Computational fluid dynamics assessment of subcooled flow boiling in internal-combustion engine-like conditions at low flow velocities with a volume-of-fluid model and a two-fluid model

The use of subcooled flow boiling is a convenient option for the thermal management of downsized engines, but proper control of the phenomenon requires the accurate prediction of heat transfer at the coolant side, for which the use of computational fluid dynamics is a suitable alternative. While in most of the applications found to engine cooling a single-fluid equivalent method is used, in this paper the performance of a twofluid method is evaluated in engine-like conditions with special interest in the low velocity range. The results indicate that the description of the process at low velocities provided by the two-fluid method is better than that of a single-fluid model, while model calibration is simpler and more robust and the computational cost is substantially reduced.The equipment used in this work was partially supported by FEDER project funds 'Dotacion de infraestructuras cientifico tecnicas para el Centro Integral de Mejora Energetica y Medioambiental de Sistemas de Transporte' (grant number FEDER-ICTS-2012-06), framed in the operational program of the unique scientific and technical infrastructure of the Ministry of Science and Innovation of Spain. This work was partially supported by Senacyt Panama (Omar Cornejo, grant 797-7-2)Torregrosa, AJ.; Olmeda González, PC.; Gil Megías, A.; Cornejo, O. (2015). Computational fluid dynamics assessment of subcooled flow boiling in internal-combustion engine-like conditions at low flow velocities with a volume-of-fluid model and a two-fluid model. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. 229(13):1830-1839. https://doi.org/10.1177/0954407015571674S1830183922913Pang, H. H., & Brace, C. J. (2004). Review of engine cooling technologies for modern engines. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 218(11), 1209-1215. doi:10.1243/0954407042580110Burke, R. D., Brace, C. J., Hawley, J. G., & Pegg, I. (2010). Review of the systems analysis of interactions between the thermal, lubricant, and combustion processes of diesel engines. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 224(5), 681-704. doi:10.1243/09544070jauto1301Steiner, H., Brenn, G., Ramstorfer, F., & Breitschadel, B. (2011). Increased Cooling Power with Nucleate Boiling Flow in Automotive Engine Applications. New Trends and Developments in Automotive System Engineering. doi:10.5772/13489Li, Z., Huang, R.-H., & Wang, Z.-W. (2011). Subcooled boiling heat transfer modelling for internal combustion engine applications. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 226(3), 301-311. doi:10.1177/0954407011417349Hawley, J. G., Wilson, M., Campbell, N. A. F., Hammond, G. P., & Leathard, M. J. (2004). Predicting boiling heat transfer using computational fluid dynamics. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 218(5), 509-520. doi:10.1243/095440704774061165Li, G., Fu, S., Liu, Y., Liu, Y., Bai, S., & Cheng, L. (2009). A homogeneous flow model for boiling heat transfer calculation based on single phase flow. Energy Conversion and Management, 50(7), 1862-1868. doi:10.1016/j.enconman.2008.12.029Chen, J. C. (1966). Correlation for Boiling Heat Transfer to Saturated Fluids in Convective Flow. Industrial & Engineering Chemistry Process Design and Development, 5(3), 322-329. doi:10.1021/i260019a023Torregrosa, A. J., Broatch, A., Olmeda, P., & Cornejo, O. (2014). Experiments on subcooled flow boiling in I.C. engine-like conditions at low flow velocities. Experimental Thermal and Fluid Science, 52, 347-354. doi:10.1016/j.expthermflusci.2013.10.004Robinson, K., Hawley, J. G., & Campbell, N. A. F. (2003). Experimental and modelling aspects of flow boiling heat transfer for application to internal combustion engines. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 217(10), 877-889. doi:10.1243/095440703769683289Lee, H. S., & O’Neill, A. T. (2009). Forced convection and nucleate boiling on a small flat heater in a rectangular duct: Experiments with two working fluids, a 50–50 ethylene glycol—water mixture, and water. Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, 223(2), 203-219. doi:10.1243/09544070jauto1008Biswas, R., & Strawn, R. C. (1998). Tetrahedral and hexahedral mesh adaptation for CFD problems. Applied Numerical Mathematics, 26(1-2), 135-151. doi:10.1016/s0168-9274(97)00092-5Hernandez-Perez, V., Abdulkadir, M., & Azzopardi, B. J. (2011). Grid Generation Issues in the CFD Modelling of Two-Phase Flow in a Pipe. The Journal of Computational Multiphase Flows, 3(1), 13-26. doi:10.1260/1757-482x.3.1.13Pioro, I. L., Rohsenow, W., & Doerffer, S. S. (2004). Nucleate pool-boiling heat transfer. II: assessment of prediction methods. International Journal of Heat and Mass Transfer, 47(23), 5045-5057. doi:10.1016/j.ijheatmasstransfer.2004.06.020Saiz Jabardo, J. M. (2010). An Overview of Surface Roughness Effects on Nucleate Boiling Heat Transfer~!2009-10-31~!2010-01-01~!2010-04-16~! The Open Transport Phenomena Journal, 2(1), 24-34. doi:10.2174/1877729501002010024Podowski, M. Z. (2012). TOWARD MECHANISTIC MODELING OF BOILING HEAT TRANSFER. Nuclear Engineering and Technology, 44(8), 889-896. doi:10.5516/net.02.2012.720Lo, S., & Osman, J. (2012). CFD Modeling of Boiling Flow in PSBT 5×5 Bundle. Science and Technology of Nuclear Installations, 2012, 1-8. doi:10.1155/2012/795935Del Valle, V. H., & Kenning, D. B. R. (1985). Subcooled flow boiling at high heat flux. International Journal of Heat and Mass Transfer, 28(10), 1907-1920. doi:10.1016/0017-9310(85)90213-3Cole, R. (1960). A photographic study of pool boiling in the region of the critical heat flux. AIChE Journal, 6(4), 533-538. doi:10.1002/aic.69006040

Multiscale computational homogenization: review and proposal of a new enhanced-first-order method

This is a copy of the author 's final draft version of an article published in the Archives of computational methods in engineering. The final publication is available at Springer via http://dx.doi.org/10.1007/s11831-016-9205-0The continuous increase of computational capacity has encouraged the extensive use of multiscale techniques to simulate the material behaviour on several fields of knowledge. In solid mechanics, the multiscale approaches which consider the macro-scale deformation gradient to obtain the homogenized material behaviour from the micro-scale are called first-order computational homogenization. Following this idea, the second-order FE2 methods incorporate high-order gradients to improve the simulation accuracy. However, to capture the full advantages of these high-order framework the classical boundary value problem (BVP) at the macro-scale must be upgraded to high-order level, which complicates their numerical solution. With the purpose of obtaining the best of both methods i.e. first-order and second-order, in this work an enhanced-first-order computational homogenization is presented. The proposed approach preserves a classical BVP at the macro-scale level but taking into account the high-order gradient of the macro-scale in the micro-scale solution. The developed numerical examples show how the proposed method obtains the expected stress distribution at the micro-scale for states of structural bending loads. Nevertheless, the macro-scale results achieved are the same than the ones obtained with a first-order framework because both approaches share the same macro-scale BVP.Peer ReviewedPostprint (author's final draft

Direct simulation of liquid-gas-solid flow with a free surface lattice Boltzmann method

Direct numerical simulation of liquid-gas-solid flows is uncommon due to the considerable computational cost. As the grid spacing is determined by the smallest involved length scale, large grid sizes become necessary -- in particular if the bubble-particle aspect ratio is on the order of 10 or larger. Hence, it arises the question of both feasibility and reasonability. In this paper, we present a fully parallel, scalable method for direct numerical simulation of bubble-particle interaction at a size ratio of 1-2 orders of magnitude that makes simulations feasible on currently available super-computing resources. With the presented approach, simulations of bubbles in suspension columns consisting of more than $100\,000$ fully resolved particles become possible. Furthermore, we demonstrate the significance of particle-resolved simulations by comparison to previous unresolved solutions. The results indicate that fully-resolved direct numerical simulation is indeed necessary to predict the flow structure of bubble-particle interaction problems correctly.Comment: submitted to International Journal of Computational Fluid Dynamic

Homogenization of plain weave composites with imperfect microstructure: Part II--Analysis of real-world materials

A two-layer statistically equivalent periodic unit cell is offered to predict a macroscopic response of plain weave multilayer carbon-carbon textile composites. Falling-short in describing the most typical geometrical imperfections of these material systems the original formulation presented in (Zeman and \v{S}ejnoha, International Journal of Solids and Structures, 41 (2004), pp. 6549--6571) is substantially modified, now allowing for nesting and mutual shift of individual layers of textile fabric in all three directions. Yet, the most valuable asset of the present formulation is seen in the possibility of reflecting the influence of negligible meso-scale porosity through a system of oblate spheroidal voids introduced in between the two layers of the unit cell. Numerical predictions of both the effective thermal conductivities and elastic stiffnesses and their comparison with available laboratory data and the results derived using the Mori-Tanaka averaging scheme support credibility of the present approach, about as much as the reliability of local mechanical properties found from nanoindentation tests performed directly on the analyzed composite samples.Comment: 28 pages, 14 figure

Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location

[EN] A large volume of the water produced for public supply is lost in the systems between sources and consumers. An important-in many cases the greatest-fraction of these losses are physical losses, mainly related to leaks and bursts in pipes and in consumer connections. Fast detection and location of bursts plays an important role in the design of operation strategies for water loss control, since this helps reduce the volume lost from the instant the event occurs until its effective repair (run time). The transient pressure signals caused by bursts contain important information about their location and magnitude, and stamp on any of these events a specific "hydraulic signature". The present work proposes and evaluates three methods to disaggregate transient signals, which are used afterwards to train artificial neural networks (ANNs) to identify burst locations and calculate the leaked flow. In addition, a clustering process is also used to group similar signals, and then train specific ANNs for each group, thus improving both the computational efficiency and the location accuracy. The proposed methods are applied to two real distribution networks, and the results show good accuracy in burst location and characterization.Manzi, D.; Brentan, BM.; Meirelles, G.; Izquierdo Sebastián, J.; Luvizotto Jr., E. (2019). Pattern Recognition and Clustering of Transient Pressure Signals for Burst Location. Water. 11(11):1-13. https://doi.org/10.3390/w11112279S1131111Creaco, E., & Walski, T. (2017). Economic Analysis of Pressure Control for Leakage and Pipe Burst Reduction. Journal of Water Resources Planning and Management, 143(12), 04017074. doi:10.1061/(asce)wr.1943-5452.0000846Campisano, A., Creaco, E., & Modica, C. (2010). RTC of Valves for Leakage Reduction in Water Supply Networks. Journal of Water Resources Planning and Management, 136(1), 138-141. doi:10.1061/(asce)0733-9496(2010)136:1(138)Campisano, A., Modica, C., Reitano, S., Ugarelli, R., & Bagherian, S. (2016). Field-Oriented Methodology for Real-Time Pressure Control to Reduce Leakage in Water Distribution Networks. Journal of Water Resources Planning and Management, 142(12), 04016057. doi:10.1061/(asce)wr.1943-5452.0000697Vítkovský, J. P., Simpson, A. R., & Lambert, M. F. (2000). Leak Detection and Calibration Using Transients and Genetic Algorithms. Journal of Water Resources Planning and Management, 126(4), 262-265. doi:10.1061/(asce)0733-9496(2000)126:4(262)Pérez, R., Puig, V., Pascual, J., Quevedo, J., Landeros, E., & Peralta, A. (2011). Methodology for leakage isolation using pressure sensitivity analysis in water distribution networks. Control Engineering Practice, 19(10), 1157-1167. doi:10.1016/j.conengprac.2011.06.004Jung, D., & Kim, J. (2017). Robust Meter Network for Water Distribution Pipe Burst Detection. Water, 9(11), 820. doi:10.3390/w9110820Colombo, A. F., Lee, P., & Karney, B. W. (2009). A selective literature review of transient-based leak detection methods. Journal of Hydro-environment Research, 2(4), 212-227. doi:10.1016/j.jher.2009.02.003Choi, D., Kim, S.-W., Choi, M.-A., & Geem, Z. (2016). Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System. Water, 8(4), 142. doi:10.3390/w8040142Christodoulou, S. E., Kourti, E., & Agathokleous, A. (2016). Waterloss Detection in Water Distribution Networks using Wavelet Change-Point Detection. Water Resources Management, 31(3), 979-994. doi:10.1007/s11269-016-1558-5Guo, X., Yang, K., & Guo, Y. (2012). Leak detection in pipelines by exclusively frequency domain method. Science China Technological Sciences, 55(3), 743-752. doi:10.1007/s11431-011-4707-3Holloway, M. B., & Hanif Chaudhry, M. (1985). Stability and accuracy of waterhammer analysis. Advances in Water Resources, 8(3), 121-128. doi:10.1016/0309-1708(85)90052-1Sanz, G., Pérez, R., Kapelan, Z., & Savic, D. (2016). Leak Detection and Localization through Demand Components Calibration. Journal of Water Resources Planning and Management, 142(2), 04015057. doi:10.1061/(asce)wr.1943-5452.0000592Zhang, Q., Wu, Z. Y., Zhao, M., Qi, J., Huang, Y., & Zhao, H. (2016). Leakage Zone Identification in Large-Scale Water Distribution Systems Using Multiclass Support Vector Machines. Journal of Water Resources Planning and Management, 142(11), 04016042. doi:10.1061/(asce)wr.1943-5452.0000661Mounce, S. R., & Machell, J. (2006). Burst detection using hydraulic data from water distribution systems with artificial neural networks. Urban Water Journal, 3(1), 21-31. doi:10.1080/15730620600578538Covas, D., Ramos, H., & de Almeida, A. B. (2005). Standing Wave Difference Method for Leak Detection in Pipeline Systems. Journal of Hydraulic Engineering, 131(12), 1106-1116. doi:10.1061/(asce)0733-9429(2005)131:12(1106)Liggett, J. A., & Chen, L. (1994). Inverse Transient Analysis in Pipe Networks. Journal of Hydraulic Engineering, 120(8), 934-955. doi:10.1061/(asce)0733-9429(1994)120:8(934)Caputo, A. C., & Pelagagge, P. M. (2002). An inverse approach for piping networks monitoring. Journal of Loss Prevention in the Process Industries, 15(6), 497-505. doi:10.1016/s0950-4230(02)00036-0Van Zyl, J. E. (2014). Theoretical Modeling of Pressure and Leakage in Water Distribution Systems. Procedia Engineering, 89, 273-277. doi:10.1016/j.proeng.2014.11.187Izquierdo, J., & Iglesias, P. . (2004). Mathematical modelling of hydraulic transients in complex systems. Mathematical and Computer Modelling, 39(4-5), 529-540. doi:10.1016/s0895-7177(04)90524-9Lin, J., Keogh, E., Wei, L., & Lonardi, S. (2007). Experiencing SAX: a novel symbolic representation of time series. Data Mining and Knowledge Discovery, 15(2), 107-144. doi:10.1007/s10618-007-0064-zNavarrete-López, C., Herrera, M., Brentan, B., Luvizotto, E., & Izquierdo, J. (2019). Enhanced Water Demand Analysis via Symbolic Approximation within an Epidemiology-Based Forecasting Framework. Water, 11(2), 246. doi:10.3390/w11020246Meirelles, G., Manzi, D., Brentan, B., Goulart, T., & Luvizotto, E. (2017). Calibration Model for Water Distribution Network Using Pressures Estimated by Artificial Neural Networks. Water Resources Management, 31(13), 4339-4351. doi:10.1007/s11269-017-1750-2Adamowski, J., & Chan, H. F. (2011). A wavelet neural network conjunction model for groundwater level forecasting. Journal of Hydrology, 407(1-4), 28-40. doi:10.1016/j.jhydrol.2011.06.013Brentan, B., Meirelles, G., Luvizotto, E., & Izquierdo, J. (2018). Hybrid SOM+ k -Means clustering to improve planning, operation and management in water distribution systems. Environmental Modelling & Software, 106, 77-88. doi:10.1016/j.envsoft.2018.02.013Calinski, T., & Harabasz, J. (1974). A dendrite method for cluster analysis. Communications in Statistics - Theory and Methods, 3(1), 1-27. doi:10.1080/0361092740882710

Identifying and classifying attributes of packaging for customer satisfaction-A Kano Model Approach

[EN] The packaging industry in India is predicted to grow at 18% annually. In recent years Packaging becomes a potential marketing tool. The marketer should design the packaging of high quality from customer perspective.  As the research in the area of packaging is very few, study of quality attributes of Packaging is the need of the hour and inevitable. An empirical research was conducted by applying Kano Model. The researcher is interested to find out the perception of the customers on 22 quality attributes of packaging. 500 respondents which were selected randomly were asked about their experience of packing on everyday commodities through a well-structured questionnaire.  The classification of attribute as must-be quality, one-dimensional quality, attractive quality, indifferent quality and reverse quality was done by three methods. Marketer should make a note of it and prioritise the attributes for customer satisfaction.Dash, SK. (2021). Identifying and classifying attributes of packaging for customer satisfaction-A Kano Model Approach. International Journal of Production Management and Engineering. 9(1):57-64. https://doi.org/10.4995/ijpme.2021.13683OJS576491Bakhitar, A.,Hannan, A., Basit, A., Ahmad, J.(2015). Prioritization of value based services of software by using AHP and fuzzy KANO model. International Conference on Computational and Social Sciences, 8, 25- 27.Basfirinci, C., Mitra, A. (2015). A cross cultural investigation of airlines service quality through integration of Servqual and the Kano model. Journal of Air Transport Management, 42(1), 239-48. https://doi.org/10.1016/j.jairtraman.2014.11.005Berger, C., Blauth, R., Boger, D., Bolster, C., Burchill, G., DuMouchel, W., Pouliot, F., Richter, R., Rubinoff, A., Shen, D., Timko, M., Walden, D. (1993). Kano's methods for understanding customer-defined quality. The Center for Quality of Management Journal, 2(4), 2-36.Brown, G.H. (1950). Measuring consumer attitudes towards products. Journal of Marketing, 14(5), 691-98. https://doi.org/10.1177/002224295001400505Chaudha, A., Jain, R., Singh, A.R., Mishra, P.K. (2011). Integration of Kano's Model into Quality Function Deployment (QFD). Journal Advice Manufacture Technology, 53, 689-698. https://doi.org/10.1007/s00170-010-2867-0Cole, R.E. (2001). From continuous improvement to continuous innovation. Quality Management Journal, 8(4), 7-21. https://doi.org/10.1080/10686967.2001.11918977Dash, S.K. (2019). Application of Kano Model in Identifying Attributes. A Case Study on School Bus Services. International Journal of Management Studies, 6(1), 31-37. https://doi.org/10.18843/ijms/v6i1(3)/03Dziuba, S.T., Śron, B. (2014). FAM-FMC system as an alternative element of the software used in a grain and flour milling enterprise. Production Engineering Archives, 4(3),29-31. https://doi.org/10.30657/pea.2014.04.08Ernzer, M., Kopp, K.(2003). Application of KANO method to life cycle design. IEEE Proceedings of Eco Design: Third International Symposium on Environmentally Conscious De-sign and Inverse Manufacturing, Tokyo Japan, December 8-11, 383-389. https://doi.org/10.1109/ECODIM.2003.1322697Feigenbaum, A.V. (1991).Total Quality Control. McGraw-Hill. Fundin, A., Nilsson, L. (2003). Using Kano's theory of attractive quality to better understand customer satisfaction with e-services. Asian Journal on Quality, 4(2), 32-49. https://doi.org/10.1108/15982688200300018Friman, M., Edvardsson, B. (2003). A content analysis of complaints and compliments. Managing Service Quality, 13(1), 20-26. https://doi.org/10.1108/09604520310456681Garvin, D.A. (1987). Competing on the eight dimensions of quality. Harvard Business Review, 65(6), 101-109.Hanan, M., Karp, P. (1989). Customer satisfaction, how to maximise, measure and market your company's "ultimate product". AMACOM.Herzberg, F., Bernard, M., Snyderman, B.B. (1959). The Motivation to Work. John Wiley and Sons.Hoch, S.J., Ha, Y.W. (1986). Consumer learning: advertising and the ambiguity of product experience. Journal of Consumer Research, 13, 221-33.https://doi.org/10.1086/209062Johnson, M.D., Nilsson, L. (2003). The Importance of Reliability and Customization from Goods to Services. Quality Management Journal, 10(1), 8-19. https://doi.org/10.1080/10686967.2003.11919049Kano, N., Seraku, N., Takahashi, F., Tsuji, S. (1984). Attractive Quality and Must-Be Quality. Journal of the Japanese Society for Quality Control, 41, 39-48.Kapalle, P.K, Lehmann, D.R. (1995). The effects of advertised and observed quality on expectations about new product quality. Journal of Marketing Research, 32(8), 280-90. https://doi.org/10.1177/002224379503200304Lee, M.C., Newcomb, J.F. (1997). Applying the Kano methodology to meet customer requirements: NASA's microgravity science program. Quality Management Journal, 4(3), 95-110. https://doi.org/10.1080/10686967.1997.11918805Löfgren, M. (2005). Winning at the first and second moments of truth: An exploratory study. Journal of Service Theory and Practice, 15(1), 102-15. https://doi.org/10.1108/09604520510575290Löfgren, M., Witell, L. (2005). Kano's Theory of Attractive Quality and Packaging. Quality Management Journal, 12(3), 7-20. https://doi.org/10.1080/10686967.2005.11919257Matzler, K., Hinterhuber, H.H., Bailom, F., Sauerwein, E. (1996). How to delight your customers. Journal of Product & Brand Management, 5(2), 6-18. https://doi.org/10.1108/10610429610119469Miarka, D., Żukowska, J., Siwek, A., Nowacka,A., Nowak, D. (2015). Microbial hazards reduction during creamy cream cheese production. Production Engineering Archives, 6(1), 39-44. https://doi.org/10.30657/pea.2015.06.10Nelson, P. (1970), Information and consumer behaviour. Journal of Political Economy, 78, 311-29. https://doi.org/10.1086/259630Nilsson-Witell, L, Fundin, A. (2005). Dynamics of service attributes: a test of Kano's theory of attractive quality. International Journal of Service Industry Management, 16(2), 152-168. https://doi.org/10.1108/09564230510592289Parasuraman, A. (1997). Reflections on gaining competitive advantage through customer value. Academy of Marketing Science Journal, 25(2), 154-61. https://doi.org/10.1007/BF02894351Parasuraman, A., Colby, C.L. (2001). Techno-Ready Marketing. Free Press.Qiting, P., Uno, N., Kubota, Y. (2013). Kano Model Analysis of Customer Needs and Satisfaction at the Shanghai Disneyland. In Proceedings of the 5th Intl Congress of the Intl Association of Societies of Design Research, Tokyo, Japan. http://design-cu.jp/iasdr2013/papers/1835-1b.pdf Accessed on January 2021.Sauerwein, E., Bailom, F., Matzler, K., Hinterhuber, H.H. (1996). The Kano Model: How to delight your Customers. Volume I of the IX. International Working Seminar on Production Economics, Innsbruck/Igls/Austria, February 19-23 1996, pp. 313-327. https://is.muni. cz/el/econ/podzim2009/MPH_MAR2/um/9899067/THE_KANO_MODEL_-_HOW_TO_DELIGHT_YOUR_CUSTOMERS.pdfShewhart, W.A. (1931). Economic Control of Quality of Manufactured Product. D. Van Nostrand Company, Inc.Underwood, R.L., Klein, N.M. (2002). Packaging as Brand Communication: Effects of Product Pictures on Consumer Responses to the Package and Brand. Journal of Marketing Theory and Practice, 10(4), 58-68. https://doi.org/10.1080/10696679.2002.11501926Underwood, R.L. Klein, N.M., Burke, R.R. (2001). Packaging communication: attentional effects of product imagery. Journal of Product & Brand Management, 10(7), 403-22. https://doi.org/10.1108/10610420110410531Watson, G.H. (2003), "Customer focus and competitiveness", in Stephens, K.S. (Ed.), Six Sigma and Related Studies in the Quality Disciplines, ASQ Quality Press, Milwaukee, WI.Williams, D. (2020). 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