• 동력기계공학회지
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• 제21권6호
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• pp.56-61
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• 2017

The check valves are mechanical valves that permit fluids to flow in only one direction, preventing flow from reversing. It is classified as one way directional valves. There are various types of check valves that used in a marine application. A lift type check valve uses the disc to open and close the passage of fluid. The disc lift up from seat as pressure below the disc increases, while drop in pressure on the inlet side or a build up of pressure on the outlet side causes the valve to close. An important concept in check valves is the cracking pressure which is the minimum upstream pressure at which the valve will operate. On the other hand, optimization is a process of finding the best set of parameters to reach a goal while not violating certain constraints. The AMESim software provides NLPQL(Nonlinear Programming by Quadratic Lagrangian) and genetic algorithm(GA) for optimization. NLPQL is the implementation of a SQP(sequential quadratic programming) algorithm. SQP is a standard method, based on the use of a gradient of objective functions and constraints to solve a non-linear optimization problem. A characteristic of the NLPQL is that it stops as soon as it finds a local minimum. Thus, the simulation results may be highly dependent on the starting point which user give to the algorithm. In this paper, we carried out optimization design of the check valve with NLPQL algorithm.

• 드라이브 ㆍ 컨트롤
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• 제14권4호
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• pp.45-50
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• 2017

The gas spring is a hydropneumatic adjusting element, consisting of a pressure tube, a piston rod, a piston and a connection fitting. The gas spring is filled with compressed nitrogen within the cylinder. The filling pressure acts on both sides of the piston and because of area difference it produces an extension force. Therefore, a gas spring is similar in function compare to mechanical coil spring. Conversely, optimization is a process of finding the best set of parameters to reach a goal while not violating certain constraints. The AMESim software provides NLPQL (Nonlinear Programming by Quadratic Lagrangian) and GA (genetic algorithm) for optimization. The NLPQL method builds a quadratic approximation to the Lagrange function and linear approximations to all output constraints at each iteration, starting with the identity matrix for the Hessian of the Lagrangian, and gradually updating it using the BFGS method. On each iteration, a quadratic programming problem is solved to find an improved design until the final convergence to the optimum design. In this study, we conducted optimization design of the gas spring reaction force with NLPQL.

• 한국자동차공학회논문집
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• 제24권4호
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• pp.392-400
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• 2016

In this paper, a study based on engine operating conditions versus hybrid excavator engines was conducted about the engine performance and fuel consumption via the 1-D engine simulation model. First of all, engine operating points with performance and emission were determined by driving patterns. The 1-D HFEM(High Frequency Engine Model) was developed for deep insight into engine combustion and the energy conversion phenomena. In accordance with changing operating points, especially High Idle and Rated output conditions, engine parameters and systems such as turbocharger(Waste Gate Turbocharger and Variable Geometry Turbocharger) injection strategies and EGR(Exhaust Gas Recirculation) should be considered. Therefore, various configurations and parametric analysis with optimization methods in hybrid excavator were simulated and optimized by NLPQL(Non-linear Programming by Quadratic Lagrangian algorithm) in 1-D HFEM. As a result, the fuel consumption with the developed hybrid electric excavator engine could be significantly decreased and bsfc(Brake Specific Fuel Consumption) was also reduced about 5 % to 7 % without any performance degradation.