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The effect of dynamic operating conditions on nano-particle emissions from a light-duty diesel engine applicable to prime and auxiliary machines on marine vessels

ABSTRACT

This study presents the nano-sized particle emission characteristics from a small turbocharged common rail diesel engine applicable to prime and auxiliary machines on marine vessels. The experiments were conducted under dynamic engine operating conditions, such as steady-state, cold start, and transient conditions. The particle number and size distributions were analyzed with a high resolution PM analyzer. The diesel oxidation catalyst (DOC) had an insignificant effect on the reduction in particle number, but particle number emissions were drastically reduced by 3 to 4 orders of magnitude downstream of the diesel particulate filter (DPF) at various steady conditions. Under high speed and load conditions, the particle filtering efficiency was decreased by the partial combustion of trapped particles inside the DPF because of the high exhaust temperature caused by the increased particle number concentration. Retarded fuel injection timing and higher EGR rates led to increased particle number emissions. As the temperature inside the DPF increased from 25 °C to 300 °C, the peak particle number level was reduced by 70% compared to cold start conditions. High levels of nucleation mode particle generation were found in the deceleration phases during the transient tests.

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Mode set focused hybrid estimation

Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans 4 (5): 917-931. de Freitas, N. (2002). Rao-Blackwellised particle filtering for fault diagnosis, Proceedings of the IEEE Aerospace Conference 2002, Big Sky, MT, USA , Vol. 4, pp. 1767-1772. Dearden, R. and Clancy, D. (2002). Particle filters for real-time fault detection in planetary rovers, 13th International Workshop on Principles of Diagnosis, DX02, Semmering, Austria , pp. 1-6. Georges, J.-P., Theilliol, D., Cocquempot, V., Ponsart, J.-C. and Aubrun, C. (2011

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Synthesizing of models for identification of teletraffic Markov chains by artificial neural networks and decision tree method

Mining , Sydney, NSW, pp.334-343, 2010. [6] D. Lowd and J. Davis, “Improving Markov Network Structure Learning Using Decision Trees”, Journal of Machine Learning Research (JMLR) , no.15, pp.501-532, 2014. [7] B. Lakshminarayanan, D. Roy and Y. The, “Top-Down Particle Filtering for Bayesian Decision Trees”, Proceedings of the 30th International Conference on Machine Learning , Atlanta, Georgia, USA, JMLR: W&CP, vol.28, pp.1-9, 2013. [8] D. Bacciu, “Hidden Tree Markov Networks: Deep and Wide Learning for Structured Data”, Proceedings of the IEEE

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Construction Method of the Topographical Features Model for Underwater Terrain Navigation

of Defense Technology, 2004. 8. Kjetil Bergh Anonsen, Ove Kent Hagen. Recent developments in the HUGIN AUV terrain navigation system, OCEANS, Vol. 9, pp. 1-7, 2011. 9. Nordlund, P.J. and Gustafsson, F., Marginalized particle filter for accurate and reliable terrain-aided navigation, Aerospace and Electronic Systems, Vol. 45, no. 4, pp. 1385-1399, 2009. 10. Marvin, W., Roe, M.E., and Trenchard, M.C.L., Integrating vector overlay information into naval digital map systems. IEEE/AIAA 30th Digital Avionics Systems

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Pedestrian Detection and Analysis with Scale-Space and Distance Transform

-1963, September 2004 [6] D.M. Gavrila, V. Philomin, Real-time Object Detection for Smart Vehicles, Proc. of IEEE International Conference on Computer Vision, Kerkyra, Greece, pp. 87-93, 1999 [7] B.K.P. Horn, B.G. Schunck, Determining optical flow, Artificial Intelligence, Vol 17, pp 185-203, 1981 [8] I. Laptev, T Lindeberg, Tracking of Multi-state Hand Models Using Particle Filtering and a Hierarchy of Multi-scale Image Features, In Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision

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Mems Technology Quality Requirements as Applied to Multibeam Echosounder

. 15. Demkowicz J.: Particle Filter Modification using Kalman Optimal Filtering Method as Applied to Road Detection from Satellite Images. MIKON 2014, 20th International Conference on Microwaves, Radar and Wireless Communications. 16. Analog Devices Technical Articles. (source: www.analog.com). 17. Seatex MRU Calibration Certificate, Kongsberg Gruppen 18. Seube N., Levilly S., de Jong K.: Automatic Estimation of Boresight Angles Between IMU and Multi-Beam Echo Sounder Systems, Quantitative Monitoring of the Underwater

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Machine Vision System Measuring the Trajectory of Upper Limb Motion Applying the Matlab Software

University. [10] Kuryło, P., Cyganiuk, J., Tertel, E., Frankovský, P. (2016). Machine vision investigate the trajectory of the motion human body – review of the methods. Acta Mechatronica , 1 (2), 7–13. [11] Deutscher, J., Blake, A., Reid, I. (2000). Articulated body motion capture by annealed particle filtering. In Proceedings of IEEE Conference on Computer Vision and Pattern Recognition . IEEE, 126–133. [12] Schmidt, J., Fritsch, J., Kwolek, B. (2006). Kernel particle filter for real-time 3D body tracking in monocular color images. In Proceedings of

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Modified Polymer Materials for Use in Selected Personal Protective Equipment Products

-473. [9] Dean, N. (2011) Shoe insoles with flexible inserts. Patent US20110162234 A1. USA [10] Deeds, W.E. (1992). Charging apparatus for meltblown webs. Patent US5122048 (A). USA. [11] Dutkiewicz, J. (2002). Superabsorbent materials from shellfish waste - A review. Journal of Biomedical Material Research, 63(3), 245–381. [12] EN 13274-3:2001. Respiratory protective devices - Methods of test - Determination of breathing resistance [13] EN 13274-7:2008. Respiratory protective devices - methods of test - part 7: determination of particle filter

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Landmark Sequence Data Association for Simultaneous Localization and Mapping of Robots

References 1. Uyen, H. S. V., J. W. Jeon. Combine Kalman Filter and Particle Filter to Improve Color Tracking Algorithm. - In: Proc. of International Conference on Control, Automation and Systems 2007, 558-561. 2. Anati, R., D. Scar amu z z a, K. G. Derpanis, K. Daniilidis. Robot Localization Using Soft Object Detection. - In: Proc. of IEEE International Conference on Robotics and Automation (ICRA’2012), 2012, 4992-4999. 3. Ivanjko, E., M. Uasak, I. Petrovic. Kalman Filter Theory Based Mobile Robot Pose Tracking

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Macroscopic Traffic Flow Control Via State Estimation

Conference on Control and Automation (MED.07), 2007. http://hal.inria.fr/inria/00142588/fr/ 17. Mihaylova, L., R. B o e l. A Particle Filter for Freeway Traffic Estimation. - In: Proc. of 43rd IEEE Conf. on Decision and Control, 14-17 December 2004, Atlantis, Paradise Island, Bahamas, pp. 2106-2111. 18. Nahi, N.E., A. N. Trivedi. Recursive Estimation of Traffic Variables: Section Density and Average Speed. Tech. Report, 1976. University of Southern California, Los Angeles. 19. Rolink, M., T. Boukhobza, D. Sauter. High Order

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