Intelligent and Sustainable Manufacturing Open Access

ISSN: 3005-8066 (Online)

3005-8058 (Print)

Intelligent and Sustainable Manufacturing is an international, peer-reviewed, open access journal, which publishes papers on all aspects of research and engineering applications of intelligent and sustainable manufacturing, including sustainable manufacturing, energy management and assessment, energy field assisted manufacturing, mechanisms of manufacturing process, process performance of difficult-to-cutting materials and intelligent manufacturing and equipment, et al. It is published biannually online by SCIEPublish.

Editors-in-Chief

Articles (31) All articles

Review

01 April 2025

Sustainable Manufacturing and Applications of Wide-Bandgap Semiconductors—A Review

Wide-bandgap (WBG) semiconductors such as silicon carbide (SiC) and gallium nitride (GaN) are revolutionizing high-power electronics due to their superior thermal conductivity, breakdown voltage, and energy efficiency. These materials are critical in electric vehicles, renewable energy systems, and high-frequency applications like 5G infrastructure. However, their production processes are resource-intensive and present significant environmental challenges. This review evaluates recent advancements in sustainable WBG semiconductor manufacturing, focusing on low-energy epitaxial growth, closed-loop recycling, and the mitigation of toxic by-products. Additionally, it highlights the role of Industry 4.0 innovations, such as AI-driven process optimization and IoT-based resource management, in enhancing sustainability. The review identifies research gaps in cost reduction, alternative WBG materials like Gallium Oxide (Ga2O3) and Diamond, and scalable green manufacturing solutions. It underscores the necessity for industry-wide collaboration and regulatory frameworks to drive the adoption of eco-friendly semiconductor fabrication. The findings of this study provide a roadmap for advancing sustainability in WBG semiconductor production, ensuring their long-term viability in the transition toward energy-efficient technologies.

Article

24 March 2025

A Shear Stress Model for Magnetorheological Fluid with High Volume Fraction

Shear stress prediction in high-concentration magnetorheological fluids (MRFs) faces limitations due to the oversimplified magnetic dipole interactions and neglect of multibody effects in classical single-chain models, particularly under conditions (3040 vol.%) where stress prediction errors start escalating nonlinearly. To address this gap, based on the classic single-chain model, this study proposed a new revised calculation method that integrates three novel components: (1) a distance-weighted dipole interaction model incorporating material-specific correction factors, (2) dynamic chain reconstruction mechanisms accounting for magnetic aggregation under shear deformation, and (3) transverse field overlap parameters quantifying anisotropic field distributions. Validated against Lord Corp.’s MRF-132DG, the proposed approach reduces shear stress prediction root-mean-square error (RMSE) by 71.7% (from 27.40 kPa to 7.76 kPa). It rectifies the R-square metric from −0.9236 to 0.8457, outperforming existing models in high-concentration regimes. The work resolves the bottleneck of modeling chain-to-network transition behaviors through Monte Carlo simulations with energy barrier analysis, revealing how localized dipole rearrangement governs macroscopic rheological responses. The methodology’s adaptability to pre-saturation magnetization stages further enables systematic evaluation of multi-dipole interaction thresholds critical for high-performance MRF engineering applications.

Article

14 March 2025

A Conceptual Design of Industrial Asset Maintenance System by Autonomous Agents Enhanced with ChatGPT

This article introduces OPRA (Observation-Prompt-Response-Action) and its multi-agent extension, COPRA (Collaborative OPRA), as frameworks offering alternatives to traditional agent architectures in intelligent manufacturing systems. Designed for adaptive decision-making in dynamic environments, OPRA enables agents to request external knowledge—such as insights from large language models—to bridge gaps in understanding and guide optimal actions in real-time. When predefined rules or operational guidelines are absent, especially in contexts marked by uncertainty, complexity, or novelty, the OPRA framework empowers agents to query external knowledge systems (e.g., ChatGPT), supporting decisions that traditional algorithms or static rules cannot adequately address. COPRA extends this approach to multi-agent scenarios, where agents collaboratively share insights from prompt-driven responses to achieve coordinated, efficient actions. These frameworks offer enhanced flexibility and responsiveness, which are critical for complex, partially observable manufacturing tasks. By integrating real-time knowledge, they reduce the need for extensive training data and improve operational resilience, making them a promising approach to sustainable manufacturing. Our study highlights the added value OPRA provides over traditional agent architectures, particularly in its ability to adapt on-the-fly through knowledge-driven prompts and reduce complexity by relying on external expertise. Motivational scenarios are discussed to demonstrate OPRA’s potential in critical areas such as predictive maintenance.

Review

13 March 2025

Review on Vibration Control of Wafer Handling Robot

The wafer handling robot serves as the pivotal component of the wafer transfer system, wherein its operational speed and motion precision exert a direct influence on both the yield and productivity of wafer processing. With the semiconductor manufacturing process advancing towards nanoscale linewidths and heightened throughput, the time-varying stiffness characteristics of the flexible joints in wafer handling robots, along with the resultant end vibration issues, have emerged as critical challenges that constrain overall performance. A comprehensive understanding of the stiffness change mechanisms, coupled with enhancements in control methodologies, plays an indispensable role in the effective vibration control of wafer handling robots. To facilitate research in pertinent areas, this paper systematically reviews the cutting-edge methods for vibration suppression in variable stiffness flexible joint wafer handling robots, concentrating on the following core aspects: The impacts of diverse dynamic stiffness identification methodologies on the accuracy of stiffness identification are thoroughly examined; This paper also explores the potential of collaborative optimization strategies involving trajectory planning, control methodologies, and lightweight intelligent algorithms in enhancing real-time control. Furthermore, it evaluates the application scenarios and feasibility of passive vibration absorbers and semi-active adjustable dampers within the context of broadband vibration suppression technologies. In conclusion, this paper synthesizes and critically discusses the advantages and limitations inherent in various research findings, while also constructing a “model-control-vibration suppression” closed-loop optimization system aimed at facilitating ultra-precision vibration control of wafer handling robots under conditions of high dynamic operation. By elucidating the bottlenecks present in existing technologies alongside the trajectory for future interdisciplinary integration, this work provides theoretical support for the intelligent advancement of wafer handling robots and fosters the expedited and reliable development of wafer transfer systems.

Article

25 February 2025

Thermal Characterization Study of Double End Face Grinding Powder Metallurgy Stainless Steel 316L

Double end face grinding machining is a highly efficient surface grinding technique. And grinding temperature is an important factor affecting the surface quality of workpieces. However, it is difficult to monitor the surface temperature of the workpiece in real time because of the covered contact between the grinding wheel and the upper and lower surfaces of the workpiece during the machining process. This paper aims to conduct a mechanistic analysis and experimental investigation of the machining process to address this challenge. Initially, the paper conducts an analysis of the kinematic mechanism, modal analysis, and the grinding force mechanism specific to the double end face grinding process. Afterwards, the mechanisms leading to the generation of grinding heat and the associated heat transfer mechanisms are explored in depth. The paper then proceeds to solve the instantaneous temperature field during double end face grinding by the finite element method (FEM). Furthermore, the micro and macro profile heights of the machined workpiece surfaces are measured and analyzed. The results show that the machined workpiece surface shows a high center and low edge. This is due to the fact that the temperature at the edge of the workpiece is higher than the center during machining, resulting in more material removal. Through these investigations, the study is able to determine the optimal process parameters for the machining process. This in turn improves machining efficiency and product conformity. And these findings not only guide practical production processes but also provide a foundation for future theoretical research in this area.

Review

18 February 2025

Digital Twin and Artificial Intelligence in Machining: A Bibliometric Analysis

The past decade has witnessed an exodus toward smart and lean manufacturing methods. The trend includes integrating intelligent methods into sustainable manufacturing systems purposely to improve the machining efficiency, reduce waste and also optimize productivity. Manufacturing systems have seen transformations from conventional methods, leaning towards smart manufacturing in line with the industrial revolution 4.0. Since the manufacturing process encompasses a wide range of human development capacity, it is essential to analyze its developmental trends, thereby preparing us for future uncertainties. In this work, we have used a Bibliometric analysis technique to study the developmental trends relating to machining, digital twins and artificial intelligence techniques. The review comprises the current activities in relation to the development to this area. The article comprises a Bibliometric analysis of 464 articles that were acquired from the Web of Science database, with a search period until November 2024. The method of obtaining the data includes retrieval from the database, qualitative analysis and interpreting the data via visual representation. The raw data obtained were redrawn using the origin software, and their visual interpretations were represented using the VOSviewer software (VOSviewer_1.6.19). The results obtained indicate that the number of publications related to the searched keywords has remarkably increased since the year 2018, achieving a record maximum of over 80 articles in 2024. This is indicative of its increasing popularity. The analysis of the articles was conducted based on the author countries, journal types, journal names, institutions, article types, major and micro research areas. The findings from the analysis are meant to provide a bibliometric explanation of the developmental trends in machining systems towards achieving the IR 4.0 goals. Additionally, the results would be helpful to researchers and industrialists that intend to achieve optimum and sustainable machining using digital twin technologies.

Article

21 January 2025

Machining Characteristics of Graphene Oxide-Based Nanosuspensions in Abrasive Machining of Single-Crystal Si and SiC

Single-crystal silicon (Si) and silicon carbide (SiC) are core semiconductor materials in communication, lighting, power generation, and transportation. However, their high hardness and wear resistance combined with low fracture toughness have posed significant challenges for high-efficiency and low-damage machining. Aqueous suspensions containing nanoparticle additives have recently been developed for sustainable manufacturing due to their satisfactory tribological performance and environmentally friendly nature. In this work, nanoadditives, including two-dimensional (2D) graphene oxide (GO) nanosheets and zero-dimensional (0D) diamond nanoparticles, were ultrasonically dispersed in water to formulate different GO-based nanosuspensions for achieving high-efficiency and low-damage abrasive machining. The experimental results indicated that GO nanosuspension was a suitable coolant for grinding Si, generating a ground surface of 32 nm in Ra, owing to its great lubricity and excellent resistance against mechanical abrasion. Diamond-GO hybrid nanosuspension demonstrated a synergistic effect in abrasion, lubrication and oxidation, which was thus appropriate for polishing SiC single crystals, leading to approximate 60% and 30% improvements in removal and roughness respectively, in comparison to a commercially available diamond suspension.

Review

16 January 2025

Biological Bone and Replacement Materials in Grinding: Force Model and Processing Capability

Grinding is widely used in orthopedic surgery to remove bone tissue material, but due to the complex and brittle structure of bone, it is prone to mechanical stresses that cause cracks and damage to the bone tissue. Furthermore, bone replacement materials typically have high hardness, strength, and brittleness, which lead to increased tool wear and damage, such as cracks and deformation during grinding. Therefore, ensuring the surface quality of bone and replacement materials during the grinding process has become a critical issue. This necessitates the development of grinding force models that consider various processing parameters, such as feed rate and cutting depth, to guide industrial production. However, currently, research on the grinding force prediction models for bone tissue and its replacement materials is relatively scarce, and there is a lack of corresponding grinding force model reviews for unified guidance. Based on this, this article focuses on bone grinding technology and, conducts a critical comparative analysis of the grinding force models for bone tissue and its replacement materials, and then summarizes the grinding force prediction models in the grinding process of bone tissue and bone replacement materials. First, according to the material types and material removal mechanisms, the materials are categorized into bone tissue, bio-inert ceramics, and bio-alloys, and the material removal process during grinding is analyzed. Subsequently, the grinding force prediction models for each material and the accuracy errors of each model are summarized. The paper also reviews the application of these grinding force prediction models, explaining how processing parameters such as feed rate and cutting depth influence grinding forces and their interrelationship. Finally, in light of the current issues in the grinding of bone tissue and replacement materials, potential future research directions are proposed, aiming to provide theoretical guidance and technical support for improving the grinding quality of bone tissue and its replacement materials.

Review

14 January 2025

Artificial Intelligence and Machine Learning for Sustainable Manufacturing: Current Trends and Future Prospects

Artificial Intelligence (AI) and Machine Learning (ML) are transforming manufacturing processes, offering unprecedented opportunities to enhance sustainability and environmental stewardship. This comprehensive review analyzes the transformative impact of AI technologies on sustainable manufacturing, focusing on critical applications, including energy optimization, predictive maintenance, waste reduction, and circular economy implementation. Through systematic analysis of current research and industry practices, the study examines both the opportunities and challenges in deploying AI-driven solutions for sustainable manufacturing. The findings provide strategic insights for researchers, industry practitioners, and policymakers working towards intelligent and sustainable manufacturing systems while elucidating emerging trends and future directions in this rapidly evolving field.

Review

08 January 2025

A Review of Ultrasonic Vibration-Assisted Grinding for Advanced Materials

Ultrasonic vibration-assisted grinding (UVAG), which superimposes high-frequency, micro-amplitude ultrasonic vibration onto conventional grinding (CG), offers several advantages, including a high material removal rate, low grinding force, low surface roughness, and minimal damage. It also addresses issues such as abrasive tool clogging, thereby enhancing machining efficiency, reducing tool wear, and improving the surface quality of the workpiece. In recent years, the rapid development of advanced materials and improvements in UVAG systems have accelerated the progress of UVAG technology. However, UVAG still faces several challenges in practical applications. For example, the design and optimization of the ultrasonic vibration system to achieve high-precision, large-amplitude, and high-efficiency grinding remain key issues. Additionally, further theoretical and experimental studies are needed to better understand the material removal mechanism, the dynamics of grinding force, abrasive tool wear, and their effects on surface quality. This paper outlines the advantages of UVAG in machining advanced materials, reviews recent progress in UVAG research, and analyzes the current state of ultrasonic vibration systems and ultrasonic grinding characteristics. Finally, it summarizes the limitations of current research and suggests directions for future studies. As an emerging machining technology, UVAG faces challenges in many areas. In-depth exploration of the theoretical and experimental aspects of high-precision, large-amplitude, and high-efficiency ultrasonic vibration systems and UVAG is essential for advancing the development of this technology.

Review

14 January 2025

Artificial Intelligence and Machine Learning for Sustainable Manufacturing: Current Trends and Future Prospects

Artificial Intelligence (AI) and Machine Learning (ML) are transforming manufacturing processes, offering unprecedented opportunities to enhance sustainability and environmental stewardship. This comprehensive review analyzes the transformative impact of AI technologies on sustainable manufacturing, focusing on critical applications, including energy optimization, predictive maintenance, waste reduction, and circular economy implementation. Through systematic analysis of current research and industry practices, the study examines both the opportunities and challenges in deploying AI-driven solutions for sustainable manufacturing. The findings provide strategic insights for researchers, industry practitioners, and policymakers working towards intelligent and sustainable manufacturing systems while elucidating emerging trends and future directions in this rapidly evolving field.

VishnuVijay Kumar
KhaledShahin

Review

28 March 2024

Digital Twins Enabling Intelligent Manufacturing: From Methodology to Application

Digital twin technology develops virtual models of objects digitally, simulating their real-world behavior based on data. It aims to reduce product development cycles and costs through feedback between the virtual and real worlds, data fusion analysis, and iterative decision-making optimization. Traditional manufacturing processes often face challenges such as poor real-time monitoring and interaction during machining, difficulties in diagnosing equipment failures, and significant errors in machining. Digital twin technology offers a powerful solution to these issues. Initially, a comprehensive review of the research literature was conducted to assess the current research scope and trends. This was followed by an explanation of the basic concepts of digital twins and the technical pathway for integrating digital twins into intelligent manufacturing including outlining the essential technologies for creating a system of interaction between the virtual and real worlds, enabling multimodel fusion, data sensing, algorithm-based prediction, and intelligent decision-making. Moreover, the application of digital twins in intelligent manufacturing throughout the product life cycle was detailed, covering product design, manufacturing, and service stages. Specifically, in the manufacturing phase, a model based on heat conduction theory and visualization was used to construct a time-varying error model for the motion axis, leading to experiments predicting the time-varying error in the hole spacing of a workpiece. These experiments achieved a minimum prediction error of only 0.2 μm compared to the actual error. By compensating for time-varying errors in real time, the variability in the hole spacing error decreased by 69.19%. This paper concludes by summarizing the current state of digital twins in intelligent manufacturing and projecting future trends in key technologies, application areas, and data use, providing a basis for further research.

ShuguoHu
ChangheLi
BenkaiLi
MinYang
XiaomingWang
TengGao
WenhaoXu
Yusuf SuleimanDambatta
ZongmingZhou
PeimingXu

Review

29 December 2023

A State-of-the-art Review on the Intelligent Tool Holders in Machining

In the manufacturing process, in addition to the properties of material itself, the quality of a product is directly related to the cutting process. Cutting force and cutting heat are two crucial factors in cutting processing. Researchers can analyze various signals during cutting process, such as cutting force signal, vibration signal, temperature signal, etc., which can regulate force and temperature, optimize the cutting process, and improve product quality. Therefore, it is very important to pay attention to various signals in cutting process. Meanwhile, good-quality signal data sets will greatly reduce time, resource and labor costs for subsequent use or analysis of researchers. Therefore, how to collect high-quality signals effectively and accurately is the first step. At present, researchers prefer to use various sensors to collect signals. With the advancement of science and technology, intelligent tool holder appears in researchers’ vision. It integrates multiple systems such as sensors, data collection, data transmission, and power supply on the tool holder. It replaces traditional wired sensors, and it is highly interactive with CNC machine tools. This paper will carry out a systematic review and prospect from three aspects: the structural design of the intelligent tool holder, the signal monitoring technology of the intelligent tool holder, and the tool condition monitoring of the intelligent tool holder.

QinglongAn
JieYang
JunliLi
GangLiu
MingChen
ChangheLi

Review

13 February 2024

3D Printing Technology for Rapid Response to Climate Change: Challenges and Emergency Needs

Providing rapid, efficient, inexpensive, and resilient solutions is an eminent and urgent need for emergency relief conditions, mainly and increasingly driven by the impacts of climate change. Under such disastrous circumstances, the current practice involves preparation, dispatching and managing significant amounts of materials, resources, manpower, and transportation of basic needs, which can be hindered remarkably by infrastructure damage and massive loss of lives. However, an emerging technology known as 3D printing (3DP) can play a significant role and rapidly bring unlimited innovative solutions in such conditions with much lesser resources to meet the necessities of large populations affected. Considering the recent progress of 3DP technology and applications in different industrial and consumer sectors, this study aims to provide an analysis of the status and current progress of 3DP technology in various fields to understand and present its potential for readiness and response to disasters, emergency and relief need driven by climate change. Secondly, this study also presents a sustainability assessment of 3DP technology for such cases to evaluate economic, environmental, and social impacts. Finally, policies and strategies are suggested to adapt 3DP technology in different sectors to prepare for large-scale emergencies.

Shoukat AlimKhan
AnsAl Rashid
JasimMuhammad
FawadAli
MuammerKoç

Review

23 February 2024

Research Status and Prospect of Ultrasonic Vibration and Minimum Quantity Lubrication Processing of Nickel-based Alloys

Nickel-based alloys has important application value in modern industrial field, but there are a lot of problems that are difficult to solve in traditional processing, and it is a typical difficult-to-process material. In order to improve the machinability of nickel-based alloys, scholars try to use a variety of non-traditional processing methods to explore and study the processing of nickel-based alloys. In these studies, ultrasonic vibration assisted processing technology and minimum quantity lubrication (MQL) processing technology can achieve remarkable results. The intermittent separation cutting characteristics of ultrasonic vibration assisted processing technology can improve the processing quality by changing the tool path, while minimum quantity lubrication processing technology can improve the lubrication effect of cutting, combining ultrasonic vibration assisted MQL processing leverages the benefits of both methods, resulting in improved machinability and expanded application of nickel-based alloys. Summarize the current research status on the machining mechanism of nickel-based alloys assisted by ultrasonic vibration and micro lubrication, and anticipate its developmental trends. This provides a reference for future research on the efficient machining mechanisms and practical applications of nickel-based alloys.

GuquanGu
DazhongWang
ShujingWu
ShuZhou
BuxinZhang

Article

31 January 2024

Design of Intelligent and Sustainable Manufacturing Production Line for Automobile Wheel Hub

The wheel hub is an important part of the automobile, and machining affects its service life and driving safety. With the increasing demand for wheel productivity and machining accuracy in the automotive transport sector, automotive wheel production lines are gradually replacing human production. However, the technical difficulties of conventional automotive wheel production lines include insufficient intelligence, low machining precision, and large use of cutting fluid. This paper aims to address these research constraints. The intelligent, sustainable manufacturing production line for automobile wheel hub is designed. First, the machining of automotive wheel hubs is analyzed, and the overall layout of the production line is designed. Next, the process equipment system including the fixture and the minimum quantity lubrication (MQL) system are designed. The fixture achieves self-positioning and clamping functions through a linkage mechanism and a crank–slider mechanism, respectively, and the reliability of the mechanism is analyzed. Finally, the trajectory planning of the robot with dual clamping stations is performed by RobotStodio. Results show the machining parameters for a machining a wheel hub with a diameter of 580 mm are rotational speed of 2500 rpm, cutting depth of 4 mm, feed rate of 0.5 mm/r, and minimum clamping force of 10881.75 N. The average time to move the wheel hub between the roller table and each machine tool is 27 s, a reduction of 6 s compared with the manual handling time. The MQL system effectively reduces the use of cutting fluid. This production line can provide a basis and reference for actual production by reasonably planning the wheel hub production line.

MinkaiChen
YanbinZhang
BoLiu
ZongmingZhou
NaiqingZhang
HuhuWang
LiqiangWang

Article

01 April 2024

Cost Effectiveness of the Industrial Internet of Things Adoption in the U.S. Manufacturing SMEs

This research paper explores the financial adoption challenges of the Industrial Internet of Things (IIoT) in industry. Previous studies have mainly concentrated on designing affordable IIoT devices, reducing operational costs, and creating conceptual frameworks to assess the financial impact of IIoT adoption. The objective of this paper is to investigate whether IIoT adoption’s financial benefits outweigh the initial costs in small and medium-sized enterprises (SMEs). The data from the Industrial Assessment Centers (IAC) database were analyzed, focusing on 62 U.S. manufacturing SMEs across 10 states and 25 Standard Industrial Classifications (SICs), evaluating projected IIoT implementation costs and anticipated cost savings. Results from the analyses reveal that statistically, the difference between implementation costs and savings is significant at a 95% confidence level. Practically, this indicates that SMEs, despite facing high initial costs, can expect these investments to be counterbalanced by substantial savings. From an engineering perspective, this finding raises awareness among SMEs that, beyond overcoming financial barriers, IIoT technologies serve as a strategic enhancement to operational efficiency and competitive positioning. This study acknowledges the limitations including reliance on estimated projections and a narrow industry focus. Future research should broaden the sample and explore the lifecycle costs of IIoT.

FatemehGhafari
EhsanShourangiz
ChaoWang

Review

19 July 2024

Solid Additives to Increase the Service Life of Ceramic Cutting Tool: Methodology and Mechanism

With the development of the manufacturing industry, there is an increasing demand for high-efficiency processing, high-precision processing, and high-temperature processing. The characteristics of ceramic tools, such as high hardness and wear resistance, make them suitable for high-precision processing. Additionally, their excellent high temperature resistance perfectly meets the requirements of high temperature processing. However, ceramic tools have a relatively low strength and are prone to breakage, which limits their application in some high-strength machining fields. Their low toughness and brittleness also lead to easy cracking and reduced tool life, resulting in frequent tool changes that further limit processing efficiency. Therefore, improving the service life of ceramic tool materials is crucial to enhance processing efficiency and achieve significant economic benefits. With the development of material science, solid additives with toughening and strengthening properties have greatly improved the performance of ceramic tool materials and given ceramic tools new life-enhancing properties, such as lubrication and repair. By utilizing the combined action of one or more solid additives and employing surface coating technology, the service life of ceramic cutting tools is significantly extended. This makes the application of ceramic tools in industrial cutting more and more widely, and the demand is also growing rapidly. However, the mechanism and methods of various solid additives to increase the life of ceramic tool materials have not been systematically reviewed. The analysis of the composition and functional properties of ceramic tool materials was used as a basis to summarize the mechanism by which various solid additives improve the service life of ceramic tool materials, and to provide points for attention in their use. The aim is to assist researchers in designing and preparing new ceramic tool materials that can meet processing requirements. Finally, the research status, challenges, and prospects of enhancing the service life of ceramic cutting tools with solid additives are summarized, providing a foundation for further research.

YuxinShi
BiaoZhao
WenfengDing

Editorial

08 November 2023

Article

23 February 2024

Influence of Separation Gap on the Performance of Savonius Hydrokinetic Turbine

Despite that ocean current energy is one of the promising sources of electricity produced in the ocean, the development of ocean current energy is far behind compared to other ocean energy due to the low efficiency and high cost of installation and maintenance. Among many converting devices, the Savonius turbine has been proven to be effective and competitive in harnessing ocean current energy. The primary purpose of the present study is to search for the optimum shape of a Savonius rotor based on CFD simulation (Star-CCM+). A Savonius turbine composed of two rotating cup-shaped rotors is selected as a numerical model. We focus on the effect of two geometry parameters such as the overlap and gap ratio on the power coefficient. Throughout the parametric study, the shape of a Savonius rotor affects the power performance, and two geometry parameters with an overlap ratio of 0.15 and a gap ratio of −0.03 are found to be the optimum design. It demonstrates stable performance within the wide TSR (Tip Speed Ratio) range of 0.6 to 1.6, with the maximum power coefficient Cp of 0.34 achieved at a TSR of 0.8. According to the numerical results based on the new CFD model, the presence of a bottom wall does not significantly affect the performance of a Savonius turbine. It means that the present unbounded CFD model can be acceptable in the initial design stage for the determination of the geometry parameters of a Savonius turbine.

MafiraAyuRamdhani
ArunGeorge
IlHyoung Cho

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