• Proceedings of the KSME Conference
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• 2007.05b
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• pp.2690-2695
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• 2007

This work presents numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm (NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis. Reynolds-averaged Navier-Stokes (RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.

• Environmental Sciences Bulletin of The Korean Environmental Sciences Society
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• v.4 no.1
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• pp.11-23
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• 2000

Photochemical air quality models are essential tools in predicting future air quality and assessing air pollution control strategies. To evaluate air quality using a photochemical air quality model, emission inventories are important inputs to these models. Since most emission inventories are provided at a county-level, these emission inventories need to be geographically allocated to the computational grid cells of the model prior to running the model. The conventional method for the spatial allocation of these emissions uses "spatial surrogate indicators", such as population for mobile source emissions and county area for biogenic source emissions. In order to examine the applicability of such approximations, more detailed spatial surrogate indicators were developed using Geographic Information System(GIS) tools to improve the spatial allocation of mobile and boigenic source emissions, The proposed spatial surrogate indicators appear to be more appropriate than conventional spatial surrogate indicators in allocating mobile and biogenic source emissions. However, they did not provide a substantial improvement in predicting ground-level ozone(O3) concentrations. As for the carbon monoxide(CO) concentration predictions, certain differences between the conventional and new spatial allocation methods were found, yet a detailed model performance evaluation was prevented due to a lack of sufficient observed data. The use of the developed spatial surrogate indicators led to higher O3 and CO concentration estimates in the biogenic source emission allocation than in the mobile source emission allocation.llocation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.10
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• pp.3887-3907
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• 2015

This paper introduces a full duplex single secondary user multiple-input multiple-output (FD-SSU-MIMO) cognitive radio network, where secondary user (SU) opportunistically accesses the authorized spectrum unoccupied by primary user (PU) and transmits data based on FD-MIMO mode. Then we study the network achievable average sum-rate maximization problem under sum transmit power budget constraint at SU communication nodes. In order to solve the trade-off problem between SU's sensing time and data transmission time based on opportunistic spectrum access (OSA) and the power allocation problem based on FD-MIMO transmit mode, we propose a simple trisection algorithm to obtain the optimal sensing time and apply an alternating optimization (AO) algorithm to tackle the FD-MIMO based network achievable sum-rate maximization problem. Simulation results show that our proposed sensing time optimization and AO-based optimal power allocation strategies obtain a higher achievable average sum-rate than sequential convex approximations for matrix-variable programming (SCAMP)-based power allocation for the FD transmission mode, as well as equal power allocation for the half duplex (HD) transmission mode.

• International Journal of Air-Conditioning and Refrigeration
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• v.16 no.1
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• pp.1-8
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• 2008

Numerical optimization for design of a blade stacking line of a low speed axial flow fan with a fast and elitist Non-Dominated Sorting of Genetic Algorithm(NSGA-II) of multi-objective optimization using three-dimensional Navier-Stokes analysis is presented in this work. Reynolds-averaged Navier-Stokes(RANS) equations with ${\kappa}-{\varepsilon}$ turbulence model are discretized with finite volume approximations and solved on unstructured grids. Regression analysis is performed to get second order polynomial response which is used to generate Pareto optimal front with help of NSGA-II and local search strategy with weighted sum approach to refine the result obtained by NSGA-II to get better Pareto optimal front. Four geometric variables related to spanwise distributions of sweep and lean of blade stacking line are chosen as design variables to find higher performed fan blade. The performance is measured in terms of the objectives; total efficiency, total pressure and torque. Hence the motive of the optimization is to enhance total efficiency and total pressure and to reduce torque.