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Keywords: genetic algorithmClose
Proc. ASME. GT2021, Volume 4: Controls, Diagnostics, and Instrumentation; Cycle Innovations; Cycle Innovations: Energy Storage; Education; Electric Power, V004T06A007, June 7–11, 2021
Paper No: GT2021-59194
... cement plant, with the raw-clinker preheater as the waste-heat source. The primary objective is to maximize the net-power output using genetic algorithms. A comparative performance analysis of the two ORCs with working fluids: R134a and Propane, the simply recuperated S-CO 2 cycle (RC) and recompressed...
Proc. ASME. GT2020, Volume 2A: Turbomachinery, V02AT32A007, September 21–25, 2020
Paper No: GT2020-14263
...RESEARCH ON OPTIMIZATION OF THE DESIGN(INVERSE) ISSUE OF S2 SURFACE OF AXIAL COMPRESSOR BASED ON GENETIC ALGORITHM Zijing Chen, Bo Liu, Xiaoxiong Wu School of Power and Energy, Northwestern Polytechnical University, 710129Xi an, China ABSTRACT In order to further improve the effectiveness of design...
Proc. ASME. GT2019, Volume 9: Oil and Gas Applications; Supercritical CO2 Power Cycles; Wind Energy, V009T48A006, June 17–21, 2019
Paper No: GT2019-91044
... to dust deposition and sand erosion. Subsequently, a two-objective genetic algorithm is developed in MATLAB 16.0 and used to customize the airfoil geometry, enhancing the lift-to-drag ratio while simultaneously minimizing the deposition and erosion rates. The whole optimization process is realized through...
Proc. ASME. GT2013, Volume 2: Aircraft Engine; Coal, Biomass and Alternative Fuels; Cycle Innovations, V002T07A013, June 3–7, 2013
Paper No: GT2013-94799
... Genetic Algorithm in which the maximum cycle efficiency is defined as the objective function. The optimization process is comprehensive, i.e., the decision variables such as temperature and pressure of turbines, compressors, re-heaters, inter-coolers, and the pinch point temperature difference...
Proc. ASME. GT2010, Volume 3: Controls, Diagnostics and Instrumentation; Cycle Innovations; Marine, 745-752, June 14–18, 2010
Paper No: GT2010-23026
... of the operating cost, related to the fuel consumption. Subsequently, different pars of objective function have been expressed in terms of decision variables. Finally, the optimal values of decision variables were obtained by minimizing the objective function using Evolutionary algorithm such as Genetic Algorithm...
Proc. ASME. GT2008, Volume 2: Controls, Diagnostics and Instrumentation; Cycle Innovations; Electric Power, 431-440, June 9–13, 2008
Paper No: GT2008-50175
... performance modelling and a Genetic Algorithm has been developed in order to estimate the design point component parameters and match the available gas path measurements of real engines. In the approach, the initially unknown component parameters may be compressor pressure ratios and efficiencies, turbine...
Proc. ASME. GT2008, Volume 6: Turbomachinery, Parts A, B, and C, 2659-2668, June 9–13, 2008
Paper No: GT2008-51301
... for multi-objective robust optimization. In this framework, probabilistic representation of design variables are introduced and Kriging models are used to approximate relations between design variables with uncertainty and multiple design objectives. A multi-objective genetic algorithm optimizes the mean...
Proc. ASME. GT2007, Volume 4: Turbo Expo 2007, Parts A and B, 683-691, May 14–17, 2007
Paper No: GT2007-27889
... technique, Genetic Algorithm (GA) is applied to search and optimize geometrical parameters of the OFIF HE. The minimum total volume or minimum total annual cost of such OFIF HE is taken as an objective function in the GA respectively. The results show that the optimized OFIF HE provides lower total volume...
Proc. ASME. GT2002, Volume 2: Turbo Expo 2002, Parts A and B, 19-27, June 3–6, 2002
Paper No: GT2002-30021
... recuperated engine with variable geometry similar to the ICR-WR21cycle. A detailed analysis of the technique applied to simple cycle and advanced cycle will be presented. Diagnostics Simulation Genetic Algorithm 1 Proceedings of ASME TURBO EXPO 2002 L in rb ng y, of a fault diagnostics...
Proc. ASME. GT2003, Volume 1: Turbo Expo 2003, 351-359, June 16–19, 2003
Paper No: GT2003-38300
... transient model, under similar operating conditions own faults through a Cumulative Deviation. The tive Deviations obtained from the comparisons are ed for the best match using Genetic Algorithm. The Algorithm has been tailored to use real coding and to meet the requirements of the new procedure...
Proc. ASME. GT2003, Volume 6: Turbo Expo 2003, Parts A and B, 369-378, June 16–19, 2003
Paper No: GT2003-38036
... using NACA65 profiles. ds: optimization, genetic algorithm, axial ATURE axial velocity density ratio camber line static pressure rise coefficient form factor interpolated profile optimized profile pressure side direction flow angle with respect to axial direction operating range stall safety...
Proc. ASME. GT2004, Volume 2: Turbo Expo 2004, 749-758, June 14–17, 2004
Paper No: GT2004-53914
... 25 11 2008 This paper presents the development of an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using Genetic Algorithm and Artificial Neural Network. The diagnostics model operates in two distinct stages. The first stage uses...
Proc. ASME. GT2005, Volume 4: Turbo Expo 2005, 899-906, June 6–9, 2005
Paper No: GT2005-69048
... 03 11 2008 This study utilizes genetic algorithm to minimize the condition number of Hermitian matrix of influence coefficient (HMIC) to reduce the computation errors in balancing procedure. Then, the optimal locations of balancing planes and sensors would be obtained as fulfilling...