Journal Vol – 14 No -5, October 2019

The Space – Time is Flat at an Absolute Free Space. It is the Mass that Makes Space – Time Curved in. The Physical Time is Discrete or Continuous is An Observer Dependent Realism only

Authors:

Prasenjit Debnath

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00001

Abstract:

According to Einstein, the astronomical bodies try to move in a straight line – it is the curved space – time that makes their paths curved in. This paper proposes that the space – time is originally a flat space – time (at an absolute free space), it is the presence of mass that makes space – time curved in. Whether the physical time is discrete or continuous, is an observer dependent realism only. An observer like human being uses neither too small units of time nor too big units of time. An observer like human being uses average or moderate units of time which makes time continuous and flat. The physical time is discrete and flat for too small units of time. The physical time is continuous and curved in for too big units of time. The space – time can be curved in into a point for infinite mass concentrated into a point. Theoretically, it should be the center of our universe.

Keywords:

Absolute free space,Discrete,Continuous,The physical time,Infinite mass,

Refference:

I. Roger Penrose, “Cycles of Time”, Vintage Books, London, pp. 50-56
II. Stephen Hawking, “The Beginning of Time”, A Lecture.
III. Stephen Hawking, “A Briefer History of Time”, Bantam Books, London, pp.
1-49.
IV. Stephen Hawking, “Black holes and Baby Universes and other essays”,
Bantam Press, London 2013, ISBN 978-0-553-40663-4
V. Stephen Hawking, “The Grand Design”, Bantam Books, London 2011
VI. Stephen Hawking, “A Brief History of Time”, Bantam Books, London 2011,
pp. 156-157. ISBN-978-0-553-10953-5
VII. Stephen Hawking, “The Universe in a Nutshell”, Bantam Press, London
2013, pp. 58-61, 63, 82-85, 90-94, 99, 196. ISBN 0-553-80202-X
VIII. Stephen Hawking, “A stubbornly persistent illusion-The essential scientific
works of Albert Einstein”, Running Press Book Publishers, Philadelphia,
London 2011.
IX. Stephen Hawking, “Stephen Hawking’s Universe: Strange Stuff Explained”,
PBS site on imaginary time.

View Download

Characterization of Individual Mobility and Society Using CDR Data

Authors:

Mohammed Zohdy Abdulhady, Loay E. George

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00002

Abstract:

Through the previous years, a large number of cell phones information has become in the hand for the analysis patterns of people movements. This information’s carry a massive assurance for realizing behavior of human on a very large scale, as well as with an accuracy and precision never happened before can be allowed with surveys, censuses or other available data selection techniques. There are a number of researches that has open key advance into analyzing mobility of human utilizing this available recent data source, as well as there have been multiple various calculations of mobility applied. Mobility of human, or motion over large or short distances for narrow or vast durations of time, is an essential until continuous study for occurrence in the sciences of demographic and social systems. Meanwhile there have been harmonious progresses in compassionate migration (consider continuous pattern of mobility) as well as its effect on people happiness, social organizations, economic, and political organization, progresses in researches of mobility have been embarrass by complexity in measuring and recording how people move on a second and in detailed range. In this paper, the ability of using mobile network records will been described for analyzing the mobility of people and society for various objectives such as monitoring the mobility in cities and builds the suitable infrastructure for them. The mobility of individuals will be very benefit for observation the behavior of peoples and their effect in security issues. In order to test the system performance, a set of tests was applied on Zain calls dataset. The results indicates for the society mobility has been exported for the Baghdad Karkh area peoples. The results have been exported for two phases, one phases when the number of people’s routes where only 10 movement and the second phase when the people routes where 3 routes.

Keywords:

Call phones,Mobile network records,Mobility,Human's behavior,Zain,

Refference:

I. Chen Zhou, Xu, Z., & Huang, B., ”Activity Recognition from Call Detail Record:
Relation Between Mobile Behavior Pattern And Social Attribute Using
Hierarchical Conditional Random Fields”,International Conference on Green
Computing and Communications & IEEE/ACM International Conference on
Cyber, Physical and Social Computing, 2010.

II. Ghotekar, N., “Analysis and Data Mining of Call Detail Records using Big Data
Technology”, International Journal of Advanced Research in Computer and
Communication Engineering, Vol. 5, December 2016.
III. M.Donato, K..,”Current trends and patterns of female migration: Evidence from
Mexico”. International Migration Review, 27(4), 748-771, 1993.
IV. Massey DS.,”Social structure, household strategies, and the cumulative causation
of migrateon”. Population Index. 56:3–26. 10.2307/3644186, 1990.
V. Massey, D. S., & Espinosa, K. E., ”What’s driving Mexico-US migration? A
theoretical, empirical, and policy analysis”, American journal of sociology,
102(4), 939-999, 1997.
VI. Massey, D. S., Williams, N., Axinn, W. G., & Ghimire, D. J., ”Community
services and out-migration”. International Migration. 48(3), 1-41, 2010.
VII. Martin B.,”Mean and Standard Deviation”, report of applied statics, 2006
VIII. R., .Harris, J. & Todaro, M. P., ”Migration, unemployment and development: a
two-sector analysis” , The American economic review, 126-142, 1970.
IX. Ratul Sikder, Uddin, M. J., & Halder, S., ”An Efficient Approach of Identifying
Tourist by Call Detail Record Analysis” International Workshop on
Computational Intelligence (IWCI) 12-13 Dhaka, Bangladesh, December 2016.
X. S. Massey, D., Arango, J., Hugo, G., Kouaouci, A., Pellegrino, A., & Taylor, J.
E., ”Theories of international migration: A review and appraisal”, Population
and development review, 431–466, 1993.
XI. Stark, O., & Bloom, D. E., ”The new economics of labor migration”. The
american Economic review, 75(2), 173-178.1985.
XII. Stark O, Taylor JE. ”Migration incentives, migration types: The role of relative
deprivation”. The Economic Journal. 101:1163–1178. 10.2307/2234433, 1985.
XIII. Sara B. Elagib, Hashim, A. H. A., & Olanrewaju, R. F.”CDR Analysis using Big
Data Technology”, International Conference on Computing, Control,
Networking, Electronics and Embedded Systems Engineering, 2015
XIV. W. Kandel, J .Durand, Parrado, E. A., & Massey, D. S., ”International
migration and development in Mexican communities”. Demography, 33(2), 249-
264, 1996.
XV. Xuzhao Wang, Dong, H., Zhou, Y., Liu, K., Jia, L., & Qin, Y., ”Travel Distance
Characteristics Analysis Using Call Detail Record Data”, 29th Chinese Control
And Decision Conference (CCDC), 2017.
XVI. Zhang, S., Yin, D., Zhang, Y., & Zhou, W., “Computing on Base Station
Behavior Using Erlang Measurement and Call Detail Record”, IEEE transactions
on emerging topics in computing, 3(3), 444-453 2015.

View Download

Team Building and Organizational Ambidexterity: A Relational Analysis

Authors:

Namrata Nanda, Siddharth Misra, Rajith K.R

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00003

Abstract:

This paper aims to identify and test the relationship of Team Building andOrganizational Ambidexterity by prompting bank employees to engage in commitment towards change.A structured questionnaire was prepared and distributed among employees of selected public and private banks across the country. A total of 240 valid responses were gathered from this survey using snowball and convenience sampling techniques. Descriptive statistics, regression analysis and factor analysis was used to interpret the results of the collected data. The analysis of data has been carried by using IBM SPSS and AMOS 20 version. The major takeaway of this research highlights the private sector banks where the commitment of employee towards change impacted team building leading to high ambidexterity as compared to that of public sector banks. Also, the results of the hypotheses formulated, holds true to the relationship of Team Building and Organizational Ambidexterity becomes stronger with a mediator Employee Commitment to Change and moderator, Psychological Safety in place.This research reflects on the importance of managing interpersonal threats hidden within every committed employee with the help of psychologically safe work environment and thus, promoting a strong culture of team spirit and being an ambidextrous organization. This paper confirms the effect of Team Building on Organizational Ambidexterity through Employee Commitment to Change and unlocks the dark box of how organizations can become ambidextrous by adding novelty to this research with the presence of Psychological Safety as a moderator.

Keywords:

Team Building,Organizational Ambidexterity,Psychological Safety,Employee Commitment to change,Moderated mediation,

Refference:

I. Beckman, C. M. (2006). The influence of founding team company affiliations
on firm behavior. Academy of Management Journal, 49(4), 741-758.
II. Beer, M., Voelpel, S. C., Leibold, M., &Tekie, E. B. (2005). Strategic
management as organizational learning: Developing fit and alignment
through a disciplined process. Long Range Planning, 38(5), 445-465.
III. Burgelman, R. A. (1983). Corporate entrepreneurship and strategic
management: Insights from a process study. Management science, 29(12),
1349-1364.
IV. Carmeli, A., &Halevi, M. Y. (2009). How top management team behavioral
integration and behavioral complexity enable organizational ambidexterity:
The moderating role of contextual ambidexterity. The Leadership
Quarterly, 20(2), 207-218.
V. Carmeli, A., &Halevi, M. Y. (2009). How top management team behavioral
integration and behavioral complexity enable organizational ambidexterity:
The moderating role of contextual ambidexterity. The Leadership
Quarterly, 20(2), 207-218.
VI. Carson, J. B., Tesluk, P. E., &Marrone, J. A. (2007). Shared leadership in
teams: An investigation of antecedent conditions and performance. Academy
of management Journal, 50(5), 1217-1234.
VII. Černe, M., Hernaus, T., Dysvik, A., &Škerlavaj, M. (2017). The role of
multilevel synergistic interplay among team mastery climate, knowledge
hiding, and job characteristics in stimulating innovative work
behavior. Human Resource Management Journal, 27(2), 281-299.
VIII. Denis, J. L., Lamothe, L., & Langley, A. (2001). The dynamics of collective
leadership and strategic change in pluralistic organizations. Academy of
Management journal, 44(4), 809-837.
IX. Ensley, M. D., Pearson, A., & Pearce, C. L. (2003). Top management team
process, shared leadership, and new venture performance: A theoretical
model and research agenda. Human Resource Management Review, 13(2),
329-346.

X. Ensley, M. D., Pearson, A., & Pearce, C. L. (2003). Top management team
process, shared leadership, and new venture performance: A theoretical
model and research agenda. Human Resource Management Review, 13(2),
329-346.
XI. Forbes, D. P. (2007). Reconsidering the strategic implications of decision
comprehensiveness. Academy of Management Review, 32(2), 361-376.
XII. Fredrickson, J. W., & Mitchell, T. R. (1984). Strategic decision processes:
Comprehensiveness and performance in an industry with an unstable
environment. Academy of Management journal, 27(2), 399-423.
XIII. Gibson, C. B., &Birkinshaw, J. (2004). The antecedents, consequences, and
mediating role of organizational ambidexterity. Academy of management
Journal, 47(2), 209-226.
XIV. Gupta, A. K., Smith, K. G., &Shalley, C. E. (2006). The interplay between
exploration and exploitation. Academy of management journal, 49(4), 693-
706.
XV. He, Z. L., & Wong, P. K. (2004). Exploration vs. exploitation: An empirical
test of the ambidexterity hypothesis. Organization science, 15(4), 481-494.
XVI. Herscovitch, L., & Meyer, J. P. (2002). Commitment to organizational
change: Extension of a three-component model. Journal of applied
psychology, 87(3), 474.
XVII. Hitt, M. A., Ireland, R. D., Camp, S. M., & Sexton, D. L. (2001). Strategic
entrepreneurship: Entrepreneurial strategies for wealth creation. Strategic
management journal, 22(6‐7), 479-491.
XVIII. Ireland, R. D., & Webb, J. W. (2007). Strategic entrepreneurship: Creating
competitive advantage through streams of innovation. Business
horizons, 50(1), 49-59.
XIX. Ireland, R. D., Hitt, M. A., &Sirmon, D. G. (2003). A model of strategic
entrepreneurship: The construct and its dimensions. Journal of
management, 29(6), 963-989.
XX. Jansen, J. J., George, G., Van den Bosch, F. A., &Volberda, H. W. (2008).
Senior team attributes and organizational ambidexterity: The moderating role
of transformational leadership. Journal of Management Studies, 45(5), 982-
1007.
XXI. Jaros, S. (2010). Commitment to organizational change: A critical
review. Journal of Change Management, 10(1), 79-108.
XXII. KetchenJr, D. J., Ireland, R. D., & Snow, C. C. (2007). Strategic
entrepreneurship, collaborative innovation, and wealth creation. Strategic
entrepreneurship journal, 1(3-4), 371-385.
XXIII. Kleinbaum, A. M., &Tushman, M. L. (2007). Building bridges: The social
structure of interdependent innovation. Strategic Entrepreneurship
Journal, 1(1‐2), 103-122.

XXIV. Kour, H., &Gakhar, K. (2015). Innovative HRM Practices: A Comparison of
Public and Private Sector Banks of India. MANTHAN: Journal of Commerce
and Management, 2(1), 1-28.
XXV. Levitt, B., & March, J. G. (1988). Organizational learning. Annual review of
sociology, 14(1), 319-338.
XXVI. Lubatkin, M. H., Simsek, Z., Ling, Y., &Veiga, J. F. (2006). Ambidexterity
and performance in small-to medium-sized firms: The pivotal role of top
management team behavioral integration. Journal of management, 32(5),
646-672.
XXVII. Makumbe, W. (2016). Predictors of effective change management: A
literature review. African Journal of Business Management, 10(23), 585-593.
XXVIII. March, J. G. (1991). Exploration and exploitation in organizational
learning. Organization science, 2(1), 71-87.
XXIX. Netemeyer, R. G., Boles, J. S., McKee, D. O., &McMurrian, R. (1997). An
investigation into the antecedents of organizational citizenship behaviors in a
personal selling context. Journal of marketing, 61(3), 85-98.
XXX. O’Reilly III, C. A., &Tushman, M. L. (2008). Ambidexterity as a dynamic
capability: Resolving the innovator’s dilemma. Research in organizational
behavior, 28, 185-206.
XXXI. Pearce, C. L., & Sims Jr, H. P. (2002). Vertical versus shared leadership as
predictors of the effectiveness of change management teams: An examination
of aversive, directive, transactional, transformational, and empowering leader
behaviors. Group dynamics: Theory, research, and practice, 6(2), 172.
XXXII. Perry, M. L., Pearce, C. L., & Sims Jr, H. P. (1999). Empowered selling
teams: How shared leadership can contribute to selling team
outcomes. Journal of Personal Selling & Sales Management, 19(3), 35-51.
XXXIII. Schoorman, F. D., Mayer, R. C., & Davis, J. H. (2007). An integrative model
of organizational trust: Past, present, and future.
XXXIV. Shin, Y., Kim, M., Choi, J. N., & Lee, S. H. (2016). Does team culture
matter? Roles of team culture and collective regulatory focus in team task
and creative performance. Group & Organization Management, 41(2), 232-
265.
XXXV. Siren, C. A., Kohtamäki, M., &Kuckertz, A. (2012). Exploration and
exploitation strategies, profit performance, and the mediating role of strategic
learning: Escaping the exploitation trap. Strategic Entrepreneurship
Journal, 6(1), 18-41.
XXXVI. Slaby, J., Mühlhoff, R., &Wüschner, P. (2019). Affective
arrangements. Emotion Review, 11(1), 3-12.
XXXVII. Smith, W. K., &Tushman, M. L. (2005). Managing strategic contradictions:
A top management model for managing innovation streams. Organization
science, 16(5), 522-536.

XXXVIII. Stinglhamber, F., Bentein, K., &Vandenberghe, C. (2002). Extension of the
Three-Component Model of Commitment to Five Foci: Development of
measures and substantive test. European journal of psychological
assessment, 18(2), 123.
XXXIX. Taneja, S., Pryor, M. G., & Toombs, L. A. (2011). Frederick W. Taylor’s
scientific management principles: Relevance and validity. Journal of Applied
Management and Entrepreneurship, 16(3), 60.
XL. Tushman, M. L., & O’Reilly III, C. A. (1996). Ambidextrous organizations:
Managing evolutionary and revolutionary change. California management
review, 38(4), 8-29.
XLI. Wang, C. L., &Rafiq, M. (2009). Organizational diversity and shared vision:
Resolving the paradox of exploratory and exploitative learning. European
Journal of Innovation Management, 12(1), 86-101.
XLII. Yu, M. C., Mai, Q., Tsai, S. B., & Dai, Y. (2018). An empirical study on the
organizational trust, employee-organization relationship and innovative
behavior from the integrated perspective of social exchange and
organizational sustainability. Sustainability, 10(3), 864.
XLIII. Yukl, G. (2008). How leaders influence organizational effectiveness. The
leadership quarterly, 19(6), 708-722.

View Download

FRAMEWORK FOR ASSESSING SEISMIC RESILIENCE OF CITIES

Authors:

Yaseen Mahmood, Khan Shahzada, Usama Ali, Abdul Farhan, Syed Shujaat Ali Shah, Fawad Ahmad

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00004

Abstract:

This paper focuses on a framework for the seismic resilience of cities which incorporates the quantification of the seismic losses and developing models for assessing such losses(economic and human losses). By convolution of seismic hazard curve and fragility curve, a seismic loss curve has been obtained. Also the recovery paths have been chosen for the cities situated in south Asian countries by considering the pre-defined recovery curve.A general concept of resilience in cities has been presented by combining the losses and recovery in a in a single graph showing the resilience for the required city.

Keywords:

Resilience,Seismic, Hazards,Risks, Fragility,Losses,Recovery,Functionality,

Refference:

I. Federal Emergency Management Agency (FEMA), “HAZUS-MH MR4
Multi-Hazard Loss Estimation Methodology – Earthquake Model: Technical
Manual. Department of Homeland Security,” Fed. Emerg. Manag. Agency,
Washington, 2003.
II. G. P. Cimellaro, A. M. Reinhorn, and M. Bruneau, “Framework for analytical
quantification of disaster resilience,” Eng. Struct., vol. 32, no. 11, pp. 3639–
3649, 2010.
III. M. Bruneau and A. M. Reinhorn, “Overview of the resilience Concept,”
Proc. 8th US Natl. Conf. Earthq. Eng., no. 2040, pp. 2–6, 2006.
IV. N. Ahmad, “Development of a seismic risk / loss model for Mansehra city ,
Pakistan DEVELOPMENT OF A SEISMIC RISK / LOSS MODEL FOR
MANSEHRA CITY , PAKISTAN A Dissertation Submitted in
PartialFulfilment of the Requirements IstitutoUniveritario di StudiSuperiori,”
Thesis, no. June, 2014.
V. S. B. Manyena, “The concept of resilience revisited,” Disasters, 30, 434. vol.
30, no. 4, pp. 433–450, 2006.
VI. S. E. Chang and M. Shinozuka, “Measuring Improvements in the Disaster
Resilience of Communities Measuring and Improving the Disaster Resilience
of Communities,” Earthq. Spectra, vol. 98195, no. 206, 2004.
VII. S. L. Kramer, GEOTECHNICAL EARTHQUAKE ENGINEERING. Prenticehall
international series, 1996.
VIII. T. M. Frankie, B. Gencturk, and A. S. Elnashai, “Simulation-based fragility
relationships for unreinforced masonry buildings,” J. Struct. Eng. (United
States), vol. 139, no. 3, pp. 400–410, 2013.
IX. U. Ali, N. Ahmad, Y. Mahmood, H. Mustafa, and M. Munir, “A comparison
of Seismic Behavior of Reinforced Concrete Special Moment Resisting
Beam-Column Joints vs. Weak Beam Column Joints Using Seismostruct,” J.
Mech. Contin. Math. Sci., vol. 14, no. 3, 2019.
X. Y. K. Wen, B. R. Ellingwood, and J. Bracci, “Vulnerability Function
Framework for Consequence-based Engineering.” pp. 1–101, 2004.

View Download

Visions and Global Developments in Artificial Intelligence for Identifying Intelligent Behavior in Machines

Authors:

B. V. V. Siva Prasad, B. Suresh Kumar, Ratna Raju Mukiri, Akshat Agrawal

DOI NO:

https://doi.org/10.26782/jmcms.2019.10.00005

Abstract:

Novel strategies of deep learning are assuring to also enhance the suggestion of AI outfitted with functionalities of self-improvement. However what are actually the greater social ramifications of this particular growth and to what extent are classical AI ideas still relevant? This paper talks about these issues consisting of an outline on standard principles as well as notions of AI in connection with big records. Particular emphasis lies on the functions, societal repercussions and also risks of machine and also deep learning. The newspaper says that the increasing significance of AI in culture bears significant threats of deep hands free operation prejudice enhanced through not enough machine learning quality, lacking mathematical responsibility and also shared risks of confounding up to incrementally aggravating conflicts in decision-making between human beings and also equipments. Big amounts of sensing unit readings as well as hyperspectral photos of plants may be utilized to pinpoint drought health conditions and to gain understandings in to when and also exactly how worry effects vegetation growth as well as progression and consequently how to an eye for an eye the trouble of planet appetite. Video game data can switch pixels right into activities within computer game, while empirical records may help enable robotics to comprehend complicated and also disorganized settings and to know manipulation skills.

Keywords:

Artificial Intelligence,machine learning,deep learning,

Refference:

I. Al-Hmouz, Ahmed. “An adaptive framework to provide personalization for
mobile learners.”(2012).
II. Al-Hmouz, Ahmed, Jun Shen, and Jun Yan. “A machine learning based
framework for adaptive mobile learning.” Advances in Web Based Learning–
ICWL 2009. Springer Berlin Heidelberg, 2009.34-43.
III. Amodei, D., et al., Concrete Problems in AI Safety, Cornell University, 2016:
https://arxiv.org/abs/1606.06565.
IV. Broder, Andrei, and Vanja Josifovski. “Introduction to computational
advertising.”(2010).
V. Cunningham, Sally Jo, James Littin, and Ian H. Witten. “Applications of
machine learning in information retrieval.”(1997).
VI. ICRC,Autonomy,ArtificialIntelligence(AI)andRobotics:TechnicalAspectsofHuman
Control,reportofanexpertmeeting,2019 (forthcoming).
VII. ICRC, Ethics and autonomous weapon systems: An Ethical Basis for Human
Control?, op. cit. p. 13.

View Download

Natural Convection Cooling of PCB Equipped with Perforated Fins Heat Sink including Inclination and Vibration Effects

Authors:

HibaMudhafarHashim, Ihsan Y. Hussain

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00006

Abstract:

A numerical simulation is proposed to investigate the thermal behavior ofa Central Processer Unit (CPU) as a single electronic component placed on Printed Circuit Board (PCB) equipped with a heat sink. Two types of heat sinks were used; the first is with solid fins and the other with perforated fins. Natural convection cooling is considered, with the inclusion of vibration and inclination effects. The power dissipated from the electronic component is (30W). In order to study the thermal behavior during the vibration effect, a frequency values of (0,2,5,9,16HZ) with constant amplitude (3 mm) was considered. The inclination effect is investigated with and without the vibration effect. The results showed that the vibration causesa decrease in the temperature of the component. The temperature of the component decreases with increasing the angle of inclination, Verification of the results gave good agreement.

Keywords:

PCB,Perforated Fins Heat Sink,Inclination,Vibration,Natural Convection.,

Refference:

I. Abbas J.Al-Jessani,Hussein.R.Al-Bugharbee “an experimental
investigation of free convection heat transfer rate enhancement of
rectangular fins with circular perforations”, international conference on
advances in sustainable engineering and applications ,IEEE(2018).
II. B. Keshavarzian and M. Khorsavi “numerical investigation of the
structural frequencies effects on flow induced vibration and heat
transfer “J.Mater.Enviro.Sci6(7)(2015),1949-1956,ISSN:2028-
2508,JMESCN.
III. C.B.Baxi, A.Ramachandrn”effect of vibration on heat transfer from
spheres”,journal of heat transfer, August (1969).
IV. Cengel Y.A. Heat transfer a practical approach (MGH, 2002)
V. F A Gdhaidh, K Hussain and H S Qi “numerical study of conjugate
natural convection heat transfer using one phase liquid cooling”
materials science and engineering 65 (2014) 012012, IOP publishing.
VI. K.A.Rajput and A.V.Kulkarni “finite element analysis of convective
heat transfer augmentation from rectangular fin by circular perforation
“,international journal on recent technologies in mechanical and
electrical engineering,ISSN:2349-7947,037-042.
VII. K.H.Dhanawade, H.S.Dhanawade “enhancement of forced convection
heat transfer from fin arrays with circular perforation “,IEEE(2010).
VIII. MdRuhul Amin Rana “numerical and experimental study on
orientation dependency of free convection heat sinks”M.SC. Thesis
(University of British Columbia) 2015
IX. N.D.Jadeja, Ta-Cheng Loo “heat induced vibration of a rectangular
plate”,journal of engineering for industry ,August 1974.
X. P.K.Nag ,A.Bhattacharya “effect of vibration on natural convection
heat transfer from vertical fin arrays “letters in heat and mass transfer ,
vol.9,pp.487-498,1982.
XI. Rodrigo G.L.JoseA.F.and Douglas M.R.”natural convection of vertical
flat plates “,from the internet 2003
XII. ShrikantChavan and AnilkumaeSathe “natural convection cooling of
electronic enclosure” ,international journal of trend in research and
development, volume 3(4), ISSN:2394-9333, 2016.
XIII. ThamirK.Ibrahim.etal “experimental study on the effect of perforations
shapes on vertical heated fins performance under forced convectionheat transfer “,international journal of heat and mass transfer
118(2018),832-846.
XIV. Wu.ShurgFu,andBao-Hong Tong “numerical investigation of heat
transfer from a heated oscillating cylinder in a cross flow “,
international journal of heat and mass transfer 45 (2002),3033-3043.
XV. Wu-Shung Fu, Chien-Ping Huang “effects of vibrational heat surface
on natural convection in a vertical channel flow”, international journal
of heat and mass transfer 49 (2006) 1340-1349
XVI. Z.Staliulionis,Z.Zhang,R.Pittini,M.A.E.Andersen,P.Tarvydas,A.Noreik
a “investigation of heat sink efficiency for electronic component
cooling applications” ELEKTRONIK IR ELEKTROTECHNKA,ISSN
1392-1215, VOL.20, NO.1, 2014.

View Download

An Efficient Emergency Vehicle Clearance Mechanism for Smart Cities

Authors:

Biru Rajak, Shrabani Mallick, Dharmender SinghKushwaha

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00007

Abstract:

The transportation management system is becoming an overwhelming task across the globe due to Globalization and population growth. Increased traffic congestion poses several problems. The extended waiting time at traffic jam leading to air and noise pollution due to the amassed vehicle is a serious threat to human health and the environment. This situation aggravates the clearance of any emergency vehicle resulting in grave consequences for the patient. A better control over the transportation system can be achieved through the Internet of Thing (IoT) based smart infrastructure. To deal with such emergency situations, this paper proposes a framework for automatic emergency vehicle clearance system. Traffic signal dynamically suspends the routine movement of traffic flow to create a "Green Corridor" to pass the ambulance without any delay at the traffic junctions. IoT based RFID tag and reader at vehicle and traffic junction respectively is used to identify the ambulance at the traffic junction. The work is simulated in SUMO and detection of RFID is analyzed in NS2 with the integration of SUMO. Considering the criticality of the issue, a simulation of the proposed work does not suffice. Therefore to check the robustness of the proposed system, it has been tested in a laboratory environment. The average reduction in travel time for five different simulations for an emergency vehicle from source to destination is 254.6%, which is substantial.

Keywords:

Emergency vehicle,Green Corridor,RFID,Smart traffic management,SUMO,Traffic congestion,

Refference:

I. A. Chattaraj, S. Bansal & A. Chandra, “An intelligent traffic control system using
RFID”, Potentials, IEEE 28.3 (2009): 40-43.
II. A.K. Mittal and D. Bhandari, “A novel approach to implement green wave system
and detection of stolen vehicles,” Proc. IEEE 3rd Int. Adv. Comput., Feb. 2013, pp.
1055–1059.
III. A.R. Dobre, A.V. Nita, A. Ciobanu, C. Negrescu, D. Stanomir, “Low computational
methods for siren detection” , Proceedings of the IEEE 21st International
Symposium for Design and Technology in Electronicpackaging (SIITME), Brasov,
Romania, 22–25 October 2015; pp. 291–295.
IV. A.S.Eltayeb, H.O Abubakr & T. A. Attia, “A GPS based traffic light pre-emption
control system for emergency vehicles” 2013 International Conference on
Computing, Electrical and Electronic Engineering (ICCEEE). IEEE, 2013.
V. B. Fazenda, H. Atmoko, F. Gu, L. Guan, A. Ball, “Acoustic based safety emergency
vehicle detection for intelligent transport systems”, Proceedings of the IEEE
International Conference ICROS-SICE, Fukuoka,Japan, 18–21 August 2009; pp.
4250–4255.
VI. D. Smith, S. Djahel & J. Murphy, “A sumo based evaluation of road incidents’
impact on traffic congestion level in smart cities”, 39th Annual IEEE Conference on
Local Computer Networks Workshops, pages 702–710. IEEE, 2014.
VII. F. Meucci, L. Pierucci, E. del Re, L. Lastrucci, P. Desii, “Areal-time siren detection
to improve safety of guide in traffic environment”, Proceedings of the IEEE 16th
International Conference on European SignalProcessing, Lausanne, Switzerland, 25–
29 August 2008; pp. 1–5.
VIII. F.W.Cathey & D.J. Dailey, “A novel technique to dynamically measure vehicle
speed using uncalibrated roadway cameras”, IEEE Proceedings. Intelligent Vehicles
Symposium, 2005. (pp. 777-782). IEEE.
IX. G. Palubinskas, F. Kurz, & P. Reinartz, “Detection of traffic congestion in optical
remote sensing imagery” , IGARSS 2008-2008 IEEE International Geoscience and
Remote Sensing Symposium (Vol. 2, pp. II-426). IEEE.
X. http://www.atmel.com/Images/Atmel-42735-8-bit-AVR-Microcontroller –
ATmega328-328P_Summary.pdf.
XI. http://www.merinews.com/mobile/article/India/2014/10/17/give-way-to-ambulanceeducates-
people-that-saving-time-is-saving-life/15902114.
XII. K. Nellore, G. Hancke, “Traffic management for emergency vehicle priority based
on visual sensing.” Sensors 16.11 (2016): 1892.

XIII. N. Singh, “An Efficient Approach for Handwritten Devanagari Character
Recognition based on Artificial Neural Network”, 2018 5th International
Conference on Signal Processing and Integrated Networks (SPIN), Noida, 2018, pp.
894-897.
XIV. N. Singh & T. Kumar. “An Improved Intelligent Transportation System: An
Approach for Bilingual License Plate Recognition.” Information and
Communication Technology for Intelligent Systems. Springer, Singapore, 2019. 29-
38.
XV. P. Priya, A. Jose, and G. Sumathy, “Traffic light pre-emption control system for
emergency vehicles.” SSRG International Journal of Electronics and Communication
Engineering (SSRG-IJECE) 2.2 (2015).
XVI. R. Sundar, S. Hebbar & V. Golla, “Implementing Intelligent Traffic Control System
for Congestion Control, Ambulance Clearance, and Stolen Vehicle Detection”, IEEE
Sensor Journal, Vol. 15, No. 2, Febuary 2015.
XVII. S. Sharma, A. Pithora, G. Gupta, M. Goel, & M. Sinha, “Traffic light priority control
for emergency vehicle using RFID,” Int. J. Innov. Eng. Technol., vol. 2, no. 2, pp.
363–366, 2013.
XVIII. T. Idé, T. Katsuki, T. Morimura & R. Morris, “City-wide traffic flow estimation
from a limited number of low-quality cameras” IEEE Transactions on Intelligent
Transportation Systems, 18(4), 950-959.
XIX. T.J. Hall, M.A. Schwartz & S.M. Hamer, “Gps-based traffic control preemption
system.” U.S. Patent No. 5,539,398. 23 Jul. 1996.
XX. T. Kumar & D.S. Kushwaha, “An Approach for Traffic Congestion Detection and
Traffic Control System”, Proceedings of Third International Conference on ICTCS
2017.
XXI. T. Kumar & D.S. Kushwaha, “An efficient approach for detection and speed
estimation of moving vehicles”, Procedia Computer Science, 89, 726-731.
XXII. T. Kumar, R. K. Sachan, D. S. & Kushwaha, “Smart City Traffic Management and
Surveillance System for Indian Scenario in Recent Advances” Mathematics,
Statistics and Computer Science (2016) (pp. 486-493).
XXIII. T. Miyazaki, Y. Kitazono, M. Shimakawa, “Ambulance siren detection using FFT
and dsPIC”, Proceedings of the First IEEE/IIAE International Conference on
Intelligent System and Image processing, Kitakyushu,Japan, 26–27 September 2013;
pp. 266–269.

View Download

All-Optical Logic Gates Based on Graphene Interferometric Waveguide

Authors:

Hassan FalahFakhruldeen, TahreerSafa’a Mansour, Yousif I. Hammadi

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00008

Abstract:

Novel types of all-optical logic gates based on graphene surface plasmonpolaritons (SSPs) are proposed in this study by utilizing linear constructive and destructive interferences among SSP waves in spatially separated graphene sheets. The realized logic gates are OR, AND, and XOR gates. The suggested transmission value threshold between the two states logic 0 and logic 1 is 0.5. Small modification in the structure has been conducted to implement the XOR gate with the same wavelength for all the proposed gates. The structure performance is measured on the basis of transmission efficiency of each implemented gate. The state of each input port can be easily controlled by switching the external gate voltage either ON or OFF. The function of the proposed gates can be achieved by modifying the chemical potential ( c  ), coupling length ( c L ), orinter spacing among the graphene sheets (d). These compact-sized logic gates are considered an important part in the integration of nanoscale photonic devices.

Keywords:

Graphene,Surface plasmonpolaritons (SPPs),,All-optical logic gate,Nanophotonic devices,Plasmonic logic gates,

Refference:

I. A. F. Aguiar, D. M. d. C. Neves, and J. B. R. Silva, “All-optical logic gates
devices based on SPP coupling between graphene sheets,” Journal of
Microwaves, Optoelectronics and Electromagnetic Applications, vol. 17, pp.
208-216, 2018.
II. A. Vakil and N. Engheta, “Transformation optics using graphene,” Science,
vol. 332, pp. 1291-1294, 2011.
III. B. Wang and G. P. Wang, “Surface plasmon polariton propagation in nanoscale
metal gap waveguides,” Optics letters, vol. 29, pp. 1992-1994, 2004.
IV. D. A. Miller, “The role of optics in computing,” Nature Photonics, vol. 4, p.
406, 2010.
V. F. Wang, Y. Zhang, C. Tian, C. Girit, A. Zettl, M. Crommie, et al., “Gatevariable
optical transitions in graphene,” science, vol. 320, pp. 206-209, 2008.
VI. H. J. Caulfield and S. Dolev, “Why future supercomputing requires optics,”
Nature Photonics, vol. 4, p. 261, 2010.
VII. H. J. Caulfield, C. S. Vikram, and A. Zavalin, “Optical logic redux,” Optik-
International Journal for Light and Electron Optics, vol. 117, pp. 199-209,
2006.
VIII. H. Wei, Z. Wang, X. Tian, M. Käll, and H. Xu, “Cascaded logic gates in
nanophotonic plasmon networks,” Nature communications, vol. 2, p. 387,
2011.
IX. Hassan Falah Fakhrulden and Tahreer Safa’a Mansour, “All-optical NoT Gate
Based on Nanoring Silver-Air Plasmonic Waveguide,” International Joural of
Engineering & Technology, vol. 7, pp.2818-2821, 2018.
X. K. J. Ooi, H. S. Chu, L. K. Ang, and P. Bai, “Mid-infrared active graphene
nanoribbon plasmonic waveguide devices,” JOSA B, vol. 30, pp. 3111-3116,
2013.
XI. K. J. Ooi, H. S. Chu, P. Bai, and L. K. Ang, “Electro-optical graphene
plasmonic logic gates,” Optics letters, vol. 39, pp. 1629-1632, 2014.

XII. M. Jablan, H. Buljan, and M. Soljačić, “Plasmonics in graphene at infrared
frequencies,” Physical review B, vol. 80, p. 245435, 2009.
XIII. M. L. Brongersma and P. G. Kik, Surface plasmon nanophotonics vol. 131:
Springer, 2007.
XIV. M. W. McCutcheon, G. W. Rieger, J. F. Young, D. Dalacu, P. J. Poole, and R.
L. Williams, “All-optical conditional logic with a nonlinear photonic crystal
nanocavity,” Applied Physics Letters, vol. 95, p. 221102, 2009.
XV. M. Yarahmadi, M. K. Moravvej-Farshi, and L. Yousefi, “Subwavelength
graphene-based plasmonic THz switches and logic gates,” IEEE Transactions
on Terahertz Science and Technology, vol. 5, pp. 725-731, 2015.
XVI. optics,” nature, vol. 424, p. 824, 2003.
XVII. S. H. Abdulnabi and M. N. Abbas, “All-optical logic gates based on nanoring
insulator–metal–insulator plasmonic waveguides at optical communications
band,” Journal of Nanophotonics, vol. 13, p. 016009, 2019.
XVIII. S. I. Bozhevolnyi, V. S. Volkov, E. Devaux, J.-Y. Laluet, and T. W. Ebbesen,
“Channel plasmon subwavelength waveguide components including
interferometers and ring resonators,” Nature, vol. 440, p. 508, 2006.
XIX. X. Wu, J. Tian, and R. Yang, “A type of all-optical logic gate based on
graphene surface plasmon polaritons,” Optics Communications, vol. 403, pp.
185-192, 2017.
XX. Y. Fu, X. Hu, C. Lu, S. Yue, H. Yang, and Q. Gong, “All-optical logic gates
based on nanoscale plasmonic slot waveguides,” Nano letters, vol. 12, pp.
5784-5790, 2012.
XXI. Y. Liu, F. Qin, Z.-M. Meng, F. Zhou, Q.-H. Mao, and Z.-Y. Li, “All-optical
logic gates based on two-dimensional low-refractive-index nonlinear photonic
crystal slabs,” Optics express, vol. 19, pp. 1945-1953, 2011.
XXII. Yousif I. Hammadi and Tahreer S. Mansour., “Multiwavelength Erbium doped
fiber laser based on microfiber Mach-Zehnder interferometer,” Journal of
Optoelectronics and Advanced Materials-Rapid Communications, vol.13, no.3-
4, pp.156 – 160, April 2019.
XXIII. Z. Li, E. A. Henriksen, Z. Jiang, Z. Hao, M. C. Martin, P. Kim, et al., “Dirac
charge dynamics in graphene by infrared spectroscopy,” Nature Physics, vol. 4,
p. 532, 2008.

View Download

A Composite Feature Set Based Blood Vessel Segmentation in Retinal Images through Supervised Learning

Authors:

Y. Madhu Sudhana Reddy, R. S. Ernest Ravindran

DOI NO:

http://doi.org/10.26782/jmcms.2019.10.00009

Abstract:

Retinal image analysis has gained a significant research interest due to its widespread applicability in the diagnosis of different eye related diseases. This paper focused in the analysis of Diabetic Retinopathy through different features (Optic Disk, Retinal Vessels, and Exudates etc.,) of retinal image. Towards this objective, a new Retinal Vessel Segmentation mechanism is introduced in this paper. The proposed mechanism accomplished the Gabor Filter for Feature Extraction and Support Vector Machine Algorithm for classification. Here the Gabor Filter ensures a more resilience to the scaling and orientation issues in the retinal image. Afterwards, a feature set consists of thirteen features is extracted from retinal image to provide a proper differentiation between the image pixels and background pixels. Based on these features, the SVM classifier classifies the vessel pixels and background pixels more effectively which improves the classification accuracy and reduces false positive rate. An extensive simulation carried out over the proposed approach through two standard datasets, DRIVE and STARE reveals the outstanding performance with respect to the performance metrics sensitivity, specificity and accuracy.

Keywords:

retinal vessel segmentation,Gabor filter,Support vector machine,Gradient features,Correlation Accuracy,

Refference:

I. A. Bhuiyan, B. Nath, J. Chua, and R. Kotagiri, “Blood vessel
segmentation from color retinal images using unsupervised texture
classification,” in 2007 IEEE International Conference on Image
Processing, vol. 5, pp. 521–524, 2007.
II. A. Budai, G. Michelson, and J. Hornegger, “Multiscale blood vessel
segmentation in retinal fundus
images,” Proc. Bildverarbeitungfr die Med., pp. 261–265, March 2010.
III. A. Budai, R. Bock, A. Maier, J. Hornegger, and G. Michelson, “Robust
vessel segmentation in fundus images,” International Journal of
Biomedical Imaging, no. 154860, 2013.
IV. A.Fathi, A.R.N Naghsh-Nilchi, Automatic wavelet-based retinal blood
vessels segmentation and vessel diameter estimation ,Biomed. Signal
Process. Control 8(1)(2013) 71-80
V. A. F. Frangi, W. J. Niessen, K. L. Vincken, and M. A. Viergever,
“Multiscale vessel enhancement filtering,” in International Conference on
Medical Image Computing and Computer- Assisted Intervention, pp.
130–137, Springer, Berlin Heidelberg, 1998.
VI. A. Hoover, “Locating blood vessels in retinal images by piecewise
threshold probing of a matched filter response,” IEEE Transactions on
Medical Imaging, vol. 19, no. 3, pp. 203–210, 2000.
VII. A.Hoover, Structured Analysis of the Retina
STARE,http://www.ces.clemson.edu/~ahoover/stare/, 2015.
VIII. B D Barkana, “Performance analysis of descriptive statistical features in
retinal vessel segmentation via fuzzy logic, ANN, SVM, and classifier
fusion”, Knowledge-Based Systems, Volume 118, 15February 2017,
Pages 165-176
IX. B.R. Mcclintic, J.I. Mcclintic, B.S. Ba, J.D. Bisognano, R.C. Block, A
relationship between microvascular abnormalities and coronary disease –
a review, Am. J. Med. 123 (4) (2011) 1–12.

X. D. Marin, A. Aquino, M. Gegundez-Arias, and J. Bravo, “A new
supervised method for blood vessel segmentation in retinal images by
using gray-level and moment invariants-based features,” IEEE
Transactions on Medical Imaging, vol. 30, no. 1, pp. 146–158, 2011.
XI. F. Zana and J. C. Klein, “Segmentation of vessel-like pattern using
mathematical morphology and curvature evaluation,” IEEE Transactions
on Image Processing, vol. 10, no. 7, pp. 1010–1019, 2001.
XII. G. Azzopardi, N. Strisciuglio, M. Vento, and N. Petkov, “Trainable
COSFIRE filters for vessel delineation with application to retinal
images,” Med. Image Anal., vol. 19, no. 1, pp. 46–57, 2015.
XIII. G. B. Kande, P. V. Subbaiah, and T. S. Savithri, “Unsupervised fuzzy
based vessel segmentation in pathological digital fundus images,” Journal
of Medical Systems, vol. 34, no. 5, pp. 849–858, 2010.
XIV. Liskowski P, Krawiec K. Segmenting Retinal Blood Vessels with Deep
Neural Networks. IEEE Transactions on Medical Imaging. 2016.
XV. Mohan V, Shah SN, Joshi SR, Seshiah V, Sahay BK, Banerjee S, Current
status of management, control, complications and psychosocial aspects of
patients with diabetes in India: Results from the DiabCare India 2011
Study. Indian J EndocrinolMetab 2014; 18:370-8.
XVI. N. Memari, A R Ramil, M I B saripan, S. Mashohor, M Moghbel,
Supervised retinal vessel segmentation from color fundus images based
on matched filtering and AdaBoost classifier, PLoS ONE 12(12):
e0188939, 2017.
XVII. Orlando J, Prokofyeva E, Blaschko M. A Discriminatively Trained Fully
Connected Conditional Random Field Model for Blood Vessel
Segmentation in Fundus Images. IEEE Transactions on Biomedical
Engineering. 2016.
XVIII. Peter Sekulic, M Bajceta, S Dukanovic, “Retinal blood vessels
segmentation using support vector machine and modified line detector”,
22nd International Scientific-Professional Conference
Information Technology, 2017.
XIX. P. Rani, N. Priyadarshini, E. R. Rajkumar, and K. Rajamani, “Retinal
vessel segmentation under
pathological conditions using supervised machine learning,” in 2016
International Conference on, Systems in Medicine and Biology (ICSMB),
pp. 62–66, 2016.
XX. Roychowdhury S, Koozekanani DD, Parhi KK. Blood vessel
segmentation of fundus images by major vessel extraction and subimage
classification. IEEE journal of biomedical and health informatics. 2015
May; 19(3):1118–28. pmid:25014980
XXI. S. A. A. Shah, T. B. Tang, I. Faye, and A. Laude, “Blood vessel
segmentation in color fundus images based on regional and Hessian
features,” Graefe’s Archive for Clinical and Experimental
Ophthalmology, pp. 1– 9, 2017.

XXII. S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum,
“Detection of blood vessels in retinal images using two-dimensional
matched filters,” IEEE Transactions on Medical Imaging, vol. 8, no. 3,
pp. 263–269, 1989.
XXIII. SoumiaBelhadi and NadjiaBenblidia, “Automated Retinal Vessel
Segmentation using Entropic Thresholding Based Spatial Correlation
Histogram of Gray Level Images”, The International Arab Journal of
Information Technology, Vol. 12, No. 5, September 2015.
XXIV. SumathiThangaraj, VivekanandamPeriyasamy, RavikanthBalaji,
“Retinal vessel segmentation using neural network”, IET Image
Processing, Volume: 12, Issue: 5, pp.669-678. 2018
XXV. T. Chakraborti, D. K. Jha, A. S. Chowdhury, and X. Jiang, “A selfadaptive
matched filter for retinal blood vessel detection,” Machine
Vision and Applications, pp. 1–14, 2014.
XXVI. T. Jintasuttisak, and S. Intajag, “Color Retinal Image Enhancement by
Rayleigh Contrast-Limited Adaptive Histogram Equalization”, In: Proc.
of International Conf. on Control, Automation and Systems, Korea, pp.
22-25 2014.
XXVII. T. Mapayi, S. Viriri, and J. R. Tapamo, “Adaptive thresholding technique
for retinal vessel segmentation based on glcm-energy information,”
Computational and Mathematical Methods in Medicine, vol. 2015,
Article ID 597475, 11 pages, 2015.
XXVIII. T. Walter and J.-C. Klein, “Segmentation of color fundus images of
the human retina: detection of the optic disc and the vascular tree using
morphological techniques,” Medical Data Analysis, pp. 282–287, 2001.
XXIX. W. S. Oliveira, J. V. Teixeira, T. I. Ren, G. D. C. Cavalcanti, and J.
Sijbers, “Unsupervised retinal vessel segmentation using combined
filters,” PLoS One, vol. 11, no. 2, article e0149943, 2016.
XXX. X. Jiang and D. Mojon, “Adaptive local thresholding by verificationbased
multi-threshold probing with application to vessel detection in
retinal images,” IEEE Transactions on Pattern Analysis and Machine
Intelligence, vol. 25, no. 1, pp. 131–137, 2003.
XXXI. Y. MadhuSudhana Reddy, R. S. Ernest Ravindran, Kakarla. Hari
Kishore, “Spatial Mutual Relationship Based Retinal Image Contrast
Enhancement for Efficient Diagnosis of Diabetic Retinopathy”,
International journal of Intelligent Engineering systems, Vol.11, issue.4,
2018.
XXXII. Y. Qian Zhao, X. Hong Wang, X. Fang Wang, F.Y. Shih, Retinal vessels
segmentation based on level set and region growing, Pattern Recognit. 47
(7) (2014) 2437–2446.
XXXIII. Y. Wang, G. Ji, P. Lin, E. Trucco, Retinal vessel segmentation using
multiwavelet kernels and multiscale hierarchical decomposition,
PatternRecognit. 46 (8) (2013) 2117–2133.

XXXIV. Y. Wang, G. Ji, P. Lin, and E. Trucco, “Retinal vessel segmentation
using multiwavelet kernels and multiscale hierarchical
decomposition,” Pattern Recognition, vol. 46, no. 8, pp. 2117–2133,
2013
XXXV. Y. Yin, M. Adel, and S. Bourennane, “Automatic segmentation and
measurement of vasculature in retinal fundus images using probabilistic
formulation,” Computational and Mathematical Methods in Medicine,
vol. 2013, Article ID 260410, 16 pages, 2013.
XXXVI. Z. Xiao, M. Adel, and S. Bourennane, “Bayesian method with spatial
constraint for retinal vessel segmentation,” Computational and
Mathematical Methods in Medicine, vol. 2013, Article ID 401413, 9
pages, 2013.

View Download

Mixed mode crack KI, KII on pipe wall subjected to water hammer modeled by four equations fluid structure interaction

Authors:

N. Brahmia, D. Daas

DOI NO:

http://doi.org/10.26782/jmcms.2019.08.00010

Abstract:

In this paper, we studied the failure of the pipe during the transient flow. The pipe is made of ductile cast iron. To simulate the flow, a model includes an upstream tank connected to pipe with a valve at the end is presented; the transient flow is caused by fast time closure of the valve. The governing equations of water hammer are given from the mass and movement continuity conservation laws for fluid and mechanical behaviors laws for pipe structure. This mathematical model is a system of nonlinear hyperbolic partial differential equations where have solved by the method of characteristic along finite difference schema. To understand the behavior of material against surge pressure, we introduce the strain energy density theory (SEDT) S. The available mechanical propriety of ductile cast iron is used from previous study to get the critical value of strain energy density Sc. At the variance of stress intensity factor KIC criterion, the benefit of strain energy density S; that it can predict the crack growth initiation and direction when the applied stress does not coincide with the crack plane.

Keywords:

Water hammer,transient flow,method of characteristics,finite differences,strain energy density,

Refference:

I. Abott MB, An introduction to the method of the characteristics. New
York: American Elsevier, 1966.
II. A. Ductile iron pipes productions. EN 545:2002 standards, Greater cairo
foundries.
III. B Chaitanya K Desai, Dilip C Patel, Kalpesh D Maniya, “Experimental
analysis of mixed mode fracture: the strain energy density concept”.
Proceedings of the International Conference on Mechanical Engineering
Dhaka, Bangladesh, 28- 30 December, 2005.

IV. Bouaziz MA, Guidara MA, Schmitt C, Hadj-Taïeb E, Azari Z, “Water
hammer effects on a gray cast iron water network after adding pumps”.
Engineering Failure Analysis, Vo. 44, 2014, 1–16.
V. BRAHMIA, N. et DJEMILI, A, “Etude de l’influence de l’ancrage de la
conduite sur la variation de la pression et des contrainte lors de
l’écoulement transitoire”. Université de Badji Mokhtar ANNABA,
Algerie, 2013.M
VI. Daniela Ristić, Marko Bojanić, “Application of the Effective Strain
Energy Density Factor in the Estimation of the Fatigue Life of Notched
Specimens”. Scientific Technical Review,Vol. LVIII, 2008, No.1.
VII. Fröberg CE, Introduction to numerical analysis. 2nd ed. Addison-Wesley
Publishing Company; 1979.
VIII. J. M. Makar et al, “Failure Modes and Mechanisms in Gray Cast Iron
Pipes”. Institute for Research in Construction, National Research Council
Canada, Ottawa, Ontario, Canada, Infrastructure Research, Waterloo,
Ontario, June 10-13, 2001.
IX. M.H. Afshar, and M. Rohani, “Water hammer simulation by implicit
method of characteristic”. International Journal of Pressure Vessels and
Piping, Vo. 85, 2008, 851-859.
X. M. Dallali et al, “Accuracy and security analysis of transient flows in
relatively long pipelines”. Engineering Failure Analysis, Vo. 51, 2015,
69–82.
XI. Pluvinage G, Fracture and fatigue emanating from stress concentrators.
Kluwer Editor; 2001.
XII. R. Lacalle et al, “Analysis of the failure of a cast iron pipe during its
pressure test”. Engineering Failure Analysis, Vo. 31, 2013, 168–178.
XIII. Schmitt C, et al, “Pipeline failure due to water hammer effects”. Fatigue
Fracture Eng Mater Struct; 29, 2006, 1075–82.
XIV. SIH,G.C. and BARTHELEMY,O.C, “Mixed mode fatigue crack growth
predictions”. Engineering. Fracture Mechanics, Vo. 13, 1980, 439-451.
Wylie EB, Streeter VL, Suo L. Fluid transients in system. New Jersey,
Prentice Hall, 1993.
XV. SIH, G.C. and MACDONALD.B, “Fracture Mechanics Applied to
Engineering Problems- Strain Energy Density Fracture Criterion”.
Engineering. Fracture Mechanics, Vo. 6, 1974, 361-386.
.XVI. Streeter VL, Wylie EB. Hydraulic transients. New York: McGraw-Hill
Book Compagny; 1967. V.
XVII Tijsseling, AS, “Water hammer with fluid-structure interaction in thickwalled
pipes”. Computers and Structures, Vo. 85, 2007, 844-851.
XVIII. Wylie EB, Streeter VL. Fluid transients. New York: Mac Graw-Hill
Company; 1978.

 

View Download