw over a period of movement and the change in the compression stroke, which ensures the force interaction of the working surface of the jaws with the material. Originality. It is proved that the quadrilateral closed circuit of the fourth-class lever mechanism can be used as a crushing chamber of a two-jaw crusher. Two links of a closed circuit operate as jaws which break pieces of material. At the same time, the nip angle can vary within fairly narrow limits, ensuring the optimality of the crushing process. It was established that the considered option of the fourth-class six-link mechanism geometry has only two folds. An algorithm is presented for searching for assemblies with the purpose of identifying those that are closely adjacent. Practical value. The results of the conducted research provide the theoretical background and the algorithm for determining the optimal parameters of a two-jaw crusher which can be used at the stage of designing similar crushers. The developed design solution for adjusting the crusher opening expands the scope of application of double-jaw crushers. The obtained dependences of the change in the angle of inclination and the working surface of the jaw over a period make it possible to develop a rational mode of crushing the material

Progress on Si‐based photoelectrodes for industrial production of green hydrogen by solar‐driven water splitting

AbstractSolar power has been regarded as the ultimate green‐energy source because of its inexhaustibility and eco‐friendliness. The solar‐driven water‐splitting technology for green hydrogen production is considered to be one of effective ways for solar energy harvesting and storage, which may provide solutions for the energy crisis and environmental issues. In the past decades, great progress has been achieved in this area. Photoelectrochemical (PEC) water splitting is especially promising for the production of solar fuels because of expected large‐scale industrial application. Silicon (Si), as an ideal candidate for the photoelectrode, is the most suitable material for the PEC device in industrial photocatalytic water splitting because of its abundance, mature fabrication technology, and suitable band gap. Here, we give a systematic review on the recent progress for Si‐based photoelectrodes for water splitting with a focus on the industrial application. Particularly, the strategies, such as band‐alignment control, morphology design, and surface engineering, are summarized to enhance the PEC performance and durability for practical application. Furthermore, the perspective for the design of commercial Si‐based PEC devices with high PEC performance, long‐term stability, large‐size, and low cost are given at the end, which shall guide the development of PEC water splitting for industrial application.

Biomedical Engineering Innovation, Design, and Entrepreneurship Alliance (BME-IDEA): 20 Years of Impact and Mapping the Path for the Next 20

Biomedical Engineering Innovation, Design, and Entrepreneurship Alliance (BME-IDEA): 20 Years of Impact and Mapping the Path for the Next 20

An Improved Multi-Objective Approach Based on Competitive Optimization Algorithm and Its Engineering Design Application

An Improved Multi-Objective Approach Based on Competitive Optimization Algorithm and Its Engineering Design Application

Study on Evaluation and Prediction Model of Long-term Mechanical Properties of Fine-grained Saline Soil Subgrade

In this thorough examination, we dive deep into the long-lasting mechanical characteristics of fine-grained saline soil subgrades, aspiring to establish a precise and reliable collection of predictive models. Our objective is to provide a solid scientific footing for the design and ongoing upkeep of road networks within saline soil environments. Analyzing prolonged monitoring data across diverse highway subgrades within a prototypical saline soil locale, we unveil the intricate temporal fluctuations and environmental sensitivities of the soil’s mechanical properties under continuous load. Precisely, the subgrade’s compressive modulus dwindled by 15%, while shear strength declined by 8% over a five-year period. These trends intensify during rainy and scorching seasons, with drops surpassing 20% and 12% respectively. Leveraging this intricate data, we deploy nonlinear regression analysis and sophisticated machine learning algorithms to construct a predictive model tailored for the long-term mechanical properties of fine-grained saline soil roadbeds. This model integrates a multitude of factors, including load duration, temperature, humidity, and more, delivering accurate forecasts of key subgrade indicators like compressive modulus, shear strength, and beyond. In the verification stage, compared with the measured data, the error rate of the model prediction results is controlled within 5%, showing high prediction accuracy and stability. In addition, we also carried on the sensitivity analysis to the model, found that the load size and the duration of the impact on the mechanical properties of the roadbed is the most significant. Therefore, in the design of road engineering in saline soil areas, the influence of these factors should be fully considered, and reasonable engineering measures should be taken to ensure the safety and durability of roads. This study not only provides effective data support for the long-term mechanical performance evaluation of fine-grained saline soil roadbed, but also provides an important theoretical reference for engineering practice in related fields.

Fair Energy Trading in Blockchain-Inspired Smart Grid: Technological Barriers and Future Trends in the Age of Electric Vehicles

The global electricity demand from electric vehicles (EVs) increased by 3631% over the last decade, from 2600 gigawatt hours (GWh) in 2013 to 97,000 GWh in 2023. The global electricity demand from EVs will rise to 710,000 GWh by 2030. These EVs will depend on smart grids (SGs) for their charging requirements. Like EVs, SGs are a booming market. In 2021, SG technologies were valued at USD 43.1 billion and are projected to reach USD 103.4 billion by 2026. As EVs become more prevalent, they introduce additional complexity to the SG landscape, with EVs not only consuming energy, but also potentially supplying it back to the grid through vehicle-to-grid (V2G) technologies. The entry of numerous independent sellers and buyers, including EV owners, into the market will lead to intense competition, resulting in rapid fluctuations in electricity prices and constant energy transactions to maximize profit for both buyers and sellers. Blockchain technology will play a crucial role in securing data publishing and transactions in this evolving scenario, ensuring transparent and efficient interactions between EVs and the grid. This survey paper explores key research challenges from an engineering design perspective of SG operation, such as the potential for voltage instability due to the integration of numerous EVs and distributed microgrids with fluctuating generation capacities and load demands. This paper also delves into the need for a synergistic balance to optimize the energy supply and demand equation. Additionally, it discusses policies and incentives that may be enforced by national electricity carriers to maintain grid reliability and manage the influx of EVs. Furthermore, this paper addresses emerging issues of SG technology providing primary charging infrastructure for EVs, such as incentivizing green energy, the technical difficulties in integrating diverse hetero-microgrids based on HVAC and HVDC technologies, challenges related to the speed of energy transaction processing during fluctuating prices, and vulnerabilities concerning cyber-attacks on blockchain-based SG architectures. Finally, future trends are discussed, including the impact of increased EV penetration on SGs, advancements in V2G technologies, load-shaping techniques, dynamic pricing mechanisms, and AI-based stability enhancement measures in the context of widespread SG adoption.

Graph masked self-distillation learning for prediction of mutation impact on protein–protein interactions

Assessing mutation impact on the binding affinity change (ΔΔG) of protein–protein interactions (PPIs) plays a crucial role in unraveling structural-functional intricacies of proteins and developing innovative protein designs. In this study, we present a deep learning framework, PIANO, for improved prediction of ΔΔG in PPIs. The PIANO framework leverages a graph masked self-distillation scheme for protein structural geometric representation pre-training, which effectively captures the structural context representations surrounding mutation sites, and makes predictions using a multi-branch network consisting of multiple encoders for amino acids, atoms, and protein sequences. Extensive experiments demonstrated its superior prediction performance and the capability of pre-trained encoder in capturing meaningful representations. Compared to previous methods, PIANO can be widely applied on both holo complex structures and apo monomer structures. Moreover, we illustrated the practical applicability of PIANO in highlighting pathogenic mutations and crucial proteins, and distinguishing de novo mutations in disease cases and controls in PPI systems. Overall, PIANO offers a powerful deep learning tool, which may provide valuable insights into the study of drug design, therapeutic intervention, and protein engineering.

A Framework for Prioritising the Performance Criteria of Natural Fibre Composite Materials: Incorporation of CRITIC-TOPSIS Method

Selecting an appropriate Multi-Criteria Decision-Making (MCDM) method to provide a solution to assist design engineers in prioritising the right criteria in the early design process is essential. Part of the aim of this study is to establish an integration CRITIC-TOPIS for selecting the most efficient framework to choose performance criteria, namely density, tensile strength, Young’s modulus, cellulose, and elongation at break for natural fibre material intended for cap toe shoes like abaca, bamboo, coir, jute, kenaf, sisal. Hence, a new framework was proposed and tested based on integrating Criteria Importance Through Inter Criteria Correlation (CRITIC)-Technique Order Preference by Similarity to Ideal Solution (TOPSIS). Therefore, this proposed framework consists of two phases: the first involves determining the weights of attributes using the CRITIC method, and the second consists of making material criteria decisions using the TOPSIS method. Meanwhile, to achieve this objective, numerical validation was obtained using data from selected past case studies, which were then replicated to validate the output of the proposed framework. According to the validation conducted using CRITIC-TOPSIS, the results show a significant level of similarity, with the rankings being consistent. Therefore, the proposed methodology may provide imprecise and ambiguous information for prioritising the performance criteria of natural fibre composite materials. Moreover, design engineers can utilise this framework in the composite industry to create the best possible evaluation model for composite material criteria selection for various applications.

Advances in Two-Electron Water Oxidation Reaction for Hydrogen Peroxide Production: Catalyst Design and Interface Engineering.

Hydrogen peroxide (H2O2) is a versatile and zero-emission material that is widely used in the industrial, domestic, and healthcare sectors. It is clear that it plays a critical role in advancing environmental sustainability, acting as a green energy source, and protecting human health. Conventional production techniques focused on anthraquinone oxidation, however, electrocatalytic synthesis has arisen as a means of utilizing renewable energy sources in conjunction with available resources like oxygen and water. These strides represent a substantial change toward more environmentally and energy-friendly H2O2 manufacturing techniques that are in line with current environmental and energy goals. This work reviews recent advances in two-electron water oxidation reaction (2e-WOR) electrocatalysts, including design principles and reaction mechanisms, examines catalyst design alternatives and experimental characterization techniques, proposes standardized assessment criteria, investigates the impact of the interfacial milieu on the reaction, and discusses the value of in situ characterization and molecular dynamics simulations as a supplement to traditional experimental techniques and theoretical simulations, as shown in Figure 1. The review also emphasizes the importance of device design, interface, and surface engineering in improving the production of H2O2. Through adjustments to the chemical microenvironment, catalysts can demonstrate improved performance, opening the door for commercial applications that are scalable through tandem cell development.

Trends and Developments in Engineering Education: A Bibliometric Analysis of Students' Competencies Research (2015-2024)

In this study, CiteSpace and VOSviewer bibliometrics software were used to analyze 257 papers on the topic of student competencies in engineering education included in the Core Collection of Web of Science databases from 2015 to 2024, revealing the research hotspots and development trends in this field. The study found that innovative research in engineering education mainly focuses on the areas of interdisciplinary integration, technology application and sustainable development, especially the application of project- based learning (PBL), augmented reality (AR) technology, and modeling tools have shown significant results in enhancing students' practical ability and innovative thinking. the extensive application of models such as BIM and LCSD further enhances students' professional skills in system analysis and engineering design. Meanwhile, the innovative curriculum design and the cultivation of innovative self-efficacy played an important role in the development of students' creativity. This study not only summarizes the hotspots of existing research, but also proposes future research directions in technology integration, global competency development and social responsibility education, providing a strong theoretical basis for the future development of engineering education.

Analysis of the Spatial Constraints of Urban Underpass Channels Based on BIM

The construction of urban underpasses faces various spatial constraints, which will lead to deformation or failure of the structures if not satisfied. The introduction of BIM (Building Information Modeling) and other modern information technology to achieve rapid and accurate spatial constraint analysis of the design scheme is of great significance to ensure the quality and safety of the underpass and adjacent structures. Firstly, through extensive literature collection and survey, the key spatial constraint indicators of underpasses in dense urban spaces and the evaluation rules of each indicator are determined. Then, an SQL (Structured Query Language)-based database of spatial constraint rules for underpasses in dense urban space areas is constructed, and the spatial constraint analysis process for underpasses in dense urban space areas is proposed. Finally, based on Building Information Modeling, programming support, and a development environment provided by C# and Visual Studio, a spatial constraint analysis system for underpasses in dense urban space areas is developed to realize complex spatial constraint analysis functions. The application practice of the system is carried out by taking a city underpass project as an example. The results show that in the design of urban underpass engineering, the spatial constraint analysis system designed in this paper and the calculation method adopted can simplify the model transmission process, save on labor costs, and reduce the error of the calculation results.

Needs Companion: A Novel Approach to Continuous User Needs Sensing Using Virtual Agents and Large Language Models

In today’s world, services are essential in daily life, and identifying each person’s unique needs is key to creating a human-centered society. Traditional research has used machine learning to recommend services based on user behavior logs without directly detecting individual needs. This study introduces a system called Needs Companion, which automatically detects individual service needs, laying the groundwork for accurate needs sensing. The system defines a needs data model based on the 6W1H framework, uses virtual agents for needs elicitation, and applies large language models (LLMs) to analyze and automatically extract needs. Experiments showed that the system could detect needs accurately and quickly. This research provides interpretable data for personalized services and contributes to fields like machine learning, human-centered design, and requirements engineering.

Analysis and evaluation of suitability of high-pressure dynamic constitutive model for concrete under blast and impact loading

Concrete demonstrates complex dynamic mechanical behaviors under the influence of blast or impact loads, and there are inherent limitations in experimental and theoretical methods when dealing with highly nonlinear problems. As computational technologies and mechanics continue to evolve, it is possible to conduct high-fidelity simulations of the transient response of concrete structures subjected to intense dynamic loads. Such simulations play a crucial role in revealing the propagation laws of stress waves, the progression of damage, the mechanisms of structural failure, and in conducting protective engineering design. An accurate concrete material model is essential for conducting numerical studies. This article reviews the development of high-pressure dynamic constitutive models for concrete in recent years from both experimental research and theoretical modeling perspectives, focusing on the analysis and evaluation of the modeling methods and main shortcomings of the equation of state(EOS), strength model, and damage model. Using single-element numerical simulations under a single loading path and numerical calculations of engineering cases under complex loading paths, a systematic analysis and comparison were conducted on the predictive capabilities of the HJC, RHT, KCC, and CSC models, as well as the newly developed Kong-Fang and Yan-Chen models. It pointed out the impact of high-pressure mechanical behavior of concrete and the cumulative effect of damage under hydrostatic pressure on the calculation results. Finally, a discussion was conducted on the inherent flaws, applicability, and research difficulties of local constitutive models of concrete in the finite element method. This provides a reference for the selection and research of constitutive models when conducting numerical analysis of the blast and impact resistance of concrete structures.

Skeleton design engineering of covalent organic frameworks to enable in situ incorporation of coordination sites for improved extraction of uranyl ions

The development of advanced porous materials for effectively separating uranium from nuclear wastewater or seawater is a highly coveted yet formidable task. However, the importance of the covalent organic frameworks (COFs) skeletons and the specific arrangement of adjacent atoms is often disregarded when configuring coordination settings for chelating molecules. In this study, a novel material for oxygen-enriched COFs was developed. The optimized oxygen-rich COFs showed aligned neighboring bonds and triazine groups along the skeletons, resulting in an increase in uranyl binding sites and a threefold rise in the total number of binding sites. The synergistic interaction of ether bonds and triazine groups in the π-conjugated skeletons significantly reduced the energy band gap, leading to improved uranium affinity and recovery. The adsorption capacity reached an impressive 660 mg/g, surpassing that of most COF-based adsorbents using a chemical coordination mechanism in uranium-containing aqueous solutions. This study provides valuable insights into designing novel adsorbents for uranium separation.

Deformation Evolution and Perceptual Prediction for Additive Manufacturing of Lightweight Composite Driven by Hybrid Digital Twins

This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins (HDT). In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering, the HDT meaning hybridization of physical and digital domains, including deformation and energy efficiency can be built, where the essential parameters can be perceptually predicted in advance, by virtue of the fusion of physical sensors and digital information. The long short term memory (LSTM) can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks, thereby laying a foundation for the HDT. The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation. The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy. The manufacturing efficiency and comprehensive costs are accounted as consideration factors, which are perceptually predicted via LSTM. The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation. The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling (FDM). The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes. The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine (CMM) and 3D optical scanner. The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption, which have been verified with a three-way solenoid valve element, in which the maximum deformation was reduced by 39.78% and the mean absolute percentage error for perceptual prediction was 3.76%.

Optimal Control of a Domestic Frost-Free Refrigerator Inlet Freezer from the Perspective of Reducing Energy Consumption

This study is dedicated to the development of a mathematical model based on shape optimal control of the inlet domestic frost-free refrigerator in the context of energy consumption reduction. A three-dimensional thermofluid model is established for the numerical studies. As the physical system (with shelves and fruits) can be seen as a porous medium, the coupled state equations of the transport mechanism (for velocity and temperature fields) are governed by Navier–Stokes–Forchheimer/Fourier equations. Then, based on the finite element method, the numerical scheme computation is made with FreeFem++ software (Version 4.6). Numerical results are shown for three different cases: empty without shelves, empty with shelves, and loaded with foods. For each case, the velocity and temperature fields’ results are discussed for the optimal configurations. The characterization of these physical parameters would help engineers in domestic frost-free refrigerator design.