Field Of Engineering Research Articles

Effect of Zr dopant on in vitro cytotoxicity and neurogenic property of spray-dried bioactive glass microspheres

Bioactive glasses (BGs) have been extensively applied in dental and tissue engineering fields due to their bioactivity, biocompatibility, and degradability. Despite the advantages, the material itself lacks functionalities such as angiogenesis and antibacterial properties which could limit its applications. To address this, trace elements like copper (Cu), manganese (Mn), boron (B), and zinc (Zn) have been incorporated to enhance their therapeutic applications. Further, to explore the potential of neurogenic applications, this study fabricated both spray-dried undoped and Zr-doped BG specimens, which were then characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), and Fourier-transform infrared spectroscopy (FTIR). The in vitro bioactivity was assessed with simulated body fluid (SBF), cytotoxicity with L929 fibroblast cells, and neurogenic properties via the neuronal marker Tuj1+. Finally, the study discusses the particle formation mechanism and their in vitro biological properties, aiming to enhance the understanding of BGs in neurogenesis.

Simulation analysis and optimization of pilot type electro-hydraulic proportional directional valve based on multi field coupling

The pilot-operated electro-hydraulic proportional directional valve is widely used in complex and precision engineering fields. Its operational stability and reliability determine the overall performance of the hydraulic system. During the working process of the pilot-operated electro-hydraulic proportional directional valve, the spool is deformed due to the influence of pressure and temperature, resulting in problems such as clamping or stagnation. In order to solve this problem, this study uses the fluid-solid-thermal coupling simulation method to analyze the flow field characteristics and thermal deformation of the spool with different structural throttling grooves, and optimizes the U-shaped throttling groove structure. The results indicate that as the valve opening increases, the front section of the throttling groove becomes the primary region where the maximum radial deformation of the spool occurs. In this paper, the steady-state flow force of the valve core is taken as the measurement standard, the constraint range of the structural parameters is determined, and the radial thermal deformation is included in the optimization analysis of the structural size. It’s verified that the optimized flow characteristics are improved by about 32 % compared with that before optimization, and the maximum steady-state flow force after considering thermal deformation is reduced by about 4N compared with that before optimization. This provides an important design basis and reference for the structural design and optimization of the pilot electro-hydraulic proportional directional valve.

A guidance of solid element application in predicting the ultimate strength of flat plates in compression

With the increasing demand in ocean engineering field for the ultimate limit state (ULS) analysis of thick plates, the solid (3D) element has been more frequently used in the ULS analysis. Compared to 2D element, 3D element can better consider the transverse shearing effect during the analysis which usually occurs on thick plates, due to its reliable reflection of geometrical characteristics and kinematic equations of the structures. Besides, 3D element can also facilitate the introduction of influencing factors into the ULS analysis, such as the erosions, cracks and residual stress. This may improve the simulation precision of ULS analysis of thick plate models. Until now, there is a lack of 3D element modelling technique in the ULS analysis of flat/stiffened plates. The present study aims to provide helpful information on 3D elements in the ULS analysis of flat plates under axial compression load. A total of 350 plate scenarios were adopted in the parametric study to consider the effects of 3D element shape (αyz). It is found that the element shape significantly influences the ULS analysis of flat plates, where planar-like 3D element is not recommended. An empirical formula for determining the optimal 3D element shape of the finite element (FE) model is proposed based on the parametric results. A guidance for implementing the 3D elements is then documented, which may help engineers further understand the ultimate strength characteristic of very thick flat plates.

Wave-transparent and eco-friendly flame-retardant phosphorus-based phthalonitrile/quartz composites by a synchronized self-curing strategy with cyano and alkynyl groups

The robust human–machine interaction systems of the aerospace industry necessitate the development of thermal resistant wave-transparent composites, with a pivotal focus on organic resin matrix. However, enhancing the heat resistance of such materials while preserving other critical attributes has been a formidable challenge. In this study, a novel designed strategy of a dual-curing functional phosphorus-based phthalonitrile monomer (P-ALK-PN) has been proposed. The optimized curing temperatures for intra- or intermolecular Diels-Alder reactions of alkynyl groups and polyaddition reaction of cyano groups resulted in a homogeneous and highly crosslinked N-enriched phthalonitrile system. After curing at 450 °C, the resin, namely P-ALK-PN-450 °C, demonstrated exceptional thermal stability (T5% = 644 °C), thermo-oxidative stability (T5% = 554 °C), and char yield at 800 °C (90.7 % in N2 and 67.5 % in air). Their quartz fiber reinforced composites (P-ALK-PN/QFs) were subsequently fabricated by the hot-pressing process. Upon post-curing at 420 °C, P-ALK-PN-420 °C/QF displayed elevated glass transition temperature (550 °C), flexural strength (319 MPa), and relatively low dielectric properties with a dielectric constant (Dk) of approximately 4.2 and a dissipation factor (Df) less than 0.02 across a wide temperature ranging from 25 °C to 600 °C. Especially, it exhibited excellent flame retardancy (only a 6.7 % weight loss at ambient temperatures exceeding 1300 °C for 200 s) as well, stemming from release of eco-friendly inert gases (NH3) and formation of a graphitized protective layer during pyrolysis. These promising results highlight the potential applications of P-ALK-PN resins in aerospace and high-performance engineering fields.

DEVELOPMENT OF ADVANCED COMPOSITIONS INCORPORATING WOLLASTONITE, THERMOVERMICULITE, SILICON IV OXIDE, AND AEROSIL

This study presents the development of innovative composite materials by incorporating wollastonite, thermovermiculite, silicon IV oxide (SiO₂), and aerosil. The primary objective was to formulate high-performance materials with enhanced mechanical, thermal, and structural properties suitable for industrial and construction applications. Wollastonite and thermovermiculite, known for their excellent reinforcing properties, were combined with SiO₂ and aerosil to create a hybrid matrix. The materials were synthesized and characterized using techniques such as X-ray diffraction (XRD), scanning electron microscopy (SEM), and thermal gravimetric analysis (TGA). Experimental results revealed that the new compositions exhibited superior heat resistance, improved strength, and enhanced durability compared to traditional materials. The inclusion of aerosil, with its fine particle size and high surface area, contributed to improved dispersion and homogeneity within the matrix, resulting in enhanced thermal stability and mechanical performance. These advanced composites hold great potential for applications in areas requiring robust thermal and structural integrity, including insulation, construction materials, and high-temperature industrial components. Future work will focus on further optimizing these formulations and exploring their use in specialized engineering fields.

Truss sizing optimum design using a metaheuristic approach: Connected banking system

Several methods have been used to solve structural optimum design problems since the creation of a need for light weight design of structures and there is still no single method for solving the optimum design problems in structural engineering field that is capable of providing efficient solutions to all of the structural optimum design problems. Therefore, there are several proposed and utilized methods to deal with optimum design issues and problems, that sometimes give promising results and sometimes the solutions are quite unacceptable. This issue with metaheuristic algorithms, which are suitable approaches to solve these set of problems, is quite usual and is supported by the “No Free Lunch theorem”. Researchers try harder than the past to propose methods capable of presenting robust and optimal solutions in a wider range of structural optimum design problems, so that to find an algorithm that can cover a wider range of structural optimization problems and obtain a better optimum design. Truss structures are one of these problems which have extremely complex search spaces to conduct search procedures by metaheuristic algorithms. This paper proposes a method for optimum design of truss sizing problems. The presented method is used against 6 well-known benchmark truss structures (10 bar, 17 bar, 18 bar, 25 bar, 72 bar and 120 bar) and its results are compared with some of the available studies in the literature. The performance of the presented algorithm can be considered as very acceptable.

Chronic Respiratory Diseases: Innovations in Treatment and Management

Chronic Respiratory Diseases (CRDs) include diseases of the airways and the lung and are among the major causes of morbidity and mortality worldwide. The major CRDs include asthma, chronic obstructive pulmonary disease (COPD), pulmonary sarcoidosis, pneumoconiosis and interstitial lung diseases (ILDs). The common symptoms of CRDs include wheezing, chest tightness, shortness of breath and cough [1]. These diseases affect millions of people across the globe and have significant impacts on patients’ quality of life and healthcare. Over the past few decades, however, improvement in the pharmacotherapy has provided a considerable progress. For example, biologics have given a new insight in the management of severe asthma by modulating certain inflammatory processes. Medications like omalizumab, dupilumab, and Mepolizumab are aimed at adjusting the immune response and significantly decrease the frequency of exacerbations and increase overall lung function. In addition, innovations in the field of stem cell therapy and tissue engineering are targeted to repair or replace diseased lung tissue, thus, being aimed at treating the origin of the diseases rather than simply alleviating the symptoms. Furthermore, Nanoparticles can be orally given, intravenously injected or inhaled using nanoparticle aerosols. Nanoparticles can also be used in the molecular imaging of chronic lung diseases such as COPD. Advanced inhalers, developed with sensors, can monitor the usage of medication and provide feedback in real-time to ensure compliance to prescriptions and other salient treatment plans. COPD pulmonary rehabilitation programs of exercise and education together with psychological support for individuals have been documented to enhance quality of life. The use of AI and a machine learning system has now moved to the ability to forecast disease exacerbations and provide individualized recommendations for treatment of CRDs. As a result, people with COPD receive care through various healthcare professions like doctors, nurses, and physiotherapists, which may have diverse roles, such as prescribing medications, supporting their self‐management or patient education, or delivering exercise training. The aim of an Integrated Disease Management (IDM) programme is that various aspects of care through which healthcare providers are working in coordination to deliver improved and optimum care to patients. Similarly Advancements in the treatment of ILD are being conducted, and recently, some new FDA-approved drugs have been advised for the management of patients. Among these are Nintedanib is a tyrosine kinase inhibitor, Pirfenidone an anti-fibrotic and anti-thrombotic agent and Treprostinil is an analogue of prostacyclin. These drugs have opened a new path for the treatment of ILD and have improved the recovery outcomes of patients [2]. Rituximab has also been found to be effective in more than half of patients treated in a small prospective open-label trial of refractory pulmonary disease in sarcoidosis[3]. Overall, there is a renewed emphasis on CRD management as a burgeoning field. With the advancement in the research in the personalized, proactive and integrative strategy the efforts made will lay down new avenues of better health outcomes for millions of people suffering from the CRDs. The future of respiratory health is bright as it is a growing field that has no sign of discontinuing its steady development and pushing for the comforts and breakthroughs for many patients across the globe.

Review of approximation formulas for fractional derivatives of variable order

Fractional differential equations of variable order, depending on a time or space variable, are successfully used to study time and/or spatial dynamics. The purpose of this review is to consider the latest research and results related to the basic definitions and approximation formulas for fractional derivatives of variable order. The review begins with an examination of existing definitions based on various physical and practical knowledge. The following are formulas for discretizing fractional derivatives, since they play a key role in numerical modeling, providing an effective means of describing complex systems with long-term memory, heterogeneous parameters and non-local interactions. Their use not only improves the accuracy of models, but also facilitates the numerical solution of differential equations, which is essential for analyzing and predicting the behavior of real processes in various fields of science and engineering. This review is intended to help readers select appropriate definitions and discretization formulas to effectively solve specific physical and engineering problems.

Диагностика тесноты междисциплинарных связей по семестровым экзаменационным оценкам студентов в период смешанных форм обучения в вузе

The analysis of the closeness of interdisciplinary connections, built with the help of exam grades received by students in previous and subsequent disciplines, according to the sequence of their study according to the curriculum. The influence of the knowledge of mathematical disciplines on the knowledge of general engineering disciplines and the dependence of knowledge between the disciplines of a special block, achieved both in full-time and distance learning, is considered. The closeness of interdisciplinary ties was established on the basis of the results of the bachelor’s examinations in the field of Computer Science and Computer Engineering, conducted both before the outbreak and during the development of the pandemic.

LITERATURE REVIEW: ADVANTAGES AND DISADVANTAGES OF BLACK BOX AND WHITE BOX TESTING METHODS

Software testing methods play a crucial role in ensuring the quality, security, and performance of applications. The two main approaches often used are black box and white box testing. This article presents a comprehensive literature review of the characteristics, advantages and disadvantages of both testing methods with the aim of providing in-depth insights for practitioners and researchers in the field of software engineering. The research method used is Systematic Literature Review (SLR), which is a technique of collecting information through the process of searching for literature based on research questions, filtering search results, evaluating, and analyzing relevant research from various academic database sources to gain a comprehensive understanding. Through the analysis of various literature studies, the results of this research are the characteristics of each testing method that is more effectively used in best practice to achieve efficient testing with the coverage of literature review sources in the range of 2020 to 2024. The review of these characteristics is based on seven criteria, namely: test focus, test base, tester's point of view, test case design, program error identification method, program code knowledge, and test effectiveness. In addition, this article presents a comparison of the two methods so that the differences in testing methods can be clearly recognised in terms of how they are implemented. Based on the results of review literature, both methods are better used together in order to ensure that the functional and internal code structure of the software is functioning properly.

A NOVEL FRAMEWORK FOR THE APPLICATION OF TOPOLOGY OPTIMIZATION IN LIGHTWEIGHT STRUCTURES

Introduction: Topology optimization has been widely used in the fields of mechanical and structural engineering. In the field of architecture, especially in the context of lightweight structures, a strong understanding of programming is essential for meaningful involvement. The purpose of the study was to establish a fundamental framework that facilitates the seamless implementation of topology optimization in the design of lightweight structures by architects. Methods: This work employed a deductive approach to analyze six case studies that involve the application of topology optimization in various lightweight constructions. The analysis was conducted based on a predefined set of criteria. Additionally, the deductive technique was used to establish a framework for implementing topology optimization in the design of lightweight structures. Finally, the framework was used to create an optimized lightweight structure (a pentagonal Roman vault). Findings: An analysis of all case studies was conducted using two distinct processes: the form-finding process and the fabrication process. This inquiry aimed to determine the procedural framework involved in the design and fabrication process of each case study. The underlying framework was derived through an analytical comparison of these six case studies. This framework enables the production of an optimized lightweight structure. Novelty: This study presents significant findings on topology optimization and its use in lightweight structures, offering essential insights for architects seeking to create aesthetically pleasing and distinctive architectural forms that prioritize high stiffness and low mass.

Microbial Fuel Cells for Bioelectricity Generation: Current Innovations, Challenges, and Future Prospects

Abstract: Microbial Fuel Cells (MFCs) have emerged as a compelling technological advancement that utilizes microbial metabolic processes to produce electricity from organic waste substrates. This review examines the latest advancements, prevailing challenges, and prospective developments of MFCs in relation to renewable energy generation and ecological sustainability. MFCs present a dual benefit by facilitating wastewater treatment while concurrently generating bioelectricity, thereby rendering them appealing for decentralized energy frameworks and waste management strategies. Recent progress in the fields of genetic engineering and synthetic biology has culminated in the development of optimized microbial strains and improved biofilm stability, which significantly enhances the efficiency of electron transfer. Innovations in electrode materials, including carbon nanotubes and graphene, have further augmented the performance metrics of these systems. Nevertheless, obstacles persist in augmenting power output, minimizing material costs, and scaling MFCs for larger industrial applications. This review also elucidates the environmental and economic implications of MFCs, particularly their capacity to mitigate carbon emissions and generate financial savings in the domain of wastewater treatment. Lastly, we delineate future research trajectories, concentrating on synthetic biology, hybrid renewable systems, and commercialization strategies that will catalyze the scalability and wider acceptance of MFC technology. The prospects for MFCs are indeed promising, providing innovative solutions to the pressing global challenges of energy production and waste management.

Birth of Strange Non-chaotic Attractors in Fractional-Order Systems

In this study, we present the construction of a quasiperiodically driven fractional-order Chua system and investigate its dynamical behavior by varying the fractional order parameters q1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q_1$$\\end{document} and q2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$q_2$$\\end{document} using various characterization techniques. Our observations suggest that selecting appropriate combinations of fractional orders allows us to observe bifurcation phenomena for different values of the forcing amplitude. Through the use of Poincaré sections, we visualize the formation of tori and their doubling bifurcation route to strange nonchaotic attractor. Additionally, we employ numerical and statistical tools, such as singular continuous spectra, separation of nearby trajectories, and fast Fourier transform, to verify the occurrence and understand the mechanism behind the birth of strange nonchaotic attractor. We differentiate torus, strange nonchaos, and chaos with the help of 0–1 test and recurrence analysis. Our results provide valuable insights into the dynamical behavior of quasiperiodically driven fractional-order systems, with potential implications for various fields of science and engineering.

Optimizing Underground Coal Mine Safety: Leveraging Advanced Computational Algorithms for Roof Fall Rate Prediction and Risk Mitigation

AbstractThe utilization and consumption of coal in various nations have emphasized the pivotal role played by coal mines. However, aside from the substantial contribution of coal mines, miners, engineers, and craftsmen in this industry have long been exposed to numerous risks and financial losses resulting from roof collapses in underground coal mines. Hence, due to the heightened sensitivity surrounding this issue, the accurate and low-error forecasting and assessment of the roof fall rate (RFR) are deemed crucial and of utmost importance. Nonetheless, due to the intricate and uncertain inherent characteristics of the rock formations, assessing the RFR has encountered multiple challenges that cannot be precisely approximated through traditional methods. In this paper, algorithms such as the harmony search algorithm (HS) and the invasive weed Optimization algorithm (IWO) are harnessed to address the aforementioned challenges. To model the RFR, a total of 109 data points were used, incorporating input parameters such as primary roof support (PRSUP), depth of cover (D), coal mine roof rating (CMRR), mine height (MH), and intersection diagonal span (IS). For effective data analysis and model development, the dataset was split into two separate groups: one for training and the other for testing. Specifically, 80% of the data was used to build the model, while the remaining 20% was allocated for model evaluation and validation. Based on the outcomes of three statistical metrics R2, MSE, and RMSE, it is evident that the deployment of HS and IWO algorithms demonstrates high performance, with predicted values closely aligning with actual ones. Consequently, the utilization of intelligent algorithms in the field of rock engineering is positioned as a potent tool for researchers and engineers. In conclusion, a sensitivity analysis is carried out with the help of the @RISK software as a means of ranking the influence that the input parameters have on the output of the model. Its results indicate that among different parameters, the CMRR parameter with a sensitivity degree of 0.11 has the most impact on the model, even with the smallest change in this parameter, a significant change is made in the model output.

Foretelling the compressive strength of bamboo using machine learning techniques

PurposeThe purpose of this research was to develop and evaluate a machine learning (ML) algorithm to accurately predict bamboo compressive strength (BCS). Using a dataset of 150 bamboo samples with features such as cross-sectional area, dry weight, density, outer diameter, culm thickness and load, various ML algorithms including artificial neural network (ANN), extreme learning machine (ELM) and support vector regression (SVR) were tested. The ELM algorithm outperformed others, showing superior accuracy based on metrics like R2, MSE, RMSE, MAE and MAPE. The study highlights the efficacy of ELM in enhancing the precision and reliability of BCS predictions, establishing it as a valuable tool for assessing bamboo strength.Design/methodology/approachThis study experimentally created a dataset of 150 bamboo samples to predict BCS using ML algorithms. Key predictive features included cross-sectional area, dry weight, density, outer diameter, culm thickness and load. The performance of various ML algorithms, including ANN, ELM and SVR, was evaluated. ELM demonstrated superior performance based on metrics such as coefficient of determination (R2), mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE), establishing its robustness in predicting BCS accurately.FindingsThe study found that the ELM algorithm outperformed other ML algorithms, including ANN and SVR, in predicting BCS. ELM achieved the highest accuracy based on key metrics such as R2, MSE, RMSE, MAE and MAPE. These results indicate that ELM is a highly effective and reliable tool for predicting the compressive strength of bamboo, thereby enhancing the precision and dependability of BCS evaluations.Originality/valueThis study is original in its application of the ELM algorithm to predict BCS using experimentally derived data. By comparing ELM with other ML algorithms like ANN and SVR, the research establishes ELM’s superior performance and reliability. The findings demonstrate the significant potential of ELM in material strength prediction, offering a novel and robust approach to evaluating bamboo’s compressive properties. This contributes valuable insights into the field of material science and engineering, particularly in the context of sustainable construction materials.

Fault identification of rolling bearing based on improved salp swarm algorithm

Due to the rapid development of industrial manufacturing technology, modern mechanical equipment involves complex operating conditions and structural characteristics of hardware systems. Therefore, the state of components directly affects the stable operation of mechanical parts. To ensure engineering reliability improvement and economic benefits, bearing diagnosis has always been a concern in the field of mechanical engineering. Therefore, this article studies an effective machine learning method to extract useful fault feature information from actual bearing vibration signals and identify bearing faults. Firstly, variational mode decomposition decomposes the source signal into several intrinsic mode functions according to the actual situation. The vibration signal of the bearing is decomposed and reconstructed. By iteratively solving the variational model, the optimal modulus function can be obtained, which can better describe the characteristics of the original signal. Then, the feature subset is efficiently searched using the wrapper method of feature selection and the improved binary salp swarm algorithm (IBSSA) to effectively reduce redundant feature vectors, thereby accurately extracting fault feature frequency signals. Finally, support vector machines are used to classify and identify fault types, and the advantages of support vector machines are verified through extensive experiments, improving the ability of global search potential solutions. The experimental findings demonstrate the superior fault recognition performance of the IBSSA algorithm, with a highest recognition accuracy of 97.5%. By comparing different recognition methods, it is concluded that this method can accurately identify bearing failure.

Engineering Origami: A Systematic Literature Review in the context of Space Technology, Design, and Applications

Abstract: Origami is the ancient art of Japanese paper folding. For years it has been used to create stunning works of art. But origami has also been used in more surprising ways, like car airbags, stents, and even space exploration. Origami is a bit more than just a paper crane. Engineers use it for use, solve the interesting problems. Origami teaches how to fold very large thin sheets into very small spaces. And that's exactly what space engineers need. The reason for incorporating origami for space applications is to launch structures in space that are just amazingly big using flashers, starshade folding, tessellations, etc. So being able to fold these structures such that they fit neatly within our rocket and then unfold when they get to space. The researcher has attempted to do a literature review and to demonstrate how origami can be used in aerospace engineering and related fields. Innovative aerospace solutions such as morphing aircraft wings and deployable space constructions are made possible by the principles of origami. Using sophisticated modelling and simulation tools is essential for creating intricate origami structures for aerospace applications.

Technological Profile of Microreactor Based on Patent Analysis

AbstractSince the concept of microreactor was first proposed in the 1980s, it has been entering the chemistry and chemical engineering fields at a high speed and is favored by both scientific and commercial fields. The development of microreactors has been extensively studied in the literature, but less analysis has been carried out from the perspective of industrial technology. This paper aims to identify, analyze, and map the technological profile of microreactors from the perspective of patents. Based on the text mining techniques, a quantitative analysis of 16,327 microreactor‐related patents in the Derwent Innovations Index database was conducted. The analysis reveals a consistent annual increase in the number of microreactor‐related patents from 1982 to 2021. Specifically, China emerges as the leading country in terms of patent disclosures, whereas the United States and the World Intellectual Property Organization (WIPO) play a crucial role in knowledge transfer. Moreover, this study identifies the technological evolution path for microreactors and analyzes in detail the key patents along this evolution path. Individuation, modularization, and greening are the developing directions of microreactor technology in the future. This research offers valuable guidance for researchers and industry professionals in decision‐making and driving advancements in microreactor technology.

Finite Element Model Calibration with Surrogate Model-Based Bayesian Updating: A Case Study of Motor FEM Model

This paper introduces an advanced methodology for the calibration of Finite Element Models (FEM) utilizing a surrogate model-based Bayesian updating framework. The approach is exemplified through a case study on motor design, where precise FEM calibration is essential for predicting and optimizing motor performance. Traditional calibration techniques are often computationally expensive due to the iterative nature of the simulation process. To mitigate this, the proposed method integrates surrogate models to approximate FEM simulations, significantly reducing the computational burden without sacrificing accuracy. Bayesian updating is then employed to iteratively refine the surrogate model by incorporating new data, thereby enhancing prediction accuracy. This dual approach not only accelerates the calibration process but also ensures a high level of precision, making it highly suitable for complex engineering applications requiring both efficiency and reliability. The case study underscores the effectiveness of this methodology, demonstrating its potential to streamline the design process in motor development and other FEM-dependent engineering fields. The findings suggest that the surrogate model-based Bayesian updating approach achieves robust calibration with significantly fewer simulations, thereby optimizing both time and computational resources.

Sound Source Identification of Vehicle Noise Based on a Microphone Array With a Modified Multiple Signal Classification Algorithm

In this paper, a modified multiple signal classification (MUSIC) algorithm tailored for sound source identification (SSI) of vehicle noise is introduced and experimentally validated. A uniform planar microphone array (UPMA) is formulated for mathematical modeling, with its SSI-oriented parameters selected based on the primary frequency spectrum of vehicle noise. Simulations are conducted to compare the SSI accuracy of two conventional spatial spectra estimation (SSE) algorithms: the Capon algorithm and the MUSIC algorithm. The results demonstrate that the MUSIC algorithm, which relies on the eigenvalues of a covariance matrix to estimate signal direction, exhibits superior SSI resolution under low signal-to-noise ratio (SNR) conditions. However, it faces challenges in distinguishing between coherent or closely spaced signals. To address this, a modified MUSIC algorithm is proposed by reconstructing the covariance matrix of received signals and the SSE function. Simulation outcomes indicate that the modified MUSIC significantly outperforms the conventional version, owing to its enhanced SSI resolution. The accuracy of the SSI system, incorporating the UPMA and the modified MUSIC, is verified using a low-frequency volume source. Ultimately, the devised SSI system is successfully deployed to identify noise sources in a vehicle at different operational conditions, further validating the efficacy of the modified MUSIC. The UPMA and the modified MUSIC presented in this study have direct applicability in vehicle noise source identification and may be extended to other sound-related engineering fields for SSI purposes.