Search results

Filters

  • Journals
  • Authors
  • Keywords
  • Date
  • Type

Search results

Number of results: 2
items per page: 25 50 75
Sort by:
Download PDF Download RIS Download Bibtex

Abstract

Water resources are the main component of natural systems affected by climate change in the Middle East. Due to a lack of water, steam power plants that use wet cooling towers have inevitably reduced their output power. This article investigates the replacement of wet cooling towers in Isfahan Thermal Power Plant (ITPP) with Heller natural dry draft cooling towers. The thermodynamic cycle of ITPP is simulated and the effect of condenser temperature on efficiency and output power of ITPP is evaluated. For various reasons, the possibility of installing the Heller tower without increasing in condenser temperature and without changing the existing components of the power plant was rejected. The results show an increase in the condenser temperature by removing the last row blades of the low-pressure turbine. However, by replacing the cooling tower without removing the blades of the last row of the turbine, the output power and efficiency of the power plant have decreased about 12.4 MW and 1.68 percent, respectively.
Go to article

Bibliography

[1] B. Dziegielewski and D. Baumann. Tapping alternatives: The benefits of managing urban water demands. Environment: Science and Policy for Sustainable Development, 34(9):6–41, 2010. doi: 10.1080/00139157.1992.9930929.
[2] D. Marmer. Water conservation equals energy conservation. Energy Engineering, 115(5):48–63, 2018. doi: 10.1080/01998595.2018.12027708.
[3] J.M. Burns, D.C. Burns, and J.S. Burns. Retrofitting cooling towers: estimates required to achieve the next level of CWA 316(b) compliance. In Proceedings of the ASME Power Conference, pages 25–33, 2004. doi: 10.1115/POWER2004-52051.
[4] A. Loew, P. Jaramillo, and H. Zhai. Marginal costs of water savings from cooling system retrofits: a case study for Texas power plants. Environmental Research Letters, 11(10):104004, 2016. doi: 10.1088/1748-9326/11/10/104004.
[5] A.E. Conradie and D.G. Kröger. Performance evaluation of dry-cooling systems for power plant applications. Applied Thermal Engineering, 16(3):219–232, 1996. doi: 10.1016/1359-4311(95)00068-2.
[6] A.E. Conradie, J.D. Buys, and D.G. Kröger. Performance optimization of dry-cooling systems for power plants through SQP methods. Applied Thermal Engineering, 18(1-2):25–45, 1998. doi: 10.1016/S1359-4311(97)00020-3.
[7] J.D. Buys and D.G. Kröger. Dimensioning heat exchangers for existing dry cooling towers. Energy Conversion and Management, 29(1):63–71, 1989. doi: 10.1016/0196-8904(89)90014-9.
[8] Z. Zou, Z. Guan, H. Gurgenci, and Y. Lu. Solar enhanced natural draft dry cooling tower for geothermal power applications. Solar Energy, 86(9):2686–2694, 2012. doi: 10.1016/j.solener.2012.06.003.
[9] S. Bagheri and M. Nikkhoo. Investigation of the optimum location for adding two extra Heller-type cooling towers in Shazand power plant. Proceedings of the 17th IAHR International Conference on Cooling Tower and Heat, pages. 74–83, Australia, 2015.
[10] W. Peng and O.K. Sadaghiani. Presentation of an integrated cooling system for enhancement of cooling capability in Heller cooling tower with thermodynamic analyses and optimization. International Journal of Refrigeration, 131:786–802, 2021. doi: 10.1016/j.ijrefrig.2021.07.016.
[11] M.A. Ardekani, F. Farhani, and M. Mazidi. Effects of cross wind conditions on efficiency of Heller dry cooling tower. Experimental Heat Transfer, 28(4):344–353, 2015. doi: 10.1080/08916152.2014.883449.
[12] A. Jahangiri, A. Borzooee, and E. Armoudli. Thermal performance improvement of the three aligned natural draft dry cooling towers by wind breaking walls and flue gas injection under different crosswind conditions. International Journal of Thermal Sciences, 137:288–298, 2019. doi: 10.1016/j.ijthermalsci.2018.11.028.
[13] A.R. Seifi, O.A. Akbari, A.A. Alrashed, F. Afshari, G.A.S. Shabani, R. Seifi, M. Goodarzi, and F. Pourfattah. Effects of external wind breakers of Heller dry cooling system in power plants. Applied Thermal Engineering, 129: 1124–1134, 2018. doi: 10.1016/j.applthermaleng.2017.10.118.
[14] R.A. Kheneslu, A. Jahangiri, and M. Ameri. Interaction effects of natural draft dry cooling tower (NDDCT) performance and 4E (energy, exergy, economic and environmental) analysis of steam power plant under different climatic conditions. Sustainable Energy Technologies and Assessments, 37:100599, 2020. doi: 10.1016/j.seta.2019.100599.
[15] A. Jahangiri and F. Rahmani. Power production limitations due to the environmental effects on the thermal effectiveness of NDDCT in an operating powerplant. Applied Thermal Engineering, 141:444–455, 2018. doi: 10.1016/j.applthermaleng.2018.05.108.
[16] A.D. Samani. Combined cycle power plant with indirect dry cooling tower forecasting using artificial neural network. Decision Science Letters, 7:131–142, 2018. doi: 10.5267/j.dsl.2017.6.004.
[17] T.L. Bergman, F.P. Incropera, D.P. DeWitt, and A.S. Lavine. Fundamentals of Heat and Mass Transfer. John Wiley & Sons, 2011.
[18] Archive of Isfahan Mohammad Montazeri Power Station. Isfahan, Iran, 1984.
[19] H. Ahmadikia and G. Iravani. Numerical and analytical study of natural dry cooling tower in a steam power plant. Journal of Advanced Materials in Engineering (Esteghlal), 26(1):183–195, 2007. (in Persian).
[20] H.G. Zavaragh, M.A. Ceviz, and M.T.S. Tabar. Analysis of windbreaker combinations on steam power plant natural draft dry cooling towers. Applied Thermal Engineering, 99:550–559, 2016. doi: 10.1016/j.applthermaleng.2016.01.103.
[21] K.F. Reinschmidt and R. Narayanan. The optimum shape of cooling towers. Computers & Structures, 5(5-6):321–325, 1975. doi: 10.1016/0045-7949(75)90039-5.
[22] Isfahan Thermal Power Plant documents, No. C.583 and C.749, Islam Abad Power Plant, Isfahan, Iran, 1988.
[23] I.H. Shames. Mechanics of Fluids. 4th ed. McGraw-Hill, New York, 2003.
[24] C.R.F. Azevedo and A. Sinátora. Erosion-fatigue of steam turbine blades. Engineering Failure Analysis, 16(2):2290–2303, 2009. doi: 10.1016/j.engfailanal.2009.03.007.
[25] H. Kim. Crack evaluation of the fourth stage blade in a low-pressure steam turbine. Engineering Failure Analysis, 18(3):907–913, 2011. doi: 10.1016/j.engfailanal.2010.11.004.
[26] L.K. Bhagi, P. Gupta, and V. Rastogi. Fractographic investigations of the failure of L-1 pressure steam turbine blade. Case Studies in Engineering Failure Analysis, 1(2):72–78, 2013. doi: 10.1016/j.csefa.2013.04.007.
Go to article

Authors and Affiliations

Mohamad Hasan Malekmohamadi
1 2
Hossein Ahmadikia
1
ORCID: ORCID
Siavash Golmohamadi
2
Hamed Khodadadi
3

  1. University of Isfahan, Isfahan, Iran
  2. Isfahan Thermal Power Plant, Isfahan, Iran
  3. Department of Electrical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran
Download PDF Download RIS Download Bibtex

Abstract

Unmanned, battery-powered quadrotors have a limited onboard energy resources. However, flight duration might be increased by reasonable energy expenditure. A reliable mathematical model of the drone is required to plan the optimum energy management during the mission. In this paper, the theoretical energy consumption model was proposed. A small, low-cost DJI MAVIC 2 Pro quadrotor was used as a test platform. Model parameters were obtained experimentally in laboratory conditions. Next, the model was implemented in MATLAB/Simulink and then validated using the data collected during real flight trials in outdoor conditions. Finally, the Monte-Carlo simulation was used to evaluate the model reliability in the presence of modeling uncertainties. It was obtained that the parameter uncertainties could affect the amount of total consumed energy by less than 8% of the nominal value. The presented model of energy consumption might be practically used to predict energy expenditure, battery state of charge, and voltage in a typical mission of a drone.
Go to article

Bibliography

[1] Z. He and L. Zhao. A simple attitude control of quadrotor helicopter based on Ziegler-Nichols rules for tuning PD parameters. The Scientific World Journal, 2014: 280180, 2014. doi: 10.1155/2014/280180.
[2] P. Jimenez, P. Lichota, D. Agudelo, and K. Rogowski. Experimental validation of total energy control system for UAVs. Energies, 13(1):14, 2020. doi: 10.3390/en13010014.
[3] C. Aoun, N. Daher, and E. Shammas. An energy optimal path-planning scheme for quadcopters in forests. 2019 IEEE 58th Conference on Decision and Control (CDC), pages 8323–8328, Nice, France, 11–13 December 2019. doi: 10.1109/CDC40024.2019.9029345.
[4] T.A. Rodrigues, J. Patrikar, A. Choudhry, J. Feldgoise, V. Arcot, A. Gahlaut, S. Lau, B. Moon, B. Wagner, H. S. Matthews, S. Scherer, and C. Samaras. In-flight positional and energy use data set of a DJI Matrice 100 quadcopter for small package delivery. Scientific Data, 8:155, 2021. doi: 10.1038/s41597-021-00930-x.
[5] F. Yacef, N. Rizoug, and L. Degaa. Energy-efficiency path planning for quadrotor UAV under wind conditions. 2020 7th International Conference on Control, Decision and Information Technologies (CoDIT), pages 1133–1138, Prague, Czech Republic, 29 June–2 July 2020. doi: 10.1109/CoDIT49905.2020.9263968.
[6] F. Yacef, O. Bouhali, M. Hamerlain, and N. Rizoug. Observer-based adaptive fuzzy backstepping tracking control of quadrotor unmanned aerial vehicle powered by Li-ion battery. Journal of Intelligent and Robotic Systems, 84(1–4):179–197, 2016. doi: 10.1007/s10846-016-0345-0.
[7] F. Yacef, N. Rizoug, O. Bouhali, and M. Hamerlain. Optimization of energy consumption for quadrotor UAV. International Micro Air Vehicle Conference and Flight Competition (IMAV) 2017, Toulouse, France, 18-21 September 2017.
[8] F. Yacef, N. Rizoug, L. Degaa, O. Bouhali, and M. Hamerlain. Trajectory optimisation for a quadrotor helicopter considering energy consumption. 2017 4th International Conference on Control, Decision and Information Technologies (CoDIT), pages 1030–1035, Barcelona, Spain, 5–7 April 2017. doi: 10.1109/CoDIT.2017.8102734.
[9] G. Jia, S. Gong, R. Guo, and M. Li. Energy consumption model of BLDC quadrotor UAVs for mobile communication trajectory planning. TechRxiv. doi: 10.36227/techrxiv.19181228.v1.
[10] F. Morbidi, R. Cano, and D. Lara. Minimum-energy path generation for a quadrotor UAV. 2016 Ieee International Conference on Robotics and Automation (ICRA), pages 1492–1498, Stockholm, Sweden, 16–21 May 2016. doi: 10.1109/ICRA.2016.7487285.
[11] S. Jee and H. Cho. Comparing energy consumption following flight pattern for quadrotor. Journal of IKEEE, 22(3):747–753, 2018. doi: 10.7471/ikeee.2018.22.3.747.
[12] C.W. Chan and T.Y. Kam. A procedure for power consumption estimation of multi-rotor unmanned aerial vehicle. Journal of Physics: Conference Series, 1509:012015, 2020. doi: 10.1088/1742-6596/1509/1/012015.
[13] Y. Wang, Y. Wang, and B. Ren. Energy saving quadrotor control for field inspection. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 52(3):1768–1777, 2020. doi: 10.1109/TSMC.2020.3037071.
[14] H. Lu, K. Chen, X.B. Zhai, B. Chen, and Y. Zhao. Tradeoff between duration and energy optimization for speed control of quadrotor unmanned aerial vehicle. 2018 IEEE Symposium on Product Compliance Engineering - Asia (ISPCE-CN), pages 1–5, Shenzhen, China, 5–7 December 2018. doi: 10.1109/ISPCE-CN.2018.8805801.
[15] N. Bezzo, K. Mohta, C. Nowzari, I. Lee, V. Kumar, and G. Pappas. Online planning for energy-efficient and disturbance-aware UAV operations. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5027–5033, Daejeon, Korea, 9–14 October 2016. doi: 10.1109/IROS.2016.7759738.
[16] V. Agarwal and R.R. Tewari. Improving energy efficiency in UAV attitude control using deep reinforcement learning. Journal of Scientific Research, 65(3):209–219, 2021.
[17] A. Korneyev, M. Gorobetz, I. Alps, and L. Ribickis. Adaptive traction drive control algorithm for electrical energy consumption minimisation of autonomous unmanned aerial vehicle. Electrical, Control and Communication Engineering, 15(2):62–70, 2019. doi: 10.2478/ecce-2019-0009.
[18] J.F. Roberts, J.-C. Zufferey, and D. Floreano. Energy management for indoor hovering robots. 2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, pages 1242–1247, Nice, France, 22–26 September 2008. doi: 10.1109/IROS.2008.4650856.
[19] A.S. Prasetia, R.-J. Wai, Y.-L. Wen, and Y.-K. Wang. Mission-based energy consumption prediction of multirotor UAV. IEEE Access, 7:33055–33063, 2019. doi: 10.1109/ACCESS.2019.2903644.
[20] X. Wu, J. Zeng, A. Tagliabue, and M. W. Mueller. Model-free online motion adaptation for energy-efficient flight of multicopters. Arxiv. doi: 10.48550/arXiv.2108.03807.
[21] C. Di Franco and G. Buttazzo. Energy-aware coverage path planning of UAVs. 2015 IEEE International Conference on Autonomous Robot Systems and Competitions, pages 111–117, Vila Real, Portugal, 08–10 April 2015. doi: 10.1109/ICARSC.2015.17.
[22] T. Dietrich, S. Krug, and A. Zimmermann. An empirical study on generic multicopter energy consumption profiles. 2017 Annual IEEE International Systems Conference (SysCon), pages 1–6, Montreal, QC, Canada, 24–27 April 2017. doi: 10.1109/SYSCON.2017.7934762.
[23] H.V. Abeywickrama, B.A. Jayawickrama, Y. He, and E. Dutkiewicz. Empirical power consumption model for UAVs. 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall), pages 1-5, Chicago, IL, USA, 27–30 August 2018. doi: 10.1109/VTCFall.2018.8690666.
[24] R. Shivgan and Z. Dong. Energy-efficient drone coverage path planning using genetic algorithm. 2020 IEEE 21st International Conference on High Performance Switching and Routing (HPSR), pages 1–6, Newark, NJ, USA, 11–14 May 2020. doi: 10.1109/HPSR48589.2020.9098989.
[25] C. Di Franco and G. Buttazzo. Coverage path planning for UAVs photogrammetry with energy and resolution constraints. Journal of Intelligent & Robotic Systems, 83:445–462, 2016. doi: 10.1007/s10846-016-0348-x.
[26] N. Gao, Y. Zeng, J. Wang, D. Wu, C. Zhang, Q. Song, J. Qian and S. Jin. Energy model for UAV communications: Experimental validation and model generalization. China Communications, 18(7):253–264, 2021. doi: 10.23919/JCC.2021.07.020.
[27] N. Kreciglowa, K. Karydis, and V. Kumar, Energy efficiency of trajectory generation methods for stop-and-go aerial robot navigation. 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pages 656–662, Miami, USA, 13–16 June 2017. doi: 10.1109/ICUAS.2017.7991496.
[28] P. Pradeep, S.G. Park, and P. Wei. Trajectory optimization of multirotor agricultural. 2018 IEEE Aerospace Conference, pages 1–7, Big Sky, USA, 3–10 March 2018. doi: 10.1109/AERO.2018.8396617.
[29] M-h. Hwang, H-R. Cha and S.Y. Jung. Practical endurance estimation for minimizing energy consumption of multirotor unmanned aerial vehicles. Energies, 11(9):2221, 2018. doi: 10.3390/en11092221.
[30] A. Abdilla, A. Richards, and S. Burrow. Power and endurance modelling of battery-powered rotorcraft. 2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 675–680, Hamburg, Germany, 28 September–2 October 2015. doi: 10.1109/IROS.2015.7353445.
[31] J. Apeland, D. Pavlou, and T. Hemmingsen. Suitability Analysis of implementing a fuel cell on a multirotor drone. Journal of Aerospace Technology and Management, 12:e3220, 2020. doi: 10.5028/jatm.v12.1172.
[32] Z. Liu, R. Sengupta and A. Kurzhanskiy. A power consumption model for multi-rotor small unmanned aircraft systems. 2017 International Conference on Unmanned Aircraft Systems (ICUAS), pages 310–315, Miami, FL, USA, 13–16 June 2017. doi: 10.1109/ICUAS.2017.7991310.
[33] L. Zhang, A. Celik, S. Dang, and B. Shihada. Energy-efficient trajectory optimization for UAV-assisted IoT networks. IEEE Transactions on Mobile Computing, 21(12):4323–4337, 2022. doi: 10.1109/TMC.2021.3075083.
[34] Y. Chen, D. Baek, A. Bocca, A. Macii, E. Macii, and M. Poncino. A case for a battery-aware model of drone energy consumption. 2018 IEEE International Telecommunications Energy Conference (INTELEC), pages 1–8, Turino, Italy, 7–11 October 2018. doi: 10.1109/INTLEC.2018.8612333.
[35] [Online]. https://www.dji.com/pl/mavic-2/info, [Accessed on: 13 July 2021].
[36] National Aeronautics and Space Administration, U.S. Standard Atmosphere, 1976, Washington, D.C., 1976.
[37] P.H. Zipfel. Modeling and Simulation of Aerospace Vehicle Dynamics. American Institute of Aeronautics and Astronautics. Reston, USA, 2000.
[38] D. Allerton. Principles of Flight Simulation. John Wiley and Sons, 2009.
[39] B.L. Stevens, F.L. Lewis, and E.N. Johnson. Aircraft Control and Simulation. Dynamics, Controls Design, and Autonomous Systems. John Wiley and Sons, 2015.
[40] M. Dreier. Introduction to Helicopter and Tiltrotor Simulation. American Institute of Aeronautics and Astronautics. Reston, USA, 2007.
[41] P. Lichota, F. Dul, and A. Karbowski. System identification and LQR controller design with incomplete state observation for aircraft trajectory tracking. Energies, 13(20):5354, 2020. doi: 10.3390/en13205354.
[42] M. Abzug. Computational Flight Dynamics. American Institute of Aeronautics and Astronautics. Reston, USA, 1998.
[43] S.K. Phang, C. Cai, B.M. Chen, and T.H. Lee. Design and Mathematical Modeling of a 4-Standard-Propeller (4SP) Quadrotor. In: Proceedings of the 10th World Congress on Intelligent Control and Automation, pages 3270–3275, Beijing, China, 6–8 July 2012. doi: 10.1109/WCICA.2012.6358437.
[44] J. Sanketi, R. Kasliwal, S. Raghavan, and S. Awan. Modelling and simulation of a multi-quadcopter concept. International Journal of Engineering Research & Technology (IJERT), 5(10):566–571, 2016.
[45] N.M. Salma and K. Osman. Modelling and PID control system integration for quadcopter DJIF450 attitude stabilization. Indonesian Journal of Electrical Engineering and Computer Science, 19(3):1235–1244. doi: 10.11591/ijeecs.v19.i3.pp1235-1244.
[46] P. Pounds, R. Mahony, and P. Corke. Modelling and control of a large quadrotor robot. Control Engineering Practice, 18(7):691–699, 2010. doi: 10.1016/j.conengprac.2010.02.008.
[47] Z. Benić, P. Piljek, and D. Kotarski. Mathematical modelling of unmanned aerial vehicles with four rotors. Interdisciplinary Description of Complex Systems, 14(1):88–100, 2016. doi: 10.7906/indecs.14.1.9.
[48] I.M. Salameh, E.M. Ammar, and T.A. Tutunji. Identification of quadcopter hovering using experimental data. 2015 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), pages 1–6, Amman, Jordan, 3–5 November 2015. doi: 10.1109/AEECT.2015.7360559.
[49] [Online]. Available: https://airdata.com [Accessed on: 27 January 2022].
[50] W. Jaafar and H. Yanikomeroglu. Dynamics of quadrotor UAVs for aerial networks: An energy perspective. Arxiv, 2019. doi: 10.48550/arXiv.1905.06703.
[51] P. Pradeep and P. Wei. Energy efficient arrival with rta constraint for multirotor eVTOL in urban air mobility. Journal of Aerospace Information Systems, 16(7):1–15, 2019. doi: 10.2514/1.I010710.
[52] F. Morbidi and D. Pisarski. Practical and accurate generation of energy-optimal trajectories for a planar quadrotor. 2021 IEEE International Conference on Robotics and Automation (ICRA), pages 355–361, Xi'an, China, 30 May–5 June 2021. doi: 10.1109/ICRA48506.2021.9561395.
[53] T. Mesbahi, N. Rizoug, P. Bartholomeus, and P. Le Moigne. Li-ion battery emulator for electric vehicle applications. 2013 IEEE Vehicle Power and Propulsion Conference (VPPC), pages 1–8, Beijing, China, 15–18 October 2013. doi: 10.1109/VPPC.2013.6671688.
[54] F. Li, W.-P. Song, B.-F. Song, and H. Zhang, Dynamic modeling, simulation, and parameter study of electric quadrotor system of Quad-Plane UAV in wind disturbance environment. International Journal of Micro Air Vehicles, 13:1–23, 2021. doi: 10.1177/17568293211022211.
[55] O. Tremblay and L-A. Dessaint. Experimental validation of a battery dynamic model for ev applications. World Electric Vehicle Journal, 3(2):289–298, 2009. doi: 10.3390/wevj3020289.
[56] S.M. Mousavi and M. Nikdel. Various battery models for various simulation studies and applications. Renewable and Sustainable Energy Reviews, 32:477–485, 2014. doi: 10.1016/j.rser.2014.01.048.
[57] S.M. Azam. Battery Identification, Prediction and Modelling. Master Thesis, Colorado State University, Fort Collins, Colorado, USA, 2018.
[58] E. Raszmann, K. Baker, Y. Shi, and D. Christensen. Modeling stationary lithium-ion batteries for optimization and predictive control. 2017 IEEE Power and Energy Conference at Illinois (PECI), pages 1–7, Champaign, IL, USA, 23–24 February 2017. doi: 10.1109/PECI.2017.7935755.
[59] H. Hemi, N.K. M’Sirdi, and A. Naamane. A new proposed shepherd model of a li-ion open circuit battery based on data fitting. IMAACA 2019, Lisbon, Portugal, 2019.
[60] [Online]: https://www.mathworks.com/help/physmod/sps/powersys/ref/battery.html. [Accessed on: 9 October 2021].
[61] H. Hinz. Comparison of lithium-ion battery models for simulating storage systems in distributed power generation. Inventions, 4(3):41, 2019. doi: 10.3390/inventions4030041.
[62] L.E. Romero, D.F. Pozo, and J.A. Rosales. Quadcopter stabilization by using PID controllers. Maskana, 5:175–186, 2016.
[63] A. Rodić and G. Mester. The modeling and simulation of an autonomous quad-rotor microcopter in a virtual outdoor scenario. A cta Polytechnica Hungarica, 8(4):107–122, 2011.
[64] A.L. Salih, M. Moghavvemi, H.A.F. Mohamed, and K.S. Gaeid. Flight PID controller design for a UAV quadrotor. Scientific Research and Essays, 5(23):3660–3667, 2010.
[65] V. Brito, A. Brito, L.B. Palma, and P. Gil. Quadcopter control approaches and performance analysis. In Proceedings of the 15th International Conference on Informatics in Control, Automation and–Robotics - Volume 1: ICINCO, pages 86–93, Porto, Portugal, 29–31 July, 2018. doi: 10.5220/0006902600960103.
[66] [Online]. Available: https://www.dji.com/pl/downloads/products/mavic-2. [Accessed on: 7 August 2021].
[67] M. Jacewicz, M. Żugaj, R. Głębocki, and P. Bibik. Quadrotor model for energy consumption analysis. Energies, 15(19):7136, 2022. doi: 10.3390/en15197136.
[68] M. Jacewicz, P. Lichota, D. Miedziński, and R. Głębocki. Study of model uncertainties influence on the impact point dispersion for a gasodynamicaly controlled projectile. Sensors, 22(9):3257, 2022. doi: 10.3390/s22093257.
[69] M. Jacewicz, R. Głębocki, and R. Ożóg. Monte-Carlo based lateral thruster parameters optimization for 122 mm rocket. In: R. Szewczyk, C. Zieliński, M. Kaliczyńska (eds) Automation 2020: Towards Industry of the Future. AUTOMATION 2020. Advances in Intelligent Systems and Computing, volume 1140, pages 125–134, Springer, 2020. doi: 10.1007/978-3-030-40971-5_12.
[70] C. Coulombe, J.-F. Gamache, A. Mohebbi, C. Abolfazl, U. Chouinard and S. Achiche, Applying robust design methodology to a quadrotor drone. In Proceedings of the 21st International Conference on Engineering Design (ICED17), Vol 4: Design Methods and Tools, Vancouver, Canada, 21–25 August 2017.
Go to article

Authors and Affiliations

Robert Głębocki
1
ORCID: ORCID
Marcin Żugaj
1
ORCID: ORCID
Mariusz Jacewicz
1
ORCID: ORCID

  1. Warsaw University of Technology, Faculty of Power and Aeronautical Engineering, Warsaw, Poland

This page uses 'cookies'. Learn more