• Nuclear Engineering and Technology
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• v.53 no.6
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• pp.2066-2073
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• 2021

The use of nuclear reactors is a large studied possible solution for thermochemical water splitting cycles. Nevertheless, there are several problems that have to be solved. One of them is to increase the efficiency of the cycles. Hence, in this paper, a thermal energy optimization of a Sulfur-Iodine nuclear hydrogen production cycle was performed by means a heuristic method with the aim of minimizing the energy targets of the heat exchanger network at different minimum temperature differences. With this method, four different heat exchanger networks are proposed. A reduction of the energy requirements for cooling ranges between 58.9-59.8% and 52.6-53.3% heating, compared to the reference design with no heat exchanger network. With this reduction, the thermal efficiency of the cycle increased in about 10% in average compared to the reference efficiency. This improves the use of thermal energy of the cycle.

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2125-2138
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• 2023

The printed circuit heat exchanger (PCHE) with airfoil fins has the benefits of high compactness, high efficiency and superior heat transfer performance. A novel multi-objective optimization approach is presented to design the airfoil fin PCHE in this paper. Three optimization design variables (the vertical number, the horizontal number and the staggered number) are obtained by means of dimensionless airfoil fin arrangement parameters. And the optimization objective is to maximize the Nusselt number (Nu) and minimize the Fanning friction factor (f). Firstly, in order to investigate the impact of design variables on the thermal-hydraulic performance, a parametric study via the design of experiments is proposed. Subsequently, the relationships between three optimization design variables and two objective functions (Nu and f) are characterized by an improved particle swarm optimization-backpropagation artificial neural network. Finally, a multi-objective optimization is used to construct the Pareto optimal front, in which the non-dominated sorting genetic algorithm II is used. The comprehensive performance is found to be the best when the airfoil fins are completely staggered arrangement. And the best compromise solution based on the TOPSIS method is identified as the optimal solution, which can achieve the requirement of high heat transfer performance and low flow resistance.

• Journal of the Korean Geosynthetics Society
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• v.6 no.1
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• pp.39-43
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• 2007

Geocomposites of needle punched and spunbonded nonwovens having the reinforcement and drainage functions were manufactured by use of thermal bonding method. The physical properties (e.g. tensile, tear and bursting strength, permittivity) of these multi-layered nonwovens were dependent on the processing parameters of temperatures, pressures, bonding periods etc. - in manufacturing by use of thermal bonding method. Therefore, it is very meaningful to optimize the processing parameters and physical properties of the geocomposites by thermal bonding method. In this study, an algorithm has been developed to optimize the process of the geocomposites using an artificial neural network (ANN). Geocomposites were employed to examine the effects of manufacturing methods on the analysis results and the neural network simulations have been applied to predict the changes of the nonwovens performances by varying the processing parameters.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.327-334
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• 2003

Thermal Prestressing Method for continuous composite girder bridges is a new design and construction method developed to induce initial composite stresses in the concrete slab at negative bending regions. Due to the induced initial stresses, prevention of tensile cracks at concrete slab, reduction of steel girder section, and reduction of reinforcing bars are possible. Thus, economical and construction efficiency can be improved. Method for determining optimum heating region of Thermal Prestressing Method, has not been established although such method is essential for increasing efficiency of the designing process. Trial-and-error method used in previous studies is far from efficient and more rational method for computing optimal heating region is required. In this study, efficient method for determining optimum heating region in the use of Thermal Prestressing Method is developed based on artificial neural network algorithm, which is widely adopted to pattern recognition, optimization, diagnosis, and estimation problems in various fields. Back-propagation algorithm, which is commonly used as a learning algorithm in neural network problems, is used for training of the neural network. Through case studies of 2-span continuous and 3-span continuous composite girder bridges using the developed process, the optimal heating regions are obtained.

• International Journal of Fluid Machinery and Systems
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• v.9 no.4
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• pp.370-381
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• 2016

As a single-channel pump is used for wastewater treatment, this particular pump type can prevent performance reduction or damage caused by foreign substances. However, the design methods for single-channel pumps are different and more difficult than those for general pumps. In this study, a design optimization method to improve the hydrodynamic performance of a single-channel pump impeller is implemented. Numerical analysis was carried out by solving three-dimensional steady-state incompressible Reynolds-averaged Navier-Stokes equations using the shear stress transport turbulence model. As a state-of-the-art impeller design method, two design variables related to controlling the internal cross-sectional flow area of a single-channel pump impeller were selected for optimization. Efficiency was used as the objective function and was numerically assessed at twelve design points selected by Latin hypercube sampling in the design space. An optimization process based on a radial basis neural network model was conducted systematically, and the performance of the optimum model was finally evaluated through an experimental test. Consequently, the optimum model showed improved performance compared with the base model, and the unstable flow components previously observed in the base model were suppressed remarkably well.

• ETRI Journal
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• v.42 no.1
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• pp.118-128
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• 2020

A semitransparent photovoltaic-thermal (PV/T) air collector can produce electricity and heat simultaneously. To maximize the thermal and overall efficiency of the semitransparent PV/T air collector, its availability should be maximum; this can be determined through a Markov analysis. In this paper, a Markov model is developed to select an optimized number of semitransparent PV modules in service with five states and two states by considering two parameters, namely failure rate (λ) and repair rate (μ). Three artificial neural network (ANN) models are developed to obtain the minimum cost, minimum temperature, and maximum thermal efficiency of the semitransparent PV/T air collector by setting its type appropriately and optimizing the number of photovoltaic modules and cost. An attempt is also made to achieve maximum thermal and overall efficiency for the semitransparent PV/T air collector by using ANN after obtaining its minimum temperature and available solar radiation.

• 한국신재생에너지학회:학술대회논문집
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• 2009.11a
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• pp.207-207
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• 2009

Aspen plus는 Aspentech사에서 개발한 공정모사용 프로그램으로서 다양한 화학종의 열역학적 자료를 기반으로 공정설계, 공정최적화, 공정모니터링 등 공정개발에 활용되고 있다. 연료개질기는 수증기 개질반응, 수성가스전이반응, 선택적화학반응으로 구성된 소규모 수소생산공정에 해당된다. 따라서 Aspen 전산모사를 통해 다양한 조건에서의 운전결과를 모사하여 개질기에 미치는 영향을 분석함으로써 운전조건을 최적화 할 수 있다. 연료개질기의 성능에 영향을 미치는 주요인자는 주로 수증기개질 촉매층 출구온도 및 수증기/탄소 비이다. 수증기개질 촉매층의 출구온도를 $660{\sim}740^{\circ}C$로 변화시키면서 개질가스의 조성, 카본 전환율, 개질효율 등을 비교 분석하였다. 또한 수증기/탄소 비를 3~5의 범위에서 변화시키면서 영향을 살펴보았다. 수증기개질 촉매층의 온도가 높을수록 수소생산량이 증가에 따른 효율 증가가 나타났으며 수증기/탄소 비가 증가할 경우에도 개질효율에 긍정적인 영향을 미치는 것을 확인하였다. 하지만 실제 개질기의 운전에서는 소재의 제약에 따라 운전 온도에 제약이 있으며 수증기/탄소비의 증가 역시 개질기의 부피 증가로 이어지는 단점이 있다는 것을 고려해야 한다. 따라서 반응기 재질, 크기, 운전온도와 개질효율과의 상관관계를 파악하여 개질기의 특성을 최적화 하여야 한다.

• Current Optics and Photonics
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• v.6 no.5
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• pp.479-488
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• 2022

To account for the internal thermal effects of solid-state lasers, a method using a back propagation (BP) neural network integrated with a particle swarm optimization (PSO) algorithm is developed, which is a new wavefront distortion correction technique. In particular, by using a slab laser model, a series of fiber pumped sources are employed to form a controlled array to pump the gain medium, allowing the internal temperature field of the gain medium to be designed by altering the power of each pump source. Furthermore, the BP artificial neural network is employed to construct a nonlinear mapping relationship between the power matrix of the pump array and the thermally induced wavefront aberration. Lastly, the suppression of thermally induced wavefront distortion can be achieved by changing the power matrix of the pump array and obtaining the optimal pump light intensity distribution combined using the PSO algorithm. The minimal beam quality β can be obtained by optimally distributing the pumping light. Compared with the method of designing uniform pumping light into the gain medium, the theoretically computed single pass beam quality β value is optimized from 5.34 to 1.28. In this numerical analysis, experiments are conducted to validate the relationship between the thermally generated wavefront and certain pumping light distributions.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1997.10a
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• pp.213-218
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• 1997

Thermal deformation of machine origin of machine tools due to internal and external heat sources has been the most important problem to fabricate products with higher accuracy and performance. In order to solve this problem, GMDH models were constructed to estimate thermal deformation of machine origin for a vertical machining ceneter through measurement of temperature data of specific points on the machine tool. These models are nonlinear equations with high-order polynomials and implemented in a multilayered perceptron type network structure. Input variables and orders are automatically selected by correlation and optimization procedure. Sensors with small influence are deleted automatically in this algorithm. It was shown that the points of temperature measurement can be reduced without sacrificing the estimation accuracy of $\pm$5${\mu}{\textrm}{m}$. From the experimental result, it was confirmed that GMDH methodology was superior to least square models to estimate the thermal behavior of machine tools.

• KIEE International Transactions on Power Engineering
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• v.5A no.4
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• pp.331-338
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• 2005

In this paper, an improved maintenance scheduling approach suitable for the competitive environment is proposed by taking account of profits and costs of generation companies and the formulated combinatorial optimization problem is solved by using Reactive Tabu search (RTS). In competitive power markets, electricity prices are determined by the balance between demand and supply through electric power exchanges or by bilateral contracts. Therefore, in decision makings, it is essential for system operation planners and market participants to take the volatility of electricity price into consideration. In the proposed maintenance scheduling approach, firstly, electricity prices over the targeted period are forecasted based on Artificial Neural Network (ANN) and also a newly proposed aggregated bidding curve. Secondary, the maintenance scheduling is formulated as a combinatorial optimization problem with a novel objective function by which the most profitable maintenance schedule would be attained. As an objective function, Opportunity Loss by Maintenance (OLM) is adopted to maximize the profit of generation companies (GENCOS). Thirdly, the combinatorial optimization maintenance scheduling problem is solved by using Reactive Tabu Search in the light of the objective functions and forecasted electricity prices. Finally, the proposed maintenance scheduling is applied to a practical test power system to verify the advantages and practicability of the proposed method.