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Journal articles on the topic 'Mechanical diagnosis'

To see the other types of publications on this topic, follow the link: Mechanical diagnosis.

Author: Grafiati

Published: 6 June 2023

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1

Remmerbach, Torsten W., Falk Wottawah, Julia Dietrich, Bryan Lincoln, Christian Wittekind, and Jochen Guck. "Oral Cancer Diagnosis by Mechanical Phenotyping." Cancer Research 69, no. 5 (February 17, 2009): 1728–32. http://dx.doi.org/10.1158/0008-5472.can-08-4073.

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2

Donelson, Ronald. "Mechanical Diagnosis and Therapy for Radiculopathy." Physical Medicine and Rehabilitation Clinics of North America 22, no. 1 (February 2011): 75–89. http://dx.doi.org/10.1016/j.pmr.2010.11.001.

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3

Zhu, Hong. "Machine-Learning-Based Mechanical Fault Diagnosis Method." Advanced Materials Research 1044-1045 (October 2014): 798–800. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.798.

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With the development of science and technology, the theoretical content of mechanical fault diagnosis technology has been initially improved and established a scientific research system. Combining the mechanical diagnostic techniques with the current advanced science and technology, a variety of mechanical fault diagnosis methods have been researched and developed. Mechanical fault diagnosis evolved from empirical diagnosis to mechanical diagnosis and then to the current intelligent learning diagnosis. Now mechanical fault diagnosis collects mechanical failure data precisely mainly by a variety of sensors, uses a variety of fault diagnosis model to conduct diversified and intelligent diagnosis.

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4

Jing, Hong Lei, Jing Nie, and Nian Zhang. "Augmented-Reality-Based Mechanical Fault Diagnosis Method." Advanced Materials Research 1044-1045 (October 2014): 720–22. http://dx.doi.org/10.4028/www.scientific.net/amr.1044-1045.720.

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With the rapid development of modern society, the industrial mechanized production reached unprecedented climax in this era. Science and technology advance increasingly, modern equipment from structure to function tends to be complex and improved, and gradually achieve a high degree of automation. However, due to the inevitable factors such as wear and tear, abrasion and chemicals infection, machinery equipment will inevitably appear unforeseen fault, causing the machine to detract from the performance, or even causing serious economic losses. Therefore, mechanical fault diagnosis can reduce equipment accident rate and ensure the long-term stable operation of the device. And applying the augmented reality to machinery fault diagnosis method research can maximize the efficiency of mechanical fault diagnosis and equipment efficiency. This article explores the prospects for the development of mechanical fault diagnosis methods based on the theoretical basis and application value of augmented reality.

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5

Yu, Guangwei, Xianzhi Wang, and Chunlin Da. "Compound mechanical fault diagnosis based on CMDE." Advances in Mechanical Engineering 14, no. 2 (February 2022): 168781322210805. http://dx.doi.org/10.1177/16878132221080560.

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The fault diagnosis technique is of important for the safety operation of the rotating machinery. In the fault diagnosis framework, the entropy-based method is a promising tool for the feature extraction and signal processing. Among the entropy-based methods, the diversity entropy has arisen increasing attention due to its merits of high consistency, strong robustness, and high calculation efficiency. However, it suffers the defect that the multiscale procedure leads to unstable complexity estimation at higher scales. This induces a poor cluster performance in analyzing the compound mechanical fault signals. To address this issue, this paper presents a novel feature extraction method called composite multiscale diversity entropy (CMDE). The proposed CMDE utilizes the mean complexity value of multiple sliding windows for each scale to enhance the stability, which enables the diversity entropy could dig richer fault information from deeper scales for the compound fault diagnosis of rotating machinery. Then, the stability of CMDE has been evaluated using synthetic gear signals. At last, the proposed CMDE has been applied in the compound mechanical fault diagnosis. The experimental results show that the CMDE achieves the highest diagnosis accuracy compared to the existing entropy-based feature extraction methods.

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6

Xu, Gang. "MECHANICAL FAILURE DIAGNOSIS IN UNSTEADY OPERATING CONDITIONS." Chinese Journal of Mechanical Engineering 37, no. 12 (2001): 104. http://dx.doi.org/10.3901/jme.2001.12.104.

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7

Wu, Wen Bing, Shu Qun Yang, and Yi Jian Huang. "Application of Bipectrum in Mechanical Fault Diagnosis." Advanced Materials Research 538-541 (June 2012): 2645–48. http://dx.doi.org/10.4028/www.scientific.net/amr.538-541.2645.

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Bispectrum produced by third order cumulant contains the asymmetric and nonlinear information of signal, which can be used to describe the nonlinear phase coupling, especially the quadratic phase coupling, has been widely applied in fault diagnosis. The features of fault signals and normal signals are fetched by 2-D wavelet in different directions, then these features are used to diagnose fault. The experiment shows that the method can achieve satisfactory result.

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8

CHEN, Yushu. "Nonlinear dynamical principle of mechanical fault diagnosis." Chinese Journal of Mechanical Engineering 43, no. 01 (2007): 25. http://dx.doi.org/10.3901/jme.2007.01.025.

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9

Gelman, L., S. Gorpinich, and C. Thompson. "Adaptive diagnosis of the bilinear mechanical systems." Mechanical Systems and Signal Processing 23, no. 5 (July 2009): 1548–53. http://dx.doi.org/10.1016/j.ymssp.2009.01.007.

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10

Remmerbach, T. W., J. Guck, and J. Käs. "O33. Oral cancer diagnosis by mechanical phenotyping." Oral Oncology 47 (July 2011): S39. http://dx.doi.org/10.1016/j.oraloncology.2011.06.144.

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11

Holenstein, Anton P. "Diagnosis of mechanical seals in large pumps." Sealing Technology 1996, no. 33 (September 1996): 9–12. http://dx.doi.org/10.1016/1350-4789(96)84413-8.

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12

Fillmore, Maggie. "The Upper Limb: Mechanical Diagnosis and Therapy." Physical Therapy 77, no. 11 (November 1, 1997): 1667–68. http://dx.doi.org/10.1093/ptj/77.11.1667a.

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13

IRIE, TAKASUKE. "Ultrasonic diagnosis." Journal of the Japan Society for Precision Engineering 53, no. 4 (1987): 522–25. http://dx.doi.org/10.2493/jjspe.53.522.

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14

MATSUYAMA, Hisayoshi. "Diagnosis algorithm." Journal of the Japan Society for Precision Engineering 57, no. 3 (1991): 433–35. http://dx.doi.org/10.2493/jjspe.57.433.

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15

R.S, Kiran, Sarmukh S, and Azmi H. "Gallstone ileus: Rare Entity for Mechanical Small Bowel Obstruction." Journal of Surgical Case Reports and Images 4, no. 8 (October 23, 2021): 01–04. http://dx.doi.org/10.31579/2690-1897/088.

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Gallstone ileus is common in elderly female population. To obtain a diagnosis of gallstone ileus is a challenge requiring clinical and radiological assistance. It’s a rare cause of intestinal obstruction, accounts approximately 1-4%. Here we report a case of 56 years old lady presented with intestinal obstruction sign and symptoms. Per abdomen examination revealed generalised tenderness with sluggish bowel sound. Abdominal X-ray revealed prominent small bowel with presence of gas till rectum. CT abdomen noted intraluminal mass over distal small bowel loops mimickering intusseption. Exploratory laparotomy with small bowel enterotomy was performed. Intra-operative finding noted impacted gallstone measuring 2x3cm, 360cm from duodenal-jejunal flexure and 50cm from terminal ileum. Post-operative patient had speedy recovery and discharged home. Here we emphasize in elderly female patient presented with sign and symptoms of intestinal obstruction, diagnosis of gallstone ileus should be one of differential diagnosis.

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16

See, Isaac, Julia Chang, Nicole Gualandi, Genevieve L. Buser, Pamela Rohrbach, Debra A. Smeltz, Mary Jo Bellush, et al. "Clinical Correlates of Surveillance Events Detected by National Healthcare Safety Network Pneumonia and Lower Respiratory Infection Definitions—Pennsylvania, 2011–2012." Infection Control & Hospital Epidemiology 37, no. 7 (April 13, 2016): 818–24. http://dx.doi.org/10.1017/ice.2016.74.

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OBJECTIVETo determine the clinical diagnoses associated with the National Healthcare Safety Network (NHSN) pneumonia (PNEU) or lower respiratory infection (LRI) surveillance eventsDESIGNRetrospective chart reviewSETTINGA convenience sample of 8 acute-care hospitals in PennsylvaniaPATIENTSAll patients hospitalized during 2011–2012METHODSMedical records were reviewed from a random sample of patients reported to the NHSN to have PNEU or LRI, excluding adults with ventilator-associated PNEU. Documented clinical diagnoses corresponding temporally to the PNEU and LRI events were recorded.RESULTSWe reviewed 250 (30%) of 838 eligible PNEU and LRI events reported to the NHSN; 29 reported events (12%) fulfilled neither PNEU nor LRI case criteria. Differences interpreting radiology reports accounted for most misclassifications. Of 81 PNEU events in adults not on mechanical ventilation, 84% had clinician-diagnosed pneumonia; of these, 25% were attributed to aspiration. Of 43 adult LRI, 88% were in mechanically ventilated patients and 35% had no corresponding clinical diagnosis (infectious or noninfectious) documented at the time of LRI. Of 36 pediatric PNEU events, 72% were ventilator associated, and 70% corresponded to a clinical pneumonia diagnosis. Of 61 pediatric LRI patients, 84% were mechanically ventilated and 21% had no corresponding clinical diagnosis documented.CONCLUSIONSIn adults not on mechanical ventilation and in children, most NHSN-defined PNEU events corresponded with compatible clinical conditions documented in the medical record. In contrast, NHSN LRI events often did not. As a result, substantial modifications to the LRI definitions were implemented in 2015.Infect Control Hosp Epidemiol 2016;37:818–824

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17

Shen, Wei Hua. "Mechanical Fault Diagnosis Based on Data Mining Technology." Advanced Materials Research 320 (August 2011): 663–68. http://dx.doi.org/10.4028/www.scientific.net/amr.320.663.

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Data mining is a variety of analysis tools using the data found in the mass relationship between model and data technology, and relationships of these models can be used to predict. Organically with the combination of expert system, can solve the problem of automatic acquisition of knowledge. It from a large number, incomplete, noisy, fuzzy, random data mining the hidden decision-making has important reference value to generate the information. Data mining technology used in machinery fault diagnosis system, the scene of large quantities of raw data into valuable knowledge for specialists to explore the information they are interested in, but also describes the evolution trend of mechanical malfunction, the expert decision-making support information. And, after years of development, data mining technology has matured and widely used. The purpose of this study is that data mining technology used in machinery fault diagnosis and to study its feasibility. If applicable, must be able to open up a mechanical fault diagnosis, a new diagnostic method, which greatly promote the development of mechanical fault diagnosis technology.

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18

Huang, S. N., K. K. Tan, and T. H. Lee. "Automated Fault Detection and Diagnosis in Mechanical Systems." IEEE Transactions on Systems, Man and Cybernetics, Part C (Applications and Reviews) 37, no. 6 (November 2007): 1360–64. http://dx.doi.org/10.1109/tsmcc.2007.900623.

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19

Pandey, Arvind K., and Lisa Mendes. "MECHANICAL MITRAL VALVE DYSFUNCTION: STUCK ON A DIAGNOSIS." Journal of the American College of Cardiology 67, no. 13 (April 2016): 1127. http://dx.doi.org/10.1016/s0735-1097(16)31128-7.

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20

Li, Yong, Jing Lin, Xiufeng Wang, and Yaguo Lei. "Biphase randomization wavelet bicoherence for mechanical fault diagnosis." Measurement 49 (March 2014): 407–20. http://dx.doi.org/10.1016/j.measurement.2013.12.012.

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21

Remmerbach, T., J. Liese, J. Dietrich, F. Wottawah, and J. Guck. "O.499 Oral cancer diagnosis by mechanical phenotyping." Journal of Cranio-Maxillofacial Surgery 36 (September 2008): S125. http://dx.doi.org/10.1016/s1010-5182(08)71623-8.

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22

Wong, Pak Kin, James Lam, Dingli Yu, Xiangyang Ji, and Chi Man Vong. "Intelligent monitoring, diagnosis and control in mechanical engineering." Advances in Mechanical Engineering 10, no. 11 (November 2018): 168781401881211. http://dx.doi.org/10.1177/1687814018812112.

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23

Qiu, Zhibin, Jiangjun Ruan, and Daochun Huang. "Mechanical fault diagnosis of outdoor high-voltage disconnector." IEEJ Transactions on Electrical and Electronic Engineering 11, no. 5 (July 11, 2016): 556–63. http://dx.doi.org/10.1002/tee.22273.

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24

Cao, Huiteng. "Big Data Technology Application in Mechanical Intelligent Fault Diagnosis." Journal of Physics: Conference Series 2066, no. 1 (November 1, 2021): 012064. http://dx.doi.org/10.1088/1742-6596/2066/1/012064.

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Abstract With the rapid implementation of made in China 2025 plan and the rapid development and application of information technology such as artificial intelligence, big data technology, industrial Internet of things and 5G, information technology has been integrated into every link of the whole life management cycle of mechanical products, such as tool condition detection and mechanical fault diagnosis in machining process. Based on this, the purpose of this study is to study the application of big data technology in mechanical intelligent fault diagnosis. In the process of this study, the decision number algorithm and data mining algorithm are used to study the experiment, and some mechanical faults in the past are analyzed and studied. Summary of the experimental results show that the use of decision number algorithm and data mining algorithm in the experiment has achieved good results, through these methods and big data technology, we can quickly diagnose the fault of mechanical equipment, accurately locate the fault location of mechanical equipment. Mechanical intelligent fault diagnosis based on big data technology can improve the efficiency of fault diagnosis, reduce enterprise costs and improve economic performance.

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25

Menon, A., and S. May. "Shoulder pain: Differential diagnosis with mechanical diagnosis and therapy extremity assessment – A case report." Manual Therapy 18, no. 4 (August 2013): 354–57. http://dx.doi.org/10.1016/j.math.2012.06.011.

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26

Shi, Lian, and Lei Liu. "Vibration Test Measures for Pump Fault Diagnosis." Journal of Electronic Research and Application 5, no. 6 (November 30, 2021): 6–10. http://dx.doi.org/10.26689/jera.v5i6.2683.

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The vibration measuring standard for compound machinery utilized in modern industrial production will be employed for the application of detecting technologies. The vibration intensity can be obtained by selecting the detecting method to obtain the speed of mechanical vibration, and technicians can examine whether the vibrating machinery is in a proper functioning state based on the value of vibration intensity, allowing for thorough fault diagnosis. In order to provide useful diagnosis ideas for technicians, this study examines the measurement of mechanical vibration and investigates the calculating method of mechanical vibration intensity.

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27

Xu, Yun Jie. "Research on Internet-Based Reconfigurable Distance Mechanical System Fault Diagnosis." Applied Mechanics and Materials 52-54 (March 2011): 109–14. http://dx.doi.org/10.4028/www.scientific.net/amm.52-54.109.

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In order to meet requirements of increasingly high-speed, large and intelligent mechanical equipments on fault diagnosis, the Internet-based reconfigurable mechanical system fault diagnosis program was presented. The overall structure and networking schema of distance mechanical fault diagnosis system were analyzed, and the distance fault diagnosis network model based on J2EE framework was also described. The structural model and reconfigurable manner of the reconfigurable distance diagnosis system was provided, which used CORBA component technology to achieve reconfiguration. The detail steps of system that take some type of diesel engine as diagnosis object was described, and the intelligent diagnosing methods were also researched. The Internet-based fault diagnosis technology effectively improves the efficiency and accuracy of diagnostic systems.

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28

Cai, Yuqi, and Liang Jin. "Reviews of Research on Mechanical Fault Diagnosis in GIS." E3S Web of Conferences 155 (2020): 01015. http://dx.doi.org/10.1051/e3sconf/202015501015.

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Gas Insulated Switchgear (GIS) has been widely used in recent years due to their advantages of small floor area, high reliability and good safety. It has arised a series of problems. At present, researchers at home and abroad have done more in-depth research on insulation fault, but research on mechanical fault is insufficient. Firstly, this paper reviews the research status of mechanical fault detection technology in GIS at home and abroad. Secondly, the aspect of the principles of mechanical fault in GIS, fault judgment and location technology, and early fault prediction and status evaluation in GIS were summarized. Finally, the paper prospected the future research direction on this basis.

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29

Pang, Tian Yang, Tian Xiang Yu, and Bi Feng Song. "Fault diagnosis for mechanical system using dynamic Bayesian network." IOP Conference Series: Materials Science and Engineering 1043, no. 3 (January 1, 2021): 032062. http://dx.doi.org/10.1088/1757-899x/1043/3/032062.

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30

Jinde, ZHENG, PAN Haiyang, CHENG Junsheng, BAO Jiahan, LIU Qingyun, and DING Keqin. "Adaptive Empirical Fourier Decomposition Based Mechanical Fault Diagnosis Method." Journal of Mechanical Engineering 56, no. 9 (2020): 125. http://dx.doi.org/10.3901/jme.2020.09.125.

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31

ANDOU, MASASHI. "Mechanical Diagnosis of Low Back Pain by McKenzie Approach." Rigakuryoho Kagaku 16, no. 4 (2001): 239–48. http://dx.doi.org/10.1589/rika.16.239.

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32

Uller, L., S. M. Bastos, O. F. Ferreira, Marize Varella de Oliveira, I. C. Abud, and R. O. Centeno. "Diagnosis of Mechanical and Corrosion Wear in Sugar Industry." Key Engineering Materials 20-28 (January 1991): 2509–17. http://dx.doi.org/10.4028/www.scientific.net/kem.20-28.2509.

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33

Hong, Geonkyo, and Dongjun Suh. "Supervised-Learning-Based Intelligent Fault Diagnosis for Mechanical Equipment." IEEE Access 9 (2021): 116147–62. http://dx.doi.org/10.1109/access.2021.3104189.

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34

Wei, Yadong, Tuzhi Long, Xiaoman Cai, Shaohui Zhang, Dejan Gjorgjevikj, and Chuan Li. "Mechanical fault diagnosis by using dynamic transfer adversarial learning." Measurement Science and Technology 32, no. 10 (June 17, 2021): 104005. http://dx.doi.org/10.1088/1361-6501/ac0184.

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35

S. Kapitonov, Sergei, Sergei Y. Grigorovich, Sergei A. Medvedev, and Ivan I. Kurbakov. "Diagnosis of internal combustion engine based on mechanical noise." International Journal of Engineering & Technology 7, no. 2.2 (March 5, 2018): 9. http://dx.doi.org/10.14419/ijet.v7i2.2.9889.

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The work of sensors is investigated in the article when they are used to detect malfunctions of automobile engines by the method of analyzing noise spectrum. The method of diagnostics of internal combustion engines for noise and vibration has been developed. This method allows to determine the fault at an early stage in a short period of time (the diagnostic process takes 5-10 minutes). The proposed solution allows to reduce the degree of influence of the human factor. Such an approach to diagnostics makes it possible to determine a huge number of faults due to the use of modern computer technologies. There is no need to dismantle the unit to determine a malfunction, which also contributes to a significant reduction in overall repair time. The model of the hardware part of the complex is based on the developed method for diagnosing engine malfunctions by noise and vibration. Technologies of National Instruments were used to create the layout. The program part is developed in the visual programming environment LabVIEW. The investigation of different types of noise sensors for use in the measuring complex was carried out using the developed hardware and software. Three types of noise sensors were investigated. The conclusion can be drawn based on the results of the study, that a piezoelectric pickup is better suited for diagnosing or analyzing noise. The analysis of the most frequently encountered car faults was carried out with the aim of forming real engine operating modes and testing the model.

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36

Jung, Hoon, and Ju-Won Kim. "A Machine Learning Approach for Mechanical Motor Fault Diagnosis." Journal of Society of Korea Industrial and Systems Engineering 40, no. 1 (March 31, 2017): 57–64. http://dx.doi.org/10.11627/jkise.2017.40.1.057.

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37

Rus, Guillermo, Juan M. Melchor, Inas Faris, Antonio Callejas, Miguel Riveiro, Francisca Molina, and Jorge Torres. "Mechanical biomarkers by torsional shear ultrasound for medical diagnosis." Journal of the Acoustical Society of America 144, no. 3 (September 2018): 1747. http://dx.doi.org/10.1121/1.5067744.

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38

Cai, HongJuan, and Hengqiang Gao. "Mechanical fault diagnosis based on digital image processing technology." International Journal of Information and Communication Technology 15, no. 1 (2019): 68. http://dx.doi.org/10.1504/ijict.2019.10023623.

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39

Gao, Hengqiang, and HongJuan Cai. "Mechanical fault diagnosis based on digital image processing technology." International Journal of Information and Communication Technology 15, no. 1 (2019): 68. http://dx.doi.org/10.1504/ijict.2019.102055.

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40

Johanson, W. G., John J. Seidenfeld, Paula Gomez, Ruben De Los Santos, and Jacqueline J. Coalson. "Bacteriologic Diagnosis of Nosocomial Pneumonia Following Prolonged Mechanical Ventilation." American Review of Respiratory Disease 137, no. 2 (February 1988): 259–64. http://dx.doi.org/10.1164/ajrccm/137.2.259.

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41

Lv, Chun Jie, and Yong Yu Yao. "Mechanical Diagnosis Based on Similarity Extraction of Time Series." Advanced Materials Research 753-755 (August 2013): 2159–63. http://dx.doi.org/10.4028/www.scientific.net/amr.753-755.2159.

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The intelligent diagnosis emphasizes the processing method of knowledge of the historical data. The capability of an intelligent diagnosis system depends on the knowledge possessed by the system, especially by the specific knowledge in application. Currently, most of the important equipment have their own the inspection systems. With the help of these systems, plenty of historical data can be collected in real time. This paper discusses the possibility of the application of similarity extraction and pattern discovery of time series in fault diagnosis by using these historical data, presents the method of time series feature extraction and pattern matching, and advances the possibility of data clustering and pattern discovery based on dimension reduction.

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42

Brown, Amy, and Lynn Snyder-Mackler. "Diagnosis of Mechanical Low Back Pain in a Laborer." Journal of Orthopaedic & Sports Physical Therapy 29, no. 9 (September 1999): 534–39. http://dx.doi.org/10.2519/jospt.1999.29.9.534.

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43

Huang, Sunan, Kok Kiong Tan, and Mingbo Xiao. "Automated Fault Diagnosis and Accommodation Control for Mechanical Systems." IEEE/ASME Transactions on Mechatronics 20, no. 1 (February 2015): 155–65. http://dx.doi.org/10.1109/tmech.2014.2322652.

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44

Weeks, Susan M. "The Cervical and Thoracic Spine – Mechanical diagnosis and therapy." Physiotherapy 77, no. 9 (September 1991): 654. http://dx.doi.org/10.1016/s0031-9406(10)60403-8.

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45

Sheng, Chenxing, Zhixiong Li, Li Qin, Zhiwei Guo, and Yuelei Zhang. "Recent Progress on Mechanical Condition Monitoring and Fault Diagnosis." Procedia Engineering 15 (2011): 142–46. http://dx.doi.org/10.1016/j.proeng.2011.08.029.

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46

Zhang, Zhongwei, Huaihai Chen, Shunming Li, and Zenghui An. "Sparse filtering based domain adaptation for mechanical fault diagnosis." Neurocomputing 393 (June 2020): 101–11. http://dx.doi.org/10.1016/j.neucom.2020.02.049.

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47

HONG, Wei, Wenjian CAI, Shaoping WANG, and Mileta M. TOMOVIC. "Mechanical wear debris feature, detection, and diagnosis: A review." Chinese Journal of Aeronautics 31, no. 5 (May 2018): 867–82. http://dx.doi.org/10.1016/j.cja.2017.11.016.

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48

Buckley, D. E., W. G. MacKinnon, R. E. Cranston, and H. A. Christian. "Problems with piston core sampling: Mechanical and geochemical diagnosis." Marine Geology 117, no. 1-4 (March 1994): 95–106. http://dx.doi.org/10.1016/0025-3227(94)90008-6.

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49

Wang, W. J. "Wavelet Transform in Vibration Analysis for Mechanical Fault Diagnosis." Shock and Vibration 3, no. 1 (1996): 17–26. http://dx.doi.org/10.1155/1996/375635.

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The wavelet transform is introduced to indicate short-time fault effects in associated vibration signals. The time-frequency and time-scale representations are unified in a general form of a three-dimensional wavelet transform, from which two-dimensional transforms with different advantages are treated as special cases derived by fixing either the scale or frequency variable. The Gaussian enveloped oscillating wavelet is recommended to extract different sizes of features from the signal. It is shown that the time-frequency and time-scale distributions generated by the wavelet transform are effective in identifying mechanical faults.

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50

Zagrai, Andrei N., and Hakan Çakan. "Magneto-mechanical impedance identification and diagnosis of metallic structures." International Journal of Engineering Science 48, no. 10 (October 2010): 888–908. http://dx.doi.org/10.1016/j.ijengsci.2010.05.010.

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