Twin Screw Expanders Profile Optimization Using Surrogate-‎Based Modelling

Document Type : Research Paper


Department of Mechanical Engineering, Iran University of Science and Technology (IUST), Narmak, Tehran 16844, Iran


Twin screw machines can be used as an expander to recover the lost power in various processes causing pressure energy loss. Twin screw expanders (TSEs) have caught the attention of many researchers due to low capital, maintenance and operation costs, long lifespan and their application in two-phase fluids. However, substantial efforts required to enhance their performance. This research describes the optimization of the profile of a TSE with 4-6 lobe configuration - using surrogate-based modeling (SBM). To do so, based on the in-house code developed within FORTRAN, a TSE profile is designed and validated against available data. Then, a mathematical model is developed viaof experiments (DOE). Next, the effects of four main profile parameters are investigated on the expander performance in the entire design space. Finally, an optimized combination of parameters is offered using a multi-objective genetic algorithm. 3D computational fluid dynamics (CFD) results show that the optimized profile had more than 7% exergy efficiency compared to the base profile.


Main Subjects

Publisher’s Note Shahid Chamran University of Ahvaz remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  

‎[1] Smith, I. K., Stosic, N., and Kovacevic, A., Screw expanders increase output and decrease the cost of ‎geothermal binary power plant systems, Transactions - Geothermal Resources Council, 29 October, 2005, ‎‎787–794.‎
‎[2] Brummer, A., Influence of liquid in clearances on the operational behavior of twin screw expanders, 9th International Conference on Compressors and their Systems, 12060, 2015.‎
‎[3] Sangfors, B., Analytical Modeling of Helical Screw Machine for Analysis and Performance Prediction, ‎Compressor Technology Conference, Purdue, 482–487, 1982.‎
‎[4] Steidel, R. F., Pankow, D. H., Brown, K. A., The empirical modelling of a Lysholm screw expander, Proc. 18th Intersociety Energy Conversion Engng. Conf. on Energy for the Marketplace, Florida, 286–293, 1983.‎
‎[5] Smith, I. K., Development of the Trilateral Flash Cycle System: Part 1: Fundamental Considerations, ‎Proceedings of the Institution of Mechanical Engineers, Part A: Journal of Power and Energy, 207, 1993, 179–194.‎
‎[6] Smith, I. K., Stosic, N., and Aldis, C. A., Development of the Trilateral Flash Cycle System: Part 3: The Design ‎of High-Efficiency Two-Phase Screw Expanders, Proceedings of the Institution of Mechanical Engineers Part A: Journal of Power and Energy, 210(1), 1996, 75–93.‎
‎[7] Smith, I. K., Stosic, N., Aldis, C. A., Kovacevic A., Twin Screw Two ­ Phase Expanders in Large Chiller Units, Imeche Conference Transactions, 105–114, 1999.‎
‎[8] Stosic, N., Smith, I. K., Kovacevic,A., A Twin Screw Combined Compressor And Expander For CO2 ‎Refrigeration Systems, International Compressor Engineering Conference, London, 1-9, 2002.‎
‎[9] Smith, I. K., Stosic, N., and Kovacevic, A., An improved system for power recovery from higher enthalpy ‎liquid dominated fields, Proceedings World Geothermal Congress, London, 1-5, 2005.‎
‎[10] Brümmer, A. and Hütker, J., Influence of geometric parameters on inlet losses during the filling process of ‎screw-type motors, Developments in Mechanical Engineering, 4, 2009, 105–121.‎
‎[11] Hutker, J. and Brummer, A., Thermodynamic Design of Screw Motors for Constant Waste Heat Flow at ‎Medium Temperature Level, International Compressor Engineering Conference, London, 1478, 2012.‎
‎[12] Kovacevic, A. and Rane, S., 3D CFD Analysis of a Twin Screw Expander, 8th International Conference on Compressors and their Systems, Woodhead Publishing Limited, 417-429, 2013.‎
‎[13] Papes, I., Degroote, J., and Vierendeels, J., New insights in twin screw expander performance for small scale ‎ORC systems from 3D CFD analysis, Applied Thermal Engineering, 91, 2015, 535–546.‎
‎[14] Papes, I., Degroote, J., and Vierendeels, J., Development of a thermodynamic low order model for a twin ‎screw expander with emphasis on pulsations in the inlet pipe, Applied Thermal Engineering, 103, 2016, 909–‎‎919.‎
‎[15] Andres, R. J. H.,CFD Simulation of a Twin Screw Expander including Leakage Flows, International Compressor Engineering Conference, Purdue, 2497, 2016. ‎
‎[16]‎ Yildiz, A. R. and Ozturk, F., Hybrid taguchi-harmony search approach for shape optimization, In: Geem Z (ed) ‎Recent advances in Harmony search algorithm, Springer, Berlin, 89-98, 2010‎
‎[17] Yildiz, A. R., Comparison of evolutionary-based optimization algorithms for structural design optimization, ‎Engineering Applications of Artificial Intelligence, 26(1), 2013, 327–333.‎
‎[18] Abolhasani, H., Moghimi, M., and Ebrahimi, M., Exergetic optimization of twin screw expanders, International Journal of Exergy, 30(4), 2019, 360-375.‎
‎[19] Kiani, M. and Yildiz, A. R., A Comparative Study of Non-traditional Methods for Vehicle Crashworthiness and ‎NVH Optimization, Archives of Computational Methods in Engineering, 23(4), 2016, 723–734.‎
‎[20] Pholdee, N., Bureerat, S., and Yildiz, A. R., Hybrid real-code population-based incremental learning and ‎differential evolution for many-objective optimization of an automotive floor-frame, International Journal of Vehicle Design, 73(1), 2017, 20–53.‎
‎[21] Karagöz, S. and Yildiz, A. R., A comparison of recent metaheuristic algorithms for crashworthiness ‎optimisation of vehicle thin-walled tubes considering sheet metal forming effects, International Journal of Vehicle Design, 73(1), 2017, 179–188.‎
‎[22] Yildiz, B. S. and Yildiz, A. R., Moth-flame optimization algorithm to determine optimal machining parameters ‎in manufacturing processes, Materials Testing, 59(5), 2017, 425–429.‎
‎[23] Gopakumar, A. M., Balachandran, P. V., Xue, D., Gubernatis, J. E., and Lookman, T., Multi-objective ‎Optimization for Materials Discovery via Adaptive Design, Scientific Reports, 8(1), 2018, 3738.‎
‎[24] Kaya, M. and Hajimirza, S., Rapid Optimization of External Quantum Efficiency of Thin Film Solar Cells Using ‎Surrogate Modeling of Absorptivity, Scientific Reports, 8(1), 2018, 8170.‎
‎[25] Hamza, F., Abderazek, H., Lakhdar, S., Ferhat, D., and Yildiz, A. R., Optimum design of cam-roller follower ‎mechanism using a new evolutionary algorithm, International Journal of Advanced Manufacturing Technology, 99(5–8), 2018, 1267–1282.‎
‎[26] Yildiz, B. S. and Yildiz, A. R., Comparison of grey Wolf, whale, water cycle, ant lion and sine-cosine ‎algorithms for the optimization of a vehicle engine connecting rod, Materials Testing, 60(3), 2018, 311–315.‎
‎[27] Noack, M. M., Yager, K. G., Fukuto, M., Doerk, G. S., Li, R., and Sethian, J. A., A Kriging-Based Approach to ‎Autonomous Experimentation with Applications to X-Ray Scattering, Scientific Reports, 9(1), 2019, 11809.‎
‎[28] Nyshadham, C., Rupp, M., Bekker, B., Shapeev, A. V., Mueller, T., Rosenbrock, C. W., Csanyi, G., Wingate, D. ‎W., and Hart, G. L. W., Machine-learned multi-system surrogate models for materials prediction, npj Computational Materials, 5(1), 2019, 51.‎
‎[29] Awad, N. H., Ali, M. Z., Mallipeddi, R., and Suganthan, P. N. An improved differential evolution algorithm using efficient adapted surrogate model for numerical optimization, Information Sciences, 451, 2018, 326-347.
‎[30] Yildiz, A. R., A novel hybrid whale–Nelder–Mead algorithm for optimization of design and manufacturing ‎problems, International Journal of Advanced Manufacturing Technology, 105(12), 2019, 5091–5104.‎
‎[31] Yildiz, A. R., Abderazek, H., and Mirjalili, S., A Comparative Study of Recent Non-traditional Methods for ‎Mechanical Design Optimization, Archives of Computational Methods in Engineering, 27, 2019, 1031–1048.‎
‎[32] Yildiz, B. S. and Yildiz, A. R., The Harris hawks optimization algorithm, salp swarm algorithm, grasshopper ‎optimization algorithm and dragonfly algorithm for structural design optimization of vehicle components, ‎Materials Testing, 61(8), 2019, 744–748.‎
‎[33] Yildiz, A. R., Yildiz, B. S., Sait, S. M., Bureerat, S., and Pholdee, N., A new hybrid Harris hawks-Nelder-Mead ‎optimization algorithm for solving design and manufacturing problems, Materials Testing, 61(8), 2019, 735–‎‎743.‎
‎[34] Moctezuma, L. A. and Molinas, M., Multi-objective optimization for EEG channel selection and accurate ‎intruder detection in an EEG-based subject identification system, Scientific Reports, 10(1), 2020, 5850.‎
‎[35] Abolhasani, H., Moghimi, M., and Ebrahimi, M., Power Recovery in a Gas Pressure Reduction Station using 3D CFD Modeling of a Twin Screw Expander, Journal of Applied Fluid Mechanics, 13(4), 2020,1179-1191.
‎[36] Kurtuluş, E., Yildiz, A. R., Sait, S. M., and Bureerat, S., A novel hybrid Harris hawks-simulated annealing ‎algorithm and RBF-based metamodel for design optimization of highway guardrails, Materials Testing, 62(3), ‎‎2020, 251–260.‎
‎[37] Champasak, P., Panagant, N., Pholdee, N., Bureerat, S., and Yildiz, A. R., Self-adaptive many-objective meta-‎heuristic based on decomposition for many-objective conceptual design of a fixed wing unmanned aerial ‎vehicle, Aerospace Science and Technology, 100, 2020, 105783.‎
‎[38] Abderazek, H., Yildiz, A. R., and Mirjalili, S., Comparison of recent optimization algorithms for design ‎optimization of a cam-follower mechanism, Knowledge-Based Systems, 2020, 105, 105237. ‎
‎[39] Forrester, A. I. J. and Keane, A. J., Recent advances in surrogate-based optimization, Progress in Aerospace Sciences, 45(1–3), 2009, 50–79.‎
‎[40] Zhu, G. J., Guo, P. C., Luo, X. Q., and Feng, J. J., The multi-objective optimization of the horizontal-axis marine ‎current turbine based on NSGA-II algorithm, IOP Conference Series: Earth and Environmental Science, ‎‎15(4), 2012, 42039.‎
‎[41] Bellary, S. A. I., Husain, A., and Samad, A., Effectiveness of meta-models for multi-objective optimization of ‎centrifugal impeller, Journal of Mechanical Science and Technology, 28(12), 2014, 4947–4957.‎
‎[42] Bellary, S. A. I., Samad, A., Couckuyt, I., and Dhaene, T., A comparative study of kriging variants for the ‎optimization of a turbomachinery system, Engineering with Computers, 32(1), 2016, 49–59.‎
‎[43] Tosin, S., Friedrichs, J., Sperling, J., and Kozulović, D., Aerodynamic optimization of turboprop turbine blades ‎using a response surface methodology based algorithm, ASME  12th International Conference on Nano channels, Microchannel, and Mini channels, American Society of Mechanical Engineers, V01AT02A009-‎V01AT02A009, 2014.‎
‎[44] Gaiser, K., Erickson, P., Stroeve, P., and Delplanque, J.P., An experimental investigation of design parameters ‎for pico-hydro Turgo turbines using a response surface methodology, Renewable Energy, 85, 2016, 406–418 .‎
‎[45] Badhurshah, R. and Samad, A., Multiple surrogate based optimization of a bidirectional impulse turbine for ‎wave energy conversion, Renewable Energy, 74, 2014, 749–760.‎
‎[46] Tang, Y., Computer Aided Design of Twin Screw Compressors, Ph. D. Thesis, 1995.‎
‎[47] Zhao, Q., Chen, Z. H., Huang, Z. G., Zhang, H. H., and Ma, J., Optimization of the aerodynamic configuration of ‎a tubular projectile based on blind kriging, Scientia Iranica, 26(1B), 2019, 311–322.‎
‎[48] Sóbester, A., Forrester, A. I. J., Toal, D. J. J., Tresidder, E., and Tucker, S., Engineering design applications of ‎surrogate-assisted optimization techniques, Optimization and Engineering, 15(1), 2014, 243–265 .‎
‎[49] Jakobsson, S., Patriksson, M., Rudholm, J., and Wojciechowski, A., A method for simulation based ‎optimization using radial basis functions, Optimization and Engineering, 11(4), 2010, 501–532.‎
‎[50]‎ Forrester, A. I. J., Sbester, A., and Keane, A. J., Engineering Design via Surrogate Modelling, John Wiley and ‎Sons Ltd, 2008.‎
‎[51] Mengistu, T. and Ghaly, W., Aerodynamic optimization of turbomachinery blades using evolutionary ‎methods and ANN-based surrogate models, Optimization and Engineering, 9(3), 2008, 239–255.‎
‎[52] Kovacevic, A., Three-Dimensional Numerical Analysis for Flow Prediction in Positive Displacement Screw Machines, Ph.D. Thesis, School of Engineering and Mathematical Sciences, City University of London, 2002.‎