Applied sciences

Archive of Mechanical Engineering

Content

Archive of Mechanical Engineering | 2017 | vol. 64 | No 1 |

Download PDF Download RIS Download Bibtex

Abstract

The paper describes the design and multibody dynamic analysis of a mechanically interconnected suspension, as applied to a small off-road vehicle. Interconnected suspensions use some sort of connection between the axles of a vehicle in order improve ride quality or vehicle handling. In principle, the connection may be hydraulic, pneumatic, or mechanical, but for installation in a typical passenger car, a mechanical connection would likely be impractical due to weight and complexity. In this paper, the vehicle in question is the University of Windsor SAE Baja off-road competition vehicle, and novel mechanical design is proposed. A multibody dynamic analysis is performed on the proposed design using the EoM open source multibody software developed by theUniversity ofWindsorVehicle Dynamics and Control research group in order to assess any potential performance improvements.

Go to article

Bibliography

[1] E. Zapletal. Balanced suspension. SAE Technical Paper 2000-01-3572, 2000.
[2] J.F. Buj. Integral suspension system for motor vehicles based on passive components. SAE Technical Paper 2002-01-3105, 2002.
[3] G. Rideout, R.J. Anderson. Experimental testing and mathematical modeling of the interconnected hydragas suspension system. SAE Technical Paper 2003-01-0312, 2003.
[4] J.R. Wilde, G.J. Heydinger, D.A. Guenther, T.P. Mallin, A.M. Devenish. Experimental evaluation of fishhook maneuver performance of a kinetic suspension system. SAE Technical Paper 2005- 01-0392, 2005.
[5] J.R. Wilde, G.J. Heydinger, D.A. Guenther. ADAMS simulation of ride and handling performance of the Kinetic™ suspension system. SAE Technical Paper 2006-01-1972, 2006.
[6] M.C. Smith, G.W. Walker. Interconnected vehicle suspension. Journal of Automobile Engineering, 219(3):295–307, 2005. doi: 10.1243/095440705X6578.
[7] B. Mavroudakis, P. Eberhard. Mode decoupling in vehicle suspensions applied to race cars. In Proceedings of the III European Conference on Computational Mechanics Solids, Structures and Coupled Problems in Engineering, Lisbon 2006.
[8] D. Cao, S. Rakheja, C.Y. Su. Roll and pitch-plane coupled hydro-pneumatic suspension. Vehicle System Dynamics, 48(3):361–386, 2010. doi: 10.1080/00423110902883251.
[9] B.P. Minaker, X. Yang, S. Li. Design optimization of an SAE Baja vehicle using the EoM open source multibody dynamics code. Proceedings of The Canadian Society for Mechanical Engineering International Congress, 2014
Go to article

Authors and Affiliations

Bruce P. Minaker
1
Zheng Yao
1

  1. Department of Mechanical,Automotive & Materials Engineering, University of Windsor, Canada
Download PDF Download RIS Download Bibtex

Abstract

This article investigates unstable tiltrotor in hover system identification from flight test data. The aircraft dynamics was described by a linear model defined in Body-Fixed-Coordinate System. Output Error Method was selected in order to obtain stability and control derivatives in lateral motion. For estimating model parameters both time and frequency domain formulations were applied. To improve the system identification performed in the time domain, a stabilization matrix was included for evaluating the states. In the end, estimates obtained from various Output Error Method formulations were compared in terms of parameters accuracy and time histories. Evaluations were performed in MATLAB R2009b environment.

Go to article

Bibliography


[1] J. Kaletka. BO-105 identification results. In Rotorcraft System Identification, AGARD LS-178, AGARD LS-178, Paper 9, November 1991.
[2] R.K. Mehra, R.K. Prasanth, and S. Gopalaswamy. XV-15 tiltrotor flight control system design using model predictive control. In I EEE Aerospace Conference, volume 2, pages 139–148, March 1998. doi: 10.1109/AERO.1998.687905.
[3] R.E. Maine and J.E. Murray. Application of parameter estimation to highly unstable aircraft. Journal of Guidance, Control, and Dynamics, 11(3):213–219, May 1988. doi: 10.2514/3.20296.
[4] S. Weiss, H. Friehmelt, E. Plaetschke, and D. Rohlf. X-31A system identification using single-surface excitation at high angles of attack. J ournal of Aircraft, 33(3):485–490, May 1996. doi: 10.2514/3.46970.
[5] E. Özger. Parameter estimation of highly unstable aircraft assuming linear errors. In AIAA Atmospheric Flight Mechanics Conference, Minneapolis, MN, August 2012. doi: 10.2514/6.2012-4511.
[6] B. Mettler, T. Kanade, and M. Tischler. System identification modeling of a model-scale helicopter. Technical Report CMU-RI-TR-00-03, Robotics Institute, Pittsburgh, PA, January 2000.
[7] S.K. Kim and D.M. Tilbury. Mathematical modeling and experimental identification of an unmanned helicopter robot with flybar dynamics. Journal of Robotic Systems, 21(3):95–116, March 2004. doi: 10.1002/rob.20002.
[8] A. Ji and K. Turkoglu. Development of a low-cost experimental quadcopter testbed using an arduino controller for video surveillance. In AIAA Infotech @ Aerospace. AIAA, January 2015. doi: 10.2514/6.2015-0716.
[9] V. Hrishikeshavan, M. Benedict, and I. Chopra. Identification of flight dynamics of a cylcocopter micro air vehicle in hover. Journal of Aircraft, 52(1):116–129, April 2014. doi: 10.2514/1.C032633.
[10] K. Rogowski and R. Maroński. CFD computation of the Savonius rotor. Journal of Theoretical and Applied Mechanics, 53(1):37–45, 2015. doi: 10.15632/jtam-pl.53.1.37.
[11] M.B. Tischler and R.K. Remple. Aircraft and Rotorcraft System Identification. AIAA Education Series. AIAA, Washington, DC, 2 edition, August 2012. doi: /10.2514/4.868207.
[12] B. Etkin. Dynamics of Atmospheric Flight. Dover Publications, Mineola, NY, 2005.
[13] L.A. Zadeh. From circuit theory to system theory. Proceeding of the IRE, 50(5):856–865, May 1962. doi: 10.1109/JRPROC.1962.288302 .
[14] T. Söderstörm and P. Stoica. System Identification. Prentice Hall International, New York, 2001.
[15] G.C. Goodwin and R.L. Payne. Dynamic System Identification: Experiment Design and Data Analysis. Academic Press Inc., New York, 1977.
[16] R.V. Jategaonkar. Flight Vehicle System Identification: A Time Domain Methodology. Progess in Astronautics and Aeronautics. AIAA, Reston, VA, 2 edition, 2015. doi: 10.2514/4.102790.
[17] R.E. Maine and K.W. Iliff. Application of parameter estimation to aircraft stability and control: The output-error approach. Technical Report NASA-RP-1168, NASA, Edwards, CA, June 1986.
[18] V. Klein and E.A. Morelli. Aircraft System Identification: Theory and Practice. AIAA Education Series. AIAA, Reston, VA, August 2006.
[19] P. Stoica and R. Moses. Introduction to Spectral Analysis. Prentice Hall, Upper Saddle River, NJ, 2 edition, 2005.
[20] L.R. Rabiner and B. Gold. Theory and Application of Digital Signal Processing. Prentice Hall Inc., Englewood Cliffs, NJ, 1 edition, 1975.
[21] P. Young and R.J. Patton. Frequency domain identification of remotely-piloted helicopter dynamics using frequency-sweep and schroeder-phased test signals. In AIAA Atmospheric Flight Mechanics Conference, Minneapolis, MN, August 1988. AIAA. doi: 10.2514/6.1988-4349.
Go to article

Authors and Affiliations

Piotr Lichota
1
Joanna Szulczyk
1

  1. Warsaw University of Technology, Institute of Aeronautics and Applied Mechanics, Poland
Download PDF Download RIS Download Bibtex

Abstract

The article presents the analyses of the flights carried out the by the Unmanned Aerial Vehicle (UAV) named PW-ZOOM used to perform a photogrammetric mission and monitoring of fauna in Antarctic areas. The analyses focus on the deviations of the optical axis of the photo-camera which occurred during photogrammetric flights carried out on the same route but during several Antarctic expeditions performed in subsequent years (2014 and 2015). The results were subjected to correlation tests with weather conditions (wind speed and variability). The basis for these analyses are the data from the onboard signal recorder integrated with an autopilot.

Go to article

Bibliography

[1] K.J. Chwedorzewska. Terrestrial antarctic ecosystems at the changing world – an overview. Polish Polar Research, 30(3):263–276, 2009. doi: 10.4202/ppres.2009.13 .
[2] A. Zmarz, M. Korczak-Abshire, R. Storvold, M. Rodzewicz, and I. Kędzierska. Indicator species population monitoring in Antarctica with UAV. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-1/W4:189–193, 2015. doi: 10.5194/isprsarchives-XL-1-W4-189-2015 .
[3] A. Zmarz, M. Korczak-Abshire, and K. Chwedorzewska. UAV Antarctic survey. Academia 1(45), 2015. http://www.scienceonline.pl/nasze-teksty/nauki-o-ziemi/item/449-uav-antarctic- survey.
[4] A. Kidawa, M. Korczak-Abshire, A. Zmarz, R. Storvold, M. Rodzewicz, K. Chwedorzewska, S.-R. Karlsen, and A. Znój. UAV for monitoring environmental changes on King George Island (South Shetland Islands) Antarctica: preliminary study on wildlife disturbance. Doc Number: WG-EMM-15/50, Commission for the Conservation of Antarctic Marine Living Resources, 06 July, 2015.
[5] T. Schenk. Introduction to Photogrammetry. Department of Civil and Environmental Engineering and Geodetic Science, The Ohio State University, Columbus, Autumn Quarter 2005.
[6] A. Zmarz and J. Hajduk. UAV application for photogrametric purposes. In 34th EARSeL Symposium: European remote sensing – new opportunities for science and practice, Abstract and Programme Book, Warsaw, Poland, 16-20 June 2014.
[7] D. Głowacki, J. Hajduk, and M. Rodzewicz. Methods of flight-path planning for UAV photogrammetry missions with consideration of aircraft dynamic properties. In 5th CEAS Air&Space Conference “Challenges in European Aerospace”, Delft, The Netherlands, 7-11 September 2015. Paper no. 23.
[8] Goetzendorf-Grabowski T. and M. Rodzewicz. Methods of flight-path planning for UAV photogrammetry missions with consideration of aircraft dynamic properties. In 5th CEAS Air&Space Conference “Challenges in European Aerospace”, Delft, The Netherlands, 7-11 September 2015. Paper no. 21.
[9] S. Park, J. Deyst, and J.P. How. A new nonlinear guidance logic for trajectory tracking. In AIAA Guidance, Navigation, and Control Conference and Exhibit, Providence, Rhode Island, USA, 16-19 August 2004. Paper AIAA 2004-4900.
[10] D. Głowacki. I nvestigation of the load spectra of the unmaned aircraft controlled by the autopilot. Ph.D. Thesis, Warsaw University of Technology, Faculty of Power and Aeronautical Engineering, 2013. (in Polish)
Go to article

Authors and Affiliations

Mirosław Rodzewicz
1
Dominik Głowacki
1
Jarosław Hajduk
2

  1. Warsaw University of Technology, Institute of Aeronautics and Applied Mechanics, Poland
  2. Airforce Institute of Technology, Warsaw, Poland
Download PDF Download RIS Download Bibtex

Abstract

The accuracy of the Moment Method for imposing no-slip boundary conditions in the lattice Boltzmann algorithm is investigated numerically using lid-driven cavity flow. Boundary conditions are imposed directly upon the hydrodynamic moments of the lattice Boltzmann equations, rather than the distribution functions, to ensure the constraints are satisfied precisely at grid points. Both single and multiple relaxation time models are applied. The results are in excellent agreement with data obtained from state-of-the-art numerical methods and are shown to converge with second order accuracy in grid spacing.

Go to article

Bibliography

[1] F. Higuera and J. Jimenez. Boltzmann approach to lattice gas simulations. Europhys. Lett., 9:663, 1989.
[2] YH. Qian, D. d’Humières, and P. Lallemand. Lattice BGK models for Navier-Stokes equation. Europhys. Lett., 17:479, 1992.
[3] X. Shan, X.F. Yuan, and H. Chen. Kinetic theory representation of hydrodynamics: a way beyond the Navier–Stokes equation. J. Fluid Mech., 550:413–441, 2006.
[4] X. He and L.S. Luo. A priori derivation of the lattice Boltzmann equation. Phys. Rev. E, 55:R6333, 1997.
[5] D. d’Humieress. Generalized lattice-Boltzmann equations. Prog. Astronaut. Aeronaut., pages 450–458, 1992.
[6] P. Lallemand and L.S. Luo. Theory of the lattice Boltzmann method: Dispersion, dissipation, isotropy, Galilean invariance, and stability. Phys. Rev. E, 61:6546, 2000.
[7] P.J. Dellar. Incompressible limits of lattice Boltzmann equations using multiple relaxation times. J. Comput. Phys., 190:351–370, 2003.
[8] A. JC. Ladd. Numerical simulations of particulate suspensions via a discretized Boltzmann equation. part 1. theoretical foundation. J. Fluid Mech., 271:285–309, 1994.
[9] Z. Guo and C. Shu. Lattice Boltzmann method and its applications in engineering. World Scientific, 2013.
[10] X.Y. He, Q.S. Zou, L.S. Luo, and M. Dembo. Analytic solutions of simple flows and analysis of nonslip boundary conditions for the lattice Boltzmann BGK model. J. Stat. Phys., 87:115–136, 1997.
[11] I. Ginzbourg and P.M. Adler. Boundary flow condition analysis for the three-dimensional lattice Boltzmann model. J. Phys. II. France, 4:191–214, 1994.
[12] J.E. Broadwell. Study of rarefied shear flow by the discrete velocity method. J. Fluid Mech., 19:401–414, 1964.
[13] R. Gatignol. Kinetic theory boundary conditions for discrete velocity gases. Phys. Fluids (1958-1988), 20:2022–2030, 1977.
[14] S. Ansumali and I. V Karlin. Kinetic boundary conditions in the lattice Boltzmann method. Phys. Rev. E, 66:026311, 2002.
[15] Q. Zou and X. He. On pressure and velocity boundary conditions for the lattice Boltzmann BGK model. Phys. Fluids., 9:1591–1598, 1997.
[16] S. Bennett. A lattice Boltzmann model for diffusion of binary gas mixtures. PhD thesis, University of Cambridge, 2010.
[17] D.R. Noble, S. Chen, G. Georgiadis, and R.O. Buckius. A consistent hydrodynamic boundary condition for the lattice Boltzmann method. Phys. Fluids, 7(1):203–209, 1995.
[18] S. Bennett, P. Asinari, and P.J. Dellar. A lattice Boltzmann model for diffusion of binary gas mixtures that includes diffusion slip. I nt. J. Numer. Meth. Fluids, 69:171–189, 2012.
[19] R. Allen and T. Reis. Moment-based boundary conditions for lattice Boltzmann simulations of natural convection in cavities. Prog. Comp. Fluid Dyn.: An Int. J., 16:216–231, 2016.
[20] T. Reis and P.J. Dellar. Moment-based formulation of Navier–Maxwell slip boundary conditions for lattice Boltzmann simulations of rarefied flows in microchannels. Phys. Fluids, 2012.
[21] A. Hantsch, T. Reis, and U. Gross. Moment method boundary conditions for multiphase lattice Boltzmann simulations with partially-wetted walls. Int. J. Multiphase Flow, 7:1–14, 2015.
[22] U. Ghia, K.N. Ghia, and C.T. Shin. High-resolutions for incompressible flow using the Navier- Stokes equations and a multigrid method. J. Comput. Phys., 48:387–411, 1982.
[23] M. Sahin and R.G. Owens. A novel fully implicit finite volume method applied to the lid-driven cavity problem—part i: High Reynolds number flow calculations. Int. J. Numer. Meth. Fluids, 42:57–77, 2003.
[24] O. Botella and R. Peyret. Benchmark spectral results on the lid-driven cavity flow. Comput. Fluids, 27:421–433, 1998.
[25] S. Hou, Q. Zou, G.D. Chen, S.and Doolen, and A.C. Cogley. Simulation of cavity flow by the lattice Boltzmann method. J. Comput. Phys., 118:329–347, 1995.
[26] M.A. Mussa, S.Abdullah, C.S.N. Azwadi,N. Muhamad, K. Sopian, S. Kartalopoulos, A. Buikis, N. Mastorakis, and L. Vladareanu. Numerical simulation of lid-driven cavity flow using the lattice Boltzmann method. In WSEAS International Conference. Proceedings. Mathematics and Computers in Science and Engineering. WSEAS, 2008.
[27] L.S. Luo,W. Liao, X. Chen,Y. Peng,W. Zhang, et al. Numerics of the lattice Boltzmann method: Effects of collision models on the lattice Boltzmann simulations. Phys. Rev. E, 83(5):056710, 2011.
[28] X. He, X. Shan, and G.D. Doolen. Discrete Boltzmann equation model for nonideal gases. Phys. Rev. E, 57:R13, 1998.
[29] R. Benzi, S. Succi, and M. Vergassola. Turbulence modelling by nonhydrodynamic variables. Europhys. Lett., 13:727, 1990.
[30] P. J Dellar. Nonhydrodynamic modes and a priori construction of shallow water lattice Boltzmann equations. Phys. Rev. E, 65:036309, 2002.
[31] J. Latt and B. Chopard. Lattice Boltzmann method with regularized pre-collision distribution functions. Comput. Fluid., 72:165–168, 2006.
[32] C.H. Bruneau and C. Jouron. An efficient scheme for solving steady incompressible Navier- Stokes equations. J. Comput. Phys., 89:389–413, 1990.
[33] S. Hou, Q. Zou, S. Chen, G. D. Doolen, and A.C. Cogley. Simulation of cavity flow by the lattice boltzmann method. J. Comp. Phys., 118(2)(2):329 –347, 1995.
[34] G. Deng, J. Piquet, P. Queutey, and M. Visonneau. Incompressible flow calculations with a consistent physical interpolation finite volume approach. Comput. Fluids, 23:1029–1047, 1994.
Go to article

Authors and Affiliations

Seemaa Mohammed
1
Tim Reis
2

  1. School of Computing Electronics and Mathematics, Plymouth University, UK
  2. Department of Mathematical Sciences, University of Greenwich, UK
Download PDF Download RIS Download Bibtex

Abstract

The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.

Go to article

Bibliography

[1] G. Renner and A. Ekárt. Genetic algorithms in computer aided design. Computer-Aided Design, 35(8):709–726, 2003. doi: 10.1016/S0010-4485(03)00003-4.
[2] V. Schramm. Labyrinth Seals of Maximum Sealing: A Approach to Computer-Based Form Optimization, volume 46. Logos Verlag Berlin GmbH, 2011. (in German).
[3] W. Wróblewski, S. Dykas, K. Bochon, and S. Rulik. Optimization of tip seal with honeycomb land in LP counter rotating gas turbine engine. Task Quarterly, 14(3):189–207, 2010.
[4] G. Nowak and W. Wróblewski. Cooling system optimisation of turbine guide vane. Applied Thermal Engineering, 29(2-2):567–572, 2009. doi: 10.1016/j.applthermaleng.2008.03.015.
[5] G. Nowak, W. Wróblewski, and I. Nowak. Convective cooling optimization of a blade for a supercritical steam turbine. International Journal of Heat and Mass Transfer, 55(17-18):4511– 4520, 2012. doi: 10.1016/j.ijheatmasstransfer.2012.03.072.
[6] G. Nowak and A. Rusin. Shape and operation optimisation of a supercritical steam turbine rotor. Energy Conversion and Management, 74:417–425, 2013. doi: 10.1016/j.enconman.2013.06.037.
[7] A. Jahangirian and A. Shahrokhi. Aerodynamic shape optimization using efficient evolutionary algorithms and unstructured CFD solver. Computers & Fluids, 46(1):270–276, 2011. doi: 10.1016/j.compfluid.2011.02.010.
[8] J. Antony. Design of experiments for engineers and scientists. Elsevier, 2nd edition, 2014.
[9] L. Eriksson, E. Johansson, N. Kettaneh-Wold, C. Wikström, and S. Wold. Design of Experiments, Principles and Applications. Umetrics AB, Sweden, 2000.
[10] H.B. Demuth, M.H. Beale, O. De Jess, and M.T. Hagan. Neural Network Design. Martin Hagan, USA, 2nd edition, 2014.
[11] T. Back. Evolutionary algorithms in theory and practice. Oxford University Press, 1996.
[12] Z. Michalewicz. Genetic Algorithms + Data Structures = Evolution Programs. Springer, 1996.
[13] V. Schramm, K. Willenborg, S. Kim, and S. Wittig. Influence of a honeycomb facing on the flow through a stepped labyrinth seal. In ASME Turbo Expo 2000: Power for Land, Sea, and Air, pages V003T01A092–V003T01A092. ASME, 2000. doi: 10.1115/2000-GT-0291.
[14] M.D. Morris. Factorial sampling plans for preliminary computational experiments. Technometrics, 33(2):161–174, 1991.
[15] B. Iooss and P. Lemaître. A review on global sensitivity analysis methods. In Dellino G. and Meloni C., editors, Uncertainty Management in Simulation-Optimization of Complex Systems, chapter 5, pages 101–122. Springer, 2015.
[16] F. Campolongo and J. Cariboni. Sensitivity analysis: How to detect important factors in large models. Technical report, 2007. http://publications.jrc.ec.europa.eu/repository/ handle/ JRC37120.
[17] F. Pianosi, F. Sarrazin, and T. Wagener. A Matlab toolbox for global sensitivity analysis. Environmental Modelling & Software, 70:80–85, 2015. doi: 10.1016/j.envsoft.2015.04.009.
Go to article

Authors and Affiliations

Sebastian Rulik
1
Włodzimierz Wróblewski
1
Daniel Frączek
1

  1. Silesian University of Technology, Institute of Power Engineering and Technology, Gliwice, Poland
Download PDF Download RIS Download Bibtex

Abstract

In the article, the authors analyze and discuss several models used to the calculation of air gauge characteristics. The model based on the actual mass flow (which is smaller than the theoretical one) was proposed, too. Calculations have been performed with a dedicated software with the second critical parameters included. The air gauge static characteristics calculated with 6 different models were compared with the experimental data. It appeared that the second critical parameters model (SCP) provided the characteristics close to the experimental ones, with an error of ca. 3% within the air gauge measuring range.

Go to article

Bibliography

[1] M. Mennesson. High precision measurement method of lengths and thicknesses. Comptes Rendus des Seances de l’Academie des Sciences, 194(25.4.1932):1459–1461, 1932.
[2] W.J. Gluchow and A.A. Tupolew. Non-contact pneumatic measuring control devices for the production of workpieces with discontinuous surfaces. Feingeratetechnik, 23(2):69–73, 1974. (in German).
[3] E.I. Ped. Optimization of the constructional elements choice of the air gauges designed for the dynamic measurements. Measurement Techniques, 7:29–31, 1981. (in Russian).
[4] F.T. Farago and Curtis M.A. Handbook of Dimensional Measurement. Industrial Press Inc., New York, 1994.
[5] G. Schuetz. Pushing the limits of air gaging-and keeping them there. Quality, 54(7):22–26, 2015.
[6] G. Schuetz. Air gaging gets better with age. Quality, 3:28–32, 2008.
[7] L. Finkelstein. Reflections on a century of measurement science as an academic discipline. Metrology and Measurement Systems, 14(4):635–638, 2007.
[8] M. Rucki, B. Barisic, and G. Varga. Air gauges as a part of the dimensional inspection systems. Measurement, 43(1):83–91, 2010. doi: 10.1016/j.measurement.2009.07.001.
[9] T. Janiczek and J. Janiczek. Linear dynamic system identification in the frequency domain using fractional derivatives. Metrology and Measurement Systems, 17(2):279–288, 2010. doi: 10.2478/v10178-010-0024-6.
[10] V.B. Bokov. Pneumatic gauge steady-state modelling by theoretical and empirical methods. Measurement, 44(2):303–311, 2011. doi: 10.1016/j.measurement.2009.01.015.
[11] B. Dobrowolski, Z. Kabza, and A. Spyra. Digital simulation of air flow through a nozzle of pneumatic gauge. In Proc. 33rd Annual Conference JUREMA, pages 67–70, 1988.
[12] M.N. Abhari, M. Ghodsian, M. Vaghefi, and N. Panahpur. Experimental and numerical simulation of flow in a 90° bend. Flow Measurement and Instrumentation, 21(3):292–298, 2010. doi: 10.1016/j.flowmeasinst.2010.03.002.
[13] J. Peng, X. Fu, and Y. Chen. Response of a swirlmeter to oscillatory flow. Flow Measurement and Instrumentation, 19(2):107–115, 2008. doi: 10.1016/j.flowmeasinst.2007.10.002.
[14] C. Crnojevic, G. Roy, A. Bettahar, and P. Florent. The influence of the regulator diameter and injection nozzle geometry on the flow structure in pneumatic dimensional control systems. Journal of Fluids Engineering, 119:609–615, 1997. doi: 10.1115/1.2819288.
[15] C. Jermak, editor. Theory and Practice of Air Gauging. Poznan University of Technology, 2011.
[16] T. Kiczkowiak and S. Grymek. Critical pressure ratio b as defined in iso 6358 and iso 6953 standards. Pomiary Automatyka Kontrola (Measurement, Automation, Monitoring), 57:559– 562, 2011. (in Polish).
[17] A Cellary and C.J. Jermak. Dynamics of a cascade pneumatic sensor for length measurements. In Proc. of Optoelectronic and Electronic Sensors II, pages 36–39. International Society for Optics and Photonics, 1997. doi: 10.1117/12.266719.
[18] A.V. Deych. Technical gasodynamics. Gosenergoizdat, Moscow, 1961. (in Russian).
[19] M. Kabacinski, C. T Lachowicz, and J. Pospolita. Numerical analysis of flow averaging tubes in the vortex-shedding regime. Archive of Mechanical Engineering, 60(2):283–297, 2013. doi: 10.2478/meceng-2013-0018.
[20] Koscielny W. and C. Wozniak. Models of the flow characteristics of the pneumatic restrictors. In Proc. PNEUMA’95, pages 73–82, 1995. (in Polish).
[21] Koscielny W. and C. Wozniak. Experimental evaluation of the models of the pneumatic restrictors flow characteristics. In Proc. PNEUMA’95, pages 83–92, 1995. (in Polish).
[22] Automation of the pneumatic dimensional measurement in mechanical engineering. Mashinostroyeniye, Moscow, 1964. (in Russian).
[23] C.J. Jermak. Methods of shaping the metrological characteristics of air gages. Strojniski Vestnik/Journal of Mechanical Engineering, 56(6):385–390, 2010.
[24] R.J. Soboczynski. Investigations on the metrological properties of high pressure air gauges. PhD thesis, Wrocław Technical University, 1977. (in Polish).
[25] Calculation of the high pressure air gauges characteristics. Journal Measuring Techniques, 6:107, 1971.
[26] Guide to the expression of uncertainty in measurement. Warszawa, Główny Urząd Miar, 1999. (in Polish).
[27] C.J. Jermak and M. Rucki. Air gauging: Static and dynamic characteristics. IFSA, Barcelona, Spain, 2012.
[28] C.J. Jermak and M. Rucki. Air gauging: Still some room for development. AASCIT Communication, 2(2):29–34, 2015.
Go to article

Authors and Affiliations

Czeslaw Janusz Jermak
1
Ryszard Piątkowski
2
Janusz Dereżyński
1
Miroslaw Rucki
3

  1. Institute of Mechanical Technology, Poznan University of Technology, Poland
  2. Chair of Thermal Engineering, Poznan Univesity of Technology, Poland
  3. Faculty of Mechanical Engineering, Kazimierz Pulaski University of Technology and Humanities in Radom, Poland
Download PDF Download RIS Download Bibtex

Abstract

This article reports the effects of CuO/water based coolant on specific fuel consumption and exhaust emissions of four stroke single cylinder diesel engine. The CuO nanoparticles of 27 nm were used to prepare the nanofluid-based engine coolant. Three different volume concentrations (i.e 0.05%, 0.1%, and 0.2%) of CuO/water nanofluids were prepared by using two-step method. The purpose of this study is to investigate the exhaust emissions (NOx), exhaust gas temperature and specific fuel consumption under different load conditions with CuO/water nanofluid. After a series of experiments, it was observed that the CuO/water nanofluids, even at low volume concentrations, have a significant influence on exhaust emissions. The experimental results revealed that, at full load condition, the specific fuel consumption was reduced by 8.6%, 15.1% and 21.1% for the addition of 0.05%, 0.1% and 0.2% CuO nanoparticles with water, respectively. Also, the emission tests were concluded that 881 ppm, 853 ppm and 833 ppm of NOx emissions were observed at high load with 0.05%, 0.1% and 0.2% volume concentrations of CuO/water nanofluids, respectively.

Go to article

Bibliography

[1] S.U.S. Choi and J.A. Eastman. Enhancing thermal conductivity of fluids with nanoparticles. In 1995 International mechanical engineering congress and exhibition. ASME, 12-17 Nov. 1995.
[2] M.A. Akhavan-Behabadi, F. Hekmatipour, S.M. Mirhabibi, and B. Sajadi. Experimental investigation of thermal–rheological properties and heat transfer behavior of the heat transfer oil–copper oxide (HTO–CuO) nanofluid in smooth tubes. Experimental Thermal and Fluid Science, 68:681–688, 2015.
[3] M.T.Naik, S.S. Fahad, L.S. Sundar, and M.K. Singh. Comparative study on thermal performance of twisted tape and wire coil inserts in turbulent flow using CuO/water nanofluid. Experimental Thermal and Fluid Science, 57:65–76, 2014.
[4] M.T. Naik, G.R. Janardana, and L.S. Sundar. Experimental investigation of heat transfer and friction factor with water–propylene glycol based CuO nanofluid in a tube with twisted tape inserts. International Communications in Heat and Mass Transfer, 46:13–21, 2013.
[5] J.J. Michael and S. Iniyan. Performance analysis of a copper sheet laminated photovoltaic thermal collector using copper oxide–water nanofluid. Solar Energy, 119:439–451, 2015.
[6] M. Bouhalleb and H. Abbassi. Natural convection in an inclined rectangular enclosure filled by CuO-H2O nanofluid, with sinusoidal temperature distribution. International Journal of Hydrogen Energy, 40(39):13676–13684, 2015.
[7] K. Goudarzi, E. Shojaeizadeh, and F. Nejati. An experimental investigation on the simultaneous effect of CuO-H2O nanofluid and receiver helical pipe on the thermal efficiency of a cylindrical solar collector. Applied Thermal Engineering, 73(1):1236–1243, 2014.
[8] Y. Abbassi, A.S. Shirani, and S. Asgarian. Two-phase mixture simulation of Al2O3/water nanofluid heat transfer in a non-uniform heat addition test section. Progress in Nuclear Energy, 83:356–364, 2015.
[9] H.K. Gupta, G.D. Agrawal, and J. Mathur. An experimental investigation of a low temperature Al2O3-H2O nanofluid based direct absorption solar collector. Solar Energy, 118:390–396, 2015.
[10] E. Shojaeizadeh, F. Veysi, and A. Kamandi. Exergy efficiency investigation and optimization of an Al2O3-water nanofluid based flat-plate solar collector. Energy and Buildings, 101:12–23, 2015.
[11] M.H. Esfe, A. Karimipour, W.-M. Yan, M. Akbari, M.R. Safaei, and M. Dahari. Experimental study on thermal conductivity of ethylene glycol based nanofluids containing Al2O3 nanoparticles. International Journal of Heat and Mass Transfer, 88:728–734, 2015.
[12] M.H. Esfe, S. Saedodin, M. Akbari, A. Karimipour, M. Afrand, S. Wongwises, M.R. Safaei, and M. Dahari. Experimental investigation and development of new correlations for thermal conductivity of cuo/eg–water nanofluid. International Communications in Heat and Mass Transfer, 65:47–51, 2015.
[13] L. S. Sundar, Md.H. Farooky, S.N. Sarada, and M.K. Singh. Experimental thermal conductivity of ethylene glycol and water mixture based low volume concentration of Al2O3 and CuO nanofluids. International Communications in Heat and Mass Transfer, 41:41–46, 2013.
[14] R.S. Khedkar, S.S. Sonawane, and K.L.Wasewar. Influence of CuO nanoparticles in enhancing the thermal conductivity of water and monoethylene glycol based nanofluids. International Communications in Heat and Mass Transfer, 39(5):665–669, 2012.
[15] M.N. Rashin and J. Hemalatha. A novel ultrasonic approach to determine thermal conductivity in CuO-ethylene glycol nanofluids. Journal of Molecular Liquids, 197:257–262, 2014.
[16] R. Karthik, R.H. Nagarajan, B. Raja, and P. Damodharan. Thermal conductivity of CuO-DI water nanofluids using 3-ω measurement technique in a suspended micro-wire. Experimental Thermal and Fluid Science, 40:1–9, 2012.
[17] S. Harikrishnan and S. Kalaiselvam. Preparation and thermal characteristics of CuO-oleic acid nanofluids as a phase change material. Thermochimica Acta, 533:46–55, 2012.
[18] M. Saeedinia, M.A. Akhavan-Behabadi, and P. Razi. Thermal and rheological characteristics of CuO-base oil nanofluid flow inside a circular tube. I nternational Communications in Heat and Mass Transfer, 39(1):152–159, 2012.
[19] M.-S. Liu, M.C.-C. Lin, I.-T. Huang, and C.-C. Wang. Enhancement of thermal conductivity with CuO for nanofluids. Chemical Engineering & Technology, 29(1):72–77, 2006.
[20] M.-S. Liu, M.C.-C. Lin, and C.-C. Wang. Enhancements of thermal conductivities with Cu, CuO, and carbon nanotube nanofluids and application of MWNT/water nanofluid on a water chiller system. Nanoscale Research Letters, 6(1):1–13, 2011.
[21] H.E. Patel, T. Sundararajan, and S.K. Das. An experimental investigation into the thermal conductivity enhancement in oxide and metallic nanofluids. Journal of Nanoparticle Research, 12(3):1015–1031, 2010.
[22] D.P. Kulkarni, R.S. Vajjha, D.K. Das, and D. Oliva. Application of aluminum oxide nanofluids in diesel electric generator as jacket water coolant. Applied Thermal Engineering, 28(14-15):1774–1781, 2008.
[23] M. Raja, R. Vijayan, S. Suresh, and R. Vivekananthan. Effect of heat transfer enhancement and NOx emission using Al2O3/water nanofluid as coolant in CI engine. Indian Journal of Engineering & Materials Sciences, 20:443–449, 2013.
[24] S.M. Peyghambarzadeh, S.H. Hashemabadi, S.M. Hoseini, and M.S. Jamnani. Experimental study of heat transfer enhancement using water/ethylene glycol based nanofluids as a newcoolant for car radiators. International Communications in Heat and Mass Transfer, 38(9):1283–1290, 2011.
[25] S. Suresh, M. Chandrasekar, and S.C. Sekhar. Experimental studies on heat transfer and friction factor characteristics of CuO/water nanofluid under turbulent flow in a helically dimpled tube. Experimental Thermal and Fluid Science, 35(3):542–549, 2011.
[26] M. Naraki, S.M. Peyghambarzadeh, S.H. Hashemabadi, and Y. Vermahmoudi. Parametric study of overall heat transfer coefficient of Cuo/water nanofluids in a car radiator. International Journal of Thermal Sciences, 66:82–90, 2013.
[27] B. Xiao, Y. Yang, and L. Chen. Developing a novel form of thermal conductivity of nanofluids with Brownian motion effect by means of fractal geometry. Powder Technology, 239:409–414, 2013.
[28] C. Sayin and M. Canakci. Effects of injection timing on the engine performance and exhaust emissions of a dual-fuel diesel engine. Energy Conversion and Management, 50(1):203–213, 2009.
Go to article

Authors and Affiliations

S. Senthilraja
1
KCK Vijayakumar
2
R. Gangadevi
3

  1. Faculty of Mechanical Engineering, Anna University, Chennai, India
  2. Department of Mechanical Engineering, Vivekanandha Institute of Engineering & Technology for Women, Tiruchengode, India
  3. Department of Mechatronics Engineering, SRM University, Chennai, India
Download PDF Download RIS Download Bibtex

Abstract

Discontinuous coefficients in the Poisson equation lead to the weak discontinuity in the solution, e.g. the gradient in the field quantity exhibits a rapid change across an interface. In the real world, discontinuities are frequently found (cracks, material interfaces, voids, phase-change phenomena) and their mathematical model can be represented by Poisson type equation. In this study, the extended finite element method (XFEM) is used to solve the formulated discontinuous problem. The XFEM solution introduce the discontinuity through nodal enrichment function, and controls it by additional degrees of freedom. This allows one to make the finite element mesh independent of discontinuity location. The quality of the solution depends mainly on the assumed enrichment basis functions. In the paper, a new set of enrichments are proposed in the solution of the Poisson equation with discontinuous coefficients. The global and local error estimates are used in order to assess the quality of the solution. The stability of the solution is investigated using the condition number of the stiffness matrix. The solutions obtained with standard and new enrichment functions are compared and discussed.

Go to article

Bibliography

[1] T.P. Fries and H.G. Matthies. Classification and overview of meshfree methods. Informatikbericht Nr.: 2003-3. Technical University Braunschweig, Brunswick, Germany, 2004.
[2] M.A. Schweitzer. Meshfree and generalized finite element methods. Postdoctoral dissertation. Mathematisch–Naturwissenschaftlichen Fakultat der Rheinischen Friedrich-Wilhelms-Universitat, Bonn, Germany, 2008.
[3] Vinh Phu Nguyen, C. Anitescu, S. Bordas, and T. Rabczuk. Isogeometric analysis: An overview and computer implementation aspects. Mathematics and Computers in Simulation, 117:89–116, 2015. doi: 10.1016/j.matcom.2015.05.008.
[4] T. Belytschko and T. Black. Elastic crack growth in finite elements with minimal remeshing. International Journal for Numerical Methods in Engineering, 45(5):601–620, 1999.
[5] R. Merle and J. Dolbow. Solving thermal and phase change problems with the eXtended finite element method. Computational Mechanics, 28(5):339–350, 2002. doi: 10.1007/s00466-002-0298-y.
[6] J. Chessa, P. Smolinski, and T. Belytschko. The extended finite element method (XFEM) for solidification problems. International Journal for Numerical Methods in Engineering, 53(8):1959–1977, 2002. doi: 10.1002/nme.386.
[7] P. Stapór. The XFEM for nonlinear thermal and phase change problems. International Journal of Numerical Methods for Heat & Fluid Flow, 25(2):400–421, 2015. doi: 10.1108/HFF-02-2014-0052.
[8] J.Y. Wu and F.B. Li. An improved stable XFEM (Is-XFEM) with a novel enrichment function for the computational modeling of cohesive cracks. Computer Methods in Applied Mechanics and Engineering, 295:77–107, 2015. doi: 10.1016/j.cma.2015.06.018.
[9] P. Hansbo, M.G. Larson, and S. Zahedi. A cut finite element method for a stokes interface problem. Applied Numerical Mathematics, 85:90–114, 2014. doi: 10.1016/j.apnum.2014.06.009.
[10] E. Wadbro, S. Zahedi, G. Kreiss, and M. Berggren. A uniformly well-conditioned, unfitted Nitsche method for interface problems. BIT Numerical Mathematics, 53(3):791–820, 2013. doi: 10.1007/s10543-012-0417-x.
[11] I. Babuška and U. Banerjee. Stable generalized finite element method (SGFEM). Computer Methods in Applied Mechanics and Engineering, 201:91–111, 2012. doi: 10.1016/j.cma.2011.09.012.
[12] K. Kergrene, I. Babuška, and U. Banerjee. Stable generalized finite element method and associated iterative schemes; application to interface problems. Computer Methods in Applied Mechanics and Engineering, 305:1–36, 2016. doi: 10.1016/j.cma.2016.02.030.
[13] G. Zi and T. Belytschko. New crack-tip elements for XFEM and applications to cohesive cracks. International Journal for Numerical Methods in Engineering, 57(15):2221–2240, 2003. doi: 10.1002/nme.849.
[14] G. Ventura, E. Budyn, and T. Belytschko. Vector level sets for description of propagating cracks in finite elements. International Journal for Numerical Methods in Engineering, 58(10):1571–1592, 2003. doi: 10.1002/nme.829.
[15] J.E. Tarancón, A.Vercher, E. Giner, and F.J. Fuenmayor. Enhanced blending elements for XFEM applied to linear elastic fracture mechanics. International Journal for Numerical Methods in Engineering, 77(1):126–148, 2009. doi: 10.1002/nme.2402.
[16] T.P. Fries. A corrected XFEM approximation without problems in blending elements. International Journal for Numerical Methods in Engineering, 75(5):503–532, 2008. doi: 10.1002/nme.2259.
[17] P. Stąpór. Application of XFEM with shifted-basis approximation to computation of stress intensity factors. Archive of Mechanical Engineering, 58(4):447–483, 2011. doi: 10.2478/v10180-011-0028-0.
[18] N. Moës, M. Cloirec, P. Cartraud, and J.-F. Remacle. A computational approach to handle complex microstructure geometries. Computer Methods in Applied Mechanics and Engineering, 192(28):3163–3177, 2003. doi: 10.1016/S0045-7825(03)00346-3.
[19] J. Dolbow, N. Moës, and T. Belytschko. Discontinuous enrichment in finite elements with a partition of unity method. Finite Elements in Analysis and Design, 36(3):235–260, 2000. doi: 10.1016/S0168-874X(00)00035-4.
[20] B.A. Saxby. High-order XFEM with applications to two-phase flows. PhD thesis, The University of Manchester, Manchester, UK, 2014. www.escholar.manchester.ac.uk/uk-ac-manscw:234445.
Go to article

Authors and Affiliations

Paweł Stąpór
1

  1. Faculty of Management and Computer Modelling, Kielce University of Technology, Poland

Instructions for authors

About the Journal
Archive of Mechanical Engineering is an international journal publishing works of wide significance, originality and relevance in most branches of mechanical engineering. The journal is peer-reviewed and is published both in electronic and printed form. Archive of Mechanical Engineering publishes original papers which have not been previously published in other journal, and are not being prepared for publication elsewhere. The publisher will not be held legally responsible should there be any claims for compensation. The journal accepts papers in English.

Archive of Mechanical Engineering is an Open Access journal. The journal does not have article processing charges (APCs) nor article submission charges.

Original high quality papers on the following topics are preferred:

  • Mechanics of Solids and Structures,
  • Fluid Dynamics,
  • Thermodynamics, Heat Transfer and Combustion,
  • Machine Design,
  • Computational Methods in Mechanical Engineering,
  • Robotics, Automation and Control,
  • Mechatronics and Micro-mechanical Systems,
  • Aeronautics and Aerospace Engineering,
  • Heat and Power Engineering.

All submissions to the AME should be made electronically via Editorial System - an online submission and peer review system at: https://www.editorialsystem.com/ame

More detailed instructions for Authors can be found there.

Reviewers

The Editorial Board of the Archive of Mechanical Engineering (AME) sincerely expresses gratitude to the following individuals who devoted their time to review papers submitted to the journal. Particularly, we express our gratitude to those who reviewed papers several times.

List of reviewers of volume 68 (2021)

Ahmad ABDALLA – Huaiyin Institute of Technology, China
Sara ABDELSALAM – University of California, Riverside, United States
Muhammad Ilman Hakimi Chua ABDULLAH – Universiti Teknikal Malaysia Melaka, Malaysia
Hafiz Malik Naqash AFZAL – University of New South Wales, Sydney, Australia
Reza ANSARI – University of Guilan, Rasht, Iran
Jeewan C. ATWAL – Indian Institute of Technology Delhi, New Delhi, India
Hadi BABAEI – Islamic Azad University, Tehran, Iran
Sakthi BALAN – K. Ramakrishnan college of Engineering, Trichy, India
Leszek BARANOWSKI – Military University of Technology, Warsaw, Poland
Elias BRASSITOS – Lebanese American University, Byblos, Lebanon
Tadeusz BURCZYŃSKI – Institute of Fundamental Technological Research, Warsaw, Poland
Nguyen Duy CHINH – Hung Yen University of Technology and Education, Hung Yen, Vietnam
Dorota CHWIEDUK – Warsaw University of Technology, Poland
Adam CISZKIEWICZ – Cracow University of Technology, Poland
Meera CS – University of Petroleum and Energy Studies, Duhradun, India
Piotr CYKLIS – Cracow University of Technology, Poland
Abanti DATTA – Indian Institute of Engineering Science and Technology, Shibpur, India
Piotr DEUSZKIEWICZ – Warsaw University of Technology, Poland
Dinesh DHANDE – AISSMS College of Engineering, Pune, India
Sufen DONG – Dalian University of Technology, China
N. Godwin Raja EBENEZER – Loyola-ICAM College of Engineering and Technology, Chennai, India
Halina EGNER – Cracow University of Technology, Poland
Fehim FINDIK – Sakarya University of Applied Sciences, Turkey
Artur GANCZARSKI – Cracow University of Technology, Poland
Peng GAO – Northeastern University, Shenyang, China
Rafał GOŁĘBSKI – Czestochowa University of Technology, Poland
Andrzej GRZEBIELEC – Warsaw University of Technology, Poland
Ngoc San HA – Curtin University, Perth, Australia
Mehmet HASKUL – University of Sirnak, Turkey
Michal HATALA – Technical University of Košice, Slovak Republic
Dewey HODGES – Georgia Institute of Technology, Atlanta, United States
Hamed HONARI – Johns Hopkins University, Baltimore, United States
Olga IWASINSKA – Warsaw University of Technology, Poland
Emmanuelle JACQUET – University of Franche-Comté, Besançon, France
Maciej JAWORSKI – Warsaw University of Technology, Poland
Xiaoling JIN – Zhejiang University, Hangzhou, China
Halil Burak KAYBAL – Amasya University, Turkey
Vladis KOSSE – Queensland University of Technology, Brisbane, Australia
Krzysztof KUBRYŃSKI – Air Force Institute of Technology, Warsaw, Poland
Waldemar KUCZYŃSKI – Koszalin University of Technology, Poland
Igor KURYTNIK – State Higher School in Oswiecim, Poland
Daniel LESNIC – University of Leeds, United Kingdom
Witold LEWANDOWSKI – Gdańsk University of Technology, Poland
Guolu LI – Hebei University of Technology, Tianjin, China
Jun LI – Xi’an Jiaotong University, China
Baiquan LIN – China University of Mining and Technology, Xuzhou, China
Dawei LIU – Yanshan University, Qinhuangdao, China
Luis Norberto LÓPEZ DE LACALLE – University of the Basque Country, Bilbao, Spain
Ming LUO – Northwestern Polytechnical University, Xi’an, China
Xin MA – Shandong University, Jinan, China
Najmuldeen Yousif MAHMOOD – University of Technology, Baghdad, Iraq
Arun Kumar MAJUMDER – Indian Institute of Technology, Kharagpur, India
Paweł MALCZYK – Warsaw University of Technology, Poland
Miloš MATEJIĆ – University of Kragujevac, Serbia
Norkhairunnisa MAZLAN – Universiti Putra Malaysia, Serdang, Malaysia
Dariusz MAZURKIEWICZ – Lublin University of Technology, Poland
Florin MINGIREANU – Romanian Space Agency, Bucharest, Romania
Vladimir MITYUSHEV – Pedagogical University of Cracow, Poland
Adis MUMINOVIC – University of Sarajevo, Bosnia and Herzegovina
Baraka Olivier MUSHAGE – Université Libre des Pays des Grands Lacs, Goma, Congo (DRC)
Tomasz MUSZYŃSKI – Gdansk University of Technology, Poland
Mohamed NASR – National Research Centre, Giza, Egypt
Driss NEHARI – University of Ain Temouchent, Algeria
Oleksii NOSKO – Bialystok University of Technology, Poland
Grzegorz NOWAK – Silesian University of Technology, Gliwice, Poland
Iwona NOWAK – Silesian University of Technology, Gliwice, Poland
Samy ORABY – Pharos University in Alexandria, Egypt
Marcin PĘKAL – Warsaw University of Technology, Poland
Bo PENG – University of Huddersfield, United Kingdom
Janusz PIECHNA – Warsaw University of Technology, Poland
Maciej PIKULIŃSKI – Warsaw University of Technology, Poland
T.V.V.L.N. RAO – The LNM Institute of Information Technology, Jaipur, India
Andrzej RUSIN – Silesian University of Technology, Gliwice, Poland
Artur RUSOWICZ – Warsaw University of Technology, Poland
Benjamin SCHLEICH – Friedrich-Alexander-Universität Erlangen-Nürnberg, Germany
Jerzy SĘK – Lodz University of Technology, Poland
Reza SERAJIAN – University of California, Merced, USA
Artem SHAKLEIN – Udmurt Federal Research Center, Izhevsk, Russia
G.L. SHI – Guangxi University of Science and Technology, Liuzhou, China
Muhammad Faheem SIDDIQUI – Vrije University, Brussels, Belgium
Jarosław SMOCZEK – AGH University of Science and Technology, Cracow, Poland
Josip STJEPANDIC – PROSTEP AG, Darmstadt, Germany
Pavel A. STRIZHAK – Tomsk Polytechnic University, Russia
Vadym STUPNYTSKYY – Lviv Polytechnic National University, Ukraine
Miklós SZAKÁLL – Johannes Gutenberg-Universität Mainz, Germany
Agnieszka TOMASZEWSKA – Gdansk University of Technology, Poland
Artur TYLISZCZAK – Czestochowa University of Technology, Poland
Aneta USTRZYCKA – Institute of Fundamental Technological Research, Warsaw, Poland
Alper UYSAL – Yildiz Technical University, Turkey
Gabriel WĘCEL – Silesian University of Technology, Gliwice, Poland
Marek WĘGLOWSKI – Welding Institute, Gliwice, Poland
Frank WILL – Technische Universität Dresden, Germany
Michał WODTKE – Gdańsk University of Technology, Poland
Marek WOJTYRA – Warsaw University of Technology, Poland
Włodzimierz WRÓBLEWSKI – Silesian University of Technology, Gliwice, Poland
Hongtao WU – Nanjing University of Aeronautics and Astronautics, China
Jinyang XU – Shanghai Jiao Tong University, China
Zhiwu XU – Harbin Institute of Technology, China
Zbigniew ZAPAŁOWICZ – West Pomeranian University of Technology, Szczecin, Poland
Zdzislaw ZATORSKI – Polish Naval Academy, Gdynia, Poland
Wanming ZHAI – Southwest Jiaotong University, Chengdu, China
Xin ZHANG – Wenzhou University of Technology, China
Su ZHAO – Ningbo Institute of Materials Technology and Engineering, China

This page uses 'cookies'. Learn more