Conventional Design Methods Research Articles

Synergistic Optimization Design Method to Improve the Overload Capacity and Efficiency of Canned Permanent Magnet Synchronous Motor for Vacuum Pump

The efficiency and overload capacity are crucial performance factors during the design of the canned permanent magnet synchronous motors (CPMSMs) intended for driving vacuum pumps, due to their unique operational characteristics. The conventional multi-objective optimization design method is not suitable for the CPMSM due to the need to carefully consider additional influential factors, including the flat structure, eddy current losses in the cans, can thickness, and can material. To enhance the efficiency and overload capacity of the CPMSM, this paper introduces a synergistic optimization design approach that integrates the equivalent magnetic circuit method and the finite element method. A design example of a 1.5 kW CPMSM is presented, and experiments are performed to verify the effectiveness of the proposed method.

An improved acoustic transponder system via LCCLC impedance matching network and equalization control strategy

AbstractThe underwater acoustic transducer (UAT) generates a large amount of reactive power during the operation, greatly limiting the output capability of the power amplifier. It is necessary to match the UAT and improve its load characteristics. The conventional design methods are not powerful enough to solve nonlinear or parameter‐sensitive problems, lacking enough convergence ability for the network design problem. This article proposes an optimization design method for LCCLC impedance matching network based on the improved chaos optimization algorithm and the corresponding equalization control strategy. First, the impedance model of the UAT is proposed, and then the matching principle of the LCCLC network is analyzed. Based on these, the optimization design for LCCLC network is described in detail. Then, to solve the problem of the calculation of the command signal, a control method based on the FIR filter principle is proposed. Finally, the proposed methods are verified by an experimental platform. The results show that the proposed design method and its corresponding control strategy successfully solve the matching and emission problems.

BIM as a means of end-to-end design, construction, and operation

Contemporary technologies are developing at a tremendous speed. The growing computing power of modern processors and rapid technological development – all of this undoubtedly requires new information processing and systematization tools. BIM modeling technology is an essential tool in the design, construction, and even operation of buildings and structures. This technology allows the end-to-end design of various objects, as well as systematization of information in a single information model. This work investigates the effectiveness of BIM technologies in the processes of design, construction, and operation of buildings and structures, as well as the development of information modeling technologies. The research is carried out using the examples of two hospital complexes, i.e., the HUS Bridge in Helsinki and the National Medical Research Center of Pediatric Hematology, Oncology and Immunology named after Dmitry Rogachev). Advantages of applying information modeling methods in the construction of socially significant and critical facilities are demonstrated. The conducted study proved the efficiency of modern BIM solutions, particularly in terms of reducing the construction period, determining the unified information and communication space, eliminating conflicts, and automated calculation of the most important design, construction, and operational parameters. BIM outperforms conventional design methods in terms of the possibility of developing a strategy for the implementation of the construction project, integrated management of graphic data, and creating structurally and architecturally complex facilities.

Design and Analysis of High Power Density On-Board Charger with Active Power Decoupling Circuit for Electric Vehicles

This article presents a design method for the active power decoupling (APD) circuit of a PFC converter for high power density on-board chargers (OBCs) utilized in electric vehicles (EVs). The utilization of electrolytic capacitors to mitigate power ripple at the input is a common practice in PFC converters. However, these electrolytic capacitors are associated with issues such as limited lifetime and low current ratings, resulting in a significant portion of the OBC’s volume being occupied by them. To address these challenges and achieve power density, the relationship between the power of the APD circuit and DC-link voltage is derived, and a design method for the APD circuit for high power density is proposed. The feasibility of this design approach is validated through the PFC converter prototype designed for 6.6 kW OBC. Consequently, a substantial volume reduction of 19.7% is realized when compared to the utilization of the electrolytic capacitor approach, and a reduction of 36.2% is achieved in comparison to the conventional APD design method. This reduction in volume proves advantageous for fulfilling the requisites of high power density OBCs.

A Novel MIMO Radar Orthogonal Waveform Design Algorithm Based on the Multi-Objective Improved Archimedes Optimization Algorithm

Realization and enhancement of detection techniques for multiple-input–multiple-output (MIMO) radar systems require polyphase code sequences with excellent orthogonality characteristics. Therefore, orthogonal waveform design is the key to realizing MIMO radar. Conventional orthogonal waveform design methods fail to ensure acceptable orthogonal characteristics by individually optimizing the autocorrelation sidelobe peak level and the cross-correlation sidelobe peak level. In this basis, the multi-objective Archimedes optimization algorithm (MOIAOA) is proposed for orthogonal waveform optimization while simultaneously minimizing the total autocorrelation sidelobe peak energy and total cross-correlation peak energy. A novel optimal individual selection method is proposed to select those individuals that best match the weight vectors and lead the evolution of these individuals to their respective neighborhoods. Then, new exploration and development phases are introduced to improve the algorithm’s ability to increase its convergence speed and accuracy. Subsequently, novel incentive functions are formulated based on distinct evolutionary phases, followed by the introduction of a novel environmental selection method aimed at comprehensively enhancing the algorithm’s convergence and distribution. Finally, a weight updating method based on the shape of the frontier surface is proposed to dynamically correct the shape of the overall frontier, further enhancing the overall distribution. The results of experiments on the orthogonal waveform design show that the multi-objective improved Archimedes optimization algorithm (MOIAOA) achieves superior orthogonality, yielding lower total autocorrelation sidelobe peak energy and total cross-correlation peak energy than three established methods.

A Rule-Based Design Approach to Generate Mass Housing in Rural Areas of the North China Plain

Affected by the development strategy of Rural Space Reconstruction in China, the demand for rural mass housing has peaked in the North China Plain in the past 20 years. However, due to the inefficiency of conventional design methods, the rural houses built appear to have a noticeable trend of urbanization and homogeneity. To propose a more effective design approach to change the hitherto unsuccessful homogenized phenomenon of rural design, the study is based on investigating the composition, configuration and characteristics of the dwellings in some traditional villages of the eastern Shandong Province, and it compares and analyzes the differences between conventional methods and generation methods through three design tests: Test 1 is for the reappearance of a general mode of planning, Test 2 is based on the definition of shape-grammar-based rules and Test 3 is mainly used for the optimizing and programming of rules. Furthermore, based on the three prototypes of homestead combination, three-level rules are determined through the three tests mentioned above: Level-1 describes the housing prototype consisting of four homesteads, which generates a variety of spatial relations through the translation of homesteads. Level-2 describes a neighborhood prototype consisting of 16 homesteads, which generates various samples through splitting prototypes and expanding homesteads. Level-3 describes a block prototype consisting of 64 homesteads, which controls open space and identifies a given base during sample filling. Through the analysis of the tests results, the rationality and feasibility of the generative design approach are verified, proving that this approach effectively solves the design monotony problem that commonly exists in rural mass housing in the North China Plain.

The effectiveness of using a mechanically stabilised layer for unpaved roads in Vietnam: Some case studies

The construction of onshore wind powers requires an access road network, whose pavement design is challenging due to the complexity of ground conditions subjected to heavy vehicles. A thick pavement structure, therefore, is often required following the conventional design method, leading to uneconomic designs and solutions. The use of multi-axial stabilisation geogrids to form a Mechanical Stabilised Layer (MSL) was considered to optimise the pavement structures as well as to reduce material costs, construction time, and carbon footprint of construction activities while maintaining the technical requirements. This paper presents experimental tests conducted in wind power projects in Vietnam to evaluate the pavement performance with and without MSL. A comparison between stabilised and conventional sections is analysed in terms of the modulus of elasticity, rutting depth, and carbon emission reduction. The study results can be applied to pavement design procedures for residential roads, industrial roads, and highway projects.

Bottom-up approaches for rapid on-demand design of modular metaporous structures with tailored absorption

As a new trend in the studies on sound-absorbing structures design, customizable metaporous structures (MPSs) have drawn a great deal of interest due to their distinct advantages, which include designable broadband absorption, light weight, and low thickness. Conventional finite element-based design methods, such as trial-and-error and iterative optimization, are inefficient and expensive in terms of computational resources. Inspired by the widely used bottom-up method, in this study, we develop a data-driven metaheuristic optimization framework to accomplish the rapid on-demand design for a novel modular metaporous structure (MMPS) of only 30mm thickness from the local to the global level. Two cascade variants of the genetic algorithm (GA) are employed for local optimization and global customized design, accordingly. The local design level involves the development of a surrogate-assisted genetic algorithm (SAGA) for design space optimization. On the basis of the structural dataset, a well-tuned convolutional neural network (CNN) model is developed for expediting the forward prediction of the acoustic properties of the two-dimensional (2D) MPS. In the subsequent phase, the validated CNN model is combined with SAGA to expedite the fitness evaluation for optimizing the structural units. At the global design stage, the developed SAGA is coupled with the population initialization process of an elitist genetic algorithm (EGA) to achieve inverse design for the MMPS with customizable broadband absorption based on the optimized design space. Furthermore, an acoustic measurement is performed on the fabricated MMPS samples to validate the finite element method (FEM) simulation and impedance design method. The utilization of this optimization method opens up fresh possibilities not solely for the customized design of sound-absorbing structures, but also for optimization processes in various other domains. Meanwhile, the proposed MMPS with customizable broadband absorption in a thickness of 30mm could well be utilized for noise reduction in a variety of practical applications.

Accurate Microwave Circuit Co-Simulation Method Based on Simplified Equivalent Circuit Modeling.

A new co-simulation method is proposed for active devices and electromagnetic resonant circuits at microwave frequency range. For the measured and extracted device parameters, three steps of equivalent circuit models are processed of the general, simplified, and EM RLC models. To overcome the limited lumped element simulation in an electromagnetic simulator, the simplified equivalent circuit model is established by mathematical computation. The co-simulation procedures are described and experimentally verified for commercial diodes. The application circuit is designed and implemented using the proposed co-simulation method. The experimental results verify that design using the proposed co-simulated method presented excellent agreement for a wideband frequency range of 0-4 GHz, compared with that using a conventional design method. The proposed co-simulation method can be applied to any commercial EM simulation tools without active model error.

Discrete sizing optimization method based on dividing rectangles algorithm and local response surface for steel frame structures

It has always been the goal of structural engineers to construct safe and stable buildings using the least amount of materials. Utilizing a quick and effective way to optimize the cross-section size is crucial because conventional building design methods are impacted by the designers' knowledge, and it is challenging to prevent material waste. Due to its implicit optimization function, discrete design variables, and expensive individual evaluation, sizing optimization of high-rise buildings is very challenging to achieve. In order to effectively handle this optimization problem, a two-stage discrete sizing optimization method for high-rise buildings based on the DIviding RECTangles (DIRECT) algorithm and local response surface is proposed in this paper. The optimization method suggested in this research consists of two key stages: the global search stage and the local search stage. In the global search stage, the entire design domain is divided using a modified DIRECT algorithm to swiftly identify potentially optimal subregions that may contain the best points. In the local search stage, the local response surface model is constructed to approximate the objective and constraint functions using the sampling points from the previous stage, and the discrete optimal solution is rapidly found through mathematical iterative solving. A sizing optimization calculation program for high-rise buildings was developed in Microsoft Visual Studio 2015 on the basis of C++ to achieve automatic optimization. The new method was applied to optimize three high-rise steel frame buildings with different heights and plan shapes. The results showed that the material cost could be successfully saved compared with the conventional design, and the over-limit constraints could be adjusted automatically, which demonstrated the viability and efficacy of the two-stage optimization method.

Design method of an ultra-thin two-dimensional geometrical waveguide near-eye display based on forward-ray-tracing and maximum FOV analysis.

A two-dimensional geometrical waveguide enables ultra-thin augmented reality (AR) near-eye display (NED) with wide field of view (FOV) and large exit-pupil diameter (EPD). A conventional design method can efficiently design waveguides that meet the requirements, but is unable to fully utilize the potential display performance of the waveguide. A forward-ray-tracing waveguide design method with maximum FOV analysis is proposed, enabling two-dimensional geometrical waveguides to achieve their maximum FOV while maintaining minimum dimensions. Finally, the designed stray-light-suppressed waveguide NED has a thickness of 1.7 mm, a FOV of 50.00°H × 29.92°V, and an eye-box of 12mm × 12 mm at an eye-relief of 18 mm.

Cyclic elastoplasticity and XFEM based fatigue life assessment of out-of-plane gusset welded joints

Conventional design methods for high-cycle fatigue failure of engineering components often assume a linear-elastic material behaviour. The different driving forces of crack initiation and propagation fail to be considered, resulting in less accurate predictions of fatigue life. To address this issue, the present study proposes a novel method to predict the fatigue life of out-of-plane gusset welded joints based on cyclic elastoplasticity and extended finite element method (XFEM). Incorporating the welding residual stress simulated by thermal–mechanical analysis, the cyclic stress–strain response of out-of-plane gusset welded joints was assessed by Fatigue SS (FSS) constitutive model. The crack initiation life was therefore predicted by the local strain approach considering the mean stress correction. Based on the converged cyclic stress–strain response, the crack propagation behaviour and life were predicted by XFEM in combination with Virtual Crack Closure Technique (VCCT). The influence of mean stress, mixed-mode crack and residual stress on crack initiation and propagation life was also investigated. The results show that the proposed method can predict the fatigue life of out-of-plane gusset welded joints under the pure tension loading with high accuracy against experimental results.

Computational Complexity and Its Influence on Predictive Capabilities of Machine Learning Models for Concrete Mix Design.

The design of concrete mixtures is crucial in concrete technology, aiming to produce concrete that meets specific quality and performance criteria. Modern standards require not only strength but also eco-friendliness and production efficiency. Based on the Three Equation Method, conventional mix design methods involve analytical and laboratory procedures but are insufficient for contemporary concrete technology, leading to overengineering and difficulty predicting concrete properties. Machine learning-based methods offer a solution, as they have proven effective in predicting concrete compressive strength for concrete mix design. This paper scrutinises the association between the computational complexity of machine learning models and their proficiency in predicting the compressive strength of concrete. This study evaluates five deep neural network models of varying computational complexity in three series. Each model is trained and tested in three series with a vast database of concrete mix recipes and associated destructive tests. The findings suggest a positive correlation between increased computational complexity and the model's predictive ability. This correlation is evidenced by an increment in the coefficient of determination (R2) and a decrease in error metrics (mean squared error, Minkowski error, normalized squared error, root mean squared error, and sum squared error) as the complexity of the model increases. The research findings provide valuable insights for increasing the performance of concrete technical feature prediction models while acknowledging this study's limitations and suggesting potential future research directions. This research paves the way for further refinement of AI-driven methods in concrete mix design, enhancing the efficiency and precision of the concrete mix design process.

A novel performance-based method to design asphalt mixtures

Conventional asphalt mix design methods require numerous experiments to verify the efficacy of various recipes, resulting in significant time and labour costs. Therefore, there is a need for an alternative method that is both efficient and convenient. To address this issue, this paper proposes a novel asphalt mix design method that reduces the reliance on time-consuming and resource-intensive physical and mechanical tests to determine the properties of new asphalt mixtures. The method focuses on the quantity of aggregates and the volume of mortar to estimate the air voids content, resilient stiffness modulus, Marshall stability, and flow requirements in accordance with industry standards. Statistical analysis shows that the key properties of asphalt mixtures are primarily influenced by the number of aggregates and volume of mortar. Nomographs are provided to predict these properties based on the mixture’s gradation and mortar volume. The proposed method’s feasibility is demonstrated by accurately predicting the gradations and properties of six different mixtures. Additionally, the paper suggests the use of 3D models for asphalt mixtures, allowing for the observation of aggregate distribution and air voids before testing the mixtures’ mechanical properties.

Data-driven electronic packaging structure inverse design with an adaptive surrogate model

PurposeThe conventional design method relies on a priori knowledge, which limits the rapid and efficient development of electronic packaging structures. The purpose of this study is to propose a hybrid method of data-driven inverse design, which couples adaptive surrogate model technology with optimization algorithm to to enable an efficient and accurate inverse design of electronic packaging structures.Design/methodology/approachThe multisurrogate accumulative local error-based ensemble forward prediction model is proposed to predict the performance properties of the packaging structure. As the forward prediction model is adaptive, it can identify respond to sensitive regions of design space and sample more design points in those regions, getting the trade-off between accuracy and computation resources. In addition, the forward prediction model uses the average ensemble method to mitigate the accuracy degradation caused by poor individual surrogate performance. The Particle Swarm Optimization algorithm is then coupled with the forward prediction model for the inverse design of the electronic packaging structure.FindingsBenchmark testing demonstrated the superior approximate performance of the proposed ensemble model. Two engineering cases have shown that using the proposed method for inverse design has significant computational savings while ensuring design accuracy. In addition, the proposed method is capable of outputting multiple structure parameters according to the expected performance and can design the packaging structure based on its extreme performance.Originality/valueBecause of its data-driven nature, the inverse design method proposed also has potential applications in other scientific fields related to optimization and inverse design.

Machine learning in design of broadband terahertz absorbers based on composite structures

Metasurface absorbers have gained increasing attention in the field of electromagnetic (EM) absorption in recent years. The conventional design method often involves designing various structures and comparing absorption bandwidth and intensity to determine the absorber's structural parameters. The absorption of EM waves by absorbers involves a complicated process of electric field excitation and impedance matching. Although design experience can reduce the number of design iterations, the selection of artificial parameters is often imprecise and can result in design bias. Fortunately, machine learning (ML) algorithms offer the possibility of automating the design of critical parameters for metasurfaces, which can effectively reduce the laborious design process and the high cost of trial and error. In this study, a pyramid-based metasurface absorber (PMA) and an inverted pyramid-based metasurface absorber (IPMA) were designed and optimized using the random forest (RF) algorithm. The results show that the absorption bandwidths of the two absorber models are 2.68 THz and 2.42 THz, respectively, and the mean absolute percentage error (MAPE) is only 0.60%, which is significantly better than other classical ML and deep learning (DL) algorithms. This study offers a new approach for designing complex systems related to EM wave absorption, reflection, and transmission propagation.

A Wideband Circularly Polarized Filtering Antenna Based on Slot-Patch Structure

This letter presents a wideband circularly polarized (CP) antenna with filtering radiation response based on a slot-patch structure. The proposed CP filtering antenna is composed of a top patch, a middle microstrip-to- (“+”-shape) slot structure, and a bottom metallic reflector. The middle layer not only functions as a slot-coupled feeding structure but also operates as a driven slot CP antenna. The top and bottom layers are used to reduce the slot antenna backside radiation and enhance the antenna axial ratio (AR) bandwidth. In addition, a diagonal slot etched on the patch and an additional E-shaped slot on the middle layer can generate the lower- and upper-band radiation zeros, respectively. For verification, the antenna is implemented. The measured results show that the overlapping bandwidth of the impedance and AR bandwidth is 46.9% (3.35-5.40 GHz). The measured average CP gain is about 7.6 dBic. High frequency selectivity and better than 10 dB out-of-band radiation rejection level is obtained. Compared to the conventional filtering CP design method which employs the filter or resonator feeding circuit, the proposed design features a simple structure, higher realized gain and wider AR/impedance overlapping bandwidth.

Bayesian design of mosaic-based mode multiplexers for various wavelength bands.

Ultracompact mode multiplexers based on mosaic structure for various wavelength bands designed by Bayesian technique are investigated. C-, O-, and C + O band, TE0-TE1 2-mode multiplexers can be designed with the same footprint, by only changing the mosaic-pattern, showing the great flexibility of mosaic-based devices. Bayesian direct binary search method is used for the design, and it is demonstrated that the Bayesian technique is superior to conventional design method in terms of the best-structure search for the same number of iterations. The designed devices are fabricated for Si-waveguide platform, and the proof-of-concept results are obtained. These results indicate that the mosaic-based devices are promising candidates for future compact optical transceivers.

Self-Powered wireless sensor node based on RF energy harvesting and management combined design

Environmental energy-harvesting technologies, such as solar, vibration, and radio frequency (RF) energies, can effectively increase the lifespan of wireless sensor nodes while reducing their size, weight, and cost. Generally, RF energy harvester design methods are based on ideal energy sources and resistive loads, whereas real RF energy conversion and management are extremely complex and cannot be verified using conventional design methods. In this study, an RF energy harvesting and management combined design method is proposed to attain the requisite output parameters of an RF energy harvester to enable load operation during the design phase. Based on the proposed method, a highly integrated high-performance RF energy-harvesting wireless sensor node (RF-EH WSN) is constructed. The experimental results indicate that the input energies for both the simulation and measurements are equal and consistent (-10 dBm) for identical expected output voltages and energies. The proposed WSN can achieve self-powered operation at a distance of 13.5 m from a 27 dBm RF energy source.

Digital-twin-enabled JIT design of rock tunnel: Methodology and application

An accurate and timely design of individual processes in drill and blast tunneling is essential for robust and low-risk construction of rock tunnels. Conventional dynamic design method of rock tunnel focuses on the modification of preliminary design schemes using exposed on-site data. A novel conceptual model, just-in-time (JIT) design of rock tunnel, is proposed in this study to denote the intrinsic requirement that tunnel support parameters should be determined in a JIT way when the rock mass are excavated. Specifically, implementation of JIT design of rock tunnel can be divided into three steps, i.e., acquisition of on-site raw data, interpretation of on-site raw data, and aggregation of the interpreted results to obtain a revised design scheme. Time consumption, accuracy and degree of automation are selected as three main indicators to measure the performance of such conceptual model. Moreover, to further improve the efficiency and accuracy, digital twin (DT) is integrated into the implementation of JIT design of rock tunnel, where Scene-Entity-Relationship-Incident-Control (SERIC) modelling method is proposed as a new DT application mode for share and reuse across the domain. The proposed DT-enabled JIT design of rock tunnel is applied into a practical engineering project, where several advanced data acquisition, interpretation and aggregation approaches are integrated using DT. Results show that the design efficiency and accuracy have been largely improved and the proposed method could facilitate information sharing between end-users and stakeholders.